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CircularGreenSimCity
CircularGreenSimCity
Commits
a7a6e0e0
Commit
a7a6e0e0
authored
Apr 17, 2023
by
Eric Duminil
Browse files
monthly_precipitation_variation_wuerzburg
parent
6c7b5e4b
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python_scripts/get_dwd_precipitation/Parse Niederschlag.ipynb
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a7a6e0e0
%% Cell type:markdown id:d52465eb tags:
# Niederschlag from DWD
[stundenwerte_RR_05705_hist.zip](https://www.dwd.de/DE/leistungen/_config/leistungsteckbriefPublication.zip?view=nasPublication&nn=16102&imageFilePath=675349470684952943382999480164406894835507349402593906628786280354581421008571928705011438849092751258299962952312473511744392814375916459681379337607540679777573246835730326122056459559982440454207144420355774317891799102452512290249737221071767178890331035105331501334382788925&download=true)
from [Klimadaten Deutschland - Stundenwerte (Archiv)](https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivstunden.html) "Würzburg, Niederschlag, Historisch"
%% Cell type:code id:18d75296 tags:
``` python
import pandas as pd
from datetime import datetime
dateparse = lambda x: datetime.strptime(x, '%Y%m%d%H')
df = pd.read_csv('produkt_rr_stunde_19950901_20211231_05705.txt',
sep=';',
skipinitialspace=True,
usecols = [1, 3],
names = ['datetime', 'precipitation'],
skiprows = 1,
parse_dates = ['datetime'],
date_parser=dateparse,
index_col = 'datetime'
)
df
```
%%%% Output: execute_result
precipitation
datetime
1995-09-01 00:00:00 0.0
1995-09-01 01:00:00 0.2
1995-09-01 02:00:00 0.2
1995-09-01 03:00:00 0.1
1995-09-01 04:00:00 0.0
... ...
2021-12-31 19:00:00 0.0
2021-12-31 20:00:00 0.0
2021-12-31 21:00:00 0.0
2021-12-31 22:00:00 0.0
2021-12-31 23:00:00 0.0
[230227 rows x 1 columns]
%% Cell type:code id:52f73e31 tags:
``` python
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20, 10)
```
%% Cell type:code id:672300ca tags:
``` python
df2 = df[df.precipitation >= 0]
```
%% Cell type:code id:0fdf4a6f tags:
``` python
df2.resample('d').sum().plot(title='Niederschlag in Würzburg [mm / Tag]');
```
%%%% Output: display_data
%% Cell type:code id:9a68733c tags:
``` python
df2.resample('M').sum().plot(title='Niederschlag in Würzburg [mm / Monat]', ylim=(0, None));
```
%%%% Output: display_data
%% Cell type:code id:929bacb6 tags:
``` python
yearly_values = df2[df2.index.year >= 1996].resample('Y').sum()
```
%% Cell type:code id:c4dd7535 tags:
``` python
yearly_values.describe()
```
%%%% Output: execute_result
precipitation
count 26.000000
mean 568.538462
std 96.303059
min 410.500000
25% 516.575000
50% 548.900000
75% 614.400000
max 807.100000
%% Cell type:code id:0a1cc458 tags:
``` python
yearly_values.plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
```
%%%% Output: display_data
%% Cell type:code id:c74631eb tags:
``` python
yearly_values.boxplot()
plt.title("Niederschlag in Würzburg, zwischen 1996 & 2021 [mm / Jahr]")
plt.savefig('wurzburg_yearly_precipitations_1996_2021.png', facecolor='w', bbox_inches='tight')
```
%%%% Output: display_data
%% Cell type:code id:faf431eb tags:
``` python
df3 = df2[df2.index.year == 2021]
```
%% Cell type:code id:2509790f tags:
``` python
rain2021 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear)
rain2021
```
%%%% Output: execute_result
datetime 1 2 3 4 5 6 7 8 9 10 ... 356 357 \
00:00:00 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.2 0.0 0.0 ... 0.0 0.0
01:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 ... 0.0 0.0
02:00:00 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 ... 0.0 0.0
03:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
04:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
05:00:00 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
