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Edit and run

  • No reampling is done for this case, we are directly using the ocean color 8 day datasets.
  • various rates of the chlor-a concentration
  • "fixed scale" seems more illustrative than the "min-max scale"




In [1]:



    


%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt
import seaborn as sns
from datetime import datetime
import datetime



    











In [2]:



    


# load the floats data 
# ********************
# *** CSV files ***
# ********************
# load CSV output    
#plt = plt.figure(8,6)
plt.close('all')
plt.cla()  # axis
plt.clf()  # figure
plt.show()

freq = 8
pref = 'df_chl_dist_out_OC_'
surf =  '_modisa.csv'
infile = pref + str(freq) + 'D' + surf

folder = '../data_collector_modisa_chla9km/df_multidimInterpolation/'
direc = folder + infile
direc

print('in_filename and path:', direc)

df_tmp = pd.read_csv(direc, index_col='index')
df_tmp.sort_values(['id', 'time'])[:20]



    


















    








<matplotlib.figure.Figure at 0x120a4ee48>










    






in_filename and path: ../data_collector_modisa_chla9km/df_multidimInterpolation/df_chl_dist_out_OC_8D_modisa.csv










    
Out[2]:










  
    

      
      time
      id
      temp
      var_lat
      var_tmp
      vn
      var_lon
      ve
      lat
      lon
      ...
      chlor_a_log_e
      chl_rate
      chl_log_e_rate
      chl_rate_stand
      chl_log_e_rate_stand
      chl_rate_week
      chl_log_e_rate_week
      week
      week_rotate
      dist
    

    

      index
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      1
      2002-11-01
      10206
      NaN
      0.000352
      1000.000000
      6.509875
      0.000996
      -6.823625
      10.873656
      67.351188
      ...
      -2.001974
      -0.001848
      -0.012985
      0.013562
      -0.178561
      -0.012938
      -0.090897
      44
      1
      519.077549
    

    

      260
      2002-11-09
      10206
      NaN
      0.000984
      1000.000000
      1.562844
      0.003551
      -0.786375
      11.155719
      67.108219
      ...
      -2.009126
      -0.000120
      -0.000894
      0.021090
      0.020181
      -0.000842
      -0.006259
      45
      2
      544.277694
    

    

      519
      2002-11-17
      10206
      NaN
      0.000691
      1000.000000
      0.728813
      0.002426
      -1.222031
      11.179438
      67.022562
      ...
      -1.998155
      0.000185
      0.001371
      0.022420
      0.057419
      0.001295
      0.009600
      46
      3
      553.600884
    

    

      778
      2002-11-25
      10206
      NaN
      0.000877
      1000.000000
      2.751937
      0.003086
      -5.805906
      11.310312
      66.887437
      ...
      -1.963044
      0.000606
      0.004389
      0.024253
      0.107016
      0.004239
      0.030722
      48
      5
      568.418257
    

    

      1037
      2002-12-03
      10206
      NaN
      0.001989
      1000.000000
      7.625781
      0.008155
      -13.705094
      11.619656
      66.202937
      ...
      -1.863798
      0.001832
      0.012406
      0.029594
      0.238787
      0.012821
      0.086840
      49
      6
      644.469466
    

    

      1296
      2002-12-11
      10206
      NaN
      0.001419
      1000.000000
      6.657594
      0.005590
      -9.692125
      12.094781
      65.421156
      ...
      -1.999938
      -0.002467
      -0.017018
      0.010864
      -0.244838
      -0.017271
      -0.119123
      50
      7
      734.343511
    

    

      1555
      2002-12-19
      10206
      NaN
      0.001140
      1000.000000
      4.556469
      0.004192
      -8.368125
      12.434812
      64.896875
      ...
      -1.852240
      0.002693
      0.018462
      0.033346
      0.338339
      0.018849
      0.129236
      51
      8
      796.212237
    

    

      1814
      2002-12-27
      10206
      NaN
      0.000417
      1000.000000
      -1.765500
      0.001212
      -11.493313
      12.549094
      64.271031
      ...
      -1.783050
      0.001405
      0.008649
      0.027735
      0.177034
      0.009834
      0.060541
      52
      9
      865.389879
    

    

      2073
      2003-01-04
      10206
      NaN
      0.000691
      1000.000000
      -5.715375
      0.002190
      -11.053437
      12.280437
      63.550156
      ...
      -1.238337
      0.015218
      0.068089
      0.087918
      1.154049
      0.106524
      0.476624
      1
      11
      938.950903
    

    

      2332
      2003-01-12
      10206
      NaN
      0.000878
      1000.000000
      -9.232062
      0.002898
      -10.050531
      11.877375
      62.833531
      ...
      -1.157548
      0.003049
      0.010099
      0.034898
      0.200867
      0.021341
      0.070691
      2
      12
      905.309926
    

    

