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In [1]:



    


import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import pandas as pd



    











In [76]:



    


filename = 'data_meteoblue/'+'history_export_2017-07-10T14_30_52.csv'



    











In [77]:



    


data = pd.read_csv(filename, sep=';' , header=10)#, delimiter=None, header='infer'



    











In [78]:



    


data



    


















    
Out[78]:











  
    

      
      Year
      Month
      Day
      Hour
      Minute
      Temperature  [2 m above gnd]
      Relative humidity  [2 m above gnd]
      Total Precipitation  [sfc]
      Total cloud cover  [sfc]
      High cloud cover  [high cld lay]
      Medium cloud cover  [mid cld lay]
      Low cloud cover  [low cld lay]
      Shortwave Radiation - backwards  [sfc]
      Wind speed  [10 m above gnd]
      Wind direction  [10 m above gnd]
      Wind speed  [900 mb]
      Wind direction  [900 mb]
      Wind Gust  [sfc]
    

  
  
    

      0
      2017
      6
      26
      0
      0
      18.59
      66.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.00
      0.70
      180.00
      3.94
      66.04
      0.9
    

    

      1
      2017
      6
      26
      1
      0
      18.34
      66.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.00
      1.48
      208.30
      3.95
      81.25
      1.3
    

    

      2
      2017
      6
      26
      2
      0
      17.16
      72.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.00
      0.30
      180.00
      3.21
      95.36
      0.8
    

    

      3
      2017
      6
      26
      3
      0
      16.18
      77.0
      0.0
      5.7
      19.0
      0.0
      0.0
      0.00
      0.30
      180.00
      2.94
      107.82
      2.0
    

    

      4
      2017
      6
      26
      4
      0
      15.55
      79.0
      0.0
      8.1
      27.0
      0.0
      0.0
      0.00
      0.45
      206.57
      2.55
      138.18
      2.1
    

    

      5
      2017
      6
      26
      5
      0
      16.37
      77.0
      0.0
      2.1
      7.0
      0.0
      0.0
      76.54
      0.32
      18.43
      2.32
      172.57
      1.0
    

    

      6
      2017
      6
      26
      6
      0
      18.02
      76.0
      0.0
      7.8
      26.0
      0.0
      0.0
      232.29
      0.28
      135.00
      2.19
      204.23
      1.2
    

    

      7
      2017
      6
      26
      7
      0
      20.30
      72.0
      0.0
      2.7
      9.0
      0.0
      0.0
      390.71
      0.57
      45.00
      1.53
      168.69
      0.8
    

    

      8
      2017
      6
      26
      8
      0
      22.60
      65.0
      0.0
      100.0
      0.0
      100.0
      0.0
      531.33
      0.32
      161.57
      1.70
      183.37
      1.7
    

    

      9
      2017
      6
      26
      9
      0
      24.53
      58.0
      0.0
      67.0
      0.0
      67.0
      0.0
      631.90
      0.50
      36.87
      2.82
      173.88
      1.5
    

    

      10
      2017
      6
      26
      10
      0
      26.51
      49.0
      0.0
      14.0
      0.0
      14.0
      0.0
      771.63
      0.51
      11.31
      2.82
      173.88
      0.6
    

    

      11
      2017
      6
      26
      11
      0
      28.29
      42.0
      0.0
      0.0
      0.0
      0.0
      0.0
      765.40
      0.51
      11.31
      2.57
      193.50
      0.8
    

    

      12
      2017
      6
      26
      12
      0
      28.93
      46.0
      0.0
      10.0
      0.0
      10.0
      0.0
      595.41
      0.70
      360.00
      3.75
      189.21
      1.0
    

    

      13
      2017
      6
      26
      13
      0
      28.94
      50.0
      0.5
      100.0
      1.0
      100.0
      75.0
      789.43
      1.75
      336.37
      2.14
      232.59
      4.7
    

    

      14
      2017
      6
      26
      14
      0
      27.96
      74.0
      0.8
      100.0
      100.0
      100.0
      79.0
      180.67
      0.92
      347.47
      3.56
      231.84
      2.7
    

    

      15
      2017
      6
      26
      15
      0
      26.34
      84.0
      1.3
      100.0
      100.0
      100.0
      0.0
      185.12
      1.75
      59.04
      2.55
      168.69
      2.3
    

    

      16
      2017
      6
      26
      16
      0
      27.48
      60.0
      0.4
      100.0
      100.0
      100.0
      81.0
      492.17
      0.57
      135.00
      2.53
      189.09
      0.3
    

