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WRF_TS.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import datetime\n",
"import matplotlib.pyplot as plt\n",
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"# set default figure size with 10 (width) x 6 (height) inches\n",
"plt.rcParams['figure.figsize'] = [10, 6]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rain TS"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# http://web.lmd.jussieu.fr/trac-LMDZ_WRF/browser/lmdz_wrf/tags/version-0.0/WRFV3/run/README.tslist\n",
"columns = ['id', 'ts_hour', 'id_tsloc', \n",
" 'ix', 'iy', 't', 'q', 'u', 'v', 'psfc', \n",
" 'glw', 'gsw', 'hfx', 'lh', 'tsk', 'tslb(1)', \n",
" 'rainc', 'rainnc', 'clw']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>id</th>\n",
" <th>ts_hour</th>\n",
" <th>id_tsloc</th>\n",
" <th>ix</th>\n",
" <th>iy</th>\n",
" <th>t</th>\n",
" <th>q</th>\n",
" <th>u</th>\n",
" <th>v</th>\n",
" <th>psfc</th>\n",
" <th>glw</th>\n",
" <th>gsw</th>\n",
" <th>hfx</th>\n",
" <th>lh</th>\n",
" <th>tsk</th>\n",
" <th>tslb(1)</th>\n",
" <th>rainc</th>\n",
" <th>rainnc</th>\n",
" <th>clw</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.016667</td>\n",
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" </tr>\n",
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" <th>2</th>\n",
" <td>1</td>\n",
" <td>0.050000</td>\n",
" <td>41</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0.083333</td>\n",
" <td>41</td>\n",
" <td>186</td>\n",
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" <td>96633.02344</td>\n",
" <td>389.02536</td>\n",
" <td>380.74539</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" id ts_hour id_tsloc ix iy t q u v \\\n",
"0 1 0.016667 41 186 178 300.78195 0.01999 -2.35945 -2.55723 \n",
"1 1 0.033333 41 186 178 298.49945 0.01977 -3.01607 -3.28876 \n",
"2 1 0.050000 41 186 178 298.43820 0.01966 -2.93889 -3.27360 \n",
"3 1 0.066667 41 186 178 298.40662 0.01958 -2.97670 -3.31790 \n",
"4 1 0.083333 41 186 178 298.37662 0.01953 -2.98620 -3.37016 \n",
"\n",
" psfc glw gsw hfx lh tsk \\\n",
"0 96597.82031 389.02536 380.74539 53.93742 218.03540 304.28552 \n",
"1 96657.67969 389.02536 380.74539 72.99442 191.83598 301.77521 \n",
"2 96648.75781 389.02536 380.74539 75.46490 193.32562 301.42719 \n",
"3 96639.21875 389.02536 380.74539 77.06808 193.84723 301.24045 \n",
"4 96633.02344 389.02536 380.74539 78.22997 193.79803 301.14343 \n",
"\n",
" tslb(1) rainc rainnc clw \n",
"0 300.18881 0.00000 0.0 0.00976 \n",
"1 300.18832 0.00000 0.0 0.01000 \n",
"2 300.18884 0.00000 0.0 0.01006 \n",
"3 300.18945 0.00000 0.0 0.00999 \n",
"4 300.19019 0.00062 0.0 0.00992 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# tslist = ['Com1.d01.TS', 'Com1.d02.TS' ]\n",
"# def read_ts(filepath, names=columns, header=0):\n",
"# return pd.read_fwf(filepath, names=names, header=header)\n",
"\n",
"# dfs = [read_ts()]\n",
"df1 = pd.read_fwf('Com1.d01.TS', names=columns, header=0, infer_nrows=4000)\n",
"df2 = pd.read_fwf('Com1.d02.TS', names=columns, header=0, infer_nrows=4000)\n",
