def get(self):
ee.Initialize(config.EE_CREDENTIALS)
ee.data.setDeadline(180000) # 3 minutes
import loggerFunctions
logger = loggerFunctions.set_logger('MAX DATES')
try:
maxDates = collection_dataStore.get_all_maxDates()
except:
time.sleep(30)
maxDates = collection_dataStore.get_all_maxDates()
logger.info(maxDates)
for key, value in maxDates.iteritems():
#Check that date is of format yyyy-mm-dd
err = 0
try:
dt.datetime.strptime(value, "%Y-%m-%d")
except:
err = 1
if err:
# 3600s = 1 hr..in seconds (not milliseconds)
#memcache.add(key=key, value=value, time=3600)
memcache.add(key=key, value=value, time=0) #no expiration
#logger.info('added'+key+value)
#else #wait until next time
out_str = '<h4>Max Dates</h4>'
for key, value in sorted(maxDates.iteritems()):
out_str += '{} = {}'.format(key, value) + '<br><br>'
self.response.out.write(out_str)
def get(self):
ee.Initialize(config.EE_CREDENTIALS)
ee.data.setDeadline(180000) # 3 minutes
import loggerFunctions
logger = loggerFunctions.set_logger('MAX DATES')
try:
maxDates = collection_dataStore.get_all_maxDates()
except:
time.sleep(30)
maxDates = collection_dataStore.get_all_maxDates()
logger.info(maxDates)
for key, value in maxDates.iteritems():
#Check that date is of format yyyy-mm-dd
err = 0
try:
dt.datetime.strptime(value, "%Y-%m-%d")
except:
err = 1
if err:
# 3600s = 1 hr..in seconds (not milliseconds)
#memcache.add(key=key, value=value, time=3600)
memcache.add(key=key, value=value, time=0) #no expiration
#logger.info('added'+key+value)
#else #wait until next time
out_str = '<h4>Max Dates</h4>'
for key, value in sorted(maxDates.iteritems()):
out_str += '{} = {}'.format(key, value) + '<br><br>'
self.response.out.write(out_str)
def get_time_series(template_values, shape_type):
"""
Time series request for points or fusion tables
(shape_type p or ft, respectively)
Args:
template_values: a dictionary of user and system input
shape_type: p (points) or ft (fusion tables)
Returns:
updated template_values with time series data
"""
#================================
logger = loggerFunctions.set_logger('points_debug')
#Catch threading error with error
error = None
#Set the time variables variables (depends on timeSeriesCalc)
time_vars = set_time_vars_for_processing(template_values, 1)
#Set collection and update template_values
productTS = template_values['productTS']
collection = set_collection(template_values, productTS,
template_values['variableTS'],
template_values['modelTS'],
template_values['scenarioTS'], time_vars, 1,
logger)
if productTS == 'MACA' or productTS == 'NASANEX':
scenarioTS = template_values['scenarioTS']
collection = collection.filterMetadata("scenario", "equals",
scenarioTS)
collection2 = None
if template_values['variable2display'] != 'none':
time_vars2 = set_time_vars_for_processing(template_values, 2)
product2TS = template_values['product2TS']
scenario2TS = template_values['scenario2TS']
collection2 = set_collection(template_values, product2TS,
template_values['variable2TS'],
template_values['model2TS'],
template_values['scenario2TS'],
time_vars2, 2, logger)
if product2TS == 'MACA' or product2TS == 'NASANEX':
collection2 = collection2.filterMetadata("scenario", "equals",
scenario2TS)
#================================
#Run threads and update template variables
extra_template_values, timeSeriesTextData, timeSeriesGraphData = run_threads(
collection, template_values, time_vars, 1, shape_type, logger)
extra_template_values['timeSeriesTextData'] = timeSeriesTextData
extra_template_values['timeSeriesGraphData'] = json.dumps(
timeSeriesGraphData)
template_values.update(extra_template_values)
if collection2:
extra_template_values, timeSeriesTextData, timeSeriesGraphData = run_threads(
collection2, template_values, time_vars2, 2, shape_type, logger)
extra_template_values['timeSeriesTextData2'] = timeSeriesTextData
extra_template_values['timeSeriesGraphData2'] = json.dumps(
timeSeriesGraphData)
template_values.update(extra_template_values)
#================================
return template_values
def processPointData(template_values, extra_template_values, time_vars, point_info, logger,timeSeriesTextData,timeSeriesGraphData,varnum):
logger = loggerFunctions.set_logger('info')
name = point_info['name']
altname = point_info['altname']
marker_color = point_info['marker_color']
#Initialize the data dicts
data_dict_ts = formatData.initialize_timeSeriesTextDataDict(name, altname=altname)
data_dict_graph = formatData.initialize_timeSeriesGraphDataDict(name,marker_color,altname=altname)
