From 0bb7562c58292993eda142cc16d0fed58ad491fb Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 6 May 2024 13:57:25 -0400 Subject: [PATCH 01/13] remove duplicate cell --- ...s - Module Standoff for IEC TS 63126.ipynb | 29 +------------------ 1 file changed, 1 insertion(+), 28 deletions(-) diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb index b8eacf2f..a0c64b42 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -644,34 +644,7 @@ "\n", "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", "\n", - "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your results will be stored in c:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\TEMP\\results\n", - "The folder must already exist or the file will not be created\n" - ] - } - ], - "source": [ - "print (\"Your results will be stored in %s\" % output_folder)\n", - "print ('The folder must already exist or the file will not be created')\n", - "\n", - "pvdeg.weather.write(data_df=WEATHER_df, metadata=META, savefile=os.path.join(output_folder, 'WeatherFile.csv')) #Writes the meterological data to an *.csv file.\n", - "\n", - "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", - "\n", - "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv'))\n" + "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv')) #Writes a file with the Tilt Azimuth scan calculations of T98.\n" ] } ], From e94efa711baacc88daead5ef972b8bc3e8849881 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 6 May 2024 14:39:52 -0400 Subject: [PATCH 02/13] re-run all notebooks --- pvdeg/weather.py | 2 +- .../_build/.doctrees/README.doctree | Bin 10895 -> 10617 bytes .../_build/.doctrees/environment.pickle | Bin 106171 -> 0 bytes .../_build/.doctrees/intro.doctree | Bin 5873 -> 6341 bytes .../1 - Basics, Humidity, Design.doctree | Bin 0 -> 111332 bytes .../2 - Degradation.doctree | Bin 0 -> 36054 bytes .../3 - Spectral Degradation.doctree | Bin 0 -> 20393 bytes .../4 - Weather Database Access.doctree | Bin 0 -> 32540 bytes ....1e8bd061cd6da7fc9cf755528e8ffc24.min.css} | 2 +- .../1 - Basics, Humidity, Design.ipynb | 1764 +++++++++++++++++ .../tutorials_and_tools/2 - Degradation.ipynb | 399 ++++ .../3 - Spectral Degradation.ipynb | 260 +++ .../4 - Weather Database Access.ipynb | 264 +++ .../1 - Basics, Humidity, Design.ipynb | 119 +- .../tutorials_and_tools/2 - Degradation.ipynb | 22 +- .../3 - Spectral Degradation.ipynb | 44 +- .../4 - Weather Database Access.ipynb | 75 +- .../tutorials_and_tools/ASTM Live Demo.ipynb | 94 +- .../B-O LID - Accelerated Test.ipynb | 204 +- .../LETID - Accelerated Test.ipynb | 596 +++--- .../LETID - Outdoor LETID.ipynb | 555 +++--- ...rio Based on Accelerated Test Result.ipynb | 949 +++++---- .../LETID - Passivated Wafer.ipynb | 244 ++- .../Monte Carlo - Arrhenius.ipynb | 111 +- .../Monte Carlo - Standoff.ipynb | 99 +- ...s - Module Standoff for IEC TS 63126.ipynb | 32 +- .../Van't Hoff Degradation Model.ipynb | 37 +- 27 files changed, 4371 insertions(+), 1501 deletions(-) delete mode 100644 tutorials_and_tools/_build/.doctrees/environment.pickle create mode 100644 tutorials_and_tools/_build/.doctrees/tutorials_and_tools/1 - Basics, Humidity, Design.doctree create mode 100644 tutorials_and_tools/_build/.doctrees/tutorials_and_tools/2 - Degradation.doctree create mode 100644 tutorials_and_tools/_build/.doctrees/tutorials_and_tools/3 - Spectral Degradation.doctree create mode 100644 tutorials_and_tools/_build/.doctrees/tutorials_and_tools/4 - Weather Database Access.doctree rename tutorials_and_tools/_build/html/_sphinx_design_static/{design-style.4045f2051d55cab465a707391d5b2007.min.css => design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css} (99%) create mode 100644 tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb create mode 100644 tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/2 - Degradation.ipynb create mode 100644 tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/3 - Spectral Degradation.ipynb create mode 100644 tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/4 - Weather Database Access.ipynb diff --git a/pvdeg/weather.py b/pvdeg/weather.py index 5543cc0a..6f911dc7 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -134,7 +134,7 @@ def read(file_in, file_type, map_variables=True, **kwargs): [psm3, tmy3, epw, h5, csv] """ - META_MAP = {"elevation": "altitude", "Local Time Zone": "timezone"} + META_MAP = {"elevation": "altitude", "Local Time Zone": "tz"} supported = ["psm3", "tmy3", "epw", "h5", "csv"] file_type = file_type.upper() diff --git a/tutorials_and_tools/_build/.doctrees/README.doctree b/tutorials_and_tools/_build/.doctrees/README.doctree index bd2136cf1ad1afc2a05eb80f3658c49991716fc3..dc723b125ad26bc812603c5eec2f7fc2f007acb1 100644 GIT binary patch delta 2590 zcmb_eTW=dh6z;C$k{G42>*nqxEQ zphD?GgCbd_;usJDfhr^bP!t&fa zok&+yr6{cFTKZt};GxW{P*C|iuS<%&swgEbeL~VjBbQFS^p6?c&=pnUOIn7P^BLVl zFVD@)o|)sy`Fah$VHTK!u*7zbCdlg9)y3sgGxIA1vBZo_B9&@hC`9YxQ|Ibppk_il z9)d!K{d46jX4kG46qS?&UX`&9qASGE1Y&X`x~M8fK_rTy5|`3ALA+|n7F-kvU*)9| zpDPKt7|YtazHfn%B}n#r4#lr_FoS|^fBcv6*A-QY_^knv5C%Yv+r6NwYJagwd#$1LWAx7(kE 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Read in necessary weather data\n", + "2. Generate solar position, POA, and module temperature\n", + "3. Generate module humidities\n", + "4. Calculate edge seal width" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_26996\\3936321414.py:2: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "import pvdeg \n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Reading in Weather Data and Site Meta-Data\n", + "Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file:\n", + "\n", + "- date-time index\n", + "- DNI\n", + "- DHI\n", + "- GHI\n", + "- Temperature, Dry-Bulb\n", + "- Temperature, Dew-Point\n", + "- Wind Speed\n", + "- Relative Humidity\n", + "\n", + "And the following site-specific metadata. This should be contained in the weather file header.\n", + "\n", + "- Latitude\n", + "- Longitude\n", + "- Altitude" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "PSM_FILE = os.path.join(DATA_DIR,'psm3_demo.csv')\n", + "\n", + "WEATHER, META = pvdeg.weather.read(PSM_FILE,'psm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.39
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.84
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.98
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.01
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.81
\n", + "
" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \n", + "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", + "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", + "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", + "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", + "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "WEATHER.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Source': 'NSRDB',\n", + " 'Location ID': '145809',\n", + " 'City': '-',\n", + " 'State': '-',\n", + " 'Country': '-',\n", + " 'Clearsky DHI Units': 'w/m2',\n", + " 'Clearsky DNI Units': 'w/m2',\n", + " 'Clearsky GHI Units': 'w/m2',\n", + " 'Dew Point Units': 'c',\n", + " 'DHI Units': 'w/m2',\n", + " 'DNI Units': 'w/m2',\n", + " 'GHI Units': 'w/m2',\n", + " 'Solar Zenith Angle Units': 'Degree',\n", + " 'Temperature Units': 'c',\n", + " 'Pressure Units': 'mbar',\n", + " 'Relative Humidity Units': '%',\n", + " 'Precipitable Water Units': 'cm',\n", + " 'Wind Direction Units': 'Degrees',\n", + " 'Wind Speed Units': 'm/s',\n", + " 'Cloud Type -15': 'N/A',\n", + " 'Cloud Type 0': 'Clear',\n", + " 'Cloud Type 1': 'Probably Clear',\n", + " 'Cloud Type 2': 'Fog',\n", + " 'Cloud Type 3': 'Water',\n", + " 'Cloud Type 4': 'Super-Cooled Water',\n", + " 'Cloud Type 5': 'Mixed',\n", + " 'Cloud Type 6': 'Opaque Ice',\n", + " 'Cloud Type 7': 'Cirrus',\n", + " 'Cloud Type 8': 'Overlapping',\n", + " 'Cloud Type 9': 'Overshooting',\n", + " 'Cloud Type 10': 'Unknown',\n", + " 'Cloud Type 11': 'Dust',\n", + " 'Cloud Type 12': 'Smoke',\n", + " 'Fill Flag 0': 'N/A',\n", + " 'Fill Flag 1': 'Missing Image',\n", + " 'Fill Flag 2': 'Low Irradiance',\n", + " 'Fill Flag 3': 'Exceeds Clearsky',\n", + " 'Fill Flag 4': 'Missing CLoud Properties',\n", + " 'Fill Flag 5': 'Rayleigh Violation',\n", + " 'Surface Albedo Units': 'N/A',\n", + " 'Version': '3.0.6',\n", + " 'latitude': 39.73,\n", + " 'longitude': -105.18,\n", + " 'altitude': 1820,\n", + " 'tz': -7}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "META" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be working with a lot of time series data, generally with a date-time index. If you're\n", + "processing an entire year of data, you don't need to sort or filter the data. However, you may want\n", + "to filter the data by a few different criteria. Below are some common examples.\n", + "- Filter by date\n", + " - month\n", + " - range of months\n", + "- Filter by sun-up hours" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-06-30 19:30:00-07:00199963019300.00.00.021.06.00.648.54
1999-06-30 20:30:00-07:00199963020300.00.00.019.07.00.455.36
1999-06-30 21:30:00-07:00199963021300.00.00.017.07.00.566.32
1999-06-30 22:30:00-07:00199963022300.00.00.015.07.00.875.74
1999-06-30 23:30:00-07:00199963023300.00.00.013.07.00.880.16
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720 rows × 12 columns

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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-09-30 19:30:00-07:00199993019300.00.00.08.0-1.02.658.57
1999-09-30 20:30:00-07:00199993020300.00.00.07.0-2.02.758.17
1999-09-30 21:30:00-07:00199993021300.00.00.07.0-3.02.954.27
1999-09-30 22:30:00-07:00199993022300.00.00.07.0-4.03.050.29
1999-09-30 23:30:00-07:00199993023300.00.00.07.0-5.03.046.91
\n", + "

2928 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", + "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", + "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", + "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", + "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "1999-09-30 19:30:00-07:00 1999 9 30 19 30 0.0 0.0 0.0 \n", + "1999-09-30 20:30:00-07:00 1999 9 30 20 30 0.0 0.0 0.0 \n", + "1999-09-30 21:30:00-07:00 1999 9 30 21 30 0.0 0.0 0.0 \n", + "1999-09-30 22:30:00-07:00 1999 9 30 22 30 0.0 0.0 0.0 \n", + "1999-09-30 23:30:00-07:00 1999 9 30 23 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \n", + "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", + "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", + "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", + "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", + "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", + "... ... ... ... ... \n", + "1999-09-30 19:30:00-07:00 8.0 -1.0 2.6 58.57 \n", + "1999-09-30 20:30:00-07:00 7.0 -2.0 2.7 58.17 \n", + "1999-09-30 21:30:00-07:00 7.0 -3.0 2.9 54.27 \n", + "1999-09-30 22:30:00-07:00 7.0 -4.0 3.0 50.29 \n", + "1999-09-30 23:30:00-07:00 7.0 -5.0 3.0 46.91 \n", + "\n", + "[2928 rows x 12 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's filter the summer months for the northern hemisphere\n", + "summer_months = [6,7,8,9]\n", + "summer_weather = WEATHER[ WEATHER.index.month.isin( summer_months ) ]\n", + "summer_weather" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 08:30:00-07:0019991183065.065.076.01.0-3.04.776.86
1999-01-01 09:30:00-07:00199911930503.093.0246.02.0-2.06.380.37
1999-01-01 10:30:00-07:001999111030617.0109.0355.03.0-2.07.074.87
1999-01-01 11:30:00-07:001999111130497.0161.0385.04.0-3.06.870.73
1999-01-01 12:30:00-07:0019991112300.0128.0128.04.0-4.06.561.80
.......................................
1999-12-31 12:30:00-07:001999123112300.020.020.09.0-8.01.631.45
1999-12-31 13:30:00-07:00199912311330562.0125.0354.08.0-6.00.540.25
1999-12-31 14:30:00-07:00199912311430530.094.0263.07.0-4.00.453.19
1999-12-31 15:30:00-07:00199912311530636.038.0159.05.0-5.01.057.10
1999-12-31 16:30:00-07:001999123116300.021.021.04.0-6.01.356.22
\n", + "

4301 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi \\\n", + "1999-01-01 08:30:00-07:00 1999 1 1 8 30 65.0 65.0 \n", + "1999-01-01 09:30:00-07:00 1999 1 1 9 30 503.0 93.0 \n", + "1999-01-01 10:30:00-07:00 1999 1 1 10 30 617.0 109.0 \n", + "1999-01-01 11:30:00-07:00 1999 1 1 11 30 497.0 161.0 \n", + "1999-01-01 12:30:00-07:00 1999 1 1 12 30 0.0 128.0 \n", + "... ... ... ... ... ... ... ... \n", + "1999-12-31 12:30:00-07:00 1999 12 31 12 30 0.0 20.0 \n", + "1999-12-31 13:30:00-07:00 1999 12 31 13 30 562.0 125.0 \n", + "1999-12-31 14:30:00-07:00 1999 12 31 14 30 530.0 94.0 \n", + "1999-12-31 15:30:00-07:00 1999 12 31 15 30 636.0 38.0 \n", + "1999-12-31 16:30:00-07:00 1999 12 31 16 30 0.0 21.0 \n", + "\n", + " ghi temp_air dew_point wind_speed \\\n", + "1999-01-01 08:30:00-07:00 76.0 1.0 -3.0 4.7 \n", + "1999-01-01 09:30:00-07:00 246.0 2.0 -2.0 6.3 \n", + "1999-01-01 10:30:00-07:00 355.0 3.0 -2.0 7.0 \n", + "1999-01-01 11:30:00-07:00 385.0 4.0 -3.0 6.8 \n", + "1999-01-01 12:30:00-07:00 128.0 4.0 -4.0 6.5 \n", + "... ... ... ... ... \n", + "1999-12-31 12:30:00-07:00 20.0 9.0 -8.0 1.6 \n", + "1999-12-31 13:30:00-07:00 354.0 8.0 -6.0 0.5 \n", + "1999-12-31 14:30:00-07:00 263.0 7.0 -4.0 0.4 \n", + "1999-12-31 15:30:00-07:00 159.0 5.0 -5.0 1.0 \n", + "1999-12-31 16:30:00-07:00 21.0 4.0 -6.0 1.3 \n", + "\n", + " relative_humidity \n", + "1999-01-01 08:30:00-07:00 76.86 \n", + "1999-01-01 09:30:00-07:00 80.37 \n", + "1999-01-01 10:30:00-07:00 74.87 \n", + "1999-01-01 11:30:00-07:00 70.73 \n", + "1999-01-01 12:30:00-07:00 61.80 \n", + "... ... \n", + "1999-12-31 12:30:00-07:00 31.45 \n", + "1999-12-31 13:30:00-07:00 40.25 \n", + "1999-12-31 14:30:00-07:00 53.19 \n", + "1999-12-31 15:30:00-07:00 57.10 \n", + "1999-12-31 16:30:00-07:00 56.22 \n", + "\n", + "[4301 rows x 12 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets just select hours from the year where the sun is above the horizon\n", + "sunup_weather = WEATHER[ WEATHER['ghi'] > 0 ]\n", + "sunup_weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Solar Position, POA, and Module Temperature\n", + "\n", + "Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", + "\n", + "poa_df = pvdeg.spectral.poa_irradiance(weather_df=WEATHER, meta=META)#, solar_position=sol_pos)\n", + "\n", + "temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META)#, poa=poa_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`.\n", + "\n", + "Lets inspect the output from each of these functions\n", + "\n", + "`solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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apparent_zenithzenithapparent_elevationelevationazimuthequation_of_time
1999-01-01 00:30:00-07:00162.416069162.416069-72.416069-72.41606920.152470-3.300994
1999-01-01 01:30:00-07:00155.313029155.313029-65.313029-65.31302953.821029-3.320654
1999-01-01 02:30:00-07:00144.992553144.992553-54.992553-54.99255372.531199-3.340306
1999-01-01 03:30:00-07:00133.692678133.692678-43.692678-43.69267884.789909-3.359948
1999-01-01 04:30:00-07:00122.172658122.172658-32.172658-32.17265894.462003-3.379582
.....................
1999-12-31 19:30:00-07:00120.682889120.682889-30.682889-30.682889264.307279-3.093347
1999-12-31 20:30:00-07:00132.197914132.197914-42.197914-42.197914273.785399-3.113209
1999-12-31 21:30:00-07:00143.553813143.553813-53.553813-53.553813285.552685-3.133063
1999-12-31 22:30:00-07:00154.080531154.080531-64.080531-64.080531303.037057-3.152909
1999-12-31 23:30:00-07:00161.842482161.842482-71.842482-71.842482334.208238-3.172747
\n", + "