06:00:00 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
07:00:00 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.1 0.0 0.0 ... 0.0 0.0
08:00:00 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
09:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
10:00:00 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 ... 0.0 0.0
11:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 ... 0.0 0.0
12:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
13:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
14:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
15:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
16:00:00 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
17:00:00 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
18:00:00 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.1 0.0 0.0 ... 0.0 0.0
19:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
20:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
21:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
22:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.1
23:00:00 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
datetime 358 359 360 361 362 363 364 365
00:00:00 0.0 0.0 0.0 0.0 0.2 1.5 0.0 0.0
01:00:00 0.0 0.0 0.0 1.1 0.0 1.2 0.1 0.0
02:00:00 0.3 0.0 0.0 0.1 0.0 0.3 0.0 0.0
03:00:00 0.1 0.0 0.0 0.1 0.0 0.3 0.0 0.0
04:00:00 0.1 0.0 0.0 0.3 0.0 0.0 0.0 0.0
05:00:00 0.0 0.7 0.0 0.1 0.0 0.0 0.0 0.0
06:00:00 0.0 2.4 0.0 0.0 0.0 0.0 0.0 0.0
07:00:00 0.0 2.7 0.0 0.0 1.1 0.0 0.0 0.0
08:00:00 0.0 0.8 0.0 0.0 2.8 0.0 1.1 0.0
09:00:00 0.0 0.4 0.0 0.0 1.5 0.0 0.1 0.0
10:00:00 0.0 0.3 0.0 0.0 0.6 0.0 0.0 0.0
11:00:00 0.0 0.2 0.0 0.0 0.7 0.1 0.0 0.0
12:00:00 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
13:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14:00:00 0.0 0.2 0.1 0.0 0.0 0.0 0.0 0.0
15:00:00 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16:00:00 0.1 0.0 0.0 0.0 1.1 0.0 0.0 0.0
17:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
18:00:00 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0
19:00:00 0.0 0.0 0.0 0.1 0.0 1.1 0.0 0.0
20:00:00 0.0 0.0 0.0 0.3 0.4 0.7 0.0 0.0
21:00:00 0.0 0.0 0.1 0.2 1.0 0.2 0.0 0.0
22:00:00 0.0 0.0 0.0 0.1 1.1 0.1 0.0 0.0
23:00:00 0.0 0.0 0.1 0.6 1.1 0.0 0.0 0.0
[24 rows x 365 columns]
%% Cell type:code id:42ddd82d tags:
``` python
import seaborn as sns
```
%% Cell type:code id:f1b17ef2 tags:
``` python
sns.heatmap(rain2021, annot=False, cmap='viridis', vmax=5)
plt.title('mm/h Regen in Würzburg, 2021')
plt.show()
```
%%%% Output: display_data
%% Cell type:code id:29be9114 tags:
``` python
df4 = df2[df2.index.year >= 2010].resample('M').sum()
df4
```
%%%% Output: execute_result
precipitation
datetime
2010-01-31 35.9
2010-02-28 28.5
2010-03-31 29.7
2010-04-30 23.7
2010-05-31 68.4
... ...
2021-08-31 82.0
2021-09-30 6.0
2021-10-31 42.5
2021-11-30 26.3
2021-12-31 60.5
[144 rows x 1 columns]
%% Cell type:code id:
7f474ad
0 tags:
%% Cell type:code id:
283cd89
0 tags:
``` python
import calendar
df4['Year'] = df4.index.year
df4['Month'] = df4.index.month
df4['Month'] = df4['Month'].apply(lambda x: calendar.month_abbr[x])
df4
```
%%%% Output: execute_result
precipitation Year Month
datetime
2010-01-31 35.9 2010 Jan
2010-02-28 28.5 2010 Feb
2010-03-31 29.7 2010 Mar
2010-04-30 23.7 2010 Apr
2010-05-31 68.4 2010 May
... ... ... ...
2021-08-31 82.0 2021 Aug
2021-09-30 6.0 2021 Sep
2021-10-31 42.5 2021 Oct
2021-11-30 26.3 2021 Nov
2021-12-31 60.5 2021 Dec
[144 rows x 3 columns]
%% Cell type:code id:
a1f5385
1 tags:
%% Cell type:code id:
28a0fd6
1 tags:
``` python
df4 = df4.set_index(['Year', 'Month'])
df4
```
%%%% Output: execute_result
precipitation
Year Month
2010 Jan 35.9
Feb 28.5
Mar 29.7
Apr 23.7
May 68.4
... ...