      2591
      2003-01-20
      10206
      NaN
      0.000889
      1000.000000
      -8.603719
      0.003055
      -9.272062
      11.255250
      62.273156
      ...
      -1.149046
      0.000335
      0.001063
      0.023076
      0.052344
      0.002348
      0.007439
      4
      14
      854.207958
    

    

      2850
      2003-01-28
      10206
      NaN
      0.000514
      1000.000000
      -13.636000
      0.001431
      -15.887000
      10.962500
      61.932000
      ...
      -1.731196
      -0.017483
      -0.072769
      -0.054560
      -1.161214
      -0.122383
      -0.509381
      5
      15
      824.233600
    

    

      3
      2002-11-01
      11089
      28.969813
      0.000057
      0.003679
      4.337844
      0.000106
      -11.122000
      14.269219
      65.099156
      ...
      -1.886046
      0.002755
      0.019628
      0.033618
      0.357495
      0.019285
      0.137394
      44
      1
      823.181574
    

    

      262
      2002-11-09
      11089
      28.742188
      0.000065
      0.003868
      -6.951906
      0.000126
      -17.539250
      14.220969
      64.193281
      ...
      -1.586824
      0.006613
      0.037403
      0.050427
      0.649662
      0.046291
      0.261820
      45
      2
      859.436899
    

    

      521
      2002-11-17
      11089
      28.087750
      0.000063
      0.003611
      -11.627562
      0.000119
      -32.685906
      13.495938
      62.557250
      ...
      -1.194386
      0.012289
      0.049055
      0.075159
      0.841183
      0.086026
      0.343383
      46
      3
      790.367112
    

    

      780
      2002-11-25
      11089
      27.942594
      0.000063
      0.003698
      6.087531
      0.000121
      -31.819562
      13.312875
      60.531781
      ...
      -1.203347
      -0.000338
      -0.001120
      0.020143
      0.016465
      -0.002364
      -0.007841
      48
      5
      644.744428
    

    

      1557
      2002-12-19
      11089
      27.076188
      0.000060
      0.003500
      0.775969
      0.000114
      -17.933438
      13.934437
      59.069375
      ...
      -1.204513
      -0.004708
      -0.014792
      0.001100
      -0.208257
      -0.032958
      -0.103544
      51
      8
      490.265590
    

    

      1816
      2002-12-27
      11089
      26.580312
      0.000065
      0.003657
      13.433844
      0.000123
      4.662594
      14.770719
      58.528844
      ...
      -1.400422
      -0.006668
      -0.024489
      -0.007439
      -0.367640
      -0.046677
      -0.171420
      52
      9
      381.228340
    

    

      2075
      2003-01-04
      11089
      26.104250
      0.000070
      0.003628
      -26.206813
      0.000134
      5.199344
      14.353250
      59.107094
      ...
      -1.390129
      0.000319
      0.001287
      0.023003
      0.056024
      0.002231
      0.009006
      1
      11
      456.224079
    

    

      2334
      2003-01-12
      11089
      26.045250
      0.000077
      0.003933
      -15.677625
      0.000150
      -5.146563
      12.791187
      58.996656
      ...
      -1.160179
      0.008048
      0.028744
      0.056681
      0.507334
      0.056338
      0.201206
      2
      12
      484.576008
    

  



20 rows × 22 columns



















In [3]:



    


df_tmp['time'] = pd.to_datetime(df_tmp['time']) # ,format='%m/%d/%y %I:%M%p'
df_timed = df_tmp.set_index('time')
# add a new column to the dataset
df_timed['year'] = df_timed.index.year
df_timed[:20]

year_max = df_timed.index.year.max()
year_min = df_timed.index.year.min()
print('year_max', year_max)
print('year_min', year_min)
#print(df_timed)



    


















    






year_max 2016
year_min 2002

















In [4]:



    


# [print(i) for i in range(year_min,year_max+1)]
# for  each year


for current_year in range(year_min, year_max+1):
    mask = df_timed.year == current_year
    df_yearly = df_timed[mask]

    print('\n summary statistics of chl_rate_week for the year %d \n' % (current_year) )
    print(df_yearly.chl_rate_week.describe())


    plt.figure(figsize=(8,6))
    axes1=df_yearly.groupby(['week_rotate'])['chl_rate_week'].mean().plot(linestyle="-",color='b', linewidth=1)
    df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.75).plot(linestyle="--",color='g', linewidth=0.35)
    df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.50).plot(linestyle="--",color='r', linewidth=0.75)
    df_yearly.groupby(['week_rotate'])['chl_rate_week'].quantile(.25).plot(linestyle="--",color='g', linewidth=0.35)
    axes1.set_ylim(-10, 10)
    axes1.set_title("Year " + str(current_year) + ": Line plot of the weekly data on the rate of change per week of the $Chl_a$ Concentration",  fontsize=10)
    plt.xlabel('week', fontsize=10)
    plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
    plt.yticks(np.arange(-10, 10, 0.5))
    plt.xticks(np.arange(1, 25, 1))
    plt.show()
    plt.close()