8760 rows × 6 columns

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" + ], + "text/plain": [ + " apparent_zenith zenith apparent_elevation \\\n", + "1999-01-01 00:30:00-07:00 162.416069 162.416069 -72.416069 \n", + "1999-01-01 01:30:00-07:00 155.313029 155.313029 -65.313029 \n", + "1999-01-01 02:30:00-07:00 144.992553 144.992553 -54.992553 \n", + "1999-01-01 03:30:00-07:00 133.692678 133.692678 -43.692678 \n", + "1999-01-01 04:30:00-07:00 122.172658 122.172658 -32.172658 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 120.682889 120.682889 -30.682889 \n", + "1999-12-31 20:30:00-07:00 132.197914 132.197914 -42.197914 \n", + "1999-12-31 21:30:00-07:00 143.553813 143.553813 -53.553813 \n", + "1999-12-31 22:30:00-07:00 154.080531 154.080531 -64.080531 \n", + "1999-12-31 23:30:00-07:00 161.842482 161.842482 -71.842482 \n", + "\n", + " elevation azimuth equation_of_time \n", + "1999-01-01 00:30:00-07:00 -72.416069 20.152470 -3.300994 \n", + "1999-01-01 01:30:00-07:00 -65.313029 53.821029 -3.320654 \n", + "1999-01-01 02:30:00-07:00 -54.992553 72.531199 -3.340306 \n", + "1999-01-01 03:30:00-07:00 -43.692678 84.789909 -3.359948 \n", + "1999-01-01 04:30:00-07:00 -32.172658 94.462003 -3.379582 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 -30.682889 264.307279 -3.093347 \n", + "1999-12-31 20:30:00-07:00 -42.197914 273.785399 -3.113209 \n", + "1999-12-31 21:30:00-07:00 -53.553813 285.552685 -3.133063 \n", + "1999-12-31 22:30:00-07:00 -64.080531 303.037057 -3.152909 \n", + "1999-12-31 23:30:00-07:00 -71.842482 334.208238 -3.172747 \n", + "\n", + "[8760 rows x 6 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sol_pos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globalpoa_directpoa_diffusepoa_sky_diffusepoa_ground_diffuse
1999-01-01 13:30:00-07:0090.4265380.090.42653887.5687172.857821
1999-01-01 14:30:00-07:0021.9215850.021.92158521.2287800.692805
1999-01-01 15:30:00-07:0057.5441600.057.54416055.7255471.818613
1999-01-01 16:30:00-07:007.3071950.07.3071957.0762600.230935
1999-01-01 17:30:00-07:000.0000000.00.0000000.0000000.000000
\n", + "
" + ], + "text/plain": [ + " poa_global poa_direct poa_diffuse \\\n", + "1999-01-01 13:30:00-07:00 90.426538 0.0 90.426538 \n", + "1999-01-01 14:30:00-07:00 21.921585 0.0 21.921585 \n", + "1999-01-01 15:30:00-07:00 57.544160 0.0 57.544160 \n", + "1999-01-01 16:30:00-07:00 7.307195 0.0 7.307195 \n", + "1999-01-01 17:30:00-07:00 0.000000 0.0 0.000000 \n", + "\n", + " poa_sky_diffuse poa_ground_diffuse \n", + "1999-01-01 13:30:00-07:00 87.568717 2.857821 \n", + "1999-01-01 14:30:00-07:00 21.228780 0.692805 \n", + "1999-01-01 15:30:00-07:00 55.725547 1.818613 \n", + "1999-01-01 16:30:00-07:00 7.076260 0.230935 \n", + "1999-01-01 17:30:00-07:00 0.000000 0.000000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poa_df.loc['1999-01-01 13:30:00-07:00':'1999-01-01 17:30:00-07:00']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models.\n", + "\n", + "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html\n", + "\n", + "The optional arguments are:\n", + "- poa : manually enter the POA Irradiance\n", + "- temp_model : choose a PVLIB compatible temperature model\n", + "- conf : choose a recognized module configuration\n", + "- wind_speed_factor : change the empirical wind speed fit paramter" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "module() got an unexpected keyword argument 'wind_speed_factor'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m temp_mod \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweather_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mWEATHER\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mMETA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mpoa\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpoa_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemp_model\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msapm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mopen_rack_glass_polymer\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mwind_speed_factor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m temp_mod\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1999-01-01 13:30:00-07:00\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1999-01-01 17:30:00-07:00\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[1;31mTypeError\u001b[0m: module() got an unexpected keyword argument 'wind_speed_factor'" + ] + } + ], + "source": [ + "temp_mod = pvdeg.temperature.module(weather_df = WEATHER, meta = META,\n", + " poa = poa_df,\n", + " temp_model = 'sapm',\n", + " conf= 'open_rack_glass_polymer',\n", + " wind_speed_factor = 1)\n", + "\n", + "temp_mod.loc['1999-01-01 13:30:00-07:00':'1999-01-01 17:30:00-07:00']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Module Humidities\n", + "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.a. Automatic\n", + "Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RH_surface_outsideRH_front_encapRH_back_encapRH_backsheet
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" + ], + "text/plain": [ + " RH_surface_outside RH_front_encap RH_back_encap \\\n", + "1999-01-01 00:30:00-07:00 79.39 44.198787 79.390000 \n", + "1999-01-01 01:30:00-07:00 80.84 44.198787 79.415220 \n", + "1999-01-01 02:30:00-07:00 82.98 44.198787 79.477223 \n", + "1999-01-01 03:30:00-07:00 85.01 44.198787 79.573455 \n", + "1999-01-01 04:30:00-07:00 85.81 44.198787 79.681927 \n", + "\n", + " RH_backsheet \n", + "1999-01-01 00:30:00-07:00 79.390000 \n", + "1999-01-01 01:30:00-07:00 80.127610 \n", + "1999-01-01 02:30:00-07:00 81.228611 \n", + "1999-01-01 03:30:00-07:00 82.291727 \n", + "1999-01-01 04:30:00-07:00 82.745964 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META)\n", + "rh_module.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.b. Manual\n", + "Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order:\n", + "1. Surface Outside\n", + "2. Front Encapsulant\n", + "2. Back Encapsulant\n", + "3. Backsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "rh_surface_outside = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_front_encap = pvdeg.humidity.front_encap(rh_ambient=rh_surface_outside,\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_back_encap = pvdeg.humidity.back_encap(rh_ambient=rh_surface_outside,\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_backsheet = pvdeg.humidity.backsheet_from_encap(rh_back_encap=rh_back_encap,\n", + " rh_surface_outside=WEATHER['relative_humidity'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Design: Edge Seal Width\n", + "`pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7170984345065904" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META)\n", + "edge_seal_width" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets look at the calculation step by step. To get the estimated edge seal width, we need the following:\n", + "1. Humidity Saturation Point\n", + "2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7170984345065904\n" + ] + } + ], + "source": [ + "psat, psat_avg = pvdeg.humidity.psat(WEATHER['temp_air'])\n", + "\n", + "k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg)\n", + "\n", + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META,\n", + " k=k, years=25)\n", + "\n", + "print(edge_seal_width)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alternate Edge Seal Width\n", + "If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4499535870958669" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META, from_dew_point=True)\n", + "edge_seal_width" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/2 - Degradation.ipynb b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/2 - Degradation.ipynb new file mode 100644 index 00000000..84b99f0b --- /dev/null +++ b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/2 - Degradation.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Degradation\n", + "Degradation and Solder Fatigue\n", + "\n", + "**Requirements**:\n", + "- compatible weather file (PSM3, TMY3, EPW)\n", + "- Accelerated testing chamber parameters\n", + " - chamber irradiance [W/m^2]\n", + " - chamber temperature [C]\n", + " - chamber humidity [%]\n", + "- Activation energies for test material [kJ/mol]\n", + "\n", + "**Objectives**:\n", + "1. Read in the weather data\n", + "2. Generate basic modeling data\n", + "3. Calculate VantHoff degradation acceleration factor\n", + "4. Calculate Arrhenius degradation acceleration factor\n", + "5. Quick Method\n", + "5. Solder Fatigue" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_28564\\3801537021.py:2: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "import pvdeg\n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read In the Weather File\n", + "\n", + "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "PSM_FILE = os.path.join(DATA_DIR,'psm3_demo.csv')\n", + "WEATHER, META = pvdeg.weather.read(PSM_FILE,'psm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Generate Basic Modeling Data\n", + "\n", + "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", + "\n", + "poa_df = pvdeg.spectral.poa_irradiance(weather_df=WEATHER, meta=META, sol_position=sol_pos)\n", + "\n", + "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df)\n", + "\n", + "temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. VantHoff Degradation\n", + "\n", + "Van 't Hoff Irradiance Degradation\n", + "\n", + "For one year of degredation the controlled environmnet lamp settings will need to be set to IWa.\n", + "\n", + "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# chamber irradiance (W/m^2)\n", + "I_chamber = 1000\n", + "# chamber temperature (C)\n", + "temp_chamber = 60\n", + "\n", + "# calculate the VantHoff Acceleration factor\n", + "vantHoff_deg = pvdeg.degradation.vantHoff_deg(weather_df=WEATHER, meta=META,\n", + " I_chamber=I_chamber,\n", + " temp_chamber=temp_chamber,\n", + " poa=poa_df,\n", + " temp=temp_cell)\n", + "\n", + "# calculate the VantHoff weighted irradiance\n", + "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META,\n", + " poa=poa_df,\n", + " temp=temp_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Arrhenius\n", + "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", + "\n", + "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", + "\n", + "Equation:\n", + "$$ AF = N * \\frac{ I_{chamber}^x * RH_{chamber}^n * e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (I_{POA}^x * RH_{outdoor}^n * e^{\\frac{-E_a}{k T_outdoor}}) }$$\n", + "\n", + "Function to calculate IWa, the Environment Characterization (W/m²). For one year of degredation the controlled environmnet lamp settings will need to be set at IWa.\n", + "\n", + "Equation:\n", + "$$ I_{WA} = [ \\frac{ \\Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# relative humidity within chamber (%)\n", + "rh_chamber = 15\n", + "# arrhenius activation energy (kj/mol)\n", + "Ea = 40\n", + "\n", + "rh_surface = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_module)\n", + "\n", + "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=rh_surface,\n", + " I_chamber=I_chamber,\n", + " rh_chamber=rh_chamber,\n", + " temp_chamber=temp_chamber,\n", + " poa=poa_df,\n", + " temp=temp_cell,\n", + " Ea=Ea)\n", + "\n", + "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=WEATHER, meta=META,\n", + " poa=poa_df,\n", + " rh_outdoor=WEATHER['relative_humidity'],\n", + " temp=temp_cell,\n", + " Ea=Ea)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Quick Method (Degradation)\n", + "\n", + "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "# chamber settings\n", + "I_chamber= 1000\n", + "temp_chamber=60\n", + "rh_chamber=15\n", + "\n", + "# activation energy\n", + "Ea = 40\n", + "\n", + "vantHoff_deg = pvdeg.degradation.vantHoff_deg(weather_df=WEATHER, meta=META,\n", + " I_chamber=I_chamber,\n", + " temp_chamber=temp_chamber)\n", + "\n", + "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "rh_surface = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_module)\n", + "\n", + "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=rh_surface,\n", + " I_chamber=I_chamber,\n", + " rh_chamber=rh_chamber,\n", + " temp_chamber=temp_chamber,\n", + " Ea=Ea)\n", + "\n", + "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=WEATHER['relative_humidity'],\n", + " Ea=Ea)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Solder Fatigue\n", + "\n", + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n", + "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n", + "\n", + "1. Lets create a datetime-index for the month of June.\n", + "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n", + "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "# select the month of June\n", + "time_range = WEATHER.index[WEATHER.index.month == 6]\n", + "\n", + "# calculate cell temperature over our selected date-time range.\n", + "# specify the module configuration\n", + "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER.loc[time_range], meta=META,\n", + " temp_model='sapm',\n", + " conf='insulated_back_glass_polymer')\n", + "\n", + "\n", + "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META,\n", + " time_range = time_range,\n", + " temp_cell = temp_cell)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/3 - Spectral Degradation.ipynb new file mode 100644 index 00000000..55d509cf --- /dev/null +++ b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/3 - Spectral Degradation.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 - Spectral Degradation\n", + "\n", + "**Requirements:**\n", + "- spectral irradiance (measured or simulated)\n", + "- wavelengths of spectral irradiance data\n", + "- module RH\n", + "- module temperature\n", + "\n", + "\n", + "**Objectives:**\n", + "1. Read in spectral irradiance\n", + "2. Calculate spectral degradation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_31964\\1750438778.py:2: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pvdeg \n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read in spectral irradiance data\n", + "\n", + "Spectral degradation has 4 main requirements:\n", + "- Spectral Irradiance [W/m^2 nm]\n", + "- Wavelength [nm] \n", + "- Module Relative Humidity [%]\n", + "- Module Temperature [C]\n", + "\n", + "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", + "\n", + "\n", + "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Spectra \\\n", + "timestamp \n", + "2021-03-09 10:00:00 [0.6892146677599185, 0.40215646494410884, 0.67... \n", + "2021-03-09 11:00:00 [0.15575709102178648, 0.5464374649246564, 0.68... \n", + "2021-03-09 12:00:00 [0.22782105874481207, 0.9056495270031296, 0.26... \n", + "2021-03-09 13:00:00 [0.3741943134512433, 0.035830980984344674, 0.4... \n", + "2021-03-09 14:00:00 [0.40321187996337626, 0.6473167864022122, 0.69... \n", + "\n", + " Temperature RH \n", + "timestamp \n", + "2021-03-09 10:00:00 45 59 \n", + "2021-03-09 11:00:00 44 56 \n", + "2021-03-09 12:00:00 59 39 \n", + "2021-03-09 13:00:00 44 13 \n", + "2021-03-09 14:00:00 25 39 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wavelengths = np.array(range(280,420,20))\n", + "\n", + "SPECTRA = pd.read_csv(os.path.join(DATA_DIR,'spectra.csv'), header=0, index_col=0)\n", + "SPECTRA.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Calculate Degradation\n", + "\n", + "The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing brackets from spectral irradiance data\n" + ] + } + ], + "source": [ + "degradation = pvdeg.degradation.degradation(spectra=SPECTRA['Spectra'],\n", + " rh_module=SPECTRA['RH'],\n", + " temp_module=SPECTRA['Temperature'],\n", + " wavelengths=wavelengths)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/4 - Weather Database Access.ipynb new file mode 100644 index 00000000..bb200a9d --- /dev/null +++ b/tutorials_and_tools/_build/jupyter_execute/tutorials_and_tools/4 - Weather Database Access.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4 - Weather Database Access\n", + "\n", + "**Requirements:**\n", + "- Internet access\n", + "- NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/\n", + "- Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method.\n", + "\n", + "**Objectives:**\n", + "\n", + "Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database.\n", + "1. Single Location, NSRDB via NREL's high performance computer, Eagle.\n", + "2. Single Location via NSRDB public API key.\n", + "3. Single Location via the PVGIS public database\n", + "\n", + "**Background:**\n", + "\n", + "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**.\n", + "\n", + "For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", + "\n", + "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single location example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + " # !pip install pvdeg==0.3.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. NSRDB - HSDS on Eagle\n", + "\n", + "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", + "\n", + "In this step:\n", + "\n", + "First we select a database. Here, we will use the NSRDB. Since we are modeling a single location, we can pass the `weather_id` as tuple (lat, long). A location gid can be used as well. 'gid' is a unique identifier to a geographic location within the NSRDB. We'll look at how to find gids later on.\n", + "\n", + "Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "Couldn't find NSRDB input files! \nSearched for: '/datasets/NSRDB/full_disc\\*_2021.h5'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 13\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m#weather_id = 1933572\u001b[39;00m\n\u001b[0;32m 8\u001b[0m weather_arg \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msatellite\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGOES\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 9\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2021\u001b[39m,\n\u001b[0;32m 10\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNREL_HPC\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattributes\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mair_temperature\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwind_speed\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdhi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mghi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdni\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelative_humidity\u001b[39m\u001b[38;5;124m'\u001b[39m]}\n\u001b[1;32m---> 13\u001b[0m weather_df, meta \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweather\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweather_db\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweather_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mweather_arg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\weather.py:70\u001b[0m, in \u001b[0;36mget\u001b[1;34m(database, id, geospatial, **kwargs)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m geospatial:\n\u001b[0;32m 66\u001b[0m \u001b[38;5;66;03m# TODO: decide wether to follow NSRDB or pvlib conventions...\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;66;03m# e.g. temp_air vs. air_temperature\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;66;03m# \"map variables\" will guarantee PVLIB conventions (automatic in coming update) which is \"temp_air\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m database \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSRDB\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 70\u001b[0m weather_df, meta \u001b[38;5;241m=\u001b[39m \u001b[43mget_NSRDB\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m database \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPVGIS\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 72\u001b[0m URL \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://re.jrc.ec.europa.eu/api/v5_2/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[1;32m~\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\weather.py:584\u001b[0m, in \u001b[0;36mget_NSRDB\u001b[1;34m(satellite, names, NREL_HPC, gid, location, geospatial, attributes, **_)\u001b[0m\n\u001b[0;32m 582\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe satellite is \u001b[39m\u001b[38;5;124m\"\u001b[39m, satellite)\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m geospatial:\n\u001b[1;32m--> 584\u001b[0m nsrdb_fnames, hsds \u001b[38;5;241m=\u001b[39m \u001b[43mget_NSRDB_fnames\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 585\u001b[0m \u001b[43m \u001b[49m\u001b[43msatellite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msatellite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNREL_HPC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNREL_HPC\u001b[49m\n\u001b[0;32m 586\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 588\u001b[0m dattr \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 589\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, file \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(nsrdb_fnames):\n", + "File \u001b[1;32m~\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\weather.py:528\u001b[0m, in \u001b[0;36mget_NSRDB_fnames\u001b[1;34m(satellite, names, NREL_HPC, **_)\u001b[0m\n\u001b[0;32m 525\u001b[0m nsrdb_fnames \u001b[38;5;241m=\u001b[39m glob\u001b[38;5;241m.\u001b[39mglob(nsrdb_fp)\n\u001b[0;32m 527\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(nsrdb_fnames) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 528\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[0;32m 529\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find NSRDB input files! \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mSearched for: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(nsrdb_fp)\n\u001b[0;32m 530\u001b[0m )\n\u001b[0;32m 532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nsrdb_fnames, hsds\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: Couldn't find NSRDB input files! \nSearched for: '/datasets/NSRDB/full_disc\\*_2021.h5'" + ] + } + ], + "source": [ + "# Get weather data\n", + "weather_db = 'NSRDB'\n", + "\n", + "#Latitude and Longitude\n", + "weather_id = (33.448376, -112.074036)\n", + "weather_id = (39.741931, -105.169891)\n", + "#weather_id = 1933572\n", + "weather_arg = {'satellite': 'GOES',\n", + " 'names': 2021,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested.\n", + "\n", + "Lets verify the weather data we fetched by running a familiar calculation; standoff distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "print(pvdeg.standards.interpret_standoff(res))\n", + "print(meta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. NSRDB - API\n", + "\n", + "To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/\n", + "\n", + "Key Notes:\n", + "- set `attributes = []` to return all possible attributes (weather fields)\n", + "- There are 2 major methods with the API\n", + " - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point.\n", + " - names = '2019' : collect a weather dataframe including measured relative humidity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "API_KEY = 'your_api_key_here'\n", + "# The example API key here is for demonstation and is rate-limited per IP.\n", + "# To get your own API key, visit https://developer.nrel.gov/signup/\n", + "\n", + "weather_db = 'PSM3'\n", + "weather_id = (39.741931, -105.169891)\n", + "weather_arg = {'api_key': 'DEMO_KEY',\n", + " 'email': 'user@mail.com',\n", + " 'names': 'tmy',\n", + " 'attributes': [],\n", + " 'map_variables': True}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "# Perform calculation and output interpretation or results\n", + "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "print(pvdeg.standards.interpret_standoff(res))\n", + "print(meta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. PVGIS\n", + "\n", + "This method uses the PVGIS database, a public resource. It requires no API key or user account." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weather_db = 'PVGIS'\n", + "#weather_id = (39.741931, -105.169891)\n", + "weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "#weather_arg = {'map_variables': True}\n", + "\n", + "# TMY\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id)\n", + "\n", + "# Perform calculation\n", + "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "print(pvdeg.standards.interpret_standoff(res))\n", + "print(meta)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb index 7b7d5608..16b6880a 100644 --- a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -30,22 +30,33 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "\n", - "import pvdeg \n", + "import pvdeg\n", "from pvdeg import DATA_DIR" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -80,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -131,7 +142,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " \n", @@ -224,7 +235,7 @@ "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", "\n", - " temp_air Dew Point wind_speed relative_humidity \n", + " temp_air dew_point wind_speed relative_humidity \n", "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", @@ -232,7 +243,7 @@ "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 " ] }, - "execution_count": 3, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -261,8 +272,6 @@ " 'City': '-',\n", " 'State': '-',\n", " 'Country': '-',\n", - " 'Time Zone': -7,\n", - " 'Local Time Zone': -7,\n", " 'Clearsky DHI Units': 'w/m2',\n", " 'Clearsky DNI Units': 'w/m2',\n", " 'Clearsky GHI Units': 'w/m2',\n", @@ -301,10 +310,11 @@ " 'Version': '3.0.6',\n", " 'latitude': 39.73,\n", " 'longitude': -105.18,\n", - " 'altitude': 1820}" + " 'altitude': 1820,\n", + " 'tz': -7}" ] }, - "execution_count": 4, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -328,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -361,7 +371,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " \n", @@ -551,7 +561,7 @@ "1999-06-30 22:30:00-07:00 1999 6 30 22 30 0.0 0.0 0.0 \n", "1999-06-30 23:30:00-07:00 1999 6 30 23 30 0.0 0.0 0.0 \n", "\n", - " temp_air Dew Point wind_speed relative_humidity \n", + " temp_air dew_point wind_speed relative_humidity \n", "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", @@ -567,7 +577,7 @@ "[720 rows x 12 columns]" ] }, - "execution_count": 5, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -580,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -613,7 +623,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " \n", @@ -803,7 +813,7 @@ "1999-09-30 22:30:00-07:00 1999 9 30 22 30 0.0 0.0 0.0 \n", "1999-09-30 23:30:00-07:00 1999 9 30 23 30 0.0 0.0 0.0 \n", "\n", - " temp_air Dew Point wind_speed relative_humidity \n", + " temp_air dew_point wind_speed relative_humidity \n", "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", @@ -819,7 +829,7 @@ "[2928 rows x 12 columns]" ] }, - "execution_count": 6, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -833,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -866,7 +876,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " \n", @@ -1056,7 +1066,7 @@ "1999-12-31 15:30:00-07:00 1999 12 31 15 30 636.0 38.0 \n", "1999-12-31 16:30:00-07:00 1999 12 31 16 30 0.0 21.0 \n", "\n", - " ghi temp_air Dew Point wind_speed \\\n", + " ghi temp_air dew_point wind_speed \\\n", "1999-01-01 08:30:00-07:00 76.0 1.0 -3.0 4.7 \n", "1999-01-01 09:30:00-07:00 246.0 2.0 -2.0 6.3 \n", "1999-01-01 10:30:00-07:00 355.0 3.0 -2.0 7.0 \n", @@ -1085,7 +1095,7 @@ "[4301 rows x 12 columns]" ] }, - "execution_count": 7, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1107,9 +1117,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], "source": [ "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", "\n", @@ -1131,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1298,7 +1319,7 @@ "[8760 rows x 6 columns]" ] }, - "execution_count": 9, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1316,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1408,7 +1429,7 @@ "1999-01-01 17:30:00-07:00 0.000000 0.000000 " ] }, - "execution_count": 10, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1434,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1448,7 +1469,7 @@ "dtype: float64" ] }, - "execution_count": 11, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1457,8 +1478,7 @@ "temp_mod = pvdeg.temperature.module(weather_df = WEATHER, meta = META,\n", " poa = poa_df,\n", " temp_model = 'sapm',\n", - " conf= 'open_rack_glass_polymer',\n", - " wind_speed_factor = 1)\n", + " conf= 'open_rack_glass_polymer')\n", "\n", "temp_mod.loc['1999-01-01 13:30:00-07:00':'1999-01-01 17:30:00-07:00']" ] @@ -1481,9 +1501,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n" + ] + }, { "data": { "text/html": [ @@ -1567,7 +1594,7 @@ "1999-01-01 04:30:00-07:00 82.745964 " ] }, - "execution_count": 12, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1591,7 +1618,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1621,7 +1648,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1630,7 +1657,7 @@ "0.7170984345065904" ] }, - "execution_count": 14, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1651,7 +1678,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1683,7 +1710,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1692,7 +1719,7 @@ "0.4499535870958669" ] }, - "execution_count": 17, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1719,7 +1746,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb b/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb index 50cea86b..03684b44 100644 --- a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -295,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "metadata": {}, "outputs": [ { diff --git a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb index ca810800..343fbd4d 100644 --- a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb @@ -30,22 +30,48 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_40480\\1750438778.py:2: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], "source": [ "import os\n", "import pandas as pd\n", "import numpy as np\n", - "import pvdeg \n", + "import pvdeg\n", "from pvdeg import DATA_DIR" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -75,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -163,7 +189,7 @@ "2021-03-09 14:00:00 25 39 " ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -186,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -221,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb index e6846aa6..cb4c755c 100644 --- a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -76,8 +76,8 @@ "output_type": "stream", "text": [ "Working on a Windows 10\n", - "Python version 3.11.4 | packaged by Anaconda, Inc. | (main, Jul 5 2023, 13:38:37) [MSC v.1916 64 bit (AMD64)]\n", - "pvdeg version 0.2.0+12.g277eda3.dirty\n" + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" ] } ], @@ -93,7 +93,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. NSRDB - HSDS on Eagle\n", + "## 1. NSRDB - HSDS on Kestrel\n", "\n", "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", "\n", @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -115,14 +115,20 @@ "\n", "#Latitude and Longitude\n", "weather_id = (33.448376, -112.074036)\n", - "weather_id = (39.741931, -105.169891)\n", "#weather_id = 1933572\n", "weather_arg = {'satellite': 'GOES',\n", " 'names': 2021,\n", " 'NREL_HPC': True,\n", " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n" + "# Uncomment the following when working on NREL Kestrel\n", + "\n", + "# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "\n", + "# res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + "# conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "# print(pvdeg.standards.interpret_standoff(res))\n", + "# print(meta)" ] }, { @@ -134,18 +140,6 @@ "Lets verify the weather data we fetched by running a familiar calculation; standoff distance." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", - " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", - "print(pvdeg.standards.interpret_standoff(res))\n", - "print(meta)\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -163,9 +157,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The estimated T₉₈ of an insulated-back module is 73.5°C. \n", + "The estimated T₉₈ of an open-rack module is 50.0°C. \n", + "Level 0 certification is valid for a standoff greather than 1.0 cm. \n", + "Level 1 certification is required for a standoff less than 1.0 cm. \n", + "Level 2 certification is never required for this temperature profile.\n", + "{'Source': 'NSRDB', 'Location ID': '145809', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 39.73, 'longitude': -105.18, 'altitude': 1820, 'tz': -7, 'wind_height': 2}\n" + ] + } + ], "source": [ "API_KEY = 'your_api_key_here'\n", "# The example API key here is for demonstation and is rate-limited per IP.\n", @@ -198,9 +207,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", + "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", + "The estimated T₉₈ of an open-rack module is 63.8°C. \n", + "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", + "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", + "Level 2 certification is required for a standoff less than 3.0 cm.\n", + "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" + ] + } + ], "source": [ "weather_db = 'PVGIS'\n", "#weather_id = (39.741931, -105.169891)\n", @@ -241,7 +264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb index 8ffb595b..fb830f8b 100644 --- a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb @@ -51,9 +51,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -133,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -176,8 +188,8 @@ "weather_id = (33.4484, -112.0740)\n", "weather_arg = {'api_key': NREL_API_KEY,\n", " 'email': 'user@mail.com',\n", - " 'names': '2021', # tmy is another common option; right now requesting only 2021 data \n", - " 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi', \n", + " 'names': '2021', # tmy is another common option; right now requesting only 2021 data\n", + " 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi',\n", " # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed'\n", " 'map_variables': True,\n", " 'leap_day': False}\n", @@ -188,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -616,7 +628,7 @@ "[8760 rows x 24 columns]" ] }, - "execution_count": 13, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -627,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -681,7 +693,7 @@ " 'altitude': 334}" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -692,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -918,7 +930,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -929,12 +941,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -947,7 +959,7 @@ "fig, ax1 = plt.subplots(figsize=(9, 6))\n", "# Instantiate a second axes that shares the same x-axis\n", "ax1.plot(weather_df.loc['2021-06-28 05:00:00-07:00':'2021-06-28 20:00:00-07:00']['dni_clear'], label='DNI')\n", - "ax2 = ax1.twinx() \n", + "ax2 = ax1.twinx()\n", "ax2.plot(weather_df.loc['2021-06-28 05:00:00-07:00':'2021-06-28 20:00:00-07:00']['temp_air'], 'r', label='Temperature')\n", "ax1.set_ylim([0,1000])\n", "ax2.set_ylim([0,50])\n", @@ -967,23 +979,32 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], "source": [ "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Minimum installation distance: 0 8.260315\n", + "Minimum installation distance: 0 11.12151\n", "Name: x, dtype: float64\n" ] } @@ -1011,31 +1032,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], "source": [ - "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta,\n", - " level=2,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model='isotropic',\n", - " temp_model='sapm',\n", - " module_type='glass_polymer',\n", - " x_0=6.1,\n", - " wind_speed_factor=1)\n" + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Minimum installation distance: 0 3.029357\n", + "Minimum installation distance: 0 11.12151\n", "Name: x, dtype: float64\n" ] } @@ -1054,9 +1076,9 @@ ], "metadata": { "kernelspec": { - "display_name": "pvdeg (py310)", + "display_name": "pvdeg_311", "language": "python", - "name": "py310" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1068,7 +1090,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb index 58f1cc72..58bca182 100644 --- a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,27 @@ "import os\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n" + "import matplotlib.pyplot as plt\n", + "import pvlib\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -73,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -131,16 +144,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.03495240552145925, 5.6638736096459965, 5.4659076024193585)" + "(0.03495240755084558, 5.663466529792824, 5.465514739492932)" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -193,7 +206,7 @@ " 'Lit BO-LID + fit to Qcells destab'])" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -233,9 +246,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_42092\\928104463.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps_initial = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-01 00:10:00', freq = 'S') # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], "source": [ "temperature = 50 # degrees celsius\n", "suns = 0.05 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc.\n", @@ -264,16 +286,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -282,7 +304,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -302,7 +324,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -319,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -334,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -384,11 +406,11 @@ " 0.000000\n", " 0.000000e+00\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " 0.000000\n", " \n", @@ -401,11 +423,11 @@ " 0.015634\n", " 0.000000e+00\n", " 114.980390\n", - " 41.593800\n", - " 0.666326\n", - " 10.107293\n", + " 41.590936\n", + " 0.666324\n", + " 10.106597\n", " 0.840986\n", - " 5.663833\n", + " 5.663426\n", " 0.999993\n", " 0.000012\n", " \n", @@ -418,11 +440,11 @@ " 0.031265\n", " 1.016487e-07\n", " 114.960790\n", - " 41.593739\n", - " 0.666322\n", - " 10.107279\n", + " 41.590875\n", + " 0.666321\n", + " 10.106583\n", " 0.840986\n", - " 5.663793\n", + " 5.663386\n", " 0.999986\n", " 0.000023\n", " \n", @@ -435,11 +457,11 @@ " 0.046893\n", " 3.049294e-07\n", " 114.941200\n", - " 41.593678\n", - " 0.666319\n", - " 10.107264\n", + " 41.590814\n", + " 0.666317\n", + " 10.106568\n", " 0.840985\n", - " 5.663752\n", + " 5.663345\n", " 0.999979\n", " 0.000035\n", " \n", @@ -452,11 +474,11 @@ " 0.062520\n", " 6.098257e-07\n", " 114.921620\n", - " 41.593617\n", - " 0.666316\n", - " 10.107249\n", - " 0.840985\n", - " 5.663712\n", + " 41.590753\n", + " 0.666314\n", + " 10.106553\n", + " 0.840984\n", + " 5.663305\n", " 0.999971\n", " 0.000046\n", " \n", @@ -486,11 +508,11 @@ " 82.313555\n", " 1.732221e+01\n", " 60.591179\n", - " 41.283350\n", - " 0.653104\n", - " 10.031854\n", + " 41.280510\n", + " 0.653103\n", + " 10.031164\n", " 0.838627\n", - " 5.494558\n", + " 5.494163\n", " 0.970106\n", " 0.413889\n", " \n", @@ -503,11 +525,11 @@ " 82.284870\n", " 1.735431e+01\n", " 60.601170\n", - " 41.283455\n", - " 0.653108\n", - " 10.031879\n", - " 0.838628\n", - " 5.494606\n", + " 41.280615\n", + " 0.653106\n", + " 10.031189\n", + " 0.838627\n", + " 5.494211\n", " 0.970115\n", " 0.414583\n", " \n", @@ -520,11 +542,11 @@ " 82.256164\n", " 1.738640e+01\n", " 60.611172\n", - " 41.283559\n", - " 0.653112\n", - " 10.031905\n", + " 41.280719\n", + " 0.653110\n", + " 10.031215\n", " 0.838628\n", - " 5.494654\n", + " 5.494259\n", " 0.970123\n", " 0.415278\n", " \n", @@ -537,11 +559,11 @@ " 82.227438\n", " 1.741847e+01\n", " 60.621185\n", - " 41.283664\n", - " 0.653115\n", - " 10.031930\n", + " 41.280824\n", + " 0.653113\n", + " 10.031240\n", " 0.838629\n", - " 5.494702\n", + " 5.494307\n", " 0.970132\n", " 0.415972\n", " \n", @@ -554,11 +576,11 @@ " 82.198691\n", " 1.745053e+01\n", " 60.631208\n", - " 41.283769\n", - " 0.653119\n", - " 10.031956\n", - " 0.838630\n", - " 5.494750\n", + " 41.280929\n", + " 0.653117\n", + " 10.031266\n", + " 0.838629\n", + " 5.494355\n", " 0.970140\n", " 0.416667\n", " \n", @@ -582,35 +604,35 @@ "1191 2022-01-01 10:00:00 50 0.05 0.350776 82.198691 \n", "\n", " NC tau Jsc Voc Isc FF \\\n", - "0 0.000000e+00 115.000000 41.593861 0.666329 10.107308 0.840987 \n", - "1 0.000000e+00 114.980390 41.593800 0.666326 10.107293 0.840986 \n", - "2 1.016487e-07 114.960790 41.593739 0.666322 10.107279 0.840986 \n", - "3 3.049294e-07 114.941200 41.593678 0.666319 10.107264 0.840985 \n", - "4 6.098257e-07 114.921620 41.593617 0.666316 10.107249 0.840985 \n", + "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000e+00 114.980390 41.590936 0.666324 10.106597 0.840986 \n", + "2 1.016487e-07 114.960790 41.590875 0.666321 10.106583 0.840986 \n", + "3 3.049294e-07 114.941200 41.590814 0.666317 10.106568 0.840985 \n", + "4 6.098257e-07 114.921620 41.590753 0.666314 10.106553 0.840984 \n", "... ... ... ... ... ... ... \n", - "1187 1.732221e+01 60.591179 41.283350 0.653104 10.031854 0.838627 \n", - "1188 1.735431e+01 60.601170 41.283455 0.653108 10.031879 0.838628 \n", - "1189 1.738640e+01 60.611172 41.283559 0.653112 10.031905 0.838628 \n", - "1190 1.741847e+01 60.621185 41.283664 0.653115 10.031930 0.838629 \n", - "1191 1.745053e+01 60.631208 41.283769 0.653119 10.031956 0.838630 \n", + "1187 1.732221e+01 60.591179 41.280510 0.653103 10.031164 0.838627 \n", + "1188 1.735431e+01 60.601170 41.280615 0.653106 10.031189 0.838627 \n", + "1189 1.738640e+01 60.611172 41.280719 0.653110 10.031215 0.838628 \n", + "1190 1.741847e+01 60.621185 41.280824 0.653113 10.031240 0.838629 \n", + "1191 1.745053e+01 60.631208 41.280929 0.653117 10.031266 0.838629 \n", "\n", " Pmp Pmp_norm time (days) \n", - "0 5.663874 1.000000 0.000000 \n", - "1 5.663833 0.999993 0.000012 \n", - "2 5.663793 0.999986 0.000023 \n", - "3 5.663752 0.999979 0.000035 \n", - "4 5.663712 0.999971 0.000046 \n", + "0 5.663467 1.000000 0.000000 \n", + "1 5.663426 0.999993 0.000012 \n", + "2 5.663386 0.999986 0.000023 \n", + "3 5.663345 0.999979 0.000035 \n", + "4 5.663305 0.999971 0.000046 \n", "... ... ... ... \n", - "1187 5.494558 0.970106 0.413889 \n", - "1188 5.494606 0.970115 0.414583 \n", - "1189 5.494654 0.970123 0.415278 \n", - "1190 5.494702 0.970132 0.415972 \n", - "1191 5.494750 0.970140 0.416667 \n", + "1187 5.494163 0.970106 0.413889 \n", + "1188 5.494211 0.970115 0.414583 \n", + "1189 5.494259 0.970123 0.415278 \n", + "1190 5.494307 0.970132 0.415972 \n", + "1191 5.494355 0.970140 0.416667 \n", "\n", "[1192 rows x 14 columns]" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -633,12 +655,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -694,7 +716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb index 37a08780..3ceef372 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -43,14 +43,26 @@ "import os\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import pvdeg" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -70,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -127,14 +139,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.03572222214916342\n" + "0.03495240755084558\n" ] } ], @@ -152,14 +164,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "38.681200110979546\n" + "41.59099692285122\n" ] } ], @@ -181,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -212,7 +224,7 @@ " 'Lit BO-LID + fit to Qcells destab'])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -252,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -449,7 +461,7 @@ "[30241 rows x 7 columns]" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -468,16 +480,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -486,7 +498,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -506,7 +518,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -523,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -538,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -588,11 +600,11 @@ " 0.000000\n", " 0.000000\n", " 115.000000\n", - " 38.681200\n", - " 0.664464\n", - " 9.399532\n", - " 0.840659\n", - " 5.250459\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", + " 0.840987\n", + " 5.663467\n", " 1.000000\n", " 0.000000\n", " \n", @@ -601,16 +613,16 @@ " 2022-01-01 00:01:00\n", " 75\n", " 0.1\n", - " 99.949243\n", - " 0.050757\n", + " 99.945424\n", + " 0.054576\n", " 0.000000\n", - " 114.936358\n", - " 38.681002\n", - " 0.664453\n", - " 9.399484\n", - " 0.840657\n", - " 5.250335\n", - " 0.999976\n", + " 114.931573\n", + " 41.590784\n", + " 0.666316\n", + " 10.106561\n", + " 0.840985\n", + " 5.663325\n", + " 0.999975\n", " 0.000694\n", " \n", " \n", @@ -618,16 +630,16 @@ " 2022-01-01 00:02:00\n", " 75\n", " 0.1\n", - " 99.898533\n", - " 0.101465\n", + " 99.890904\n", + " 0.109094\n", " 0.000002\n", - " 114.872848\n", - " 38.680805\n", - " 0.664442\n", - " 9.399436\n", - " 0.840655\n", - " 5.250211\n", - " 0.999953\n", + " 114.863300\n", + " 41.590572\n", + " 0.666304\n", + " 10.106509\n", + " 0.840983\n", + " 5.663184\n", + " 0.999950\n", " 0.001389\n", " \n", " \n", @@ -635,16 +647,16 @@ " 2022-01-01 00:03:00\n", " 75\n", " 0.1\n", - " 99.847871\n", - " 0.152123\n", - " 0.000005\n", - " 114.809470\n", - " 38.680607\n", - " 0.664431\n", - " 9.399388\n", - " 0.840653\n", - " 5.250087\n", - " 0.999929\n", + " 99.836439\n", + " 0.163555\n", + " 0.000006\n", + " 114.795178\n", + " 41.590359\n", + " 0.666292\n", + " 10.106457\n", + " 0.840981\n", + " 5.663043\n", + " 0.999925\n", " 0.002083\n", " \n", " \n", @@ -652,16 +664,16 @@ " 2022-01-01 00:04:00\n", " 75\n", " 0.1\n", - " 99.797257\n", - " 0.202732\n", - " 0.000011\n", - " 114.746224\n", - " 38.680410\n", - " 0.664421\n", - " 9.399340\n", - " 0.840651\n", - " 5.249964\n", - " 0.999906\n", + " 99.782028\n", + " 0.217959\n", + " 0.000012\n", + " 114.727209\n", + " 41.590147\n", + " 0.666281\n", + " 10.106406\n", + " 0.840979\n", + " 5.662902\n", + " 0.999900\n", " 0.002778\n", " \n", " \n", @@ -686,16 +698,16 @@ " 2022-01-21 23:56:00\n", " 75\n", " 0.1\n", - " 0.009655\n", - " 58.346523\n", - " 41.643822\n", - " 70.271599\n", - " 38.459016\n", - " 0.654399\n", - " 9.345541\n", - " 0.838862\n", - " 5.130235\n", - " 0.977102\n", + " 0.006066\n", + " 54.974093\n", + " 45.019841\n", + " 71.887698\n", + " 41.381392\n", + " 0.656754\n", + " 10.055678\n", + " 0.839286\n", + " 5.542735\n", + " 0.978682\n", " 20.997222\n", " \n", " \n", @@ -703,16 +715,16 @@ " 2022-01-21 23:57:00\n", " 75\n", " 0.1\n", - " 0.009652\n", - " 58.345271\n", - " 41.645077\n", - " 70.272185\n", - " 38.459021\n", - " 0.654399\n", - " 9.345542\n", - " 0.838862\n", - " 5.130237\n", - " 0.977103\n", + " 0.006065\n", + " 54.972773\n", + " 45.021162\n", + " 71.888345\n", + " 41.381397\n", + " 0.656754\n", + " 10.055679\n", + " 0.839286\n", + " 5.542738\n", + " 0.978683\n", " 20.997917\n", " \n", " \n", @@ -720,16 +732,16 @@ " 2022-01-21 23:58:00\n", " 75\n", " 0.1\n", - " 0.009650\n", - " 58.344019\n", - " 41.646331\n", - " 70.272772\n", - " 38.459026\n", - " 0.654399\n", - " 9.345543\n", - " 0.838862\n", - " 5.130240\n", - " 0.977103\n", + " 0.006064\n", + " 54.971454\n", + " 45.022482\n", + " 71.888992\n", + " 41.381402\n", + " 0.656754\n", + " 10.055681\n", + " 0.839286\n", + " 5.542740\n", + " 0.978683\n", " 20.998611\n", " \n", " \n", @@ -737,16 +749,16 @@ " 2022-01-21 23:59:00\n", " 75\n", " 0.1\n", - " 0.009648\n", - " 58.342767\n", - " 41.647585\n", - " 70.273358\n", - " 38.459030\n", - " 0.654399\n", - " 9.345544\n", - " 0.838862\n", - " 5.130242\n", - " 0.977103\n", + " 0.006063\n", + " 54.970135\n", + " 45.023802\n", + " 71.889638\n", + " 41.381407\n", + " 0.656755\n", + " 10.055682\n", + " 0.839286\n", + " 5.542743\n", + " 0.978684\n", " 20.999306\n", " \n", " \n", @@ -754,16 +766,16 @@ " 2022-01-22 00:00:00\n", " 75\n", " 0.1\n", - " 0.009646\n", - " 58.341516\n", - " 41.648839\n", - " 70.273944\n", - " 38.459035\n", - " 0.654400\n", - " 9.345545\n", - " 0.838862\n", - " 5.130244\n", - " 0.977104\n", + " 0.006061\n", + " 54.968816\n", + " 45.025123\n", + " 71.890285\n", + " 41.381411\n", + " 0.656755\n", + " 10.055683\n", + " 0.839286\n", + " 5.542745\n", + " 0.978684\n", " 21.000000\n", " \n", " \n", @@ -772,49 +784,49 @@ "" ], "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 2022-01-01 00:00:00 75 0.1 100.000000 0.000000 \\\n", - "1 2022-01-01 00:01:00 75 0.1 99.949243 0.050757 \n", - "2 2022-01-01 00:02:00 75 0.1 99.898533 0.101465 \n", - "3 2022-01-01 00:03:00 75 0.1 99.847871 0.152123 \n", - "4 2022-01-01 00:04:00 75 0.1 99.797257 0.202732 \n", + " Datetime Temperature Injection NA NB \\\n", + "0 2022-01-01 00:00:00 75 0.1 100.000000 0.000000 \n", + "1 2022-01-01 00:01:00 75 0.1 99.945424 0.054576 \n", + "2 2022-01-01 00:02:00 75 0.1 99.890904 0.109094 \n", + "3 2022-01-01 00:03:00 75 0.1 99.836439 0.163555 \n", + "4 2022-01-01 00:04:00 75 0.1 99.782028 0.217959 \n", "... ... ... ... ... ... \n", - "30236 2022-01-21 23:56:00 75 0.1 0.009655 58.346523 \n", - "30237 2022-01-21 23:57:00 75 0.1 0.009652 58.345271 \n", - "30238 2022-01-21 23:58:00 75 0.1 0.009650 58.344019 \n", - "30239 2022-01-21 23:59:00 75 0.1 0.009648 58.342767 \n", - "30240 2022-01-22 00:00:00 75 0.1 0.009646 58.341516 \n", + "30236 2022-01-21 23:56:00 75 0.1 0.006066 54.974093 \n", + "30237 2022-01-21 23:57:00 75 0.1 0.006065 54.972773 \n", + "30238 2022-01-21 23:58:00 75 0.1 0.006064 54.971454 \n", + "30239 2022-01-21 23:59:00 75 0.1 0.006063 54.970135 \n", + "30240 2022-01-22 00:00:00 75 0.1 0.006061 54.968816 \n", "\n", - " NC tau Jsc Voc Isc FF \n", - "0 0.000000 115.000000 38.681200 0.664464 9.399532 0.840659 \\\n", - "1 0.000000 114.936358 38.681002 0.664453 9.399484 0.840657 \n", - "2 0.000002 114.872848 38.680805 0.664442 9.399436 0.840655 \n", - "3 0.000005 114.809470 38.680607 0.664431 9.399388 0.840653 \n", - "4 0.000011 114.746224 38.680410 0.664421 9.399340 0.840651 \n", - "... ... ... ... ... ... ... \n", - "30236 41.643822 70.271599 38.459016 0.654399 9.345541 0.838862 \n", - "30237 41.645077 70.272185 38.459021 0.654399 9.345542 0.838862 \n", - "30238 41.646331 70.272772 38.459026 0.654399 9.345543 0.838862 \n", - "30239 41.647585 70.273358 38.459030 0.654399 9.345544 0.838862 \n", - "30240 41.648839 70.273944 38.459035 0.654400 9.345545 0.838862 \n", + " NC tau Jsc Voc Isc FF \\\n", + "0 0.000000 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000 114.931573 41.590784 0.666316 10.106561 0.840985 \n", + "2 0.000002 114.863300 41.590572 0.666304 10.106509 0.840983 \n", + "3 0.000006 114.795178 41.590359 0.666292 10.106457 0.840981 \n", + "4 0.000012 114.727209 41.590147 0.666281 10.106406 0.840979 \n", + "... ... ... ... ... ... ... \n", + "30236 45.019841 71.887698 41.381392 0.656754 10.055678 0.839286 \n", + "30237 45.021162 71.888345 41.381397 0.656754 10.055679 0.839286 \n", + "30238 45.022482 71.888992 41.381402 0.656754 10.055681 0.839286 \n", + "30239 45.023802 71.889638 41.381407 0.656755 10.055682 0.839286 \n", + "30240 45.025123 71.890285 41.381411 0.656755 10.055683 0.839286 \n", "\n", " Pmp Pmp_norm time (days) \n", - "0 5.250459 1.000000 0.000000 \n", - "1 5.250335 0.999976 0.000694 \n", - "2 5.250211 0.999953 0.001389 \n", - "3 5.250087 0.999929 0.002083 \n", - "4 5.249964 0.999906 0.002778 \n", + "0 5.663467 1.000000 0.000000 \n", + "1 5.663325 0.999975 0.000694 \n", + "2 5.663184 0.999950 0.001389 \n", + "3 5.663043 0.999925 0.002083 \n", + "4 5.662902 0.999900 0.002778 \n", "... ... ... ... \n", - "30236 5.130235 0.977102 20.997222 \n", - "30237 5.130237 0.977103 20.997917 \n", - "30238 5.130240 0.977103 20.998611 \n", - "30239 5.130242 0.977103 20.999306 \n", - "30240 5.130244 0.977104 21.000000 \n", + "30236 5.542735 0.978682 20.997222 \n", + "30237 5.542738 0.978683 20.997917 \n", + "30238 5.542740 0.978683 20.998611 \n", + "30239 5.542743 0.978684 20.999306 \n", + "30240 5.542745 0.978684 21.000000 \n", "\n", "[30241 rows x 14 columns]" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -837,12 +849,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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9lS5duhicUxSF1157jerVq1O5cmWDa2PGjKFZs2YG53x9fQ1eX758mddff50ZM2ZQrVo1PvzwQxYuXMikSZP0ZTZt2sTAgQMxNzfnpZdewt/fn8zMTPbt28fEiRM5c+YMS5cuLfTzDBs2jF9++YVx48ZRs2ZNVqxYQY8ePdi1axdt2rS573tjYmKYOXMmVatWpWHDhuzevbvQ9y+IEydOANCgQYNSqV+UvwMHDvDhhx8ybNgwHBwcHlj+xRdfZPDgwZibmxf6XsV5b0Hc71lK+95CPEokGRQVVsuWLWnZsqXBuX379pGamsqQIUNylW/bti0DBgy4b53Hjh2jS5cuvP322wCYmpryv//9T58MhoeHM3jwYKpVq8bOnTvx8PDQv/fNN9/k4sWLbNq0qdDPcuTIEX788Uc+/fRT3nnnHQB9ojlp0iQOHDhw3/d7eHhw8+ZN3N3dOXbsWK6k915xcXHs2rWLfv365Xl93bp19O7dG2tra4PzJ0+exMzMjDp16hTi6SqelJSUXM9WEd0vzoryDMbGxhgbG5f5e4urPO8txMNGuonFQ2Xt2rVoNBqef/75PK8nJSWRnZ2d7/tr1KjBv//+y7Zt2wgNDWXp0qXUrFlTf33evHkkJyezbNkyg0Qwh6+vL2PHji103L/88gvGxsa88sor+nMWFhaMGDGCgwcPcu3atfu+39zcHHd39wLf7+uvv+bZZ5/lt99+y3Vt2bJlPP/886xcuTLXtRMnTuDn54epqWmB71UU+/bto1mzZlhYWODj48O3336bq8yVK1d44403qF27NpaWllSqVImBAwfmGgOWMy4wJCSE559/HkdHx/u2tF65cgVfX1/8/f2Jioq6b5wRERGMGDECT09PzM3N8fb25vXXX9cPLRg2bBjVq1fP9b68xj/eL84HPUNERAQvv/wybm5umJubU69ePZYvX55n/RcvXtS3lNnb2zN8+HBSU1P1ZSZOnAiAt7e3fjjF/cbV5TX2riD3yu+9BX2eB33+H/Qsed37+PHjPPXUU9jZ2WFjY0Pnzp3zHEpS0OcT4lEhLYPioZGVlcXPP/9Mq1at8vwFPHz4cJKTkzE2NqZt27Z8+umnNG3a1KBM48aNGTJkCN26dQPU7tC7x979+eef1KhRg1atWpVo7MePH6dWrVrY2dkZnG/evDkAwcHBeHl5ldj93n33XY4cOcJzzz3Hpk2b6Ny5MwC//vorr776Ki+88AKvv/66wXsyMzMJDQ1l8ODBJRZHXk6dOkW3bt1wcXFhxowZZGdnM336dNzc3AzKHT16lAMHDjB48GCqVKnC5cuX+eabb+jQoQMhISFYWVkZlB84cCA1a9Zk9uzZKIqS573DwsLo1KkTTk5ObNu2DWdn53zjvHHjBs2bNyc+Pp5XXnkFPz8/IiIi+OWXX0hNTcXMzKxIz3+/OPO6FhUVxZNPPolGo2H06NG4uLjwzz//MGLECBITExk3bpxBHc8++yze3t7MmTOHoKAg/ve//+Hq6srcuXPp168f58+fZ926dXz++ef653dxcSnSs9zvXvkp6PM86PNf2Gc5c+YMbdu2xc7OjkmTJmFqasq3335Lhw4d2LNnDy1atCiR5xPioaQI8ZD4888/FUD5+uuvDc7v379f6d+/v7Js2TLljz/+UObMmaNUqlRJsbCwUIKCgvKsKywsTAkMDFSysrL05xISEhRAeeaZZ0o89nr16imdOnXKdf7MmTMKoCxZsqTAdR09elQBlO+///6+5dLS0pT27dsrNjY2yqFDh5Tt27cr5ubmSs+ePQ2eO8fx48cVQPn0008LHEtR9OnTR7GwsFCuXLmiPxcSEqIYGxsrd/9ISk1NzfXegwcPKoDyww8/6M9Nnz5dAZTnnnsuV/mca9HR0crZs2cVT09PpVmzZkpsbOwD43zppZcUIyMj5ejRo7mu6XQ6RVEUZejQoUq1atXyvW9e5+4XZ17XRowYoXh4eCgxMTEG5wcPHqzY29vrP085dbz88ssG5fr27atUqlRJ//rTTz9VACU8PDzvB7/H999/n6t8Qe+V13sL+jwF+fzf71nuvXefPn0UMzMzJSwsTF/mxo0biq2trdKuXTuD9xb0+YR4VEg3sXhorF27FlNTU5599lmD861ateKXX37h5Zdfpnfv3kyePJlDhw6h0WiYMmVKnnXVqFGDxo0bY2Jyp3E8MTERAFtb2xKPPS0tLc+B7BYWFvrrJc3CwoKNGzdSq1YtevToQZ8+fWjRogU///yzwXPnOHnyJAANGzYs8VhyaLVatmzZQp8+fahatar+fJ06dQgICDAoa2lpqf84KyuL27dv4+vri4ODA0FBQbnqfu211/K97+nTp2nfvj3Vq1dn+/btODo63jdOnU7H77//Tq9evXK1LgPFWgLnfnHee01RFDZs2ECvXr1QFIWYmBj9ERAQQEJCQq7Pxb11tG3bltu3b+u/v0tSYe9V0Ocp6c+/Vqtl69at9OnThxo1aujPe3h48Pzzz7Nv3748Yy7Lz6UQ5UmSQfFQSE5O5o8//iAgIIBKlSo9sLyvry/PPPMMu3btQqvVFugeOV24SUlJxYo1L5aWlmRkZOQ6n56err9eGuzs7Pjss8+IjY0lOTmZhQsX5nuvvGYSL126FH9/f2xtbXF1dc01QScmJoa33noLLy8v7OzsqFevHvPmzcv3cx4dHU1aWprBOM0ctWvXNnidlpbGtGnT8PLywtzcHGdnZ1xcXIiPjychISHX+729vfP9PPTq1QtbW1u2bNmSq6s+vzgTExPx9/d/YNnCul+c916Ljo4mPj6epUuX4uLiYnAMHz4cgFu3bhm85+4kG9AnvnFxcSURfrHuVdDnKenPf3R0NKmpqbm+x0D9Q0Sn0+U5brcsP5dClCcZMygeCr///nu+s4jz4+XlRWZmJikpKQVKAOzs7PD09OT06dPFCTVPHh4e+nUR73bz5k0APD09S/yeAJcuXWLIkCH4+fmRlpZG//792b9/f56TY06ePImbm5t+7N7ixYtZtmwZ69evx8/Pj+vXr7Njxw59+Vu3btG6dWs6dOjA4cOH8fT05MSJE3zwwQf6gf3F8dZbb/H9998zbtw4WrZsib29PRqNhsGDB6PT6XKVv19C3b9/f1auXMmaNWt49dVXix1bjvxaqO73B8j94rz3Ws5zvvDCCwwdOjTP99y7DFB+M2iVfMZRFkdh71XQ5ymNWIuiLD+XQpQnSQbFQ2HNmjXY2NjQu3fvAr/n0qVLWFhYYGNjU+D39OzZk6VLl3Lw4MFcy9oUR6NGjdi1axeJiYkGienhw4f110vazZs36dq1K6ampmzbto20tDTatGlDt27d2LNnD05OTgblT548adBFvGLFCsaMGaNfZsbLy4thw4bpr48ePZrGjRvz3Xff6c81bNiQjRs35huTi4sLlpaWXLhwIde10NBQg9e//PILQ4cOZf78+fpz6enpRVpg+NNPP8XExIQ33ngDW1vbfGej3x2nnZ3dA/8wcHR0zDOeK1euFDrG/OKwtbVFq9XmWnOzqEp6l5fCKOjz6HS6An3+C/osLi4uWFlZ5foeAzh37hxGRkYlOoFLiIeNdBOLCi86Oprt27fTt2/fXDNIc67f68SJE2zcuJFu3bphZFTwb/NJkyZhbW3NyJEj81x2JCwsjC+++KJwDwAMGDAArVZrsFh1RkYG33//PS1atDD4RZSamsq5c+eKtYVaXFwcAQEBJCcns337dqpUqULNmjXZsmUL165d4+mnnyYlJUVfPjIyklu3bhkkgxYWFnz99dds3Lgx1xipsLAwNmzYwMyZMwsVl7GxMQEBAfz+++9cvXpVf/7s2bNs2bIlV9l7W2AWLVpU4G7/u2k0GpYuXcqAAQMYOnTofRNWACMjI/r06cOff/7JsWPHcl3PicvHx4eEhAT9eEtQk/C8lvQpCmNjY/r378+GDRvyTIzy+t5/kJy1C8tj146CPk9BP/8FfRZjY2O6devGH3/8YbDUTFRUFGvXrqVNmzYF6j0Q4lElLYOiwvvpp5/Izs7Ot4t40KBBWFpa0qpVK1xdXQkJCWHp0qVYWVnxySefFOpePj4+rF27lkGDBlGnTh2DHUgOHDjA+vXrDVrHQE002rdvf99dQVq0aMHAgQOZMmUKt27dwtfXl5UrV3L58mWWLVtmUPbIkSN07NiR6dOnM2PGDP35xYsXEx8fz40bNwB1GZzr168Dapeqvb29vuzXX3/NtWvX2L17t8H4vEaNGvHXX3/RrVs3Vq5cyRtvvAHkPV5w3bp1zJ49m1GjRpGQkMCLL77IV199hZmZGTt27KBmzZp5jsF6kA8//JDNmzfTtm1b3njjDbKzs1m0aBH16tUzSKp69uzJqlWrsLe3p27duhw8eJDt27cXaMxoXoyMjFi9ejV9+vTh2Wef5e+//6ZTp075lp89ezZbt26lffv2vPLKK9SpU4ebN2+yfv169u3bh4ODA4MHD+bdd9+lb9++jBkzhtTUVL755htq1aqV5ySXovjkk0/YtWsXLVq0YNSoUdStW5fY2FiCgoLYvn07sbGxhaqvSZMmALz//vsMHjwYU1NTevXqVWYLXBf0eQry+S/Ms8yaNYtt27bRpk0b3njjDUxMTPj222/JyMhg3rx5ZfLsQlRY5TSLWYgCe/LJJxVXV1clOzs7z+tffPGF0rx5c8XJyUkxMTFRPDw8lBdeeEG5cOFCke95/vx5ZdSoUUr16tUVMzMzxdbWVmndurWyaNEiJT09XV8uKSlJAZTBgwc/sM60tDTlnXfeUdzd3RVzc3OlWbNmyubNm3OV27VrlwIo06dPNzhfrVo1BcjzuHdpjaysLOX06dP5xnLixAn98hyKoijz5s1TACU4ODhXWa1Wq2zdulWxsLBQ1qxZoyiKonz00UdK586dH/jM+dmzZ4/SpEkTxczMTKlRo4ayZMmSXMuxxMXFKcOHD1ecnZ0VGxsbJSAgQDl37pxSrVo1ZejQofpydy8fc6+8rqWmphosuXM/V65cUV566SXFxcVFMTc3V2rUqKG8+eabSkZGhr7M1q1bFX9/f8XMzEypXbu2snr16vsuLVPQOO8WFRWlvPnmm4qXl5diamqquLu7K507d1aWLl36wDryWt7lo48+UipXrqwYGRk9cJmZ+y0t86B75fXegj6PohTs85/fs+R176CgICUgIECxsbFRrKyslI4dOyoHDhzI9cyF+VwK8SjQKIqMhBWiqP7++2969uzJiRMnqF+/fnmHU6qqV6/O1KlTGTlyJD/88AMff/xxnmOwhMixbNkyRo4cybVr16hSpUp5hyOEyIeMGRSiGHbt2sXgwYMfuURwzpw5HD58mKysLJKTk/n4449JSUnhmWeeAdSlWhISEvj4449JTU1Fp9Nx9OhRxo8fX86Ri4rk5s2baDSaXJOVhBAVi4wZFKIYPv300/IOoVTExcUxZMgQIiMjsbOzo127dhw8eFC/1ZejoyM7duzgnXfe4fPPP0en0+Hr63vfBZXF4yMqKopffvmFJUuW0LJlyzwnfgkhKg7pJhZCCFGidu/eTY8ePWjevDnfffddnouMCyEqDkkGhRBCCCEeYzJmUAghhBDiMSbJoBBCCCHEY0wmkADZ2dkcP34cNze3Qu1WIYQQQojyo9PpiIqK4oknnsDERFKaopLPHHD8+HGaN29e3mEIIYQQogiOHDlCs2bNyjuMh5Ykg4CbmxugfjN5eHiUczRCCCGEKIibN2/SvHlz/e9xUTSSDIK+a9jDw0NWyRdCCCEeMjLEq3jksyeEEEII8RiTZFAIIYQQ4jEmyaAQQgghxGNMxgwWkE6nIzMzs7zDKHempqYYGxuXdxhCiHKm1WrJysoq7zDEI05+55QNSQYLIDMzk/DwcHQ6XXmHUiE4ODjg7u6ORqMp71CEEGVMURQiIyOJj48v71DEY0J+55Q+SQYfQFEUbt68ibGxMV5eXo/1jCVFUUhNTeXWrVsAsgyPEI+hnETQ1dUVKysr+QUtSo38zik7kgw+QHZ2NqmpqXh6emJlZVXe4ZQ7S0tLAG7duoWrq6s03wvxGNFqtfpEsFKlSuUdjngMyO+csvH4NnMVkFarBcDMzKycI6k4cpJiGS8kxOMl5/+8/GEsypL8zil9kgwWkHSF3CGfCyEeb/IzQJQl+X4rfZIMCiGEEEI8xso1Gfz333/p1asXnp6eaDQafv/9d4PriqIwbdo0PDw8sLS0pEuXLly4cMGgTGxsLEOGDMHOzg4HBwdGjBhBcnJyGT6FEEIIIR4GD8o78rJ7924aN26Mubk5vr6+rFixIleZr776iurVq2NhYUGLFi04cuSIwfX09HTefPNNKlWqhI2NDf379ycqKqqEnqr4yjUZTElJoWHDhnz11Vd5Xp83bx5ffvklS5Ys4fDhw1hbWxMQEEB6erq+zJAhQzhz5gzbtm3jr7/+4t9//+WVV14pq0eo8Nq3b49Go2HdunUG5xctWoSnp2c5RSWEEKIgOnTowLhx4/J9XRb3fJQ8KO+4V3h4OE8//TQdO3YkODiYcePGMXLkSLZs2aIv89NPPzFhwgSmT59OUFAQDRs2JCAgQD8LGmD8+PH8+eefrF+/nj179nDjxg369etX4s9XZEoFASi//fab/rVOp1Pc3d2VTz/9VH8uPj5eMTc3V9atW6coiqKEhIQogHL06FF9mX/++UfRaDRKREREge997do1BVCuXbuW61paWpoSEhKipKWlFfqZbkZFKafOnFWSk1MK/d6SoNPpFFtbW8XDw0Pp37+/wbWhQ4cqTz/9dJHqLc7nRAjx8HqY/+8PHTpUAZQ5c+YYnP/tt9+UCvSrMJf27dsrY8eO1b++ffu2kpiYWKb3zEu7du0UQAEUExMTpVatWsqaNWtKJZ77fd/d7/f3g9ybd+Rl0qRJSr169QzODRo0SAkICNC/bt68ufLmm2/qX2u1WsXT01P/vRYfH6+Ympoq69ev15c5e/asAigHDx4sdNylocKOGQwPDycyMpIuXbroz9nb29OiRQsOHjwIwMGDB3FwcKBp06b6Ml26dMHIyIjDhw/nW3dGRgaJiYn6IykpqVSeoUmTZtSvV4fA4OBSqf9BLly4QFJSElOnTuWff/4hNTVVfy0oKIgmTZqUS1xCCFEeLCwsmDt3LnFxcSVWZ1nvTOXk5IStrW2Z3vNeiqJw/Phx5syZw82bNwkNDaVly5YMHTqU8PDwcokpKSnJ4Pd6RkZGidR78OBBgzwEICAgQJ+HZGZmEhgYaFDGyMiILl266MsEBgaSlZVlUMbPz4+qVavqy5S3CpsMRkZGAuDm5mZw3s3NTX8tMjISV1dXg+smJiY4OTnpy+Rlzpw52Nvb64+6deuWcPQqO3t7AG7HxpdK/Q8SGBiIhYUFI0eOxM7Ojn/++QdQxy6cPXuWxo0bl0tcQghRHrp06YK7uztz5szJt0xGRgZjxozB1dUVCwsL2rRpw9GjR/XXO3TowOjRoxk3bhzOzs4EBAToz7/11luMGzcOR0dH3Nzc+O6770hJSWH48OHY2tri6+ur/zmcY/PmzbRp0wYHBwcqVapEz549CQsLyze+u7twL1++jEajyXV06NABULdRnTNnDt7e3lhaWtKwYUN++eUXg/pSUlJ46aWXsLGxwcPDg/nz5z/w85jT0NCmTRvc3d2pUaMGU6ZMITs7m5MnTz7w/aWhbt26Br/X7/c1LozIyMg885DExETS0tKIiYlBq9U+MFcxMzPDwcEh3zLlrcImg6VpypQpJCQk6I+QkJACv1dRFFIzswt02NqpyWBU9O0Cv+d+h6IohXrOoKAgGjRogJmZGX379tX/EDhx4gTZ2dm5ksFnn32W1q1bF+oeQgjxsDA2Nmb27NksWrSI69ev51lm0qRJbNiwgZUrVxIUFISvry8BAQHExsbqy6xcuRIzMzP279/PkiVLDM47Oztz5MgR3nrrLV5//XUGDhxIq1atCAoKolu3brz44osGvTQpKSlMmDCBY8eOsWPHDoyMjOjbt2+Btj/18vLi5s2b+uP48eNUqlSJdu3aAWrDxw8//MCSJUs4c+YM48eP54UXXmDPnj36OiZOnMiePXv4448/2Lp1K7t37yYoKOi+9w0MDESj0dCgQQP9uZzP571JUVkJCQkx+L0+ZcqUconjYVVhdyBxd3cHICoqymALmqioKBo1aqQvc/cATVB3DImNjdW/Py/m5uaYm5vrXycmJhY4rrQsLXWnbXlwQeBWTDYA7/98hE/OOxX4HvkJmRmAlVnBv2RBQUH6hK9fv37069ePjIwMgoKCcHFxwcvLS1/25MmTXLt2jYsXLxY7TiHE40H5b7uw8lDUrfD69u1Lo0aNmD59OsuWLTO4lpKSwjfffMOKFSt46qmnAPjuu+/Ytm0by5YtY+LEiQDUrFmTefPm5aq7YcOGTJ06FVAbHT755BOcnZ0ZNWoUANOmTeObb77h5MmTPPnkkwD079/foI7ly5fj4uJCSEgI/v7+930WY2Nj/e+69PR0+vTpQ8uWLZkxYwYZGRnMnj2b7du307JlSwBq1KjBvn37+Pbbb2nfvj3JycksW7aM1atX07lzZ0BNaKtUqXLf+wYFBeHt7Y2dnR0A586dY9KkSTRq1IjmzZuzYMEC3n77bU6fPk29evUAePPNN/n666+JjY3F0dGRBQsWMH/+fJydnUlNTeWrr76iW7du973v/dja2urjKUnu7u65Zv1GRUVhZ2eHpaUlxsbGGBsb51km52vj7u5OZmYm8fHxBq2Dd5cpbxW2ZdDb2xt3d3d27NihP5eYmMjhw4f139gtW7YkPj6ewMBAfZmdO3ei0+lo0aJFmcd8LyNzawB0GeWz1M3d4wI7dOiAqakpW7ZsITAwMFer4LRp05g9ezYODg7cvHmzPMIVQjxkUlNTsbGxKZejOEno3LlzWblyJWfPnjU4HxYWRlZWlkEPiampKc2bNzcom99467tbyoyNjalUqRL169fXn8tpNbu7EePChQs899xz1KhRAzs7O6pXrw7A1atXC/VML7/8MklJSaxduxYjIyMuXrxIamoqXbt2Nfi8/fDDD/pu6LCwMDIzMw1+Xzo5OVG7du373isoKIjLly9jY2ODhYUFTzzxBPXr12fz5s0YGRlx+vRp6tevT2hoqP5ZDh48SJUqVXB0dATg9OnTzJ8/nxMnTvDpp58yY8aMQj1vWWnZsqVBHgKwbds2fR5iZmZGkyZNDMrodDp27NihL9OkSRNMTU0NyoSGhnL16lV9mfJWri2DycnJBi1R4eHhBAcH4+TkRNWqVRk3bhyzZs2iZs2aeHt788EHH+Dp6UmfPn0AqFOnDt27d2fUqFEsWbKErKwsRo8ezeDBg0tt2RRLU2NCZgYUqOxb8X+z/PQOnq3vxOcFfM+D7l1Qly5dIj4+Xp/0mZiY0Lt3bzZs2MCpU6f0f/UCHD16lOTkZDp27Iifnx8hISGyIbgQ4pHVrl07AgICmDJlCsOGDSv0+62trfM8b2pqavBao9EYnMtpyby7C7hXr15Uq1aN7777Dk9PT3Q6Hf7+/oWamDJr1iy2bNnCkSNH9JNLctbb3bRpE5UrVzYof3fPWFEEBQUxceJERo4ciZWVFR4eHgattKdPn+bZZ5/VJ4MfffQRffv2Zf/+/QZlxowZA0DlypX1W7+WtgflHVOmTCEiIoIffvgBgNdee43FixczadIkXn75ZXbu3MnPP//Mpk2b9HVMmDCBoUOH0rRpU5o3b87ChQv1Y0VBnfw6YsQIJkyYgJOTE3Z2drz11lu0bNlS30Jc3so1GTx27BgdO3bUv54wYQIAQ4cOZcWKFUyaNImUlBReeeUV4uPjadOmDZs3b8bCwkL/njVr1jB69Gg6d+6MkZER/fv358svvyy1mDUaTYG7ap2d1L+AkpMSC9W9WxICAwMxMzMz6Gbo37+/frzK+++/rz8/bdo0PvroIwB9MpjTZSCEEPmxsrIqt0X+i7s/8ieffEKjRo0MWsF8fHz0YwGrVasGqPvhHj16tFTW3bt9+zahoaF89913tG3bFoB9+/YVqo4NGzYwc+ZM/vnnH3x8fPTn69ati7m5OVevXqV9+/Z5vtfHxwdTU1MOHz5M1apVAYiLi+P8+fP5vienoaFr1674+vrmuq4oCpcvX6Znz558/vnnXLp0iUuXLtGiRQv97yNFUTh37hy1a9dGq9XyzTff0KNHj0I9d1E9KO+4efOmQaust7c3mzZtYvz48XzxxRdUqVKF//3vf/qJQwCDBg0iOjqaadOmERkZSaNGjdi8ebPB+MnPP/9cn6NkZGQQEBDA119/XQZPXDDlmgx26NDhvpMiNBoNM2fOZObMmfmWcXJyYu3ataURXrE5OjoAkJiQUOb3DgoKwt/fHzMzM/25rl27otVqyczM1LcY7t+/nz179vDcc88B6l9NAwcOLPN4hRAPH41Gk28rWUVXv359hgwZYtB4YG1tzeuvv87EiRP1LUXz5s0jNTWVESNGlHgMjo6OVKpUiaVLl+Lh4cHVq1eZPHlygd9/+vRpXnrpJd59913q1aunn5lqZmaGk5MT77zzDuPHj0en09GmTRsSEhLYv38/dnZ2DB06FBsbG0aMGMHEiROpVKkSrq6uvP/++xgZ5T+CLGfyyN1Lut0tPDwcLy8v6tSpQ1hYGB999BEffPABv/32m/73Tnh4OBkZGbRs2RIzMzO6dOlSqOcujgflHXntLtKhQweOHz9+33pHjx7N6NGj871uYWHBV199VeDFrstahR0z+Chw+m9sREI5JINz5swxGEsJatdAYmIiiqLg7e0NwAcffMD27du5fPkyly9fZtOmTYWaXS2EEA+rmTNn5pq1+8knn+h7URo3bszFixfZsmWLfqxbSTIyMuLHH38kMDAQf39/xo8fz6efflrg9x87dozU1FRmzZqFh4eH/sjZ2SInEZszZ45+WNWmTZv0P/8BPv30U9q2bUuvXr3o0qULbdq0ue8atDkzrO3/WzrtXqdPn8bf3x9zc3NSUlKIiIigQ4cO+vM5ZXr16kVwcDBHjhxh9uzZxe66FsWjUQq7Xskj6Pr163h5eXHt2rVcs6jS09MJDw/H29vboHu6IDZs2MCAAQNo1LQ5xw4fwtio8DPfStPOnTuZNWsWO3fu1J+LjY2ldu3aREdH5/u+4nxOhBAPL/m/Lx5k9uzZmJqaMnHiRH766Sfq1auHv78/Hh4eXLp0CUtLS2bPnk1WVhbTp08vUJ33+7673+9vUXDSMliKcv6STExMJLsAa0aVtU6dOhkkgqB2u98vERRCCCHyc3cL4KBBg/D39+fWrVvY2tpiaWmpL3P3LGtR/irsOoOPgpxkMCkhgWytgrl8toUQQjzC8hrD7+rqyvnz5+9bRpQvaRksRTmLSyYlJpCte+x744UQQghRAUkyWIpyBtimp6WSmpZeztEIIYQQQuQmyWApuntrnNj4+PILRAghhBAiH5IMliITExNs/lsNPjYuvnyDEUIIIYTIgySDpSynqzhOkkEhhBBCVECSDJYy+/8mkUgyKIR4VMjytKIsyfdb6ZNksJQ52DsAEB8fV76BCCFEMZmamgKQmppazpGIx0nO91vO958oebLyXSnL2Z84Pj4BRVHQaCrWLiRCCFFQxsbGODg4cOvWLQCsrKzkZ5ooNYqikJqayq1bt3BwcMDY2Li8Q3pkSTJYyhwd/tuFJCEenaJgLD84hRAPMXd3dwB9QihEaXNwcNB/34nSIclgKXNwUCeQJCUmkKVVMJaOeSHEQ0yj0eDh4YGrqytZWVnlHY54xJmamkqLYBmQZLCU5exCkpiobklHGQ95aN++Pf/++y9r167lueee059ftGgRc+bM4caNG2UbkBDikWBsbCy/pIV4REg7VSnLSQaTkxLJ0unK9N6KonD8+HE8PDzYsGGDwbXAwEAaN25cpvEIIYQQouKRZLCUGexPrC3bZPDChQskJSUxdepU/vnnH4MZgEFBQTRp0qRM4xFCCCFExSPJYFFlpuR/ZN3Zh1ifDMbHkZ2enEfZtILVWwSBgYFYWFgwcuRI7Ozs+OeffwBIT0/n7Nmz0jIohBBCCBkzWGSzPfO/VrMbDFkP3EkGs64ew+Mrn9xlq7WB4ZvuvF5YH1Jv5y43I6HQIQYFBdGgQQPMzMzo27cvv/zyC/379+fEiRNkZ2frk8EFCxawYMECnJ2dSUhIoEePHnz11VeFvp8QQgghHj7SMljKcpLBuLSyX0E9KChIn/D169ePTZs2kZGRQVBQEC4uLnh5eQFw+vRpvvjiC4KDgwkJCWHFihUkJyeXebxCCCGEKHvSMlhU791nFq7mzgw7JycnAG5nWxD6cii13W3vKXtPPj7uVElFSFBQkH4GcYcOHTA1NWXLli25Jo+cPn2a999/H4Dg4GB8fHywsbEpsTiEEEIIUXFJMlhUZtYFKpaTDKanpZGUpUExfcCK/QWs90EuXbpEfHy8PukzMTGhd+/ebNiwgVOnTvHUU08B6ozj0NBQ+vfvT2pqKvHx8ezbt69EYhBCCCFExSfdxKXMzs5OvxZXfFwsWl3ZdBcHBgZiZmaGv7+//lz//v3ZuHEjZ86c0SeJ4eHh1KlTh+DgYM6fP8+YMWP4/PPPyyRGIYQQQpQ/SQZLmUajwdFR3ZIuIT6OrDJaXiYoKAh/f3/MzMz057p27YpWqyUzM1OfDJ4+fZratWvry9SvX5+oqKgyiVEIIYQQ5U+SwTKQ01WcGB9HlrZsWgbnzJlDYGCgwTlzc3MSExNRFAVvb2/AMBnUarWsW7eOzp07l0mMQgghhCh/MmawDFSqVAmAhPj4Mt+F5EFOnz7Nvn37WL9+PRqNhqeeeorXX3+9vMMSQgghRBmRZLAM5LQMJsTHqfsTVyBr164t7xCEEEIIUY6km7gMGHYTV6yWQSGEEEI83iQZLAN3twyW1ZhBIYQQQoiCkGSwDOiTwYQ4sqVlUAghhBAViCSDZUCfDMZJy6AQQgghKhZJBsuAwQQSnQ6dIgmhEEIIISoGSQbLQM7SMonxcQDSVSyEEEKICkOSwTJwp2UwHkC6ioUQQghRYUgyWAbuXloGkOVlhBBCCFFhSDJYBnKSwZSUZLIyMyUZFEIIIUSFIclgGbC3t0ej0QCQmBAv3cRCCCGEqDAkGSwDRkZGODo6AhAfF0tmdtm1DLZv3x6NRsO6desMzi9atAhPT88yi0MIIYQQFZMkg2UkZ0ZxQhluSacoCsePH8fDw4MNGzYYXAsMDKRx48ZlEocQQgghKi5JBsuI4f7EZdNNfOHCBZKSkpg6dSr//PMPqamp+mtBQUE0adKkTOIQQgghRMUlyWARpWalFuqwd7QH1OVlsnU6MrOzSM1KJT07vUD1FkVgYCAWFhaMHDkSOzs7/vnnHwDS09M5e/astAwKIYQQApPyDuBh1WJti0KVv5ZwDYDEBHV5mW1XtjN53ySaujXl++7f68t139CduIy4XO8/NfRUoWMMCgqiQYMGmJmZ0bdvX3755Rf69+/PiRMnyM7ONkgGV6xYwaJFi9Bqteh0Ot5++22GDh1a6HsKIYQQ4uEiLYNlxNjaGIDkhHgAsnWl31UcFBSkT/j69evHpk2byMjIICgoCBcXF7y8vAD49ttvWb58OVu2bCE4OJhdu3ahyJZ5QgghxGNBWgaL6PDzhwtV/uOLHzN7+2x9y2BL9w4cfv4wRhrDfHxz/80lFmNQUBDPPfccAB06dMDU1JQtW7YYTB6Ji4tj6tSpnDhxAmdnZ0Cd7DJs2LASi0MIIYSoKL766is+/fRTIiMjadiwIYsWLaJ58+Z5ls3KymLOnDmsXLmSiIgIateuzdy5c+nevbu+TFJSEh988AG//fYbt27d4oknnuCLL76gWbNm+jLDhg1j5cqVBnUHBASweXPJ/c4vDkkGi8jK1KpQ5T3cPABIiI0FQFGMsDK1KHa9+bl06RLx8fH6pM/ExITevXuzYcMGTp06xVNPPQXAb7/9RseOHWWZGSGEEI+8n376iQkTJrBkyRJatGjBwoULCQgIIDQ0FFdX11zlp06dyurVq/nuu+/w8/Njy5Yt9O3blwMHDvDEE08AMHLkSE6fPs2qVavw9PRk9erVdOnShZCQECpXrqyvq3v37nz//Z1hYebm5qX/wAUk3cRlJKfVLS7uNgBZpbzWYGBgIGZmZvj7++vP9e/fn40bN3LmzBl9knj69GkaNWpUqrEIIYQQFcGCBQsYNWoUw4cPp27duixZsgQrKyuWL1+eZ/lVq1bx3nvv0aNHD2rUqMHrr79Ojx49mD9/PgBpaWls2LCBefPm0a5dO3x9fZkxYwa+vr588803BnWZm5vj7u6uP3LWH64IJBksIy4uLgDExsQAkFnKaw0GBQXh7++PmZmZ/lzXrl3RarVkZmbqk0Fra2t0OtkeTwghxKMtMzOTwMBAunTpoj9nZGREly5dOHjwYJ7vycjIwMLCsBfP0tKSffv2AZCdnY1Wq71vmRy7d+/G1dWV2rVr8/rrr3P79u2SeKwSIclgGclJBm/fVpPB0l5rcM6cOQQGBhqcMzc3JzExEUVR8Pb2BuCpp55i7dq1+m/KxMREVq9eXaqxCSGEECUpKSmJxMRE/ZGRkZGrTExMDFqtFjc3N4Pzbm5uREZG5llvQEAACxYs4MKFC+h0OrZt28avv/7KzZs3AbC1taVly5Z89NFH3LhxA61Wy+rVqzl48KC+DKhdxD/88AM7duxg7ty57Nmzh6eeegqtVluCn4WikzGDZSSnmzj29m10Oh1ZWg2Kouj3LC4vrVq1YsKECXTs2FEfz/jx48s1pkdCZgqkJ0JmMmQkQlY6KFrQZYNOB76dIedrf+scpN4GE3MwNgVjc7CwA3M7MLO+U04IIUSe6tata/B6+vTpzJgxo9j1fvHFF4waNQo/Pz80Gg0+Pj4MHz7coFt51apVvPzyy1SuXBljY2MaN27Mc889Z9AgM3jwYP3H9evXp0GDBvj4+LB79246d+5c7DiLS5LBMpKTDGq1WpISErB3dESrUzAxLv9f9CNHjmTkyJHlHcbDITsT4q9A7CX1SIyA5FuQlQqD7mpR/fkluLg9/3qmxYJGXW6IvZ/BqfV5l9MYwzvnwVr9/iFwBVw9DDauYOOm/mvrrn5sXwVMLUvkMYUQ4mFy72SNvCZnODs7Y2xsTFRUlMH5qKgo3N3d86zXxcWF33//nfT0dG7fvo2npyeTJ0+mRo0a+jI+Pj7s2bOHlJQUEhMT8fDwYNCgQQZl7lWjRg2cnZ25ePGiJIOPEzMzM+zt7UlISCAxLhZ7R0eytDpMjKWnvkJSFEi8AfZ3friwfhiE/AFKPmMsszPB5L8xmua2oDFS/zW3U1v9jEzUQ2MEOi0Y/ZcMWrtCJV/QZoI2C7LSICNJbUlUtGodOS7vyz9xBBh/Rk0KQU1G46+CQ1Vw8lH/zbmnEEI8QmxtbbGzs7tvGTMzM5o0acKOHTvo06cPADqdjh07djB69Oj7vtfCwoLKlSuTlZXFhg0bePbZZ3OVsba2xtramri4OLZs2cK8efPyre/69evcvn0bDw+PBz9cGZBksAw5OzuTkJBAUsJtwIdMrYK041QQ2Rlw4zhc2Q9XDsC1o5CVAu/dUBM5AFNrNRE0tQanGuDkDfZeYOumtsxx1zjQvkthwPcF6+LtPls97qYoamtjesKd+wM0HAyuddXWyOSoO/8m3YTsdLC566/b4HVw+pc7r43NoZIPONeESjWhzXgwtyn0p0oIIR5WEyZMYOjQoTRt2pTmzZuzcOFCUlJSGD58OAAvvfQSlStXZs6cOQAcPnyYiIgIGjVqREREBDNmzECn0zFp0iR9nVu2bEFRFGrXrs3FixeZOHEifn5++jqTk5P58MMP6d+/P+7u7oSFhTFp0iR8fX0JCAgo+09CHiQZLEMuLi6EhYWRGK+uNZhVyjOKRQGEbIRjy+DqITWZupuxGcRdBpfa6uuO70HnD9TE70FJnonZ/a8/iEajjhc0szY879tFPe6lKGriaHzXf+nKjdUxi3H/dWtrM+BWiHoYmUCHyXfKbv8Qos+BWz1w81cPJ29pSRRCPFIGDRpEdHQ006ZNIzIykkaNGrF582b9pJKrV69iZHSnxy49PZ2pU6dy6dIlbGxs6NGjB6tWrcLBwUFfJiEhgSlTpnD9+nWcnJzo378/H3/8MaampgAYGxtz8uRJVq5cSXx8PJ6ennTr1o2PPvqowqw1KMlgGcqZUZwYq87czSzltQbFPRQFIk+qXbI5SVZsGFzarX5s7QJVW0K11lD1STUxMja98/67u4wrGo0GLB0Mz7V8Uz1A7ZaOvwq3L0LMeUiLN3y2sJ1wMxhC/75zztQKXOuAe314+nMwkiENQoiH3+jRo/PtFt69e7fB6/bt2xMSEnLf+p599tk8u41zWFpasmXLlkLHWZYkGSygktirV7/w9EOeDD50+xbHhqvj7E6tVxOhfv+DBgPVa3V6g5Ep1OwKzrUe3Zm7RsZqS5+Tt/qs93pqLtw8AZGnIOoM3DqrdlNHBEJanGEiuOlttbvcszF4PgEufoYtkkIIIR4q8hP8AYyN1W6yzMxMLC2LN8Ivp2UwIU7tJi7thadLS2pqKoC+CbxC0mbD+c1qF3DYzjvnTSwg8fqd15V8oNX9Bw4/Fqo+qR45dFq1aznqtPq51J/Xwcmf1eVy+G9pBVMrcG+gdkt7t4PaT5Vp6EIIIYqnQieDWq2WGTNmsHr1aiIjI/H09GTYsGFMnTpVvz6foihMnz6d7777jvj4eFq3bs0333xDzZo1SyQGExMTrKysiI6OxtTU1GAsQWHljDGIvhWFkp1JhlZDWppJua81WFCKopCamsqtW7dwcHDQJ8oVTlo8fNNKXfYFAA3U6AANngW/nuoafuL+jIzViSbO9/w/UnTQ+0uICFIn3NwIhswkuHZIPW6dNUwGg9eq3e2u9aT1UAghKqgK/dN57ty5fPPNN6xcuZJ69epx7Ngxhg8fjr29PWPGjAFg3rx5fPnll6xcuRJvb28++OADAgICCAkJybU9TFFoNBo8PDwIDw/nypUrxaorp3s14vo1bt1QW6eMki0wMno4ksEcDg4O+a7JVG4yU8HMSv3Y0gEcvdUJIU+8AE2Gq92joviMTaBeX/UAtaXw9kW4EQTXj4H7nb2wSb4Fv7+ufmxmA5WbgFeL/47mkpQLIUQFUaGTwQMHDvDMM8/w9NNPA1C9enXWrVvHkSNHADW5WrhwIVOnTuWZZ54B4IcffsDNzY3ff//dYMXv4jAzM6NmzZpkZmYWq546deoA6sbWXxyJJy4lk2+GNMHH3fYB76w4TE1NK1aLYEoM/PsZnFgHo4+BjdoVT99v1Fm/JhVjptYjy8gIXGqpR8N7/r+lJ4JPZ7h+VO1WDt+jHqCutdhhCrSflLtOIYQQZapCJ4OtWrVi6dKlnD9/nlq1anHixAn27dvHggULAAgPDycyMtJg02l7e3tatGjBwYMH800GMzIyDPYtTEpKemAsRkZGxW5pzFkdPSYmhmpm5kREpnE9KZsG1YvfgvnYyUyBg1/D/i/UbkqAkN+h+Sj1Y4eq5Raa+I+zL7z4q9p6GH3uv67kI3D1oLpkz91foxvH4c+xUL2tOpu7WkuwdCy30IUQ4nFSoZPByZMnk5iYiJ+fH8bGxmi1Wj7++GOGDBkCoN9YujCbTgPMmTOHDz/8sPQCz0fObOLo6Gi8nKwIuhrPtdjUMo/joaYocPIn2DZNXWwZwKMhdJkBNTqWa2giH0ZG4FZXPZq+rJ5LiDDcWSV8rzqb+eYJOLgY0KhdztXbQvU26r/SrSyEEKWiQMnghAkTCl3x1KlTcXJyKvT77vbzzz+zZs0a1q5dS7169QgODmbcuHF4enoydOjQItc7ZcoUg2eKiIjItcl1aciZTZyWlobrfxOTr8VJMlhgOi2s7ndnXUDH6tDpA6jXT9bAe9jcu2Zjw8Fg5wmX98Ll/XD7grrMTeQpOPQ1DNukJoUAqbHqOpEyBEAIIUpEgZLBhQsX0rJlS8zMCrarwr59+xg9enSxk8GJEycyefJkfXdv/fr1uXLlCnPmzGHo0KH6SQxRUVEG+/tFRUXRqFGjfOs1Nzc3WPU7MTGxWHEWlI2NDebm5mRkZGBHGgDXYtPK5N6PBCNjcK4NVw9D+4nQcrQkBI8KG1eoP0A9AJIi1a0BL++H60egSrM7ZffMhcCValdyjQ7q4VZf/iAQQogiKnA38W+//Yarq2uBytralsyEiNTU1FxLuRgbG6PTqevzeXt74+7uzo4dO/TJX2JiIocPH+b1118vkRhKkkajwdnZmYiICKx0aovgdWkZvL/I02p3omM19XWn96HlG2qroHh02bqDf3/1uFfkachOU9ePzFlD0qqSusZhjQ7wxIuyjZ4QQhRCgZLB77//Hnt7+wJX+u233+Yax1cUvXr14uOPP6Zq1arUq1eP48ePs2DBAl5+WR13pNFoGDduHLNmzaJmzZr6pWU8PT3p06dPse9fGlxcXIiIiMAkMwkw43pcGoqiPDRrDZYZnRYOfAk7P1ZbhYb9pf6Ct7BXD/H4GvaXup5h+B51yMDlfZB6G878BtcDofFdQ0iuB6prJcp4QyGEyFeBksHCjs97/vnnixTMvRYtWsQHH3zAG2+8wa1bt/D09OTVV19l2rRp+jKTJk0iJSWFV155hfj4eNq0acPmzZtLZI3B0pAzblCXnoiRxpmMbB3RSRm42lXMeMtFcjRsGHFnGRJLR3VrNPOHZwkeUYo0mjsTUp58HbRZ6rZ5l3aDqeWdLQW12eoY08xkdW1D387g20W6lIUQ4h4apRgbzZ4+fZo9e/ag1Wpp3bo1TZo0KcnYysz169fx8vLi2rVrVKlSpVTvNWTIENauXctnn33Gr1lPEBGfxobXW9KkWvHGVz4yrh6C9cMg6aa6zVmPT6HRkEd3z2BReuKvwg99IDbM8Ly1K/h0Unek8e1cLqEJIUpGWf7+fpQV+c/jr776is6dO7Nnzx527dpFp06d+Pjjj0sytkfS3ZNevJzUKcUyiQR1yZhD38D3PdRE0LkWjNql7iAiiaAoCoeqMCYIxgTD0/Ohdg8wtYaUW3DyR/UPjxwZyerru/dhFkKIx0SBJ5Bcu3YNLy8v/evFixdz5swZ/dp5Bw8epHfv3rz//vslH+UjJGcsZWRkJF7NrDhErKw1CJCVBseWg6JVJw30+hLMbco7KvEocPIGp5HQbCRkZ8K1w3BxO9TpeafMpd3w0xAwt4ca7dXuZN8uuZfAEUKIR1CBk8EuXbrwxhtvMGbMGDQaDZUqVWLz5s0MHDiQzMxMtm/frh8PJ/KXkwxGRUXR1EndS1fWGkTdV/j5n+HCNnUXEWkNFKXBxAy826rH3VJvq2NT0+Lg7Eb1AHDzh5rd1ERSEkMhxCOqwN3ER48eJTQ0lBYtWhAcHMzSpUv5/PPPsbS0xMHBgZ9++omVK1eWZqyPhJxu4sjISKpVUpPBy7cf02QwIQJOrr/z2skbWrwiiaAoe02GwsQwGLlD3TO5SjNAA1GnYd8C0N7ZvpLYS+rC10II8YgocMugnZ0dX3/9NQcOHGDYsGF06tSJvXv3otVq0Wq1ODg4lGKYj467WwarVbIG4MrtlPIMqXzcDlMH9ydeV1tkanZ54FuEKFVGxlClqXp0mAwptyFsh7pvslONO+W2fgChf6sJY81uUCtAbUGUP2KEEA+pQk8gadWqFceOHcPR0ZEnnniCf//9VxLBQshpGYyOjqaqg7qcTFRiBqmZj9HA9agQ+P4pSLgKjt7gUqu8IxIiN+tK6ozj7nPunFMUSIkGRaeOPdz5ESxpA5/Xgz/Hwvkt5RevEEIUUYGTwezsbL7++mveeustVqxYwXvvvceff/7J/PnzGThwIFFRUaUZ5yPD2dkZjUaDTqcjKzUBBytTAK48Ll3FN47Dih6QHAWu9eDlzeqsTyEeBhoNjNgK407D0wugVncwsYTECAhcAXsXGJZPvFEuYQohRGEUOBkcMWIEixcvxtramu+//57x48dTq1Ytdu7cSffu3WnZsiXffPNNacb6SDAxMdHPwI6KiqL649RVHHUGVvVVB+lXbqruJGFTsC0OhahQHLyg2Qh4/id4NxyG/ALNX4FGz90pkxqrthguagKb34NLe9QFsoUQooIp8JjBP/74g4MHD1KnTh1SU1OpX78+X375JaAmir169WLcuHEVck/gisbd3Z3o6GgiIyOpXsmF4GvxhMc84i2DiTfhh2fuJIIv/iZbhIlHg6kl1OyqHne7GQwaI7h9UT0OfaUuXePbGWo/pZa3dCyXkIUQ4m4Fbhl0c3Nj69atZGZmsnPnTipVqmRw3dXVlbVr15Z4gI+ix3ISia07+A8A9/rwwi+SCIpHn08nmHQJBq6Ahs+DlTNkJMCZX+HXURDyx52yOm25hSmEEAVuGVy8eDFDhgxhwoQJeHh48PPPP5dmXI+0u5eXqemvJoOXH/VkUKNRB+Jnpshi0uLxYWEP9fqqh06r7qEc+o860aRW9zvljnwHR5aqLYa1nwKvJ8G4wD+ehRCiWAr806Zr165ERUURExMji0sX090tg11y1hp8FLuJs9LgwCJoPRZMzNWEUBJB8bgyMgav5urRZbrhtQtb1T2UDy5WDwsHtRu59lPqTigW9uUSshDi8VCoPz01Go0kgiXg7mTQ21ltGYxMTCctU4ulmXF5hlZydFq1K+zsnxB5EgatLu+IhKi4Bq5Q1zQM3awmhmmxcGq9ephaq93NphblHaUQ4hFVoDGDjRs3Ji4ursCVtmnThoiIiCIH9ai7u5vYwcoMe8v/lpeJfYS6irdNUxNBYzN48o3yjkaIis3CTu1K7vctTLwIwzerLerOtaFqC8NEcP1w9f/XlYMy1lAIUSIK1DIYHBzMiRMncHJyKlClwcHBZGRkPLjgY+rulkGA6pWsOHE9gcsxqfi5PwITK4LXqV1dAH2XQLVW5RuPEA8TI2Oo1lI9us6EzLuGkCTeUCegAOz/Aiyd1B1QanVXJ6zIxCwhRBEUuJu4c+fOKIpSoLIa2Zbpvu5uGQSo7mzNiesJj8aM4hvH4a9x6sftJ4N//3INR4iHnpnVnY8t7GHAcnUSyoVtanfyiXXqYWQKHadA27fLL1YhxEOpQMlgeHh4oSuuUqVKod/zuMhpGYyJiSE7O1u/vMxDP6M4JQZ+ehGy09WWivbvlndEQjxazKzVP7D8+4M2G64dUhPD0H/UCSgO1e6UjbkAwWuhdg+o3ASMCr37qBDiMVGgZLBatWoPLiQKzNnZGSMjI3Q6HdHR0Xg7PyIzim9fhPREcPKBvt/KLx8hSpOxCVRvox4BH6vJn637netnN8K+Beph7aJ2J9fuATU6qEmlEEL8RxayKgfGxsa4uLgQFRX138LTarL90LcMVn0SXtkFumywdCjvaIR4vDjXNHzt+QTU6wcXt0NKNBxfrR7G5mpC2Gsh2HmWR6RCiApGksFy4ubmRlRUFJGRkbSoWReAmwnppGZmY2X2kH1ZFEVdQxCgkk/5xiKEUPl0Uo/sTLh64E53cvwVuLIfrO7aRSpsp/ravcGd/8tCiMfGQ5Z1PDo8PT05efIkN2/exNHajErWZtxOyeRSdAr+lR+iBWbT4mBVP+j4Xu69WYUQ5c/ETG0JrNEBun8Ct87C7QvqQvA5/p6knrOrrI73rf0UVG8raxsK8ZiQQV3lxNNT7Z7JWY/Rx0XdmSMsOrncYio0RYE/x8KNIPhnktoCIYSouDQacKsLdZ+5cy4zFVxqg6kVJEbAsWWwZgDMqwE/DjHcQ1kI8UgqdDJ47do1rl+/rn995MgRxo0bx9KlS0s0sEddTjJ448YNAHxc1QHdYdEP0bjBU+vVXxRGJtD/f2oLhBDi4WJmBYPXqLucPP8zNBkOth6QlQLn/lLHHObQ6SA6VP1DUAjxyCh0N/Hzzz/PK6+8wosvvkhkZCRdu3alXr16rFmzhsjISKZNm1YacT5yKleuDNyVDD5sLYNJUfD3RPXj9pPVpSuEEA8vU8v/FrAOUJO9m8HqGEPv9nfKRATCsi7g6K3OTK7dHaq2BGPTcgtbCFF8hW4ZPH36NM2bNwfg559/xt/fnwMHDrBmzRpWrFhR0vE9svLtJr71ECSDigJ/jYf0ePBoCG3GlXdEQoiSpNGos5E7vgfVW985H3Ne3WIyLhwOfQUre8GnPvDLCDj1i7q0lBDioVPoZDArKwtzc3Xg8fbt2+nduzcAfn5+3Lx5s2Sje4Tl6ib+Lxm8FJOCVlfBu2BO/QKhm9QdD575WloFhHhcPDFE7U5+dhU0GqLOQE5PgNO/wIYREHXmTlltVvnFKYQolEJ3E9erV48lS5bw9NNPs23bNj766CNATWoqVar0gHeLHDndxJGRkWi1Wio7WmJmYkRmto6IuDSqVrJ6QA3l6Nph9d/2k8Ddv3xjEUKULXNbqNtbPXRauH4MQv+Gq4fAq/mdcv+8C1cOqF3J+l1QjMsvbiFEvgqdDM6dO5e+ffvy6aefMnToUBo2bAjAxo0b9d3H4sFcXV31u5BERUXh6elJDWdrzkUmERadXLGTwac/U5ee8G5X3pEIIcqTkTFUbaEed1MUuLgN4q9C9FnY9zlYOf+3bE13qNERzG3KJ2YhRC6FTgY7dOhATEwMiYmJODo66s+/8sorWFlV4ASmgjE2Nsbd3Z0bN25w48YNPD098XGx0SeDHf1cyzvE+/PtXN4RCCEqKo0GXv0XLu5QJ6Fc2AapMRC8Wj1c/ODNw+UdpRDiP0VaZ1BRFAIDA/n2229JSkoCwMzMTJLBQso9ozhneZkKOIlEmwVbP4BEGRcqhCgAS0eoPwAGLINJYTD0T3jyDXCsru6MkiM7E77vAbvmwI1gWbZGiHJQ6JbBK1eu0L17d65evUpGRgZdu3bF1taWuXPnkpGRwZIlS0ojzkdSrhnFrjkziivgWoOHl8CBL9V1Bcccl7E/QoiCMzZVh5V4t4OA2ZCdcefa1QPq9nhX9sOeT8DWU+1KrvXfUBTZBUWIUlfolsGxY8fStGlT4uLisLS01J/v27cvO3bsKNHgHnX5zSiucC2DiTdh9yfqx+0mSiIohCg6jcYwwXNvAH2+gTq9wNQakm7AseWwdiDM81ZXLxCiBH311VdUr14dCwsLWrRowZEjR/Itm5WVxcyZM/Hx8cHCwoKGDRuyefNmgzJJSUmMGzeOatWqYWlpSatWrTh69KhBGUVRmDZtGh4eHlhaWtKlSxcuXLhQKs9XFIVOBvfu3cvUqVMxMzPcbaJ69er6Fi5RMPd2E9f4r5v4dkomcSkVaGu3XbMgMxmqNFOXkxBCiJJi5QSNnodBq9Vla4ZsgKYj1H2Ss1LBudadsuH/wt75EBUi3cmiSH766ScmTJjA9OnTCQoKomHDhgQEBHDr1q08y0+dOpVvv/2WRYsWERISwmuvvUbfvn05fvy4vszIkSPZtm0bq1at4tSpU3Tr1o0uXboY5ETz5s3jyy+/ZMmSJRw+fBhra2sCAgJIT08v9WcuEKWQHBwclDNnziiKoig2NjZKWFiYoiiKsnfvXsXV1bWw1VUI165dUwDl2rVrZXrf5cuXK4ASEBCgP9dqzg6l2rt/Kccu3y7TWPJ185SiTLdXlOl2inL1SHlHI4R4XOh0inLjhPpvjg2vqD+Lptspyuf1FeXvSYoStktRsjPLLUxRvgr7+7t58+bKm2++qX+t1WoVT09PZc6cOXmW9/DwUBYvXmxwrl+/fsqQIUMURVGU1NRUxdjYWPnrr78MyjRu3Fh5//33FUVRFJ1Op7i7uyuffvqp/np8fLxibm6urFu3rkBxl7ZCtwx269aNhQsX6l9rNBqSk5OZPn06PXr0KLEk9XFwb8sg3GkdvBBVAbqKFQW2TgUUqNcXvJqVd0RCiMeFRgMeDdR/c/h0gprdwNgc4q+oY5l/eAbm1YD1w9XJKOKxlJSURGJiov7IyMjIVSYzM5PAwEC6dOmiP2dkZESXLl04ePBgnvVmZGRgYWE4btXS0pJ9+/YBkJ2djVarvW+Z8PBwIiMjDe5rb29PixYt8r1vWSt0Mjh//nz2799P3bp1SU9P5/nnn9d3Ec+dO7c0Ynxk3TtmEKC2my0AoVFJ5RKTgYs74NIudfupztPLOxohxOOu4SAYsh7eDYdBa+CJF8DaBTIS1a3yTO4avhSyEW6HlV+sokzVrVsXe3t7/TFnzpxcZWJiYtBqtbi5uRmcd3NzIzIyMs96AwICWLBgARcuXECn07Ft2zZ+/fVX/Y5rtra2tGzZko8++ogbN26g1WpZvXo1Bw8e1JfJqbsw9y1rhZ5NXKVKFU6cOMGPP/7IyZMnSU5OZsSIEQwZMsRgQol4sJxk8Pbt26Snp2NhYUEtdzUZPF8RksEqTaH1WDAyASfv8o5GCCFUZtZQp6d66HQQEQiZd/3MzEyBDSNBm6GOOaz13y4oXs1lAtwjKiQkRN/bBui3zS2uL774glGjRuHn54dGo8HHx4fhw4ezfPlyfZlVq1bx8ssvU7lyZYyNjWncuDHPPfccgYGBJRJDWSh0MghgYmLCCy+8UNKxPHYcHR2xsLAgPT2dmzdv4u3tfadlMLICdBNbOkDXmeUdhRBC5M/IKPcQlpRoqPqkulxNzHn1OPAlWDpBrQC1RbF6m/KJV5QKW1tb7Ozs7lvG2dkZY2NjoqKiDM5HRUXh7u6e53tcXFz4/fffSU9P5/bt23h6ejJ58mRq1KihL+Pj48OePXtISUkhMTERDw8PBg0apC+TU3dUVBQeHh4G923UqFFRHrfEFToZ3LhxY57nNRoNFhYW+Pr64u0trUgFodFo8PT05NKlS0RERODt7U1NN3V5mZjkDG4nZ1DJpmT+uimUnFl6d4/VEUKIh4VjdRi6EdLiISxnF5StkBYLJ9aBm/+dZDA9EdITwMGrPCMWZcDMzIwmTZqwY8cO+vTpA4BOp2PHjh2MHj36vu+1sLCgcuXKZGVlsWHDBp599tlcZaytrbG2tiYuLo4tW7Ywb948ALy9vXF3d2fHjh365C8xMZHDhw/z+uuvl+gzFlWhk8E+ffqg0WhQ7pnWn3NOo9HQpk0bfv/9d4Pt6kTevLy8uHTpEteuXQPAysyEqk5WXI1N5XxUMi3LIxk8vgpO/ASdp+Xec1QIIR4Wlg7g3189tFlw9RCc3wx+T98pc3Yj/PEmuNVXF7uu/RR4PKG2OIpHzoQJExg6dChNmzalefPmLFy4kJSUFIYPHw7ASy+9ROXKlfVjDg8fPkxERASNGjUiIiKCGTNmoNPpmDRpkr7OLVu2oCgKtWvX5uLFi0ycOBE/Pz99nRqNhnHjxjFr1ixq1qyJt7c3H3zwAZ6envqktLwV+rt927ZtNGvWjG3btpGQkEBCQgLbtm2jRYsW/PXXX/z777/cvn2bd955pzTifeR4eal/jeYkgwC13Mpx3GB2Juz5FK7sg4hjZX9/IYQoDcam4N0WAj42HAMdewk0RhB1Cv79FL7rBAv8YOMYtUUxO/esVPHwGjRoEJ999hnTpk2jUaNGBAcHs3nzZv3kjqtXr+onfgCkp6czdepU6tatS9++falcuTL79u3DwcFBXyYhIYE333wTPz8/XnrpJdq0acOWLVswNTXVl5k0aRJvvfUWr7zyCs2aNSM5OZnNmzfnmoVcXjTKvU18D+Dv78/SpUtp1aqVwfn9+/fzyiuvcObMGbZv387LL7/M1atXSzTY0nL9+nW8vLy4du0aVapUKdN7v//++8yePZs333yTxYsXA/DplnN8tSuM51tUZXbf+mUaD0f/B5veBht3GBsMpjIpSAjxiEu5rXYjh/4NYTvVRfYB0MDEi2DtrL7UZqlJpagwyvP396Ok0N3EYWFheQ7StLOz49KlSwDUrFmTmJiY4kf3GLhvy2BkGbcMZmfAv/PVj9u+LYmgEOLxYF0JGj2nHtkZcHkvhG6G1Jg7iSDAqr6QlfZfd3IPcK0rY6tFmdu+fTvTp09Hp9PRo0cPJk+ebNAKWRSF7iZu0qQJEydOJDo6Wn8uOjqaSZMm0ayZOqPrwoUL+iRH3F/VqlUBDFpRa7vfWWuwkA23xXNinbovqK0nNBladvcVQoiKwsQcfLvA05/BwBV3zqcnwtWD6vCZnbPgm1bwRQP4exKE7ZIFr0WZefPNN5k0aRKLFi3i8uXLfPjhh8Wus9DJ4LJlywgPD6dKlSr4+vri6+tLlSpVuHz5Mv/73/8ASE5OZurUqcUO7nGQVzJYw9kGEyMNSenZRCaW0b6FOi3s/1L9uNVo9QeiEEIIlYUdjDsNvb5Q1y00sYD4q3DkW1jVBzaMKO8IxWPCysqKZ555hqZNm7J06VK2b99e7DoL3U1cu3ZtQkJC2Lp1K+fPn9ef69q1K0b/zb6qKLNjHgY5LaixsbGkpKRgbW2NmYkR3s7WXLiVTGhkEh72ZdBdG/o3xIaBhQM0llZBIYTIxc4DmgxTj8xUuLQbzv+jdin7dLpTLu6yuuh1zQCo1Q3cG0h3sigx0dHRrF+/ntq1a1O7dm0yM4vfKl2kRaeNjIzo3r073bt3L3YAjzt7e3vs7OxITEzk2rVr+Pn5AVDL3ZYLt5I5H5VEh9qupR9Ire7QZwlkp4G5TenfTwghHmZmVuDXQz10OtBl37l2fitcP6oeu2aBrYe6p3KtAPBuLz9jRbG8/fbbbN68mc8++4xz586RkZHBs88+S/369alfv36RGuSKlAympKSwZ88erl69misjHTNmTFGqfKx5eXlx5swZg2Swtpstm7hZdjuRGJuqg6eFEEIUjpERGN21L3Ld3urP1Atb1dbDpJsQtFI9jM3gpT+gWqt8qxPifsaPH2/wOjw8nNOnT3P69Gl++eWXskkGjx8/To8ePUhNTSUlJQUnJydiYmKwsrLC1dVVksEiqFq1KmfOnDEYN5gzozg0KrH0A9DpZIFVIYQoKbbu0HS4emSlq+u2nt+qLnideAPc71oy7Ogyda3DWgFQtaUsXSMeKCEhAXt7e/1rb29vvL296dWrV5HrLHQyOH78eHr16sWSJUuwt7fn0KFDmJqa8sILLzB27NgiB/I4y2sSSR2PnIWnk8nS6jA1LqVkLfI0/PQCPPkGtHildO4hhBCPK1MLdXaybxd4ai4kXAdz2zvXj6+CG8fh4GIwtwOfjuqwHd+uYONSfnGLCsvR0REvLy/q1auHv78/9evXx9/fn7p162JuXrTJn4XOMIKDg3n77bcxMjLC2NiYjIwMvLy8mDdvHu+9916Rgnjc5bXWoJejFbbmJmRm6wiLLsWu4iPfQlw4XD1QevcQQgihTiK5dw/k1uOg4fNg5QwZiRDyB/z+OnxWE1b1K5cwRcX2+uuv4+zsTOvWrfHz8yMwMJCJEyfi7e2tH2pWWIVOBk1NTfWzhl1dXfWtWfb29gbJjCi4vFoGjYw01PFQF/cOuVFKXcWpsXDyZ/XjFq+Vzj2EEELkr14f6PsNvHMBRu6AdpPAoyGggMWdrkAUBba8D2f/hIxy2KpUVBhfffUVv//+O1euXGHdunX069eP7du3c+PGDfbu3VukOgvdTfzEE09w9OhRatasSfv27Zk2bRoxMTGsWrUKf3//IgXxuMurZRCgrqcdRy7HEnIjkX6NS+HGQSshO139wePVohRuIIQQokCMjKBKU/Xo9D4k3oSs1DvXo8+pXckHF4ORKVRv/d/SNQFQyaf84hblwsvLi6VLlxIeHs6sWbOYM2cO8+fPp27dukWqr9DJ4OzZs0lKUv8q+fjjj3nppZd4/fXXqVmzJsuWLStSEI+7u1sGFUVB8996VHU91ZbBM6XRMqjNhiPqIuG0eE3WwBJCiIrEzsPwtYkFtHhdnYQSF67OUr60G7ZMgUq+0Hm6OotZPPLOnj1LaGgooaGhnD17lrCwMFJSUjhz5kzZJYNNmzbVf+zq6srmzZuLdGNxR+XKldFoNKSnpxMTE4OLizpouG5ON/HNRIMksUSEboLE6+o4lXoyLkUIISo0J2946hPoPgduX4TzW+DCFrhyQH1tanWnbNQZiAiCml3Vmc3ikVKvXj0aNGjAs88+y4QJE6hTp06x9yYudDLYqVMnfv31VxwcHAzOJyYm0qdPH3bu3FmsgB5H5ubmuLm5ERkZybVr1/TJYE03dVu6hLQsbiSkU9mhBHciOfyt+m/T4epsNyGEEBWfRgPONdWj1WhIT1D3Rq7e5k6ZE+vgwCL1Y/f66oLXvl2hSjMwLtLywqIC+eyzzzhz5gx//PEHX3zxBVWrVsXf319/BAQEFLrOQk8g2b17d55bn6Snpxd54OL9RERE8MILL1CpUiUsLS2pX78+x44d019XFIVp06bh4eGBpaUlXbp04cKFCyUeR2nL6Sq+cuWK/py5iTE1/1tv8ExEQsnesMNkqD8Qmgwv2XqFEEKUHQt7dRLK3X/UO9UAz8aABiJPwd758H13+LQGrB8GafHlE6sositXrvDnn39y5MgRJkyYwLJlyzh8+DBRUVH8/PPP9OvXj8zMTFatWlWk+gv8J8LJkyf1H4eEhBAZGal/rdVq2bx5M5UrVy5SEPmJi4ujdevWdOzYkX/++QcXFxcuXLiAo6Ojvsy8efP48ssvWblyJd7e3nzwwQcEBAQQEhKChcXD0+Ll7e3NkSNHCA8PNzhf18OOszcTCbmZSLd6Jdjc791OPYQQQjxamr6sHikxcHGHuhNK2A5Ii4PL+9X1DHOE/AE27urEFSPj8otZ5GvdunUMGzaMrKwsNBoNTzzxhD4ngjJedLpRo0ZoNBo0Gg2dOnXKdd3S0pJFixYVOZC8zJ07Fy8vL77//nv9OW9vb/3HiqKwcOFCpk6dyjPPPAPADz/8gJubG7///juDBw8u0XhKU85zXbp0yeB8XU87NgSV4vIyQgghHk3WztBwkHrotOo4wqSbd3ac0ulg0zuQcgssHMCn039dyp3BxrVcQxd3fPjhhzz//PNMmTKFa9eu8e677zJ58uQSnbRb4GQwPDwcRVGoUaMGR44c0WekAGZmZri6umJsXLJ/VWzcuJGAgAAGDhzInj17qFy5Mm+88QajRo3SxxQZGUmXLl3077G3t6dFixYcPHgw32QwIyODjIwM/euc2dHlKScZvLdlsF5JzygO/Qcu7VHHCrrULpk6hRBCVGxGxuDVzPBcRiJ4t1VbD9Pj4cyv6gHg0QgavwTNRpR1pOIely5dYvPmzVSvXp1atWqxevVqmjRpUj7JYLVq1QDQ6XQldvMHuXTpEt988w0TJkzgvffe4+jRo4wZMwYzMzOGDh2q76p2c3MzeF/OZIz8zJkzhw8//LBUYy+s/JLBnIWnI+LTSEjNwt6qmPtWHv4WLu0CcxvoNLV4dQkhhHh4WTrAgOXqUmMRgXBxm9qlfPME3AyG+PZ3ymalqV3KPp1lm7wylp2djZXVndnifn5+6HQ6IiMjcXcvmeFjRZpWdOHCBXbt2sWtW7dyJYfTpk0rkcBATTybNm3K7NmzAXXB69OnT7NkyRKGDh1a5HqnTJnChAkT9K8jIiKKvDZPSclJBi9fvmywjIy9pSlVHC25HpdGyM1EWvpUKvpN4i6riSDAEy8UM2IhhBCPBGMTqNpCPTpNhaQouLgdPBvdKXN5H/z2KqABzyfUZWt8u0LlxjLWsAysXLmS1q1b06BBA2xsbDAxMSE1NfXBbyygQieD3333nX5fPHd3d4O17zQaTYkmgx4eHrmStDp16rBhwwYAfUYcFRWFh8edBTqjoqJo1KhRvvWam5sbbOacmFj+4/GqVq2KRqMhLS2NqKgog2y/nqcd1+PSOB2RULxk8Phq9d8aHcGxevECFkII8WiydYMnhhie02nBvQFEnoQbQeqxZy5YOqljDNu+A65F2xdX3F/btm2ZNWsWSUlJGBkZ4e3tTXp6OsuWLaNLly40bdoUW1vbYt2j0EvLzJo1i48//pjIyEiCg4M5fvy4/ggKCipWMPdq3bo1oaGhBufOnz+v77L29vbG3d2dHTt26K8nJiZy+PBhWrZsWaKxlDYzMzOqVKkC5O4qblDFAYCTxVleRqeDEz+qHzd+sej1CCGEePzU7g6v7YW3Q+GZr6DuM2BuD2mxcGq94S5WN0/C9UD1944otj179pCQkEBoaCirV6+mb9++tG/fnm+++YbOnTvj6OhInTp1inWPQrcMxsXFMXDgwGLdtKDGjx9Pq1atmD17Ns8++yxHjhxh6dKlLF26FFBbIseNG8esWbOoWbOmfmkZT09P+vTpUyYxliRvb2+uXbtGeHi4QTLboIq6WfnJ6/FFr/zqQUi4pi4pULtHMSMVQgjxWLJ1V4cZPfECaLPg+lG4sh+ca90ps28BnPkNrCqpM5R9u6h7KFs65l+veKCaNWtSs2ZNg8mx4eHhHDt2jOPHjxer7kK3DA4cOJCtW7cW66YF1axZM3777TfWrVuHv78/H330EQsXLmTIkDvN15MmTeKtt97ilVdeoVmzZiQnJ7N58+aHao3BHPlNImlQ2QGAK7dTiU/NveB3gZz8r1Ww7jNgWoI7mQghhHg8GZtCtVbQbqJhy6CZDZjZQupttdXwt1ch5mL5xfkI8/b2ZuDAgfq5FUVV6JZBX19fPvjgAw4dOkT9+vVz7Yc3ZsyYYgV0r549e9KzZ898r2s0GmbOnMnMmTNL9L7lIb9k0N7KlOqVrLh8O5WT1xNoV6sIM7ls3MHaBRoMKolQhRBCiLw9sxh6fg7XjqgTUa4dUSeaiAqr0Mng0qVLsbGxYc+ePezZs8fgmkajKfFk8HGSXzIIUL+Kw3/JYHzRksFO70P7d0FT6MZgIYQQonCMTaF6a/UQFV6hk8G8EhVRMu6XDDasYs+fJ25w8noxJpHIBuVCCCGEuEeRm4kyMzMJDQ0lOzu7JON5rOUkg1evXs31edXPKC5sMpgaC5d2q8sCCCGEEELco9BNRampqbz11lusXLkSUJd6qVGjBm+99RaVK1dm8uTJJR7k48LT0xMzMzMyMzO5du2awT7M/pXtMNJAZGI6txLTcbUr4ASZM7/CprfV2VwvbCilyIUQQghR0u7eIONBFixYUOT7FDoZnDJlCidOnGD37t10795df75Lly7MmDFDksFiMDIyolq1aly4cIHw8HCDZNDKzISarraERiVx4noCXesWNBn8Xf3Xu13JByyEEEKIUnPvkjFBQUFkZ2dTu3ZtQG2QMzY2pkmTJsW6T6GTwd9//52ffvqJJ5980mD3kXr16hEWFlasYATUqFGDCxcucOnSJTp16mRwrUEVe0Kjkjh5PZ6udd3yqeEuybfU9Z9AXVJGCCGEEA+NXbt26T9esGABtra2rFy5EkdHdc3GuLg4hg8fTtu2bYt1n0KPGYyOjsbV1TXX+ZSUFIPkUBSNr68vABcv5l6T6c7i0wUcN3j2T1B06j6Ssv2cEEII8dCaP38+c+bM0SeCAI6OjsyaNYv58+cXq+5CJ4NNmzZl06ZN+tc5CeD//ve/h24LuIqoZs2aAFy4cCHXtTuTSOJRFOXBlYX8rv5bt0/JBCeEEEKIcpGYmEh0dHSu89HR0SQlJRWr7kJ3E8+ePZunnnqKkJAQsrOz+eKLLwgJCeHAgQO51h0UhXe/ZLCOhx1mJkbEpWZx5XYq1Z2t868oJQYu71M/rtenFCIVQgghRFnp27cvw4cPZ/78+TRv3hyAw4cPM3HiRPr161esugvdMtimTRuCg4PJzs6mfv36bN26FVdXVw4ePFjsAYziTjJ48eJFdPds8m1mYkT9ympXceCVuPtXdGGr2kXs0Ui6iIUQQoiH3JIlS3jqqad4/vnnqVatGtWqVeP555+ne/fufP3118WqW6MUqL/x0Xb9+nW8vLy4du0aVapUKddYsrKysLS0RKvVcv36dSpXrmxwffbfZ1n67yWeb1GV2X3r51+RokBEIGSmQI32pRy1EEIIUfYq0u/vspKSkqKfsOvj44O19X16CQuo0N3Ef//9N8bGxgQEBBic37JlCzqdjqeeeqrYQT3OTE1NqV69OmFhYVy4cCFXMti4qjpwNOhBLYMaDVRpWlphCiGEEKIcWFtb06BBgxKts9DdxJMnT0arzb2bhaIossZgCbnfuMHG1RwACI1KIik9qyzDEkIIIUQ52rt3Ly+88AItW7YkIiICgFWrVrFv375i1VvoZPDChQvUrVs313k/P788l0MRhXe/ZNDV1gIvJ0sUBYKvxeddwV/j4Y/REB1ailEKIYQQoqxs2LCBgIAALC0tOX78OBkZGQAkJCQwe/bsYtVd6GTQ3t6eS5cu5Tp/8eLFEum3FvdPBgGa6LuK43NfzEqHEz/B8VWQlVpaIQohhBCiDM2aNYslS5bw3XffYWpqqj/funVrgoKCilV3oZPBZ555hnHjxhnsNnLx4kXefvttevfuXaxghOpByWDjamoyGHg1j3GDl/dBVgrYeqgziYUQQgjx0AsNDaVdu9xby9rb2xMfH1+sugudDM6bNw9ra2v8/Pzw9vbG29ubOnXqUKlSJT777LNiBSNUOclgWFhYruVl4M4kkuNX49Dp7pkMHvq3+m+t7uokEiGEEEI89Nzd3fMcjrdv3z5q1KhRrLoLPZvY3t6eAwcOsG3bNk6cOIGlpSUNGjTIM1sVRVOtWjVMTExIT08nIiICLy8vg+t+7rZYmRmTlJ7NxehkarnZqhcUBc5vVj+u3aOMoxZCCCFEaRk1ahRjx45l+fLlaDQabty4wcGDB3nnnXf44IMPilV3oZLBnDXwgoOD6datG926dSvWzUXeTExMqFGjBufPn+fChQu5kkETYyMaVnHg4KXbBF6Ju5MMRp6ExAgwtQJvSc6FEEKIR8XkyZPR6XR07tyZ1NRU2rVrh7m5Oe+88w5vvfVWseouVDexqakpVatWzXNpGVGyHjiJ5L9xg8cu3zVuMPQf9V+fTmBqUarxCSGEEA+jr776iurVq2NhYUGLFi04cuRIvmWzsrKYOXMmPj4+WFhY0LBhQzZv3mxQRqvV8sEHH+Dt7Y2lpSU+Pj589NFH3L2nx7Bhw9BoNAZH9+7dCxW3RqPh/fffJzY2ltOnT3Po0CGio6P56KOPCvcJyEOhxwy+//77vPfee8TGxhb75iJ/Ocng+fPn87zepLqaDB69fNfXwaoSONeG2rLwtxBCCHGvn376iQkTJjB9+nSCgoJo2LAhAQEB3Lp1K8/yU6dO5dtvv2XRokWEhITw2muv0bdvX44fP64vM3fuXL755hsWL17M2bNnmTt3LvPmzWPRokUGdXXv3p2bN2/qj3Xr1hUq9k6dOvHhhx9iZmZG3bp1ad68OTY2NsTFxdGpU6fCfzLuUugxg4sXL+bixYt4enpSrVq1XMvJFHd6s1D5+fkBcO7cuTyvN63miJEGrsamcjMhDQ97S2g+Sj3ymHQihBBCPO4WLFjAqFGjGD58OKDu97tp0yaWL1+e58YZq1at4v3336dHD3Uc/uuvv8727duZP38+q1evBuDAgQM888wzPP300wBUr16ddevW5WpxNDc3x93dvcix7969m1OnTnH8+HHWrFmjz78yMzPZs2dPkeuFIiSDffr0KdYNRcHkJINnz57N87qthSn+le05eT2BI+GxPNPorm3rjArd4CuEEEI8tJKSkkhMTNS/Njc3x9zc3KBMZmYmgYGBTJkyRX/OyMiILl26cPDgwTzrzcjIwMLCcNiVpaWlwY4frVq1YunSpZw/f55atWpx4sQJ9u3bx4IFCwzet3v3blxdXXF0dKRTp07MmjWLSpUqFeo5t2/fzquvvsqTTz7Jn3/+SfXq1Qv1/vwUOhmcPn16idxY3F+dOnUAuHz5MmlpaVhaWuYq07y6EyevJ3DoUizPeCaCo7eMFRRCCPHYuXdntOnTpzNjxgyDczExMWi1Wtzc3AzOu7m55dsLFxAQwIIFC2jXrh0+Pj7s2LGDX3/91WDuxOTJk0lMTMTPzw9jY2O0Wi0ff/wxQ4YM0Zfp3r07/fr1w9vbm7CwMN577z2eeuopDh48iLGxcYGf08PDgz179jB8+HCaNWvG+vXr9flCcRQ6GQSIj4/nl19+ISwsjIkTJ+Lk5ERQUBBubm5Urlz5wRWIB3JxccHJyYnY2FhCQ0Np1KhRrjItalTif/vCOXIpGi6OgswUGLUD3OqVfcBCCCFEOQkJCTHIP+5tFSyqL774glGjRuHn54dGo8HHx4fhw4ezfPlyfZmff/6ZNWvWsHbtWurVq0dwcDDjxo3D09OToUOHAjB48GB9+fr169OgQQN8fHzYvXs3nTt3LlAsmv/WDjY3N2ft2rXMmjWL7t278+677xb7OQvdn3jy5Elq1arF3Llz+eyzz/SrXv/6668GTa+ieDQajT7bz6+ruHl1JzQasLgdAqkxYGQMlWqWZZhCCCFEubO1tcXOzk5/5JUMOjs7Y2xsTFRUlMH5qKiofMfyubi48Pvvv5OSksKVK1c4d+4cNjY2Bos8T5w4kcmTJzN48GDq16/Piy++yPjx45kzZ06+8daoUQNnZ+c8F5HOz92zk0Gd3LJmzRrmz59f4DryU+hkcMKECQwbNowLFy4Y9KP36NGDf//9t9gBiTselAzaW5ni525He6OT6gnvdmBiVlbhCSGEEA8NMzMzmjRpwo4dO/TndDodO3bsoGXLlvd9r4WFBZUrVyY7O5sNGzbwzDPP6K+lpqZidM9YfWNj4zx3EMtx/fp1bt++jYeHR4HjDw8Px9nZ2eBc//79OXTokEFLZVEUupv46NGjfPvtt7nOV65cmcjIyGIFIww9KBkEaOHtRPvbJ9QXPsWbWi6EEEI8yiZMmMDQoUNp2rQpzZs3Z+HChaSkpOhnF7/00ktUrlxZ36p3+PBhIiIiaNSoEREREcyYMQOdTsekSZP0dfbq1YuPP/6YqlWrUq9ePY4fP86CBQt4+eWXAUhOTubDDz+kf//+uLu7ExYWxqRJk/D19SUgIKDAsVerVi3P8/7+/vj7+xf1UwIUIRk0Nzc3mLGT4/z587i4uBQrGGGoIMlg6ypmNA78b2Fq3y5lEZYQQgjxUBo0aBDR0dFMmzaNyMhIGjVqxObNm/WTSq5evWrQypeens7UqVO5dOkSNjY29OjRg1WrVuHg4KAvs2jRIj744APeeOMNbt26haenJ6+++irTpk0D1FbCkydPsnLlSuLj4/H09KRbt2589NFHDxzbOGHCBD766COsra2ZMGHCfcveO3u5MDTKvZ3QDzBy5Ehu377Nzz//jJOTEydPnsTY2Jg+ffrQrl07Fi5cWORgysv169fx8vLi2rVrVKlSpbzD0bt8+TLe3t6YmpqSmpqKiUnu3D0x+Hfsfh9KuM4N+3dP42Qt3cRCCCEeDxX193dJ6dixI7/99hsODg507Ngx33IajYadO3cW+T6FbhmcP38+AwYMwNXVlbS0NNq3b09kZCQtW7bk448/LnIgIreqVatiaWlJWloaly5dolatWrnK2EWoax39q2uAW3gs3f2LvqClEEIIISqOXbt25flxSSt0Mmhvb8+2bdvYt28fJ0+eJDk5mcaNG9Oli3RRljQjIyNq165NcHAw586dyzMZpOnLbI4w449wF+qHxUgyKIQQQohCKdI6gwBt2rShTZs2JRmLyEOdOnUIDg7m7Nmz9O7dO3cBt7rQagxBlwJJuBhT9gEKIYQQolQ8aJzg3YozZrBIyeCOHTv4/PPP9RMb6tSpw7hx46R1sBQUZBJJS59KGGkgLDrlzj7FQgghhHioHT9+vEDlchakLqpCJ4Nff/01Y8eOZcCAAYwdOxaAQ4cO0aNHDz7//HPefPPNYgUkDOVssRMSEpL7YvA60Giw9+1CgyoOBF+LZ9+FGAY29SrjKIUQQghR0kpznODdCp0Mzp49m88//5zRo0frz40ZM4bWrVsze/ZsSQZLWM7aQWfOnEGr1RruYfjvpxAbBs/9SBvfGgRfi2f/RUkGhRBCiEdVSEgIV69eJTMzU39Oo9HQq1evItdZ6GQwPj6e7t275zrfrVu3EtkfTxjy9fXFwsKC1NRULl26RM2a/203l3hDTQQ1RlCtFW1Ms1m86yL7Lt5GUZRiNxkLIYQQouK4dOkSffv25dSpU2g0Gv32dDm/77VabZHrLvR2dL179+a3337Ldf6PP/6gZ8+eRQ5E5M3Y2Jh69eoBcOrUqTsXwveq/3o0BAt7nqjqgKWpMTHJGYRGJZVDpEIIIYQoLWPHjsXb25tbt25hZWXFmTNn+Pfff2natCm7d+8uVt2FbhmsW7cuH3/8Mbt379bv5Xfo0CH279/P22+/zZdffqkvO2bMmGIFJ1T169cnMDCQU6dO0a9fP/Xk5f/2ga7eFgBzE2Na1HBid2g0+y7E4OduV07RCiGEEKKkHTx4kJ07d+Ls7IyRkRFGRka0adOGOXPmMGbMmAJPNslLoZPBZcuW4ejoSEhIiMGkBgcHB5YtW6Z/rdFoJBksIfXr1wfg5MmTd07mtAx6t9OfauPrrCaDF2MY2bZGWYYohBBCiFKk1WqxtbUFwNnZmRs3blC7dm2qVatGaGhoseoudDIYHh5erBuKwmvQoAFwVzdx/DWIvwIaY6j6pL5ca19nAA5fiiUzW4eZSaFHAQghhBCiAvL39+fEiRN4e3vTokUL5s2bh5mZGUuXLqVGjeI1AEm28BDIaRm8ePEiqampcDNYveDREMxt9eX83G1xtjEnLUvLsSux5RCpEEIIIUrD1KlT0el0AMycOZPw8HDatm3L33//bTBEryiKvAOJKDtubm64uLgQHR3NmTNnaNasF7xzAVKiDcppNBra1XLm16AI9oRG08rHuZwiFkIIIURJCggI0H/s6+vLuXPniI2NxdHRsdgriEjL4EMiV1exjSu41ctVrmNtVwB2hd4qs9iEEEIIUfacnJxKZCk5aRl8SNSvX58dO3YYLi+Th3Y1XTA20nA+KpnrcalUcbQqowiFEEIIUZrS09M5efIkt27d0ncZ5+jdu3eR65Vk8CGRM27w1JF/4Yc+UKcnNBuZq5y9lSlNqjpy5HIsu0KjefHJamUcqRBCCCFK2ubNm3nppZeIiYnJdU2j0ZTtotMAe/fu5YUXXqBly5ZEREQAsGrVKvbt21fkQMT95XQTnwwJhUu74HpgvmU7+LkAsPucdBULIYQQj4K33nqLgQMHcvPmTXQ6ncFRnEQQipAMbtiwgYCAACwtLTl+/DgZGRkAJCQkMHv27GIFI/JXt25djIyMiI5P4WaSzmBJmXvljBvcHxZDelbxvkGEEEIIUf6ioqKYMGECbm5uJV53oZPBWbNmsWTJEr777jtMTU3151u3bk1QUFCJBifusLKyws+vNgBBN7X3TQb93G3xsLcgPUvHoUu3yypEIYQQQpSSAQMGFHvbufwUesxgaGgo7dq1y3Xe3t6e+Pj4kohJ5KOxX3VCQs4SFGPG05Vq5ltOo9HQobYr645cZXdoNB3+aykUQgghxMNp8eLFDBw4kL1791K/fn2DBjko3hbAhU4G3d3duXjxItWrVzc4v2/fvmKvgC3ur3FlC1YDQXE2YHT/Rt2OtV1Yd+QqO8/dYnqvuiUy9VwIIYQQ5WPdunVs3boVCwsLdu/ebfB7vbhbABc6GRw1ahRjx45l+fLlaDQabty4wcGDB3nnnXf44IMPihyIeLAmTkkABF1LfWDZ1r7OmBkbcTU2lYu3kqnpZvvA9wghhBCiYnr//ff58MMPmTx5MkYPaBAqrEIng5MnT0an09G5c2dSU1Np164d5ubmvPPOO7z11lslGpww1MjHA4CrUXHExMTg7Jz/DiPW5ia08q3E7tBotoZESTIohBBCPMQyMzMZNGhQiSeCUIQJJBqNhvfff5/Y2FhOnz7NoUOHiI6O5qOPPirx4IQhuxd/oGZNdazg8ePHH1g+oJ47AFvORJZqXEIIIUR+FEUp7xAeCUOHDuWnn34qlboL3TL48ssv88UXX2Bra0vdunX151NSUnjrrbdYvnx5iQYoDDVu3JgLFy4QGBhI165d71u2Sx033tOc4uT1BG7Ep+HpYFlGUQohhHic3U67zYEbB9gbsZeT0Sf5s8+fmBqbPviNIl9arZZ58+axZcsWGjRokGsCyYIFC4pcd6GTwZUrV/LJJ59ga2vY7ZiWlsYPP/wgyWBpyc4EEzMaN27MTz/9VKBlfFxszWlS1ZFjV+LYFhLF0FbVSz9OIYQQjx2tTsupmFPsjdjL/oj9nLl9xuD68VvHae7RvJyiezScOnWKJ554AoDTp08bXCvuJNECJ4OJiYkoioKiKCQlJWFhYaG/ptVq+fvvv3F1Ld0lTD755BOmTJnC2LFjWbhwIaDu0/f222/z448/kpGRQUBAAF9//XWpLMpYrtb0h/hrNPZ4EaDAazp2q+fGsStxbA2JlGRQCCFEidsYtpG5R+aSmJlocL6OUx1aV25Nm8ptaOjSsJyie3Ts2rWr1OoucDLo4OCARqNBo9FQq1atXNc1Gg0ffvhhiQZ3t6NHj/Ltt9/qt2XLMX78eDZt2sT69euxt7dn9OjR9OvXj/3795daLGVOp4OI45CZxBNdWwAQFhZGfHw8Dg4O931rQD13Zv99jkOXYolPzcTByqwMAhZCCPEoOh1zmp1Xd9Kpaif8nf0BqGRRicTMRGzNbGnl2Yo2ldvQ2rM1LlYu5RztoyMrK4vu3buzZMkS/dyBklTgZHDXrl0oikKnTp3YsGEDTk5O+mtmZmZUq1YNT0/PEg8QIDk5mSFDhvDdd98xa9Ys/fmEhASWLVvG2rVr6dSpEwDff/89derU4dChQzz5ZP67dDxUbl+AzCQwtaaSX0uqVavGlStXCA4OpkOHDvd9a7VK1vi523IuMomd527Rr3GVsolZCCHEQy8mLQYnCyeMNOp803Xn1rExbCNZuix9MtjUvSk/PPUD9Z3rY2JU6NFnogBMTU05efJkqdVf4NnE7du3p0OHDoSHh/PMM8/Qvn17/dGyZctSSwQB3nzzTZ5++mm6dOlicD4wMJCsrCyD835+flStWpWDBw+WWjxlLiJQ/dezERgZ06RJE0BtLS2IbnXVLnOZVSyEEOJ+tDotJ6JPsOj4Ip7981k6/tyRc7Hn9Ne7VuvKU9WfoolbE/05c2NznnB9QhLBUvbCCy+wbNmyUqm70F+5atWqAZCamsrVq1fJzMw0uH5vN25x/fjjjwQFBeWZ+ERGRmJmZparq9TNzY3IyPwTn4yMDDIyMvSvk5KSSizeUnH9mPpv5cYANG/enF9//ZXDhw8X6O3d6rnz5c6L7DkfTWpmNlZm8h9WCCGEKiEjgf0R+/WTP+Iy4gyuh9wOoW4ldfWQDl4d6ODVoRyiFNnZ2Sxfvpzt27fTpEkTrK2tDa6X6Wzi6Ohohg8fzj///JPnda1WW+Rg7nXt2jXGjh3Ltm3bDCasFNecOXNKdXxjictpGays/iXWooU6brCgyWA9TzuqOllxNTaVHWdv0ath6bXiCiGEqNgUReF83Hn+vf4veyP2ciL6BDpFp79ua2pLq8qtaFu5La0rt8bZMv8NDkTZOX36NI0bq41C58+fN7hWZrOJc4wbN474+HgOHz5Mhw4d+O2334iKimLWrFnMnz+/WMHcKzAwkFu3bukfHtRk899//2Xx4sVs2bKFzMzMXBMpoqKicHd3z7feKVOmMGHCBP3riIgIgzUTK5SsdIj6bwp55aYANG3aFCMjI65fv05ERASVK1e+bxUajYaeDTz4encYf528IcmgEEI8ppaeXMpP537iVtotg/O+Dr60rdKWdpXb0dC1IaZGsiZgRVMhZhPn2LlzJ3/88Yc+IalWrRpdu3bFzs6OOXPm8PTTT5dYcJ07d+bUqVMG54YPH46fnx/vvvsuXl5emJqasmPHDvr37w9AaGgoV69epWXLlvnWa25ujrm5uf51YmJivmXLXWYKPPECxF4Ce3Xyh42NDf7+/pw8eZLDhw/Tr1+/B1bTs4EnX+8OY1doNEnpWdhayH90IYR4VCmKwuXEy+y9vpeBtQdiaaJuOpCSlcKttFtYmljSwr0Fbau0pW3ltnjYeJRzxKIg4uPjWbZsGWfPngWgXr16vPzyy9jb2xer3kIngykpKfr1BB0dHYmOjqZWrVrUr1+/wGvfFZStrS3+/v4G56ytralUqZL+/IgRI5gwYQJOTk7Y2dnx1ltv0bJly0dnJrF1Jej1Ra7TLVq0KFQyWMfDFh8Xa8KiU9gWEiWzioUQ4hGj1WkxNjLWv35126vcTLlJdfvqtKvSDoB+NfvR3L05Td2bYm5snl9VogI6duwYAQEBWFpa0ry5uoD3ggUL+Pjjj9m6datBL2phFXpv4tq1axMaGgpAw4YN+fbbb4mIiGDJkiV4eJT9Xxaff/45PXv2pH///rRr1w53d3d+/fXXMo+jrBV23KDaVax2D/918mapxSWEEKLs3Ei+wU/nfmL0jtEEbAggW5cNqD/zO1XtxJMeTxokfdXsqtG6cmtJBB9C48ePp3fv3ly+fJlff/2VX3/9lfDwcHr27Mm4ceOKVbdGKeQO0qtXryY7O5thw4YRGBhI9+7diY2NxczMjBUrVjBo0KBiBVQerl+/jpeXF9euXaNKlQrWYhYdCo7VwcTwP+6ZM2fw9/fH2tqahIQEjI2N837/XS7eSqLLgn8xNdZw9P0usgC1EEI8ZLJ0WQTfCmbv9b3sjdjLxfiLBtdXPbWKRq6NALWruLgTCyq6Cv37u4RZWlpy/Phx/Pz8DM6HhITQtGlTUlNTi1x3obuJX3jhBf3HTZo04cqVK5w7d46qVavi7CwzjkpUdiYsaQOKAmNPgP2diSJ+fn7Y2tqSlJTEmTNnCrSkj6+rrX4B6i1nIhnUrGppRi+EEKIExKTFsC9iH/9e/5eDNw6SnJWsv2akMaKRSyP92L9ajnd2CHvUE8HHjZ2dHVevXs2VDF67dg1bW9ti1V3oZHDmzJm88847WFlZAWBlZUXjxo1JS0tj5syZTJs2rVgBibtEnwNtJpjbg53hDGBjY2OaNWvGzp07OXToUIHXd+zV0JNzkaH8dfKmJINCCFFBKYrC0pNL2X1tN6dvnza45mjuSJvKbWhbpS2tPFthb168yQPi4TBo0CBGjBjBZ599RqtWrQDYv38/EydO5LnnnitW3YUeM/jhhx+SnJyc63xqaurDtXbfwyDyv61nPBpAHn/h5UySKei4QYCeDdRxnfsvxnArMb34MQohhCi2tOw0gm8F619rNBr2XN+jTwTrVqrLaw1fY02PNex6dhez287mKe+nJBF8jHz22Wf069ePl156ierVq1O9enWGDRvGgAEDmDt3brHqLnTLYH5jEE6cOGGwX7EoAf9v777DorrSB45/Z+gd6YJSrNi72LGggEZFTYyJm6hJTGI0G2M2iamm7boxG+OamLL5xZKiSYyCHVQQ7A17ww6KFLHQFBhm7u8P4lVEo+jAUN7P88wDc8+Ze19ghnnn3Hvek7a/5KvXnUf9bkwiKc/Se36udrT3dWZPylWW7bvA+F4NHjpMIYQQDy7rehZhS8LQG/RsHLURB8uSU35jWowhX5dPT5+euNu6mzhKYWqWlpb897//Zfr06Zw8eRKNRkPDhg3VM7UP476TwTp16qDRaNBoNDRp0qRUQqjX68nLy+PFF1986IDELdJujAy2uWNz9+7dATh69ChZWVn3fc3miA712JNylSV7zvNczwC5rkQIISqBQTFwKOsQCecTKNIX8VrH1wBws3HD296bguICzuWeU5d+C/UPNWW4ooqytbU1+tK/950Mzpo1C0VReOaZZ/jwww9LFTi0tLTE39//Lws9i3IyGCD9z4Lbde/8R3d1daV58+YcOXKEzZs3ExERcV+7fqSVNx+uOMKx9FwOX8ihpY+cZhBCiIqQr8tn24VtJJxPYOP5jVwuuAyAjbkNk9pNUku8zA+bTx2rOvLhXJSh1Wrv+bzQaDQUFxc/8DHuOxkcM2YMAAEBAXTv3h1z83KfYRblcfkU6PLB3AZcG9+1W48ePcqdDDrZWtC/mSerDqaxZM95SQaFEMKIzueeV5O/Xem70Bl0apu9hT3dvLsRXD+41GNcrOUyq8oyZ84cPvvsM9LT02nTpg1ffvmlWsT5djqdjunTp7NgwQJSU1Np2rQpn376KWFhYWofvV7PBx98wM8//0x6ejre3t6MHTuWd999V03iFEVh2rRpfP/991y9epXu3bvzzTff0Ljx3d/fb4iMjLxr27Zt25g9ezYGg+Gufe5HuTM6BwcHjh49SqtWrQBYtmwZ8+bNo3nz5nzwwQdYWkrtOqOwcoCQD6AwD8zu/mfq2bMn//vf/9i0aVO5dj+igw+rDqaxfN8F3h7YDAuzcs8lEkIIQcnKH/sv7ifhfAIJ5xI4lX2qVHt9h/oE1wumd/3etPdoj4WZLAdqKr/99htTpkzh22+/JSgoiFmzZhEaGkpSUpK6utqt3n33XX7++We+//57AgMDiYmJYdiwYWzdupV27doB8Omnn/LNN9+wYMECWrRowe7duxk3bhxOTk78/e9/B2DGjBnMnj2bBQsWEBAQwHvvvUdoaChHjhzB2tr6L2MeOnRomW1JSUlMnTqVFStWMHr0aD766KOH+8Uo5dSxY0fljz/+UBRFUU6dOqVYWVkpTzzxhNKoUSPllVdeKe/uqoRz584pgHLu3DlTh1JuZ8+eVQDF3NxcycvLu+/H6Yr1SoeP1yp+b65U1h1Or8AIhRCi5inWF6vfZxdmK20XtFVazm+ptJzfUmmzoI0yds1YZd7Becrpq6cVg8FgwkhrtvK+f3fu3FmZOHGiel+v1yve3t7K9OnT79i/bt26yldffVVq2/Dhw5XRo0er9wcNGqQ888wzd+1jMBgULy8v5bPPPlPbr169qlhZWSmLFi26r7hvSE1NVZ577jnFwsJCeeSRR5SDBw+W6/F3U+7hoOPHj9O2bVsAFi9eTHBwMAsXLmT+/PksWbLk4TJTUW5+fn7Ur1+f4uJitm/fft+PMzfTMrRtSRHrJXvOV1R4QghRoxy/cpxnYp5hXMw4dZujpSN9fPswMGAgn/b8lITHE5gXNo+xLccS4CST9CpDbm4uOTk56q2wsLBMn6KiIhITEwkJCVG3abVaQkJC7lqVo7CwsMzInY2NDZs3b1bvd+vWjdjYWI4fPw6UVFfZvHkz4eHhAJw5c4b09PRSx3VyciIoKOi+q4FkZ2fz5ptv0qhRIw4fPkxsbCwrVqygZcuW9/X4eyl3Mqgoinpuev369QwcOBCA+vXrk5WVZZSgBHBsNWSdLJlIcg89evQAKP+p4vYlS/fEHs3kcn5R+WMUQogarNhQzO703ezN3Ktuc7ZyZlf6LvZl7uNKwRV1+8zeM/m016cMbDBQav+ZQPPmzXFyclJv06dPL9MnKysLvV6Pp6dnqe2enp6kp6ffcb+hoaHMnDmTEydOYDAYWLduHUuXLiUtLU3tM3XqVEaNGkVgYCAWFha0a9eOyZMnM3r0aAB13+U57q1mzJhBgwYNWLlyJYsWLWLr1q307Nnzno8rj3JfM9ixY0c++eQTQkJCSEhI4JtvvgFKMt/bf1DxgK5dhl//rCb+1vmS6wf/Qs+ePVm0aFG5k8Hm3o608nHiYGo2SxLPS81BIUStl6/LZ0vqFuLPxbMxdSPZhdl08+7Gd/2/A8DD1oN/9fgXbd3bUse6jmmDFaojR47g43NzyVYrKyuj7Pe///0v48ePJzAwUK3rN27cOObOnav2+f333/nll19YuHAhLVq0YN++fUyePBlvb2918u3DmDp1KjY2NjRq1IgFCxawYMGCO/ZbunTpAx+j3MngrFmzGD16NFFRUbzzzjs0atQIgD/++ENdHkU8pIzDJV+d/e6ZCALqJ4Tt27ej0+mwsLj/i5OfDPLlraUHWbQzRWoOCiFqpfT8dOLPxRN/Lp6d6TtLzf51snKirl3dUgsuDG442DSBirtycHDA0dHxL/u4ublhZmZGRkZGqe0ZGRl4eXnd8THu7u5ERUVRUFDApUuX8Pb2ZurUqTRocHPw5PXXX1dHBwFatWpFcnIy06dPZ8yYMeq+MzIyqFu3bqnj3rjs7q88/fTTFf7eXO5ksHXr1hw8eLDM9s8++wwzMzOjBFXrZR4p+erZ4r66N2/enDp16nDlyhUSExPVZerux+A23nyy8gins/LZfvoyXRu6PkjEQghRbSiKwrHLx4g/F8+Gcxs4evloqXZfB1/61O9DH98+tHFvg7lWSqnVBJaWlnTo0IHY2Fi1FJvBYCA2NpZJkyb95WOtra3x8fFBp9OxZMkSRo4cqbZdu3YNrbb0VXdmZmbqJXUBAQF4eXkRGxurJn85OTns2LGDCRMm3DPu+fPn3/8P+YCM9gy/19RoUQ43RgY9mt9Xd61WS3BwMFFRUcTFxZUrGbS3MmdoOx8W7khh4c4USQaFEDXaiSsnmLB+AhnXbo4OadDQ1qMtvev3pnf93gQ4ylmSmmrKlCmMGTOGjh070rlzZ2bNmkV+fj7jxpVMCHr66afx8fFRrzncsWMHqamptG3bltTUVD744AMMBgNvvPGGus/Bgwfzz3/+E19fX1q0aMHevXuZOXMmzzzzDFBSEHry5Ml88sknNG7cWC0t4+3tfd/1gSuafNypitSRwftLBgH69eunJoNvv/12uQ73ZGdfFu5IIeZQOpfyCnG1N861FkIIYUrZhdlsPL8Rc6054QElMzvrOdQjuzAbG3Mbunl3o3f93vT06YmrjXwQrg0ef/xxLl68yPvvv096ejpt27YlOjpanfOQkpJSapSvoKCAd999l9OnT2Nvb8/AgQP56aefcHZ2Vvt8+eWXvPfee7z00ktkZmbi7e3NCy+8wPvvv6/2eeONN8jPz+f555/n6tWr9OjRg+jo6CozkKZRFEUxdRCmdv78eerXr8+5c+eoV6+eaYMxGODf9aEoD17aAR6B9/Wwo0eP0rx5c6ytrbly5Uq5n2CDv9zMwdRs3h4YyPO9Gj5I5EIIYXJ6gx4zbcklSytPr+StTW/RyLkRkUNvruJwKOsQjes0VpeCE9VXlXr/rsZk2YmqJjulJBE0swTX+0/KAgMDqVu3LgUFBWzdurXch30yyBeARTvPYTDU+s8HQohqQm/Qsy9zH18kfsHQqKHMOzxPbevp05NAl0D61O9DseHmuq0t3VpKIijELcqdDH700Udcu3atzPbr168//HIoAmxdYeSPEPovKMeSRRqNhr59+wIQGxtb7sMObuONvZU5Z7Ly2XxS6kUKIaqu68XXiUuJ4/0t79N3cV+eWvMUcw/N5XT2aTae36j2c7JyYvHgxfy9/d9lEogQf6Hcp4nNzMxIS0srs4bfpUuX8PDwQK/XGzXAylBThpnnzZvHM888Q5cuXe67qvmtPlh+mPlbz9KnqTvzxt150W4hhDCFrOtZJJxLIP5cPNvStlGov7nChIOFAz3q9aBP/T509+mOo+VflxgRNUdNef82tXJ/VLq11tKt9u/fj4uLi1GCEg+mX79+AOzatYucnJx71ly63dhu/izYdpYNSRc5fTGPBu72FRGmEELcl4z8DFaeXkncuTgOXDxQqs3bzps+vn3oXb83HTw6YFGOMylCiNLuOxmsU6cOGo0GjUZDkyZNSiWEer2evLw8XnzxxQoJslbZPQ8cvcG/B1jaleuhvr6+NGrUiJMnT5KQkMDgweUrjOrvZkffph7EHstkwdazfDjUOGseCiHE/TAoBgqKC7C1sAXgbM5ZZu2Zpba3dG2pln9pUqeJlH8RwkjuOxmcNWsWiqLwzDPP8OGHH+LkdHPtRUtLS/z9/enatWuFBFlrFBfB6n+AoRhePVzuZBBKRgdPnjxJbGxsuZNBgHHdA4g9lskfied5LbQpjtbyaVsIUfFWnFrBF4lfMMB/AFM7TwWgvWd7+vn2o2vdrvSu3xtPO1nyVIiKcN/J4I319QICAujevTvm5nIxrtFdPlWSCFo6gKPPvfvfQUhICN999x1r1659oMd3b+RKYw97TmTmsXj3eZ7tEfBA+xFCiLvJK8pjc+pmAl0C8XfyB8Dewp6L1y+yI22H2s9Ca8GsPrNME6QQtUi5ZxPn5+ffcbZqTEwMa9asMUpQtdbFpJKv7k3gAU9/hISEYGZmxtGjRzl79my5H6/RaBjb3R+ABVvPopcyM0III7h47SK/J/3Oi+tfpOdvPXl94+ssP7Vcbe/q3ZWv+33Nb4/8ZsIohaidyp0MTp069Y4zhhVFYerUqUYJqtbKOl7y1a3pA+/C2dmZbt26ATxwcj68XT2cbCxIuXyNtYfTHzgWIUTtdib7DD8c/IHRq0fTd3FfPt7+MVtSt1BsKMbf0b/Uqh/W5tb0rNcTSzNLE0YsRO1U7nO9J06coHnzssukBQYGcvLkSaMEVWvdOjL4EMLDw9m0aRNr1qy5r0Wwb2djacbTXf34Mu4k3yacIqyll1yoLYS4J4Ni4FDWIeJS4og7F8eZ7DOl2lu7taaPbx/6+valgVMDE0UphLhduZNBJycnTp8+jb+/f6ntJ0+exM6u/BMexC3UkcGHSwYHDhzI22+/TWxsLIWFhVhZlb/S/thu/ny/6TT7z2ez7dQlujVye6iYhBA1W1xKHJ9s/4SL1y+q28y15gR5BdHXty+96/fGw9bjL/YghDCVcp8mHjp0KJMnT+bUqVPqtpMnT/Laa68xZMgQowZXqxgMkHWi5PuHOE0M0Lp1a7y9vbl27RobN2689wPuwNXeisc71gfgm4RT9+gthKhN8oryiD4bzeGsw+o2D1sPLl6/iJ2FHWH+YczoNYONj2/k2/7fMrLpSEkEhajCyp0MzpgxAzs7OwIDAwkICCAgIIBmzZrh6urKf/7zn4qIsfZ4JhqGfw91/B9qNxqNhvDwcODBrxsEeK5nA8y0GjadyOLg+eyHikkIUXPM2TeH1xNe59ekX9VtzV2b813Id2x8fCOfBX9GeEA4DpYOJoxSCHG/yp0MOjk5sXXrVlatWsVLL73Ea6+9RmxsLHFxcTg7O1dAiLWEVgvebaH1SDB7+LI9N5LB1atXP/A+6rvYMrh1XQC+ldFBIWqdcznnmH9oPn9b/Td2pe9St/ep3wd/R3/8HP3UbVqNlm4+3WQCiBDVULnXJr5VQUEBVlZW1X5yQU1c2zA7Oxs3NzeKi4s5deoUDRo82MXax9JzCJu1CY0GYqcEyxJ1QtRgiqJw4uoJYpNjWZ+ynuNXjqttf2v2N97s/Kbar7r/3xc1Q018/zaFco8MGgwGPv74Y3x8fLC3t+fMmZLZYu+99x4//PCD0QOsNQ78Dtu/gUvGGYFzcnKiR48eAKxYseKB9xPo5UjfQA8UBeZskNFBIWoag2LgwMUDzEycySORjzBi+Qi+3v81x68cx0xjRlDdIN4JeoexLcaqj5FEUIiapdznIz/55BMWLFjAjBkzGD9+vLq9ZcuWzJo1i2effdaoAdYau+dCyjawdQPXhkbZZUREBPHx8URGRvLKK6888H7+3q8xcccyidx7nol9GsrooBDVXLGhmD0Ze1ifsp7YlFgyr2WqbZZaS7p5d6OfXz961+uNs7Wz6QIVQlSKco8M/vjjj/zvf/9j9OjRmJmZqdvbtGnDsWPHjBpcrXKjrMxD1hi81dChQwHYtGkTWVlZD7yftvWd6RfogUGBL+OklqQQ1d3PR37m2bXPsujYIjKvZWJrbku4fzifBX/GxlEb+bLfl0Q0ipBEUIhaotzJYGpqKo0aNSqz3WAwoNPpjBJUrZN/Ca5dKvnetezv9kH5+/vTrl07DAbDQ50qBpgcUpKkLtuXysnMXGOEJ4SoBFtTt/J6wuusS16nbutdvzfOVs4MazSMOf3msHHURmYEzyDMPww7C6kXK0RtU+5ksHnz5mzatKnM9j/++IN27doZJahaJ+vPlUecfMHSuP+IIyIiAIiKinqo/bSq50T/5p4YFPhvrIwOClFVXS24SpG+SL2/O2M30WejWXPmZpkpfyd/4kfG81H3j+hVrxdWZuUvTC+EqDnKfc3g+++/z5gxY0hNTcVgMLB06VKSkpL48ccfWblyZUXEWPOpK480Nvquhw0bxrRp01i7di35+fkPtUrM5JDGrDuSwcoDF3i5byOaeEoNMSGqgoz8DOLOxRGbHMvujN180fsL+vj2ASA8IBy9omeA34BSjzHTmt1pV0KIWuiBViBZsWIF69evx87Ojvfff5+jR4+yYsUK+vfvXxEx1nw3ZhBXQDLYsmVLGjRoQEFBATExMQ+1rxbeToS18EJR4PO1SUaKUAjxIFJyUph7aC6jV40m5I8Q/rXjX+xI34Fe0XMw66Dar3Gdxrza4VVauLUwYbRCiKrsvkYGZ8+ezfPPP4+1tTUpKSn06NGDdevW3fuB4v5cPl3y1cX4C7drNBqGDRvG559/TmRkJMOHD3+o/U0Z0IS1R9KJOZxBYvJlOvi5GClSIcRfURSF41eOE5tSUgPwxJUTpdrburclxC+Evr59qe9Q30RRCiGqo/sqOm1ubs6FCxfw8PDAzMyMtLQ0PDxqzjqTJi9aef0KXDoNjt7gWNfou9+8eTM9e/bEycmJjIwMrKwe7vqgqUsO8Ouuc3Twq8MfL3aVmmNCVKDjV46z4tQKYlNiOZd7Tt1upjGjs1dn+vn2o69vX9xt3U0YpRB3d/36dRITE9Xat8Zk8vfvGuK+Rga9vb1ZsmQJAwcORFEUzp8/T0FBwR37+vr6GjXAWsGmDtTrUGG779atGz4+PqSmphIdHa2WnHlQr/ZvQtS+VBKTrxBzOJ2wlsZPYIWorfQGPXpFry7rlpiRyPzD8wGwMrOim3c3QvxCCK4XjJOVkwkjFeLurly5wurVq4mMjCQ6Oprr16+Tnp6Ou7t8aKmK7isZfPfdd3n55ZeZNGkSGo2GTp06lelzY3kivV5v9CDFw9FqtTz++OPMnDmTX3/99aGTQU9Ha8b3bMCXcSf5NDqJfs08sTAr9+WnQojbLDi8gHmH5vFS25cY2XQkACG+IezN2EuIXwg9fHpga2Fr4iiFuLPU1FSWLVtGZGQk8fHxFBcXq2316tXj1KlTkgxWUfe9NnFubi7Jycm0bt2a9evX4+rqesd+bdq0MWqAlcGkw8xpB2Dvz1CvI7QeWWGH2bVrF507d8bW1pbMzMyHmlUMkFugo/dn8VzKL+LjoS14qqu/cQIVopbQ6XXsSN9BG/c2OFiWzMyfe2guXyR+QZ/6fZjdd7aJIxTi3o4ePUpUVBRRUVHs3LmzVFuLFi2IiIggIiKCDh06VMglRXKa2Djuu7SMg4MDLVu2ZN68eXTv3v2hrzsTf0rdDTu/gytnKjQZ7NixIw0bNuTUqVOsWLGCUaNGPdT+HKwteCWkMe8vO8wX608wpK0PTjYWRopWiJqpUF/ItgvbWJe8jg3nNpBblMu/evyLwQ0HAzAoYBBN6zSls1dnE0cqxJ0ZDAZ27dpFZGQkUVFRJCXdrCyh0Wjo0qULw4YNIyIigsaNjV8hQ1SMctcZHDNmDFevXuWnn37i1KlTvP7667i4uLBnzx48PT3x8fGpiDhrrhtlZVyMsx7x3Wg0GkaNGsU///lPfv3114dOBgGe6OzLj9uSOZmZxxfrjvPBECldIcTtrhdfZ0vqFtYmr2Xj+Y3k6/LVNjcbNwr1hep9TztPPO08TRGmEHdVVFSkrnO/bNky0tLS1DYLCwv69evHsGHDGDx4MHXryjXk1VG5k8EDBw4QEhKCk5MTZ8+eZfz48bi4uLB06VJSUlL48ccfKyLOmutGWRnXik0GATUZXLNmDVevXsXZ2fmh9mdhpuWDwS342w87+Gl7MqM61yfQy9E4wQpRjeXr8tl4fiPrktexOXUz14uvq20eth4M8BtAiF8Ibd3bSvFnUSXl5uYSHR1NVFQUq1atIjs7W21zcHBg4MCBDBs2jPDwcBwd5f9+dVfuZPDVV19l7NixzJgxAweHmytQDBw4kCeffNKowdUKFVhj8HYtW7akRYsWHD58mMjISMaNG/fQ++zR2I3wll6sOZTOtGWH+fX5LlJqRtRau9J38dORn9iSuoUiw80l4Xzsfejv158QvxBaubVCq5EJV6LqyczMZPny5URFRbF+/XoKC28Ztfb0ZOjQoURERNC3b1+5VKyGKXcyuHv3bv73v/+V2e7j40N6erpRgqo1DAa4fKbk+0oYGQR44oknePfdd/nxxx+NkgwCvDOoGRuSMtlx5jIrDqQxpI23UfYrRFV3teAqGo1GLfGSnp/OhnMbAPB18GWAf8kIYHOX5vIhSVRJp0+fJioqisjISLZs2cKtc0obNWqkXv/XpUsXtFr5EFNTlTsZtLKyIicnp8z248ePy5Tx8so5D/pC0FqAY+XMgnr66ad57733iI+P5/Tp0zRo8PAjkvXq2PJS70bMXHecf606Sr9AD+ysyv3UEqJamZU4i/mH5/NK+1cY17Lkg1Vw/WBeaP0C/f3606ROE0kARZWjKAr79u1TE8CDBw+Wau/QoYOaADZvLh9iaotyp/lDhgzho48+QqfTASUTE1JSUnjzzTcZMWKE0QOs0W6MCtbxB7PKSZ7q169PSEgIAAsWLDDafp/v1QBfF1vScwqYue640fYrRFWQeS2TRccWkXU9S91W164uekXP8Ss3n++Olo5MajeJpi5N5U1UVBnFxcXEx8czefJkAgICaN++PR999BEHDx7EzMyMvn37Mnv2bJKTk9m9ezfvvPMOLVq0kOdwLXLfdQZvyM7O5tFHH2X37t3k5ubi7e1Neno6Xbt2ZfXq1Q9dv84UTFanSFHg2mW4dgncm1TaYRcuXMjo0aPx8/Pj9OnTRhv6Tzh+kTFzd6LVwNKXutO2vrNR9iuEKaTlpbEueR3rU9azL3MfCgrvBL3DqMCSmfg5RTlcKbiCn6OfiSMVoqzr16+zbt06IiMjWbFiBZcuXVLbbGxsCAsLIyIigkGDBt21bnB1IHUGjaPcw1FOTk6sW7eOzZs3c+DAAfLy8mjfvr062iTKQaMBO9eSWyUaNmwYTk5OJCcnEx8fT9++fY2y3+Am7gxr50Pk3lSmLjnAipd7yMokolq5kHeBdcnriDkbw8Gs0qfP2ri3wdXm5mvV0dIRR0uZRSmqjsuXL7Nq1SqioqKIjo7m2rVrapuLiwuDBw9m2LBh9O/fH1tbWclG3PTA5yZ79OhRIYtOi4pnY2PDqFGj+O6775g/f77RkkGA9x5pTsLxixxLz+V/G08zsU8jo+1biIpwtwRQg4b2nu3p79effr798LLzMmGUQtzZ+fPn1RVA4uPjSy0J6+vrS0REBMOGDaNHjx6Ym8u13OLOynWa2GAwMH/+fJYuXcrZs2fRaDQEBATw6KOP8tRTT1Xb6wtMNsy8+g3QaKHrS+DsW3nHBbZv307Xrl2xsbEhPT3dqHWiovamMvm3fViaa4l+pScN3O2Ntm8hjGXT+U18s/+bMglgB88ODPAfQH+//rjZuJkwQiHKUhSFo0ePqiuA7N69u1R7q1at1CXg2rVrV23fl++XnCY2jvv+mKAoCkOGDGH16tW0adOGVq1aqU/KsWPHsnTpUqKioiow1BpGUUrWJNblQ6dnK/3wQUFBNGvWjKNHj/Lzzz/z0ksvGW3fQ9t6E7k3lYTjF3njjwP89kJXzLQ1+x+SqPou5F3A0sxSTfB0Bh0Hsw5KAiiqPIPBwI4dO9QE8MSJE2qbRqOhW7duDBs2jKFDh9KokZyNEeV338ng/Pnz2bhxI7GxsfTp06dUW1xcHBEREfz44488/fTTRg+yRrp2qSQRBHCqX+mH12g0vPjii7zyyit88803TJgwwWifIDUaDf8c1pKwWZvYnXyF7zae4qXe8g9KmM7M3TOZd3gez7d+npfbvQxAd5/uvBP0DiF+IZIAiiqnsLCQDRs2EBUVxbJly0rV8bW0tCQkJISIiAiGDBmCp6csYSgezn1f3b9o0SLefvvtMokgQN++fZk6dSq//PKLUYObPn06nTp1wsHBAQ8PDyIiIkotig1QUFDAxIkTcXV1xd7enhEjRpCRkWHUOCrEleSSrw51wcLaJCE8/fTT2NracujQITZv3mzUfderY8u0wc0B+GLdcQ5fyL7HI4QwjrS8NBYcXkBqXqq6rYlLEzRoyLyWqW6zMrNiVOAoSQRFlZGTk8Nvv/3GE088gYeHB+Hh4Xz33XfqpTxPPPEEv/32G1lZWaxatYrx48dLIiiM4r6TwQMHDhAWFnbX9vDwcPbv32+UoG5ISEhg4sSJbN++nXXr1qHT6RgwYAD5+TcXen/11VdZsWIFixcvJiEhgQsXLjB8+HCjxlEhrp4t+VrH32QhODs7M3r0aAC+/vpro+//0Q71GNDcE51eYcpv+ynQ6e/9ICEewI0EcPSq0QxYMoD/7P4Pa86sUdv7+fYjbmQcH3f/2IRRClFWRkYG//vf/xg4cCDu7u6MGjWKX3/9lZycHOrWrcuLL75IdHQ0Fy9eZOHChYwcObLUUrBCGMN9nya+fPnyX34C8fT05MqVK0YJ6obo6OhS9+fPn4+HhweJiYn06tWL7OxsfvjhBxYuXKjOiJ03bx7NmjVj+/btdOnSxajxGNWNkUFn09Yoe+mll/j+++9ZsmQJGRkZRv2UqdFomD68FXtSrpCUkcvMdcd5e2Azo+1f1G5peWmsTV7L2rNrOZB1QN1+Yxawr8PNSVk25jbYmNuYIkwhyjh58qS6Asi2bdtKLQHXpEkTdQWQzp07yxJwolLcdzKo1+v/clq6mZkZxcXFRgnqbrKzS041uri4AJCYmIhOpytV4zAwMBBfX1+2bdt212SwsLCw1ALcubm5FRj1XVw5W/K1jmmTwbZt29KlSxe2b9/ODz/8wNtvv23U/bvaW/Hv4a157sfdfL/pNL0au9OjsZyWEw9GTQCT13LgYtkEMNQ/lBDfENxtZWlMUXUoisKePXvUEjCHDh0q1d6pUye1BExgYGCNnwEsqp5yzSYeO3YsVlZWd2y/NbmqCAaDgcmTJ9O9e3datmwJQHp6OpaWljg7O5fq6+npWepi29tNnz6dDz/8sCLDvbfcP+Mz8cgglIwObt++nW+//ZbXX38dCwsLo+4/pLknTwb5snBHCpN/28vqv/fEw9E010mK6ml72na+3PulJICi2iguLmbTpk3qDOBz586pbebm5vTu3ZuIiAiGDh0qJVEq2Zw5c/jss89IT0+nTZs2fPnll3Tu3PmOfXU6HdOnT2fBggWkpqbStGlTPv3001KXzfn7+5OcnFzmsS+99BJz5swBoHfv3iQkJJRqf+GFF/j222+N+JM9uPtOBseMGXPPPhU5k3jixIlGm+jw1ltvMWXKFPV+amoqzZs3f+j9lsuTv0F+lskmj9zqscce4x//+Afnzp3jjz/+4IknnjD6Md5/pDl7kq9wLD2XV37dx8/PBUm5GXFXaXlpaDQatdCzFi0HLh5QE8ABfiVlYCQBFFXJtWvXWLt2LZGRkaxcuZLLly+rbba2toSHh6tLwNWpU8eEkdZev/32G1OmTOHbb78lKCiIWbNmERoaSlJSEh4eHmX6v/vuu/z88898//33BAYGEhMTw7Bhw9i6dSvt2rUDYNeuXaWKfR86dIj+/fvz2GOPldrX+PHj+eijj9T7VWoVGKUamDhxolKvXj3l9OnTpbbHxsYqgHLlypVS2319fZWZM2fe9/7PnTunAMq5c+eMEW619NFHHymA0r59e8VgMFTIMU5m5irN3luj+L25Upm5NqlCjiGqv6/3fq20nN9Smb5jurqtWF+s/HbsNyUzP9OEkQlRVlZWljJ//nwlIiJCsbGxUQD15urqqowbN05Zvny5cu3aNVOHWiOV9/27c+fOysSJE9X7er1e8fb2VqZPn37H/nXr1lW++uqrUtuGDx+ujB49+q7HeOWVV5SGDRuWei8NDg5WXnnllfuK0RSq9JWpiqIwadIkIiMjiYuLIyAgoFR7hw4dsLCwIDY2Vt2WlJRESkoKXbt2rexwq7UJEyZgY2PDnj17ygxlG0tDd3v+NawVALPjTrDlZFaFHEdUH+n56fx4+EdOXz2tbmvu2hwNGi5dv6RuM9OaMbLpSBkJFFVCSkoKs2fPpm/fvnh6ejJ27FiioqK4fv06fn5+TJ48mfj4eNLT05k7dy6DBw/GxkYmMFWk3NxccnJy1NudLl0rKioiMTGx1DwDrVZLSEgI27Ztu+N+CwsLsbYufQbPxsbmrmcpi4qK+Pnnn3nmmWfKXPv5yy+/4ObmRsuWLXnrrbdKrR1talV6ocKJEyeycOFCli1bhoODg3odoJOTEzY2Njg5OfHss88yZcoUXFxccHR05OWXX6Zr165VeyZx6h7Y+B+o1xF6Trl3/0rg5ubGuHHj+Prrr/nPf/5D7969K+Q4Ee182H76Er/uOsfLi/ayfFJ36tWpQkPlosJlXc9iXfI6os9EsydzDwBXCq/wSvtXAOjm3Y3Yx2Il8RNVhqIoHD58WJ0BvGfPnlLtrVu3VmcAt2nTRiaAmMDtl3pNmzaNDz74oNS2rKws9Hp9maoZnp6eHDt27I77DQ0NZebMmfTq1YuGDRsSGxvL0qVLS50WvlVUVBRXr15l7NixpbY/+eST+Pn54e3tzYEDB3jzzTdJSkpi6dKl5ftBK0iVTga/+eYbgDKJybx589Rf9BdffIFWq2XEiBEUFhYSGhpaITXzjCrzCCStAt21KpMMQknNxm+++YZVq1Zx5MiRCruO8oMhLTh0IZtDqTk8/2MiSyZ0w8bSrEKOJaqGqwVXWZ+ynugz0ezK2IVBMaht7T3a09i5sXrfwsxCEkFhcnq9nu3bt6sJ4KlTp9Q2jUZDjx491CXgGjRoYMJIBcCRI0fw8fFR799tsmt5/fe//2X8+PHqLO+GDRsybtw45s6de8f+P/zwA+Hh4Xh7e5fa/vzzz6vft2rVirp169KvXz9OnTpFw4YNjRLrw6jSyaByS+2lu7G2tmbOnDnqjJ1q4UaNQROXlbldo0aNiIiIIDIykv/85z93fbI/LGsLM757qiNDvtzMkbQcXv9jP18+UfMXVK9tcopyiEuJI/psNDsu7KBYuVl6qpVbK8L8wxjgP0CdJCKEqRUWFhIbG0tUVBTLly8vtZqVlZUV/fv3JyIigsGDB99xsoEwHQcHBxwdHf+yj5ubG2ZmZmVWKcvIyMDL687/h9zd3YmKiqKgoIBLly7h7e3N1KlT7/gBIDk5mfXr19/XaF9QUBBQUnNSksHa6kaNwSpQVuZ2r7/+OpGRkfz000+8//77+Pv7V8hxfJxt+OZvHXjy++2sPJBGc29HWb+4hjhy6Qjf7P+GLalb0Bl06vZmLs0I9Q8l1D+Ueg5SSkNUDdnZ2axZs4bIyEhWr15NXl6e2ubk5MQjjzxCREQEYWFh2NvbmzBS8bAsLS3p0KEDsbGxREREACVl62JjY5k0adJfPtba2hofHx90Oh1Llixh5MiRZfrMmzcPDw8PBg0adM9Y9u3bB0DdunXL/XNUBEkGTeFq1RwZBOjatSshISGsX7+e6dOn891331XYsToHuPDBkBa8G3WIz2KSaORuz4AWMkpU3Vwvvk6+Ll9d41dRFOLPxQPQ0KkhYQFhhPmH4e/kb7IYhbhVWloay5cvVycn6nQ3P7R4e3szdOhQhg0bRnBwMJaWliaMVBjblClTGDNmDB07dqRz587MmjWL/Px8xo0bB5SUyPPx8WH69OkA7Nixg9TUVNq2bUtqaioffPABBoOBN954o9R+DQYD8+bNY8yYMWUW6Dh16hQLFy5k4MCBuLq6cuDAAV599VV69epF69atK+cHvxfTTmauGiq9tMznzRRlmqOinNtVOccrp02bNimAYmFhoSQnJ1f48d5aekDxe3Ol0vTd1crelCsVfjxhPJEnIpVOP3dS3t70trrNYDAoPxz8QTl++bgJIxOitKSkJOXTTz9Vunbtqmg0mlIlYAIDA5WpU6cqO3bsUPR6valDFeXwIO/fX375peLr66tYWloqnTt3VrZv3662BQcHK2PGjFHvx8fHK82aNVOsrKwUV1dX5amnnlJSU1PL7DMmJkYBlKSksmXTUlJSlF69eikuLi6KlZWV0qhRI+X1119XsrOzy/fDViCNotzHhXk13Pnz56lfvz7nzp2r+Erweh184gGKAV47Dg7GWwvYmPr27cuGDRuYMGFChU/I0ekNPLdgNwnHL+JqZ8nSl7rh52pXoccU5acz6NiRtgNvO28aOJdcL7M7fTfjYsbRtE5TFg9eLNd9iipDURR2796tLgF35MiRUu1BQUFEREQQERFBYGCgiaIUD6tS379rMEkGqeQnU84FmN0eFD28kwFVdBHyhIQEevfujaWlJadOnarw30teYTGPf7eNwxdyCHCzY8mEbrjYyekZU9Mb9OzO2M2aM2uITYnlauFVRjUdxTtd3gHAoBg4nHWYlm4tJREUJqfT6di4cSORkZEsW7aM8+fPq23m5ub07duXiIgIhgwZUmrmqai+JBk0DrlmsLI5esM7aXD9SpVNBAGCg4MJDg4mISGBTz75pMLXT7S3Mmfe2E4M+3orZ7LyeW7BLn5+LghbS3mKVjaDYmBf5j7WnFnDuuR1XCq4WfzZxdoFJysn9b5Wo6WVeytThCkEAPn5+cTExBAVFcXKlSu5cuWK2mZnZ0d4eDjDhg1j4MCBZdaxF0KUkJFB5JPF3WzatIlevXphZmbG4cOHadq0aYUf82RmLsO/3kpOQTE9G7vx/dMdsbaQGoQVTVEUDmUdYs3ZNaw9u5aMazdLLzhZORHiG0JYQBgdPTtirpUEXZhWVlYWK1asICoqirVr11JQUKC2ubu7M2TIEIYNG0a/fv3KrB4hahZ5/zYOSQaRJ9NfGTJkCCtWrGD48OEsWbKkUo6ZmHyFp37YwbUiPSHNPPnmb+2xMKu6o6jVWUZ+BguPLSTmbAypeanqdnsLe/r69iXMP4wu3l2w0FqYMEoh4OzZs+r1f5s2bcJguFm4PCAgQF0BpFu3bpiZyQfI2kLev41DkkEq+cm0+Qs4vxvaj4EmAyr2WEZw+PBhWrdujcFgYNu2bZW2zN/WU1mMm7eLwmIDQ9p488XjbTHTyjVpxnC9+Do25iXrpJ7NPsvgqMEA2Jjb0Lt+b8L8w+ju0x0rM+NU8BfiQSiKwsGDB9UVQG7UZbuhbdu2agLYqlUruWa1lpJk0DjkfE9lS94GJ2KgcX9TR3JfWrRowdixY5k7dy5vvPEGCQkJlfJPt1tDN775W3ue/zGR5fsvYG2h5d/DW6OVhPCB7UzbyfSd0/F39OeLPl8A4O/kz9gWY2np1pJe9XqpSaIQpqDX69m6das6Anj69Gm1TavV0rNnT3UGcEUVxBeiNpJksLJl/zm7zan6fIL58MMPWbhwIZs2bWLZsmVq5faK1jfQk/+OasfLi/bw++7zFOsVZjzaGnM5ZXxfUvNS0Rv0+Dr6AuBo5cjJqydJz0+nSF+EpVnJbO3XOr5myjBFLVdQUEBsbCyRkZEsX76cixcvqm3W1tYMGDBAXQLOzc3NhJEKUXNJMljZbiSDjtUnGaxXrx5TpkzhX//6F1OmTCE0NBQbm8oZQRrUui4GpR2Tf9vH0r2pFOoNzHq8rVxDeBcZ+RmsTV5L9JloDmQdYHCDwfyr578AaFqnKZ/1+oyu3l3VRFAIU7h69SqrV68mMjKSNWvWkJ+fr7Y5OzszePBgIiIiCA0Nxc5Oao4KUdEkGaxMBTlQmF3yvVP1qnH11ltvsWDBAs6cOcN//vMf3nvvvUo79uA23liYaXl50R5WHUijqNjAV0+2w8pcLhIHyLqexbrkdUSfiWZv5l4USi4D1mq05OlurrOq0WgICwgzVZiilrtw4QLLli0jKiqKuLg4iouL1TYfHx8iIiIYNmwYvXr1wsJCJiwJUZlkAgmVeAFq5lH4ugtYO8PU5Io7TgX57bffGDVqFNbW1hw7dgw/v8pdW3nDsUxe+DmRomIDPRu78c3fOmBvVTs/z1wtuMr6lPVEn41mV/ouDMrNmZXtPdoT6h/KAP8B6nrBQphCUlISkZGRREVFsWPHjlJtzZs3VxPADh06yAQQ8UBkAolx1M53UlNRrxesb9o4HtDIkSP59ttviY+P57XXXuOPP/6o1OP3CfRg7phOjP9xN5tOZPH4d9uYN64THg61o47YNd21khHAs9Fsv7CdYuXmyEort1aE+ocS6h+Kl52XCaMUtZnBYGD37t1qAnjs2LFS7V27dlUngDRp0sREUQohbifJYGUqyAZz62o1eeRWGo2G2bNn065dO5YsWcKaNWsIDw+v1Bh6NHbj1+e78Mz8XRy+kMPwr7fy4zOdaeBuX6lxVBZFUdQRkzxdHu9teU89DRzoEqgmgPUdqucHDFH96XQ64uPjiYqKYtmyZaSm3qxXaWFhQb9+/dQl4OrWrWvCSIUQdyOniankYWZFAd11sLSt2ONUoNdee42ZM2dSv359Dh8+jIODQ6XHkHwpn6fn7iT50jXq2Frwf2M60cGvTqXHUVFOZ59mzt45FBmK+LLvl+r2aVun4WXnRZh/GAFOASaMUNRmeXl5REdHq0vAZWdnq2329vYMHDiQYcOGER4ejpOT01/sSYiHI6eJjUOSQeTJVF75+fm0bt2a06dPM3HiRL766iuTxJGVV8iz83ex/3w2lmZa/jW8FY92qJ5/vyJ9EVcLr+Jh6wHAudxzDFw6EK1GS9xjcbjauJo4QlHbXbx4kRUrVhAZGcm6desoLCxU2zw8PBg6dCgRERH069cPKyspWC4qh7x/G4ckg8iT6UHExcXRr18/ADZu3EjPnj1NEse1omIm/7qPtUdK1tJ9rkcAU8MDq0UtQp1Bx860naw5s4a4lDg6eHUoNQr405Gf6ODZgWYuzeTiemESp0+fZtmyZURGRrJly5ZSS8A1bNhQXQGkS5cusgScMAl5/zYOSQapxCfToidAo4XQf0Id/4o7TiUZP348//d//0eTJk3Yt29fpdUevJ3BoDAr9gSzY08A0LOxG1890R4n26pXnkJv0JOYkcias2tYn7yeq4VX1TYfex9WDFsh6wALk1EUhb1796orgBw8eLBUe/v27dUEsEWLFvIhRZicJIPGIckglfRkUhT42B0MOph8EJx9K+Y4lejq1as0b96ctLQ0Jk2axJdffnnvB1Wg1QfTeO33/VzX6fF1sWXOk+1pVc/01ysZFAP7L+5nzZk1rEteR9b1LLXNxdqF/n79CfUPpb1He8y0MroiKpdOp2PTpk1qAnju3Dm1zczMjODgYPUUsK9v9f+/JWoWSQaNQ2YTV5Zrl0oSQQD7mlH6w9nZmXnz5hEWFsZXX31FeHg4AwcONFk8A1vVxc/Vlhd+SiTl8jVGfLOVdx9pxlNd/Cp9BENRFA5fOsyaM2uIORtDxrUMtc3R0pEQvxDC/MPo5NUJc628DEXlysvLIyYmRp0AcvXqVbXN1taWsLAwIiIiGDRoEC4uLqYLVAhRKeRdqLLkXCj5aucO5jVnKbDQ0FAmT57MrFmzGDduHAcOHMDT09Nk8bTwdmLVyz35xx/7WXckg/eXHWb76Uv8e0RrHK0r7/Rrni6Pp9c8je7PDwB2Fnb0rd+XsIAwutbtioWZnAoWlSszM5MVK1YQFRVVZgKIu7s7Q4YMYejQoYSEhJjskg8hhGlIMlhZctNLvjrUvDpb06dPJzY2loMHD/LMM8+wcuVKk15L5GRrwf+e6sDcLWeZvvooqw+mcyg1h5kj29DR3/ijHJcLLrPo2CKSc5KZ0WsGAA6WDoT4haAoCmEBYfTw6YGVmcywFJXr5MmT6unfrVu3cutVQQ0aNFCv/+vatatMABGiFpNksLLk/jky6Oht2jgqgLW1NQsXLqRjx46sXr2aGTNm8Oabb5o0Jo1Gw7M9Amjv68ykhXtJuXyNkd9t4/leDXm1f+OHXte4oLgAa/OSlU8MioH/HfgfBsXA39v9nXoOJdetfNrzU7nAXlQqRVFITExUE8DDhw+Xau/QoYO6AohMABFC3CDJYGXJSSv56lAzrhe8XcuWLZk9ezYvvPACb7/9Nh07dlRLz5hSO986rJnckw+XH2HJnvN8m3CK+KRMZo5sS3Nvx3LtKzUvlZizMUSficbe0p65oXMBcLNx47lWz+Hv6I+L9c2RR3mjFZWhqKiIhISEO64AYm5uTu/evdUVQOrXl5VqhBBlSTJYWfRFJUvROdS8kcEbxo8fz/bt25k3bx6jRo1iz549VeLNx9Hags9HtqF/c0/eiTzIsfRchs7ZzEu9GzGhd0OsLe4+SpiRn8Ha5LVEn4nmQNYBdbuF1oLswmycrEpmK7/c7uUK/zmEuCE3N1ddAWTVqlWlVgCxs7MjPDyciIgIBg4cSJ06NWdlHiFExZDSMlTi1HRFAUMx1ODJA9evX6d79+7s3buXzp07k5CQgLW1tanDUmXlFfLW0oOs+7NIdQM3O/45rBVdG95c4SPrehbrktcRfSaavZl71bWANWjo5NWJUP9QQvxCSo0CClHR0tPT1Qkg69evp6ioSG27dQWQvn37VqnXnBAVSUrLGIckg8iTydhOnz5Nx44duXLlCk888QS//PJLlTplqigKqw+m88GKw1zMLZlRObS9E+2bnWfThfXsytiFQbm50kJ7j/aE+ocywH8AbjZupgpb1DKKonDo0CGWL1/O8uXL2blzZ6n2Ro0aqRNAgoKCZAKIqJXk/ds45DSxMLoGDRqwePFiwsLCWLRoEY0aNeKjjz4ydVgqjUbDoNZ16dHYjc9ijvHLjjPE5k0hbs91tU8rt1aE+ocS6h+Kl13NvM5TVD1FRUVs3LhRTQCTk5NLtXfq1EmdANKsmSxTKIQwDkkGK0NxIcwNKykrM+L/wNLW1BFVuH79+vHdd9/x7LPP8vHHH9OwYUPGjBlj6rAo1BeyLnkdezP28m6Xd/kkohXD2tVjQkxL8nSpFOe0xseiCy907UVwE3dThytqgcuXL7NmzRpWrFjBmjVryMnJUdusra0JCQlhyJAhPPLII9StW/NKUwkhTE+SwcqQmw4X9oCZFVjUnmKuzzzzDCdPnmT69Ok899xzeHl5ERoaWulxKIqijqDo9DqmbZlGkaGIkU1H0tSlKR386hA/9muW7slg5rrjnLlUxJi5O+nT1J23BjajiadDpccsaraTJ0+yYsUKli9fzqZNm9Dr9Wqbh4cHgwcPZsiQIYSEhGBrW/M/PAohTEuSwcqQe0tZmVp2WueTTz7hzJkz/PrrrwwbNoyYmBh69uxZ4cct1Bey+fxmos9Gk3ktkwXhCwCwt7Tn8cDHsbOww9nKWe1va2nN37r4MbiNN1/FnWD+1rNsSLpI/PGLRLT1YXJIY/xc7So8blEz6fV6duzYoZ7+PXr0aKn2li1bMmTIEAYPHkznzp3RarUmilQIURtJMlgZcmpuwel70Wq1LFiwgOzsbNasWcOgQYOIi4ujY8eORj+WTq9j64WtRJ+NZsO5DeTr8tW2lJwUfB19AXij0xt33YeTjQXvDGrOk0F+zIg+xppD6UTuTWX5/guM7FiPl/s2xtu59ozuigeXl5fHunXrWL58OatWreLixYtqm7m5OcHBwQwePJjBgwfToEEDE0YqhKjtJBmsDOpSdLVzIoKlpSVLliwhPDychIQEwsLCiIuLo3Xr1g+9b51Bx860nUSfjSY2JZbcoly1zcvOi1C/UMICwqjvUL56hwFudnzztw4cPJ/N5+uSiE+6yKKd51iSmMpjHevxfK8GMlIoykhJSWHVqlWsXLmS2NjYUuv/Ojs7Ex4ezpAhQwgLC8PZ2dl0gQohxC0kGawMN5aiq8EFp+/FxsaG5cuXExISwq5du+jduzdr1659oBHCYkMxO9N3svbsWuJS4rhSeEVtc7dxZ4D/AML8w2jt3hqt5uFOt7Wq58T8cZ3ZffYy/1mbxPbTl/llRwqLdqYwqLU3LwY3oIW300MdQ1RfxcXFbNu2jVWrVrFq1SoOHTpUqr1BgwYMGTKEIUOG0KNHDywsam6NUSFE9SXJYGW4MTLoWLtnAjo6OhITE0N4eDg7duygb9++rF69mh49etz3PhRF4bEVj3Hy6kl1m4u1C/39+hPqH0p7j/aYaY1fb62jvwuLxndh55nLfJNwiviki6zYf4EV+y8Q3MSd8T0b0L2Rq5T6qAWysrKIjo5m1apVxMTEcOXKzQ8jWq2Wrl27MmjQIIYOHSrlX4QQ1YIkg5XF3KaktEwtV6dOHdatW8cjjzzCxo0bCQ0NJTIykgEDBpTpqzfo2Z62nc2pm3m90+toNVo0Gg0dPDtw6fol+vn1Y4DfADp5dcJcW/FPZY1GQ1ADV4IauHLkQg7fJpxi5YELJBy/SMLxizR0t+Pprv4Mb++Dg7WMANUUiqKwf/9+dfRv+/bt3Fqr38XFhfDwcAYNGsSAAQNwdXX9i70JIUTVIyuQUEkVzBUFFANUwKhVdXTt2jWGDx9OTEwM5ubmfPvttzz77LOlysAU6YsI/i2YPF0eC8IW0N6zPQA5RTnYmttWSgJ4LymXrvHD5tMs2ZNKXmExAHaWZozoUI+nuvjRWMrSVEt5eXmsX7+eVatWsXr1ai5cuFCqvU2bNgwaNIhBgwbJ6h9CmJCsQGIckgwiTyZTKSws5Nlnn+WXX34BoNvfulH/sfosGrxITQg/2/UZBcUFjG4+mgZOVXfGZW6Bjsi9qSzYepZTF2/OYm5b35nHOtZjcBtvHGW0sMpSFIUTJ06wZs0aVq1aRUJCQqm1f21tbQkJCWHQoEEMHDhQ/k8IUUXI+7dxSDKIPJlMQafXsS1tG9Fnovnxix9JjUwFwLGTI7GLY+noZ/zSM5VBURS2nrrEgq1niTuWSbGh5OVlZa4lrKUXj3WoT9eGrphp5ToyU8vNzSUuLo7o6GhiYmI4c+ZMqfYGDRqoo3/BwcFYW1ubKFIhxN3I+7dxSDJIBT+ZriTD4rFQxx8em2fcfVczNxLAmLMxbDi3oVQZGN12HSe+P4FBb6B58+YsWbKEwMBAE0b78C7mFrJsXyq/7z7H8Yw8dbu7gxUDW3rxSBtvOvjWQSuJYaUwGAzs379fTf62bNlCcXGx2m5paUmPHj3UBLBJkyYy+UOIKk6SQeOQZJAKfjIlb4N5YVAnAF7ZZ9x9VwPXdNfYnLqZ2JRYNp7fSJ7uZlLkZuNGiG8IA/wH0N6jPdu3bWfkyJFcuHABe3t75s6dy2OPPWbC6I1DURQOnM9mceI5VuxPI/u6Tm3zcrRmYKu6DGpdl3b1nSUxNLKLFy+ydu1aYmJiiImJITMzs1R7o0aNCAsLIywsjN69e2NnJ7UjhahOJBk0DkkGqeAn05Fl8PvTUL8LPBtj3H1XAyOWj+D4lePqfXcbd0L8QhjgN4B2Hu3KlIHJyMhg1KhRxMfHAzB+/HhmzpyJvb19ZYZdYYqKDWw5mcXKA2msPZxObuHNkSk3eyv6BrrTr5knPRu7YWtp+gky1Y1Op2P79u3ExMQQHR3Nnj17Ss38tbOzo1+/foSGhhIaGkrDhg1NGK0Q4mFJMmgc8m5T0fL+HImw9zBtHBVMp9cReTKSTamb+Dz4cyzNLAHo4dODa7prhPiF0M+33z0LQXt6erJu3TreffddZsyYwffff09sbCw//fQT3bp1q6wfp8JYmmvpE+hBn0APCotbsvF4FqsOXGD90Uyy8gr5ffd5ft99HktzLV0buBLSzIPgJh74utqaOvQqyWAwcOjQIWJjY1m/fj0bN24kLy+vVJ+2bdsSGhpKWFgY3bp1w9LS0kTRCiFE1SQjg1TwJ4u4T2DjZ9DpORj0uXH3bWI5RTk4WjoCYFAMhCwO4eL1i8zpN4de9XoBUKgvxFJr+UDXXsXHxzNmzBhSUlLQarW89tprTJs2rUaeyisqNrDr7GXWH80g9mgmKZevlWqvV8eGHo3c6N7IjW4NXXG1tzJRpKZ39uxZ1q9fT2xsLHFxcWVO/bq5udG/f3/CwsIYMGAAXl61cxlIIWoDGRk0DkkGqeAn0/KXYc+P0OcdCH7DuPuuZIqicPLqSWJTYolNiSXzWiZxj8Wpp3oXHF6AXtEzMGAgXnbGeQPOzs7m73//Oz/++CMAvr6+fPXVVwwePNgo+6+KFEXhZGYe649msuFYJnvPXUGnL/0ybVbXkW4NXenkX4cOfi64O9Tc5DArK4u4uDh19O/06dOl2m1tbenVqxchISH069eP1q1bo9U+3DKEQojqQZJB45BkkAp+Mi18HI5Hw+D/Qoexxt13JdAZdOzJ2EP8uXg2nNtAal6q2qbVaPn9kd9p6tK0wuNYtWoVEydOJDk5GYChQ4fy2Wef0bhx4wo/tqnlFxaz8+xltpzIYvPJLI6l55bp4+dqSwe/OnTyd6GjXx0auttX28koly5dYtOmTSQkJBAfH8++fftKtZuZmREUFKQmf126dJFTv0LUUg/y/j1nzhw+++wz0tPTadOmDV9++SWdO3e+Y1+dTsf06dNZsGABqampNG3alE8//ZSwsDC1j7+/v/redKuXXnqJOXPmAFBQUMBrr73Gr7/+SmFhIaGhoXz99dd4eno+wE9tfJIMUsHJ4K+j4VQcPDoXmoYbd98VJKcohy2pW9hwbgObz28mV3cz+bDUWhJUN4j+fv3pXb83dazrVFpc+fn5fPzxx3z++ecUFxdjbm7Oiy++yHvvvYeHR82+JvNWWXmFbDmZxc4zl0lMvkJSRi63v4odrc1p6eNEq3pOtPJxorWPM/VdbKpkqZSMjAw2btxIQkICCQkJHDp0qEyfVq1a0a9fP/r160evXr1wdHQ0QaRCiKqmvO/fv/32G08//TTffvstQUFBzJo1i8WLF5OUlHTH95E333yTn3/+me+//57AwEBiYmKYMmUKW7dupV27dkBJ1QK9Xq8+5tChQ/Tv358NGzbQu3dvACZMmMCqVauYP38+Tk5OTJo0Ca1Wy5YtW4zzi3hIkgxSicvRVcE34hsURWHhsYVsSNlAYkYixcrNWa4u1i70qteL3vV609W7K7YWpp3McPjwYd544w1Wr14NgIODA1OmTOHvf/87Li4uJo3NFLKv69iTcoXEs1fYnXyZfeeuUqAzlOnnZGNBSx9HWvk406yuA029HGjgZo+leeWeUk1NTVUTv4SEBJKSksr0adasGcHBwQQHB9OnT58q8+lZCFG1lPf9OygoiE6dOvHVV18BJZPQ6tevz8svv8zUqVPL9Pf29uadd95h4sSJ6rYRI0ZgY2PDzz//fMdjTJ48mZUrV3LixAk0Gg3Z2dm4u7uzcOFCHn30UQCOHTtGs2bN2LZtG126dHmQH92oZDZxZaliiaDOoOP01dPqKV6NRsPyU8s5cukIAA2dGhJcP5g+9fvQyq1VmRIwptSiRQtWrVpFXFwcb7zxBomJiXz44Yd8/vnnTJgwgVdffZW6deuaOsxK42RjQZ+mHvRpWvKpVqc3kJSey8HUbA6mZnMoNZtjablkX9ex5eQltpy8pD7WXKshwM2OJl4ONPV0oIlnSZLo62JrlFVSiouLOXjwIFu3bmXbtm1s3bq1zEofAK1btyY4OJhevXrRq1evWjXSK4R4eLm5ueTk5Kj3rayssLIqfS11UVERiYmJvPXWW+o2rVZLSEgI27Ztu+N+CwsLy6w+ZGNjw+bNm+/Yv6ioiJ9//pkpU6aoZ2ISExPR6XSEhISo/QIDA/H19ZVkUJjOpeuXeCTyEQqKC9g0ahP2liU1/P7W7G9cKbhC7/q98XX0NXGU99a3b1927tzJkiVL+Oc//8n+/fv57LPPmD17Nk8++SQTJ06kQ4cOpg6z0lmYaWnp40RLHyee+HNbUbGB4xm5HDifzaEL2RxPzyUpPZfcwmJOZOZxIjOPVaTdsg8N9V1saeBmh7+rHf5udiXfu9nh5Wh91+sRL126xPbt29XEb+fOneTn55fqo9Vqadu2rTry17Nnz1o5oiuEMJ7mzZuXuj9t2jQ++OCDUtuysrLQ6/VlzjR4enpy7NixO+43NDSUmTNn0qtXLxo2bEhsbCxLly4tdVr4VlFRUVy9epWxY8eq29LT07G0tMTZ2bnMcdPT0+/vB6xgkgxWpEunYMmz4NIQHv2h0g+v0+vYm7mXzambMSgG/tHpHwC42rjiYu1CTlEOyTnJtHBrAcDghtVvhq5Wq+Wxxx7j0UcfZfXq1fzzn/9k27ZtzJs3j3nz5tG5c2cmTJjAY489ViNL0twvS/ObCeINiqKQll1AUkZuSXKYkcvxjFxOZORRWGzg9MV8Tl/ML7Mvawstfi52eNlrsbh6jmsXjnPx9BFOHdnHmZPHy/R3dHSkS5cudO3alW7duhEUFISTk1OZfkII8aCOHDmCj4+Pev/2UcEH9d///pfx48cTGBiIRqOhYcOGjBs3jrlz596x/w8//EB4eDje3t5GOX5lkWSwIuWkwoW9UFT2DbUiGBQDJ66cYGf6Tnam7WRn+k6uFZfUq7M1t+WV9q9gYWYBwA+hP+Bu416lTv8+DI1Gw6BBgxg4cCDbtm3j66+/ZvHixezcuZOdO3cyceJEIiIiGD16NP3798fCwsLUIZucRqPB29kGb2cb9RQzgMGgcCH7OmezrnEmK48zWdc4lXGVw4cOkpx0iEsXjnM6/SS6rGQwlP10bOHig2uDVvg1b0uLth1pFtgMrzp2eDpa4+FgxXWssNMbMDeT8i9CCONwcHC458QyNzc3zMzMyMjIKLU9IyPjrvVI3d3diYqKoqCggEuXLuHt7c3UqVNp0KBBmb7JycmsX7+epUuXltru5eVFUVERV69eLTU6+FfHrWySDFYkdfWRirn4XVEUzuWeY3vadnam72RX+i4uF1wu1cfF2oXu3t3p4dMDhZtzhYxVB7Cq0Wg0dOvWjW7dujFz5kzmzp3L//3f/3Hq1CkWLlzIwoULcXNz45FHHuGRRx6hf//+MjP1NpcvX+L4gQMcPHiQAwcOsH//fg4ePEhRUVGZvnaOdXALaI513UYY3BpS4NIQrU3JqF86kJ4BsRllrxHUaMDVzgpPRys8HKzwcLDGxd4SVztL6thalvre1d5SluYTQjw0S0tLOnToQGxsLBEREUDJBJLY2FgmTZr0l4+1trbGx8cHnU7HkiVLGDlyZJk+8+bNw8PDg0GDBpXa3qFDBywsLIiNjWXEiBEAJCUlkZKSQteuXY3zwz0kmU1MBc4m3vY1xLwFLUeUlJYxksxrmcxMnEliRiLp+aWvN7Axt6G9Z3uCvIIIqhtEoEvgXy7/VhsoisLOnTtZuHAhv/76a6kVKywsLAgODiYkJITg4GD1RVsb5Obmcvz4cQ4fPsyBW5K/u13DUqdOHTp27FjqVr9+/VLlagqL9aRnF3D+ynVSr1wnPaeAjJwCMnMLycwpICOnkIt5hegN5fu3Y22hxeXPJNHFzgpnGwucbCxwtDHH0doCRxsLHK3LbnOwNsdCRiCFqLEepLTMmDFj+O677+jcuTOzZs3i999/59ixY3h6evL000/j4+PD9OnTAdixYwepqam0bduW1NRUPvjgA86cOcOePXtKjfIZDAYCAgJ44okn+Pe//13muBMmTGD16tXMnz8fR0dHXn75ZQC2bt1qnF/EQ6oxH7fLU0Sy0uT9ORT9ECODZ7LPEH02Gg8bD0Y0KflEYWdhx5ozazAoBsy15rRxb0NQ3SCCvIJo5dZKPRUsSmg0GoKCgggKCuLzzz9n48aNrFq1ihUrVnDixAnWr1/P+vXrgZLVLG6MLLZv35727dtTr169Klmf734UFRWRnJzM8ePHSUpKUm/Hjx8nLS3tro9r2LAhrVq1onXr1rRq1YoOHTrg7+9/z9+DlbkZfq52+Lne/fpMvUHhcn7Rn0liAZk5hWTmFnI5v+iOtyK9gQKdgQvZBVzILij378DO0gxHGwvsrcyxszLHzsoMW0tz7CzNsLUyx97KHFtLM+wszbG1Mvvz/q3tZlhb3LxZmWslwRSimnr88ce5ePEi77//Punp6bRt25bo6Gh1UsmN5U9vKCgo4N133+X06dPY29szcOBAfvrppzKTQdavX09KSgrPPPPMHY/7xRdfoNVqGTFiRKmi01VFjRgZLG8RydtV2Mhg5ATYvxD6TYOeU+7ardhQzIW8C5y6eopjl4/R17evWvJl5emVvLXpLdq4t+HngTdrGv167Fd8HX1p59EOG3Mb48Vcyxw/fpzVq1eTkJDAxo0buXz5cpk+rq6utGvXjsDAQBo3bkzjxo1p0qQJfn5+mJub7vOUwWDg0qVLZGRkcP78eZKTk0lOTubs2bPq9xcuXOCvXuKenp4EBgaqSV/r1q1p0aIF9vb2lfiT3J2iKOQX6bmcV8Tla0Vczi/kUl4R2dd15BQUk3NdV3Ir0JFzvZicAl1J23Ud+UV3nu1nDGZaDdbm2psJooUWa3MzrC20tySOJdus/vzeytwMS3MtlmYaLMxKEkqL2++babE0v+2+mRaLP7dZqts1fz5Wi1ajwVyrqbYrzgjxMGQ5OuOoEclgeYtI3q7Cnkw/DSf9bDy5Ie9T2DiE3KJcsq5ncfH6RS5eu0hqXipnc85yLvccxYabRZ7/0fEfjGkxBoDknGTm7JtDZ6/OPNrkUePFJsowGAwcOXKEjRs3smvXLvbs2cORI0coLi6+Y3+tVounpyfe3t7UrVuXunXr4uXlhZOTk3pzdHTE0dERS0tLzM3NsbCwwNzcXE0idTodRUVFpb7m5+eTk5NDdnZ2qa+XL18mIyNDvd1e9f5ubGxsaNKkCU2bNlW/Nm3alMaNG5f5dFuTFOsN5BYUk329JEHMLywmv0jPtaJi8gqLuVaoJ7+omGtF+pK2W9rzC29+zSsspkCnp7C4bCHvqkSjKakbeSM5NNNqML8lWSy5X/LVTHPrfS1mGjDXatVttz9Gq7lxA61Gg+aW77VaSt/XaNCo39+hv+ZG/z+3ae/RX3vbY/kz6dWAhpJ9abhZylXzZ5+b9zU3updqu/UxlNqmHqFk241j3tJ+t/3djKv0/m7GdeOYpR9z+4j77Wn9rc2a21rvddLi9vZ7Pb7c/e9xvFt7+LrYGr3IvSSDxlHtTxM/aBHJwsJC9X5ubtm1Xo3CzJKX6npy4vgPcPyvS8tYmVnh5+hHoEsgjZwbqdv9HP2Y0WtGxcQnStFqtbRs2ZKWLVuq2woKCjh06BD79+/n+PHjnDhxghMnTnDy5EkKCgpIS0v7y9OtlcHV1RVvb2/8/PzUm7+/v/q9h4dHtT3N/TDMzbTUsbOkjp1x1i02GJQ/T1nrKdD9+bX4lu//3F5YrKdQZ/iz7dZ2A8UGAzq9gaJiBZ3eoN6K9Aq64tvu32gvNlCkN1BUbED35/biO1xzqSig0yuAQmHZ8IUwubjXgmngXjXOOojSqn0y+CBFJKdPn86HH35Y8cE9+SvOMc9Q58pJrMytsDO3w83GDXdbd9xt3PGy88LfyZ8AxwA87Txr/USPqsja2lqdLHErg8FARkYGaWlpXLhwQf2amZlJdna2esvJySEnJwedTkdxcbH6tbi4GEVRsLS0xNLSEgsLC/WrjY1NqZHFW0caPT09S908PDxqzYQXU9NqNVhrS04Bm5rBoKAzlCSHekPpW7HBcNv9st8XGwwYDKh9iw0KhlLtt94vST4VBQzKza8G9X7J93rDze9vtJfcv7XvjbZb2g337q833NwGJf0UShLgG2mxoraBgqKu133rfQXUB9y6TX3srftUlFv2fUv/vzgmtzz+Row3j3d73Df7c8tx/sqtJ/Ju73r7Y2+tHlG27a8fS7kee/8xaWvhh9Lqotongw/irbfeYsqUm9fwpaamlqlebixzQ403i1hUHVqtVj013L59e1OHI2oZrVaDldYMq1r5H1wIYWzV/l/JgxSRvH3NwlvXMxRCCCGEqE2q/XnJW4tI3nCjiGRVKeYohBBCCFFVVfuRQYApU6YwZswYOnbsqBaRzM/PZ9y4caYOTQghhBCiSqsRyeC9ikgKIYQQQog7qxHJIMCkSZPuubagEEIIIYQordpfMyiEEEIIIR6cJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELVYjVmB5GEYDAYA0tLSTByJEEIIIe7XjfftG+/j4sFIMghkZGQA0LlzZxNHIoQQQojyysjIwNfX19RhVFsaRVEUUwdhasXFxezduxdPT0+0WuOdOc/NzaV58+YcOXIEBwcHo+1XVAz5e1Uf8reqPuRvVb1Ut7+XwWAgIyODdu3aYW4u41sPSpLBCpSTk4OTkxPZ2dk4OjqaOhxxD/L3qj7kb1V9yN+qepG/V+0kE0iEEEIIIWoxSQaFEEIIIWoxSQYrkJWVFdOmTcPKysrUoYj7IH+v6kP+VtWH/K2qF/l71U5yzaAQQgghRC0mI4NCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJIMVaM6cOfj7+2NtbU1QUBA7d+40dUjiNh988AEajabULTAw0NRhiT9t3LiRwYMH4+3tjUajISoqqlS7oii8//771K1bFxsbG0JCQjhx4oRpgq3l7vW3Gjt2bJnXWlhYmGmCreWmT59Op06dcHBwwMPDg4iICJKSkkr1KSgoYOLEibi6umJvb8+IESPUpVtFzSPJYAX57bffmDJlCtOmTWPPnj20adOG0NBQMjMzTR2auE2LFi1IS0tTb5s3bzZ1SOJP+fn5tGnThjlz5tyxfcaMGcyePZtvv/2WHTt2YGdnR2hoKAUFBZUcqbjX3wogLCys1Gtt0aJFlRihuCEhIYGJEyeyfft21q1bh06nY8CAAeTn56t9Xn31VVasWMHixYtJSEjgwoULDB8+3IRRiwqliArRuXNnZeLEiep9vV6veHt7K9OnTzdhVOJ206ZNU9q0aWPqMMR9AJTIyEj1vsFgULy8vJTPPvtM3Xb16lXFyspKWbRokQkiFDfc/rdSFEUZM2aMMnToUJPEI/5aZmamAigJCQmKopS8jiwsLJTFixerfY4ePaoAyrZt20wVpqhAMjJYAYqKikhMTCQkJETdptVqCQkJYdu2bSaMTNzJiRMn8Pb2pkGDBowePZqUlBRThyTuw5kzZ0hPTy/1OnNyciIoKEheZ1VUfHw8Hh4eNG3alAkTJnDp0iVThySA7OxsAFxcXABITExEp9OVem0FBgbi6+srr60aSpLBCpCVlYVer8fT07PUdk9PT9LT000UlbiToKAg5s+fT3R0NN988w1nzpyhZ8+e5Obmmjo0cQ83XkvyOqsewsLC+PHHH4mNjeXTTz8lISGB8PBw9Hq9qUOr1QwGA5MnT6Z79+60bNkSKHltWVpa4uzsXKqvvLZqLnNTByCEKYWHh6vft27dmqCgIPz8/Pj999959tlnTRiZEDXLqFGj1O9btWpF69atadiwIfHx8fTr18+EkdVuEydO5NChQ3KtdC0nI4MVwM3NDTMzszIzrzIyMvDy8jJRVOJ+ODs706RJE06ePGnqUMQ93HgtyeusemrQoAFubm7yWjOhSZMmsXLlSjZs2EC9evXU7V5eXhQVFXH16tVS/eW1VXNJMlgBLC0t6dChA7Gxseo2g8FAbGwsXbt2NWFk4l7y8vI4deoUdevWNXUo4h4CAgLw8vIq9TrLyclhx44d8jqrBs6fP8+lS5fktWYCiqIwadIkIiMjiYuLIyAgoFR7hw4dsLCwKPXaSkpKIiUlRV5bNZScJq4gU6ZMYcyYMXTs2JHOnTsza9Ys8vPzGTdunKlDE7f4xz/+weDBg/Hz8+PChQtMmzYNMzMznnjiCVOHJihJzm8dOTpz5gz79u3DxcUFX19fJk+ezCeffELjxo0JCAjgvffew9vbm4iICNMFXUv91d/KxcWFDz/8kBEjRuDl5cWpU6d44403aNSoEaGhoSaMunaaOHEiCxcuZNmyZTg4OKjXATo5OWFjY4OTkxPPPvssU6ZMwcXFBUdHR15++WW6du1Kly5dTBy9qBCmns5ck3355ZeKr6+vYmlpqXTu3FnZvn27qUMSt3n88ceVunXrKpaWloqPj4/y+OOPKydPnjR1WOJPGzZsUIAytzFjxiiKUlJe5r333lM8PT0VKysrpV+/fkpSUpJpg66l/upvde3aNWXAgAGKu7u7YmFhofj5+Snjx49X0tPTTR12rXSnvxOgzJs3T+1z/fp15aWXXlLq1Kmj2NraKsOGDVPS0tJMF7SoUBpFUZTKT0GFEEIIIURVINcMCiGEEELUYpIMCiGEEELUYpIMCiGEEELUYpIMCiGEEELUYpIMCiGEEELUYpIMCiGEEELUYpIMCiGEEELUYpIMCiEq3NixY02yKsj8+fPRaDRoNBomT578l339/f2ZNWtWpcR143g3Yrt9DVghhKhMshydEOKhaDSav2yfNm0a//3vfzFVfXtHR0eSkpKws7MzyfHvZteuXWzatIkRI0aYOhQhRC0nyaAQ4qGkpaWp3//222+8//77JCUlqdvs7e2xt7c3RWhASbLq5eVlsuPfjbu7Oy4uLqYOQwgh5DSxEOLheHl5qTcnJyc1+bpxs7e3L3OauHfv3rz88stMnjyZOnXq4Onpyffff09+fj7jxo3DwcGBRo0asWbNmlLHOnToEOHh4djb2+Pp6clTTz1FVlZWuWPOzMxk8ODB2NjYEBAQwC+//FKmz8yZM2nVqhV2dnbUr1+fl156iby8PADy8/NxdHTkjz/+KPWYqKgo7OzsyM3NpaioiEmTJlG3bl2sra3x8/Nj+vTp5Y5VCCEqmiSDQgiTWLBgAW5ubuzcuZOXX36ZCRMm8Nhjj9GtWzf27NnDgAEDeOqpp7h27RoAV69epW/fvrRr147du3cTHR1NRkYGI0eOLPexx44dy7lz59iwYQN//PEHX3/9NZmZmaX6aLVaZs+ezeHDh1mwYAFxcXG88cYbANjZ2TFq1CjmzZtX6jHz5s3j0UcfxcHBgdmzZ7N8+XJ+//13kpKS+OWXX/D393+wX5YQQlQkRQghjGTevHmKk5NTme1jxoxRhg4dqt4PDg5WevTood4vLi5W7OzslKeeekrdlpaWpgDKtm3bFEVRlI8//lgZMGBAqf2eO3dOAZSkpKT7jicpKUkBlJ07d6rbjh49qgDKF198cdefbfHixYqrq6t6f8eOHYqZmZly4cIFRVEUJSMjQzE3N1fi4+MVRVGUl19+Wenbt69iMBjuus8NGzYogHLlypW79hFCiIomI4NCCJNo3bq1+r2ZmRmurq60atVK3ebp6Qmgjtjt37+fDRs2qNcg2tvbExgYCMCpU6fu+7hHjx7F3NycDh06qNsCAwNxdnYu1W/9+vX069cPHx8fHBwceOqpp7h06ZI6Utm5c2datGjBggULAPj555/x8/OjV69eQMno4759+2jatCl///vfWbt27X3HKIQQlUmSQSGESVhYWJS6r9FoSm27MUvZYDAAkJeXx+DBg9m3b1+p24kTJ9QEzFjOnj3LI488QuvWrVmyZAmJiYnMmTMHgKKiIrXfc889x/z584GSU8Tjxo1T427fvj1nzpzh448/5vr164wcOZJHH33UqHEKIYQxyGxiIUS10L59e5YsWYK/vz/m5g/+ryswMJDi4mISExPp1KkTAElJSaVq/SUmJmIwGPj888/Raks+M//+++9l9vW3v/2NN954g9mzZ3PkyBHGjBlTqt3R0ZHHH3+cxx9/nEcffZSwsDAuX74ss4iFEFWKjAwKIaqFiRMncvnyZZ544gl27drFqVOniImJYdy4cej1+vveT9OmTQkLC+OFF15gx44dJCYm8txzz2FjY6P2adSoETqdji+//JLTp0/z008/8e2335bZV506dRg+fDivv/46AwYMoF69emrbzJkzWbRoEceOHeP48eMsXrwYLy+vMqejhRDC1CQZFEJUC97e3mzZsgW9Xs+AAQNo1aoVkydPxtnZWR29u1/z5s3D29ub4OBghg8fzvPPP4+Hh4fa3qZNG2bOnMmnn35Ky5Yt+eWXX+5aFubZZ5+lqKiIZ555ptR2BwcHZsyYQceOHenUqRNnz55l9erV5Y5VCCEqmkZRTLQsgBBCVLD58+czefLkCl3u7aeffuLVV1/lwoULWFpaluux8fHx9OnThytXrsiIoRDCZOQjqhCiRsvOzsbe3p4333zTqPu9du0ap06d4t///jcvvPBCuRPBFi1aEB4ebtSYhBDiQcjIoBCixsrNzSUjIwMAZ2dn3NzcjLbvDz74gH/+85/06tWLZcuWlXvJveTkZHQ6HQANGjSQ08dCCJORZFAIIYQQohaTj6JCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELWYJINCCCGEELXY/wN+1C7O6giNQwAAAABJRU5ErkJggg==", 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", 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" ] @@ -890,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -932,83 +944,83 @@ " \n", " \n", " 0\n", - " 2023-05-24 17:20:59.177108\n", + " 2024-05-06 14:27:54.077274\n", " 75\n", " 0.1\n", " 100.000000\n", " 0.000000\n", " 0.000000\n", " 115.000000\n", - " 38.681200\n", - " 0.664464\n", - " 9.244807\n", - " 0.840659\n", - " 5.164032\n", + " 41.590997\n", + " 0.666327\n", + " 9.940248\n", + " 0.840987\n", + " 5.570241\n", " 1.000000\n", " \n", " \n", " 1\n", - " 2023-05-24 17:21:59.177108\n", + " 2024-05-06 14:28:54.077274\n", " 75\n", " 0.1\n", - " 99.949243\n", - " 0.050757\n", + " 99.945424\n", + " 0.054576\n", " 0.000000\n", - " 114.936358\n", - " 38.681200\n", - " 0.664464\n", - " 9.244807\n", - " 0.840659\n", - " 5.164032\n", + " 114.931573\n", + " 41.590997\n", + " 0.666327\n", + " 9.940248\n", + " 0.840987\n", + " 5.570241\n", " 1.000000\n", " \n", " \n", " 2\n", - " 2023-05-24 17:22:59.177108\n", + " 2024-05-06 14:29:54.077274\n", " 75\n", " 0.1\n", - 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" 9.244665\n", - " 0.840653\n", - " 5.163666\n", - " 0.999929\n", + " 99.782028\n", + " 0.217959\n", + " 0.000012\n", + " 114.727209\n", + " 41.590359\n", + " 0.666292\n", + " 9.940096\n", + " 0.840981\n", + " 5.569824\n", + " 0.999925\n", " \n", " \n", " ...\n", @@ -1028,83 +1040,83 @@ " \n", " \n", " 30236\n", - " 2023-06-14 17:16:59.177108\n", + " 2024-05-27 14:23:54.077274\n", " 75\n", " 0.1\n", - " 0.009655\n", - " 58.346523\n", - " 41.643822\n", - " 70.271599\n", - " 38.459012\n", - " 0.654399\n", - " 9.191704\n", - " 0.838862\n", - " 5.045785\n", - " 0.977102\n", + " 0.006066\n", + " 54.974093\n", + " 45.019841\n", + " 71.887698\n", + " 41.381387\n", + " 0.656754\n", + " 9.890151\n", + " 0.839286\n", + " 5.451494\n", + " 0.978682\n", " \n", " \n", " 30237\n", - " 2023-06-14 17:17:59.177108\n", + " 2024-05-27 14:24:54.077274\n", " 75\n", " 0.1\n", - " 0.009652\n", - " 58.345271\n", - " 41.645077\n", - " 70.272185\n", - " 38.459016\n", - " 0.654399\n", - " 9.191705\n", - " 0.838862\n", - " 5.045787\n", - " 0.977102\n", + " 0.006065\n", + " 54.972773\n", + " 45.021162\n", + " 71.888345\n", + " 41.381392\n", + " 0.656754\n", + " 9.890153\n", + " 0.839286\n", + " 5.451497\n", + " 0.978682\n", " \n", " \n", " 30238\n", - " 2023-06-14 17:18:59.177108\n", + " 2024-05-27 14:25:54.077274\n", " 75\n", " 0.1\n", - " 0.009650\n", - " 58.344019\n", - " 41.646331\n", - " 70.272772\n", - " 38.459021\n", - " 0.654399\n", - " 9.191706\n", - " 0.838862\n", - " 5.045789\n", - " 0.977103\n", + " 0.006064\n", + " 54.971454\n", + " 45.022482\n", + " 71.888992\n", + " 41.381397\n", + " 0.656754\n", + " 9.890154\n", + " 0.839286\n", + " 5.451499\n", + " 0.978683\n", " \n", " \n", " 30239\n", - " 2023-06-14 17:19:59.177108\n", + " 2024-05-27 14:26:54.077274\n", " 75\n", " 0.1\n", - " 0.009648\n", - " 58.342767\n", - " 41.647585\n", - " 70.273358\n", - " 38.459026\n", - " 0.654399\n", - " 9.191707\n", - " 0.838862\n", - " 5.045791\n", - " 0.977103\n", + " 0.006063\n", + " 54.970135\n", + " 45.023802\n", + " 71.889638\n", + " 41.381402\n", + " 0.656754\n", + " 9.890155\n", + " 0.839286\n", + " 5.451502\n", + " 0.978683\n", " \n", " \n", " 30240\n", - " 2023-06-14 17:20:59.177108\n", + " 2024-05-27 14:27:54.077274\n", " 75\n", " 0.1\n", - " 0.009646\n", - " 58.341516\n", - " 41.648839\n", - " 70.273944\n", - " 38.459030\n", - " 0.654399\n", - " 9.191708\n", - " 0.838862\n", - " 5.045793\n", - " 0.977103\n", + " 0.006061\n", + " 54.968816\n", + " 45.025123\n", + " 71.890285\n", + " 41.381407\n", + " 0.656755\n", + " 9.890156\n", + " 0.839286\n", + " 5.451504\n", + " 0.978684\n", " \n", " \n", "\n", @@ -1112,49 +1124,49 @@ "" ], "text/plain": [ - " Datetime Temperature Injection NA \n", - "0 2023-05-24 17:20:59.177108 75 0.1 100.000000 \\\n", - "1 2023-05-24 17:21:59.177108 75 0.1 99.949243 \n", - "2 2023-05-24 17:22:59.177108 75 0.1 99.898533 \n", - "3 2023-05-24 17:23:59.177108 75 0.1 99.847871 \n", - "4 2023-05-24 17:24:59.177108 75 0.1 99.797257 \n", + " Datetime Temperature Injection NA \\\n", + "0 2024-05-06 14:27:54.077274 75 0.1 100.000000 \n", + "1 2024-05-06 14:28:54.077274 75 0.1 99.945424 \n", + "2 2024-05-06 14:29:54.077274 75 0.1 99.890904 \n", + "3 2024-05-06 14:30:54.077274 75 0.1 99.836439 \n", + "4 2024-05-06 14:31:54.077274 75 0.1 99.782028 \n", "... ... ... ... ... \n", - "30236 2023-06-14 17:16:59.177108 75 0.1 0.009655 \n", - "30237 2023-06-14 17:17:59.177108 75 0.1 0.009652 \n", - "30238 2023-06-14 17:18:59.177108 75 0.1 0.009650 \n", - "30239 2023-06-14 17:19:59.177108 75 0.1 0.009648 \n", - "30240 2023-06-14 17:20:59.177108 75 0.1 0.009646 \n", + "30236 2024-05-27 14:23:54.077274 75 0.1 0.006066 \n", + "30237 2024-05-27 14:24:54.077274 75 0.1 0.006065 \n", + "30238 2024-05-27 14:25:54.077274 75 0.1 0.006064 \n", + "30239 2024-05-27 14:26:54.077274 75 0.1 0.006063 \n", + "30240 2024-05-27 14:27:54.077274 75 0.1 0.006061 \n", "\n", - " NB NC tau Jsc Voc Isc \n", - "0 0.000000 0.000000 115.000000 38.681200 0.664464 9.244807 \\\n", - "1 0.050757 0.000000 114.936358 38.681200 0.664464 9.244807 \n", - "2 0.101465 0.000002 114.872848 38.681002 0.664453 9.244760 \n", - "3 0.152123 0.000005 114.809470 38.680805 0.664442 9.244712 \n", - "4 0.202732 0.000011 114.746224 38.680607 0.664431 9.244665 \n", + " NB NC tau Jsc Voc Isc \\\n", + "0 0.000000 0.000000 115.000000 41.590997 0.666327 9.940248 \n", + "1 0.054576 0.000000 114.931573 41.590997 0.666327 9.940248 \n", + "2 0.109094 0.000002 114.863300 41.590784 0.666316 9.940197 \n", + "3 0.163555 0.000006 114.795178 41.590572 0.666304 9.940147 \n", + "4 0.217959 0.000012 114.727209 41.590359 0.666292 9.940096 \n", "... ... ... ... ... ... ... \n", - "30236 58.346523 41.643822 70.271599 38.459012 0.654399 9.191704 \n", - "30237 58.345271 41.645077 70.272185 38.459016 0.654399 9.191705 \n", - "30238 58.344019 41.646331 70.272772 38.459021 0.654399 9.191706 \n", - "30239 58.342767 41.647585 70.273358 38.459026 0.654399 9.191707 \n", - "30240 58.341516 41.648839 70.273944 38.459030 0.654399 9.191708 \n", + "30236 54.974093 45.019841 71.887698 41.381387 0.656754 9.890151 \n", + "30237 54.972773 45.021162 71.888345 41.381392 0.656754 9.890153 \n", + "30238 54.971454 45.022482 71.888992 41.381397 0.656754 9.890154 \n", + "30239 54.970135 45.023802 71.889638 41.381402 0.656754 9.890155 \n", + "30240 54.968816 45.025123 71.890285 41.381407 0.656755 9.890156 \n", "\n", " FF Pmp Pmp_norm \n", - "0 0.840659 5.164032 1.000000 \n", - "1 0.840659 5.164032 1.000000 \n", - "2 0.840657 5.163910 0.999976 \n", - "3 0.840655 5.163788 0.999953 \n", - "4 0.840653 5.163666 0.999929 \n", + "0 0.840987 5.570241 1.000000 \n", + "1 0.840987 5.570241 1.000000 \n", + "2 0.840985 5.570102 0.999975 \n", + "3 0.840983 5.569963 0.999950 \n", + "4 0.840981 5.569824 0.999925 \n", "... ... ... ... \n", - "30236 0.838862 5.045785 0.977102 \n", - "30237 0.838862 5.045787 0.977102 \n", - "30238 0.838862 5.045789 0.977103 \n", - "30239 0.838862 5.045791 0.977103 \n", - "30240 0.838862 5.045793 0.977103 \n", + "30236 0.839286 5.451494 0.978682 \n", + "30237 0.839286 5.451497 0.978682 \n", + "30238 0.839286 5.451499 0.978683 \n", + "30239 0.839286 5.451502 0.978683 \n", + "30240 0.839286 5.451504 0.978684 \n", "\n", "[30241 rows x 13 columns]" ] }, - "execution_count": 11, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1180,7 +1192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb index 433647eb..c414cb5e 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,14 +46,27 @@ "import os\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import pvdeg" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -74,19 +87,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# load weather and location data, use pvlib read_psm3 function with map_variables = True\n", "\n", "sam_file = 'psm3.csv'\n", - "weather, meta = pvlib.iotools.read_psm3(os.path.join(DATA_DIR, sam_file), map_variables = True)" + "weather, meta = pvdeg.weather.read(os.path.join(DATA_DIR, sam_file), file_type='PSM3', map_variables = True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -119,7 +132,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " poa_global\n", @@ -345,7 +358,7 @@ "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", "\n", - " temp_air Dew Point wind_speed relative_humidity \\\n", + " temp_air dew_point wind_speed relative_humidity \\\n", "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", @@ -374,7 +387,7 @@ "[8760 rows x 15 columns]" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -385,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -586,7 +599,7 @@ "[8760 rows x 7 columns]" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -597,12 +610,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection.\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients. \n", + "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", "\n", "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", "from pvlib.location import Location\n", @@ -620,14 +633,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# set up system in pvlib\n", "lat = meta['latitude']\n", "lon = meta['longitude']\n", - "tz = meta['Local Time Zone']\n", + "tz = meta['tz']\n", "elevation = meta['altitude']\n", "surface_tilt = lat # fixed, latitude tilt\n", "surface_azimuth = 180 # south-facing\n", @@ -643,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -663,7 +676,7 @@ " losses_model: no_extra_losses" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -686,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -702,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -713,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -831,7 +844,7 @@ "[4301 rows x 3 columns]" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -850,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -869,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -890,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -911,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -938,12 +951,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " #calc device parameters for first row\n", @@ -952,7 +965,7 @@ " voc = letid.calc_voc_from_tau(tau, wafer_thickness, s_rear, jsc, temperature = 25)\n", " timesteps.at[index, 'Jsc'] = jsc\n", " timesteps.at[index, 'Voc'] = voc\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -961,7 +974,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -981,7 +994,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -1003,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1018,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1067,11 +1080,11 @@ " 0.000000\n", " 0.000000e+00\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", @@ -1079,15 +1092,15 @@ " 1999-01-01 09:30:00-07:00\n", " 12.378439\n", " 0.031607\n", - " 99.997546\n", - " 0.002454\n", + " 99.997547\n", + " 0.002453\n", " 0.000000e+00\n", " 114.996922\n", - " 41.593851\n", - " 0.666329\n", - " 10.107306\n", + " 41.590987\n", + " 0.666327\n", + " 10.106610\n", " 0.840987\n", - " 5.663867\n", + " 5.663460\n", " 0.999999\n", " \n", " \n", @@ -1095,15 +1108,15 @@ " 1999-01-01 10:30:00-07:00\n", " 16.595081\n", " 0.044220\n", - " 99.991952\n", - " 0.008048\n", - " 6.073443e-09\n", - " 114.989904\n", - " 41.593829\n", - " 0.666327\n", - " 10.107301\n", - " 0.840987\n", - " 5.663853\n", + " 99.991953\n", + " 0.008047\n", + " 6.072523e-09\n", + " 114.989905\n", + " 41.590966\n", + " 0.666326\n", + " 10.106605\n", + " 0.840986\n", + " 5.663446\n", " 0.999996\n", " \n", " \n", @@ -1111,15 +1124,15 @@ " 1999-01-01 11:30:00-07:00\n", " 17.457240\n", " 0.043201\n", - " 99.985928\n", - " 0.014072\n", - " 2.755674e-08\n", - " 114.982349\n", - " 41.593806\n", - " 0.666326\n", - " 10.107295\n", + " 99.985929\n", + " 0.014071\n", + " 2.755257e-08\n", + " 114.982350\n", + " 41.590942\n", + " 0.666324\n", + " 10.106599\n", " 0.840986\n", - " 5.663837\n", + " 5.663430\n", " 0.999994\n", " \n", " \n", @@ -1127,15 +1140,15 @@ " 1999-01-01 12:30:00-07:00\n", " 6.392783\n", " 0.007124\n", - " 99.985663\n", - " 0.014337\n", - " 2.864736e-08\n", - " 114.982017\n", - " 41.593805\n", - " 0.666326\n", - " 10.107295\n", + " 99.985664\n", + " 0.014336\n", + " 2.864303e-08\n", + " 114.982018\n", + " 41.590941\n", + " 0.666324\n", + " 10.106599\n", " 0.840986\n", - " 5.663836\n", + " 5.663429\n", " 0.999993\n", " \n", " \n", @@ -1159,15 +1172,15 @@ " 1999-12-31 12:30:00-07:00\n", " 9.515577\n", " 0.001151\n", - " 42.030312\n", - " 53.765479\n", - " 4.204209e+00\n", - " 72.485121\n", - " 41.388750\n", - " 0.656931\n", - " 10.057466\n", - " 0.839318\n", - " 5.545421\n", + " 42.029575\n", + " 53.766632\n", + " 4.203792e+00\n", + " 72.484546\n", + " 41.385898\n", + " 0.656929\n", + " 10.056773\n", + " 0.839317\n", + " 5.545020\n", " 0.979086\n", " \n", " \n", @@ -1175,47 +1188,47 @@ " 1999-12-31 13:30:00-07:00\n", " 28.119796\n", " 0.044499\n", - " 42.030968\n", - " 53.764500\n", - " 4.204532e+00\n", - " 72.485609\n", - " 41.388754\n", - " 0.656931\n", - " 10.057467\n", + " 42.030231\n", + " 53.765654\n", + " 4.204115e+00\n", + " 72.485034\n", + " 41.385901\n", + " 0.656929\n", + " 10.056774\n", " 0.839318\n", - " 5.545423\n", - " 0.979087\n", + " 5.545022\n", + " 0.979086\n", " \n", " \n", " 4298\n", " 1999-12-31 14:30:00-07:00\n", " 23.314672\n", " 0.035130\n", - " 42.037048\n", - " 53.758278\n", - " 4.204673e+00\n", - " 72.488710\n", - " 41.388777\n", - " 0.656932\n", - " 10.057473\n", + " 42.036311\n", + " 53.759433\n", + " 4.204257e+00\n", + " 72.488135\n", + " 41.385924\n", + " 0.656930\n", + " 10.056780\n", " 0.839318\n", - " 5.545435\n", - " 0.979089\n", + " 5.545034\n", + " 0.979088\n", " \n", " \n", " 4299\n", " 1999-12-31 15:30:00-07:00\n", " 17.890528\n", " 0.027891\n", - " 42.049612\n", - " 53.745658\n", - " 4.204730e+00\n", - " 72.495002\n", - " 41.388824\n", - " 0.656933\n", - " 10.057484\n", + " 42.048873\n", + " 53.746813\n", + " 4.204313e+00\n", + " 72.494425\n", + " 41.385972\n", + " 0.656932\n", + " 10.056791\n", " 0.839318\n", - " 5.545459\n", + " 5.545058\n", " 0.979093\n", " \n", " \n", @@ -1223,15 +1236,15 @@ " 1999-12-31 16:30:00-07:00\n", " 4.552365\n", " 0.001188\n", - " 42.050184\n", - " 53.745086\n", - " 4.204730e+00\n", - " 72.495287\n", - " 41.388826\n", - " 0.656934\n", - " 10.057485\n", + " 42.049445\n", + " 53.746242\n", + " 4.204314e+00\n", + " 72.494710\n", + " 41.385974\n", + " 0.656932\n", + " 10.056792\n", " 0.839318\n", - " 5.545460\n", + " 5.545059\n", " 0.979093\n", " \n", " \n", @@ -1242,47 +1255,47 @@ "text/plain": [ " Datetime Temperature Injection NA NB \\\n", "0 1999-01-01 08:30:00-07:00 3.348774 0.006239 100.000000 0.000000 \n", - "1 1999-01-01 09:30:00-07:00 12.378439 0.031607 99.997546 0.002454 \n", - "2 1999-01-01 10:30:00-07:00 16.595081 0.044220 99.991952 0.008048 \n", - "3 1999-01-01 11:30:00-07:00 17.457240 0.043201 99.985928 0.014072 \n", - "4 1999-01-01 12:30:00-07:00 6.392783 0.007124 99.985663 0.014337 \n", + "1 1999-01-01 09:30:00-07:00 12.378439 0.031607 99.997547 0.002453 \n", + "2 1999-01-01 10:30:00-07:00 16.595081 0.044220 99.991953 0.008047 \n", + "3 1999-01-01 11:30:00-07:00 17.457240 0.043201 99.985929 0.014071 \n", + "4 1999-01-01 12:30:00-07:00 6.392783 0.007124 99.985664 0.014336 \n", "... ... ... ... ... ... \n", - "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151 42.030312 53.765479 \n", - "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499 42.030968 53.764500 \n", - "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130 42.037048 53.758278 \n", - "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891 42.049612 53.745658 \n", - "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188 42.050184 53.745086 \n", + "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151 42.029575 53.766632 \n", + "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499 42.030231 53.765654 \n", + "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130 42.036311 53.759433 \n", + "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891 42.048873 53.746813 \n", + "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188 42.049445 53.746242 \n", "\n", " NC tau Jsc Voc Isc FF \\\n", - "0 0.000000e+00 115.000000 41.593861 0.666329 10.107308 0.840987 \n", - "1 0.000000e+00 114.996922 41.593851 0.666329 10.107306 0.840987 \n", - "2 6.073443e-09 114.989904 41.593829 0.666327 10.107301 0.840987 \n", - "3 2.755674e-08 114.982349 41.593806 0.666326 10.107295 0.840986 \n", - "4 2.864736e-08 114.982017 41.593805 0.666326 10.107295 0.840986 \n", + "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000e+00 114.996922 41.590987 0.666327 10.106610 0.840987 \n", + "2 6.072523e-09 114.989905 41.590966 0.666326 10.106605 0.840986 \n", + "3 2.755257e-08 114.982350 41.590942 0.666324 10.106599 0.840986 \n", + "4 2.864303e-08 114.982018 41.590941 0.666324 10.106599 0.840986 \n", "... ... ... ... ... ... ... \n", - "4296 4.204209e+00 72.485121 41.388750 0.656931 10.057466 0.839318 \n", - "4297 4.204532e+00 72.485609 41.388754 0.656931 10.057467 0.839318 \n", - "4298 4.204673e+00 72.488710 41.388777 0.656932 10.057473 0.839318 \n", - "4299 4.204730e+00 72.495002 41.388824 0.656933 10.057484 0.839318 \n", - "4300 4.204730e+00 72.495287 41.388826 0.656934 10.057485 0.839318 \n", + "4296 4.203792e+00 72.484546 41.385898 0.656929 10.056773 0.839317 \n", + "4297 4.204115e+00 72.485034 41.385901 0.656929 10.056774 0.839318 \n", + "4298 4.204257e+00 72.488135 41.385924 0.656930 10.056780 0.839318 \n", + "4299 4.204313e+00 72.494425 41.385972 0.656932 10.056791 0.839318 \n", + "4300 4.204314e+00 72.494710 41.385974 0.656932 10.056792 0.839318 \n", "\n", " Pmp Pmp_norm \n", - "0 5.663874 1.000000 \n", - "1 5.663867 0.999999 \n", - "2 5.663853 0.999996 \n", - "3 5.663837 0.999994 \n", - "4 5.663836 0.999993 \n", + "0 5.663467 1.000000 \n", + "1 5.663460 0.999999 \n", + "2 5.663446 0.999996 \n", + "3 5.663430 0.999994 \n", + "4 5.663429 0.999993 \n", "... ... ... \n", - "4296 5.545421 0.979086 \n", - "4297 5.545423 0.979087 \n", - "4298 5.545435 0.979089 \n", - "4299 5.545459 0.979093 \n", - "4300 5.545460 0.979093 \n", + "4296 5.545020 0.979086 \n", + "4297 5.545022 0.979086 \n", + "4298 5.545034 0.979088 \n", + "4299 5.545058 0.979093 \n", + "4300 5.545059 0.979093 \n", "\n", "[4301 rows x 13 columns]" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1309,12 +1322,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1362,7 +1375,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1386,7 +1399,6 @@ " \n", " \n", " \n", - " Datetime\n", " Temperature\n", " Injection\n", " NA\n", @@ -1400,86 +1412,96 @@ " Pmp\n", " Pmp_norm\n", " \n", + " \n", + " time\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " 1999-01-01 00:30:00-07:00\n", + " 1999-01-01 00:30:00-07:00\n", " 0.0\n", " NaN\n", " 100.000000\n", " 0.000000e+00\n", " 0.000000e+00\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", - " 1\n", - " 1999-01-01 01:30:00-07:00\n", + " 1999-01-01 01:30:00-07:00\n", " 0.0\n", " NaN\n", " 100.000000\n", " 1.702422e-15\n", " 0.000000e+00\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", - " 2\n", - " 1999-01-01 02:30:00-07:00\n", + " 1999-01-01 02:30:00-07:00\n", " 0.0\n", " NaN\n", " 100.000000\n", " 3.404843e-15\n", " 5.403329e-36\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", - " 3\n", - " 1999-01-01 03:30:00-07:00\n", + " 1999-01-01 03:30:00-07:00\n", " 0.0\n", " NaN\n", " 100.000000\n", " 5.107265e-15\n", " 1.620999e-35\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", - " 4\n", - " 1999-01-01 04:30:00-07:00\n", + " 1999-01-01 04:30:00-07:00\n", " 0.0\n", " NaN\n", " 100.000000\n", " 6.809686e-15\n", " 3.241997e-35\n", " 115.000000\n", - " 41.593861\n", - " 0.666329\n", - " 10.107308\n", + " 41.590997\n", + " 0.666327\n", + " 10.106612\n", " 0.840987\n", - " 5.663874\n", + " 5.663467\n", " 1.000000\n", " \n", " \n", @@ -1496,137 +1518,134 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 8755\n", - " 1999-12-31 19:30:00-07:00\n", + " 1999-12-31 19:30:00-07:00\n", " 0.0\n", " NaN\n", - " 27.832909\n", - " 6.778494e+01\n", - " 4.382156e+00\n", - " 66.112015\n", - " 41.336694\n", - " 0.654976\n", - " 10.044817\n", + " 27.833654\n", + " 6.778463e+01\n", + " 4.381716e+00\n", + " 66.112142\n", + " 41.333851\n", + " 0.654974\n", + " 10.044126\n", " 0.838966\n", - " 5.519653\n", + " 5.519257\n", " 0.974537\n", " \n", " \n", - " 8756\n", - " 1999-12-31 20:30:00-07:00\n", + " 1999-12-31 20:30:00-07:00\n", " 0.0\n", " NaN\n", - " 27.832909\n", - " 6.778494e+01\n", - " 4.382156e+00\n", - " 66.112015\n", - " 41.336694\n", - " 0.654976\n", - " 10.044817\n", + " 27.833654\n", + " 6.778463e+01\n", + " 4.381716e+00\n", + " 66.112142\n", + " 41.333851\n", + " 0.654974\n", + " 10.044126\n", " 0.838966\n", - " 5.519653\n", + " 5.519257\n", " 0.974537\n", " \n", " \n", - " 8757\n", - " 1999-12-31 21:30:00-07:00\n", + " 1999-12-31 21:30:00-07:00\n", " 0.0\n", " NaN\n", - " 27.832909\n", - " 6.778494e+01\n", - " 4.382156e+00\n", - " 66.112015\n", - " 41.336694\n", - " 0.654976\n", - " 10.044817\n", + " 27.833654\n", + " 6.778463e+01\n", + " 4.381716e+00\n", + " 66.112142\n", + " 41.333851\n", + " 0.654974\n", + " 10.044126\n", " 0.838966\n", - " 5.519653\n", + " 5.519257\n", " 0.974537\n", " \n", " \n", - " 8758\n", - " 1999-12-31 22:30:00-07:00\n", + " 1999-12-31 22:30:00-07:00\n", " 0.0\n", " NaN\n", - " 27.832909\n", - " 6.778494e+01\n", - " 4.382156e+00\n", - " 66.112015\n", - " 41.336694\n", - " 0.654976\n", - " 10.044817\n", + " 27.833654\n", + " 6.778463e+01\n", + " 4.381716e+00\n", + " 66.112142\n", + " 41.333851\n", + " 0.654974\n", + " 10.044126\n", " 0.838966\n", - " 5.519653\n", + " 5.519257\n", " 0.974537\n", " \n", " \n", - " 8759\n", - " 1999-12-31 23:30:00-07:00\n", + " 1999-12-31 23:30:00-07:00\n", " 0.0\n", " NaN\n", - " 27.832909\n", - " 6.778494e+01\n", - " 4.382156e+00\n", - " 66.112015\n", - " 41.336694\n", - " 0.654976\n", - " 10.044817\n", + " 27.833654\n", + " 6.778463e+01\n", + " 4.381716e+00\n", + " 66.112142\n", + " 41.333851\n", + " 0.654974\n", + " 10.044126\n", " 0.838966\n", - " 5.519653\n", + " 5.519257\n", " 0.974537\n", " \n", " \n", "\n", - "

