diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..036896e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Matthew Graber
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/M3project_Graber.ipynb b/M3project_Graber.ipynb
new file mode 100644
index 0000000..6c838c7
--- /dev/null
+++ b/M3project_Graber.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e6dd90cf-cf07-4ef4-b9d5-ae8764e53b80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary packages\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from cartopy import crs as ccrs\n",
+ "from datetime import datetime\n",
+ "from pathlib import Path\n",
+ "import fsspec"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "71a11aab-06be-445b-a52f-17be8969919f",
+ "metadata": {},
+ "source": [
+ "(1) Adapt the code from class that reads GHCN Daily Data from Amazon Web Services and write a function that will download the station you want (called with a GHCN station ID), and calculate the all time record high and low and the normal (mean) high and low temperature for the 1991-2020 period for the desired station and returns a pandas data frame with the columns ['record_min_temp', average_min_temp', 'average_max_temp', record_max_temp']. Write a code that can call this function and successfully demonstrate that it works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "312549fb-7284-4672-877e-0eca6c72b08e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "( ID LATITUDE LONGITUDE ELEVATION STATE NAME \\\n",
+ " 0 ACW00011604 17.1167 -61.7833 10.1 ST JOHNS COOLIDGE FLD \n",
+ " 1 ACW00011647 17.1333 -61.7833 19.2 ST JOHNS \n",
+ " 2 AE000041196 25.3330 55.5170 34.0 SHARJAH INTER. AIRP \n",
+ " 3 AEM00041194 25.2550 55.3640 10.4 DUBAI INTL \n",
+ " 4 AEM00041217 24.4330 54.6510 26.8 ABU DHABI INTL \n",
+ " \n",
+ " GSN_FLAG HCN_CRN_FLAG WMO_ID \n",
+ " 0 NaN NaN NaN \n",
+ " 1 NaN NaN NaN \n",
+ " 2 GSN NaN 41196 \n",
+ " 3 NaN NaN 41194 \n",
+ " 4 NaN NaN 41217 ,\n",
+ " ID LAT LON ELEMENT FIRSTYEAR LASTYEAR\n",
+ " 0 ACW00011604 17.1167 -61.7833 TMAX 1949 1949\n",
+ " 1 ACW00011604 17.1167 -61.7833 TMIN 1949 1949\n",
+ " 2 ACW00011604 17.1167 -61.7833 PRCP 1949 1949\n",
+ " 3 ACW00011604 17.1167 -61.7833 SNOW 1949 1949\n",
+ " 4 ACW00011604 17.1167 -61.7833 SNWD 1949 1949)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load in the appropriate txt files\n",
+ "S3_STATIONS_TXT = \"s3://noaa-ghcn-pds/ghcnd-stations.txt\"\n",
+ "S3_INVENTORY_TXT = \"s3://noaa-ghcn-pds/ghcnd-inventory.txt\"\n",
+ "S3_BY_STATION = \"s3://noaa-ghcn-pds/csv/by_station/{id}.csv\"\n",
+ "STOR = {\"anon\": True}\n",
+ "\n",
+ "colspecs = [(0,11),(12,20),(21,30),(31,37),(38,40),(41,71),(72,75),(76,79),(80,85)]\n",
+ "names = ['ID','LATITUDE','LONGITUDE','ELEVATION','STATE','NAME','GSN_FLAG','HCN_CRN_FLAG','WMO_ID']\n",
+ "\n",
+ "stations = pd.read_fwf(S3_STATIONS_TXT, colspecs=colspecs, names=names, dtype={'ID':str,'STATE':str,'WMO_ID':str}, storage_options=STOR)\n",
+ "stations['NAME'] = stations['NAME'].str.strip(); stations['STATE'] = stations['STATE'].fillna('').str.strip()\n",
+ "\n",
+ "inventory = pd.read_csv(\n",
+ " S3_INVENTORY_TXT, sep=r'\\s+', names=['ID','LAT','LON','ELEMENT','FIRSTYEAR','LASTYEAR'],\n",
+ " dtype={'ID':str,'ELEMENT':str,'FIRSTYEAR':int,'LASTYEAR':int}, engine='python', storage_options=STOR\n",
+ ")\n",
+ "\n",
+ "stations.head(), inventory.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "9f054604-8ad5-40a4-be42-4213d8805e45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ID \n",
+ " NAME \n",
+ " STATE \n",
+ " LATITUDE \n",
+ " LONGITUDE \n",
+ " ELEVATION \n",
+ " first \n",
+ " last \n",
+ " years \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2017 \n",
+ " USW00093823 \n",
+ " TERRE HAUTE CAA AP \n",
+ " IN \n",
+ " 39.4500 \n",
+ " -87.3000 \n",
+ " 175.3 \n",
+ " 1875 \n",
+ " 2025 \n",
+ " 151 \n",
+ " \n",
+ " \n",
+ " 1852 \n",
+ " USC00126001 \n",
+ " MT VERNON \n",
+ " IN \n",
+ " 37.9286 \n",
+ " -87.8956 \n",
+ " 108.8 \n",
+ " 1888 \n",
+ " 2025 \n",
+ " 138 \n",
+ " \n",
+ " \n",
+ " 1816 \n",
+ " USC00125117 \n",
+ " LOGANSPORT CICOTT ST \n",
+ " IN \n",
+ " 40.7478 \n",
+ " -86.3878 \n",
+ " 182.9 \n",
+ " 1891 \n",
+ " 2025 \n",
+ " 135 \n",
+ " \n",
+ " \n",
+ " 1821 \n",
+ " USC00125237 \n",
+ " MADISON SEWAGE PLT \n",
+ " IN \n",
