def best(patterns, extended):
# Enumberate daytimes
dts = collections.defaultdict(set)
all = []
for p in patterns:
all.extend(p[0].each())
for p in all:
for dt in p.getDayTimesRaw():
dts[dt.data()].add(p)
# For each daytime, iterate patterns to find termweeks
dt_tw = {}
dt_tw_sz = {}
for (dt, ps) in dts.iteritems():
tws = collections.defaultdict(set)
for p in ps:
for (term, week) in p.getTermWeeks().each():
tws[term].add(week)
dt_tw[dt] = tws
dt_tw_sz[dt] = reduce(lambda tot, item: tot + len(item),
tws.itervalues(), 0)
# restrict to at most max_trials (longest)
dt_use = set()
dt_candidates = dt_tw.keys()
for i in range(0, max_trials):
if len(dt_candidates) == 0:
break
use = max(dt_candidates, key=lambda k: dt_tw_sz[k])
dt_candidates.remove(use)
dt_use.add(use)
# find longest range of each, using 1-8,9-16,17-24 type ranges to allow term overlap
dt_longest = {}
for dt in dt_use:
# build termy week numbers (1-24)
week_nums = set()
for (term, weeks) in dt_tw[dt].iteritems():
for week in filter(lambda x: x > 0 and x < 9, weeks):
week_nums.add(term * 8 + week)
ranges = sorted(util.ranges(week_nums),
key=lambda x: x[1],
reverse=True)
if len(ranges) == 0:
dt_longest[dt] = set()
else:
dt_longest[dt] = set(
range(ranges[0][0], ranges[0][0] + ranges[0][1]))
# permute through including and excluding date ranges to see which gives best coverage (EXPONENTIAL!)
best_score = None
best = None
for dts in util.powerset(dt_use):
if len(dts) == 0:
continue
all = set(range(1, 25))
for dt in dts:
all &= dt_longest[dt]
score = len(all) * len(dts)
if best_score == None or score > best_score:
best_score = score
best = dts
# Generate pattern
if best is None:
logger.error("No common in %s" % all)
return None
p = patternatom.PatternAtom(False)
for b in best:
p.addDayTimeRange(b[0], b[1][0], b[1][1], b[2][0], b[2][1])
p.setAllYear()
# Extend to include out-of-term dates, where required
if extended:
for q in patterns:
for qa in q[0].blast():
p.expand_back_to(qa)
p.expand_forward_to(qa)
return p
def main(ini_path, start_date = None, end_date = None, crop_str = ''):
"""Compute Growing Season Statistics
Args:
ini_path (str): file path of project INI file
start_date (str): ISO format date string (YYYY-MM-DD)
end_date (str): ISO format date string (YYYY-MM-DD)
crop_str (str): comma separate list or range of crops to compare
Returns:
None
"""
# Field names
date_field = 'Date'
doy_field = 'DOY'
year_field = 'Year'
# month_field = 'Month'
# day_field = 'Day'
season_field = 'Season'
# Output file/folder names
gs_summary_name = 'growing_season_full_summary.csv'
gs_mean_annual_name = 'growing_season_mean_annual.csv'
baddata_name = 'growing_season_bad_data.txt'
# Delimiter
sep = ','
# sep = r"\s*"
logging.info('\nComputing growing season statistics')
logging.info(' INI: {}'.format(ini_path))
# Check that INI file can be read
crop_et_sec = 'CROP_ET'
config = util.read_ini(ini_path, crop_et_sec)
# Get project workspace and daily ET folder from INI file
# project workspace can use old or new ini file
try:
project_ws = config.get('PROJECT', 'project_folder')
except:
try:
project_ws = config.get(crop_et_sec, 'project_folder')
except:
logging.error(
'ERROR: project_folder ' +
'parameter is not set in INI file')
sys.exit()
def get_config_param(config, param_name, section):
""""""
try:
param_value = config.get(section, param_name)
except:
logging.error(('ERROR: {} parameter is not set' +
' in INI file').format(param_name))
sys.exit()
return param_value
daily_stats_ws = os.path.join(
project_ws, get_config_param(
config, 'daily_output_folder', crop_et_sec))
gs_stats_ws = os.path.join(
project_ws, get_config_param(config, 'gs_output_folder', crop_et_sec))
try:
name_format = config.get(crop_et_sec, 'name_format')
if name_format is None or name_format == 'None':
# name_format = '%s_daily_crop_%c.csv'
name_format = '%s_crop_%c.csv'
except:
# name_format = '%s_daily_crop_%c.csv'
name_format = '%s_crop_%c.csv'
if '%s' not in name_format or '%c' not in name_format:
logging.error("crop et file name format requires"
" '%s' and '%c' wildcards.")
sys.exit()
swl = name_format.index('%s')
cwl = name_format.index('%c')
prefix = name_format[(swl + 2):cwl]
suffix = name_format[(cwl + 2):len(name_format)]
suf_no_ext = suffix[:(suffix.index('.'))]
# Check workspaces
if not os.path.isdir(daily_stats_ws):
logging.error(('\nERROR: daily ET stats folder {0} ' +
'could be found\n').format(daily_stats_ws))
sys.exit()
if not os.path.isdir(gs_stats_ws):
os.mkdir(gs_stats_ws)
# Range of data to use
try:
year_start = dt.datetime.strptime(start_date, '%Y-%m-%d').year
logging.info(' Start Year: {0}'.format(year_start))
except:
year_start = None
try:
year_end = dt.datetime.strptime(end_date, '%Y-%m-%d').year
logging.info(' End Year: {0}'.format(year_end))
except:
year_end = None
if year_start and year_end and year_end <= year_start:
logging.error('\n ERROR: End date must be after start date\n')
sys.exit()
# Allow user to subset crops from INI
try:
crop_skip_list = sorted(list(util.parse_int_set(
config.get(crop_et_sec, 'crop_skip_list'))))
except:
crop_skip_list = []
# crop_skip_list = [44, 45, 46, 55, 56, 57]
try:
crop_test_list = sorted(list(util.parse_int_set(
config.get(crop_et_sec, 'crop_test_list'))))
except:
crop_test_list = []
# Overwrite INI crop list with user defined values
# Could also append to INI crop list
if crop_str:
try:
crop_test_list = list(util.parse_int_set(crop_str))
# try:
# crop_test_list = sorted(list(set(
# crop_test_list + list(util.parse_int_set(crop_str)))
except:
pass
logging.debug('\n crop_test_list = {0}'.format(crop_test_list))
logging.debug(' crop_skip_list = {0}'.format(crop_skip_list))
# Output file paths
gs_summary_path = os.path.join(gs_stats_ws, gs_summary_name)
gs_mean_annual_path = os.path.join(gs_stats_ws, gs_mean_annual_name)
baddata_path = os.path.join(gs_stats_ws, baddata_name)
# Initialize output data arrays and open bad data log file
gs_summary_data = []
gs_mean_annual_data = []
all_cuttings=pd.DataFrame()
baddata_file = open(baddata_path, 'w')
# make used file list using name_format attributes
data_file_list = []
for item in os.listdir(daily_stats_ws):
if prefix in item and suffix in item:
if not item in data_file_list:
data_file_list.append(os.path.join(daily_stats_ws, item))
if len(data_file_list) < 1:
logging.info('No files found')
sys.exit()
data_file_list = sorted(data_file_list)
# Process each file
for file_path in data_file_list:
file_name = os.path.basename(file_path)
logging.debug('')
logging.info(' Processing {0}'.format(file_name))
station, crop_num = os.path.splitext(file_name)[0].split(prefix)
# crop_num = int(crop_num[:crop_num.index(suf_no_ext)])
crop_num = int(crop_num)
logging.debug(' Station: {0}'.format(station))
logging.debug(' Crop Num: {0}'.format(crop_num))
if station == 'temp': continue
# Get crop name
with open(file_path, 'r') as file_f:
crop_name = file_f.readline().split('-', 1)[1].strip()
logging.debug(' Crop: {0}'.format(crop_name))
# Read data from file into record array (structured array)
daily_df = pd.read_csv(file_path, header=0, comment='#',
sep=sep)
logging.debug(' Fields: {0}'.format(
', '.join(daily_df.columns.values)))
daily_df[date_field] = pd.to_datetime(daily_df[date_field])
daily_df.set_index(date_field, inplace=True)
daily_df[year_field] = daily_df.index.year
# daily_df[year_field] = daily_df[date_field].map(lambda x: x.year)
# Build list of unique years
year_array = np.sort(np.unique(
np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' All Years: {0}'.format(
', '.join(list(util.ranges(year_array.tolist())))))
# logging.debug(' All Years: {0}'.format(
# ','.join(map(str, year_array.tolist()))))
# Don't include first year in stats
crop_year_start = min(daily_df[year_field])
logging.debug(' Skipping {}, first year'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
# Check if start and end years have >= 365 days
crop_year_start = min(daily_df[year_field])
crop_year_end = max(daily_df[year_field])
if sum(daily_df[year_field] == crop_year_start) < 365:
logging.debug(' Skipping {}, missing days'.format(
crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
if sum(daily_df[year_field] == crop_year_end) < 365:
logging.debug(' Skipping {}, missing days'.format(
crop_year_end))
daily_df = daily_df[daily_df[year_field] < crop_year_end]
del crop_year_start, crop_year_end
# Only keep years between year_start and year_end
if year_start:
daily_df = daily_df[daily_df[year_field] >= year_start]
if year_end:
daily_df = daily_df[daily_df[year_field] <= year_end]
year_sub_array = np.sort(np.unique(np.array(daily_df[year_field])
.astype(np.int)))
logging.debug(' Data Years: {0}'.format(
', '.join(list(util.ranges(year_sub_array.tolist())))))
# logging.debug(' Data Years: {0}'.format(
# ','.join(map(str, year_sub_array.tolist()))))
# Get separate date related fields
date_array = daily_df.index.date
year_array = daily_df[year_field].values.astype(np.int)
doy_array = daily_df[doy_field].values.astype(np.int)
# Remove leap days
# leap_array = (doy_array == 366)
# doy_sub_array = np.delete(doy_array, np.where(leap_array)[0])
# Build separate arrays for each set of crop specific fields
season_array = np.array(daily_df[season_field])
# Original code from growing_season script
# Initialize mean annual growing season length variables
gs_sum, gs_cnt, gs_mean = 0, 0, 0
start_sum, start_cnt, start_mean = 0, 0, 0
end_sum, end_cnt, end_mean = 0, 0, 0
# Process each year
for year_i, year in enumerate(year_sub_array):
year_crop_str = "Crop: {0:2d} {1:32s} Year: {2}".format(
crop_num, crop_name, year)
logging.debug(year_crop_str)
# Extract data for target year
year_mask = (year_array == year)
date_sub_array = date_array[year_mask]
doy_sub_array = doy_array[year_mask]
season_sub_mask = season_array[year_mask]
field_names=list(daily_df.columns.values)
