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')
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