    

      17
      2017
      6
      26
      17
      0
      25.93
      73.0
      0.7
      100.0
      100.0
      100.0
      81.0
      95.23
      0.10
      90.00
      6.73
      191.14
      2.7
    

    

      18
      2017
      6
      26
      18
      0
      25.46
      72.0
      0.0
      100.0
      100.0
      100.0
      83.0
      51.62
      1.24
      194.04
      9.62
      196.93
      4.5
    

    

      19
      2017
      6
      26
      19
      0
      24.34
      72.0
      0.0
      100.0
      100.0
      100.0
      86.0
      30.26
      1.84
      192.53
      8.23
      204.39
      5.9
    

    

      20
      2017
      6
      26
      20
      0
      24.63
      66.0
      0.0
      100.0
      100.0
      100.0
      80.0
      1.78
      0.50
      216.87
      3.81
      203.20
      3.7
    

    

      21
      2017
      6
      26
      21
      0
      24.61
      64.0
      0.0
      100.0
      100.0
      100.0
      83.0
      0.00
      0.73
      344.05
      2.33
      189.87
      0.4
    

    

      22
      2017
      6
      26
      22
      0
      24.26
      60.0
      0.0
      100.0
      100.0
      100.0
      3.0
      0.00
      0.82
      345.96
      0.82
      194.04
      6.0
    

    

      23
      2017
      6
      26
      23
      0
      22.87
      62.0
      0.0
      100.0
      100.0
      100.0
      100.0
      0.00
      2.69
      311.99
      0.86
      305.54
      2.5
    

    

      24
      2017
      6
      27
      0
      0
      19.59
      81.0
      0.0
      100.0
      30.0
      100.0
      4.0
      0.00
      2.55
      348.69
      2.91
      63.43
      3.6
    

    

      25
      2017
      6
      27
      1
      0
      21.48
      67.0
      0.0
      100.0
      0.0
      100.0
      0.0
      0.00
      0.41
      194.04
      1.81
      96.34
      0.6
    

    

      26
      2017
      6
      27
      2
      0
      20.04
      74.0
      0.0
      62.0
      0.0
      62.0
      0.0
      0.00
      1.20
      180.00
      0.90
      180.00
      1.9
    

    

      27
      2017
      6
      27
      3
      0
      19.85
      78.0
      0.0
      28.0
      0.0
      28.0
      3.0
      0.00
      0.64
      51.34
      2.77
      154.36
      0.3
    

    

      28
      2017
      6
      27
      4
      0
      19.29
      77.0
      0.0
      11.0
      0.0
      11.0
      0.0
      0.00
      0.22
      63.43
      2.40
      180.00
      0.5
    

    

      29
      2017
      6
      27
      5
      0
      20.16
      76.0
      0.0
      5.0
      0.0
      5.0
      0.0
      71.20
      0.10
      360.00
      2.38
      202.25
      0.9
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      306
      2017
      7
      8
      18
      0
      32.13
      38.0
      0.9
      100.0
      100.0
      100.0
      0.0
      206.48
      0.22
      153.43
      0.50
      126.87
      0.8
    

    

      307
      2017
      7
      8
      19
      0
      30.18
      50.0
      0.7
      100.0
      100.0
      100.0
      74.0
      78.32
      0.10
      360.00
      1.12
      280.30
      0.1
    

    

      308
      2017
      7
      8
      20
      0
      29.58
      56.0
      0.0
      74.0
      100.0
      74.0
      74.0
      2.67
      0.63
      341.57
      2.70
      360.00
      1.0
    

    

      309
      2017
      7
      8
      21
      0
      28.59
      59.0
      0.0
      78.0
      100.0
      78.0
      0.0
      0.00
      1.10
      354.81
      4.47
      10.30
      2.2
    

    

      310
      2017
      7
      8
      22
      0
      28.06
      59.0
      0.1
      100.0
      100.0
      100.0
      15.0
      0.00
      0.36
      326.31
      3.09
      29.05
      2.1
    

    

      311
      2017
      7
      8
      23
      0
      27.65
      57.0
      0.0
      44.0
      100.0
      44.0
      0.0
      0.00
      0.28
      225.00
      3.10
      104.93
      1.2
    

    

      312
      2017
      7
      9
      0
      0
      26.53
      58.0
      0.0
      87.0
      100.0
      87.0
      0.0
      0.00
      1.30
      184.40
      2.56
      159.44
      1.8
    

    