# Only Run if Cutting in field_names else fill with blanks
# Max of 6 cuttings?
# Initial arrays with nans (is np.full better?)
if 'Cutting' in field_names :
cutting_dates = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
cutting_dates_doy = [np.nan, np.nan, np.nan, np.nan, np.nan,
np.nan]
cutting_sub_array = daily_df.Cutting[year_mask]
cutting_number = len(cutting_sub_array[cutting_sub_array>0])
cutting_dates[0:cutting_number] = \
date_sub_array[cutting_sub_array>0]
cutting_dates_doy[0:cutting_number] = \
doy_sub_array[cutting_sub_array>0]
else:
cutting_dates=[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
cutting_number=[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
cutting_sub_array=[np.nan, np.nan, np.nan, np.nan, np.nan,
np.nan]
cutting_dates_doy=[np.nan, np.nan, np.nan, np.nan, np.nan,
np.nan]
# Track all cutting doy for mean annual by crop
# Each column is different cutting 1-6)
cutting_dates_temp=pd.DataFrame(cutting_dates_doy).transpose()
all_cuttings=all_cuttings.append(cutting_dates_temp)
# print(cutting_dates)
# print('Break Line 269')
# sys.exit()
# Look for transitions in season value
# Start transitions up day before actual start
# End transitions down on end date
try:
start_i = np.where(np.diff(season_sub_mask) == 1)[0][0] + 1
except:
start_i = None
try:
end_i = np.where(np.diff(season_sub_mask) == -1)[0][0]
except:
end_i = None
# If start transition is not found, season starts on DOY 1
if start_i is None and end_i is not None:
start_i = 0
# If end transition is not found, season ends on DOY 365/366
elif start_i is not None and end_i is None:
end_i = -1
# If neither transition is found, season is always on
# elif start_i is None and end_i is None:
# , end_i = 0, -1
# Calculate start and stop day of year
# Set start/end to 0 if season never gets set to 1
if not np.any(season_sub_mask):
skip_str = " Skipping, season flag was never set to 1"
logging.debug(skip_str)
baddata_file.write(
'{0} {1} {2}\n'.format(station, year_crop_str, skip_str))
start_doy, end_doy = 0, 0
start_date, end_date = "", ""
elif np.all(season_sub_mask):
start_doy, end_doy = doy_sub_array[0], doy_sub_array[-1]
start_date = date_sub_array[0].isoformat()
end_date = date_sub_array[-1].isoformat()
else:
start_doy, end_doy = doy_sub_array[start_i],\
doy_sub_array[end_i]
start_date = date_sub_array[start_i].isoformat()
end_date = date_sub_array[end_i].isoformat()
gs_length = sum(season_sub_mask)
logging.debug("Start: {0} ({1}) End: {2} ({3})".format(
start_doy, start_date, end_doy, end_date))
# Track growing season length and mean annual g.s. length
if start_doy > 0 and end_doy > 0 and year_i != 0:
start_sum += start_doy
end_sum += end_doy
gs_sum += gs_length
start_cnt += 1
end_cnt += 1
gs_cnt += 1
# Append data to list
gs_summary_data.append(
[station, crop_num, crop_name, year,
start_doy, end_doy, start_date, end_date, gs_length,
cutting_dates[0],
cutting_dates[1],
cutting_dates[2],
cutting_dates[3],
cutting_dates[4],
cutting_dates[5]])
# Cleanup
del year_mask, doy_sub_array, season_sub_mask
del start_doy, end_doy, start_date, end_date, gs_length
# Calculate mean annual growing season start/end/length
if gs_cnt > 0:
mean_start_doy = int(round(float(start_sum) / start_cnt))
mean_end_doy = int(round(float(end_sum) / end_cnt))
mean_length = int(round(float(gs_sum) / gs_cnt))
mean_start_date = util.doy_2_date(year, mean_start_doy)
mean_end_date = util.doy_2_date(year, mean_end_doy)
else:
mean_start_doy, mean_end_doy, mean_length = 0, 0, 0
mean_start_date, mean_end_date = "", ""
#Take mean of all doy cuttings columns
mean_cuttings=all_cuttings.mean(skipna=True)
# print(mean_cuttings)
# print(round(mean_cuttings[4],0))
# sys.exit()
# Append mean annual growing season data to list
gs_mean_annual_data.append(
[station, crop_num, crop_name,
mean_start_doy, mean_end_doy,
mean_start_date, mean_end_date, mean_length,
round(mean_cuttings[0], 0),
round(mean_cuttings[1], 0),
round(mean_cuttings[2], 0),
round(mean_cuttings[3], 0),
round(mean_cuttings[4], 0),
round(mean_cuttings[5], 0)])
# Cleanup
del season_array
del gs_sum, gs_cnt, gs_mean
del start_sum, start_cnt, start_mean
del end_sum, end_cnt, end_mean
del mean_start_doy, mean_end_doy, mean_length
del mean_start_date, mean_end_date
del year_array, year_sub_array, doy_array
del daily_df
del cutting_dates, cutting_number, cutting_sub_array
del all_cuttings, mean_cuttings
all_cuttings=pd.DataFrame()
logging.debug("")
# Close bad data file log
baddata_file.close()
# Build output record array file
# https://stackoverflow.com/questions/3348460/
# csv-file-written-with-python-has-blank-lines-between-each-row/3348664
gs_summary_csv = csv.writer(open(gs_summary_path, 'w', newline=''))
gs_summary_csv.writerow(
['STATION', 'CROP_NUM', 'CROP_NAME', 'YEAR',
'START_DOY', 'END_DOY', 'START_DATE', 'END_DATE', 'GS_LENGTH',
'CUTTING_1','CUTTING_2','CUTTING_3','CUTTING_4','CUTTING_5',
'CUTTING_6'])
gs_summary_csv.writerows(gs_summary_data)
# Build output record array file
gs_mean_annual_csv = csv.writer(open(gs_mean_annual_path, 'w', newline=''))
gs_mean_annual_csv.writerow(
['STATION', 'CROP_NUM', 'CROP_NAME', 'MEAN_START_DOY', 'MEAN_END_DOY',
'MEAN_START_DATE', 'MEAN_END_DATE', 'MEAN_GS_LENGTH',
'MEAN_CUTTING_1','MEAN_CUTTING_2','MEAN_CUTTING_3','MEAN_CUTTING_4',
'MEAN_CUTTING_5','MEAN_CUTTING_6'])
gs_mean_annual_csv.writerows(gs_mean_annual_data)
# Cleanup
del gs_summary_path, gs_summary_name
del gs_summary_csv, gs_summary_data
del gs_mean_annual_path, gs_mean_annual_name
del gs_mean_annual_csv, gs_mean_annual_data
def main(ini_path,
show_flag=False,
save_flag=True,
label_flag=False,
figure_size=(12, 12),
figure_dpi=300,
start_date=None,
end_date=None,
crop_str='',
simplify_tol=None,
area_threshold=0):
"""Plot crop summary maps using daily output files
Args:
ini_path (str): file path of the project INI file
show_flag (bool): if True, show maps
save_flag (bool): if True, save maps to disk
label_flag (bool): if True, label maps with cell values
figure_size (tuple): width, height tuple [inches]
start_date (str): ISO format date string (YYYY-MM-DD)
end_date (str): ISO format date string (YYYY-MM-DD)
crop_str (str): comma separate list or range of crops to compare
simplify_tol (float): simplify tolerance [in the units of ET Cells]
area_threshold (float): CDL area threshold [acres]
Returns:
None
"""
# ET Cells field names
cell_id_field = 'CELL_ID'
crop_area_field = 'AG_ACRES'
# Input field names
date_field = 'Date'
doy_field = 'DOY'
year_field = 'Year'
# month_field = 'Month'
# day_field = 'Day'
# pmeto_field = 'PMETo'
# precip_field = 'PPT'
# t30_field = 'T30'
etact_field = 'ETact'
# etpot_field = 'ETpot'
# etbas_field = 'ETbas'
# irrig_field = 'Irrigation'
season_field = 'Season'
cutting_field = 'Cutting'
# runoff_field = 'Runoff'
# dperc_field = 'DPerc'
# niwr_field = 'NIWR'
# kc_field = 'Kc'
# kcb_field = 'Kcb'
# Output field names
annual_et_field = 'Annual_ET'
seasonal_et_field = 'Seasonal_ET'
gs_start_doy_field = 'Start_DOY'
gs_end_doy_field = 'End_DOY'
gs_length_field = 'GS_Length'
# Number of header lines in data file
# header_lines = 2
# Additional figure controls
# figure_dynamic_size = False
# figure_ylabel_size = '12pt'
# Delimiter
sep = ','
# sep = r"\s*"
daily_input_re = re.compile(
'(?P<cell_id>\w+)_daily_crop_(?P<crop_num>\d{2}).csv', re.I)
# gs_input_re = re.compile(
# '(?P<cell_id>\w+)_gs_crop_(?P<crop_num>\d{2}).csv', re.I)
logging.info('\nGenerate crop summary maps from daily data')
logging.info(' INI: {}'.format(ini_path))
# Check that the INI file can be read
crop_et_sec = 'CROP_ET'
config = util.read_ini(ini_path, crop_et_sec)
# Get the project workspace and daily ET folder from the INI file
def get_config_param(config, param_name, section):
""""""
try:
param_value = config.get(section, param_name)
except:
logging.error(('ERROR: The {} parameter is not set' +
' in the INI file').format(param_name))
sys.exit()
return param_value
cells_path = get_config_param(config, 'cells_path', crop_et_sec)
project_ws = get_config_param(config, 'project_folder', crop_et_sec)
daily_stats_ws = os.path.join(
project_ws, get_config_param(config, 'daily_output_folder',
crop_et_sec))
try:
output_ws = os.path.join(
project_ws, config.get(crop_et_sec, 'summary_maps_folder'))
except:
if 'stats' in daily_stats_ws:
output_ws = daily_stats_ws.replace('stats', 'maps')
else:
output_ws = os.path.join(project_ws, 'summary_maps_folder')
# Check workspaces
if not os.path.isdir(daily_stats_ws):
logging.error(('\nERROR: The daily ET stats folder {0} ' +
'could be found\n').format(daily_stats_ws))
sys.exit()
if not os.path.isfile(cells_path):
logging.error(('\nERROR: The cells shapefile {0} ' +
'could be found\n').format(cells_path))
sys.exit()
if not os.path.isdir(output_ws):
os.mkdir(output_ws)
# Range of data to plot
try:
year_start = dt.datetime.strptime(start_date, '%Y-%m-%d').year
logging.info(' Start Year: {0}'.format(year_start))
except:
year_start = None
try:
year_end = dt.datetime.strptime(end_date, '%Y-%m-%d').year
logging.info(' End Year: {0}'.format(year_end))
except:
year_end = None
if year_start and year_end and year_end < year_start:
logging.error('\n ERROR: End date must be after start date\n')
sys.exit()
# Allow user to subset crops from INI
try:
crop_skip_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_skip_list'))))
except:
crop_skip_list = []
# crop_skip_list = [44, 45, 46]
try:
crop_test_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_test_list'))))
except:
crop_test_list = []
# Allow user to subset cells from INI
try:
cell_skip_list = config.get(crop_et_sec, 'cell_skip_list').split(',')
cell_skip_list = sorted([c.strip() for c in cell_skip_list])
except:
cell_skip_list = []
try:
cell_test_list = config.get(crop_et_sec, 'cell_test_list').split(',')
cell_test_list = sorted([c.strip() for c in cell_test_list])
except:
cell_test_list = []
# Overwrite INI crop list with user defined values
# Could also append to the INI crop list
if crop_str:
try:
crop_test_list = list(util.parse_int_set(crop_str))
# try:
# crop_test_list = sorted(list(set(
# crop_test_list + list(util.parse_int_set(crop_str)))
except:
pass
logging.debug('\n crop_test_list = {0}'.format(crop_test_list))
logging.debug(' crop_skip_list = {0}'.format(crop_skip_list))
logging.debug(' cell_test_list = {0}'.format(cell_test_list))
logging.debug(' cell_test_list = {0}'.format(cell_test_list))
# Build list of all daily ET files
daily_path_dict = defaultdict(dict)
for f_name in os.listdir(daily_stats_ws):
f_match = daily_input_re.match(os.path.basename(f_name))
