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  • resampling frequency is 2 Days
  • various rates of the chlor-a concentration
  • "fixed scale" seems more illustrative than the "min-max scale"




In [2]:



    


%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 [3]:



    


# 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 = 2
pref = 'df_chl_dist_out_'
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 0x11367ff28>










    






in_filename and path: ../data_collector_modisa_chla9km/df_multidimInterpolation/df_chl_dist_out_2D_modisa.csv










    
Out[3]:










  
    

      
      time
      id
      lat
      temp
      var_tmp
      vn
      var_lon
      lon
      var_lat
      ve
      ...
      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
      10.737250
      NaN
      1000.000000
      6.374250
      0.000719
      67.512250
      0.000272
      -4.832250
      ...
      -2.014689
      -0.001486
      -0.011020
      -0.019168
      -0.057326
      -0.010402
      -0.077139
      44
      1
      502.866078
    

    

      260
      2002-11-03
      10206
      10.818500
      NaN
      1000.000000
      7.311375
      0.000778
      67.399375
      0.000295
      -8.207500
      ...
      -1.960301
      0.003727
      0.027194
      -0.015864
      0.091292
      0.026090
      0.190358
      44
      1
      514.302886
    

    

      519
      2002-11-05
      10206
      10.902250
      NaN
      1000.000000
      5.807000
      0.002068
      67.291000
      0.000666
      -7.410625
      ...
      -1.940023
      0.001442
      0.010139
      -0.017312
      0.024963
      0.010096
      0.070973
      45
      2
      525.418388
    

    

      778
      2002-11-07
      10206
      11.036625
      NaN
      1000.000000
      6.546875
      0.000420
      67.202125
      0.000174
      -6.844125
      ...
      -2.039786
      -0.006822
      -0.049882
      -0.022551
      -0.208465
      -0.047755
      -0.349172
      45
      2
      534.353712
    

    

      1037
      2002-11-09
      10206
      11.130625
      NaN
      1000.000000
      4.402625
      0.002828
      67.113250
      0.000866
      -0.825125
      ...
      -1.998625
      0.002733
      0.020581
      -0.016494
      0.065572
      0.019128
      0.144065
      45
      2
      543.770628
    

    

      1296
      2002-11-11
      10206
      11.153750
      NaN
      1000.000000
      0.028125
      0.001112
      67.132250
      0.000387
      0.870375
      ...
      -2.057866
      -0.003898
      -0.029621
      -0.020697
      -0.129667
      -0.027283
      -0.207344
      46
      3
      541.655587
    

    

      1555
      2002-11-13
      10206
      11.153250
      NaN
      1000.000000
      2.077500
      0.000949
      67.105875
      0.000329
      -3.435000
      ...
      -1.958813
      0.006650
      0.049526
      -0.014011
      0.178145
      0.046548
      0.346684
      46
      3
      544.537084
    

    

      1814
      2002-11-15
      10206
      11.185250
      NaN
      1000.000000
      -0.256875
      0.009313
      67.081500
      0.002355
      0.244250
      ...
      -1.951589
      0.000511
      0.003612
      -0.017902
      -0.000421
      0.003579
      0.025285
      46
      3
      547.160169
    

    

      3368
      2002-11-27
      10206
      11.290375
      NaN
      1000.000000
      2.209500
      0.004046
      66.954125
      0.001114
      -2.474250
      ...
      -2.011486
      0.001250
      0.009433
      -0.017434
      0.022217
      0.008751
      0.066030
      48
      5
      561.105028
    

    

      3627
      2002-11-29
      10206
      11.334500
      NaN
      1000.000000
      3.017375
      0.000849
      66.869375
      0.000309
      -6.904000
      ...
      -1.994817
      0.001124
      0.008334
      -0.017514
      0.017945
      0.007871
      0.058341
      48
      5
      570.438783
    

    

      3886
      2002-12-01
      10206
      11.370375
      NaN
      1000.000000
      2.757500
      0.002439
      66.731125
      0.000727
      -12.013000
      ...
      -1.965238
      0.002042
      0.014790
      -0.016932
      0.043050
      0.014294
      0.103528
      48
      5
      585.622607
    

    

      5440
      2002-12-13
      10206
      12.050500
      NaN
      1000.000000
      7.001750
      0.001724
      65.475625
      0.000548
      -10.719000
      ...
      -1.972290
      0.001632
      0.011869
      -0.017192
      0.031689
      0.011423
      0.083080
      50
      7
      727.854567
    

    

      5699
      2002-12-15
      10206
      12.151500
      NaN
      1000.000000
      7.548875
      0.002947
      65.335500
      0.000857
      -7.489875
      ...
      -2.025179
      -0.003584
      -0.026444
      -0.020498
      -0.117314
      -0.025086
      -0.185109
      50
      7
      744.401621
    

    

      6994
      2002-12-25
      10206
      12.539250
      NaN
      1000.000000
      3.442250
      0.001216
      64.699250
      0.000419
      -9.392000
      ...
      -1.886846
      -0.002969
      -0.019220
      -0.020109
      -0.089218
      -0.020786
      -0.134540
      52
      9
      819.312377
    

    

      7253
      2002-12-27
      10206
      12.583875
      NaN
      1000.000000
      2.296375
      0.000739
      64.523500
      0.000278
      -10.462250
      ...
      -1.818675
      0.005346
      0.034085
      -0.014838
      0.118093
      0.037420
      0.238597
      52
      9
      838.974759
    

    