      313
      2017
      7
      9
      1
      0
      24.87
      59.0
      0.0
      10.8
      36.0
      7.0
      0.0
      0.00
      0.10
      90.00
      1.99
      197.53
      1.1
    

    

      314
      2017
      7
      9
      2
      0
      24.14
      61.0
      0.0
      54.0
      20.0
      54.0
      1.0
      0.00
      0.00
      180.00
      2.53
      161.57
      1.1
    

    

      315
      2017
      7
      9
      3
      0
      24.13
      64.0
      0.0
      100.0
      37.0
      100.0
      0.0
      0.00
      1.36
      17.10
      2.35
      102.26
      1.6
    

    

      316
      2017
      7
      9
      4
      0
      24.55
      60.0
      0.0
      100.0
      100.0
      100.0
      0.0
      0.00
      1.46
      254.05
      0.91
      186.34
      0.6
    

    

      317
      2017
      7
      9
      5
      0
      22.78
      65.0
      0.2
      100.0
      100.0
      100.0
      100.0
      6.23
      1.70
      220.24
      10.22
      183.37
      6.7
    

    

      318
      2017
      7
      9
      6
      0
      24.47
      68.0
      0.3
      100.0
      100.0
      100.0
      4.0
      103.24
      1.71
      339.44
      4.00
      90.00
      1.3
    

    

      319
      2017
      7
      9
      7
      0
      25.11
      65.0
      0.0
      100.0
      64.0
      100.0
      2.0
      243.86
      0.45
      116.57
      5.80
      147.68
      1.7
    

    

      320
      2017
      7
      9
      8
      0
      25.98
      60.0
      0.0
      100.0
      100.0
      100.0
      0.0
      331.97
      1.50
      3.81
      2.58
      125.54
      1.8
    

    

      321
      2017
      7
      9
      9
      0
      28.08
      53.0
      0.0
      37.0
      68.0
      37.0
      2.0
      351.55
      1.06
      41.19
      0.91
      173.66
      1.2
    

    

      322
      2017
      7
      9
      10
      0
      28.41
      50.0
      0.1
      100.0
      100.0
      100.0
      7.0
      561.59
      1.20
      94.76
      8.20
      178.60
      8.7
    

    

      323
      2017
      7
      9
      11
      0
      30.05
      36.0
      1.1
      100.0
      100.0
      100.0
      5.0
      467.25
      4.00
      177.14
      9.37
      168.93
      10.3
    

    

      324
      2017
      7
      9
      12
      0
      24.17
      61.0
      0.9
      100.0
      100.0
      100.0
      100.0
      131.72
      3.36
      329.62
      1.94
      258.11
      3.4
    

    

      325
      2017
      7
      9
      13
      0
      25.86
      56.0
      0.2
      100.0
      100.0
      100.0
      9.0
      202.92
      0.94
      327.99
      2.40
      163.07
      1.6
    

    

      326
      2017
      7
      9
      14
      0
      25.65
      60.0
      0.5
      100.0
      100.0
      100.0
      65.0
      218.94
      0.50
      233.13
      6.51
      197.88
      6.5
    

    

      327
      2017
      7
      9
      15
      0
      25.66
      67.0
      0.1
      100.0
      0.0
      100.0
      0.0
      165.54
      1.50
      360.00
      2.92
      210.96
      5.9
    

    

      328
      2017
      7
      9
      16
      0
      26.09
      62.0
      0.0
      100.0
      0.0
      100.0
      61.0
      226.06
      1.36
      306.03
      4.07
      204.68
      3.7
    

    

      329
      2017
      7
      9
      17
      0
      25.81
      64.0
      0.0
      100.0
      0.0
      100.0
      69.0
      191.35
      0.36
      303.69
      6.86
      198.70
      5.2
    

    

      330
      2017
      7
      9
      18
      0
      25.03
      63.0
      0.3
      100.0
      0.0
      100.0
      69.0
      103.24
      0.32
      198.43
      7.40
      196.48
      5.1
    

    

      331
      2017
      7
      9
      19
      0
      24.62
      63.0
      0.0
      100.0
      0.0
      100.0
      4.0
      45.39
      0.20
      90.00
      6.99
      199.21
      4.4
    

    

      332
      2017
      7
      9
      20
      0
      23.82
      68.0
      0.2
      72.0
      0.0
      72.0
      72.0
      6.23
      0.20
      180.00
      8.10
      197.99
      3.9
    

    

