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Application - Usage

Info

A version of this page for the Juptyter Notebook can be found at https://github.com/GAD-DIMNT-CPTEC/readDiag/blob/master/notebooks/readDiag_Radiance_tutorial_complete-en_us.ipynb.

If you want to interact with the notebook, click on the button below to open Google Colab.

Open In Colab

Using the read_diag Class

Required Libraries

To start using readDiag, first load the necessary libraries for its usage:

  • gsidiag: it is the library that contains the read_diag and plot_diag classes;
  • pandas: it is the library that provides tabulated data structures used by readDiag;
  • matplotlib: it is the library from which figures are created;
  • datetime: it is the library used for date manipulation.

The %matplotlib inline instruction is a Jupyter magic command and only adjusts the environment so that the plt.show() command is not needed whenever figures are displayed within the notebook. If you are using readDiag within a Python script, this directive can be omitted, and the plt.show() command should be used as needed.

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import gsidiag as gd

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

Main Variables

In the table below, the default values of the variables used throughout this tutorial are listed. Note that the variables have different types, and some can be declared as None.

Variables Values Type
DIRdiag = /pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout string
varName = uv string
varType = 220 integer
dateIni = 2019121000 string
dateFin = 2019121118 string
nHour = 6 string
vminOMA = -2.0 float
vmaxOMA = 2.0 float
vminSTD = 0.0 float
vmaxSTD = 14.0 float
Level = 1000 integer or None
Lay = 15 integer or None
SingleL = OneL string

Thus, in the following cell, the variables are adjusted according to the values in the table above. Additionally, the date values are also adjusted to compose the path and names of the GSI diagnostic files:

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# For use in Egeon
#DIRdiag = "/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout"

# For use in Itapemirim (Colorado, Ilopolis, and other virtual machines)
#DIRdiag = "/share/das/dist/joao.gerd/EXP18/GSI/dataout"

# For use on a local machine (in this case, the remote directory should be mounted locally with the sshfs command)
# Example:
# $ sshfs username@egeon.cptec.inpe.br:/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout
# or
# $ sshfs username@itapemirim.cptec.inpe.br:/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout
DIRdiag = '/extra2/EGEON_EXP18_GSI_dataout'

varName = 'uv'
varType = 220
dateIni = '2019121000'
dateFin = '2019121118'
nHour = '6'
vminOMA = -2.0
vmaxOMA = 2.0
vminSTD = 0.0
vmaxSTD = 14.0
Level = 1000
Lay = 15
SingleL = 'OneL'

datei = datetime.strptime(str(dateIni), '%Y%m%d%H')
datef = datetime.strptime(str(dateFin), '%Y%m%d%H')
dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6H').tolist()]

print(dates)

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['2019121000', '2019121006', '2019121012', '2019121018', '2019121100', '2019121106', '2019121112', '2019121118']

Generating the path and pathc variables where the complete paths (including the file name) of the diagnostic files for the first (OmF) and last (OmA) outer loops will be:

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paths, pathsc = [], []

OuterL = '01'
[paths.append(DIRdiag + '/' + dt + '/diag_conv_' + OuterL + '.' + dt) for dt in dates]

OuterLc = '03'
[pathsc.append(DIRdiag + '/' + dt + '/diag_conv_' + OuterLc + '.' + dt) for dt in dates]

print(paths)
print('')
print(pathsc)

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['/extra2/EGEON_EXP18_GSI_dataout/2019121000/diag_conv_01.2019121000', '/extra2/EGEON_EXP18_GSI_dataout/2019121006/diag_conv_01.2019121006', '/extra2/EGEON_EXP18_GSI_dataout/2019121012/diag_conv_01.2019121012', '/extra2/EGEON_EXP18_GSI_dataout/2019121018/diag_conv_01.2019121018', '/extra2/EGEON_EXP18_GSI_dataout/2019121100/diag_conv_01.2019121100', '/extra2/EGEON_EXP18_GSI_dataout/2019121106/diag_conv_01.2019121106', '/extra2/EGEON_EXP18_GSI_dataout/2019121112/diag_conv_01.2019121112', '/extra2/EGEON_EXP18_GSI_dataout/2019121118/diag_conv_01.2019121118']

['/extra2/EGEON_EXP18_GSI_dataout/2019121000/diag_conv_03.2019121000', '/extra2/EGEON_EXP18_GSI_dataout/2019121006/diag_conv_03.2019121006', '/extra2/EGEON_EXP18_GSI_dataout/2019121012/diag_conv_03.2019121012', '/extra2/EGEON_EXP18_GSI_dataout/2019121018/diag_conv_03.2019121018', '/extra2/EGEON_EXP18_GSI_dataout/2019121100/diag_conv_03.2019121100', '/extra2/EGEON_EXP18_GSI_dataout/2019121106/diag_conv_03.2019121106', '/extra2/EGEON_EXP18_GSI_dataout/2019121112/diag_conv_03.2019121112', '/extra2/EGEON_EXP18_GSI_dataout/2019121118/diag_conv_03.2019121118']

Reading Diagnostic Files

Warning

Reading diagnostic files using the values adjusted for the parameters above requires at least 8GB of RAM. If necessary, adjust the parameters to consider a smaller interval.

Reading diagnostic files with the read_diag() function from the readDiag package. In the following code snippet, note that the read_diag() function is used within a loop that iterates over all the files in the paths and pathsc lists defined in the previous step. At the end of the loop, the gdf_list list is generated, which will contain all the files read by readDiag:

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read = True

if read:
    gdf_list = []
    print('')

    print('Please wait; the estimated total time for reading the files is ' +
          str(int((float(len(paths))*20)/60)) + ' minutes and ' +
          str(int((float(len(paths))*20)%60)) + ' seconds.')

    print('')

    for path, pathc in zip(paths, pathsc):
        print('Reading ' + path)

        gdf = gd.read_diag(path, pathc)

        gdf_list.append(gdf)

    print('Done!')

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Please wait; the estimated total time for reading the files is 2 minutes and 40 seconds.

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121000/diag_conv_01.2019121000

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121006/diag_conv_01.2019121006

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121012/diag_conv_01.2019121012

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121018/diag_conv_01.2019121018

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121100/diag_conv_01.2019121100

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121106/diag_conv_01.2019121106

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121112/diag_conv_01.2019121112

>>> GSI DIAG <<<

Reading /extra2/EGEON_EXP18_GSI_dataout/2019121118/diag_conv_01.2019121118

>>> GSI DIAG <<<

Done!

The gdf_list variable is a list of dataframes containing the data from each diagnostic file. To work with a single time, simply refer to the list with a fixed index, for example: gdf_list[0]:

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gdf_list

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[<gsidiag.__main__.read_diag at 0x7f9005378790>,
 <gsidiag.__main__.read_diag at 0x7f8ff18e65d0>,
 <gsidiag.__main__.read_diag at 0x7f8fda8d2f50>,
 <gsidiag.__main__.read_diag at 0x7f8fc0b68bd0>,
 <gsidiag.__main__.read_diag at 0x7f8fabb26e10>,
 <gsidiag.__main__.read_diag at 0x7f8f97e89b90>,
 <gsidiag.__main__.read_diag at 0x7f8f83823350>,
 <gsidiag.__main__.read_diag at 0x7f8f67a70b90>]

Setting tidx = 0 retrieves the first object from the gdf_list:

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tidx = 0
gdf_list[tidx]

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<gsidiag.__main__.read_diag at 0x7f9005378790>

Obtaining File Information

Use the pfileinfo() function to get a list of observations and their respective types (kx) contained within the file:

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gdf_list[tidx].pfileinfo()

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Variable Name: ps
              └── kx => 181  120  187  180  183

Variable Name: t
              └── kx => 181  120  187  180  183  130  126

Variable Name: q
              └── kx => 181  120  187  180  183

Variable Name: uv
              └── kx => 257  259  258  244  281  253  254  220  287  280  221  284  230  243  210  252  242  250  229  224  282  245  246  247  240

In addition to the pfileinfo() method, other methods and functions can also be used to access information about the opened files. To get a list of available methods and functions, type gdf_list[tidx]. and press the <TAB> key twice on the keyboard:

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>>> gdf_list[tidx].
gdf_list[tidx].close(      gdf_list[tidx].obsInfo     gdf_list[tidx].pfileinfo(  gdf_list[tidx].zlevs
gdf_list[tidx].tocsv(      gdf_list[tidx].overview(   gdf_list[tidx].varNames
gdf_list[tidx].obs

The built-in methods and functions have documentation, which can be accessed as follows:

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print(object.function_name.__doc__)

or

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help(object.function_name)

For example:

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print(gdf_list[tidx].pfileinfo.__doc__)

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Prints a fancy list of the existing variables and types.

Usage: pfileinfo()

or alternatively:

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help(gdf_list[tidx].pfileinfo)

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Help on method pfileinfo in module gsidiag.__main__:

pfileinfo() method of gsidiag.__main__.read_diag instance
    Prints a fancy list of the existing variables and types.