8760 rows × 13 columns

\n", + "

8760 rows × 12 columns

\n", "" ], "text/plain": [ - " Datetime Temperature Injection NA \\\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 \n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", - "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 27.832909 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 27.832909 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 27.832909 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 27.832909 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 27.832909 \n", + " Temperature Injection NA NB \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", + "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.702422e-15 \n", + "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.404843e-15 \n", + "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.107265e-15 \n", + "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.809686e-15 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 20:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 21:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 22:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 23:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", "\n", - " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 115.000000 41.593861 0.666329 10.107308 \n", - "1 1.702422e-15 0.000000e+00 115.000000 41.593861 0.666329 10.107308 \n", - "2 3.404843e-15 5.403329e-36 115.000000 41.593861 0.666329 10.107308 \n", - "3 5.107265e-15 1.620999e-35 115.000000 41.593861 0.666329 10.107308 \n", - "4 6.809686e-15 3.241997e-35 115.000000 41.593861 0.666329 10.107308 \n", - "... ... ... ... ... ... ... \n", - "8755 6.778494e+01 4.382156e+00 66.112015 41.336694 0.654976 10.044817 \n", - "8756 6.778494e+01 4.382156e+00 66.112015 41.336694 0.654976 10.044817 \n", - "8757 6.778494e+01 4.382156e+00 66.112015 41.336694 0.654976 10.044817 \n", - "8758 6.778494e+01 4.382156e+00 66.112015 41.336694 0.654976 10.044817 \n", - "8759 6.778494e+01 4.382156e+00 66.112015 41.336694 0.654976 10.044817 \n", + " NC tau Jsc Voc \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", + "1999-01-01 01:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", + "1999-01-01 02:30:00-07:00 5.403329e-36 115.000000 41.590997 0.666327 \n", + "1999-01-01 03:30:00-07:00 1.620999e-35 115.000000 41.590997 0.666327 \n", + "1999-01-01 04:30:00-07:00 3.241997e-35 115.000000 41.590997 0.666327 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 20:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 21:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 22:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 23:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", "\n", - " FF Pmp Pmp_norm \n", - "0 0.840987 5.663874 1.000000 \n", - "1 0.840987 5.663874 1.000000 \n", - "2 0.840987 5.663874 1.000000 \n", - "3 0.840987 5.663874 1.000000 \n", - "4 0.840987 5.663874 1.000000 \n", - "... ... ... ... \n", - "8755 0.838966 5.519653 0.974537 \n", - "8756 0.838966 5.519653 0.974537 \n", - "8757 0.838966 5.519653 0.974537 \n", - "8758 0.838966 5.519653 0.974537 \n", - "8759 0.838966 5.519653 0.974537 \n", + " Isc FF Pmp Pmp_norm \n", + "time \n", + "1999-01-01 00:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 01:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 02:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 03:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 04:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 20:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 21:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 22:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 23:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", "\n", - "[8760 rows x 13 columns]" + "[8760 rows x 12 columns]" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1664,7 +1683,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb index 2f9f0dc8..134c1ef5 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -57,9 +57,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -78,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -202,7 +213,7 @@ "5 7 235.8 9.76 31.98 75.51 0.666250 40.164609 -0.381679" ] }, - "execution_count": 3, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -233,7 +244,7 @@ "0.6611301062313933" ] }, - "execution_count": 4, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -290,7 +301,7 @@ "(235.89622533600513, 236.7)" ] }, - "execution_count": 6, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -313,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -322,7 +333,7 @@ "0.6538686361228122" ] }, - "execution_count": 7, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -348,7 +359,7 @@ "(224.09272908700694, 224.8)" ] }, - "execution_count": 8, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -364,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -373,7 +384,7 @@ "0.050036816961295416" ] }, - "execution_count": 9, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -395,19 +406,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# load weather and location data, use pvlib read_psm3 function\n", "\n", "sam_file = 'psm3.csv'\n", - "weather, meta = pvlib.iotools.read_psm3(os.path.join(DATA_DIR,sam_file), map_variables = True)\n" + "weather, meta = pvdeg.weather.read(os.path.join(DATA_DIR, sam_file), file_type='PSM3', map_variables = True)\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -440,7 +451,7 @@ " dhi\n", " ghi\n", " temp_air\n", - " Dew Point\n", + " dew_point\n", " wind_speed\n", " relative_humidity\n", " poa_global\n", @@ -666,7 +677,7 @@ "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", "\n", - " temp_air Dew Point wind_speed relative_humidity \\\n", + " temp_air dew_point wind_speed relative_humidity \\\n", "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", @@ -695,7 +706,7 @@ "[8760 rows x 15 columns]" ] }, - "execution_count": 11, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -706,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -717,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -906,7 +917,7 @@ "[8760 rows x 7 columns]" ] }, - "execution_count": 14, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -925,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1264,7 +1275,7 @@ "[9 rows x 25 columns]" ] }, - "execution_count": 15, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1283,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1293,12 +1304,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# import the rest of the pvlib stuff\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients. \n", + "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", "\n", "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", "from pvlib.location import Location\n", @@ -1313,14 +1324,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "# set up system in pvlib\n", "lat = meta['latitude']\n", "lon = meta['longitude']\n", - "tz = meta['Local Time Zone']\n", + "tz = meta['tz']\n", "elevation = meta['altitude']\n", "surface_tilt = lat # fixed, latitude tilt\n", "surface_azimuth = 180 # south-facing\n", @@ -1336,7 +1347,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1356,7 +1367,7 @@ " losses_model: no_extra_losses" ] }, - "execution_count": 19, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1379,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1408,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -1421,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -1440,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1471,8 +1482,6 @@ " NB\n", " NC\n", " tau\n", - " Jsc\n", - " Voc\n", " \n", " \n", " \n", @@ -1485,8 +1494,6 @@ " 0.0\n", " 0.0\n", " 120.0\n", - " 41.454173\n", - " 0.661317\n", " \n", " \n", " 1\n", @@ -1497,8 +1504,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 2\n", @@ -1509,8 +1514,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 3\n", @@ -1521,8 +1524,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 4\n", @@ -1533,8 +1534,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " ...\n", @@ -1545,8 +1544,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 8755\n", @@ -1557,8 +1554,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 8756\n", @@ -1569,8 +1564,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 8757\n", @@ -1581,8 +1574,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 8758\n", @@ -1593,8 +1584,6 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", " 8759\n", @@ -1605,45 +1594,30 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", - " 0.000000\n", " \n", " \n", "\n", - "

8760 rows × 9 columns

\n", + "

