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aliaksei135/vmc.ipynb

Created March 20, 2024 16:58
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Rough calculation of proportion of VMC
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vmc.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: meteostat in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (1.6.7)\n",
"Requirement already satisfied: pandas>=1.1 in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from meteostat) (2.2.1)\n",
"Requirement already satisfied: pytz in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from meteostat) (2024.1)\n",
"Requirement already satisfied: numpy in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from meteostat) (1.26.4)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from pandas>=1.1->meteostat) (2.8.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from pandas>=1.1->meteostat) (2024.1)\n",
"Requirement already satisfied: six>=1.5 in /home/aliaksei/PycharmProjects/URE/venv/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas>=1.1->meteostat) (1.16.0)\n"
]
}
],
"source": [
"!pip install meteostat\n",
"\n",
"import meteostat as ms\n",
"from datetime import datetime,timedelta"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"Warning: Cannot load hourly/2017/03866.csv.gz from https://bulk.meteostat.net/v2/\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
"FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n"
]
}
],
"source": [
"START = datetime(2017, 1, 1)\n",
"END = datetime(2023, 12, 31)\n",
"LOC = ms.Point(50.777934, -1.086725, 100) # Portsmouth, UK\n",
"\n",
"data = ms.Hourly(LOC, START, END)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df = data.fetch()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hours between 2017-01-01 00:00:00 and 2023-12-31 00:00:00: 61320\n"
]
}
],
"source": [
"print(f'Hours between {START} and {END}: {(END-START).days * 24}')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>temp</th>\n",
" <th>dwpt</th>\n",
" <th>rhum</th>\n",
" <th>prcp</th>\n",
" <th>snow</th>\n",
" <th>wdir</th>\n",
" <th>wspd</th>\n",
" <th>wpgt</th>\n",
" <th>pres</th>\n",
" <th>tsun</th>\n",
" <th>coco</th>\n",
" <th>hour</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-01-01 13:00:00</th>\n",
" <td>8.5</td>\n",
" <td>6.5</td>\n",
" <td>87.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>230.0</td>\n",
" <td>7.6</td>\n",
" <td>NaN</td>\n",
" <td>1014.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01 15:00:00</th>\n",
" <td>7.5</td>\n",
" <td>6.4</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>5.4</td>\n",
" <td>NaN</td>\n",
" <td>1016.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01 16:00:00</th>\n",
" <td>3.5</td>\n",
" <td>2.5</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10.0</td>\n",
" <td>20.5</td>\n",
" <td>NaN</td>\n",
" <td>1017.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01 17:00:00</th>\n",
" <td>2.5</td>\n",
" <td>1.5</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>18.4</td>\n",
" <td>NaN</td>\n",
" <td>1018.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-01 18:00:00</th>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>18.4</td>\n",
" <td>NaN</td>\n",
" <td>1019.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-12-30 15:00:00</th>\n",
" <td>11.5</td>\n",
" <td>9.6</td>\n",
" <td>88.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>180.0</td>\n",
