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Aplicação - Uso

Informação

Uma versão desta página para o Jupyter Notebook pode ser encontrada em https://github.com/GAD-DIMNT-CPTEC/readDiag/blob/master/notebooks/readDiag_Radiancia_tutorial_completo-pt_br.ipynb.

Se preferir interagir com o notebook, clique no botão abaixo para utilizar o Google Colab.

Open In Colab

Utilização da classe read_diag

Bibliotecas necessárias

Para iniciar a utilização do readDiag, carregue primeiro as bibliotecas necessárias para a sua utilização:

  • gsidiag: é a biblioteca que contém as classes read_diag e plot_diag;
  • pandas: é a biblioteca que fornece as estruturas de dados tabulados utilizadas pelo readDiag;
  • matplotlib: é a biblioteca a partir da qual são confeccionadas as figuras;
  • datetime: é a biblioteca utilizada para manipular datas.

A instrução %matplotlib inline é um comando mágico do Jupyter e apenas ajusta o ambiente para que não seja necessário utilizar o comando plt.show() sempre que figuras forem mostradas dentro do notebook. Se você estiver utilizando o readDiag dentro de um script Python, esta diretiva pode ser suprimida e o comando plt.show() deve ser utilizado, a depender da situação.

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import gsidiag as gd

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

Variáveis principais

Na tabela a seguir, estão relacionadas os valores padrão das variáveis utilizadas ao longo deste tutorial. Observe que as variáveis possuem diferentes tipos e que algumas podem ser declaradas como None.

Variáveis Valores Tipo
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 ou None
Lay = 15 integer ou None
SingleL = OneL string

Dessa forma, na célula a seguir são ajustadas as variáveis seguindo os valores da tabela acima. Além disso, são ajustados também os valores das datas para compor o caminho e os nomes dos arquivos de diagnóstico do GSI:

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# Para uso na Egeon
#DIRdiag = "/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout"

# Para uso na Itapemirim (Colorado, Ilopolis e outras máquinas virtuais)
#DIRdiag = "/share/das/dist/joao.gerd/EXP18/GSI/dataout"

# Para uso em máquina local (neste caso, o diretório remoto deverá ser montado localmente com o comando sshfs)
# Exemplo: 
# $ sshfs username@egeon.cptec.inpe.br:/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout
# ou
# $ 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']

Gerando as variáveis path e pathc onde estarão os caminhos completos (incluindo nome do arquivo) dos arquivos diagnósticos do primeiro (OmF) e último (OmA) outer loop:

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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']

Leitura dos arquivos de diagnósticos

Atenção

A leitura dos arquivos de diagnóstico seguindo os valores ajustados para os parâmetros acima, requer pelo menos 8GB de memória RAM. Caso seja necessário, ajuste os parâmetros para considerar um intervalo menor.

Lendo os arquivos diagnósticos com a função read_diag() do pacote readDiag. No trecho de código a seguir, observe que a função read_diag() é utilizada dentro de um loop que varia sobre todos os arquivos das listas paths e pathsc definidas no passo anterior. No final do loop, é gerada a lista gdf_list que conterá todos os arquivos lidos pelo readDiag:

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read = True

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

    print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +
          str(int((float(len(paths))*20)/60)) + ' minutos e ' +
          str(int((float(len(paths))*20)%60)) + ' segundos.')

    print('')

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

        gdf = gd.read_diag(path, pathc)

        gdf_list.append(gdf)

    print('Pronto!')    

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Aguarde, o tempo total estimado para a leitura dos arquivos é de 2 minutos e 40 segundos.

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 <<<

Pronto!