8760 rows × 7 columns

\n", "" ], "text/plain": [ - " Datetime Temperature Injection NA NB NC \\\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.0 0.0 \n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "... ... ... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN NaN \n", - "\n", - " tau Jsc Voc \n", - "0 120.0 41.454173 0.661317 \n", - "1 NaN NaN 0.000000 \n", - "2 NaN NaN 0.000000 \n", - "3 NaN NaN 0.000000 \n", - "4 NaN NaN 0.000000 \n", - "... ... ... ... \n", - "8755 NaN NaN 0.000000 \n", - "8756 NaN NaN 0.000000 \n", - "8757 NaN NaN 0.000000 \n", - "8758 NaN NaN 0.000000 \n", - "8759 NaN NaN 0.000000 \n", + " Datetime Temperature Injection NA NB NC tau\n", + "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.0 0.0 120.0\n", + "1 1999-01-01 01:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "2 1999-01-01 02:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "3 1999-01-01 03:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "4 1999-01-01 04:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "... ... ... ... ... ... ... ...\n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", "\n", - "[8760 rows x 9 columns]" + "[8760 rows x 7 columns]" ] }, - "execution_count": 30, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1662,16 +1636,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -1680,7 +1654,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, srv_rear, generation, depth)\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -1700,7 +1674,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -1709,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1724,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1773,11 +1747,11 @@ " 0.000000e+00\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", - " 0.840102\n", - " 5.545822\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", + " 0.840101\n", + " 5.545424\n", " 1.000000\n", " \n", " \n", @@ -1789,11 +1763,11 @@ " 1.683090e-15\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", - " 0.840102\n", - " 5.545822\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", + " 0.840101\n", + " 5.545424\n", " 1.000000\n", " \n", " \n", @@ -1805,11 +1779,11 @@ " 3.366181e-15\n", " 5.269266e-36\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", - " 0.840102\n", - " 5.545822\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", + " 0.840101\n", + " 5.545424\n", " 1.000000\n", " \n", " \n", @@ -1821,11 +1795,11 @@ " 5.049271e-15\n", " 1.580780e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", - " 0.840102\n", - " 5.545822\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", + " 0.840101\n", + " 5.545424\n", " 1.000000\n", " \n", " \n", @@ -1837,11 +1811,11 @@ " 6.732362e-15\n", " 3.161560e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", - " 0.840102\n", - " 5.545822\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", + " 0.840101\n", + " 5.545424\n", " 1.000000\n", " \n", " \n", @@ -1865,15 +1839,15 @@ " 1999-12-31 19:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", - " 0.839288\n", - " 5.487995\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", + " 0.839287\n", + " 5.487602\n", " 0.989573\n", " \n", " \n", @@ -1881,15 +1855,15 @@ " 1999-12-31 20:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", - " 0.839288\n", - " 5.487995\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", + " 0.839287\n", + " 5.487602\n", " 0.989573\n", " \n", " \n", @@ -1897,15 +1871,15 @@ " 1999-12-31 21:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", - " 0.839288\n", - " 5.487995\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", + " 0.839287\n", + " 5.487602\n", " 0.989573\n", " \n", " \n", @@ -1913,15 +1887,15 @@ " 1999-12-31 22:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", - " 0.839288\n", - " 5.487995\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", + " 0.839287\n", + " 5.487602\n", " 0.989573\n", " \n", " \n", @@ -1929,15 +1903,15 @@ " 1999-12-31 23:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", - " 0.839288\n", - " 5.487995\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", + " 0.839287\n", + " 5.487602\n", " 0.989573\n", " \n", " \n", @@ -1953,42 +1927,42 @@ "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.469717 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.469717 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.469717 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.469717 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.469717 \n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.470917 \n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.470917 \n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.470917 \n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.470917 \n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.470917 \n", "\n", " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "1 1.683090e-15 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "2 3.366181e-15 5.269266e-36 120.000000 41.454173 0.661317 9.982165 \n", - "3 5.049271e-15 1.580780e-35 120.000000 41.454173 0.661317 9.982165 \n", - "4 6.732362e-15 3.161560e-35 120.000000 41.454173 0.661317 9.982165 \n", + "0 0.000000e+00 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "1 1.683090e-15 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "2 3.366181e-15 5.269266e-36 120.000000 41.451328 0.661315 9.981480 \n", + "3 5.049271e-15 1.580780e-35 120.000000 41.451328 0.661315 9.981480 \n", + "4 6.732362e-15 3.161560e-35 120.000000 41.451328 0.661315 9.981480 \n", "... ... ... ... ... ... ... \n", - "8755 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8756 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8757 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8758 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8759 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", + "8755 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8756 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8757 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8758 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8759 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", "\n", " FF Pmp Pmp_norm \n", - "0 0.840102 5.545822 1.000000 \n", - "1 0.840102 5.545822 1.000000 \n", - "2 0.840102 5.545822 1.000000 \n", - "3 0.840102 5.545822 1.000000 \n", - "4 0.840102 5.545822 1.000000 \n", + "0 0.840101 5.545424 1.000000 \n", + "1 0.840101 5.545424 1.000000 \n", + "2 0.840101 5.545424 1.000000 \n", + "3 0.840101 5.545424 1.000000 \n", + "4 0.840101 5.545424 1.000000 \n", "... ... ... ... \n", - "8755 0.839288 5.487995 0.989573 \n", - "8756 0.839288 5.487995 0.989573 \n", - "8757 0.839288 5.487995 0.989573 \n", - "8758 0.839288 5.487995 0.989573 \n", - "8759 0.839288 5.487995 0.989573 \n", + "8755 0.839287 5.487602 0.989573 \n", + "8756 0.839287 5.487602 0.989573 \n", + "8757 0.839287 5.487602 0.989573 \n", + "8758 0.839287 5.487602 0.989573 \n", + "8759 0.839287 5.487602 0.989573 \n", "\n", "[8760 rows x 13 columns]" ] }, - "execution_count": 33, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2008,7 +1982,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2065,9 +2039,9 @@ " 0.000000e+00\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2089,9 +2063,9 @@ " 1.683090e-15\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2113,9 +2087,9 @@ " 3.366181e-15\n", " 5.269266e-36\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2137,9 +2111,9 @@ " 5.049271e-15\n", " 1.580780e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2161,9 +2135,9 @@ " 6.732362e-15\n", " 3.161560e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2205,13 +2179,13 @@ " 1999-12-31 19:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2229,13 +2203,13 @@ " 1999-12-31 20:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2253,13 +2227,13 @@ " 1999-12-31 21:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2277,13 +2251,13 @@ " 1999-12-31 22:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2301,13 +2275,13 @@ " 1999-12-31 23:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2333,24 +2307,24 @@ "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.469717 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.469717 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.469717 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.469717 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.469717 \n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.470917 \n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.470917 \n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.470917 \n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.470917 \n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.470917 \n", "\n", " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "1 1.683090e-15 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "2 3.366181e-15 5.269266e-36 120.000000 41.454173 0.661317 9.982165 \n", - "3 5.049271e-15 1.580780e-35 120.000000 41.454173 0.661317 9.982165 \n", - "4 6.732362e-15 3.161560e-35 120.000000 41.454173 0.661317 9.982165 \n", + "0 0.000000e+00 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "1 1.683090e-15 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "2 3.366181e-15 5.269266e-36 120.000000 41.451328 0.661315 9.981480 \n", + "3 5.049271e-15 1.580780e-35 120.000000 41.451328 0.661315 9.981480 \n", + "4 6.732362e-15 3.161560e-35 120.000000 41.451328 0.661315 9.981480 \n", "... ... ... ... ... ... ... \n", - "8755 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8756 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8757 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8758 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8759 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", + "8755 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8756 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8757 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8758 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8759 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", "\n", " ... p_mp i_x i_xx Effective irradiance ghi dhi dni wind_speed \\\n", "0 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", @@ -2381,7 +2355,7 @@ "[8760 rows x 27 columns]" ] }, - "execution_count": 34, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -2403,12 +2377,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 66, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2442,12 +2416,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2480,12 +2454,31 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 68, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_15280\\3402272254.py:45: DeprecationWarning: You are passing x=0 0.0\n", + "1 1.0\n", + "2 2.0\n", + "3 3.0\n", + "4 4.0\n", + " ... \n", + "8755 8755.0\n", + "8756 8756.0\n", + "8757 8757.0\n", + "8758 8758.0\n", + "8759 8759.0\n", + "Name: Timedelta, Length: 8760, dtype: float64 as a positional argument. Please change your invocation to use keyword arguments. From SciPy 1.14, passing these as positional arguments will result in an error.\n", + " energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2495,7 +2488,7 @@ } ], "source": [ - "import matplotlib.dates as mdates \n", + "import matplotlib.dates as mdates\n", "from scipy.integrate import cumulative_trapezoid, simpson\n", "\n", "\n", @@ -2560,7 +2553,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -2584,7 +2577,6 @@ " \n", " \n", " \n", - " Datetime\n", " Temperature\n", " Injection\n", " NA\n", @@ -2598,86 +2590,96 @@ " Pmp\n", " Pmp_norm\n", " \n", + " \n", + " time\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " 1999-01-01 00:30:00-07:00\n", + " 1999-01-01 00:30:00-07:00\n", " 0.0\n", " NaN\n", - " 100.00000\n", + " 100.000000\n", " 0.000000e+00\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.907547\n", - " 0.840102\n", - " 5.504366\n", + " 41.451328\n", + " 0.661315\n", + " 10.072673\n", + " 0.840101\n", + " 5.596088\n", " 1.000000\n", " \n", " \n", - " 1\n", - " 1999-01-01 01:30:00-07:00\n", + " 1999-01-01 01:30:00-07:00\n", " 0.0\n", " NaN\n", - " 100.00000\n", + " 100.000000\n", " 1.683090e-15\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.907547\n", - " 0.840102\n", - " 5.504366\n", + " 41.451328\n", + " 0.661315\n", + " 10.072673\n", + " 0.840101\n", + " 5.596088\n", " 1.000000\n", " \n", " \n", - " 2\n", - " 1999-01-01 02:30:00-07:00\n", + " 1999-01-01 02:30:00-07:00\n", " 0.0\n", " NaN\n", - " 100.00000\n", + " 100.000000\n", " 3.366181e-15\n", " 5.269266e-36\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.907547\n", - " 0.840102\n", - " 5.504366\n", + " 41.451328\n", + " 0.661315\n", + " 10.072673\n", + " 0.840101\n", + " 5.596088\n", " 1.000000\n", " \n", " \n", - " 3\n", - " 1999-01-01 03:30:00-07:00\n", + " 1999-01-01 03:30:00-07:00\n", " 0.0\n", " NaN\n", - " 100.00000\n", + " 100.000000\n", " 5.049271e-15\n", " 1.580780e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.907547\n", - " 0.840102\n", - " 5.504366\n", + " 41.451328\n", + " 0.661315\n", + " 10.072673\n", + " 0.840101\n", + " 5.596088\n", " 1.000000\n", " \n", " \n", - " 4\n", - " 1999-01-01 04:30:00-07:00\n", + " 1999-01-01 04:30:00-07:00\n", " 0.0\n", " NaN\n", - " 100.00000\n", + " 100.000000\n", " 6.732362e-15\n", " 3.161560e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.907547\n", - " 0.840102\n", - " 5.504366\n", + " 41.451328\n", + " 0.661315\n", + " 10.072673\n", + " 0.840101\n", + " 5.596088\n", " 1.000000\n", " \n", " \n", @@ -2694,137 +2696,134 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 8755\n", - " 1999-12-31 19:30:00-07:00\n", + " 1999-12-31 19:30:00-07:00\n", " 0.0\n", " NaN\n", - " 29.74847\n", - " 6.666315e+01\n", - " 3.588381e+00\n", - " 90.001187\n", - " 41.345459\n", - " 0.656725\n", - " 9.881565\n", + " 29.749662\n", + " 6.666236e+01\n", + " 3.587983e+00\n", + " 90.001455\n", + " 41.342623\n", + " 0.656723\n", + " 10.046257\n", " 0.839281\n", - " 5.446490\n", - " 0.989485\n", + " 5.537249\n", + " 0.989486\n", " \n", " \n", - " 8756\n", - " 1999-12-31 20:30:00-07:00\n", + " 1999-12-31 20:30:00-07:00\n", " 0.0\n", " NaN\n", - " 29.74847\n", - " 6.666315e+01\n", - " 3.588381e+00\n", - " 90.001187\n", - " 41.345459\n", - " 0.656725\n", - " 9.881565\n", + " 29.749662\n", + " 6.666236e+01\n", + " 3.587983e+00\n", + " 90.001455\n", + " 41.342623\n", + " 0.656723\n", + " 10.046257\n", " 0.839281\n", - " 5.446490\n", - " 0.989485\n", + " 5.537249\n", + " 0.989486\n", " \n", " \n", - " 8757\n", - " 1999-12-31 21:30:00-07:00\n", + " 1999-12-31 21:30:00-07:00\n", " 0.0\n", " NaN\n", - " 29.74847\n", - " 6.666315e+01\n", - " 3.588381e+00\n", - " 90.001187\n", - " 41.345459\n", - " 0.656725\n", - " 9.881565\n", + " 29.749662\n", + " 6.666236e+01\n", + " 3.587983e+00\n", + " 90.001455\n", + " 41.342623\n", + " 0.656723\n", + " 10.046257\n", " 0.839281\n", - " 5.446490\n", - " 0.989485\n", + " 5.537249\n", + " 0.989486\n", " \n", " \n", - " 8758\n", - " 1999-12-31 22:30:00-07:00\n", + " 1999-12-31 22:30:00-07:00\n", " 0.0\n", " NaN\n", - " 29.74847\n", - " 6.666315e+01\n", - " 3.588381e+00\n", - " 90.001187\n", - " 41.345459\n", - " 0.656725\n", - " 9.881565\n", + " 29.749662\n", + " 6.666236e+01\n", + " 3.587983e+00\n", + " 90.001455\n", + " 41.342623\n", + " 0.656723\n", + " 10.046257\n", " 0.839281\n", - " 5.446490\n", - " 0.989485\n", + " 5.537249\n", + " 0.989486\n", " \n", " \n", - " 8759\n", - " 1999-12-31 23:30:00-07:00\n", + " 1999-12-31 23:30:00-07:00\n", " 0.0\n", " NaN\n", - " 29.74847\n", - " 6.666315e+01\n", - " 3.588381e+00\n", - " 90.001187\n", - " 41.345459\n", - " 0.656725\n", - " 9.881565\n", + " 29.749662\n", + " 6.666236e+01\n", + " 3.587983e+00\n", + " 90.001455\n", + " 41.342623\n", + " 0.656723\n", + " 10.046257\n", " 0.839281\n", - " 5.446490\n", - " 0.989485\n", + " 5.537249\n", + " 0.989486\n", " \n", " \n", "\n", - "

8760 rows × 13 columns

\n", + "

8760 rows × 12 columns

\n", "" ], "text/plain": [ - " Datetime Temperature Injection NA \\\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.00000 \n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.00000 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.00000 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.00000 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.00000 \n", - "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 29.74847 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 29.74847 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 29.74847 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 29.74847 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 29.74847 \n", + " Temperature Injection NA NB \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", + "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.683090e-15 \n", + "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.366181e-15 \n", + "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.049271e-15 \n", + "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.732362e-15 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 20:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 21:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 22:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 23:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", "\n", - " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 120.000000 41.454173 0.661317 9.907547 \n", - "1 1.683090e-15 0.000000e+00 120.000000 41.454173 0.661317 9.907547 \n", - "2 3.366181e-15 5.269266e-36 120.000000 41.454173 0.661317 9.907547 \n", - "3 5.049271e-15 1.580780e-35 120.000000 41.454173 0.661317 9.907547 \n", - "4 6.732362e-15 3.161560e-35 120.000000 41.454173 0.661317 9.907547 \n", - "... ... ... ... ... ... ... \n", - "8755 6.666315e+01 3.588381e+00 90.001187 41.345459 0.656725 9.881565 \n", - "8756 6.666315e+01 3.588381e+00 90.001187 41.345459 0.656725 9.881565 \n", - "8757 6.666315e+01 3.588381e+00 90.001187 41.345459 0.656725 9.881565 \n", - "8758 6.666315e+01 3.588381e+00 90.001187 41.345459 0.656725 9.881565 \n", - "8759 6.666315e+01 3.588381e+00 90.001187 41.345459 0.656725 9.881565 \n", + " NC tau Jsc Voc \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", + "1999-01-01 01:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", + "1999-01-01 02:30:00-07:00 5.269266e-36 120.000000 41.451328 0.661315 \n", + "1999-01-01 03:30:00-07:00 1.580780e-35 120.000000 41.451328 0.661315 \n", + "1999-01-01 04:30:00-07:00 3.161560e-35 120.000000 41.451328 0.661315 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 20:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 21:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 22:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 23:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", "\n", - " FF Pmp Pmp_norm \n", - "0 0.840102 5.504366 1.000000 \n", - "1 0.840102 5.504366 1.000000 \n", - "2 0.840102 5.504366 1.000000 \n", - "3 0.840102 5.504366 1.000000 \n", - "4 0.840102 5.504366 1.000000 \n", - "... ... ... ... \n", - "8755 0.839281 5.446490 0.989485 \n", - "8756 0.839281 5.446490 0.989485 \n", - "8757 0.839281 5.446490 0.989485 \n", - "8758 0.839281 5.446490 0.989485 \n", - "8759 0.839281 5.446490 0.989485 \n", + " Isc FF Pmp Pmp_norm \n", + "time \n", + "1999-01-01 00:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 01:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 02:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 03:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 04:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 20:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 21:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 22:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 23:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", "\n", - "[8760 rows x 13 columns]" + "[8760 rows x 12 columns]" ] }, - "execution_count": 39, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -2837,7 +2836,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -2894,9 +2893,9 @@ " 0.000000e+00\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2918,9 +2917,9 @@ " 1.683090e-15\n", " 0.000000e+00\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2942,9 +2941,9 @@ " 3.366181e-15\n", " 5.269266e-36\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2966,9 +2965,9 @@ " 5.049271e-15\n", " 1.580780e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2990,9 +2989,9 @@ " 6.732362e-15\n", " 3.161560e-35\n", " 120.000000\n", - " 41.454173\n", - " 0.661317\n", - " 9.982165\n", + " 41.451328\n", + " 0.661315\n", + " 9.981480\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3034,13 +3033,13 @@ " 1999-12-31 19:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3058,13 +3057,13 @@ " 1999-12-31 20:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3082,13 +3081,13 @@ " 1999-12-31 21:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3106,13 +3105,13 @@ " 1999-12-31 22:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3130,13 +3129,13 @@ " 1999-12-31 23:30:00-07:00\n", " 0.0\n", " NaN\n", - " 30.469717\n", - " 6.606672e+01\n", - " 3.463559e+00\n", - " 90.202937\n", - " 41.346420\n", - " 0.656763\n", - " 9.956218\n", + " 30.470917\n", + " 6.606591e+01\n", + " 3.463173e+00\n", + " 90.203213\n", + " 41.343585\n", + " 0.656761\n", + " 9.955535\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3162,24 +3161,24 @@ "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.469717 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.469717 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.469717 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.469717 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.469717 \n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.470917 \n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.470917 \n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.470917 \n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.470917 \n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.470917 \n", "\n", " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "1 1.683090e-15 0.000000e+00 120.000000 41.454173 0.661317 9.982165 \n", - "2 3.366181e-15 5.269266e-36 120.000000 41.454173 0.661317 9.982165 \n", - "3 5.049271e-15 1.580780e-35 120.000000 41.454173 0.661317 9.982165 \n", - "4 6.732362e-15 3.161560e-35 120.000000 41.454173 0.661317 9.982165 \n", + "0 0.000000e+00 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "1 1.683090e-15 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "2 3.366181e-15 5.269266e-36 120.000000 41.451328 0.661315 9.981480 \n", + "3 5.049271e-15 1.580780e-35 120.000000 41.451328 0.661315 9.981480 \n", + "4 6.732362e-15 3.161560e-35 120.000000 41.451328 0.661315 9.981480 \n", "... ... ... ... ... ... ... \n", - "8755 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8756 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8757 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8758 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", - "8759 6.606672e+01 3.463559e+00 90.202937 41.346420 0.656763 9.956218 \n", + "8755 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8756 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8757 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8758 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8759 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", "\n", " ... i_x i_xx Effective irradiance ghi dhi dni wind_speed \\\n", "0 ... 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", @@ -3210,7 +3209,7 @@ "[8760 rows x 28 columns]" ] }, - "execution_count": 38, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -3243,7 +3242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb b/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb index 499704fa..fa61923a 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -48,9 +48,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", @@ -70,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -100,12 +111,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "mechanism_params = utilities.get_kinetics('repins')\n", - "injection = 0.5 # " + "injection = 0.5 #" ] }, { @@ -118,16 +129,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2.5706584437713456e+21" + "2.57051594134322e+21" ] }, - "execution_count": 4, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -149,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -158,7 +169,7 @@ "7.064647199192719e+20" ] }, - "execution_count": 5, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -206,7 +217,7 @@ "1.2" ] }, - "execution_count": 6, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -225,16 +236,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4.711342509616992" + "4.711029107876425" ] }, - "execution_count": 7, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -245,16 +256,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4.711342509616992" + "4.711029107876425" ] }, - "execution_count": 8, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -316,9 +327,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\530898593.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], "source": [ "temperature = 125 # degrees celsius\n", "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", @@ -340,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -506,7 +526,7 @@ "[86401 rows x 7 columns]" ] }, - "execution_count": 12, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -525,16 +545,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -543,7 +563,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " j_gen = j_gen\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -564,7 +584,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -585,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -598,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -651,10 +671,10 @@ " 2022-01-01 00:00:01\n", " 125\n", " 0.5\n", - " 9.942175e+01\n", - " 5.782480e-01\n", + " 9.942178e+01\n", + " 5.782160e-01\n", " 0.000000\n", - " 335.383917\n", + " 335.384693\n", " 0.000125\n", " 1.0\n", " \n", @@ -663,10 +683,10 @@ " 2022-01-01 00:00:02\n", " 125\n", " 0.5\n", - " 9.887086e+01\n", - " 1.128658e+00\n", + " 9.887092e+01\n", + " 1.128597e+00\n", " 0.000486\n", - " 322.562131\n", + " 322.563499\n", " 0.000243\n", " 2.0\n", " \n", @@ -675,10 +695,10 @@ " 2022-01-01 00:00:03\n", " 125\n", " 0.5\n", - " 9.834396e+01\n", - " 1.654652e+00\n", + " 9.834404e+01\n", + " 1.654564e+00\n", " 0.001392\n", - " 311.192917\n", + " 311.194745\n", " 0.000356\n", " 3.0\n", " \n", @@ -687,10 +707,10 @@ " 2022-01-01 00:00:04\n", " 125\n", " 0.5\n", - " 9.783834e+01\n", - " 2.159000e+00\n", + " 9.783845e+01\n", + " 2.158887e+00\n", " 0.002663\n", - " 301.019637\n", + " 301.021824\n", " 0.000465\n", " 4.0\n", " \n", @@ -711,8 +731,8 @@ " 2022-01-01 23:59:56\n", " 125\n", " 0.5\n", - " 5.771633e-34\n", - " 3.352902e-27\n", + " 5.800845e-34\n", + " 3.370010e-27\n", " 100.000000\n", " 350.000000\n", " 0.000000\n", @@ -723,8 +743,8 @@ " 2022-01-01 23:59:57\n", " 125\n", " 0.5\n", - " 5.766526e-34\n", - " 3.349935e-27\n", + " 5.795712e-34\n", + " 3.367028e-27\n", " 100.000000\n", " 350.000000\n", " 0.000000\n", @@ -735,8 +755,8 @@ " 2022-01-01 23:59:58\n", " 125\n", " 0.5\n", - " 5.761423e-34\n", - " 3.346971e-27\n", + " 5.790584e-34\n", + " 3.364048e-27\n", " 100.000000\n", " 350.000000\n", " 0.000000\n", @@ -747,8 +767,8 @@ " 2022-01-01 23:59:59\n", " 125\n", " 0.5\n", - " 5.756325e-34\n", - " 3.344009e-27\n", + " 5.785460e-34\n", + " 3.361072e-27\n", " 100.000000\n", " 350.000000\n", " 0.000000\n", @@ -759,8 +779,8 @@ " 2022-01-02 00:00:00\n", " 125\n", " 0.5\n", - " 5.751231e-34\n", - " 3.341050e-27\n", + " 5.780341e-34\n", + " 3.358098e-27\n", " 100.000000\n", " 350.000000\n", " 0.000000\n", @@ -774,23 +794,23 @@ "text/plain": [ " Datetime Temperature Injection NA NB \\\n", "0 2022-01-01 00:00:00 125 0.5 1.000000e+02 0.000000e+00 \n", - "1 2022-01-01 00:00:01 125 0.5 9.942175e+01 5.782480e-01 \n", - "2 2022-01-01 00:00:02 125 0.5 9.887086e+01 1.128658e+00 \n", - "3 2022-01-01 00:00:03 125 0.5 9.834396e+01 1.654652e+00 \n", - "4 2022-01-01 00:00:04 125 0.5 9.783834e+01 2.159000e+00 \n", + "1 2022-01-01 00:00:01 125 0.5 9.942178e+01 5.782160e-01 \n", + "2 2022-01-01 00:00:02 125 0.5 9.887092e+01 1.128597e+00 \n", + "3 2022-01-01 00:00:03 125 0.5 9.834404e+01 1.654564e+00 \n", + "4 2022-01-01 00:00:04 125 0.5 9.783845e+01 2.158887e+00 \n", "... ... ... ... ... ... \n", - "86396 2022-01-01 23:59:56 125 0.5 5.771633e-34 3.352902e-27 \n", - "86397 2022-01-01 23:59:57 125 0.5 5.766526e-34 3.349935e-27 \n", - "86398 2022-01-01 23:59:58 125 0.5 5.761423e-34 3.346971e-27 \n", - "86399 2022-01-01 23:59:59 125 0.5 5.756325e-34 3.344009e-27 \n", - "86400 2022-01-02 00:00:00 125 0.5 5.751231e-34 3.341050e-27 \n", + "86396 2022-01-01 23:59:56 125 0.5 5.800845e-34 3.370010e-27 \n", + "86397 2022-01-01 23:59:57 125 0.5 5.795712e-34 3.367028e-27 \n", + "86398 2022-01-01 23:59:58 125 0.5 5.790584e-34 3.364048e-27 \n", + "86399 2022-01-01 23:59:59 125 0.5 5.785460e-34 3.361072e-27 \n", + "86400 2022-01-02 00:00:00 125 0.5 5.780341e-34 3.358098e-27 \n", "\n", " NC tau NDD time (s) \n", "0 0.000000 350.000000 0.000000 0.0 \n", - "1 0.000000 335.383917 0.000125 1.0 \n", - "2 0.000486 322.562131 0.000243 2.0 \n", - "3 0.001392 311.192917 0.000356 3.0 \n", - "4 0.002663 301.019637 0.000465 4.0 \n", + "1 0.000000 335.384693 0.000125 1.0 \n", + "2 0.000486 322.563499 0.000243 2.0 \n", + "3 0.001392 311.194745 0.000356 3.0 \n", + "4 0.002663 301.021824 0.000465 4.0 \n", "... ... ... ... ... \n", "86396 100.000000 350.000000 0.000000 86396.0 \n", "86397 100.000000 350.000000 0.000000 86397.0 \n", @@ -801,7 +821,7 @@ "[86401 rows x 9 columns]" ] }, - "execution_count": 15, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -819,12 +839,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -876,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -885,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -894,9 +914,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\241191725.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], "source": [ "temperature = temperatures[1] # degrees celsius\n", "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", @@ -918,16 +947,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -936,7 +965,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " j_gen = j_gen\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -957,7 +986,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -966,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -977,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -986,9 +1015,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\950821369.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], "source": [ "temperature = temperatures[2] # degrees celsius\n", "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", @@ -1010,16 +1048,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "for index, timestep in timesteps.iterrows():\n", - " \n", + "\n", " # first row tau has already been assigned\n", " if index == 0:\n", " pass\n", - " \n", + "\n", " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", " else:\n", " n_A = timesteps.at[index-1, 'NA']\n", @@ -1028,7 +1066,7 @@ "\n", " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", " j_gen = j_gen\n", - " \n", + "\n", " temperature = timesteps.at[index, 'Temperature']\n", " injection = timesteps.at[index, 'Injection']\n", "\n", @@ -1049,7 +1087,7 @@ " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", "\n", " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", - " \n", + "\n", " # assign new defect state percentages\n", " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", @@ -1058,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1069,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1078,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1087,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -1099,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -1108,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -1124,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1133,12 +1171,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1157,7 +1195,7 @@ " ax.legend()\n", "\n", "\n", - "marker = itertools.cycle(('o', 's', 'v', 'o', '*')) \n", + "marker = itertools.cycle(('o', 's', 'v', 'o', '*'))\n", "\n", "for name, group in grouped_data:\n", " ax.scatter(group['X'], group['Y'], label = f\"Wyller NDD {name}$\\degree$C\", marker = next(marker))\n", @@ -1173,12 +1211,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1199,7 +1237,7 @@ " ax.legend(prop={'size': 12})\n", "\n", "\n", - "marker = itertools.cycle(('o', 's', 'v', 'o', '*')) \n", + "marker = itertools.cycle(('o', 's', 'v', 'o', '*'))\n", "\n", "for name, group in grouped_data:\n", " ax.scatter(group['X'], group['Y'], label = f\"Literature data {name}$\\degree$C\", color = next(colors), marker = next(marker), s = 70)\n", @@ -1246,7 +1284,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "vscode": { "interpreter": { diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb b/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb index 0ef838c9..22bcedae 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -48,10 +48,10 @@ "output_type": "stream", "text": [ "Working on a Windows 10\n", - "Python version 3.11.4 | packaged by Anaconda, Inc. | (main, Jul 5 2023, 13:38:37) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.1.0\n", - "Pvlib version 0.9.5\n", - "pvdeg version 0.2.0+40.g968e483.dirty\n" + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "Pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" ] } ], @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -122,7 +122,7 @@ "pvdeg.montecarlo.Corr" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -173,17 +173,17 @@ "output_type": "stream", "text": [ " Ea LnR0 X\n", - "0 43.310964 20.082424 -0.076470\n", - "1 63.858966 13.259692 0.024310\n", - "2 59.968897 14.428516 0.042583\n", - "3 73.746862 9.652391 0.295242\n", - "4 67.954485 11.666279 0.029110\n", + "0 77.061268 8.761775 -0.033741\n", + "1 68.815017 11.551990 0.032976\n", + "2 57.913704 15.284075 -0.090303\n", + "3 61.471572 13.806868 0.132937\n", + "4 69.933636 11.081281 0.146191\n", "... ... ... ...\n", - "19995 57.022304 15.367897 0.105352\n", - "19996 69.850571 11.244541 0.012642\n", - "19997 55.831637 16.003457 0.004917\n", - "19998 51.458692 17.205392 0.125170\n", - "19999 52.547601 16.893998 0.240680\n", + "19995 63.283418 13.339625 -0.103397\n", + "19996 61.727045 13.872809 -0.146122\n", + "19997 61.658618 13.845784 0.040761\n", + "19998 57.356771 15.267059 0.048293\n", + "19999 46.224012 19.113392 -0.029546\n", "\n", "[20000 rows x 3 columns]\n" ] @@ -206,31 +206,31 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Ea : mean 62.00571434068475, stdev 7.2945584039777325\n", - "LnR0 : mean 13.747061024151163, stdev 2.4430079919211964\n", - "X : mean 0.0341427590574392, stdev 0.09887113563275718\n", + "Ea : mean 62.09114091002211, stdev 7.3423207010176\n", + "LnR0 : mean 13.718327678222028, stdev 2.4591817361029067\n", + "X : mean 0.0341455765799867, stdev 0.099555706536666\n", "\n", - "Ea_X 0.026648561044721943\n", - "Ea_lnR0 -0.9994904431591315\n", - "X_lnR0 -0.03999643337451283\n" + "Ea_X 0.02855949815906674\n", + "Ea_lnR0 -0.9994870223941739\n", + "X_lnR0 -0.04195086545006722\n" ] } ], "source": [ - "# mean and standard deviation match inputs \n", + "# mean and standard deviation match inputs\n", "for col in mc_inputs.columns:\n", " print(f\"{col} : mean {mc_inputs[col].mean()}, stdev {mc_inputs[col].std()}\")\n", "\n", "print()\n", "\n", - "# come up with a better way of checking \n", + "# come up with a better way of checking\n", "print('Ea_X', np.corrcoef(mc_inputs['Ea'], mc_inputs['X'])[0][1])\n", "print('Ea_lnR0', np.corrcoef(mc_inputs['Ea'], mc_inputs['LnR0'])[0][1])\n", "print('X_lnR0', np.corrcoef(mc_inputs['X'], mc_inputs['LnR0'])[0][1])" @@ -250,15 +250,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", - "{'Source': 'NSRDB', 'Location ID': '1060699', 'City': '-', 'State': '-', 'Country': '-', 'Time Zone': -5, 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 25.77, 'longitude': -80.18, 'altitude': 0, 'timezone': -5}\n" + "Column \"relative_humidity\" not found in DataFrame. Calculating...\n" ] } ], @@ -283,9 +282,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], "source": [ "sol_pos = pvdeg.spectral.solar_position(weather_df, meta)\n", "poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df, meta)\n", @@ -298,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -326,24 +334,23 @@ "text": [ "Help on function simulate in module pvdeg.montecarlo:\n", "\n", - "simulate(func: Callable, correlated_samples: pandas.core.frame.DataFrame, **function_kwargs)\n", - " Applies a funtion to preform a monte carlo simulation\n", + "simulate(func: Callable, correlated_samples: pandas.core.frame.DataFrame, **function_kwargs) -> pandas.core.series.Series\n", + " Applies a target function to data to preform a monte carlo simulation. If you get a key error and the target function has default parameters, \n", + " try adding them to your ``func_kwargs`` dictionary instead of using the default value from the target function.\n", " \n", " Parameters\n", " ----------\n", " func : function\n", " Function to apply for monte carlo simulation\n", " correlated_samples : pd.DataFrame \n", - " Dataframe of correlated samples with named columns for each appropriate modeling constant\n", - " trials : int\n", - " Number of monte carlo iterations to run\n", - " func_kwargs : dict\n", - " Keyword arguments to pass to func.\n", + " Dataframe of correlated samples with named columns for each appropriate modeling constant, can be generated using generateCorrelatedSamples()\n", + " function_kwargs : dict\n", + " Keyword arguments to pass to func, only include arguments not named in your correlated_samples columns\n", " \n", " Returns\n", " -------\n", - " res : pandas.DataFrame\n", - " DataFrame with monte carlo results\n", + " res : pandas.Series\n", + " Series with monte carlo results from target function\n", "\n" ] } @@ -366,13 +373,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "results = pvdeg.montecarlo.simulate(\n", " func=pvdeg.degradation.vecArrhenius,\n", - " correlated_samples=mc_inputs, \n", + " correlated_samples=mc_inputs,\n", " **function_kwargs)" ] }, @@ -386,12 +393,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -438,7 +445,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb b/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb index 7aa22099..0ad5f68a 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -43,10 +43,10 @@ "output_type": "stream", "text": [ "Working on a Windows 10\n", - "Python version 3.10.9 | packaged by Anaconda, Inc. | (main, Mar 8 2023, 10:42:25) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.1.2\n", - "Pvlib version 0.10.2\n", - "Pvdeg version 0.2.0+12.g9849aa2.dirty\n" + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "Pvlib version 0.10.3\n", + "Pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" ] } ], @@ -75,14 +75,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \r" + "Column \"relative_humidity\" not found in DataFrame. Calculating...\n" ] } ], @@ -90,7 +90,7 @@ "weather_db = 'PSM3'\n", "weather_id = (40.633365593159226, -73.9945801019899) # Manhattan, NYC\n", "weather_arg = {'api_key': 'DEMO_KEY',\n", - " 'email': 'user@mail.com', \n", + " 'email': 'user@mail.com',\n", " 'names': 'tmy',\n", " 'attributes': [],\n", " 'map_variables': True}\n", @@ -100,17 +100,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " x T98_0 T98_inf T98\n", - "0 0.0 67.0331 45.660409 70.0\n", - " x T98_0 T98_inf T98\n", - "0 0.0 69.139133 46.496454 70.0\n" + "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", + " x T98_0 T98_inf\n", + "0 0.0 69.139133 46.496454\n", + " x T98_0 T98_inf\n", + "0 0.0 69.139133 46.496454\n" ] } ], @@ -148,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -173,17 +176,17 @@ "output_type": "stream", "text": [ " X_0 wind_factor\n", - "0 0.898718 0.459997\n", - "1 6.956492 0.335493\n", - "2 7.103368 0.598136\n", - "3 7.305353 -0.527580\n", - "4 2.698503 -0.051998\n", + "0 6.566013 -0.137416\n", + "1 7.907630 0.095923\n", + "2 0.867071 0.094729\n", + "3 6.643706 0.799062\n", + "4 4.784092 0.766851\n", ".. ... ...\n", - "495 -0.534005 0.620055\n", - "496 4.696580 0.518921\n", - "497 1.657083 0.041645\n", - "498 3.767393 0.368091\n", - "499 3.309681 0.483557\n", + "495 1.744402 -0.366729\n", + "496 3.642750 0.976478\n", + "497 2.513120 0.275430\n", + "498 4.124216 0.274358\n", + "499 8.787583 0.758527\n", "\n", "[500 rows x 2 columns]\n" ] @@ -204,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -221,12 +224,12 @@ " 'conf_0' : \"insulated_back_glass_polymer\", # or is it conf_inf\n", " 'conf_inf' : \"open_rack_glass_polymer\",\n", " 'T98' : 70,\n", - "} \n", + "}\n", "\n", "# notice how we left off parts we want to use in the monte carlo simulation because they are already contained in the dataframe\n", "\n", "results = pvdeg.montecarlo.simulate(\n", - " func=pvdeg.standards.standoff, \n", + " func=pvdeg.standards.standoff,\n", " correlated_samples=samples, # in this case correlated_samples is a misnomer, they are not required to be correlated\n", " **function_kwargs)" ] @@ -243,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -263,27 +266,27 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " x T98_0 T98_inf T98\n", - "0 0.000000 65.508742 44.608501 70.0\n", - "1 0.000000 66.973564 45.611088 70.0\n", - "2 0.000000 63.445939 43.421469 70.0\n", - "3 1.337077 73.952652 50.320063 70.0\n", - "4 0.120627 70.988196 48.383881 70.0\n", - ".. ... ... ... ...\n", - "495 0.160900 63.027109 43.196684 70.0\n", - "496 0.000000 64.583898 44.101643 70.0\n", - "497 0.025518 70.345243 47.753195 70.0\n", - "498 0.000000 66.674085 45.392219 70.0\n", - "499 0.000000 65.243865 44.415142 70.0\n", + " x T98_0 T98_inf\n", + "0 0.478822 71.598257 48.872775\n", + "1 0.000000 69.767202 47.442516\n", + "2 0.000000 69.773189 47.445724\n", + "3 0.000000 59.685666 41.423457\n", + "4 0.000000 60.371287 41.775550\n", + ".. ... ... ...\n", + "495 0.239998 72.969946 49.864180\n", + "496 0.000000 56.305886 39.623323\n", + "497 0.000000 67.751698 46.091127\n", + "498 0.000000 67.764896 46.102988\n", + "499 0.000000 60.570653 41.847434\n", "\n", - "[500 rows x 4 columns]\n" + "[500 rows x 3 columns]\n" ] } ], @@ -301,12 +304,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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xCwAAKo0ePXrowQcf1Pjx41W1alXFxsbqjTfeUE5Oju6++26Fh4erYcOG+vrrr53P2bVrl/r376+wsDDFxsZq+PDhOnbsmHP/8uXL1a1bN0VFRalatWq68cYblZaW5ty/f/9+mUwmffbZZ7r++usVGhqqNm3aaMOGDV69dngGxS0AAFcCw5BycnxzM4xyhfr222+revXq2rx5sx588EGNGTNGf/7zn9W5c2dt27ZNSUlJGj58uHJzc3Xq1Cn17NlT7dq105YtW7RkyRIdOXJEt9xyi/N4ubm5Gj9+vLZs2aIVK1bIbDbr5ptvluMPf0F94okn9PDDD2vHjh1q3LixbrvtNhUWFnok/fAextwCAHAlyM2VwsJ8c+7sbKlKFZebt2nTRk8++aQkaeLEiXruuedUvXp1jR49WpI0efJkzZ07Vzt37tTy5cvVrl07/eMf/3A+/6233lJCQoJ++uknNWzYUDfddJMiIiJk/n2Worfeeks1atTQjz/+qJYtWzqf9/DDD+uGG26QJE2dOlUtWrTQL7/8oqZNm15yCuA99NwCAIBKpXXr1s77FotF1apVU6tWrZzbYmNjJUmZmZn67rvvtGrVKoWFhTlv54vR80MP0tLSdPvtt6t+/fqKiIhQ3bp1JUkHDx4s9bzx8fHOc+DyQs8tAABXgtDQcz2ovjp3OVit1iKPTSZTkW0mk0mS5HA4lJ2drYEDB+r5558vdpzzBeptt92munXr6o033lDNmjXlcDjUsmVLFRQUlHreC8+BywvFLQAAVwKTqVxDAy4XV199tT799FPVrVtXAQHFy5qjR4/q559/1htvvKHu3btLktauXevtMOFFDEvwMqvFrHvaNNXxZS1YoQwAgEs0duxYnThxQrfddpu+/fZbpaWlaenSpbr77rtlt9tVtWpVRUdH64033tAvv/yilStXKjk52ddhowJRXXmZ1WJWvwa1lb29LsUtAACXqGbNmlq3bp3sdruSkpLUqlUrjR8/XlFRUTKbzTKbzXrzzTe1bds2tWzZUhMmTNCLL77o67BRgRiWAAAAKo3U1NRi2/bv319sm3HB9GKNGjXSZ599VuLxDMNQjx49tGvXLudsCX98ft26dYs8lqSoqKhi23B5oLj1MrvD0A9HTygoIUAOI9zX4QAAAPgV/i7uZfmFdk39Zovibt+oArvd1+EAAAD4FYpbAAAA+A2KWwAAAPgNilsAAAD4DYpbAAAA+A2fFrdz585V69atFRERoYiICCUmJurrr7927s/Ly9PYsWNVrVo1hYWFaejQoTpy5EiRYxw8eFA33HCDQkNDFRMTo0ceeUSFhYXevhQAAABUAj4tbmvVqqXnnntOW7du1ZYtW9SzZ08NGjRIP/zwgyRpwoQJ+uKLL/Txxx9r9erVOnz4sIYMGeJ8vt1u1w033KCCggKtX79eb7/9tubPn6/Jkyf76pIAAADgQz4tbgcOHKgBAwaoUaNGaty4sZ599lmFhYVp48aNOn36tN58803NmDFDPXv2VPv27TVv3jytX79eGzdulCQtW7ZMP/74o9577z21bdtW/fv319NPP605c+aooKDAl5dWqgCzWXe2bKSTq5qyQhkAAICHVZrqym6364MPPlBOTo4SExO1detW2Ww29e7d29mmadOmql27tjZs2CBJ2rBhg1q1aqXY2Fhnm759+yorK8vZ+1vZBAaYdVPjesra3IDiFgAAH+nRo4fGjx/vfFy3bl3NmjXLZ/HAc3y+Qtn333+vxMRE5eXlKSwsTAsXLlTz5s21Y8cOBQYGKioqqkj72NhYZWRkSJIyMjKKFLbn95/fV5r8/Hzl5+c7H2dlZUmSbDabbDabJy6rTDZboSSrbLZClXa6c8OGrSostJXapoKCk9V51ybvnrzinX99vfE6ozjy7zvk3rd8kX+bzSbDMORwOORwOLx23srm/BK653Pxx33nt23atElVqlS5onNVEcrKf0kcDocMw5DNZpPFYimyz9WfH58Xt02aNNGOHTt0+vRpffLJJxoxYoRWr15doeecPn26pk6dWmz7smXLFBoaWqHndhjSxj1hCoxrrw0bt+vYsawS26WlRUrqobVr1yk9/XSFxnQhS16ebvz9/tKlS2UPDvbaub0pJSXF1yFc0ci/75B73/Jm/gMCAhQXF6fs7OxKO1TPm86cOVPkcWFhoQoKCpwdXEFBQSosLHQ+hmf9Mf+lKSgo0NmzZ7VmzZpiEwTk5ua6dAyfF7eBgYFq2LChJKl9+/b69ttv9fLLL+vWW29VQUGBTp06VaT39siRI4qLi5MkxcXFafPmzUWOd342hfNtSjJx4kQlJyc7H2dlZSkhIUFJSUmKiIjw1KWVKLegUBM2rlT8iHVq16G7unUOKrHd9u3n/u3atYvatavQkIrKyXHe7du3r1SlihdPXvFsNptSUlLUp08fWa3Wiz8BHkX+fYfc+5Yv8p+Xl6dDhw4pLCxMwZdRR0XPnj3VsmVLWSwWvfPOOwoMDNS0adN0++2368EHH9Snn36q2NhYvfzyy+rfv78kadeuXXr00Ue1du1aValSRX369NGMGTNUvXp1GYahjIwMPf7441q4cKHCw8P1t7/9TQEBAQoMDHT+v1+/fn399a9/1V//+ldJ0syZMzV//nzt3btX0dHRuvHGG/X8888rLCxMkjR//nwlJyfr//2//6fk5GQdOnRIXbp00VtvvaX4+HjfJK8SMgxDZ86cUXh4uEwm00Xb5+XlKSQkRN26dSv2vnX1Fw+fF7d/5HA4lJ+fr/bt28tqtWrFihUaOnSoJGnPnj06ePCgEhMTJUmJiYl69tlnlZmZqZiYGEnnfiuOiIhQ8+bNSz1HUFCQgoKKF5VWq7XCP3Ssxv9eWKvVUur5AgLO/2uVV/8fuuBkVqtV3j2593jjtUbpyL/vkHvf8mb+7Xa7TCaTzGazzBd8xyO3oPTpMs0mk4KtFo+2DQ0sf6nxzjvv6NFHH9XmzZv14YcfauzYsfr88891880364knntDMmTM1YsQIHTx4UAUFBerdu7fuvfdezZo1S2fPntVjjz2mYcOGaeXKlXI4HJo8ebLWrFmjzz//XDExMfr73/+ubdu2qW3btkVycz5fkmSxWPTKK6+oXr162rt3r/7yl7/o8ccf16uvvnru+s1m5ebmasaMGXr33XdlNpt155136tFHH9X7779f7mv2V+eHIlyY27KYzWaZTKYSf1Zc/dnxaXE7ceJE9e/fX7Vr19aZM2e0YMECpaamaunSpYqMjNSoUaOUnJys6OhoRURE6MEHH1RiYqKuvfZaSVJSUpKaN2+u4cOH64UXXlBGRoaefPJJjR07tsTiFQCAK13zyUtL3Xd9kxqad3dH5+P2Ty/XWZu9xLad6kXrw/sTnY+7Pr9KJ3KKD3/Y/9wN5Y6xTZs2evLJJyWdqxWee+45Va9eXaNHj5YkTZ48WXPnztXOnTu1fPlytWvXTv/4xz+cz3/rrbeUkJCgn376SXFxcXrvvff0zjvvqFevXpKkt99+W7Vq1Sozhj9+2eyZZ57RAw884CxupXM98q+99poaNGggSRo3bpymTZtW7uuFZ/m0uM3MzNRdd92l9PR0RUZGqnXr1lq6dKn69Okj6dyfBMxms4YOHar8/Hz17du3yJvKYrFo8eLFGjNmjBITE1WlShWNGDGCNxYAAJex1q1bO+9bLBZVq1ZNrVq1cm47/+XxzMxMfffdd1q1apVzuMCF0tLSlJOTo4KCAnXq1Mm5PTo6Wk2aNCkzhuXLl2v69On673//q6ysLBUWFiovL0+5ubnO7+eEhoY6C1tJio+PV2ZmpnsXDY/xaXH75ptvlrk/ODhYc+bM0Zw5c0ptU6dOHX311VeeDg0AAL/047S+pe4z/2FM5NZJvUtpWbzt2seuv7TALvDHPz+f/zP1hY+lc3/yzs7O1sCBA/X8888XO058fLx++umncp9///79uvHGGzVmzBg9++yzio6O1tq1azVq1CgVFBQ4i9uS4jw/OwB8p9KNuQUAABWnPGNgK6qtJ1199dX69NNPVbduXQUEFI+hQYMGslqt2rRpk+rWrStJOnnypH766Sd17969xGNu3bpVDodDL730knOc6EcffVRh1wDPYhUBAABw2Ro7dqxOnDih2267Td9++63S0tK0dOlS3X333bLb7QoLC9Odd96pxx57TCtXrtSuXbs0cuTIMr/c1LBhQ9lsNs2ePVt79+7Vu+++q9dee82LV4VLQXHrZQFms/7UtL5OrW3ECmUAAFyimjVrat26dbLb7UpKSlKrVq00fvx4RUVFOQvYadOmqWvXrho4cKB69+6trl27qn379qUes02bNpoxY4aef/55tWzZUu+//76mT5/urUvCJWJYgpcFBph1S/OGemmdVQFmVgoCAOBCqampxbbt37+/2LYLx7Y2atRIn332WYnHMwxDYWFheuedd4r01j7yyCNlnmPChAmaMGFCkW3Dhw933h85cqRGjhxZZP/gwYMZc1sJ0HUIAAAAv0HPrZc5HIYOZWXLWj1ADoO5eAEAADyJnlsvyyu062/L16vmqDUqsJc8MTYAAADcQ3ELAAAAv0FxCwAAAL9BcQsAgB/j2/u4nHji/UpxCwCAH7JYLJKkgoICH0cCuC43N1dS8aWNy4PZEgAA8EMBAQEKDQ3V0aNHZbVay1yRy585HA4VFBQoLy/vis2BL7maf8MwlJubq8zMTEVFRTl/OXMHxS0AAH7IZDIpPj5e+/bt04EDB3wdjs8YhqGzZ88qJCREJpPJ1+Fcccqb/6ioKMXFxV3SOSluvSzAbNbARnX03nsWBQzmN0gAQMUJDAxUo0aNruihCTabTWvWrFG3bt0u6U/dcE958m+1Wi+px/Y8ilsvCwwwa3irJvpXKsvvAgAqntlsVnBwsK/D8BmLxaLCwkIFBwdT3PqAL/JP1yEAAAD8Bj23XuZwGMrMOStLhE0Og/QDAAB4EtWVl+UV2jVu6TeqNUYqsPf0dTgAAAB+hWEJAAAA8BsUtwAAAPAbFLcAAADwGxS3AAAA8BsUtwAAAPAbFLcAAADwGxS3XmYxm5RUP0FnttWRxUT6AQAAPInqysuCAiy6t20znUhpKauF9AMAAHgS1RUAAAD8BiuUeZlhGMrKL5A5xCHDMPk6HAAAAL9CcetlZ2123ftlqhIekvJZfhcAAMCjGJYAAAAAv0FxCwAAAL9BcQsAAAC/QXELAAAAv0FxCwAAAL9BcQsAAAC/QXHrZRazSd1r11T297VYfhcAAMDDqK68LCjAorEdWur4V21YfhcAAMDDqK4AAADgN1ihzMsMw1BeYaFMVpMMw/B1OAAAAH6F4tbLztrsuus/K1U7meV3AQAAPI1hCQAAAPAbFLcAAADwGxS3AAAA8BsUtwAAAPAbFLcAAADwGxS3AAAA8BsUt15mNpl07VWxyvlvnMwmk6/DAQAA8CsUt14WbLUouVMbHfu8vQItFl+HAwAA4FcobgEAAOA3KG4BAADgN1h+18tyCwp1y2fLVOcxKa+wpySrr0MCAADwG/TcAgAAwG9Q3AIAAMBvUNwCAADAb1DcAgAAwG9Q3AIAAMBvUNwCAADAb/i0uJ0+fbquueYahYeHKyYmRoMHD9aePXuKtOnRo4dMJlOR2wMPPFCkzcGDB3XDDTcoNDRUMTExeuSRR1RYWOjNS3GZ2WRSu9jqyk2rwfK7AAAAHubT4nb16tUaO3asNm7cqJSUFNlsNiUlJSknJ6dIu9GjRys9Pd15e+GFF5z77Ha7brjhBhUUFGj9+vV6++23NX/+fE2ePNnbl+OSYKtFE7tcraOfdGT5XQAAAA/z6SIOS5YsKfJ4/vz5iomJ0datW9WtWzfn9tDQUMXFxZV4jGXLlunHH3/U8uXLFRsbq7Zt2+rpp5/WY489pilTpigwMLBCrwEAAACVR6Vaoez06dOSpOjo6CLb33//fb333nuKi4vTwIEDNWnSJIWGhkqSNmzYoFatWik2NtbZvm/fvhozZox++OEHtWvXrth58vPzlZ+f73yclZUlSbLZbLLZbB6/rj+y2QolWWWzFaq0050bVWFVYaGt1DYVFJxzzTSbzSbvnrzinX99vfE6ozjy7zvk3rfIv++Qe9/yZP5dPUalKW4dDofGjx+vLl26qGXLls7tt99+u+rUqaOaNWtq586deuyxx7Rnzx599tlnkqSMjIwiha0k5+OMjIwSzzV9+nRNnTq12PZly5Y5i+aKkm+X/r45QAkTLFq7MUjHjmWV2C4tLVJSD61du07p6acrNKYLWfLydOPv95cuXSp7cLDXzu1NKSkpvg7hikb+fYfc+xb59x1y71ueyH9ubq5L7SpNcTt27Fjt2rVLa9euLbL9vvvuc95v1aqV4uPj1atXL6WlpalBgwZunWvixIlKTk52Ps7KylJCQoKSkpIUERHh3gW4KLegUI9uXilzoF0dOlyjbp2DSmy3ffu5f7t27aISOp8rzgXjnfv27StVqeLFk1c8m82mlJQU9enTR1ar9eJPgEeRf98h975F/n2H3PuWJ/N//i/tF1Mpittx48Zp8eLFWrNmjWrVqlVm206dOkmSfvnlFzVo0EBxcXHavHlzkTZHjhyRpFLH6QYFBSkoqHhRabVaK/yNbzX+N0OC1Wop9XwBAef/tcqrP4sXnMxqtcq7J/ceb7zWKB359x1y71vk33fIvW95Iv+uPt+nsyUYhqFx48Zp4cKFWrlyperVq3fR5+zYsUOSFB8fL0lKTEzU999/r8zMTGeblJQURUREqHnz5hUSNwAAAConn/bcjh07VgsWLNDnn3+u8PBw5xjZyMhIhYSEKC0tTQsWLNCAAQNUrVo17dy5UxMmTFC3bt3UunVrSVJSUpKaN2+u4cOH64UXXlBGRoaefPJJjR07tsTeWQAAAPgvn/bczp07V6dPn1aPHj0UHx/vvH344YeSpMDAQC1fvlxJSUlq2rSp/va3v2no0KH64osvnMewWCxavHixLBaLEhMTdeedd+quu+7StGnTfHVZAAAA8BGf9twahlHm/oSEBK1evfqix6lTp46++uorT4UFAACAy5RPe26vRGaTSc2rV1XewWiW3wUAAPAwilsvC7ZaNKXbNTry/xJZfhcAAMDDKG4BAADgNyhuAQAA4DcqxSIOV5LcgkKNWrxKtR40Ka+wqyQmlAYAAPAUilsfOFNgkyXU11EAAAD4H4YlAAAAwG9Q3AIAAMBvUNwCAADAb1DcAgAAwG9Q3AIAAMBvUNx6mdlkUoOoCOWnR7L8LgAAgIdR3HpZsNWi6T2vVcY7XVl+FwAAwMMobgEAAOA3KG4BAADgN1ihzMvOFtg1dskaXfWASfmFncXyuwAAAJ5Dcetlhgwdzc1TQOS5+wAAAPAchiUAAADAb1DcAgAAwG9Q3AIAAMBvUNwCAADAb1DcAgAAwG9Q3HqZSSbVCq+igmNhMonldwEAADyJ4tbLQgItmtGni9Lf7K6gAJbfBQAA8CS357k9ePCgDhw4oNzcXNWoUUMtWrRQUFCQJ2MDAAAAyqVcxe3+/fs1d+5cffDBB/r1119lGP9bhCAwMFDXXXed7rvvPg0dOlRmM53CAAAA8C6XK9CHHnpIbdq00b59+/TMM8/oxx9/1OnTp1VQUKCMjAx99dVX6tq1qyZPnqzWrVvr22+/rci4L1tnC+xKTlmn+FGrlV9o93U4AAAAfsXlntsqVapo7969qlatWrF9MTEx6tmzp3r27KmnnnpKS5Ys0aFDh3TNNdd4NFh/YMjQr2dyFFid5XcBAAA8zeXidvr06S4ftF+/fm4FAwAAAFwKtwbGnj17Vrm5uc7HBw4c0KxZs7R06VKPBQYAAACUl1vF7aBBg/TOO+9Ikk6dOqVOnTrppZde0uDBgzV37lyPBggAAAC4yq3idtu2bbruuuskSZ988oliY2N14MABvfPOO3rllVc8GiAAAADgKreK29zcXIWHh0uSli1bpiFDhshsNuvaa6/VgQMHPBogAAAA4Cq3ituGDRtq0aJFOnTokJYuXaqkpCRJUmZmpiIiIjwaoL8xyaQaocEqPB3C8rsAAAAe5lZxO3nyZD388MOqW7euOnXqpMTEREnnenHbtWvn0QD9TUigRXP6ddNvr/Vk+V0AAAAPc2v53T/96U/q2rWr0tPT1aZNG+f2Xr166eabb/ZYcAAAAEB5uFXcSlJcXJzi4uKKbOvYseMlBwQAAAC4y+XidsiQIS4f9LPPPnMrmCtBns2uiSs3Ku4ukwrsHSRZfR0SAACA33C5uI2MjKzIOK4YDsNQ2qksBcWfuw8AAADPcbm4nTdvXkXGAQAAAFwyt2ZLAAAAACojt79Q9sknn+ijjz7SwYMHVVBQUGTftm3bLjkwAAAAoLzc6rl95ZVXdPfddys2Nlbbt29Xx44dVa1aNe3du1f9+/f3dIwAAACAS9wqbl999VW9/vrrmj17tgIDA/Xoo48qJSVFDz30kE6fPu3pGAEAAACXuFXcHjx4UJ07d5YkhYSE6MyZM5Kk4cOH6//9v//nuej8VHigVfbcQF+HAQAA4HfcKm7j4uJ04sQJSVLt2rW1ceNGSdK+fftkML1VmUIDA/Tmjdfr19l9FBzg9pBnAAAAlMCt4rZnz576z3/+I0m6++67NWHCBPXp00e33nory+8CAADAZ9zqOnz99dflcDgkSWPHjlW1atW0fv163XTTTbr//vs9GiAAAADgKreKW7PZLLP5f52+w4YN07BhwzwWlD/Ls9k1Zc23ir3NpAJ7O7H8LgAAgOe4XNzu3LlTLVu2lNls1s6dO8ts27p160sOzF85DEM/Hjup4NosvwsAAOBpLhe3bdu2VUZGhmJiYtS2bVuZTKYSvzxmMplkt9s9GiQAAADgCpeL23379qlGjRrO+wAAAEBl43JxW6dOHUmSzWbT1KlTNWnSJNWrV6/CAgMAAADKq9xTgVmtVn366acVEQsAAABwSdya53bw4MFatGiRh0MBAAAALo1bU4E1atRI06ZN07p169S+fXtVqVKlyP6HHnrII8H5qyCLWWfPmnwdBgAAgN9xq7h98803FRUVpa1bt2rr1q1F9plMJorbMoQGBujdQb3VqZNVwcNsvg4HAADAr7g1LGHfvn2l3vbu3evycaZPn65rrrlG4eHhiomJ0eDBg7Vnz54ibfLy8pyroIWFhWno0KE6cuRIkTYHDx7UDTfcoNDQUMXExOiRRx5RYWGhO5cGAACAy5hbxe20adOUm5tbbPvZs2c1bdo0l4+zevVqjR07Vhs3blRKSopsNpuSkpKUk5PjbDNhwgR98cUX+vjjj7V69WodPnxYQ4YMce632+264YYbVFBQoPXr1+vtt9/W/PnzNXnyZHcuDQAAAJcxt4rbqVOnKjs7u9j23NxcTZ061eXjLFmyRCNHjlSLFi3Upk0bzZ8/XwcPHnQOdTh9+rTefPNNzZgxQz179lT79u01b948rV+/Xhs3bpQkLVu2TD/++KPee+89tW3bVv3799fTTz+tOXPmqKCgwJ3Lq1B5Nrumr9umGn/arAIWuwAAAPAot8bcGoYhk6n4F6K+++47RUdHux3M6dOnJcl5jK1bt8pms6l3797ONk2bNlXt2rW1YcMGXXvttdqwYYNatWql2NhYZ5u+fftqzJgx+uGHH9SuXbti58nPz1d+fr7zcVZWlqRzc/jabBU7Dja/oFDbjxxTaINz90s737lRFVYVFtpUwSEVZbPJ6rxrk3dPXvHO57uiX2eUjPz7Drn3LfLvO+TetzyZf1ePUa7itmrVqjKZTDKZTGrcuHGRAtdutys7O1sPPPBA+SL9ncPh0Pjx49WlSxe1bNlSkpSRkaHAwEBFRUUVaRsbG6uMjAxnmwsL2/P7z+8ryfTp00vsYV62bJlCQ0Pdit9V+XbpfNq3bPlW2aeySmyXlhYpqYfWrl2n9PTTFRrThSx5ebrx9/tLly6VPTjYa+f2ppSUFF+HcEUj/75D7n2L/PsOufctT+S/pCGxJSlXcTtr1iwZhqF77rlHU6dOVWRkpHNfYGCg6tatq8TExPJF+ruxY8dq165dWrt2rVvPL4+JEycqOTnZ+TgrK0sJCQlKSkpSREREhZ47t6BQj25eKUnq0OEadescVGK77dvP/du1axeV0PlccS4Y79y3b1/pD9O8Xe5sNptSUlLUp08fWa3Wiz8BHkX+fYfc+xb59x1y71uezP/5v7RfTLmK2xEjRkiS6tWrpy5duiggwK1RDcWMGzdOixcv1po1a1SrVi3n9ri4OBUUFOjUqVNFem+PHDmiuLg4Z5vNmzcXOd752RTOt/mjoKAgBQUVLyqtVmuFv/Gtxv96u61WS6nnO5/agACrvPqzeMHJrFarvHty7/HGa43SkX/fIfe+Rf59h9z7lify7+rz3fpCWffu3T1S2BqGoXHjxmnhwoVauXKl6tWrV2R/+/btZbVatWLFCue2PXv26ODBg84e4sTERH3//ffKzMx0tklJSVFERISaN29+yTECAADg8uGZrlc3jR07VgsWLNDnn3+u8PBw5xjZyMhIhYSEKDIyUqNGjVJycrKio6MVERGhBx98UImJibr22mslSUlJSWrevLmGDx+uF154QRkZGXryySc1duzYEntnAQAA4L98WtzOnTtXktSjR48i2+fNm6eRI0dKkmbOnCmz2ayhQ4cqPz9fffv21auvvupsa7FYtHjxYo0ZM0aJiYmqUqWKRowYUa75dgEAAOAffFrcGoZx0TbBwcGaM2eO5syZU2qbOnXq6KuvvvJkaBUmNDBAHw1JOrf87hCmJQEAAPAkt8bcAgAAAJWRWz23OTk5eu6557RixQplZmbK4XAU2b93716PBAcAAACUh1vF7b333qvVq1dr+PDhio+PL3G1MpQsz2bXjE3fqfogkwrsLSQxLQkAAICnuFXcfv311/ryyy/VpUsXT8fj9xyGoY2/HVGVppLDYKoyAAAAT3JrzG3VqlUVHR3t6VgAAACAS+JWcfv0009r8uTJLq/xCwAAAHiDW8MSXnrpJaWlpSk2NlZ169Ytthzatm3bPBIcAAAAUB5uFbeDBw/2cBgAAADApXOruH3qqac8HQcAAABwyVjEAQAAAH7Dp8vvXolCrBa9c1NP9ehhVdDNjos/AQAAAC6j59bLTCaTggMCZNgCWPwCAADAw1wubrOysioyDgAAAOCSuVzcVq1aVZmZmZKknj176tSpUxUVk1/LL7RrzpZdqjbgO9nsDEsAAADwJJeL27CwMB0/flySlJqaKpvNVmFB+TO7w9Dqg4cV1upX2Q2KWwAAAE9y+QtlvXv31vXXX69mzZpJkm6++WYFBgaW2HblypWeiQ4AAAAoB5eL2/fee09vv/220tLStHr1arVo0UKhoaEVGRsAAABQLi4XtyEhIXrggQckSVu2bNHzzz+vqKioiooLAAAAKDe35rldtWqV875hGJLEtFYAAADwObfnuX3nnXfUqlUrhYSEKCQkRK1bt9a7777rydgAAACAcnGr53bGjBmaNGmSxo0bpy5dukiS1q5dqwceeEDHjh3ThAkTPBokAAAA4Aq3itvZs2dr7ty5uuuuu5zbbrrpJrVo0UJTpkyhuC1DiNWif9/QQ337BijoZoZyAAAAeJJbwxLS09PVuXPnYts7d+6s9PT0Sw7Kn5lMJkUEBcpxNohxygAAAB7mVnHbsGFDffTRR8W2f/jhh2rUqNElBwUAAAC4w61hCVOnTtWtt96qNWvWOMfcrlu3TitWrCix6MX/5Bfa9e8duxXdxyybnV8EAAAAPMmtntuhQ4dq06ZNql69uhYtWqRFixapevXq2rx5s26++WZPx+hX7A5Dy/YeUvjVB1h+FwAAwMPc6rmVpPbt2+u9997zZCwAAADAJXF7nlsAAACgsqG4BQAAgN+guAUAAIDfoLgFAACA33CruL3nnnt05syZYttzcnJ0zz33XHJQAAAAgDvcKm7ffvttnT17ttj2s2fP6p133rnkoPxZcIBF/+p7nX6de70CLRZfhwMAAOBXyjUVWFZWlgzDkGEYOnPmjIKDg5377Ha7vvrqK8XExHg8SH9iNpsUUyVE9iyrzCabr8MBAADwK+UqbqOiomQymWQymdS4ceNi+00mk6ZOneqx4AAAAIDyKFdxu2rVKhmGoZ49e+rTTz9VdHS0c19gYKDq1KmjmjVrejxIf1JQ6NC73+9RVA+LCh31fR0OAACAXylXcdu9e3dJ0r59+1S7dm2ZTKYKCcqfFToc+uLnA4rsJBU66vo6HAAAAL/i1vK7Bw4c0IEDB0rd361bN7cDAgAAANzlVnHbo0ePYtsu7MW12+1uBwQAAAC4y62pwE6ePFnklpmZqSVLluiaa67RsmXLPB0jAAAA4BK3em4jIyOLbevTp48CAwOVnJysrVu3XnJgAAAAQHl5dPnd2NhY7dmzx5OHBAAAAFzmVs/tzp07izw2DEPp6el67rnn1LZtW0/EBQAAAJSbW8Vt27ZtZTKZZBhGke3XXnut3nrrLY8E5q+CAyx6qXdn3XZbgAJvZvldAAAAT3KruN23b1+Rx2azWTVq1CiyHC9KZjablBARJtsxlt8FAADwNLeK2zp16ng6DgAAAOCSuf2FstWrV2vgwIFq2LChGjZsqJtuuknffPONJ2PzSwWFDn304y+K7PKTCh0OX4cDAADgV9wqbt977z317t1boaGheuihh/TQQw8pJCREvXr10oIFCzwdo18pdDj0yX/3KqrrzxS3AAAAHubWsIRnn31WL7zwgiZMmODc9tBDD2nGjBl6+umndfvtt3ssQAAAAMBVbvXc7t27VwMHDiy2/aabbir2ZTMAAADAW9wqbhMSErRixYpi25cvX66EhIRLDgoAAABwh1vDEv72t7/poYce0o4dO9S5c2dJ0rp16zR//ny9/PLLHg0QAAAAcJVbxe2YMWMUFxenl156SR999JEkqVmzZvrwww81aNAgjwYIAAAAuMqt4laSbr75Zt18882ejAUAAAC4JC6Puf3jUrtwT1CARf/o0Unpb3dRoIXldwEAADzJ5eK2RYsW+uCDD1RQUFBmu59//lljxozRc889d8nB+SOL2aSG0ZEqyIiS2WTydTgAAAB+xeXidvbs2frnP/+puLg43XrrrXrxxRf1/vvv69NPP9W///1vJScnq2PHjmrbtq0iIiI0ZsyYix5zzZo1GjhwoGrWrCmTyaRFixYV2T9y5EiZTKYit379+hVpc+LECd1xxx2KiIhQVFSURo0apezsbFcvCwAAAH7E5TG3vXr10pYtW7R27Vp9+OGHev/993XgwAGdPXtW1atXV7t27XTXXXfpjjvuUNWqVV06Zk5Ojtq0aaN77rlHQ4YMKbFNv379NG/ePOfjoKCgIvvvuOMOpaenKyUlRTabTXfffbfuu+++SrtSWkGhQ//5aZ8iOlpU6Kjl63AAAAD8Srm/UNa1a1d17drVIyfv37+/+vfvX2aboKAgxcXFlbhv9+7dWrJkib799lt16NBB0rke5gEDBuif//ynatas6ZE4PanQ4dB7u35W1eulQkfliw8AAOBy5tYiDt6UmpqqmJgYNWnSRGPGjNHx48ed+zZs2KCoqChnYStJvXv3ltls1qZNm3wRLgAAAHzI7anAvKFfv34aMmSI6tWrp7S0NP39739X//79tWHDBlksFmVkZCgmJqbIcwICAhQdHa2MjIxSj5ufn6/8/Hzn46ysLEmSzWaTzWarmIv5nc1WeMF9e6nnKyyUJKsKC22q4JCKstlkdd61ybsnr3jn813RrzNKRv59h9z7Fvn3HXLvW57Mv6vHqNTF7bBhw5z3W7VqpdatW6tBgwZKTU1Vr1693D7u9OnTNXXq1GLbly1bptDQULeP64p8u3Q+7Vu2fKvsU1kltktLi5TUQ2vXrlN6+ukKjelClrw83fj7/aVLl8oeHOy1c3tTSkqKr0O4opF/3yH3vkX+fYfc+5Yn8p+bm+tSu0pd3P5R/fr1Vb16df3yyy/q1auX4uLilJmZWaRNYWGhTpw4Ueo4XUmaOHGikpOTnY+zsrKUkJCgpKQkRUREVFj8kpRbUKhHN6+UJHXocI26dQ4qsd327ef+7dq1i9q1q9CQisrJcd7t27evVKWKF09e8Ww2m1JSUtSnTx9ZrdaLPwEeRf59h9z7Fvn3HXLvW57M//m/tF/MZVXc/vrrrzp+/Lji4+MlSYmJiTp16pS2bt2q9u3bS5JWrlwph8OhTp06lXqcoKCgYrMuSJLVaq3wN77V+N/ctlarpdTzBQSc/9cqr/4sXnAyq9Uq757ce7zxWqN05N93yL1vkX/fIfe+5Yn8u/p8t75Qtm3bNn3//ffOx59//rkGDx6sv//97xdd5OFC2dnZ2rFjh3bs2CFJ2rdvn3bs2KGDBw8qOztbjzzyiDZu3Kj9+/drxYoVGjRokBo2bHiuR1FSs2bN1K9fP40ePVqbN2/WunXrNG7cOA0bNqxSzpQAAACAiuVWcXv//ffrp59+kiTt3btXw4YNU2hoqD7++GM9+uijLh9ny5Ytateundr9/nf35ORktWvXTpMnT5bFYtHOnTt10003qXHjxho1apTat2+vb775pkiv6/vvv6+mTZuqV69eGjBggLp27arXX3/dncvyiqAAi566roMyFlzL8rsAAAAe5tawhJ9++klt27aVJH388cfq1q2bFixYoHXr1mnYsGGaNWuWS8fp0aOHDMModf/SpUsveozo6OhKu2BDSSxmk1rUiFb+IavMJr65CQAA4Elu9dwahiGHwyFJWr58uQYMGCBJSkhI0LFjxzwXHQAAAFAObhW3HTp00DPPPKN3331Xq1ev1g033CDp3JjZ2NhYjwbob2x2h5akHVRYu/0q/P0XBAAAAHiGW8XtzJkztW3bNo0bN05PPPGEGjZsKEn65JNP1LlzZ48G6G9sdofe+u6/qpb0A8UtAACAh7k15rZNmzZFZks478UXX1RAwGU1uxgAAAD8iFs9t/Xr19fx48eLbc/Ly1Pjxo0vOSgAAADAHW4Vt/v375fdbi+2PT8/X7/++uslBwUAAAC4o1xjCP7zn/847y9dulSRkZHOx3a7XStWrFC9evU8Fx0AAABQDuUqbgcPHixJMplMGjFiRJF9VqtVdevW1UsvveSx4AAAAIDyKFdxe35u23r16unbb79V9erVKyQoAAAAwB1ujbndt28fha2bAi1mPZ7YTpkfd5DV7Fb6AQAAUAq35+1asWKFVqxYoczMTGeP7nlvvfXWJQfmrwIsZl0dX0Nn91plMbP8LgAAgCe5VdxOnTpV06ZNU4cOHRQfHy+TyeTpuAAAAIByc6u4fe211zR//nwNHz7c0/H4PZvdodQDv6lKS4sKHTG+DgcAAMCvuDXos6CggGV23WSzO/Tq1h9U/YadLL8LAADgYW4Vt/fee68WLFjg6VgAAACAS+LWsIS8vDy9/vrrWr58uVq3bi2r1Vpk/4wZMzwSHAAAAFAebhW3O3fuVNu2bSVJu3btKrKPL5cBAADAV9wqbletWuXpOAAAAIBLxioCAAAA8Btu9dxef/31ZQ4/WLlypdsBAQAAAO5yq7g9P972PJvNph07dmjXrl0aMWKEJ+LyW4EWsyZ0bK2//z1A1sF0nAMAAHiSW8XtzJkzS9w+ZcoUZWdnX1JA/i7AYlZirTjl7mH5XQAAAE/zaNfhnXfeqbfeesuThwQAAABc5lbPbWk2bNig4OBgTx7S7xTaHdrwa4ZCmwTI7oj2dTgAAAB+xa3idsiQIUUeG4ah9PR0bdmyRZMmTfJIYP6qwO7QzM07VWOwZHP09HU4AAAAfsWt4jYyMrLIY7PZrCZNmmjatGlKSkrySGAAAABAeblV3M6bN8/TcQAAAACX7JLG3G7dulW7d++WJLVo0ULt2rXzSFAAAACAO9wqbjMzMzVs2DClpqYqKipKknTq1Cldf/31+uCDD1SjRg1PxggAAAC4xK2pwB588EGdOXNGP/zwg06cOKETJ05o165dysrK0kMPPeTpGAEAAACXuNVzu2TJEi1fvlzNmjVzbmvevLnmzJnDF8oAAADgM2713DocDlmt1mLbrVarHA7HJQflz6wWs/7SvoWOfdlaAWaW3wUAAPAkt6qrnj176q9//asOHz7s3Pbbb79pwoQJ6tWrl8eC80dWi1k96lylnF0JFLcAAAAe5lZ19a9//UtZWVmqW7euGjRooAYNGqhevXrKysrS7NmzPR0jAAAA4BK3xtwmJCRo27ZtWr58uf773/9Kkpo1a6bevXt7NDh/VGh3aFv6UYXUt8juiPJ1OAAAAH7F7XluTSaT+vTpoz59+ngyHr9XYHfouQ3bFfNnlt8FAADwtHINS1i5cqWaN2+urKysYvtOnz6tFi1a6JtvvvFYcAAAAEB5lKu4nTVrlkaPHq2IiIhi+yIjI3X//fdrxowZHgsOAAAAKI9yFbffffed+vXrV+r+pKQkbd269ZKDAgAAANxRruL2yJEjJc5ve15AQICOHj16yUEBAAAA7ihXcXvVVVdp165dpe7fuXOn4uPjLzkoAAAAwB3lKm4HDBigSZMmKS8vr9i+s2fP6qmnntKNN97oseAAAACA8ijXVGBPPvmkPvvsMzVu3Fjjxo1TkyZNJEn//e9/NWfOHNntdj3xxBMVEqi/sFrMuqdNU734okUBg1mhDAAAwJPKVdzGxsZq/fr1GjNmjCZOnCjDMCSdm/O2b9++mjNnjmJjYyskUH9htZjVr0FtPbXdqgCzzdfhAAAA+JVyL+JQp04dffXVVzp58qR++eUXGYahRo0aqWrVqhURHwAAAOAyt1coq1q1qq655hpPxnJFsDsM/XD0hIISAuQwwn0dDgAAgF9h0KeX5RfaNfWbLYq7faMK7HZfhwMAAOBXKG4BAADgNyhuAQAA4DcobgEAAOA3KG4BAADgNyhuAQAA4DcobgEAAOA3KG69LMBs1p0tG+nkqqYKMJN+AAAAT6K68rLAALNualxPWZsbUNwCAAB4GNUVAAAA/Ibby+/CPXaHoV9OnFZgXIAcRqivwwEAAPArPu25XbNmjQYOHKiaNWvKZDJp0aJFRfYbhqHJkycrPj5eISEh6t27t37++ecibU6cOKE77rhDERERioqK0qhRo5Sdne3Fqyif/EK7/p66SfEj1rH8LgAAgIf5tLjNyclRmzZtNGfOnBL3v/DCC3rllVf02muvadOmTapSpYr69u2rvLw8Z5s77rhDP/zwg1JSUrR48WKtWbNG9913n7cuAQAAAJWIT4cl9O/fX/379y9xn2EYmjVrlp588kkNGjRIkvTOO+8oNjZWixYt0rBhw7R7924tWbJE3377rTp06CBJmj17tgYMGKB//vOfqlmzpteuBQAAAL5Xacfc7tu3TxkZGerdu7dzW2RkpDp16qQNGzZo2LBh2rBhg6KiopyFrST17t1bZrNZmzZt0s0331zisfPz85Wfn+98nJWVJUmy2Wyy2WwVdEX6/RyFF9y3l3q+wkJJsqqw0KYKDqkom01W512bvHvyinc+3xX9OqNk5N93yL1vkX/fIfe+5cn8u3qMSlvcZmRkSJJiY2OLbI+NjXXuy8jIUExMTJH9AQEBio6OdrYpyfTp0zV16tRi25ctW6bQ0Ir9kle+XTqf9i1bvlX2qawS26WlRUrqobVr1yk9/XSFxnQhS16ebvz9/tKlS2UPDvbaub0pJSXF1yFc0ci/75B73yL/vkPufcsT+c/NzXWpXaUtbivSxIkTlZyc7HyclZWlhIQEJSUlKSIiokLPnVtQqEc3r5Qkdehwjbp1Diqx3fbt5/7t2rWL2rWr0JCKyslx3u3bt69UpYoXT17xbDabUlJS1KdPH1mt1os/AR5F/n2H3PsW+fcdcu9bnsz/+b+0X0ylLW7j4uIkSUeOHFF8fLxz+5EjR9S2bVtnm8zMzCLPKyws1IkTJ5zPL0lQUJCCgooXlVartcLf+FbDdMH5LKWeLyDg/L9WefVn8YKTWa1Weffk3uON1xqlI/++Q+59i/z7Drn3LU/k39XnV9pFHOrVq6e4uDitWLHCuS0rK0ubNm1SYmKiJCkxMVGnTp3S1q1bnW1Wrlwph8OhTp06eT1mVwSYzfpT0/o6tbYRK5QBAAB4mE97brOzs/XLL784H+/bt087duxQdHS0ateurfHjx+uZZ55Ro0aNVK9ePU2aNEk1a9bU4MGDJUnNmjVTv379NHr0aL322muy2WwaN26chg0bVmlnSggMMOuW5g310jqrAswMbgcAAPAknxa3W7Zs0fXXX+98fH4c7IgRIzR//nw9+uijysnJ0X333adTp06pa9euWrJkiYIv+JLT+++/r3HjxqlXr14ym80aOnSoXnnlFa9fCwAAAHzPp8Vtjx49ZBhGqftNJpOmTZumadOmldomOjpaCxYsqIjwKoTDYehQVras1QPkMEr+MhkAAADcw6BPL8srtOtvy9er5qg1LL8LAADgYRS3AAAA8BsUtwAAAPAbFLcAAADwGxS3AAAA8BsUtwAAAPAbFLcAAADwGxS3XhZgNmtgozo6vak+y+8CAAB4GNWVlwUGmDW8VROdSm1GcQsAAOBhVFcAAADwGz5dfvdK5HAYysw5K0uETQ6D9AMAAHgS1ZWX5RXaNW7pN6o1Riqw9/R1OAAAAH6FYQkAAADwGxS3AAAA8BsUtwAAAPAbFLcAAADwGxS3AAAA8BsUtwAAAPAbFLdeZjGblFQ/QWe21ZHFRPoBAAA8ierKy4ICLLq3bTOdSGkpq4X0AwAAeBLVFQAAAPwGK5R5mWEYysovkDnEIcMw+TocAAAAv0Jx62VnbXbd+2WqEh6S8ll+FwAAwKMYlgAAAAC/QXELAAAAv0FxCwAAAL9BcQsAAAC/QXELAAAAv0FxCwAAAL9BcetlFrNJ3WvXVPb3tVh+FwAAwMOorrwsKMCisR1a6vhXbVh+FwAAwMOorgAAAOA3WKHMywzDUF5hoUxWkwzD8HU4AAAAfoXi1svO2uy66z8rVTuZ5XcBAAA8jWEJAAAA8BsUtwAAAPAbFLcAAADwGxS3AAAA8BsUtwAAAPAbFLcAAADwGxS3XmY2mXTtVbHK+W+czCaTr8MBAADwKxS3XhZstSi5Uxsd+7y9Ai0WX4cDAADgVyhuAQAA4DcobgEAAOA3WH7Xy3ILCnXLZ8tU5zEpr7CnJKuvQwIAAPAb9NwCAADAb1DcAgAAwG9Q3AIAAMBvUNwCAADAb1DcAgAAwG9Q3AIAAMBvUNx6mdlkUrvY6spNq8HyuwAAAB5GcetlwVaLJna5Wkc/6cjyuwAAAB5GcQsAAAC/QXELAAAAv8Hyu16WW1Co4Z8vV8IEk/IKu4vldwEAADyH4tYH8u0OmQN9HQUAAID/qdTDEqZMmSKTyVTk1rRpU+f+vLw8jR07VtWqVVNYWJiGDh2qI0eO+DBiAAAA+FKlLm4lqUWLFkpPT3fe1q5d69w3YcIEffHFF/r444+1evVqHT58WEOGDPFhtAAAAPClSj8sISAgQHFxccW2nz59Wm+++aYWLFignj17SpLmzZunZs2aaePGjbr22mu9HSoAAAB8rNIXtz///LNq1qyp4OBgJSYmavr06apdu7a2bt0qm82m3r17O9s2bdpUtWvX1oYNG8osbvPz85Wfn+98nJWVJUmy2Wyy2WwVdzGSbLbCC+7bSz1fYaEkWVVYaFMFh1SUzeb8ipvNZpN3T17xzue7ol9nlIz8+w659y3y7zvk3rc8mX9Xj1Gpi9tOnTpp/vz5atKkidLT0zV16lRdd9112rVrlzIyMhQYGKioqKgiz4mNjVVGRkaZx50+fbqmTp1abPuyZcsUGhrqyUsoJt8unU/7li3fKvtUVont0tIiJfXQ2rXrlJ5+ukJjupAlL083/n5/6dKlsgcHe+3c3pSSkuLrEK5o5N93yL1vkX/fIfe+5Yn85+bmutSuUhe3/fv3d95v3bq1OnXqpDp16uijjz5SSEiI28edOHGikpOTnY+zsrKUkJCgpKQkRUREXFLMF5Nns2te2lZt325WxwGt1DUxqMR227ef+7dr1y5q165CQyoqJ8d5t2/fvlKVKl48ecWz2WxKSUlRnz59ZLUyDZu3kX/fIfe+Rf59h9z7lifzf/4v7RdTqYvbP4qKilLjxo31yy+/qE+fPiooKNCpU6eK9N4eOXKkxDG6FwoKClJQUPGi0mq1Vvgb32q1amr3jur0qFVVxttKPV9AwPl/rfLqz+IFJ7NarfLuyb3HG681Skf+fYfc+xb59x1y71ueyL+rz6/0syVcKDs7W2lpaYqPj1f79u1ltVq1YsUK5/49e/bo4MGDSkxM9GGUAAAA8JVK3XP78MMPa+DAgapTp44OHz6sp556ShaLRbfddpsiIyM1atQoJScnKzo6WhEREXrwwQeVmJjITAkAAABXqEpd3P7666+67bbbdPz4cdWoUUNdu3bVxo0bVaNGDUnSzJkzZTabNXToUOXn56tv37569dVXfRx12XILCjVq8SrVetCkvMKuYvldAAAAz6nUxe0HH3xQ5v7g4GDNmTNHc+bM8VJEnnGmwCZLxU7KAAAAcEW6rMbcAgAAAGWhuAUAAIDfoLgFAACA36C4BQAAgN+guAUAAIDfoLj1MrPJpAZREcpPj5TZZPJ1OAAAAH6F4tbLgq0WTe95rTLe6apAi8XX4QAAAPgVilsAAAD4DYpbAAAA+I1KvUKZPzpbYNfYJWt01QMm5Rd2FsvvAgAAeA7FrZcZMnQ0N08BkefuAwAAwHMYlgAAAAC/QXELAAAAv0FxCwAAAL9BcQsAAAC/QXELAAAAv0Fx62UmmVQrvIoKjoXJJJbfBQAA8CSKWy8LCbRoRp8uSn+zu4ICWH4XAADAkyhuAQAA4DcobgEAAOA3WKHMy84W2JWcsk7xo0zKL+wklt8FAADwHIpbLzNk6NczOQqszvK7AAAAnsawBAAAAPgNilsAAAD4DYpbAAAA+A2KWwAAAPgNilsAAAD4DYpbLzPJpBqhwSo8HcLyuwAAAB5GcetlIYEWzenXTb+91pPldwEAADyM4hYAAAB+g+IWAAAAfoMVyrwsz2bXxJUbFXeXSQX2DrrY8ru7d1/8mNWrS7VreyY+AACAyxnFrZc5DENpp7IUFH/ufmmqV5dCQ6U777z4MUNDzxXBFLgAAOBKR3FbSdWufa5gPXas7Ha7d58rgI8do7gFAACguK3EatemYAUAACgPitsrxMGDF+8FlqQaoVJCxYcDAABQIShurwAHD0rNmkm5uRdvWz1EOlrxIQEAAFQIitsrwLFj5wrb9947V+SWZvdu6T4XvsAGAABQWVHc+kB4oFWnTnl26d2ypgw7v69ZM+nqqz16WgAAgEqF4tbLQgMD9OaN16tTJ6uC77Rd8vFcnTIsNPRcWwAAAH9GcXuZc3XKMBZ6AAAAVwKKWz/AlGEAAADnUNx6WZ7NrilrvlXsbSYV2NvpYsvv+tKOHZIjpOR93u4JdnUqM3qoAQC4slHcepnDMPTjsZMKrl328ru+UL26FBoi6ey5x126SqXNHubNJX/LM5UZSxEDAHBlo7iFU+3a0rZtkn6fLmzd2pJ7br295G95pjJjKWIAAK5sFLcoIuGC5cnatpVUxVeRFMdUZgAA4GLMvg4AAAAA8BSKWwAAAPgNhiWgwrg6w8HFlLX6GgAAwIUobn0gyGLW2bOeXX63sinPDAeuYIU1AADgCopbLwsNDNC7g3qfW3532KUvv1tZuTrDgavKM39tWT29hYXS0aOlTN4LAAAuexS3qFDenOGgevVzPbx33llWK6uCgnrq+usNNWjgnbgAAID3UNzCb9Sufa7Xtqxxvt9/X6iRIwO0dm2hTp8u+3isdgYAwOWH4tbL8mx2TV+3TTX+ZFKBvbUq8/K7l6PatcsuSCMjDQUFnStwL4bVzvyHK19u5JcZAPAPFLde5jAMbT9yTKENKt/yu1eC2rWlf/1rpVq3vl4BAaX/YnF+tbNvvil7zLCrBZGrM0dczgWWK9foizHPrn650ZO/zFwJrzcAVFYUt7ji1KhxVu3aSdYyOs1dG7/rWkFUnpkjLtfeYtev0ftjnl35cqMnl26+El5vAKjMKG6BErgyftfVgsjVmSNc7S12lSd7BS/WE7l7t2vXeH7M8/HjNq9/oc9bX24s7+vtiYJauvhrxEwh8Fe89/FHflPczpkzRy+++KIyMjLUpk0bzZ49Wx07dvR1WH6trCm3/GHhhYuN3z3vYtd6fv/FiitXe4tdFRoqffaZVKNG2ee82DWW58/6111X9vEKCyv3UBxX3reu/tLgajF9sXN67jWq3DOFMC4a7vCH9z48zy+K2w8//FDJycl67bXX1KlTJ82aNUt9+/bVnj17FBMT4+vw/E55/mTvzwsvlKcYdSUXrvQWu+roUWnIEKlfv4vHdbE/i7vaE1m+uYhNCvDSp4+rBWt5Xsuyfmlw9Re78vwcXeyXFFd6zsvTa+7JMcOuHOv8+7Uyjov2lMJCKS0tUtu366LvfW8X8d7OheS5a3Tl88mXfzGqjK6E7wT4RXE7Y8YMjR49Wnfffbck6bXXXtOXX36pt956S48//riPo/M/rhZhl/MPhivKU4y6mgtXe4td4alhFed54s/61arJ5dkqPOliv1y4+lqW55cGT/wy4+r5zp+zrJ5zV3vNPTlmuLzHWrKk7F8afDEu2nOsknq41NKbY7F9kwvPX2NZn0+V/S9G3nSlfCfgsi9uCwoKtHXrVk2cONG5zWw2q3fv3tqwYUOJz8nPz1d+fr7z8enfJzw9ceKEbLaKXTUst6BQjvxz76pTp07q+HEvf6JcTE6Oc3Iy2/HjUl5eic2qVDl3u5jjxz0XmifYbDbl5ubq+PHjspb1jTIXuZoHyfu5uFhsp05JklWbNxfq1KnSP/x/+skkKUCnTtku+RqqVLHpxRc3qEmTzgrwVtetpOjoc7koK35XXss6daR166QTJy79fK6c09XzuXLOU6cKJYXo22/zlZ1tLvU4P/1kUm5ugObOLVTjxmW/L8aMCdBXX5XeztVjnY8/IaH0/a6+X11Rnrg8pbCwUFu2bFGHDh3KfO+7kldP8kUuPHmNrnw+ufrevxJ48udbkmJjpbi4ss/pyf93z5w5I0kyLjbblHGZ++233wxJxvr164tsf+SRR4yOHTuW+JynnnrKkMSNGzdu3Lhx48btMrsdOnSozNrwsu+5dcfEiROVnJzsfOxwOHTixAlVq1ZNJpOpws+flZWlhIQEHTp0SBERERV+PvwPufct8u875N63yL/vkHvf8mT+DcPQmTNnVLNmzTLbXfbFbfXq1WWxWHTkyJEi248cOaK4UvrKg4KCFBQUVGRbVFRURYVYqoiICH7QfITc+xb59x1y71vk33fIvW95Kv+RkZEXbXPZDz4JDAxU+/bttWLFCuc2h8OhFStWKDEx0YeRAQAAwNsu+55bSUpOTtaIESPUoUMHdezYUbNmzVJOTo5z9gQAAABcGfyiuL311lt19OhRTZ48WRkZGWrbtq2WLFmi2NhYX4dWoqCgID311FPFhkag4pF73yL/vkPufYv8+w659y1f5N9kGBebTwEAAAC4PFz2Y24BAACA8yhuAQAA4DcobgEAAOA3KG4BAADgNyhuK8icOXNUt25dBQcHq1OnTtq8eXOZ7T/++GM1bdpUwcHBatWqlb766isvRep/ypP7+fPny2QyFbkFBwd7MVr/sWbNGg0cOFA1a9aUyWTSokWLLvqc1NRUXX311QoKClLDhg01f/78Co/TX5U3/6mpqcXe+yaTSRkZGd4J2I9Mnz5d11xzjcLDwxUTE6PBgwdrz549F30en/ue4U7++ez3jLlz56p169bOBRoSExP19ddfl/kcb7zvKW4rwIcffqjk5GQ99dRT2rZtm9q0aaO+ffsqMzOzxPbr16/XbbfdplGjRmn79u0aPHiwBg8erF27dnk58stfeXMvnVs1JT093Xk7cOCAFyP2Hzk5OWrTpo3mzJnjUvt9+/bphhtu0PXXX68dO3Zo/Pjxuvfee7V06dIKjtQ/lTf/5+3Zs6fI+z8mJqaCIvRfq1ev1tixY7Vx40alpKTIZrMpKSlJOTk5pT6Hz33PcSf/Ep/9nlCrVi0999xz2rp1q7Zs2aKePXtq0KBB+uGHH0ps77X3vQGP69ixozF27FjnY7vdbtSsWdOYPn16ie1vueUW44YbbiiyrVOnTsb9999foXH6o/Lmft68eUZkZKSXortySDIWLlxYZptHH33UaNGiRZFtt956q9G3b98KjOzK4Er+V61aZUgyTp486ZWYriSZmZmGJGP16tWltuFzv+K4kn8++ytO1apVjX//+98l7vPW+56eWw8rKCjQ1q1b1bt3b+c2s9ms3r17a8OGDSU+Z8OGDUXaS1Lfvn1LbY+SuZN7ScrOzladOnWUkJBQ5m+c8Cze95VD27ZtFR8frz59+mjdunW+DscvnD59WpIUHR1dahve/xXHlfxLfPZ7mt1u1wcffKCcnBwlJiaW2MZb73uKWw87duyY7HZ7sdXRYmNjSx3LlpGRUa72KJk7uW/SpIneeustff7553rvvffkcDjUuXNn/frrr94I+YpW2vs+KytLZ8+e9VFUV474+Hi99tpr+vTTT/Xpp58qISFBPXr00LZt23wd2mXN4XBo/Pjx6tKli1q2bFlqOz73K4ar+eez33O+//57hYWFKSgoSA888IAWLlyo5s2bl9jWW+97v1h+F3BXYmJikd8wO3furGbNmun//u//9PTTT/swMqBiNWnSRE2aNHE+7ty5s9LS0jRz5ky9++67Pozs8jZ27Fjt2rVLa9eu9XUoVyRX889nv+c0adJEO3bs0OnTp/XJJ59oxIgRWr16dakFrjfQc+th1atXl8Vi0ZEjR4psP3LkiOLi4kp8TlxcXLnao2Tu5P6PrFar2rVrp19++aUiQsQFSnvfR0REKCQkxEdRXdk6duzIe/8SjBs3TosXL9aqVatUq1atMtvyue955cn/H/HZ777AwEA1bNhQ7du31/Tp09WmTRu9/PLLJbb11vue4tbDAgMD1b59e61YscK5zeFwaMWKFaWOQUlMTCzSXpJSUlJKbY+SuZP7P7Lb7fr+++8VHx9fUWHid7zvK58dO3bw3neDYRgaN26cFi5cqJUrV6pevXoXfQ7vf89xJ/9/xGe/5zgcDuXn55e4z2vve49+PQ2GYRjGBx98YAQFBRnz5883fvzxR+O+++4zoqKijIyMDMMwDGP48OHG448/7my/bt06IyAgwPjnP/9p7N6923jqqacMq9VqfP/99766hMtWeXM/depUY+nSpUZaWpqxdetWY9iwYUZwcLDxww8/+OoSLltnzpwxtm/fbmzfvt2QZMyYMcPYvn27ceDAAcMwDOPxxx83hg8f7my/d+9eIzQ01HjkkUeM3bt3G3PmzDEsFouxZMkSX13CZa28+Z85c6axaNEi4+effza+//57469//athNpuN5cuX++oSLltjxowxIiMjjdTUVCM9Pd15y83Ndbbhc7/iuJN/Pvs94/HHHzdWr15t7Nu3z9i5c6fx+OOPGyaTyVi2bJlhGL5731PcVpDZs2cbtWvXNgIDA42OHTsaGzdudO7r3r27MWLEiCLtP/roI6Nx48ZGYGCg0aJFC+PLL7/0csT+ozy5Hz9+vLNtbGysMWDAAGPbtm0+iPryd35qqT/ezud7xIgRRvfu3Ys9p23btkZgYKBRv359Y968eV6P21+UN//PP/+80aBBAyM4ONiIjo42evToYaxcudI3wV/mSsq7pCLvZz73K447+eez3zPuueceo06dOkZgYKBRo0YNo1evXs7C1jB89743GYZheLYvGAAAAPANxtwCAADAb1DcAgAAwG9Q3AIAAMBvUNwCAADAb1DcAgAAwG9Q3AIAAMBvUNwCAADAb1DcAsAl6NGjh8aPH1+u5yxatEgNGzaUxWJxPrekba4wmUxatGiRJGn//v0ymUzasWNHueK5HPTo0UMmk8kj1zdy5Ejnsc7nDoD/oLgF4FdGjhypwYMH+zqMMt1///3605/+pEOHDunpp58udVt5JSQkKD09XS1btrxo28uxEB49erTL11eWl19+Wenp6R6KCkBlE+DrAADgSpKdna3MzEz17dtXNWvWLHWbOywWi+Li4jwVaqUTGhrqkeuLjIxUZGSkByICUBnRcwvgsvPJJ5+oVatWCgkJUbVq1dS7d2/l5ORoypQpevvtt/X55587/+ycmpoqSXrsscfUuHFjhYaGqn79+po0aZJsNpvzmFOmTFHbtm317rvvqm7duoqMjNSwYcN05swZZ5ucnBzdddddCgsLU3x8vF566aVisZ08eVJ33XWXqlatqtDQUPXv318///yzJCk1NVXh4eGSpJ49ezrjK2lbSX7++Wd169ZNwcHBat68uVJSUors/2Nv7MmTJ3XHHXeoRo0aCgkJUaNGjTRv3jxJUr169SRJ7dq1k8lkUo8ePSRJ3377rfr06aPq1asrMjJS3bt317Zt24qcx2Qy6d///rduvvlmhYaGqlGjRvrPf/5TpM0PP/ygG2+8UREREQoPD9d1112ntLQ05/5///vfatasmYKDg9W0aVO9+uqrJV5zWVJTU2UymbR06VK1a9dOISEh6tmzpzIzM/X111+rWbNmioiI0O23367c3NxyHx/A5YniFsBlJT09Xbfddpvuuece7d69W6mpqRoyZIgMw9DDDz+sW265Rf369VN6errS09PVuXNnSVJ4eLjmz5+vH3/8US+//LLeeOMNzZw5s8ix09LStGjRIi1evFiLFy/W6tWr9dxzzzn3P/LII1q9erU+//xzLVu2TKmpqcUKv5EjR2rLli36z3/+ow0bNsgwDA0YMEA2m02dO3fWnj17JEmffvqpM76Stv2Rw+HQkCFDFBgYqE2bNum1117TY489VmauJk2apB9//FFff/21du/erblz56p69eqSpM2bN0uSli9frvT0dH322WeSpDNnzmjEiBFau3atNm7cqEaNGmnAgAFFinxJmjp1qm655Rbt3LlTAwYM0B133KETJ05Ikn777Td169ZNQUFBWrlypbZu3ap77rlHhYWFkqT3339fkydP1rPPPqvdu3frH//4hyZNmqS33367zOspzZQpU/Svf/1L69ev16FDh3TLLbdo1qxZWrBggb788kstW7ZMs2fPduvYAC5DBgBcRrZu3WpIMvbv31/i/hEjRhiDBg266HFefPFFo3379s7HTz31lBEaGmpkZWU5tz3yyCNGp06dDMMwjDNnzhiBgYHGRx995Nx//PhxIyQkxPjrX/9qGIZh/PTTT4YkY926dc42x44dM0JCQpzPO3nypCHJWLVqlbNNSdv+aOnSpUZAQIDx22+/Obd9/fXXhiRj4cKFhmEYxr59+wxJxvbt2w3DMIyBAwcad999d4nH+2Pb0tjtdiM8PNz44osvnNskGU8++aTzcXZ2tiHJ+Prrrw3DMIyJEyca9erVMwoKCko8ZoMGDYwFCxYU2fb0008biYmJpcbRvXt3Z57PW7VqlSHJWL58uXPb9OnTDUlGWlqac9v9999v9O3bt9gxL8wdAP/BmFsAl5U2bdqoV69eatWqlfr27aukpCT96U9/UtWqVct83ocffqhXXnlFaWlpys7OVmFhoSIiIoq0qVu3rnOIgCTFx8crMzNT0rle3YKCAnXq1Mm5Pzo6Wk2aNHE+3r17twICAoq0qVatmpo0aaLdu3df0nXv3r1bCQkJRcbkJiYmlvmcMWPGaOjQodq2bZuSkpI0ePDgEnuFL3TkyBE9+eSTSk1NVWZmpux2u3Jzc3Xw4MEi7Vq3bu28X6VKFUVERDhztWPHDl133XWyWq3Fjp+Tk6O0tDSNGjVKo0ePdm4vLCx0exzshbHExsY6h55cuO18TzUA/8ewBACXFYvFopSUFH399ddq3ry5Zs+erSZNmmjfvn2lPmfDhg264447NGDAAC1evFjbt2/XE088oYKCgiLt/liMmUwmORyOCrkOb+jfv78OHDigCRMm6PDhw+rVq5cefvjhMp8zYsQI7dixQy+//LLWr1+vHTt2qFq1auXKVUhISKnHz87OliS98cYb2rFjh/O2a9cubdy40Z3LLBKLyWTyu9cRQPlQ3AK47JhMJnXp0kVTp07V9u3bFRgYqIULF0qSAgMDZbfbi7Rfv3696tSpoyeeeEIdOnRQo0aNdODAgXKds0GDBrJardq0aZNz28mTJ/XTTz85Hzdr1kyFhYVF2hw/flx79uxR8+bN3bnUIsc+dOhQkSmsXCkGa9SooREjRui9997TrFmz9Prrr0s6lydJxXK1bt06PfTQQxowYIBatGihoKAgHTt2rFyxtm7dWt98802RL+ydFxsbq5o1a2rv3r1q2LBhkdv5L7kBwKVgWAKAy8qmTZu0YsUKJSUlKSYmRps2bdLRo0fVrFkzSeeGFixdulR79uxRtWrVFBkZqUaNGungwYP64IMPdM011+jLL790FsOuCgsL06hRo/TII4+oWrVqiomJ0RNPPCGz+X99BI0aNdKgQYM0evRo/d///Z/Cw8P1+OOP66qrrtKgQYMu6bp79+6txo0ba8SIEXrxxReVlZWlJ554osznTJ48We3bt1eLFi2Un5+vxYsXO/MUExOjkJAQLVmyRLVq1VJwcLAzV++++646dOigrKwsPfLII2X2xJZk3Lhxmj17toYNG6aJEycqMjJSGzduVMeOHdWkSRNNnTpVDz30kCIjI9WvXz/l5+dry5YtOnnypJKTk93OEQBI9NwCuMxERERozZo1GjBggBo3bqwnn3xSL730kvr37y/p3ET/TZo0UYcOHVSjRg2tW7dON910kyZMmKBx48apbdu2Wr9+vSZNmlTuc7/44ou67rrrNHDgQPXu3Vtdu3ZV+/bti7SZN2+e2rdvrxtvvFGJiYkyDENfffVVieNPy8NsNmvhwoU6e/asOnbsqHvvvVfPPvtsmc8JDAzUxIkT1bp1a3Xr1k0Wi0UffPCBJCkgIECvvPKK/u///k81a9Z0Ft9vvvmmTp48qauvvlrDhw/XQw89pJiYmHLFWq1aNa1cuVLZ2dnq3r272rdvrzfeeMOZg3vvvVf//ve/NW/ePLVq1Urdu3fX/Pnz6bkF4BEmwzAMXwcBAEBZevToobZt22rWrFkeO6bJZNLChQsr/Yp2AMqHnlsAwGXh1VdfVVhYmL7//vtLOs4DDzygsLAwD0UFoLKh5xYAUOn99ttvOnv2rCSpdu3azi/EuSMzM1NZWVmSzk33VqVKFY/ECKByoLgFAACA32BYAgAAAPwGxS0AAAD8BsUtAAAA/AbFLQAAAPwGxS0AAAD8BsUtAAAA/AbFLQAAAPwGxS0AAAD8BsUtAAAA/Mb/B0rlaU+/pz1QAAAAAElFTkSuQmCC", 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N0unTp3XVVVepW7duCg8Pv+B74XQ6ZRiG7Ha7rFZrkXXu/uxZDHentyglP/zwg5KSkpSXl6fw8HDNmzdPffv2lSS99dZbqlWrlqpXr64dO3Zo/Pjxateunes3l/vuu08HDhzQkiVLXMfLzc1VpUqV9PXXX6tPnz4XPOeUKVM0derUYsvnzZtXZNhDach3SI9uPBtoR1QLU6N6WZfdJy0tSv/3f1310kupqlv3VKnWJ0nWvDzdOGSIJGnRRx/Jcd7AegBA+RAYGKj4+HglJiYW+UvmoUMWHT/uu+98xMQ4lZjo16iBcqSgoECHDh1SRkZGse9Q5ebm6rbbbtOpU6cuOczB7+G2oKBABw8e1KlTp/TZZ5/pn//8p1atWqUmTZoU23bFihXq3r279u3bp7p163ocbi/Uc5uYmKijR4+W+piQ3IJCtXhyhSTpnT5d1LlD8GX32bZNat/epg0b7LpIZ7R35eTIVrmyJMl+4oRUqZIPTmo+drtdKSkp6tmzZ7EB+TAX2rpiKG/tnJeXp0OHDql27dpFvv2PyzMMQ6dPn1ZERMQF706A0pOXl6dffvlFiYmJxT63WVlZqlq16mXDrd+HJQQFBalevXqSpNatW7tudvyPf/yj2Lbt27eXJFe4jY+P18aNG4tsc+TIEUm65H3kgoODFRxcPFTabLZS/w+WzfjfD4nNZnXrfOe+dBYYaJNP/nt63klsNpt8c1Lz8sXnCmUDbV0xlJd2djgcslgsCggI8NpY0Iri3J/Lz71/8J2AgABZLJYL/py5+3NX5lrM6XRedFaS7du3Szp7jzXp7L3nfvjhhyKzdaSkpCgyMvKCPb8AAAAwN7/23E6YMEF9+vRRzZo1dfr0ac2bN0+pqalasmSJ0tLSXONvq1Spoh07dmjMmDHq3Lmz6wbMycnJatKkiYYOHarnn39eGRkZmjhxokaOHHnBnlkAAACYm1/DbWZmpu644w6lp6crKipKzZs315IlS9SzZ08dOnRIy5Yt04wZM5STk6PExEQNHjy4yFRxVqtVixYt0ogRI5SUlKRKlSpp2LBhRe6LCwAAgIrDr+H2nXfeuei6xMRErVq16rLHqFWrlr7++mtvlgUAAIByqsyNuTU7mzVAD7ZuqqNfNVcgg9QBAAC8inTlYzZrgLrWuko5OxMJtwAAAF5GugIAAIBp+P0+txVNocOprem/K/RqqxzOaH+XAwAAYCr03PpYgcOpZ9dtU+yfN8vuzkTbAAAAcBvhFgAAlBldu3bVQw89pNGjR6ty5cqKi4vT22+/rZycHN11112KiIhQvXr19M0337j22blzp/r06aPw8HDFxcVp6NChOnr0qGv94sWL1alTJ0VHR6tKlSq68cYblZaW5lr/yy+/yGKxaP78+brhhhsUHh6uTp06ad26dT69dngH4RYAgIrAMKScHP88DKNEpb733nuqWrWqNm7cqIceekgjRozQn//8Z3Xo0EFbt25VcnKyhg4dqtzcXJ08eVLdunVTq1attHnzZi1evFhHjhzRLbfc4jpeTk6Oxo4dq82bN2v58uUKCAjQzTff7Jpm95zHH39c48aN09atW1WvXj3dfvvtKiws9MrbD99hzC0AABVBbq4UHu6fc2dnS5Uqub15ixYtXJM2TZgwQc8++6yqVq2qe++9V5I0efJkzZ49Wzt27NCyZcvUqlUrPfPMM6793333XSUmJurHH39UgwYNNHjw4CLHf/fdd1WtWjXt3r1b11xzjWv5uHHj1K9fPzmdTj322GNKSkrSvn371KhRoyu5evgYPbcAAKBMad68ueu51WpVlSpV1KxZM9eyuLg4SWdnOv3++++1cuVKhYeHux7nwui5oQc//fSTbr31Vl199dWKjIxU7dq1JUkHDx686Hnj4+Nd50D5Qs8tAAAVQVjY2R5Uf527BGw2W5HXFoulyDKLxSJJcjqdys7OVv/+/fXcc88VO05CQoIkqX///qpVq5befvttVa9eXU6nU9dcc40KCgouet7zz4HyhXALAEBFYLGUaGhAeXHttdfq888/V+3atRUYWDzWHDt2THv37tXbb7+t66+/XpK0evVqX5cJH2JYgo/ZrAG6u0UjHVvalBnKAAC4QiNHjtTx48d16623atOmTUpLS9OSJUt01113yeFwqHLlyqpSpYreeust7du3TytWrNDYsWP9XTZKEenKx2zWAPWuW1PZ22oTbgEAuELVq1fXmjVr5HA4lJycrGbNmmn06NGKjo5WQECAAgIC9NFHH2nLli265pprNGbMGL3wwgv+LhuliGEJAACgzEhNTS227Jdffim2zDjv9mL169fX/PnzL3rMHj16aPfu3Rfdv3bt2kVeS1JUVJQcDocC6Igqdwi3PuZwGtr1+3EFJwbKaUT4uxwAAABT4dcRH8svdGjqd5sVf9t6FTgc/i4HAADAVAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcOtjgQEB+us19XViZSNmKAMAAPAy0pWPBQUG6KYGdZS1sS7hFgAAP+natatGjx7tel27dm3NmDHDb/XAe5ihDAAAVHibNm1SpUqV/F0GvIBw62MOp6F9x08pKD5QTiPM3+UAAABJ1apV83cJ8BL+Lu5j+YUO/T11gxKGrWH6XQAA/qBr16566KGHNHr0aFWuXFlxcXF6++23lZOTo7vuuksRERGqV6+evvnmG9c+O3fuVJ8+fRQeHq64uDgNHTpUR48eda3PycnRHXfcofDwcCUkJOill14qdt4/DkuYNWuWWrRooUqVKikxMVEPPvigsrOzXevnzp2r6OhoLVmyRI0bN1Z4eLh69+6t9PT00nlj4DbCLQAAFUhuQeFFH3l2h9e39cR7772nqlWrauPGjXrooYc0YsQI/fnPf1aHDh20detWJScna+jQocrNzdXJkyfVrVs3tWrVSps3b9bixYt15MgR3XLLLa7jPfLII1q1apW++OILLV26VKmpqdq6deslawgICNCMGTO0a9cuvffee1qxYoUeffTRotecm6sXX3xR77//vr799lsdPHhQ48aN8+ia4T0MSwAAoAJpMnnJRdfd0LCa5tzVzvW69ZPLdMZ+4b8ytq8To4/vT3K97vTcSh3PKSi23S/P9itxjS1atNDEiRMlSRMmTNCzzz6rqlWr6t5775UkTZ48WbNnz9aOHTu0bNkytWrVSs8884xr/3fffVeJiYn68ccfVb16db3zzjv64IMP1L17d0lnw3ONGjUuWcOIESMUGRmpgIAA1a5dW0899ZQeeOABvfHGG65t7Ha73nzzTdWtW1eSNGrUKE2bNq3E1wvvItwCAIAypXnz5q7nVqtVVapUUbNmzVzL4uLiJEmZmZn6/vvvtXLlSoWHhxc7Tlpams6cOaOCggK1b9/etTwmJkYNGza8ZA2pqamaOXOm/vOf/ygrK0uFhYXKy8tTbm6uwsLOfmcmLCzMFWwlKSEhQZmZmZ5dNLyGcAsAQAWye1qvi64LsFiKvN4yqYfb264ef8OVFXYem81W5LXFYimyzPLfczudTmVnZ6t///567rnnih0nISFB+/btK/H5f/nlFw0ZMkQPPPCAnn76acXExGj16tUaPny4CgoKXOH2QnUahlHi88G7CLcAAFQgYUHu/6+/tLb1pmuvvVaff/65ateurcDA4jXUrVtXNptNGzZsUM2aNSVJJ06c0I8//qguXbpc8JhbtmyR0+nUiy++6DrmJ598UnoXAa/iC2UAAKDcGjlypI4fP65bb71VmzZtUlpampYsWaK77rpLDodD4eHhGj58uB555BGtWLFCO3fu1J133qmAS0ykVK9ePdntdr3++uv6+eef9f777+vNN9/04VXhShBufSwwIEB/anS1Tq6uzwxlAABcoerVq2vNmjVyOBxKTk5Ws2bNNHr0aEVHR7sC7AsvvKDrr79e/fv3V48ePdSpUye1bt36osds0aKFnn76aT3//PO65ppr9OGHH2r69Om+uiRcIYvB4BBlZWUpKipKp06dUmRkZKmfb+NGu9q3t2nDBrvatbNddvutW6XWraUtW6Rrry318qScHOncwPzsbIkZWzxit9v19ddfq2/fvsXGZcFcaOuKoby1c15envbv3686deooJCTE3+WUK06nU1lZWa67JcB3LvW5dTev0WIAAAAwDb5Q5mNOp6FDWdmyVQ2U0wj2dzkAAACmQs+tj+UVOvR/y9aq+vBvmX4XAADAywi3AAAAMA3CLQAAAEyDcAsAgIlxUySUJ974vBJuAQAwIavVKkkqKCjwcyWA+3JzcyUVn9q4JLhbAgAAJhQYGKiwsDD9/vvvstls3K+1BJxOpwoKCpSXl8f75iOGYSg3N1eZmZmKjo52/XLmCcItAAAmZLFYlJCQoP379+vAgQP+LqdcMQxDZ86cUWhoqCwWi7/LqVCio6MVHx9/Rccg3PpYYECA+tevpQ8+sCpwIL8NAgBKT1BQkOrXr8/QhBKy2+369ttv1blz53IxG51Z2Gy2K+qxPYdw62NBgQEa2qyhXk+1KTDA7u9yAAAmFxAQwPS7JWS1WlVYWKiQkBDCbTlE1yEAAABMg55bH3M6DWXmnJE10i6nwdsPAADgTaQrH8srdGjUku9UY4RU4Ojm73IAAABMhWEJAAAAMA3CLQAAAEyDcAsAAADT8Gu4nT17tpo3b67IyEhFRkYqKSlJ33zzjWt9Xl6eRo4cqSpVqig8PFyDBw/WkSNHihzj4MGD6tevn8LCwhQbG6tHHnlEhYWFvr4UAAAAlAF+Dbc1atTQs88+qy1btmjz5s3q1q2bBgwYoF27dkmSxowZo3//+9/69NNPtWrVKh0+fFiDBg1y7e9wONSvXz8VFBRo7dq1eu+99zR37lxNnjzZX5cEAAAAP/Lr3RL69+9f5PXTTz+t2bNna/369apRo4beeecdzZs3T926nb2rwJw5c9S4cWOtX79e1113nZYuXardu3dr2bJliouLU8uWLfXkk09q/PjxmjJlioKCgvxxWQAAAPCTMnMrMIfDoU8//VQ5OTlKSkrSli1bZLfb1aNHD9c2jRo1Us2aNbVu3Tpdd911WrdunZo1a6a4uDjXNr169dKIESO0a9cutWrV6oLnys/PV35+vut1VlaWpLPT7dntpTtrmNPhVI/aV2nB/EA5+zrdOt/ZURY2FRbaVcrlnWW3y+Z6apdvTmo+59q2tD9T8D/aumKgnSsO2rpscrc9/B5uf/jhByUlJSkvL0/h4eFasGCBmjRpou3btysoKEjR0dFFto+Li1NGRoYkKSMjo0iwPbf+3LqLmT59uqZOnVps+dKlSxUWFnaFV3R5TXKj9E5KV23tnapTJ05ddvu0tChJXbV69Rqlp19++ytlzcvTjf99vmTJEjmYtvGKpKSk+LsE+AhtXTHQzhUHbV225ObmurWd38Ntw4YNtX37dp06dUqfffaZhg0bplWrVpXqOSdMmKCxY8e6XmdlZSkxMVHJycmKjIws1XNL0saNZ7/w1r59e7Vrd/km2Lbt7L+dOnXURTqjvSsnx/W0V69eUqVKPjip+djtdqWkpKhnz57MTW5ytHXFQDtXHLR12XTuL+2X4/dwGxQUpHr16kmSWrdurU2bNunVV1/VX/7yFxUUFOjkyZNFem+PHDmi+Ph4SVJ8fLw2btxY5Hjn7qZwbpsLCQ4OVnBwcLHlNput1D/EhmHojLNAAaH5Cgy0unW+wMBz/9rkk5+x805is9nkm5Oaly8+VygbaOuKgXauOGjrssXdtihz97l1Op3Kz89X69atZbPZtHz5cte6vXv36uDBg0pKSpIkJSUl6YcfflBmZqZrm5SUFEVGRqpJkyY+r90dZ+wO3fNVqhIfXqZ8h8Pf5QAAAJiKX3tuJ0yYoD59+qhmzZo6ffq05s2bp9TUVC1ZskRRUVEaPny4xo4dq5iYGEVGRuqhhx5SUlKSrrvuOklScnKymjRpoqFDh+r5559XRkaGJk6cqJEjR16wZxYAAADm5tdwm5mZqTvuuEPp6emKiopS8+bNtWTJEvXs2VOS9MorryggIECDBw9Wfn6+evXqpTfeeMO1v9Vq1aJFizRixAglJSWpUqVKGjZsmKZNm+avSwIAAIAf+TXcvvPOO5dcHxISolmzZmnWrFkX3aZWrVr6+uuvvV0aAAAAyqEyN+YWAAAA8BThFgAAAKZBuAUAAIBpEG59zBpgUZea1ZX9Qw1ZLbz9AAAA3kS68rHgQKtGtrlGx75uIZuVtx8AAMCbSFcAAAAwDb9Pv1vRGIahvMJCWWwWGYbh73IAAABMhXDrY2fsDt3x5QrVHCvlO7r5uxwAAABTYVgCAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3PpYgMWi666KU85/4hVgsfi7HAAAAFMh3PpYiM2qse1b6OgXrRVktfq7HAAAAFMh3AIAAMA0CLcAAAAwDabf9bHcgkLdMn+pao2X8gq7SbL5uyQAAADToOcWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbn0swGJRq7iqyk2rxvS7AAAAXka49bEQm1UTOl6r3z9rx/S7AAAAXka4BQAAgGkQbgEAAGAaTL/rY7kFhRr6xTIljrEor7CLmH4XAADAewi3fpDvcCogyN9VAAAAmA/DEgAAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsfC7BY1KRqZeUdjGH6XQAAAC8j3PpYiM2qKZ3b6sj/S2L6XQAAAC8j3AIAAMA0CLcAAAAwDWYo87HcgkINX7RSNR6yKK+wk5h+FwAAwHsIt35wusAua5i/qwAAADAfhiUAAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3PhZgsahudKTy06OYfhcAAMDLCLc+FmKzanq365Txr05MvwsAAOBlhFsAAACYBuEWAAAApuHXcDt9+nS1bdtWERERio2N1cCBA7V3794i23Tt2lUWi6XI44EHHiiyzcGDB9WvXz+FhYUpNjZWjzzyiAoLC315KW47U+DQyMXf6qoHVii/0OHvcgAAAEzFr9Pvrlq1SiNHjlTbtm1VWFiov//970pOTtbu3btVqVIl13b33nuvpk2b5nodFva/uWsdDof69eun+Ph4rV27Vunp6brjjjtks9n0zDPP+PR63GHI0O+5eQqMOvscAAAA3uPXcLt48eIir+fOnavY2Fht2bJFnTt3di0PCwtTfHz8BY+xdOlS7d69W8uWLVNcXJxatmypJ598UuPHj9eUKVMUFBRUqtcAAACAssOv4faPTp06JUmKiYkpsvzDDz/UBx98oPj4ePXv31+TJk1y9d6uW7dOzZo1U1xcnGv7Xr16acSIEdq1a5datWpV7Dz5+fnKz893vc7KypIk2e122e12r1/X+ez2wvOeO9w639kRFjYVFtpVyuWdZbfL5npql29Oaj7n2ra0P1PwP9q6YqCdKw7aumxytz3KTLh1Op0aPXq0OnbsqGuuuca1/LbbblOtWrVUvXp17dixQ+PHj9fevXs1f/58SVJGRkaRYCvJ9TojI+OC55o+fbqmTp1abPnSpUuLDHkoDfkO6dzbvnnzJmWfzLrsPmlpUZK6avXqNUpPP1Wq9UmSNS9PN/73+ZIlS+QICSn1c5pZSkqKv0uAj9DWFQPtXHHQ1mVLbm6uW9uVmXA7cuRI7dy5U6tXry6y/L777nM9b9asmRISEtS9e3elpaWpbt26Hp1rwoQJGjt2rOt1VlaWEhMTlZycrMjISM8uwE25BYV6dOMKSVKbNm3VuUPwZffZtu3sv506ddQFOqK9LyfH9bRXr17SeeOf4T673a6UlBT17NlTNpvt8jug3KKtKwbaueKgrcumc39pv5wyEW5HjRqlRYsW6dtvv1WNGjUuuW379u0lSfv27VPdunUVHx+vjRs3FtnmyJEjknTRcbrBwcEKDi4eKm02W6l/iG3G/2Yls9msbp0vMPDcvzb55GfsvJPYbDb55qTm5YvPFcoG2rpioJ0rDtq6bHG3Lfx6KzDDMDRq1CgtWLBAK1asUJ06dS67z/bt2yVJCQkJkqSkpCT98MMPyszMdG2TkpKiyMhINWnSpFTqvhIWWVQjopIKjobLIqbfBQAA8Ca/9tyOHDlS8+bN0xdffKGIiAjXGNmoqCiFhoYqLS1N8+bNU9++fVWlShXt2LFDY8aMUefOndW8eXNJUnJyspo0aaKhQ4fq+eefV0ZGhiZOnKiRI0desHfW30KDrHq5Z0e1b29T8H0MVAcAAPAmj8PtwYMHdeDAAeXm5qpatWpq2rRpicPk7NmzJZ2dqOF8c+bM0Z133qmgoCAtW7ZMM2bMUE5OjhITEzV48GBNnDjRta3VatWiRYs0YsQIJSUlqVKlSho2bFiR++ICAACgYihRuP3ll180e/ZsffTRR/r1119lGP+bhCAoKEjXX3+97rvvPg0ePFgBAZcf8XD+/heSmJioVatWXfY4tWrV0tdff335CwAAAICpuT3m9uGHH1aLFi20f/9+PfXUU9q9e7dOnTqlgoICZWRk6Ouvv1anTp00efJkNW/eXJs2bSrNusutMwUOjU1Zo4Thq5h+FwAAwMvc7rmtVKmSfv75Z1WpUqXYutjYWHXr1k3dunXTE088ocWLF+vQoUNq27atV4s1A0OGfj2do6CqTL8LAADgbW6H2+nTp7t90N69e3tUDAAAAHAlPLoV2JkzZ4rMEnHgwAHNmDFDS5Ys8VphAAAAQEl5FG4HDBigf/3rX5KkkydPqn379nrppZc0cOBA1x0QAAAAAF/zKNxu3bpV119/vSTps88+U1xcnA4cOKB//etfeu2117xaIAAAAOAuj8Jtbm6uIiIiJElLly7VoEGDFBAQoOuuu04HDhzwaoEAAACAuzwKt/Xq1dPChQt16NAhLVmyRMnJyZKkzMxMRUZGerVAs7HIomphISo8Fcr0uwAAAF7mUbidPHmyxo0bp9q1a6t9+/ZKSkqSdLYXt1WrVl4t0GxCg6ya1buzfnuzm4IDrf4uBwAAwFQ8mn73T3/6kzp16qT09HS1aNHCtbx79+66+eabvVYcAAAAUBIehVtJio+PV3x8fJFl7dq1u+KCAAAAAE+5HW4HDRrk9kHnz5/vUTEVQZ7doQkr1iv+DosKHG0k2fxdEgAAgGm4HW6joqJKs44Kw2kYSjuZpeCEs88BAADgPW6H2zlz5pRmHQAAAMAV8+huCQAAAEBZ5PEXyj777DN98sknOnjwoAoKCoqs27p16xUXBgAAAJSURz23r732mu666y7FxcVp27ZtateunapUqaKff/5Zffr08XaNAAAAgFs8CrdvvPGG3nrrLc2cOVNBQUF69NFHlZKSoocfflinTp3ydo0AAACAWzwKtwcPHlSHDh0kSaGhoTp9+rQkaejQofp//+//ea86k4oIssmRG+TvMgAAAEzHo3AbHx+v48ePS5Jq1qyp9evXS5L2798vg9tbXVJYUKDeufEG/Tqzp0ICPR7yDAAAgAvwKNx269ZNX375pSTprrvu0pgxY9SzZ0/95S9/YfpdAAAA+I1HXYdvvfWWnE6nJGnkyJGqUqWK1q5dq5tuukn333+/VwsEAAAA3OVRuA0ICFBAwP86fYcMGaIhQ4Z4rSgzy7M7NOXbTYq71aICRysx/S4AAID3uB1ud+zYoWuuuUYBAQHasWPHJbdt3rz5FRdmVk7D0O6jJxRSk+l3AQAAvM3tcNuyZUtlZGQoNjZWLVu2lMViueCXxywWixwOh1eLBAAAANzhdrjdv3+/qlWr5noOAAAAlDVuh9tatWpJkux2u6ZOnapJkyapTp06pVYYAAAAUFIlvhWYzWbT559/Xhq1AAAAAFfEo/vcDhw4UAsXLvRyKQAAAMCV8ehWYPXr19e0adO0Zs0atW7dWpUqVSqy/uGHH/ZKcWYVbA3QmTMWf5cBAABgOh6F23feeUfR0dHasmWLtmzZUmSdxWIh3F5CWFCg3h/QQ+3b2xQyxO7vcgAAAEzFo3DL3RIAAABQFnk05nbatGnKzc0ttvzMmTOaNm3aFRcFAAAAeMKjcDt16lRlZ2cXW56bm6upU6decVFmlmd3aPqarar2p40qYLILAAAAr/JoWIJhGLJYin8h6vvvv1dMTMwVF2VmTsPQtiNHFVaX6XcBAAC8rUThtnLlyrJYLLJYLGrQoEGRgOtwOJSdna0HHnjA60UCAAAA7ihRuJ0xY4YMw9Ddd9+tqVOnKioqyrUuKChItWvXVlJSkteLBAAAANxRonA7bNgwSVKdOnXUsWNHBQZ6NKoBAAAAKBUepdMuXbp4uw4AAADginl0twQAAACgLCLcAgAAwDQYNOtjYUGB+mRQ8tnpdwcx/S4AAIA30XMLAAAA0/Co5zYnJ0fPPvusli9frszMTDmdziLrf/75Z68UBwAAAJSER+H2nnvu0apVqzR06FAlJCRccLYyXFie3aGXN3yvqgMsKnA0lWTzd0kAAACm4VG4/eabb/TVV1+pY8eO3q7H9JyGofW/HVGlRpLTaOLvcgAAAEzFozG3lStXVkxMjLdrAQAAAK6IR+H2ySef1OTJk5Wbm+vtegAAAACPeTQs4aWXXlJaWpri4uJUu3Zt2WxFx41u3brVK8UBAAAAJeFRuB04cKCXywAAAACunEfh9oknnvB2HQAAAMAVYxIHAAAAmIZfw+306dPVtm1bRUREKDY2VgMHDtTevXuLbJOXl6eRI0eqSpUqCg8P1+DBg3XkyJEi2xw8eFD9+vVTWFiYYmNj9cgjj6iwsNCXl+K2UJtV/7qpmw6+3EvBVqu/ywEAADAVv4bbVatWaeTIkVq/fr1SUlJkt9uVnJysnJwc1zZjxozRv//9b3366adatWqVDh8+rEGDBrnWOxwO9evXTwUFBVq7dq3ee+89zZ07V5MnT/bHJV2WxWJRSGCgDHsgk18AAAB4mdtjbrOyshQZGenVky9evLjI67lz5yo2NlZbtmxR586dderUKb3zzjuaN2+eunXrJkmaM2eOGjdurPXr1+u6667T0qVLtXv3bi1btkxxcXFq2bKlnnzySY0fP15TpkxRUFCQV2sGAABA2eV2uK1cubLS09MVGxurbt26af78+YqOjvZqMadOnZIk1wQRW7Zskd1uV48ePVzbNGrUSDVr1tS6det03XXXad26dWrWrJni4uJc2/Tq1UsjRozQrl271KpVq2Lnyc/PV35+vut1VlaWJMlut8tut3v1moqdu9CpmRt3qkrfQOXm1ZM7pzs7wsKmwkK7W9tfMbvdNSmw3W6Xb05qPuc+S6X9mYL/0dYVA+1ccdDWZZO77eF2uA0PD9exY8cUGxur1NRUrze40+nU6NGj1bFjR11zzTWSpIyMDAUFBRUL0XFxccrIyHBtc36wPbf+3LoLmT59uqZOnVps+dKlSxUWFnall3JJ+Q7pu18DFd5M2rDpuE6dyLrsPmlpUZK6avXqNUpPP1Wq9UmSNS9PN/73+ZIlS+QICSn1c5pZSkqKv0uAj9DWFQPtXHHQ1mWLu5OHuR1ue/TooRtuuEGNGzeWJN18880X/ZP/ihUr3D2sy8iRI7Vz506tXr26xPuW1IQJEzR27FjX66ysLCUmJio5OdnrQy/+KLegUI9uPPv+tGnTVp07BF92n23bzv7bqVNHXaAj2vvOG/Pcq1cvqVIlH5zUfOx2u1JSUtSzZ89iE53AXGjrioF2rjho67Lp3F/aL8ftcPvBBx/ovffeU1pamlatWqWmTZt6rZdz1KhRWrRokb799lvVqFHDtTw+Pl4FBQU6efJkkd7bI0eOKD4+3rXNxo0bixzv3N0Uzm3zR8HBwQoOLh4qbTZbqX+Ibcb/vkRms1ndOl9g4Ll/bfLJz9h5J7HZbPLNSc3LF58rlA20dcVAO1cctHXZ4m5buB1uQ0ND9cADD0iSNm/erOeee+6Kx9wahqGHHnpICxYsUGpqqurUqVNkfevWrWWz2bR8+XINHjxYkrR3714dPHhQSUlJkqSkpCQ9/fTTyszMVGxsrKSzf0aIjIxUkyZNrqg+AAAAlC8ezVC2cuVK13PDMCTJo9tajRw5UvPmzdMXX3yhiIgI1xjZqKgohYaGKioqSsOHD9fYsWMVExOjyMhIPfTQQ0pKStJ1110nSUpOTlaTJk00dOhQPf/888rIyNDEiRM1cuTIC/bOAgAAwLw8vs/tv/71LzVr1kyhoaEKDQ1V8+bN9f7775foGLNnz9apU6fUtWtXJSQkuB4ff/yxa5tXXnlFN954owYPHqzOnTsrPj5e8+fPd623Wq1atGiRrFarkpKS9Ne//lV33HGHpk2b5umlAQAAoJzyqOf25Zdf1qRJkzRq1Ch17NhRkrR69Wo98MADOnr0qMaMGePWcc71+l5KSEiIZs2apVmzZl10m1q1aunrr792r3gAAACYlkfhdubMmZo9e7buuOMO17KbbrpJTZs21ZQpU9wOtxVRqM2qf/brql69AhV8MzOUAQAAeJNHwxLS09PVoUOHYss7dOig9PT0Ky7KzCwWiyKDg+Q8E8z0uwAAAF7mUbitV6+ePvnkk2LLP/74Y9WvX/+KiwIAAAA84dGwhKlTp+ovf/mLvv32W9eY2zVr1mj58uUXDL34n/xCh/65fY9iegbI7uAXAQAAAG/yqOd28ODB2rBhg6pWraqFCxdq4cKFqlq1qjZu3Kibb77Z2zWaisNpaOnPhxRx7QE5DKe/ywEAADAVj3pupbMTLHzwwQferAUAAAC4Ih7f5xYAAAAoawi3AAAAMA3CLQAAAEyDcAsAAADT8Cjc3n333Tp9+nSx5Tk5Obr77ruvuCgAAADAEx6F2/fee09nzpwptvzMmTP617/+dcVFmVlIoFWv97pev86+QUFWq7/LAQAAMJUS3QosKytLhmHIMAydPn1aISEhrnUOh0Nff/21YmNjvV6kmQQEWBRbKVSOLJsCLHZ/lwMAAGAqJQq30dHRslgsslgsatCgQbH1FotFU6dO9VpxAAAAQEmUKNyuXLlShmGoW7du+vzzzxUTE+NaFxQUpFq1aql69epeL9JMCgqdev+HvYrualWh82p/lwMAAGAqJQq3Xbp0kSTt379fNWvWlMViKZWizKzQ6dS/fzqgqPZSobO2v8sBAAAwFY+m3z1w4IAOHDhw0fWdO3f2uCAAAADAUx6F265duxZbdn4vrsPh8LggAAAAwFMe3QrsxIkTRR6ZmZlavHix2rZtq6VLl3q7RgAAAMAtHvXcRkVFFVvWs2dPBQUFaezYsdqyZcsVFwYAAACUlFen342Li9PevXu9eUgAAADAbR713O7YsaPIa8MwlJ6ermeffVYtW7b0Rl0AAABAiXkUblu2bCmLxSLDMIosv+666/Tuu+96pTCzCgm06qUeHXTrrYEKupnpdwEAALzJo3C7f//+Iq8DAgJUrVq1ItPx4sICAixKjAyX/SjT7wIAAHibR+G2Vq1a3q4DAAAAuGIef6Fs1apV6t+/v+rVq6d69erppptu0nfffefN2kypoNCpT3bvU1THH1XodPq7HAAAAFPxKNx+8MEH6tGjh8LCwvTwww/r4YcfVmhoqLp376558+Z5u0ZTKXQ69dl/flZ0p58ItwAAAF7m0bCEp59+Ws8//7zGjBnjWvbwww/r5Zdf1pNPPqnbbrvNawUCAAAA7vKo5/bnn39W//79iy2/6aabin3ZDAAAAPAVj8JtYmKili9fXmz5smXLlJiYeMVFAQAAAJ7waFjC//3f/+nhhx/W9u3b1aFDB0nSmjVrNHfuXL366qteLRAAAABwl0fhdsSIEYqPj9dLL72kTz75RJLUuHFjffzxxxowYIBXCwQAAADc5VG4laSbb75ZN998szdrAQAAAK6I22Nu/zjVLjwTHGjVM13bK/29jgqyMv0uAACAN7kdbps2baqPPvpIBQUFl9zup59+0ogRI/Tss89ecXFmZA2wqF5MlAoyohVgsfi7HAAAAFNxe1jCzJkzNX78eD344IPq2bOn2rRpo+rVqyskJEQnTpzQ7t27tXr1au3atUujRo3SiBEjSrNuAAAAoBi3w2337t21efNmrV69Wh9//LE+/PBDHThwQGfOnFHVqlXVqlUr3XHHHbr99ttVuXLl0qy5XCsodOrLH/crsp1Vhc4a/i4HAADAVEr8hbJOnTqpU6dOpVFLhVDodOqDnT+p8g1SobO6v8sBAAAwFY8mcQAAAADKIsItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDY/C7datW/XDDz+4Xn/xxRcaOHCg/v73v192kgcAAACgtHgUbu+//379+OOPkqSff/5ZQ4YMUVhYmD799FM9+uijXi3QbIIDrXri+jbKmHcd0+8CAAB4mUfh9scff1TLli0lSZ9++qk6d+6sefPmae7cufr888+9WZ/pWAMsalotRvmHqjD9LgAAgJd5FG4Nw5DT6ZQkLVu2TH379pUkJSYm6ujRo96rDgAAACgBj8JtmzZt9NRTT+n999/XqlWr1K9fP0nS/v37FRcX59UCzcbucGpx2kGFt/pFhf/9BQEAAADe4VG4feWVV7R161aNGjVKjz/+uOrVqydJ+uyzz9ShQwevFmg2dodT737/H1VJ3kW4BQAA8LJAT3Zq0aJFkbslnPPCCy8oMNCjQwIAAABXzKOe26uvvlrHjh0rtjwvL08NGjS44qIAAAAAT3gUbn/55Rc5HI5iy/Pz8/Xrr7+6fZxvv/1W/fv3V/Xq1WWxWLRw4cIi6++8805ZLJYij969exfZ5vjx47r99tsVGRmp6OhoDR8+XNnZ2Z5cFgAAAMq5Eo0h+PLLL13PlyxZoqioKNdrh8Oh5cuXq06dOm4fLycnRy1atNDdd9+tQYMGXXCb3r17a86cOa7XwcHBRdbffvvtSk9PV0pKiux2u+666y7dd999mjdvntt1AAAAwBxKFG4HDhwoSbJYLBo2bFiRdTabTbVr19ZLL73k9vH69OmjPn36XHKb4OBgxcfHX3Ddnj17tHjxYm3atElt2rSRJM2cOVN9+/bViy++qOrVq7tdCwAAAMq/EoXbc/e2rVOnjjZt2qSqVauWSlHnS01NVWxsrCpXrqxu3brpqaeeUpUqVSRJ69atU3R0tCvYSlKPHj0UEBCgDRs26Oabb77gMfPz85Wfn+96nZWVJUmy2+2y2+2leDWS3V543nOHW+crLJQkmwoL7Srl8s6y22VzPbXLNyc1n3NtW9qfKfgfbV0x0M4VB21dNrnbHh7d2mD//v2e7FZivXv31qBBg1SnTh2lpaXp73//u/r06aN169bJarUqIyNDsbGxRfYJDAxUTEyMMjIyLnrc6dOna+rUqcWWL126VGFhYV6/jvM5DKl/VCW9889m2lZtk7JPZl12n7S0KEldtXr1GqWnnyrV+iTJmpenG//7fMmSJXKEhJT6Oc0sJSXF3yXAR2jrioF2rjho67IlNzfXre08vm/X8uXLtXz5cmVmZrp6dM959913PT1sEUOGDHE9b9asmZo3b666desqNTVV3bt39/i4EyZM0NixY12vs7KylJiYqOTkZEVGRl5Rze6Iq1ao1/8eqqTrItWu3eWbYNu2s/926tRRrVqVcnGSlJPjetqrVy+pUiUfnNR87Ha7UlJS1LNnT9lstsvvgHKLtq4YaOeKg7Yum879pf1yPAq3U6dO1bRp09SmTRslJCTIYrF4cpgSu/rqq1W1alXt27dP3bt3V3x8vDIzM4tsU1hYqOPHj190nK50dhzvH7+YJp0dN+yLD/G5U9hsgW6d79ytgwMDbfLJz9h5J7HZbPLNSc3LV58r+B9tXTHQzhUHbV22uNsWHoXbN998U3PnztXQoUM92d1jv/76q44dO6aEhARJUlJSkk6ePKktW7aodevWkqQVK1bI6XSqffv2Pq3NXXaHU6kHflOla6wqdMZefgcAAAC4zaNwW1BQ4JVpdrOzs7Vv3z7X6/3792v79u2KiYlRTEyMpk6dqsGDBys+Pl5paWl69NFHVa9evbN/KpfUuHFj9e7dW/fee6/efPNN2e12jRo1SkOGDCmzd0qwO5x6Y8suVe0nFTq7+bscAAAAU/FoEod77rnHK/eR3bx5s1q1aqVW/x1IOnbsWLVq1UqTJ0+W1WrVjh07dNNNN6lBgwYaPny4Wrdure+++67IkIIPP/xQjRo1Uvfu3dW3b1916tRJb7311hXXBgAAgPLHo57bvLw8vfXWW1q2bJmaN29ebAzEyy+/7NZxunbtKsMwLrp+yZIllz1GTEwMEzYAAABAkofhdseOHWrZsqUkaefOnUXW+erLZQAAAMAfeRRuV65c6e06AAAAgCvm0ZhbAAAAoCzyqOf2hhtuuOTwgxUrVnhcEAAAAOApj8LtufG259jtdm3fvl07d+7UsGHDvFGXaQVZAzSmXXP9/e+Bsg2k4xwAAMCbPAq3r7zyygWXT5kyRdnZ2VdUkNkFWgOUVCNeuXttsgbY/V0OAACAqXi16/Cvf/2r3n33XW8eEgAAAHCbRz23F7Nu3TqFhIR485CmU+hwat2vGQprGCiHM8bf5QAAAJiKR+F20KBBRV4bhqH09HRt3rxZkyZN8kphZlXgcOqVjTtUbaBkZ/pdAAAAr/Io3EZFRRV5HRAQoIYNG2ratGlKTk72SmEAAABASXkUbufMmePtOgAAAIArdkVjbrds2aI9e/ZIkpo2bapWrVp5pSgAAADAEx6F28zMTA0ZMkSpqamKjo6WJJ08eVI33HCDPvroI1WrVs2bNQIAAABu8ehWYA899JBOnz6tXbt26fjx4zp+/Lh27typrKwsPfzww96uEQAAAHCLRz23ixcv1rJly9S4cWPXsiZNmmjWrFl8oQwAAAB+41HPrdPplM1mK7bcZrPJ6XRecVFmZrMG6MHWTXX0q+YKDGD6XQAAAG/yKF1169ZNf/vb33T48GHXst9++01jxoxR9+7dvVacGdmsAepa6yrl7Ewk3AIAAHiZR+nq9ddfV1ZWlmrXrq26deuqbt26qlOnjrKysjRz5kxv1wgAAAC4xaMxt4mJidq6dauWLVum//znP5Kkxo0bq0ePHl4tzowKHU5tTf9doVdb5XBG+7scAAAAU/H4PrcWi0U9e/ZUz549vVmP6RU4nHp23TbF/pnpdwEAALytRMMSVqxYoSZNmigrK6vYulOnTqlp06b67rvvvFYcAAAAUBIlCrczZszQvffeq8jIyGLroqKidP/99+vll1/2WnEAAABASZQo3H7//ffq3bv3RdcnJydry5YtV1wUAAAA4IkShdsjR45c8P625wQGBur333+/4qIAAAAAT5Qo3F511VXauXPnRdfv2LFDCQkJV1wUAAAA4IkShdu+fftq0qRJysvLK7buzJkzeuKJJ3TjjTd6rTgAAACgJEp0K7CJEydq/vz5atCggUaNGqWGDRtKkv7zn/9o1qxZcjgcevzxx0ulULOwWQN0d4tGeuEFqwIHMkMZAACAN5Uo3MbFxWnt2rUaMWKEJkyYIMMwJJ29522vXr00a9YsxcXFlUqhZmGzBqh33Zp6YptNgQF2f5cDAABgKiWexKFWrVr6+uuvdeLECe3bt0+GYah+/fqqXLlyadQHAAAAuM3jGcoqV66stm3berOWCsHhNLTr9+MKTgyU04jwdzkAAACmwqBPH8svdGjqd5sVf9t6FTgc/i4HAADAVAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcOtjgQEB+us19XViZSMFBvD2AwAAeBPpyseCAgN0U4M6ytpYl3ALAADgZaQrAAAAmIbH0+/CMw6noX3HTykoPlBOI8zf5QAAAJgKPbc+ll/o0N9TNyhh2Bqm3wUAAPAywi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDc+lhgQID+1OhqnVxdnxnKAAAAvIx05WNBgQG6pUk9nVrTgHALAADgZaQrAAAAmAbT7/qY02noUFa2bFUD5TSC/V0OAACAqfi15/bbb79V//79Vb16dVksFi1cuLDIesMwNHnyZCUkJCg0NFQ9evTQTz/9VGSb48eP6/bbb1dkZKSio6M1fPhwZWdn+/AqSiav0KH/W7ZW1Yd/y/S7AAAAXubXcJuTk6MWLVpo1qxZF1z//PPP67XXXtObb76pDRs2qFKlSurVq5fy8vJc29x+++3atWuXUlJStGjRIn377be67777fHUJAAAAKEP8OiyhT58+6tOnzwXXGYahGTNmaOLEiRowYIAk6V//+pfi4uK0cOFCDRkyRHv27NHixYu1adMmtWnTRpI0c+ZM9e3bVy+++KKqV6/us2sBAACA/5XZMbf79+9XRkaGevTo4VoWFRWl9u3ba926dRoyZIjWrVun6OhoV7CVpB49eiggIEAbNmzQzTfffMFj5+fnKz8/3/U6KytLkmS322W320vpivTfcxSe99zh1vkKCyXJpsJCu0q5vLPsdtlcT+3yzUnN51zblvZnCv5HW1cMtHPFQVuXTe62R5kNtxkZGZKkuLi4Isvj4uJc6zIyMhQbG1tkfWBgoGJiYlzbXMj06dM1derUYsuXLl2qsLCwKy39kvId0rm3ffPmTco+mXXZfdLSoiR11erVa5SefqpU65Mka16ebvzv8yVLlsgRElLq5zSzlJQUf5cAH6GtKwbaueKgrcuW3Nxct7Yrs+G2NE2YMEFjx451vc7KylJiYqKSk5MVGRlZqufOLSjUoxtXSJLatGmrzh0uf8eEbdvO/tupU0e1alWa1f1XTo7raa9evaRKlXxwUvOx2+1KSUlRz549ZbPZLr8Dyi3aumKgnSsO2rpsOveX9ssps+E2Pj5eknTkyBElJCS4lh85ckQtW7Z0bZOZmVlkv8LCQh0/fty1/4UEBwcrOLh4qLTZbKX+IbYZlvPOZ3XrfIGB5/61ySc/Y+edxGazyTcnNS9ffK5QNtDWFQPtXHHQ1mWLu21RZidxqFOnjuLj47V8+XLXsqysLG3YsEFJSUmSpKSkJJ08eVJbtmxxbbNixQo5nU61b9/e5zW7IzAgQP3r19KpDVczQxkAAICX+bXnNjs7W/v27XO93r9/v7Zv366YmBjVrFlTo0eP1lNPPaX69eurTp06mjRpkqpXr66BAwdKkho3bqzevXvr3nvv1Ztvvim73a5Ro0ZpyJAhZfZOCUGBARrarKFeT7UpMICB6gAAAN7k13C7efNm3XDDDa7X58bBDhs2THPnztWjjz6qnJwc3XfffTp58qQ6deqkxYsXK+S8Lzh9+OGHGjVqlLp3766AgAANHjxYr732ms+vBQAAAP7n13DbtWtXGYZx0fUWi0XTpk3TtGnTLrpNTEyM5s2bVxrllQqn01BmzhlZI+1yGmV2yDMAAEC5RLrysbxCh0Yt+U41RkgFjm7+LgcAAMBU+EYTAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3PqYNcCi5KsTdXprLVktvP0AAADeRLryseBAq+5p2VjHU66RzcrbDwAA4E2kKwAAAJgGM5T5mGEYysovUECoU4Zh8Xc5AAAApkK49bEzdofu+SpViQ9L+Uy/CwAA4FUMSwAAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbH7MGWNSlZnVl/1CD6XcBAAC8jHTlY8GBVo1sc42Ofd2C6XcBAAC8jHQFAAAA02CGMh8zDEN5hYWy2CwyDMPf5QAAAJgK4dbHztgduuPLFao5lul3AQAAvI1hCQAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3DrYwEWi667Kk45/4lXgMXi73IAAABMhXDrYyE2q8a2b6GjX7RWkNXq73IAAABMhXALAAAA0yDcAgAAwDSYftfHcgsKdcv8pao1Xsor7CbJ5u+SAAAATIOeWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbj1sQCLRa3iqio3rRrT7wIAAHgZ4dbHQmxWTeh4rX7/rB3T7wIAAHgZ4RYAAACmQbgFAACAaTD9ro/lFhRq6BfLlDjGorzCLmL6XQAAAO8h3PpBvsOpgCB/VwEAAGA+DEsAAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaZTpcDtlyhRZLJYij0aNGrnW5+XlaeTIkapSpYrCw8M1ePBgHTlyxI8VX16AxaImVSsr72AM0+8CAAB4WZkOt5LUtGlTpaenux6rV692rRszZoz+/e9/69NPP9WqVat0+PBhDRo0yI/VXl6IzaopndvqyP9LYvpdAAAALyvz97kNDAxUfHx8seWnTp3SO++8o3nz5qlbt26SpDlz5qhx48Zav369rrvuOl+XCgAAAD8r8+H2p59+UvXq1RUSEqKkpCRNnz5dNWvW1JYtW2S329WjRw/Xto0aNVLNmjW1bt26S4bb/Px85efnu15nZWVJkux2u+x2e+ldzH/Z7YWSbLLbC+XO6QoLJcmmwkK7W9tfMbvdNW+a3W6Xb05qPuc+S774TMG/aOuKgXauOGjrssnd9ijT4bZ9+/aaO3euGjZsqPT0dE2dOlXXX3+9du7cqYyMDAUFBSk6OrrIPnFxccrIyLjkcadPn66pU6cWW7506VKFhYV58xKKyXdIkzcFqsZDQVq9PlBHj2Zddp+0tChJXbV69Rqlp58q1fokyZqXpxv/+3zJkiVyhISU+jnNLCUlxd8lwEdo64qBdq44aOuyJTc3163tLIZhGKVci9ecPHlStWrV0ssvv6zQ0FDdddddRXpgJaldu3a64YYb9Nxzz130OBfquU1MTNTRo0cVGRlZavVLUm5BoVo8uUKS9E6fLurcIfiy+2zbJrVvb9OGDXa1alWq5Z2VkyNb5cqSJPuJE1KlSj44qfnY7XalpKSoZ8+estlsl98B5RZtXTHQzhUHbV02ZWVlqWrVqjp16tQl81qZ7rn9o+joaDVo0ED79u1Tz549VVBQoJMnTxbpvT1y5MgFx+ieLzg4WMHBxUOlzWYr9Q+xzfjfHRJsNqtb5wsMPPevTT75GTvvJDabTb45qXn54nOFsoG2rhho54qDti5b3G2LMn+3hPNlZ2crLS1NCQkJat26tWw2m5YvX+5av3fvXh08eFBJSUl+rBIAAAD+UqZ7bseNG6f+/furVq1aOnz4sJ544glZrVbdeuutioqK0vDhwzV27FjFxMQoMjJSDz30kJKSkrhTAgAAQAVVpsPtr7/+qltvvVXHjh1TtWrV1KlTJ61fv17VqlWTJL3yyisKCAjQ4MGDlZ+fr169eumNN97wc9UAAADwlzIdbj/66KNLrg8JCdGsWbM0a9YsH1UEAACAsqxcjbk1gwCLRXWjI5WfHsX0uwAAAF5GuPWxEJtV07tdp4x/dWL6XQAAAC8j3AIAAMA0CLcAAAAwjTL9hTIzOlPg0MjF3+qqByzKL+wgiZtDAwAAeAvh1scMGfo9N0+BUWefAwAAwHsYlgAAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINz6mEUW1YiopIKj4bKI6XcBAAC8iXDrY6FBVr3cs6PS3+mi4ECm3wUAAPAmwi0AAABMg3ALAAAA02CGMh87U+DQ2JQ1ShhuUX5hezH9LgAAgPcQbn3MkKFfT+coqCrT7wIAAHgbwxIAAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbH7PIomphISo8Fcr0uwAAAF5GuPWx0CCrZvXurN/e7Mb0uwAAAF5GuAUAAIBpEG4BAABgGsxQ5mN5docmrFiv+DssKnC0EdPvAgAAeA/h1sechqG0k1kKTjj7HAAAAN7DsAQAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuHWDyKCbHLkBvm7DAAAANPhVmA+FhYUqHduvEHt29sU8le7v8sBAAAwFXpuAQAAYBr03MJjBw9KR4+6v33VqlLNmqVXj1Q2awIAAL5DuPWxPLtDU77dpLhbLSpwtFJ5nX734EGpcWMpN9f9fcLCpD17Si9MlsWaAACAbxFufcxpGNp99IRCapbu9Lsl7cGUStaLefTo2RD5wQdnA+Xl7Nkj/fWvZ/crrSBZFmsCAAC+Rbg1IU96MKXzejGruL9P48bStdeW7DylrSzWBAAAfINwa0Il7cGU/tCLWYJwCwAAUJYQbk2svPdglnRoxZ49pVcLAAAoHwi3KGLPHingjNTyv6+3b5ecoRferjRdydCKqlVLpyYAAFD2EW7LEXcDpSfBs2rVs8Hwr3+VwiTl/Hd5x07SxfKlJ0GyJNdQ0qEVErf2AgCgoiPc+kGwNUBnzljc3v784OmukgbPmjXPBsqjR8/23KrT2eVrVl+45/ZcXe4GSU+v4frrCasAAMB9hFsfCwsK1PsDepydfneIe9Pvnh883eVJD2bNmv/dJ+d/y1q2lFSpZMe52LF9cQ0AAKBiI9yWE67gWY6Z4Rp8gVnWAADwHOEWKIGSBM/CQiktLUrbtkmBbv6k/f67NGgQs6wBAOApwq2P5dkdmr5mq6r9yaICR3OV1+l3K6KS38HBJqlric8TFiYtXixVq3b5bX0xy1ppz3YHAIA3EW59zGkY2nbkqMLqlu70u/C+kk6OUVho1+rVa9SpU0cFBrr/S0xZCoZXPNtdGbkOAEDFQbgFSsjdyTHsdik9/ZRatZJs5bSD/kpmu/vuO/f3KUuBHgBQvhFuUeGV5v2DzaIks915etu30uzp9WRoRUkUFkq//36Re+aVI3yZEYAZmCbczpo1Sy+88IIyMjLUokULzZw5U+3atfN3WSjDfHH/YF8pSfAu7UBS0tu+lfa4YU+HVpSMTcHB3XTDDYbq1i3N85QeT96nsDBp/nz3xodLvgnDpfmLjFl+iSmLSvsXUIlfxioSU4Tbjz/+WGPHjtWbb76p9u3ba8aMGerVq5f27t2r2NhYf5eHMsoM9971NKC7G0g87a325LZvpdUz7ulsdyXxww+FuvPOQK1eXahTp9zbp6SfpdL+Yl9Jh6Ccu7NH797u11PSMFxSntxtpGQ8+yXGF8GtrCnJZ883v4CW7PNnll9kKupfY0wRbl9++WXde++9uuuuuyRJb775pr766iu9++67euyxx/xcHcqy8n7v3ZIGdE8DSWn2VnsS0EuqtGe7i4oyFBx8NuCWpCZ3h2L48ot9JRmCUtqfPU+U5G4jJXXul5hjx+xuh1tfBbeypiSfPU/G9pdUyT9/FfevMWb4MnC5D7cFBQXasmWLJkyY4FoWEBCgHj16aN26dRfcJz8/X/n5+a7Xp/7b1XL8+HHZ7e7NGuap3IJCOfPPftJOnjyhY8fK4H/xcnJcNyizHzsm5eX5tZzyym63Kzc3V8eOHZOtFL9RVqnS2Yc7atWS1qyRjh93//gxMWePf+yYZ/VdTqVKJa+ppEr/Gux64YV1atiwgwLduKnxjz9aNGJEoL7+ulANGlz+rik//mhRbm6gZs92b3tPzyEF6uRJu9vvU2l/9jwREyMlJpbOsY8eLZQUqk2b8pWdHeDWPp60XXnn6WcvIcGuWrVKp6aSfv727HFq1KhgLV6cr8aN3Wvrsqakn72StpskxcVJ8fFXWqn7Tp8+LUkyLne3KaOc++233wxJxtq1a4ssf+SRR4x27dpdcJ8nnnjCkMSDBw8ePHjw4MGjnD0OHTp0yWxY7ntuPTFhwgSNHTvW9drpdOr48eOqUqWKLBZLqZ8/KytLiYmJOnTokCIjI0v9fPAP2rnioK0rBtq54qCtyybDMHT69GlVr179ktuV+3BbtWpVWa1WHTlypMjyI0eOKP4ifeXBwcEKDg4usiw6Orq0SryoyMhIfmgqANq54qCtKwbaueKgrcueqKioy25TPgeSnCcoKEitW7fW8uXLXcucTqeWL1+upKQkP1YGAAAAXyv3PbeSNHbsWA0bNkxt2rRRu3btNGPGDOXk5LjungAAAICKwRTh9i9/+Yt+//13TZ48WRkZGWrZsqUWL16suLg4f5d2QcHBwXriiSeKDY2AudDOFQdtXTHQzhUHbV2+WQzjcvdTAAAAAMqHcj/mFgAAADiHcAsAAADTINwCAADANAi3AAAAMA3CbSmZNWuWateurZCQELVv314bN2685PaffvqpGjVqpJCQEDVr1kxff/21jyrFlShJO8+dO1cWi6XIIyQkxIfVwhPffvut+vfvr+rVq8tisWjhwoWX3Sc1NVXXXnutgoODVa9ePc2dO7fU68SVK2lbp6amFvuZtlgsysjI8E3B8Mj06dPVtm1bRUREKDY2VgMHDtTevXsvux//ny4/CLel4OOPP9bYsWP1xBNPaOvWrWrRooV69eqlzMzMC26/du1a3XrrrRo+fLi2bdumgQMHauDAgdq5c6ePK0dJlLSdpbOz3aSnp7seBw4c8GHF8EROTo5atGihWbNmubX9/v371a9fP91www3avn27Ro8erXvuuUdLliwp5UpxpUra1ufs3bu3yM91bGxsKVUIb1i1apVGjhyp9evXKyUlRXa7XcnJycrJybnoPvx/upwx4HXt2rUzRo4c6XrtcDiM6tWrG9OnT7/g9rfccovRr1+/Isvat29v3H///aVaJ65MSdt5zpw5RlRUlI+qQ2mQZCxYsOCS2zz66KNG06ZNiyz7y1/+YvTq1asUK4O3udPWK1euNCQZJ06c8ElNKB2ZmZmGJGPVqlUX3Yb/T5cv9Nx6WUFBgbZs2aIePXq4lgUEBKhHjx5at27dBfdZt25dke0lqVevXhfdHv7nSTtLUnZ2tmrVqqXExEQNGDBAu3bt8kW58CF+niueli1bKiEhQT179tSaNWv8XQ5K6NSpU5KkmJiYi27Dz3X5Qrj1sqNHj8rhcBSbHS0uLu6i47AyMjJKtD38z5N2btiwod5991198cUX+uCDD+R0OtWhQwf9+uuvvigZPnKxn+esrCydOXPGT1WhNCQkJOjNN9/U559/rs8//1yJiYnq2rWrtm7d6u/S4Can06nRo0erY8eOuuaaay66Hf+fLl9MMf0uUB4kJSUpKSnJ9bpDhw5q3Lix/vGPf+jJJ5/0Y2UAPNGwYUM1bNjQ9bpDhw5KS0vTK6+8ovfff9+PlcFdI0eO1M6dO7V69Wp/lwIvoufWy6pWrSqr1aojR44UWX7kyBHFx8dfcJ/4+PgSbQ//86Sd/8hms6lVq1bat29faZQIP7nYz3NkZKRCQ0P9VBV8pV27dvxMlxOjRo3SokWLtHLlStWoUeOS2/L/6fKFcOtlQUFBat26tZYvX+5a5nQ6tXz58iK9dudLSkoqsr0kpaSkXHR7+J8n7fxHDodDP/zwgxISEkqrTPgBP88V2/bt2/mZLuMMw9CoUaO0YMECrVixQnXq1LnsPvxclzP+/kabGX300UdGcHCwMXfuXGP37t3GfffdZ0RHRxsZGRmGYRjG0KFDjccee8y1/Zo1a4zAwEDjxRdfNPbs2WM88cQThs1mM3744Qd/XQLcUNJ2njp1qrFkyRIjLS3N2LJlizFkyBAjJCTE2LVrl78uAW44ffq0sW3bNmPbtm2GJOPll182tm3bZhw4cMAwDMN47LHHjKFDh7q2//nnn42wsDDjkUceMfbs2WPMmjXLsFqtxuLFi/11CXBTSdv6lVdeMRYuXGj89NNPxg8//GD87W9/MwICAoxly5b56xLghhEjRhhRUVFGamqqkZ6e7nrk5ua6tuH/0+Ub4baUzJw506hZs6YRFBRktGvXzli/fr1rXZcuXYxhw4YV2f6TTz4xGjRoYAQFBRlNmzY1vvrqKx9XDE+UpJ1Hjx7t2jYuLs7o27evsXXrVj9UjZI4d7unPz7Ote2wYcOMLl26FNunZcuWRlBQkHH11Vcbc+bM8XndKLmStvVzzz1n1K1b1wgJCTFiYmKMrl27GitWrPBP8XDbhdpYUpGfU/4/Xb5ZDMMwfN1bDAAAAJQGxtwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAV6Br164aPXp0ifZZuHCh6tWrJ6vV6tr3QsvcYbFYtHDhQknSL7/8IovFou3bt5eonvKga9euslgsXrm+O++803Wsc+8dAPMg3AIwlTvvvFMDBw70dxmXdP/99+tPf/qTDh06pCeffPKiy0oqMTFR6enpuuaaay67bXkMwvfee6/b13cpr776qtLT071UFYCyJtDfBQBARZKdna3MzEz16tVL1atXv+gyT1itVsXHx3ur1DInLCzMK9cXFRWlqKgoL1QEoCyi5xZAufPZZ5+pWbNmCg0NVZUqVdSjRw/l5ORoypQpeu+99/TFF1+4/uycmpoqSRo/frwaNGigsLAwXX311Zo0aZLsdrvrmFOmTFHLli31/vvvq3bt2oqKitKQIUN0+vRp1zY5OTm64447FB4eroSEBL300kvFajtx4oTuuOMOVa5cWWFhYerTp49++uknSVJqaqoiIiIkSd26dXPVd6FlF/LTTz+pc+fOCgkJUZMmTZSSklJk/R97Y0+cOKHbb79d1apVU2hoqOrXr685c+ZIkurUqSNJatWqlSwWi7p27SpJ2rRpk3r27KmqVasqKipKXbp00datW4ucx2Kx6J///KduvvlmhYWFqX79+vryyy+LbLNr1y7deOONioyMVEREhK6//nqlpaW51v/zn/9U48aNFRISokaNGumNN9644DVfSmpqqiwWi5YsWaJWrVopNDRU3bp1U2Zmpr755hs1btxYkZGRuu2225Sbm1vi4wMonwi3AMqV9PR03Xrrrbr77ru1Z88epaamatCgQTIMQ+PGjdMtt9yi3r17Kz09Xenp6erQoYMkKSIiQnPnztXu3bv16quv6u2339Yrr7xS5NhpaWlauHChFi1apEWLFmnVqlV69tlnXesfeeQRrVq1Sl988YWWLl2q1NTUYsHvzjvv1ObNm/Xll19q3bp1MgxDffv2ld1uV4cOHbR3715J0ueff+6q70LL/sjpdGrQoEEKCgrShg0b9Oabb2r8+PGXfK8mTZqk3bt365tvvtGePXs0e/ZsVa1aVZK0ceNGSdKyZcuUnp6u+fPnS5JOnz6tYcOGafXq1Vq/fr3q16+vvn37Fgn5kjR16lTdcsst2rFjh/r27avbb79dx48flyT99ttv6ty5s4KDg7VixQpt2bJFd999twoLCyVJH374oSZPnqynn35ae/bs0TPPPKNJkybpvffeu+T1XMyUKVP0+uuva+3atTp06JBuueUWzZgxQ/PmzdNXX32lpUuXaubMmR4dG0A5ZABAObJlyxZDkvHLL79ccP2wYcOMAQMGXPY4L7zwgtG6dWvX6yeeeMIICwszsrKyXMseeeQRo3379oZhGMbp06eNoKAg45NPPnGtP3bsmBEaGmr87W9/MwzDMH788UdDkrFmzRrXNkePHjVCQ0Nd+504ccKQZKxcudK1zYWW/dGSJUuMwMBA47fffnMt++abbwxJxoIFCwzDMIz9+/cbkoxt27YZhmEY/fv3N+66664LHu+P216Mw+EwIiIijH//+9+uZZKMiRMnul5nZ2cbkoxvvvnGMAzDmDBhglGnTh2joKDggsesW7euMW/evCLLnnzySSMpKemidXTp0sX1Pp+zcuVKQ5KxbNky17Lp06cbkoy0tDTXsvvvv9/o1atXsWOe/94BMA/G3AIoV1q0aKHu3burWbNm6tWrl5KTk/WnP/1JlStXvuR+H3/8sV577TWlpaUpOztbhYWFioyMLLJN7dq1XUMEJCkhIUGZmZmSzvbqFhQUqH379q71MTExatiwoev1nj17FBgYWGSbKlWqqGHDhtqzZ88VXfeePXuUmJhYZExuUlLSJfcZMWKEBg8erK1btyo5OVkDBw68YK/w+Y4cOaKJEycqNTVVmZmZcjgcys3N1cGDB4ts17x5c9fzSpUqKTIy0vVebd++Xddff71sNlux4+fk5CgtLU3Dhw/Xvffe61peWFjo8TjY82uJi4tzDT05f9m5nmoA5sewBADlitVqVUpKir755hs1adJEM2fOVMOGDbV///6L7rNu3Trdfvvt6tu3rxYtWqRt27bp8ccfV0FBQZHt/hjGLBaLnE5nqVyHL/Tp00cHDhzQmDFjdPjwYXXv3l3jxo275D7Dhg3T9u3b9eqrr2rt2rXavn27qlSpUqL3KjQ09KLHz87OliS9/fbb2r59u+uxc+dOrV+/3pPLLFKLxWIxXTsCKBnCLYByx2KxqGPHjpo6daq2bdumoKAgLViwQJIUFBQkh8NRZPu1a9eqVq1aevzxx9WmTRvVr19fBw4cKNE569atK5vNpg0bNriWnThxQj/++KPrdePGjVVYWFhkm2PHjmnv3r1q0qSJJ5da5NiHDh0qcgsrd8JgtWrVNGzYMH3wwQeaMWOG3nrrLUln3ydJxd6rNWvW6OGHH1bfvn3VtGlTBQcH6+jRoyWqtXnz5vruu++KfGHvnLi4OFWvXl0///yz6tWrV+Rx7ktuAHAlGJYAoFzZsGGDli9fruTkZMXGxmrDhg36/fff1bhxY0lnhxYsWbJEe/fuVZUqVRQVFaX69evr4MGD+uijj9S2bVt99dVXrjDsrvDwcA0fPlyPPPKIqlSpotjYWD3++OMKCPhfH0H9+vU1YMAA3XvvvfrHP/6hiIgIPfbYY7rqqqs0YMCAK7ruHj16qEGDBho2bJheeOEFZWVl6fHHH7/kPpMnT1br1q3VtGlT5efna9GiRa73KTY2VqGhoVq8eLFq1KihkJAQ13v1/vvvq02bNsrKytIjjzxyyZ7YCxk1apRmzpypIUOGaMKECYqKitL69evVrl07NWzYUFOnTtXDDz+sqKgo9e7dW/n5+dq8ebNOnDihsWPHevweAYBEzy2AciYyMlLffvut+vbtqwYNGmjixIl66aWX1KdPH0lnb/TfsGFDtWnTRtWqVdOaNWt00003acyYMRo1apRatmyptWvXatKkSSU+9wsvvKDrr79e/fv3V48ePdSpUye1bt26yDZz5sxR69atdeONNyopKUmGYejrr7++4PjTkggICNCCBQt05swZtWvXTvfcc4+efvrpS+4TFBSkCRMmqHnz5urcubOsVqs++ugjSVJgYKBee+01/eMf/1D16tVd4fudd97RiRMndO2112ro0KF6+OGHFRsbW6Jaq1SpohUrVig7O1tdunRR69at9fbbb7veg3vuuUf//Oc/NWfOHDVr1kxdunTR3Llz6bkF4BUWwzAMfxcBAMCldO3aVS1bttSMGTO8dkyLxaIFCxaU+RntAJQMPbcAgHLhjTfeUHh4uH744YcrOs4DDzyg8PBwL1UFoKyh5xYAUOb99ttvOnPmjCSpZs2ari/EeSIzM1NZWVmSzt7urVKlSl6pEUDZQLgFAACAaTAsAQAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKbx/wGwXyWSUASLmgAAAABJRU5ErkJggg==", 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" ] @@ -346,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb index a0c64b42..62392923 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -224,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -255,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -389,14 +389,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.9% complete" + "\r" ] }, { @@ -442,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -512,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -552,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -622,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": { "scrolled": true }, diff --git a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb b/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb index ae334363..2f830d7c 100644 --- a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -38,24 +38,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_40744\\3936321414.py:2: DeprecationWarning: \n", - "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", - "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", - "but was not found to be installed on your system.\n", - "If this would cause problems for you,\n", - "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", - " \n", - " import pandas as pd\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import pandas as pd\n", @@ -75,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -103,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -141,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": { "scrolled": false }, @@ -205,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -247,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -281,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -325,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -357,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": { "collapsed": true }, From c981cd337d72665354cd6700822de1213d7bee0c Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 6 May 2024 15:30:37 -0400 Subject: [PATCH 03/13] run jupyter-book --- .pre-commit-config.yaml | 1 + .../_build/.doctrees/environment.pickle | Bin 0 -> 1130779 bytes .../_build/.doctrees/intro.doctree | Bin 6341 -> 6282 bytes .../1 - Basics, Humidity, Design.doctree | Bin 111332 -> 110789 bytes .../2 - Degradation.doctree | Bin 36054 -> 36054 bytes .../3 - Spectral Degradation.doctree | Bin 20393 -> 20391 bytes .../4 - Weather Database Access.doctree | Bin 32540 -> 31040 bytes .../ASTM Live Demo.doctree | Bin 0 -> 93219 bytes .../B-O LID - Accelerated Test.doctree | Bin 0 -> 55132 bytes .../LETID - Accelerated Test.doctree | Bin 0 -> 84649 bytes .../LETID - Outdoor LETID.doctree | Bin 0 -> 109634 bytes ...o Based on Accelerated Test Result.doctree | Bin 0 -> 209126 bytes .../LETID - Passivated Wafer.doctree | Bin 0 -> 100671 bytes ...- Module Standoff for IEC TS 63126.doctree | Bin 0 -> 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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.39
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.84
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.98
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.01
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.81
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" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \n", + "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", + "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", + "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", + "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", + "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "WEATHER.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Source': 'NSRDB',\n", + " 'Location ID': '145809',\n", + " 'City': '-',\n", + " 'State': '-',\n", + " 'Country': '-',\n", + " 'Clearsky DHI Units': 'w/m2',\n", + " 'Clearsky DNI Units': 'w/m2',\n", + " 'Clearsky GHI Units': 'w/m2',\n", + " 'Dew Point Units': 'c',\n", + " 'DHI Units': 'w/m2',\n", + " 'DNI Units': 'w/m2',\n", + " 'GHI Units': 'w/m2',\n", + " 'Solar Zenith Angle Units': 'Degree',\n", + " 'Temperature Units': 'c',\n", + " 'Pressure Units': 'mbar',\n", + " 'Relative Humidity Units': '%',\n", + " 'Precipitable Water Units': 'cm',\n", + " 'Wind Direction Units': 'Degrees',\n", + " 'Wind Speed Units': 'm/s',\n", + " 'Cloud Type -15': 'N/A',\n", + " 'Cloud Type 0': 'Clear',\n", + " 'Cloud Type 1': 'Probably Clear',\n", + " 'Cloud Type 2': 'Fog',\n", + " 'Cloud Type 3': 'Water',\n", + " 'Cloud Type 4': 'Super-Cooled Water',\n", + " 'Cloud Type 5': 'Mixed',\n", + " 'Cloud Type 6': 'Opaque Ice',\n", + " 'Cloud Type 7': 'Cirrus',\n", + " 'Cloud Type 8': 'Overlapping',\n", + " 'Cloud Type 9': 'Overshooting',\n", + " 'Cloud Type 10': 'Unknown',\n", + " 'Cloud Type 11': 'Dust',\n", + " 'Cloud Type 12': 'Smoke',\n", + " 'Fill Flag 0': 'N/A',\n", + " 'Fill Flag 1': 'Missing Image',\n", + " 'Fill Flag 2': 'Low Irradiance',\n", + " 'Fill Flag 3': 'Exceeds Clearsky',\n", + " 'Fill Flag 4': 'Missing CLoud Properties',\n", + " 'Fill Flag 5': 'Rayleigh Violation',\n", + " 'Surface Albedo Units': 'N/A',\n", + " 'Version': '3.0.6',\n", + " 'latitude': 39.73,\n", + " 'longitude': -105.18,\n", + " 'altitude': 1820,\n", + " 'tz': -7}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "META" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be working with a lot of time series data, generally with a date-time index. If you're\n", + "processing an entire year of data, you don't need to sort or filter the data. However, you may want\n", + "to filter the data by a few different criteria. Below are some common examples.\n", + "- Filter by date\n", + " - month\n", + " - range of months\n", + "- Filter by sun-up hours" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
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1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-06-30 19:30:00-07:00199963019300.00.00.021.06.00.648.54
1999-06-30 20:30:00-07:00199963020300.00.00.019.07.00.455.36
1999-06-30 21:30:00-07:00199963021300.00.00.017.07.00.566.32
1999-06-30 22:30:00-07:00199963022300.00.00.015.07.00.875.74
1999-06-30 23:30:00-07:00199963023300.00.00.013.07.00.880.16
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720 rows × 12 columns

