data_dir = data_dir_noJumps
f_name = "Eastern_WA_SF_" + str(SF_year) + "_70cloud_" + indeks + ".csv"
a_df = pd.read_csv(data_dir + f_name, low_memory=False)
##################################################################
##################################################################
####
#### 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 + "/"
for NASS_out in NASS_out_options:
for last_survey_date in last_survey_date_options:
for double_potential in double_potential_options:
for irrigated in irrigated_options:
for SF_year in SF_years:
all_data = pd.DataFrame()
for county in counties:
file_name = county + "_" + SF_year + postfix_1 + postfix_2
a_df = pd.read_csv(data_dir + file_name,
low_memory=False)
print(file_name)
print(a_df.SF_year.unique())
if NASS_out == True:
a_df = rc.filter_out_NASS(a_df)
out_name_NASS = "NASSOut"
else:
out_name_NASS = "NASSIn"
if last_survey_date == True:
a_df = a_df[a_df['LstSrvD'].str.contains(SF_year)]
out_name_survey = "LastSrvCorrect"
else:
out_name_survey = "LastSrvFalse"
if irrigated == True:
a_df = rc.filter_out_nonIrrigated(a_df)
out_name_irrigated = "JustIrr"
else:
out_name_irrigated = "IrrAndNonIrr"
elif filter_lastSurDate == False:
print("4")
last_part_name = "NassIn_NotCorrectYears"
print(last_part_name)
print("filter_NASS is " + str(filter_NASS))
print("filter_lastSurDate is " + str(filter_lastSurDate))
if (filter_NASS == True):
a_df = rc.filter_by_lastSurvey(dt_df_surv=a_df, year=2018)
print(
"After filtering by last survey date, a_df is of dimension {fileShape}."
.format(fileShape=a_df.shape))
if (filter_lastSurDate == True):
a_df = rc.filter_out_NASS(dt_df_NASS=a_df)
print("After filtering out NASS, a_df is of dimension {fileShape}.".format(
fileShape=a_df.shape))
######################
output_dir = data_dir + "/savitzky/Grant_Irrigated_EVI_2018_" + last_part_name + \
"/delta" + str(delt) + "_Sav_win" + str(Sav_win_size) + "_Order" + str(sav_order) + "/"
plot_dir_base = output_dir
print("plot_dir_base is " + plot_dir_base)
os.makedirs(output_dir, exist_ok=True)
os.makedirs(plot_dir_base, exist_ok=True)
######################
a_df['year'] = 2018
output['parameters'] = delta_windows_degrees
####
#### Build shapeFile info accordingly
####
curr_SF = WSDA_DataTable.copy()
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(