"df3 = pd.read_fwf('Com.d01.TS', names=columns, header=0, infer_nrows=4000)\n",
"df4 = pd.read_fwf('Com.d02.TS', names=columns, header=0, infer_nrows=4000)\n",
"df5 = pd.read_fwf('Com.d03.TS', names=columns, header=0, infer_nrows=4000)\n",
"df1.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: I deduced the column names from: http://web.lmd.jussieu.fr/trac-LMDZ_WRF/browser/lmdz_wrf/tags/version-0.0/WRFV3/run/README.tslist \n",
"\n",
"Is this correct?? Maybe, maybe not!!\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>ts_hour</th>\n",
" <th>id_tsloc</th>\n",
" <th>ix</th>\n",
" <th>iy</th>\n",
" <th>t</th>\n",
" <th>q</th>\n",
" <th>u</th>\n",
" <th>v</th>\n",
" <th>psfc</th>\n",
" <th>glw</th>\n",
" <th>gsw</th>\n",
" <th>hfx</th>\n",
" <th>lh</th>\n",
" <th>tsk</th>\n",
" <th>tslb(1)</th>\n",
" <th>rainc</th>\n",
" <th>rainnc</th>\n",
" <th>clw</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>193.84723</td>\n",
" <td>301.24045</td>\n",
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" <td>41</td>\n",
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],
"text/plain": [
" id ts_hour id_tsloc ix iy t q u v \\\n",
"0 1 0.016667 41 186 178 300.78195 0.01999 -2.35945 -2.55723 \n",
"1 1 0.033333 41 186 178 298.49945 0.01977 -3.01607 -3.28876 \n",
"2 1 0.050000 41 186 178 298.43820 0.01966 -2.93889 -3.27360 \n",
"3 1 0.066667 41 186 178 298.40662 0.01958 -2.97670 -3.31790 \n",
"4 1 0.083333 41 186 178 298.37662 0.01953 -2.98620 -3.37016 \n",
"\n",
" psfc glw gsw hfx lh tsk \\\n",
"0 96597.82031 389.02536 380.74539 53.93742 218.03540 304.28552 \n",
"1 96657.67969 389.02536 380.74539 72.99442 191.83598 301.77521 \n",
"2 96648.75781 389.02536 380.74539 75.46490 193.32562 301.42719 \n",
"3 96639.21875 389.02536 380.74539 77.06808 193.84723 301.24045 \n",
"4 96633.02344 389.02536 380.74539 78.22997 193.79803 301.14343 \n",
"\n",
" tslb(1) rainc rainnc clw \n",
"0 300.18881 0.00000 0.0 0.00976 \n",
"1 300.18832 0.00000 0.0 0.01000 \n",
"2 300.18884 0.00000 0.0 0.01006 \n",
"3 300.18945 0.00000 0.0 0.00999 \n",
"4 300.19019 0.00062 0.0 0.00992 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.concat([df1, df5])\n",
"df = df.sort_values(by='ts_hour')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>id</th>\n",
" <th>ts_hour</th>\n",
" <th>id_tsloc</th>\n",
" <th>ix</th>\n",
" <th>iy</th>\n",
" <th>t</th>\n",
" <th>q</th>\n",
" <th>u</th>\n",
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" <th>hfx</th>\n",
" <th>lh</th>\n",
" <th>tsk</th>\n",
" <th>tslb(1)</th>\n",
" <th>rainc</th>\n",
" <th>rainnc</th>\n",
" <th>clw</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>193.84723</td>\n",
" <td>301.24045</td>\n",
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" <td>96633.02344</td>\n",
" <td>389.02536</td>\n",
" <td>380.74539</td>\n",
" <td>78.22997</td>\n",
" <td>193.79803</td>\n",
" <td>301.14343</td>\n",
" <td>300.19019</td>\n",
" <td>0.00062</td>\n",
" <td>0.0</td>\n",
" <td>0.00992</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id ts_hour id_tsloc ix iy t q \\\n",
