diff --git a/merge_climate_datasets_exercise.ipynb b/merge_climate_datasets_exercise.ipynb new file mode 100644 index 0000000..3b251be --- /dev/null +++ b/merge_climate_datasets_exercise.ipynb @@ -0,0 +1,3759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aa24b099", + "metadata": {}, + "source": [ + "# Merging Climate Datasets Exercise\n", + "\n", + "Work through this notebook to practice harmonizing and merging two climate datasets that differ in temporal cadence and spatial resolution.\n", + "\n", + "You will: \n", + "- Load two public NOAA datasets directly from the cloud\n", + "- Subset to the continental US (use 230°E–300°E in longitude since the data span 0–360°)\n", + "- Use `xr.resample` to aggregate time and `xr.interp` to match grids\n", + "- Combine the variables with `xr.merge` for joint analysis\n", + "\n", + "Refer back to the answer key after attempting each step.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d6f677f5", + "metadata": {}, + "source": [ + "## 1. Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a656265", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "\n", + "try:\n", + " import cartopy.crs as ccrs\n", + " import cartopy.feature as cfeature\n", + "except ImportError:\n", + " ccrs = None\n", + " cfeature = None\n", + "\n", + "TEMP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2020.nc\"\n", + "PRECIP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/cpc_global_precip/precip.2020.nc\"\n", + "\n", + "LAT_RANGE = (50, 20) # degrees North\n", + "LON_RANGE_360 = (230, 300) # degrees East (equivalent to -130° to -60°)\n", + "LON_RANGE_180 = (-130, -60) # convenience if a dataset uses -180° to 180°\n", + "\n", + "TIME_RANGE = slice(\"2020-06-01\", \"2020-06-30\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "45f8536b", + "metadata": {}, + "source": [ + "## 2. Load the datasets\n", + "\n", + "Open both remote datasets with `xr.open_dataset`, passing a reasonable chunk size for the time dimension. Assign the resulting objects to `air` and `precip`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3270985f", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 62MB\n",
+              "Dimensions:  (time: 1464, lat: 73, lon: 144)\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0\n",
+              "  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
+              "  * time     (time) datetime64[ns] 12kB 2020-01-01 ... 2020-12-31T18:00:00\n",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 62MB dask.array<chunksize=(8, 73, 144), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 62MB\n", + "Dimensions: (time: 1464, lat: 73, lon: 144)\n", + "Coordinates:\n", + " * lat (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0\n", + " * lon (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n", + " * time (time) datetime64[ns] 12kB 2020-01-01 ... 2020-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 62MB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: load the air temperature and precipitation datasets.\n", + "# Example: air = xr.open_dataset(..., chunks={\"time\": 8})\n", + "#raise NotImplementedError(\"Assign datasets to `air` and `precip`.\")\n", + "air = xr.open_dataset(\n", + " TEMP_URL,\n", + " chunks={\"time\":8}\n", + ")\n", + "\n", + "precip = xr.open_dataset(\n", + " PRECIP_URL,\n", + " chunks={\"time\": 8}\n", + ")\n", + "\n", + "air\n" + ] + }, + { + "cell_type": "markdown", + "id": "761ec85f", + "metadata": {}, + "source": [ + "## 3. Subset to the continental United States and June 2020\n", + "\n", + "Select the bounding box provided above and limit the time range to June 2020 for both datasets. Store the results in `air_us` and `precip_us`.\n", + "Remember that longitude runs from 0° to 360°, so select 230°E–300°E. Check whether each coordinate is ascending or descending before building the slice.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "264d9641", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'precip' (time: 30, lat: 60, lon: 140)> Size: 1MB\n",
+              "dask.array<getitem, shape=(30, 60, 140), dtype=float32, chunksize=(8, 60, 140), chunktype=numpy.ndarray>\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+              "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "Attributes:\n",
+              "    units:         mm\n",
+              "    var_desc:      Precipitation\n",
+              "    level_desc:    Surface\n",
+              "    statistic:     Total\n",
+              "    parent_stat:   Other\n",
+              "    long_name:     Daily total of precipitation\n",
+              "    cell_methods:  time: sum\n",
+              "    valid_range:   [   0. 1000.]\n",
+              "    avg_period:    0000-00-01 00:00:00\n",
+              "    actual_range:  [  0.   776.75]\n",
