def work_3():
data_in = datetime(2010,6,16,8,00,0)
data_fin = datetime(2010,6,16,8,05,0)
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
data_in = datetime(2010,6,18,8,00,0)
data_fin = datetime(2010,6,18,22,00,0)
dati2 = df.query_db('greenhouse.db','data',data_in,data_fin)
dati = np.concatenate((dati,dati2))
data_in = datetime(2010,6,21,8,00,0)
data_fin = datetime(2010,6,25,22,00,0)
dati2 = df.query_db('greenhouse.db','data',data_in,data_fin)
dati = np.concatenate((dati,dati2))
data_in = datetime(2010,6,27,8,00,0)
data_fin = datetime(2010,6,28,22,00,0)
dati2 = df.query_db('greenhouse.db','data',data_in,data_fin)
dati = np.concatenate((dati,dati2))
Is = dati['rad_int_sup_solar']
lista_to_filter = df.smooht_Is(Is)
Is_2 = df.smooth_value(Is,lista_to_filter)
tra_P_M = mf.transpiration_P_M(Is_2,dati['rad_int_inf_solar'],0.64,2.96,((dati['temp_1']+dati['temp_2'])/2)+273.15,(dati['RH_1']+dati['RH_2'])/200)
delta_peso = np.diff(dati['peso_balanca'])
fr,lista_irr,lista_irr_free = mf.find_irrigation_point(delta_peso,dati['data'])
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],20)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
a,r2 = df.linear_reg(tra_weigh_avg,tra_P_M_avg,True)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M],tra_P_M_avg,"penman",tra_weigh_avg,"balancae")
print r2
def work_2():
data_in = datetime(2010,6,21,8,00,0)
data_fin = datetime(2010,6,26,8,00,0)
dati_station = df.query_db('greenhouse.db','station_data',data_in,data_fin)
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
Is_green = np.array(df.avg_(dati['rad_int_sup_solar'],12))
Is_station = station_dati['net_eadiation']
print len(Is_green)
print len(Is_station)
model,R2 = df.linear_reg(Is_station[:-1],Is_green,True)
print model
print R2
data_plot.plot_time_data_2_y_same_axis(station_dati['data'][:-1],Is_station,'Is_station',Is_green,'greenhouse')
def work_1():
data_in = datetime(2010,6,24,8,00,0)
data_fin = datetime(2010,6,24,22,00,0)
#np.concatenate((dati,dati2))
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
Is = dati['rad_int_sup_solar']
lista_to_filter = df.smooht_Is(Is)
Is_2 = df.smooth_value(Is,lista_to_filter)
tra_P_M = mf.transpiration_P_M(Is_2,dati['rad_int_inf_solar'],0.64,2.96,((dati['temp_1']+dati['temp_2'])/2)+273.15,(dati['RH_1']+dati['RH_2'])/200)
tra_weight = mf.transpiration_from_balance(dati['peso_balanca'],300,2260000)
delta_peso = np.diff(dati['peso_balanca'])
fr,lista_irr,lista_irr_free = mf.find_irrigation_point(delta_peso,dati['data'])
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],50)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M], tra_P_M_avg, 'tra Penman', tra_weigh_avg, 'trans weight')
RMSE = df.RMSE(tra_P_M_avg, tra_weigh_avg)
print "RMSE is", RMSE
print "RRMSE is", df.RRMSE(RMSE, tra_weigh_avg)
date = dati['data'][time_P_M].astype(object)
dates= pylab.date2num(date)
pylab.plot_date(dates,tra_weigh_avg,'rx')
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],20)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
a,r2 = df.linear_reg(tra_weigh_avg,tra_P_M_avg,True)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M],tra_P_M_avg,"penman",tra_weigh_avg,"balancae")
print r2
if (__name__=="__main__"):
data_in = datetime(2010,6,23,8,00,0)
data_fin = datetime(2010,6,24,22,05,0)
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
Is = dati['rad_int_sup_solar']
lista_to_filter = df.smooht_Is(Is)
Is_2 = df.smooth_value(Is,lista_to_filter)
tra_P_M = mf.transpiration_P_M(Is_2,dati['rad_int_inf_solar'],0.64,3.96,((dati['temp_1']+dati['temp_2'])/2)+273.15,(dati['RH_1']+dati['RH_2'])/200)
delta_peso = np.diff(dati['peso_balanca'])
fr,lista_irr,lista_irr_free = mf.find_irrigation_point(delta_peso,dati['data'])
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],50)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
a,r2 = df.linear_reg(tra_weigh_avg,tra_P_M_avg,True)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M],tra_P_M_avg,"penman",tra_weigh_avg,"balancae")
print r2