A variável gdf_list é uma lista de dataframes contendo os dados de cada arquivo diagnóstico. Para trabalhar com um único tempo basta informar a lista com o índice fixo, por exemplo: 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>]

Fazendo tidx = 0, obtemos o primeiro objeto da lista gdf_list:

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tidx = 0
gdf_list[tidx]

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<gsidiag.__main__.read_diag at 0x7f9005378790>

Obtendo informações dos arquivos

Utilize a função pfileinfo() para obter uma lista das observações e seus respectivos tipos (kx) que estão contidos dentro do arquivo:

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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

Além do método pfileinfo(), outros métodos e funções também podem ser utilizados para acessar as informações sobre os arquivos abertos. Para obter uma lista dos métodos e funções disponíveis, digite gdf_list[tidx]. e pressione a tecla <TAB> duas vezes no teclado:

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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

Os métodos e funções embutidas possuem documentação, as quais podem ser acessadas da seguinte forma:

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print(object.function_name.__doc__)

ou

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help(object.function_name)

Por exemplo:

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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()

ou ainda:

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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()

Para obter um dicionário com todas as informações sobre as variáveis e tipos contidos no arquivo, utilize o método obsInfo:

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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]}

Para acessar uma variável específica (ex: uv), faça:

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print('Variável: ', varName)

gdf_list[tidx].obsInfo[varName]

Variável: 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

Para acessar variável e tipo específicos (ex: uv do tipo 220), faça:

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print('Variável: ', varName, ' e Tipo: ', varType)

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

Variável: uv e Tipo: 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

O parâmetro varType pode ser também uma lista, ex: varType=[220,221]:

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varTypes = [220,221]

print('Variável: ', varName, ' e Tipos: ', varTypes)

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

Variável: uv e Tipos: [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

Para acessar o valor da observação, utilize o método obs:

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print('Variável: ', varName, ' e Tipo: ', varType)

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

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Variável:  uv  e Tipo:  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

Utilização da classe plot_diag

Distribuição espacial

A utilização das funções da classe plot_diag é apresentada abaixo, junto com os comandos para gerar diversos tipos de figuras.

Gerando uma figura com os valores das observações (param='obs') para a variável e tipo selecionados:

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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

Para salvar a figura, defina o seu nome (figname) e execute os comandos a seguir:

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

plt.tight_layout()
plt.savefig(figname)

Gerando a mesma figura, mas considerando vários tipos diferentes (kx) da observação selecionada:

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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

Gerando uma figura com diferentes variáveis, considerando a máscara 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

Histograma

Utilize a função pcount() da classe plot_diag para obter um histograma com a contagem do número de observações para uma determinada variável:

1
gd.plot_diag.pcount(gdf_list[tidx], varName)

png

Utilize a função vcount() da classe plot_diag para obter um histograma com a contagem do número de observações para todos os tipos de variáveis:

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gd.plot_diag.vcount(gdf_list[tidx])

png

De forma semelhante, utilize a função kxcount() da classe plot_diag para obter um histograma com a contagem do número de observações por tipo:

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gd.plot_diag.kxcount(gdf_list[tidx])

png

Série temporal

A seguir são apresentadas as opções de figuras com a função time_series(), incluída na classe plot_diag. Inicialmente é gerada uma figura com os parâmetros já fixados nesta seção. Em seguida são apresentadas figuras alterando os parâmetros Level, Lay e SingleL.

Plotando uma série temporal do OmA e 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

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png

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No caso anterior, o parâmetro Level estava fixo em 1000 hPa com SingleL igual a All, ou seja, toda a atmosfera foi considerada como uma única camada e o valor 1000 hPa funciona apenas como uma flag para indicar que não existe variação em altura. Ainda com Level=1000, pode-se usar SingleL='OneL' para uma única camada em torno do valor de Level, neste caso 1000 hPa, variando entre Level-Lay e Level+Lay (na definição de variáveis, Lay foi fixado com 15 hPa; no caso de ser None, o valor padrão de 50 hPa é utilizado).

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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

Observe como no exemplo acima a quantidade de dados diminui, uma vez que agora está sendo utilizada uma camada entre 1015 e 985 hPa.