if not f_match:
continue
cell_id = f_match.group('cell_id')
crop_num = int(f_match.group('crop_num'))
if f_match.group('cell_id') == 'test':
continue
elif crop_skip_list and crop_num in crop_skip_list:
continue
elif crop_test_list and crop_num not in crop_test_list:
continue
elif cell_skip_list and cell_id in cell_skip_list:
continue
elif cell_test_list and cell_id not in cell_test_list:
continue
else:
daily_path_dict[crop_num][cell_id] = os.path.join(
daily_stats_ws, f_name)
if not daily_path_dict:
logging.error(' ERROR: No daily ET files were found\n' +
' ERROR: Check the folder_name parameters\n')
sys.exit()
# Read ET Cells into memory with fiona and shapely
# Convert multi-polygons to list of polygons
cell_geom_dict = defaultdict(list)
cell_data_dict = dict()
cell_extent = []
with fiona.open(cells_path, "r") as cell_f:
cell_extent = cell_f.bounds[:]
# Fiona is printing a debug statement here "Index: N"
for item in cell_f:
cell_id = item['properties'][cell_id_field]
cell_data_dict[cell_id] = item['properties']
# Simplify the geometry
if simplify_tol is not None:
item_geom = shape(item['geometry']).simplify(
simplify_tol, preserve_topology=False)
else:
item_geom = shape(item['geometry'])
# Unpack multipolygons to lists of polygons
if item_geom.is_empty:
continue
elif item_geom.geom_type == 'MultiPolygon':
# Order the geometries from largest to smallest area
item_geom_list = sorted([[g.area, g]
for g in item_geom if not g.is_empty],
reverse=True)
for item_area, item_poly in item_geom_list:
cell_geom_dict[cell_id].append(item_poly)
elif item_geom.geom_type == 'Polygon':
cell_geom_dict[cell_id].append(item_geom)
else:
logging.error('Invalid geometry type')
continue
if not cell_geom_dict:
logging.error('ET Cell shapefile not read in')
sys.exit()
# Plot keyword arguments
plot_kwargs = {
'extent': cell_extent,
'fig_size': figure_size,
'fig_dpi': figure_dpi,
'save_flag': save_flag,
'show_flag': show_flag,
'label_flag': label_flag,
}
# Plot CELL_ID
logging.info('\nPlotting total crop acreage')
cell_id_dict = {k: k.replace(' ', '\n') for k in cell_data_dict.iterkeys()}
# cell_id_dict = {k:k for k in cell_data_dict.iterkeys()}
cell_plot_func(os.path.join(output_ws, 'cell_id.png'),
cell_geom_dict,
cell_id_dict,
cmap=None,
title_str='CELL_ID',
clabel_str='',
label_size=6,
**plot_kwargs)
# Plot total CDL crop acreages
logging.info('\nPlotting total crop acreage')
crop_area_dict = {
k: v[crop_area_field]
for k, v in cell_data_dict.iteritems()
}
# crop_area_dict = {
# :v[crop_area_field] for k,v in cell_data_dict.iteritems()
# v[crop_area_field] > area_threshold}
cell_plot_func(os.path.join(output_ws, 'total_crop_acreage.png'),
cell_geom_dict,
crop_area_dict,
cmap=cm.YlGn,
title_str='Total CDL Crop Area',
clabel_str='acres',
label_size=6,
**plot_kwargs)
# Plot PMETo
# pmeto_dict = {
# :v[crop_area_field]
# k,v in cell_data_dict.iteritems()}
# cell_plot_func(
# .path.join(output_ws, 'eto.png'),
# , pmeto_dict, cmap=cm.YlGn,
# ='Reference ET', clabel_str='mm',
# =8, **plot_kwargs)
# Build an empty dataframe to write the total area weighted ET
# columns_dict = {cell_id_field:sorted(cell_data_dict.keys())}
columns_dict = {
'CROP_{0:02d}'.format(k): None
for k in daily_path_dict.keys()
}
columns_dict[cell_id_field] = sorted(cell_data_dict.keys())
crop_area_df = pd.DataFrame(columns_dict).set_index(cell_id_field)
annual_et_df = pd.DataFrame(columns_dict).set_index(cell_id_field)
seasonal_et_df = pd.DataFrame(columns_dict).set_index(cell_id_field)
# First process by crop
logging.info('')
for crop_num in sorted(daily_path_dict.keys()):
crop_column = 'CROP_{0:02d}'.format(crop_num)
logging.info('Crop Num: {0:2d}'.format(crop_num))
# First threshold CDL crop areas
# Check all cell_id's against crop_area_dict keys
crop_area_dict = {
k: v[crop_column]
for k, v in cell_data_dict.iteritems()
if (k in daily_path_dict[crop_num].keys()
and v[crop_column] > area_threshold)
}
# crop_area_dict = {
# k: v[crop_column] for k,v in cell_data_dict.iteritems()
# if k in daily_path_dict[crop_num].keys()}
# Build an empty dataframe to write to
crop_output_df = pd.DataFrame({
cell_id_field:
sorted(
list(
set(daily_path_dict[crop_num].keys())
& set(crop_area_dict.keys()))),
annual_et_field:
None,
seasonal_et_field:
None,
gs_start_doy_field:
None,
gs_end_doy_field:
None,
gs_length_field:
None,
cutting_field:
None
})
crop_output_df.set_index(cell_id_field, inplace=True)
# Process each cell
for cell_id, input_path in sorted(daily_path_dict[crop_num].items()):
logging.debug(' Cell ID: {0}'.format(cell_id))
# Skip if crop area is below threshold
if cell_id not in crop_area_dict.keys():
logging.debug(' Area below threshold, skipping')
continue
# Get crop name from the first line of the output file
# DEADBEEF - This may not exist in the output file...
with open(input_path, 'r') as file_f:
crop_name = file_f.readline().split('-', 1)[1].strip()
crop_name = crop_name.replace('--', ' - ')
crop_name = crop_name.replace(' (', ' - ').replace(')', '')
logging.debug(' Crop: {0}'.format(crop_name))
logging.debug(' {0}'.format(os.path.basename(input_path)))
# Read data from file into record array (structured array)
daily_df = pd.read_table(input_path,
header=0,
comment='#',
sep=sep)
logging.debug(' Fields: {0}'.format(', '.join(
daily_df.columns.values)))
daily_df[date_field] = pd.to_datetime(daily_df[date_field])
daily_df.set_index(date_field, inplace=True)
# Build list of unique years
year_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' All Years: {0}'.format(', '.join(
list(util.ranges(year_array.tolist())))))
# logging.debug(' All Years: {0}'.format(
# ','.join(map(str, year_array.tolist()))))
# Don't include the first year in the stats
crop_year_start = min(daily_df[year_field])
logging.debug(
' Skipping {}, first year'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
# Check if start and end years have >= 365 days
crop_year_start = min(daily_df[year_field])
crop_year_end = max(daily_df[year_field])
if sum(daily_df[year_field] == crop_year_start) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
if sum(daily_df[year_field] == crop_year_end) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_end))
daily_df = daily_df[daily_df[year_field] < crop_year_end]
del crop_year_start, crop_year_end
# Only keep years between year_start and year_end
if year_start:
daily_df = daily_df[daily_df[year_field] >= year_start]
if year_end:
daily_df = daily_df[daily_df[year_field] <= year_end]
year_sub_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' Plot Years: {0}'.format(', '.join(
list(util.ranges(year_sub_array.tolist())))))
# logging.debug(' Plot Years: {0}'.format(
# ','.join(map(str, year_sub_array.tolist()))))
# Seasonal/Annual ET
crop_seasonal_et_df = daily_df[
daily_df[season_field] > 0].resample('AS',
how={etact_field: np.sum})
crop_annual_et_df = daily_df.resample('AS',
how={etact_field: np.sum})
crop_output_df.set_value(cell_id, seasonal_et_field,
float(crop_seasonal_et_df.mean()))
crop_output_df.set_value(cell_id, annual_et_field,
float(crop_annual_et_df.mean()))
del crop_seasonal_et_df, crop_annual_et_df
# Compute growing season start and end DOY from dailies
crop_gs_df = daily_df[[year_field, season_field
]].resample('AS', how={year_field: np.mean})
crop_gs_df[gs_start_doy_field] = None
crop_gs_df[gs_end_doy_field] = None
crop_gs_fields = [year_field, doy_field, season_field]
crop_gs_groupby = daily_df[crop_gs_fields].groupby([year_field])
for year, group in crop_gs_groupby:
if not np.any(group[season_field].values):
logging.debug(' Skipping, season flag was never set to 1')
continue
# Identify "changes" in season flag
season_diff = np.diff(group[season_field].values)
# Growing season start
try:
start_i = np.where(season_diff == 1)[0][0] + 1
gs_start_doy = float(group.ix[start_i, doy_field])
except:
gs_start_doy = float(min(group[doy_field].values))
crop_gs_df.set_value(group.index[0], gs_start_doy_field,
gs_start_doy)
# Growing season end
try:
end_i = np.where(season_diff == -1)[0][0] + 1
gs_end_doy = float(group.ix[end_i, doy_field])
except:
gs_end_doy = float(max(group[doy_field].values))
crop_gs_df.set_value(group.index[0], gs_end_doy_field,
gs_end_doy)
del season_diff
# Write mean growing season start and end DOY
crop_output_df.set_value(
cell_id, gs_start_doy_field,
int(round(crop_gs_df[gs_start_doy_field].mean(), 0)))
crop_output_df.set_value(
cell_id, gs_end_doy_field,
int(round(crop_gs_df[gs_end_doy_field].mean(), 0)))
# Growing season length
crop_output_df.set_value(
cell_id, gs_length_field,
int(round(crop_gs_groupby[season_field].sum().mean(), 0)))
# Crop cuttings
# Maybe only sum cuttings that are in season
if (cutting_field in list(daily_df.columns.values)
and np.any(daily_df[cutting_field].values)):
gs_input_fields = [year_field, cutting_field]
crop_gs_groupby = daily_df[gs_input_fields].groupby(
[year_field])
crop_output_df.set_value(
cell_id, cutting_field,
int(round(crop_gs_groupby[cutting_field].sum().mean(), 0)))
# Cleanup
del crop_gs_groupby, crop_gs_df, crop_gs_fields
# Make the maps
logging.debug('')
title_fmt = 'Crop {0:02d} - {1} - {2}'.format(crop_num, crop_name,
'{}')
# Crop acreages
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_cdl_acreage.png'.format(crop_num)),
cell_geom_dict,
crop_area_dict,
cmap=cm.YlGn,
clabel_str='acres',
title_str=title_fmt.format('CDL Area'),
**plot_kwargs)
# Annual/Seasonal ET
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_et_actual.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[annual_et_field].to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str=title_fmt.format('Annual Evapotranspiration'),
**plot_kwargs)
cell_plot_func(
os.path.join(output_ws,
'crop_{0:02d}_et_seasonal.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[seasonal_et_field].to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str=title_fmt.format('Seasonal Evapotranspiration'),
**plot_kwargs)
# Growing Season Start/End/Length
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_gs_start_doy.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[gs_start_doy_field].to_dict(),
cmap=cm.RdYlBu,
clabel_str='Day of Year',
title_str=title_fmt.format('Growing Season Start'),
**plot_kwargs)
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_gs_end_doy.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[gs_end_doy_field].to_dict(),
cmap=cm.RdYlBu_r,
clabel_str='Day of Year',
title_str=title_fmt.format('Growing Season End'),
**plot_kwargs)
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_gs_length.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[gs_length_field].to_dict(),