#logger.info('**********timeSeriesCalc************'+template_values['timeSeriesCalc'])
climoData=[]
percentileData = []
#process data according to what timeSereisCalc is
if template_values['timeSeriesCalc'] == 'days':
data_dict_ts['Data'],data_dict_graph['Data'] = process_daily_threadData(
point_info['data'],template_values,varnum)
elif template_values['timeSeriesCalc'] == 'interannual':
sS_doy = time_vars['seasonStart_doy']
sE_doy = time_vars['seasonEnd_doy']
data_dict_ts['Data'],data_dict_graph['Data'] = process_interannual_threadData(
point_info['data'], template_values, sS_doy, sE_doy, varnum,logger)
elif template_values['timeSeriesCalc'] == 'intraannual':
#logger.info('**********inside intrannaul************')
#get data for all years stored in dict with keys year
doyS = time_vars['doyStart']
doyE = time_vars['doyEnd']
yS = time_vars['yearStart']
yE = time_vars['yearEnd']
year_dict_ts,year_dict_graph, climoData, percentileData =process_intraannual_threadData(
point_info['data'], template_values,doyS, doyE, yS, yE, varnum,logger)
for year in range(int(yS),int(yE) +1):
'''
if year in year_dict_ts.keys() and year_dict_ts[year]:
data_dict_ts['Data'].append(year_dict_ts[year])
if year in year_dict_graph.keys() and year_dict_graph[year]:
data_dict_graph['Data'].append(year_dict_graph[year])
'''
data_dict_ts['Data'].append(year_dict_ts[year])
data_dict_graph['Data'].append(year_dict_graph[year])
timeSeriesTextData.append(data_dict_ts)
timeSeriesGraphData.append(data_dict_graph)
if climoData:
if varnum == 1:
extra_template_values['climoData'] = json.dumps(climoData)
if varnum == 2:
extra_template_values['climoData2'] = json.dumps(climoData)
if percentileData:
if varnum == 1:
extra_template_values['percentileData'] = json.dumps(percentileData)
if varnum == 2:
extra_template_values['percentileData2'] = json.dumps(percentileData)
return extra_template_values,timeSeriesTextData,timeSeriesGraphData
def get_time_series(template_values, shape_type):
"""
Time series request for points or fusion tables
(shape_type p or ft, respectively)
Args:
template_values: a dictionary of user and system input
shape_type: p (points) or ft (fusion tables)
Returns:
updated template_values with time series data
"""
#================================
logger = loggerFunctions.set_logger('points_debug')
#Catch threading error with error
error = None
#Set the time variables variables (depends on timeSeriesCalc)
time_vars = set_time_vars_for_processing(template_values,1)
#Set collection and update template_values
productTS = template_values['productTS']
collection = set_collection(
template_values,productTS, template_values['variableTS'],
template_values['modelTS'], template_values['scenarioTS'],
time_vars, 1, logger)
if productTS =='MACA' or productTS=='NASANEX':
scenarioTS = template_values['scenarioTS']
collection = collection.filterMetadata("scenario", "equals",scenarioTS)
collection2 = None
if template_values['variable2display'] != 'none':
time_vars2 = set_time_vars_for_processing(template_values,2)
product2TS = template_values['product2TS']
scenario2TS = template_values['scenario2TS']
collection2 = set_collection(
template_values, product2TS, template_values['variable2TS'],
template_values['model2TS'], template_values['scenario2TS'],
time_vars2, 2, logger)
if product2TS =='MACA' or product2TS=='NASANEX':
collection2 = collection2.filterMetadata("scenario", "equals",scenario2TS)
#================================
#Run threads and update template variables
extra_template_values,timeSeriesTextData, timeSeriesGraphData = run_threads(
collection,template_values, time_vars,1,shape_type,logger)
extra_template_values['timeSeriesTextData'] = timeSeriesTextData
extra_template_values['timeSeriesGraphData'] = json.dumps(timeSeriesGraphData)
template_values.update(extra_template_values)
if collection2:
extra_template_values,timeSeriesTextData, timeSeriesGraphData = run_threads(
collection2,template_values, time_vars2,2,shape_type,logger)
extra_template_values['timeSeriesTextData2'] = timeSeriesTextData
extra_template_values['timeSeriesGraphData2'] = json.dumps(timeSeriesGraphData)
template_values.update(extra_template_values)
#================================
return template_values
def get_all_maxDates():
import loggerFunctions
logger_temp = loggerFunctions.set_logger('maxDates')
threads = [None for p in default_maxDateCalc]
maxDates = {}