+ " 38.7369 \n",
+ " -85.3942 \n",
+ " 140.2 \n",
+ " 1892 \n",
+ " 2025 \n",
+ " 134 \n",
+ " \n",
+ " \n",
+ " 1620 \n",
+ " USC00120200 \n",
+ " ANGOLA \n",
+ " IN \n",
+ " 41.6639 \n",
+ " -85.0183 \n",
+ " 310.9 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1669 \n",
+ " USC00121747 \n",
+ " COLUMBUS \n",
+ " IN \n",
+ " 39.1661 \n",
+ " -85.9228 \n",
+ " 185.6 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1712 \n",
+ " USC00122825 \n",
+ " FARMLAND 5 NNW \n",
+ " IN \n",
+ " 40.2539 \n",
+ " -85.1483 \n",
+ " 294.1 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1823 \n",
+ " USC00125337 \n",
+ " MARION 2 N \n",
+ " IN \n",
+ " 40.5800 \n",
+ " -85.6586 \n",
+ " 240.8 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1879 \n",
+ " USC00126580 \n",
+ " OOLITIC PURDUE EX FM \n",
+ " IN \n",
+ " 38.8886 \n",
+ " -86.5517 \n",
+ " 197.8 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1902 \n",
+ " USC00127125 \n",
+ " 1 WSW PRINCETON \n",
+ " IN \n",
+ " 38.3517 \n",
+ " -87.5925 \n",
+ " 137.2 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1918 \n",
+ " USC00127522 \n",
+ " ROCKVILLE \n",
+ " IN \n",
+ " 39.7594 \n",
+ " -87.2269 \n",
+ " 210.0 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ " 1930 \n",
+ " USC00127935 \n",
+ " SEYMOUR 1 WSW \n",
+ " IN \n",
+ " 38.9542 \n",
+ " -85.9044 \n",
+ " 172.8 \n",
+ " 1893 \n",
+ " 2025 \n",
+ " 133 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " date \n",
+ " record_max_temp \n",
+ " record_min_temp \n",
+ " avg_max_temp \n",
+ " avg_min_temp \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1991-01-01 \n",
+ " 17.8 \n",
+ " -20.0 \n",
+ " 4.950000 \n",
+ " -4.120000 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1991-01-02 \n",
+ " 16.1 \n",
+ " -22.2 \n",
+ " 4.400000 \n",
+ " -6.310000 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1991-01-03 \n",
+ " 19.4 \n",
+ " -22.2 \n",
+ " 4.730000 \n",
+ " -4.273333 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1991-01-04 \n",
+ " 18.9 \n",
+ " -17.8 \n",
+ " 5.203333 \n",
+ " -5.193333 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1991-01-05 \n",
+ " 19.4 \n",
+ " -18.3 \n",
+ " 4.280000 \n",
+ " -5.736667 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Make a seasonality time series plot that plots the 4 time series in the daily dataframe\n",
+ "# Shade in the area between the min/max time series and the area between the average time series\n",
+ "days = np.arange(1, daily.shape[0] + 1)\n",
+ "maxT = daily['record_max_temp']\n",
+ "minT = daily['record_min_temp']\n",
+ "avg_maxT = daily['avg_max_temp']\n",
+ "avg_minT = daily['avg_min_temp']\n",
+ "\n",
+ "plt.figure(figsize = (12,8))\n",
+ "plt.plot(days, maxT, color = 'red')\n",
+ "plt.plot(days, minT, color = 'red')\n",
+ "plt.plot(days, avg_maxT, color = 'blue')\n",
+ "plt.plot(days, avg_minT, color = 'blue')\n",
+ "plt.fill_between(days, minT, maxT, color='red', alpha=0.15, label='Min/Max Temperature Range')\n",
+ "plt.fill_between(days, avg_minT, avg_maxT, color='blue', alpha=0.15, label='Average Temperature Range')\n",
+ "\n",
+ "month_starts = pd.date_range('1991-01-01', periods=12, freq='MS')\n",
+ "xtick_pos = month_starts.dayofyear.values \n",
+ "xtick_labels = month_starts.strftime('%b 1') \n",
+ "\n",
+ "plt.legend()\n",
+ "plt.xticks(xtick_pos, xtick_labels)\n",
+ "plt.xlabel('Calendar Day', fontsize = 13)\n",
+ "plt.ylabel('Temperature [°C]', fontsize = 13)\n",
+ "plt.title('Temperature Seasonality for West Lafayette, IN \\n1991-2020', fontsize = 14)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35bcea21-88d6-4c5d-8cd9-0349233179c5",
+ "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": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 3dbf7ef..2ef7298 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,3 @@
-# ATMS 523 Module-3
+# ATMS 523 Module-3 Project
-Code, notebooks, and homework for ATMS 523 Module 3.
+This repository contains my project for module 3. The notebook contains code that takes in data from the Purdue station and calculates temperature statistics in the 1991-2020 period including: minimum temperature, maximum temperature, average maximum temperature, and average minimum temperature. This is done for each calendar day and culminates with a seasonality plot to illustrate the results.