+              "    dataset:       CPC Global Precipitation\n",
+              "    _ChunkSizes:   [  1 360 720]
" + ], + "text/plain": [ + " Size: 1MB\n", + "dask.array\n", + "Coordinates:\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n", + " * time (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n", + "Attributes:\n", + " units: mm\n", + " var_desc: Precipitation\n", + " level_desc: Surface\n", + " statistic: Total\n", + " parent_stat: Other\n", + " long_name: Daily total of precipitation\n", + " cell_methods: time: sum\n", + " valid_range: [ 0. 1000.]\n", + " avg_period: 0000-00-01 00:00:00\n", + " actual_range: [ 0. 776.75]\n", + " dataset: CPC Global Precipitation\n", + " _ChunkSizes: [ 1 360 720]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: subset both datasets using `sel`, handling coordinate ordering as needed.\n", + "air_us = air.sel(\n", + "time = slice(\"2020-06-01\", \"2020-06-30\"), \n", + "lat = slice(50,20), \n", + "lon = slice(230,300)\n", + ")\n", + "\n", + "precip_us = precip.sel(\n", + "time = slice(\"2020-06-01\", \"2020-06-30\"), \n", + "lat = slice(50,20), \n", + "lon = slice(230,300)\n", + ")\n", + "\n", + "precip_us.precip\n", + "#raise NotImplementedError(\"Create `air_us` and `precip_us`.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "0fd517a9", + "metadata": {}, + "source": [ + "## 4. Align temporal cadence\n", + "\n", + "Aggregate the six-hourly air temperatures to daily means with `xr.resample`. Name the resulting DataArray `air_daily`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "96529bc0", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 46kB\n",
+              "Dimensions:  (time: 30, lat: 13, lon: 29)\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+              "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 45kB dask.array<chunksize=(1, 13, 29), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 46kB\n", + "Dimensions: (time: 30, lat: 13, lon: 29)\n", + "Coordinates:\n", + " * lat (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n", + " * lon (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n", + " * time (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n", + "Data variables:\n", + " air (time, lat, lon) float32 45kB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: use xr.resample to create daily means.\n", + "air_daily = air_us.resample(time = '1D').mean()\n", + "air_daily\n", + "# raise NotImplementedError(\"Create `air_daily`.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4bc3f03", + "metadata": {}, + "source": [ + "## 5. Interpolate to the precipitation grid\n", + "\n", + "Use `xr.interp` to interpolate the daily air temperatures onto the precipitation grid (`precip_us.lat` and `precip_us.lon`). Store the interpolated result in `air_interp`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "52eb7321", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 1MB\n",
+              "Dimensions:  (time: 30, lat: 60, lon: 140)\n",
+              "Coordinates:\n",
+              "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+              "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+              "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 1MB\n", + "Dimensions: (time: 30, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air (time, lat, lon) float32 1MB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: interpolate the resampled temperature field onto the precipitation grid.\n", + "air_interp = air_daily.interp(lat = precip_us.lat, lon = precip_us.lon, method = 'linear', assume_sorted = False)\n", + "air_interp\n", + "#raise NotImplementedError(\"Create `air_interp`.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e1bcf4b", + "metadata": {}, + "source": [ + "## 6. Merge the datasets\n", + "\n", + "Convert the aligned arrays into datasets with clear variable names and merge them with `xr.merge`. Save the output as `merged`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b766aff5", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 2MB\n",
+              "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+              "Coordinates:\n",
+              "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
+              "  * lat              (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n",
+              "  * lon              (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n",
+              "Data variables:\n",
