################################################################## #### #### plots has to be exact. So, we need #### to filter out NASS, and filter by last survey date #### ################################################################## ################################################################## a_df = a_df[a_df['county'] == given_county] # Filter given_county a_df = rc.filter_out_NASS(a_df) # Toss NASS a_df = rc.filter_by_lastSurvey(a_df, year=SF_year) # filter by last survey date a_df['SF_year'] = SF_year if irrigated_only == True: a_df = rc.filter_out_nonIrrigated(a_df) output_Irr = "irrigated_only" else: output_Irr = "non_irrigated_only" a_df = rc.filter_out_Irrigated(a_df) ################################################################## if jumps == "yes": output_dir = "/data/hydro/users/Hossein/remote_sensing/02_Eastern_WA_plots_tbls/" + \ "2Yr_plt_70cloud_notRegular_wJump/" + given_county + "_" + str(SF_year) + "_raw_" + output_Irr + "_" + indeks + "/" else: output_dir = "/data/hydro/users/Hossein/remote_sensing/02_Eastern_WA_plots_tbls/" + \ "2Yr_plt_70cloud_notRegular_noJump/" + given_county + "_" + str(SF_year) + "_noJump_" + output_Irr + "_" + indeks + "/" plot_dir_base = output_dir print("plot_dir_base is " + plot_dir_base)
if double_by_Note == False: dbl_name = "_dblNotFiltered_" else: curr_SF = rc.filter_double_by_Notes( curr_SF) dbl_name = "_onlyDblByNotes_" if NASS_out == True: curr_SF = rc.filter_out_NASS(curr_SF) NASS_name = "NASSOut_" else: NASS_name = "NASSin_" if non_Irr_out == True: curr_SF = rc.filter_out_nonIrrigated( curr_SF) non_Irr_name = "JustIrr" else: non_Irr_name = "BothIrr" if perennials_out == True: print("line 165") print(curr_SF.shape) curr_SF = curr_SF[curr_SF.CropTyp.isin( double_crop_potens['Crop_Type'])] print("line 167") print(curr_SF.shape) Pere_name = "_PereOut_" else: Pere_name = "_PereIn_"