    Usage: pfileinfo()

To get a dictionary with all the information about the variables and types contained in the file, use the obsInfo method:

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gdf_list[tidx].obsInfo

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{'ps':                   lat         lon    elev         prs     hgt  press   time  \
 kx  points
 181 0      -72.000000    2.520000  1290.0  852.099976  1290.0  800.0    0.0
     1      -70.760002   11.830000   123.0  987.700012   123.0  900.0    0.0
     2      -72.000000    2.520000  1290.0  852.400024  1290.0  800.0   60.0
     3      -72.000000    2.520000  1290.0  852.099976  1290.0  800.0  120.0
     4      -72.000000    2.520000  1290.0  852.400024  1290.0  800.0  -60.0
 ...               ...         ...     ...         ...     ...    ...    ...
 183 3600    65.680000  341.929993    27.0  982.299988    27.0  900.0 -180.0
     3601    63.779999  341.929993    38.0  977.599976    38.0  900.0 -180.0
     3602    66.449997  344.049988    10.0  989.299988    10.0  900.0 -180.0
     3603    63.779999  341.929993    38.0  972.599976    38.0  900.0    0.0
     3604    66.449997  344.049988    10.0  984.200012    10.0  900.0    0.0

             idqc  iuse  iusev  ...       inp_err       adj_err        inverr  \
 kx  points                     ...
 181 0        2.0   1.0    0.0  ...  1.000000e+00  1.000000e+00  2.684909e-01
     1        2.0   1.0    0.0  ...  1.000000e+00  1.000000e+00  5.507368e-01
     2        2.0   1.0    0.0  ...  1.000000e+00  1.000000e+00  2.689506e-01
     3        2.0   1.0    0.0  ...  1.000000e+00  1.000000e+00  2.738240e-01
     4        2.0   1.0    0.0  ...  1.000000e+00  1.000000e+00  2.748610e-01
 ...          ...   ...    ...  ...           ...           ...           ...
 183 3600     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  1.000000e-11
     3601     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  1.000000e-11
     3602     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  1.000000e-11
     3603     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  1.000000e-11
     3604     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  1.000000e-11

                      oer         obs       omf       oma           imp  \
 kx  points
 181 0       3.689821e+00  852.099976  2.616960  1.882513 -8.956059e-01
     1       1.795848e+00  987.700012  1.048871  0.143297 -6.011624e-01
     2       3.683941e+00  852.400024  2.931195  2.196748 -1.022330e+00
     3       3.618501e+00  852.099976  2.632484  1.898037 -9.195598e-01
     4       3.603527e+00  852.400024  2.977133  2.242685 -1.063870e+00
 ...                  ...         ...       ...       ...           ...
 183 3600    1.000000e+11  982.299988  4.375452  0.995740 -1.815308e-10
     3601    1.000000e+11  977.599976 -0.614942 -3.658909  1.300946e-10
     3602    1.000000e+11  989.299988  4.233233  1.677821 -1.510518e-10
     3603    1.000000e+11  972.599976 -1.276419 -4.320014  1.703327e-10
     3604    1.000000e+11  984.200012  4.378825  1.823362 -1.584946e-10

                      dfs                    geometry
 kx  points
 181 0      -5.208976e-01   POINT (2.52000 -72.00000)
     1      -5.289034e-01  POINT (11.83000 -70.76000)
     2      -5.843764e-01   POINT (2.52000 -72.00000)
     3      -5.343153e-01   POINT (2.52000 -72.00000)
     4      -6.067804e-01   POINT (2.52000 -72.00000)
 ...                  ...                         ...
 183 3600   -1.478777e-10  POINT (-18.07001 65.68000)
     3601    1.871863e-11  POINT (-18.07001 63.78000)
     3602   -1.081766e-10  POINT (-15.95001 66.45000)
     3603    3.884901e-11  POINT (-18.07001 63.78000)
     3604   -1.118992e-10  POINT (-15.95001 66.45000)

 [82566 rows x 21 columns],
 't':                   lat        lon    elev         prs     hgt  press   time  \
 kx  points
 181 0      -72.000000    2.52000  1290.0  852.099976  1290.0  800.0    0.0
     1      -70.760002   11.83000   123.0  987.700012   123.0  900.0    0.0
     2      -72.000000    2.52000  1290.0  852.400024  1290.0  800.0   60.0
     3      -72.000000    2.52000  1290.0  852.099976  1290.0  800.0  120.0
     4      -72.000000    2.52000  1290.0  852.400024  1290.0  800.0  -60.0
 ...               ...        ...     ...         ...     ...    ...    ...
 126 3351    39.709999  280.98999   763.0  892.900024  1054.0  800.0  150.0
     3352    39.709999  280.98999   763.0  886.000000  1118.0  800.0  150.0
     3353    39.709999  280.98999   763.0  879.000000  1183.0  800.0  150.0
     3354    39.709999  280.98999   763.0  872.099976  1247.0  800.0  150.0
     3355    39.709999  280.98999   763.0  865.299988  1312.0  800.0  150.0

             idqc  iuse  iusev  ...       inp_err       adj_err        inverr  \
 kx  points                     ...
 181 0        9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  9.090909e-12
     1        9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  9.090909e-12
     2        9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  9.090909e-12
     3        9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  9.090909e-12
     4        9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-11  9.090909e-12
 ...          ...   ...    ...  ...           ...           ...           ...
 126 3351     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-17  1.000000e-17
     3352     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-17  1.000000e-17
     3353     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-17  1.000000e-17
     3354     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-17  1.000000e-17
     3355     9.0  -1.0  101.0  ...  1.000000e-11  1.000000e-17  1.000000e-17

                      oer         obs       omf       oma           imp  \
 kx  points
 181 0       1.100000e+11  266.750000  5.638822  5.409732 -2.301008e-11
     1       1.100000e+11  273.850006  6.480910  6.229341 -2.906831e-11
     2       1.100000e+11  267.049988  5.925752  5.696663 -2.420519e-11
     3       1.100000e+11  267.149994  6.012683  5.783593 -2.456731e-11
     4       1.100000e+11  266.549988  5.074802  4.845712 -2.066080e-11
 ...                  ...         ...       ...       ...           ...
 126 3351    1.000000e+17  287.250000  4.286700  2.850528 -1.025029e-16
     3352    1.000000e+17  286.350006  3.588516  2.029374 -8.759085e-17
     3353    1.000000e+17  286.350006  3.845302  2.211233 -9.896797e-17
     3354    1.000000e+17  286.149994  3.865214  2.203462 -1.008463e-16
     3355    1.000000e+17  285.149994  3.036773  1.390132 -7.289526e-17

                      dfs                    geometry
 kx  points
 181 0      -1.174360e-11   POINT (2.52000 -72.00000)
     1      -1.482183e-11  POINT (11.83000 -70.76000)
     2      -1.234115e-11   POINT (2.52000 -72.00000)
     3      -1.252222e-11   POINT (2.52000 -72.00000)
     4      -1.056896e-11   POINT (2.52000 -72.00000)
 ...                  ...                         ...
 126 3351   -6.156439e-17  POINT (-79.01001 39.71000)
     3352   -5.595004e-17  POINT (-79.01001 39.71000)
     3353   -6.283490e-17  POINT (-79.01001 39.71000)
     3354   -6.423026e-17  POINT (-79.01001 39.71000)
     3355   -5.000478e-17  POINT (-79.01001 39.71000)

 [116439 rows x 21 columns],
 'q':                   lat         lon    elev         prs     hgt  press   time  \
 kx  points
 181 0      -72.000000    2.520000  1290.0  852.099976  1290.0  800.0    0.0
     1      -70.760002   11.830000   123.0  987.700012   123.0  900.0    0.0
     2      -72.000000    2.520000  1290.0  852.400024  1290.0  800.0   60.0
     3      -72.000000    2.520000  1290.0  852.099976  1290.0  800.0  120.0
     4      -72.000000    2.520000  1290.0  852.400024  1290.0  800.0  -60.0
 ...               ...         ...     ...         ...     ...    ...    ...
 183 2660    65.680000  341.929993    27.0  982.299988    27.0  900.0 -180.0
     2661    63.779999  341.929993    38.0  977.599976    38.0  900.0 -180.0
     2662    66.449997  344.049988    10.0  989.299988    10.0  900.0 -180.0
     2663    63.779999  341.929993    38.0  972.599976    38.0  900.0    0.0
     2664    66.449997  344.049988    10.0  984.200012    10.0  900.0    0.0

             idqc  iuse  iusev  ...   inp_err   adj_err    inverr  \
 kx  points                     ...
 181 0        9.0  -1.0  101.0  ...  0.000054  0.000054  0.000054
     1        9.0  -1.0  101.0  ...  0.000039  0.000039  0.000039
     2        9.0  -1.0  101.0  ...  0.000054  0.000054  0.000054
     3        9.0  -1.0  101.0  ...  0.000054  0.000054  0.000054
     4        9.0  -1.0  101.0  ...  0.000053  0.000053  0.000053
 ...          ...   ...    ...  ...       ...       ...       ...
 183 2660     9.0  -1.0  102.0  ...  0.000028  0.000028  0.000028
     2661     9.0  -1.0  102.0  ...  0.000024  0.000024  0.000024
     2662     9.0  -1.0  102.0  ...  0.000024  0.000024  0.000024
     2663     9.0  -1.0  102.0  ...  0.000024  0.000024  0.000024
     2664     9.0  -1.0  102.0  ...  0.000025  0.000025  0.000025

                      oer    obs       omf       oma           imp  \
 kx  points
 181 0       1.871621e+10  1.562  0.147178  0.146827 -5.506964e-15
     1       2.613980e+10  2.092  0.246853  0.246150 -1.326650e-14
     2       1.876213e+10  1.523  0.143555  0.143204 -5.358170e-15
     3       1.880805e+10  1.462  0.117932  0.117581 -4.389752e-15
     4       1.926510e+10  1.667  0.163603  0.163252 -5.947479e-15
 ...                  ...    ...       ...       ...           ...
 183 2660    3.369109e+10  3.083 -0.273933 -0.258595 -2.424459e-13
     2661    3.843422e+10  4.159  0.867337  0.867106 -1.045875e-14
     2662    3.916033e+10  3.604 -0.224209 -0.135418 -8.154096e-13
     2663    3.862750e+10  4.241  1.041719  1.041487 -1.249572e-14
     2664    3.823060e+10  3.955 -0.032830  0.132701  4.324247e-13

                      dfs                    geometry
 kx  points
 181 0      -2.756724e-15   POINT (2.52000 -72.00000)
     1      -6.642705e-15  POINT (11.83000 -70.76000)
     2      -2.682395e-15   POINT (2.52000 -72.00000)
     3      -2.198141e-15   POINT (2.52000 -72.00000)
     4      -2.976934e-15   POINT (2.52000 -72.00000)
 ...                  ...                         ...
 183 2660   -1.247146e-13  POINT (-18.07001 65.68000)
     2661   -5.229694e-15  POINT (-18.07001 63.78000)
     2662   -5.083661e-13  POINT (-15.95001 66.45000)
     2663   -6.249719e-15  POINT (-18.07001 63.78000)
     2664   -1.421482e-13  POINT (-15.95001 66.45000)

 [102785 rows x 21 columns],
 'uv':                   lat         lon     elev    prs   hgt  press   time  idqc  \
 kx  points
 257 0      -78.699997   14.860000  61068.0  487.0  30.0  400.0  -66.0   2.0
     1      -78.769997   16.080000  60056.0  487.0  54.0  400.0  -66.0   2.0
     2      -78.870003   16.120001  72079.0  500.0  37.0  500.0  -66.0   2.0
     3      -80.500000   12.490000  80084.0  562.0  47.0  500.0  -66.0   2.0
     4      -80.290001   13.620000  64059.0  512.0  56.0  500.0  -66.0   2.0
 ...               ...         ...      ...    ...   ...    ...    ...   ...
 240 7107    13.980000  245.070007  74084.0  925.0  47.0  900.0  171.0   2.0
     7108    13.690000  241.919998  79080.0  887.0  64.0  800.0  171.0   2.0
     7109    13.710000  244.139999  57051.0  887.0  70.0  800.0  171.0   2.0
     7110    13.520000  247.809998  56054.0  906.0  65.0  900.0  171.0   2.0
     7111    13.510000  250.479996  80087.0  906.0  60.0  900.0  171.0   2.0

             iuse  iusev  ...       inp_err       adj_err        inverr  \
 kx  points               ...
 257 0       -1.0    0.0  ...  2.307337e-01  2.307337e-01  2.938736e-39
     1       -1.0    0.0  ...  2.307337e-01  2.307337e-01  2.938736e-39
     2       -1.0    0.0  ...  2.439024e-01  2.439024e-01  2.938736e-39
     3       -1.0    0.0  ...  2.500000e-01  2.500000e-01  2.938736e-39
     4       -1.0    0.0  ...  2.453386e-01  2.453386e-01  2.938736e-39
 ...          ...    ...  ...           ...           ...           ...
 240 7107    -1.0  100.0  ...  1.000000e-09  1.000000e-09  1.000000e-09
     7108    -1.0  100.0  ...  1.000000e-09  1.000000e-09  1.000000e-09
     7109    -1.0  100.0  ...  1.000000e-09  1.000000e-09  1.000000e-09
     7110    -1.0  100.0  ...  1.000000e-09  1.000000e-09  1.000000e-09
     7111    -1.0  100.0  ...  1.000000e-09  1.000000e-09  1.000000e-09

                      oer       obs       omf       oma           imp  \
 kx  points
 257 0                NaN -2.250000 -5.155063 -5.807309           NaN
     1                NaN  0.934964 -2.355014 -2.984500           NaN
     2                NaN  0.988129 -2.418580 -3.046497           NaN
     3                NaN  3.983431  0.595628  0.257419           NaN
     4                NaN  3.680218  0.498253  0.008589           NaN
 ...                  ...       ...       ...       ...           ...
 240 7107    1.000000e+09 -7.203239 -0.806275 -0.955513  2.629265e-10
     7108    1.000000e+09 -7.751757 -1.638531 -1.589939 -1.568761e-10
     7109    1.000000e+09 -7.044833 -4.839164 -4.851346  1.180420e-10
     7110    1.000000e+09 -7.018799 -2.621243 -2.872725  1.381637e-09
     7111    1.000000e+09 -4.978588 -1.019387 -1.283688  6.087035e-10

                      dfs                     geometry
 kx  points
 257 0                NaN   POINT (14.86000 -78.70000)
     1                NaN   POINT (16.08000 -78.77000)
     2                NaN   POINT (16.12000 -78.87000)
     3                NaN   POINT (12.49001 -80.50000)
     4                NaN   POINT (13.62000 -80.29000)
 ...                  ...                          ...
 240 7107    1.203272e-10  POINT (-114.92999 13.98000)
     7108   -7.961867e-11  POINT (-118.08002 13.69000)
     7109    5.894722e-11  POINT (-115.85999 13.71000)
     7110    6.591968e-10  POINT (-112.19000 13.52000)
     7111    2.694244e-10  POINT (-109.52002 13.51000)

 [435598 rows x 21 columns]}

To access a specific variable (e.g., uv), do the following:

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print('Variable: ', varName)

gdf_list[tidx].obsInfo[varName]

Variable: uv

lat lon elev prs hgt press time idqc iuse iusev ... inp_err adj_err inverr oer obs omf oma imp dfs geometry
kx points
257 0 -78.699997 14.860000 61068.0 487.0 30.0 400.0 -66.0 2.0 -1.0 0.0 ... 2.307337e-01 2.307337e-01 2.938736e-39 NaN -2.250000 -5.155063 -5.807309 NaN NaN POINT (14.86000 -78.70000)
1 -78.769997 16.080000 60056.0 487.0 54.0 400.0 -66.0 2.0 -1.0 0.0 ... 2.307337e-01 2.307337e-01 2.938736e-39 NaN 0.934964 -2.355014 -2.984500 NaN NaN POINT (16.08000 -78.77000)
2 -78.870003 16.120001 72079.0 500.0 37.0 500.0 -66.0 2.0 -1.0 0.0 ... 2.439024e-01 2.439024e-01 2.938736e-39 NaN 0.988129 -2.418580 -3.046497 NaN NaN POINT (16.12000 -78.87000)
3 -80.500000 12.490000 80084.0 562.0 47.0 500.0 -66.0 2.0 -1.0 0.0 ... 2.500000e-01 2.500000e-01 2.938736e-39 NaN 3.983431 0.595628 0.257419 NaN NaN POINT (12.49001 -80.50000)
4 -80.290001 13.620000 64059.0 512.0 56.0 500.0 -66.0 2.0 -1.0 0.0 ... 2.453386e-01 2.453386e-01 2.938736e-39 NaN 3.680218 0.498253 0.008589 NaN NaN POINT (13.62000 -80.29000)
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
240 7107 13.980000 245.070007 74084.0 925.0 47.0 900.0 171.0 2.0 -1.0 100.0 ... 1.000000e-09 1.000000e-09 1.000000e-09 1.000000e+09 -7.203239 -0.806275 -0.955513 2.629265e-10 1.203272e-10 POINT (-114.92999 13.98000)
7108 13.690000 241.919998 79080.0 887.0 64.0 800.0 171.0 2.0 -1.0 100.0 ... 1.000000e-09 1.000000e-09 1.000000e-09 1.000000e+09 -7.751757 -1.638531 -1.589939 -1.568761e-10 -7.961867e-11 POINT (-118.08002 13.69000)
7109 13.710000 244.139999 57051.0 887.0 70.0 800.0 171.0 2.0 -1.0 100.0 ... 1.000000e-09 1.000000e-09 1.000000e-09 1.000000e+09 -7.044833 -4.839164 -4.851346 1.180420e-10 5.894722e-11 POINT (-115.85999 13.71000)
7110 13.520000 247.809998 56054.0 906.0 65.0 900.0 171.0 2.0 -1.0 100.0 ... 1.000000e-09 1.000000e-09 1.000000e-09 1.000000e+09 -7.018799 -2.621243 -2.872725 1.381637e-09 6.591968e-10 POINT (-112.19000 13.52000)
7111 13.510000 250.479996 80087.0 906.0 60.0 900.0 171.0 2.0 -1.0 100.0 ... 1.000000e-09 1.000000e-09 1.000000e-09 1.000000e+09 -4.978588 -1.019387 -1.283688 6.087035e-10 2.694244e-10 POINT (-109.52002 13.51000)

435598 rows × 21 columns

To access specific variable and type (e.g., uv of type 220), do:

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print('Variable: ', varName, ' and Type: ', varType)

gdf_list[tidx].obsInfo[varName].loc[varType]

Variable: uv and Type: 220

lat lon elev prs hgt press time idqc iuse iusev ... inp_err adj_err inverr oer obs omf oma imp dfs geometry
points
0 -33.980000 18.600000 52.0 1004.000000 1.000000e+11 1000.0 -45.000000 2.0 1.0 0.0 ... 0.714286 0.553566 1.418077e-01 7.787335 -1.0 5.123084 2.253895 -2.717996 -1.887564 POINT (18.60001 -33.98000)
1 -33.979839 18.599819 52.0 1000.000000 1.000000e+11 1000.0 -44.880001 2.0 1.0 0.0 ... 0.714286 0.553565 2.351083e-01 4.988861 -3.5 2.621643 -0.247218 -1.365421 -1.507585 POINT (18.59982 -33.97984)
2 -33.978512 18.598080 52.0 975.700012 3.000000e+02 900.0 -44.173199 2.0 1.0 0.0 ... 0.714286 0.714286 7.142857e-01 1.400000 -4.0 9.577692 3.444868 -57.046478 -41.955929 POINT (18.59808 -33.97851)
3 -33.977551 18.596769 52.0 942.400024 6.000000e+02 900.0 -43.173000 2.0 -1.0 0.0 ... 0.666667 0.652960 2.938736e-39 1.531486 0.0 16.411770 9.015676 -122.798218 -79.258301 POINT (18.59677 -33.97755)
4 -33.977921 18.596710 52.0 925.000000 1.000000e+11 900.0 -42.640198 2.0 -1.0 0.0 ... 0.666667 0.619352 2.938736e-39 1.614591 -0.4 13.814748 6.904759 -88.673553 -59.123161 POINT (18.59671 -33.97792)
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
37461 75.934608 344.023560 14.0 8.200000 1.000000e+11 0.0 48.249001 2.0 1.0 0.0 ... 0.476190 0.396825 3.968254e-01 2.520000 15.0 -3.926741 -4.877170 3.320431 1.480986 POINT (-15.97644 75.93461)
37462 75.892578 344.104126 14.0 7.400000 1.000000e+11 0.0 50.231998 2.0 1.0 0.0 ... 0.476190 0.396825 3.968254e-01 2.520000 21.6 3.278691 2.275991 -2.210190 -1.304581 POINT (-15.89587 75.89258)
37463 75.865311 344.162476 14.0 6.900000 1.000000e+11 0.0 51.583801 2.0 1.0 0.0 ... 0.476190 0.396825 3.968254e-01 2.520000 17.4 -0.492477 -1.526073 0.827923 0.201993 POINT (-15.83752 75.86531)
37464 75.810112 344.267883 14.0 6.100000 1.000000e+11 0.0 53.967602 2.0 1.0 0.0 ... 0.476190 0.346399 3.463990e-01 2.886844 22.6 5.281235 3.752217 -4.784572 -2.797208 POINT (-15.73212 75.81011)
37465 75.793587 344.295898 14.0 5.900000 1.000000e+11 0.0 54.613201 2.0 1.0 0.0 ... 0.476190 0.348412 3.484119e-01 2.870166 16.9 -0.252810 -1.935140 1.282453 0.148183 POINT (-15.70410 75.79359)