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" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", + "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", + "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", + "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", + "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "1999-06-30 19:30:00-07:00 1999 6 30 19 30 0.0 0.0 0.0 \n", + "1999-06-30 20:30:00-07:00 1999 6 30 20 30 0.0 0.0 0.0 \n", + "1999-06-30 21:30:00-07:00 1999 6 30 21 30 0.0 0.0 0.0 \n", + "1999-06-30 22:30:00-07:00 1999 6 30 22 30 0.0 0.0 0.0 \n", + "1999-06-30 23:30:00-07:00 1999 6 30 23 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \n", + "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", + "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", + "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", + "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", + "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", + "... ... ... ... ... \n", + "1999-06-30 19:30:00-07:00 21.0 6.0 0.6 48.54 \n", + "1999-06-30 20:30:00-07:00 19.0 7.0 0.4 55.36 \n", + "1999-06-30 21:30:00-07:00 17.0 7.0 0.5 66.32 \n", + "1999-06-30 22:30:00-07:00 15.0 7.0 0.8 75.74 \n", + "1999-06-30 23:30:00-07:00 13.0 7.0 0.8 80.16 \n", + "\n", + "[720 rows x 12 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's select the month of June\n", + "june_weather = WEATHER[ WEATHER.index.month == 6 ]\n", + "june_weather" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-09-30 19:30:00-07:00199993019300.00.00.08.0-1.02.658.57
1999-09-30 20:30:00-07:00199993020300.00.00.07.0-2.02.758.17
1999-09-30 21:30:00-07:00199993021300.00.00.07.0-3.02.954.27
1999-09-30 22:30:00-07:00199993022300.00.00.07.0-4.03.050.29
1999-09-30 23:30:00-07:00199993023300.00.00.07.0-5.03.046.91
\n", + "

2928 rows × 12 columns

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" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", + "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", + "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", + "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", + "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "1999-09-30 19:30:00-07:00 1999 9 30 19 30 0.0 0.0 0.0 \n", + "1999-09-30 20:30:00-07:00 1999 9 30 20 30 0.0 0.0 0.0 \n", + "1999-09-30 21:30:00-07:00 1999 9 30 21 30 0.0 0.0 0.0 \n", + "1999-09-30 22:30:00-07:00 1999 9 30 22 30 0.0 0.0 0.0 \n", + "1999-09-30 23:30:00-07:00 1999 9 30 23 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \n", + "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", + "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", + "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", + "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", + "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", + "... ... ... ... ... \n", + "1999-09-30 19:30:00-07:00 8.0 -1.0 2.6 58.57 \n", + "1999-09-30 20:30:00-07:00 7.0 -2.0 2.7 58.17 \n", + "1999-09-30 21:30:00-07:00 7.0 -3.0 2.9 54.27 \n", + "1999-09-30 22:30:00-07:00 7.0 -4.0 3.0 50.29 \n", + "1999-09-30 23:30:00-07:00 7.0 -5.0 3.0 46.91 \n", + "\n", + "[2928 rows x 12 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's filter the summer months for the northern hemisphere\n", + "summer_months = [6,7,8,9]\n", + "summer_weather = WEATHER[ WEATHER.index.month.isin( summer_months ) ]\n", + "summer_weather" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 08:30:00-07:0019991183065.065.076.01.0-3.04.776.86
1999-01-01 09:30:00-07:00199911930503.093.0246.02.0-2.06.380.37
1999-01-01 10:30:00-07:001999111030617.0109.0355.03.0-2.07.074.87
1999-01-01 11:30:00-07:001999111130497.0161.0385.04.0-3.06.870.73
1999-01-01 12:30:00-07:0019991112300.0128.0128.04.0-4.06.561.80
.......................................
1999-12-31 12:30:00-07:001999123112300.020.020.09.0-8.01.631.45
1999-12-31 13:30:00-07:00199912311330562.0125.0354.08.0-6.00.540.25
1999-12-31 14:30:00-07:00199912311430530.094.0263.07.0-4.00.453.19
1999-12-31 15:30:00-07:00199912311530636.038.0159.05.0-5.01.057.10
1999-12-31 16:30:00-07:001999123116300.021.021.04.0-6.01.356.22
\n", + "

4301 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi \\\n", + "1999-01-01 08:30:00-07:00 1999 1 1 8 30 65.0 65.0 \n", + "1999-01-01 09:30:00-07:00 1999 1 1 9 30 503.0 93.0 \n", + "1999-01-01 10:30:00-07:00 1999 1 1 10 30 617.0 109.0 \n", + "1999-01-01 11:30:00-07:00 1999 1 1 11 30 497.0 161.0 \n", + "1999-01-01 12:30:00-07:00 1999 1 1 12 30 0.0 128.0 \n", + "... ... ... ... ... ... ... ... \n", + "1999-12-31 12:30:00-07:00 1999 12 31 12 30 0.0 20.0 \n", + "1999-12-31 13:30:00-07:00 1999 12 31 13 30 562.0 125.0 \n", + "1999-12-31 14:30:00-07:00 1999 12 31 14 30 530.0 94.0 \n", + "1999-12-31 15:30:00-07:00 1999 12 31 15 30 636.0 38.0 \n", + "1999-12-31 16:30:00-07:00 1999 12 31 16 30 0.0 21.0 \n", + "\n", + " ghi temp_air dew_point wind_speed \\\n", + "1999-01-01 08:30:00-07:00 76.0 1.0 -3.0 4.7 \n", + "1999-01-01 09:30:00-07:00 246.0 2.0 -2.0 6.3 \n", + "1999-01-01 10:30:00-07:00 355.0 3.0 -2.0 7.0 \n", + "1999-01-01 11:30:00-07:00 385.0 4.0 -3.0 6.8 \n", + "1999-01-01 12:30:00-07:00 128.0 4.0 -4.0 6.5 \n", + "... ... ... ... ... \n", + "1999-12-31 12:30:00-07:00 20.0 9.0 -8.0 1.6 \n", + "1999-12-31 13:30:00-07:00 354.0 8.0 -6.0 0.5 \n", + "1999-12-31 14:30:00-07:00 263.0 7.0 -4.0 0.4 \n", + "1999-12-31 15:30:00-07:00 159.0 5.0 -5.0 1.0 \n", + "1999-12-31 16:30:00-07:00 21.0 4.0 -6.0 1.3 \n", + "\n", + " relative_humidity \n", + "1999-01-01 08:30:00-07:00 76.86 \n", + "1999-01-01 09:30:00-07:00 80.37 \n", + "1999-01-01 10:30:00-07:00 74.87 \n", + "1999-01-01 11:30:00-07:00 70.73 \n", + "1999-01-01 12:30:00-07:00 61.80 \n", + "... ... \n", + "1999-12-31 12:30:00-07:00 31.45 \n", + "1999-12-31 13:30:00-07:00 40.25 \n", + "1999-12-31 14:30:00-07:00 53.19 \n", + "1999-12-31 15:30:00-07:00 57.10 \n", + "1999-12-31 16:30:00-07:00 56.22 \n", + "\n", + "[4301 rows x 12 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets just select hours from the year where the sun is above the horizon\n", + "sunup_weather = WEATHER[ WEATHER['ghi'] > 0 ]\n", + "sunup_weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Solar Position, POA, and Module Temperature\n", + "\n", + "Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", + "\n", + "poa_df = pvdeg.spectral.poa_irradiance(weather_df=WEATHER, meta=META)#, solar_position=sol_pos)\n", + "\n", + "temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META)#, poa=poa_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`.\n", + "\n", + "Lets inspect the output from each of these functions\n", + "\n", + "`solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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apparent_zenithzenithapparent_elevationelevationazimuthequation_of_time
1999-01-01 00:30:00-07:00162.416069162.416069-72.416069-72.41606920.152470-3.300994
1999-01-01 01:30:00-07:00155.313029155.313029-65.313029-65.31302953.821029-3.320654
1999-01-01 02:30:00-07:00144.992553144.992553-54.992553-54.99255372.531199-3.340306
1999-01-01 03:30:00-07:00133.692678133.692678-43.692678-43.69267884.789909-3.359948
1999-01-01 04:30:00-07:00122.172658122.172658-32.172658-32.17265894.462003-3.379582
.....................
1999-12-31 19:30:00-07:00120.682889120.682889-30.682889-30.682889264.307279-3.093347
1999-12-31 20:30:00-07:00132.197914132.197914-42.197914-42.197914273.785399-3.113209
1999-12-31 21:30:00-07:00143.553813143.553813-53.553813-53.553813285.552685-3.133063
1999-12-31 22:30:00-07:00154.080531154.080531-64.080531-64.080531303.037057-3.152909
1999-12-31 23:30:00-07:00161.842482161.842482-71.842482-71.842482334.208238-3.172747
\n", + "