2021 Aug 82.0
Sep 6.0
Oct 42.5
Nov 26.3
Dec 60.5
[144 rows x 1 columns]
%% Cell type:code id:
3c9b3b93
tags:
%% Cell type:code id:
8e3bd7d1
tags:
``` python
df_precipitation = df4.reset_index().pivot_table(columns='Year',index='Month',values='precipitation', sort=False)
df_precipitation
```
%%%% Output: execute_result
Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 \
Month
Jan 35.9 38.6 66.6 29.8 31.1 53.3 62.2 14.7 56.1 37.2 23.2
Feb 28.5 31.4 12.2 35.1 39.6 8.4 60.1 21.2 12.4 10.6 106.5
Mar 29.7 6.2 5.7 26.1 7.7 43.3 36.5 41.6 46.0 44.8 27.0
Apr 23.7 14.2 11.8 42.1 40.7 17.3 49.6 20.9 34.5 27.8 10.9
May 68.4 6.6 34.7 99.7 73.7 22.4 66.9 118.4 48.4 72.0 41.2
Jun 21.5 110.0 67.2 44.7 16.7 39.3 53.4 49.3 27.3 44.6 75.6
Jul 75.2 94.6 66.8 36.3 77.5 26.7 47.5 85.0 54.4 25.3 15.5
Aug 185.1 43.4 28.0 108.9 95.1 67.6 30.6 61.1 22.5 39.2 61.9
Sep 55.3 28.3 44.8 88.9 31.2 26.2 32.5 68.3 24.1 31.2 31.9
Oct 27.6 36.1 41.9 56.5 39.2 28.5 38.1 36.1 8.8 65.9 41.6
Nov 87.0 0.6 79.7 60.2 35.3 87.8 66.0 62.4 11.4 37.2 9.6
Dec 106.5 109.5 78.5 30.2 41.3 27.6 7.4 49.9 86.2 54.2 48.3
Year 2021
Month
Jan 54.6
Feb 48.6
Mar 35.8
Apr 17.6
May 69.8
Jun 100.9
Jul 138.7
Aug 82.0
Sep 6.0
Oct 42.5
Nov 26.3
Dec 60.5
%% Cell type:code id:
a2c4799e
tags:
%% Cell type:code id:
fa12c1b9
tags:
``` python
ax=sns.heatmap(df_precipitation, cmap='viridis_r')
ax.invert_yaxis()
plt.title("Precipitation in Würzburg [mm / month]");
```
%%%% Output: display_data
%% Cell type:code id:
06014eb4
tags:
%% Cell type:code id:
470cf56e
tags:
``` python
df_precipitation.sum().plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
```
%%%% Output: display_data
%% Cell type:code id:
bf62c2f5
tags:
%% Cell type:code id:
40727a96
tags:
``` python
df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\n1966 -> 2021\n[mm / Monat]',
ylim=(0, None));
```
%%%% Output: display_data
%% Cell type:code id:
13fe43a8
tags:
%% Cell type:code id:
0a195120
tags:
``` python
df_precipitation.transpose().plot.box(showfliers=False, ylim=(0, None))
duration = "2010 -> 2021"
plt.title(f"Variation of monthly precipitation in Würzburg, {duration}")
plt.ylabel("[mm / month]");
plt.savefig('monthly_precipitation_variation_wuerzburg.png', facecolor="w", bbox_inches = 'tight')
```
%%%% Output: display_data
%% Cell type:code id:
c49bdeb
5 tags:
%% Cell type:code id:
e5d682d
5 tags:
``` python
# TODO: Compare 1995 -> 2021 with 2015 -> 2021
# TODO: Get duration from min max index
# TODO: Ridgeline for each month?
# TODO: apply to Feuerbach
# TODO: Push and document
# TODO: Show on map
```
%% Cell type:code id:
1542246a
tags:
%% Cell type:code id:
803223b4
tags:
``` python
```
...
...
python_scripts/get_dwd_precipitation/monthly_precipitation_variation_wuerzburg.png
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python_scripts/get_dwd_precipitation/wurzburg_yearly_precipitations_1996_2021.png
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