In [1]:

    

import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import scipy as sp
from hydromet.io import read_predictors, read_predictands
from hydromet.system import get_model_config, get_model_dir
%matplotlib inline
from phildb.database import PhilDB
db = PhilDB('hm_tsdb')


    

    




/Users/amacdon/anaconda/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

In [2]:

    

config = get_model_config('project', 'LinearRegression')


    

In [3]:

    

config['calibration']['end_date']


    

    
Out[3]:





datetime.date(2008, 12, 1)

In [4]:

    

predictors = read_predictors(db, config, '410730', 'calibration')


    

In [5]:

    

predictands = read_predictands(db, config, '410730', 'calibration')


    

In [6]:

    

from hydromet.models.linear_regression import LinearRegressionModel


    

In [7]:

    

lrm = LinearRegressionModel()


    

In [8]:

    

lrm.calibrate(predictors, predictands)


    

In [9]:

    

fc_date = '2010-06-01'


    

In [10]:

    

for fc_date in pd.date_range('2010-01-01', '2011-12-01', freq='MS'):
    p = read_predictors(db, config, '410730', idx = slice(fc_date, fc_date))
    print(lrm.forecast(p)[0][0] - read_predictands(db, config, '410730', idx = slice(fc_date, fc_date))['Q'].values[0])


    

    




326.488413169
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332.15095665
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317.536393439
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286.47629382
264.665804855
5.52403147837
-503.222118183
-185.396177992
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4192.81126957
-3919.17968903

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