+              "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+              "    daily_precip     (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 2MB\n", + "Dimensions: (time: 30, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air_temperature (time, lat, lon) float32 1MB dask.array\n", + " daily_precip (time, lat, lon) float32 1MB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: build datasets and merge them into one object named `merged`.\n", + "merged = xr.merge([air_interp, precip_us])\n", + "merged = merged.rename({'air': 'air_temperature', 'precip':'daily_precip'})\n", + "merged\n", + "#raise NotImplementedError(\"Create `merged`.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa374697", + "metadata": {}, + "source": [ + "## 7. Inspect your result\n", + "\n", + "Once your pipeline runs without `NotImplementedError`, evaluate the following cell to sanity-check the merged dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "70410d84", + "metadata": { + "tags": [ + "validation" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 2MB\n", + "Dimensions: (time: 30, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air_temperature (time, lat, lon) float32 1MB dask.array\n", + " daily_precip (time, lat, lon) float32 1MB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time\n" + ] + } + ], + "source": [ + "# The assertions below should pass once you have completed the exercise.\n", + "assert set(merged.data_vars) == {\"air_temperature\", \"daily_precip\"}\n", + "assert merged.air_temperature.dims == merged.daily_precip.dims\n", + "print(merged)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b789f53", + "metadata": {}, + "source": [ + "## 8. Check In\n", + "\n", + "- Render both variables at the first timestep on a `cartopy` map to verify alignment visually (PlateCarree works well).\n", + "- Build a scatter plot comparing colocated temperature and precipitation values across the merged domain.\n", + "- Save the merged output with `to_netcdf` for future analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1a1c3eeb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First timesetp map\n", + "import numpy as np\n", + "plot_precip = merged['daily_precip'].isel(time = 0)\n", + "plot_temp = (merged['air_temperature'].isel(time = 0))\n", + "proj = ccrs.PlateCarree()\n", + "fig, axs = plt.subplots(2,1, subplot_kw = dict(projection=proj))\n", + "lon = merged['lon'].values\n", + "lat = merged['lat'].values\n", + "\n", + "x, y = np.meshgrid(lon, lat)\n", + "\n", + "cmap = 'viridis'\n", + "axs[0].coastlines()\n", + "axs[0].add_feature(cfeature.STATES)\n", + "gl = axs[0].gridlines(draw_labels=True, lw = 0.5)\n", + "gl.right_labels= False\n", + "gl.top_labels=False\n", + "t = axs[0].pcolormesh(x,y,plot_precip , cmap=cmap)\n", + "fig.colorbar(t, shrink = 0.6)\n", + "axs[0].set_title('Total Precipitation (mm)')\n", + "\n", + "axs[1].coastlines()\n", + "axs[1].add_feature(cfeature.STATES)\n", + "gl = axs[1].gridlines(draw_labels=True, lw = 0.5)\n", + "gl.right_labels= False\n", + "gl.top_labels=False\n", + "t = axs[1].pcolormesh(x,y,plot_temp , cmap=cmap)\n", + "fig.colorbar(t, shrink = 0.6)\n", + "axs[1].set_title('Average Temperature (Kelvin)')\n", + "plt.suptitle('2020-06-01')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8480a361", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter Plot\n", + "\n", + "merged.plot.scatter( \n", + " x = 'daily_precip',\n", + " y = 'air_temperature'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5d59da0", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "NetCDF: String match to name in use", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmerged\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmerged_9.28.25\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/core/dataset.py:2102\u001b[39m, in \u001b[36mDataset.to_netcdf\u001b[39m\u001b[34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf, auto_complex)\u001b[39m\n\u001b[32m 2099\u001b[39m encoding = {}\n\u001b[32m 2100\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[32m-> \u001b[39m\u001b[32m2102\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value] # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[32m 2103\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 2104\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2105\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2106\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 2107\u001b[39m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2108\u001b[39m \u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2109\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2110\u001b[39m \u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[43m=\u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2111\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2112\u001b[39m \u001b[43m \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 2113\u001b[39m \u001b[43m \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m=\u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2114\u001b[39m \u001b[43m \u001b[49m\u001b[43mauto_complex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauto_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2115\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/backends/api.py:2107\u001b[39m, in \u001b[36mto_netcdf\u001b[39m\u001b[34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf, auto_complex)\u001b[39m\n\u001b[32m 2102\u001b[39m \u001b[38;5;66;03m# TODO: figure out how to refactor this logic (here and in save_mfdataset)\u001b[39;00m\n\u001b[32m 2103\u001b[39m \u001b[38;5;66;03m# to avoid this mess of conditionals\u001b[39;00m\n\u001b[32m 2104\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 2105\u001b[39m \u001b[38;5;66;03m# TODO: allow this work (setting up the file for writing array data)\u001b[39;00m\n\u001b[32m 2106\u001b[39m \u001b[38;5;66;03m# to be parallelized with dask\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2107\u001b[39m \u001b[43mdump_to_store\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2108\u001b[39m \u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[43m=\u001b[49m\u001b[43munlimited_dims\u001b[49m\n\u001b[32m 2109\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m autoclose:\n\u001b[32m 2111\u001b[39m store.close()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/backends/api.py:2157\u001b[39m, in \u001b[36mdump_to_store\u001b[39m\u001b[34m(dataset, store, writer, encoder, encoding, unlimited_dims)\u001b[39m\n\u001b[32m 2154\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m encoder:\n\u001b[32m 2155\u001b[39m variables, attrs = encoder(variables, attrs)\n\u001b[32m-> \u001b[39m\u001b[32m2157\u001b[39m \u001b[43mstore\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_encoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[43m=\u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/backends/common.py:527\u001b[39m, in \u001b[36mAbstractWritableDataStore.store\u001b[39m\u001b[34m(self, variables, attributes, check_encoding_set, writer, unlimited_dims)\u001b[39m\n\u001b[32m 523\u001b[39m writer = ArrayWriter()\n\u001b[32m 525\u001b[39m variables, attributes = \u001b[38;5;28mself\u001b[39m.encode(variables, attributes)\n\u001b[32m--> \u001b[39m\u001b[32m527\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mset_attributes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattributes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 528\u001b[39m \u001b[38;5;28mself\u001b[39m.set_dimensions(variables, unlimited_dims=unlimited_dims)\n\u001b[32m 529\u001b[39m \u001b[38;5;28mself\u001b[39m.set_variables(\n\u001b[32m 530\u001b[39m variables, check_encoding_set, writer, unlimited_dims=unlimited_dims\n\u001b[32m 531\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/backends/common.py:544\u001b[39m, in \u001b[36mAbstractWritableDataStore.set_attributes\u001b[39m\u001b[34m(self, attributes)\u001b[39m\n\u001b[32m 534\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 535\u001b[39m \u001b[33;03mThis provides a centralized method to set the dataset attributes on the\u001b[39;00m\n\u001b[32m 536\u001b[39m \u001b[33;03mdata store.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 541\u001b[39m \u001b[33;03m Dictionary of key/value (attribute name / attribute) pairs\u001b[39;00m\n\u001b[32m 542\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 543\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m attributes.items():\n\u001b[32m--> \u001b[39m\u001b[32m544\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mset_attribute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/envs/xarray-climate/lib/python3.13/site-packages/xarray/backends/netCDF4_.py:555\u001b[39m, in \u001b[36mNetCDF4DataStore.set_attribute\u001b[39m\u001b[34m(self, key, value)\u001b[39m\n\u001b[32m 553\u001b[39m \u001b[38;5;28mself\u001b[39m.ds.setncattr_string(key, value)\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m555\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mds\u001b[49m\u001b[43m.\u001b[49m\u001b[43msetncattr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32msrc/netCDF4/_netCDF4.pyx:3087\u001b[39m, in \u001b[36mnetCDF4._netCDF4.Dataset.setncattr\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32msrc/netCDF4/_netCDF4.pyx:1882\u001b[39m, in \u001b[36mnetCDF4._netCDF4._set_att\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32msrc/netCDF4/_netCDF4.pyx:2164\u001b[39m, in \u001b[36mnetCDF4._netCDF4._ensure_nc_success\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mAttributeError\u001b[39m: NetCDF: String match to name in use" + ] + } + ], + "source": [ + "merged.to_netcdf('merged_9.28.25')" + ] + }, + { + "cell_type": "markdown", + "id": "d1c320a1", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/merged_9.28.25 b/merged_9.28.25 new file mode 100644 index 0000000..f4d24b9 Binary files /dev/null and b/merged_9.28.25 differ