8760 rows × 6 columns

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" + ], + "text/plain": [ + " apparent_zenith zenith apparent_elevation \\\n", + "1999-01-01 00:30:00-07:00 162.416069 162.416069 -72.416069 \n", + "1999-01-01 01:30:00-07:00 155.313029 155.313029 -65.313029 \n", + "1999-01-01 02:30:00-07:00 144.992553 144.992553 -54.992553 \n", + "1999-01-01 03:30:00-07:00 133.692678 133.692678 -43.692678 \n", + "1999-01-01 04:30:00-07:00 122.172658 122.172658 -32.172658 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 120.682889 120.682889 -30.682889 \n", + "1999-12-31 20:30:00-07:00 132.197914 132.197914 -42.197914 \n", + "1999-12-31 21:30:00-07:00 143.553813 143.553813 -53.553813 \n", + "1999-12-31 22:30:00-07:00 154.080531 154.080531 -64.080531 \n", + "1999-12-31 23:30:00-07:00 161.842482 161.842482 -71.842482 \n", + "\n", + " elevation azimuth equation_of_time \n", + "1999-01-01 00:30:00-07:00 -72.416069 20.152470 -3.300994 \n", + "1999-01-01 01:30:00-07:00 -65.313029 53.821029 -3.320654 \n", + "1999-01-01 02:30:00-07:00 -54.992553 72.531199 -3.340306 \n", + "1999-01-01 03:30:00-07:00 -43.692678 84.789909 -3.359948 \n", + "1999-01-01 04:30:00-07:00 -32.172658 94.462003 -3.379582 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 -30.682889 264.307279 -3.093347 \n", + "1999-12-31 20:30:00-07:00 -42.197914 273.785399 -3.113209 \n", + "1999-12-31 21:30:00-07:00 -53.553813 285.552685 -3.133063 \n", + "1999-12-31 22:30:00-07:00 -64.080531 303.037057 -3.152909 \n", + "1999-12-31 23:30:00-07:00 -71.842482 334.208238 -3.172747 \n", + "\n", + "[8760 rows x 6 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sol_pos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globalpoa_directpoa_diffusepoa_sky_diffusepoa_ground_diffuse
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1999-01-01 16:30:00-07:007.3071950.07.3071957.0762600.230935
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" + ], + "text/plain": [ + " poa_global poa_direct poa_diffuse \\\n", + "1999-01-01 13:30:00-07:00 90.426538 0.0 90.426538 \n", + "1999-01-01 14:30:00-07:00 21.921585 0.0 21.921585 \n", + "1999-01-01 15:30:00-07:00 57.544160 0.0 57.544160 \n", + "1999-01-01 16:30:00-07:00 7.307195 0.0 7.307195 \n", + "1999-01-01 17:30:00-07:00 0.000000 0.0 0.000000 \n", + "\n", + " poa_sky_diffuse poa_ground_diffuse \n", + "1999-01-01 13:30:00-07:00 87.568717 2.857821 \n", + "1999-01-01 14:30:00-07:00 21.228780 0.692805 \n", + "1999-01-01 15:30:00-07:00 55.725547 1.818613 \n", + "1999-01-01 16:30:00-07:00 7.076260 0.230935 \n", + "1999-01-01 17:30:00-07:00 0.000000 0.000000 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poa_df.loc['1999-01-01 13:30:00-07:00':'1999-01-01 17:30:00-07:00']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models.\n", + "\n", + "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html\n", + "\n", + "The optional arguments are:\n", + "- poa : manually enter the POA Irradiance\n", + "- temp_model : choose a PVLIB compatible temperature model\n", + "- conf : choose a recognized module configuration\n", + "- wind_speed_factor : change the empirical wind speed fit paramter" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1999-01-01 13:30:00-07:00 4.639740\n", + "1999-01-01 14:30:00-07:00 2.425271\n", + "1999-01-01 15:30:00-07:00 1.249262\n", + "1999-01-01 16:30:00-07:00 -0.829008\n", + "1999-01-01 17:30:00-07:00 -2.000000\n", + "dtype: float64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_mod = pvdeg.temperature.module(weather_df = WEATHER, meta = META,\n", + " poa = poa_df,\n", + " temp_model = 'sapm',\n", + " conf= 'open_rack_glass_polymer')\n", + "\n", + "temp_mod.loc['1999-01-01 13:30:00-07:00':'1999-01-01 17:30:00-07:00']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Module Humidities\n", + "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.a. Automatic\n", + "Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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RH_surface_outsideRH_front_encapRH_back_encapRH_backsheet
1999-01-01 00:30:00-07:0079.3944.19878779.39000079.390000
1999-01-01 01:30:00-07:0080.8444.19878779.41522080.127610
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1999-01-01 03:30:00-07:0085.0144.19878779.57345582.291727
1999-01-01 04:30:00-07:0085.8144.19878779.68192782.745964
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" + ], + "text/plain": [ + " RH_surface_outside RH_front_encap RH_back_encap \\\n", + "1999-01-01 00:30:00-07:00 79.39 44.198787 79.390000 \n", + "1999-01-01 01:30:00-07:00 80.84 44.198787 79.415220 \n", + "1999-01-01 02:30:00-07:00 82.98 44.198787 79.477223 \n", + "1999-01-01 03:30:00-07:00 85.01 44.198787 79.573455 \n", + "1999-01-01 04:30:00-07:00 85.81 44.198787 79.681927 \n", + "\n", + " RH_backsheet \n", + "1999-01-01 00:30:00-07:00 79.390000 \n", + "1999-01-01 01:30:00-07:00 80.127610 \n", + "1999-01-01 02:30:00-07:00 81.228611 \n", + "1999-01-01 03:30:00-07:00 82.291727 \n", + "1999-01-01 04:30:00-07:00 82.745964 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META)\n", + "rh_module.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.b. Manual\n", + "Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order:\n", + "1. Surface Outside\n", + "2. Front Encapsulant\n", + "2. Back Encapsulant\n", + "3. Backsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "rh_surface_outside = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_front_encap = pvdeg.humidity.front_encap(rh_ambient=rh_surface_outside,\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_back_encap = pvdeg.humidity.back_encap(rh_ambient=rh_surface_outside,\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_mod)\n", + "\n", + "rh_backsheet = pvdeg.humidity.backsheet_from_encap(rh_back_encap=rh_back_encap,\n", + " rh_surface_outside=WEATHER['relative_humidity'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Design: Edge Seal Width\n", + "`pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7170984345065904" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META)\n", + "edge_seal_width" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets look at the calculation step by step. To get the estimated edge seal width, we need the following:\n", + "1. Humidity Saturation Point\n", + "2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7170984345065904\n" + ] + } + ], + "source": [ + "psat, psat_avg = pvdeg.humidity.psat(WEATHER['temp_air'])\n", + "\n", + "k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg)\n", + "\n", + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META,\n", + " k=k, years=25)\n", + "\n", + "print(edge_seal_width)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alternate Edge Seal Width\n", + "If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4499535870958669" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META, from_dew_point=True)\n", + "edge_seal_width" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb new file mode 100644 index 00000000..91acb74b --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Degradation\n", + "Degradation and Solder Fatigue\n", + "\n", + "**Requirements**:\n", + "- compatible weather file (PSM3, TMY3, EPW)\n", + "- Accelerated testing chamber parameters\n", + " - chamber irradiance [W/m^2]\n", + " - chamber temperature [C]\n", + " - chamber humidity [%]\n", + "- Activation energies for test material [kJ/mol]\n", + "\n", + "**Objectives**:\n", + "1. Read in the weather data\n", + "2. Generate basic modeling data\n", + "3. Calculate VantHoff degradation acceleration factor\n", + "4. Calculate Arrhenius degradation acceleration factor\n", + "5. Quick Method\n", + "5. Solder Fatigue" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "import pvdeg\n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read In the Weather File\n", + "\n", + "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "PSM_FILE = os.path.join(DATA_DIR,'psm3_demo.csv')\n", + "WEATHER, META = pvdeg.weather.read(PSM_FILE,'psm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Generate Basic Modeling Data\n", + "\n", + "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", + "\n", + "poa_df = pvdeg.spectral.poa_irradiance(weather_df=WEATHER, meta=META, sol_position=sol_pos)\n", + "\n", + "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df)\n", + "\n", + "temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. VantHoff Degradation\n", + "\n", + "Van 't Hoff Irradiance Degradation\n", + "\n", + "For one year of degredation the controlled environmnet lamp settings will need to be set to IWa.\n", + "\n", + "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# chamber irradiance (W/m^2)\n", + "I_chamber = 1000\n", + "# chamber temperature (C)\n", + "temp_chamber = 60\n", + "\n", + "# calculate the VantHoff Acceleration factor\n", + "vantHoff_deg = pvdeg.degradation.vantHoff_deg(weather_df=WEATHER, meta=META,\n", + " I_chamber=I_chamber,\n", + " temp_chamber=temp_chamber,\n", + " poa=poa_df,\n", + " temp=temp_cell)\n", + "\n", + "# calculate the VantHoff weighted irradiance\n", + "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META,\n", + " poa=poa_df,\n", + " temp=temp_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Arrhenius\n", + "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", + "\n", + "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", + "\n", + "Equation:\n", + "$$ AF = N * \\frac{ I_{chamber}^x * RH_{chamber}^n * e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (I_{POA}^x * RH_{outdoor}^n * e^{\\frac{-E_a}{k T_outdoor}}) }$$\n", + "\n", + "Function to calculate IWa, the Environment Characterization (W/m²). For one year of degredation the controlled environmnet lamp settings will need to be set at IWa.\n", + "\n", + "Equation:\n", + "$$ I_{WA} = [ \\frac{ \\Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# relative humidity within chamber (%)\n", + "rh_chamber = 15\n", + "# arrhenius activation energy (kj/mol)\n", + "Ea = 40\n", + "\n", + "rh_surface = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_module)\n", + "\n", + "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=rh_surface,\n", + " I_chamber=I_chamber,\n", + " rh_chamber=rh_chamber,\n", + " temp_chamber=temp_chamber,\n", + " poa=poa_df,\n", + " temp=temp_cell,\n", + " Ea=Ea)\n", + "\n", + "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=WEATHER, meta=META,\n", + " poa=poa_df,\n", + " rh_outdoor=WEATHER['relative_humidity'],\n", + " temp=temp_cell,\n", + " Ea=Ea)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Quick Method (Degradation)\n", + "\n", + "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "# chamber settings\n", + "I_chamber= 1000\n", + "temp_chamber=60\n", + "rh_chamber=15\n", + "\n", + "# activation energy\n", + "Ea = 40\n", + "\n", + "vantHoff_deg = pvdeg.degradation.vantHoff_deg(weather_df=WEATHER, meta=META,\n", + " I_chamber=I_chamber,\n", + " temp_chamber=temp_chamber)\n", + "\n", + "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "rh_surface = pvdeg.humidity.surface_outside(rh_ambient=WEATHER['relative_humidity'],\n", + " temp_ambient=WEATHER['temp_air'],\n", + " temp_module=temp_module)\n", + "\n", + "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=rh_surface,\n", + " I_chamber=I_chamber,\n", + " rh_chamber=rh_chamber,\n", + " temp_chamber=temp_chamber,\n", + " Ea=Ea)\n", + "\n", + "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=WEATHER, meta=META,\n", + " rh_outdoor=WEATHER['relative_humidity'],\n", + " Ea=Ea)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Solder Fatigue\n", + "\n", + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n", + "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n", + "\n", + "1. Lets create a datetime-index for the month of June.\n", + "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n", + "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "# select the month of June\n", + "time_range = WEATHER.index[WEATHER.index.month == 6]\n", + "\n", + "# calculate cell temperature over our selected date-time range.\n", + "# specify the module configuration\n", + "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER.loc[time_range], meta=META,\n", + " temp_model='sapm',\n", + " conf='insulated_back_glass_polymer')\n", + "\n", + "\n", + "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META,\n", + " time_range = time_range,\n", + " temp_cell = temp_cell)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb new file mode 100644 index 00000000..1367a259 --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 - Spectral Degradation\n", + "\n", + "**Requirements:**\n", + "- spectral irradiance (measured or simulated)\n", + "- wavelengths of spectral irradiance data\n", + "- module RH\n", + "- module temperature\n", + "\n", + "\n", + "**Objectives:**\n", + "1. Read in spectral irradiance\n", + "2. Calculate spectral degradation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_40480\\1750438778.py:2: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pvdeg\n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read in spectral irradiance data\n", + "\n", + "Spectral degradation has 4 main requirements:\n", + "- Spectral Irradiance [W/m^2 nm]\n", + "- Wavelength [nm] \n", + "- Module Relative Humidity [%]\n", + "- Module Temperature [C]\n", + "\n", + "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", + "\n", + "\n", + "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2021-03-09 10:00:00[0.6892146677599185, 0.40215646494410884, 0.67...4559
2021-03-09 11:00:00[0.15575709102178648, 0.5464374649246564, 0.68...4456
2021-03-09 12:00:00[0.22782105874481207, 0.9056495270031296, 0.26...5939
2021-03-09 13:00:00[0.3741943134512433, 0.035830980984344674, 0.4...4413
2021-03-09 14:00:00[0.40321187996337626, 0.6473167864022122, 0.69...2539
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" + ], + "text/plain": [ + " Spectra \\\n", + "timestamp \n", + "2021-03-09 10:00:00 [0.6892146677599185, 0.40215646494410884, 0.67... \n", + "2021-03-09 11:00:00 [0.15575709102178648, 0.5464374649246564, 0.68... \n", + "2021-03-09 12:00:00 [0.22782105874481207, 0.9056495270031296, 0.26... \n", + "2021-03-09 13:00:00 [0.3741943134512433, 0.035830980984344674, 0.4... \n", + "2021-03-09 14:00:00 [0.40321187996337626, 0.6473167864022122, 0.69... \n", + "\n", + " Temperature RH \n", + "timestamp \n", + "2021-03-09 10:00:00 45 59 \n", + "2021-03-09 11:00:00 44 56 \n", + "2021-03-09 12:00:00 59 39 \n", + "2021-03-09 13:00:00 44 13 \n", + "2021-03-09 14:00:00 25 39 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wavelengths = np.array(range(280,420,20))\n", + "\n", + "SPECTRA = pd.read_csv(os.path.join(DATA_DIR,'spectra.csv'), header=0, index_col=0)\n", + "SPECTRA.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Calculate Degradation\n", + "\n", + "The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing brackets from spectral irradiance data\n" + ] + } + ], + "source": [ + "degradation = pvdeg.degradation.degradation(spectra=SPECTRA['Spectra'],\n", + " rh_module=SPECTRA['RH'],\n", + " temp_module=SPECTRA['Temperature'],\n", + " wavelengths=wavelengths)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb new file mode 100644 index 00000000..d59d5f1d --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb @@ -0,0 +1,272 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4 - Weather Database Access\n", + "\n", + "**Requirements:**\n", + "- Internet access\n", + "- NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/\n", + "- Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method.\n", + "\n", + "**Objectives:**\n", + "\n", + "Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database.\n", + "1. Single Location, NSRDB via NREL's high performance computer, Eagle.\n", + "2. Single Location via NSRDB public API key.\n", + "3. Single Location via the PVGIS public database\n", + "\n", + "**Background:**\n", + "\n", + "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**.\n", + "\n", + "For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", + "\n", + "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single location example" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + " # !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. NSRDB - HSDS on Kestrel\n", + "\n", + "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", + "\n", + "In this step:\n", + "\n", + "First we select a database. Here, we will use the NSRDB. Since we are modeling a single location, we can pass the `weather_id` as tuple (lat, long). A location gid can be used as well. 'gid' is a unique identifier to a geographic location within the NSRDB. We'll look at how to find gids later on.\n", + "\n", + "Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Get weather data\n", + "weather_db = 'NSRDB'\n", + "\n", + "#Latitude and Longitude\n", + "weather_id = (33.448376, -112.074036)\n", + "#weather_id = 1933572\n", + "weather_arg = {'satellite': 'GOES',\n", + " 'names': 2021,\n", + " 'NREL_HPC': True,\n", + " 'attributes': ['air_temperature', 'wind_speed', 'dhi', 'ghi', 'dni', 'relative_humidity']}\n", + "\n", + "# Uncomment the following when working on NREL Kestrel\n", + "\n", + "# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "\n", + "# res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + "# conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "# print(pvdeg.standards.interpret_standoff(res))\n", + "# print(meta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested.\n", + "\n", + "Lets verify the weather data we fetched by running a familiar calculation; standoff distance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. NSRDB - API\n", + "\n", + "To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/\n", + "\n", + "Key Notes:\n", + "- set `attributes = []` to return all possible attributes (weather fields)\n", + "- There are 2 major methods with the API\n", + " - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point.\n", + " - names = '2019' : collect a weather dataframe including measured relative humidity." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The estimated T₉₈ of an insulated-back module is 73.5°C. \n", + "The estimated T₉₈ of an open-rack module is 50.0°C. \n", + "Level 0 certification is valid for a standoff greather than 1.0 cm. \n", + "Level 1 certification is required for a standoff less than 1.0 cm. \n", + "Level 2 certification is never required for this temperature profile.\n", + "{'Source': 'NSRDB', 'Location ID': '145809', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 39.73, 'longitude': -105.18, 'altitude': 1820, 'tz': -7, 'wind_height': 2}\n" + ] + } + ], + "source": [ + "API_KEY = 'your_api_key_here'\n", + "# The example API key here is for demonstation and is rate-limited per IP.\n", + "# To get your own API key, visit https://developer.nrel.gov/signup/\n", + "\n", + "weather_db = 'PSM3'\n", + "weather_id = (39.741931, -105.169891)\n", + "weather_arg = {'api_key': 'DEMO_KEY',\n", + " 'email': 'user@mail.com',\n", + " 'names': 'tmy',\n", + " 'attributes': [],\n", + " 'map_variables': True}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "# Perform calculation and output interpretation or results\n", + "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "print(pvdeg.standards.interpret_standoff(res))\n", + "print(meta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. PVGIS\n", + "\n", + "This method uses the PVGIS database, a public resource. It requires no API key or user account." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", + "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", + "The estimated T₉₈ of an open-rack module is 63.8°C. \n", + "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", + "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", + "Level 2 certification is required for a standoff less than 3.0 cm.\n", + "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" + ] + } + ], + "source": [ + "weather_db = 'PVGIS'\n", + "#weather_id = (39.741931, -105.169891)\n", + "weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "#weather_arg = {'map_variables': True}\n", + "\n", + "# TMY\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id)\n", + "\n", + "# Perform calculation\n", + "res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", + " conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", + "print(pvdeg.standards.interpret_standoff(res))\n", + "print(meta)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb new file mode 100644 index 00000000..fb830f8b --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb @@ -0,0 +1,1098 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ASTM Demonstration\n", + "\n", + "![PVDeg Logo](../PVD_logo.png)\n", + "\n", + "\n", + "**Steps:**\n", + "1. Import weather data\n", + "2. Calculate installation standoff\n", + "3. Calculate installation standoff - with more detail\n", + "\n", + "**Background:**\n", + "\n", + "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", + "\n", + "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "#!pip install pvdeg==0.1.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pvlib\n", + "import pvdeg\n", + "import pandas as pd\n", + "from pvdeg import DATA_DIR\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvlib version \", pvlib.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Import Weather Data\n", + "\n", + "The function has two minimum requirements:\n", + "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed\n", + "- Site meta-data containing (at least) Latitude, Longitude, Time Zone\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Where to get _Free_ Solar Irradiance Data?\n", + "\n", + "There are many different sources of solar irradiance data. For your projects, these are some of the most common:\n", + "\n", + "- [NSRDB](https://maps.nrel.gov/nsrdb-viewer/) - National Solar Radiation Database. You can access data through the website for many locations accross the world, or you can use their [web API](https://developer.nrel.gov/docs/solar/nsrdb/) to download data programmatically. An \"API\" is an [\"application programming interface\"](https://en.wikipedia.org/wiki/API), and a \"web API\" is a programming interface that allows you to write code to interact with web services like the NSRDB.\n", + "\n", + "- [EPW](https://www.energy.gov/eere/buildings/downloads/energyplus-0) - Energy Plus Weather data is available for many locations accross the world. It's in its own format file ('EPW') so you can't open it easily in a spreadsheet program like Excel, but you can use [`pvlib.iotools.read_epw()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_epw.html) to get it into a dataframe and use it.\n", + "\n", + "- [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/) - Free global weather data provided by the European Union and derived from many govermental agencies including the NSRDB. PVGIS also provides a web API. You can get PVGIS TMY data using [`pvlib.iotools.get_pvgis_tmy()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_pvgis_tmy.html).\n", + "\n", + "- Perhaps another useful link: https://sam.nrel.gov/weather-data.html\n", + "\n", + "### Where else can you get historical irradiance data?\n", + "\n", + "There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python.\n", + "\n", + "* [SolarAnywhere](https://www.solaranywhere.com/)\n", + "* [SolarGIS](https://solargis.com/)\n", + "* [Vaisala](https://www.vaisala.com/en)\n", + "* [Meteonorm](https://meteonorm.com/en/)\n", + "* [DNV Solar Resource Compass](https://src.dnv.com/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NREL API Key\n", + "At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do...\n", + "\n", + "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n", + "\n", + "### Application Programming Interface (API)\n", + "What exactly is an API? Nowadays, the phrase is used interchangeably with a \"web API\" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers.\n", + "\n", + "A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18).\n", + "\n", + "https://developer.nrel.gov/api/pvwatts/v6.json?api_key=DEMO_KEY&lat=40&lon=-105&system_capacity=4&azimuth=180&tilt=40&array_type=1&module_type=1&losses=10\n", + "\n", + "In addition to just using your browser, you can also access web APIs programmatically. The most popular Python package to interact with web APIs is [requests](https://docs.python-requests.org/en/master/). There's also free open source command-line tools like [cURL](https://curl.se/) and [HTTPie](https://httpie.io/), and a popular nagware/freemium GUI application called [Postman](https://www.postman.com/).\n", + "\n", + "**If you have an NREL API key please enter it in the next cell.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "NREL_API_KEY = None # <-- please set your NREL API key here\n", + "\n", + "# note you must use \"quotes\" around your key, for example:\n", + "# NREL_API_KEY = 'DEMO_KEY' # single or double both work fine\n", + "\n", + "# during the live tutorial, we've stored a dedicated key on our server\n", + "if NREL_API_KEY is None:\n", + " try:\n", + " NREL_API_KEY = os.environ['NREL_API_KEY'] # get dedicated key for tutorial from servier\n", + " except KeyError:\n", + " NREL_API_KEY = 'DEMO_KEY' # OK for this demo, but better to get your own key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetching TMYs from the NSRDB\n", + "\n", + "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). \n", + "\n", + "This function uses [`pvdeg.weather.get()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.weather.html#pvdeg.weather.get), which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data.\n", + "\n", + "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib.\n", + "# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it.\n", + "'''\n", + "weather_db = 'PSM3'\n", + "weather_id = (33.4484, -112.0740)\n", + "weather_arg = {'api_key': NREL_API_KEY,\n", + " 'email': 'user@mail.com',\n", + " 'names': '2021', # tmy is another common option; right now requesting only 2021 data\n", + " 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi',\n", + " # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed'\n", + " 'map_variables': True,\n", + " 'leap_day': False}\n", + "\n", + "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "''';" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "weather_df, meta = pvlib.iotools.get_psm3(\n", + " latitude=33.4484, longitude=-112.0740,\n", + " api_key=NREL_API_KEY,\n", + " email='silvana.ovaitt@nrel.gov', # <-- any email works here fine\n", + " names='2021',\n", + " map_variables=True,\n", + " attributes=[],\n", + " leap_day=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2021-01-01 04:30:00-07:002021114304.70.00.00.00...0.039.97127.210.16969.01.046.01.80.00.0
..................................................................
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2021-12-31 23:30:00-07:002021123123307.00.00.00.00...0.033.22162.850.16968.01.034.01.60.00.0
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" + ], + "text/plain": [ + " Year Month Day Hour Minute temp_air \\\n", + "2021-01-01 00:30:00-07:00 2021 1 1 0 30 6.5 \n", + "2021-01-01 01:30:00-07:00 2021 1 1 1 30 6.0 \n", + "2021-01-01 02:30:00-07:00 2021 1 1 2 30 5.5 \n", + "2021-01-01 03:30:00-07:00 2021 1 1 3 30 5.1 \n", + "2021-01-01 04:30:00-07:00 2021 1 1 4 30 4.7 \n", + "... ... ... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 2021 12 31 19 30 9.1 \n", + "2021-12-31 20:30:00-07:00 2021 12 31 20 30 8.5 \n", + "2021-12-31 21:30:00-07:00 2021 12 31 21 30 7.8 \n", + "2021-12-31 22:30:00-07:00 2021 12 31 22 30 7.4 \n", + "2021-12-31 23:30:00-07:00 2021 12 31 23 30 7.0 \n", + "\n", + " dhi_clear dni_clear ghi_clear Cloud Type ... \\\n", + "2021-01-01 00:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "2021-01-01 01:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 02:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 03:30:00-07:00 0.0 0.0 0.0 4 ... \n", + "2021-01-01 04:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "... ... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 20:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 21:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 22:30:00-07:00 0.0 0.0 0.0 7 ... \n", + "2021-12-31 23:30:00-07:00 0.0 0.0 0.0 0 ... \n", + "\n", + " ghi relative_humidity solar_zenith albedo \\\n", + "2021-01-01 00:30:00-07:00 0.0 35.09 169.51 0.16 \n", + "2021-01-01 01:30:00-07:00 0.0 36.34 163.48 0.16 \n", + "2021-01-01 02:30:00-07:00 0.0 37.37 152.07 0.16 \n", + "2021-01-01 03:30:00-07:00 0.0 38.47 139.71 0.16 \n", + "2021-01-01 04:30:00-07:00 0.0 39.97 127.21 0.16 \n", + "... ... ... ... ... \n", + "2021-12-31 19:30:00-07:00 0.0 27.14 114.11 0.16 \n", + "2021-12-31 20:30:00-07:00 0.0 28.57 126.45 0.16 \n", + "2021-12-31 21:30:00-07:00 0.0 29.85 138.95 0.16 \n", + "2021-12-31 22:30:00-07:00 0.0 31.44 151.32 0.16 \n", + "2021-12-31 23:30:00-07:00 0.0 33.22 162.85 0.16 \n", + "\n", + " pressure precipitable_water wind_direction \\\n", + "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", + "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", + "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", + "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", + "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", + "... ... ... ... \n", + "2021-12-31 19:30:00-07:00 967.0 0.9 28.0 \n", + "2021-12-31 20:30:00-07:00 967.0 0.9 31.0 \n", + "2021-12-31 21:30:00-07:00 967.0 0.9 33.0 \n", + "2021-12-31 22:30:00-07:00 968.0 1.0 33.0 \n", + "2021-12-31 23:30:00-07:00 968.0 1.0 34.0 \n", + "\n", + " wind_speed \\\n", + "2021-01-01 00:30:00-07:00 1.8 \n", + "2021-01-01 01:30:00-07:00 1.8 \n", + "2021-01-01 02:30:00-07:00 1.8 \n", + "2021-01-01 03:30:00-07:00 1.7 \n", + "2021-01-01 04:30:00-07:00 1.8 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 1.2 \n", + "2021-12-31 20:30:00-07:00 1.3 \n", + "2021-12-31 21:30:00-07:00 1.4 \n", + "2021-12-31 22:30:00-07:00 1.5 \n", + "2021-12-31 23:30:00-07:00 1.6 \n", + "\n", + " Global Horizontal UV Irradiance (280-400nm) \\\n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 0.0 \n", + "2021-12-31 20:30:00-07:00 0.0 \n", + "2021-12-31 21:30:00-07:00 0.0 \n", + "2021-12-31 22:30:00-07:00 0.0 \n", + "2021-12-31 23:30:00-07:00 0.0 \n", + "\n", + " Global Horizontal UV Irradiance (295-385nm) \n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "... ... \n", + "2021-12-31 19:30:00-07:00 0.0 \n", + "2021-12-31 20:30:00-07:00 0.0 \n", + "2021-12-31 21:30:00-07:00 0.0 \n", + "2021-12-31 22:30:00-07:00 0.0 \n", + "2021-12-31 23:30:00-07:00 0.0 \n", + "\n", + "[8760 rows x 24 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_df" + 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Type 4': 'Super-Cooled Water',\n", + " 'Cloud Type 5': 'Mixed',\n", + " 'Cloud Type 6': 'Opaque Ice',\n", + " 'Cloud Type 7': 'Cirrus',\n", + " 'Cloud Type 8': 'Overlapping',\n", + " 'Cloud Type 9': 'Overshooting',\n", + " 'Cloud Type 10': 'Unknown',\n", + " 'Cloud Type 11': 'Dust',\n", + " 'Cloud Type 12': 'Smoke',\n", + " 'Fill Flag 0': 'N/A',\n", + " 'Fill Flag 1': 'Missing Image',\n", + " 'Fill Flag 2': 'Low Irradiance',\n", + " 'Fill Flag 3': 'Exceeds Clearsky',\n", + " 'Fill Flag 4': 'Missing CLoud Properties',\n", + " 'Fill Flag 5': 'Rayleigh Violation',\n", + " 'Surface Albedo Units': 'N/A',\n", + " 'Version': 'v3.2.2',\n", + " 'latitude': 33.45,\n", + " 'longitude': -112.06,\n", + " 'altitude': 334}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meta" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutetemp_airdhi_cleardni_clearghi_clearCloud Type...ghirelative_humiditysolar_zenithalbedopressureprecipitable_waterwind_directionwind_speedGlobal Horizontal UV Irradiance (280-400nm)Global Horizontal UV Irradiance (295-385nm)
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2021-01-01 03:30:00-07:002021113305.10.00.00.04...0.038.47139.710.16968.01.046.01.70.00.0
2021-01-01 04:30:00-07:002021114304.70.00.00.00...0.039.97127.210.16969.01.046.01.80.00.0
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5 rows × 24 columns

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"\n", + " pressure precipitable_water wind_direction \\\n", + "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", + "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", + "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", + "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", + "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", + "\n", + " wind_speed \\\n", + "2021-01-01 00:30:00-07:00 1.8 \n", + "2021-01-01 01:30:00-07:00 1.8 \n", + "2021-01-01 02:30:00-07:00 1.8 \n", + "2021-01-01 03:30:00-07:00 1.7 \n", + "2021-01-01 04:30:00-07:00 1.8 \n", + "\n", + " Global Horizontal UV Irradiance (280-400nm) \\\n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "\n", + " Global Horizontal UV Irradiance (295-385nm) \n", + "2021-01-01 00:30:00-07:00 0.0 \n", + "2021-01-01 01:30:00-07:00 0.0 \n", + "2021-01-01 02:30:00-07:00 0.0 \n", + "2021-01-01 03:30:00-07:00 0.0 \n", + "2021-01-01 04:30:00-07:00 0.0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax1 = plt.subplots(figsize=(9, 6))\n", + "# Instantiate a second axes that shares the same x-axis\n", + "ax1.plot(weather_df.loc['2021-06-28 05:00:00-07:00':'2021-06-28 20:00:00-07:00']['dni_clear'], label='DNI')\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(weather_df.loc['2021-06-28 05:00:00-07:00':'2021-06-28 20:00:00-07:00']['temp_air'], 'r', label='Temperature')\n", + "ax1.set_ylim([0,1000])\n", + "ax2.set_ylim([0,50])\n", + "ax1.set_ylabel('DNI')\n", + "ax2.set_ylabel('Temperature $\\degree$C');\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Installation Standoff - Level 1\n", + "\n", + "We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum installation distance: 0 11.12151\n", + "Name: x, dtype: float64\n" + ] + } + ], + "source": [ + "print(\"Minimum installation distance:\", standoff['x'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate Installation Standoff - Level 2\n", + "\n", + "Let's take a closer look at the function and some optional parameters.\n", + "\n", + "- level : 1 or 2 (see IEC TS 63216)\n", + "- tilt and azimuth : tilt from horizontal of PV module and azimuth in degrees from North\n", + "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez')\n", + "- temp_model : pvlib compatible module temperature model (Options: 'sapm', 'pvsyst', 'faiman', 'sandia')\n", + "- module_type : basic module construction (Options: 'glass_polymer', 'glass_glass')\n", + "- x_0 : thermal decay constant [cm] (see documentation)\n", + "- wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum installation distance: 0 11.12151\n", + "Name: x, dtype: float64\n" + ] + } + ], + "source": [ + "print(\"Minimum installation distance:\", standoff['x'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg_311", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb new file mode 100644 index 00000000..ab068fdc --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb @@ -0,0 +1,729 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# B-O LID - Accelerated Test\n", + "\n", + "## Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests\n", + "\n", + "\n", + "This library can also be used to model B-O LID, as the defect states and transitions can be modeled in the same way as LETID. See kinetic_parameters.json for B-O LID kinetic parameters used in this example.\n", + "\n", + "In this example, we will model B-O LID progression in a test similar to [IEC 61215 MQT 19.1](https://webstore.iec.ch/publication/61215) IEC 61215 MQT 19.1, which prescribes $\\ge $ 10 $kWh/m^2$ of 1-sun illumination with maximum power point tracking at $50\\degree C$.\n", + "\n", + "**Objectives:**\n", + "1. Define necessary solar cell device parameters\n", + "2. Define necessary degradation parameters: degraded lifetime and defect states, this time using B-O LID kinetics\n", + "3. Create timeseries of temperature and current injection\n", + "4. Run through timeseries, calculating defect states\n", + "5. Calculate device degradation and plot\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from pvdeg import letid, collection, utilities, DATA_DIR\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pvlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "import pvdeg\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvlib version \", pvlib.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Device parameters\n", + "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "wafer_thickness = 180 # um\n", + "s_rear = 46 # cm/s\n", + "cell_area = 243 # cm^2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Other device parameters \n", + "\n", + "Other required parameters are base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Degradation parameters\n", + "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", + "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how much maximum power degradation these parameters will result in:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.03495240755084558, 5.663466529792824, 5.465514739492932)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "letid.calc_pmp_loss_from_tau_loss(tau_0, tau_deg, cell_area, wafer_thickness, s_rear) # returns % power loss, pmp_0, pmp_deg\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remaining degradation parameters:\n", + "\n", + "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", + "\n", + "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for B-O LID" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# starting defect state percentages\n", + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('Choose a set of kinetic parameters:',\n", + " ['repins',\n", + " 'repins_best_case',\n", + " 'kwapil',\n", + " 'bredemeier',\n", + " 'wyller_wafer',\n", + " 'wyller_cell',\n", + " 'graf',\n", + " 'dark letid',\n", + " 'bo-lid',\n", + " 'Lit BO-LID + fit to Qcells destab'])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", + "utilities.get_kinetics()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mechanism': 'BO-LID', 'v_ab': 4000.0, 'v_ba': 10000000000000.0, 'v_bc': 12500000000.0, 'v_cb': 532000.0, 'ea_ab': 0.475, 'ea_ba': 1.32, 'ea_bc': 0.98, 'ea_cb': 0.87, 'suns_ab': 0.1, 'suns_bc': 2.7, 'temperature_ab': 400, 'temperature_bc': 434, 'tau_ab': 140, 'tau_bc': 165, 'x_ab': 0, 'x_ba': 1, 'x_bc': 0, 'structure_ab': 'wafer', 'structure_bc': 'wafer', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 0, 'srv_bc': 0, 'comments': ''}\n" + ] + } + ], + "source": [ + "mechanism_params = utilities.get_kinetics('bo-lid')\n", + "print(mechanism_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up timeseries\n", + "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection. \n", + "\n", + "We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_42092\\928104463.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps_initial = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-01 00:10:00', freq = 'S') # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], + "source": [ + "temperature = 50 # degrees celsius\n", + "suns = 0.05 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc.\n", + "\n", + "timesteps_initial = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-01 00:10:00', freq = 'S') # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", + "timesteps = pd.date_range(start = '2022-01-01 00:10:00', end = '2022-01-01 10:00:00', freq = 'min') # a total of 10 hours of exposure\n", + "timesteps = pd.DataFrame(timesteps, columns = ['Datetime'])\n", + "timesteps_initial = pd.DataFrame(timesteps_initial, columns = ['Datetime'])\n", + "\n", + "timesteps = pd.concat([timesteps_initial, timesteps]) # concatenate the two time series\n", + "timesteps = timesteps.sort_values(by = 'Datetime')\n", + "timesteps.reset_index(inplace = True, drop = True)\n", + "\n", + "temps = np.full(len(timesteps), temperature)\n", + "injection = np.full(len(timesteps), suns)\n", + "\n", + "timesteps['Temperature'] = temps\n", + "timesteps['Injection'] = injection\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor(tau, 'ab', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_ba = letid.carrier_factor(tau, 'ba', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_bc = letid.carrier_factor(tau, 'bc', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finish calculating degraded device parameters.\n", + "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "\n", + "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", + "for index, timestep in timesteps.iterrows():\n", + " jsc_now = collection.calculate_jsc_from_tau_cp(timesteps.at[index, 'tau'], wafer_thickness, d_base, s_rear, generation, depth)\n", + " timesteps.at[index, 'Jsc'] = jsc_now\n", + " timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(timesteps.at[index, 'tau'], wafer_thickness, s_rear, jsc_now, temperature = 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_normtime (days)
02022-01-01 00:00:00500.05100.0000000.0000000.000000e+00115.00000041.5909970.66632710.1066120.8409875.6634671.0000000.000000
12022-01-01 00:00:01500.0599.9843660.0156340.000000e+00114.98039041.5909360.66632410.1065970.8409865.6634260.9999930.000012
22022-01-01 00:00:02500.0599.9687350.0312651.016487e-07114.96079041.5908750.66632110.1065830.8409865.6633860.9999860.000023
32022-01-01 00:00:03500.0599.9531060.0468933.049294e-07114.94120041.5908140.66631710.1065680.8409855.6633450.9999790.000035
42022-01-01 00:00:04500.0599.9374800.0625206.098257e-07114.92162041.5907530.66631410.1065530.8409845.6633050.9999710.000046
.............................................
11872022-01-01 09:56:00500.050.36423282.3135551.732221e+0160.59117941.2805100.65310310.0311640.8386275.4941630.9701060.413889
11882022-01-01 09:57:00500.050.36082082.2848701.735431e+0160.60117041.2806150.65310610.0311890.8386275.4942110.9701150.414583
11892022-01-01 09:58:00500.050.35744082.2561641.738640e+0160.61117241.2807190.65311010.0312150.8386285.4942590.9701230.415278
11902022-01-01 09:59:00500.050.35409282.2274381.741847e+0160.62118541.2808240.65311310.0312400.8386295.4943070.9701320.415972
11912022-01-01 10:00:00500.050.35077682.1986911.745053e+0160.63120841.2809290.65311710.0312660.8386295.4943550.9701400.416667
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1192 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB \\\n", + "0 2022-01-01 00:00:00 50 0.05 100.000000 0.000000 \n", + "1 2022-01-01 00:00:01 50 0.05 99.984366 0.015634 \n", + "2 2022-01-01 00:00:02 50 0.05 99.968735 0.031265 \n", + "3 2022-01-01 00:00:03 50 0.05 99.953106 0.046893 \n", + "4 2022-01-01 00:00:04 50 0.05 99.937480 0.062520 \n", + "... ... ... ... ... ... \n", + "1187 2022-01-01 09:56:00 50 0.05 0.364232 82.313555 \n", + "1188 2022-01-01 09:57:00 50 0.05 0.360820 82.284870 \n", + "1189 2022-01-01 09:58:00 50 0.05 0.357440 82.256164 \n", + "1190 2022-01-01 09:59:00 50 0.05 0.354092 82.227438 \n", + "1191 2022-01-01 10:00:00 50 0.05 0.350776 82.198691 \n", + "\n", + " NC tau Jsc Voc Isc FF \\\n", + "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000e+00 114.980390 41.590936 0.666324 10.106597 0.840986 \n", + "2 1.016487e-07 114.960790 41.590875 0.666321 10.106583 0.840986 \n", + "3 3.049294e-07 114.941200 41.590814 0.666317 10.106568 0.840985 \n", + "4 6.098257e-07 114.921620 41.590753 0.666314 10.106553 0.840984 \n", + "... ... ... ... ... ... ... \n", + "1187 1.732221e+01 60.591179 41.280510 0.653103 10.031164 0.838627 \n", + "1188 1.735431e+01 60.601170 41.280615 0.653106 10.031189 0.838627 \n", + "1189 1.738640e+01 60.611172 41.280719 0.653110 10.031215 0.838628 \n", + "1190 1.741847e+01 60.621185 41.280824 0.653113 10.031240 0.838629 \n", + "1191 1.745053e+01 60.631208 41.280929 0.653117 10.031266 0.838629 \n", + "\n", + " Pmp Pmp_norm time (days) \n", + "0 5.663467 1.000000 0.000000 \n", + "1 5.663426 0.999993 0.000012 \n", + "2 5.663386 0.999986 0.000023 \n", + "3 5.663345 0.999979 0.000035 \n", + "4 5.663305 0.999971 0.000046 \n", + "... ... ... ... \n", + "1187 5.494163 0.970106 0.413889 \n", + "1188 5.494211 0.970115 0.414583 \n", + "1189 5.494259 0.970123 0.415278 \n", + "1190 5.494307 0.970132 0.415972 \n", + "1191 5.494355 0.970140 0.416667 \n", + "\n", + "[1192 rows x 14 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", + "timesteps = letid.calc_device_params(timesteps, cell_area = 243)\n", + "timesteps['time (days)'] = (timesteps['Datetime'] - timesteps.iloc[0]['Datetime']).dt.total_seconds()/86400 # create a column for days elapsed\n", + "\n", + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cycler import cycler\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n", + "\n", + "ax.plot(timesteps['time (days)'], timesteps[['NA', 'NB', 'NC']].values)\n", + "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$'], loc = 'upper left')\n", + "ax.set_ylabel('Defect state percentages [%]')\n", + "ax.set_xlabel('Time [days]')\n", + "\n", + "ax2 = ax.twinx()\n", + "ax2.plot(timesteps['time (days)'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized $P_{MP}$')\n", + "ax2.legend(loc = 'upper right')\n", + "ax2.set_ylabel('Normalized $P_{MP}$')\n", + "\n", + "ax.set_title('Accelerated B-O LID Test\\n'fr'{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ injection')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb new file mode 100644 index 00000000..7786944b --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb @@ -0,0 +1,1205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LETID - Accelerated Tests\n", + "\n", + "\n", + "Several standardized accelerated tests have been developed for LETID. These include [IEC TS 63342](https://webstore.iec.ch/publication/67332) for c-Si photovoltaic modules, and [IEC TS 63202-4](https://webstore.iec.ch/publication/67612) for c-Si photovoltaic cells.\n", + "Both procedures essentially prescribe exposure to constant light or current injection at constant elevated temperature for a prescribed duration of time. This notebook demonstrates how to use this library to model device behavior in such a procedure.\n", + "\n", + "**Requirements:**\n", + "- `pandas`, `numpy`, `matplotlib`\n", + "\n", + "**Objectives:**\n", + "1. Define necessary solar cell device parameters\n", + "2. Define necessary degradation parameters: degraded lifetime and defect states\n", + "3. Create timeseries of temperature and current injection\n", + "4. Run through timeseries, calculating defect states\n", + "5. Calculate device degradation and plot\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pvdeg import letid, collection, utilities, DATA_DIR\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Device parameters\n", + "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). The values defined below are representative of a typical PERC solar cell." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "wafer_thickness = 180 # um\n", + "s_rear = 46 # cm/s\n", + "cell_area = 243 # cm^2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Other device parameters\n", + "Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Degradation parameters\n", + "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", + "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how much maximum power degradation these parameters will result in: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.03495240755084558\n" + ] + } + ], + "source": [ + "loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss(tau_0, tau_deg, cell_area, wafer_thickness, s_rear) # returns % power loss, pmp_0, pmp_deg\n", + "print(loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Check to see the device's current collection " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41.59099692285122\n" + ] + } + ], + "source": [ + "jsc_0 = collection.calculate_jsc_from_tau_cp(tau_0, wafer_thickness, d_base, s_rear, generation, depth) # returns short-circuit current (Jsc) in mA/cm^2 given required cell parameters\n", + "print(jsc_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Remaining degradation parameters:\n", + "\n", + "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", + "\n", + "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# starting defect state percentages\n", + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('Choose a set of kinetic parameters:',\n", + " ['repins',\n", + " 'repins_best_case',\n", + " 'kwapil',\n", + " 'bredemeier',\n", + " 'wyller_wafer',\n", + " 'wyller_cell',\n", + " 'graf',\n", + " 'dark letid',\n", + " 'bo-lid',\n", + " 'Lit BO-LID + fit to Qcells destab'])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", + "utilities.get_kinetics()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" + ] + } + ], + "source": [ + "mechanism_params = utilities.get_kinetics('repins')\n", + "print(mechanism_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up timeseries\n", + "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection. \n", + "\n", + "We will create a pandas datetime series and calculate the changes in defect states for each timestep." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "temperature = 75 # degrees celsius\n", + "suns = 0.1 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", + "\n", + "duration='3W'\n", + "freq='min'\n", + "start='2022-01-01'\n", + "\n", + "# default is 3 weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", + "timesteps = pd.date_range(start,end=pd.to_datetime(start)+pd.to_timedelta(duration), freq=freq)\n", + "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", + "\n", + "\n", + "temps = np.full(len(timesteps), temperature)\n", + "injection = np.full(len(timesteps), suns)\n", + "\n", + "timesteps['Temperature'] = temps\n", + "timesteps['Injection'] = injection\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB NC tau\n", + "0 2022-01-01 00:00:00 75 0.1 100.0 0.0 0.0 115.0\n", + "1 2022-01-01 00:01:00 75 0.1 NaN NaN NaN NaN\n", + "2 2022-01-01 00:02:00 75 0.1 NaN NaN NaN NaN\n", + "3 2022-01-01 00:03:00 75 0.1 NaN NaN NaN NaN\n", + "4 2022-01-01 00:04:00 75 0.1 NaN NaN NaN NaN\n", + "... ... ... ... ... ... ... ...\n", + "30236 2022-01-21 23:56:00 75 0.1 NaN NaN NaN NaN\n", + "30237 2022-01-21 23:57:00 75 0.1 NaN NaN NaN NaN\n", + "30238 2022-01-21 23:58:00 75 0.1 NaN NaN NaN NaN\n", + "30239 2022-01-21 23:59:00 75 0.1 NaN NaN NaN NaN\n", + "30240 2022-01-22 00:00:00 75 0.1 NaN NaN NaN NaN\n", + "\n", + "[30241 rows x 7 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run through timesteps\n", + "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor(tau, 'ab', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_ba = letid.carrier_factor(tau, 'ba', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_bc = letid.carrier_factor(tau, 'bc', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finish calculating degraded device parameters.\n", + "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "\n", + "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", + "for index, timestep in timesteps.iterrows():\n", + " jsc_now = collection.calculate_jsc_from_tau_cp(timesteps.at[index, 'tau'], wafer_thickness, d_base, s_rear, generation, depth)\n", + " timesteps.at[index, 'Jsc'] = jsc_now\n", + " timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(timesteps.at[index, 'tau'], wafer_thickness, s_rear, jsc_now, temperature = 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_normtime (days)
02022-01-01 00:00:00750.1100.0000000.0000000.000000115.00000041.5909970.66632710.1066120.8409875.6634671.0000000.000000
12022-01-01 00:01:00750.199.9454240.0545760.000000114.93157341.5907840.66631610.1065610.8409855.6633250.9999750.000694
22022-01-01 00:02:00750.199.8909040.1090940.000002114.86330041.5905720.66630410.1065090.8409835.6631840.9999500.001389
32022-01-01 00:03:00750.199.8364390.1635550.000006114.79517841.5903590.66629210.1064570.8409815.6630430.9999250.002083
42022-01-01 00:04:00750.199.7820280.2179590.000012114.72720941.5901470.66628110.1064060.8409795.6629020.9999000.002778
.............................................
302362022-01-21 23:56:00750.10.00606654.97409345.01984171.88769841.3813920.65675410.0556780.8392865.5427350.97868220.997222
302372022-01-21 23:57:00750.10.00606554.97277345.02116271.88834541.3813970.65675410.0556790.8392865.5427380.97868320.997917
302382022-01-21 23:58:00750.10.00606454.97145445.02248271.88899241.3814020.65675410.0556810.8392865.5427400.97868320.998611
302392022-01-21 23:59:00750.10.00606354.97013545.02380271.88963841.3814070.65675510.0556820.8392865.5427430.97868420.999306
302402022-01-22 00:00:00750.10.00606154.96881645.02512371.89028541.3814110.65675510.0556830.8392865.5427450.97868421.000000
\n", + "

30241 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB \\\n", + "0 2022-01-01 00:00:00 75 0.1 100.000000 0.000000 \n", + "1 2022-01-01 00:01:00 75 0.1 99.945424 0.054576 \n", + "2 2022-01-01 00:02:00 75 0.1 99.890904 0.109094 \n", + "3 2022-01-01 00:03:00 75 0.1 99.836439 0.163555 \n", + "4 2022-01-01 00:04:00 75 0.1 99.782028 0.217959 \n", + "... ... ... ... ... ... \n", + "30236 2022-01-21 23:56:00 75 0.1 0.006066 54.974093 \n", + "30237 2022-01-21 23:57:00 75 0.1 0.006065 54.972773 \n", + "30238 2022-01-21 23:58:00 75 0.1 0.006064 54.971454 \n", + "30239 2022-01-21 23:59:00 75 0.1 0.006063 54.970135 \n", + "30240 2022-01-22 00:00:00 75 0.1 0.006061 54.968816 \n", + "\n", + " NC tau Jsc Voc Isc FF \\\n", + "0 0.000000 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000 114.931573 41.590784 0.666316 10.106561 0.840985 \n", + "2 0.000002 114.863300 41.590572 0.666304 10.106509 0.840983 \n", + "3 0.000006 114.795178 41.590359 0.666292 10.106457 0.840981 \n", + "4 0.000012 114.727209 41.590147 0.666281 10.106406 0.840979 \n", + "... ... ... ... ... ... ... \n", + "30236 45.019841 71.887698 41.381392 0.656754 10.055678 0.839286 \n", + "30237 45.021162 71.888345 41.381397 0.656754 10.055679 0.839286 \n", + "30238 45.022482 71.888992 41.381402 0.656754 10.055681 0.839286 \n", + "30239 45.023802 71.889638 41.381407 0.656755 10.055682 0.839286 \n", + "30240 45.025123 71.890285 41.381411 0.656755 10.055683 0.839286 \n", + "\n", + " Pmp Pmp_norm time (days) \n", + "0 5.663467 1.000000 0.000000 \n", + "1 5.663325 0.999975 0.000694 \n", + "2 5.663184 0.999950 0.001389 \n", + "3 5.663043 0.999925 0.002083 \n", + "4 5.662902 0.999900 0.002778 \n", + "... ... ... ... \n", + "30236 5.542735 0.978682 20.997222 \n", + "30237 5.542738 0.978683 20.997917 \n", + "30238 5.542740 0.978683 20.998611 \n", + "30239 5.542743 0.978684 20.999306 \n", + "30240 5.542745 0.978684 21.000000 \n", + "\n", + "[30241 rows x 14 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", + "timesteps = letid.calc_device_params(timesteps, cell_area = 243)\n", + "timesteps['time (days)'] = (timesteps['Datetime'] - timesteps.iloc[0]['Datetime']).dt.total_seconds()/86400 # create a column for days elapsed\n", + "\n", + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cycler import cycler\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n", + "\n", + "ax.plot(timesteps['time (days)'], timesteps[['NA', 'NB', 'NC']].values)\n", + "\n", + "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$', '80 % regeneration'], loc = 'upper left')\n", + "ax.set_ylabel('Defect state percentages [%]')\n", + "ax.set_xlabel('Time [days]')\n", + "\n", + "ax2 = ax.twinx()\n", + "ax2.plot(timesteps['time (days)'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized $P_{MP}$')\n", + "ax2.legend(loc = 'upper right')\n", + "ax2.set_ylabel('Normalized $P_{MP}$')\n", + "\n", + "#ax.axvline(pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), linestyle = ':' , c = 'grey')\n", + "#ax.annotate('80% regeneration', (pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), 80),\n", + "# xytext=(0.5, 0.8), textcoords='axes fraction',\n", + "# arrowprops=dict(facecolor='black', shrink=0.1),\n", + "# horizontalalignment='right', verticalalignment='top')\n", + "\n", + "ax.set_title('Accelerated LETID Test\\n'fr'{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ dark current injection')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `calc_letid_lab` wraps all of the steps above into a single function:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
02024-05-06 15:13:53.364135750.1100.0000000.0000000.000000115.00000041.5909970.6663279.9402480.8409875.5702411.000000
12024-05-06 15:14:53.364135750.199.9454240.0545760.000000114.93157341.5909970.6663279.9402480.8409875.5702411.000000
22024-05-06 15:15:53.364135750.199.8909040.1090940.000002114.86330041.5907840.6663169.9401970.8409855.5701020.999975
32024-05-06 15:16:53.364135750.199.8364390.1635550.000006114.79517841.5905720.6663049.9401470.8409835.5699630.999950
42024-05-06 15:17:53.364135750.199.7820280.2179590.000012114.72720941.5903590.6662929.9400960.8409815.5698240.999925
..........................................
302362024-05-27 15:09:53.364135750.10.00606654.97409345.01984171.88769841.3813870.6567549.8901510.8392865.4514940.978682
302372024-05-27 15:10:53.364135750.10.00606554.97277345.02116271.88834541.3813920.6567549.8901530.8392865.4514970.978682
302382024-05-27 15:11:53.364135750.10.00606454.97145445.02248271.88899241.3813970.6567549.8901540.8392865.4514990.978683
302392024-05-27 15:12:53.364135750.10.00606354.97013545.02380271.88963841.3814020.6567549.8901550.8392865.4515020.978683
302402024-05-27 15:13:53.364135750.10.00606154.96881645.02512371.89028541.3814070.6567559.8901560.8392865.4515040.978684
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30241 rows × 13 columns

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" + ], + "text/plain": [ + " Datetime Temperature Injection NA \\\n", + "0 2024-05-06 15:13:53.364135 75 0.1 100.000000 \n", + "1 2024-05-06 15:14:53.364135 75 0.1 99.945424 \n", + "2 2024-05-06 15:15:53.364135 75 0.1 99.890904 \n", + "3 2024-05-06 15:16:53.364135 75 0.1 99.836439 \n", + "4 2024-05-06 15:17:53.364135 75 0.1 99.782028 \n", + "... ... ... ... ... \n", + "30236 2024-05-27 15:09:53.364135 75 0.1 0.006066 \n", + "30237 2024-05-27 15:10:53.364135 75 0.1 0.006065 \n", + "30238 2024-05-27 15:11:53.364135 75 0.1 0.006064 \n", + "30239 2024-05-27 15:12:53.364135 75 0.1 0.006063 \n", + "30240 2024-05-27 15:13:53.364135 75 0.1 0.006061 \n", + "\n", + " NB NC tau Jsc Voc Isc \\\n", + "0 0.000000 0.000000 115.000000 41.590997 0.666327 9.940248 \n", + "1 0.054576 0.000000 114.931573 41.590997 0.666327 9.940248 \n", + "2 0.109094 0.000002 114.863300 41.590784 0.666316 9.940197 \n", + "3 0.163555 0.000006 114.795178 41.590572 0.666304 9.940147 \n", + "4 0.217959 0.000012 114.727209 41.590359 0.666292 9.940096 \n", + "... ... ... ... ... ... ... \n", + "30236 54.974093 45.019841 71.887698 41.381387 0.656754 9.890151 \n", + "30237 54.972773 45.021162 71.888345 41.381392 0.656754 9.890153 \n", + "30238 54.971454 45.022482 71.888992 41.381397 0.656754 9.890154 \n", + "30239 54.970135 45.023802 71.889638 41.381402 0.656754 9.890155 \n", + "30240 54.968816 45.025123 71.890285 41.381407 0.656755 9.890156 \n", + "\n", + " FF Pmp Pmp_norm \n", + "0 0.840987 5.570241 1.000000 \n", + "1 0.840987 5.570241 1.000000 \n", + "2 0.840985 5.570102 0.999975 \n", + "3 0.840983 5.569963 0.999950 \n", + "4 0.840981 5.569824 0.999925 \n", + "... ... ... ... \n", + "30236 0.839286 5.451494 0.978682 \n", + "30237 0.839286 5.451497 0.978682 \n", + "30238 0.839286 5.451499 0.978683 \n", + "30239 0.839286 5.451502 0.978683 \n", + "30240 0.839286 5.451504 0.978684 \n", + "\n", + "[30241 rows x 13 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "letid.calc_letid_lab(tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, 'repins')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb new file mode 100644 index 00000000..bda70ff1 --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb @@ -0,0 +1,1696 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LETID - Outdoor Environments \n", + "\n", + "This is an example on how to model LETID progression in outdoor environments\n", + "\n", + "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", + "\n", + "This will illustrate the potential of \"Temporary Recovery\", i.e., the backwards transition of the LETID defect B->A that can take place with carrier injection at lower temperatures.\n", + "\n", + "\n", + "**Requirements:**\n", + "- `pvlib`, `pandas`, `numpy`, `matplotlib`\n", + "\n", + "**Objectives:**\n", + "1. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", + "2. Define necessary solar cell device parameters\n", + "3. Define necessary degradation parameters: degraded lifetime and defect states\n", + "4. Run through timeseries, calculating defect states\n", + "5. Calculate device degradation and plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pvdeg import letid, collection, utilities, DATA_DIR\n", + "\n", + "import pvlib\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvlib version 0.10.3\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvlib version \", pvlib.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll use pvlib to create and run a model system, and use the irradiance, temperature, and operating point of that model to set up our LETID model\n", + "For this example, we'll model a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# load weather and location data, use pvlib read_psm3 function with map_variables = True\n", + "\n", + "sam_file = 'psm3.csv'\n", + "weather, meta = pvdeg.weather.read(os.path.join(DATA_DIR, sam_file), file_type='PSM3', map_variables = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humiditypoa_globaltemp_celltemp_module
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.390.00.00.0
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.840.00.00.0
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1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.810.00.00.0
................................................
1999-12-31 19:30:00-07:001999123119300.00.00.00.0-3.00.983.630.00.00.0
1999-12-31 20:30:00-07:001999123120300.00.00.00.0-3.01.286.820.00.00.0
1999-12-31 21:30:00-07:001999123121300.00.00.00.0-4.01.683.780.00.00.0
1999-12-31 22:30:00-07:001999123122300.00.00.00.0-4.01.781.220.00.00.0
1999-12-31 23:30:00-07:001999123123300.00.00.00.0-5.01.879.430.00.00.0
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8760 rows × 15 columns

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" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", + "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", + "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", + "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", + "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \\\n", + "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", + "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", + "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", + "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", + "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", + "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", + "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", + "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", + "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", + "\n", + " poa_global temp_cell temp_module \n", + "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", + "\n", + "[8760 rows x 15 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#if our weather file doesn't have precipitable water, calculate it with pvlib\n", + "if not 'precipitable_water' in weather.columns:\n", + " weather['precipitable_water'] = pvlib.atmosphere.gueymard94_pw(weather['temp_air'], weather['relative_humidity'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# rename some columns for pvlib if they haven't been already\n", + "weather.rename(columns = {'GHI':'ghi', 'DNI':'dni', 'DHI':'dhi', 'Temperature':'temp_air', 'Wind Speed':'wind_speed', 'Relative Humidity':'relative_humidity', 'Precipitable Water':'precipitable_water'}, inplace = True)\n", + "weather = weather[['ghi', 'dni', 'dhi', 'temp_air', 'wind_speed', 'relative_humidity', 'precipitable_water']]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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........................
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" + ], + "text/plain": [ + " ghi dni dhi temp_air wind_speed \\\n", + "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", + "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", + "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", + "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", + "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", + "... ... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", + "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", + "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", + "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", + "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", + "\n", + " relative_humidity precipitable_water \n", + "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", + "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", + "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", + "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", + "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", + "... ... ... \n", + "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", + "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", + "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", + "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", + "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", + "\n", + "[8760 rows x 7 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection.\n", + "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", + "\n", + "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", + "from pvlib.location import Location\n", + "from pvlib.pvsystem import PVSystem\n", + "from pvlib.modelchain import ModelChain\n", + "\n", + "cec_modules = pvlib.pvsystem.retrieve_sam('CECMod')\n", + "cec_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')\n", + "\n", + "cec_module = cec_modules['Jinko_Solar_Co___Ltd_JKM260P_60']\n", + "cec_inverter = cec_inverters['ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_']\n", + "\n", + "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# set up system in pvlib\n", + "lat = meta['latitude']\n", + "lon = meta['longitude']\n", + "tz = meta['tz']\n", + "elevation = meta['altitude']\n", + "surface_tilt = lat # fixed, latitude tilt\n", + "surface_azimuth = 180 # south-facing\n", + "\n", + "location = Location(lat, lon, tz, elevation, 'Golden, CO, USA')\n", + "\n", + "system = PVSystem(surface_tilt = surface_tilt, surface_azimuth = surface_azimuth,\n", + " module_parameters = cec_module,\n", + " inverter_parameters = cec_inverter,\n", + " temperature_model_parameters = temperature_model_parameters,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelChain: \n", + " name: None\n", + " clearsky_model: ineichen\n", + " transposition_model: haydavies\n", + " solar_position_method: nrel_numpy\n", + " airmass_model: kastenyoung1989\n", + " dc_model: cec\n", + " ac_model: sandia_inverter\n", + " aoi_model: physical_aoi_loss\n", + " spectral_model: first_solar_spectral_loss\n", + " temperature_model: sapm_temp\n", + " losses_model: no_extra_losses" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create and run pvlib modelchain\n", + "mc = ModelChain(system, location, aoi_model = \"physical\")\n", + "mc.run_model(weather)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up timeseries\n", + "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", + "\n", + "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "ee = mc.results.effective_irradiance\n", + "#injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000)\n", + "injection = letid.calc_injection_outdoors(mc.results)\n", + "temperature = mc.results.cell_temperature\n", + "\n", + "timesteps = pd.DataFrame({'Temperature':temperature, 'Injection':injection}) # create a DataFrame with cell temperature and injection\n", + "timesteps.reset_index(inplace = True) # reset the index so datetime is a column. I prefer integer indexing.\n", + "timesteps.rename(columns = {'index':'Datetime'}, inplace = True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# filter out times when injection is NaN, these won't progress LETID, and it'll make the calculations below run faster\n", + "timesteps = timesteps[timesteps['Injection'].notnull()]\n", + "timesteps.reset_index(inplace = True, drop = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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............
42961999-12-31 12:30:00-07:009.5155770.001151
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" + ], + "text/plain": [ + " Datetime Temperature Injection\n", + "0 1999-01-01 08:30:00-07:00 3.348774 0.006239\n", + "1 1999-01-01 09:30:00-07:00 12.378439 0.031607\n", + "2 1999-01-01 10:30:00-07:00 16.595081 0.044220\n", + "3 1999-01-01 11:30:00-07:00 17.457240 0.043201\n", + "4 1999-01-01 12:30:00-07:00 6.392783 0.007124\n", + "... ... ... ...\n", + "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151\n", + "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499\n", + "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130\n", + "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891\n", + "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188\n", + "\n", + "[4301 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Device parameters\n", + "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "wafer_thickness = 180 # um\n", + "s_rear = 46 # cm/s\n", + "cell_area = 243 # cm^2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Other device parameters \n", + "Other required device parameters: base diffusivity (in cm2/s), and optical generation profile, which allow us to estimate current collection in the device." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Degradation parameters\n", + "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", + "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remaining degradation parameters: \n", + "\n", + "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", + "\n", + "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the kinetic parameters for LETID degradation from Repins." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# starting defect state percentages\n", + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0\n", + "\n", + "mechanism_params = utilities.get_kinetics('repins')\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run through timesteps\n", + "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " #calc device parameters for first row\n", + " tau = tau_0\n", + " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", + " voc = letid.calc_voc_from_tau(tau, wafer_thickness, s_rear, jsc, temperature = 25)\n", + " timesteps.at[index, 'Jsc'] = jsc\n", + " timesteps.at[index, 'Voc'] = voc\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, s_rear, generation, depth)\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor(tau, 'ab', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_ba = letid.carrier_factor(tau, 'ba', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + " x_bc = letid.carrier_factor(tau, 'bc', temperature, injection, jsc, wafer_thickness, s_rear, mechanism_params)\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step\n", + "\n", + " #calculate device parameters\n", + " timesteps.at[index, 'tau'] = tau\n", + " timesteps.at[index, 'Jsc'] = jsc\n", + " timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(tau, wafer_thickness, s_rear, jsc, temperature = 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finish calculating degraded device parameters.\n", + "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "\n", + "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", + "for index, timestep in timesteps.iterrows():\n", + " jsc_now = collection.calculate_jsc_from_tau_cp(timesteps.at[index, 'tau'], wafer_thickness, d_base, s_rear, generation, depth)\n", + " timesteps.at[index, 'Jsc'] = jsc_now\n", + " timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(timesteps.at[index, 'tau'], wafer_thickness, s_rear, jsc_now, temperature = 25)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
01999-01-01 08:30:00-07:003.3487740.006239100.0000000.0000000.000000e+00115.00000041.5909970.66632710.1066120.8409875.6634671.000000
11999-01-01 09:30:00-07:0012.3784390.03160799.9975470.0024530.000000e+00114.99692241.5909870.66632710.1066100.8409875.6634600.999999
21999-01-01 10:30:00-07:0016.5950810.04422099.9919530.0080476.072523e-09114.98990541.5909660.66632610.1066050.8409865.6634460.999996
31999-01-01 11:30:00-07:0017.4572400.04320199.9859290.0140712.755257e-08114.98235041.5909420.66632410.1065990.8409865.6634300.999994
41999-01-01 12:30:00-07:006.3927830.00712499.9856640.0143362.864303e-08114.98201841.5909410.66632410.1065990.8409865.6634290.999993
..........................................
42961999-12-31 12:30:00-07:009.5155770.00115142.02957553.7666324.203792e+0072.48454641.3858980.65692910.0567730.8393175.5450200.979086
42971999-12-31 13:30:00-07:0028.1197960.04449942.03023153.7656544.204115e+0072.48503441.3859010.65692910.0567740.8393185.5450220.979086
42981999-12-31 14:30:00-07:0023.3146720.03513042.03631153.7594334.204257e+0072.48813541.3859240.65693010.0567800.8393185.5450340.979088
42991999-12-31 15:30:00-07:0017.8905280.02789142.04887353.7468134.204313e+0072.49442541.3859720.65693210.0567910.8393185.5450580.979093
43001999-12-31 16:30:00-07:004.5523650.00118842.04944553.7462424.204314e+0072.49471041.3859740.65693210.0567920.8393185.5450590.979093
\n", + "

4301 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB \\\n", + "0 1999-01-01 08:30:00-07:00 3.348774 0.006239 100.000000 0.000000 \n", + "1 1999-01-01 09:30:00-07:00 12.378439 0.031607 99.997547 0.002453 \n", + "2 1999-01-01 10:30:00-07:00 16.595081 0.044220 99.991953 0.008047 \n", + "3 1999-01-01 11:30:00-07:00 17.457240 0.043201 99.985929 0.014071 \n", + "4 1999-01-01 12:30:00-07:00 6.392783 0.007124 99.985664 0.014336 \n", + "... ... ... ... ... ... \n", + "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151 42.029575 53.766632 \n", + "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499 42.030231 53.765654 \n", + "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130 42.036311 53.759433 \n", + "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891 42.048873 53.746813 \n", + "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188 42.049445 53.746242 \n", + "\n", + " NC tau Jsc Voc Isc FF \\\n", + "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", + "1 0.000000e+00 114.996922 41.590987 0.666327 10.106610 0.840987 \n", + "2 6.072523e-09 114.989905 41.590966 0.666326 10.106605 0.840986 \n", + "3 2.755257e-08 114.982350 41.590942 0.666324 10.106599 0.840986 \n", + "4 2.864303e-08 114.982018 41.590941 0.666324 10.106599 0.840986 \n", + "... ... ... ... ... ... ... \n", + "4296 4.203792e+00 72.484546 41.385898 0.656929 10.056773 0.839317 \n", + "4297 4.204115e+00 72.485034 41.385901 0.656929 10.056774 0.839318 \n", + "4298 4.204257e+00 72.488135 41.385924 0.656930 10.056780 0.839318 \n", + "4299 4.204313e+00 72.494425 41.385972 0.656932 10.056791 0.839318 \n", + "4300 4.204314e+00 72.494710 41.385974 0.656932 10.056792 0.839318 \n", + "\n", + " Pmp Pmp_norm \n", + "0 5.663467 1.000000 \n", + "1 5.663460 0.999999 \n", + "2 5.663446 0.999996 \n", + "3 5.663430 0.999994 \n", + "4 5.663429 0.999993 \n", + "... ... ... \n", + "4296 5.545020 0.979086 \n", + "4297 5.545022 0.979086 \n", + "4298 5.545034 0.979088 \n", + "4299 5.545058 0.979093 \n", + "4300 5.545059 0.979093 \n", + "\n", + "[4301 rows x 13 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps = letid.calc_device_params(timesteps, cell_area = 243) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", + "\n", + "timesteps\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. This isn't a robust performance model of a degraded module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cycler import cycler\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n", + "\n", + "ax.plot(timesteps['Datetime'], timesteps[['NA', 'NB', 'NC']].values)\n", + "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$'], loc = 'upper left')\n", + "ax.set_ylabel('Defect state percentages [%]')\n", + "ax.set_xlabel('Datetime')\n", + "\n", + "ax2 = ax.twinx()\n", + "ax2.plot(timesteps['Datetime'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized STC $P_{MP}$')\n", + "ax2.legend(loc = 'upper right')\n", + "ax2.set_ylabel('Normalized STC $P_{MP}$')\n", + "\n", + "ax.set_title('Outdoor LETID \\n'f'{location.name}')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The example data provided for Golden, CO, shows how $N_A$ increases in cold weather, and power temporarily recovers, due to temporary recovery of LETID (B->A)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.......................................
1999-12-31 19:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
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" + ], + "text/plain": [ + " Temperature Injection NA NB \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", + "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.702422e-15 \n", + "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.404843e-15 \n", + "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.107265e-15 \n", + "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.809686e-15 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 20:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 21:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 22:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "1999-12-31 23:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", + "\n", + " NC tau Jsc Voc \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", + "1999-01-01 01:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", + "1999-01-01 02:30:00-07:00 5.403329e-36 115.000000 41.590997 0.666327 \n", + "1999-01-01 03:30:00-07:00 1.620999e-35 115.000000 41.590997 0.666327 \n", + "1999-01-01 04:30:00-07:00 3.241997e-35 115.000000 41.590997 0.666327 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 20:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 21:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 22:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "1999-12-31 23:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", + "\n", + " Isc FF Pmp Pmp_norm \n", + "time \n", + "1999-01-01 00:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 01:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 02:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 03:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "1999-01-01 04:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 20:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 21:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 22:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "1999-12-31 23:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", + "\n", + "[8760 rows x 12 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0\n", + "mechanism_params = 'repins'\n", + "\n", + "letid.calc_letid_outdoors(tau_0, tau_deg,wafer_thickness, s_rear, nA_0, nB_0, nC_0, weather, meta, mechanism_params, generation_df, module_parameters = cec_module)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb new file mode 100644 index 00000000..87994afb --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb @@ -0,0 +1,3255 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LETID - Outdoor Scenario Based on Accelerated Test\n", + "\n", + "This is an example for using a test result to model LETID progression in outdoor environments\n", + "\n", + "One important use case for this library is to use data from a LETID test (e.g. [IEC TS 63342](https://webstore.iec.ch/publication/67332)) to model how a module may degrade and regenerate in the field.\n", + "\n", + "We will take some data from module testing presented in [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573), and use it to estimate device parameters.\n", + "\n", + "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", + "\n", + "\n", + "\n", + "**Requirements:**\n", + "- `pvlib`, `pandas`, `numpy`, `matplotlib`, `scipy`\n", + "\n", + "**Objectives:**\n", + "1. Load data from example test results\n", + "2. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", + "3. Define necessary solar cell device parameters\n", + "4. Define necessary degradation parameters: degraded lifetime and defect states\n", + "5. Run through timeseries, calculating defect states\n", + "6. Calculate device degradation and plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "from pvdeg import letid, collection, utilities, DATA_DIR\n", + "\n", + "import pvdeg\n", + "import pvlib\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of \"Type S\" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "cell_area = 243 # cm^2" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Week Pmp Isc Voc FF cell Voc Jsc % degradation\n", + "0 0 236.7 9.91 31.86 75.01 0.663750 40.781893 0.000000\n", + "1 1 230.9 9.82 31.63 74.38 0.658958 40.411523 -2.511910\n", + "2 2 228.5 9.72 31.54 74.50 0.657083 40.000000 -3.588621\n", + "3 3 226.9 9.70 31.56 74.10 0.657500 39.917695 -4.319083\n", + "4 4 224.8 9.62 31.48 74.22 0.655833 39.588477 -5.293594\n", + "5 7 235.8 9.76 31.98 75.51 0.666250 40.164609 -0.381679" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(os.path.join(DATA_DIR, 'module test data.csv'))\n", + "df['cell Voc'] = df['Voc']/48\n", + "df['Jsc'] = df['Isc']/cell_area*1000\n", + "df['% degradation'] = (df['Pmp']-df['Pmp'].iloc[0])/df['Pmp']*100\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6611301062313933" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us.\n", + "\n", + "wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um.\n", + "\n", + "srv_rear = 100 # [cm/s] a reasonable value for rear surface recombination velocity for passivated emitter and rear cell (PERC) solar cells.\n", + "# Typical PERC cells might have a rear SRV anywhere from 50 to 100 cm/s. Higher values for early PERC, lower values for more modern, higher efficiency PERC.\n", + "# Other device structures will have different SRV values. E.g., aluminum back surface field (Al-BSF) cells could be ~500 cm/s. TopCON or other high efficiency cell structures will be lower, e.g. 10 cm/s.\n", + "# Note that all of these values are intepreted as \"lumped\" values for the entire rear surface.\n", + "\n", + "isc_0 = df.query(\"Week == 0\")['Isc'].item() # [A] we'll use the short circuit current from the Week 0 test data, instead of trying to calculate it\n", + "cell_area = 240.8 # [cm^2] typical cell size for 6-inch pseudosquare monocrystalline silicon wafers\n", + "jsc_0 = isc_0/cell_area*1000 # [mA/cm^2] short circuit current density\n", + "\n", + "temperature = 25 # [C] standard measurement temperature\n", + "\n", + "voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature)\n", + "voc_0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty close!" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(235.89622533600513, 236.7)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check to make sure power is close to the measured Week 0 power\n", + "\n", + "ff_0 = df.query(\"Week == 0\")['FF'].item()# [%] fill factor\n", + "\n", + "pmp_0 = (voc_0*48)*(jsc_0*cell_area/1000)*ff_0/100 # [W] maximum power\n", + "pmp_0, df.query(\"Week == 0\")['Pmp'].item()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6538686361228122" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tau_deg = 80 # [us] degraded bulk lifetime\n", + "\n", + "isc_deg = df.query(\"Week == 4\")['Isc'].item() # [A] we'll use the short circuit current from the Week 4 test data, instead of trying to calculate it\n", + "jsc_deg = isc_deg/cell_area*1000 # [mA/cm^2] short circuit current density\n", + "\n", + "voc_deg = letid.calc_voc_from_tau(tau_deg, wafer_thickness, srv_rear, jsc_deg, temperature)\n", + "voc_deg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224.09272908700694, 224.8)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check to make sure power is close to the measured Week 4 power\n", + "\n", + "ff_deg = df.query(\"Week == 4\")['FF'].item()# [%] fill factor\n", + "\n", + "pmp_deg = (voc_deg*48)*(jsc_deg*cell_area/1000)*ff_deg/100 # [W] maximum power\n", + "pmp_deg, df.query(\"Week == 4\")['Pmp'].item()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.050036816961295416" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(pmp_0 - pmp_deg) / pmp_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So for modeling this module, we will use ```tau_0``` = 120 $\\mu s$, ```tau_deg``` = 80 $\\mu s$, with ```wafer_thickness``` = 180 $\\mu m$ and ```srv_rear``` = 100 cm/s.\n", + "\n", + "Great!\n", + "\n", + "The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# load weather and location data, use pvlib read_psm3 function\n", + "\n", + "sam_file = 'psm3.csv'\n", + "weather, meta = pvdeg.weather.read(os.path.join(DATA_DIR, sam_file), file_type='PSM3', map_variables = True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humiditypoa_globaltemp_celltemp_module
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.390.00.00.0
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.840.00.00.0
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.980.00.00.0
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.010.00.00.0
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.810.00.00.0
................................................
1999-12-31 19:30:00-07:001999123119300.00.00.00.0-3.00.983.630.00.00.0
1999-12-31 20:30:00-07:001999123120300.00.00.00.0-3.01.286.820.00.00.0
1999-12-31 21:30:00-07:001999123121300.00.00.00.0-4.01.683.780.00.00.0
1999-12-31 22:30:00-07:001999123122300.00.00.00.0-4.01.781.220.00.00.0
1999-12-31 23:30:00-07:001999123123300.00.00.00.0-5.01.879.430.00.00.0
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8760 rows × 15 columns

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" + ], + "text/plain": [ + " Year Month Day Hour Minute dni dhi ghi \\\n", + "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", + "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", + "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", + "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", + "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", + "\n", + " temp_air dew_point wind_speed relative_humidity \\\n", + "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", + "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", + "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", + "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", + "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", + "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", + "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", + "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", + "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", + "\n", + " poa_global temp_cell temp_module \n", + "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", + "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", + "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", + "\n", + "[8760 rows x 15 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "#if our weather file doesn't have precipitable water, calculate it with pvlib\n", + "if not 'precipitable_water' in weather.columns:\n", + " weather['precipitable_water'] = pvlib.atmosphere.gueymard94_pw(weather['temp_air'], weather['relative_humidity'])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# drop unneeded columns\n", + "weather = weather[['ghi', 'dni', 'dhi', 'temp_air', 'wind_speed', 'relative_humidity', 'precipitable_water']]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ghidnidhitemp_airwind_speedrelative_humidityprecipitable_water
1999-01-01 00:30:00-07:000.00.00.00.01.879.390.891869
1999-01-01 01:30:00-07:000.00.00.00.01.780.840.908158
1999-01-01 02:30:00-07:000.00.00.00.01.582.980.932199
1999-01-01 03:30:00-07:000.00.00.00.01.385.010.955004
1999-01-01 04:30:00-07:000.00.00.00.01.385.810.963991
........................
1999-12-31 19:30:00-07:000.00.00.00.00.983.630.939501
1999-12-31 20:30:00-07:000.00.00.00.01.286.820.975338
1999-12-31 21:30:00-07:000.00.00.00.01.683.780.941186
1999-12-31 22:30:00-07:000.00.00.00.01.781.220.912427
1999-12-31 23:30:00-07:000.00.00.00.01.879.430.892318
\n", + "

8760 rows × 7 columns

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" + ], + "text/plain": [ + " ghi dni dhi temp_air wind_speed \\\n", + "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", + "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", + "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", + "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", + "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", + "... ... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", + "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", + "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", + "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", + "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", + "\n", + " relative_humidity precipitable_water \n", + "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", + "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", + "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", + "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", + "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", + "... ... ... \n", + "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", + "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", + "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", + "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", + "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", + "\n", + "[8760 rows x 7 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up PVlib model\n", + "Note that the module we select here is NOT the same \"Type S\" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TechnologyBifacialSTCPTCA_cLengthWidthN_sI_sc_refV_oc_ref...a_refI_L_refI_o_refR_sR_sh_refAdjustgamma_rBIPVVersionDate
Gintech_Energy_GIN_M6_48_220Mono-c-Si0220.48194.51.3131.3240.992489.3131.3...1.5211519.32280.00.10608777.16406322.488531-0.55NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_P6_48_220Multi-c-Si0220.378199.21.3131.3240.992489.2831.3...1.3798159.3791740.00.254869374.7245188.824213-0.49NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_M6_48_225Mono-c-Si0225.844199.11.3131.3240.992489.4531.5...1.5305969.4619890.00.10090579.53174622.462965-0.55NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG230N8K_G4Mono-c-Si0230.127212.91.2741.30.98489.930.0...1.11089410.3120510.00.307502314.5515142.112491-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG235N8K_G4Mono-c-Si0235.216217.61.2741.30.98489.9630.2...1.12468810.3759430.00.278936250.7994693.374386-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG240N8K_G4Mono-c-Si0240.1222.41.2741.30.984810.0130.5...1.13498310.3139960.00.2634533976.5139163.244429-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG245N8K_G4Mono-c-Si0245.016227.11.2741.30.984810.0630.8...1.15045410.367120.00.2460411112.8308114.077489-0.38NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_279BSTCMono-c-Si1220.472199.31.6681.6950.984489.1531.0...1.2663699.150290.00.2109126628.00537113.999823-0.4399NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_286BSTCMono-c-Si1225.298204.01.6681.6950.984489.3531.0...1.2663479.3502750.00.2064037016.81640613.996228-0.4399NSAM 2018.11.11 r21/3/2019
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9 rows × 25 columns

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" + ], + "text/plain": [ + " Technology Bifacial STC PTC \\\n", + "Gintech_Energy_GIN_M6_48_220 Mono-c-Si 0 220.48 194.5 \n", + "Gintech_Energy_GIN_P6_48_220 Multi-c-Si 0 220.378 199.2 \n", + "Gintech_Energy_GIN_M6_48_225 Mono-c-Si 0 225.844 199.1 \n", + "LG_Electronics_Inc__LG230N8K_G4 Mono-c-Si 0 230.127 212.9 \n", + "LG_Electronics_Inc__LG235N8K_G4 Mono-c-Si 0 235.216 217.6 \n", + "LG_Electronics_Inc__LG240N8K_G4 Mono-c-Si 0 240.1 222.4 \n", + "LG_Electronics_Inc__LG245N8K_G4 Mono-c-Si 0 245.016 227.1 \n", + "Prism_Solar_Technologies_Bi48_279BSTC Mono-c-Si 1 220.472 199.3 \n", + "Prism_Solar_Technologies_Bi48_286BSTC Mono-c-Si 1 225.298 204.0 \n", + "\n", + " A_c Length Width N_s I_sc_ref \\\n", + "Gintech_Energy_GIN_M6_48_220 1.313 1.324 0.992 48 9.31 \n", + "Gintech_Energy_GIN_P6_48_220 1.313 1.324 0.992 48 9.28 \n", + "Gintech_Energy_GIN_M6_48_225 1.313 1.324 0.992 48 9.45 \n", + "LG_Electronics_Inc__LG230N8K_G4 1.274 1.3 0.98 48 9.9 \n", + "LG_Electronics_Inc__LG235N8K_G4 1.274 1.3 0.98 48 9.96 \n", + "LG_Electronics_Inc__LG240N8K_G4 1.274 1.3 0.98 48 10.01 \n", + "LG_Electronics_Inc__LG245N8K_G4 1.274 1.3 0.98 48 10.06 \n", + "Prism_Solar_Technologies_Bi48_279BSTC 1.668 1.695 0.984 48 9.15 \n", + "Prism_Solar_Technologies_Bi48_286BSTC 1.668 1.695 0.984 48 9.35 \n", + "\n", + " V_oc_ref ... a_ref I_L_ref \\\n", + "Gintech_Energy_GIN_M6_48_220 31.3 ... 1.521151 9.3228 \n", + "Gintech_Energy_GIN_P6_48_220 31.3 ... 1.379815 9.379174 \n", + "Gintech_Energy_GIN_M6_48_225 31.5 ... 1.530596 9.461989 \n", + "LG_Electronics_Inc__LG230N8K_G4 30.0 ... 1.110894 10.312051 \n", + "LG_Electronics_Inc__LG235N8K_G4 30.2 ... 1.124688 10.375943 \n", + "LG_Electronics_Inc__LG240N8K_G4 30.5 ... 1.134983 10.313996 \n", + "LG_Electronics_Inc__LG245N8K_G4 30.8 ... 1.150454 10.36712 \n", + "Prism_Solar_Technologies_Bi48_279BSTC 31.0 ... 1.266369 9.15029 \n", + "Prism_Solar_Technologies_Bi48_286BSTC 31.0 ... 1.266347 9.350275 \n", + "\n", + " I_o_ref R_s R_sh_ref \\\n", + "Gintech_Energy_GIN_M6_48_220 0.0 0.106087 77.164063 \n", + "Gintech_Energy_GIN_P6_48_220 0.0 0.254869 374.724518 \n", + "Gintech_Energy_GIN_M6_48_225 0.0 0.100905 79.531746 \n", + "LG_Electronics_Inc__LG230N8K_G4 0.0 0.307502 314.551514 \n", + "LG_Electronics_Inc__LG235N8K_G4 0.0 0.278936 250.799469 \n", + "LG_Electronics_Inc__LG240N8K_G4 0.0 0.263453 3976.513916 \n", + "LG_Electronics_Inc__LG245N8K_G4 0.0 0.246041 1112.830811 \n", + "Prism_Solar_Technologies_Bi48_279BSTC 0.0 0.210912 6628.005371 \n", + "Prism_Solar_Technologies_Bi48_286BSTC 0.0 0.206403 7016.816406 \n", + "\n", + " Adjust gamma_r BIPV \\\n", + "Gintech_Energy_GIN_M6_48_220 22.488531 -0.55 N \n", + "Gintech_Energy_GIN_P6_48_220 8.824213 -0.49 N \n", + "Gintech_Energy_GIN_M6_48_225 22.462965 -0.55 N \n", + "LG_Electronics_Inc__LG230N8K_G4 2.112491 -0.38 N \n", + "LG_Electronics_Inc__LG235N8K_G4 3.374386 -0.38 N \n", + "LG_Electronics_Inc__LG240N8K_G4 3.244429 -0.38 N \n", + "LG_Electronics_Inc__LG245N8K_G4 4.077489 -0.38 N \n", + "Prism_Solar_Technologies_Bi48_279BSTC 13.999823 -0.4399 N \n", + "Prism_Solar_Technologies_Bi48_286BSTC 13.996228 -0.4399 N \n", + "\n", + " Version Date \n", + "Gintech_Energy_GIN_M6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", + "Gintech_Energy_GIN_P6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", + "Gintech_Energy_GIN_M6_48_225 SAM 2018.11.11 r2 1/3/2019 \n", + "LG_Electronics_Inc__LG230N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", + "LG_Electronics_Inc__LG235N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", + "LG_Electronics_Inc__LG240N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", + "LG_Electronics_Inc__LG245N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", + "Prism_Solar_Technologies_Bi48_279BSTC SAM 2018.11.11 r2 1/3/2019 \n", + "Prism_Solar_Technologies_Bi48_286BSTC SAM 2018.11.11 r2 1/3/2019 \n", + "\n", + "[9 rows x 25 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cec_modules = pvlib.pvsystem.retrieve_sam('CECMod').T\n", + "cec_modules[cec_modules['STC'].between(220, 250) & (cec_modules['N_s'] == 48)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4'" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "cec_modules = cec_modules.T\n", + "cec_module = cec_modules['LG_Electronics_Inc__LG235N8K_G4']" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# import the rest of the pvlib stuff\n", + "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", + "\n", + "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", + "from pvlib.location import Location\n", + "from pvlib.pvsystem import PVSystem\n", + "from pvlib.modelchain import ModelChain\n", + "\n", + "cec_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')\n", + "cec_inverter = cec_inverters['ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_']\n", + "\n", + "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# set up system in pvlib\n", + "lat = meta['latitude']\n", + "lon = meta['longitude']\n", + "tz = meta['tz']\n", + "elevation = meta['altitude']\n", + "surface_tilt = lat # fixed, latitude tilt\n", + "surface_azimuth = 180 # south-facing\n", + "\n", + "location = Location(lat, lon, tz, elevation, 'Golden, CO, USA')\n", + "\n", + "system = PVSystem(surface_tilt = surface_tilt, surface_azimuth = surface_azimuth,\n", + " module_parameters = cec_module,\n", + " inverter_parameters = cec_inverter,\n", + " temperature_model_parameters = temperature_model_parameters,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelChain: \n", + " name: None\n", + " clearsky_model: ineichen\n", + " transposition_model: haydavies\n", + " solar_position_method: nrel_numpy\n", + " airmass_model: kastenyoung1989\n", + " dc_model: cec\n", + " ac_model: sandia_inverter\n", + " aoi_model: physical_aoi_loss\n", + " spectral_model: first_solar_spectral_loss\n", + " temperature_model: sapm_temp\n", + " losses_model: no_extra_losses" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create and run pvlib modelchain\n", + "mc = ModelChain(system, location, aoi_model = \"physical\")\n", + "mc.run_model(weather)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up timeseries\n", + "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", + "\n", + "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "ee = mc.results.effective_irradiance\n", + "#injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000)\n", + "injection = letid.calc_injection_outdoors(mc.results)\n", + "temperature = mc.results.cell_temperature\n", + "\n", + "timesteps = pd.DataFrame({'Temperature':temperature, 'Injection':injection}) # create a DataFrame with cell temperature and injection\n", + "timesteps.reset_index(inplace = True) # reset the index so datetime is a column. I prefer integer indexing.\n", + "timesteps.rename(columns = {'index':'Datetime'}, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Remaining degradation parameters:\n", + "We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the \"default\" values provided here should be sufficient for most use cases.\n", + "\n", + "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", + "\n", + "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# starting defect state percentages\n", + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0\n", + "\n", + "mechanism_params = utilities.get_kinetics('repins')\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB NC tau\n", + "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.0 0.0 120.0\n", + "1 1999-01-01 01:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "2 1999-01-01 02:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "3 1999-01-01 03:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "4 1999-01-01 04:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "... ... ... ... ... ... ... ...\n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", + "\n", + "[8760 rows x 7 columns]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run through timesteps\n", + "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " jsc = collection.calculate_jsc_from_tau_cp(tau, wafer_thickness, d_base, srv_rear, generation, depth)\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor(tau, 'ab', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n", + " x_ba = letid.carrier_factor(tau, 'ba', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n", + " x_bc = letid.carrier_factor(tau, 'bc', temperature, injection, jsc, wafer_thickness, srv_rear, mechanism_params)\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "\n", + "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", + "for index, timestep in timesteps.iterrows():\n", + " jsc_now = collection.calculate_jsc_from_tau_cp(timesteps.at[index, 'tau'], wafer_thickness, d_base, srv_rear, generation, depth)\n", + " timesteps.at[index, 'Jsc'] = jsc_now\n", + " timesteps.at[index, 'Voc'] = letid.calc_voc_from_tau(timesteps.at[index, 'tau'], wafer_thickness, srv_rear, jsc_now, temperature = 25)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Datetime Temperature Injection NA \\\n", + "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 \n", + "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 \n", + "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 \n", + "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", + "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", + "... ... ... ... ... \n", + "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.470917 \n", + "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.470917 \n", + "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.470917 \n", + "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.470917 \n", + "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.470917 \n", + "\n", + " NB NC tau Jsc Voc Isc \\\n", + "0 0.000000e+00 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "1 1.683090e-15 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", + "2 3.366181e-15 5.269266e-36 120.000000 41.451328 0.661315 9.981480 \n", + "3 5.049271e-15 1.580780e-35 120.000000 41.451328 0.661315 9.981480 \n", + "4 6.732362e-15 3.161560e-35 120.000000 41.451328 0.661315 9.981480 \n", + "... ... ... ... ... ... ... \n", + "8755 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8756 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8757 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8758 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "8759 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", + "\n", + " ... p_mp i_x i_xx Effective irradiance ghi dhi dni wind_speed \\\n", + "0 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", + "1 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", + "2 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.5 \n", + "3 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", + "4 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "8755 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.9 \n", + "8756 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.2 \n", + "8757 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.6 \n", + "8758 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", + "8759 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", + "\n", + " temp_air precipitable_water \n", + "0 0.0 0.891869 \n", + "1 0.0 0.908158 \n", + "2 0.0 0.932199 \n", + "3 0.0 0.955004 \n", + "4 0.0 0.963991 \n", + "... ... ... \n", + "8755 0.0 0.939501 \n", + "8756 0.0 0.975338 \n", + "8757 0.0 0.941186 \n", + "8758 0.0 0.912427 \n", + "8759 0.0 0.892318 \n", + "\n", + "[8760 rows x 27 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps = timesteps.merge(mc.results.dc, left_on = 'Datetime', right_index=True)\n", + "timesteps = timesteps.merge(pd.DataFrame(mc.results.effective_irradiance, columns= ['Effective irradiance']), left_on = 'Datetime', right_index=True)\n", + "timesteps = timesteps.merge(mc.results.weather, left_on = 'Datetime', right_index=True)\n", + "\n", + "timesteps\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cycler import cycler\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n", + "\n", + "ax.plot(timesteps['Datetime'], timesteps[['NA', 'NB', 'NC']].values)\n", + "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$'], loc = 'upper left')\n", + "ax.set_ylabel('Defect state percentages [%]')\n", + "ax.set_xlabel('Datetime')\n", + "\n", + "ax2 = ax.twinx()\n", + "ax2.plot(timesteps['Datetime'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized STC $P_{MP}$')\n", + "ax2.legend(loc = 'upper right')\n", + "ax2.set_ylabel('Normalized STC $P_{MP}$')\n", + "\n", + "ax.set_title('Simulated Outdoor LETID progression based on accelerated test results \\n'f'{location.name}')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.dates as mdates\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(timesteps['Datetime'], timesteps['Pmp_norm'], c = 'black', label = 'Normalized STC $P_{MP}$')\n", + "ax.plot(timesteps['Datetime'], np.ones(len(timesteps)), c = 'black')\n", + "ax.fill_between(timesteps['Datetime'], np.ones(len(timesteps)), timesteps['Pmp_norm'], color = 'grey', alpha = 0.5)\n", + "\n", + "\n", + "ax.set_ylabel('Normalized STC $P_{MP}$')\n", + "\n", + "ax.set_title('Energy Loss')\n", + "\n", + "loss = letid.calc_energy_loss(timesteps)\n", + "\n", + "ax.text(mdates.datestr2num('1999-08-02'), .994, s = f\"Energy loss = {loss*100:.2f}%\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_15280\\3402272254.py:45: DeprecationWarning: You are passing x=0 0.0\n", + "1 1.0\n", + "2 2.0\n", + "3 3.0\n", + "4 4.0\n", + " ... \n", + "8755 8755.0\n", + "8756 8756.0\n", + "8757 8757.0\n", + "8758 8758.0\n", + "8759 8759.0\n", + "Name: Timedelta, Length: 8760, dtype: float64 as a positional argument. Please change your invocation to use keyword arguments. From SciPy 1.14, passing these as positional arguments will result in an error.\n", + " energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.dates as mdates\n", + "from scipy.integrate import cumulative_trapezoid, simpson\n", + "\n", + "\n", + "fig, axs = plt.subplots(3, 1, figsize = (10,10), sharex = True)\n", + "ax1, ax2, ax3 = axs\n", + "\n", + "ax1.plot(timesteps['Datetime'], timesteps[\"p_mp\"], label = 'Base $P_{MP}$')\n", + "ax1.plot(timesteps['Datetime'], timesteps[\"p_mp\"]*timesteps['Pmp_norm'], label = 'Degraded $P_{MP}$', alpha = 0.7)\n", + "ax1.legend(loc = 'lower left')\n", + "\n", + "axin1 = ax1.inset_axes([.55, 0.25, 0.43, 0.43])\n", + "axin1.plot(timesteps['Datetime'], timesteps[\"p_mp\"])\n", + "axin1.plot(timesteps['Datetime'], timesteps[\"p_mp\"]*timesteps['Pmp_norm'], alpha = 0.7)\n", + "\n", + "\n", + "axin1.set_xlim(mdates.datestr2num('1999-12-04'), mdates.datestr2num('1999-12-11'))\n", + "axin1.set_ylim(50,240)\n", + "axin1.tick_params(axis='x', labelrotation = 15)\n", + "ax1.indicate_inset_zoom(axin1)\n", + "\n", + "start = timesteps['Datetime'].iloc[0]\n", + "timesteps['Timedelta'] = [(d - start).total_seconds()/3600 for d in timesteps['Datetime']]\n", + "\n", + "ax2.plot(timesteps['Datetime'], cumulative_trapezoid(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'], initial = 0), label = 'Cumulative energy, base')\n", + "ax2.plot(timesteps['Datetime'], cumulative_trapezoid((timesteps[\"p_mp\"]*timesteps['Pmp_norm'])/1000, timesteps['Timedelta'], initial = 0), label = 'Cumulative energy, degraded')\n", + "ax2.fill_between(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000), (cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), alpha = 0.5, color = 'C1', label = 'Energy loss')\n", + "ax2.legend()\n", + "\n", + "axin2 = ax2.inset_axes([.55, 0.25, 0.43, 0.43])\n", + "axin2.plot(timesteps['Datetime'], cumulative_trapezoid(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'], initial = 0))\n", + "axin2.plot(timesteps['Datetime'], cumulative_trapezoid((timesteps[\"p_mp\"]*timesteps['Pmp_norm'])/1000, timesteps['Timedelta'], initial = 0))\n", + "axin2.fill_between(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000), (cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), alpha = 0.5, color = 'C1')\n", + "\n", + "axin2.set_xlim(mdates.datestr2num('1999-12-04'), mdates.datestr2num('1999-12-11'))\n", + "axin2.set_ylim(412, 427)\n", + "axin2.tick_params(axis='x', labelrotation = 15)\n", + "ax2.indicate_inset_zoom(axin2)\n", + "\n", + "ax3.set_xlabel('Datetime')\n", + "ax3.plot(timesteps['Datetime'], (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps['Timedelta'], initial = 0)/1000)-(cumulative_trapezoid(timesteps[\"p_mp\"]*timesteps['Pmp_norm'], timesteps['Timedelta'], initial = 0)/1000), label = 'Cumulative energy loss')\n", + "ax3.legend()\n", + "\n", + "loss = letid.calc_energy_loss(timesteps)\n", + "energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n", + "ax3.text(mdates.datestr2num('1999-03-02'), 2, s = f\"Energy loss = {loss*energy:.1f} kWh ({loss*100:.2f}%)\")\n", + "\n", + "ax1.set_ylabel('Module $P_{MP}$ [W]')\n", + "ax2.set_ylabel('Cumulative Energy [kWh]')\n", + "ax3.set_ylabel('Cumulative Energy losses [kWh]')\n", + "\n", + "ax1.set_title('Module power, cumulative energy, and loss due to LETID')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
time
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1999-01-01 04:30:00-07:000.0NaN100.0000006.732362e-153.161560e-35120.00000041.4513280.66131510.0726730.8401015.5960881.000000
.......................................
1999-12-31 19:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
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8760 rows × 12 columns