Agora vamos alterar para não fixar em um único nível ou camada, ou seja, altear a variável Level para None ou Zlevs. A opção None busca os dados em cada nível existente nos arquivos e faz a figura para todos esses níveis, porém os valores no eixo y (ordenada) são apenas dos níveis padrão. É importante esclarecer que os chamados níveis padrão estão definidos na classe read_diag e podem ser acessados utilizando o método zlevs da seguinte forma:

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]

Dessa forma, pode-se utilizar o parâmetro Level='Zlevs' para produzir uma série temporal por níveis:

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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

Se Lay=None, então as camadas são preenchidas variando entre o valor médio considerando as camadas inferiores e superiores. Por exemplo, para o nível de 700 hPa é construída uma camada entre 750 e 650 hPa, uma vez que os níveis inferiores e superiores são 800 e 600 hPa, respectivamente. Para o nível de 1000 hPa a camada varia entre 1050 e 950 hPa.

O mesmo exemplo anterior, mas considerando 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

Finalmente, existe a opção de se considerar todos os níveis, ou seja, Level=None. Esta opção traz certa dificuldade na visualização da informação na figura por conta da grande quantidade de níveis e distribuição dos dados em todos os níveis:

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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, 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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
 Levels:  [1, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, 31, 32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 37, 38, 38, 38, 38, 39, 39, 39, 39, 40, 40, 40, 40, 41, 41, 41, 41, 42, 42, 42, 42, 43, 43, 43, 43, 44, 44, 44, 44, 45, 45, 45, 45, 46, 46, 46, 46, 47, 47, 47, 47, 48, 48, 48, 48, 49, 49, 49, 49, 50, 50, 50, 50, 51, 51, 51, 51, 52, 52, 52, 52, 53, 53, 53, 53, 54, 54, 54, 54, 55, 55, 55, 55, 56, 56, 56, 56, 57, 57, 57, 57, 58, 58, 58, 58, 59, 59, 59, 59, 60, 60, 60, 60, 61, 61, 61, 61, 62, 62, 62, 62, 63, 63, 63, 63, 64, 64, 64, 64, 65, 65, 65, 65, 66, 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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

Finalizado o uso dos arquivos, feche-os para liberar a memória utilizada:

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

Radiância

Nesta seção apresentam-se as funções do readDiag construídas e modificadas para acesso aos arquivos de diagnóstico da radiância gerados pelo GSI.

A Classe read_diag

A classe read_diag não foi alterada. Abaixo as ferramentas desta classe são utilizadas como exemplo para visualização do diagnóstico de dados de radiância.

A Classe plot_diag

Na classe plot_diag as funções abaixo foram construídas ou modificadas para leitura e análise dos dados de radiância:

  1. plot(self, varName, varType, param, minVal=None, maxVal=None, mask=None, area=None, **kwargs): a função plot gera uma figura para a variável varName (ex: amsua), varType (ex: n19 (satélite)) e param, que pode ser várias opções como: param="obs" para o valor da observação, param="omf" para observação menos background ou param="oma" para observação menos análise. Foram inseridas as variáveis opicionais minVal e maxVal para fixar o range de cores do colormap. Também é possível mascarar os dados com as variáveis iuse e idqc, que indicam se o dado de radiância foi (iuse>=1 & idqc=0) assimilado ou rejeitado (iuse>=1 & idqc!=0), ou ainda, se o dado é monitorado (iuse=-1). Utilizando o mask ainda é possível especificar o canal nchan (nº do canal). Abaixo há um exemplo executado para varName="amsua", varType="n19", param="obs" e 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): a função time_series_radi gera figuras que mostram a variação temporal das médias e desvios padrões dos parâmetros OmF e OmA dos dados de radiância. Essa função pode ser utilizada considerando-se três situações: quando channel é o nº de um canal (4 Figuras são geradas utilizando apenas as observações do canal espeificado), quando channel é uma lista de canais (6 diagramas de Hovmoller são criados considerando apenas os canais listados) e quando channel é None (são gerados 6 diagramas de Hovmoller considerando todos os canais disponíveis (1 à 15)).

    A seguir são apresentados os parâmetros de entrada na função.

    Parâmetro Exemplo Descrição
    self ['/home/user/diag_amsua_n19_01.2024020100'] Lista com todos os caminhos completos (caminho/nome_do_arquivo) de cada tempo da série temporal.
    varName amsua Nome da variável
    varType n19 Tipo da variável
    dateIni 2024020100 Data inicial da série temporal
    dateFin 2024020218 Data final da série temporal
    nHour 6 Intervalo de tempo em horas entre cada arquivo na lista self
    vminOMA -2.0 Valor mínimo da escala y (ordenada) para OmF e OmA
    vmaxOMA 2.0 Valor máximo da escala y (ordenada) para OmF e OmA
    vminSTD 0.0 Valor mínimo da escala y (ordenada) para o desvio-padrão
    vmaxSTD 14.0 Valor máximo da escala y (ordenada) para o desvio-padrão
    channel 14 Valor(es) do canal a ser feita a série temporal, opções: valor numérico referente ao canal, ex.: 14 para plotar um canal; [6, 9, 10, 13, 14] para plotar uma lista de canais específica; None para plotar todos os canais.
    Clean True ou False Se True, após gerar e salvar a figura, a janela da figura é reiniciada (plt.clf()) ou fechada (plt.close()), se False, este procedimento é eliminado e a figura continua disponível para visualização com plt.show().

  3. statcount(self, varName=None, varType=None, noiqc=False, dateIni=None, dateFin=None, nHour="06", channel=None, figTS=False, figMap=False, **kwargs): a função statcount gera dois tipos de figuras. Se figTS é True uma figura com a série temporal da quantidade de dados assimilados, rejeitados e monitorados é gerada. Se figMap é True uma sequência temporal de figuras indicando no mapa a geolocalização das observações assimiladas, rejeitadas e monitoradas são produzidas. Nesta função, quando os dados são de radiância, a variável channel recebe o número específico de um canal e quando se trata de dados convencionais a variável channel deve ser None. Quando channel = nº canal os dados monitorados (iuse=-1) são subclassificados em dois grupos: observações monitoradas que seriam assimiladas (monitorado-assimilado) pois, idqc=0 e observações monitoradas que seriam rejeitadas (monitorado-rejeitado) pois, idqc!=0. Logo, para os dados de radiância, se figMap=True podem ser geradas duas figuras, uma com a geolocalização das observações assimiladas e rejeitadas e outra com a geolocalização das observações monitoradas-assimiladas e monitoradas-rejeitadas. A variável noiqc é um parâmetro da lista de nomes do GSI sendo utilizada na classificação dos dados convencionais e pode receber os valores True ou False (não interfere na classificação da radiância). Abaixo há um exemplo executado para varName="amsua", varType="n19", noiqc=False, dateIni="2024020100", dateFin="2024020218", nHour="06", channel=6, figTS=True, figMap=True.

figMap=True

figTS=True

No decorrer deste notebook são mostrados exemplos com recortes de código para exemplificar o uso das funções acima elencadas.

Utilização da classe read_diag

Bibliotecas necessárias

Para iniciar a utilização do readDiag, carregue primeiro as bibliotecas necessárias para a sua utilização:

  • gsidiag: é a biblioteca que contém as classes read_diag e plot_diag;
  • pandas: é a biblioteca que fornece as estruturas de dados tabulados utilizadas pelo readDiag;
  • matplotlib: é a biblioteca a partir da qual são confeccionadas as figuras;
  • datetime: é a biblioteca utilizada para manipular datas;
  • datasources: é a biblioteca utilizada para analisar e armazenar informações de um arquivo contendo dados de observações;
  • os: é a biblioteca que fornece diversas interfaces do sistema operacional.

A instrução %matplotlib inline é um comando mágico do Jupyter e apenas ajusta o ambiente para que não seja necessário utilizar o comando plt.show() sempre que figuras forem mostradas dentro do notebook. Se você estiver utilizando o readDiag dentro de um script Python, esta diretiva pode ser suprimida e o comando plt.show() deve ser utilizado, a depender da situação.