"0 1 2017-09-19 12:01:00.001199961 41 186 178 300.78195 0.01999 \n",
"1 1 2017-09-19 12:01:59.998800039 41 186 178 298.49945 0.01977 \n",
"2 1 2017-09-19 12:03:00.000000000 41 186 178 298.43820 0.01966 \n",
"3 1 2017-09-19 12:04:00.001199961 41 186 178 298.40662 0.01958 \n",
"4 1 2017-09-19 12:04:59.998800039 41 186 178 298.37662 0.01953 \n",
"\n",
" u v psfc glw gsw hfx lh \\\n",
"0 -2.35945 -2.55723 96597.82031 389.02536 380.74539 53.93742 218.03540 \n",
"1 -3.01607 -3.28876 96657.67969 389.02536 380.74539 72.99442 191.83598 \n",
"2 -2.93889 -3.27360 96648.75781 389.02536 380.74539 75.46490 193.32562 \n",
"3 -2.97670 -3.31790 96639.21875 389.02536 380.74539 77.06808 193.84723 \n",
"4 -2.98620 -3.37016 96633.02344 389.02536 380.74539 78.22997 193.79803 \n",
"\n",
" tsk tslb(1) rainc rainnc clw \n",
"0 304.28552 300.18881 0.00000 0.0 0.00976 \n",
"1 301.77521 300.18832 0.00000 0.0 0.01000 \n",
"2 301.42719 300.18884 0.00000 0.0 0.01006 \n",
"3 301.24045 300.18945 0.00000 0.0 0.00999 \n",
"4 301.14343 300.19019 0.00062 0.0 0.00992 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert time id hours into dates\n",
"df['ts_hour'] = df['ts_hour'] * 3600\n",
"df['ts_hour'] = pd.to_datetime(df['ts_hour'], unit='s', \n",
" origin=pd.Timestamp('2017-09-19 12'))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"df['windspeed'] = 1.944 * np.sqrt(df['u']**2 + df['v']**2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>ts_hour</th>\n",
" <th>id_tsloc</th>\n",
" <th>ix</th>\n",
" <th>iy</th>\n",
" <th>t</th>\n",
" <th>q</th>\n",
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" <th>rainc</th>\n",
" <th>rainnc</th>\n",
" <th>clw</th>\n",
" <th>windspeed</th>\n",
" <th>Rainfall</th>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>1</td>\n",
" <td>2017-09-19 12:04:00.001199961</td>\n",
" <td>41</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
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" <td>0.01958</td>\n",
" <td>-2.97670</td>\n",
" <td>-3.31790</td>\n",
" <td>96639.21875</td>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2017-09-19 12:04:59.998800039</td>\n",
" <td>41</td>\n",
" <td>186</td>\n",
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" <td>0.000024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" id ts_hour id_tsloc ix iy t q \\\n",
"0 1 2017-09-19 12:01:00.001199961 41 186 178 300.78195 0.01999 \n",
"1 1 2017-09-19 12:01:59.998800039 41 186 178 298.49945 0.01977 \n",
"2 1 2017-09-19 12:03:00.000000000 41 186 178 298.43820 0.01966 \n",
"3 1 2017-09-19 12:04:00.001199961 41 186 178 298.40662 0.01958 \n",
"4 1 2017-09-19 12:04:59.998800039 41 186 178 298.37662 0.01953 \n",
"\n",
" u v psfc ... gsw hfx lh \\\n",
"0 -2.35945 -2.55723 96597.82031 ... 380.74539 53.93742 218.03540 \n",
"1 -3.01607 -3.28876 96657.67969 ... 380.74539 72.99442 191.83598 \n",
"2 -2.93889 -3.27360 96648.75781 ... 380.74539 75.46490 193.32562 \n",
"3 -2.97670 -3.31790 96639.21875 ... 380.74539 77.06808 193.84723 \n",
"4 -2.98620 -3.37016 96633.02344 ... 380.74539 78.22997 193.79803 \n",
