##################################################################
####
#### 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_"