37466 rows × 21 columns

The varType parameter can also be a list, for example: varType=[220, 221]:

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varTypes = [220,221]

print('Variable: ', varName, ' e Types: ', varTypes)

gdf_list[tidx].obsInfo[varName].loc[varTypes]

Variable: uv e Types: [220, 221]

lat lon elev prs hgt press time idqc iuse iusev ... inp_err adj_err inverr oer obs omf oma imp dfs geometry
kx points
220 0 -33.980000 18.600000 52.0 1004.000000 1.000000e+11 1000.0 -45.000000 2.0 1.0 0.0 ... 0.714286 0.553566 1.418077e-01 7.787335 -1.0 5.123084 2.253895 -2.717996 -1.887564 POINT (18.60001 -33.98000)
1 -33.979839 18.599819 52.0 1000.000000 1.000000e+11 1000.0 -44.880001 2.0 1.0 0.0 ... 0.714286 0.553565 2.351083e-01 4.988861 -3.5 2.621643 -0.247218 -1.365421 -1.507585 POINT (18.59982 -33.97984)
2 -33.978512 18.598080 52.0 975.700012 3.000000e+02 900.0 -44.173199 2.0 1.0 0.0 ... 0.714286 0.714286 7.142857e-01 1.400000 -4.0 9.577692 3.444868 -57.046478 -41.955929 POINT (18.59808 -33.97851)
3 -33.977551 18.596769 52.0 942.400024 6.000000e+02 900.0 -43.173000 2.0 -1.0 0.0 ... 0.666667 0.652960 2.938736e-39 1.531486 0.0 16.411770 9.015676 -122.798218 -79.258301 POINT (18.59677 -33.97755)
4 -33.977921 18.596710 52.0 925.000000 1.000000e+11 900.0 -42.640198 2.0 -1.0 0.0 ... 0.666667 0.619352 2.938736e-39 1.614591 -0.4 13.814748 6.904759 -88.673553 -59.123161 POINT (18.59671 -33.97792)
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
221 639 7.378550 352.432678 336.0 616.083801 4.200000e+03 600.0 -32.119801 2.0 1.0 0.0 ... 0.526316 0.526316 5.263158e-01 1.900000 -10.0 -1.387435 0.471839 -0.895970 -1.357695 POINT (-7.56732 7.37855)
640 7.382580 352.415344 336.0 551.021729 5.100000e+03 500.0 -29.119799 2.0 1.0 0.0 ... 0.500000 0.500000 5.000000e-01 2.000000 -11.3 -0.546905 1.363278 0.779710 -0.522345 POINT (-7.58466 7.38258)
641 7.385930 352.406006 336.0 500.000000 1.000000e+11 500.0 -27.538200 2.0 1.0 0.0 ... 0.476190 0.412661 4.126614e-01 2.423294 -10.3 -3.802699 -2.725824 -2.901176 -1.689862 POINT (-7.59399 7.38593)
642 7.386930 352.403564 336.0 510.839386 5.700000e+03 500.0 -27.119999 2.0 1.0 0.0 ... 0.476190 0.412666 4.126657e-01 2.423269 -10.9 -3.664526 -2.379657 -3.204753 -1.943009 POINT (-7.59644 7.38693)
643 7.392140 352.394043 336.0 473.089569 6.300000e+03 400.0 -25.120199 2.0 1.0 0.0 ... 0.434783 0.434783 4.347826e-01 2.300000 -6.5 2.720051 3.510464 2.141166 0.934767 POINT (-7.60596 7.39214)

38110 rows × 21 columns

To access the observation value, use the obs method:

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print('Variable: ', varName, ' e Type: ', varType)

gdf_list[tidx].obsInfo[varName].loc[varType].obs

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Variable  uv e Type:  220

points
0        -1.0
1        -3.5
2        -4.0
3         0.0
4        -0.4
         ...
37461    15.0
37462    21.6
37463    17.4
37464    22.6
37465    16.9
Name: obs, Length: 37466, dtype: float32

Using the plot_diag Class

Spatial Distribution

The usage of the plot_diag class functions is presented below, along with commands to generate various types of figures.

Generating a figure with observation values (param='obs') for the selected variable and type:

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param = 'obs'

gd.plot_diag.plot(gdf_list[tidx],
                  varName=varName,
                  varType=varType,
                  param=param,
                  mask='iuse == 1',
                  legend='true')

png

To save the figure, define its name (figname) and execute the following commands:

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figname = varName + '_' + param + '_' + 'plot.png'

plt.tight_layout()
plt.savefig(figname)

Generating the same figure, but considering various different types (kx) of the selected observation:

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varTypes = [254,242,221,220,257,258,281,280]
idschar = '_'.join([str(item) for item in varTypes])

gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)

figname = varName + '_' + idschar + '_' + 'ptmap.png'

plt.tight_layout()
plt.savefig(figname)

png

Generating a figure with different variables, considering the mask iuse==1:

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varNames = ['uv','ps','t']
idschar = '_'.join([str(item) for item in varNames])

gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iuse==1')

figname = idschar + '_pvmap.png'

plt.tight_layout()
plt.savefig(figname)

png

Histogram

Use the pcount() function from the plot_diag class to obtain a histogram with the count of the number of observations for a specific variable:

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gd.plot_diag.pcount(gdf_list[tidx], varName)

png

Use the vcount() function from the plot_diag class to obtain a histogram with the count of the number of observations for all types of variables:

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gd.plot_diag.vcount(gdf_list[tidx])

png

Similarly, use the kxcount() function from the plot_diag class to obtain a histogram with the count of the number of observations per type:

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gd.plot_diag.kxcount(gdf_list[tidx])

png

Time Series

Below are the options for figures using the time_series() function, included in the plot_diag class. Initially, a figure is generated with the parameters already set in this section. Figures are then presented by changing the Level, Lay, and SingleL parameters.

Plotting a time series for OmA and OmF:

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gd.plot_diag.time_series(gdf_list,
                         varName=varName,
                         varType=varType,
                         dateIni=dateIni,
                         dateFin=dateFin,
                         nHour=nHour,
                         vminOMA=vminOMA,
                         vmaxOMA=vmaxOMA,
                         vminSTD=vminSTD,
                         vmaxSTD=vmaxSTD,
                         Level=Level,
                         Lay=Lay,
                         SingleL=SingleL,
                         Clean=False)

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 =====================================================================================================
 Analyzing data of variable: uv  ||  type: 220  ||  Dropssonda  ||  check: OmF
 =====================================================================================================

[1000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50, 0]
 Preparing data for: 2019-12-10:00  - Level:  1000
 Preparing data for: 2019-12-10:06  - Level:  1000
 Preparing data for: 2019-12-10:12  - Level:  1000
 Preparing data for: 2019-12-10:18  - Level:  1000
 Preparing data for: 2019-12-11:00  - Level:  1000
 Preparing data for: 2019-12-11:06  - Level:  1000
 Preparing data for: 2019-12-11:12  - Level:  1000
 Preparing data for: 2019-12-11:18  - Level:  1000

 =====================================================================================================

 Calculating for 2019-12-10:00
 Calculating for 2019-12-10:06
 Calculating for 2019-12-10:12
 Calculating for 2019-12-10:18
 Calculating for 2019-12-11:00
 Calculating for 2019-12-11:06
 Calculating for 2019-12-11:12
 Calculating for 2019-12-11:18

 =====================================================================================================

 Making Graphics...
 Done!

png

png

png

png

In the previous case, the Level parameter was fixed at 1000 hPa with SingleL equal to All, meaning that the entire atmosphere was considered as a single layer, and the value 1000 hPa serves as a flag to indicate that there is no variation in height. Even with Level=1000, you can use SingleL='OneL' for a single layer around the Level value, in this case, 1000 hPa, varying between Level-Lay and Level+Lay (in the variable definition, Lay was fixed at 15 hPa; in case it is None, the default value of 50 hPa is used).

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SingleL = 'OneL'
Lay = 15

gd.plot_diag.time_series(gdf_list,
                         varName=varName,
                         varType=varType,
                         dateIni=dateIni,
                         dateFin=dateFin,
                         nHour=nHour,
                         vminOMA=vminOMA,
                         vmaxOMA=vmaxOMA,
                         vminSTD=vminSTD,
                         vmaxSTD=vmaxSTD,
                         Level=Level,
                         Lay=Lay,
                         SingleL=SingleL,
                         Clean=False)

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 =====================================================================================================
 Analyzing data of variable: uv  ||  type: 220  ||  Dropssonda  ||  check: OmF
 =====================================================================================================

[1000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50, 0]
 Preparing data for: 2019-12-10:00  - Level:  1000
 Preparing data for: 2019-12-10:06  - Level:  1000
 Preparing data for: 2019-12-10:12  - Level:  1000
 Preparing data for: 2019-12-10:18  - Level:  1000
 Preparing data for: 2019-12-11:00  - Level:  1000
 Preparing data for: 2019-12-11:06  - Level:  1000
 Preparing data for: 2019-12-11:12  - Level:  1000
 Preparing data for: 2019-12-11:18  - Level:  1000

 =====================================================================================================

 Calculating for 2019-12-10:00
 Calculating for 2019-12-10:06
 Calculating for 2019-12-10:12
 Calculating for 2019-12-10:18
 Calculating for 2019-12-11:00
 Calculating for 2019-12-11:06
 Calculating for 2019-12-11:12
 Calculating for 2019-12-11:18

 =====================================================================================================

 Making Graphics...
 Done!

png

png

png

png

Notice how in the example above, the amount of data decreases since now a layer between 1015 and 985 hPa is being used.

Now, let's change it to not fix it at a single level or layer, i.e., change the Level variable to None or Zlevs. The None option looks for data at each level existing in the files and creates the plot for all these levels, but the values on the y-axis (ordinate) are only for the standard levels. It's important to clarify that the so-called standard levels are defined in the read_diag class and can be accessed using the zlevs method as follows:

1
gdf_list[tidx].zlevs

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[1000.0,
 900.0,
 800.0,
 700.0,
 600.0,
 500.0,
 400.0,
 300.0,
 250.0,
 200.0,
 150.0,
 100.0,
 50.0,
 0.0]

This way, you can use the Level='Zlevs' parameter to produce a time series by levels:

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Level = 'Zlevs'
Lay = 15

gd.plot_diag.time_series(gdf_list,
                         varName=varName,
                         varType=varType,
                         dateIni=dateIni,
                         dateFin=dateFin,
                         nHour=nHour,
                         vminOMA=vminOMA,
                         vmaxOMA=vmaxOMA,
                         vminSTD=vminSTD,
                         vmaxSTD=vmaxSTD,
                         Level=Level,
                         Lay=Lay,
                         SingleL=SingleL,
                         Clean=False)

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 =====================================================================================================
 Analyzing data of variable: uv  ||  type: 220  ||  Dropssonda  ||  check: OmF
 =====================================================================================================

[1000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50, 0]
 Preparing data for: 2019-12-10:00
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:06
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:12
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:18
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:00
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:06
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:12
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:18
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]


 =====================================================================================================

 Calculating for 2019-12-10:00
 Calculating for 2019-12-10:06
 Calculating for 2019-12-10:12
 Calculating for 2019-12-10:18
 Calculating for 2019-12-11:00
 Calculating for 2019-12-11:06
 Calculating for 2019-12-11:12
 Calculating for 2019-12-11:18

 =====================================================================================================

 Making Graphics...
 Done!

png

png

If Lay=None, then the layers are filled, varying between the mean value considering the lower and upper layers. For example, for the 700 hPa level, a layer is constructed between 750 and 650 hPa, since the lower and upper levels are 800 and 600 hPa, respectively. For the 1000 hPa level, the layer varies between 1050 and 950 hPa.

The same previous example, but considering Level='Zlevs':

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Level = 'Zlevs'
Lay = None

gd.plot_diag.time_series(gdf_list,
                         varName=varName,
                         varType=varType,
                         dateIni=dateIni,
                         dateFin=dateFin,
                         nHour=nHour,
                         vminOMA=vminOMA,
                         vmaxOMA=vmaxOMA,
                         vminSTD=vminSTD,
                         vmaxSTD=vmaxSTD,
                         Level=Level,
                         Lay=Lay,
                         SingleL=SingleL,
                         Clean=False)

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 =====================================================================================================
 Analyzing data of variable: uv  ||  type: 220  ||  Dropssonda  ||  check: OmF
 =====================================================================================================

[1000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50, 0]
 Preparing data for: 2019-12-10:00
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:06
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:12
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-10:18
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:00
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:06
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:12
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]

 Preparing data for: 2019-12-11:18
 Levels:  [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000]


 =====================================================================================================

 Calculating for 2019-12-10:00
 Calculating for 2019-12-10:06
 Calculating for 2019-12-10:12
 Calculating for 2019-12-10:18
 Calculating for 2019-12-11:00
 Calculating for 2019-12-11:06
 Calculating for 2019-12-11:12
 Calculating for 2019-12-11:18

 =====================================================================================================

 Making Graphics...
 Done!

png

png

Finally, there is the option to consider all levels, i.e., Level=None. This option brings some difficulty in visualizing the information in the figure due to the large number of levels and distribution of data across all levels:

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Level = None

gd.plot_diag.time_series(gdf_list,
                         varName=varName,
                         varType=varType,
                         dateIni=dateIni,
                         dateFin=dateFin,
                         nHour=nHour,
                         vminOMA=vminOMA,
                         vmaxOMA=vmaxOMA,
                         vminSTD=vminSTD,
                         vmaxSTD=vmaxSTD,
                         Level=Level,
                         Lay=Lay,
                         SingleL=SingleL,
                         Clean=False)

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 =====================================================================================================
 Analyzing data of variable: uv  ||  type: 220  ||  Dropssonda  ||  check: OmF
 =====================================================================================================

[1000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50, 0]
 Preparing data for: 2019-12-10:00
 Levels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026]

 Preparing data for: 2019-12-10:06
 Levels:  [1, 2, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 82, 82, 83, 83, 84, 84, 85, 85, 86, 86, 87, 87, 88, 88, 89, 89, 90, 90, 91, 91, 92, 92, 93, 93, 94, 94, 95, 95, 96, 96, 97, 97, 98, 99, 100, 100, 101, 101, 102, 102, 103, 103, 104, 105, 105, 106, 106, 107, 107, 108, 108, 109, 109, 110, 110, 111, 112, 112, 113, 113, 114, 115, 115, 116, 116, 117, 117, 118, 119, 119, 120, 121, 121, 122, 122, 123, 123, 124, 125, 126, 126, 127, 127, 128, 128, 129, 129, 130, 130, 131, 131, 132, 132, 133, 133, 134, 134, 135, 135, 136, 136, 137, 137, 138, 138, 139, 139, 140, 140, 141, 141, 142, 142, 143, 144, 144, 145, 145, 146, 147, 147, 148, 148, 149, 149, 150, 150, 151, 152, 152, 153, 153, 154, 154, 155, 155, 156, 156, 157, 158, 158, 159, 159, 160, 160, 161, 161, 162, 162, 163, 163, 164, 164, 165, 165, 166, 166, 167, 167, 168, 169, 170, 170, 171, 171, 172, 172, 173, 173, 174, 174, 175, 176, 176, 177, 178, 178, 179, 179, 180, 180, 181, 181, 182, 182, 183, 183, 184, 184, 185, 185, 186, 187, 187, 188, 188, 189, 189, 190, 190, 191, 191, 192, 192, 193, 193, 194, 194, 195, 195, 196, 196, 197, 198, 198, 199, 199, 200, 200, 201, 202, 203, 203, 204, 205, 205, 206, 206, 207, 207, 208, 208, 209, 209, 210, 211, 211, 212, 213, 213, 214, 215, 215, 216, 216, 217, 218, 218, 219, 220, 220, 221, 221, 222, 222, 223, 223, 224, 225, 225, 226, 226, 227, 227, 228, 228, 229, 229, 230, 230, 231, 231, 232, 233, 233, 234, 234, 235, 236, 236, 237, 237, 238, 238, 239, 240, 240, 241, 241, 242, 242, 243, 244, 245, 245, 246, 246, 247, 247, 248, 249, 249, 250, 250, 251, 251, 252, 252, 253, 253, 254, 254, 255, 255, 256, 256, 257, 258, 258, 259, 260, 261, 261, 262, 262, 263, 263, 264, 264, 265, 266, 266, 267, 267, 268, 268, 269, 269, 270, 271, 272, 272, 273, 273, 274, 274, 275, 275, 276, 276, 277, 277, 278, 278, 279, 280, 281, 281, 282, 282, 283, 283, 284, 284, 285, 286, 286, 287, 287, 288, 289, 290, 291, 292, 292, 293, 294, 294, 295, 296, 297, 297, 298, 299, 300, 300, 301, 302, 302, 303, 304, 305, 306, 307, 307, 308, 308, 309, 310, 311, 311, 312, 313, 314, 315, 316, 317, 317, 318, 319, 320, 320, 321, 321, 322, 323, 324, 324, 325, 326, 327, 327, 328, 329, 329, 330, 330, 331, 331, 332, 332, 333, 334, 334, 335, 336, 337, 337, 338, 338, 339, 339, 340, 340, 341, 341, 342, 343, 344, 345, 346, 347, 348, 348, 349, 349, 350, 351, 352, 353, 354, 354, 355, 355, 356, 357, 358, 358, 359, 360, 361, 361, 362, 363, 363, 364, 365, 365, 366, 367, 368, 368, 369, 370, 370, 371, 371, 372, 372, 373, 374, 375, 376, 376, 377, 377, 378, 379, 379, 380, 380, 381, 382, 383, 384, 384, 385, 386, 387, 387, 388, 389, 390, 391, 392, 393, 393, 394, 395, 396, 397, 398, 399, 400, 400, 401, 401, 402, 402, 403, 403, 404, 404, 405, 406, 407, 408, 409, 410, 410, 411, 411, 412, 413, 414, 414, 415, 416, 417, 418, 419, 420, 421, 422, 422, 423, 423, 424, 424, 425, 426, 427, 427, 428, 429, 430, 431, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 441, 442, 443, 444, 444, 445, 446, 447, 447, 448, 449, 450, 450, 451, 452, 453, 454, 455, 455, 456, 456, 457, 458, 458, 459, 460, 461, 461, 462, 462, 463, 464, 465, 466, 466, 467, 468, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 489, 490, 490, 491, 491, 492, 493, 493, 494, 495, 496, 497, 498, 499, 500, 500, 501, 502, 503, 504, 505, 506, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 517, 518, 519, 520, 521, 522, 523, 524, 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 Preparing data for: 2019-12-10:12
 Levels:  [1, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30, 30, 31, 31, 31, 32, 32, 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 44, 45, 45, 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49, 49, 50, 50, 50, 51, 51, 51, 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55, 56, 56, 56, 57, 57, 57, 58, 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62, 62, 62, 63, 63, 63, 64, 64, 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68, 68, 69, 69, 69, 70, 70, 70, 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74, 75, 75, 75, 76, 76, 76, 77, 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81, 81, 81, 82, 82, 82, 83, 83, 83, 84, 84, 84, 85, 85, 85, 86, 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 Preparing data for: 2019-12-10:18
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 Preparing data for: 2019-12-11:00
 Levels:  [1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 53, 53, 53, 53, 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 Preparing data for: 2019-12-11:06
 Levels:  [1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 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 Preparing data for: 2019-12-11:12
 Levels:  [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 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 Preparing data for: 2019-12-11:18
 Levels:  [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 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 Calculating for 2019-12-10:00
 Calculating for 2019-12-10:06
 Calculating for 2019-12-10:12
 Calculating for 2019-12-10:18
 Calculating for 2019-12-11:00
 Calculating for 2019-12-11:06
 Calculating for 2019-12-11:12
 Calculating for 2019-12-11:18

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 Making Graphics...
 Done!

png

png

After finishing the use of the files, close them to release the used memory:

1
2
for file in gdf_list:
    file.close()

Radiance

This section presents the readDiag functions that have been created and modified for accessing the radiance diagnostic files generated by GSI.

The read_diag Class

The read_diag class has not been altered. The tools in this class are used as examples below to visualize the diagnostic data of radiance.

The plot_diag Class

In the plot_diag class, the following functions have been created or modified for reading and analyzing radiance data:

  1. plot(self, varName, varType, param, minVal=None, maxVal=None, mask=None, area=None, **kwargs): The plot function generates a figure for the variable varName (e.g., amsua), varType (e.g., n19 (satellite)), and param, which can include options such as: param="obs" for observation value, param="omf" for observation minus background, or param="oma" for observation minus analysis. Optional variables minVal and maxVal have been added to set the color range of the colormap. Data can also be masked using the variables iuse and idqc, which indicate whether the radiance data was assimilated (iuse>=1 & idqc=0) or rejected (iuse>=1 & idqc!=0), or whether the data is monitored (iuse=-1). Additionally, the mask parameter allows specification of the channel nchan (channel number). Below is an example executed for varName="amsua", varType="n19", param="obs", and mask="(nchan==14) & (iuse >= 1 & idqc == 0)".


  2. time_series_radi(self, varName=None, varType=None, mask=None, dateIni=None, dateFin=None, nHour="06", vminOMA=None, vmaxOMA=None, vminSTD=0.0, vmaxSTD=14.0, channel=None, Clean=None): The time_series_radi function generates figures showing the temporal variation of the means and standard deviations of the OmF and OmA parameters of radiance data. This function can be used in three scenarios: when channel is a single channel number (4 figures are generated using only the observations from the specified channel), when channel is a list of channels (6 Hovmöller diagrams are created considering only the listed channels), and when channel is None (6 Hovmöller diagrams are generated considering all available channels (1 to 15)).

    The input parameters for the function are presented below.

    Parameter Example Description
    self ['/home/user/diag_amsua_n19_01.2024020100'] List of all full paths (path/filename) for each time in the time series.
    varName amsua Variable name
    varType n19 Variable type
    dateIni 2024020100 Start date of the time series
    dateFin 2024020218 End date of the time series
    nHour 6 Time interval in hours between each file in the self list
    vminOMA -2.0 Minimum y-scale value for OmF and OmA
    vmaxOMA 2.0 Maximum y-scale value for OmF and OmA
    vminSTD 0.0 Minimum y-scale value for standard deviation
    vmaxSTD 14.0 Maximum y-scale value for standard deviation
    channel 14 Channel value(s) for the time series. Options: numeric value representing the channel, e.g., 14 for a single channel; [6, 9, 10, 13, 14] for a list of specific channels; None to plot all channels.
    Clean True or False If True, after generating and saving the figure, the figure window is reset (plt.clf()) or closed (plt.close()); if False, this step is skipped, and the figure remains available for viewing with plt.show().

  3. statcount(self, varName=None, varType=None, noiqc=False, dateIni=None, dateFin=None, nHour="06", channel=None, figTS=False, figMap=False, **kwargs): The statcount function generates two types of figures. If figTS is True, a time series figure showing the number of assimilated, rejected, and monitored data points is generated. If figMap is True, a temporal sequence of figures indicating the geographical location of the assimilated, rejected, and monitored observations is produced. In this function, when the data is radiance, the channel variable is assigned the specific channel number, and for conventional data, the channel variable should be None. When channel = channel number, the monitored data (iuse=-1) is subclassified into two groups: monitored observations that would be assimilated (monitored-assimilated) since idqc=0, and monitored observations that would be rejected (monitored-rejected) since idqc!=0. Thus, for radiance data, if figMap=True, two figures can be generated—one showing the geographical location of the assimilated and rejected observations and another showing the location of the monitored-assimilated and monitored-rejected observations. The noiqc variable is a parameter from the GSI name list used for classifying conventional data and can take values True or False (it does not affect radiance classification). Below is an example executed for varName="amsua", varType="n19", noiqc=False, dateIni="2024020100", dateFin="2024020218", nHour="06", channel=6, figTS=True, figMap=True.

figMap=True

figTS=True

Throughout this notebook, examples with code snippets are provided to illustrate the use of the above-listed functions.

Using the read_diag Class

Required Libraries

To start using readDiag, first load the necessary libraries:

  • gsidiag: the library containing the read_diag and plot_diag classes;
  • pandas: the library that provides the tabular data structures used by readDiag;
  • matplotlib: the library used for creating figures;
  • datetime: the library used for manipulating dates;
  • datasources: the library used to analyze and store information from a file containing observation data;
  • os: the library that provides various operating system interfaces.

The %matplotlib inline directive is a Jupyter magic command that allows figures to be displayed inline without needing to use plt.show() each time. If you are using readDiag within a Python script, you can omit this directive and use plt.show() as needed.

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import gsidiag as gd

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from gsidiag.datasources import getVarInfo

import os

%matplotlib inline

Main Variables

The table below lists the default values of the variables used throughout this tutorial. Note that the variables have different types, and some can be declared as None.

Variables Values Type
DIRdiag = /pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout string
varName = amsua string
varType = n19 string
dateIni = 2024021000 string
dateFin = 2024021018 string
nHour = 6 string
vminOMA = -2.0 float
vmaxOMA = 2.0 float
vminSTD = 0.0 float
vmaxSTD = 14.0 float
channel = 6 integer or None

The following cell sets these variables according to the values in the table above. Additionally, it adjusts the dates to form the path and filenames of the GSI diagnostic files:

Tip

To test the readDiag functionalities related to radiances, download the following files into the data directory of your readDiag installation:

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cd readDiag/data

wget https://dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19_amsua.tar.gz
wget https://dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19_conv_0201.tar.gz
wget https://dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19_conv_0202.tar.gz

tar -zxvf SMNAexp19_amsua.tar.gz 
tar -zxvf SMNAexp19_conv_0201.tar.gz
tar -zxvf SMNAexp19_conv_0202.tar.gz 

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DIRdiag = os.path.join(os.getcwd(), '../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout')

varName = 'amsua'
varType = 'n19'
dateIni = '2024020100' 
dateFin = '2024020218'
nHour = '6'          
vminOMA = -2.0       
vmaxOMA = 2.0        
vminSTD = 0.0        
vmaxSTD = 14.0       
channel = 6

datei = datetime.strptime(str(dateIni), '%Y%m%d%H')
datef = datetime.strptime(str(dateFin), '%Y%m%d%H')
dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6H').tolist()]

print(dates)

['2024020100', '2024020106', '2024020112', '2024020118', '2024020200', '2024020206', '2024020212', '2024020218']

Generating the paths and pathsc variables that will contain the full paths (including filenames) of the diagnostic files for the first (OmF) and last (OmA) outer loops of the radiance data:

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paths, pathsc = [], []

OuterL = '01'        
[paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]

OuterLc = '03'
[pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterLc + '.' + dt) for dt in dates]

print(paths)
print('')
print(pathsc)

['/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020100/diag_amsua_n19_01.2024020100', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020106/diag_amsua_n19_01.2024020106', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020112/diag_amsua_n19_01.2024020112', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020118/diag_amsua_n19_01.2024020118', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020200/diag_amsua_n19_01.2024020200', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020206/diag_amsua_n19_01.2024020206', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020212/diag_amsua_n19_01.2024020212', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020218/diag_amsua_n19_01.2024020218']

['/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020100/diag_amsua_n19_03.2024020100', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020106/diag_amsua_n19_03.2024020106', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020112/diag_amsua_n19_03.2024020112', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020118/diag_amsua_n19_03.2024020118', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020200/diag_amsua_n19_03.2024020200', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020206/diag_amsua_n19_03.2024020206', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020212/diag_amsua_n19_03.2024020212', '/extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020218/diag_amsua_n19_03.2024020218']

Reading Diagnostic Files

Reading the radiance diagnostic files using the read_diag() function from the readDiag package. In the following code snippet, notice that the read_diag() function is used within a loop that iterates over all the files in the paths and pathsc lists defined in the previous step. At the end of the loop, the gdf_list list is generated, which will contain all the files read by readDiag:

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read = True

if read:        
    gdf_list = []
    print('')

    print('Please wait, the estimated total time to read the files is ' +
          str(int((float(len(paths))*20)/60)) + ' minutes and ' +
          str(int((float(len(paths))*20)%60)) + ' seconds.')

    print('')

    for path, pathc in zip(paths, pathsc):
        print('Reading ' + path)

        gdf = gd.read_diag(path, pathc)

        gdf_list.append(gdf)

    print('Done!')  

Please wait, the estimated total time to read the files is 2 minutes and 40 seconds.

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020100/diag_amsua_n19_01.2024020100

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020106/diag_amsua_n19_01.2024020106

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020112/diag_amsua_n19_01.2024020112

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020118/diag_amsua_n19_01.2024020118

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020200/diag_amsua_n19_01.2024020200

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020206/diag_amsua_n19_01.2024020206

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020212/diag_amsua_n19_01.2024020212

>>> GSI DIAG <<<

Reading /extra3/GitHub/readDiag.issuefix_27/notebooks/../data/dataserver.cptec.inpe.br/dataserver_dimnt/das/testecase/SMNAexp19/GSI/dataout/2024020218/diag_amsua_n19_01.2024020218

>>> GSI DIAG <<<

Done!

The gdf_list variable is a list of dataframes containing the data from each diagnostic file. To work with a single time point, simply specify the list with a fixed index, for example: gdf_list[0]:

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gdf_list

[<gsidiag.__main__.read_diag at 0x7e85a4f46e20>,
 <gsidiag.__main__.read_diag at 0x7e85e73735e0>,
 <gsidiag.__main__.read_diag at 0x7e85a4f46d90>,
 <gsidiag.__main__.read_diag at 0x7e85a5fbe340>,
 <gsidiag.__main__.read_diag at 0x7e85a4f2a280>,
 <gsidiag.__main__.read_diag at 0x7e85a4f46c70>,
 <gsidiag.__main__.read_diag at 0x7e85a4f1d640>,
 <gsidiag.__main__.read_diag at 0x7e85a4f2ab80>]

By setting tidx = 0, we obtain the first object in the gdf_list:

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tidx = 0
gdf_list[tidx]

<gsidiag.__main__.read_diag at 0x7e85a4f46e20>

Retrieving File Information

Use the pfileinfo() function to get information such as the sensor type and its corresponding satellite (n19) contained within the file:

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Variable Name : amsua
              └── kx => n19

In addition to the pfileinfo() method, other methods and functions can also be used to access information about the opened files. To get a list of available methods and functions, type gdf_list[tidx]. and press <TAB> twice on your keyboard:

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>>> gdf_list[tidx].
gdf_list[tidx].close(      gdf_list[tidx].obsInfo     gdf_list[tidx].pfileinfo(  gdf_list[tidx].zlevs       
gdf_list[tidx].tocsv(      gdf_list[tidx].overview(   gdf_list[tidx].varNames    
gdf_list[tidx].obs

To obtain a dictionary with all the information about the variables and types contained in the file, use the obsInfo method:

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gdf_list[tidx].obsInfo

{'amsua':                     lat         lon         elev  nchan      time  iuse  idqc  \
 SatId points                                                                    
 n19   0      -87.446198    6.644900  2647.032227    1.0  2.885000   1.0   8.0   
       1      -87.446198    6.644900  2647.032227    2.0  2.885000   1.0   8.0   
       2      -87.446198    6.644900  2647.032227    3.0  2.885000   1.0   8.0   
       3      -87.446198    6.644900  2647.032227    4.0  2.885000   1.0   8.0   
       4      -87.446198    6.644900  2647.032227    5.0  2.885000   1.0   8.0   
 ...                 ...         ...          ...    ...       ...   ...   ...   
       137830  56.754601  336.250885     0.055578   11.0 -1.522778   1.0   0.0   
       137831  56.754601  336.250885     0.055578   12.0 -1.522778   1.0   0.0   
       137832  56.754601  336.250885     0.055578   13.0 -1.522778   1.0   0.0   
       137833  56.754601  336.250885     0.055578   14.0 -1.522778   4.0   0.0   
       137834  56.754601  336.250885     0.055578   15.0 -1.522778   1.0  51.0   

                 inverr       oer         obs        omf   omf_nobc     emiss  \
 SatId points                                                                   
 n19   0       0.000000       NaN  187.330002   2.591137  -7.817700  0.813564   
       1       0.000000       NaN  189.190002   4.114823  -3.598879  0.801515   
       2       0.000000       NaN  211.460007  -1.666820   2.795111  0.791668   
       3       0.000000       NaN  225.820007   0.495127  -0.318058  0.791438   
       4       0.000000       NaN  229.089996   0.241357  -1.846274  0.790830   
 ...                ...       ...         ...        ...        ...       ...   
       137830  2.499857  0.400023  219.949997   0.632804  -0.609745  0.856901   
       137831  1.817697  0.550150  219.039993   0.488474  -0.474023  0.856901   
       137832  1.248405  0.801043  217.449997  -1.116789  -1.843264  0.856901   
       137833  0.248456  4.025372  219.979996  -0.762485  -0.762485  0.856901   
       137834  0.000000       NaN  229.039993 -23.291994 -41.154434  0.950000   

                     oma   oma_nobc       imp       dfs  \
 SatId points                                             
 n19   0        2.803686  -7.640147       NaN       NaN   
       1        4.388880  -3.414203       NaN       NaN   
       2       -1.364057   3.049402       NaN       NaN   
       3        0.594996  -0.230114       NaN       NaN   
       4        0.297319  -1.806047       NaN       NaN   
 ...                 ...        ...       ...       ...   
       137830   0.451392  -0.792813 -0.491687 -0.286979   
       137831   0.409210  -0.554484 -0.129336 -0.070378   
       137832  -0.789088  -1.543222 -0.779681 -0.456870   
       137833   0.452604   0.452604 -0.093540 -0.230162   
       137834 -23.673958 -41.430882       NaN       NaN   

                                 geometry  
 SatId points                              
 n19   0        POINT (6.64490 -87.44620)  
       1        POINT (6.64490 -87.44620)  
       2        POINT (6.64490 -87.44620)  
       3        POINT (6.64490 -87.44620)  
       4        POINT (6.64490 -87.44620)  
 ...                                  ...  
       137830  POINT (-23.74915 56.75460)  
       137831  POINT (-23.74915 56.75460)  
       137832  POINT (-23.74915 56.75460)  
       137833  POINT (-23.74915 56.75460)  
       137834  POINT (-23.74915 56.75460)  

 [137835 rows x 18 columns]}

To access a specific variable (e.g., amsua), do the following:

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print('Variable: ', varName)

gdf_list[tidx].obsInfo[varName]

Variable: amsua

lat lon elev nchan time iuse idqc inverr oer obs omf omf_nobc emiss oma oma_nobc imp dfs geometry
SatId points
n19 0 -87.446198 6.644900 2647.032227 1.0 2.885000 1.0 8.0 0.000000 NaN 187.330002 2.591137 -7.817700 0.813564 2.803686 -7.640147 NaN NaN POINT (6.64490 -87.44620)
1 -87.446198 6.644900 2647.032227 2.0 2.885000 1.0 8.0 0.000000 NaN 189.190002 4.114823 -3.598879 0.801515 4.388880 -3.414203 NaN NaN POINT (6.64490 -87.44620)
2 -87.446198 6.644900 2647.032227 3.0 2.885000 1.0 8.0 0.000000 NaN 211.460007 -1.666820 2.795111 0.791668 -1.364057 3.049402 NaN NaN POINT (6.64490 -87.44620)
3 -87.446198 6.644900 2647.032227 4.0 2.885000 1.0 8.0 0.000000 NaN 225.820007 0.495127 -0.318058 0.791438 0.594996 -0.230114 NaN NaN POINT (6.64490 -87.44620)
...
137832 56.754601 336.250885 0.055578 13.0 -1.522778 1.0 0.0 1.248405 0.801043 217.449997 -1.116789 -1.843264 0.856901 -0.789088 -1.543222 -0.779681 -0.456870 POINT (-23.74915 56.75460)
137833 56.754601 336.250885 0.055578 14.0 -1.522778 4.0 0.0 0.248456 4.025372 219.979996 -0.762485 -0.762485 0.856901 0.452604 0.452604 -0.093540 -0.230162 POINT (-23.74915 56.75460)
137834 56.754601 336.250885 0.055578 15.0 -1.522778 1.0 51.0 0.000000 NaN 229.039993 -23.291994 -41.154434 0.950000 -23.673958 -41.430882 NaN NaN POINT (-23.74915 56.75460)

137835 rows × 18 columns

To access a specific variable and type (e.g., amsua of type n19), do the following:

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print('Variable: ', varName, ' and Type: ', varType)

gdf_list[tidx].obsInfo[varName].loc[varType]
lat lon elev nchan time iuse idqc inverr oer obs omf omf_nobc emiss oma oma_nobc imp dfs geometry
points
0 -87.446198 6.644900 2647.032227 1.0 2.885000 1.0 8.0 0.000000 NaN 187.330002 2.591137 -7.817700 0.813564 2.803686 -7.640147 NaN NaN POINT (6.64490 -87.44620)
1 -87.446198 6.644900 2647.032227 2.0 2.885000 1.0 8.0 0.000000 NaN 189.190002 4.114823 -3.598879 0.801515 4.388880 -3.414203 NaN NaN POINT (6.64490 -87.44620)
2 -87.446198 6.644900 2647.032227 3.0 2.885000 1.0 8.0 0.000000 NaN 211.460007 -1.666820 2.795111 0.791668 -1.364057 3.049402 NaN NaN POINT (6.64490 -87.44620)
3 -87.446198 6.644900 2647.032227 4.0 2.885000 1.0 8.0 0.000000 NaN 225.820007 0.495127 -0.318058 0.791438 0.594996 -0.230114 NaN NaN POINT (6.64490 -87.44620)
...
137832 56.754601 336.250885 0.055578 13.0 -1.522778 1.0 0.0 1.248405 0.801043 217.449997 -1.116789 -1.843264 0.856901 -0.789088 -1.543222 -0.779681 -0.456870 POINT (-23.74915 56.75460)
137833 56.754601 336.250885 0.055578 14.0 -1.522778 4.0 0.0 0.248456 4.025372 219.979996 -0.762485 -0.762485 0.856901 0.452604 0.452604 -0.093540 -0.230162 POINT (-23.74915 56.75460)
137834 56.754601 336.250885 0.055578 15.0 -1.522778 1.0 51.0 0.000000 NaN 229.039993 -23.291994 -41.154434 0.950000 -23.673958 -41.430882 NaN NaN POINT (-23.74915 56.75460)

137835 rows × 18 columns

To access the observation value, use the obs method:

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print('Variable: ', varName, ' and Type: ', varType)

gdf_list[tidx].obsInfo[varName].loc[varType].obs

Variable:  amsua  and Type:  n19
[13]:
points
0         187.330002
1         189.190002
2         211.460007
3         225.820007
4         229.089996
             ...    
137830    219.949997
137831    219.039993
137832    217.449997
137833    219.979996
137834    229.039993
Name: obs, Length: 137835, dtype: float32

To access the quality control value of the observation in a specific channel, use the idqc method with the mask instruction in the query function:

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mask = 'nchan == 2'
gdf_list[tidx].obsInfo[varName].query(mask).loc[varType].idqc

points
1          8.0
16         8.0
31        51.0
46        51.0
61        51.0
          ... 
137761    51.0
137776    51.0
137791    51.0
137806    51.0
137821    51.0
Name: idqc, Length: 9189, dtype: float32

Using the plot_diag class

Spatial Distribution

Below is an example of using functions from the plot_diag class that have been modified or created for radiance data diagnostics, along with commands to generate various types of figures.