"\n",
" tsk tslb(1) rainc rainnc clw windspeed Rainfall \n",
"0 304.28552 300.18881 0.00000 0.0 0.00976 6.764011 0.000000 \n",
"1 301.77521 300.18832 0.00000 0.0 0.01000 8.674820 0.000000 \n",
"2 301.42719 300.18884 0.00000 0.0 0.01006 8.552171 0.000000 \n",
"3 301.24045 300.18945 0.00000 0.0 0.00999 8.665358 0.000000 \n",
"4 301.14343 300.19019 0.00062 0.0 0.00992 8.753478 0.000024 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate Rainfall \n",
"df['Rainfall'] = 0.0393701*(df['rainc'] + df['rainnc'])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">Rainfall</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ts_hour</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>19</th>\n",
" <td>719.0</td>\n",
" <td>0.044800</td>\n",
" <td>0.045967</td>\n",
" <td>0.000000</td>\n",
" <td>0.000125</td>\n",
" <td>0.034919</td>\n",
" <td>0.078226</td>\n",
" <td>0.186541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10000.0</td>\n",
" <td>1.692485</td>\n",
" <td>0.554350</td>\n",
" <td>0.183818</td>\n",
" <td>1.385554</td>\n",
" <td>1.564032</td>\n",
" <td>1.905379</td>\n",
" <td>4.492998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>6481.0</td>\n",
" <td>1.161761</td>\n",
" <td>0.423063</td>\n",
" <td>0.386042</td>\n",
" <td>0.797715</td>\n",
" <td>1.187073</td>\n",
" <td>1.494939</td>\n",
" <td>2.136233</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rainfall \\\n",
" count mean std min 25% 50% 75% \n",
"ts_hour \n",
"19 719.0 0.044800 0.045967 0.000000 0.000125 0.034919 0.078226 \n",
"20 10000.0 1.692485 0.554350 0.183818 1.385554 1.564032 1.905379 \n",
"21 6481.0 1.161761 0.423063 0.386042 0.797715 1.187073 1.494939 \n",
"\n",
" \n",
" max \n",
"ts_hour \n",
"19 0.186541 \n",
"20 4.492998 \n",
"21 2.136233 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(df.ts_hour.dt.day)[['Rainfall', 'ts_hour']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Rainfall', 'ts_hour']][df['ts_hour'].dt.day == 19].ts_hour.dt.hour.unique()#.plot(x='ts_hour')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Rainfall', 'ts_hour']][df['ts_hour'].dt.day == 20].ts_hour.dt.hour.unique()#.plot(x='ts_hour')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Rainfall', 'ts_hour']][df['ts_hour'].dt.day == 21].ts_hour.dt.hour.unique()#.plot(x='ts_hour')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Rainfall in Comerío, P.R')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.plot(df['ts_hour'], df['Rainfall'], color='green', marker='o', \n",
" markersize=3)\n",
"plt.margins(0.2)\n",
"plt.subplots_adjust(bottom=0.15)\n",
"plt.xticks(rotation=45)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Rainfall (in)\")\n",
"plt.xlim(df['ts_hour'].min(), df['ts_hour'].max())\n",
"plt.title(\"Rainfall in Comerío, P.R\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Rainfall (in)')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Same plot using built-in pandas plotting utilities\n",
"ax = df[['ts_hour', 'Rainfall']].plot(x='ts_hour', title=\"Rainfall in Comerío, P.R\",\n",