cmap=cm.RdYlBu_r,
clabel_str='Days',
title_str=title_fmt.format('Growing Season Length'),
**plot_kwargs)
# Crop cuttings
if np.any(crop_output_df[cutting_field].values):
cell_plot_func(os.path.join(
output_ws, 'crop_{0:02d}_cuttings.png'.format(crop_num)),
cell_geom_dict,
crop_output_df[cutting_field].to_dict(),
cmap=cm.RdYlBu_r,
clabel_str='Cuttings',
title_str=title_fmt.format('Crop Cuttings'),
**plot_kwargs)
# Crop area weighted ET
crop_area_df[crop_column] = pd.Series(crop_area_dict)
annual_et_df[crop_column] = crop_output_df[annual_et_field]
seasonal_et_df[crop_column] = crop_output_df[seasonal_et_field]
# Compute and plot crop weighted average ET
annual_et = ((annual_et_df * crop_area_df).sum(axis=1) /
crop_area_df.sum(axis=1))
seasonal_et = ((seasonal_et_df * crop_area_df).sum(axis=1) /
crop_area_df.sum(axis=1))
cell_plot_func(
os.path.join(output_ws, 'et_actual.png'),
cell_geom_dict,
annual_et[annual_et.notnull()].to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str='Crop Area Weighted Annual Evapotranspiration',
**plot_kwargs)
cell_plot_func(
os.path.join(output_ws, 'et_seasonal.png'),
cell_geom_dict,
seasonal_et[seasonal_et.notnull()].to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str='Crop Area Weighted Seasonal Evapotranspiration',
**plot_kwargs)
del annual_et, seasonal_et
# Cleanup
del crop_output_df
gc.collect()
# Compute and plot crop weighted average ET
annual_et_df *= crop_area_df
seasonal_et_df *= crop_area_df
annual_et_df = annual_et_df.sum(axis=1) / crop_area_df.sum(axis=1)
seasonal_et_df = seasonal_et_df.sum(axis=1) / crop_area_df.sum(axis=1)
annual_et_df = annual_et_df[annual_et_df.notnull()]
seasonal_et_df = seasonal_et_df[seasonal_et_df.notnull()]
cell_plot_func(os.path.join(output_ws, 'et_actual.png'),
cell_geom_dict,
annual_et_df.to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str='Crop Area Weighted Annual Evapotranspiration',
**plot_kwargs)
cell_plot_func(os.path.join(output_ws, 'et_seasonal.png'),
cell_geom_dict,
seasonal_et_df.to_dict(),
cmap=cm.YlGn,
clabel_str='mm',
title_str='Crop Area Weighted Seasonal Evapotranspiration',
**plot_kwargs)
# Cleanup
del crop_area_df, annual_et_df, seasonal_et_df
def main(ini_path,
figure_show_flag=False,
figure_save_flag=True,
figure_size=(1000, 300),
start_date=None,
end_date=None,
crop_str=''):
"""Plot full daily data by crop
Args:
ini_path (str): file path ofproject INI file
figure_show_flag (bool): if True, show figures
figure_save_flag (bool): if True, save figures
figure_size (tuple): width, height of figure in pixels
start_date (str): ISO format date string (YYYY-MM-DD)
end_date (str): ISO format date string (YYYY-MM-DD)
crop_str (str): comma separate list or range of crops to compare
Returns:
None
"""
# Input/output names
# input_folder = 'daily_stats'
# output_folder = 'daily_plots'
# Only process subset of crops
crop_keep_list = list(util.parse_int_set(crop_str))
# These crops will not be processed (if set)
crop_skip_list = [44, 45, 46]
# Input field names
date_field = 'Date'
doy_field = 'DOY'
year_field = 'Year'
# month_field = 'Month'
# day_field = 'Day'
# pmeto_field = 'PMETo'
precip_field = 'PPT'
# t30_field = 'T30'
etact_field = 'ETact'
etpot_field = 'ETpot'
etbas_field = 'ETbas'
irrig_field = 'Irrigation'
season_field = 'Season'
runoff_field = 'Runoff'
dperc_field = 'DPerc'
# niwr_field = 'NIWR'
# Number of header lines in data file
# header_lines = 2
# Additional figure controls
# figure_dynamic_size = False
figure_ylabel_size = '12pt'
# Delimiter
sep = ','
# sep = r"\s*"
sub_x_range_flag = True
logging.info('\nPlot mean daily data by crop')
logging.info(' INI: {}'.format(ini_path))
# Check that INI file can be read
crop_et_sec = 'CROP_ET'
config = util.read_ini(ini_path, crop_et_sec)
# Get to get project workspace and daily ET folder from INI file
# project workspace can use old or new ini file
try:
project_ws = config.get('PROJECT', 'project_folder')
except:
try:
project_ws = config.get(crop_et_sec, 'project_folder')
except:
logging.error('ERROR: project_folder ' +
'parameter is not set in INI file')
sys.exit()
try:
input_ws = os.path.join(project_ws,
config.get(crop_et_sec, 'daily_output_folder'))
except:
logging.error('ERROR:daily_output_folder ' +
'parameter is not set inINI file')
sys.exit()
try:
output_ws = os.path.join(project_ws,
config.get(crop_et_sec, 'daily_plots_folder'))
except:
if 'stats' in input_ws:
output_ws = input_ws.replace('stats', 'plots')
else:
output_ws = os.path.join(project_ws, 'daily_stats_folder')
# Check workspaces
if not os.path.isdir(input_ws):
logging.error(('\nERROR:input ET folder {0} ' +
'could be found\n').format(input_ws))
sys.exit()
if not os.path.isdir(output_ws):
os.mkdir(output_ws)
try:
name_format = config.get(crop_et_sec, 'name_format')
if name_format is None or name_format == 'None':
# name_format = '%s_daily_crop_%c.csv'
name_format = '%s_crop_%c.csv'
except:
# name_format = '%s_daily_crop_%c.csv'
name_format = '%s_crop_%c.csv'
if '%s' not in name_format or '%c' not in name_format:
logging.error(
"crop et file name format requires '%s' and '%c' wildcards.")
sys.exit()
swl = name_format.index('%s')
cwl = name_format.index('%c')
prefix = name_format[(swl + 2):cwl]
suffix = name_format[(cwl + 2):len(name_format)]
suf_no_ext = suffix[:(suffix.index('.'))]
# Range of data to plot
try:
year_start = dt.datetime.strptime(start_date, '%Y-%m-%d').year
logging.info(' Start Year: {0}'.format(year_start))
except:
year_start = None
try:
year_end = dt.datetime.strptime(end_date, '%Y-%m-%d').year
logging.info(' End Year: {0}'.format(year_end))
except:
year_end = None
if year_start and year_end and year_end < year_start:
logging.error('\n ERROR: End date must be after start date\n')
sys.exit()
# # Windows only a
# if figure_dynamic_size:
# :
# logging.info('Setting plots width/height dynamically')
# from win32api import GetSystemMetrics
# figure_width = int(0.92 * GetSystemMetrics(0))
# figure_height = int(0.28 * GetSystemMetrics(1))
# logging.info(' {0} {1}'.format(GetSystemMetrics(0), GetSystemMetrics(1)))
# logging.info(' {0} {1}'.format(figure_width, figure_height))
# :
# figure_width = 1200
# figure_height = 300
# make used file list using name_format attributes
data_file_list = []
for item in os.listdir(input_ws):
if prefix in item and suffix in item:
if not item in data_file_list:
data_file_list.append(os.path.join(input_ws, item))
if len(data_file_list) < 1:
logging.info('No files found')
sys.exit()
data_file_list = sorted(data_file_list)
# Process each file
for file_count, file_path in enumerate(data_file_list):
file_name = os.path.basename(file_path)
logging.debug('')
logging.info(' Processing {0}'.format(file_name))
station, crop_num = os.path.splitext(file_name)[0].split(prefix)
# crop_num = int(crop_num[:crop_num.index(suf_no_ext)])
crop_num = int(crop_num)
logging.debug(' Station: {0}'.format(station))
logging.debug(' Crop Num: {0}'.format(crop_num))
if station == 'temp':
logging.debug(' Skipping')
continue
elif crop_skip_list and crop_num in crop_skip_list:
logging.debug(' Skipping, crop number in crop_skip_list')
continue
elif crop_keep_list and crop_num not in crop_keep_list:
logging.debug(' Skipping, crop number not in crop_keep_list')
continue
# Get crop name
with open(file_path, 'r') as file_f:
crop_name = file_f.readline().split('-', 1)[1].strip()
logging.debug(' Crop: {0}'.format(crop_name))
# Read data from file into record array (structured array)
daily_df = pd.read_csv(file_path, header=0, comment='#', sep=sep)
logging.debug(' Fields: {0}'.format(', '.join(
daily_df.columns.values)))
daily_df[date_field] = pd.to_datetime(daily_df[date_field])
# workaround for data before 1970 on a pc
if not year_start or year_start < 1970:
# test if a pc
if os.getenv('OS') is not None and os.getenv('OS') == 'Windows_NT':
# check if data exist before 1970
data_sy = daily_df[date_field][0].year
if data_sy < 1970:
# add multiple of 4 years to actual dates
years_to_add = 1970 - data_sy + ((1970 - data_sy) % 4)
daily_df[date_field] = daily_df[date_field] + pd.Timedelta(
days=int(years_to_add * 365.25))
if file_count == 0:
logging.info(' Added {0} years to input dates'.format(
years_to_add))
if year_start and file_count == 0:
year_start += years_to_add
if year_end and file_count == 0:
year_end += years_to_add
del years_to_add
del data_sy
daily_df.set_index(date_field, inplace=True)
daily_df[year_field] = daily_df.index.year
# daily_df[year_field] = daily_df[date_field].map(lambda x: x.year)
#Get PMET type from fieldnames in daily .csv
field_names = daily_df.columns
PMET_str = field_names[4]
# if 'PMETr' in field_names:
# PMET_str='PMETr'
# else:
# PMET_str='PMETo'
# Build list of unique years
year_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' All Years: {0}'.format(', '.join(
list(util.ranges(year_array.tolist())))))
# Don't include first year in plots
crop_year_start = min(daily_df[year_field])
logging.debug(' Skipping {}, first year'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
# Check if start and end years have >= 365 days
crop_year_start = min(daily_df[year_field])
crop_year_end = max(daily_df[year_field])
if sum(daily_df[year_field] == crop_year_start) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
if sum(daily_df[year_field] == crop_year_end) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_end))
daily_df = daily_df[daily_df[year_field] < crop_year_end]
# Only keep years between year_start and year_end
# Adjust crop years
if year_start:
daily_df = daily_df[daily_df[year_field] >= year_start]
crop_year_start = max(year_start, crop_year_start)
if year_end:
daily_df = daily_df[daily_df[year_field] <= year_end]
crop_year_end = min(year_end, crop_year_end)
year_sub_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' Plot Years: {0}'.format(', '.join(
list(util.ranges(year_sub_array.tolist())))))
# Initial range of time series to show
# For now default to last ~8 year
if sub_x_range_flag:
x_range = Range1d(
np.datetime64(
dt.datetime(max(crop_year_end - 9, crop_year_start), 1, 1),
's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1), 's'),
bounds=(np.datetime64(dt.datetime(crop_year_start, 1, 1), 's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1),
's')))
else:
x_range = Range1d(
np.datetime64(dt.datetime(crop_year_start, 1, 1), 's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1), 's'))
# Build separate arrays for each field of non-crop specific data
dt_array = daily_df.index.date
doy_array = daily_df[doy_field].values.astype(np.int)