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" + ], + "text/plain": [ + " Temperature Injection NA NB \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", + "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.683090e-15 \n", + "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.366181e-15 \n", + "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.049271e-15 \n", + "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.732362e-15 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 20:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 21:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 22:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "1999-12-31 23:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", + "\n", + " NC tau Jsc Voc \\\n", + "time \n", + "1999-01-01 00:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", + "1999-01-01 01:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", + "1999-01-01 02:30:00-07:00 5.269266e-36 120.000000 41.451328 0.661315 \n", + "1999-01-01 03:30:00-07:00 1.580780e-35 120.000000 41.451328 0.661315 \n", + "1999-01-01 04:30:00-07:00 3.161560e-35 120.000000 41.451328 0.661315 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 20:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 21:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 22:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "1999-12-31 23:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", + "\n", + " Isc FF Pmp Pmp_norm \n", + "time \n", + "1999-01-01 00:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 01:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 02:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 03:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "1999-01-01 04:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", + "... ... ... ... ... \n", + "1999-12-31 19:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 20:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 21:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 22:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "1999-12-31 23:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", + "\n", + "[8760 rows x 12 columns]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mechanism_params = 'repins'\n", + "\n", + "letid.calc_letid_outdoors(tau_0, tau_deg, wafer_thickness, srv_rear, nA_0, nB_0, nC_0, weather, meta, mechanism_params, generation_df, module_parameters = cec_module)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauJscVocIsc...i_xi_xxEffective irradianceghidhidniwind_speedtemp_airprecipitable_waterTimedelta
01999-01-01 00:30:00-07:000.0NaN100.0000000.000000e+000.000000e+00120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.80.00.8918690.0
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Define necessary wafer device parameters\n", + "2. Define necessary degradation parameters: degraded lifetime and defect states\n", + "3. Create timeseries of temperature and injection\n", + "4. Run through timeseries, calculating defect states\n", + "5. Calculate device degradation and plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from pvdeg import letid, collection, utilities, DATA_DIR\n", + "import pvdeg\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Device parameters\n", + "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "wafer_thickness = 180 # um\n", + "tau = 350 # us, lifetime of the wafer for demonstration purposes\n", + "\n", + "generation_df = pd.read_excel(os.path.join(DATA_DIR, 'PVL_GenProfile.xlsx'), header = 0) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", + "generation = generation_df['Generation (cm-3s-1)']\n", + "depth = generation_df['Depth (um)']\n", + "\n", + "j_gen = collection.generation_current(generation, depth)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Degradation parameters\n", + "Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\\frac{\\Delta n}{\\tau}$) must equal generation per volume ($\\frac{Jq}{W}$), which gives $\\Delta n = \\frac{Jq\\tau}{W}$\n", + "\n", + "The term $\\Delta n^{x_{ij}}$ is calculated for wafers by the function ```letid.carrier_factor_wafer```, which adjusts the rate of the transition $i \\rightarrow j$ for $\\Delta n$.\n", + "\n", + "Since kinetic parameters are taken from literature that may have used different $\\Delta n$, we adjust transitions by the ratio $\\frac{\\Delta n}{\\Delta n_{lit}}$.\n", + "\n", + "See [Repins *et al.* 2023](https://doi.org/10.1557/s43577-022-00438-8) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "mechanism_params = utilities.get_kinetics('repins')\n", + "injection = 0.5 #" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Demonstrate calculation of dn^x_ij\n", + "1. Calculate $\\Delta n$ of the wafer" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.57051594134322e+21" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.constants import elementary_charge as q # Elementary charge, C\n", + "\n", + "dn = (((j_gen * 0.001 * 10000 * injection) * (tau * 1e-6))/ (wafer_thickness * 1e-6)/ q) # calculate excess carrier density\n", + "dn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Calculate $\\Delta n_{lit}$, the excess carrier density from the literature experiments where kinetic parameters were determined.\n", + "Assuming literature experiments were carried out on cells, this requires using the `calc_dn` function" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.064647199192719e+20" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.constants import convert_temperature\n", + "\n", + "transition = 'bc' # for this example we'll look at the B -> C transition\n", + "\n", + "meas_tau = mechanism_params[f\"tau_{transition}\"]\n", + "meas_temp = mechanism_params[f\"temperature_{transition}\"]\n", + "meas_temp = convert_temperature(meas_temp, 'K', 'C')\n", + "meas_suns = mechanism_params[f\"suns_{transition}\"]\n", + "meas_jsc = 40\n", + "meas_wafer_thickness = mechanism_params[f\"thickness_{transition}\"]\n", + "meas_srv = mechanism_params[f\"srv_{transition}\"]\n", + "meas_structure = mechanism_params[f\"structure_{transition}\"]\n", + "\n", + "dn_lit = letid.calc_dn(\n", + " meas_tau,\n", + " meas_temp,\n", + " meas_suns,\n", + " meas_jsc,\n", + " wafer_thickness=meas_wafer_thickness,\n", + " s_rear=meas_srv,\n", + " )\n", + "dn_lit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Return the exponent $x_{ij}$ of the transition in question\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exponent = mechanism_params[f\"x_{transition}\"]\n", + "exponent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Calculate $(\\frac{\\Delta n}{\\Delta n_{lit}})^{x_{ij}}$. This is the acceleration factor of the transition due to excess carriers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.711029107876425" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(dn/dn_lit)**exponent" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.711029107876425" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the \"carrier_factor_wafer\" function yields the same result\n", + "\n", + "letid.carrier_factor_wafer(tau, transition, injection, j_gen, wafer_thickness, mechanism_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Degradation parameters\n", + "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$), and starting defect state percentages" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "tau_0 = 350 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", + "tau_deg = 41 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" + ] + } + ], + "source": [ + "# starting defect state percentages\n", + "nA_0 = 100\n", + "nB_0 = 0\n", + "nC_0 = 0\n", + "\n", + "mechanism_params = utilities.get_kinetics('repins')\n", + "print(mechanism_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up timeseries\n", + "In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367)." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\530898593.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], + "source": [ + "temperature = 125 # degrees celsius\n", + "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", + "\n", + "timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", + "timesteps = pd.DataFrame(timesteps, columns = ['Datetime'])\n", + "\n", + "temps = np.full(len(timesteps), temperature)\n", + "injection = np.full(len(timesteps), suns)\n", + "\n", + "timesteps['Temperature'] = temps\n", + "timesteps['Injection'] = injection\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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86401 rows × 7 columns

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" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB NC tau\n", + "0 2022-01-01 00:00:00 125 0.5 100.0 0.0 0.0 350.0\n", + "1 2022-01-01 00:00:01 125 0.5 NaN NaN NaN NaN\n", + "2 2022-01-01 00:00:02 125 0.5 NaN NaN NaN NaN\n", + "3 2022-01-01 00:00:03 125 0.5 NaN NaN NaN NaN\n", + "4 2022-01-01 00:00:04 125 0.5 NaN NaN NaN NaN\n", + "... ... ... ... ... ... ... ...\n", + "86396 2022-01-01 23:59:56 125 0.5 NaN NaN NaN NaN\n", + "86397 2022-01-01 23:59:57 125 0.5 NaN NaN NaN NaN\n", + "86398 2022-01-01 23:59:58 125 0.5 NaN NaN NaN NaN\n", + "86399 2022-01-01 23:59:59 125 0.5 NaN NaN NaN NaN\n", + "86400 2022-01-02 00:00:00 125 0.5 NaN NaN NaN NaN\n", + "\n", + "[86401 rows x 7 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run through timesteps\n", + "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " j_gen = j_gen\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor_wafer(tau, 'ab', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_ba = letid.carrier_factor_wafer(tau, 'ba', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_bc = letid.carrier_factor_wafer(tau, 'bc', injection, j_gen, wafer_thickness, mechanism_params)\n", + " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finish calculating degraded device parameters.\n", + "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate\n", + "\n", + "For wafers, results are often presented in terms of normalized defect density (NDD), where\n", + "\n", + "$NDD(t) = \\frac{1}{\\tau (t)} - \\frac{1}{\\tau _0}$" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "\n", + "timesteps['NDD'] = letid.calc_ndd(timesteps['tau'].iloc[0], timesteps['tau'])\n", + "\n", + "timesteps['time (s)'] = (timesteps['Datetime'] - timesteps.iloc[0]['Datetime']).dt.total_seconds() # create a column for seconds elapsed\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatetimeTemperatureInjectionNANBNCtauNDDtime (s)
02022-01-01 00:00:001250.51.000000e+020.000000e+000.000000350.0000000.0000000.0
12022-01-01 00:00:011250.59.942178e+015.782160e-010.000000335.3846930.0001251.0
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42022-01-01 00:00:041250.59.783845e+012.158887e+000.002663301.0218240.0004654.0
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863962022-01-01 23:59:561250.55.800845e-343.370010e-27100.000000350.0000000.00000086396.0
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86401 rows × 9 columns