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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

Variáveis principais

Na tabela a seguir, estão relacionadas os valores padrão das variáveis utilizadas ao longo deste tutorial. Observe que as variáveis possuem diferentes tipos e que algumas podem ser declaradas como None.

Variáveis Valores Tipo
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 ou None

Dessa forma, na célula a seguir são ajustadas as variáveis seguindo os valores da tabela acima. Além disso, são ajustados também os valores das datas para compor o caminho e os nomes dos arquivos de diagnóstico do GSI:

Dica

Para testar as funcionalidades do readDiag relacionadas com as radiâncias, baixe os arquivos a seguir dentro do diretório data da sua instalação do readDiag:

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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']

Gerando as variáveis path e pathc onde estarão os caminhos completos (incluindo nome do arquivo) dos arquivos diagnósticos do primeiro (OmF) e último (OmA) outer loop dos dados de radiância:

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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']

Leitura dos arquivos de diagnósticos

Lendo os arquivos diagnósticos de radiância com a função read_diag() do pacote readDiag. No trecho de código a seguir, observe que a função read_diag() é utilizada dentro de um loop que varia sobre todos os arquivos das listas paths e pathsc definidas no passo anterior. No final do loop, é gerada a lista gdf_list que conterá todos os arquivos lidos pelo readDiag:

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read = True

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

    print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +
          str(int((float(len(paths))*20)/60)) + ' minutos e ' +
          str(int((float(len(paths))*20)%60)) + ' segundos.')

    print('')

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

        gdf = gd.read_diag(path, pathc)

        gdf_list.append(gdf)

    print('Pronto!')  

Aguarde, o tempo total estimado para a leitura dos arquivos é de 2 minutos e 40 segundos.

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 <<<

Pronto!

A variável gdf_list é uma lista de dataframes contendo os dados de cada arquivo diagnóstico. Para trabalhar com um único tempo basta informar a lista com o índice fixo, por exemplo: 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>]

Fazendo tidx = 0, obtemos o primeiro objeto da lista gdf_list:

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tidx = 0
gdf_list[tidx]

<gsidiag.__main__.read_diag at 0x7e85a4f46e20>

Obtendo informações dos arquivos

Utilize a função pfileinfo() para obter informação como o tipo de sensor e seu respectivo satélite (n19) que estão contidos dentro do arquivo:

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gdf_list[tidx].pfileinfo()

Variable Name : amsua
              └── kx => n19

Além do método pfileinfo(), outros métodos e funções também podem ser utilizados para acessar as informações sobre os arquivos abertos. Para obter uma lista dos métodos e funções disponíveis, digite gdf_list[tidx]. e pressione a tecla <TAB> duas vezes no teclado:

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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

Para obter um dicionário com todas as informações sobre as variáveis e tipos contidos no arquivo, utilize o método obsInfo:

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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]}

Para acessar uma variável específica (ex: amsua), faça:

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print('Variável: ', varName)

gdf_list[tidx].obsInfo[varName]

Variável: 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

Para acessar variável e tipo específicos (ex: amsua do tipo n19), faça:

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print('Variável: ', varName, ' e Tipo: ', 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

Para acessar o valor da observação, utilize o método obs:

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print('Variável: ', varName, ' e Tipo: ', varType)

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

Variável:  amsua  e Tipo:  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

Para acessar o valor do controle de qualidade da observação em um canal específico, utilize o método idqc com a instrução mask na função query:

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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

Utilização da classe plot_diag

Distribuição espacial

A utilização das funções da classe plot_diag que foram alteradas/construídas para diagnóstico de dados de radiância é apresentada abaixo, junto com os comandos para gerar diversos tipos de figuras.

Gerando uma figura com os valores das observações (param='obs') para a variável e tipo selecionados:

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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

Histograma

As funções pcount() e vcount() da classe plot_diag também podem ser utilizadas para obter um histograma com a contagem do número de observações para a variável, porém, no caso da radiância, como há apenas os dados de uma variável (sensor) e um tipo (satélite) nos arquivos, ambas as funções geram o mesmo histograma com uma única coluna:

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

png

Utilize a função vcount() da classe plot_diag para obter um histograma com a contagem do número de observações para todos os tipos de variáveis:

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gd.plot_diag.vcount(gdf_list[tidx])

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Série temporal

A seguir são apresentadas as opções de figuras com a função time_series_radi() (nova função), incluída na classe plot_diag. Inicialmente é gerada uma figura com os parâmetros já fixados nesta seção. Em seguida são apresentadas figuras alterando o parâmetro channel.

Plotando uma série temporal do OmA e OmF para os dados do canal setado na célula de definição das variáveis:

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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

No caso anterior, o parâmetro channel estava fixo no canal 6. Os valores de vminOMA, vmaxOMA, vminSTD e vmaxSTD, fixados inicialmente, são alterados para melhorar a visualização da série temporal. 

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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

Agora, a variável channel é alterada para considerar mais de um canal. A opção channel = None busca os dados em cada canal existente nos arquivos e faz um diagrama de Hovmoller com as médias, desvios padrão e quantidade de dados assimilados em todos os canais (observe que os valores no eixo y (ordenada) identifica todos os canais de 1 à 15). Quando channel é uma lista específica de canais, indicada pelo usuário, o diagrama de Hovmoller é construído apenas com os canais indicados pelo usuário.

Dessa forma, pode-se utilizar o parâmetro channels=[6,10,11,12,13,14] para produzir uma série temporal para mais de um canal:

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channels = [6, 10, 11, 12, 13, 14]

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=channels, 
                             Clean=False)

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

 Preparing data for: Canais de radiancia2024-02-01:00
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-01:06
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-01:12
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-01:18
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-02:00
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-02:06
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-02:12
 Channels:  [6, 10, 11, 12, 13, 14]

 Preparing data for: Canais de radiancia2024-02-02:18
 Channels:  [6, 10, 11, 12, 13, 14]


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

channels =  [6, 10, 11, 12, 13, 14]
 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

Considerando todos os canais, ou seja, 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: Canais de radiancia2024-02-01:00
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-01:06
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-01:12
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-01:18
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-02:00
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-02:06
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-02:12
 Channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

 Preparing data for: Canais de radiancia2024-02-02:18
 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

A seguir são apresentadas as opções de figuras obtidas pela função statcount(). A função statcount() fornece dois tipos de figuras: com figMap=True é possível gerar uma sequência de imagens com os pontos geográficos das observações assimiladas, rejeitadas e monitoradas em um período de tempo dateIni à dateFin com intervalo de tempo nHour. Uma outra figura contendo uma série temporal da quantidade de dados assimilados, rejeitados e monitorados é gerada se figTS=True. Quando os arquivos utilizados são de radiância, deve-se atribuir a variável channel o número do canal desejado (channel=6), enquanto que, se os dados analisados forem convencionais a variável channel deve receber None (channel=None).

Nos dados de radiância a classificação dos dados em assimilado, rejeitado e monitorado é realizada considerando dois tipos de observações monitoradas:

  1. monitorado-assimilado: observações que são monitoradas, iuse = -1, que poderiam ser assimiladas pois idqc = 0.
  2. monitorado-rejeitado: observações que são monitoradas, iuse = -1, que poderiam ser rejeitadas pois idqc != 0.

A tabela abaixo detalha como a classificação dos dados de radiância é realizada.

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

A seguir um exemplo que gera uma sequência de figuras com a geolocalização dos dados de radiância assimilados, rejeitados e monitorados (figMap=True) em um intervalo de tempo dentro do período de dados carregado no início do 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

Quando figTS=True apenas uma figura com a série temporal da quantidade de dados assimilados, rejeitados e monitorados é retornada.

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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

Finalizado o uso dos arquivos, feche-os para liberar a memória utilizada:

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

O readDiag é um pacote em desenvolvimento e em constante atualização. Novas funcionalidades serão adicionadas e demonstradas por meio deste notebook.