pmet_array = daily_df[PMET_str].values
precip_array = daily_df[precip_field].values
# Remove leap days
# leap_array = (doy_array == 366)
# doy_sub_array = np.delete(doy_array, np.where(leap_array)[0])
# Build separate arrays for each set of crop specific fields
etact_array = daily_df[etact_field].values
etpot_array = daily_df[etpot_field].values
etbas_array = daily_df[etbas_field].values
irrig_array = daily_df[irrig_field].values
season_array = daily_df[season_field].values
runoff_array = daily_df[runoff_field].values
dperc_array = daily_df[dperc_field].values
kc_array = etact_array / pmet_array
kcb_array = etbas_array / pmet_array
# NIWR is ET - precip + runoff + deep percolation
# Don't include deep percolation when irrigating
# niwr_array = etact_array - (precip_array - runoff_array)
# niwr_array[irrig_array==0] += dperc_array[irrig_array == 0]
# Remove leap days
# etact_sub_array = np.delete(etact_array, np.where(leap_array)[0])
# niwr_sub_array = np.delete(niwr_array, np.where(leap_array)[0])
# Time series figures of daily data
output_name = '{0}_crop_{1:02d}_{2}-{3}'.format(
station, int(crop_num), crop_year_start, crop_year_end)
output_path = os.path.join(output_ws, output_name + '.html')
f = output_file(output_path, title=output_name)
TOOLS = 'xpan,xwheel_zoom,box_zoom,reset,save'
f1 = figure(x_axis_type='datetime',
x_range=x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
# title='Evapotranspiration', x_axis_type='datetime',
f1.line(dt_array, etact_array, color='blue', legend_label='ETact')
f1.line(dt_array, etbas_array, color='green', legend_label='ETbas')
f1.line(dt_array,
pmet_array,
color='black',
legend_label=PMET_str,
line_dash="dotted")
# line_dash="dashdot")
# f1.title = 'Evapotranspiration [mm]'
f1.grid.grid_line_alpha = 0.3
f1.yaxis.axis_label = 'Evapotranspiration [mm]'
f1.yaxis.axis_label_text_font_size = figure_ylabel_size
f2 = figure(x_axis_type="datetime",
x_range=f1.x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
f2.line(dt_array, kc_array, color='blue', legend_label='Kc')
f2.line(dt_array, kcb_array, color='green', legend_label='Kcb')
f2.line(dt_array,
season_array,
color='black',
legend_label='Season',
line_dash="dashed")
f2.grid.grid_line_alpha = 0.3
f2.yaxis.axis_label = 'Kc and Kcb (dimensionless)'
f2.yaxis.axis_label_text_font_size = figure_ylabel_size
f3 = figure(x_axis_type="datetime",
x_range=f1.x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
f3.line(dt_array, precip_array, color='blue', legend_label='PPT')
f3.line(dt_array,
irrig_array,
color='black',
legend_label='Irrigation',
line_dash="dotted")
f3.grid.grid_line_alpha = 0.3
f3.yaxis.axis_label = 'PPT and Irrigation [mm]'
f3.yaxis.axis_label_text_font_size = figure_ylabel_size
if figure_save_flag:
# save(column([f1, f2, f3], sizing_mode = 'stretch_both'))
save(column([f1, f2, f3], sizing_mode='stretch_both'),
validate=True)
if figure_show_flag:
# Open in browser
show(column([f1, f2, f3], sizing_mode='stretch_both'))
# Cleanup
del f1, f2, f3, f
del etact_array, etpot_array, etbas_array
del irrig_array, season_array
del runoff_array, dperc_array
del kc_array, kcb_array
del file_path
del dt_array, year_array, year_sub_array, doy_array
del pmet_array
del precip_array
gc.collect()
def main(ini_path,
figure_show_flag=False,
figure_save_flag=True,
figure_size=(1000, 300),
start_date=None,
end_date=None,
crop_str='',
overwrite_flag=False):
"""Plot full daily data by crop
Args:
ini_path (str): file path of the project INI file
figure_show_flag (bool): if True, show figures
figure_save_flag (bool): if True, save figures
figure_size (tuple): width, height of figure in pixels
start_date (str): ISO format date string (YYYY-MM-DD)
end_date (str): ISO format date string (YYYY-MM-DD)
crop_str (str): comma separate list or range of crops to compare
overwrite_flag (bool): If True, overwrite existing files
Returns:
None
"""
# Input/output names
# input_folder = 'daily_stats'
# output_folder = 'daily_plots'
# Only process a subset of the crops
crop_keep_list = list(util.parse_int_set(crop_str))
# These crops will not be processed (if set)
crop_skip_list = [44, 45, 46]
# Input field names
date_field = 'Date'
doy_field = 'DOY'
year_field = 'Year'
# month_field = 'Month'
# day_field = 'Day'
pmeto_field = 'PMETo'
precip_field = 'PPT'
# t30_field = 'T30'
etact_field = 'ETact'
etpot_field = 'ETpot'
etbas_field = 'ETbas'
irrig_field = 'Irrigation'
season_field = 'Season'
runoff_field = 'Runoff'
dperc_field = 'DPerc'
# niwr_field = 'NIWR'
# Number of header lines in data file
# header_lines = 2
# Additional figure controls
# figure_dynamic_size = False
figure_ylabel_size = '12pt'
# Delimiter
sep = ','
# sep = r"\s*"
sub_x_range_flag = True
logging.info('\nPlot mean daily data by crop')
logging.info(' INI: {}'.format(ini_path))
# Check that the INI file can be read
crop_et_sec = 'CROP_ET'
config = util.read_ini(ini_path, crop_et_sec)
# Get the project workspace and daily ET folder from the INI file
try:
project_ws = config.get(crop_et_sec, 'project_folder')
except:
logging.error('ERROR: The project_folder ' +
'parameter is not set in the INI file')
sys.exit()
try:
input_ws = os.path.join(project_ws,
config.get(crop_et_sec, 'daily_output_folder'))
except:
logging.error('ERROR: The daily_output_folder ' +
'parameter is not set in the INI file')
sys.exit()
try:
output_ws = os.path.join(project_ws,
config.get(crop_et_sec, 'daily_plots_folder'))
except:
if 'stats' in input_ws:
output_ws = input_ws.replace('stats', 'plots')
else:
output_ws = os.path.join(project_ws, 'daily_stats_folder')
# Check workspaces
if not os.path.isdir(input_ws):
logging.error(('\nERROR: The input ET folder {0} ' +
'could be found\n').format(input_ws))
sys.exit()
if not os.path.isdir(output_ws):
os.mkdir(output_ws)
# Range of data to plot
try:
year_start = dt.datetime.strptime(start_date, '%Y-%m-%d').year
logging.info(' Start Year: {0}'.format(year_start))
except:
year_start = None
try:
year_end = dt.datetime.strptime(end_date, '%Y-%m-%d').year
logging.info(' End Year: {0}'.format(year_end))
except:
year_end = None
if year_start and year_end and year_end < year_start:
logging.error('\n ERROR: End date must be after start date\n')
sys.exit()
# # Windows only a
# if figure_dynamic_size:
# :
# logging.info('Setting plots width/height dynamically')
# from win32api import GetSystemMetrics
# figure_width = int(0.92 * GetSystemMetrics(0))
# figure_height = int(0.28 * GetSystemMetrics(1))
# logging.info(' {0} {1}'.format(GetSystemMetrics(0), GetSystemMetrics(1)))
# logging.info(' {0} {1}'.format(figure_width, figure_height))
# :
# figure_width = 1200
# figure_height = 300
# Regular expressions
data_re = re.compile('(?P<CELLID>\w+)_daily_crop_(?P<CROP>\d+).csv$', re.I)
# Build list of all data files
data_file_list = sorted([
os.path.join(input_ws, f_name) for f_name in os.listdir(input_ws)
if data_re.match(f_name)
])
if not data_file_list:
logging.error(' ERROR: No daily ET files were found\n' +
' ERROR: Check the folder_name parameters\n')
sys.exit()
# Process each file
for file_path in data_file_list:
file_name = os.path.basename(file_path)
logging.debug('')
logging.info(' {0}'.format(file_name))
station, crop_num = os.path.splitext(file_name)[0].split(
'_daily_crop_')
crop_num = int(crop_num)
logging.debug(' Station: {0}'.format(station))
logging.debug(' Crop Num: {0}'.format(crop_num))
if station == 'temp':
logging.debug(' Skipping')
continue
elif crop_skip_list and crop_num in crop_skip_list:
logging.debug(' Skipping, crop number in crop_skip_list')
continue
elif crop_keep_list and crop_num not in crop_keep_list:
logging.debug(' Skipping, crop number not in crop_keep_list')
continue
# Get crop name
with open(file_path, 'r') as file_f:
crop_name = file_f.readline().split('-', 1)[1].strip()
logging.debug(' Crop: {0}'.format(crop_name))
# Read data from file into record array (structured array)
daily_df = pd.read_table(file_path, header=0, comment='#', sep=sep)
logging.debug(' Fields: {0}'.format(', '.join(
daily_df.columns.values)))
daily_df[date_field] = pd.to_datetime(daily_df[date_field])
daily_df.set_index(date_field, inplace=True)
daily_df[year_field] = daily_df.index.year
# daily_df[year_field] = daily_df[date_field].map(lambda x: x.year)
# Build list of unique years
year_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' All Years: {0}'.format(', '.join(
list(util.ranges(year_array.tolist())))))
# logging.debug(' All Years: {0}'.format(
# ','.join(map(str, year_array.tolist()))))
# Don't include the first year in the stats
crop_year_start = min(daily_df[year_field])
logging.debug(' Skipping {}, first year'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
# Check if start and end years have >= 365 days
crop_year_start = min(daily_df[year_field])
crop_year_end = max(daily_df[year_field])
if sum(daily_df[year_field] == crop_year_start) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
if sum(daily_df[year_field] == crop_year_end) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_end))
daily_df = daily_df[daily_df[year_field] < crop_year_end]
# Only keep years between year_start and year_end
# Adjust crop years
if year_start:
daily_df = daily_df[daily_df[year_field] >= year_start]
crop_year_start = max(year_start, crop_year_start)
if year_end:
daily_df = daily_df[daily_df[year_field] <= year_end]
crop_year_end = min(year_end, crop_year_end)
year_sub_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' Plot Years: {0}'.format(', '.join(
list(util.ranges(year_sub_array.tolist())))))
# logging.debug(' Plot Years: {0}'.format(
# ','.join(map(str, year_sub_array.tolist()))))
# Initial range of timeseries to show
# For now default to last ~8 year
if sub_x_range_flag:
x_range = Range1d(
np.datetime64(
dt.datetime(max(crop_year_end - 9, crop_year_start), 1, 1),
's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1), 's'),
bounds=(np.datetime64(dt.datetime(crop_year_start, 1, 1), 's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1),
's')))
else:
x_range = Range1d(
np.datetime64(dt.datetime(crop_year_start, 1, 1), 's'),
np.datetime64(dt.datetime(crop_year_end + 1, 1, 1), 's'))
# Build separate arrays for each field of non-crop specific data
dt_array = daily_df.index.date
doy_array = daily_df[doy_field].values.astype(np.int)
pmeto_array = daily_df[pmeto_field].values
precip_array = daily_df[precip_field].values
# Remove leap days
# leap_array = (doy_array == 366)
# doy_sub_array = np.delete(doy_array, np.where(leap_array)[0])
# Build separate arrays for each set of crop specific fields
etact_array = daily_df[etact_field].values
etpot_array = daily_df[etpot_field].values
etbas_array = daily_df[etbas_field].values
irrig_array = daily_df[irrig_field].values
season_array = daily_df[season_field].values
runoff_array = daily_df[runoff_field].values
dperc_array = daily_df[dperc_field].values
kc_array = etact_array / pmeto_array
kcb_array = etbas_array / pmeto_array
# NIWR is ET - precip + runoff + deep percolation
# Don't include deep percolation when irrigating
# niwr_array = etact_array - (precip_array - runoff_array)
# niwr_array[irrig_array==0] += dperc_array[irrig_array == 0]
# Remove leap days
# etact_sub_array = np.delete(etact_array, np.where(leap_array)[0])
# niwr_sub_array = np.delete(niwr_array, np.where(leap_array)[0])
# Timeseries figures of daily data
output_name = '{0}_crop_{1:02d}_{2}-{3}'.format(
station, int(crop_num), crop_year_start, crop_year_end)
output_path = os.path.join(output_ws, output_name + '.html')
if overwrite_flag and os.path.isfile(output_path):
os.remove(output_path)
f = output_file(output_path, title=output_name)
TOOLS = 'xpan,xwheel_zoom,box_zoom,reset,save'
f1 = figure(x_axis_type='datetime',
x_range=x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
# title='Evapotranspiration', x_axis_type='datetime',
f1.line(dt_array, etact_array, color='blue', legend='ETact')
f1.line(dt_array, etbas_array, color='green', legend='ETbas')
f1.line(dt_array,
pmeto_array,
color='black',
legend='ETos',
line_dash="dotted")
# line_dash="dashdot")
# f1.title = 'Evapotranspiration [mm]'
f1.grid.grid_line_alpha = 0.3
f1.yaxis.axis_label = 'Evapotranspiration [mm]'
f1.yaxis.axis_label_text_font_size = figure_ylabel_size
# f1.xaxis.bounds = x_bounds
f2 = figure(x_axis_type="datetime",
x_range=f1.x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
f2.line(dt_array, kc_array, color='blue', legend='Kc')
f2.line(dt_array, kcb_array, color='green', legend='Kcb')
f2.line(dt_array,
season_array,
color='black',
legend='Season',
line_dash="dashed")
# f2.title = 'Kc and Kcb (dimensionless)'
f2.grid.grid_line_alpha = 0.3
f2.yaxis.axis_label = 'Kc and Kcb (dimensionless)'
f2.yaxis.axis_label_text_font_size = figure_ylabel_size
f3 = figure(x_axis_type="datetime",
x_range=f1.x_range,
width=figure_size[0],
height=figure_size[1],
tools=TOOLS,
toolbar_location="right",
active_scroll="xwheel_zoom")
f3.line(dt_array, precip_array, color='blue', legend='PPT')
f3.line(dt_array,
irrig_array,
color='black',
legend='Irrigation',
line_dash="dotted")
# f3.title = 'PPT and Irrigation [mm]'
f3.grid.grid_line_alpha = 0.3
# f3.xaxis.axis_label = 'Date'
f3.yaxis.axis_label = 'PPT and Irrigation [mm]'
f3.yaxis.axis_label_text_font_size = figure_ylabel_size
if figure_show_flag:
# Open in a browser
show(column([f1, f2, f3], sizing_mode='stretch_both'))
# show(vplot(f1, f2, f3))
if figure_save_flag:
save(column([f1, f2, f3], sizing_mode='stretch_both'))
# save(vplot(f1, f2, f3))
del f1, f2, f3, f
# Cleanup
del etact_array, etpot_array, etbas_array
del irrig_array, season_array
del runoff_array, dperc_array
del kc_array, kcb_array
# del niwr_array
# del etact_sub_array, niwr_sub_array
# Cleanup
del file_path, daily_df
del dt_array, year_array, year_sub_array, doy_array
del pmeto_array
del precip_array
gc.collect()
def main(ini_path,
zone_type='huc8',
area_threshold=10,
dairy_cuttings=5,
beef_cuttings=4,
crop_str='',
remove_empty_flag=True,
overwrite_flag=False,
cleanup_flag=False):
"""Build a feature class for each crop and set default crop parameters
Apply the values in the CropParams.txt as defaults to every cell
Args:
ini_path (str): file path of the project INI file
zone_type (str): Zone type (huc8, huc10, county)
area_threshold (float): CDL area threshold [acres]
dairy_cuttings (int): Initial number of dairy hay cuttings
beef_cuttings (int): Initial number of beef hay cuttings
crop_str (str): comma separate list or range of crops to compare
overwrite_flag (bool): If True, overwrite existing output rasters
cleanup_flag (bool): If True, remove temporary files
Returns:
None
"""
logging.info('\nCalculating ET-Demands Spatial Crop Parameters')
remove_empty_flag = True
# Input paths
# DEADBEEF - For now, get cropET folder from INI file
# This function may eventually be moved into the main cropET code
config = util.read_ini(ini_path, section='CROP_ET')
crop_et_sec = 'CROP_ET'
project_ws = config.get(crop_et_sec, 'project_folder')
gis_ws = config.get(crop_et_sec, 'gis_folder')
cells_path = config.get(crop_et_sec, 'cells_path')
# try: cells_path = config.get(crop_et_sec, 'cells_path')
# except: cells_path = os.path.join(gis_ws, 'ETCells.shp')
stations_path = config.get(crop_et_sec, 'stations_path')
crop_et_ws = config.get(crop_et_sec, 'crop_et_folder')
bin_ws = os.path.join(crop_et_ws, 'bin')
try:
template_ws = config.get(crop_et_sec, 'template_folder')
except:
template_ws = os.path.join(os.path.dirname(crop_et_ws), 'static')
try:
calibration_ws = config.get(crop_et_sec, 'spatial_cal_folder')
except:
calibration_ws = os.path.join(project_ws, 'calibration')
# Sub folder names
static_ws = os.path.join(project_ws, 'static')
pmdata_ws = os.path.join(project_ws, 'pmdata')
crop_params_path = os.path.join(static_ws, 'CropParams.txt')
# Input units
cell_elev_units = 'FEET'
station_elev_units = 'FEET'
# Field names
cell_id_field = 'CELL_ID'
cell_name_field = 'CELL_NAME'
crop_acres_field = 'CROP_ACRES'
dairy_cutting_field = 'Dairy_Cut'
beef_cutting_field = 'Beef_Cut'
# Only keep the following ET Cell fields
keep_field_list = [cell_id_field, cell_name_field, 'AG_ACRES']
# keep_field_list = ['NLDAS_ID', 'CELL_ID', 'HUC8', 'COUNTY', 'AG_ACRES']
# keep_field_list = ['FIPS', 'COUNTY']
# The maximum crop name was ~50 characters
string_field_len = 50
# Check input folders
if not os.path.isdir(crop_et_ws):
logging.error(('ERROR: The INI cropET folder ' +
'does not exist\n {}').format(crop_et_ws))
sys.exit()
elif not os.path.isdir(bin_ws):
logging.error('\nERROR: The Bin workspace {0} ' +
'does not exist\n'.format(bin_ws))
sys.exit()
elif not os.path.isdir(project_ws):
logging.error(('ERROR: The project folder ' +
'does not exist\n {}').format(project_ws))
sys.exit()
elif not os.path.isdir(gis_ws):
logging.error(
('ERROR: The GIS folder ' + 'does not exist\n {}').format(gis_ws))
sys.exit()
if '.gdb' not in calibration_ws and not os.path.isdir(calibration_ws):
os.makedirs(calibration_ws)
logging.info('\nGIS Workspace: {0}'.format(gis_ws))
logging.info('Project Workspace: {0}'.format(project_ws))
logging.info('CropET Workspace: {0}'.format(crop_et_ws))
logging.info('Bin Workspace: {0}'.format(bin_ws))
logging.info('Calib. Workspace: {0}'.format(calibration_ws))
# Check input files
if not os.path.isfile(crop_params_path):
logging.error('\nERROR: The crop parameters file {} ' +
'does not exist\n'.format(crop_params_path))
sys.exit()
elif not arcpy.Exists(cells_path):
logging.error(('\nERROR: The ET Cell shapefile {} ' +
'does not exist\n').format(cells_path))
sys.exit()
elif not os.path.isfile(stations_path) or not arcpy.Exists(stations_path):
logging.error(('ERROR: The NLDAS station shapefile ' +
'does not exist\n %s').format(stations_path))
sys.exit()
logging.debug('Crop Params Path: {0}'.format(crop_params_path))
logging.debug('ET Cells Path: {0}'.format(cells_path))
logging.debug('Stations Path: {0}'.format(stations_path))
# For now, only allow calibration parameters in separate shapefiles
ext = '.shp'
# # Build output geodatabase if necessary
# if calibration_ws.endswith('.gdb'):
# .debug('GDB Path: {0}'.format(calibration_ws))
# = ''
# arcpy.Exists(calibration_ws) and overwrite_flag:
# try: arcpy.Delete_management(calibration_ws)
# except: pass
# calibration_ws is not None and not arcpy.Exists(calibration_ws):
# arcpy.CreateFileGDB_management(
# os.path.dirname(calibration_ws),
# os.path.basename(calibration_ws))
# else:
# = '.shp'
# Field Name, Property, Field Type
# Property is the string of the CropParameter class property value
# It will be used to access the property using getattr
dairy_cutting_field = 'Dairy_Cut'
beef_cutting_field = 'Beef_Cut'
param_list = [
# ['Name', 'name', 'STRING'],
# ['ClassNum', 'class_number', 'LONG'],
# ['IsAnnual', 'is_annual', 'SHORT'],
# ['IrrigFlag', 'irrigation_flag', 'SHORT'],
# ['IrrigDays', 'days_after_planting_irrigation', 'LONG'],
# ['Crop_FW', 'crop_fw', 'LONG'],
# ['WinterCov', 'winter_surface_cover_class', 'SHORT'],
# ['CropKcMax', 'kc_max', 'FLOAT'],
['MAD_Init', 'mad_initial', 'LONG'],
['MAD_Mid', 'mad_midseason', 'LONG'],
# ['RootDepIni', 'rooting_depth_initial', 'FLOAT'],
# ['RootDepMax', 'rooting_depth_max', 'FLOAT'],
# ['EndRootGrw', 'end_of_root_growth_fraction_time', 'FLOAT'],
# ['HeightInit', 'height_initial', 'FLOAT'],
# ['HeightMax', 'height_max', 'FLOAT'],
# ['CurveNum', 'curve_number', 'LONG'],
# ['CurveName', 'curve_name', 'STRING'],
# ['CurveType', 'curve_type', 'SHORT'],
# ['PL_GU_Flag', 'flag_for_means_to_estimate_pl_or_gu', 'SHORT'],
['T30_CGDD', 't30_for_pl_or_gu_or_cgdd', 'FLOAT'],
['PL_GU_Date', 'date_of_pl_or_gu', 'FLOAT'],
['CGDD_Tbase', 'tbase', 'FLOAT'],
['CGDD_EFC', 'cgdd_for_efc', 'LONG'],
['CGDD_Term', 'cgdd_for_termination', 'LONG'],
['Time_EFC', 'time_for_efc', 'LONG'],
['Time_Harv', 'time_for_harvest', 'LONG'],
['KillFrostC', 'killing_frost_temperature', 'Float'],
# ['InvokeStrs', 'invoke_stress', 'SHORT'],
# ['CN_Coarse', 'cn_coarse_soil', 'LONG'],
# ['CN_Medium', 'cn_medium_soil', 'LONG'],
# ['CN_Fine', 'cn_fine_soil', 'LONG']
]
# if calibration_ws.endswith('.gdb'):
# _cutting_field = 'Dairy_Cuttings'
# _cutting_field = 'Beef_Cuttings'
# _list = [
# # ['Name', 'name', 'STRING'],
# # ['Class_Number', 'class_number', 'LONG'],
# # ['Is_Annual', 'is_annual', 'SHORT'],
# # ['Irrigation_Flag', 'irrigation_flag', 'SHORT'],
# # ['Irrigation_Days', 'days_after_planting_irrigation', 'LONG'],
# # ['Crop_FW', 'crop_fw', 'LONG'],
# # ['Winter_Cover_Class', 'winter_surface_cover_class', 'SHORT'],
# # ['Crop_Kc_Max', 'kc_max', 'FLOAT'],
# # ['MAD_Initial', 'mad_initial', 'LONG'],
# # ['MAD_Midseason', 'mad_midseason', 'LONG'],
# # ['Root_Depth_Ini', 'rooting_depth_initial', 'FLOAT'],
# # ['Root_Depth_Max', 'rooting_depth_max', 'FLOAT'],
# # ['End_Root_Growth', 'end_of_root_growth_fraction_time', 'FLOAT'],
# # ['Height_Initial', 'height_initial', 'FLOAT'],
# # ['Height_Maximum', 'height_max', 'FLOAT'],
# # ['Curve_Number', 'curve_number', 'LONG'],
# # ['Curve_Name', 'curve_name', 'STRING'],
# # ['Curve_Type', 'curve_type', 'SHORT'],
# # ['PL_GU_Flag', 'flag_for_means_to_estimate_pl_or_gu', 'SHORT'],
# ['T30_CGDD', 't30_for_pl_or_gu_or_cgdd', 'FLOAT'],
# ['PL_GU_Date', 'date_of_pl_or_gu', 'FLOAT'],
# ['CGDD_Tbase', 'tbase', 'FLOAT'],
# ['CGDD_EFC', 'cgdd_for_efc', 'LONG'],
# ['CGDD_Termination', 'cgdd_for_termination', 'LONG'],
# ['Time_EFC', 'time_for_efc', 'LONG'],
# ['Time_Harvest', 'time_for_harvest', 'LONG'],
# ['Killing_Crost_C', 'killing_frost_temperature', 'Float'],
# # ['Invoke_Stress', 'invoke_stress', 'SHORT'],
# # ['CN_Coarse_Soil', 'cn_coarse_soil', 'LONG'],
# # ['CN_Medium_Soil', 'cn_medium_soil', 'LONG'],
# # ['CN_Fine_Soil', 'cn_fine_soil', 'LONG']
# ]
# Allow user to subset crops and cells from INI
try:
crop_skip_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_skip_list'))))
except:
crop_skip_list = []
try:
crop_test_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_test_list'))))
except:
crop_test_list = []
try:
cell_skip_list = config.get(crop_et_sec, 'cell_skip_list').split(',')
cell_skip_list = sorted([c.strip() for c in cell_skip_list])
except:
cell_skip_list = []
try:
cell_test_list = config.get(crop_et_sec, 'cell_test_list').split(',')
cell_test_list = sorted([c.strip() for c in cell_test_list])
except:
cell_test_list = []
# Overwrite INI crop list with user defined values
# Could also append to the INI crop list
if crop_str:
try:
crop_test_list = sorted(list(util.parse_int_set(crop_str)))
# try:
# crop_test_list = sorted(list(set(
# crop_test_list + list(util.parse_int_set(crop_str)))
except:
pass
# Don't build crop parameter files for non-crops
crop_skip_list = sorted(
list(set(crop_skip_list + [44, 45, 46, 55, 56, 57])))
# crop_test_list = sorted(list(set(crop_test_list + [46])))
logging.debug('\ncrop_test_list = {0}'.format(crop_test_list))
logging.debug('crop_skip_list = {0}'.format(crop_skip_list))
logging.debug('cell_test_list = {0}'.format(cell_test_list))
logging.debug('cell_test_list = {0}'.format(cell_test_list))
# Read crop parameters using ET Demands functions/methods
logging.info('\nReading Default Crop Parameters')
sys.path.append(bin_ws)
import crop_parameters
crop_param_dict = crop_parameters.read_crop_parameters(crop_params_path)
# arcpy.CheckOutExtension('Spatial')
# arcpy.env.pyramid = 'NONE 0'
arcpy.env.overwriteOutput = overwrite_flag
arcpy.env.parallelProcessingFactor = 8
# Get list of crops specified in ET cells
# Currently this may only be crops with CDL acreage
crop_field_list = [
field.name for field in arcpy.ListFields(cells_path)
if re.match('CROP_\d{2}', field.name)
]
logging.debug('Cell crop fields: {}'.format(', '.join(crop_field_list)))
crop_number_list = [
int(f_name.split('_')[1]) for f_name in crop_field_list
]
crop_number_list = [
crop_num for crop_num in crop_number_list
if not ((crop_test_list and crop_num not in crop_test_list) or
(crop_skip_list and crop_num in crop_skip_list))
]
logging.info('Cell crop numbers: {}'.format(', '.join(
list(util.ranges(crop_number_list)))))
# Get crop acreages for each cell
crop_acreage_dict = defaultdict(dict)
field_list = [cell_id_field] + crop_field_list
with arcpy.da.SearchCursor(cells_path, field_list) as cursor:
for row in cursor:
for i, crop_num in enumerate(crop_number_list):
crop_acreage_dict[crop_num][row[0]] = row[i + 1]
# Make an empty template crop feature class
logging.info('')
crop_template_path = os.path.join(calibration_ws, 'crop_00_template' + ext)
if overwrite_flag and arcpy.Exists(crop_template_path):
logging.debug('Overwriting template crop feature class')
arcpy.Delete_management(crop_template_path)
if arcpy.Exists(crop_template_path):
logging.info('Template crop feature class already exists, skipping')
else:
logging.info('Building template crop feature class')
arcpy.CopyFeatures_management(cells_path, crop_template_path)
# Remove unneeded et cell fields
for field in arcpy.ListFields(crop_template_path):
if (field.name not in keep_field_list and field.editable
and not field.required):
logging.debug(' Delete field: {0}'.format(field.name))
arcpy.DeleteField_management(crop_template_path, field.name)
field_list = [f.name for f in arcpy.ListFields(crop_template_path)]
# Add crop acreage field
if crop_acres_field not in field_list:
logging.debug(' Add field: {0}'.format(crop_acres_field))
arcpy.AddField_management(crop_template_path, crop_acres_field,
'Float')
arcpy.CalculateField_management(crop_template_path,
crop_acres_field, '0',
'PYTHON_9.3')
# Add crop parameter fields if necessary
for param_field, param_method, param_type in param_list:
logging.debug(' Add field: {0}'.format(param_field))
if param_field not in field_list:
arcpy.AddField_management(crop_template_path, param_field,
param_type)
# if dairy_cutting_field not in field_list:
# .debug(' Add field: {0}'.format(dairy_cutting_field))
# .AddField_management(crop_template_path, dairy_cutting_field, 'Short')
# .CalculateField_management(
# crop_template_path, dairy_cutting_field, dairy_cuttings, 'PYTHON')
# if beef_cutting_field not in field_list:
# .debug(' Add field: {0}'.format(beef_cutting_field))
# .AddField_management(crop_template_path, beef_cutting_field, 'Short')
# .CalculateField_management(
# crop_template_path, beef_cutting_field, beef_cuttings, 'PYTHON')
# Add an empty/zero crop field for the field mappings below
# if len(arcpy.ListFields(cells_path, 'CROP_EMPTY')) == 0:
# .AddField_management(cells_path, 'CROP_EMPTY', 'Float')
# .CalculateField_management(
# cells_path, 'CROP_EMPTY', '0', 'PYTHON_9.3')
# Process each crop
logging.info('\nBuild crop feature classes')
for crop_num in crop_number_list:
try:
crop_param = crop_param_dict[crop_num]
except:
continue
logging.info('{0:>2d} {1}'.format(crop_num, crop_param))
# Replace other characters with spaces, then remove multiple spaces
crop_name = re.sub('[-"().,/~]', ' ', str(crop_param.name).lower())
crop_name = ' '.join(crop_name.strip().split()).replace(' ', '_')
crop_path = os.path.join(
calibration_ws,
'crop_{0:02d}_{1}{2}'.format(crop_num, crop_name, ext))
crop_field = 'CROP_{0:02d}'.format(crop_num)
# Skip if all zone crop areas are below threshold
if all(
[v < area_threshold
for v in crop_acreage_dict[crop_num].values()]):
logging.info(' All crop acreaeges below threshold, skipping crop')
continue
# Remove existing shapefiles if necessary
if overwrite_flag and arcpy.Exists(crop_path):
logging.debug(' Overwriting: {}'.format(
os.path.basename(crop_path)))
arcpy.Delete_management(crop_path)
# Don't check skip list until after existing files are removed
# if ((crop_test_list and crop_num not in crop_test_list) or
# _skip_list and crop_num in crop_skip_list)):
# .debug(' Skipping')
#
# Copy ET cells for each crop if needed
if arcpy.Exists(crop_path):
logging.debug(' Shapefile already exists, skipping')
continue
else:
# logging.debug(' {0}'.format(crop_path))
arcpy.Copy_management(crop_template_path, crop_path)
# Remove extra fields
# for field in arcpy.ListFields(crop_path):
# field.name not in keep_field_list:
# # logging.debug(' {0}'.format(field.name))
# arcpy.DeleteField_management(crop_path, field.name)
# Add alfalfa cutting field
if crop_num in [1, 2, 3, 4]:
if len(arcpy.ListFields(crop_path, dairy_cutting_field)) == 0:
logging.debug(' Add field: {0}'.format(dairy_cutting_field))
arcpy.AddField_management(crop_path, dairy_cutting_field,
'Short')
arcpy.CalculateField_management(crop_path, dairy_cutting_field,
dairy_cuttings, 'PYTHON')
if len(arcpy.ListFields(crop_path, beef_cutting_field)) == 0:
logging.debug(' Add field: {0}'.format(beef_cutting_field))
arcpy.AddField_management(crop_path, beef_cutting_field,
'Short')
arcpy.CalculateField_management(crop_path, beef_cutting_field,
beef_cuttings, 'PYTHON')
# Write default crop parameters to file
field_list = [p[0]
for p in param_list] + [cell_id_field, crop_acres_field]
with arcpy.da.UpdateCursor(crop_path, field_list) as cursor:
for row in cursor:
# Skip and/or remove zones without crop acreage
if crop_acreage_dict[crop_num][row[-2]] < area_threshold:
if remove_empty_flag:
cursor.deleteRow()
continue
# Write parameter values
for i, (param_field, param_method,
param_type) in enumerate(param_list):
row[i] = getattr(crop_param, param_method)
# Write crop acreage
row[-1] = crop_acreage_dict[crop_num][row[-2]]
cursor.updateRow(row)
def main(ini_path,
start_date=None,
end_date=None,
crop_str='',
overwrite_flag=False):
"""Compuate Growing Season Statistics
Args:
ini_path (str): file path of the project INI file
start_date (str): ISO format date string (YYYY-MM-DD)
end_date (str): ISO format date string (YYYY-MM-DD)
crop_str (str): comma separate list or range of crops to compare
overwrite_flag (bool): If True, overwrite existing files
Returns:
None
"""
# Field names
date_field = 'Date'
doy_field = 'DOY'
year_field = 'Year'
# month_field = 'Month'
# day_field = 'Day'
season_field = 'Season'
# Output file/folder names
gs_summary_name = 'growing_season_full_summary.csv'
gs_mean_annual_name = 'growing_season_mean_annual.csv'
baddata_name = 'growing_season_bad_data.txt'
# Number of header lines in data file
# header_lines = 2
# Delimiter
sep = ','
# sep = r"\s*"
logging.info('\nComputing growing season statistics')
logging.info(' INI: {}'.format(ini_path))
# Check that the INI file can be read
crop_et_sec = 'CROP_ET'
config = util.read_ini(ini_path, crop_et_sec)
# Get the project workspace and daily ET folder from the INI file
def get_config_param(config, param_name, section):
""""""
try:
param_value = config.get(section, param_name)
except:
logging.error(('ERROR: The {} parameter is not set' +
' in the INI file').format(param_name))
sys.exit()
return param_value
project_ws = get_config_param(config, 'project_folder', crop_et_sec)
daily_stats_ws = os.path.join(
project_ws, get_config_param(config, 'daily_output_folder',
crop_et_sec))
gs_stats_ws = os.path.join(
project_ws, get_config_param(config, 'gs_output_folder', crop_et_sec))
# Check workspaces
if not os.path.isdir(daily_stats_ws):
logging.error(('\nERROR: The daily ET stats folder {0} ' +
'could be found\n').format(daily_stats_ws))
sys.exit()
if not os.path.isdir(gs_stats_ws):
os.mkdir(gs_stats_ws)
# Range of data to plot
try:
year_start = dt.datetime.strptime(start_date, '%Y-%m-%d').year
logging.info(' Start Year: {0}'.format(year_start))
except:
year_start = None
try:
year_end = dt.datetime.strptime(end_date, '%Y-%m-%d').year
logging.info(' End Year: {0}'.format(year_end))
except:
year_end = None
if year_start and year_end and year_end < year_start:
logging.error('\n ERROR: End date must be after start date\n')
sys.exit()
# Allow user to subset crops from INI
try:
crop_skip_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_skip_list'))))
except:
crop_skip_list = []
# crop_skip_list = [44, 45, 46, 55, 56, 57]
try:
crop_test_list = sorted(
list(util.parse_int_set(config.get(crop_et_sec,
'crop_test_list'))))
except:
crop_test_list = []
# Overwrite INI crop list with user defined values
# Could also append to the INI crop list
if crop_str:
try:
crop_test_list = list(util.parse_int_set(crop_str))
# try:
# crop_test_list = sorted(list(set(
# crop_test_list + list(util.parse_int_set(crop_str)))
except:
pass
logging.debug('\n crop_test_list = {0}'.format(crop_test_list))
logging.debug(' crop_skip_list = {0}'.format(crop_skip_list))
# Output file paths
gs_summary_path = os.path.join(gs_stats_ws, gs_summary_name)
gs_mean_annual_path = os.path.join(gs_stats_ws, gs_mean_annual_name)
baddata_path = os.path.join(gs_stats_ws, baddata_name)
# Build list of site files
# site_file_re = '^RG\d{8}ETca.dat$'
# site_file_list = sorted([item for item in os.listdir(workspace)
# if re.match(site_file_re, item)])
# site_file_list = sorted([
# item for item in os.listdir(daily_stats_ws)
# if re.match('\w+_daily_crop_\d{2}.csv$', item)])
# Initialize output data arrays and open bad data log file
gs_summary_data = []
gs_mean_annual_data = []
baddata_file = open(baddata_path, 'w')
# Regular expressions
data_re = re.compile('(?P<CELLID>\w+)_daily_crop_(?P<CROP>\d+).csv$', re.I)
# Build list of all data files
data_file_list = sorted([
os.path.join(daily_stats_ws, f_name)
for f_name in os.listdir(daily_stats_ws) if data_re.match(f_name)
])
if not data_file_list:
logging.error(' ERROR: No daily ET files were found\n' +
' ERROR: Check the folder_name parameters\n')
sys.exit()
# Process each file
for file_path in data_file_list:
file_name = os.path.basename(file_path)
logging.debug('')
logging.info(' {0}'.format(file_name))
station, crop_num = os.path.splitext(file_name)[0].split(
'_daily_crop_')
crop_num = int(crop_num)
logging.debug(' Station: {0}'.format(station))
logging.debug(' Crop Num: {0}'.format(crop_num))
if station == 'temp':
logging.debug(' Skipping')
continue
# Get crop name
with open(file_path, 'r') as file_f:
crop_name = file_f.readline().split('-', 1)[1].strip()
logging.debug(' Crop: {0}'.format(crop_name))
# Read data from file into record array (structured array)
daily_df = pd.read_table(file_path, header=0, comment='#', sep=sep)
logging.debug(' Fields: {0}'.format(', '.join(
daily_df.columns.values)))
daily_df[date_field] = pd.to_datetime(daily_df[date_field])
daily_df.set_index(date_field, inplace=True)
daily_df[year_field] = daily_df.index.year
# daily_df[year_field] = daily_df[date_field].map(lambda x: x.year)
# Build list of unique years
year_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' All Years: {0}'.format(', '.join(
list(util.ranges(year_array.tolist())))))
# logging.debug(' All Years: {0}'.format(
# ','.join(map(str, year_array.tolist()))))
# Don't include the first year in the stats
crop_year_start = min(daily_df[year_field])
logging.debug(' Skipping {}, first year'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
# Check if start and end years have >= 365 days
crop_year_start = min(daily_df[year_field])
crop_year_end = max(daily_df[year_field])
if sum(daily_df[year_field] == crop_year_start) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_start))
daily_df = daily_df[daily_df[year_field] > crop_year_start]
if sum(daily_df[year_field] == crop_year_end) < 365:
logging.debug(
' Skipping {}, missing days'.format(crop_year_end))
daily_df = daily_df[daily_df[year_field] < crop_year_end]
del crop_year_start, crop_year_end
# Only keep years between year_start and year_end
if year_start:
daily_df = daily_df[daily_df[year_field] >= year_start]
if year_end:
daily_df = daily_df[daily_df[year_field] <= year_end]
year_sub_array = np.sort(
np.unique(np.array(daily_df[year_field]).astype(np.int)))
logging.debug(' Plot Years: {0}'.format(', '.join(
list(util.ranges(year_sub_array.tolist())))))
# logging.debug(' Plot Years: {0}'.format(
# ','.join(map(str, year_sub_array.tolist()))))
# Get separate date related fields
date_array = daily_df.index.date
year_array = daily_df[year_field].values.astype(np.int)
doy_array = daily_df[doy_field].values.astype(np.int)
# Remove leap days
# leap_array = (doy_array == 366)
# doy_sub_array = np.delete(doy_array, np.where(leap_array)[0])
# Build separate arrays for each set of crop specific fields
season_array = np.array(daily_df[season_field])
# # Original code from growing_season script
# Initialize mean annual growing season length variables
gs_sum, gs_cnt, gs_mean = 0, 0, 0
start_sum, start_cnt, start_mean = 0, 0, 0
end_sum, end_cnt, end_mean = 0, 0, 0
# Process each year
for year_i, year in enumerate(year_sub_array):
year_crop_str = "Crop: {0:2d} {1:32s} Year: {2}".format(
crop_num, crop_name, year)
logging.debug(year_crop_str)
# Extract data for target year
year_mask = (year_array == year)
date_sub_array = date_array[year_mask]
doy_sub_array = doy_array[year_mask]
season_sub_mask = season_array[year_mask]
# Look for transitions in season value
# Start transitions up the day before the actual start
# End transitions down on the end date
try:
start_i = np.where(np.diff(season_sub_mask) == 1)[0][0] + 1
except:
start_i = None
try:
end_i = np.where(np.diff(season_sub_mask) == -1)[0][0]
except:
end_i = None
# If start transition is not found, season starts on DOY 1
if start_i is None and end_i is not None:
start_i = 0
# If end transition is not found, season ends on DOY 365/366
elif start_i is not None and end_i is None:
end_i = -1
# If neither transition is found, season is always on
# elif start_i is None and end_i is None:
# , end_i = 0, -1
# Calculate start and stop day of year
# Set start/end to 0 if season never gets set to 1
if not np.any(season_sub_mask):
skip_str = " Skipping, season flag was never set to 1"
logging.debug(skip_str)
baddata_file.write('{0} {1} {2}\n'.format(
station, year_crop_str, skip_str))
start_doy, end_doy = 0, 0
start_date, end_date = "", ""
elif np.all(season_sub_mask):
start_doy, end_doy = doy_sub_array[0], doy_sub_array[-1]
start_date = date_sub_array[0].isoformat()
end_date = date_sub_array[-1].isoformat()
else:
start_doy, end_doy = doy_sub_array[start_i], doy_sub_array[
end_i]
start_date = date_sub_array[start_i].isoformat()
end_date = date_sub_array[end_i].isoformat()
gs_length = sum(season_sub_mask)
logging.debug("Start: {0} ({1}) End: {2} ({3})".format(
start_doy, start_date, end_doy, end_date))
# Track growing season length and mean annual g.s. length
if start_doy > 0 and end_doy > 0 and year_i != 0:
start_sum += start_doy
end_sum += end_doy
gs_sum += gs_length
start_cnt += 1
end_cnt += 1
gs_cnt += 1
# Append data to list
gs_summary_data.append([
station, crop_num, crop_name, year, start_doy, end_doy,
start_date, end_date, gs_length
])
# Cleanup
del year_mask, doy_sub_array, season_sub_mask
del start_doy, end_doy, start_date, end_date, gs_length
# Calculate mean annual growing season start/end/length
if gs_cnt > 0:
mean_start_doy = int(round(float(start_sum) / start_cnt))
mean_end_doy = int(round(float(end_sum) / end_cnt))
mean_length = int(round(float(gs_sum) / gs_cnt))
mean_start_date = util.doy_2_date(year, mean_start_doy)
mean_end_date = util.doy_2_date(year, mean_end_doy)
else:
mean_start_doy, mean_end_doy, mean_length = 0, 0, 0
mean_start_date, mean_end_date = "", ""
# Append mean annual growing season data to list
gs_mean_annual_data.append([
station, crop_num, crop_name, mean_start_doy, mean_end_doy,
mean_start_date, mean_end_date, mean_length
])
# Cleanup
del season_array
del gs_sum, gs_cnt, gs_mean
del start_sum, start_cnt, start_mean
del end_sum, end_cnt, end_mean
del mean_start_doy, mean_end_doy, mean_length
del mean_start_date, mean_end_date
del year_array, year_sub_array, doy_array
del daily_df
logging.debug("")
# Close bad data file log
baddata_file.close()
# Build output record array file
gs_summary_csv = csv.writer(open(gs_summary_path, 'wb'))
gs_summary_csv.writerow([
'STATION', 'CROP_NUM', 'CROP_NAME', 'YEAR', 'START_DOY', 'END_DOY',
'START_DATE', 'END_DATE', 'GS_LENGTH'
])
gs_summary_csv.writerows(gs_summary_data)
# Build output record array file
gs_mean_annual_csv = csv.writer(open(gs_mean_annual_path, 'wb'))
gs_mean_annual_csv.writerow([
'STATION', 'CROP_NUM', 'CROP_NAME', 'MEAN_START_DOY', 'MEAN_END_DOY',
'MEAN_START_DATE', 'MEAN_END_DATE', 'MEAN_GS_LENGTH'
])
gs_mean_annual_csv.writerows(gs_mean_annual_data)
# Cleanup
del gs_summary_path, gs_summary_name
del gs_summary_csv, gs_summary_data
del gs_mean_annual_path, gs_mean_annual_name
del gs_mean_annual_csv, gs_mean_annual_data
def best(patterns,extended):
# Enumberate daytimes
dts = collections.defaultdict(set)
all = []
for p in patterns:
all.extend(p[0].each())
for p in all:
for dt in p.getDayTimesRaw():
dts[dt.data()].add(p)
# For each daytime, iterate patterns to find termweeks
dt_tw = {}
dt_tw_sz = {}
for (dt,ps) in dts.iteritems():
tws = collections.defaultdict(set)
for p in ps:
for (term,week) in p.getTermWeeks().each():
tws[term].add(week)
dt_tw[dt] = tws
dt_tw_sz[dt] = reduce(lambda tot,item: tot+len(item),tws.itervalues(),0)
# restrict to at most max_trials (longest)
dt_use = set()
dt_candidates = dt_tw.keys()
for i in range(0,max_trials):
if len(dt_candidates) == 0:
break
use = max(dt_candidates,key = lambda k: dt_tw_sz[k])
dt_candidates.remove(use)
dt_use.add(use)
# find longest range of each, using 1-8,9-16,17-24 type ranges to allow term overlap
dt_longest = {}
for dt in dt_use:
# build termy week numbers (1-24)
week_nums = set()
for (term,weeks) in dt_tw[dt].iteritems():
for week in filter(lambda x: x>0 and x<9,weeks):
week_nums.add(term*8+week)
ranges = sorted(util.ranges(week_nums),key = lambda x: x[1],reverse = True)
if len(ranges) == 0:
dt_longest[dt] = set()
else:
dt_longest[dt] = set(range(ranges[0][0],ranges[0][0]+ranges[0][1]))
# permute through including and excluding date ranges to see which gives best coverage (EXPONENTIAL!)
best_score = None
best = None
for dts in util.powerset(dt_use):
if len(dts) == 0:
continue
all = set(range(1,25))
for dt in dts:
all &= dt_longest[dt]
score = len(all) * len(dts)
if best_score == None or score > best_score:
best_score = score
best = dts
# Generate pattern
if best is None:
logger.error("No common in %s" % all)
return None
p = patternatom.PatternAtom(False)
for b in best:
p.addDayTimeRange(b[0],b[1][0],b[1][1],b[2][0],b[2][1])
p.setAllYear()
# Extend to include out-of-term dates, where required
if extended:
for q in patterns:
for qa in q[0].blast():
p.expand_back_to(qa)
p.expand_forward_to(qa)
return p