" xlim=(df['ts_hour'].min(), df['ts_hour'].max()),\n",
" figsize=(10, 6))\n",
"\n",
"ax.set_xlabel(\"Time\")\n",
"ax.set_ylabel(\"Rainfall (in)\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Wind Speed (kt)')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Same plot using built-in pandas plotting utilities\n",
"ax = df[['ts_hour', 'windspeed']].plot(x='ts_hour', title=\"Wind Speed in Comerío, P.R\",\n",
" xlim=(df['ts_hour'].min(), df['ts_hour'].max()),\n",
" figsize=(10, 6))\n",
"\n",
"ax.set_xlabel(\"Time\")\n",
"ax.set_ylabel(\"Wind Speed (kt)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wind vertical velocity"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.read_fwf('Com1.d01.WW', header=0, infer_nrows=4000)\n",
"df2 = pd.read_fwf('Com1.d02.WW', header=0, infer_nrows=4000)\n",
"df3 = pd.read_fwf('Com.d01.WW', header=0, infer_nrows=4000)\n",
"df4 = pd.read_fwf('Com.d02.WW', header=0, infer_nrows=4000)\n",
"df5 = pd.read_fwf('Com.d03.WW', header=0, infer_nrows=4000)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\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>Unnamed: 0</th>\n",
" <th>Unnamed: 1</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
" <th>Unnamed: 4</th>\n",
" <th>Unnamed: 5</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>Unnamed: 10</th>\n",
" <th>Unnamed: 11</th>\n",
" <th>Unnamed: 12</th>\n",
" <th>Unnamed: 13</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.016667</td>\n",
" <td>0.10314</td>\n",
" <td>0.11203</td>\n",
" <td>0.11444</td>\n",
" <td>0.11735</td>\n",
" <td>0.11878</td>\n",
" <td>0.11466</td>\n",
" <td>0.10703</td>\n",
" <td>0.09949</td>\n",
" <td>0.09254</td>\n",
" <td>0.08225</td>\n",
" <td>0.06480</td>\n",
" <td>0.04771</td>\n",
" <td>0.03644</td>\n",
" <td>0.02751</td>\n",
" <td>0.02312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.033333</td>\n",
" <td>0.10920</td>\n",
" <td>0.12400</td>\n",
" <td>0.12689</td>\n",
" <td>0.12890</td>\n",
" <td>0.12816</td>\n",
" <td>0.11972</td>\n",
" <td>0.10514</td>\n",
" <td>0.09117</td>\n",
" <td>0.08060</td>\n",
" <td>0.06982</td>\n",
" <td>0.05629</td>\n",
" <td>0.04208</td>\n",
" <td>0.03037</td>\n",
" <td>0.02262</td>\n",
" <td>0.01887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.050000</td>\n",
" <td>0.10549</td>\n",
" <td>0.12064</td>\n",
" <td>0.12084</td>\n",
" <td>0.11915</td>\n",
" <td>0.11534</td>\n",
" <td>0.10405</td>\n",
" <td>0.08606</td>\n",
" <td>0.06807</td>\n",
" <td>0.05400</td>\n",
" <td>0.04060</td>\n",
" <td>0.02423</td>\n",
" <td>0.00760</td>\n",
" <td>-0.00492</td>\n",
" <td>-0.01194</td>\n",
" <td>-0.01537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.066667</td>\n",
" <td>0.10697</td>\n",
" <td>0.12375</td>\n",
" <td>0.12298</td>\n",
" <td>0.11971</td>\n",
" <td>0.11590</td>\n",
" <td>0.10572</td>\n",
" <td>0.08803</td>\n",
" <td>0.06944</td>\n",
" <td>0.05402</td>\n",
" <td>0.03909</td>\n",
" <td>0.02140</td>\n",
" <td>0.00335</td>\n",
" <td>-0.01089</td>\n",
" <td>-0.02004</td>\n",
" <td>-0.02548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.083333</td>\n",
" <td>0.10864</td>\n",
" <td>0.12592</td>\n",
" <td>0.12429</td>\n",
" <td>0.12013</td>\n",
" <td>0.11660</td>\n",
" <td>0.10733</td>\n",
" <td>0.09015</td>\n",
" <td>0.07122</td>\n",
" <td>0.05452</td>\n",
" <td>0.03811</td>\n",
" <td>0.01952</td>\n",
" <td>0.00087</td>\n",
" <td>-0.01388</td>\n",
" <td>-0.02342</td>\n",
" <td>-0.02963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 \\\n",
"0 0.016667 0.10314 0.11203 0.11444 0.11735 0.11878 \n",
"1 0.033333 0.10920 0.12400 0.12689 0.12890 0.12816 \n",
"2 0.050000 0.10549 0.12064 0.12084 0.11915 0.11534 \n",
"3 0.066667 0.10697 0.12375 0.12298 0.11971 0.11590 \n",
"4 0.083333 0.10864 0.12592 0.12429 0.12013 0.11660 \n",
"\n",
" Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10 Unnamed: 11 \\\n",
"0 0.11466 0.10703 0.09949 0.09254 0.08225 0.06480 \n",
"1 0.11972 0.10514 0.09117 0.08060 0.06982 0.05629 \n",
"2 0.10405 0.08606 0.06807 0.05400 0.04060 0.02423 \n",
"3 0.10572 0.08803 0.06944 0.05402 0.03909 0.02140 \n",
"4 0.10733 0.09015 0.07122 0.05452 0.03811 0.01952 \n",
"\n",
" Unnamed: 12 Unnamed: 13 Unnamed: 14 Unnamed: 15 \n",
"0 0.04771 0.03644 0.02751 0.02312 \n",
"1 0.04208 0.03037 0.02262 0.01887 \n",
"2 0.00760 -0.00492 -0.01194 -0.01537 \n",
"3 0.00335 -0.01089 -0.02004 -0.02548 \n",
"4 0.00087 -0.01388 -0.02342 -0.02963 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.concat([df1, df3])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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>ts_hour</th>\n",
" <th>VV-lev-1</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Unnamed: 3</th>\n",
" <th>Unnamed: 4</th>\n",
" <th>Unnamed: 5</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>Unnamed: 10</th>\n",
" <th>Unnamed: 11</th>\n",
" <th>Unnamed: 12</th>\n",
" <th>Unnamed: 13</th>\n",
" <th>Unnamed: 14</th>\n",
" <th>Unnamed: 15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-09-19 12:01:00.001199961</td>\n",
" <td>0.200488</td>\n",
" <td>0.11203</td>\n",
" <td>0.11444</td>\n",
" <td>0.11735</td>\n",
" <td>0.11878</td>\n",
" <td>0.11466</td>\n",
" <td>0.10703</td>\n",
" <td>0.09949</td>\n",
" <td>0.09254</td>\n",
" <td>0.08225</td>\n",
" <td>0.06480</td>\n",
" <td>0.04771</td>\n",
" <td>0.03644</td>\n",
" <td>0.02751</td>\n",
" <td>0.02312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-09-19 12:01:59.998800039</td>\n",
" <td>0.212267</td>\n",
" <td>0.12400</td>\n",
" <td>0.12689</td>\n",
" <td>0.12890</td>\n",
" <td>0.12816</td>\n",
" <td>0.11972</td>\n",
" <td>0.10514</td>\n",
" <td>0.09117</td>\n",
" <td>0.08060</td>\n",
" <td>0.06982</td>\n",
" <td>0.05629</td>\n",
" <td>0.04208</td>\n",
" <td>0.03037</td>\n",
" <td>0.02262</td>\n",
" <td>0.01887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-09-19 12:03:00.000000000</td>\n",
" <td>0.205056</td>\n",
" <td>0.12064</td>\n",
" <td>0.12084</td>\n",
" <td>0.11915</td>\n",
" <td>0.11534</td>\n",
" <td>0.10405</td>\n",
" <td>0.08606</td>\n",
" <td>0.06807</td>\n",
" <td>0.05400</td>\n",