" <td>24.1</td>\n",
" <td>46.3</td>\n",
" <td>1001.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-12-30 16:00:00</th>\n",
" <td>11.5</td>\n",
" <td>9.6</td>\n",
" <td>88.0</td>\n",
" <td>0.1</td>\n",
" <td>NaN</td>\n",
" <td>180.0</td>\n",
" <td>24.1</td>\n",
" <td>46.3</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-12-30 17:00:00</th>\n",
" <td>11.5</td>\n",
" <td>9.6</td>\n",
" <td>88.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>180.0</td>\n",
" <td>27.7</td>\n",
" <td>46.3</td>\n",
" <td>998.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-12-30 18:00:00</th>\n",
" <td>11.5</td>\n",
" <td>9.6</td>\n",
" <td>88.0</td>\n",
" <td>0.1</td>\n",
" <td>NaN</td>\n",
" <td>170.0</td>\n",
" <td>22.3</td>\n",
" <td>46.3</td>\n",
" <td>997.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-12-30 19:00:00</th>\n",
" <td>11.5</td>\n",
" <td>9.6</td>\n",
" <td>88.0</td>\n",
" <td>0.2</td>\n",
" <td>NaN</td>\n",
" <td>160.0</td>\n",
" <td>24.1</td>\n",
" <td>48.2</td>\n",
" <td>995.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27119 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" temp dwpt rhum prcp snow wdir wspd wpgt pres \\\n",
"time \n",
"2017-01-01 13:00:00 8.5 6.5 87.0 NaN NaN 230.0 7.6 NaN 1014.0 \n",
"2017-01-01 15:00:00 7.5 6.4 93.0 NaN NaN 20.0 5.4 NaN 1016.0 \n",
"2017-01-01 16:00:00 3.5 2.5 93.0 NaN NaN 10.0 20.5 NaN 1017.0 \n",
"2017-01-01 17:00:00 2.5 1.5 93.0 NaN NaN 20.0 18.4 NaN 1018.0 \n",
"2017-01-01 18:00:00 1.5 0.5 93.0 NaN NaN 20.0 18.4 NaN 1019.0 \n",
"... ... ... ... ... ... ... ... ... ... \n",
"2023-12-30 15:00:00 11.5 9.6 88.0 0.0 NaN 180.0 24.1 46.3 1001.0 \n",
"2023-12-30 16:00:00 11.5 9.6 88.0 0.1 NaN 180.0 24.1 46.3 1000.0 \n",
"2023-12-30 17:00:00 11.5 9.6 88.0 0.0 NaN 180.0 27.7 46.3 998.0 \n",
"2023-12-30 18:00:00 11.5 9.6 88.0 0.1 NaN 170.0 22.3 46.3 997.0 \n",
"2023-12-30 19:00:00 11.5 9.6 88.0 0.2 NaN 160.0 24.1 48.2 995.0 \n",
"\n",
" tsun coco hour \n",
"time \n",
"2017-01-01 13:00:00 NaN 8.0 13 \n",
"2017-01-01 15:00:00 NaN 7.0 15 \n",
"2017-01-01 16:00:00 NaN 7.0 16 \n",
"2017-01-01 17:00:00 NaN 7.0 17 \n",
"2017-01-01 18:00:00 NaN 7.0 18 \n",
"... ... ... ... \n",
"2023-12-30 15:00:00 NaN 4.0 15 \n",
"2023-12-30 16:00:00 NaN 7.0 16 \n",
"2023-12-30 17:00:00 NaN 8.0 17 \n",
"2023-12-30 18:00:00 NaN 8.0 18 \n",
"2023-12-30 19:00:00 NaN 8.0 19 \n",
"\n",
"[27119 rows x 12 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_df = df.dropna(subset=['coco'])\n",
"clean_df = clean_df.assign(hour=clean_df.index.hour)\n",
"clean_df = clean_df[(clean_df['hour'] > 6) & (clean_df['hour'] < 20)]\n",
"clean_df"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hours with VMC: 20305\n",
"Hours with IMC: 6814\n",
"Percentage of VMC: 74.87370478262473%\n"
]
}
],
"source": [
"# Some very generous assumptions\n",
"VMC_CODES = [\n",
" 1,\n",
" 2,\n",
" 3,\n",
" 7,\n",
" 14,\n",
"] # https://dev.meteostat.net/formats.html#weather-condition-codes\n",
"\n",
"vmc = clean_df[clean_df['coco'].isin(VMC_CODES)].shape[0]\n",
"print(f'Hours with VMC: {vmc}')\n",
"print(f'Hours with IMC: {clean_df.shape[0] - vmc}')\n",
"print(f'Percentage of VMC: {vmc / clean_df.shape[0] * 100}%')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.11.8"
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"nbformat": 4,
"nbformat_minor": 2
}
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