Validacion climMAPcore

En el siguiente ejercicio vamos a generar ciertos indicadores estadisticos para la validacion de las salidas de informacion de la aplicacion climMAPcore

Procedimiento

Los valores a comparar son el valor diario de la estacion vs el valor del modelo WRF. La base que se utilizara se encuentra en la carpeta data de nombre dataFromAguascalientestTest.csv la cual incluye los siguientes campos:

Station : numero de la estacion
State : estado
Lat : latitud
Long : longitud
Year : anio
Month : mes
Day : dia
Rain : precipitacion estacion
Hr : humedad relativa estacion
Tpro : temperatura promedio estacion
RainWRF : precipitacion modelo WRF
HrWRF : humedad relativa modelo WRF
TproWRF : tmperatura promedio modelo WRF



In [1]:

    
# librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
%matplotlib inline
plt.style.use('ggplot')



In [2]:

    
# leer archivo
data = pd.read_csv('../data/dataFromAguascalientestTest.csv')



In [3]:

    
# verificar su contenido
data.head()



In [4]:

    
# diferencia entre valores de precipitacion, humedad relativa y temperatura promedio
data['diffRain'] = data['Rain'] - data['RainWRF']
data['diffHr'] = data['Hr'] - data['HrWRF']
data['diffTpro'] = data['Tpro'] - data['TproWRF']



In [5]:

    
# verificar contenido
data.head()



In [11]:

    
# histograma de diferencias Hr
data['diffHr'].hist()









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x117295d30>



In [22]:

    
# comportamiento de los datos por mes
data.groupby(['Month']).mean()[['Hr','HrWRF']]



In [21]:

    
# visualizar los datos en grafica
data.groupby(['Month']).mean()[['Hr','HrWRF']].plot.bar()









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x11bee7400>



In [12]:

    
# histograma de diferencias Tpro
data['diffTpro'].hist()









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x117dbf3c8>



In [23]:

    
# comportamiento de los datos por mes
data.groupby(['Month']).mean()[['Tpro','TproWRF']]



In [20]:

    
# visualizar los datos en grafica
data.groupby(['Month']).mean()[['Tpro','TproWRF']].plot.bar()









    Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x11af4e780>



In [15]:

    
# histograma de diferencias Rain
data['diffRain'].hist()









    Out[15]:





<matplotlib.axes._subplots.AxesSubplot at 0x119165d30>



In [19]:

    
# comportamiento de los datos por mes
data.groupby(['Month']).mean()[['Rain','RainWRF']]



In [18]:

    
# visualizar los datos en grafica
data.groupby(['Month']).mean()[['Rain','RainWRF']].plot.bar()









    Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x11a949e80>

Regresion Lineal



In [25]:

    
# librerias seabron as sns
import seaborn as sns



In [36]:

    
# Hr
sns.lmplot(x='Hr',y='HrWRF',data=data, col='Month', aspect=0.6, size=8)









    Out[36]:





<seaborn.axisgrid.FacetGrid at 0x1370a1eb8>



In [31]:

    
# Tpro
sns.lmplot(x='Tpro',y='TproWRF',data=data, col='Month', aspect=0.6, size=8)









    Out[31]:





<seaborn.axisgrid.FacetGrid at 0x124980c18>



In [32]:

    
# Rain
sns.lmplot(x='Rain',y='RainWRF',data=data, col='Month', aspect=0.6, size=8)









    Out[32]:





<seaborn.axisgrid.FacetGrid at 0x124b4a1d0>



In [37]:

    
# Rain polynomial regression
sns.lmplot(x='Rain',y='RainWRF',data=data, col='Month', aspect=0.6, size=8, order=2)









    Out[37]:





<seaborn.axisgrid.FacetGrid at 0x137c4fa20>

Regresion lineal con p y pearsonr



In [42]:

    
# Hr
sns.jointplot("Hr", "HrWRF", data=data, kind="reg")









    Out[42]:





<seaborn.axisgrid.JointGrid at 0x139318240>



In [38]:

    
# Tpro
sns.jointplot("Tpro", "TproWRF", data=data, kind="reg")









    Out[38]:





<seaborn.axisgrid.JointGrid at 0x138394048>



In [39]:

    
# Rain
sns.jointplot("Rain", "RainWRF", data=data, kind="reg")









    Out[39]:





<seaborn.axisgrid.JointGrid at 0x1391c5f28>

OLS Regression



In [44]:

    
# HR
result = sm.ols(formula='HrWRF ~ Hr', data=data).fit()



In [47]:

    
print(result.params)









    



Intercept    8.961574
Hr           0.908297
dtype: float64



In [48]:

    
print(result.summary())









    



                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  HrWRF   R-squared:                       0.735
Model:                            OLS   Adj. R-squared:                  0.735
Method:                 Least Squares   F-statistic:                 1.706e+04
Date:                Tue, 24 Oct 2017   Prob (F-statistic):               0.00
Time:                        10:39:45   Log-Likelihood:                -21078.
No. Observations:                6154   AIC:                         4.216e+04
Df Residuals:                    6152   BIC:                         4.217e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      8.9616      0.273     32.862      0.000       8.427       9.496
Hr             0.9083      0.007    130.614      0.000       0.895       0.922
==============================================================================
Omnibus:                       12.704   Durbin-Watson:                   1.087
Prob(Omnibus):                  0.002   Jarque-Bera (JB):               12.784
Skew:                           0.109   Prob(JB):                      0.00168
Kurtosis:                       2.949   Cond. No.                         113.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