      333
      2017
      7
      9
      21
      0
      22.61
      71.0
      0.9
      100.0
      0.0
      0.0
      100.0
      0.00
      1.00
      216.87
      8.19
      199.98
      3.4
    

    

      334
      2017
      7
      9
      22
      0
      21.81
      74.0
      0.2
      100.0
      0.0
      83.0
      100.0
      0.00
      1.40
      270.00
      6.40
      208.97
      3.2
    

    

      335
      2017
      7
      9
      23
      0
      20.37
      77.0
      0.2
      100.0
      0.0
      10.0
      100.0
      0.00
      1.84
      292.38
      4.18
      222.09
      3.2
    

  



336 rows × 18 columns



















In [80]:



    


data.index = pd.to_datetime( data[['Year', 'Month', 'Day', 'Hour', 'Minute']] )

data = data.drop( ['Year', 'Month', 'Day', 'Hour', 'Minute'], axis=1 )



    











In [81]:



    


data.columns



    


















    
Out[81]:








Index(['Temperature  [2 m above gnd]', 'Relative humidity  [2 m above gnd]',
       'Total Precipitation  [sfc]', 'Total cloud cover  [sfc]',
       'High cloud cover  [high cld lay]', 'Medium cloud cover  [mid cld lay]',
       'Low cloud cover  [low cld lay]',
       'Shortwave Radiation - backwards  [sfc]',
       'Wind speed  [10 m above gnd]', 'Wind direction  [10 m above gnd]',
       'Wind speed  [900 mb]', 'Wind direction  [900 mb]', 'Wind Gust  [sfc]'],
      dtype='object')

















In [82]:



    


data['Total cloud cover  [sfc]'].plot()



    


















    
Out[82]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647cdc9198>










    
























In [92]:



    


import weatherfeed as wf
import sunradiation as sun



    











In [38]:



    


coords_grenoble = (45.1973288, 5.7139923)

startday = pd.to_datetime('26/06/2017', format='%d/%m/%Y').tz_localize('Europe/Paris')
lastday =  pd.to_datetime('10/07/2017', format='%d/%m/%Y').tz_localize('Europe/Paris')



    











In [39]:



    


weatherdata = wf.buildmultidayDF(startday, lastday, coords_grenoble )



    


















    






26, 27, 28, 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, done

















In [83]:



    


weatherdata['temperature'].plot( figsize=(14, 6))
data['Temperature  [2 m above gnd]'].plot(style='r')



    


















    
Out[83]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647ce56a58>










    
























In [84]:



    


data.index.dayofyear



    


















    
Out[84]:








Int64Index([177, 177, 177, 177, 177, 177, 177, 177, 177, 177,
            ...
            190, 190, 190, 190, 190, 190, 190, 190, 190, 190],
           dtype='int64', length=336)

















In [87]:



    


zoom_mask = data.index.map(lambda x: x.dayofyear) == 185



    











In [88]:



    


weatherdata['temperature'].loc[ zoom_mask].plot( figsize=(14, 6))
data['Temperature  [2 m above gnd]'].loc[ zoom_mask].plot(style='r')



    


















    
Out[88]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647cc93a20>










    
























In [89]:



    


weatherdata['cloudCover'].plot( figsize=(14, 6))
cc = data['Total cloud cover  [sfc]']/100
cc.plot(style='r')



    


















    
Out[89]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647cbe1d68>










    
























In [106]:



    


zoom_mask = data.index.map(lambda x: x.dayofyear) == 181
data['Shortwave Radiation - backwards  [sfc]'].loc[zoom_mask].plot(figsize=(14, 6))



    


















    
Out[106]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647c528b70>










    
























In [93]:



    


sundata = sun.buildmultidayDF( coords_grenoble, weatherdata.index, cloudCover = weatherdata['cloudCover']  )



    


















    






/home/etienne/Projets/thermique_appart/py3/lib/python3.5/site-packages/pysolar/time.py:105: UserWarning: I don't know about leap seconds after 2015
  (leap_seconds_base_year + len(leap_seconds_adjustments) - 1)

















In [94]:



    


sundata['I0'].plot()



    


















    
Out[94]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647caf37b8>










    
























In [107]:



    


zoom_mask = data.index.map(lambda x: x.dayofyear) == 181
data['Shortwave Radiation - backwards  [sfc]'].loc[zoom_mask].plot(figsize=(14, 6))
sundata['I0'].loc[zoom_mask].plot()

# surface horizontal  vs   dans direction du soleil



    


















    
Out[107]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f647c6a1358>










    
























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Content source: xdze2/thermique_appart

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