      7512
      2002-12-29
      10206
      12.588375
      NaN
      1000.000000
      -1.934375
      0.002552
      64.366625
      0.000804
      -10.133750
      ...
      -1.835596
      -0.001361
      -0.008460
      -0.019089
      -0.047372
      -0.009527
      -0.059222
      52
      9
      855.859231
    

    

      7771
      2002-12-31
      10206
      12.543625
      NaN
      1000.000000
      -3.439750
      0.001012
      64.190250
      0.000368
      -11.078750
      ...
      -1.769552
      0.005445
      0.033022
      -0.014774
      0.113958
      0.038118
      0.231155
      1
      11
      873.959197
    

    

      10361
      2003-01-20
      10206
      11.464500
      NaN
      1000.000000
      -8.722125
      0.001504
      62.478250
      0.000509
      -7.808750
      ...
      -1.351429
      -0.058614
      -0.186762
      -0.055383
      -0.740809
      -0.410298
      -1.307333
      4
      14
      872.473960
    

    

      11138
      2003-01-26
      10206
      11.070000
      NaN
      1000.000000
      -9.359500
      0.000926
      62.063375
      0.000337
      -10.658875
      ...
      -1.036614
      0.030110
      0.093044
      0.000861
      0.347391
      0.210770
      0.651307
      4
      14
      835.689016
    

    

      521
      2002-11-05
      11089
      14.298875
      28.89325
      0.003938
      4.507125
      0.000097
      65.041250
      0.000053
      -10.529750
      ...
      -1.842034
      0.007208
      0.047681
      -0.013657
      0.170968
      0.050456
      0.333766
      45
      2
      830.177225
    

  



20 rows × 22 columns



















In [5]:



    


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 [13]:



    


# [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    90.000000
mean      0.164260
std       1.127157
min      -1.637868
25%      -0.041247
50%       0.007013
75%       0.064822
max       9.750447
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x102af4f28>










    

















    






 summary statistics of chl_rate_week for the year 2003 

count    108.000000
mean      -0.023719
std       11.692654
min      -69.154118
25%       -0.208309
50%        0.011123
75%        0.216892
max       46.663716
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116f78a20>










    

















    






 summary statistics of chl_rate_week for the year 2004 

count    8.000000
mean     0.030804
std      0.116175
min     -0.081375
25%     -0.044966
50%      0.002731
75%      0.071391
max      0.277428
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x117690c88>










    

















    






 summary statistics of chl_rate_week for the year 2005 

count    106.000000
mean       0.010666
std        0.296527
min       -0.879874
25%       -0.085455
50%       -0.002009
75%        0.097460
max        1.163664
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116fa0b00>










    

















    






 summary statistics of chl_rate_week for the year 2006 

count    201.000000
mean      -0.038071
std        1.721764
min      -16.158397
25%       -0.110084
50%       -0.010593
75%        0.062009
max       14.456993
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116a9e208>










    

















    






 summary statistics of chl_rate_week for the year 2007 

count    206.000000
mean       0.051636
std        0.438560
min       -1.730095
25%       -0.076108
50%        0.006083
75%        0.123297
max        2.967473
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1170bc1d0>










    

















    






 summary statistics of chl_rate_week for the year 2008 

count    293.000000
mean       0.283979
std       16.263207
min      -94.308617
25%       -0.123734
50%       -0.005309
75%        0.111467
max       79.725910
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116bc0ac8>










    

















    






 summary statistics of chl_rate_week for the year 2009 

count    89.000000
mean     -0.443150
std       3.993296
min     -36.562424
25%      -0.070949
50%      -0.006755
75%       0.077960
max       7.036528
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x102a74a58>










    

















    






 summary statistics of chl_rate_week for the year 2010 

count    149.000000
mean      -0.023245
std        0.218063
min       -1.007590
25%       -0.059863
50%       -0.007450
75%        0.051831
max        0.612091
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x11367ff60>










    

















    






 summary statistics of chl_rate_week for the year 2011 

count    30.000000
mean      0.251446
std       1.063232
min      -0.380810
25%      -0.027995
50%      -0.000409
75%       0.118452
max       5.737800
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116bb4f60>










    

















    






 summary statistics of chl_rate_week for the year 2012 

count    92.000000
mean     -0.040408
std       9.686297
min     -45.659181
25%      -0.367191
50%       0.002440
75%       0.535387
max      63.718582
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x1160e8438>










    

















    






 summary statistics of chl_rate_week for the year 2013 

count    101.000000
mean       0.081242
std        1.239407
min       -7.956510
25%       -0.158827
50%       -0.001653
75%        0.275481
max        7.508984
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116f273c8>










    

















    






 summary statistics of chl_rate_week for the year 2014 

count    415.000000
mean       0.044642
std       16.615929
min     -118.908850
25%       -0.386717
50%       -0.010398
75%        0.320548
max      144.372604
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116b076d8>










    

















    






 summary statistics of chl_rate_week for the year 2015 

count    245.000000
mean       1.474546
std       14.973212
min      -72.187329
25%       -0.166308
50%        0.026891
75%        0.266506
max      139.555398
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x116a95198>










    

















    






 summary statistics of chl_rate_week for the year 2016 

count    103.000000
mean       0.012770
std        1.641607
min      -10.818189
25%       -0.039095
50%        0.007569
75%        0.054186
max       10.526400
Name: chl_rate_week, dtype: float64










    

















    








<matplotlib.figure.Figure at 0x102adfcf8>










    

















    








<matplotlib.figure.Figure at 0x116fa8048>

















In [ ]:

Content source: vyan2000/ocml-public

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