Generating a figure with the observation values (param='obs') for the selected variable and type:

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param = 'obs'

gd.plot_diag.plot(gdf_list[tidx], 
                  varName=varName, 
                  varType=varType, 
                  param=param,  
                  mask="(nchan=="+str(channel)+") & (iuse >= 1 & idqc == 0)", 
                  markersize=4.80, 
                  legend='true')

png

Histogram

The pcount() and vcount() functions from the plot_diag class can also be used to obtain a histogram with the observation count for the variable. However, in the case of radiance data, since there is only one variable (sensor) and one type (satellite) in the files, both functions generate the same histogram with a single column:

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gd.plot_diag.pcount(gdf_list[tidx], varName)

png

Use the vcount() function from the plot_diag class to obtain a histogram with the observation count for all types of variables:

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gd.plot_diag.vcount(gdf_list[tidx])

png

Time Series

Below are the options for figures using the time_series_radi() function (new function) included in the plot_diag class. Initially, a figure is generated with the parameters already set in this section. Next, figures are presented with the channel parameter altered.

Plotting a time series of OmA and OmF for the data from the channel set in the variable definition cell:

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gd.plot_diag.time_series_radi(gdf_list,
                             varName=varName, 
                             varType=varType, 
                             mask='idqc==0 & iuse>=1', 
                             dateIni=dateIni, 
                             dateFin=dateFin, 
                             nHour=nHour, 
                             vminOMA=vminOMA, 
                             vmaxOMA=vmaxOMA, 
                             vminSTD=vminSTD, 
                             vmaxSTD=vmaxSTD, 
                             channel=channel, 
                             Clean=False)

 =============================================================================================================
 Variable: amsua  ||  type: n19  ||  Radiance - AMSU-A - NOAA-19.  ||  check: OmF
 =============================================================================================================

 Preparing data for: 2024-02-01:00  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:06  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:12  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:18  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:00  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:06  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:12  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:18  - Channel de radiancia:  6

 =============================================================================================================

channels =  [6]
 Calculating for 2024-02-01:00
 Calculating for 2024-02-01:06
 Calculating for 2024-02-01:12
 Calculating for 2024-02-01:18
 Calculating for 2024-02-02:00
 Calculating for 2024-02-02:06
 Calculating for 2024-02-02:12
 Calculating for 2024-02-02:18

 =============================================================================================================

 Making Graphics...
 Done!

png png png png

In the previous example, the channel parameter was set to channel 6. The initial values for vminOMA, vmaxOMA, vminSTD, and vmaxSTD were then adjusted to improve the time series visualization. 

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vminOMA = -0.5 
vmaxOMA = 0.5 
vminSTD = 0.0
vmaxSTD = 1.0

gd.plot_diag.time_series_radi(gdf_list,
                             varName=varName, 
                             varType=varType, 
                             mask='idqc==0 & iuse>=1', 
                             dateIni=dateIni, 
                             dateFin=dateFin, 
                             nHour=nHour, 
                             vminOMA=vminOMA, 
                             vmaxOMA=vmaxOMA, 
                             vminSTD=vminSTD, 
                             vmaxSTD=vmaxSTD, 
                             channel=channel, 
                             Clean=False)

 =============================================================================================================
 Variable: amsua  ||  type: n19  ||  Radiance - AMSU-A - NOAA-19.  ||  check: OmF
 =============================================================================================================

 Preparing data for: 2024-02-01:00  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:06  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:12  - Channel de radiancia:  6
 Preparing data for: 2024-02-01:18  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:00  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:06  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:12  - Channel de radiancia:  6
 Preparing data for: 2024-02-02:18  - Channel de radiancia:  6

 =============================================================================================================

channels =  [6]
 Calculating for 2024-02-01:00
 Calculating for 2024-02-01:06
 Calculating for 2024-02-01:12
 Calculating for 2024-02-01:18
 Calculating for 2024-02-02:00
 Calculating for 2024-02-02:06
 Calculating for 2024-02-02:12
 Calculating for 2024-02-02:18

 =============================================================================================================

 Making Graphics...
 Done!

png png png png

Now, the channel variable is changed to consider more than one channel. The channel = None option fetches data for each existing channel in the files and creates a Hovmoller diagram with the means, standard deviations, and assimilated data counts for all channels (note that the values on the y-axis identify all channels from 1 to 15). When channel is a specific list of channels specified by the user, the Hovmoller diagram is constructed only with the channels indicated by the user.

Thus, you can use the `channels

= None` parameter to consider all channels. Use the same vminOMA, vmaxOMA, vminSTD, and vmaxSTD values for better visualization:

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channel = None 

gd.plot_diag.time_series_radi(gdf_list,
                             varName=varName, 
                             varType=varType, 
                             mask='idqc==0 & iuse>=1', 
                             dateIni=dateIni, 
                             dateFin=dateFin, 
                             nHour=nHour, 
                             vminOMA=vminOMA, 
                             vmaxOMA=vmaxOMA, 
                             vminSTD=vminSTD, 
                             vmaxSTD=vmaxSTD, 
                             channel=channel, 
                             Clean=False)

 =============================================================================================================
 Variable: amsua  ||  type: n19  ||  Radiance - AMSU-A - NOAA-19.  ||  check: OmF
 =============================================================================================================

 Preparing data for: 2024-02-01:00  - All available channels.
 Preparing data for: 2024-02-01:06  - All available channels.
 Preparing data for: 2024-02-01:12  - All available channels.
 Preparing data for: 2024-02-01:18  - All available channels.
 Preparing data for: 2024-02-02:00  - All available channels.
 Preparing data for: 2024-02-02:06  - All available channels.
 Preparing data for: 2024-02-02:12  - All available channels.
 Preparing data for: 2024-02-02:18  - All available channels.

 =============================================================================================================

channels =  [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Calculating for 2024-02-01:00
 Calculating for 2024-02-01:06
 Calculating for 2024-02-01:12
 Calculating for 2024-02-01:18
 Calculating for 2024-02-02:00
 Calculating for 2024-02-02:06
 Calculating for 2024-02-02:12
 Calculating for 2024-02-02:18

 =============================================================================================================

 Making Graphics...
 Done!

png png png png

"Considering all channels, that is, channel = None:"

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channel = None

gd.plot_diag.time_series_radi(gdf_list,
                             varName=varName, 
                             varType=varType, 
                             mask='idqc==0 & iuse>=1', 
                             dateIni=dateIni, 
                             dateFin=dateFin, 
                             nHour=nHour, 
                             vminOMA=vminOMA, 
                             vmaxOMA=vmaxOMA, 
                             vminSTD=vminSTD, 
                             vmaxSTD=vmaxSTD, 
                             channel=channel, 
                             Clean=False)

 =============================================================================================================
 Variable: amsua  ||  type: n19  ||  Radiance - AMSU-A - NOAA-19.  ||  check: OmF
 =============================================================================================================

 Preparing data for: Radiance channels2024-02-01:00
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-01:06
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-01:12
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-01:18
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-02:00
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-02:06
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-02:12
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Radiance channels2024-02-02:18
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]


 =============================================================================================================

channels =  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
 Calculating for 2024-02-01:00
 Calculating for 2024-02-01:06
 Calculating for 2024-02-01:12
 Calculating for 2024-02-01:18
 Calculating for 2024-02-02:00
 Calculating for 2024-02-02:06
 Calculating for 2024-02-02:12
 Calculating for 2024-02-02:18

 =============================================================================================================

 Making Graphics...
 Done!

png png png

STATCOUNT

The following are the figure options obtained by the statcount() function. The statcount() function provides two types of figures: with figMap=True, it is possible to generate a sequence of images with the geographical points of the assimilated, rejected, and monitored observations over a period from dateIni to dateFin with a time interval of nHour. Another figure, containing a time series of the number of assimilated, rejected, and monitored data, is generated if figTS=True. When the files used are radiance data, the channel variable should be assigned the desired channel number (channel=6), while if the data analyzed are conventional, the channel variable should be set to None (channel=None).

In radiance data, the classification of data into assimilated, rejected, and monitored is performed considering two types of monitored observations:

  1. monitored-assimilated: observations that are monitored, iuse = -1, which could be assimilated since idqc = 0.
  2. monitored-rejected: observations that are monitored, iuse = -1, which could be rejected since idqc != 0.

The table below details how the classification of radiance data is performed.

idqc iuse
Assimilated == 0 >= 1
Monitored-assimilated == 0 >= -1 and < 1
Monitored-rejected != 0 >= -1 and < 1
Rejected != 0 >= 1

Below is an example that generates a sequence of figures showing the geolocation of assimilated, rejected, and monitored radiance data (figMap=True) over a time interval within the data period loaded at the beginning of the notebook.

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channel = 6
dateIni = '2024020100'
dateFin = '2024020118'

gd.plot_diag.statcount(gdf_list, 
                        varName=varName, 
                        varType=varType, 
                        noiqc=False, 
                        dateIni=dateIni, 
                        dateFin=dateFin, 
                        nHour=nHour, 
                        channel=channel, 
                        figTS=False, 
                        figMap=True, 
                        markersize=4.80)

png png png png

When figTS=True, only one figure with the time series of the quantity of assimilated, rejected, and monitored data is returned.

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channel = 6
dateIni = '2024020100'
dateFin = '2024020218'

gd.plot_diag.statcount(gdf_list, 
                        varName=varName, 
                        varType=varType, 
                        noiqc=False, 
                        dateIni=dateIni, 
                        dateFin=dateFin, 
                        nHour=nHour, 
                        channel=channel, 
                        figTS=True, 
                        figMap=False, 
                        markersize=4.80)

channel = 8
gd.plot_diag.statcount(gdf_list, 
                        varName=varName, 
                        varType=varType, 
                        noiqc=False, 
                        dateIni=dateIni, 
                        dateFin=dateFin, 
                        nHour=nHour, 
                        channel=channel, 
                        figTS=True, 
                        figMap=False, 
                        markersize=4.80)

png png

After using the files, close them to free up the memory used:

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for file in gdf_list:
    if file._FNumber != None:
            file.close()

readDiag is a package under development and constantly being updated. New features will be added and demonstrated through this notebook.