" <td>0.04060</td>\n",
" <td>0.02423</td>\n",
" <td>0.00760</td>\n",
" <td>-0.00492</td>\n",
" <td>-0.01194</td>\n",
" <td>-0.01537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-09-19 12:04:00.001199961</td>\n",
" <td>0.207933</td>\n",
" <td>0.12375</td>\n",
" <td>0.12298</td>\n",
" <td>0.11971</td>\n",
" <td>0.11590</td>\n",
" <td>0.10572</td>\n",
" <td>0.08803</td>\n",
" <td>0.06944</td>\n",
" <td>0.05402</td>\n",
" <td>0.03909</td>\n",
" <td>0.02140</td>\n",
" <td>0.00335</td>\n",
" <td>-0.01089</td>\n",
" <td>-0.02004</td>\n",
" <td>-0.02548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-09-19 12:04:59.998800039</td>\n",
" <td>0.211179</td>\n",
" <td>0.12592</td>\n",
" <td>0.12429</td>\n",
" <td>0.12013</td>\n",
" <td>0.11660</td>\n",
" <td>0.10733</td>\n",
" <td>0.09015</td>\n",
" <td>0.07122</td>\n",
" <td>0.05452</td>\n",
" <td>0.03811</td>\n",
" <td>0.01952</td>\n",
" <td>0.00087</td>\n",
" <td>-0.01388</td>\n",
" <td>-0.02342</td>\n",
" <td>-0.02963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ts_hour VV-lev-1 Unnamed: 2 Unnamed: 3 Unnamed: 4 \\\n",
"0 2017-09-19 12:01:00.001199961 0.200488 0.11203 0.11444 0.11735 \n",
"1 2017-09-19 12:01:59.998800039 0.212267 0.12400 0.12689 0.12890 \n",
"2 2017-09-19 12:03:00.000000000 0.205056 0.12064 0.12084 0.11915 \n",
"3 2017-09-19 12:04:00.001199961 0.207933 0.12375 0.12298 0.11971 \n",
"4 2017-09-19 12:04:59.998800039 0.211179 0.12592 0.12429 0.12013 \n",
"\n",
" Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10 \\\n",
"0 0.11878 0.11466 0.10703 0.09949 0.09254 0.08225 \n",
"1 0.12816 0.11972 0.10514 0.09117 0.08060 0.06982 \n",
"2 0.11534 0.10405 0.08606 0.06807 0.05400 0.04060 \n",
"3 0.11590 0.10572 0.08803 0.06944 0.05402 0.03909 \n",
"4 0.11660 0.10733 0.09015 0.07122 0.05452 0.03811 \n",
"\n",
" Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14 Unnamed: 15 \n",
"0 0.06480 0.04771 0.03644 0.02751 0.02312 \n",
"1 0.05629 0.04208 0.03037 0.02262 0.01887 \n",
"2 0.02423 0.00760 -0.00492 -0.01194 -0.01537 \n",
"3 0.02140 0.00335 -0.01089 -0.02004 -0.02548 \n",
"4 0.01952 0.00087 -0.01388 -0.02342 -0.02963 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert time id hours into dates\n",
"df = df.rename(index=str, columns={'Unnamed: 0': 'ts_hour', 'Unnamed: 1': 'VV-lev-1'})\n",
"df['ts_hour'] = df['ts_hour'] * 3600\n",
"df['ts_hour'] = pd.to_datetime(df['ts_hour'], unit='s', \n",
" origin=pd.Timestamp('2017-09-19 12'))\n",
"\n",
"df['VV-lev-1'] = df['VV-lev-1'] * 1.94384\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Vertical Velocity (kt)')"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Same plot using built-in pandas plotting utilities\n",
"ax = df[['ts_hour', 'VV-lev-1']].plot(x='ts_hour', title=\"Vertical Velocity in Comerío, P.R\",\n",
" xlim=(df['ts_hour'].min(), df['ts_hour'].max()),\n",
" figsize=(10, 6))\n",
"\n",
"ax.set_xlabel(\"Time\")\n",
"ax.set_ylabel(\"Vertical Velocity (kt)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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