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" + ], + "text/plain": [ + " Datetime Temperature Injection NA NB \\\n", + "0 2022-01-01 00:00:00 125 0.5 1.000000e+02 0.000000e+00 \n", + "1 2022-01-01 00:00:01 125 0.5 9.942178e+01 5.782160e-01 \n", + "2 2022-01-01 00:00:02 125 0.5 9.887092e+01 1.128597e+00 \n", + "3 2022-01-01 00:00:03 125 0.5 9.834404e+01 1.654564e+00 \n", + "4 2022-01-01 00:00:04 125 0.5 9.783845e+01 2.158887e+00 \n", + "... ... ... ... ... ... \n", + "86396 2022-01-01 23:59:56 125 0.5 5.800845e-34 3.370010e-27 \n", + "86397 2022-01-01 23:59:57 125 0.5 5.795712e-34 3.367028e-27 \n", + "86398 2022-01-01 23:59:58 125 0.5 5.790584e-34 3.364048e-27 \n", + "86399 2022-01-01 23:59:59 125 0.5 5.785460e-34 3.361072e-27 \n", + "86400 2022-01-02 00:00:00 125 0.5 5.780341e-34 3.358098e-27 \n", + "\n", + " NC tau NDD time (s) \n", + "0 0.000000 350.000000 0.000000 0.0 \n", + "1 0.000000 335.384693 0.000125 1.0 \n", + "2 0.000486 322.563499 0.000243 2.0 \n", + "3 0.001392 311.194745 0.000356 3.0 \n", + "4 0.002663 301.021824 0.000465 4.0 \n", + "... ... ... ... ... \n", + "86396 100.000000 350.000000 0.000000 86396.0 \n", + "86397 100.000000 350.000000 0.000000 86397.0 \n", + "86398 100.000000 350.000000 0.000000 86398.0 \n", + "86399 100.000000 350.000000 0.000000 86399.0 \n", + "86400 100.000000 350.000000 0.000000 86400.0 \n", + "\n", + "[86401 rows x 9 columns]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timesteps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cycler import cycler\n", + "plt.style.use('default')\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.set_prop_cycle(cycler('color', ['tab:blue', 'tab:orange', 'tab:green']) + cycler('linestyle', ['-', '--', '-.']))\n", + "\n", + "ax.plot(timesteps['time (s)'], timesteps[['NA', 'NB', 'NC']].values)\n", + "ax.legend(labels = ['$N_A$', '$N_B$', '$N_C$'], loc = 'upper left')\n", + "ax.set_ylabel('Defect state percentages [%]')\n", + "ax.set_xlabel('Time [s]')\n", + "\n", + "ax2 = ax.twinx()\n", + "c_norm = 1.35 #Normalization constants \"C_norm\". See Repins 2023 for details\n", + "ax2.plot(timesteps['time (s)'], timesteps['NDD']*c_norm, c = 'black', label = 'NDD modeled')\n", + "ax.set_ylabel('NDD [$\\mu s^{-1}$]')\n", + "\n", + "literature_data_file = os.path.join(DATA_DIR, 'wyller data.csv')\n", + "data = pd.read_csv(literature_data_file, header = 0)\n", + "filtered = data[data['Series'] == '0.5 sun 125C']\n", + "ax2.scatter(filtered['X'], filtered['Y'], label = 'NDD data from Wyller', marker = 'v', c = 'black')\n", + "\n", + "ax2.legend(loc = 'center left')\n", + "\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_title('Accelerated LETID Test (wafer)\\n'fr'{temperature}$\\degree$C, {suns} suns illumination')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare model results to experimental data\n", + "Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367).\n", + "Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8)." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "temperatures = [125, 150, 175] # degrees celsius" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps125 = timesteps #we already modeled the 125C experiment above" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\241191725.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], + "source": [ + "temperature = temperatures[1] # degrees celsius\n", + "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", + "\n", + "timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", + "timesteps = pd.DataFrame(timesteps, columns = ['Datetime'])\n", + "\n", + "temps = np.full(len(timesteps), temperature)\n", + "injection = np.full(len(timesteps), suns)\n", + "\n", + "timesteps['Temperature'] = temps\n", + "timesteps['Injection'] = injection\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " j_gen = j_gen\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor_wafer(tau, 'ab', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_ba = letid.carrier_factor_wafer(tau, 'ba', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_bc = letid.carrier_factor_wafer(tau, 'bc', injection, j_gen, wafer_thickness, mechanism_params)\n", + " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "timesteps['NDD'] = letid.calc_ndd(timesteps['tau'].iloc[0], timesteps['tau'])\n", + "timesteps['time (s)'] = (timesteps['Datetime'] - timesteps.iloc[0]['Datetime']).dt.total_seconds() # create a column for seconds elapsed" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps150 = timesteps" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\950821369.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", + " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" + ] + } + ], + "source": [ + "temperature = temperatures[2] # degrees celsius\n", + "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", + "\n", + "timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", + "timesteps = pd.DataFrame(timesteps, columns = ['Datetime'])\n", + "\n", + "temps = np.full(len(timesteps), temperature)\n", + "injection = np.full(len(timesteps), suns)\n", + "\n", + "timesteps['Temperature'] = temps\n", + "timesteps['Injection'] = injection\n", + "\n", + "timesteps[['NA', 'NB', 'NC', 'tau']] = np.nan # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", + "\n", + "timesteps.loc[0, ['NA', 'NB', 'NC']] = nA_0, nB_0, nC_0 # assign first timestep defect state percentages\n", + "timesteps.loc[0, 'tau'] = letid.tau_now(tau_0, tau_deg, nB_0) # calculate tau for the first timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "for index, timestep in timesteps.iterrows():\n", + "\n", + " # first row tau has already been assigned\n", + " if index == 0:\n", + " pass\n", + "\n", + " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", + " else:\n", + " n_A = timesteps.at[index-1, 'NA']\n", + " n_B = timesteps.at[index-1, 'NB']\n", + " n_C = timesteps.at[index-1, 'NC']\n", + "\n", + " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", + " j_gen = j_gen\n", + "\n", + " temperature = timesteps.at[index, 'Temperature']\n", + " injection = timesteps.at[index, 'Injection']\n", + "\n", + " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", + " k_AB = letid.k_ij(mechanism_params['v_ab'], mechanism_params['ea_ab'], temperature)\n", + " k_BA = letid.k_ij(mechanism_params['v_ba'], mechanism_params['ea_ba'], temperature)\n", + " k_BC = letid.k_ij(mechanism_params['v_bc'], mechanism_params['ea_bc'], temperature)\n", + " k_CB = letid.k_ij(mechanism_params['v_cb'], mechanism_params['ea_cb'], temperature)\n", + "\n", + " x_ab = letid.carrier_factor_wafer(tau, 'ab', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_ba = letid.carrier_factor_wafer(tau, 'ba', injection, j_gen, wafer_thickness, mechanism_params)\n", + " x_bc = letid.carrier_factor_wafer(tau, 'bc', injection, j_gen, wafer_thickness, mechanism_params)\n", + " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", + "\n", + " # calculate the instantaneous change in NA, NB, and NC\n", + " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", + " dN_Bdt = (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", + " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", + "\n", + " t_step = (timesteps.at[index, 'Datetime'] - timesteps.at[index-1,'Datetime']).total_seconds()\n", + "\n", + " # assign new defect state percentages\n", + " timesteps.at[index, 'NA'] = n_A + dN_Adt*t_step\n", + " timesteps.at[index, 'NB'] = n_B + dN_Bdt*t_step\n", + " timesteps.at[index, 'NC'] = n_C + dN_Cdt*t_step\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps['tau'] = letid.tau_now(tau_0, tau_deg, timesteps['NB'])\n", + "timesteps['NDD'] = letid.calc_ndd(timesteps['tau'].iloc[0], timesteps['tau'])\n", + "timesteps['time (s)'] = (timesteps['Datetime'] - timesteps.iloc[0]['Datetime']).dt.total_seconds() # create a column for seconds elapsed" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps175 = timesteps" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "timesteps = pd.concat([timesteps125, timesteps150, timesteps175])" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "normalization_constants = {125 : 1.35, 150 : 0.6, 175 : 0.35} #Normalization constants \"C_norm\". See Repins 2023 for details\n", + "\n", + "timesteps['Normalization constant'] = timesteps['Temperature'].map(normalization_constants)\n", + "timesteps['NDD-normalized'] = timesteps['NDD']*timesteps['Normalization constant']" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "grouped_model = timesteps.groupby('Temperature')" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "literature_data_file = os.path.join(DATA_DIR, 'wyller data.csv')\n", + "data = pd.read_csv(literature_data_file, header = 0)\n", + "\n", + "data['Temperature'] = pd.to_numeric(data['Series'].str[-4:-1])\n", + "#normalization_constants = {'0.5 sun 125C' : 1/1.35, '0.5 sun 150C' : 1/0.6, '0.5 sun 175C' : 1/0.35}\n", + "#data['Normalization constant'] = data['Series'].map(normalization_constants)\n", + "#data['Y-normalized'] = data['Y']*data['Normalization constant']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "grouped_data = data.groupby('Temperature')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for name, group in grouped_model:\n", + " ax.plot(group['time (s)']+.1, group['NDD-normalized'], label = f\"Modeled NDD {name}$\\degree$C\")\n", + " ax.legend()\n", + "\n", + "\n", + "marker = itertools.cycle(('o', 's', 'v', 'o', '*'))\n", + "\n", + "for name, group in grouped_data:\n", + " ax.scatter(group['X'], group['Y'], label = f\"Wyller NDD {name}$\\degree$C\", marker = next(marker))\n", + " ax.legend()\n", + "\n", + "ax.set_xscale('log')\n", + "\n", + "ax.set_xlabel('Time [s]')\n", + "ax.set_ylabel('NDD [$\\mu s^{-1}$]')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "colors = itertools.cycle(('red', 'blue', 'green'))\n", + "\n", + "for name, group in grouped_model:\n", + " ax.plot(group['time (s)']+.1, group['NDD-normalized'], color = next(colors), label = f\"Modeled {name}$\\degree$C\", linewidth = 3)\n", + " ax.legend(prop={'size': 12})\n", + "\n", + "\n", + "marker = itertools.cycle(('o', 's', 'v', 'o', '*'))\n", + "\n", + "for name, group in grouped_data:\n", + " ax.scatter(group['X'], group['Y'], label = f\"Literature data {name}$\\degree$C\", color = next(colors), marker = next(marker), s = 70)\n", + " ax.legend(prop={'size': 12})\n", + "\n", + "ax.set_xscale('log')\n", + "\n", + "ax.tick_params(axis='both', which='major', labelsize=16)\n", + "\n", + "\n", + "ax.set_xlabel('Time [s]', fontsize = 16)\n", + "ax.set_ylabel('Normalized defect \\n density [$\\mu s^{-1}$]', fontsize = 16)\n", + "\n", + "ax.set_title('Modeled vs. Literature LETID data', fontsize = 16)\n", + "\n", + "\n", + "plt.tight_layout()\n", + "\n", + "#fig.savefig('wafer data.png', dpi = 600)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb new file mode 100644 index 00000000..8b9b29de --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tools - Module Standoff for IEC TS 63126\n", + "\n", + "### Calculation of module standoff distance according to IEC TS 63126\n", + "\n", + "**Requirements:**\n", + "- Local weather data file or site longitude and latittude\n", + "\n", + "**Objectives:**\n", + "1. Import weather data.\n", + "2. Calculate installation standoff - Level 1 and Level 2.\n", + "3. Calculate $X_{eff}$ from provided module temperature data.\n", + "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n", + "\n", + "**Background:**\n", + "\n", + "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70°C or 80°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", + "\n", + " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n", + "\n", + "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", + "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" + ] + }, + { + "attachments": { + "62279573-41e3-45dd-bf62-4fa60c1e7e69.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![T98 70C standoff Map.png](attachment:62279573-41e3-45dd-bf62-4fa60c1e7e69.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "import pvdeg\n", + "import pandas as pd\n", + "from pvdeg import DATA_DIR\n", + "import dask\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n", + "dask version 2024.1.1\n", + "C:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\data\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)\n", + "print(\"dask version\", dask.__version__)\n", + "print(DATA_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Import Weather Data\n", + "\n", + "The function has these minimum requirements when using a weather data file:\n", + "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n", + "- Site meta-data containing (at least) latitude, longitude, and time zone\n", + "\n", + "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n" + ] + } + ], + "source": [ + "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", + "weather_file = os.path.join(DATA_DIR, 'psm3_demo.csv')\n", + "WEATHER_df, META = pvdeg.weather.read(weather_file,'csv')\n", + "print(META)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", + "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n" + ] + } + ], + "source": [ + "# From Tutorial 5 EXAMPLE, this works.\n", + "#API_KEY = 'your_api_key_here'\n", + "API_KEY = 'DEMO_KEY' #you can activate this line to use the demonstration API key but it has limited usage.\n", + "# The example API key here is for demonstation and is rate-limited per IP.\n", + "# To get your own API key, visit https://developer.nrel.gov/signup/\n", + "\n", + "weather_db = 'PSM3' #This pulls data from the NSRDB just for locations in North and South America.\n", + "weather_id = (33.4152, -111.8315)\n", + "weather_arg = {'api_key': API_KEY,\n", + " 'email': 'user@mail.com',\n", + " 'names': 'tmy',\n", + " 'attributes': [],\n", + " 'map_variables': True}\n", + "\n", + "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", + "print (META)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" + ] + } + ], + "source": [ + "weather_db = 'PVGIS' #This pulls data for most of the world.\n", + "weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "weather_arg = {'map_variables': True}\n", + "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id)\n", + "print(META)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", + "\n", + "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", + "\n", + "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n", + "\n", + "Once the meterological data has been obtained, the input parameter possibilities are:\n", + "\n", + "- T₉₈ : Does not necessarily need to be set at 70°C or 80°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70°C.\n", + "- tilt : tilt from horizontal of PV module. The default is 0°.\n", + "- azimuth : azimuth in degrees from North. The default is 180° for south facing.\n", + "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n", + "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n", + "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n", + "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n", + "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n", + "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", + "The estimated T₉₈ of an open-rack module is 63.8°C. \n", + "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", + "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", + "Level 2 certification is required for a standoff less than 3.0 cm.\n" + ] + } + ], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n", + "print(pvdeg.standards.interpret_standoff(standoff))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "First calculation standoff = 9.3 cm.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "Second calculation standoff = 3.0 cm.\n", + "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", + "The estimated T₉₈ of an open-rack module is 63.8°C. \n", + "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", + "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", + "Level 2 certification is required for a standoff less than 3.0 cm.\n" + ] + } + ], + "source": [ + "standoff_1 = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META,\n", + " T98=70, tilt=META['latitude'], azimuth=None,\n", + " sky_model='isotropic', temp_model='sapm', conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer',\n", + " x_0=6.5, wind_factor=0.33)\n", + "print('First calculation standoff = ', '%.1f' % standoff_1['x'].iloc[0] , ' cm.')\n", + "standoff_2 = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META,\n", + " T98=80, tilt=META['latitude'], azimuth=None,\n", + " sky_model='isotropic', temp_model='sapm', conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer',\n", + " x_0=6.5, wind_factor=0.33)\n", + "print('Second calculation standoff = ', '%.1f' % standoff_2['x'].iloc[0] , ' cm.')\n", + "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate $X_{eff}$ from provided module temperature data.\n", + "\n", + "To do this calculation, one must use a set of data with: \n", + " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", + " - ambient temperature data,\n", + " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", + " - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n", + " - geographic meta data including longitude and latitude,\n", + "\n", + "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n", + "\n", + "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The effective standoff for this system is 2.0 cm.\n" + ] + } + ], + "source": [ + "# Read the weather file\n", + "weather_file = os.path.join(DATA_DIR,'xeff_demo.csv')\n", + "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file,'csv')\n", + "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n", + "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n", + " weather_df = xeff_weather,\n", + " meta = xeff_meta,\n", + " sky_model = \"isotropic\",\n", + " temp_model = \"sapm\",\n", + " conf_0 = \"insulated_back_glass_polymer\",\n", + " conf_inf = \"open_rack_glass_polymer\",\n", + " wind_factor = 0.33)\n", + "\n", + "# Now calculate X_eff.\n", + "x_eff = pvdeg.standards.eff_gap(T_0, T_inf, xeff_weather['module_temperature'], xeff_weather[\"temp_air\"], xeff_poa, x_0=6.5, poa_min=100, t_amb_min=0)\n", + "print ('The effective standoff for this system is', '%.1f' % x_eff , 'cm.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "\n", + "Equation 2 can be reorganized as,\n", + "\n", + "$$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n", + "\n", + "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The 98ᵗʰ percential temperature is estimated to be 89.6 °C.\n" + ] + } + ], + "source": [ + "# This is the minimal function call using the common default settings to estimate T₉₈.\n", + "T_98 = pvdeg.standards.T98_estimate(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " tilt=META['latitude'],\n", + " azimuth=None,\n", + " x_eff=0,)\n", + "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % T_98 , '°C.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "\n", + "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "#Scans through all the azimuth and tilt running the minimum standoff calculation\n", + "#Set up keyword parameters for the calculation\n", + "\n", + "kwarg_x = dict( sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " T98=70,\n", + " x_0=6.5,\n", + " wind_factor=0.33)\n", + "#Run the calculation\n", + "x_azimuth_step=10\n", + "x_tilt_step=10\n", + "standoff_series = pvdeg.utilities.tilt_azimuth_scan(weather_df = WEATHER_df, meta=META,\n", + " tilt_step=x_tilt_step, azimuth_step=x_azimuth_step, func = pvdeg.standards.standoff_x, **kwarg_x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=0 : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mspringe\\\\OneDrive - NREL\\\\msp\\\\projects\\\\2023_DegradationTools\\\\Github\\\\PVDegradationTools\\\\TEMP\\\\results'\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "standoff_series_df=pd.DataFrame({'Tilt': standoff_series[:, 0],'Azimuth': standoff_series[:, 1],'Xₘᵢₙ': standoff_series[:, 2]})\n", + "x_fig = plt.figure(figsize=(16,4))\n", + "plt.title('Plot of $\\it{Xₘᵢₙ}$ for all orientations for $\\it{T₉₈}$='+'%.0f' % kwarg_x['T98']+'°C.', fontsize=15, y=1.08)\n", + "x_fig=sns.heatmap(standoff_series_df.pivot(index='Tilt', columns='Azimuth', values='Xₘᵢₙ'),\n", + " cbar_kws={'label': 'Xₘᵢₙ', 'format': '%.0f',\"pad\":0.02 }, cmap=\"Spectral_r\", vmin=0, vmax=None)\n", + "\n", + "h_ticks=37\n", + "x_number=math.ceil(360/x_azimuth_step)+1\n", + "x_ticks=[(x*(360/(h_ticks-1))/x_azimuth_step+0.5) for x in range(h_ticks-1)]\n", + "x_labels=([('%.0f'%(360/(h_ticks-1)*x)) for x in range(h_ticks)])\n", + "x_ticks.append(x_number-0.5)\n", + "x_fig.set_xticks(x_ticks)\n", + "x_fig.set_xticklabels(x_labels, rotation=90);\n", + "\n", + "v_ticks=10\n", + "y_number=math.ceil(90/x_tilt_step)+1\n", + "y_ticks=[(x*(90/(v_ticks-1))/x_tilt_step+0.5) for x in range(v_ticks-1)]\n", + "y_labels=([('%.0f'%(90/(v_ticks-1)*x)) for x in range(v_ticks)])\n", + "y_ticks.append(y_number-0.5)\n", + "x_fig.set_yticks(y_ticks)\n", + "x_fig.set_yticklabels(y_labels, rotation=0);\n", + "\n", + "x_fig.set_xlabel('Azimuth [°]', fontsize=15, labelpad=10);\n", + "x_fig.set_ylabel('Tilt [°]', fontsize=15);\n", + "x_fig.figure.axes[-1].set_ylabel('$\\it{Xₘᵢₙ}$ [cm]', size=15);\n", + "x_fig.invert_yaxis()\n", + "\n", + "output_folder = os.path.join(os.path.dirname(os.path.dirname(os.getcwd())), 'TEMP', 'results')\n", + "try:\n", + " os.mkdir(output_folder)\n", + "except OSError as error:\n", + " print(error)\n", + "\n", + "plt.savefig(output_folder+'\\Standoff_Scan.png', bbox_inches='tight') #Creates an image file of the standoff plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$.\n", + "\n", + "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "#Scans through all the azimuth and tilt running the 98ᵗʰ percentile temperature calculation.\n", + "#Set up keyword parameters for the calculation\n", + "kwarg_T = dict(sky_model=\"isotropic\", temp_model=\"sapm\", conf_0=\"insulated_back_glass_polymer\", conf_inf=\"open_rack_glass_polymer\",\n", + " x_eff=5, x_0=6.5, wind_factor=0.33)\n", + "#Run the calculation\n", + "T_azimuth_step=10\n", + "T_tilt_step=10\n", + "T98_series = pvdeg.utilities.tilt_azimuth_scan(weather_df = WEATHER_df, meta=META,\n", + " tilt_step=T_tilt_step, azimuth_step=T_azimuth_step, func = pvdeg.standards.T98_estimate, **kwarg_T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=None : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This produces the plot of the data\n", + "T98_series_df=pd.DataFrame({'Tilt': T98_series[:, 0],'Azimuth': T98_series[:, 1],'T₉₈': T98_series[:, 2]})\n", + "T98_fig = plt.figure(figsize=(16,4))\n", + "if kwarg_T['x_eff']==None:\n", + " plt.title('Plot of $\\it{T₉₈}$ for all orientations for an open-rack mounting.', fontsize=15, y=1.08)\n", + "else:\n", + " plt.title('Plot of $\\it{T₉₈}$ for all orientations for $X_{eff}$='+'%.0f' % kwarg_T['x_eff']+' cm.', fontsize=15, y=1.08)\n", + "T98_fig=sns.heatmap(T98_series_df.pivot(index='Tilt', columns='Azimuth', values='T₉₈'),\n", + " cbar_kws={'label': 'Xₘᵢₙ', 'format': '%.0f',\"pad\":0.02 }, cmap=\"Spectral_r\", vmin=None, vmax=None)\n", + "\n", + "h_ticks=37\n", + "x_number=math.ceil(360/T_azimuth_step)+1\n", + "x_ticks=[(x*(360/(h_ticks-1))/T_azimuth_step+0.5) for x in range(h_ticks-1)]\n", + "x_labels=([('%.0f'%(360/(h_ticks-1)*x)) for x in range(h_ticks)])\n", + "x_ticks.append(x_number-0.5)\n", + "T98_fig.set_xticks(x_ticks)\n", + "T98_fig.set_xticklabels(x_labels, rotation=90);\n", + "\n", + "v_ticks=10\n", + "y_number=math.ceil(90/T_tilt_step)+1\n", + "y_ticks=[(x*(90/(v_ticks-1))/T_tilt_step+0.5) for x in range(v_ticks-1)]\n", + "y_labels=([('%.0f'%(90/(v_ticks-1)*x)) for x in range(v_ticks)])\n", + "y_ticks.append(y_number-0.5)\n", + "T98_fig.set_yticks(y_ticks)\n", + "T98_fig.set_yticklabels(y_labels, rotation=0);\n", + "\n", + "T98_fig.set_xlabel('Azimuth [°]', fontsize=15, labelpad=10);\n", + "T98_fig.set_ylabel('Tilt [°]', fontsize=15);\n", + "T98_fig.figure.axes[-1].set_ylabel('$\\it{T₉₈}$ [°C]', size=15);\n", + "T98_fig.invert_yaxis()\n", + "\n", + "plt.savefig(os.path.join(output_folder, 'T98_Scan.png'), bbox_inches='tight') #Creates an image file of the standoff plot\n", + "plt.show(T98_fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Plot $X_{min}$ for Level 1, Level 2, and $T_{98}$ for a given region.\n", + "\n", + "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. This is presented as doing the computations on Amazon Web Services (AWS) for which you will need a paid account, and will be covered on a subsequent journal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Save data outputs.\n", + "\n", + "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your results will be stored in c:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\TEMP\\results\n", + "The folder must already exist or the file will not be created\n" + ] + } + ], + "source": [ + "print (\"Your results will be stored in %s\" % output_folder)\n", + "print ('The folder must already exist or the file will not be created')\n", + "\n", + "pvdeg.weather.write(data_df=WEATHER_df, metadata=META, savefile=os.path.join(output_folder, 'WeatherFile.csv')) #Writes the meterological data to an *.csv file.\n", + "\n", + "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", + "\n", + "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv')) #Writes a file with the Tilt Azimuth scan calculations of T98.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials_and_tools/_build/html/_static/basic.css b/tutorials_and_tools/_build/html/_static/basic.css index 9e364ed3..c4a952a2 100644 --- a/tutorials_and_tools/_build/html/_static/basic.css +++ b/tutorials_and_tools/_build/html/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -237,6 +237,10 @@ a.headerlink { visibility: hidden; } +a:visited { + color: #551A8B; +} + h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -324,17 +328,17 @@ aside.sidebar { p.sidebar-title { font-weight: bold; } + nav.contents, aside.topic, - div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ + nav.contents, aside.topic, - div.topic { border: 1px solid #ccc; padding: 7px; @@ -375,7 +379,6 @@ div.sidebar > :last-child, aside.sidebar > :last-child, nav.contents > :last-child, aside.topic > :last-child, - div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -385,7 +388,6 @@ div.sidebar::after, aside.sidebar::after, nav.contents::after, aside.topic::after, - div.topic::after, div.admonition::after, blockquote::after { @@ -611,25 +613,6 @@ ul.simple p { margin-bottom: 0; } -/* Docutils 0.17 and older (footnotes & citations) */ -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -/* Docutils 0.18+ (footnotes & citations) */ aside.footnote > span, div.citation > span { float: left; @@ -654,8 +637,6 @@ div.citation > p:last-of-type:after { clear: both; } -/* Footnotes & citations ends */ - dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; @@ -668,10 +649,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -697,6 +674,16 @@ dd { margin-left: 30px; } +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + dl > dd:last-child, dl > 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/tutorials_and_tools/_build/html/_static/doctools.js b/tutorials_and_tools/_build/html/_static/doctools.js index c3db08d1..4d67807d 100644 --- a/tutorials_and_tools/_build/html/_static/doctools.js +++ b/tutorials_and_tools/_build/html/_static/doctools.js @@ -4,12 +4,19 @@ * * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ "use strict"; +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + const _ready = (callback) => { if (document.readyState !== "loading") { callback(); @@ -18,73 +25,11 @@ const _ready = (callback) => { } }; -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - /** * Small JavaScript module for the documentation. */ const Documentation = { init: () => { - Documentation.highlightSearchWords(); Documentation.initDomainIndexTable(); Documentation.initOnKeyListeners(); }, @@ -126,51 +71,6 @@ const Documentation = { Documentation.LOCALE = catalog.locale; }, - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - Documentation.gettext("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - /** * helper function to focus on search bar */ @@ -210,15 +110,11 @@ const Documentation = { ) return; - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; if (!event.shiftKey) { switch (event.key) { @@ -240,10 +136,6 @@ const Documentation = { event.preventDefault(); } break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); } } diff --git a/tutorials_and_tools/_build/html/_static/documentation_options.js b/tutorials_and_tools/_build/html/_static/documentation_options.js index 30637825..eb83c716 100644 --- a/tutorials_and_tools/_build/html/_static/documentation_options.js +++ b/tutorials_and_tools/_build/html/_static/documentation_options.js @@ -1,5 +1,4 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), +const DOCUMENTATION_OPTIONS = { VERSION: '', LANGUAGE: 'en', COLLAPSE_INDEX: false, @@ -8,7 +7,7 @@ var DOCUMENTATION_OPTIONS = { LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true, + NAVIGATION_WITH_KEYS: false, SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: false, -}; \ No newline at end of file + ENABLE_SEARCH_SHORTCUTS: true, +}; diff --git a/tutorials_and_tools/_build/html/_static/language_data.js b/tutorials_and_tools/_build/html/_static/language_data.js index 2e22b06a..434cd3d5 100644 --- a/tutorials_and_tools/_build/html/_static/language_data.js +++ b/tutorials_and_tools/_build/html/_static/language_data.js @@ -5,7 +5,7 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -13,7 +13,7 @@ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, is available */ +/* Non-minified version is copied as a separate JS file, if available */ /** * Porter Stemmer @@ -196,4 +196,3 @@ var Stemmer = function() { return w; } } - diff --git a/tutorials_and_tools/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po index 2e8d6820..edae2ec4 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: ar\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "أقترح تحرير" +msgid "Theme by the" +msgstr "موضوع بواسطة" -msgid "Last updated on" -msgstr "آخر تحديث في" +msgid "Open an issue" +msgstr "افتح قضية" -msgid "Edit this page" -msgstr "قم بتحرير هذه الصفحة" +msgid "Contents" +msgstr "محتويات" -msgid "Launch" -msgstr "إطلاق" +msgid "Download notebook file" +msgstr "تنزيل ملف دفتر الملاحظات" -msgid "Print to PDF" -msgstr "طباعة إلى PDF" +msgid "Sphinx Book Theme" +msgstr "موضوع كتاب أبو الهول" -msgid "open issue" -msgstr "قضية مفتوحة" +msgid "Fullscreen mode" +msgstr "وضع ملء الشاشة" -msgid "Download notebook file" -msgstr "تنزيل ملف دفتر الملاحظات" +msgid "Edit this page" +msgstr "قم بتحرير هذه الصفحة" -msgid "Toggle navigation" -msgstr "تبديل التنقل" +msgid "By" +msgstr "بواسطة" + +msgid "Copyright" +msgstr "حقوق النشر" msgid "Source repository" msgstr "مستودع المصدر" -msgid "By the" -msgstr "بواسطة" +msgid "previous page" +msgstr "الصفحة السابقة" msgid "next page" msgstr "الصفحة التالية" +msgid "Toggle navigation" +msgstr "تبديل التنقل" + msgid "repository" msgstr "مخزن" -msgid "Sphinx Book Theme" -msgstr "موضوع كتاب أبو الهول" - -msgid "Download source file" -msgstr "تنزيل ملف المصدر" +msgid "suggest edit" +msgstr "أقترح تحرير" -msgid "Contents" -msgstr "محتويات" +msgid "open issue" +msgstr "قضية مفتوحة" -msgid "By" -msgstr "بواسطة" +msgid "Launch" +msgstr "إطلاق" -msgid "Copyright" -msgstr "حقوق النشر" +msgid "Print to PDF" +msgstr "طباعة إلى PDF" -msgid "Fullscreen mode" -msgstr "وضع ملء الشاشة" +msgid "By the" +msgstr "بواسطة" -msgid "Open an issue" -msgstr "افتح قضية" +msgid "Last updated on" +msgstr "آخر تحديث في" -msgid "previous page" -msgstr "الصفحة السابقة" +msgid "Download source file" +msgstr "تنزيل ملف المصدر" msgid "Download this page" msgstr "قم بتنزيل هذه الصفحة" - -msgid "Theme by the" -msgstr "موضوع بواسطة" diff --git a/tutorials_and_tools/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po index 56ef0ebd..1f363b9d 100644 --- a/tutorials_and_tools/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: bg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "предложи редактиране" +msgid "Theme by the" +msgstr "Тема от" -msgid "Last updated on" -msgstr "Последна актуализация на" +msgid "Open an issue" +msgstr "Отворете проблем" -msgid "Edit this page" -msgstr "Редактирайте тази страница" +msgid "Contents" +msgstr "Съдържание" -msgid "Launch" -msgstr "Стартиране" +msgid "Download notebook file" +msgstr "Изтеглете файла на бележника" -msgid "Print to PDF" -msgstr "Печат в PDF" +msgid "Sphinx Book Theme" +msgstr "Тема на книгата Sphinx" -msgid "open issue" -msgstr "отворен брой" +msgid "Fullscreen mode" +msgstr "Режим на цял екран" -msgid "Download notebook file" -msgstr "Изтеглете файла на бележника" +msgid "Edit this page" +msgstr "Редактирайте тази страница" -msgid "Toggle navigation" -msgstr "Превключване на навигацията" +msgid "By" +msgstr "От" + +msgid "Copyright" +msgstr "Авторско право" msgid "Source repository" msgstr "Хранилище на източника" -msgid "By the" -msgstr "По" +msgid "previous page" +msgstr "предишна страница" msgid "next page" msgstr "Следваща страница" +msgid "Toggle navigation" +msgstr "Превключване на навигацията" + msgid "repository" msgstr "хранилище" -msgid "Sphinx Book Theme" -msgstr "Тема на книгата Sphinx" - -msgid "Download source file" -msgstr "Изтеглете изходния файл" +msgid "suggest edit" +msgstr "предложи редактиране" -msgid "Contents" -msgstr "Съдържание" +msgid "open issue" +msgstr "отворен брой" -msgid "By" -msgstr "От" +msgid "Launch" +msgstr "Стартиране" -msgid "Copyright" -msgstr "Авторско право" +msgid "Print to PDF" +msgstr "Печат в PDF" -msgid "Fullscreen mode" -msgstr "Режим на цял екран" +msgid "By the" +msgstr "По" -msgid "Open an issue" -msgstr "Отворете проблем" +msgid "Last updated on" +msgstr "Последна актуализация на" -msgid "previous page" -msgstr "предишна страница" +msgid "Download source file" +msgstr "Изтеглете изходния файл" msgid "Download this page" msgstr "Изтеглете тази страница" - -msgid "Theme by the" -msgstr "Тема от" diff --git a/tutorials_and_tools/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po index 243ca31f..fa543728 100644 --- a/tutorials_and_tools/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po @@ -8,56 +8,56 @@ msgstr "" "Language: bn\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "Last updated on" -msgstr "সর্বশেষ আপডেট" +msgid "Theme by the" +msgstr "থিম দ্বারা" -msgid "Edit this page" -msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন" +msgid "Open an issue" +msgstr "একটি সমস্যা খুলুন" -msgid "Launch" -msgstr "শুরু করা" +msgid "Download notebook file" +msgstr "নোটবুক ফাইল ডাউনলোড করুন" -msgid "Print to PDF" -msgstr "পিডিএফ প্রিন্ট করুন" +msgid "Sphinx Book Theme" +msgstr "স্পিনিক্স বুক থিম" -msgid "open issue" -msgstr "খোলা সমস্যা" +msgid "Edit this page" +msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন" -msgid "Download notebook file" -msgstr "নোটবুক ফাইল ডাউনলোড করুন" +msgid "By" +msgstr "দ্বারা" -msgid "Toggle navigation" -msgstr "নেভিগেশন টগল করুন" +msgid "Copyright" +msgstr "কপিরাইট" msgid "Source repository" msgstr "উত্স সংগ্রহস্থল" -msgid "By the" -msgstr "দ্বারা" +msgid "previous page" +msgstr "আগের পৃষ্ঠা" msgid "next page" msgstr "পরবর্তী পৃষ্ঠা" -msgid "Sphinx Book Theme" -msgstr "স্পিনিক্স বুক থিম" +msgid "Toggle navigation" +msgstr "নেভিগেশন টগল করুন" -msgid "Download source file" -msgstr "উত্স ফাইল ডাউনলোড করুন" +msgid "open issue" +msgstr "খোলা সমস্যা" -msgid "By" -msgstr "দ্বারা" +msgid "Launch" +msgstr "শুরু করা" -msgid "Copyright" -msgstr "কপিরাইট" +msgid "Print to PDF" +msgstr "পিডিএফ প্রিন্ট করুন" -msgid "Open an issue" -msgstr "একটি সমস্যা খুলুন" +msgid "By the" +msgstr "দ্বারা" -msgid "previous page" -msgstr "আগের পৃষ্ঠা" +msgid "Last updated on" +msgstr "সর্বশেষ আপডেট" + +msgid "Download source file" +msgstr "উত্স ফাইল ডাউনলোড করুন" msgid "Download this page" msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" - -msgid "Theme by the" -msgstr "থিম দ্বারা" diff --git a/tutorials_and_tools/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po index b27a13db..22f1569a 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: ca\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "suggerir edició" - -msgid "Last updated on" -msgstr "Darrera actualització el" +msgid "Theme by the" +msgstr "Tema del" -msgid "Edit this page" -msgstr "Editeu aquesta pàgina" +msgid "Open an issue" +msgstr "Obriu un número" -msgid "Launch" -msgstr "Llançament" +msgid "Download notebook file" +msgstr "Descarregar fitxer de quadern" -msgid "Print to PDF" -msgstr "Imprimeix a PDF" +msgid "Sphinx Book Theme" +msgstr "Tema del llibre Esfinx" -msgid "open issue" -msgstr "número obert" +msgid "Edit this page" +msgstr "Editeu aquesta pàgina" -msgid "Download notebook file" -msgstr "Descarregar fitxer de quadern" +msgid "By" +msgstr "Per" -msgid "Toggle navigation" -msgstr "Commuta la navegació" +msgid "Copyright" +msgstr "Copyright" msgid "Source repository" msgstr "Dipòsit de fonts" -msgid "By the" -msgstr "Per la" +msgid "previous page" +msgstr "Pàgina anterior" msgid "next page" msgstr "pàgina següent" -msgid "Sphinx Book Theme" -msgstr "Tema del llibre Esfinx" +msgid "Toggle navigation" +msgstr "Commuta la navegació" -msgid "Download source file" -msgstr "Baixeu el fitxer font" +msgid "suggest edit" +msgstr "suggerir edició" -msgid "By" -msgstr "Per" +msgid "open issue" +msgstr "número obert" -msgid "Copyright" -msgstr "Copyright" +msgid "Launch" +msgstr "Llançament" -msgid "Open an issue" -msgstr "Obriu un número" +msgid "Print to PDF" +msgstr "Imprimeix a PDF" -msgid "previous page" -msgstr "Pàgina anterior" +msgid "By the" +msgstr "Per la" + +msgid "Last updated on" +msgstr "Darrera actualització el" + +msgid "Download source file" +msgstr "Baixeu el fitxer font" msgid "Download this page" msgstr "Descarregueu aquesta pàgina" - -msgid "Theme by the" -msgstr "Tema del" diff --git a/tutorials_and_tools/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po index 3818df97..afecd9e7 100644 --- a/tutorials_and_tools/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: cs\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "navrhnout úpravy" +msgid "Theme by the" +msgstr "Téma od" -msgid "Last updated on" -msgstr "Naposledy aktualizováno" +msgid "Open an issue" +msgstr "Otevřete problém" -msgid "Edit this page" -msgstr "Upravit tuto stránku" +msgid "Contents" +msgstr "Obsah" -msgid "Launch" -msgstr "Zahájení" +msgid "Download notebook file" +msgstr "Stáhnout soubor poznámkového bloku" -msgid "Print to PDF" -msgstr "Tisk do PDF" +msgid "Sphinx Book Theme" +msgstr "Téma knihy Sfinga" -msgid "open issue" -msgstr "otevřené číslo" +msgid "Fullscreen mode" +msgstr "Režim celé obrazovky" -msgid "Download notebook file" -msgstr "Stáhnout soubor poznámkového bloku" +msgid "Edit this page" +msgstr "Upravit tuto stránku" -msgid "Toggle navigation" -msgstr "Přepnout navigaci" +msgid "By" +msgstr "Podle" + +msgid "Copyright" +msgstr "autorská práva" msgid "Source repository" msgstr "Zdrojové úložiště" -msgid "By the" -msgstr "Podle" +msgid "previous page" +msgstr "předchozí stránka" msgid "next page" msgstr "další strana" +msgid "Toggle navigation" +msgstr "Přepnout navigaci" + msgid "repository" msgstr "úložiště" -msgid "Sphinx Book Theme" -msgstr "Téma knihy Sfinga" - -msgid "Download source file" -msgstr "Stáhněte si zdrojový soubor" +msgid "suggest edit" +msgstr "navrhnout úpravy" -msgid "Contents" -msgstr "Obsah" +msgid "open issue" +msgstr "otevřené číslo" -msgid "By" -msgstr "Podle" +msgid "Launch" +msgstr "Zahájení" -msgid "Copyright" -msgstr "autorská práva" +msgid "Print to PDF" +msgstr "Tisk do PDF" -msgid "Fullscreen mode" -msgstr "Režim celé obrazovky" +msgid "By the" +msgstr "Podle" -msgid "Open an issue" -msgstr "Otevřete problém" +msgid "Last updated on" +msgstr "Naposledy aktualizováno" -msgid "previous page" -msgstr "předchozí stránka" +msgid "Download source file" +msgstr "Stáhněte si zdrojový soubor" msgid "Download this page" msgstr "Stáhněte si tuto stránku" - -msgid "Theme by the" -msgstr "Téma od" diff --git a/tutorials_and_tools/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po index 7f20a3bd..649c78a8 100644 --- a/tutorials_and_tools/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: da\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Theme by the" +msgstr "Tema af" -msgid "Last updated on" -msgstr "Sidst opdateret den" +msgid "Open an issue" +msgstr "Åbn et problem" -msgid "Edit this page" -msgstr "Rediger denne side" +msgid "Contents" +msgstr "Indhold" -msgid "Launch" -msgstr "Start" +msgid "Download notebook file" +msgstr "Download notesbog-fil" -msgid "Print to PDF" -msgstr "Udskriv til PDF" +msgid "Sphinx Book Theme" +msgstr "Sphinx bogtema" -msgid "open issue" -msgstr "åbent nummer" +msgid "Fullscreen mode" +msgstr "Fuldskærmstilstand" -msgid "Download notebook file" -msgstr "Download notesbog-fil" +msgid "Edit this page" +msgstr "Rediger denne side" -msgid "Toggle navigation" -msgstr "Skift navigation" +msgid "By" +msgstr "Ved" + +msgid "Copyright" +msgstr "ophavsret" msgid "Source repository" msgstr "Kildelager" -msgid "By the" -msgstr "Ved" +msgid "previous page" +msgstr "forrige side" msgid "next page" msgstr "Næste side" +msgid "Toggle navigation" +msgstr "Skift navigation" + msgid "repository" msgstr "lager" -msgid "Sphinx Book Theme" -msgstr "Sphinx bogtema" - -msgid "Download source file" -msgstr "Download kildefil" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Contents" -msgstr "Indhold" +msgid "open issue" +msgstr "åbent nummer" -msgid "By" -msgstr "Ved" +msgid "Launch" +msgstr "Start" -msgid "Copyright" -msgstr "ophavsret" +msgid "Print to PDF" +msgstr "Udskriv til PDF" -msgid "Fullscreen mode" -msgstr "Fuldskærmstilstand" +msgid "By the" +msgstr "Ved" -msgid "Open an issue" -msgstr "Åbn et problem" +msgid "Last updated on" +msgstr "Sidst opdateret den" -msgid "previous page" -msgstr "forrige side" +msgid "Download source file" +msgstr "Download kildefil" msgid "Download this page" msgstr "Download denne side" - -msgid "Theme by the" -msgstr "Tema af" diff --git a/tutorials_and_tools/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po index c0027d3a..f51d2ecc 100644 --- a/tutorials_and_tools/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: de\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "vorschlagen zu bearbeiten" +msgid "Theme by the" +msgstr "Thema von der" -msgid "Last updated on" -msgstr "Zuletzt aktualisiert am" +msgid "Open an issue" +msgstr "Öffnen Sie ein Problem" -msgid "Edit this page" -msgstr "Bearbeite diese Seite" +msgid "Contents" +msgstr "Inhalt" -msgid "Launch" -msgstr "Starten" +msgid "Download notebook file" +msgstr "Notebook-Datei herunterladen" -msgid "Print to PDF" -msgstr "In PDF drucken" +msgid "Sphinx Book Theme" +msgstr "Sphinx-Buch-Thema" -msgid "open issue" -msgstr "offenes Thema" +msgid "Fullscreen mode" +msgstr "Vollbildmodus" -msgid "Download notebook file" -msgstr "Notebook-Datei herunterladen" +msgid "Edit this page" +msgstr "Bearbeite diese Seite" -msgid "Toggle navigation" -msgstr "Navigation umschalten" +msgid "By" +msgstr "Durch" + +msgid "Copyright" +msgstr "Urheberrechte ©" msgid "Source repository" msgstr "Quell-Repository" -msgid "By the" -msgstr "Bis zum" +msgid "previous page" +msgstr "vorherige Seite" msgid "next page" msgstr "Nächste Seite" +msgid "Toggle navigation" +msgstr "Navigation umschalten" + msgid "repository" msgstr "Repository" -msgid "Sphinx Book Theme" -msgstr "Sphinx-Buch-Thema" - -msgid "Download source file" -msgstr "Quelldatei herunterladen" +msgid "suggest edit" +msgstr "vorschlagen zu bearbeiten" -msgid "Contents" -msgstr "Inhalt" +msgid "open issue" +msgstr "offenes Thema" -msgid "By" -msgstr "Durch" +msgid "Launch" +msgstr "Starten" -msgid "Copyright" -msgstr "Urheberrechte ©" +msgid "Print to PDF" +msgstr "In PDF drucken" -msgid "Fullscreen mode" -msgstr "Vollbildmodus" +msgid "By the" +msgstr "Bis zum" -msgid "Open an issue" -msgstr "Öffnen Sie ein Problem" +msgid "Last updated on" +msgstr "Zuletzt aktualisiert am" -msgid "previous page" -msgstr "vorherige Seite" +msgid "Download source file" +msgstr "Quelldatei herunterladen" msgid "Download this page" msgstr "Laden Sie diese Seite herunter" - -msgid "Theme by the" -msgstr "Thema von der" diff --git a/tutorials_and_tools/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po index bdeb3270..8bec7905 100644 --- a/tutorials_and_tools/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: el\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "προτείνω επεξεργασία" +msgid "Theme by the" +msgstr "Θέμα από το" -msgid "Last updated on" -msgstr "Τελευταία ενημέρωση στις" +msgid "Open an issue" +msgstr "Ανοίξτε ένα ζήτημα" -msgid "Edit this page" -msgstr "Επεξεργαστείτε αυτήν τη σελίδα" +msgid "Contents" +msgstr "Περιεχόμενα" -msgid "Launch" -msgstr "Εκτόξευση" +msgid "Download notebook file" +msgstr "Λήψη αρχείου σημειωματάριου" -msgid "Print to PDF" -msgstr "Εκτύπωση σε PDF" +msgid "Sphinx Book Theme" +msgstr "Θέμα βιβλίου Sphinx" -msgid "open issue" -msgstr "ανοιχτό ζήτημα" +msgid "Fullscreen mode" +msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ" -msgid "Download notebook file" -msgstr "Λήψη αρχείου σημειωματάριου" +msgid "Edit this page" +msgstr "Επεξεργαστείτε αυτήν τη σελίδα" -msgid "Toggle navigation" -msgstr "Εναλλαγή πλοήγησης" +msgid "By" +msgstr "Με" + +msgid "Copyright" +msgstr "Πνευματική ιδιοκτησία" msgid "Source repository" msgstr "Αποθήκη πηγής" -msgid "By the" -msgstr "Από το" +msgid "previous page" +msgstr "προηγούμενη σελίδα" msgid "next page" msgstr "επόμενη σελίδα" +msgid "Toggle navigation" +msgstr "Εναλλαγή πλοήγησης" + msgid "repository" msgstr "αποθήκη" -msgid "Sphinx Book Theme" -msgstr "Θέμα βιβλίου Sphinx" - -msgid "Download source file" -msgstr "Λήψη αρχείου προέλευσης" +msgid "suggest edit" +msgstr "προτείνω επεξεργασία" -msgid "Contents" -msgstr "Περιεχόμενα" +msgid "open issue" +msgstr "ανοιχτό ζήτημα" -msgid "By" -msgstr "Με" +msgid "Launch" +msgstr "Εκτόξευση" -msgid "Copyright" -msgstr "Πνευματική ιδιοκτησία" +msgid "Print to PDF" +msgstr "Εκτύπωση σε PDF" -msgid "Fullscreen mode" -msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ" +msgid "By the" +msgstr "Από το" -msgid "Open an issue" -msgstr "Ανοίξτε ένα ζήτημα" +msgid "Last updated on" +msgstr "Τελευταία ενημέρωση στις" -msgid "previous page" -msgstr "προηγούμενη σελίδα" +msgid "Download source file" +msgstr "Λήψη αρχείου προέλευσης" msgid "Download this page" msgstr "Λήψη αυτής της σελίδας" - -msgid "Theme by the" -msgstr "Θέμα από το" diff --git a/tutorials_and_tools/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po index 6749f3a3..d72a0481 100644 --- a/tutorials_and_tools/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: eo\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "sugesti redaktadon" +msgid "Theme by the" +msgstr "Temo de la" -msgid "Last updated on" -msgstr "Laste ĝisdatigita la" +msgid "Open an issue" +msgstr "Malfermu numeron" -msgid "Edit this page" -msgstr "Redaktu ĉi tiun paĝon" +msgid "Contents" +msgstr "Enhavo" -msgid "Launch" -msgstr "Lanĉo" +msgid "Download notebook file" +msgstr "Elŝutu kajeran dosieron" -msgid "Print to PDF" -msgstr "Presi al PDF" +msgid "Sphinx Book Theme" +msgstr "Sfinksa Libro-Temo" -msgid "open issue" -msgstr "malferma numero" +msgid "Fullscreen mode" +msgstr "Plenekrana reĝimo" -msgid "Download notebook file" -msgstr "Elŝutu kajeran dosieron" +msgid "Edit this page" +msgstr "Redaktu ĉi tiun paĝon" -msgid "Toggle navigation" -msgstr "Ŝalti navigadon" +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "Kopirajto" msgid "Source repository" msgstr "Fonto-deponejo" -msgid "By the" -msgstr "Per la" +msgid "previous page" +msgstr "antaŭa paĝo" msgid "next page" msgstr "sekva paĝo" +msgid "Toggle navigation" +msgstr "Ŝalti navigadon" + msgid "repository" msgstr "deponejo" -msgid "Sphinx Book Theme" -msgstr "Sfinksa Libro-Temo" - -msgid "Download source file" -msgstr "Elŝutu fontodosieron" +msgid "suggest edit" +msgstr "sugesti redaktadon" -msgid "Contents" -msgstr "Enhavo" +msgid "open issue" +msgstr "malferma numero" -msgid "By" -msgstr "De" +msgid "Launch" +msgstr "Lanĉo" -msgid "Copyright" -msgstr "Kopirajto" +msgid "Print to PDF" +msgstr "Presi al PDF" -msgid "Fullscreen mode" -msgstr "Plenekrana reĝimo" +msgid "By the" +msgstr "Per la" -msgid "Open an issue" -msgstr "Malfermu numeron" +msgid "Last updated on" +msgstr "Laste ĝisdatigita la" -msgid "previous page" -msgstr "antaŭa paĝo" +msgid "Download source file" +msgstr "Elŝutu fontodosieron" msgid "Download this page" msgstr "Elŝutu ĉi tiun paĝon" - -msgid "Theme by the" -msgstr "Temo de la" diff --git a/tutorials_and_tools/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po index 71dde37f..611834b2 100644 --- a/tutorials_and_tools/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: es\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "sugerir editar" +msgid "Theme by the" +msgstr "Tema por el" -msgid "Last updated on" -msgstr "Ultima actualización en" +msgid "Open an issue" +msgstr "Abrir un problema" -msgid "Edit this page" -msgstr "Edita esta página" +msgid "Contents" +msgstr "Contenido" -msgid "Launch" -msgstr "Lanzamiento" +msgid "Download notebook file" +msgstr "Descargar archivo de cuaderno" -msgid "Print to PDF" -msgstr "Imprimir en PDF" +msgid "Sphinx Book Theme" +msgstr "Tema del libro de la esfinge" -msgid "open issue" -msgstr "Tema abierto" +msgid "Fullscreen mode" +msgstr "Modo de pantalla completa" -msgid "Download notebook file" -msgstr "Descargar archivo de cuaderno" +msgid "Edit this page" +msgstr "Edita esta página" -msgid "Toggle navigation" -msgstr "Navegación de palanca" +msgid "By" +msgstr "Por" + +msgid "Copyright" +msgstr "Derechos de autor" msgid "Source repository" msgstr "Repositorio de origen" -msgid "By the" -msgstr "Por el" +msgid "previous page" +msgstr "pagina anterior" msgid "next page" msgstr "siguiente página" +msgid "Toggle navigation" +msgstr "Navegación de palanca" + msgid "repository" msgstr "repositorio" -msgid "Sphinx Book Theme" -msgstr "Tema del libro de la esfinge" - -msgid "Download source file" -msgstr "Descargar archivo fuente" +msgid "suggest edit" +msgstr "sugerir editar" -msgid "Contents" -msgstr "Contenido" +msgid "open issue" +msgstr "Tema abierto" -msgid "By" -msgstr "Por" +msgid "Launch" +msgstr "Lanzamiento" -msgid "Copyright" -msgstr "Derechos de autor" +msgid "Print to PDF" +msgstr "Imprimir en PDF" -msgid "Fullscreen mode" -msgstr "Modo de pantalla completa" +msgid "By the" +msgstr "Por el" -msgid "Open an issue" -msgstr "Abrir un problema" +msgid "Last updated on" +msgstr "Ultima actualización en" -msgid "previous page" -msgstr "pagina anterior" +msgid "Download source file" +msgstr "Descargar archivo fuente" msgid "Download this page" msgstr "Descarga esta pagina" - -msgid "Theme by the" -msgstr "Tema por el" diff --git a/tutorials_and_tools/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po index cdcd07c7..345088f0 100644 --- a/tutorials_and_tools/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: et\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "soovita muuta" +msgid "Theme by the" +msgstr "Teema" -msgid "Last updated on" -msgstr "Viimati uuendatud" +msgid "Open an issue" +msgstr "Avage probleem" -msgid "Edit this page" -msgstr "Muutke seda lehte" +msgid "Contents" +msgstr "Sisu" -msgid "Launch" -msgstr "Käivitage" +msgid "Download notebook file" +msgstr "Laadige sülearvuti fail alla" -msgid "Print to PDF" -msgstr "Prindi PDF-i" +msgid "Sphinx Book Theme" +msgstr "Sfinksiraamatu teema" -msgid "open issue" -msgstr "avatud küsimus" +msgid "Fullscreen mode" +msgstr "Täisekraanirežiim" -msgid "Download notebook file" -msgstr "Laadige sülearvuti fail alla" +msgid "Edit this page" +msgstr "Muutke seda lehte" -msgid "Toggle navigation" -msgstr "Lülita navigeerimine sisse" +msgid "By" +msgstr "Kõrval" + +msgid "Copyright" +msgstr "Autoriõigus" msgid "Source repository" msgstr "Allikahoidla" -msgid "By the" -msgstr "Autor" +msgid "previous page" +msgstr "eelmine leht" msgid "next page" msgstr "järgmine leht" +msgid "Toggle navigation" +msgstr "Lülita navigeerimine sisse" + msgid "repository" msgstr "hoidla" -msgid "Sphinx Book Theme" -msgstr "Sfinksiraamatu teema" - -msgid "Download source file" -msgstr "Laadige alla lähtefail" +msgid "suggest edit" +msgstr "soovita muuta" -msgid "Contents" -msgstr "Sisu" +msgid "open issue" +msgstr "avatud küsimus" -msgid "By" -msgstr "Kõrval" +msgid "Launch" +msgstr "Käivitage" -msgid "Copyright" -msgstr "Autoriõigus" +msgid "Print to PDF" +msgstr "Prindi PDF-i" -msgid "Fullscreen mode" -msgstr "Täisekraanirežiim" +msgid "By the" +msgstr "Autor" -msgid "Open an issue" -msgstr "Avage probleem" +msgid "Last updated on" +msgstr "Viimati uuendatud" -msgid "previous page" -msgstr "eelmine leht" +msgid "Download source file" +msgstr "Laadige alla lähtefail" msgid "Download this page" msgstr "Laadige see leht alla" - -msgid "Theme by the" -msgstr "Teema" diff --git a/tutorials_and_tools/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po index 3c3dd089..d97a08dc 100644 --- a/tutorials_and_tools/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: fi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "ehdottaa muokkausta" +msgid "Theme by the" +msgstr "Teeman tekijä" -msgid "Last updated on" -msgstr "Viimeksi päivitetty" +msgid "Open an issue" +msgstr "Avaa ongelma" -msgid "Edit this page" -msgstr "Muokkaa tätä sivua" +msgid "Contents" +msgstr "Sisällys" -msgid "Launch" -msgstr "Tuoda markkinoille" +msgid "Download notebook file" +msgstr "Lataa muistikirjatiedosto" -msgid "Print to PDF" -msgstr "Tulosta PDF-tiedostoon" +msgid "Sphinx Book Theme" +msgstr "Sphinx-kirjan teema" -msgid "open issue" -msgstr "avoin ongelma" +msgid "Fullscreen mode" +msgstr "Koko näytön tila" -msgid "Download notebook file" -msgstr "Lataa muistikirjatiedosto" +msgid "Edit this page" +msgstr "Muokkaa tätä sivua" -msgid "Toggle navigation" -msgstr "Vaihda navigointia" +msgid "By" +msgstr "Tekijä" + +msgid "Copyright" +msgstr "Tekijänoikeus" msgid "Source repository" msgstr "Lähteen arkisto" -msgid "By the" -msgstr "Mukaan" +msgid "previous page" +msgstr "Edellinen sivu" msgid "next page" msgstr "seuraava sivu" +msgid "Toggle navigation" +msgstr "Vaihda navigointia" + msgid "repository" msgstr "arkisto" -msgid "Sphinx Book Theme" -msgstr "Sphinx-kirjan teema" - -msgid "Download source file" -msgstr "Lataa lähdetiedosto" +msgid "suggest edit" +msgstr "ehdottaa muokkausta" -msgid "Contents" -msgstr "Sisällys" +msgid "open issue" +msgstr "avoin ongelma" -msgid "By" -msgstr "Tekijä" +msgid "Launch" +msgstr "Tuoda markkinoille" -msgid "Copyright" -msgstr "Tekijänoikeus" +msgid "Print to PDF" +msgstr "Tulosta PDF-tiedostoon" -msgid "Fullscreen mode" -msgstr "Koko näytön tila" +msgid "By the" +msgstr "Mukaan" -msgid "Open an issue" -msgstr "Avaa ongelma" +msgid "Last updated on" +msgstr "Viimeksi päivitetty" -msgid "previous page" -msgstr "Edellinen sivu" +msgid "Download source file" +msgstr "Lataa lähdetiedosto" msgid "Download this page" msgstr "Lataa tämä sivu" - -msgid "Theme by the" -msgstr "Teeman tekijä" diff --git a/tutorials_and_tools/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po index b57d2fe7..88f35173 100644 --- a/tutorials_and_tools/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: fr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "suggestion de modification" +msgid "Theme by the" +msgstr "Thème par le" -msgid "Last updated on" -msgstr "Dernière mise à jour le" +msgid "Open an issue" +msgstr "Ouvrez un problème" -msgid "Edit this page" -msgstr "Modifier cette page" +msgid "Contents" +msgstr "Contenu" -msgid "Launch" -msgstr "lancement" +msgid "Download notebook file" +msgstr "Télécharger le fichier notebook" -msgid "Print to PDF" -msgstr "Imprimer au format PDF" +msgid "Sphinx Book Theme" +msgstr "Thème du livre Sphinx" -msgid "open issue" -msgstr "signaler un problème" +msgid "Fullscreen mode" +msgstr "Mode plein écran" -msgid "Download notebook file" -msgstr "Télécharger le fichier notebook" +msgid "Edit this page" +msgstr "Modifier cette page" -msgid "Toggle navigation" -msgstr "Basculer la navigation" +msgid "By" +msgstr "Par" + +msgid "Copyright" +msgstr "droits d'auteur" msgid "Source repository" msgstr "Dépôt source" -msgid "By the" -msgstr "Par le" +msgid "previous page" +msgstr "page précédente" msgid "next page" msgstr "page suivante" +msgid "Toggle navigation" +msgstr "Basculer la navigation" + msgid "repository" msgstr "dépôt" -msgid "Sphinx Book Theme" -msgstr "Thème du livre Sphinx" - -msgid "Download source file" -msgstr "Télécharger le fichier source" +msgid "suggest edit" +msgstr "suggestion de modification" -msgid "Contents" -msgstr "Contenu" +msgid "open issue" +msgstr "signaler un problème" -msgid "By" -msgstr "Par" +msgid "Launch" +msgstr "lancement" -msgid "Copyright" -msgstr "droits d'auteur" +msgid "Print to PDF" +msgstr "Imprimer au format PDF" -msgid "Fullscreen mode" -msgstr "Mode plein écran" +msgid "By the" +msgstr "Par le" -msgid "Open an issue" -msgstr "Ouvrez un problème" +msgid "Last updated on" +msgstr "Dernière mise à jour le" -msgid "previous page" -msgstr "page précédente" +msgid "Download source file" +msgstr "Télécharger le fichier source" msgid "Download this page" msgstr "Téléchargez cette page" - -msgid "Theme by the" -msgstr "Thème par le" diff --git a/tutorials_and_tools/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po index 4c425e89..fb9440ac 100644 --- a/tutorials_and_tools/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: hr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "predloži uređivanje" +msgid "Theme by the" +msgstr "Tema autora" -msgid "Last updated on" -msgstr "Posljednje ažuriranje:" +msgid "Open an issue" +msgstr "Otvorite izdanje" -msgid "Edit this page" -msgstr "Uredite ovu stranicu" +msgid "Contents" +msgstr "Sadržaj" -msgid "Launch" -msgstr "Pokrenite" +msgid "Download notebook file" +msgstr "Preuzmi datoteku bilježnice" -msgid "Print to PDF" -msgstr "Ispis u PDF" +msgid "Sphinx Book Theme" +msgstr "Tema knjige Sphinx" -msgid "open issue" -msgstr "otvoreno izdanje" +msgid "Fullscreen mode" +msgstr "Način preko cijelog zaslona" -msgid "Download notebook file" -msgstr "Preuzmi datoteku bilježnice" +msgid "Edit this page" +msgstr "Uredite ovu stranicu" -msgid "Toggle navigation" -msgstr "Uključi / isključi navigaciju" +msgid "By" +msgstr "Po" + +msgid "Copyright" +msgstr "Autorska prava" msgid "Source repository" msgstr "Izvorno spremište" -msgid "By the" -msgstr "Od strane" +msgid "previous page" +msgstr "Prethodna stranica" msgid "next page" msgstr "sljedeća stranica" +msgid "Toggle navigation" +msgstr "Uključi / isključi navigaciju" + msgid "repository" msgstr "spremište" -msgid "Sphinx Book Theme" -msgstr "Tema knjige Sphinx" - -msgid "Download source file" -msgstr "Preuzmi izvornu datoteku" +msgid "suggest edit" +msgstr "predloži uređivanje" -msgid "Contents" -msgstr "Sadržaj" +msgid "open issue" +msgstr "otvoreno izdanje" -msgid "By" -msgstr "Po" +msgid "Launch" +msgstr "Pokrenite" -msgid "Copyright" -msgstr "Autorska prava" +msgid "Print to PDF" +msgstr "Ispis u PDF" -msgid "Fullscreen mode" -msgstr "Način preko cijelog zaslona" +msgid "By the" +msgstr "Od strane" -msgid "Open an issue" -msgstr "Otvorite izdanje" +msgid "Last updated on" +msgstr "Posljednje ažuriranje:" -msgid "previous page" -msgstr "Prethodna stranica" +msgid "Download source file" +msgstr "Preuzmi izvornu datoteku" msgid "Download this page" msgstr "Preuzmite ovu stranicu" - -msgid "Theme by the" -msgstr "Tema autora" diff --git a/tutorials_and_tools/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po index 5db2ae14..9ffb56f7 100644 --- a/tutorials_and_tools/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: id\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "menyarankan edit" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "Last updated on" -msgstr "Terakhir diperbarui saat" +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Edit this page" -msgstr "Edit halaman ini" +msgid "Contents" +msgstr "Isi" -msgid "Launch" -msgstr "Meluncurkan" +msgid "Download notebook file" +msgstr "Unduh file notebook" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Sphinx Book Theme" +msgstr "Tema Buku Sphinx" -msgid "open issue" -msgstr "masalah terbuka" +msgid "Fullscreen mode" +msgstr "Mode layar penuh" -msgid "Download notebook file" -msgstr "Unduh file notebook" +msgid "Edit this page" +msgstr "Edit halaman ini" -msgid "Toggle navigation" -msgstr "Alihkan navigasi" +msgid "By" +msgstr "Oleh" + +msgid "Copyright" +msgstr "hak cipta" msgid "Source repository" msgstr "Repositori sumber" -msgid "By the" -msgstr "Oleh" +msgid "previous page" +msgstr "halaman sebelumnya" msgid "next page" msgstr "halaman selanjutnya" +msgid "Toggle navigation" +msgstr "Alihkan navigasi" + msgid "repository" msgstr "gudang" -msgid "Sphinx Book Theme" -msgstr "Tema Buku Sphinx" - -msgid "Download source file" -msgstr "Unduh file sumber" +msgid "suggest edit" +msgstr "menyarankan edit" -msgid "Contents" -msgstr "Isi" +msgid "open issue" +msgstr "masalah terbuka" -msgid "By" -msgstr "Oleh" +msgid "Launch" +msgstr "Meluncurkan" -msgid "Copyright" -msgstr "hak cipta" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "Fullscreen mode" -msgstr "Mode layar penuh" +msgid "By the" +msgstr "Oleh" -msgid "Open an issue" -msgstr "Buka masalah" +msgid "Last updated on" +msgstr "Terakhir diperbarui saat" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "Download source file" +msgstr "Unduh file sumber" msgid "Download this page" msgstr "Unduh halaman ini" - -msgid "Theme by the" -msgstr "Tema oleh" diff --git a/tutorials_and_tools/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po index 7d54fdef..04308dd2 100644 --- a/tutorials_and_tools/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: it\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "suggerisci modifica" +msgid "Theme by the" +msgstr "Tema di" -msgid "Last updated on" -msgstr "Ultimo aggiornamento il" +msgid "Open an issue" +msgstr "Apri un problema" -msgid "Edit this page" -msgstr "Modifica questa pagina" +msgid "Contents" +msgstr "Contenuti" -msgid "Launch" -msgstr "Lanciare" +msgid "Download notebook file" +msgstr "Scarica il file del taccuino" -msgid "Print to PDF" -msgstr "Stampa in PDF" +msgid "Sphinx Book Theme" +msgstr "Tema del libro della Sfinge" -msgid "open issue" -msgstr "questione aperta" +msgid "Fullscreen mode" +msgstr "Modalità schermo intero" -msgid "Download notebook file" -msgstr "Scarica il file del taccuino" +msgid "Edit this page" +msgstr "Modifica questa pagina" -msgid "Toggle navigation" -msgstr "Attiva / disattiva la navigazione" +msgid "By" +msgstr "Di" + +msgid "Copyright" +msgstr "Diritto d'autore" msgid "Source repository" msgstr "Repository di origine" -msgid "By the" -msgstr "Dal" +msgid "previous page" +msgstr "pagina precedente" msgid "next page" msgstr "pagina successiva" +msgid "Toggle navigation" +msgstr "Attiva / disattiva la navigazione" + msgid "repository" msgstr "repository" -msgid "Sphinx Book Theme" -msgstr "Tema del libro della Sfinge" - -msgid "Download source file" -msgstr "Scarica il file sorgente" +msgid "suggest edit" +msgstr "suggerisci modifica" -msgid "Contents" -msgstr "Contenuti" +msgid "open issue" +msgstr "questione aperta" -msgid "By" -msgstr "Di" +msgid "Launch" +msgstr "Lanciare" -msgid "Copyright" -msgstr "Diritto d'autore" +msgid "Print to PDF" +msgstr "Stampa in PDF" -msgid "Fullscreen mode" -msgstr "Modalità schermo intero" +msgid "By the" +msgstr "Dal" -msgid "Open an issue" -msgstr "Apri un problema" +msgid "Last updated on" +msgstr "Ultimo aggiornamento il" -msgid "previous page" -msgstr "pagina precedente" +msgid "Download source file" +msgstr "Scarica il file sorgente" msgid "Download this page" msgstr "Scarica questa pagina" - -msgid "Theme by the" -msgstr "Tema di" diff --git a/tutorials_and_tools/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po index 32b017cf..4ea190d3 100644 --- a/tutorials_and_tools/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: iw\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "מציע לערוך" +msgid "Theme by the" +msgstr "נושא מאת" -msgid "Last updated on" -msgstr "עודכן לאחרונה ב" +msgid "Open an issue" +msgstr "פתח גיליון" -msgid "Edit this page" -msgstr "ערוך דף זה" +msgid "Contents" +msgstr "תוכן" -msgid "Launch" -msgstr "לְהַשִׁיק" +msgid "Download notebook file" +msgstr "הורד קובץ מחברת" -msgid "Print to PDF" -msgstr "הדפס לקובץ PDF" +msgid "Sphinx Book Theme" +msgstr "נושא ספר ספינקס" -msgid "open issue" -msgstr "בעיה פתוחה" +msgid "Fullscreen mode" +msgstr "מצב מסך מלא" -msgid "Download notebook file" -msgstr "הורד קובץ מחברת" +msgid "Edit this page" +msgstr "ערוך דף זה" -msgid "Toggle navigation" -msgstr "החלף ניווט" +msgid "By" +msgstr "על ידי" + +msgid "Copyright" +msgstr "זכויות יוצרים" msgid "Source repository" msgstr "מאגר המקורות" -msgid "By the" -msgstr "דרך" +msgid "previous page" +msgstr "עמוד קודם" msgid "next page" msgstr "עמוד הבא" +msgid "Toggle navigation" +msgstr "החלף ניווט" + msgid "repository" msgstr "מאגר" -msgid "Sphinx Book Theme" -msgstr "נושא ספר ספינקס" - -msgid "Download source file" -msgstr "הורד את קובץ המקור" +msgid "suggest edit" +msgstr "מציע לערוך" -msgid "Contents" -msgstr "תוכן" +msgid "open issue" +msgstr "בעיה פתוחה" -msgid "By" -msgstr "על ידי" +msgid "Launch" +msgstr "לְהַשִׁיק" -msgid "Copyright" -msgstr "זכויות יוצרים" +msgid "Print to PDF" +msgstr "הדפס לקובץ PDF" -msgid "Fullscreen mode" -msgstr "מצב מסך מלא" +msgid "By the" +msgstr "דרך" -msgid "Open an issue" -msgstr "פתח גיליון" +msgid "Last updated on" +msgstr "עודכן לאחרונה ב" -msgid "previous page" -msgstr "עמוד קודם" +msgid "Download source file" +msgstr "הורד את קובץ המקור" msgid "Download this page" msgstr "הורד דף זה" - -msgid "Theme by the" -msgstr "נושא מאת" diff --git a/tutorials_and_tools/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po index 16924e19..77d5a097 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: ja\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "編集を提案する" +msgid "Theme by the" +msgstr "のテーマ" -msgid "Last updated on" -msgstr "最終更新日" +msgid "Open an issue" +msgstr "問題を報告" -msgid "Edit this page" -msgstr "このページを編集" +msgid "Contents" +msgstr "目次" -msgid "Launch" -msgstr "起動" +msgid "Download notebook file" +msgstr "ノートブックファイルをダウンロード" -msgid "Print to PDF" -msgstr "PDFに印刷" +msgid "Sphinx Book Theme" +msgstr "スフィンクスの本のテーマ" -msgid "open issue" -msgstr "未解決の問題" +msgid "Fullscreen mode" +msgstr "全画面モード" -msgid "Download notebook file" -msgstr "ノートブックファイルをダウンロード" +msgid "Edit this page" +msgstr "このページを編集" -msgid "Toggle navigation" -msgstr "ナビゲーションを切り替え" +msgid "By" +msgstr "著者" + +msgid "Copyright" +msgstr "Copyright" msgid "Source repository" msgstr "ソースリポジトリ" -msgid "By the" -msgstr "によって" +msgid "previous page" +msgstr "前のページ" msgid "next page" msgstr "次のページ" +msgid "Toggle navigation" +msgstr "ナビゲーションを切り替え" + msgid "repository" msgstr "リポジトリ" -msgid "Sphinx Book Theme" -msgstr "スフィンクスの本のテーマ" - -msgid "Download source file" -msgstr "ソースファイルをダウンロード" +msgid "suggest edit" +msgstr "編集を提案する" -msgid "Contents" -msgstr "目次" +msgid "open issue" +msgstr "未解決の問題" -msgid "By" -msgstr "著者" +msgid "Launch" +msgstr "起動" -msgid "Copyright" -msgstr "Copyright" +msgid "Print to PDF" +msgstr "PDFに印刷" -msgid "Fullscreen mode" -msgstr "全画面モード" +msgid "By the" +msgstr "によって" -msgid "Open an issue" -msgstr "問題を報告" +msgid "Last updated on" +msgstr "最終更新日" -msgid "previous page" -msgstr "前のページ" +msgid "Download source file" +msgstr "ソースファイルをダウンロード" msgid "Download this page" msgstr "このページをダウンロード" - -msgid "Theme by the" -msgstr "のテーマ" diff --git a/tutorials_and_tools/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po index 69dd18f7..6ee3d781 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: ko\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "편집 제안" +msgid "Theme by the" +msgstr "테마별" -msgid "Last updated on" -msgstr "마지막 업데이트" +msgid "Open an issue" +msgstr "이슈 열기" -msgid "Edit this page" -msgstr "이 페이지 편집" +msgid "Contents" +msgstr "내용" -msgid "Launch" -msgstr "시작하다" +msgid "Download notebook file" +msgstr "노트북 파일 다운로드" -msgid "Print to PDF" -msgstr "PDF로 인쇄" +msgid "Sphinx Book Theme" +msgstr "스핑크스 도서 테마" -msgid "open issue" -msgstr "열린 문제" +msgid "Fullscreen mode" +msgstr "전체 화면으로보기" -msgid "Download notebook file" -msgstr "노트북 파일 다운로드" +msgid "Edit this page" +msgstr "이 페이지 편집" -msgid "Toggle navigation" -msgstr "탐색 전환" +msgid "By" +msgstr "으로" + +msgid "Copyright" +msgstr "저작권" msgid "Source repository" msgstr "소스 저장소" -msgid "By the" -msgstr "에 의해" +msgid "previous page" +msgstr "이전 페이지" msgid "next page" msgstr "다음 페이지" +msgid "Toggle navigation" +msgstr "탐색 전환" + msgid "repository" msgstr "저장소" -msgid "Sphinx Book Theme" -msgstr "스핑크스 도서 테마" - -msgid "Download source file" -msgstr "소스 파일 다운로드" +msgid "suggest edit" +msgstr "편집 제안" -msgid "Contents" -msgstr "내용" +msgid "open issue" +msgstr "열린 문제" -msgid "By" -msgstr "으로" +msgid "Launch" +msgstr "시작하다" -msgid "Copyright" -msgstr "저작권" +msgid "Print to PDF" +msgstr "PDF로 인쇄" -msgid "Fullscreen mode" -msgstr "전체 화면으로보기" +msgid "By the" +msgstr "에 의해" -msgid "Open an issue" -msgstr "이슈 열기" +msgid "Last updated on" +msgstr "마지막 업데이트" -msgid "previous page" -msgstr "이전 페이지" +msgid "Download source file" +msgstr "소스 파일 다운로드" msgid "Download this page" msgstr "이 페이지 다운로드" - -msgid "Theme by the" -msgstr "테마별" diff --git a/tutorials_and_tools/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po index 9f037752..01be2679 100644 --- a/tutorials_and_tools/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: lt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "pasiūlyti redaguoti" +msgid "Theme by the" +msgstr "Tema" -msgid "Last updated on" -msgstr "Paskutinį kartą atnaujinta" +msgid "Open an issue" +msgstr "Atidarykite problemą" -msgid "Edit this page" -msgstr "Redaguoti šį puslapį" +msgid "Contents" +msgstr "Turinys" -msgid "Launch" -msgstr "Paleiskite" +msgid "Download notebook file" +msgstr "Atsisiųsti nešiojamojo kompiuterio failą" -msgid "Print to PDF" -msgstr "Spausdinti į PDF" +msgid "Sphinx Book Theme" +msgstr "Sfinkso knygos tema" -msgid "open issue" -msgstr "atviras klausimas" +msgid "Fullscreen mode" +msgstr "Pilno ekrano režimas" -msgid "Download notebook file" -msgstr "Atsisiųsti nešiojamojo kompiuterio failą" +msgid "Edit this page" +msgstr "Redaguoti šį puslapį" -msgid "Toggle navigation" -msgstr "Perjungti naršymą" +msgid "By" +msgstr "Iki" + +msgid "Copyright" +msgstr "Autorių teisės" msgid "Source repository" msgstr "Šaltinio saugykla" -msgid "By the" -msgstr "Prie" +msgid "previous page" +msgstr "Ankstesnis puslapis" msgid "next page" msgstr "Kitas puslapis" +msgid "Toggle navigation" +msgstr "Perjungti naršymą" + msgid "repository" msgstr "saugykla" -msgid "Sphinx Book Theme" -msgstr "Sfinkso knygos tema" - -msgid "Download source file" -msgstr "Atsisiųsti šaltinio failą" +msgid "suggest edit" +msgstr "pasiūlyti redaguoti" -msgid "Contents" -msgstr "Turinys" +msgid "open issue" +msgstr "atviras klausimas" -msgid "By" -msgstr "Iki" +msgid "Launch" +msgstr "Paleiskite" -msgid "Copyright" -msgstr "Autorių teisės" +msgid "Print to PDF" +msgstr "Spausdinti į PDF" -msgid "Fullscreen mode" -msgstr "Pilno ekrano režimas" +msgid "By the" +msgstr "Prie" -msgid "Open an issue" -msgstr "Atidarykite problemą" +msgid "Last updated on" +msgstr "Paskutinį kartą atnaujinta" -msgid "previous page" -msgstr "Ankstesnis puslapis" +msgid "Download source file" +msgstr "Atsisiųsti šaltinio failą" msgid "Download this page" msgstr "Atsisiųskite šį puslapį" - -msgid "Theme by the" -msgstr "Tema" diff --git a/tutorials_and_tools/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po index c9633b54..993a1e41 100644 --- a/tutorials_and_tools/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: lv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "ieteikt rediģēt" +msgid "Theme by the" +msgstr "Autora tēma" -msgid "Last updated on" -msgstr "Pēdējoreiz atjaunināts" +msgid "Open an issue" +msgstr "Atveriet problēmu" -msgid "Edit this page" -msgstr "Rediģēt šo lapu" +msgid "Contents" +msgstr "Saturs" -msgid "Launch" -msgstr "Uzsākt" +msgid "Download notebook file" +msgstr "Lejupielādēt piezīmju grāmatiņu" -msgid "Print to PDF" -msgstr "Drukāt PDF formātā" +msgid "Sphinx Book Theme" +msgstr "Sfinksa grāmatas tēma" -msgid "open issue" -msgstr "atklāts jautājums" +msgid "Fullscreen mode" +msgstr "Pilnekrāna režīms" -msgid "Download notebook file" -msgstr "Lejupielādēt piezīmju grāmatiņu" +msgid "Edit this page" +msgstr "Rediģēt šo lapu" -msgid "Toggle navigation" -msgstr "Pārslēgt navigāciju" +msgid "By" +msgstr "Autors" + +msgid "Copyright" +msgstr "Autortiesības" msgid "Source repository" msgstr "Avota krātuve" -msgid "By the" -msgstr "Ar" +msgid "previous page" +msgstr "iepriekšējā lapa" msgid "next page" msgstr "nākamā lapaspuse" +msgid "Toggle navigation" +msgstr "Pārslēgt navigāciju" + msgid "repository" msgstr "krātuve" -msgid "Sphinx Book Theme" -msgstr "Sfinksa grāmatas tēma" - -msgid "Download source file" -msgstr "Lejupielādēt avota failu" +msgid "suggest edit" +msgstr "ieteikt rediģēt" -msgid "Contents" -msgstr "Saturs" +msgid "open issue" +msgstr "atklāts jautājums" -msgid "By" -msgstr "Autors" +msgid "Launch" +msgstr "Uzsākt" -msgid "Copyright" -msgstr "Autortiesības" +msgid "Print to PDF" +msgstr "Drukāt PDF formātā" -msgid "Fullscreen mode" -msgstr "Pilnekrāna režīms" +msgid "By the" +msgstr "Ar" -msgid "Open an issue" -msgstr "Atveriet problēmu" +msgid "Last updated on" +msgstr "Pēdējoreiz atjaunināts" -msgid "previous page" -msgstr "iepriekšējā lapa" +msgid "Download source file" +msgstr "Lejupielādēt avota failu" msgid "Download this page" msgstr "Lejupielādējiet šo lapu" - -msgid "Theme by the" -msgstr "Autora tēma" diff --git a/tutorials_and_tools/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po index 9a6a41e8..81daf7c8 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: ml\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" - -msgid "Last updated on" -msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" +msgid "Theme by the" +msgstr "പ്രമേയം" -msgid "Edit this page" -msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക" +msgid "Open an issue" +msgstr "ഒരു പ്രശ്നം തുറക്കുക" -msgid "Launch" -msgstr "സമാരംഭിക്കുക" +msgid "Download notebook file" +msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" -msgid "Print to PDF" -msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" +msgid "Sphinx Book Theme" +msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം" -msgid "open issue" -msgstr "തുറന്ന പ്രശ്നം" +msgid "Edit this page" +msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക" -msgid "Download notebook file" -msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" +msgid "By" +msgstr "എഴുതിയത്" -msgid "Toggle navigation" -msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" +msgid "Copyright" +msgstr "പകർപ്പവകാശം" msgid "Source repository" msgstr "ഉറവിട ശേഖരം" -msgid "By the" -msgstr "എഴുതിയത്" +msgid "previous page" +msgstr "മുൻപത്തെ താൾ" msgid "next page" msgstr "അടുത്ത പേജ്" -msgid "Sphinx Book Theme" -msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം" +msgid "Toggle navigation" +msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" -msgid "Download source file" -msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക" +msgid "suggest edit" +msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" -msgid "By" -msgstr "എഴുതിയത്" +msgid "open issue" +msgstr "തുറന്ന പ്രശ്നം" -msgid "Copyright" -msgstr "പകർപ്പവകാശം" +msgid "Launch" +msgstr "സമാരംഭിക്കുക" -msgid "Open an issue" -msgstr "ഒരു പ്രശ്നം തുറക്കുക" +msgid "Print to PDF" +msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" -msgid "previous page" -msgstr "മുൻപത്തെ താൾ" +msgid "By the" +msgstr "എഴുതിയത്" + +msgid "Last updated on" +msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" + +msgid "Download source file" +msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക" msgid "Download this page" msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" - -msgid "Theme by the" -msgstr "പ്രമേയം" diff --git a/tutorials_and_tools/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po index ef72d8c6..fd857bff 100644 --- a/tutorials_and_tools/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: mr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "संपादन सुचवा" - -msgid "Last updated on" -msgstr "अखेरचे अद्यतनित" +msgid "Theme by the" +msgstr "द्वारा थीम" -msgid "Edit this page" -msgstr "हे पृष्ठ संपादित करा" +msgid "Open an issue" +msgstr "एक मुद्दा उघडा" -msgid "Launch" -msgstr "लाँच करा" +msgid "Download notebook file" +msgstr "नोटबुक फाईल डाउनलोड करा" -msgid "Print to PDF" -msgstr "पीडीएफवर मुद्रित करा" +msgid "Sphinx Book Theme" +msgstr "स्फिंक्स बुक थीम" -msgid "open issue" -msgstr "खुला मुद्दा" +msgid "Edit this page" +msgstr "हे पृष्ठ संपादित करा" -msgid "Download notebook file" -msgstr "नोटबुक फाईल डाउनलोड करा" +msgid "By" +msgstr "द्वारा" -msgid "Toggle navigation" -msgstr "नेव्हिगेशन टॉगल करा" +msgid "Copyright" +msgstr "कॉपीराइट" msgid "Source repository" msgstr "स्त्रोत भांडार" -msgid "By the" -msgstr "द्वारा" +msgid "previous page" +msgstr "मागील पान" msgid "next page" msgstr "पुढील पृष्ठ" -msgid "Sphinx Book Theme" -msgstr "स्फिंक्स बुक थीम" +msgid "Toggle navigation" +msgstr "नेव्हिगेशन टॉगल करा" -msgid "Download source file" -msgstr "स्त्रोत फाइल डाउनलोड करा" +msgid "suggest edit" +msgstr "संपादन सुचवा" -msgid "By" -msgstr "द्वारा" +msgid "open issue" +msgstr "खुला मुद्दा" -msgid "Copyright" -msgstr "कॉपीराइट" +msgid "Launch" +msgstr "लाँच करा" -msgid "Open an issue" -msgstr "एक मुद्दा उघडा" +msgid "Print to PDF" +msgstr "पीडीएफवर मुद्रित करा" -msgid "previous page" -msgstr "मागील पान" +msgid "By the" +msgstr "द्वारा" + +msgid "Last updated on" +msgstr "अखेरचे अद्यतनित" + +msgid "Download source file" +msgstr "स्त्रोत फाइल डाउनलोड करा" msgid "Download this page" msgstr "हे पृष्ठ डाउनलोड करा" - -msgid "Theme by the" -msgstr "द्वारा थीम" diff --git a/tutorials_and_tools/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po index e29cbe2e..b616d70f 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: ms\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "cadangkan edit" - -msgid "Last updated on" -msgstr "Terakhir dikemas kini pada" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "Edit this page" -msgstr "Edit halaman ini" +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Launch" -msgstr "Lancarkan" +msgid "Download notebook file" +msgstr "Muat turun fail buku nota" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Sphinx Book Theme" +msgstr "Tema Buku Sphinx" -msgid "open issue" -msgstr "isu terbuka" +msgid "Edit this page" +msgstr "Edit halaman ini" -msgid "Download notebook file" -msgstr "Muat turun fail buku nota" +msgid "By" +msgstr "Oleh" -msgid "Toggle navigation" -msgstr "Togol navigasi" +msgid "Copyright" +msgstr "hak cipta" msgid "Source repository" msgstr "Repositori sumber" -msgid "By the" -msgstr "Oleh" +msgid "previous page" +msgstr "halaman sebelumnya" msgid "next page" msgstr "muka surat seterusnya" -msgid "Sphinx Book Theme" -msgstr "Tema Buku Sphinx" +msgid "Toggle navigation" +msgstr "Togol navigasi" -msgid "Download source file" -msgstr "Muat turun fail sumber" +msgid "suggest edit" +msgstr "cadangkan edit" -msgid "By" -msgstr "Oleh" +msgid "open issue" +msgstr "isu terbuka" -msgid "Copyright" -msgstr "hak cipta" +msgid "Launch" +msgstr "Lancarkan" -msgid "Open an issue" -msgstr "Buka masalah" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "By the" +msgstr "Oleh" + +msgid "Last updated on" +msgstr "Terakhir dikemas kini pada" + +msgid "Download source file" +msgstr "Muat turun fail sumber" msgid "Download this page" msgstr "Muat turun halaman ini" - -msgid "Theme by the" -msgstr "Tema oleh" diff --git a/tutorials_and_tools/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po index e4844d7c..f16f4bcc 100644 --- a/tutorials_and_tools/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: nl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "suggereren bewerken" +msgid "Theme by the" +msgstr "Thema door de" -msgid "Last updated on" -msgstr "Laatst geupdate op" +msgid "Open an issue" +msgstr "Open een probleem" -msgid "Edit this page" -msgstr "bewerk deze pagina" +msgid "Contents" +msgstr "Inhoud" -msgid "Launch" -msgstr "Lancering" +msgid "Download notebook file" +msgstr "Download notebookbestand" -msgid "Print to PDF" -msgstr "Afdrukken naar pdf" +msgid "Sphinx Book Theme" +msgstr "Sphinx-boekthema" -msgid "open issue" -msgstr "open probleem" +msgid "Fullscreen mode" +msgstr "Volledig scherm" -msgid "Download notebook file" -msgstr "Download notebookbestand" +msgid "Edit this page" +msgstr "bewerk deze pagina" -msgid "Toggle navigation" -msgstr "Schakel navigatie" +msgid "By" +msgstr "Door" + +msgid "Copyright" +msgstr "auteursrechten" msgid "Source repository" msgstr "Bronopslagplaats" -msgid "By the" -msgstr "Door de" +msgid "previous page" +msgstr "vorige pagina" msgid "next page" msgstr "volgende bladzijde" +msgid "Toggle navigation" +msgstr "Schakel navigatie" + msgid "repository" msgstr "repository" -msgid "Sphinx Book Theme" -msgstr "Sphinx-boekthema" - -msgid "Download source file" -msgstr "Download het bronbestand" +msgid "suggest edit" +msgstr "suggereren bewerken" -msgid "Contents" -msgstr "Inhoud" +msgid "open issue" +msgstr "open probleem" -msgid "By" -msgstr "Door" +msgid "Launch" +msgstr "Lancering" -msgid "Copyright" -msgstr "auteursrechten" +msgid "Print to PDF" +msgstr "Afdrukken naar pdf" -msgid "Fullscreen mode" -msgstr "Volledig scherm" +msgid "By the" +msgstr "Door de" -msgid "Open an issue" -msgstr "Open een probleem" +msgid "Last updated on" +msgstr "Laatst geupdate op" -msgid "previous page" -msgstr "vorige pagina" +msgid "Download source file" +msgstr "Download het bronbestand" msgid "Download this page" msgstr "Download deze pagina" - -msgid "Theme by the" -msgstr "Thema door de" diff --git a/tutorials_and_tools/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po index d079dd9b..b1d304ee 100644 --- a/tutorials_and_tools/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: no\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Theme by the" +msgstr "Tema av" -msgid "Last updated on" -msgstr "Sist oppdatert den" +msgid "Open an issue" +msgstr "Åpne et problem" -msgid "Edit this page" -msgstr "Rediger denne siden" +msgid "Contents" +msgstr "Innhold" -msgid "Launch" -msgstr "Start" +msgid "Download notebook file" +msgstr "Last ned notatbokfilen" -msgid "Print to PDF" -msgstr "Skriv ut til PDF" +msgid "Sphinx Book Theme" +msgstr "Sphinx boktema" -msgid "open issue" -msgstr "åpent nummer" +msgid "Fullscreen mode" +msgstr "Fullskjerm-modus" -msgid "Download notebook file" -msgstr "Last ned notatbokfilen" +msgid "Edit this page" +msgstr "Rediger denne siden" -msgid "Toggle navigation" -msgstr "Bytt navigasjon" +msgid "By" +msgstr "Av" + +msgid "Copyright" +msgstr "opphavsrett" msgid "Source repository" msgstr "Kildedepot" -msgid "By the" -msgstr "Ved" +msgid "previous page" +msgstr "forrige side" msgid "next page" msgstr "neste side" +msgid "Toggle navigation" +msgstr "Bytt navigasjon" + msgid "repository" msgstr "oppbevaringssted" -msgid "Sphinx Book Theme" -msgstr "Sphinx boktema" - -msgid "Download source file" -msgstr "Last ned kildefilen" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Contents" -msgstr "Innhold" +msgid "open issue" +msgstr "åpent nummer" -msgid "By" -msgstr "Av" +msgid "Launch" +msgstr "Start" -msgid "Copyright" -msgstr "opphavsrett" +msgid "Print to PDF" +msgstr "Skriv ut til PDF" -msgid "Fullscreen mode" -msgstr "Fullskjerm-modus" +msgid "By the" +msgstr "Ved" -msgid "Open an issue" -msgstr "Åpne et problem" +msgid "Last updated on" +msgstr "Sist oppdatert den" -msgid "previous page" -msgstr "forrige side" +msgid "Download source file" +msgstr "Last ned kildefilen" msgid "Download this page" msgstr "Last ned denne siden" - -msgid "Theme by the" -msgstr "Tema av" diff --git a/tutorials_and_tools/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po index fcac51d3..80d2c896 100644 --- a/tutorials_and_tools/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: pl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "zaproponuj edycję" +msgid "Theme by the" +msgstr "Motyw autorstwa" -msgid "Last updated on" -msgstr "Ostatnia aktualizacja" +msgid "Open an issue" +msgstr "Otwórz problem" -msgid "Edit this page" -msgstr "Edytuj tę strone" +msgid "Contents" +msgstr "Zawartość" -msgid "Launch" -msgstr "Uruchomić" +msgid "Download notebook file" +msgstr "Pobierz plik notatnika" -msgid "Print to PDF" -msgstr "Drukuj do PDF" +msgid "Sphinx Book Theme" +msgstr "Motyw książki Sphinx" -msgid "open issue" -msgstr "otwarty problem" +msgid "Fullscreen mode" +msgstr "Pełny ekran" -msgid "Download notebook file" -msgstr "Pobierz plik notatnika" +msgid "Edit this page" +msgstr "Edytuj tę strone" -msgid "Toggle navigation" -msgstr "Przełącz nawigację" +msgid "By" +msgstr "Przez" + +msgid "Copyright" +msgstr "prawa autorskie" msgid "Source repository" msgstr "Repozytorium źródłowe" -msgid "By the" -msgstr "Przez" +msgid "previous page" +msgstr "Poprzednia strona" msgid "next page" msgstr "Następna strona" +msgid "Toggle navigation" +msgstr "Przełącz nawigację" + msgid "repository" msgstr "magazyn" -msgid "Sphinx Book Theme" -msgstr "Motyw książki Sphinx" - -msgid "Download source file" -msgstr "Pobierz plik źródłowy" +msgid "suggest edit" +msgstr "zaproponuj edycję" -msgid "Contents" -msgstr "Zawartość" +msgid "open issue" +msgstr "otwarty problem" -msgid "By" -msgstr "Przez" +msgid "Launch" +msgstr "Uruchomić" -msgid "Copyright" -msgstr "prawa autorskie" +msgid "Print to PDF" +msgstr "Drukuj do PDF" -msgid "Fullscreen mode" -msgstr "Pełny ekran" +msgid "By the" +msgstr "Przez" -msgid "Open an issue" -msgstr "Otwórz problem" +msgid "Last updated on" +msgstr "Ostatnia aktualizacja" -msgid "previous page" -msgstr "Poprzednia strona" +msgid "Download source file" +msgstr "Pobierz plik źródłowy" msgid "Download this page" msgstr "Pobierz tę stronę" - -msgid "Theme by the" -msgstr "Motyw autorstwa" diff --git a/tutorials_and_tools/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po index 1761db08..45ac847f 100644 --- a/tutorials_and_tools/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: pt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "sugerir edição" +msgid "Theme by the" +msgstr "Tema por" -msgid "Last updated on" -msgstr "Última atualização em" +msgid "Open an issue" +msgstr "Abra um problema" -msgid "Edit this page" -msgstr "Edite essa página" +msgid "Contents" +msgstr "Conteúdo" -msgid "Launch" -msgstr "Lançamento" +msgid "Download notebook file" +msgstr "Baixar arquivo de notebook" -msgid "Print to PDF" -msgstr "Imprimir em PDF" +msgid "Sphinx Book Theme" +msgstr "Tema do livro Sphinx" -msgid "open issue" -msgstr "questão aberta" +msgid "Fullscreen mode" +msgstr "Modo tela cheia" -msgid "Download notebook file" -msgstr "Baixar arquivo de notebook" +msgid "Edit this page" +msgstr "Edite essa página" -msgid "Toggle navigation" -msgstr "Alternar de navegação" +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "direito autoral" msgid "Source repository" msgstr "Repositório fonte" -msgid "By the" -msgstr "Pelo" +msgid "previous page" +msgstr "página anterior" msgid "next page" msgstr "próxima página" +msgid "Toggle navigation" +msgstr "Alternar de navegação" + msgid "repository" msgstr "repositório" -msgid "Sphinx Book Theme" -msgstr "Tema do livro Sphinx" - -msgid "Download source file" -msgstr "Baixar arquivo fonte" +msgid "suggest edit" +msgstr "sugerir edição" -msgid "Contents" -msgstr "Conteúdo" +msgid "open issue" +msgstr "questão aberta" -msgid "By" -msgstr "De" +msgid "Launch" +msgstr "Lançamento" -msgid "Copyright" -msgstr "direito autoral" +msgid "Print to PDF" +msgstr "Imprimir em PDF" -msgid "Fullscreen mode" -msgstr "Modo tela cheia" +msgid "By the" +msgstr "Pelo" -msgid "Open an issue" -msgstr "Abra um problema" +msgid "Last updated on" +msgstr "Última atualização em" -msgid "previous page" -msgstr "página anterior" +msgid "Download source file" +msgstr "Baixar arquivo fonte" msgid "Download this page" msgstr "Baixe esta página" - -msgid "Theme by the" -msgstr "Tema por" diff --git a/tutorials_and_tools/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po index db865c8f..532b3b84 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: ro\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "sugerează editare" +msgid "Theme by the" +msgstr "Tema de" -msgid "Last updated on" -msgstr "Ultima actualizare la" +msgid "Open an issue" +msgstr "Deschideți o problemă" -msgid "Edit this page" -msgstr "Editați această pagină" +msgid "Contents" +msgstr "Cuprins" -msgid "Launch" -msgstr "Lansa" +msgid "Download notebook file" +msgstr "Descărcați fișierul notebook" -msgid "Print to PDF" -msgstr "Imprimați în PDF" +msgid "Sphinx Book Theme" +msgstr "Tema Sphinx Book" -msgid "open issue" -msgstr "problema deschisă" +msgid "Fullscreen mode" +msgstr "Modul ecran întreg" -msgid "Download notebook file" -msgstr "Descărcați fișierul notebook" +msgid "Edit this page" +msgstr "Editați această pagină" -msgid "Toggle navigation" -msgstr "Comutare navigare" +msgid "By" +msgstr "De" + +msgid "Copyright" +msgstr "Drepturi de autor" msgid "Source repository" msgstr "Depozit sursă" -msgid "By the" -msgstr "Langa" +msgid "previous page" +msgstr "pagina anterioară" msgid "next page" msgstr "pagina următoare" +msgid "Toggle navigation" +msgstr "Comutare navigare" + msgid "repository" msgstr "repertoriu" -msgid "Sphinx Book Theme" -msgstr "Tema Sphinx Book" - -msgid "Download source file" -msgstr "Descărcați fișierul sursă" +msgid "suggest edit" +msgstr "sugerează editare" -msgid "Contents" -msgstr "Cuprins" +msgid "open issue" +msgstr "problema deschisă" -msgid "By" -msgstr "De" +msgid "Launch" +msgstr "Lansa" -msgid "Copyright" -msgstr "Drepturi de autor" +msgid "Print to PDF" +msgstr "Imprimați în PDF" -msgid "Fullscreen mode" -msgstr "Modul ecran întreg" +msgid "By the" +msgstr "Langa" -msgid "Open an issue" -msgstr "Deschideți o problemă" +msgid "Last updated on" +msgstr "Ultima actualizare la" -msgid "previous page" -msgstr "pagina anterioară" +msgid "Download source file" +msgstr "Descărcați fișierul sursă" msgid "Download this page" msgstr "Descarcă această pagină" - -msgid "Theme by the" -msgstr "Tema de" diff --git a/tutorials_and_tools/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po index 84ab6eb5..b718b482 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: ru\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "предложить редактировать" +msgid "Theme by the" +msgstr "Тема от" -msgid "Last updated on" -msgstr "Последнее обновление" +msgid "Open an issue" +msgstr "Открыть вопрос" -msgid "Edit this page" -msgstr "Редактировать эту страницу" +msgid "Contents" +msgstr "Содержание" -msgid "Launch" -msgstr "Запуск" +msgid "Download notebook file" +msgstr "Скачать файл записной книжки" -msgid "Print to PDF" -msgstr "Распечатать в PDF" +msgid "Sphinx Book Theme" +msgstr "Тема книги Сфинкс" -msgid "open issue" -msgstr "открытый вопрос" +msgid "Fullscreen mode" +msgstr "Полноэкранный режим" -msgid "Download notebook file" -msgstr "Скачать файл записной книжки" +msgid "Edit this page" +msgstr "Редактировать эту страницу" -msgid "Toggle navigation" -msgstr "Переключить навигацию" +msgid "By" +msgstr "По" + +msgid "Copyright" +msgstr "авторское право" msgid "Source repository" msgstr "Исходный репозиторий" -msgid "By the" -msgstr "Посредством" +msgid "previous page" +msgstr "Предыдущая страница" msgid "next page" msgstr "Следующая страница" +msgid "Toggle navigation" +msgstr "Переключить навигацию" + msgid "repository" msgstr "хранилище" -msgid "Sphinx Book Theme" -msgstr "Тема книги Сфинкс" - -msgid "Download source file" -msgstr "Скачать исходный файл" +msgid "suggest edit" +msgstr "предложить редактировать" -msgid "Contents" -msgstr "Содержание" +msgid "open issue" +msgstr "открытый вопрос" -msgid "By" -msgstr "По" +msgid "Launch" +msgstr "Запуск" -msgid "Copyright" -msgstr "авторское право" +msgid "Print to PDF" +msgstr "Распечатать в PDF" -msgid "Fullscreen mode" -msgstr "Полноэкранный режим" +msgid "By the" +msgstr "Посредством" -msgid "Open an issue" -msgstr "Открыть вопрос" +msgid "Last updated on" +msgstr "Последнее обновление" -msgid "previous page" -msgstr "Предыдущая страница" +msgid "Download source file" +msgstr "Скачать исходный файл" msgid "Download this page" msgstr "Загрузите эту страницу" - -msgid "Theme by the" -msgstr "Тема от" diff --git a/tutorials_and_tools/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po index e44878b5..f6c423b6 100644 --- a/tutorials_and_tools/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: sk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "navrhnúť úpravu" +msgid "Theme by the" +msgstr "Téma od" -msgid "Last updated on" -msgstr "Posledná aktualizácia dňa" +msgid "Open an issue" +msgstr "Otvorte problém" -msgid "Edit this page" -msgstr "Upraviť túto stránku" +msgid "Contents" +msgstr "Obsah" -msgid "Launch" -msgstr "Spustiť" +msgid "Download notebook file" +msgstr "Stiahnite si zošit" -msgid "Print to PDF" -msgstr "Tlač do PDF" +msgid "Sphinx Book Theme" +msgstr "Téma knihy Sfinga" -msgid "open issue" -msgstr "otvorené vydanie" +msgid "Fullscreen mode" +msgstr "Režim celej obrazovky" -msgid "Download notebook file" -msgstr "Stiahnite si zošit" +msgid "Edit this page" +msgstr "Upraviť túto stránku" -msgid "Toggle navigation" -msgstr "Prepnúť navigáciu" +msgid "By" +msgstr "Autor:" + +msgid "Copyright" +msgstr "Autorské práva" msgid "Source repository" msgstr "Zdrojové úložisko" -msgid "By the" -msgstr "Podľa" +msgid "previous page" +msgstr "predchádzajúca strana" msgid "next page" msgstr "ďalšia strana" +msgid "Toggle navigation" +msgstr "Prepnúť navigáciu" + msgid "repository" msgstr "Úložisko" -msgid "Sphinx Book Theme" -msgstr "Téma knihy Sfinga" - -msgid "Download source file" -msgstr "Stiahnite si zdrojový súbor" +msgid "suggest edit" +msgstr "navrhnúť úpravu" -msgid "Contents" -msgstr "Obsah" +msgid "open issue" +msgstr "otvorené vydanie" -msgid "By" -msgstr "Autor:" +msgid "Launch" +msgstr "Spustiť" -msgid "Copyright" -msgstr "Autorské práva" +msgid "Print to PDF" +msgstr "Tlač do PDF" -msgid "Fullscreen mode" -msgstr "Režim celej obrazovky" +msgid "By the" +msgstr "Podľa" -msgid "Open an issue" -msgstr "Otvorte problém" +msgid "Last updated on" +msgstr "Posledná aktualizácia dňa" -msgid "previous page" -msgstr "predchádzajúca strana" +msgid "Download source file" +msgstr "Stiahnite si zdrojový súbor" msgid "Download this page" msgstr "Stiahnite si túto stránku" - -msgid "Theme by the" -msgstr "Téma od" diff --git a/tutorials_and_tools/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po index 228939bc..9822dc58 100644 --- a/tutorials_and_tools/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: sl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "predlagajte urejanje" +msgid "Theme by the" +msgstr "Tema avtorja" -msgid "Last updated on" -msgstr "Nazadnje posodobljeno dne" +msgid "Open an issue" +msgstr "Odprite številko" -msgid "Edit this page" -msgstr "Uredite to stran" +msgid "Contents" +msgstr "Vsebina" -msgid "Launch" -msgstr "Kosilo" +msgid "Download notebook file" +msgstr "Prenesite datoteko zvezka" -msgid "Print to PDF" -msgstr "Natisni v PDF" +msgid "Sphinx Book Theme" +msgstr "Tema knjige Sphinx" -msgid "open issue" -msgstr "odprto vprašanje" +msgid "Fullscreen mode" +msgstr "Celozaslonski način" -msgid "Download notebook file" -msgstr "Prenesite datoteko zvezka" +msgid "Edit this page" +msgstr "Uredite to stran" -msgid "Toggle navigation" -msgstr "Preklopi navigacijo" +msgid "By" +msgstr "Avtor" + +msgid "Copyright" +msgstr "avtorske pravice" msgid "Source repository" msgstr "Izvorno skladišče" -msgid "By the" -msgstr "Avtor" +msgid "previous page" +msgstr "Prejšnja stran" msgid "next page" msgstr "Naslednja stran" +msgid "Toggle navigation" +msgstr "Preklopi navigacijo" + msgid "repository" msgstr "odlagališče" -msgid "Sphinx Book Theme" -msgstr "Tema knjige Sphinx" - -msgid "Download source file" -msgstr "Prenesite izvorno datoteko" +msgid "suggest edit" +msgstr "predlagajte urejanje" -msgid "Contents" -msgstr "Vsebina" +msgid "open issue" +msgstr "odprto vprašanje" -msgid "By" -msgstr "Avtor" +msgid "Launch" +msgstr "Kosilo" -msgid "Copyright" -msgstr "avtorske pravice" +msgid "Print to PDF" +msgstr "Natisni v PDF" -msgid "Fullscreen mode" -msgstr "Celozaslonski način" +msgid "By the" +msgstr "Avtor" -msgid "Open an issue" -msgstr "Odprite številko" +msgid "Last updated on" +msgstr "Nazadnje posodobljeno dne" -msgid "previous page" -msgstr "Prejšnja stran" +msgid "Download source file" +msgstr "Prenesite izvorno datoteko" msgid "Download this page" msgstr "Prenesite to stran" - -msgid "Theme by the" -msgstr "Tema avtorja" diff --git a/tutorials_and_tools/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po index 1a712a18..e809230c 100644 --- a/tutorials_and_tools/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: sr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "предложи уређивање" +msgid "Theme by the" +msgstr "Тхеме би" -msgid "Last updated on" -msgstr "Последње ажурирање" +msgid "Open an issue" +msgstr "Отворите издање" -msgid "Edit this page" -msgstr "Уредите ову страницу" +msgid "Contents" +msgstr "Садржај" -msgid "Launch" -msgstr "Лансирање" +msgid "Download notebook file" +msgstr "Преузмите датотеку бележнице" -msgid "Print to PDF" -msgstr "Испис у ПДФ" +msgid "Sphinx Book Theme" +msgstr "Тема књиге Спхинк" -msgid "open issue" -msgstr "отворено издање" +msgid "Fullscreen mode" +msgstr "Режим целог екрана" -msgid "Download notebook file" -msgstr "Преузмите датотеку бележнице" +msgid "Edit this page" +msgstr "Уредите ову страницу" -msgid "Toggle navigation" -msgstr "Укључи / искључи навигацију" +msgid "By" +msgstr "Од стране" + +msgid "Copyright" +msgstr "Ауторско право" msgid "Source repository" msgstr "Изворно спремиште" -msgid "By the" -msgstr "Од" +msgid "previous page" +msgstr "Претходна страница" msgid "next page" msgstr "Следећа страна" +msgid "Toggle navigation" +msgstr "Укључи / искључи навигацију" + msgid "repository" msgstr "спремиште" -msgid "Sphinx Book Theme" -msgstr "Тема књиге Спхинк" - -msgid "Download source file" -msgstr "Преузми изворну датотеку" +msgid "suggest edit" +msgstr "предложи уређивање" -msgid "Contents" -msgstr "Садржај" +msgid "open issue" +msgstr "отворено издање" -msgid "By" -msgstr "Од стране" +msgid "Launch" +msgstr "Лансирање" -msgid "Copyright" -msgstr "Ауторско право" +msgid "Print to PDF" +msgstr "Испис у ПДФ" -msgid "Fullscreen mode" -msgstr "Режим целог екрана" +msgid "By the" +msgstr "Од" -msgid "Open an issue" -msgstr "Отворите издање" +msgid "Last updated on" +msgstr "Последње ажурирање" -msgid "previous page" -msgstr "Претходна страница" +msgid "Download source file" +msgstr "Преузми изворну датотеку" msgid "Download this page" msgstr "Преузмите ову страницу" - -msgid "Theme by the" -msgstr "Тхеме би" diff --git a/tutorials_and_tools/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po index 7d2b56d9..2421b001 100644 --- a/tutorials_and_tools/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: sv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "föreslå redigering" +msgid "Theme by the" +msgstr "Tema av" -msgid "Last updated on" -msgstr "Senast uppdaterad den" +msgid "Open an issue" +msgstr "Öppna en problemrapport" -msgid "Edit this page" -msgstr "Redigera den här sidan" +msgid "Contents" +msgstr "Innehåll" -msgid "Launch" -msgstr "Lansera" +msgid "Download notebook file" +msgstr "Ladda ner notebook-fil" -msgid "Print to PDF" -msgstr "Skriv ut till PDF" +msgid "Sphinx Book Theme" +msgstr "Sphinx Boktema" -msgid "open issue" -msgstr "öppet problem" +msgid "Fullscreen mode" +msgstr "Fullskärmsläge" -msgid "Download notebook file" -msgstr "Ladda ner anteckningsbokfilen" +msgid "Edit this page" +msgstr "Redigera den här sidan" -msgid "Toggle navigation" -msgstr "Växla navigering" +msgid "By" +msgstr "Av" + +msgid "Copyright" +msgstr "Upphovsrätt" msgid "Source repository" -msgstr "Källförvar" +msgstr "Källkodsrepositorium" -msgid "By the" -msgstr "Vid" +msgid "previous page" +msgstr "föregående sida" msgid "next page" msgstr "nästa sida" -msgid "repository" -msgstr "förvar" +msgid "Toggle navigation" +msgstr "Växla navigering" -msgid "Sphinx Book Theme" -msgstr "Sphinx boktema" +msgid "repository" +msgstr "repositorium" -msgid "Download source file" -msgstr "Ladda ner källfil" +msgid "suggest edit" +msgstr "föreslå ändring" -msgid "Contents" -msgstr "Innehåll" +msgid "open issue" +msgstr "öppna problemrapport" -msgid "By" -msgstr "Förbi" +msgid "Launch" +msgstr "Öppna" -msgid "Copyright" -msgstr "upphovsrätt" +msgid "Print to PDF" +msgstr "Skriv ut till PDF" -msgid "Fullscreen mode" -msgstr "Fullskärmsläge" +msgid "By the" +msgstr "Av den" -msgid "Open an issue" -msgstr "Öppna ett problem" +msgid "Last updated on" +msgstr "Senast uppdaterad den" -msgid "previous page" -msgstr "föregående sida" +msgid "Download source file" +msgstr "Ladda ner källfil" msgid "Download this page" msgstr "Ladda ner den här sidan" - -msgid "Theme by the" -msgstr "Tema av" diff --git a/tutorials_and_tools/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po index c75ffe19..500042f4 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: ta\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "திருத்த பரிந்துரைக்கவும்" - -msgid "Last updated on" -msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" +msgid "Theme by the" +msgstr "வழங்கிய தீம்" -msgid "Edit this page" -msgstr "இந்தப் பக்கத்தைத் திருத்தவும்" +msgid "Open an issue" +msgstr "சிக்கலைத் திறக்கவும்" -msgid "Launch" -msgstr "தொடங்க" +msgid "Download notebook file" +msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" -msgid "Print to PDF" -msgstr "PDF இல் அச்சிடுக" +msgid "Sphinx Book Theme" +msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்" -msgid "open issue" -msgstr "திறந்த பிரச்சினை" +msgid "Edit this page" +msgstr "இந்தப் பக்கத்தைத் திருத்தவும்" -msgid "Download notebook file" -msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" +msgid "By" +msgstr "வழங்கியவர்" -msgid "Toggle navigation" -msgstr "வழிசெலுத்தலை நிலைமாற்று" +msgid "Copyright" +msgstr "பதிப்புரிமை" msgid "Source repository" msgstr "மூல களஞ்சியம்" -msgid "By the" -msgstr "மூலம்" +msgid "previous page" +msgstr "முந்தைய பக்கம்" msgid "next page" msgstr "அடுத்த பக்கம்" -msgid "Sphinx Book Theme" -msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்" +msgid "Toggle navigation" +msgstr "வழிசெலுத்தலை நிலைமாற்று" -msgid "Download source file" -msgstr "மூல கோப்பைப் பதிவிறக்குக" +msgid "suggest edit" +msgstr "திருத்த பரிந்துரைக்கவும்" -msgid "By" -msgstr "வழங்கியவர்" +msgid "open issue" +msgstr "திறந்த பிரச்சினை" -msgid "Copyright" -msgstr "பதிப்புரிமை" +msgid "Launch" +msgstr "தொடங்க" -msgid "Open an issue" -msgstr "சிக்கலைத் திறக்கவும்" +msgid "Print to PDF" +msgstr "PDF இல் அச்சிடுக" -msgid "previous page" -msgstr "முந்தைய பக்கம்" +msgid "By the" +msgstr "மூலம்" + +msgid "Last updated on" +msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" + +msgid "Download source file" +msgstr "மூல கோப்பைப் பதிவிறக்குக" msgid "Download this page" msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" - -msgid "Theme by the" -msgstr "வழங்கிய தீம்" diff --git a/tutorials_and_tools/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po index 2595c035..b1afebba 100644 --- a/tutorials_and_tools/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: te\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "సవరించమని సూచించండి" - -msgid "Last updated on" -msgstr "చివరిగా నవీకరించబడింది" +msgid "Theme by the" +msgstr "ద్వారా థీమ్" -msgid "Edit this page" -msgstr "ఈ పేజీని సవరించండి" +msgid "Open an issue" +msgstr "సమస్యను తెరవండి" -msgid "Launch" -msgstr "ప్రారంభించండి" +msgid "Download notebook file" +msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" -msgid "Print to PDF" -msgstr "PDF కి ముద్రించండి" +msgid "Sphinx Book Theme" +msgstr "సింహిక పుస్తక థీమ్" -msgid "open issue" -msgstr "ఓపెన్ ఇష్యూ" +msgid "Edit this page" +msgstr "ఈ పేజీని సవరించండి" -msgid "Download notebook file" -msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" +msgid "By" +msgstr "ద్వారా" -msgid "Toggle navigation" -msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" +msgid "Copyright" +msgstr "కాపీరైట్" msgid "Source repository" msgstr "మూల రిపోజిటరీ" -msgid "By the" -msgstr "ద్వారా" +msgid "previous page" +msgstr "ముందు పేజి" msgid "next page" msgstr "తరువాతి పేజీ" -msgid "Sphinx Book Theme" -msgstr "సింహిక పుస్తక థీమ్" +msgid "Toggle navigation" +msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" -msgid "Download source file" -msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి" +msgid "suggest edit" +msgstr "సవరించమని సూచించండి" -msgid "By" -msgstr "ద్వారా" +msgid "open issue" +msgstr "ఓపెన్ ఇష్యూ" -msgid "Copyright" -msgstr "కాపీరైట్" +msgid "Launch" +msgstr "ప్రారంభించండి" -msgid "Open an issue" -msgstr "సమస్యను తెరవండి" +msgid "Print to PDF" +msgstr "PDF కి ముద్రించండి" -msgid "previous page" -msgstr "ముందు పేజి" +msgid "By the" +msgstr "ద్వారా" + +msgid "Last updated on" +msgstr "చివరిగా నవీకరించబడింది" + +msgid "Download source file" +msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి" msgid "Download this page" msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" - -msgid "Theme by the" -msgstr "ద్వారా థీమ్" diff --git a/tutorials_and_tools/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po index 73cd30ea..29b8237b 100644 --- a/tutorials_and_tools/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: tg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "пешниҳод вироиш" +msgid "Theme by the" +msgstr "Мавзӯъи аз" -msgid "Last updated on" -msgstr "Last навсозӣ дар" +msgid "Open an issue" +msgstr "Масъаларо кушоед" -msgid "Edit this page" -msgstr "Ин саҳифаро таҳрир кунед" +msgid "Contents" +msgstr "Мундариҷа" -msgid "Launch" -msgstr "Оғоз" +msgid "Download notebook file" +msgstr "Файли дафтарро зеркашӣ кунед" -msgid "Print to PDF" -msgstr "Чоп ба PDF" +msgid "Sphinx Book Theme" +msgstr "Сфинкс Мавзӯи китоб" -msgid "open issue" -msgstr "барориши кушод" +msgid "Fullscreen mode" +msgstr "Ҳолати экрани пурра" -msgid "Download notebook file" -msgstr "Файли дафтарро зеркашӣ кунед" +msgid "Edit this page" +msgstr "Ин саҳифаро таҳрир кунед" -msgid "Toggle navigation" -msgstr "Гузаришро иваз кунед" +msgid "By" +msgstr "Бо" + +msgid "Copyright" +msgstr "Ҳуқуқи муаллиф" msgid "Source repository" msgstr "Анбори манбаъ" -msgid "By the" -msgstr "Бо" +msgid "previous page" +msgstr "саҳифаи қаблӣ" msgid "next page" msgstr "саҳифаи оянда" +msgid "Toggle navigation" +msgstr "Гузаришро иваз кунед" + msgid "repository" msgstr "анбор" -msgid "Sphinx Book Theme" -msgstr "Сфинкс Мавзӯи китоб" - -msgid "Download source file" -msgstr "Файли манбаъро зеркашӣ кунед" +msgid "suggest edit" +msgstr "пешниҳод вироиш" -msgid "Contents" -msgstr "Мундариҷа" +msgid "open issue" +msgstr "барориши кушод" -msgid "By" -msgstr "Бо" +msgid "Launch" +msgstr "Оғоз" -msgid "Copyright" -msgstr "Ҳуқуқи муаллиф" +msgid "Print to PDF" +msgstr "Чоп ба PDF" -msgid "Fullscreen mode" -msgstr "Ҳолати экрани пурра" +msgid "By the" +msgstr "Бо" -msgid "Open an issue" -msgstr "Масъаларо кушоед" +msgid "Last updated on" +msgstr "Last навсозӣ дар" -msgid "previous page" -msgstr "саҳифаи қаблӣ" +msgid "Download source file" +msgstr "Файли манбаъро зеркашӣ кунед" msgid "Download this page" msgstr "Ин саҳифаро зеркашӣ кунед" - -msgid "Theme by the" -msgstr "Мавзӯъи аз" diff --git a/tutorials_and_tools/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po index 0392b4ad..ac65ee05 100644 --- a/tutorials_and_tools/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: th\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "แนะนำแก้ไข" +msgid "Theme by the" +msgstr "ธีมโดย" -msgid "Last updated on" -msgstr "ปรับปรุงล่าสุดเมื่อ" +msgid "Open an issue" +msgstr "เปิดปัญหา" -msgid "Edit this page" -msgstr "แก้ไขหน้านี้" +msgid "Contents" +msgstr "สารบัญ" -msgid "Launch" -msgstr "เปิด" +msgid "Download notebook file" +msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" -msgid "Print to PDF" -msgstr "พิมพ์เป็น PDF" +msgid "Sphinx Book Theme" +msgstr "ธีมหนังสือสฟิงซ์" -msgid "open issue" -msgstr "เปิดปัญหา" +msgid "Fullscreen mode" +msgstr "โหมดเต็มหน้าจอ" -msgid "Download notebook file" -msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" +msgid "Edit this page" +msgstr "แก้ไขหน้านี้" -msgid "Toggle navigation" -msgstr "ไม่ต้องสลับช่องทาง" +msgid "By" +msgstr "โดย" + +msgid "Copyright" +msgstr "ลิขสิทธิ์" msgid "Source repository" msgstr "ที่เก็บซอร์ส" -msgid "By the" -msgstr "โดย" +msgid "previous page" +msgstr "หน้าที่แล้ว" msgid "next page" msgstr "หน้าต่อไป" +msgid "Toggle navigation" +msgstr "ไม่ต้องสลับช่องทาง" + msgid "repository" msgstr "ที่เก็บ" -msgid "Sphinx Book Theme" -msgstr "ธีมหนังสือสฟิงซ์" - -msgid "Download source file" -msgstr "ดาวน์โหลดไฟล์ต้นฉบับ" +msgid "suggest edit" +msgstr "แนะนำแก้ไข" -msgid "Contents" -msgstr "สารบัญ" +msgid "open issue" +msgstr "เปิดปัญหา" -msgid "By" -msgstr "โดย" +msgid "Launch" +msgstr "เปิด" -msgid "Copyright" -msgstr "ลิขสิทธิ์" +msgid "Print to PDF" +msgstr "พิมพ์เป็น PDF" -msgid "Fullscreen mode" -msgstr "โหมดเต็มหน้าจอ" +msgid "By the" +msgstr "โดย" -msgid "Open an issue" -msgstr "เปิดปัญหา" +msgid "Last updated on" +msgstr "ปรับปรุงล่าสุดเมื่อ" -msgid "previous page" -msgstr "หน้าที่แล้ว" +msgid "Download source file" +msgstr "ดาวน์โหลดไฟล์ต้นฉบับ" msgid "Download this page" msgstr "ดาวน์โหลดหน้านี้" - -msgid "Theme by the" -msgstr "ธีมโดย" diff --git a/tutorials_and_tools/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po index c8375b54..662d66ca 100644 --- a/tutorials_and_tools/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: tl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "iminumungkahi i-edit" - -msgid "Last updated on" -msgstr "Huling na-update noong" +msgid "Theme by the" +msgstr "Tema ng" -msgid "Edit this page" -msgstr "I-edit ang pahinang ito" +msgid "Open an issue" +msgstr "Magbukas ng isyu" -msgid "Launch" -msgstr "Ilunsad" +msgid "Download notebook file" +msgstr "Mag-download ng file ng notebook" -msgid "Print to PDF" -msgstr "I-print sa PDF" +msgid "Sphinx Book Theme" +msgstr "Tema ng Sphinx Book" -msgid "open issue" -msgstr "bukas na isyu" +msgid "Edit this page" +msgstr "I-edit ang pahinang ito" -msgid "Download notebook file" -msgstr "Mag-download ng file ng notebook" +msgid "By" +msgstr "Ni" -msgid "Toggle navigation" -msgstr "I-toggle ang pag-navigate" +msgid "Copyright" +msgstr "Copyright" msgid "Source repository" msgstr "Pinagmulan ng imbakan" -msgid "By the" -msgstr "Sa pamamagitan ng" +msgid "previous page" +msgstr "Nakaraang pahina" msgid "next page" msgstr "Susunod na pahina" -msgid "Sphinx Book Theme" -msgstr "Tema ng Sphinx Book" +msgid "Toggle navigation" +msgstr "I-toggle ang pag-navigate" -msgid "Download source file" -msgstr "Mag-download ng file ng pinagmulan" +msgid "suggest edit" +msgstr "iminumungkahi i-edit" -msgid "By" -msgstr "Ni" +msgid "open issue" +msgstr "bukas na isyu" -msgid "Copyright" -msgstr "Copyright" +msgid "Launch" +msgstr "Ilunsad" -msgid "Open an issue" -msgstr "Magbukas ng isyu" +msgid "Print to PDF" +msgstr "I-print sa PDF" -msgid "previous page" -msgstr "Nakaraang pahina" +msgid "By the" +msgstr "Sa pamamagitan ng" + +msgid "Last updated on" +msgstr "Huling na-update noong" + +msgid "Download source file" +msgstr "Mag-download ng file ng pinagmulan" msgid "Download this page" msgstr "I-download ang pahinang ito" - -msgid "Theme by the" -msgstr "Tema ng" diff --git a/tutorials_and_tools/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po index 47d7bdf7..d1ae7233 100644 --- a/tutorials_and_tools/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: tr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "düzenleme öner" +msgid "Theme by the" +msgstr "Tarafından tema" -msgid "Last updated on" -msgstr "Son güncelleme tarihi" +msgid "Open an issue" +msgstr "Bir sorunu açın" -msgid "Edit this page" -msgstr "Bu sayfayı düzenle" +msgid "Contents" +msgstr "İçindekiler" -msgid "Launch" -msgstr "Başlatmak" +msgid "Download notebook file" +msgstr "Defter dosyasını indirin" -msgid "Print to PDF" -msgstr "PDF olarak yazdır" +msgid "Sphinx Book Theme" +msgstr "Sfenks Kitap Teması" -msgid "open issue" -msgstr "Açık konu" +msgid "Fullscreen mode" +msgstr "Tam ekran modu" -msgid "Download notebook file" -msgstr "Defter dosyasını indirin" +msgid "Edit this page" +msgstr "Bu sayfayı düzenle" -msgid "Toggle navigation" -msgstr "Gezinmeyi değiştir" +msgid "By" +msgstr "Tarafından" + +msgid "Copyright" +msgstr "Telif hakkı" msgid "Source repository" msgstr "Kaynak kod deposu" -msgid "By the" -msgstr "Tarafından" +msgid "previous page" +msgstr "önceki sayfa" msgid "next page" msgstr "sonraki Sayfa" +msgid "Toggle navigation" +msgstr "Gezinmeyi değiştir" + msgid "repository" msgstr "depo" -msgid "Sphinx Book Theme" -msgstr "Sfenks Kitap Teması" - -msgid "Download source file" -msgstr "Kaynak dosyayı indirin" +msgid "suggest edit" +msgstr "düzenleme öner" -msgid "Contents" -msgstr "İçindekiler" +msgid "open issue" +msgstr "Açık konu" -msgid "By" -msgstr "Tarafından" +msgid "Launch" +msgstr "Başlatmak" -msgid "Copyright" -msgstr "Telif hakkı" +msgid "Print to PDF" +msgstr "PDF olarak yazdır" -msgid "Fullscreen mode" -msgstr "Tam ekran modu" +msgid "By the" +msgstr "Tarafından" -msgid "Open an issue" -msgstr "Bir sorunu açın" +msgid "Last updated on" +msgstr "Son güncelleme tarihi" -msgid "previous page" -msgstr "önceki sayfa" +msgid "Download source file" +msgstr "Kaynak dosyayı indirin" msgid "Download this page" msgstr "Bu sayfayı indirin" - -msgid "Theme by the" -msgstr "Tarafından tema" diff --git a/tutorials_and_tools/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po index e85f6f16..be49ab85 100644 --- a/tutorials_and_tools/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: uk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "запропонувати редагувати" +msgid "Theme by the" +msgstr "Тема від" -msgid "Last updated on" -msgstr "Останнє оновлення:" +msgid "Open an issue" +msgstr "Відкрийте випуск" -msgid "Edit this page" -msgstr "Редагувати цю сторінку" +msgid "Contents" +msgstr "Зміст" -msgid "Launch" -msgstr "Запуск" +msgid "Download notebook file" +msgstr "Завантажте файл блокнота" -msgid "Print to PDF" -msgstr "Друк у форматі PDF" +msgid "Sphinx Book Theme" +msgstr "Тема книги \"Сфінкс\"" -msgid "open issue" -msgstr "відкритий випуск" +msgid "Fullscreen mode" +msgstr "Повноекранний режим" -msgid "Download notebook file" -msgstr "Завантажте файл блокнота" +msgid "Edit this page" +msgstr "Редагувати цю сторінку" -msgid "Toggle navigation" -msgstr "Переключити навігацію" +msgid "By" +msgstr "Автор" + +msgid "Copyright" +msgstr "Авторське право" msgid "Source repository" msgstr "Джерело сховища" -msgid "By the" -msgstr "По" +msgid "previous page" +msgstr "Попередня сторінка" msgid "next page" msgstr "Наступна сторінка" +msgid "Toggle navigation" +msgstr "Переключити навігацію" + msgid "repository" msgstr "сховище" -msgid "Sphinx Book Theme" -msgstr "Тема книги \"Сфінкс\"" - -msgid "Download source file" -msgstr "Завантажити вихідний файл" +msgid "suggest edit" +msgstr "запропонувати редагувати" -msgid "Contents" -msgstr "Зміст" +msgid "open issue" +msgstr "відкритий випуск" -msgid "By" -msgstr "Автор" +msgid "Launch" +msgstr "Запуск" -msgid "Copyright" -msgstr "Авторське право" +msgid "Print to PDF" +msgstr "Друк у форматі PDF" -msgid "Fullscreen mode" -msgstr "Повноекранний режим" +msgid "By the" +msgstr "По" -msgid "Open an issue" -msgstr "Відкрийте випуск" +msgid "Last updated on" +msgstr "Останнє оновлення:" -msgid "previous page" -msgstr "Попередня сторінка" +msgid "Download source file" +msgstr "Завантажити вихідний файл" msgid "Download this page" msgstr "Завантажте цю сторінку" - -msgid "Theme by the" -msgstr "Тема від" diff --git a/tutorials_and_tools/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po index 0f90726c..94bcab33 100644 --- a/tutorials_and_tools/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po @@ -8,59 +8,59 @@ msgstr "" "Language: ur\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "ترمیم کی تجویز کریں" - -msgid "Last updated on" -msgstr "آخری بار تازہ کاری ہوئی" +msgid "Theme by the" +msgstr "کے ذریعہ تھیم" -msgid "Edit this page" -msgstr "اس صفحے میں ترمیم کریں" +msgid "Open an issue" +msgstr "ایک مسئلہ کھولیں" -msgid "Launch" -msgstr "لانچ کریں" +msgid "Download notebook file" +msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" -msgid "Print to PDF" -msgstr "پی ڈی ایف پرنٹ کریں" +msgid "Sphinx Book Theme" +msgstr "سپنکس بک تھیم" -msgid "open issue" -msgstr "کھلا مسئلہ" +msgid "Edit this page" +msgstr "اس صفحے میں ترمیم کریں" -msgid "Download notebook file" -msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" +msgid "By" +msgstr "بذریعہ" -msgid "Toggle navigation" -msgstr "نیویگیشن ٹوگل کریں" +msgid "Copyright" +msgstr "کاپی رائٹ" msgid "Source repository" msgstr "ماخذ ذخیرہ" -msgid "By the" -msgstr "کی طرف" +msgid "previous page" +msgstr "سابقہ ​​صفحہ" msgid "next page" msgstr "اگلا صفحہ" -msgid "Sphinx Book Theme" -msgstr "سپنکس بک تھیم" +msgid "Toggle navigation" +msgstr "نیویگیشن ٹوگل کریں" -msgid "Download source file" -msgstr "سورس فائل ڈاؤن لوڈ کریں" +msgid "suggest edit" +msgstr "ترمیم کی تجویز کریں" -msgid "By" -msgstr "بذریعہ" +msgid "open issue" +msgstr "کھلا مسئلہ" -msgid "Copyright" -msgstr "کاپی رائٹ" +msgid "Launch" +msgstr "لانچ کریں" -msgid "Open an issue" -msgstr "ایک مسئلہ کھولیں" +msgid "Print to PDF" +msgstr "پی ڈی ایف پرنٹ کریں" -msgid "previous page" -msgstr "سابقہ ​​صفحہ" +msgid "By the" +msgstr "کی طرف" + +msgid "Last updated on" +msgstr "آخری بار تازہ کاری ہوئی" + +msgid "Download source file" +msgstr "سورس فائل ڈاؤن لوڈ کریں" msgid "Download this page" msgstr "اس صفحے کو ڈاؤن لوڈ کریں" - -msgid "Theme by the" -msgstr "کے ذریعہ تھیم" diff --git a/tutorials_and_tools/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po index 2cb5cf3b..116236dc 100644 --- a/tutorials_and_tools/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: vi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "đề nghị chỉnh sửa" +msgid "Theme by the" +msgstr "Chủ đề của" -msgid "Last updated on" -msgstr "Cập nhật lần cuối vào" +msgid "Open an issue" +msgstr "Mở một vấn đề" -msgid "Edit this page" -msgstr "chỉnh sửa trang này" +msgid "Contents" +msgstr "Nội dung" -msgid "Launch" -msgstr "Phóng" +msgid "Download notebook file" +msgstr "Tải xuống tệp sổ tay" -msgid "Print to PDF" -msgstr "In sang PDF" +msgid "Sphinx Book Theme" +msgstr "Chủ đề sách nhân sư" -msgid "open issue" -msgstr "vấn đề mở" +msgid "Fullscreen mode" +msgstr "Chế độ toàn màn hình" -msgid "Download notebook file" -msgstr "Tải xuống tệp sổ tay" +msgid "Edit this page" +msgstr "chỉnh sửa trang này" -msgid "Toggle navigation" -msgstr "Chuyển đổi điều hướng thành" +msgid "By" +msgstr "Bởi" + +msgid "Copyright" +msgstr "Bản quyền" msgid "Source repository" msgstr "Kho nguồn" -msgid "By the" -msgstr "Bằng" +msgid "previous page" +msgstr "trang trước" msgid "next page" msgstr "Trang tiếp theo" +msgid "Toggle navigation" +msgstr "Chuyển đổi điều hướng thành" + msgid "repository" msgstr "kho" -msgid "Sphinx Book Theme" -msgstr "Chủ đề sách nhân sư" - -msgid "Download source file" -msgstr "Tải xuống tệp nguồn" +msgid "suggest edit" +msgstr "đề nghị chỉnh sửa" -msgid "Contents" -msgstr "Nội dung" +msgid "open issue" +msgstr "vấn đề mở" -msgid "By" -msgstr "Bởi" +msgid "Launch" +msgstr "Phóng" -msgid "Copyright" -msgstr "Bản quyền" +msgid "Print to PDF" +msgstr "In sang PDF" -msgid "Fullscreen mode" -msgstr "Chế độ toàn màn hình" +msgid "By the" +msgstr "Bằng" -msgid "Open an issue" -msgstr "Mở một vấn đề" +msgid "Last updated on" +msgstr "Cập nhật lần cuối vào" -msgid "previous page" -msgstr "trang trước" +msgid "Download source file" +msgstr "Tải xuống tệp nguồn" msgid "Download this page" msgstr "Tải xuống trang này" - -msgid "Theme by the" -msgstr "Chủ đề của" diff --git a/tutorials_and_tools/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po index f91f3ba0..4f4ab579 100644 --- a/tutorials_and_tools/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: zh_CN\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "提出修改建议" +msgid "Theme by the" +msgstr "主题作者：" -msgid "Last updated on" -msgstr "上次更新时间：" +msgid "Open an issue" +msgstr "创建议题" -msgid "Edit this page" -msgstr "编辑此页面" +msgid "Contents" +msgstr "目录" -msgid "Launch" -msgstr "启动" +msgid "Download notebook file" +msgstr "下载笔记本文件" -msgid "Print to PDF" -msgstr "列印成 PDF" +msgid "Sphinx Book Theme" +msgstr "Sphinx Book 主题" -msgid "open issue" -msgstr "创建议题" +msgid "Fullscreen mode" +msgstr "全屏模式" -msgid "Download notebook file" -msgstr "下载笔记本文件" +msgid "Edit this page" +msgstr "编辑此页面" -msgid "Toggle navigation" -msgstr "显示或隐藏导航栏" +msgid "By" +msgstr "作者：" + +msgid "Copyright" +msgstr "版权" msgid "Source repository" msgstr "源码库" -msgid "By the" -msgstr "作者：" +msgid "previous page" +msgstr "上一页" msgid "next page" msgstr "下一页" +msgid "Toggle navigation" +msgstr "显示或隐藏导航栏" + msgid "repository" msgstr "仓库" -msgid "Sphinx Book Theme" -msgstr "Sphinx Book 主题" - -msgid "Download source file" -msgstr "下载源文件" +msgid "suggest edit" +msgstr "提出修改建议" -msgid "Contents" -msgstr "目录" +msgid "open issue" +msgstr "创建议题" -msgid "By" -msgstr "作者：" +msgid "Launch" +msgstr "启动" -msgid "Copyright" -msgstr "版权" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Fullscreen mode" -msgstr "全屏模式" +msgid "By the" +msgstr "作者：" -msgid "Open an issue" -msgstr "创建议题" +msgid "Last updated on" +msgstr "上次更新时间：" -msgid "previous page" -msgstr "上一页" +msgid "Download source file" +msgstr "下载源文件" msgid "Download this page" msgstr "下载此页面" - -msgid "Theme by the" -msgstr "主题作者：" diff --git a/tutorials_and_tools/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/tutorials_and_tools/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po index 7833d904..42b43b86 100644 --- a/tutorials_and_tools/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po +++ b/tutorials_and_tools/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po @@ -8,68 +8,68 @@ msgstr "" "Language: zh_TW\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "suggest edit" -msgstr "提出修改建議" +msgid "Theme by the" +msgstr "佈景主題作者：" -msgid "Last updated on" -msgstr "最後更新時間：" +msgid "Open an issue" +msgstr "開啟議題" -msgid "Edit this page" -msgstr "編輯此頁面" +msgid "Contents" +msgstr "目錄" -msgid "Launch" -msgstr "啟動" +msgid "Download notebook file" +msgstr "下載 Notebook 檔案" -msgid "Print to PDF" -msgstr "列印成 PDF" +msgid "Sphinx Book Theme" +msgstr "Sphinx Book 佈景主題" -msgid "open issue" -msgstr "公開的問題" +msgid "Fullscreen mode" +msgstr "全螢幕模式" -msgid "Download notebook file" -msgstr "下載 Notebook 檔案" +msgid "Edit this page" +msgstr "編輯此頁面" -msgid "Toggle navigation" -msgstr "顯示或隱藏導覽列" +msgid "By" +msgstr "作者：" + +msgid "Copyright" +msgstr "Copyright" msgid "Source repository" msgstr "來源儲存庫" -msgid "By the" -msgstr "作者：" +msgid "previous page" +msgstr "上一頁" msgid "next page" msgstr "下一頁" +msgid "Toggle navigation" +msgstr "顯示或隱藏導覽列" + msgid "repository" msgstr "儲存庫" -msgid "Sphinx Book Theme" -msgstr "Sphinx Book 佈景主題" - -msgid "Download source file" -msgstr "下載原始檔" +msgid "suggest edit" +msgstr "提出修改建議" -msgid "Contents" -msgstr "目錄" +msgid "open issue" +msgstr "公開的問題" -msgid "By" -msgstr "作者：" +msgid "Launch" +msgstr "啟動" -msgid "Copyright" -msgstr "Copyright" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Fullscreen mode" -msgstr "全螢幕模式" +msgid "By the" +msgstr "作者：" -msgid "Open an issue" -msgstr "開啟議題" +msgid "Last updated on" +msgstr "最後更新時間：" -msgid "previous page" -msgstr "上一頁" +msgid "Download source file" +msgstr "下載原始檔" msgid "Download this page" msgstr "下載此頁面" - -msgid "Theme by the" -msgstr "佈景主題作者：" diff --git a/tutorials_and_tools/_build/html/_static/play-solid.svg b/tutorials_and_tools/_build/html/_static/play-solid.svg new file mode 100644 index 00000000..d84a7d6f --- /dev/null +++ b/tutorials_and_tools/_build/html/_static/play-solid.svg @@ -0,0 +1 @@ + diff --git a/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js b/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js index 766173ab..c07508ec 100644 --- a/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js +++ b/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js @@ -1,3 +1,3 @@ /*! 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l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], 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e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ke.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Hs),n(".dropdown-menu",Fs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(qs)?t:ke.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(document,$s,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this)||Vs.getOrCreateInstance(this).show()})),de.on(window,Is,(()=>{for(const t of ke.find(Rs))Vs.getOrCreateInstance(t)})),Qt(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){de.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),de.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(de.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),de.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=we.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=we.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Be,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>fe.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(qe).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return we.findOne(ii,this._element)}_getItems(){return we.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?We:Be:t===ze?Be:We}_orderToDirection(t){return Kt()?t===We?ze:Re:t===We?Re:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=we.getElementFromSelector(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===_e.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,Ue,(()=>{const t=we.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,di=`hidden${ai}`,ui=`click${ai}.data-api`,fi="show",pi="collapse",mi="collapsing",gi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ve{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=we.find(_i);for(const t of i){const e=we.getSelectorFromElement(t),i=we.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(mi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi,fi),this._element.style[e]="",fe.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(mi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi),fe.trigger(this._element,di)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(gi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,ui,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ei=".bs.dropdown",Ai=".data-api",Ti="ArrowUp",Ci="ArrowDown",Oi=`hide${Ei}`,xi=`hidden${Ei}`,ki=`show${Ei}`,Li=`shown${Ei}`,Si=`click${Ei}${Ai}`,Di=`keydown${Ei}${Ai}`,$i=`keyup${Ei}${Ai}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,Mi=".dropdown-menu",ji=Kt()?"top-end":"top-start",Fi=Kt()?"top-start":"top-end",Hi=Kt()?"bottom-end":"bottom-start",Bi=Kt()?"bottom-start":"bottom-end",Wi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",Ri={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},qi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Mi)[0]||we.prev(this._element,Mi)[0]||we.findOne(Mi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Ri}static get DefaultType(){return qi}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),fe.trigger(this._element,Li,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Fi:ji:e?Bi:Hi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===Ci,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ti,Ci].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:we.prev(this,Ni)[0]||we.next(this,Ni)[0]||we.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Di,Ni,Vi.dataApiKeydownHandler),fe.on(document,Di,Mi,Vi.dataApiKeydownHandler),fe.on(document,Si,Vi.clearMenus),fe.on(document,$i,Vi.clearMenus),fe.on(document,Si,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Yi="backdrop",Ki="show",Qi=`mousedown.bs.${Yi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ui={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Gi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Ui}static get NAME(){return Yi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ki),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Qi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Ji=".bs.focustrap",Zi=`focusin${Ji}`,tn=`keydown.tab${Ji}`,en="backward",nn={autofocus:!0,trapElement:null},sn={autofocus:"boolean",trapElement:"element"};class on extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return nn}static get DefaultType(){return sn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,Ji),fe.on(document,Zi,(t=>this._handleFocusin(t))),fe.on(document,tn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,Ji))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===en?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?en:"forward")}}const rn=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",an=".sticky-top",ln="padding-right",cn="margin-right";class hn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,ln,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e+t)),this._setElementAttributes(an,cn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,ln),this._resetElementAttributes(rn,ln),this._resetElementAttributes(an,cn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const dn=".bs.modal",un=`hide${dn}`,fn=`hidePrevented${dn}`,pn=`hidden${dn}`,mn=`show${dn}`,gn=`shown${dn}`,_n=`resize${dn}`,bn=`click.dismiss${dn}`,vn=`mousedown.dismiss${dn}`,yn=`keydown.dismiss${dn}`,wn=`click${dn}.data-api`,En="modal-open",An="show",Tn="modal-static",Cn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new hn,this._addEventListeners()}static get Default(){return Cn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,mn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(En),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,un).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,dn),fe.off(this._dialog,dn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Gi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new on({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,gn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,yn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,vn,(t=>{fe.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(En),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Tn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Tn),this._queueCallback((()=>{this._element.classList.remove(Tn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,mn,(t=>{t.defaultPrevented||fe.one(e,pn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),Ee(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Sn=`load${kn}${Ln}`,Dn="show",$n="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,Mn=`shown${kn}`,jn=`hide${kn}`,Fn=`hidePrevented${kn}`,Hn=`hidden${kn}`,Bn=`resize${kn}`,Wn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,Rn={backdrop:!0,keyboard:!0,scroll:!1},qn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Rn}static get DefaultType(){return qn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new hn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($n),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Dn),this._element.classList.remove($n),fe.trigger(this._element,Mn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,jn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Dn,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new hn).reset(),fe.trigger(this._element,Hn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Gi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Fn)}:null})}_initializeFocusTrap(){return new on({trapElement:this._element})}_addEventListeners(){fe.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Fn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Hn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),fe.on(window,Sn,(()=>{for(const t of we.find(Nn))Vn.getOrCreateInstance(t).show()})),fe.on(window,Bn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),Ee(Vn),Qt(Vn);const Yn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={allowList:Yn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Gn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Jn={entry:"(string|element|function|null)",selector:"(string|element)"};class Zn extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Un}static get DefaultType(){return Gn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Jn)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const ts=new Set(["sanitize","allowList","sanitizeFn"]),es="fade",is="show",ns=".modal",ss="hide.bs.modal",os="hover",rs="focus",as={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ls={allowList:Yn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},cs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class hs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ls}static get DefaultType(){return cs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(ns),ss,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[rs]=!1,this._activeTrigger[os]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(es,is),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(es),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Zn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(es)}_isShown(){return this.tip&&this.tip.classList.contains(is)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=as[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===os?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===os?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?rs:os]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?rs:os]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(ns),ss,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))ts.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=hs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(hs);const ds={...hs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},us={...hs.DefaultType,content:"(null|string|element|function)"};class fs extends hs{static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".bs.scrollspy",ms=`activate${ps}`,gs=`click${ps}`,_s=`load${ps}.data-api`,bs="active",vs="[href]",ys=".nav-link",ws=`${ys}, .nav-item > ${ys}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,gs),fe.on(this._config.target,gs,vs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(vs,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(bs),this._activateParents(t),fe.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(bs);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,ws))t.classList.add(bs)}_clearActiveClass(t){t.classList.remove(bs);const e=we.find(`${vs}.${bs}`,t);for(const t of e)t.classList.remove(bs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,_s,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Cs=".bs.tab",Os=`hide${Cs}`,xs=`hidden${Cs}`,ks=`show${Cs}`,Ls=`shown${Cs}`,Ss=`click${Cs}`,Ds=`keydown${Cs}`,$s=`load${Cs}`,Is="ArrowLeft",Ns="ArrowRight",Ps="ArrowUp",Ms="ArrowDown",js="Home",Fs="End",Hs="active",Bs="fade",Ws="show",zs=".dropdown-toggle",Rs=`:not(${zs})`,qs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Vs=`.nav-link${Rs}, .list-group-item${Rs}, [role="tab"]${Rs}, ${qs}`,Ys=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Ks extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ds,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Os,{relatedTarget:t}):null;fe.trigger(t,ks,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ls,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Bs)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,xs,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Bs)))}_keydown(t){if(![Is,Ns,Ps,Ms,js,Fs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([js,Fs].includes(t.key))i=e[t.key===js?0:e.length-1];else{const n=[Ns,Ms].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ks.getOrCreateInstance(i).show())}_getChildren(){return we.find(Vs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(zs,Hs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(Vs)?t:we.findOne(Vs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ks.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ss,qs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Ks.getOrCreateInstance(this).show()})),fe.on(window,$s,(()=>{for(const t of we.find(Ys))Ks.getOrCreateInstance(t)})),Qt(Ks);const Qs=".bs.toast",Xs=`mouseover${Qs}`,Us=`mouseout${Qs}`,Gs=`focusin${Qs}`,Js=`focusout${Qs}`,Zs=`hide${Qs}`,to=`hidden${Qs}`,eo=`show${Qs}`,io=`shown${Qs}`,no="hide",so="show",oo="showing",ro={animation:"boolean",autohide:"boolean",delay:"number"},ao={animation:!0,autohide:!0,delay:5e3};class lo extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ao}static get DefaultType(){return ro}static get 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91ad10aa..10f979d0 100644 --- a/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.LICENSE.txt +++ b/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.LICENSE.txt @@ -1,5 +1,5 @@ /*! - * Bootstrap v5.2.3 (https://getbootstrap.com/) - * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Bootstrap v5.3.2 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) */ diff --git a/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.map b/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.map index d83e2f7c..e62bcc03 100644 --- a/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.map +++ b/tutorials_and_tools/_build/html/_static/scripts/bootstrap.js.map @@ -1 +1 @@ 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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.2.3 (https://getbootstrap.com/)\n * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n\n return prefix;\n};\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n\n return selector;\n};\n\nconst getSelectorFromElement = element => {\n const selector = getSelector(element);\n\n if (selector) {\n return document.querySelector(selector) ? selector : null;\n }\n\n return null;\n};\n\nconst getElementFromSelector = element => {\n const selector = getSelector(element);\n return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n } // Get transition-duration of the element\n\n\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n } // If multiple durations are defined, take the first\n\n\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n\n return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object);\n }\n\n return null;\n};\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n const closedDetails = element.closest('details:not([open])');\n\n if (!closedDetails) {\n return elementIsVisible;\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n\n if (summary === null) {\n return false;\n }\n }\n\n return elementIsVisible;\n};\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n\n if (element.classList.contains('disabled')) {\n return true;\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n } // Can find the shadow root otherwise it'll return the document\n\n\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n\n if (element instanceof ShadowRoot) {\n return element;\n } // when we don't find a shadow root\n\n\n if (!element.parentNode) {\n return null;\n }\n\n return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n\n return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback();\n }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n\n index += shouldGetNext ? 1 : -1;\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n\n return fn.apply(element, [event]);\n };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n\n hydrateObj(event, {\n delegateTarget: target\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n\n return fn.apply(target, [event]);\n }\n }\n };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n\n return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n\n callable = wrapFunction(callable);\n }\n\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n if (!fn) {\n return;\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n\n let evt = new Event(event, {\n bubbles,\n cancelable: true\n });\n evt = hydrateObj(evt, args);\n\n if (defaultPrevented) {\n evt.preventDefault();\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n\n return evt;\n }\n\n};\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n\n get() {\n return value;\n }\n\n });\n }\n }\n\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n\n const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n\n instanceMap.set(key, instance);\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n\n return null;\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n\n if (value === 'false') {\n return false;\n }\n\n if (value === Number(value).toString()) {\n return Number(value);\n }\n\n if (value === '' || value === 'null') {\n return null;\n }\n\n if (typeof value !== 'string') {\n return value;\n }\n\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n\n return attributes;\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n\n static get DefaultType() {\n return {};\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n return config;\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return { ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property];\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n\n if (!element) {\n return;\n }\n\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n } // Public\n\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n } // Static\n\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n\n static get VERSION() {\n return VERSION;\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n } // Public\n\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n if (closeEvent.defaultPrevented) {\n return;\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n } // Private\n\n\n _destroyElement() {\n this._element.remove();\n\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n } // Public\n\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n\n return parents;\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling;\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n\n previous = previous.previousElementSibling;\n }\n\n return [];\n },\n\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n\n next = next.nextElementSibling;\n }\n\n return [];\n },\n\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n\n if (!element || !Swipe.isSupported()) {\n return;\n }\n\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n\n this._initEvents();\n } // Getters\n\n\n static get Default() {\n return Default$c;\n }\n\n static get DefaultType() {\n return DefaultType$c;\n }\n\n static get NAME() {\n return NAME$d;\n } // Public\n\n\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n } // Private\n\n\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n\n this._handleSwipe();\n\n execute(this._config.endCallback);\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n\n if (!direction) {\n return;\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n } // Static\n\n\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n this._addEventListeners();\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$b;\n }\n\n static get DefaultType() {\n return DefaultType$b;\n }\n\n static get NAME() {\n return NAME$c;\n } // Public\n\n\n next() {\n this._slide(ORDER_NEXT);\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n\n prev() {\n this._slide(ORDER_PREV);\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n\n this._clearInterval();\n }\n\n cycle() {\n this._clearInterval();\n\n this._updateInterval();\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n\n this.cycle();\n }\n\n to(index) {\n const items = this._getItems();\n\n if (index > items.length - 1 || index < 0) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n\n const activeIndex = this._getItemIndex(this._getActive());\n\n if (activeIndex === index) {\n return;\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n this._slide(order, items[index]);\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n } // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n\n this.pause();\n\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n\n const direction = KEY_TO_DIRECTION[event.key];\n\n if (direction) {\n event.preventDefault();\n\n this._slide(this._directionToOrder(direction));\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n\n if (!element) {\n return;\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n\n const activeElement = this._getActive();\n\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n if (nextElement === activeElement) {\n return;\n }\n\n const nextElementIndex = this._getItemIndex(nextElement);\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n\n const slideEvent = triggerEvent(EVENT_SLIDE);\n\n if (slideEvent.defaultPrevented) {\n return;\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return;\n }\n\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n\n this._setActiveIndicatorElement(nextElementIndex);\n\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n if (isCycling) {\n this.cycle();\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this);\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n\n if (slideIndex) {\n carousel.to(slideIndex);\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n carousel.prev();\n\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n\n this._initializeChildren();\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n\n if (this._config.toggle) {\n this.toggle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$a;\n }\n\n static get DefaultType() {\n return DefaultType$a;\n }\n\n static get NAME() {\n return NAME$b;\n } // Public\n\n\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n\n let activeChildren = []; // find active children\n\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n\n const dimension = this._getDimension();\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.style[dimension] = 0;\n\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n\n this._queueCallback(complete, this._element, true);\n\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n const dimension = this._getDimension();\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger);\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n\n this._element.style[dimension] = '';\n\n this._queueCallback(complete, this._element, true);\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n } // Private\n\n\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n\n config.parent = getElement(config.parent);\n return config;\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n for (const element of children) {\n const selected = getElementFromSelector(element);\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n } // Static\n\n\n static jQueryInterface(config) {\n const _config = {};\n\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n\n const selector = getSelectorFromElement(this);\n const selectorElements = SelectorEngine.find(selector);\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n } // Getters\n\n\n static get Default() {\n return Default$9;\n }\n\n static get DefaultType() {\n return DefaultType$9;\n }\n\n static get NAME() {\n return NAME$a;\n } // Public\n\n\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._createPopper(); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n this._element.focus();\n\n this._element.setAttribute('aria-expanded', true);\n\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n this._element.classList.add(CLASS_NAME_SHOW$6);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n\n this._completeHide(relatedTarget);\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n\n super.dispose();\n }\n\n update() {\n this._inNavbar = this._detectNavbar();\n\n if (this._popper) {\n this._popper.update();\n }\n } // Private\n\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n if (hideEvent.defaultPrevented) {\n return;\n } // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n if (this._popper) {\n this._popper.destroy();\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.setAttribute('aria-expanded', 'false');\n\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n\n _getConfig(config) {\n config = super._getConfig(config);\n\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n\n return config;\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n\n let referenceElement = this._element;\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n\n const popperConfig = this._getPopperConfig();\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n\n _getPlacement() {\n const parentDropdown = this._parent;\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n } // We need to trim the value because custom properties can also include spaces\n\n\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n if (!items.length) {\n return;\n } // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n\n if (!context || context._config.autoClose === false) {\n continue;\n }\n\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n };\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n\n context._completeHide(relatedTarget);\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n\n if (isInput && !isEscapeEvent) {\n return;\n }\n\n event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n\n instance._selectMenuItem(event);\n\n return;\n }\n\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n } // Public\n\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n\n hide() {\n const width = this.getWidth();\n\n this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n\n isOverflowing() {\n return this.getWidth() > 0;\n } // Private\n\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n\n this._element.style.overflow = 'hidden';\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n\n this._saveInitialAttribute(element, styleProperty);\n\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n } // Getters\n\n\n static get Default() {\n return Default$8;\n }\n\n static get DefaultType() {\n return DefaultType$8;\n }\n\n static get NAME() {\n return NAME$9;\n } // Public\n\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._append();\n\n const element = this._getElement();\n\n if (this._config.isAnimated) {\n reflow(element);\n }\n\n element.classList.add(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n\n dispose() {\n if (!this._isAppended) {\n return;\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n this._element.remove();\n\n this._isAppended = false;\n } // Private\n\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n\n this._element = backdrop;\n }\n\n return this._element;\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n\n _append() {\n if (this._isAppended) {\n return;\n }\n\n const element = this._getElement();\n\n this._config.rootElement.append(element);\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n } // Getters\n\n\n static get Default() {\n return Default$7;\n }\n\n static get DefaultType() {\n return DefaultType$7;\n }\n\n static get NAME() {\n return NAME$8;\n } // Public\n\n\n activate() {\n if (this._isActive) {\n return;\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n\n deactivate() {\n if (!this._isActive) {\n return;\n }\n\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n } // Private\n\n\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement);\n\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$6;\n }\n\n static get DefaultType() {\n return DefaultType$6;\n }\n\n static get NAME() {\n return NAME$7;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n this._isTransitioning = true;\n\n this._scrollBar.hide();\n\n document.body.classList.add(CLASS_NAME_OPEN);\n\n this._adjustDialog();\n\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = false;\n this._isTransitioning = true;\n\n this._focustrap.deactivate();\n\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY$4);\n }\n\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n }\n\n handleUpdate() {\n this._adjustDialog();\n } // Private\n\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n\n this._element.style.display = 'block';\n\n this._element.removeAttribute('aria-hidden');\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_SHOW$4);\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n\n if (this._config.keyboard) {\n event.preventDefault();\n this.hide();\n return;\n }\n\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n\n return;\n }\n\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n\n _hideModal() {\n this._element.style.display = 'none';\n\n this._element.setAttribute('aria-hidden', true);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n this._isTransitioning = false;\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n\n this._resetAdjustments();\n\n this._scrollBar.reset();\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n\n this._element.classList.add(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n\n this._element.focus();\n }\n /**\n * The following methods are used to handle overflowing modals\n */\n\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n const scrollbarWidth = this._scrollBar.getWidth();\n\n const isBodyOverflowing = scrollbarWidth > 0;\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n } // Static\n\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](relatedTarget);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n }); // avoid conflict when clicking modal toggler while another one is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$5;\n }\n\n static get DefaultType() {\n return DefaultType$5;\n }\n\n static get NAME() {\n return NAME$6;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n\n this._backdrop.show();\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n\n this._element.classList.add(CLASS_NAME_SHOW$3);\n\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n\n this._queueCallback(completeCallBack, this._element, true);\n }\n\n hide() {\n if (!this._isShown) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._focustrap.deactivate();\n\n this._element.blur();\n\n this._isShown = false;\n\n this._element.classList.add(CLASS_NAME_HIDING);\n\n this._backdrop.hide();\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n\n this._queueCallback(completeCallback, this._element, true);\n }\n\n dispose() {\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n } // Private\n\n\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n });\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n }\n\n return true;\n } // Check if a regular expression validates the attribute.\n\n\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n } // Getters\n\n\n static get Default() {\n return Default$4;\n }\n\n static get DefaultType() {\n return DefaultType$4;\n }\n\n static get NAME() {\n return NAME$5;\n } // Public\n\n\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n\n hasContent() {\n return this.getContent().length > 0;\n }\n\n changeContent(content) {\n this._checkContent(content);\n\n this._config.content = { ...this._config.content,\n ...content\n };\n return this;\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n\n const template = templateWrapper.children[0];\n\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n\n return template;\n } // Private\n\n\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n\n this._checkContent(config.content);\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n\n if (!templateElement) {\n return;\n }\n\n content = this._resolvePossibleFunction(content);\n\n if (!content) {\n templateElement.remove();\n return;\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n\n return;\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n\n templateElement.textContent = content;\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg;\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n\n templateElement.textContent = element.textContent;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n\n super(element, config); // Private\n\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null; // Protected\n\n this.tip = null;\n\n this._setListeners();\n\n if (!this._config.selector) {\n this._fixTitle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$3;\n }\n\n static get DefaultType() {\n return DefaultType$3;\n }\n\n static get NAME() {\n return NAME$4;\n } // Public\n\n\n enable() {\n this._isEnabled = true;\n }\n\n disable() {\n this._isEnabled = false;\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n\n this._activeTrigger.click = !this._activeTrigger.click;\n\n if (this._isShown()) {\n this._leave();\n\n return;\n }\n\n this._enter();\n }\n\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n\n this._disposePopper();\n\n super.dispose();\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n } // todo v6 remove this OR make it optional\n\n\n this._disposePopper();\n\n const tip = this._getTipElement();\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n const {\n container\n } = this._config;\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n if (this._isHovered === false) {\n this._leave();\n }\n\n this._isHovered = false;\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n hide() {\n if (!this._isShown()) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const tip = this._getTipElement();\n\n tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n if (!this._isHovered) {\n this._disposePopper();\n }\n\n this._element.removeAttribute('aria-describedby');\n\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n update() {\n if (this._popper) {\n this._popper.update();\n }\n } // Protected\n\n\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n\n return this.tip;\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n if (!tip) {\n return null;\n }\n\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n\n return tip;\n }\n\n setContent(content) {\n this._newContent = content;\n\n if (this._isShown()) {\n this._disposePopper();\n\n this.show();\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({ ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n\n return this._templateFactory;\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n } // Private\n\n\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg;\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n context._leave();\n });\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title');\n\n if (!title) {\n return;\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n this._element.removeAttribute('title');\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n\n this._isHovered = true;\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n this._isHovered = false;\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n\n config = { ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n\n return config;\n }\n\n _getDelegateConfig() {\n const config = {};\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key];\n }\n }\n\n config.selector = false;\n config.trigger = 'manual'; // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n\n return config;\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n\n this._popper = null;\n }\n\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n\n static get DefaultType() {\n return DefaultType$2;\n }\n\n static get NAME() {\n return NAME$3;\n } // Overrides\n\n\n _isWithContent() {\n return this._getTitle() || this._getContent();\n } // Private\n\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n } // Getters\n\n\n static get Default() {\n return Default$1;\n }\n\n static get DefaultType() {\n return DefaultType$1;\n }\n\n static get NAME() {\n return NAME$2;\n } // Public\n\n\n refresh() {\n this._initializeTargetsAndObservables();\n\n this._maybeEnableSmoothScroll();\n\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n\n dispose() {\n this._observer.disconnect();\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n\n return config;\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n } // unregister any previous listeners\n\n\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n } // Chrome 60 doesn't support `scrollTo`\n\n\n root.scrollTop = height;\n }\n });\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n } // The logic of selection\n\n\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n this._process(targetElement(entry));\n };\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n\n this._clearActiveClass(targetElement(entry));\n\n continue;\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n if (!parentScrollTop) {\n return;\n }\n\n continue;\n } // if we are scrolling up, pick the smallest offsetTop\n\n\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor);\n\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n\n this._clearActiveClass(this._config.target);\n\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n\n this._activateParents(target);\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both

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z_7@QS?nDfW`(-2zv=U0&LWI=*i)mC=E;z=T=(zIqr-o$%Wx{c`6HVnm%}4Nr|2QuF f?|;lS^v{3k;?_^C7k6?bqhi{WnUjlDVab05bM$4> literal 0 HcmV?d00001 diff --git a/tutorials_and_tools/_build/html/_sources/intro.md b/tutorials_and_tools/_build/html/_sources/intro.md index d83076b7..7035b3c3 100644 --- a/tutorials_and_tools/_build/html/_sources/intro.md +++ b/tutorials_and_tools/_build/html/_sources/intro.md @@ -1,13 +1,13 @@ -# Welcome to the PVDEG Tutorials +# Welcome to the PVDEG Tutorials and Tools -Here is a collection of Jupyter Journals ready to read, or run in your browser -through Google Collab. -Clicking on the rocket-icon on the top allows you to launch the journals on +Here is a collection of Jupyter Journals that include tutorials and tools. +These are ready to read, install and use, or run in your browser through Google Collab. +Clicking on the rocket-icon on the top allows you to launch the journals on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. -As per the instructions on each journal, you should uncomment the first line +As per the instructions on each journal, you should uncomment the first line that reads `pip install ...` to install the environment if you follow this mode. -You can also clone the repository and run them locally, following the +You can also clone the repository and run them locally, following the instructions on the [PVDegTools Github page](https://github.com/NREL/PVDegradationTools) ```{tableofcontents} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb index c70b6f61..9768eff1 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb @@ -29,7 +29,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb index 29db9e1b..e1c63f60 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb index 457968db..2e262b2a 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb @@ -30,7 +30,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb index aee0cf74..369d8398 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb @@ -37,7 +37,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb index eb321a5b..0fbd5b31 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb @@ -55,7 +55,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb index 96dcc671..edcd9173 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb index 37d104d3..7b44fe95 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb @@ -41,7 +41,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb index a3ace75f..5acfadd4 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb @@ -35,7 +35,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb index a1d3a3e3..740ca6c0 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb @@ -40,7 +40,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb index b0237deb..a32e4928 100644 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb +++ b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.1.1" + "# !pip install pvdeg==0.3.2" ] }, { diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb new file mode 100644 index 00000000..486fc48d --- /dev/null +++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb @@ -0,0 +1,1759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Basics, Humidity, Design\n", + "### Module Humidity and Edge Seal Width\n", + "\n", + "\n", + "**Requirements:**\n", + "- weather file (psm3 preferred) demo file is provided\n", + "\n", + "**Objectives:**\n", + "1. Read in necessary weather data\n", + "2. Generate solar position, POA, and module temperature\n", + "3. Generate module humidities\n", + "4. Calculate edge seal width" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "import pvdeg\n", + "from pvdeg import DATA_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 10\n", + "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", + "Pandas version 2.2.0\n", + "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys, platform\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Reading in Weather Data and Site Meta-Data\n", + "Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file:\n", + "\n", + "- date-time index\n", + "- DNI\n", + "- DHI\n", + "- GHI\n", + "- Temperature, Dry-Bulb\n", + "- Temperature, Dew-Point\n", + "- Wind Speed\n", + "- Relative Humidity\n", + "\n", + "And the following site-specific metadata. This should be contained in the weather file header.\n", + "\n", + "- Latitude\n", + "- Longitude\n", + "- Altitude" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "PSM_FILE = os.path.join(DATA_DIR,'psm3_demo.csv')\n", + "\n", + "WEATHER, META = pvdeg.weather.read(PSM_FILE,'psm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "