def main(ini_path, overwrite_flag=True):
"""Earth Engine Beamer ET Zonal Stats
Args:
ini_path (str):
overwrite_flag (bool): if True, overwrite existing files
Returns:
None
"""
logging.info('\nEarth Engine Beamer ET Zonal Stats')
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='BEAMER')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='ZONAL_STATS')
# Overwrite landsat products with Beamer specific values
ini['EXPORT']['landsat_products'] = [
'ndvi_toa', 'ndwi_toa', 'albedo_sur', 'ts', 'evi_sur', 'etstar_mean',
'etg_mean', 'etg_lpi', 'etg_upi', 'etg_lci', 'etg_uci', 'etg_mean',
'et_lpi', 'et_upi', 'et_lci', 'et_uci'
]
# First row of csv is header
header_list = [
'ZONE_NAME', 'ZONE_FID', 'DATE', 'SCENE_ID', 'PLATFORM', 'PATH', 'ROW',
'YEAR', 'MONTH', 'DAY', 'DOY', 'PIXEL_COUNT', 'PIXEL_TOTAL',
'FMASK_COUNT', 'FMASK_TOTAL', 'FMASK_PCT', 'ETSTAR_COUNT',
'CLOUD_SCORE', 'QA', 'NDVI_TOA', 'NDWI_TOA', 'ALBEDO_SUR', 'TS',
'EVI_SUR', 'ETSTAR_MEAN', 'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI',
'ETG_UCI', 'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI', 'WY_ETO',
'WY_PPT'
]
int_fields = [
'ZONE_FID', 'PATH', 'ROW', 'YEAR', 'MONTH', 'DAY', 'DOY',
'PIXEL_COUNT', 'PIXEL_TOTAL', 'FMASK_COUNT', 'FMASK_TOTAL',
'ETSTAR_COUNT'
]
float_fields = list(
set(header_list) - set(int_fields) -
set(['ZONE_NAME', 'DATE', 'SCENE_ID', 'PLATFORM']))
# Regular expression to pull out Landsat scene_id
# If RE has capturing groups, findall call below will fail to extract ID
landsat_re = re.compile('L[ETC]0[4578]_\d{3}XXX_\d{4}\d{2}\d{2}')
# landsat_re = re.compile('L[ETC][4578]\d{3}XXX\d{4}\d{3}')
# landsat_re = re.compile('L[ETC][4578]\d{3}\d{3}\d{4}\d{3}\D{3}\d{2}')
# Remove the existing CSV
output_path = os.path.join(ini['ZONAL_STATS']['output_ws'],
ini['BEAMER']['output_name'])
if overwrite_flag and os.path.isfile(output_path):
os.remove(output_path)
# Create an empty CSV
if not os.path.isfile(output_path):
data_df = pd.DataFrame(columns=header_list)
data_df[int_fields] = data_df[int_fields].astype(np.int64)
data_df[float_fields] = data_df[float_fields].astype(np.float32)
data_df.to_csv(output_path, index=False)
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# zone_count = len(zone_geom_list)
# output_fmt = '_{0:0%sd}.csv' % str(int(math.log10(zone_count)) + 1)
# Check if the zone_names are unique
# Eventually support merging common zone_names
if len(set([z[1] for z in zone_geom_list])) != len(zone_geom_list):
logging.error(
'\nERROR: There appear to be duplicate zone ID/name values.'
'\n Currently, the values in "{}" must be unique.'
'\n Exiting.'.format(ini['INPUTS']['zone_field']))
return False
# Filter features by FID
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Set all zone specific parameters into a dictionary
zone = {}
# Need zone_shp_path projection to build EE geometries
zone['osr'] = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone['proj'] = gdc.osr_wkt(zone['osr'])
# zone['proj'] = ee.Projection(zone['proj']).wkt().getInfo()
# zone['proj'] = zone['proj'].replace('\n', '').replace(' ', '')
# logging.debug(' Zone Projection: {}'.format(zone['proj']))
# Check that shapefile has matching spatial reference
if not gdc.matching_spatref(zone['osr'], ini['SPATIAL']['osr']):
logging.warning(' Zone OSR:\n{}\n'.format(zone['osr']))
logging.warning(' Output OSR:\n{}\n'.format(
ini['SPATIAL']['osr'].ExportToWkt()))
logging.warning(' Zone Proj4: {}'.format(
zone['osr'].ExportToProj4()))
logging.warning(' Output Proj4: {}'.format(
ini['SPATIAL']['osr'].ExportToProj4()))
logging.warning(
'\nWARNING: \n'
'The output and zone spatial references do not appear to match\n'
'This will likely cause problems!')
input('Press ENTER to continue')
else:
logging.debug(' Zone Projection:\n{}\n'.format(
zone['osr'].ExportToWkt()))
logging.debug(' Output Projection:\n{}\n'.format(
ini['SPATIAL']['osr'].ExportToWkt()))
logging.debug(' Output Cellsize: {}'.format(
ini['SPATIAL']['cellsize']))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Read in ETo and PPT data from file
if (ini['BEAMER']['eto_source'] == 'file'
or ini['BEAMER']['ppt_source'] == 'file'):
data_array = np.atleast_1d(
np.genfromtxt(ini['BEAMER']['data_path'],
delimiter=',',
names=True,
encoding=None,
dtype=None))
data_fields = data_array.dtype.names
logging.debug(' CSV fields: {}'.format(', '.join(data_fields)))
# DEADBEEF - Compare fields names assuming all upper case
data_fields = [f.upper() for f in data_fields]
eto_dict = defaultdict(dict)
ppt_dict = defaultdict(dict)
for row in data_array:
z = str(row[data_fields.index(ini['BEAMER']['data_zone_field'])])
y = row[data_fields.index(ini['BEAMER']['data_year_field'])]
if ini['BEAMER']['eto_source'] == 'file':
# DEADBEEF - Compare fields names assuming all upper case
eto_dict[z][y] = row[data_fields.index(
ini['BEAMER']['data_eto_field'].upper())]
if ini['BEAMER']['ppt_source'] == 'file':
# DEADBEEF - Compare fields names assuming all upper case
ppt_dict[z][y] = row[data_fields.index(
ini['BEAMER']['data_ppt_field'].upper())]
# Get filtered/merged/prepped Landsat collection
landsat_args = {
k: v
for section in ['INPUTS'] for k, v in ini[section].items() if k in [
'landsat4_flag', 'landsat5_flag', 'landsat7_flag', 'landsat8_flag',
'fmask_flag', 'acca_flag', 'start_year', 'end_year', 'start_month',
'end_month', 'start_doy', 'end_doy', 'scene_id_keep_list',
'scene_id_skip_list', 'path_keep_list', 'row_keep_list',
'tile_geom', 'adjust_method', 'mosaic_method', 'refl_sur_method'
]
}
landsat_args['products'] = ini['EXPORT']['landsat_products']
landsat = ee_common.Landsat(landsat_args)
# Calculate zonal stats for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone['fid'] = zone_fid
zone['name'] = zone_name.replace(' ', '_')
zone['json'] = zone_json
logging.info('ZONE: {} (FID: {})'.format(zone['name'], zone['fid']))
# zone_key used for wy_ppt and wy_eto inputs from csv file
if ini['INPUTS']['zone_field'] == 'FID':
zone_key = str(zone['fid'])
print('Using FID as zone_field')
else:
zone_key = zone['name']
print('Using Name as zone_field')
# Build EE geometry object for zonal stats
zone['geom'] = ee.Geometry(geo_json=zone['json'],
opt_proj=zone['proj'],
opt_geodesic=False)
# logging.debug(' Centroid: {}'.format(
# zone['geom'].centroid(100).getInfo()['coordinates']))
# Use feature geometry to build extent, transform, and shape
zone['extent'] = gdc.Extent(
ogr.CreateGeometryFromJson(json.dumps(zone['json'])).GetEnvelope())
# zone['extent'] = gdc.Extent(zone['geom'].GetEnvelope())
zone['extent'] = zone['extent'].ogrenv_swap()
zone['extent'] = zone['extent'].adjust_to_snap(
'EXPAND', ini['SPATIAL']['snap_x'], ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone['geo'] = zone['extent'].geo(ini['SPATIAL']['cellsize'])
zone['transform'] = gdc.geo_2_ee_transform(zone['geo'])
# zone['transform'] = '[' + ','.join(map(str, zone['transform'])) + ']'
zone['shape'] = zone['extent'].shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone['shape']))
logging.debug(' Zone Transform: {}'.format(zone['transform']))
logging.debug(' Zone Extent: {}'.format(zone['extent']))
# logging.debug(' Zone Geom: {}'.format(zone['geom'].getInfo()))
# Assume all pixels in all 14+2 images could be reduced
zone['max_pixels'] = zone['shape'][0] * zone['shape'][1]
logging.debug(' Max Pixels: {}'.format(zone['max_pixels']))
# Set output spatial reference
# Eventually allow user to manually set these
# output_crs = zone['proj']
# ini['INPUTS']['transform'] = zone['transform']
logging.debug(' Output Projection: {}'.format(ini['SPATIAL']['crs']))
logging.debug(' Output Transform: {}'.format(zone['transform']))
# Process date range by year
start_dt = datetime.datetime(ini['INPUTS']['start_year'], 1, 1)
end_dt = datetime.datetime(ini['INPUTS']['end_year'] + 1, 1,
1) - datetime.timedelta(0, 1)
iter_months = ini['BEAMER']['month_step']
for i, iter_start_dt in enumerate(
rrule.rrule(
# rrule.YEARLY, interval=interval_cnt,
rrule.MONTHLY,
interval=iter_months,
dtstart=start_dt,
until=end_dt)):
iter_end_dt = (
iter_start_dt +
# relativedelta.relativedelta(years=interval_cnt) -
relativedelta.relativedelta(months=iter_months) -
datetime.timedelta(0, 1))
if ((ini['INPUTS']['start_month']
and iter_end_dt.month < ini['INPUTS']['start_month'])
or (ini['INPUTS']['end_month']
and iter_start_dt.month > ini['INPUTS']['end_month'])):
logging.debug(' {} {} skipping'.format(
iter_start_dt.date(), iter_end_dt.date()))
continue
elif (
(ini['INPUTS']['start_doy'] and
int(iter_end_dt.strftime('%j')) < ini['INPUTS']['start_doy'])
or
(ini['INPUTS']['end_doy'] and int(iter_start_dt.strftime('%j'))
> ini['INPUTS']['end_doy'])):
logging.debug(' {} {} skipping'.format(
iter_start_dt.date(), iter_end_dt.date()))
continue
else:
logging.info(' {} {}'.format(iter_start_dt.date(),
iter_end_dt.date()))
year = iter_start_dt.year
# Filter the GRIDMET collection
wy_start_date = '{}-10-01'.format(year - 1)
wy_end_date = '{}-10-01'.format(year)
logging.debug(' WY: {} {}'.format(wy_start_date, wy_end_date))
gridmet_coll = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') \
.filterDate(wy_start_date, wy_end_date)
# # PRISM collection was uploaded as an asset
# if ini['BEAMER']['ppt_source'] == 'prism':
# def prism_time_start(input_image):
# """Set time_start property on PRISM water year PPT collection"""
# # Assume year is the 4th item separated by "_"
# wy = ee.String(
# input_image.get('system:index')).split('_').get(3)
# date_start = ee.Date(ee.String(wy).cat('-10-01'))
# return input_image.select([0], ['ppt']).setMulti({
# 'system:time_start': date_start.advance(-1, 'year').millis()
# })
# prism_coll = ee.ImageCollection('users/cgmorton/prism_800m_ppt_wy')
# prism_coll = ee.ImageCollection(prism_coll.map(prism_time_start)) \
# .filterDate(wy_start_dt, wy_end_dt)
# # prism_coll = ee.ImageCollection(
# # ee_common.MapsEngineAssets.prism_ppt_wy).filterDate(
# # wy_start_dt, wy_end_dt)
# Get water year PPT for centroid of zone or read from file
# Convert all input data to mm to match GRIDMET data
if ini['BEAMER']['ppt_source'] == 'file':
wy_ppt_input = ppt_dict[zone_key][year]
if ini['BEAMER']['data_ppt_units'] == 'mm':
pass
elif ini['BEAMER']['data_ppt_units'] == 'in':
wy_ppt_input *= 25.4
elif ini['BEAMER']['data_ppt_units'] == 'ft':
wy_ppt_input *= (25.4 * 12)
elif ini['BEAMER']['ppt_source'] == 'gridmet':
wy_ppt_input = float(
utils.ee_getinfo(
ee.ImageCollection(
gridmet_coll.select(['pr'],
['ppt']).sum()).getRegion(
zone['geom'].centroid(1),
500))[1][4])
# Calculate GRIDMET zonal mean of geometry
# wy_ppt_input = float(ee.ImageCollection(
# gridmet_coll.select(['pr'], ['ppt'])).reduceRegion(
# reducer=ee.Reducer.sum(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1).getInfo()['ppt']
# elif ini['BEAMER']['ppt_source'] == 'prism':
# # Calculate PRISM zonal mean of geometry
# wy_ppt_input = float(utils.ee_getinfo(ee.ImageCollection(
# prism_coll.map(ee_common.prism_ppt_func)).sum().reduceRegion(
# reducer=ee.Reducer.mean(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1))['ppt'])
# Get water year ETo for centroid of zone or read from file
# Convert all input data to mm for Beamer Method
if ini['BEAMER']['eto_source'] == 'FILE':
wy_eto_input = eto_dict[zone_key][year]
if ini['BEAMER']['data_eto_units'] == 'mm':
pass
elif ini['BEAMER']['data_eto_units'] == 'in':
wy_eto_input *= 25.4
elif ini['BEAMER']['data_eto_units'] == 'ft':
wy_eto_input *= (25.4 * 12)
# This assumes GRIMET data is in millimeters
elif ini['BEAMER']['eto_source'] == 'gridmet':
wy_eto_input = float(
utils.ee_getinfo(
ee.ImageCollection(gridmet_coll.select(
['eto']).sum()).getRegion(zone['geom'].centroid(1),
500))[1][4])
# wy_eto_input = float(ee.ImageCollection(
# gridmet_coll.select(['eto'])).reduceRegion(
# reducer=ee.Reducer.sum(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1).getInfo()
logging.debug(' Input ETO: {} mm PPT: {} mm'.format(
wy_eto_input, wy_ppt_input))
# Scale ETo & PPT
wy_eto_input *= ini['BEAMER']['eto_factor']
wy_ppt_input *= ini['BEAMER']['ppt_factor']
# Convert output units from mm
wy_eto_output = wy_eto_input
wy_ppt_output = wy_ppt_input
if ini['BEAMER']['ppt_units'] == 'mm':
pass
elif ini['BEAMER']['ppt_units'] == 'in':
wy_ppt_output /= 25.4
elif ini['BEAMER']['ppt_units'] == 'ft':
wy_ppt_output /= (25.4 * 12)
if ini['BEAMER']['eto_units'] == 'mm':
pass
elif ini['BEAMER']['eto_units'] == 'in':
wy_eto_output /= 25.4
elif ini['BEAMER']['eto_units'] == 'ft':
wy_eto_output /= (25.4 * 12)
logging.debug(' Output ETO: {} {} PPT: {} {}'.format(
wy_eto_output, ini['BEAMER']['eto_units'], wy_ppt_output,
ini['BEAMER']['ppt_units']))
# Initialize the Landsat object
landsat.zone_geom = zone['geom']
landsat.start_date = iter_start_dt.strftime('%Y-%m-%d')
landsat.end_date = iter_end_dt.strftime('%Y-%m-%d')
landsat_coll = landsat.get_collection()
if ee.Image(landsat_coll.first()).getInfo() is None:
logging.info(' No images, skipping')
continue
# # Print the collection SCENE_ID list
# logging.debug('{}'.format(', '.join([
# f['properties']['SCENE_ID']
# for f in landsat_coll.getInfo()['features']])))
# input('ENTER')
# Add water year ETo and PPT values to each image
def eto_ppt_func(img):
""""""
return ee.Image(img).setMulti({
'wy_eto': wy_eto_input,
'wy_ppt': wy_ppt_input
})
landsat_coll = ee.ImageCollection(landsat_coll.map(eto_ppt_func))
# Compute ETg
image_coll = ee.ImageCollection(landsat_coll.map(landsat_etg_func))
# # Get the output image URL
# output_url = ee.Image(landsat_coll.first()) \
# .select(['red', 'green', 'blue']) \
# .visualize(min=[0, 0, 0], max=[0.4, 0.4, 0.4]) \
# .getThumbUrl({'format': 'png', 'size': '600'})
# # This would load the image in your browser
# import webbrowser
# webbrowser.open(output_url)
# # webbrowser.read(output_url)
# # Show the output image
# window = tk.Tk()
# output_file = Image.open(io.BytesIO(urllib.urlopen(output_url).read()))
# output_photo = ImageTk.PhotoImage(output_file)
# label = tk.Label(window, image=output_photo)
# label.pack()
# window.mainloop()
# Compute zonal stats of polygon
def beamer_zonal_stats_func(input_image):
""""""
# Beamer function adds 5 ETg and 1 ET* band
# Landsat collection adds 3 ancillary bands
bands = len(landsat_args['products']) + 3 + 6
# .clip(zone['geom']) \
input_mean = input_image \
.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=zone['geom'],
crs=ini['SPATIAL']['crs'],
crsTransform=zone['transform'],
bestEffort=False,
tileScale=1,
maxPixels=zone['max_pixels'] * bands)
fmask_img = input_image.select(['fmask'])
input_count = fmask_img.gt(1) \
.addBands(fmask_img.gte(0).unmask()) \
.rename(['fmask', 'pixel']) \
.reduceRegion(
reducer=ee.Reducer.sum().combine(
ee.Reducer.count(), '', True),
geometry=zone['geom'],
crs=ini['SPATIAL']['crs'],
crsTransform=zone['transform'],
bestEffort=False,
tileScale=1,
maxPixels=zone['max_pixels'] * 3)
etstar_count = input_image \
.select(['etstar_mean'], ['etstar_count']) \
.lte(ini['BEAMER']['etstar_threshold']) \
.reduceRegion(
reducer=ee.Reducer.sum(),
geometry=zone['geom'],
crs=ini['SPATIAL']['crs'],
crsTransform=zone['transform'],
bestEffort=False,
tileScale=1,
maxPixels=zone['max_pixels'] * 2)
# Save as image properties
return ee.Feature(
None, {
'scene_id': ee.String(input_image.get('SCENE_ID')),
'time': input_image.get('system:time_start'),
'row': input_mean.get('row'),
'pixel_count': input_count.get('pixel_sum'),
'pixel_total': input_count.get('pixel_count'),
'fmask_count': input_count.get('fmask_sum'),
'fmask_total': input_count.get('fmask_count'),
'cloud_score': input_mean.get('cloud_score'),
'etstar_count': etstar_count.get('etstar_count'),
'ndvi_toa': input_mean.get('ndvi_toa'),
'ndwi_toa': input_mean.get('ndwi_toa'),
'albedo_sur': input_mean.get('albedo_sur'),
'ts': input_mean.get('ts'),
'evi_sur': input_mean.get('evi_sur'),
'etstar_mean': input_mean.get('etstar_mean'),
'etg_mean': input_mean.get('etg_mean'),
'etg_lpi': input_mean.get('etg_lpi'),
'etg_upi': input_mean.get('etg_upi'),
'etg_lci': input_mean.get('etg_lci'),
'etg_uci': input_mean.get('etg_uci')
})
# Calculate values and statistics
stats_coll = ee.ImageCollection(
image_coll.map(beamer_zonal_stats_func))
# # DEADBEEF - Test the function for a single image
# stats_info = beamer_zonal_stats_func(
# ee.Image(image_coll.first())).getInfo()
# print(stats_info)
# for k, v in sorted(stats_info['properties'].items()):
# logging.info('{:24s}: {}'.format(k, v))
# input('ENTER')
# return False
# # DEADBEEF - Print the stats info to the screen
# stats_info = stats_coll.getInfo()
# import pprint
# pp = pprint.PrettyPrinter(indent=4)
# for ftr in stats_info['features']:
# pp.pprint(ftr)
# input('ENTER')
# # return False
# Get the values from EE
stats_desc = utils.ee_getinfo(stats_coll)
if stats_desc is None:
logging.error(' Timeout error, skipping')
continue
# Save data for writing
row_list = []
for ftr in stats_desc['features']:
try:
count = int(ftr['properties']['pixel_count'])
except (KeyError, TypeError) as e:
# logging.debug(' Exception: {}'.format(e))
continue
if count == 0:
logging.info(' COUNT: 0, skipping')
continue
# First get scene ID and time
try:
scene_id = landsat_re.findall(
ftr['properties']['scene_id'])[0]
scene_time = datetime.datetime.utcfromtimestamp(
float(ftr['properties']['time']) / 1000)
except:
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(ftr)
input('ENTER')
# Extract and save other properties
try:
row_list.append({
'ZONE_FID':
zone_fid,
'ZONE_NAME':
zone_name,
'SCENE_ID':
scene_id,
'PLATFORM':
scene_id[0:4],
'PATH':
int(scene_id[5:8]),
'ROW':
int(ftr['properties']['row']),
# 'ROW': int(scene_id[8:11]),
'DATE':
scene_time.date().isoformat(),
'YEAR':
int(scene_time.year),
'MONTH':
int(scene_time.month),
'DAY':
int(scene_time.day),
'DOY':
int(scene_time.strftime('%j')),
'PIXEL_COUNT':
int(ftr['properties']['pixel_count']),
'PIXEL_TOTAL':
int(ftr['properties']['pixel_total']),
'FMASK_COUNT':
int(ftr['properties']['fmask_count']),
'FMASK_TOTAL':
int(ftr['properties']['fmask_total']),
'CLOUD_SCORE':
float(ftr['properties']['cloud_score']),
'ETSTAR_COUNT':
int(ftr['properties']['etstar_count']),
'NDVI_TOA':
float(ftr['properties']['ndvi_toa']),
'NDWI_TOA':
float(ftr['properties']['ndwi_toa']),
'ALBEDO_SUR':
float(ftr['properties']['albedo_sur']),
'TS':
float(ftr['properties']['ts']),
'EVI_SUR':
float(ftr['properties']['evi_sur']),
'ETSTAR_MEAN':
float(ftr['properties']['etstar_mean']),
'ETG_MEAN':
float(ftr['properties']['etg_mean']),
'ETG_LPI':
float(ftr['properties']['etg_lpi']),
'ETG_UPI':
float(ftr['properties']['etg_upi']),
'ETG_LCI':
float(ftr['properties']['etg_lci']),
'ETG_UCI':
float(ftr['properties']['etg_uci']),
'WY_ETO':
wy_eto_output,
'WY_PPT':
wy_ppt_output
})
except (KeyError, TypeError) as e:
logging.info(' ERROR: {}\n SCENE_ID: {}\n '
' There may not be an SR image to join to\n'
' {}'.format(e, scene_id, ftr['properties']))
# input('ENTER')
# Save all values to the dataframe (and export)
if row_list:
logging.debug(' Appending')
data_df = data_df.append(row_list, ignore_index=True)
# DEADBEEF
if data_df['QA'].isnull().any():
data_df.loc[data_df['QA'].isnull(), 'QA'] = 0
fmask_mask = data_df['FMASK_TOTAL'] > 0
if fmask_mask.any():
data_df.loc[fmask_mask, 'FMASK_PCT'] = 100.0 * (
data_df.loc[fmask_mask, 'FMASK_COUNT'] /
data_df.loc[fmask_mask, 'FMASK_TOTAL'])
logging.debug(' Saving')
data_df[int_fields] = data_df[int_fields].astype(np.int64)
data_df[float_fields] = data_df[float_fields].astype(
np.float32)
# Compute ET from ETg and PPT offline
# (must be after float conversion above)
data_df['ET_MEAN'] = data_df['ETG_MEAN'] + data_df['WY_PPT']
data_df['ET_LPI'] = data_df['ETG_LPI'] + data_df['WY_PPT']
data_df['ET_UPI'] = data_df['ETG_UPI'] + data_df['WY_PPT']
data_df['ET_LCI'] = data_df['ETG_LCI'] + data_df['WY_PPT']
data_df['ET_UCI'] = data_df['ETG_UCI'] + data_df['WY_PPT']
# Convert float fields to objects, set NaN to None
for field in data_df.columns.values:
if field.upper() not in float_fields:
continue
data_df[field] = data_df[field].astype(object)
null_mask = data_df[field].isnull()
data_df.loc[null_mask, field] = None
data_df.loc[~null_mask,
field] = data_df.loc[~null_mask, field].map(
lambda x: '{0:10.6f}'.format(x).strip())
# data_df.loc[~null_mask, [field]] = data_df.loc[~null_mask, [field]].apply(
# lambda x: '{0:10.6f}'.format(x[0]).strip(), axis=1)
# data_df = data_df.reindex_axis(header_list, axis=1)
data_df = data_df.reindex(header_list, axis=1)
# data_df.reset_index(drop=False, inplace=True)
data_df.sort_values(['ZONE_FID', 'DATE', 'ROW'],
ascending=True,
inplace=True)
# data_df.sort(
# ['ZONE_NAME', 'DATE'], ascending=[True, True], inplace=True)
data_df.to_csv(output_path, index=False)
del row_list
def main(ini_path, overwrite_flag=True):
"""Generate Beamer ETg summary tables
Args:
ini_path (str):
overwrite_flag (bool): if True, overwrite existing tables
Default is True (for now)
"""
logging.info('\nGenerate Beamer ETg summary tables')
# # Eventually get from INI (like ini['BEAMER']['landsat_products'])
# daily_fields = [
# 'ZONE_NAME', 'ZONE_FID', 'DATE', 'SCENE_ID', 'PLATFORM', 'PATH', 'ROW',
# 'YEAR', 'MONTH', 'DAY', 'DOY', 'WATER_YEAR',
# 'PIXEL_COUNT', 'ETSTAR_COUNT',
# 'NDVI_TOA', 'NDWI_TOA', 'ALBEDO_SUR', 'TS', 'EVI_SUR', 'ETSTAR_MEAN',
# 'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI',
# 'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI',
# 'ETO', 'PPT']
# annual_fields = [
# 'SCENE_COUNT', 'PIXEL_COUNT', 'ETSTAR_COUNT',
# 'NDVI_TOA', 'NDWI_TOA', 'ALBEDO_SUR', 'TS',
# 'EVI_SUR_MEAN', 'EVI_SUR_MEDIAN', 'EVI_SUR_MIN', 'EVI_SUR_MAX',
# 'ETSTAR_MEAN',
# 'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI',
# 'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI',
# 'ETO', 'PPT']
# For unit conversion
eto_fields = [
'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI', 'ET_MEAN',
'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI', 'ETO'
]
ppt_fields = ['PPT']
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='BEAMER')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='TABLES')
# Hardcode GRIDMET month range to the water year
ini['SUMMARY']['gridmet_start_month'] = 10
ini['SUMMARY']['gridmet_end_month'] = 9
# Output paths
output_daily_path = os.path.join(
ini['SUMMARY']['output_ws'],
ini['BEAMER']['output_name'].replace('.csv', '_daily.xlsx'))
output_annual_path = os.path.join(
ini['SUMMARY']['output_ws'],
ini['BEAMER']['output_name'].replace('.csv', '_annual.xlsx'))
# Check if files already exist
if overwrite_flag:
if os.path.isfile(output_daily_path):
os.remove(output_daily_path)
if os.path.isfile(output_annual_path):
os.remove(output_annual_path)
else:
if (os.path.isfile(output_daily_path)
and os.path.isfile(output_annual_path)):
logging.info('\nOutput files already exist and '
'overwrite is False, exiting')
return True
# Start/end year
year_list = list(
range(ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(
utils.wrapped_range(ini['INPUTS']['start_month'],
ini['INPUTS']['end_month'], 1, 12))
doy_list = list(
utils.wrapped_range(ini['INPUTS']['start_doy'],
ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(
utils.month_range(gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(', '.join(
map(str, gridmet_months))))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# # Filter features by FID before merging geometries
# if ini['INPUTS']['fid_keep_list']:
# landsat_df = landsat_df[landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_keep_list'])]
# if ini['INPUTS']['fid_skip_list']:
# landsat_df = landsat_df[~landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_skip_list'])]
logging.info('\nProcessing zones')
zone_df_dict = {}
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
zone_stats_ws = os.path.join(ini['ZONAL_STATS']['output_ws'],
zone_name)
if not os.path.isdir(zone_stats_ws):
logging.debug(
' Folder {} does not exist, skipping'.format(zone_stats_ws))
continue
# Input paths
landsat_daily_path = os.path.join(
zone_stats_ws, '{}_landsat_daily.csv'.format(zone_name))
gridmet_daily_path = os.path.join(
zone_stats_ws, '{}_gridmet_daily.csv'.format(zone_name))
gridmet_monthly_path = os.path.join(
zone_stats_ws, '{}_gridmet_monthly.csv'.format(zone_name))
if not os.path.isfile(landsat_daily_path):
logging.error(' Landsat daily CSV does not exist, skipping zone')
continue
elif (not os.path.isfile(gridmet_daily_path)
and not os.path.isfile(gridmet_monthly_path)):
logging.error(
' GRIDMET daily or monthly CSV does not exist, skipping zone')
continue
# DEADBEEF - Eventually support generating only Landsat figures
# logging.error(
# ' GRIDMET daily and/or monthly CSV files do not exist.\n'
# ' ETo and PPT will not be processed.')
logging.debug(' Reading Landsat CSV')
landsat_df = pd.read_csv(landsat_daily_path)
logging.debug(' Filtering Landsat dataframe')
landsat_df = landsat_df[landsat_df['PIXEL_COUNT'] > 0]
# QA field should have been written in zonal stats code
# Eventually this block can be removed
if 'QA' not in landsat_df.columns.values:
landsat_df['QA'] = 0
# # This assumes that there are L5/L8 images in the dataframe
# if not landsat_df.empty:
# max_pixel_count = max(landsat_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if year_list:
landsat_df = landsat_df[landsat_df['YEAR'].isin(year_list)]
if month_list:
landsat_df = landsat_df[landsat_df['MONTH'].isin(month_list)]
if doy_list:
landsat_df = landsat_df[landsat_df['DOY'].isin(doy_list)]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['path_keep_list']:
landsat_df = landsat_df[landsat_df['PATH'].isin(
ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list']
and ini['INPUTS']['row_keep_list'] != ['XXX']):
landsat_df = landsat_df[landsat_df['ROW'].isin(
ini['INPUTS']['row_keep_list'])]
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[np.logical_not(
scene_id_df.isin(ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[np.logical_not(landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list']))]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not landsat_df.empty:
logging.debug(' Maximum QA: {0}'.format(
ini['SUMMARY']['max_qa']))
landsat_df = landsat_df[
landsat_df['QA'] <= ini['SUMMARY']['max_qa']]
# Filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not landsat_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
landsat_df = landsat_df[
landsat_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not landsat_df.empty:
landsat_df['FMASK_PCT'] = 100 * (landsat_df['FMASK_COUNT'] /
landsat_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
landsat_df = landsat_df[
landsat_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not landsat_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = ((landsat_df['PLATFORM'] == 'LE07') &
((landsat_df['YEAR'] >= 2004) |
((landsat_df['YEAR'] == 2003) &
(landsat_df['DOY'] > 151))))
slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] /
landsat_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] / max_pixel_count)
landsat_df = landsat_df[(
(slc_off_pct >= ini['SUMMARY']['min_slc_off_pct'])
& slc_off_mask) | (~slc_off_mask)]
if landsat_df.empty:
logging.error(
' Empty Landsat dataframe after filtering, skipping zone')
continue
# Aggregate GRIDMET (to water year)
if os.path.isfile(gridmet_monthly_path):
logging.debug(' Reading montly GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_monthly_path)
elif os.path.isfile(gridmet_daily_path):
logging.debug(' Reading daily GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_daily_path)
logging.debug(' Computing GRIDMET summaries')
# Summarize GRIDMET for target months year
if (gridmet_start_month in [10, 11, 12]
and gridmet_end_month in [10, 11, 12]):
month_mask = ((gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
elif (gridmet_start_month in [10, 11, 12]
and gridmet_end_month not in [10, 11, 12]):
month_mask = gridmet_df['MONTH'] >= gridmet_start_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
month_mask = gridmet_df['MONTH'] <= gridmet_end_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
else:
month_mask = ((gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
# GROUP_YEAR for rows not in the GRIDMET month range will be NAN
gridmet_df = gridmet_df[~pd.isnull(gridmet_df['GROUP_YEAR'])]
if year_list:
gridmet_df = gridmet_df[gridmet_df['GROUP_YEAR'].isin(year_list)]
if gridmet_df.empty:
logging.error(
' Empty GRIDMET dataframe after filtering by year')
continue
# Group GRIDMET data by user specified range (default is water year)
gridmet_group_df = gridmet_df \
.groupby(['ZONE_NAME', 'ZONE_FID', 'GROUP_YEAR']) \
.agg({'ETO': np.sum, 'PPT': np.sum}) \
.reset_index() \
.sort_values(by='GROUP_YEAR')
# .rename(columns={'ETO': 'ETO', 'PPT': 'PPT'}) \
# Rename wasn't working when chained...
gridmet_group_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
gridmet_group_df['YEAR'] = gridmet_group_df['YEAR'].astype(int)
# # Group GRIDMET data by month
# gridmet_month_df = gridmet_df\
# .groupby(['ZONE_NAME', 'ZONE_FID', 'GROUP_YEAR', 'MONTH']) \
# .agg({'ETO': np.sum, 'PPT': np.sum}) \
# .reset_index() \
# .sort_values(by=['GROUP_YEAR', 'MONTH'])
# gridmet_month_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
# # Rename monthly PPT columns
# gridmet_month_df['MONTH'] = 'PPT_M' + gridmet_month_df['MONTH'].astype(str)
# # Pivot rows up to separate columns
# gridmet_month_df = gridmet_month_df.pivot_table(
# 'PPT', ['ZONE_NAME', 'YEAR'], 'MONTH')
# gridmet_month_df.reset_index(inplace=True)
# columns = ['ZONE_NAME', 'YEAR'] + ['PPT_M{}'.format(m) for m in gridmet_months]
# gridmet_month_df = gridmet_month_df[columns]
# del gridmet_month_df.index.name
# Merge Landsat and GRIDMET collections
zone_df = landsat_df.merge(gridmet_group_df,
on=['ZONE_NAME', 'ZONE_FID', 'YEAR'])
if zone_df is None or zone_df.empty:
logging.info(' Empty zone dataframe, not generating figures')
continue
# Compute ETg
zone_df['ETG_MEAN'] = zone_df['ETSTAR_MEAN'] * (zone_df['ETO'] -
zone_df['PPT'])
zone_df['ETG_LPI'] = zone_df['ETSTAR_LPI'] * (zone_df['ETO'] -
zone_df['PPT'])
zone_df['ETG_UPI'] = zone_df['ETSTAR_UPI'] * (zone_df['ETO'] -
zone_df['PPT'])
zone_df['ETG_LCI'] = zone_df['ETSTAR_LCI'] * (zone_df['ETO'] -
zone_df['PPT'])
zone_df['ETG_UCI'] = zone_df['ETSTAR_UCI'] * (zone_df['ETO'] -
zone_df['PPT'])
# Compute ET
zone_df['ET_MEAN'] = zone_df['ETG_MEAN'] + zone_df['PPT']
zone_df['ET_LPI'] = zone_df['ETG_LPI'] + zone_df['PPT']
zone_df['ET_UPI'] = zone_df['ETG_UPI'] + zone_df['PPT']
zone_df['ET_LCI'] = zone_df['ETG_LCI'] + zone_df['PPT']
zone_df['ET_UCI'] = zone_df['ETG_UCI'] + zone_df['PPT']
# Append zone dataframes
zone_df_dict[zone_name] = zone_df
# Export each zone to a separate tab
if not os.path.isfile(output_daily_path):
logging.info('\nWriting daily values to Excel')
excel_f = ExcelWriter(output_daily_path)
for zone_name, zone_df in sorted(zone_df_dict.items()):
logging.info(' {}'.format(zone_name))
zone_df.to_excel(excel_f,
zone_name,
index=False,
float_format='%.4f')
# zone_df.to_excel(excel_f, zone_name, index=False)
del zone_df
excel_f.save()
if not os.path.isfile(output_annual_path):
logging.info('\nComputing annual summaries')
annual_df = pd.concat(list(zone_df_dict.values())) \
.groupby(['ZONE_NAME', 'YEAR']) \
.agg({
'PIXEL_COUNT': ['count', 'mean'],
'PIXEL_TOTAL': ['mean'],
'FMASK_COUNT': 'mean',
'FMASK_TOTAL': 'mean',
'CLOUD_SCORE': 'mean',
'ETSTAR_COUNT': 'mean',
'NDVI_TOA': 'mean',
'NDWI_TOA': 'mean',
'ALBEDO_SUR': 'mean',
'TS': 'mean',
# 'EVI_SUR': 'mean',
'EVI_SUR': ['mean', 'median', 'min', 'max'],
'ETSTAR_MEAN': 'mean',
'ETG_MEAN': 'mean',
'ETG_LPI': 'mean',
'ETG_UPI': 'mean',
'ETG_LCI': 'mean',
'ETG_UCI': 'mean',
'ET_MEAN': 'mean',
'ET_LPI': 'mean',
'ET_UPI': 'mean',
'ET_LCI': 'mean',
'ET_UCI': 'mean',
'ETO': 'mean',
'PPT': 'mean'
})
annual_df.columns = annual_df.columns.map('_'.join)
annual_df = annual_df.rename(columns={
'PIXEL_COUNT_count': 'SCENE_COUNT',
'PIXEL_COUNT_mean': 'PIXEL_COUNT'
})
annual_df = annual_df.rename(
columns={
'EVI_SUR_mean': 'EVI_SUR_MEAN',
'EVI_SUR_median': 'EVI_SUR_MEDIAN',
'EVI_SUR_min': 'EVI_SUR_MIN',
'EVI_SUR_max': 'EVI_SUR_MAX'
})
annual_df.rename(columns=lambda x: str(x).replace('_mean', ''),
inplace=True)
annual_df['SCENE_COUNT'] = annual_df['SCENE_COUNT'].astype(np.int)
annual_df['PIXEL_COUNT'] = annual_df['PIXEL_COUNT'].astype(np.int)
annual_df['PIXEL_TOTAL'] = annual_df['PIXEL_TOTAL'].astype(np.int)
annual_df['FMASK_COUNT'] = annual_df['FMASK_COUNT'].astype(np.int)
annual_df['FMASK_TOTAL'] = annual_df['FMASK_TOTAL'].astype(np.int)
annual_df['ETSTAR_COUNT'] = annual_df['ETSTAR_COUNT'].astype(np.int)
annual_df = annual_df.reset_index()
# Convert ETo units
if (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'mm'):
pass
elif (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'in'):
annual_df[eto_fields] /= (25.4)
elif (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'ft'):
annual_df[eto_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not ' +
'currently supported, exiting').format(
ini['BEAMER']['eto_units'], ini['TABLES']['eto_units']))
sys.exit()
# Convert PPT units
if (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'mm'):
pass
elif (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'in'):
annual_df[ppt_fields] /= (25.4)
elif (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'ft'):
annual_df[ppt_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not ' +
'currently supported, exiting').format(
ini['BEAMER']['ppt_units'], ini['TABLES']['ppt_units']))
sys.exit()
logging.info('\nWriting annual values to Excel')
excel_f = ExcelWriter(output_annual_path)
for zone_name in sorted(zone_df_dict.keys()):
logging.info(' {}'.format(zone_name))
zone_df = annual_df[annual_df['ZONE_NAME'] == zone_name]
zone_df.to_excel(excel_f,
zone_name,
index=False,
float_format='%.4f')
del zone_df
excel_f.save()
def main(ini_path, show_flag=False, overwrite_flag=True):
"""Generate Bokeh figures
Bokeh issues:
Adjust y range based on non-muted data
https://stackoverflow.com/questions/43620837/how-to-get-bokeh-to-dynamically-adjust-y-range-when-panning
Linked interactive legends so that there is only one legend for the gridplot
Maybe hide or mute QA values above max (instead of filtering them in advance)
Args:
ini_path (str):
show_flag (bool): if True, show the figures in the browser.
Default is False.
overwrite_flag (bool): if True, overwrite existing tables.
Default is True (for now)
"""
logging.info('\nGenerate interactive timeseries figures')
# Eventually read from INI
plot_var_list = ['NDVI_TOA', 'ALBEDO_SUR', 'TS', 'NDWI_TOA', 'EVI_SUR']
# plot_var_list = [
# 'NDVI_TOA', 'ALBEDO_SUR', 'TS', 'NDWI_TOA',
# 'CLOUD_SCORE', 'FMASK_PCT']
output_folder = 'figures'
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='FIGURES')
inputs.parse_section(ini, section='BEAMER')
# Output paths
output_ws = os.path.join(ini['SUMMARY']['output_ws'], output_folder)
if not os.path.isdir(output_ws):
os.makedirs(output_ws)
# Start/end year
year_list = list(
range(ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(
utils.wrapped_range(ini['INPUTS']['start_month'],
ini['INPUTS']['end_month'], 1, 12))
doy_list = list(
utils.wrapped_range(ini['INPUTS']['start_doy'],
ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(
utils.month_range(gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(', '.join(
map(str, gridmet_months))))
# Read in the zonal stats CSV
logging.debug(' Reading zonal stats CSV file')
input_df = pd.read_csv(
os.path.join(ini['ZONAL_STATS']['output_ws'],
ini['BEAMER']['output_name']))
logging.debug(input_df.head())
logging.debug(' Filtering Landsat dataframe')
input_df = input_df[input_df['PIXEL_COUNT'] > 0]
# # This assumes that there are L5/L8 images in the dataframe
# if not input_df.empty:
# max_pixel_count = max(input_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if ini['INPUTS']['fid_keep_list']:
input_df = input_df[input_df['ZONE_FID'].isin(
ini['INPUTS']['fid_keep_list'])]
if ini['INPUTS']['fid_skip_list']:
input_df = input_df[~input_df['ZONE_FID'].
isin(ini['INPUTS']['fid_skip_list'])]
if year_list:
input_df = input_df[input_df['YEAR'].isin(year_list)]
if month_list:
input_df = input_df[input_df['MONTH'].isin(month_list)]
if doy_list:
input_df = input_df[input_df['DOY'].isin(doy_list)]
if ini['INPUTS']['path_keep_list']:
input_df = input_df[input_df['PATH'].isin(
ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list']
and ini['INPUTS']['row_keep_list'] != ['XXX']):
input_df = input_df[input_df['ROW'].isin(
ini['INPUTS']['row_keep_list'])]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])
])
input_df = input_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])
])
input_df = input_df[np.logical_not(
scene_id_df.isin(ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[~input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list'])]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not input_df.empty:
logging.debug(' Maximum QA: {0}'.format(ini['SUMMARY']['max_qa']))
input_df = input_df[input_df['QA'] <= ini['SUMMARY']['max_qa']]
# First filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not input_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
input_df = input_df[
input_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not input_df.empty:
input_df['FMASK_PCT'] = 100 * (input_df['FMASK_COUNT'] /
input_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
input_df = input_df[
input_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not input_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = ((input_df['PLATFORM'] == 'LE07') &
((input_df['YEAR'] >= 2004) |
((input_df['YEAR'] == 2003) &
(input_df['DOY'] > 151))))
slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / input_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / max_pixel_count)
input_df = input_df[((slc_off_pct >= ini['SUMMARY']['min_slc_off_pct'])
& slc_off_mask) | (~slc_off_mask)]
if input_df.empty:
logging.error(' Empty dataframe after filtering, exiting')
return False
# Process each zone separately
logging.debug(input_df.head())
zone_name_list = sorted(list(set(input_df['ZONE_NAME'].values)))
for zone_name in zone_name_list:
logging.info('ZONE: {}'.format(zone_name))
# The names are currently stored in the CSV as spaces
zone_output_name = zone_name.replace(' ', '_')
zone_df = input_df[input_df['ZONE_NAME'] == zone_name]
if zone_df.empty:
logging.info(' Empty zone dataframe, skipping zone')
continue
# Output file paths
output_doy_path = os.path.join(
output_ws, '{}_timeseries_doy.html'.format(zone_output_name))
output_date_path = os.path.join(
output_ws, '{}_timeseries_date.html'.format(zone_output_name))
# # Check for QA field
# if 'QA' not in zone_df.columns.values:
# # logging.warning(
# # ' WARNING: QA field not present in CSV\n'
# # ' To compute QA/QC values, please run "ee_summary_qaqc.py"\n'
# # ' Script will continue with no QA/QC values')
# zone_df['QA'] = 0
# # raw_input('ENTER')
# # logging.error(
# # '\nPlease run the "ee_summary_qaqc.py" script '
# # 'to compute QA/QC values\n')
# # sys.exit()
# Check that plot variables are present
for plot_var in plot_var_list:
if plot_var not in zone_df.columns.values:
logging.error(' The variable {} does not exist in the '
'dataframe'.format(plot_var))
sys.exit()
# if ini['INPUTS']['scene_id_keep_list']:
# # Replace XXX with primary ROW value for checking skip list SCENE_ID
# scene_id_df = pd.Series([
# s.replace('XXX', '{:03d}'.format(int(r)))
# for s, r in zip(zone_df['SCENE_ID'], zone_df['ROW'])])
# zone_df = zone_df[scene_id_df.isin(
# ini['INPUTS']['scene_id_keep_list']).values]
# # This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# # zone_df = zone_df[zone_df['SCENE_ID'].isin(
# # ini['INPUTS']['scene_id_keep_list'])]
# if ini['INPUTS']['scene_id_skip_list']:
# # Replace XXX with primary ROW value for checking skip list SCENE_ID
# scene_id_df = pd.Series([
# s.replace('XXX', '{:03d}'.format(int(r)))
# for s, r in zip(zone_df['SCENE_ID'], zone_df['ROW'])])
# zone_df = zone_df[np.logical_not(scene_id_df.isin(
# ini['INPUTS']['scene_id_skip_list']).values)]
# # This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# # zone_df = zone_df[np.logical_not(zone_df['SCENE_ID'].isin(
# # ini['INPUTS']['scene_id_skip_list']))]
# Compute colors for each QA value
logging.debug(' Building column data source')
qa_values = sorted(list(set(zone_df['QA'].values)))
colors = {
qa: "#%02x%02x%02x" % (int(r), int(g), int(b))
for qa, (
r, g, b,
_) in zip(qa_values, 255 *
cm.viridis(mpl.colors.Normalize()(qa_values)))
}
logging.debug(' QA values: {}'.format(', '.join(map(str, qa_values))))
# Unpack the data by QA type to support interactive legends
sources = dict()
for qa_value in qa_values:
qa_df = zone_df[zone_df['QA'] == qa_value]
qa_data = {
'INDEX':
list(range(len(qa_df.index))),
'PLATFORM':
qa_df['PLATFORM'],
'DATE':
pd.to_datetime(qa_df['DATE']),
'DATE_STR':
pd.to_datetime(
qa_df['DATE']).map(lambda x: x.strftime('%Y-%m-%d')),
'DOY':
qa_df['DOY'].values,
'QA':
qa_df['QA'].values,
'COLOR': [colors[qa] for qa in qa_df['QA'].values]
}
for plot_var in plot_var_list:
if plot_var in qa_df.columns.values:
qa_data.update({plot_var: qa_df[plot_var].values})
sources[qa_value] = bokeh.models.ColumnDataSource(qa_data)
tooltips = [("LANDSAT", "@PLATFORM"), ("DATE", "@TIME"),
("DOY", "@DOY")]
# Selection
hover_circle = Circle(fill_color='#ff0000', line_color='#ff0000')
selected_circle = Circle(fill_color='COLOR', line_color='COLOR')
nonselected_circle = Circle(fill_color='#aaaaaa', line_color='#aaaaaa')
# Plot the data by DOY
logging.debug(' Building DOY timeseries figure')
if os.path.isfile(output_doy_path):
os.remove(output_doy_path)
output_file(output_doy_path, title=zone_name)
figure_args = dict(
plot_width=750,
plot_height=250,
title=None,
tools="xwheel_zoom,xpan,xbox_zoom,reset,box_select",
# tools="xwheel_zoom,xpan,xbox_zoom,reset,tap",
active_scroll="xwheel_zoom")
plot_args = dict(size=4, alpha=0.9, color='COLOR')
if ini['SUMMARY']['max_qa'] > 0:
plot_args['legend'] = 'QA'
figures = []
for plot_i, plot_var in enumerate(plot_var_list):
if plot_i == 0:
f = figure(
# x_range=Range1d(1, 366, bounds=(1, 366)),
y_axis_label=plot_var,
**figure_args)
else:
f = figure(x_range=f.x_range,
y_axis_label=plot_var,
**figure_args)
for qa, source in sorted(sources.items()):
r = f.circle('DOY', plot_var, source=source, **plot_args)
r.hover_glyph = hover_circle
r.selection_glyph = selected_circle
r.nonselection_glyph = nonselected_circle
r.muted_glyph = nonselected_circle
# DEADBEEF - This will display high QA points as muted
# if qa > ini['SUMMARY']['max_qa']:
# r.muted = True
# # r.visible = False
f.add_tools(bokeh.models.HoverTool(tooltips=tooltips))
# if ini['SUMMARY']['max_qa'] > 0:
f.legend.location = "top_left"
f.legend.click_policy = "hide"
# f.legend.click_policy = "mute"
f.legend.orientation = "horizontal"
figures.append(f)
# Try to not allow more than 4 plots in a column
p = gridplot(figures,
ncols=len(plot_var_list) // 3,
sizing_mode='stretch_both')
if show_flag:
show(p)
save(p)
# Plot the data by DATE
logging.debug(' Building date timeseries figure')
if os.path.isfile(output_date_path):
os.remove(output_date_path)
output_file(output_date_path, title=zone_name)
figure_args = dict(
plot_width=750,
plot_height=250,
title=None,
tools="xwheel_zoom,xpan,xbox_zoom,reset,box_select",
# tools="xwheel_zoom,xpan,xbox_zoom,reset,tap",
active_scroll="xwheel_zoom",
x_axis_type="datetime",
)
plot_args = dict(size=4, alpha=0.9, color='COLOR')
if ini['SUMMARY']['max_qa'] > 0:
plot_args['legend'] = 'QA'
figures = []
for plot_i, plot_var in enumerate(plot_var_list):
if plot_i == 0:
f = figure(
# x_range=Range1d(x_limit[0], x_limit[1], bounds=x_limit),
y_axis_label=plot_var,
**figure_args)
else:
f = figure(x_range=f.x_range,
y_axis_label=plot_var,
**figure_args)
if plot_var == 'TS':
f.y_range.bounds = (270, None)
for qa, source in sorted(sources.items()):
r = f.circle('DATE', plot_var, source=source, **plot_args)
r.hover_glyph = hover_circle
r.selection_glyph = selected_circle
r.nonselection_glyph = nonselected_circle
r.muted_glyph = nonselected_circle
# DEADBEEF - This will display high QA points as muted
# if qa > ini['SUMMARY']['max_qa']:
# r.muted = True
# # r.visible = False
f.add_tools(bokeh.models.HoverTool(tooltips=tooltips))
# if ini['SUMMARY']['max_qa'] > 0:
f.legend.location = "top_left"
f.legend.click_policy = "hide"
# f.legend.click_policy = "mute"
f.legend.orientation = "horizontal"
figures.append(f)
# Try to not allow more than 4 plots in a column
p = gridplot(figures,
ncols=len(plot_var_list) // 3,
sizing_mode='stretch_both')
if show_flag:
show(p)
save(p)
# Pause after each iteration if show is True
if show_flag:
input('Press ENTER to continue')
def main(ini_path, show_flag=False, overwrite_flag=False):
"""Generate Beamer ETg summary figures
Args:
ini_path (str):
show_flag (bool): if True, show the figures in the browser.
Default is False.
overwrite_flag (bool): if True, overwrite existing figures
Default is True (for now)
"""
logging.info('\nGenerate Beamer ETg summary figures')
ncolors = [
'#348ABD', '#7A68A6', '#A60628', '#467821',
'#CF4457', '#188487', '#E24A33']
xtick_fs = 8
ytick_fs = 8
xlabel_fs = 8
ylabel_fs = 8
ms = 2
figsize = (3.0, 2.5)
output_folder = 'figures'
# For unit conversion
eto_fields = [
'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI',
'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI',
'WY_ETO']
ppt_fields = ['WY_PPT']
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='FIGURES')
inputs.parse_section(ini, section='BEAMER')
# Output paths
output_ws = os.path.join(
ini['SUMMARY']['output_ws'], output_folder)
if not os.path.isdir(output_ws):
os.makedirs(output_ws)
# Start/end year
year_list = list(range(
ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(utils.wrapped_range(
ini['INPUTS']['start_month'], ini['INPUTS']['end_month'], 1, 12))
doy_list = list(utils.wrapped_range(
ini['INPUTS']['start_doy'], ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(utils.month_range(
gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(
', '.join(map(str, gridmet_months))))
# Read in the zonal stats CSV
logging.debug(' Reading zonal stats CSV file')
input_df = pd.read_csv(os.path.join(
ini['ZONAL_STATS']['output_ws'], ini['BEAMER']['output_name']))
logging.debug(input_df.head())
logging.debug(' Filtering Landsat dataframe')
input_df = input_df[input_df['PIXEL_COUNT'] > 0]
# # This assumes that there are L5/L8 images in the dataframe
# if not input_df.empty:
# max_pixel_count = max(input_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if ini['INPUTS']['fid_keep_list']:
input_df = input_df[input_df['ZONE_FID'].isin(
ini['INPUTS']['fid_keep_list'])]
if ini['INPUTS']['fid_skip_list']:
input_df = input_df[~input_df['ZONE_FID'].isin(
ini['INPUTS']['fid_skip_list'])]
if year_list:
input_df = input_df[input_df['YEAR'].isin(year_list)]
if month_list:
input_df = input_df[input_df['MONTH'].isin(month_list)]
if doy_list:
input_df = input_df[input_df['DOY'].isin(doy_list)]
if ini['INPUTS']['path_keep_list']:
input_df = input_df[
input_df['PATH'].isin(ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list'] and
ini['INPUTS']['row_keep_list'] != ['XXX']):
input_df = input_df[
input_df['ROW'].isin(ini['INPUTS']['row_keep_list'])]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])])
input_df = input_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])])
input_df = input_df[np.logical_not(scene_id_df.isin(
ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[~input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list'])]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not input_df.empty:
logging.debug(' Maximum QA: {0}'.format(
ini['SUMMARY']['max_qa']))
input_df = input_df[input_df['QA'] <= ini['SUMMARY']['max_qa']]
# First filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not input_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
input_df = input_df[
input_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not input_df.empty:
input_df['FMASK_PCT'] = 100 * (
input_df['FMASK_COUNT'] / input_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
input_df = input_df[
input_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not input_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = (
(input_df['PLATFORM'] == 'LE07') &
((input_df['YEAR'] >= 2004) |
((input_df['YEAR'] == 2003) & (input_df['DOY'] > 151))))
slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / input_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / max_pixel_count)
input_df = input_df[
((slc_off_pct >= ini['SUMMARY']['min_slc_off_pct']) & slc_off_mask) |
(~slc_off_mask)]
if input_df.empty:
logging.error(' Empty dataframe after filtering, exiting')
return False
# Process each zone separately
logging.debug(input_df.head())
zone_name_list = sorted(list(set(input_df['ZONE_NAME'].values)))
for zone_name in zone_name_list:
logging.info('ZONE: {}'.format(zone_name))
# The names are currently stored in the CSV with spaces
zone_output_name = zone_name.replace(' ', '_')
zone_df = input_df[input_df['ZONE_NAME'] == zone_name]
if zone_df.empty:
logging.info(' Empty zone dataframe, skipping zone')
continue
logging.debug(' Computing annual summaries')
annual_df = zone_df \
.groupby(['ZONE_NAME', 'YEAR']) \
.agg({
'PIXEL_COUNT': ['count', 'mean'],
'PIXEL_TOTAL': ['mean'],
'FMASK_COUNT': 'mean',
'FMASK_TOTAL': 'mean',
'CLOUD_SCORE': 'mean',
'ETSTAR_COUNT': 'mean',
'NDVI_TOA': 'mean',
'NDWI_TOA': 'mean',
'ALBEDO_SUR': 'mean',
'TS': 'mean',
'EVI_SUR': 'mean',
'ETSTAR_MEAN': 'mean',
'ETG_MEAN': 'mean',
'ETG_LPI': 'mean',
'ETG_UPI': 'mean',
'ETG_LCI': 'mean',
'ETG_UCI': 'mean',
'ET_MEAN': 'mean',
'ET_LPI': 'mean',
'ET_UPI': 'mean',
'ET_LCI': 'mean',
'ET_UCI': 'mean',
'WY_ETO': 'mean',
'WY_PPT': 'mean'
})
annual_df.columns = annual_df.columns.map('_'.join)
annual_df = annual_df.rename(columns={
'PIXEL_COUNT_count': 'SCENE_COUNT',
'PIXEL_COUNT_mean': 'PIXEL_COUNT'})
annual_df.rename(
columns=lambda x: str(x).replace('_mean', ''), inplace=True)
annual_df['SCENE_COUNT'] = annual_df['SCENE_COUNT'].astype(np.int)
annual_df['PIXEL_COUNT'] = annual_df['PIXEL_COUNT'].astype(np.int)
annual_df['PIXEL_TOTAL'] = annual_df['PIXEL_TOTAL'].astype(np.int)
annual_df['FMASK_COUNT'] = annual_df['FMASK_COUNT'].astype(np.int)
annual_df['FMASK_TOTAL'] = annual_df['FMASK_TOTAL'].astype(np.int)
annual_df['ETSTAR_COUNT'] = annual_df['ETSTAR_COUNT'].astype(np.int)
annual_df = annual_df.reset_index()
# Convert ETo units
if (ini['BEAMER']['eto_units'] == 'mm' and
ini['FIGURES']['eto_units'] == 'mm'):
pass
elif (ini['BEAMER']['eto_units'] == 'mm' and
ini['FIGURES']['eto_units'] == 'in'):
annual_df[eto_fields] /= (25.4)
elif (ini['BEAMER']['eto_units'] == 'mm' and
ini['FIGURES']['eto_units'] == 'ft'):
annual_df[eto_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not ' +
'currently supported, exiting').format(
ini['BEAMER']['eto_units'], ini['FIGURES']['eto_units']))
sys.exit()
# Convert PPT units
if (ini['BEAMER']['ppt_units'] == 'mm' and
ini['FIGURES']['ppt_units'] == 'mm'):
pass
elif (ini['BEAMER']['ppt_units'] == 'mm' and
ini['FIGURES']['ppt_units'] == 'in'):
annual_df[ppt_fields] /= (25.4)
elif (ini['BEAMER']['ppt_units'] == 'mm' and
ini['FIGURES']['ppt_units'] == 'ft'):
annual_df[ppt_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not '
'currently supported, exiting').format(
ini['BEAMER']['ppt_units'], ini['FIGURES']['ppt_units']))
sys.exit()
logging.debug(' Generating figures')
zone_df = annual_df[annual_df['ZONE_NAME'] == zone_name]
year_min, year_max = min(zone_df['YEAR']), max(zone_df['YEAR'])
# Set default PPT min/max scaling
ppt_min = 0
if ini['FIGURES']['ppt_units'] == 'mm':
ppt_max = 100 * math.ceil((max(zone_df['WY_PPT']) + 100) / 100)
elif ini['FIGURES']['ppt_units'] == 'ft':
ppt_max = 0.2 * math.ceil((max(zone_df['WY_PPT']) + 0.1) / 0.2)
else:
ppt_max = 1.2 * max(zone_df['WY_PPT'])
logging.debug(' EVI vs PPT')
figure_path = os.path.join(
output_ws, '{}_evi.png'.format(zone_output_name))
fig = plt.figure(figsize=figsize)
ax1 = fig.add_axes([0.20, 0.21, 0.65, 0.75])
ax1.set_xlabel('Year', fontsize=xlabel_fs)
ax2 = ax1.twinx()
ax1.plot(
zone_df['YEAR'].values, zone_df['WY_PPT'],
marker='o', c='0.5', ms=ms, label='WY PPT')
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_xlim([year_min - 1, year_max + 1])
ax1.set_ylim([ppt_min, ppt_max])
ax1.tick_params(axis='y', labelsize=ytick_fs)
ax1.tick_params(axis='x', labelsize=xtick_fs)
ax1.tick_params(axis='x', which='both', top='off')
ax1.xaxis.set_minor_locator(MultipleLocator(1))
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
ax1.set_ylabel(
'PPT [{}/yr]'.format(ini['FIGURES']['ppt_units']),
fontsize=ylabel_fs)
ax2.plot(
zone_df['YEAR'].values, zone_df['EVI_SUR'].values,
marker='o', c=ncolors[0], ms=ms,
label='EVI')
ax1.plot(0, 0, marker='o', c=ncolors[0], ms=ms, label='EVI')
ax2.yaxis.tick_left()
ax2.yaxis.set_label_position("left")
ax2.set_ylim([
0.05 * math.floor((min(zone_df['EVI_SUR']) - 0.01) / 0.05),
0.05 * math.ceil((max(zone_df['EVI_SUR']) + 0.01) / 0.05)])
ax2.tick_params(axis='y', labelsize=ytick_fs)
ax2.set_ylabel('EVI [dimensionless]', fontsize=ylabel_fs)
ax1.legend(
loc='upper right', frameon=False, fontsize=6, numpoints=1)
if overwrite_flag or not os.path.isfile(figure_path):
plt.savefig(figure_path, dpi=300)
plt.close()
del fig, ax1, ax2
logging.debug(' ETo vs PPT')
figure_path = os.path.join(
output_ws, '{}_eto.png'.format(zone_output_name))
fig = plt.figure(figsize=figsize)
ax1 = fig.add_axes([0.18, 0.21, 0.67, 0.75])
ax1.set_xlabel('Year', fontsize=xlabel_fs)
ax2 = ax1.twinx()
ax1.plot(
zone_df['YEAR'].values, zone_df['WY_PPT'],
marker='o', c='0.5', ms=ms, label='WY PPT')
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_xlim([year_min - 1, year_max + 1])
ax1.set_ylim([ppt_min, ppt_max])
ax1.tick_params(axis='y', labelsize=ytick_fs)
ax1.tick_params(axis='x', labelsize=xtick_fs)
ax1.tick_params(axis='x', which='both', top='off')
ax1.xaxis.set_minor_locator(MultipleLocator(1))
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
ax1.set_ylabel(
'PPT [{}/yr]'.format(ini['FIGURES']['ppt_units']),
fontsize=ylabel_fs)
ax2.plot(
zone_df['YEAR'].values, zone_df['WY_ETO'].values,
marker='o', c=ncolors[1], ms=ms, label='ETo')
ax1.plot(0, 0, marker='o', c=ncolors[1], ms=ms, label='ETo')
ax2.yaxis.tick_left()
ax2.yaxis.set_label_position("left")
ax2.set_ylim([
max(0, 0.9 * min(zone_df['WY_ETO'])),
1.1 * max(zone_df['WY_ETO'])])
# ax2.set_ylim([
# max(0, 100 * math.floor((min(zone_df['WY_ETO']) - 100) / 100)),
# 100 * math.ceil((max(zone_df['WY_ETO']) + 100) / 100)])
ax2.tick_params(axis='y', labelsize=ytick_fs)
ax2.set_ylabel(
'ETo [{}/yr]'.format(ini['FIGURES']['eto_units']),
fontsize=ylabel_fs)
ax1.legend(loc='upper right', frameon=False, fontsize=6, numpoints=1)
if overwrite_flag or not os.path.isfile(figure_path):
plt.savefig(figure_path, dpi=300)
plt.close()
del fig, ax1, ax2
logging.debug(' ET vs PPT')
figure_path = os.path.join(
output_ws, '{}_et.png'.format(zone_output_name))
fig = plt.figure(figsize=figsize)
ax1 = fig.add_axes([0.18, 0.21, 0.67, 0.75])
ax1.set_xlabel('Year', fontsize=xlabel_fs)
ax2 = ax1.twinx()
ax1.plot(
zone_df['YEAR'].values, zone_df['WY_PPT'],
marker='o', c='0.5', ms=ms, label='WY PPT')
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_xlim([year_min - 1, year_max + 1])
ax1.set_ylim([ppt_min, ppt_max])
ax1.tick_params(axis='y', labelsize=ytick_fs)
ax1.tick_params(axis='x', labelsize=xtick_fs)
ax1.tick_params(axis='x', which='both', top='off')
ax1.xaxis.set_minor_locator(MultipleLocator(1))
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
ax1.set_ylabel(
'PPT [{}/yr]'.format(ini['FIGURES']['ppt_units']),
fontsize=ylabel_fs)
ax2.plot(
zone_df['YEAR'].values, zone_df['ET_UCI'].values,
marker='', c=ncolors[2], alpha=0.5, lw=0.75)
ax2.plot(
zone_df['YEAR'].values, zone_df['ET_LCI'].values,
marker='', c=ncolors[2], alpha=0.5, lw=0.75)
ax2.plot(
zone_df['YEAR'].values, zone_df['ET_MEAN'].values,
marker='o', c=ncolors[2], ms=ms, label='ET')
ax1.plot(0, 0, marker='o', c=ncolors[2], ms=ms, label='ET')
ax2.yaxis.tick_left()
ax2.yaxis.set_label_position("left")
ax2.set_ylim([
max(0, 0.9 * min(zone_df['ET_LCI'])),
1.1 * max(zone_df['ET_UCI'])])
# ax2.set_ylim([
# max(0, 100 * math.floor((min(zone_df['ET_MEAN']) - 100) / 100)),
# 100 * math.ceil((max(zone_df['ET_MEAN']) + 100) / 100)])
ax2.tick_params(axis='y', labelsize=ytick_fs)
ax2.set_ylabel(
'ET [{}/yr]'.format(ini['FIGURES']['eto_units']),
fontsize=ylabel_fs)
ax1.legend(loc='upper right', frameon=False, fontsize=6, numpoints=1)
if overwrite_flag or not os.path.isfile(figure_path):
plt.savefig(figure_path, dpi=300)
plt.close()
del fig, ax1, ax2
logging.debug(' ETg vs PPT')
figure_path = os.path.join(
output_ws, '{}_etg.png'.format(zone_output_name))
fig = plt.figure(figsize=figsize)
ax1 = fig.add_axes([0.18, 0.21, 0.67, 0.75])
ax1.set_xlabel('Year', fontsize=xlabel_fs)
ax2 = ax1.twinx()
ax1.plot(
zone_df['YEAR'].values, zone_df['WY_PPT'],
marker='o', c='0.5', ms=ms, label='WY PPT')
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax1.set_xlim([year_min - 1, year_max + 1])
ax1.set_ylim([ppt_min, ppt_max])
ax1.tick_params(axis='y', labelsize=ytick_fs)
ax1.tick_params(axis='x', labelsize=xtick_fs)
ax1.tick_params(axis='x', which='both', top='off')
ax1.xaxis.set_minor_locator(MultipleLocator(1))
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
tick.set_ha('right')
ax1.set_ylabel(
'PPT [{}/yr]'.format(ini['FIGURES']['ppt_units']),
fontsize=ylabel_fs)
ax2.plot(
zone_df['YEAR'].values, zone_df['ETG_UCI'].values,
marker='', c=ncolors[3], alpha=0.5, lw=0.75)
ax2.plot(
zone_df['YEAR'].values, zone_df['ETG_LCI'].values,
marker='', c=ncolors[3], alpha=0.5, lw=0.75)
ax2.plot(
zone_df['YEAR'].values, zone_df['ETG_MEAN'].values,
marker='o', c=ncolors[3], ms=ms, label='ETg')
ax1.plot(0, 0, marker='o', c=ncolors[3], ms=ms, label='ETg')
ax2.yaxis.tick_left()
ax2.yaxis.set_label_position("left")
ax2.set_ylim([
max(0, 0.9 * min(zone_df['ETG_LCI'])),
1.1 * max(zone_df['ETG_UCI'])])
# ax2.set_ylim([
# max(0, 100 * math.floor((min(zone_df['ETG_MEAN']) - 100) / 100)),
# 100 * math.ceil((max(zone_df['ETG_MEAN']) + 100) / 100)])
ax2.tick_params(axis='y', labelsize=ytick_fs)
ax2.set_ylabel(
'ETg [{}/yr]'.format(ini['FIGURES']['eto_units']),
fontsize=ylabel_fs)
ax1.legend(loc='upper right', frameon=False, fontsize=6, numpoints=1)
if overwrite_flag or not os.path.isfile(figure_path):
plt.savefig(figure_path, dpi=300)
plt.close()
del fig, ax1, ax2
logging.debug(' Complimentary')
figure_path = os.path.join(
output_ws, '{}_complimentary.png'.format(zone_output_name))
fig = plt.figure(figsize=(3, 2.5))
ax = fig.add_axes([0.18, 0.16, 0.78, 0.80])
# ax = fig.add_axes([0.18, 0.21, 0.67, 0.70])
ax.plot(
zone_df['WY_PPT'].values, zone_df['WY_ETO'].values,
linestyle='', marker='o', c=ncolors[1], ms=3, label='ETo')
ax.plot(
zone_df['WY_PPT'].values, zone_df['ET_MEAN'].values,
linestyle='', marker='o', c=ncolors[2], ms=3, label='ET')
# xmax = 100 * math.ceil(max(zone_df['WY_PPT']) / 100)
# ymax = 200 * math.ceil((max(zone_df['WY_ETO']) + 200) / 200)
ax.set_xlim([ppt_min, ppt_max])
ax.set_ylim([0, 1.2 * max(zone_df['WY_ETO'])])
ax.tick_params(axis='y', labelsize=ytick_fs)
ax.tick_params(axis='x', labelsize=xtick_fs)
ax.tick_params(axis='x', which='both', top='off')
ax.tick_params(axis='y', which='both', right='off')
ax.set_xlabel('PPT [{}/yr]'.format(
ini['FIGURES']['ppt_units']), fontsize=xlabel_fs)
ax.set_ylabel('ET and ETo [{}/yr]'.format(
ini['FIGURES']['eto_units']), fontsize=ylabel_fs)
ax.legend(loc='upper right', frameon=False, fontsize=6, numpoints=1)
if overwrite_flag or not os.path.isfile(figure_path):
plt.savefig(figure_path, dpi=300)
plt.close()
del fig, ax
def ee_image_download(ini_path=None, overwrite_flag=False):
"""Earth Engine Annual Mean Image Download
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nEarth Engine EDDI Image Download')
# 12 month EDDI
aggregation_days = 365
export_name = 'eddi_12month'
output_name = 'eddi.12month'
eddi_date_list = [
'0131', '0228', '0331', '0430', '0531', '0630', '0731', '0831', '0930',
'1031', '1130', '1231'
]
# eddi_date_list = ['0930', '1231']
# eddi_date_list = ['{:02d}01'.format(m) for m in range(1, 13)]
# eddi_date_list = []
eddi_folder = 'eddi'
# Do we need to support separate EDDI years?
# start_year = 1984
# end_year = 2016
#
climo_year_start = 1979
climo_year_end = 2017
# Read config file
# ini = inputs.ini_parse(ini_path, section='IMAGE')
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='IMAGES')
nodata_value = -9999
# Manually set output spatial reference
logging.info('\nHardcoding GRIDMET snap, cellsize and spatial reference')
ini['output_x'], ini['output_y'] = -124.79299639209513, 49.41685579737572
ini['SPATIAL']['cellsize'] = 0.041666001963701
# ini['SPATIAL']['cellsize'] = [0.041666001963701, 0.041666001489718]
# ini['output_x'] = -124.79166666666666666667
# ini['output_y'] = 25.04166666666666666667
# ini['SPATIAL']['cellsize'] = 1. / 24
ini['SPATIAL']['osr'] = gdc.epsg_osr(4326)
# ini['SPATIAL']['osr'] = gdc.epsg_osr(4269)
ini['SPATIAL']['crs'] = 'EPSG:4326'
logging.debug(' Snap: {} {}'.format(ini['output_x'], ini['output_y']))
logging.debug(' Cellsize: {}'.format(ini['SPATIAL']['cellsize']))
logging.debug(' OSR: {}'.format(ini['SPATIAL']['osr']))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Need zone_shp_path projection to build EE geometries
zone_osr = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone_proj = gdc.osr_wkt(zone_osr)
# zone_proj = ee.Projection(zone_proj).wkt().getInfo()
# zone_proj = zone_proj.replace('\n', '').replace(' ', '')
logging.debug(' Zone Projection: {}'.format(zone_proj))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get current running tasks
tasks = utils.get_ee_tasks()
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
# Build EE geometry object for zonal stats
zone_geom = ee.Geometry(zone_json, zone_proj, False)
# Project the zone_geom to the GRIDMET projection
# if zone_proj != output_proj:
zone_geom = zone_geom.transform(ini['SPATIAL']['crs'], 0.001)
# Get the extent from the Earth Engine geometry object?
zone_extent = zone_geom.bounds().getInfo()['coordinates'][0]
zone_extent = gdc.Extent([
min(zip(*zone_extent)[0]),
min(zip(*zone_extent)[1]),
max(zip(*zone_extent)[0]),
max(zip(*zone_extent)[1])
])
# # Use GDAL and geometry json to build extent, transform, and shape
# zone_extent = gdc.Extent(
# ogr.CreateGeometryFromJson(json.dumps(zone_json)).GetEnvelope())
# # zone_extent = gdc.Extent(zone_geom.GetEnvelope())
# zone_extent.ymin, zone_extent.xmax = zone_extent.xmax, zone_extent.ymin
# Adjust extent to match raster
zone_extent = zone_extent.adjust_to_snap('EXPAND', ini['output_x'],
ini['output_y'],
ini['SPATIAL']['cellsize'])
zone_geo = zone_extent.geo(ini['SPATIAL']['cellsize'])
zone_transform = gdc.geo_2_ee_transform(zone_geo)
zone_transform = '[' + ','.join(map(str, zone_transform)) + ']'
zone_shape = zone_extent.shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone_shape))
logging.debug(' Zone Transform: {}'.format(zone_transform))
logging.debug(' Zone Extent: {}'.format(zone_extent))
# logging.debug(' Geom: {}'.format(zone_geom.getInfo()))
# output_transform = zone_transform[:]
output_transform = '[' + ','.join(map(str, zone_transform)) + ']'
output_shape = '[{1}x{0}]'.format(*zone_shape)
logging.debug(' Output Projection: {}'.format(ini['SPATIAL']['crs']))
logging.debug(' Output Transform: {}'.format(output_transform))
logging.debug(' Output Shape: {}'.format(output_shape))
zone_eddi_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
eddi_folder)
if not os.path.isdir(zone_eddi_ws):
os.makedirs(zone_eddi_ws)
# GRIDMET PDSI
# Process each image in the collection by date
export_list = []
export_list = list(
date_range(datetime.datetime(ini['INPUTS']['start_year'], 1, 1),
datetime.datetime(ini['INPUTS']['end_year'], 12, 31),
skip_leap_days=True))
# Filter list to only keep last dekad of October and December
if eddi_date_list:
export_list = [
tgt_dt for tgt_dt in export_list
if tgt_dt.strftime('%m%d') in eddi_date_list
]
for tgt_dt in export_list:
date_str = tgt_dt.strftime('%Y%m%d')
logging.info('{} {}'.format(tgt_dt.strftime('%Y-%m-%d'),
output_name))
if tgt_dt >= datetime.datetime.today():
logging.info(' Date after current date, skipping')
continue
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_{}'.format(ini['INPUTS']['zone_filename'],
date_str, export_name.lower())
output_id = '{}_{}'.format(date_str, output_name)
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_eddi_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
continue
elif os.path.isfile(output_path):
logging.debug(' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
eddi_image = ee_eddi_image(tgt_dt.strftime('%Y-%m-%d'),
agg_days=aggregation_days,
variable='eddi',
year_start=climo_year_start,
year_end=climo_year_end)
logging.debug(' Building export task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=eddi_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'gdrive':
# task = ee.batch.Export.image.toCloudStorage(
# image=eddi_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
def ee_image_download(ini_path=None, overwrite_flag=False):
"""Earth Engine Annual Mean Image Download
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nEarth Engine Landsat Image Download')
images_folder = 'landsat'
if overwrite_flag:
logging.warning(
'\nAre you sure you want to overwrite existing images?')
input('Press ENTER to continue')
# Regular expression to pull out Landsat scene_id
# landsat_re = re.compile(
# 'L[ETC][4578]\d{6}(?P<YEAR>\d{4})(?P<DOY>\d{3})\D{3}\d{2}')
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='IMAGES')
nodata_value = -9999
# Float32/Float64
float_output_type = 'Float32'
float_nodata_value = np.finfo(np.float32).min
# Byte/Int16/UInt16/UInt32/Int32
int_output_type = 'Byte'
int_nodata_value = 255
int_bands = ['cloud_score', 'fmask']
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'], zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Check if the zone_names are unique
# Eventually support merging common zone_names
if len(set([z[1] for z in zone_geom_list])) != len(zone_geom_list):
logging.error(
'\nERROR: There appear to be duplicate zone ID/name values.'
'\n Currently, the values in "{}" must be unique.'
'\n Exiting.'.format(ini['INPUTS']['zone_field']))
return False
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())]]
ini['INPUTS']['zone_field'] = ''
# Need zone_shp_path projection to build EE geometries
zone_osr = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone_proj = gdc.osr_wkt(zone_osr)
# zone_proj = ee.Projection(zone_proj).wkt().getInfo()
# zone_proj = zone_proj.replace('\n', '').replace(' ', '')
# logging.debug(' Zone Projection: {}'.format(zone_proj))
# Check that shapefile has matching spatial reference
if not gdc.matching_spatref(zone_osr, ini['SPATIAL']['osr']):
logging.warning(' Zone OSR:\n{}\n'.format(zone_osr))
logging.warning(' Output OSR:\n{}\n'.format(
ini['SPATIAL']['osr']))
logging.warning(' Zone Proj4: {}'.format(zone_osr.ExportToProj4()))
logging.warning(' Output Proj4: {}'.format(
ini['SPATIAL']['osr'].ExportToProj4()))
logging.warning(
'\nWARNING: \n'
'The output and zone spatial references do not appear to match\n'
'This will likely cause problems!')
input('Press ENTER to continue')
else:
logging.debug(' Zone Projection:\n{}\n'.format(
zone_osr.ExportToWkt()))
logging.debug(' Output Projection:\n{}\n'.format(
ini['SPATIAL']['osr'].ExportToWkt()))
logging.debug(' Output Cellsize: {}'.format(
ini['SPATIAL']['cellsize']))
# Keyword arguments for ee_common.get_landsat_collection() and
# ee_common.get_landsat_image()
# Zone geom will be updated inside the loop
landsat_args = {
k: v for section in ['INPUTS']
for k, v in ini[section].items()
if k in [
'landsat4_flag', 'landsat5_flag',
'landsat7_flag', 'landsat8_flag',
'fmask_flag', 'acca_flag',
'start_year', 'end_year',
'start_month', 'end_month',
'start_doy', 'end_doy',
'scene_id_keep_list', 'scene_id_skip_list',
'path_keep_list', 'row_keep_list',
'refl_sur_method', 'adjust_method', 'mosaic_method']}
# landsat_args['start_date'] = start_date
# landsat_args['end_date'] = end_date
# For composite images, compute all components bands
landsat_args['products'] = ini['IMAGES']['download_bands'][:]
if 'refl_toa' in landsat_args['products']:
landsat_args['products'].extend([
'blue_toa', 'green_toa', 'red_toa',
'nir_toa', 'swir1_toa', 'swir2_toa'])
landsat_args['products'].remove('refl_toa')
if 'refl_sur' in landsat_args['products']:
landsat_args['products'].extend([
'blue_sur', 'green_sur', 'red_sur',
'nir_sur', 'swir1_sur', 'swir2_sur'])
landsat_args['products'].remove('refl_sur')
if 'tasseled_cap' in landsat_args['products']:
landsat_args['products'].extend([
'tc_green', 'tc_bright', 'tc_wet'])
landsat_args['products'].remove('tasseled_cap')
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get current running tasks
tasks = utils.get_ee_tasks()
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
# Build EE geometry object for zonal stats
zone_geom = ee.Geometry(
geo_json=zone_json, opt_proj=zone_proj, opt_geodesic=False)
landsat_args['zone_geom'] = zone_geom
# logging.debug(' Centroid: {}'.format(
# zone_geom.centroid(100).getInfo()['coordinates']))
# Use feature geometry to build extent, transform, and shape
zone_extent = gdc.Extent(
ogr.CreateGeometryFromJson(json.dumps(zone_json)).GetEnvelope())
# zone_extent = gdc.Extent(zone_geom.GetEnvelope())
zone_extent.ymin, zone_extent.xmax = zone_extent.xmax, zone_extent.ymin
zone_extent = zone_extent.buffer(ini['IMAGES']['image_buffer'])
zone_extent = zone_extent.adjust_to_snap(
'EXPAND', ini['SPATIAL']['snap_x'], ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone_geo = zone_extent.geo(ini['SPATIAL']['cellsize'])
zone_transform = gdc.geo_2_ee_transform(zone_geo)
zone_transform = '[' + ','.join(map(str, zone_transform)) + ']'
zone_shape = zone_extent.shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone_shape))
logging.debug(' Zone Transform: {}'.format(zone_transform))
logging.debug(' Zone Extent: {}'.format(zone_extent))
# logging.debug(' Zone Geom: {}'.format(zone_geom.getInfo()))
# output_transform = zone_transform[:]
output_transform = '[' + ','.join(map(str, zone_transform)) + ']'
output_shape = '{1}x{0}'.format(*zone_shape)
# logging.debug(' Image Transform: {}'.format(output_transform))
# logging.debug(' Image Shape: {}'.format(output_shape))
zone_images_ws = os.path.join(
ini['IMAGES']['output_ws'], zone_name, images_folder)
if not os.path.isdir(zone_images_ws):
os.makedirs(zone_images_ws)
# Move to EE common?
def get_collection_ids(image):
return ee.Feature(None, {'id': image.get('SCENE_ID')})
# Get list of available Landsat images
landsat_obj = ee_common.Landsat(landsat_args)
scene_id_list = [
f['properties']['id']
for f in landsat_obj.get_collection().map(
get_collection_ids).getInfo()['features']]
# Get list of unique image "dates"
# Keep scene_id components as string for set operation
# If not mosaicing images, include path/row in set
# otherwise set to None
if not ini['INPUTS']['mosaic_method']:
scene_id_list = set([
(image_id[12:20], image_id[0:4], image_id[5:8], image_id[8:11])
for image_id in scene_id_list])
else:
scene_id_list = set([
(image_id[12:20], image_id[0:4], None, None)
for image_id in scene_id_list])
logging.debug(' Scene Count: {}\n'.format(len(scene_id_list)))
# Process each image in the collection by date
# Leave scene_id components as strings
for date, landsat, path, row in sorted(scene_id_list):
scene_dt = datetime.datetime.strptime(date, '%Y%m%d')
year = scene_dt.strftime('%Y')
doy = scene_dt.strftime('%j')
# If not mosaicing images, include path/row in name
if not ini['INPUTS']['mosaic_method']:
landsat_str = '{}{}{}'.format(landsat, path, row)
else:
landsat_str = '{}'.format(landsat)
logging.info('{} {} (DOY {})'.format(
landsat.upper(), scene_dt.strftime('%Y-%m-%d'), doy))
zone_year_ws = os.path.join(zone_images_ws, year)
if not os.path.isdir(zone_year_ws):
os.makedirs(zone_year_ws)
# Get the prepped Landsat image by ID
landsat_image = ee.Image(landsat_obj.get_image(
landsat, year, doy, path, row))
# Clip using the feature geometry
if ini['IMAGES']['clip_landsat_flag']:
landsat_image = landsat_image.clip(zone_geom)
else:
landsat_image = landsat_image.clip(ee.Geometry.Rectangle(
list(zone_extent), ini['SPATIAL']['crs'], False))
# DEADBEEF - Display a single image
# ee_common.show_thumbnail(landsat_image.visualize(
# bands=['fmask', 'fmask', 'fmask'], min=0, max=4))
# ee_common.show_thumbnail(landsat_image.visualize(
# bands=['toa_red', 'toa_green', 'toa_blue'],
# min=0.05, max=0.35, gamma=1.4))
# return True
# Set the masked values to a nodata value
# so that the TIF can have a nodata value other than 0 set
landsat_image = landsat_image.unmask(nodata_value, False)
for band in ini['IMAGES']['download_bands']:
logging.debug(' Band: {}'.format(band))
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_{}_{}_{}'.format(
ini['INPUTS']['zone_filename'], date, doy,
landsat_str.lower(), band.lower())
output_id = '{}_{}_{}.{}'.format(
date, doy, landsat_str.lower(), band)
export_path = os.path.join(
ini['EXPORT']['export_ws'], export_id + '.tif')
output_path = os.path.join(
zone_year_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(
' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(
' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
if band in int_bands:
subprocess.check_output([
'gdalwarp',
'-ot', int_output_type, '-overwrite',
'-of', 'GTiff', '-co', 'COMPRESS=LZW',
'-srcnodata', str(nodata_value),
'-dstnodata', str(int_nodata_value),
export_path, output_path])
else:
subprocess.check_output([
'gdalwarp',
'-ot', float_output_type, '-overwrite',
'-of', 'GTiff', '-co', 'COMPRESS=LZW',
'-srcnodata', str(nodata_value),
'-dstnodata', '{:f}'.format(float_nodata_value),
export_path, output_path])
with open(os.devnull, 'w') as devnull:
subprocess.check_call(
['gdalinfo', '-stats', output_path],
stdout=devnull)
subprocess.check_output(
['gdalmanage', 'delete', export_path])
continue
elif os.path.isfile(output_path):
logging.debug(
' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(
' Task already submitted, skipping')
continue
# Should composites include Ts?
if band == 'refl_toa':
band_list = [
'blue_toa', 'green_toa', 'red_toa',
'nir_toa', 'swir1_toa', 'swir2_toa']
elif band == 'refl_sur':
band_list = [
'blue_sur', 'green_sur', 'red_sur',
'nir_sur', 'swir1_sur', 'swir2_sur']
elif band == 'tasseled_cap':
band_list = ['tc_bright', 'tc_green', 'tc_wet']
else:
band_list = [band]
band_image = landsat_image.select(band_list)
# CGM 2016-09-26 - Don't apply any cloud masks to images
# # Apply cloud mask before exporting
# if fmask_flag and band not in ['refl_sur', 'cloud', 'fmask']:
# fmask = ee.Image(landsat_image.select(['fmask']))
# cloud_mask = fmask.eq(2).Or(fmask.eq(3)).Or(fmask.eq(4)).Not()
# band_image = band_image.updateMask(cloud_mask)
logging.debug(' Building export task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
band_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'cloud':
# task = ee.batch.Export.image.toCloudStorage(
# band_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
def ee_beamer_et(ini_path=None, overwrite_flag=False):
"""Earth Engine Beamer ET Image Download
Args:
ini_path (str):
overwrite_flag (bool): if True, overwrite existing files
Returns:
None
"""
logging.info('\nEarth Engine Beamer Annual Mean ETg Image Download')
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='IMAGES')
inputs.parse_section(ini, section='BEAMER')
ini['IMAGES']['download_bands'] = [
'etg_mean', 'etg_lci', 'etg_uci', 'etg_lpi', 'etg_upi'
]
stat_list = ['mean', 'median']
nodata_value = -9999
zips_folder = 'zips'
annuals_folder = 'annuals'
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# zone_count = len(zone_geom_list)
# output_fmt = '_{0:0%sd}.csv' % str(int(math.log10(zone_count)) + 1)
# Check if the zone_names are unique
# Eventually support merging common zone_names
if len(set([z[1] for z in zone_geom_list])) != len(zone_geom_list):
logging.error(
'\nERROR: There appear to be duplicate zone ID/name values.'
'\n Currently, the values in "{}" must be unique.'
'\n Exiting.'.format(ini['INPUTS']['zone_field']))
return False
# Filter features by FID
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Set all zone specific parameters into a dictionary
zone = {}
# Need zone_shp_path projection to build EE geometries
zone['osr'] = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone['proj'] = gdc.osr_wkt(zone['osr'])
# zone['proj'] = ee.Projection(zone['proj']).wkt().getInfo()
# zone['proj'] = zone['proj'].replace('\n', '').replace(' ', '')
# logging.debug(' Zone Projection: {}'.format(zone['proj']))
# Check that shapefile has matching spatial reference
if not gdc.matching_spatref(zone['osr'], ini['SPATIAL']['osr']):
logging.warning(' Zone OSR:\n{}\n'.format(zone['osr']))
logging.warning(' Output OSR:\n{}\n'.format(
ini['SPATIAL']['osr'].ExportToWkt()))
logging.warning(' Zone Proj4: {}'.format(
zone['osr'].ExportToProj4()))
logging.warning(' Output Proj4: {}'.format(
ini['SPATIAL']['osr'].ExportToProj4()))
logging.warning(
'\nWARNING: \n'
'The output and zone spatial references do not appear to match\n'
'This will likely cause problems!')
input('Press ENTER to continue')
else:
logging.debug(' Zone Projection:\n{}\n'.format(
zone['osr'].ExportToWkt()))
logging.debug(' Output Projection:\n{}\n'.format(
ini['SPATIAL']['osr'].ExportToWkt()))
logging.debug(' Output Cellsize: {}'.format(
ini['SPATIAL']['cellsize']))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get list of path/row strings to centroid coordinates
if ini['INPUTS']['tile_keep_list']:
ini['INPUTS']['tile_geom'] = [
wrs2.tile_centroids[tile]
for tile in ini['INPUTS']['tile_keep_list']
if tile in wrs2.tile_centroids.keys()
]
ini['INPUTS']['tile_geom'] = ee.Geometry.MultiPoint(
ini['INPUTS']['tile_geom'], 'EPSG:4326')
else:
ini['INPUTS']['tile_geom'] = None
# Read in ETo and PPT data from file
if (ini['BEAMER']['eto_source'] == 'file'
or ini['BEAMER']['ppt_source'] == 'file'):
data_array = np.genfromtxt(ini['BEAMER']['data_path'],
delimiter=',',
names=True,
dtype=None)
data_fields = data_array.dtype.names
logging.debug(' CSV fields: {}'.format(', '.join(data_fields)))
# DEADBEEF - Compare fields names assuming all upper case
data_fields = [f.upper() for f in data_fields]
eto_dict = defaultdict(dict)
ppt_dict = defaultdict(dict)
for row in data_array:
z = str(row[data_fields.index(ini['BEAMER']['data_zone_field'])])
y = int(row[data_fields.index(ini['BEAMER']['data_year_field'])])
if ini['BEAMER']['eto_source'] == 'file':
# DEADBEEF - Compare fields names assuming all upper case
eto_dict[z][y] = row[data_fields.index(
ini['BEAMER']['data_eto_field'].upper())]
if ini['BEAMER']['ppt_source'] == 'file':
# DEADBEEF - Compare fields names assuming all upper case
ppt_dict[z][y] = row[data_fields.index(
ini['BEAMER']['data_ppt_field'].upper())]
# Get filtered/merged/prepped Landsat collection
landsat_args = {
k: v
for section in ['INPUTS'] for k, v in ini[section].items() if k in [
'landsat4_flag', 'landsat5_flag', 'landsat7_flag', 'landsat8_flag',
'fmask_flag', 'acca_flag', 'start_year', 'end_year', 'start_month',
'end_month', 'start_doy', 'end_doy', 'scene_id_keep_list',
'scene_id_skip_list', 'path_keep_list', 'row_keep_list',
'tile_geom', 'adjust_method', 'mosaic_method', 'refl_sur_method'
]
}
landsat_args['products'] = ['evi_sur']
landsat = ee_common.Landsat(landsat_args)
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone['fid'] = zone_fid
zone['name'] = zone_name.replace(' ', '_')
zone['json'] = zone_json
logging.info('ZONE: {} (FID: {})'.format(zone['name'], zone['fid']))
# Build EE geometry object for zonal stats
zone['geom'] = ee.Geometry(geo_json=zone['json'],
opt_proj=zone['proj'],
opt_geodesic=False)
# logging.debug(' Centroid: {}'.format(
# zone['geom'].centroid(100).getInfo()['coordinates']))
# Use feature geometry to build extent, transform, and shape
zone['extent'] = gdc.Extent(
ogr.CreateGeometryFromJson(json.dumps(zone['json'])).GetEnvelope())
# zone['extent'] = gdc.Extent(zone['geom'].GetEnvelope())
zone['extent'] = zone['extent'].ogrenv_swap()
zone['extent'] = zone['extent'].adjust_to_snap(
'EXPAND', ini['SPATIAL']['snap_x'], ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone['geo'] = zone['extent'].geo(ini['SPATIAL']['cellsize'])
zone['transform'] = gdc.geo_2_ee_transform(zone['geo'])
# zone['transform'] = '[' + ','.join(map(str, zone['transform'])) + ']'
zone['shape'] = zone['extent'].shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone['shape']))
logging.debug(' Zone Transform: {}'.format(zone['transform']))
logging.debug(' Zone Extent: {}'.format(zone['extent']))
# logging.debug(' Zone Geom: {}'.format(zone['geom'].getInfo()))
# Assume all pixels in all 14+2 images could be reduced
zone['max_pixels'] = zone['shape'][0] * zone['shape'][1]
logging.debug(' Max Pixels: {}'.format(zone['max_pixels']))
# Set output spatial reference
# Eventually allow user to manually set these
# output_crs = zone['proj']
logging.debug(' Image Projection: {}'.format(ini['SPATIAL']['crs']))
# output_transform = zone['transform'][:]
output_transform = '[' + ','.join(map(str, zone['transform'])) + ']'
output_shape = '{1}x{0}'.format(*zone['shape'])
logging.debug(' Image Transform: {}'.format(output_transform))
logging.debug(' Image Shape: {}'.format(output_shape))
zone_output_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name)
zone_zips_ws = os.path.join(zone_output_ws, zips_folder)
zone_annuals_ws = os.path.join(zone_output_ws, annuals_folder)
if not os.path.isdir(zone_zips_ws):
os.makedirs(zone_zips_ws)
if not os.path.isdir(zone_annuals_ws):
os.makedirs(zone_annuals_ws)
# Process date range by year
interval_cnt = 1
start_dt = datetime.datetime(ini['INPUTS']['start_year'], 1, 1)
end_dt = datetime.datetime(ini['INPUTS']['end_year'] + 1, 1,
1) - datetime.timedelta(0, 1)
for i, iter_start_dt in enumerate(
rrule.rrule(rrule.YEARLY,
interval=interval_cnt,
dtstart=start_dt,
until=end_dt)):
iter_end_dt = (iter_start_dt +
relativedelta.relativedelta(years=interval_cnt) -
datetime.timedelta(0, 1))
if ((ini['INPUTS']['start_month']
and iter_end_dt.month < ini['INPUTS']['start_month'])
or (ini['INPUTS']['end_month']
and iter_start_dt.month > ini['INPUTS']['end_month'])):
logging.debug(' {} {} skipping'.format(
iter_start_dt.date(), iter_end_dt.date()))
continue
elif (
(ini['INPUTS']['start_doy'] and
int(iter_end_dt.strftime('%j')) < ini['INPUTS']['start_doy'])
or
(ini['INPUTS']['end_doy'] and int(iter_start_dt.strftime('%j'))
> ini['INPUTS']['end_doy'])):
logging.debug(' {} {} skipping'.format(
iter_start_dt.date(), iter_end_dt.date()))
continue
else:
logging.info('{} {}'.format(iter_start_dt.date(),
iter_end_dt.date()))
year = iter_start_dt.year
# image_id = 'etg_{}_{}'.format(
image_id = '{}_{}'.format(zone_name.lower().replace(' ', '_'),
year)
zip_path = os.path.join(zone_zips_ws, image_id + '.zip')
# median_path = os.path.join(
# zone_output_ws, image_id + '.img')
logging.debug(' Zip: {}'.format(zip_path))
if os.path.isfile(zip_path) and overwrite_flag:
logging.debug(' Output already exists, removing zip')
os.remove(zip_path)
elif os.path.isfile(zip_path) and not overwrite_flag:
# Check that existing ZIP files can be opened
try:
with zipfile.ZipFile(zip_path, 'r') as z:
pass
except Exception as e:
logging.warning(' Zip file error, removing'.format(i))
os.remove(zip_path)
# Filter the GRIDMET collection
wy_start_date = '{}-10-01'.format(year - 1)
wy_end_date = '{}-10-01'.format(year)
logging.debug(' WY: {} {}'.format(wy_start_date, wy_end_date))
gridmet_coll = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') \
.filterDate(wy_start_date, wy_end_date)
# # PRISM collection was uploaded as an asset
# if ini['BEAMER']['ppt_source'] == 'prism':
# def prism_time_start(input_image):
# """Set time_start property on PRISM water year PPT collection"""
# # Assume year is the 4th item separated by "_"
# water_year = ee.String(input_image.get('system:index')).split('_').get(3)
# date_start = ee.Date(ee.String(water_year).cat('-10-01'))
# return input_image.select([0], ['ppt']).set({
# 'system:time_start': date_start.millis()
# })
# prism_coll = ee.ImageCollection('users/cgmorton/prism_800m_ppt_wy')
# prism_coll = prism_coll.map(prism_time_start) \
# .filterDate(wy_start_date, wy_end_date)
# Get water year PPT from file
# Convert all input data to mm to match GRIDMET data
if ini['BEAMER']['ppt_source'] == 'file':
wy_ppt_input = ppt_dict[zone_name][year]
if ini['BEAMER']['data_ppt_units'] == 'mm':
pass
elif ini['BEAMER']['data_ppt_units'] == 'in':
wy_ppt_input *= 25.4
elif ini['BEAMER']['data_ppt_units'] == 'ft':
wy_ppt_input *= (25.4 * 12)
elif ini['BEAMER']['ppt_source'] == 'gridmet':
# GET GRIDMET value at centroid of geometry
wy_ppt_input = float(
utils.ee_getinfo(
ee.ImageCollection(
gridmet_coll.select(['pr'],
['ppt']).sum()).getRegion(
zone['geom'].centroid(1),
500))[1][4])
# Calculate GRIDMET zonal mean of geometry
# wy_ppt_input = float(ee.ImageCollection(
# gridmet_coll.select(['pr'], ['ppt'])).reduceRegion(
# reducer=ee.Reducer.sum(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1).getInfo()['ppt']
# elif ini['BEAMER']['ppt_source'] == 'prism':
# # Calculate PRISM zonal mean of geometry
# wy_ppt_input = float(ee.ImageCollection(
# prism_coll.map(ee_common.prism_ppt_func)).sum().reduceRegion(
# reducer=ee.Reducer.mean(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1).getInfo()['ppt'])
# Get water year ETo read from file
# Convert all input data to mm for Beamer Method
if ini['BEAMER']['eto_source'] == 'file':
wy_eto_input = eto_dict[zone_name][year]
if ini['BEAMER']['data_eto_units'] == 'mm':
pass
elif ini['BEAMER']['data_eto_units'] == 'in':
wy_eto_input *= 25.4
elif ini['BEAMER']['data_eto_units'] == 'ft':
wy_eto_input *= (25.4 * 12)
# This assumes GRIMET data is in millimeters
elif ini['BEAMER']['eto_source'] == 'gridmet':
wy_eto_input = float(
utils.ee_getinfo(
ee.ImageCollection(gridmet_coll.select(
['eto']).sum()).getRegion(zone['geom'].centroid(1),
500))[1][4])
# wy_eto_input = float(ee.ImageCollection(
# gridmet_coll.select(['eto'])).reduceRegion(
# reducer=ee.Reducer.sum(),
# geometry=zone['geom'],
# crs=ini['SPATIAL']['crs'],
# crsTransform=zone['transform'],
# bestEffort=False,
# tileScale=1).getInfo()
logging.debug(' Input ETO: {} mm PPT: {} mm'.format(
wy_eto_input, wy_ppt_input))
# Scale ETo & PPT
wy_eto_input *= ini['BEAMER']['eto_factor']
wy_ppt_input *= ini['BEAMER']['ppt_factor']
# Convert output units from mm
wy_ppt_output = wy_ppt_input
wy_eto_output = wy_eto_input
if ini['IMAGES']['ppt_units'] == 'mm':
pass
elif ini['IMAGES']['ppt_units'] == 'in':
wy_ppt_output /= 25.4
elif ini['IMAGES']['ppt_units'] == 'ft':
wy_ppt_output /= (25.4 * 12)
if ini['IMAGES']['eto_units'] == 'mm':
pass
elif ini['IMAGES']['eto_units'] == 'in':
wy_eto_output /= 25.4
elif ini['IMAGES']['eto_units'] == 'ft':
wy_eto_output /= (25.4 * 12)
logging.debug(' Output ETO: {} {} PPT: {} {}'.format(
wy_eto_output, ini['IMAGES']['eto_units'], wy_ppt_output,
ini['IMAGES']['ppt_units']))
# Initialize the Landsat object for target zone and iteration
landsat.zone_geom = zone['geom']
landsat.start_date = iter_start_dt.strftime('%Y-%m-%d')
landsat.end_date = iter_end_dt.strftime('%Y-%m-%d')
landsat_coll = landsat.get_collection()
# print(sorted(utils.ee_getinfo(
# landsat_coll.aggregate_histogram('SCENE_ID'))))
# input('ENTER')
# Skip if Landsat collection is empty
if not utils.ee_getinfo(
landsat_coll.aggregate_histogram('SCENE_ID')):
logging.info(' Empty Landsat collection, skipping')
continue
# Add water year ETo and PPT values to each image
def eto_ppt_func(img):
""""""
return ee.Image(img).setMulti({
'wy_eto': wy_eto_output,
'wy_ppt': wy_ppt_output
})
landsat_coll = ee.ImageCollection(landsat_coll.map(eto_ppt_func))
# Build each collection separately then merge
etg_coll = ee.ImageCollection(landsat_coll.map(
ee_common.beamer_func)) \
.select(ini['IMAGES']['download_bands'])
# Clip using the feature geometry
# Set the masked values to a nodata value
# so that the TIF can have a nodata value other than 0 set
etg_image = ee.Image(etg_coll.mean()) \
.clip(zone['geom']) \
.unmask(nodata_value, False)
if not os.path.isfile(zip_path):
# Get the download URL
logging.debug(' Requesting URL')
zip_url = utils.ee_request(
etg_image.getDownloadURL({
'name': image_id,
'crs': ini['SPATIAL']['crs'],
'crs_transform': output_transform,
'dimensions': output_shape
}))
# Try downloading a few times
logging.info(' Downloading')
for i in range(1, 10):
try:
response = urlrequest.urlopen(zip_url)
with open(zip_path, 'wb') as output_f:
shutil.copyfileobj(response, output_f)
break
except Exception as e:
logging.info(' Resending query')
logging.debug(' {}'.format(e))
sleep(i**2)
os.remove(zip_path)
# Try extracting the files
try:
logging.info(' Extracting')
with zipfile.ZipFile(zip_path, 'r') as z:
z.extractall(zone_annuals_ws)
except Exception as e:
logging.warning(' Error: could not extract'.format(i))
logging.debug(' {}'.format(e))
try:
os.remove(zip_path)
except Exception as e:
pass
# Set nodata value
for item in os.listdir(zone_annuals_ws):
if item.startswith(image_id) and item.endswith('.tif'):
gdc.raster_path_set_nodata(
os.path.join(zone_annuals_ws, item), nodata_value)
raster_statistics(os.path.join(zone_annuals_ws, item))
logging.info('\nComputing composite rasters from annual means')
for stat in stat_list:
logging.info(' Stat: {}'.format(stat))
for band in ini['IMAGES']['download_bands']:
logging.info(' {}'.format(band))
image_band_list = [
os.path.join(zone_annuals_ws, item)
for item in os.listdir(zone_annuals_ws)
if item.endswith('.{}.tif'.format(band.lower()))
]
# for image_path in image_band_list:
# raster_path_set_nodata(image_path, nodata_value)
output_path = os.path.join(
# zone_output_ws, 'etg_{}_{}.{}.tif'.format(
zone_output_ws,
'{}_{}.{}.tif'.format(zone_name.lower().replace(' ', '_'),
stat.lower(), band.lower()))
logging.debug(' {}'.format(output_path))
# Use GDAL to compute the composite raster
cell_statistics(image_band_list, output_path, stat.lower())
raster_statistics(output_path)
def main(ini_path=None, overwrite_flag=True, show_flag=False):
"""Generate summary figures
Args:
ini_path (str): file path of the control file
overwrite_flag (bool): if True, overwrite existing figures
show_flag (bool): if True, show figures as they are being built
"""
logging.info('\nGenerate summary figures')
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='FIGURES')
# Band options
band_list = [
'albedo_sur',
'cloud_score',
'eto',
'evi_sur',
'fmask_count',
'fmask_total',
'ndvi_sur',
'ndvi_toa',
'ndwi_green_nir_sur',
'ndwi_green_nir_toa',
'ndwi_green_swir1_sur',
'ndwi_green_swir1_toa',
'ndwi_nir_swir1_sur',
'ndwi_nir_swir1_toa',
'ndwi_swir1_green_sur',
'ndwi_swir1_green_toa',
# 'ndwi_sur', 'ndwi_toa',
'pixel_count',
'pixel_total',
'ppt',
'tc_bright',
'tc_green',
'tc_wet',
'ts'
]
band_name = {
'albedo_sur': 'Albedo',
'cloud_score': 'Cloud Score',
'eto': 'ETo',
'evi_sur': 'EVI',
'fmask_count': 'Fmask Count',
'fmask_total': 'Fmask Total',
'ndvi_sur': 'NDVI',
'ndvi_toa': 'NDVI (TOA)',
'ndwi_green_nir_sur': 'NDWI (Green, NIR)',
'ndwi_green_nir_toa': 'NDWI (Green, NIR) (TOA)',
'ndwi_green_swir1_sur': 'NDWI (Green, SWIR1)',
'ndwi_green_swir1_toa': 'NDWI (Green, SWIR1) (TOA)',
'ndwi_nir_swir1_sur': 'NDWI (NIR, SWIR1)',
'ndwi_nir_swir1_toa': 'NDWI (NIR, SWIR1) (TOA)',
'ndwi_swir1_green_sur': 'NDWI (SWIR1, Green)',
'ndwi_swir1_green_toa': 'NDWI (SWIR1, Green) (TOA)',
# 'ndwi_sur': 'NDWI (SWIR1, GREEN)',
# 'ndwi_toa': 'NDWI (SWIR1, GREEN) (TOA)',
'pixel_count': 'Pixel Count',
'pixel_total': 'Pixel Total',
'ppt': 'PPT',
'tc_bright': 'Brightness',
'tc_green': 'Greeness',
'tc_wet': 'Wetness',
'ts': 'Ts'
}
band_unit = {
'albedo_sur': 'dimensionless',
'cloud_score': 'dimensionless',
'evi_sur': 'dimensionless',
'eto': 'mm',
'fmask_count': 'dimensionless',
'fmask_total': 'dimensionless',
'ndvi_sur': 'dimensionless',
'ndvi_toa': 'dimensionless',
'ndwi_green_nir_sur': 'dimensionless',
'ndwi_green_nir_toa': 'dimensionless',
'ndwi_green_swir1_sur': 'dimensionless',
'ndwi_green_swir1_toa': 'dimensionless',
'ndwi_nir_swir1_sur': 'dimensionless',
'ndwi_nir_swir1_toa': 'dimensionless',
'ndwi_swir1_green_sur': 'dimensionless',
'ndwi_swir1_green_toa': 'dimensionless',
# 'ndwi_sur': 'dimensionless',
# 'ndwi_toa': 'dimensionless',
'pixel_count': 'dimensionless',
'pixel_total': 'dimensionless',
'ppt': 'mm',
'tc_bright': 'dimensionless',
'tc_green': 'dimensionless',
'tc_wet': 'dimensionless',
'ts': 'K',
}
band_color = {
'albedo_sur': '#CF4457',
'cloud_score': '0.5',
'eto': '#348ABD',
'fmask_count': '0.5',
'fmask_total': '0.5',
'evi_sur': '#FFA500',
'ndvi_sur': '#A60628',
'ndvi_toa': '#A60628',
'ndwi_green_nir_sur': '#4eae4b',
'ndwi_green_nir_toa': '#4eae4b',
'ndwi_green_swir1_sur': '#4eae4b',
'ndwi_green_swir1_toa': '#4eae4b',
'ndwi_nir_swir1_sur': '#4eae4b',
'ndwi_nir_swir1_toa': '#4eae4b',
'ndwi_swir1_green_sur': '#4eae4b',
'ndwi_swir1_green_toa': '#4eae4b',
# 'ndwi_sur': '#4eae4b',
# 'ndwi_toa': '#4eae4b',
'pixel_count': '0.5',
'pixel_total': '0.5',
'ppt': '0.5',
'tc_bright': '#E24A33',
'tc_green': '#E24A33',
'tc_wet': '#E24A33',
'ts': '#188487'
}
# A couple of color palettes to sample from
# import seaborn as sns
# print(sns.color_palette('hls', 20).as_hex())
# print(sns.color_palette('husl', 20).as_hex())
# print(sns.color_palette('hsv', 20).as_hex())
# print(sns.color_palette('Set1', 20).as_hex())
# print(sns.color_palette('Set2', 20).as_hex())
# Hardcoded plot options
figures_folder = 'figures'
fig_type = 'large'
plot_dict = dict()
# Center y-labels in figure window (instead of centering on ticks/axes)
plot_dict['center_ylabel'] = False
# Axes percentages must be 0-1
plot_dict['timeseries_band_ax_pct'] = [0.3, 0.92]
plot_dict['timeseries_ppt_ax_pct'] = [0.0, 0.35]
plot_dict['complement_band_ax_pct'] = [0.0, 0.5]
plot_dict['complement_eto_ax_pct'] = [0.4, 1.0]
if fig_type.lower() == 'large':
plot_dict['title_fs'] = 12
plot_dict['xtick_fs'] = 10
plot_dict['ytick_fs'] = 10
plot_dict['xlabel_fs'] = 10
plot_dict['ylabel_fs'] = 10
plot_dict['legend_fs'] = 10
plot_dict['ts_ms'] = 3
plot_dict['comp_ms'] = 4
plot_dict['timeseries_ax'] = [0.12, 0.13, 0.78, 0.81]
plot_dict['scatter_ax'] = [0.12, 0.10, 0.82, 0.84]
plot_dict['complement_ax'] = [0.12, 0.10, 0.78, 0.84]
plot_dict['fig_size'] = (6.0, 5.0)
elif fig_type.lower() == 'small':
plot_dict['title_fs'] = 10
plot_dict['xtick_fs'] = 8
plot_dict['ytick_fs'] = 8
plot_dict['xlabel_fs'] = 8
plot_dict['ylabel_fs'] = 8
plot_dict['legend_fs'] = 8
plot_dict['ts_ms'] = 1.5
plot_dict['comp_ms'] = 2
plot_dict['timeseries_ax'] = [0.18, 0.21, 0.67, 0.70]
plot_dict['scatter_ax'] = [0.18, 0.21, 0.67, 0.70]
plot_dict['complement_ax'] = [0.18, 0.16, 0.67, 0.75]
plot_dict['fig_size'] = (3.0, 2.5)
plot_dict['fig_dpi'] = 300
plot_dict['show'] = show_flag
plot_dict['overwrite'] = overwrite_flag
# CSV parameters
landsat_annual_fields = [
'ZONE_FID',
'ZONE_NAME',
'YEAR',
'SCENE_COUNT',
'CLOUD_SCORE',
'PIXEL_COUNT',
'PIXEL_TOTAL',
'FMASK_COUNT',
'FMASK_TOTAL',
'TS',
'ALBEDO_SUR',
'NDVI_TOA',
'NDVI_SUR',
'EVI_SUR',
'NDWI_GREEN_NIR_SUR',
'NDWI_GREEN_SWIR1_SUR',
'NDWI_NIR_SWIR1_SUR',
# 'NDWI_GREEN_NIR_TOA', 'NDWI_GREEN_SWIR1_TOA', 'NDWI_NIR_SWIR1_TOA',
# 'NDWI_SWIR1_GREEN_TOA', 'NDWI_SWIR1_GREEN_SUR',
# 'NDWI_TOA', 'NDWI_SUR',
'TC_BRIGHT',
'TC_GREEN',
'TC_WET'
]
# Add merged row XXX to keep list
ini['INPUTS']['row_keep_list'].append('XXX')
# Check figure bands
timeseries_bands = ini['FIGURES']['timeseries_bands']
scatter_bands = ini['FIGURES']['scatter_bands']
complementary_bands = ini['FIGURES']['complementary_bands']
if timeseries_bands:
logging.info('Timeseries Bands:')
for band in timeseries_bands:
if band not in band_list:
logging.info(
' Invalid timeseries band: {}, exiting'.format(band))
return False
logging.info(' {}'.format(band))
if scatter_bands:
logging.info('Scatter Bands (x:y):')
for band_x, band_y in scatter_bands:
if band_x not in band_list:
logging.info(
' Invalid scatter band: {}, exiting'.format(band_x))
return False
elif band_y not in band_list:
logging.info(' Invalid band: {}, exiting'.format(band_y))
return False
logging.info(' {}:{}'.format(band_x, band_y))
if complementary_bands:
logging.info('Complementary Bands:')
for band in complementary_bands:
if band not in band_list:
logging.info(
' Invalid complementary band: {}, exiting'.format(band))
return False
logging.info(' {}'.format(band))
# Add input plot options
plot_dict['ppt_plot_type'] = ini['FIGURES']['ppt_plot_type']
plot_dict['scatter_best_fit'] = ini['FIGURES']['scatter_best_fit']
# Start/end year
year_list = list(
range(ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(
utils.wrapped_range(ini['INPUTS']['start_month'],
ini['INPUTS']['end_month'], 1, 12))
doy_list = list(
utils.wrapped_range(ini['INPUTS']['start_doy'],
ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(
utils.month_range(gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(', '.join(
map(str, gridmet_months))))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# # Filter features by FID before merging geometries
# if ini['INPUTS']['fid_keep_list']:
# landsat_df = landsat_df[landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_keep_list'])]
# if ini['INPUTS']['fid_skip_list']:
# landsat_df = landsat_df[~landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_skip_list'])]
logging.info('\nProcessing zones')
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
zone_stats_ws = os.path.join(ini['ZONAL_STATS']['output_ws'],
zone_name)
zone_figures_ws = os.path.join(ini['SUMMARY']['output_ws'], zone_name,
figures_folder)
if not os.path.isdir(zone_stats_ws):
logging.debug(
' Folder {} does not exist, skipping'.format(zone_stats_ws))
continue
elif not os.path.isdir(zone_figures_ws):
os.makedirs(zone_figures_ws)
# Input paths
landsat_daily_path = os.path.join(
zone_stats_ws, '{}_landsat_daily.csv'.format(zone_name))
gridmet_daily_path = os.path.join(
zone_stats_ws, '{}_gridmet_daily.csv'.format(zone_name))
gridmet_monthly_path = os.path.join(
zone_stats_ws, '{}_gridmet_monthly.csv'.format(zone_name))
if not os.path.isfile(landsat_daily_path):
logging.error(' Landsat daily CSV does not exist, skipping zone')
continue
elif (not os.path.isfile(gridmet_daily_path)
and not os.path.isfile(gridmet_monthly_path)):
logging.error(
' GRIDMET daily or monthly CSV does not exist, skipping zone')
continue
# DEADBEEF - Eventually support generating only Landsat figures
# logging.error(
# ' GRIDMET daily and/or monthly CSV files do not exist.\n'
# ' ETo and PPT will not be processed.')
# Output paths
landsat_summary_path = os.path.join(
zone_figures_ws, '{}_landsat_figures.csv'.format(zone_name))
gridmet_summary_path = os.path.join(
zone_figures_ws, '{}_gridmet_figures.csv'.format(zone_name))
zone_summary_path = os.path.join(
zone_figures_ws, '{}_zone_figures.csv'.format(zone_name))
logging.debug(' Reading Landsat CSV')
landsat_df = pd.read_csv(landsat_daily_path)
logging.debug(' Filtering Landsat dataframe')
landsat_df = landsat_df[landsat_df['PIXEL_COUNT'] > 0]
# QA field should have been written in zonal stats code
# Eventually this block can be removed
if 'QA' not in landsat_df.columns.values:
landsat_df['QA'] = 0
# # This assumes that there are L5/L8 images in the dataframe
# if not landsat_df.empty:
# max_pixel_count = max(landsat_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if year_list:
landsat_df = landsat_df[landsat_df['YEAR'].isin(year_list)]
if month_list:
landsat_df = landsat_df[landsat_df['MONTH'].isin(month_list)]
if doy_list:
landsat_df = landsat_df[landsat_df['DOY'].isin(doy_list)]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['path_keep_list']:
landsat_df = landsat_df[landsat_df['PATH'].isin(
ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list']
and ini['INPUTS']['row_keep_list'] != ['XXX']):
landsat_df = landsat_df[landsat_df['ROW'].isin(
ini['INPUTS']['row_keep_list'])]
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[np.logical_not(
scene_id_df.isin(ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[np.logical_not(landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list']))]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not landsat_df.empty:
logging.debug(' Maximum QA: {0}'.format(
ini['SUMMARY']['max_qa']))
landsat_df = landsat_df[
landsat_df['QA'] <= ini['SUMMARY']['max_qa']]
# Filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not landsat_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
landsat_df = landsat_df[
landsat_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not landsat_df.empty:
landsat_df['FMASK_PCT'] = 100 * (landsat_df['FMASK_COUNT'] /
landsat_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
landsat_df = landsat_df[
landsat_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not landsat_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = ((landsat_df['LANDSAT'] == 'LE7') &
((landsat_df['YEAR'] >= 2004) |
((landsat_df['YEAR'] == 2003) &
(landsat_df['DOY'] > 151))))
slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] /
landsat_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] / max_pixel_count)
landsat_df = landsat_df[(
(slc_off_pct >= ini['SUMMARY']['min_slc_off_pct'])
& slc_off_mask) | (~slc_off_mask)]
if landsat_df.empty:
logging.error(
' Empty Landsat dataframe after filtering, skipping zone')
continue
logging.debug(' Computing Landsat annual summaries')
agg_dict = {
'PIXEL_COUNT': {
'PIXEL_COUNT': 'mean',
'SCENE_COUNT': 'count'
},
'PIXEL_TOTAL': {
'PIXEL_TOTAL': 'mean'
},
'FMASK_COUNT': {
'FMASK_COUNT': 'mean'
},
'FMASK_TOTAL': {
'FMASK_TOTAL': 'mean'
},
'CLOUD_SCORE': {
'CLOUD_SCORE': 'mean'
}
}
for field in landsat_df.columns.values:
if field in landsat_annual_fields:
agg_dict.update({field: {field: 'mean'}})
landsat_df = landsat_df \
.groupby(['ZONE_NAME', 'ZONE_FID', 'YEAR']) \
.agg(agg_dict)
landsat_df.columns = landsat_df.columns.droplevel(0)
landsat_df.reset_index(inplace=True)
# landsat_df = landsat_df[landsat_annual_fields]
landsat_df['YEAR'] = landsat_df['YEAR'].astype(np.int)
landsat_df['SCENE_COUNT'] = landsat_df['SCENE_COUNT'].astype(np.int)
landsat_df['PIXEL_COUNT'] = landsat_df['PIXEL_COUNT'].astype(np.int)
landsat_df['PIXEL_TOTAL'] = landsat_df['PIXEL_TOTAL'].astype(np.int)
landsat_df['FMASK_COUNT'] = landsat_df['FMASK_COUNT'].astype(np.int)
landsat_df['FMASK_TOTAL'] = landsat_df['FMASK_TOTAL'].astype(np.int)
landsat_df.sort_values(by='YEAR', inplace=True)
# Aggregate GRIDMET (to water year)
if os.path.isfile(gridmet_monthly_path):
logging.debug(' Reading montly GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_monthly_path)
elif os.path.isfile(gridmet_daily_path):
logging.debug(' Reading daily GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_daily_path)
logging.debug(' Computing GRIDMET summaries')
# Summarize GRIDMET for target months year
if (gridmet_start_month in [10, 11, 12]
and gridmet_end_month in [10, 11, 12]):
month_mask = ((gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
elif (gridmet_start_month in [10, 11, 12]
and gridmet_end_month not in [10, 11, 12]):
month_mask = gridmet_df['MONTH'] >= gridmet_start_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
month_mask = gridmet_df['MONTH'] <= gridmet_end_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
else:
month_mask = ((gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
# GROUP_YEAR for rows not in the GRIDMET month range will be NAN
gridmet_df = gridmet_df[~pd.isnull(gridmet_df['GROUP_YEAR'])]
if year_list:
gridmet_df = gridmet_df[gridmet_df['GROUP_YEAR'].isin(year_list)]
if gridmet_df.empty:
logging.error(
' Empty GRIDMET dataframe after filtering by year')
continue
# Group GRIDMET data by user specified range (default is water year)
gridmet_group_df = gridmet_df \
.groupby(['ZONE_FID', 'ZONE_NAME', 'GROUP_YEAR']) \
.agg({'ETO': {'ETO': 'sum'}, 'PPT': {'PPT': 'sum'}})
gridmet_group_df.columns = gridmet_group_df.columns.droplevel(0)
gridmet_group_df.reset_index(inplace=True)
gridmet_group_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
gridmet_group_df.sort_values(by='YEAR', inplace=True)
# # Group GRIDMET data by month
# gridmet_month_df = gridmet_df.groupby(
# ['ZONE_FID', 'ZONE_NAME', 'GROUP_YEAR', 'MONTH']).agg({
# 'ETO': {'ETO': 'sum'}, 'PPT': {'PPT': 'sum'}})
# gridmet_month_df.columns = gridmet_month_df.columns.droplevel(0)
# gridmet_month_df.reset_index(inplace=True)
# gridmet_month_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
# # gridmet_month_df.sort_values(by=['YEAR', 'MONTH'], inplace=True)
# gridmet_month_df.reset_index(inplace=True)
# # Rename monthly PPT columns
# gridmet_month_df['MONTH'] = 'PPT_M' + gridmet_month_df['MONTH'].astype(str)
# # Pivot rows up to separate columns
# gridmet_month_df = gridmet_month_df.pivot_table(
# 'PPT', ['ZONE_FID', 'YEAR'], 'MONTH')
# gridmet_month_df.reset_index(inplace=True)
# columns = ['ZONE_FID', 'YEAR'] + ['PPT_M{}'.format(m) for m in gridmet_months]
# gridmet_month_df = gridmet_month_df[columns]
# del gridmet_month_df.index.name
# Merge Landsat and GRIDMET collections
zone_df = landsat_df.merge(gridmet_group_df,
on=['ZONE_FID', 'ZONE_NAME', 'YEAR'])
# gridmet_group_df, on=['ZONE_FID', 'YEAR'])
# zone_df = zone_df.merge(
# gridmet_month_df, on=['ZONE_FID', 'ZONE_NAME', 'YEAR'])
# gridmet_month_df, on=['ZONE_FID', 'YEAR'])
if zone_df is None or zone_df.empty:
logging.info(' Empty zone dataframe, not generating figures')
continue
# Save annual Landsat and GRIDMET tables
logging.debug(' Saving summary tables')
logging.debug(' {}'.format(landsat_summary_path))
landsat_df.sort_values(by=['YEAR'], inplace=True)
landsat_df.to_csv(landsat_summary_path, index=False)
# columns=export_fields
logging.debug(' {}'.format(gridmet_summary_path))
gridmet_group_df.sort_values(by=['YEAR'], inplace=True)
gridmet_group_df.to_csv(gridmet_summary_path, index=False)
# columns=export_fields
logging.debug(' {}'.format(zone_summary_path))
zone_df.sort_values(by=['YEAR'], inplace=True)
zone_df.to_csv(zone_summary_path, index=False)
# columns=export_fields
# Adjust year range based on data availability?
# start_year = min(zone_df['YEAR']),
# end_year = max(zone_df['YEAR'])
logging.debug(' Generating figures')
for band in timeseries_bands:
timeseries_plot(band, zone_df, zone_name, zone_figures_ws,
ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'], band_name, band_unit,
band_color, plot_dict)
for band_x, band_y in scatter_bands:
scatter_plot(band_x, band_y, zone_df, zone_name, zone_figures_ws,
band_name, band_unit, band_color, plot_dict)
for band in complementary_bands:
complementary_plot(band, zone_df, zone_name, zone_figures_ws,
band_name, band_unit, band_color, plot_dict)
del landsat_df, gridmet_df, zone_df
def ee_image_download(ini_path=None, overwrite_flag=False):
"""Earth Engine Annual Mean Image Download
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nEarth Engine GRIDMET Image Download')
# Do we need to support separate GRIDMET years?
# start_year = 1984
# end_year = 2016
gridmet_download_bands = {
'eto': 'ETo',
'pr': 'PPT',
}
# If false, script will export annual and water year total images
gridmet_monthly_flag = False
gridmet_flag = True
pdsi_flag = False
pdsi_date_list = [
'0120',
'0220',
'0320',
'0420',
'0520',
'0620',
'0720',
'0820',
'0920',
'1020',
'1120',
'1220',
]
# pdsi_date_list = ['0920', '1220']
# pdsi_date_list = []
if gridmet_monthly_flag:
gridmet_folder = 'gridmet_monthly'
else:
gridmet_folder = 'gridmet_annual'
if not pdsi_date_list:
pdsi_folder = 'pdsi_full'
else:
pdsi_folder = 'pdsi'
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='IMAGES')
nodata_value = -9999
# Manually set output spatial reference
logging.info('\nHardcoding GRIDMET snap, cellsize and spatial reference')
ini['output_x'], ini['output_y'] = -124.79299639209513, 49.41685579737572
ini['SPATIAL']['cellsize'] = 0.041666001963701
# ini['SPATIAL']['cellsize'] = [0.041666001963701, 0.041666001489718]
# ini['output_x'], ini['output_y'] = -124.79166666666666666667, 25.04166666666666666667
# ini['SPATIAL']['cellsize'] = 1. / 24
ini['SPATIAL']['osr'] = gdc.epsg_osr(4326)
# ini['SPATIAL']['osr'] = gdc.epsg_osr(4269)
ini['SPATIAL']['crs'] = 'EPSG:4326'
logging.debug(' Snap: {} {}'.format(ini['output_x'], ini['output_y']))
logging.debug(' Cellsize: {}'.format(ini['SPATIAL']['cellsize']))
logging.debug(' OSR: {}'.format(ini['SPATIAL']['osr']))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Need zone_shp_path projection to build EE geometries
zone_osr = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone_proj = gdc.osr_wkt(zone_osr)
# zone_proj = ee.Projection(zone_proj).wkt().getInfo()
# zone_proj = zone_proj.replace('\n', '').replace(' ', '')
logging.debug(' Zone Projection: {}'.format(zone_proj))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get current running tasks
tasks = utils.get_ee_tasks()
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
# Build EE geometry object for zonal stats
zone_geom = ee.Geometry(zone_json, zone_proj, False)
# Project the zone_geom to the GRIDMET projection
# if zone_proj != output_proj:
zone_geom = zone_geom.transform(ini['SPATIAL']['crs'], 0.001)
# Get the extent from the Earth Engine geometry object?
zone_extent = zone_geom.bounds().getInfo()['coordinates'][0]
zone_extent = gdc.Extent([
min(zip(*zone_extent)[0]),
min(zip(*zone_extent)[1]),
max(zip(*zone_extent)[0]),
max(zip(*zone_extent)[1])
])
# # Use GDAL and geometry json to build extent, transform, and shape
# zone_extent = gdc.Extent(
# ogr.CreateGeometryFromJson(json.dumps(zone_json)).GetEnvelope())
# # zone_extent = gdc.Extent(zone_geom.GetEnvelope())
# zone_extent.ymin, zone_extent.xmax = zone_extent.xmax, zone_extent.ymin
# Adjust extent to match raster
zone_extent = zone_extent.adjust_to_snap('EXPAND',
ini['SPATIAL']['snap_x'],
ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone_geo = zone_extent.geo(ini['SPATIAL']['cellsize'])
zone_transform = gdc.geo_2_ee_transform(zone_geo)
zone_transform = '[' + ','.join(map(str, zone_transform)) + ']'
zone_shape = zone_extent.shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone_shape))
logging.debug(' Zone Transform: {}'.format(zone_transform))
logging.debug(' Zone Extent: {}'.format(zone_extent))
# logging.debug(' Geom: {}'.format(zone_geom.getInfo()))
# output_transform = zone_transform[:]
output_transform = '[' + ','.join(map(str, zone_transform)) + ']'
output_shape = '[{1}x{0}]'.format(*zone_shape)
logging.debug(' Output Projection: {}'.format(ini['SPATIAL']['crs']))
logging.debug(' Output Transform: {}'.format(output_transform))
logging.debug(' Output Shape: {}'.format(output_shape))
zone_gridmet_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
gridmet_folder)
zone_pdsi_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
pdsi_folder)
if not os.path.isdir(zone_gridmet_ws):
os.makedirs(zone_gridmet_ws)
if not os.path.isdir(zone_pdsi_ws):
os.makedirs(zone_pdsi_ws)
# GRIDMET PPT & ETo
if gridmet_flag:
# Process each image in the collection by date
export_list = []
for year in range(ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'] + 1):
for b_key, b_name in sorted(gridmet_download_bands.items()):
if gridmet_monthly_flag:
# Monthly
for start_month in range(1, 13):
start_dt = datetime.datetime(year, start_month, 1)
end_dt = (start_dt + relativedelta(months=1) -
datetime.timedelta(0, 1))
export_list.append([
start_dt, end_dt,
'{:04d}{:02d}'.format(year, start_month),
b_key, b_name
])
else:
# Calendar year
export_list.append([
datetime.datetime(year, 1, 1),
datetime.datetime(year + 1, 1, 1),
'{:04d}'.format(year), b_key, b_name
])
# Water year
export_list.append([
datetime.datetime(year - 1, 10, 1),
datetime.datetime(year, 10, 1) -
datetime.timedelta(0, 1), '{:04d}wy'.format(year),
b_key, b_name
])
for start_dt, end_dt, date_str, b_key, b_name in export_list:
logging.info('{} {}'.format(date_str, b_name))
if end_dt > datetime.datetime.today():
logging.info(' End date after current date, skipping')
continue
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_gridmet_{}'.format(
ini['INPUTS']['zone_filename'], date_str, b_name.lower())
output_id = '{}_gridmet.{}'.format(date_str, b_name.lower())
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_gridmet_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(
' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(
' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
# subprocess.check_output([
# 'gdalwarp',
# '-ot', float_output_type, '-overwrite',
# '-of', 'GTiff', '-co', 'COMPRESS=LZW',
# '-srcnodata', str(nodata_value),
# '-dstnodata', '{:f}'.format(float_nodata_value),
# export_path, output_path])
# with open(os.devnull, 'w') as devnull:
# subprocess.check_call(
# ['gdalinfo', '-stats', output_path],
# stdout=devnull)
# subprocess.check_output(
# ['gdalmanage', 'delete', export_path])
continue
elif os.path.isfile(output_path):
logging.debug(
' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
# GRIDMET collection is available in EarthEngine
gridmet_coll = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')\
.filterDate(start_dt, end_dt) \
.select([b_key])
gridmet_image = ee.Image(gridmet_coll.sum())
logging.debug(' Starting download task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=gridmet_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'gdrive':
# task = ee.batch.Export.image.toCloudStorage(
# image=gridmet_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
# GRIDMET PDSI
if pdsi_flag:
# Process each image in the collection by date
export_list = []
b_name = 'pdsi'
for year in range(ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'] + 1):
# Dekad
for start_month in range(1, 13):
for start_day, end_day in zip([1, 10, 20], [10, 20, 30]):
if start_month == 12 and start_day == 20:
# Go to the first day of the next year (and month)
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year + 1, 1, 1)
elif start_month < 12 and start_day == 20:
# Go to the first day of the next month
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year, start_month + 1,
1)
else:
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year, start_month,
end_day)
end_dt = end_dt - datetime.timedelta(0, 1)
export_list.append([
start_dt, end_dt, '{:04d}{:02d}{:02d}'.format(
year, start_month, start_day), b_name
])
# Filter list to only keep last dekad of October and December
if pdsi_date_list:
export_list = [[
start_dt, end_dt, date_str, b_name
] for start_dt, end_dt, date_str, b_name in export_list
if start_dt.strftime('%m%d') in pdsi_date_list]
for start_dt, end_dt, date_str, b_name in export_list:
logging.info('{} {}'.format(date_str, b_name))
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_{}'.format(
os.path.splitext(
ini['INPUTS']['zone_filename'])[0].lower(), date_str,
b_name.lower())
output_id = '{}_{}'.format(date_str, b_name.lower())
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_pdsi_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(
' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(
' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
continue
elif os.path.isfile(output_path):
logging.debug(
' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
# PDSI collection is available in EarthEngine
# Index the PDSI image directly
pdsi_image = ee.Image('IDAHO_EPSCOR/PDSI/{}'.format(
start_dt.strftime('%Y%m%d')))
# pdsi_coll = ee.ImageCollection('IDAHO_EPSCOR/PDSI')\
# .filterDate(start_dt, end_dt) \
# .select(['pdsi'])
# pdsi_image = ee.Image(pdsi_coll.mean())
logging.debug(' Building export task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=pdsi_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'cloud':
# task = ee.batch.Export.image.toCloudStorage(
# image=pdsi_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
def main(ini_path, overwrite_flag=True):
"""Generate Beamer ETg summary tables
Args:
ini_path (str):
overwrite_flag (bool): if True, overwrite existing figures
Default is True (for now)
"""
logging.info('\nGenerate Beamer ETg summary tables')
# # Eventually get from INI (like ini['BEAMER']['landsat_products'])
# daily_fields = [
# 'ZONE_NAME', 'ZONE_FID', 'DATE', 'SCENE_ID', 'PLATFORM', 'PATH', 'ROW',
# 'YEAR', 'MONTH', 'DAY', 'DOY', 'WATER_YEAR',
# 'PIXEL_COUNT', 'ETSTAR_COUNT',
# 'NDVI_TOA', 'NDWI_TOA', 'ALBEDO_SUR', 'TS', 'EVI_SUR', 'ETSTAR_MEAN',
# 'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI',
# 'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI',
# 'WY_ETO', 'WY_PPT']
# annual_fields = [
# 'SCENE_COUNT', 'PIXEL_COUNT', 'ETSTAR_COUNT',
# 'NDVI_TOA', 'NDWI_TOA', 'ALBEDO_SUR', 'TS',
# 'EVI_SUR_MEAN', 'EVI_SUR_MEDIAN', 'EVI_SUR_MIN', 'EVI_SUR_MAX',
# 'ETSTAR_MEAN',
# 'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI',
# 'ET_MEAN', 'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI',
# 'WY_ETO', 'WY_PPT']
# For unit conversion
eto_fields = [
'ETG_MEAN', 'ETG_LPI', 'ETG_UPI', 'ETG_LCI', 'ETG_UCI', 'ET_MEAN',
'ET_LPI', 'ET_UPI', 'ET_LCI', 'ET_UCI', 'WY_ETO'
]
ppt_fields = ['WY_PPT']
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='BEAMER')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='TABLES')
# Output paths
output_daily_path = os.path.join(
ini['SUMMARY']['output_ws'],
ini['BEAMER']['output_name'].replace('.csv', '_daily.xlsx'))
output_annual_path = os.path.join(
ini['SUMMARY']['output_ws'],
ini['BEAMER']['output_name'].replace('.csv', '_annual.xlsx'))
# Check if files already exist
if overwrite_flag:
if os.path.isfile(output_daily_path):
os.remove(output_daily_path)
if os.path.isfile(output_annual_path):
os.remove(output_annual_path)
else:
if (os.path.isfile(output_daily_path)
and os.path.isfile(output_annual_path)):
logging.info('\nOutput files already exist and '
'overwrite is False, exiting')
return True
# Start/end year
year_list = list(
range(ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(
utils.wrapped_range(ini['INPUTS']['start_month'],
ini['INPUTS']['end_month'], 1, 12))
doy_list = list(
utils.wrapped_range(ini['INPUTS']['start_doy'],
ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(
utils.month_range(gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(', '.join(
map(str, gridmet_months))))
# Read in the zonal stats CSV
logging.debug(' Reading zonal stats CSV file')
input_df = pd.read_csv(
os.path.join(ini['ZONAL_STATS']['output_ws'],
ini['BEAMER']['output_name']))
logging.debug(' Filtering Landsat dataframe')
input_df = input_df[input_df['PIXEL_COUNT'] > 0]
# # This assumes that there are L5/L8 images in the dataframe
# if not input_df.empty:
# max_pixel_count = max(input_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if ini['INPUTS']['fid_keep_list']:
input_df = input_df[input_df['ZONE_FID'].isin(
ini['INPUTS']['fid_keep_list'])]
if ini['INPUTS']['fid_skip_list']:
input_df = input_df[~input_df['ZONE_FID'].
isin(ini['INPUTS']['fid_skip_list'])]
zone_name_list = sorted(list(set(input_df['ZONE_NAME'].values)))
if year_list:
input_df = input_df[input_df['YEAR'].isin(year_list)]
if month_list:
input_df = input_df[input_df['MONTH'].isin(month_list)]
if doy_list:
input_df = input_df[input_df['DOY'].isin(doy_list)]
if ini['INPUTS']['path_keep_list']:
input_df = input_df[input_df['PATH'].isin(
ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list']
and ini['INPUTS']['row_keep_list'] != ['XXX']):
input_df = input_df[input_df['ROW'].isin(
ini['INPUTS']['row_keep_list'])]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
input_df = input_df[input_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])
])
input_df = input_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(input_df['SCENE_ID'], input_df['ROW'])
])
input_df = input_df[np.logical_not(
scene_id_df.isin(ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# input_df = input_df[~input_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list'])]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not input_df.empty:
logging.debug(' Maximum QA: {0}'.format(ini['SUMMARY']['max_qa']))
input_df = input_df[input_df['QA'] <= ini['SUMMARY']['max_qa']]
# First filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not input_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
input_df = input_df[
input_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not input_df.empty:
input_df['FMASK_PCT'] = 100 * (input_df['FMASK_COUNT'] /
input_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
input_df = input_df[
input_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not input_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = ((input_df['PLATFORM'] == 'LE07') &
((input_df['YEAR'] >= 2004) |
((input_df['YEAR'] == 2003) &
(input_df['DOY'] > 151))))
slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / input_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (input_df['PIXEL_COUNT'] / max_pixel_count)
input_df = input_df[((slc_off_pct >= ini['SUMMARY']['min_slc_off_pct'])
& slc_off_mask) | (~slc_off_mask)]
if input_df.empty:
logging.error(' Empty dataframe after filtering, exiting')
return False
if not os.path.isfile(output_daily_path):
logging.info('\nWriting daily values to Excel')
excel_f = ExcelWriter(output_daily_path)
for zone_name in sorted(zone_name_list):
logging.info(' {}'.format(zone_name))
zone_df = input_df[input_df['ZONE_NAME'] == zone_name]
zone_df.to_excel(excel_f,
zone_name,
index=False,
float_format='%.4f')
# zone_df.to_excel(excel_f, zone_name, index=False)
del zone_df
excel_f.save()
if not os.path.isfile(output_annual_path):
logging.info('\nComputing annual summaries')
annual_df = input_df \
.groupby(['ZONE_NAME', 'YEAR']) \
.agg({
'PIXEL_COUNT': ['count', 'mean'],
'PIXEL_TOTAL': ['mean'],
'FMASK_COUNT': 'mean',
'FMASK_TOTAL': 'mean',
'CLOUD_SCORE': 'mean',
'ETSTAR_COUNT': 'mean',
'NDVI_TOA': 'mean',
'NDWI_TOA': 'mean',
'ALBEDO_SUR': 'mean',
'TS': 'mean',
# 'EVI_SUR': 'mean',
'EVI_SUR': ['mean', 'median', 'min', 'max'],
'ETSTAR_MEAN': 'mean',
'ETG_MEAN': 'mean',
'ETG_LPI': 'mean',
'ETG_UPI': 'mean',
'ETG_LCI': 'mean',
'ETG_UCI': 'mean',
'ET_MEAN': 'mean',
'ET_LPI': 'mean',
'ET_UPI': 'mean',
'ET_LCI': 'mean',
'ET_UCI': 'mean',
'WY_ETO': 'mean',
'WY_PPT': 'mean'
})
annual_df.columns = annual_df.columns.map('_'.join)
annual_df = annual_df.rename(columns={
'PIXEL_COUNT_count': 'SCENE_COUNT',
'PIXEL_COUNT_mean': 'PIXEL_COUNT'
})
annual_df = annual_df.rename(
columns={
'EVI_SUR_mean': 'EVI_SUR_MEAN',
'EVI_SUR_median': 'EVI_SUR_MEDIAN',
'EVI_SUR_min': 'EVI_SUR_MIN',
'EVI_SUR_max': 'EVI_SUR_MAX'
})
annual_df.rename(columns=lambda x: str(x).replace('_mean', ''),
inplace=True)
annual_df['SCENE_COUNT'] = annual_df['SCENE_COUNT'].astype(np.int)
annual_df['PIXEL_COUNT'] = annual_df['PIXEL_COUNT'].astype(np.int)
annual_df['PIXEL_TOTAL'] = annual_df['PIXEL_TOTAL'].astype(np.int)
annual_df['FMASK_COUNT'] = annual_df['FMASK_COUNT'].astype(np.int)
annual_df['FMASK_TOTAL'] = annual_df['FMASK_TOTAL'].astype(np.int)
annual_df['ETSTAR_COUNT'] = annual_df['ETSTAR_COUNT'].astype(np.int)
annual_df = annual_df.reset_index()
# Convert ETo units
if (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'mm'):
pass
elif (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'in'):
annual_df[eto_fields] /= (25.4)
elif (ini['BEAMER']['eto_units'] == 'mm'
and ini['TABLES']['eto_units'] == 'ft'):
annual_df[eto_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not ' +
'currently supported, exiting').format(
ini['BEAMER']['eto_units'], ini['TABLES']['eto_units']))
sys.exit()
# Convert PPT units
if (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'mm'):
pass
elif (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'in'):
annual_df[ppt_fields] /= (25.4)
elif (ini['BEAMER']['ppt_units'] == 'mm'
and ini['TABLES']['ppt_units'] == 'ft'):
annual_df[ppt_fields] /= (12 * 25.4)
else:
logging.error(
('\nERROR: Input units {} and output units {} are not ' +
'currently supported, exiting').format(
ini['BEAMER']['ppt_units'], ini['TABLES']['ppt_units']))
sys.exit()
logging.info('\nWriting annual values to Excel')
excel_f = ExcelWriter(output_annual_path)
for zone_name in sorted(zone_name_list):
logging.info(' {}'.format(zone_name))
zone_df = annual_df[annual_df['ZONE_NAME'] == zone_name]
zone_df.to_excel(excel_f,
zone_name,
index=False,
float_format='%.4f')
del zone_df
excel_f.save()
def main(ini_path, show_flag=False, overwrite_flag=True):
"""Generate Bokeh figures
Bokeh issues:
Adjust y range based on non-muted data
https://stackoverflow.com/questions/43620837/how-to-get-bokeh-to-dynamically-adjust-y-range-when-panning
Linked interactive legends so that there is only one legend for the gridplot
Maybe hide or mute QA values above max (instead of filtering them in advance)
Args:
ini_path (str):
show_flag (bool): if True, show the figures in the browser.
Default is False.
overwrite_flag (bool): if True, overwrite existing tables.
Default is True (for now)
"""
logging.info('\nGenerate interactive timeseries figures')
# Eventually read from INI
plot_var_list = ['NDVI_TOA', 'ALBEDO_SUR', 'TS', 'NDWI_TOA', 'EVI_SUR']
# plot_var_list = [
# 'NDVI_TOA', 'ALBEDO_SUR', 'TS', 'NDWI_TOA',
# 'CLOUD_SCORE', 'FMASK_PCT']
output_folder = 'figures'
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='ZONAL_STATS')
inputs.parse_section(ini, section='SUMMARY')
inputs.parse_section(ini, section='FIGURES')
inputs.parse_section(ini, section='BEAMER')
# Hardcode GRIDMET month range to the water year
ini['SUMMARY']['gridmet_start_month'] = 10
ini['SUMMARY']['gridmet_end_month'] = 9
# Start/end year
year_list = list(range(
ini['INPUTS']['start_year'], ini['INPUTS']['end_year'] + 1))
month_list = list(utils.wrapped_range(
ini['INPUTS']['start_month'], ini['INPUTS']['end_month'], 1, 12))
doy_list = list(utils.wrapped_range(
ini['INPUTS']['start_doy'], ini['INPUTS']['end_doy'], 1, 366))
# GRIDMET month range (default to water year)
gridmet_start_month = ini['SUMMARY']['gridmet_start_month']
gridmet_end_month = ini['SUMMARY']['gridmet_end_month']
gridmet_months = list(utils.month_range(
gridmet_start_month, gridmet_end_month))
logging.info('\nGridmet months: {}'.format(
', '.join(map(str, gridmet_months))))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'], zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']]
# # Filter features by FID before merging geometries
# if ini['INPUTS']['fid_keep_list']:
# landsat_df = landsat_df[landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_keep_list'])]
# if ini['INPUTS']['fid_skip_list']:
# landsat_df = landsat_df[~landsat_df['ZONE_FID'].isin(
# ini['INPUTS']['fid_skip_list'])]
logging.info('\nProcessing zones')
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
zone_stats_ws = os.path.join(
ini['ZONAL_STATS']['output_ws'], zone_name)
zone_figures_ws = os.path.join(
ini['SUMMARY']['output_ws'], zone_name, output_folder)
if not os.path.isdir(zone_stats_ws):
logging.debug(' Folder {} does not exist, skipping'.format(
zone_stats_ws))
continue
elif not os.path.isdir(zone_figures_ws):
os.makedirs(zone_figures_ws)
# Input paths
landsat_daily_path = os.path.join(
zone_stats_ws, '{}_landsat_daily.csv'.format(zone_name))
gridmet_daily_path = os.path.join(
zone_stats_ws, '{}_gridmet_daily.csv'.format(zone_name))
gridmet_monthly_path = os.path.join(
zone_stats_ws, '{}_gridmet_monthly.csv'.format(zone_name))
if not os.path.isfile(landsat_daily_path):
logging.error(' Landsat daily CSV does not exist, skipping zone')
continue
elif (not os.path.isfile(gridmet_daily_path) and
not os.path.isfile(gridmet_monthly_path)):
logging.error(
' GRIDMET daily or monthly CSV does not exist, skipping zone')
continue
# DEADBEEF - Eventually support generating only Landsat figures
# logging.error(
# ' GRIDMET daily and/or monthly CSV files do not exist.\n'
# ' ETo and PPT will not be processed.')
# Output paths
output_doy_path = os.path.join(
zone_figures_ws, '{}_timeseries_doy.html'.format(zone_name))
output_date_path = os.path.join(
zone_figures_ws, '{}_timeseries_date.html'.format(zone_name))
logging.debug(' Reading Landsat CSV')
landsat_df = pd.read_csv(landsat_daily_path)
logging.debug(' Filtering Landsat dataframe')
landsat_df = landsat_df[landsat_df['PIXEL_COUNT'] > 0]
# QA field should have been written in zonal stats code
# Eventually this block can be removed
if 'QA' not in landsat_df.columns.values:
landsat_df['QA'] = 0
# # This assumes that there are L5/L8 images in the dataframe
# if not landsat_df.empty:
# max_pixel_count = max(landsat_df['PIXEL_COUNT'])
# else:
# max_pixel_count = 0
if year_list:
landsat_df = landsat_df[landsat_df['YEAR'].isin(year_list)]
if month_list:
landsat_df = landsat_df[landsat_df['MONTH'].isin(month_list)]
if doy_list:
landsat_df = landsat_df[landsat_df['DOY'].isin(doy_list)]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['path_keep_list']:
landsat_df = landsat_df[
landsat_df['PATH'].isin(ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list'] and
ini['INPUTS']['row_keep_list'] != ['XXX']):
landsat_df = landsat_df[
landsat_df['ROW'].isin(ini['INPUTS']['row_keep_list'])]
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])])
landsat_df = landsat_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])])
landsat_df = landsat_df[np.logical_not(scene_id_df.isin(
ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[np.logical_not(landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list']))]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not landsat_df.empty:
logging.debug(' Maximum QA: {0}'.format(
ini['SUMMARY']['max_qa']))
landsat_df = landsat_df[
landsat_df['QA'] <= ini['SUMMARY']['max_qa']]
# Filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not landsat_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
landsat_df = landsat_df[
landsat_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not landsat_df.empty:
landsat_df['FMASK_PCT'] = 100 * (
landsat_df['FMASK_COUNT'] / landsat_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
landsat_df = landsat_df[
landsat_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not landsat_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = (
(landsat_df['PLATFORM'] == 'LE07') &
((landsat_df['YEAR'] >= 2004) |
((landsat_df['YEAR'] == 2003) & (landsat_df['DOY'] > 151))))
slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] / landsat_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] / max_pixel_count)
landsat_df = landsat_df[
((slc_off_pct >= ini['SUMMARY']['min_slc_off_pct']) & slc_off_mask) |
(~slc_off_mask)]
if landsat_df.empty:
logging.error(
' Empty Landsat dataframe after filtering, skipping zone')
continue
# Aggregate GRIDMET (to water year)
if os.path.isfile(gridmet_monthly_path):
logging.debug(' Reading montly GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_monthly_path)
elif os.path.isfile(gridmet_daily_path):
logging.debug(' Reading daily GRIDMET CSV')
gridmet_df = pd.read_csv(gridmet_daily_path)
logging.debug(' Computing GRIDMET summaries')
# Summarize GRIDMET for target months year
if (gridmet_start_month in [10, 11, 12] and
gridmet_end_month in [10, 11, 12]):
month_mask = (
(gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
elif (gridmet_start_month in [10, 11, 12] and
gridmet_end_month not in [10, 11, 12]):
month_mask = gridmet_df['MONTH'] >= gridmet_start_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR'] + 1
month_mask = gridmet_df['MONTH'] <= gridmet_end_month
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
else:
month_mask = (
(gridmet_df['MONTH'] >= gridmet_start_month) &
(gridmet_df['MONTH'] <= gridmet_end_month))
gridmet_df.loc[month_mask, 'GROUP_YEAR'] = gridmet_df['YEAR']
# GROUP_YEAR for rows not in the GRIDMET month range will be NAN
gridmet_df = gridmet_df[~pd.isnull(gridmet_df['GROUP_YEAR'])]
if year_list:
gridmet_df = gridmet_df[gridmet_df['GROUP_YEAR'].isin(year_list)]
if gridmet_df.empty:
logging.error(
' Empty GRIDMET dataframe after filtering by year')
continue
# Group GRIDMET data by user specified range (default is water year)
gridmet_group_df = gridmet_df \
.groupby(['ZONE_NAME', 'ZONE_FID', 'GROUP_YEAR']) \
.agg({'ETO': np.sum, 'PPT': np.sum}) \
.reset_index() \
.sort_values(by='GROUP_YEAR')
# .rename(columns={'ETO': 'ETO', 'PPT': 'ETO'}) \
# Rename wasn't working when chained...
gridmet_group_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
gridmet_group_df['YEAR'] = gridmet_group_df['YEAR'].astype(int)
# # Group GRIDMET data by month
# gridmet_month_df = gridmet_df\
# .groupby(['ZONE_NAME', 'ZONE_FID', 'GROUP_YEAR', 'MONTH']) \
# .agg({'ETO': np.sum, 'PPT': np.sum}) \
# .reset_index() \
# .sort_values(by=['GROUP_YEAR', 'MONTH'])
# gridmet_month_df.rename(columns={'GROUP_YEAR': 'YEAR'}, inplace=True)
# # Rename monthly PPT columns
# gridmet_month_df['MONTH'] = 'PPT_M' + gridmet_month_df['MONTH'].astype(str)
# # Pivot rows up to separate columns
# gridmet_month_df = gridmet_month_df.pivot_table(
# 'PPT', ['ZONE_NAME', 'YEAR'], 'MONTH')
# gridmet_month_df.reset_index(inplace=True)
# columns = ['ZONE_NAME', 'YEAR'] + ['PPT_M{}'.format(m) for m in gridmet_months]
# gridmet_month_df = gridmet_month_df[columns]
# del gridmet_month_df.index.name
# Merge Landsat and GRIDMET collections
zone_df = landsat_df.merge(
gridmet_group_df, on=['ZONE_NAME', 'ZONE_FID', 'YEAR'])
if zone_df is None or zone_df.empty:
logging.info(' Empty zone dataframe, not generating figures')
continue
# Compute ETg
zone_df['ETG_MEAN'] = zone_df['ETSTAR_MEAN'] * (
zone_df['ETO'] - zone_df['PPT'])
zone_df['ETG_LPI'] = zone_df['ETSTAR_LPI'] * (
zone_df['ETO'] - zone_df['PPT'])
zone_df['ETG_UPI'] = zone_df['ETSTAR_UPI'] * (
zone_df['ETO'] - zone_df['PPT'])
zone_df['ETG_LCI'] = zone_df['ETSTAR_LCI'] * (
zone_df['ETO'] - zone_df['PPT'])
zone_df['ETG_UCI'] = zone_df['ETSTAR_UCI'] * (
zone_df['ETO'] - zone_df['PPT'])
# Compute ET
zone_df['ET_MEAN'] = zone_df['ETG_MEAN'] + zone_df['PPT']
zone_df['ET_LPI'] = zone_df['ETG_LPI'] + zone_df['PPT']
zone_df['ET_UPI'] = zone_df['ETG_UPI'] + zone_df['PPT']
zone_df['ET_LCI'] = zone_df['ETG_LCI'] + zone_df['PPT']
zone_df['ET_UCI'] = zone_df['ETG_UCI'] + zone_df['PPT']
# ORIGINAL PLOTTING CODE
# Check that plot variables are present
for plot_var in plot_var_list:
if plot_var not in landsat_df.columns.values:
logging.error(
' The plotting variable {} does not exist in the '
'dataframe'.format(plot_var))
sys.exit()
# if ini['INPUTS']['scene_id_keep_list']:
# # Replace XXX with primary ROW value for checking skip list SCENE_ID
# scene_id_df = pd.Series([
# s.replace('XXX', '{:03d}'.format(int(r)))
# for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])])
# landsat_df = landsat_df[scene_id_df.isin(
# ini['INPUTS']['scene_id_keep_list']).values]
# # This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# # landsat_df = landsat_df[landsat_df['SCENE_ID'].isin(
# # ini['INPUTS']['scene_id_keep_list'])]
# if ini['INPUTS']['scene_id_skip_list']:
# # Replace XXX with primary ROW value for checking skip list SCENE_ID
# scene_id_df = pd.Series([
# s.replace('XXX', '{:03d}'.format(int(r)))
# for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])])
# landsat_df = landsat_df[np.logical_not(scene_id_df.isin(
# ini['INPUTS']['scene_id_skip_list']).values)]
# # This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# # landsat_df = landsat_df[np.logical_not(landsat_df['SCENE_ID'].isin(
# # ini['INPUTS']['scene_id_skip_list']))]
# Compute colors for each QA value
logging.debug(' Building column data source')
qa_values = sorted(list(set(zone_df['QA'].values)))
colors = {
qa: "#%02x%02x%02x" % (int(r), int(g), int(b))
for qa, (r, g, b, _) in zip(
qa_values,
255 * cm.viridis(mpl.colors.Normalize()(qa_values)))
}
logging.debug(' QA values: {}'.format(
', '.join(map(str, qa_values))))
# Unpack the data by QA type to support interactive legends
sources = dict()
# sources = defaultdict(dict)
# platform_list = ['LT04', 'LT05', 'LE07', 'LC08']
for qa_value in qa_values:
# for platform in platform_list:
# qa_df = zone_df[
# (zone_df['PLATFORM'] == platform) &
# (zone_df['QA'] == qa_value)]
qa_df = zone_df[zone_df['QA'] == qa_value]
qa_data = {
'INDEX': list(range(len(qa_df.index))),
'PLATFORM': qa_df['PLATFORM'],
'DATE': pd.to_datetime(qa_df['DATE']),
'TIME': pd.to_datetime(qa_df['DATE']).map(
lambda x: x.strftime('%Y-%m-%d')),
'DOY': qa_df['DOY'].values,
'QA': qa_df['QA'].values,
'COLOR': [colors[qa] for qa in qa_df['QA'].values]
}
for plot_var in plot_var_list:
if plot_var in qa_df.columns.values:
qa_data.update({plot_var: qa_df[plot_var].values})
sources[qa_value] = bokeh.models.ColumnDataSource(qa_data)
# sources[qa_value][platform] = bokeh.models.ColumnDataSource(
# qa_data)
tooltips = [
("LANDSAT", "@PLATFORM"),
("DATE", "@TIME"),
("DOY", "@DOY")]
# hover_tool = bokeh.models.HoverTool(tooltips=tooltips)
# tools="xwheel_zoom,xpan,xbox_zoom,reset,box_select"
# tools = [
# hover_tool,
# bokeh.models.WheelZoomTool(dimensions='width'),
# bokeh.models.PanTool(dimensions='width'),
# bokeh.models.BoxZoomTool(dimensions='width'),
# bokeh.models.ResetTool(),
# bokeh.models.BoxSelectTool()]
# Selection
hover_circle = Circle(
fill_color='#ff0000', line_color='#ff0000')
selected_circle = Circle(
fill_color='COLOR', line_color='COLOR')
nonselected_circle = Circle(
fill_color='#aaaaaa', line_color='#aaaaaa')
# Plot the data by DOY
logging.debug(' Building DOY timeseries figure')
if os.path.isfile(output_doy_path):
os.remove(output_doy_path)
output_file(output_doy_path, title=zone_name)
figure_args = dict(
plot_width=750, plot_height=250, title=None,
tools="xwheel_zoom,xpan,xbox_zoom,reset,box_select",
# tools="xwheel_zoom,xpan,xbox_zoom,reset,tap",
active_scroll="xwheel_zoom")
plot_args = dict(
size=4, alpha=0.9, color='COLOR')
if ini['SUMMARY']['max_qa'] > 0:
plot_args['legend'] = 'QA'
figures = []
for plot_i, plot_var in enumerate(plot_var_list):
if plot_i == 0:
f = figure(
# x_range=bokeh.models.Range1d(1, 366, bounds=(1, 366)),
y_axis_label=plot_var, **figure_args)
else:
f = figure(
x_range=f.x_range, y_axis_label=plot_var, **figure_args)
# # Add each QA level as a separate object
# for qa, platform_sources in sorted(sources.items()):
# for platform, source in platform_sources.items():
# if platform == 'LT05':
# r = f.triangle(
# 'DOY', plot_var, source=source, **plot_args)
# elif platform == 'LE07':
# r = f.square(
# 'DOY', plot_var, source=source, **plot_args)
# elif platform == 'LC08':
# r = f.circle(
# 'DOY', plot_var, source=source, **plot_args)
# else:
# r = f.diamond(
# 'DOY', plot_var, source=source, **plot_args)
# r.hover_glyph = hover_circle
# r.selection_glyph = selected_circle
# r.nonselection_glyph = nonselected_circle
# r.muted_glyph = nonselected_circle
# hover_tool.renderers.append(r)
# Add each QA level as a separate object
for qa, source in sorted(sources.items()):
r = f.circle('DOY', plot_var, source=source, **plot_args)
r.hover_glyph = hover_circle
r.selection_glyph = selected_circle
r.nonselection_glyph = nonselected_circle
r.muted_glyph = nonselected_circle
# # DEADBEEF - This will display high QA points as muted
# if qa > ini['SUMMARY']['max_qa']:
# r.muted = True
# # r.visible = False
f.add_tools(bokeh.models.HoverTool(tooltips=tooltips))
# if ini['SUMMARY']['max_qa'] > 0:
f.legend.location = "top_left"
f.legend.click_policy = "hide"
# f.legend.click_policy = "mute"
f.legend.orientation = "horizontal"
figures.append(f)
del f
# Try to not allow more than 4 plots in a column
p = gridplot(
figures, ncols=len(plot_var_list) // 3,
sizing_mode='stretch_both')
if show_flag:
show(p)
save(p)
# Plot the data by DATE
logging.debug(' Building date timeseries figure')
if os.path.isfile(output_date_path):
os.remove(output_date_path)
output_file(output_date_path, title=zone_name)
figure_args = dict(
plot_width=750, plot_height=250, title=None,
tools="xwheel_zoom,xpan,xbox_zoom,reset,box_select",
# tools="xwheel_zoom,xpan,xbox_zoom,reset,tap",
active_scroll="xwheel_zoom",
x_axis_type="datetime",)
plot_args = dict(
size=4, alpha=0.9, color='COLOR')
if ini['SUMMARY']['max_qa'] > 0:
plot_args['legend'] = 'QA'
figures = []
for plot_i, plot_var in enumerate(plot_var_list):
if plot_i == 0:
f = figure(
# x_range=bokeh.models.Range1d(x_limit[0], x_limit[1], bounds=x_limit),
y_axis_label=plot_var, **figure_args)
else:
f = figure(
x_range=f.x_range, y_axis_label=plot_var, **figure_args)
if plot_var == 'TS':
f.y_range.bounds = (270, None)
# # Add each QA level as a separate object
# for qa, platform_sources in sorted(sources.items()):
# for platform, source in sorted(platform_sources.items()):
# if platform == 'LT05':
# r = f.triangle(
# 'DATE', plot_var, source=source, **plot_args)
# elif platform == 'LE07':
# r = f.square(
# 'DATE', plot_var, source=source, **plot_args)
# elif platform == 'LC08':
# r = f.circle(
# 'DATE', plot_var, source=source, **plot_args)
# else:
# r = f.diamond(
# 'DATE', plot_var, source=source, **plot_args)
# r.hover_glyph = hover_circle
# r.selection_glyph = selected_circle
# r.nonselection_glyph = nonselected_circle
# r.muted_glyph = nonselected_circle
# hover_tool.renderers.append(r)
# Add each QA level as a separate object
for qa, source in sorted(sources.items()):
r = f.circle('DATE', plot_var, source=source, **plot_args)
r.hover_glyph = hover_circle
r.selection_glyph = selected_circle
r.nonselection_glyph = nonselected_circle
r.muted_glyph = nonselected_circle
# # DEADBEEF - This will display high QA points as muted
# if qa > ini['SUMMARY']['max_qa']:
# r.muted = True
# # r.visible = False
f.add_tools(bokeh.models.HoverTool(tooltips=tooltips))
# if ini['SUMMARY']['max_qa'] > 0:
f.legend.location = "top_left"
f.legend.click_policy = "hide"
# f.legend.click_policy = "mute"
f.legend.orientation = "horizontal"
figures.append(f)
del f
# Try to not allow more than 4 plots in a column
p = gridplot(
figures, ncols=len(plot_var_list) // 3,
sizing_mode='stretch_both')
if show_flag:
show(p)
save(p)
# Pause after each iteration if show is True
if show_flag:
input('Press ENTER to continue')
def main(ini_path=None, overwrite_flag=False):
"""Generate summary thumbnails
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nGenerate summary thumbnails')
# Inputs (eventually move to INI file?)
vis_args = {
'bands': ['red', 'green', 'blue'],
# 'bands': ['swir1', 'nir', 'red'],
'min': [0.01, 0.01, 0.01],
'max': [0.4, 0.4, 0.4],
'gamma': [1.8, 1.8, 1.8]
}
# Buffer zone polygon
zone_buffer = 240
# Generate images by DOY
doy_flag = True
# Generate images by date
date_flag = True
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='SUMMARY')
year_list = range(ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'] + 1)
month_list = list(
utils.wrapped_range(ini['INPUTS']['start_month'],
ini['INPUTS']['end_month'], 1, 12))
doy_list = list(
utils.wrapped_range(ini['INPUTS']['start_doy'],
ini['INPUTS']['end_doy'], 1, 366))
# Add merged row XXX to keep list
ini['INPUTS']['row_keep_list'].append('XXX')
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Need zone_shp_path projection to build EE geometries
zone = {}
zone['osr'] = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone['proj'] = gdc.osr_wkt(zone['osr'])
# zone['proj'] = ee.Projection(zone['proj']).wkt().getInfo()
# zone['proj'] = zone['proj'].replace('\n', '').replace(' ', '')
# logging.debug(' Zone Projection: {}'.format(zone['proj']))
# Initialize Earth Engine API key
logging.debug('')
ee.Initialize()
coll_dict = {
'LT04': 'LANDSAT/LT04/C01/T1_SR',
'LT05': 'LANDSAT/LT05/C01/T1_SR',
'LE07': 'LANDSAT/LE07/C01/T1_SR',
'LC08': 'LANDSAT/LC08/C01/T1_SR'
}
logging.info('\nProcessing zones')
for zone_fid, zone_name, zone_json in zone_geom_list:
zone['fid'] = zone_fid
zone['name'] = zone_name.replace(' ', '_')
zone['json'] = zone_json
logging.info('ZONE: {} (FID: {})'.format(zone['name'], zone['fid']))
# Build EE geometry object for zonal stats
zone['geom'] = ee.Geometry(geo_json=zone['json'],
opt_proj=zone['proj'],
opt_geodesic=False)
# logging.debug(' Centroid: {}'.format(
# zone['geom'].centroid(100).getInfo()['coordinates']))
# Use feature geometry to build extent, transform, and shape
zone['extent'] = gdc.Extent(
ogr.CreateGeometryFromJson(json.dumps(zone['json'])).GetEnvelope())
# zone['extent'] = gdc.Extent(zone['geom'].GetEnvelope())
zone['extent'] = zone['extent'].ogrenv_swap()
zone['extent'] = zone['extent'].buffer(zone_buffer)
zone['extent'] = zone['extent'].adjust_to_snap(
'EXPAND', ini['SPATIAL']['snap_x'], ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone['geo'] = zone['extent'].geo(ini['SPATIAL']['cellsize'])
zone['transform'] = gdc.geo_2_ee_transform(zone['geo'])
# zone['transform'] = '[' + ','.join(map(str, zone['transform'])) + ']'
zone['shape'] = zone['extent'].shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone['shape']))
logging.debug(' Zone Transform: {}'.format(zone['transform']))
logging.debug(' Zone Extent: {}'.format(zone['extent']))
# logging.debug(' Zone Geom: {}'.format(zone['geom'].getInfo()))
# Build an EE geometry of the extent
extent_geom = ee.Geometry.Rectangle(coords=list(zone['extent']),
proj=zone['proj'],
geodesic=False)
if 'SUMMARY' in ini.keys():
zone_output_ws = os.path.join(ini['SUMMARY']['output_ws'],
zone['name'])
elif 'EXPORT' in ini.keys():
zone_output_ws = os.path.join(ini['EXPORT']['output_ws'],
zone['name'])
else:
logging.error(
'INI file does not contain a SUMMARY or EXPORT section')
sys.exit()
if not os.path.isdir(zone_output_ws):
logging.debug(
'Folder {} does not exist, skipping'.format(zone_output_ws))
continue
landsat_daily_path = os.path.join(
zone_output_ws, '{}_landsat_daily.csv'.format(zone['name']))
if not os.path.isfile(landsat_daily_path):
logging.error(' Landsat daily CSV does not exist, skipping zone')
continue
output_doy_ws = os.path.join(zone_output_ws, 'thumbnails_doy')
output_date_ws = os.path.join(zone_output_ws, 'thumbnails_date')
if overwrite_flag and os.path.isdir(output_doy_ws):
for file_name in os.listdir(output_doy_ws):
os.remove(os.path.join(output_doy_ws, file_name))
if overwrite_flag and os.path.isdir(output_date_ws):
for file_name in os.listdir(output_date_ws):
os.remove(os.path.join(output_date_ws, file_name))
if doy_flag and not os.path.isdir(output_doy_ws):
os.makedirs(output_doy_ws)
if date_flag and not os.path.isdir(output_date_ws):
os.makedirs(output_date_ws)
logging.debug(' Reading Landsat CSV')
landsat_df = pd.read_csv(landsat_daily_path)
# landsat_df = pd.read_csv(
# landsat_daily_path, parse_dates=['DATE'], index_col='DATE')
landsat_df = landsat_df[landsat_df['PIXEL_COUNT'] > 0]
# Common summary filtering
logging.debug(' Filtering using INI SUMMARY parameters')
if year_list:
landsat_df = landsat_df[landsat_df['YEAR'].isin(year_list)]
if month_list:
landsat_df = landsat_df[landsat_df['MONTH'].isin(month_list)]
if doy_list:
landsat_df = landsat_df[landsat_df['DOY'].isin(doy_list)]
if ini['INPUTS']['path_keep_list']:
landsat_df = landsat_df[landsat_df['PATH'].isin(
ini['INPUTS']['path_keep_list'])]
if (ini['INPUTS']['row_keep_list']
and ini['INPUTS']['row_keep_list'] != ['XXX']):
landsat_df = landsat_df[landsat_df['ROW'].isin(
ini['INPUTS']['row_keep_list'])]
# Assume the default is for these to be True and only filter if False
if not ini['INPUTS']['landsat4_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT04']
if not ini['INPUTS']['landsat5_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LT05']
if not ini['INPUTS']['landsat7_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LE07']
if not ini['INPUTS']['landsat8_flag']:
landsat_df = landsat_df[landsat_df['PLATFORM'] != 'LC08']
if ini['INPUTS']['scene_id_keep_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[scene_id_df.isin(
ini['INPUTS']['scene_id_keep_list']).values]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_keep_list'])]
if ini['INPUTS']['scene_id_skip_list']:
# Replace XXX with primary ROW value for checking skip list SCENE_ID
scene_id_df = pd.Series([
s.replace('XXX', '{:03d}'.format(int(r)))
for s, r in zip(landsat_df['SCENE_ID'], landsat_df['ROW'])
])
landsat_df = landsat_df[np.logical_not(
scene_id_df.isin(ini['INPUTS']['scene_id_skip_list']).values)]
# This won't work: SCENE_ID have XXX but scene_id_skip_list don't
# landsat_df = landsat_df[np.logical_not(landsat_df['SCENE_ID'].isin(
# ini['INPUTS']['scene_id_skip_list']))]
# Filter by QA/QC value
if ini['SUMMARY']['max_qa'] >= 0 and not landsat_df.empty:
logging.debug(' Maximum QA: {0}'.format(
ini['SUMMARY']['max_qa']))
landsat_df = landsat_df[
landsat_df['QA'] <= ini['SUMMARY']['max_qa']]
# Filter by average cloud score
if ini['SUMMARY']['max_cloud_score'] < 100 and not landsat_df.empty:
logging.debug(' Maximum cloud score: {0}'.format(
ini['SUMMARY']['max_cloud_score']))
landsat_df = landsat_df[
landsat_df['CLOUD_SCORE'] <= ini['SUMMARY']['max_cloud_score']]
# Filter by Fmask percentage
if ini['SUMMARY']['max_fmask_pct'] < 100 and not landsat_df.empty:
landsat_df['FMASK_PCT'] = 100 * (landsat_df['FMASK_COUNT'] /
landsat_df['FMASK_TOTAL'])
logging.debug(' Max Fmask threshold: {}'.format(
ini['SUMMARY']['max_fmask_pct']))
landsat_df = landsat_df[
landsat_df['FMASK_PCT'] <= ini['SUMMARY']['max_fmask_pct']]
# Filter low count SLC-off images
if ini['SUMMARY']['min_slc_off_pct'] > 0 and not landsat_df.empty:
logging.debug(' Mininum SLC-off threshold: {}%'.format(
ini['SUMMARY']['min_slc_off_pct']))
# logging.debug(' Maximum pixel count: {}'.format(
# max_pixel_count))
slc_off_mask = ((landsat_df['PLATFORM'] == 'LE07') &
((landsat_df['YEAR'] >= 2004) |
((landsat_df['YEAR'] == 2003) &
(landsat_df['DOY'] > 151))))
slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] /
landsat_df['PIXEL_TOTAL'])
# slc_off_pct = 100 * (landsat_df['PIXEL_COUNT'] / max_pixel_count)
landsat_df = landsat_df[(
(slc_off_pct >= ini['SUMMARY']['min_slc_off_pct'])
& slc_off_mask) | (~slc_off_mask)]
if landsat_df.empty:
logging.error(
' Empty Landsat dataframe after filtering, skipping zone')
continue
logging.debug(' Downloading thumbnails')
for landsat, start_date in zip(landsat_df['PLATFORM'],
landsat_df['DATE']):
start_dt = datetime.datetime.strptime(start_date, '%Y-%m-%d')
end_dt = start_dt + datetime.timedelta(days=1)
end_date = end_dt.strftime('%Y-%m-%d')
output_doy_path = os.path.join(
output_doy_ws,
'{}_{}.png'.format(start_dt.strftime('%j_%Y-%m-%d'), landsat))
output_date_path = os.path.join(
output_date_ws,
'{}_{}.png'.format(start_dt.strftime('%Y-%m-%d_%j'), landsat))
# DEADBEEF - This seems like a poor approach
save_doy_flag = False
save_date_flag = False
if doy_flag and not os.path.isfile(output_doy_path):
save_doy_flag = True
if date_flag and not os.path.isfile(output_date_path):
save_date_flag = True
if not save_doy_flag and not save_date_flag:
logging.debug(
' {} - file already exists, skipping'.format(start_date))
continue
logging.debug(' {}'.format(start_date))
# logging.debug(' {}'.format(output_path))
if landsat in ['LT04', 'LT05', 'LE07']:
ee_coll = ee.ImageCollection(coll_dict[landsat]).select(
['B1', 'B2', 'B3', 'B4', 'B5', 'B7'],
['blue', 'green', 'red', 'nir', 'swir1', 'swir2'])
elif landsat in ['LC08']:
ee_coll = ee.ImageCollection(coll_dict[landsat]).select(
['B2', 'B3', 'B4', 'B5', 'B6', 'B7'],
['blue', 'green', 'red', 'nir', 'swir1', 'swir2'])
ee_coll = ee_coll.filterDate(start_date, end_date)
ee_image = ee.Image(ee_coll.median().divide(10000)) \
.visualize(**vis_args) \
.reproject(crs=zone['proj'], crsTransform=zone['transform']) \
.paint(zone['geom'], color=0.5, width=1) \
.clip(extent_geom)
# Get the image thumbnail
for i in range(10):
try:
output_url = ee_image.getThumbUrl({'format': 'png'})
break
except Exception as e:
logging.error(' Exception: {}, retry {}'.format(e, i))
logging.debug('{}'.format(e))
sleep(i**2)
for i in range(10):
try:
# DEADBEEF - This seems like a poor approach
if save_doy_flag and save_date_flag:
urllib.urlretrieve(output_url, output_doy_path)
shutil.copy(output_doy_path, output_date_path)
elif save_doy_flag:
urllib.urlretrieve(output_url, output_doy_path)
elif save_date_flag:
urllib.urlretrieve(output_url, output_date_path)
break
except Exception as e:
logging.error(' Exception: {}, retry {}'.format(e, i))
logging.debug('{}'.format(e))
sleep(i**2)