    # http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html
    #http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/
    plt.figure(figsize=(8,6))
    axes2 = df_yearly.boxplot(column='chl_rate_week', by='week_rotate')
    axes2.set_ylim(-10, 10)
    #axes2.set_title("Box plot of the weekly data \n on the rate of change per week of the $Chl_a$ Concentration",  fontsize=10)
    plt.title("Year " + str(current_year) + ": Box plot of the weekly data \n on the rate of change per week of the $Chl_a$ Concentration",  fontsize=10)
    plt.suptitle("") # remove auto-title
    plt.xlabel('week', fontsize=10)
    plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 \cdot 7days)$', fontsize=10)
    plt.yticks(np.arange(-10, 10, 0.5))
    plt.xticks(np.arange(1, 25, 1))
    plt.show()
    plt.close()
    # the rate of change is slower on the regular scale

#matplotlib.pyplot.close("all")
plt.close('all')
plt.cla()  # axis
plt.clf()  # figure
plt.show()



    


















    






 summary statistics of chl_rate_week for the year 2002 

count    45.000000
mean      0.007193
std       0.089912
min      -0.382744
25%      -0.016304
50%       0.003533
75%       0.019285
max       0.360003
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1234c2c88>










    

















    






 summary statistics of chl_rate_week for the year 2003 

count    42.000000
mean     -0.012217
std       0.562858
min      -1.348533
25%      -0.085488
50%       0.005000
75%       0.074460
max       1.654371
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x123431748>










    

















    






 summary statistics of chl_rate_week for the year 2004 

count    12.000000
mean      0.012543
std       0.047171
min      -0.071865
25%      -0.008972
50%       0.000219
75%       0.036668
max       0.100659
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x123fd0160>










    

















    






 summary statistics of chl_rate_week for the year 2005 

count    52.000000
mean      0.001395
std       0.096001
min      -0.288784
25%      -0.011939
50%       0.011187
75%       0.044616
max       0.320302
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1243166d8>










    

















    






 summary statistics of chl_rate_week for the year 2006 

count    108.000000
mean      -0.001235
std        0.208780
min       -1.073614
25%       -0.045042
50%       -0.010641
75%        0.009952
max        1.209117
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x11104fd30>










    

















    






 summary statistics of chl_rate_week for the year 2007 

count    101.000000
mean       0.020263
std        0.099526
min       -0.158987
25%       -0.015842
50%        0.004553
75%        0.026788
max        0.689046
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1246a95f8>










    

















    






 summary statistics of chl_rate_week for the year 2008 

count    164.000000
mean      -0.267231
std        2.242144
min      -11.282684
25%       -0.032930
50%       -0.005816
75%        0.013276
max        9.149188
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x123ffee48>










    

















    






 summary statistics of chl_rate_week for the year 2009 

count    43.000000
mean     -0.011376
std       0.205353
min      -1.038915
25%      -0.011553
50%       0.003382
75%       0.016298
max       0.704537
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x10fed26a0>










    

















    






 summary statistics of chl_rate_week for the year 2010 

count    72.000000
mean     -0.009643
std       0.061495
min      -0.349300
25%      -0.022902
50%      -0.006200
75%       0.011392
max       0.192485
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x12340e908>










    

















    






 summary statistics of chl_rate_week for the year 2011 

count    21.000000
mean     -0.001537
std       0.156353
min      -0.598967
25%      -0.017451
50%       0.031760
75%       0.059737
max       0.187121
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x10fe60cc0>










    

















    






 summary statistics of chl_rate_week for the year 2012 

count    39.000000
mean      0.030538
std       0.557391
min      -1.411399
25%      -0.116740
50%       0.008265
75%       0.212498
max       1.246088
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x124691cf8>










    

















    






 summary statistics of chl_rate_week for the year 2013 

count    31.000000
mean      0.045952
std       0.339103
min      -0.192211
25%      -0.063384
50%      -0.013830
75%       0.043601
max       1.822601
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x12476f550>










    

















    






 summary statistics of chl_rate_week for the year 2014 

count    192.000000
mean       0.009153
std        2.211830
min      -20.801012
25%       -0.086144
50%       -0.005429
75%        0.085154
max       18.750057
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x122e9d588>










    

















    






 summary statistics of chl_rate_week for the year 2015 

count    98.000000
mean      0.019596
std       3.101022
min     -21.651430
25%      -0.046124
50%       0.008014
75%       0.076202
max      16.909442
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1246c00f0>










    

















    






 summary statistics of chl_rate_week for the year 2016 

count    47.000000
mean     -0.005335
std       0.608121
min      -2.834149
25%      -0.039637
50%      -0.008139
75%       0.018232
max       2.801594
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x123011780>










    

















    








<matplotlib.figure.Figure at 0x124a06208>

















In [ ]:

Content source: vyan2000/ocml-public

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