In [52]:

    
# Tpro
result = sm.ols(formula='TproWRF ~ Tpro', data=data).fit()



In [53]:

    
print(result.params)









    



Intercept    0.677357
Tpro         0.879475
dtype: float64



In [54]:

    
print(result.summary())









    



                            OLS Regression Results                            
==============================================================================
Dep. Variable:                TproWRF   R-squared:                       0.855
Model:                            OLS   Adj. R-squared:                  0.855
Method:                 Least Squares   F-statistic:                 3.634e+04
Date:                Tue, 24 Oct 2017   Prob (F-statistic):               0.00
Time:                        10:40:29   Log-Likelihood:                -10994.
No. Observations:                6154   AIC:                         2.199e+04
Df Residuals:                    6152   BIC:                         2.201e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      0.6774      0.087      7.806      0.000       0.507       0.847
Tpro           0.8795      0.005    190.640      0.000       0.870       0.889
==============================================================================
Omnibus:                      108.862   Durbin-Watson:                   0.478
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              160.688
Skew:                           0.197   Prob(JB):                     1.28e-35
Kurtosis:                       3.687   Cond. No.                         88.9
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



In [55]:

    
# Rain
result = sm.ols(formula='RainWRF ~ Rain', data=data).fit()



In [56]:

    
print(result.params)









    



Intercept    0.587440
Rain         0.604637
dtype: float64



In [57]:

    
print(result.summary())









    



                            OLS Regression Results                            
==============================================================================
Dep. Variable:                RainWRF   R-squared:                       0.190
Model:                            OLS   Adj. R-squared:                  0.190
Method:                 Least Squares   F-statistic:                     1441.
Date:                Tue, 24 Oct 2017   Prob (F-statistic):          1.75e-283
Time:                        10:40:55   Log-Likelihood:                -14737.
No. Observations:                6154   AIC:                         2.948e+04
Df Residuals:                    6152   BIC:                         2.949e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      0.5874      0.034     17.152      0.000       0.520       0.655
Rain           0.6046      0.016     37.960      0.000       0.573       0.636
==============================================================================
Omnibus:                     7397.878   Durbin-Watson:                   1.200
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1297078.169
Skew:                           6.293   Prob(JB):                         0.00
Kurtosis:                      73.001   Cond. No.                         2.19
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Histogramas seaborn



In [59]:

    
# Hr
sns.distplot(data['diffHr'])









    Out[59]:





<matplotlib.axes._subplots.AxesSubplot at 0x139d5a240>



In [64]:

    
# Tpro
sns.distplot(data['diffTpro'])









    Out[64]:





<matplotlib.axes._subplots.AxesSubplot at 0x13a7bfba8>



In [65]:

    
# Rain
sns.distplot(data['diffRain'])









    Out[65]:





<matplotlib.axes._subplots.AxesSubplot at 0x13a967588>



In [ ]:

	Station	State	Lat	Long	Year	Month	Day	Hr	Tpro	RainWRF	HrWRF	TproWRF
0	22.0	AGS	21.7853	21.7853	2017.0	1.0	1.0	55.49	14.18	0.090005	58.330972	13.649469
1	22.0	AGS	21.7853	21.7853	2017.0	1.0	2.0	46.33	14.26	0.000000	53.417499	13.828637
2	22.0	AGS	21.7853	21.7853	2017.0	1.0	3.0	32.51	15.43	0.000000	39.286445	14.939110
3	22.0	AGS	21.7853	21.7853	2017.0	1.0	4.0	25.60	17.01	0.000000	39.291486	15.209583
4	22.0	AGS	21.7853	21.7853	2017.0	1.0	5.0	28.03	16.75	0.000000	31.613730	15.769464

	Station	State	Lat	Long	Year	Month	Day	Hr	Tpro	RainWRF	HrWRF	TproWRF	diffRain	diffHr	diffTpro
0	22.0	AGS	21.7853	21.7853	2017.0	1.0	1.0	55.49	14.18	0.090005	58.330972	13.649469	-0.090005	-2.840972	0.530531
1	22.0	AGS	21.7853	21.7853	2017.0	1.0	2.0	46.33	14.26	0.000000	53.417499	13.828637	0.000000	-7.087499	0.431363
2	22.0	AGS	21.7853	21.7853	2017.0	1.0	3.0	32.51	15.43	0.000000	39.286445	14.939110	0.000000	-6.776445	0.490890
3	22.0	AGS	21.7853	21.7853	2017.0	1.0	4.0	25.60	17.01	0.000000	39.291486	15.209583	0.000000	-13.691486	1.800417
4	22.0	AGS	21.7853	21.7853	2017.0	1.0	5.0	28.03	16.75	0.000000	31.613730	15.769464	0.000000	-3.583730	0.980536

	Hr	HrWRF
Month
1.0	41.450787	44.815389
2.0	36.156996	41.821895
3.0	37.344165	45.491191
4.0	27.901539	33.474755
5.0	29.969440	35.266573
6.0	47.800255	53.295624

	Tpro	TproWRF
Month
1.0	13.485769	12.241899
2.0	15.096891	13.478516
3.0	17.353890	15.635514
4.0	19.495078	17.539529
5.0	22.461708	20.859032
6.0	22.240676	21.143828

	Rain	RainWRF
Month
1.0	0.014991	0.029041
2.0	0.081513	0.195703
3.0	0.292979	0.644869
4.0	0.031078	0.094193
5.0	0.126376	0.709989
6.0	1.480784	3.068844