# Loop over default_maxDateCalc defined in STATIC section
# and start a thread for each product
for t_idx, prod_args in enumerate(default_maxDateCalc):
logger_temp.info('Running thread: ' + str(t_idx))
logger_temp.info('Product: ' + prod_args[0])
# Add thread index and thread data to arguments
thread_args = prod_args[1] + (t_idx, maxDates)
t = threading.Thread(target=maxDateWorker, args=thread_args)
# Keep track of the thread by putting it in a slot in threads
threads[t_idx] = t
# Start thread
t.start()
# Collect threads
for t_idx, t in enumerate(threads):
t.join(10)
return maxDates
def get_all_maxDates():
import loggerFunctions
logger_temp = loggerFunctions.set_logger('maxDates')
threads = [None for p in default_maxDateCalc]
maxDates = {}
# Loop over default_maxDateCalc defined in STATIC section
# and start a thread for each product
for t_idx, prod_args in enumerate(default_maxDateCalc):
logger_temp.info('Running thread: ' + str(t_idx))
logger_temp.info('Product: ' + prod_args[0])
# Add thread index and thread data to arguments
thread_args = prod_args[1] + (t_idx, maxDates)
t = threading.Thread(target=maxDateWorker, args=thread_args)
# Keep track of the thread by putting it in a slot in threads
threads[t_idx] = t
# Start thread
t.start()
# Collect threads
for t_idx, t in enumerate(threads):
t.join(10)
return maxDates
def runTool(self, applicationName):
ee.Initialize(config.EE_CREDENTIALS)
ee.data.setDeadline(180000)
import loggerFunctions
logger = loggerFunctions.set_logger('TEST')
# Initialize forms
template_values = templatevariables.set_initial_template_values(
self, applicationName)
'''
try:
template_values = templatevariables.set_initial_template_values(
self, applicationName)
except:
template_values = {}
'''
# Check user input for errors:
fieldID, input_err = formchecks.check_user_input(self, template_values)
if input_err is None:
if self.request.arguments():
# Update template values with mapid or time series data
toolAction = template_values['toolAction']
if toolAction == 'getTimeSeriesOverDateRange':
subDomainTypeTS = template_values['subDomainTypeTS']
if subDomainTypeTS == 'points':
shape_type = 'p'
elif subDomainTypeTS == 'customShapes':
shape_type = 'ft'
template_values = timeseriesMethods.get_time_series(
template_values, shape_type)
elif (toolAction == 'getMap' or
toolAction == 'downloadRectangleSubset' or
toolAction == 'showSingleValueOnMap' or
toolAction == 'downloadFusionTableSubset'):
template_values = mappingMethods.get_images(template_values)
else:
template_values['form_error'] = {fieldID: input_err}
#template_values['form_error'] = {'fieldID':str(fieldID),'error':str(input_err)}
return template_values
def runTool(self, applicationName):
ee.Initialize(config.EE_CREDENTIALS)
ee.data.setDeadline(180000)
import loggerFunctions
logger = loggerFunctions.set_logger('TEST')
# Initialize forms
template_values = templatevariables.set_initial_template_values(
self, applicationName)
'''
try:
template_values = templatevariables.set_initial_template_values(
self, applicationName)
except:
template_values = {}
'''
# Check user input for errors:
fieldID, input_err = formchecks.check_user_input(self, template_values)
if input_err is None:
if self.request.arguments():
# Update template values with mapid or time series data
toolAction = template_values['toolAction']
if toolAction == 'getTimeSeriesOverDateRange':
subDomainTypeTS = template_values['subDomainTypeTS']
if subDomainTypeTS == 'points':
shape_type = 'p'
elif subDomainTypeTS == 'customShapes':
shape_type = 'ft'
template_values = timeseriesMethods.get_time_series(
template_values, shape_type)
elif (toolAction == 'getMap'
or toolAction == 'downloadRectangleSubset'
or toolAction == 'showSingleValueOnMap'
or toolAction == 'downloadFusionTableSubset'):
template_values = mappingMethods.get_images(template_values)
else:
template_values['form_error'] = {fieldID: input_err}
#template_values['form_error'] = {'fieldID':str(fieldID),'error':str(input_err)}
return template_values
def get_climatology(collection, product, variable, dateStart, dateEnd,
statistic, calculation, yearStartClim, yearEndClim,
logger=None):
"""Return the climatology image
Args:
collection: EarthEngine collection to process (has already selected variable)
product: string of the product ()
variable: string of the variable ()
dateStart: string of the start date isoformat (YYYY-MM-DD)
dateEnd: string of the end date isoformat (YYYY-MM-DD)
statistic: string of the statistic (Mean, Median, Total, etc.)
calculation: string of the calculation type
(anom, value, anompercentof,anompercentchange,clim)
yearStartClim: string of the climatology start year
yearEndClim: string of the climatology end year
logger:
Returns:
EarthEngine image object
String of additional notes about the collection
"""
#==============
logger = loggerFunctions.set_logger('get_images_debug')
yearStartClim = int(yearStartClim)
yearEndClim = int(yearEndClim)
#Build python datetime objects from the date string
dateStart_dt = dt.datetime.strptime(dateStart, '%Y-%m-%d')
dateEnd_dt = dt.datetime.strptime(dateEnd, '%Y-%m-%d')
#==============
#Check timedelta between start and end is greater than 1 year
def yearsahead(years, start_date):
try:
return start_date.replace(year=start_date.year + years)
except: # Must be 2/29!
assert from_date.month == 2 and from_date.day == 29 # can be removed
return from_date.replace(month=2, day=28, year=start_date.year+years)
#Climo products will have less than a year to avg over too
if dateEnd_dt <= yearsahead(1, dateStart_dt) or product in ['MACA', 'NASANEX']:
sub_year_flag = False
doyStart = dateStart_dt.timetuple().tm_yday
doyEnd = dateEnd_dt.timetuple().tm_yday
dayStart = dateStart[5:]
dayEnd = dateEnd[5:]
if(doyStart<doyEnd):
num_days = len(range(doyStart,doyEnd))
else:
num_days = len(range(doyStart,366))+len(range(1,doyEnd))
else:
sub_year_flag = True
doyStart = 1
doyEnd = 366
num_days = 366
#==============
if sub_year_flag:
#List sequence is inclusive (i.e. don't advance yearEnd)
yearListClim = ee.List.sequence(yearStartClim,yearEndClim) #list inclusive
num_years =yearEndClim - yearStartClim + 1
else:
yearListClim = ee.List.sequence(yearStartClim,yearEndClim-1)#list inclusive
num_years =yearEndClim -1 - yearStartClim + 1
#==============Not technically correct.. takes min over a single year.. not min over the time period
# these are both resulting in 429 errors right now and 500 server errors too
doy_filter = ee.Filter.calendarRange(doyStart, doyEnd, 'day_of_year')
if statistic == 'Min':
def min_climatology_func(year):
"""For each year, return an image of the minimum value over the DOY range"""
year_filter =ee.Filter.calendarRange(year, year, 'year')
return ee.Image(collection.filter(year_filter).filter(doy_filter).min())
climatology_coll = ee.ImageCollection.fromImages(
yearListClim.map(min_climatology_func))
climatology_img = get_statistic(climatology_coll, 'Mean', logger=logger)
#==============Not technically correct.. takes max over a single year.. not max over the time period
# these are both resulting in 429 errors right now... too many requests. and 500 server errors too
elif statistic == 'Max':
def max_climatology_func(year):
"""For each year, return an image of the minimum value over the DOY range"""
year_filter =ee.Filter.calendarRange(year, year, 'year')
return ee.Image(collection.filter(year_filter).filter(doy_filter).max())
climatology_coll = ee.ImageCollection.fromImages(
yearListClim.map(max_climatology_func))
climatology_img = get_statistic(climatology_coll, 'Mean', logger=logger)
#==============
elif (statistic == 'Mean' or statistic == 'Total' or statistic == 'Median'):
#FilterDate needs an extra day on the high end,Set yearEnd to Jan 1st of next year
yearStartClimUTC = dt.datetime(yearStartClim, 1, 1)
yearEndClimUTC = dt.datetime(yearEndClim+1, 1, 1)
#===================
climatology_coll=collectionMethods.get_climatology_collection(product, variable)
if climatology_coll=='None' or (product=='G' and (yearEndClim!=1981 or yearStartClim!=2010)): #no pre-calculated climos
climatology_coll_temp = 'None'
climatology_coll = collection.filterDate(
yearStartClimUTC, yearEndClimUTC).filter(doy_filter)
else: #yes pre-calculated climos
climatology_coll_temp = 'Some'
#climatology_coll = climatology_coll.filterMetadata('doy','greater_than',doyStart) \
# .filterMetadata('doy','not_greater_than',doyEnd)
if doyStart <= doyEnd:
# Date range is in same year
climatology_coll = climatology_coll.filter(ee.Filter.And(
ee.Filter.gte('doy', doyStart),
ee.Filter.lte('doy', doyEnd)))
else:
# Date range wraps around to next year
climatology_coll = climatology_coll.filter(ee.Filter.Or(
ee.Filter.gte('doy', doyStart),
ee.Filter.lte('doy', doyEnd)))
#===================
if not sub_year_flag:
climatology_img = get_statistic(
climatology_coll, statistic, logger=logger)
if statistic=='Total' and climatology_coll_temp=='None':
climatology_img =climatology_img.divide(num_years)
else:
climatology_img = get_statistic(
climatology_coll, statistic, logger=logger)
climatologyNote = 'Average calculated from {0}-{1}'.format(str(yearStartClim), str(yearEndClim))
return climatology_img, climatology_coll,climatologyNote,num_days
def get_images(template_values):
""""""
#from forms import stateLat, stateLong
#set up logger
logger = loggerFunctions.set_logger('get_images_debug')
TV = {}
for key, val in template_values.iteritems():
TV[key] = val
product = TV['product']
var = TV['variable']
model = TV['model']
scenario = TV['scenario']
calculation = TV['calculation']
toolAction = TV['toolAction']
yearStartClim = TV['yearStartClim']
yearEndClim = TV['yearEndClim']
statistic = TV['statistic']
units = TV['units']
palette = TV['palette']
minColorbar = template_values['minColorbar']
maxColorbar = template_values['maxColorbar']
colorbarType = template_values['colorbarType']
colorbarmap = template_values['colorbarmap']
colorbarTicks = template_values['colorbarTicks']
scale = TV['scale']
downloadFilename = TV['downloadFilename']
mask = TV['mask']
if mask !='none':
maskMin = TV['maskMin']
maskMax = TV['maskMax']
# Build EarthEngine date objects from date strings and explicitly set GMT
# Note, by default EarthEngine date objects are already GMT
if product in ['MACA', 'NASANEX']:
monthList = [
'', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul',
'Aug', 'Sept', 'Oct', 'Nov', 'Dec']
yearStartClimFut = TV['yearStartClimFut']
yearEndClimFut = TV['yearEndClimFut']
yearStartClim = TV['yearStartClim']
yearEndClim = TV['yearEndClim']
#these are for calculating the source title
if calculation in ['anom', 'anompercentof', 'anompercentchange', 'percentile','value']:
dS = yearStartClimFut+'-'+TV['monthStartFut']+'-'+TV['dayStartFut']
dE = yearEndClimFut+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
else:
dS = yearStartClim+'-'+TV['monthStartFut']+'-'+TV['dayStartFut']
dE = yearEndClim+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
#just need to get the doyfilter right with these
dSFut = yearStartClimFut+'-'+TV['monthStartFut']+'-'+TV['dayStartFut']
dSHist = yearStartClim+'-'+TV['monthStartFut']+'-'+TV['dayStartFut']
if int(TV['monthEndFut']) > int(TV['monthStartFut']):
dEFut = yearStartClimFut+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
dEHist = yearStartClim+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
else:
dEFut = str(int(yearStartClimFut)+1)+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
dEHist = str(int(yearStartClim)+1)+'-'+TV['monthEndFut']+'-'+TV['dayEndFut']
dSUTC = ee.Date(dSHist, 'GMT')
dEUTC = ee.Date(dEHist, 'GMT')
else:
dS = TV['dateStart']
dE = TV['dateEnd']
dSUTC = ee.Date(dS, 'GMT')
dEUTC = ee.Date(dE, 'GMT')
#==============
#Initial Collection
#==============
frequency='daily'
if product=='MACA' or product=='NASANEX':
frequency='monthly'
collection, coll_name, coll_desc, var_desc, notes = collectionMethods.get_collection(
product, var, model, scenario, frequency,logger=logger)
#if product =='NVET':
# frequency='yearly'
#collection, coll_name, coll_desc, notes = collectionMethods.get_collection(
# product, var, model, scenario, frequency,logger=logger)
#==============
#Calculation:Values,Climatology,Anomalies
#==============
#get_statistic returns ee.ImageCollection
if calculation in ['value']:
if product in ['MACA', 'NASANEX']:
# This is future average
# FilterDate is exclusive on the high end, include an extra day on dEUTC
collection = collection.filterMetadata('scenario', 'equals', scenario)
calc_img,clim_temp,clim_notes_temp,num_days = get_climatology(
collection, product, var, dSFut, dEFut, statistic,
calculation, yearStartClimFut, yearEndClimFut, logger=logger)
elif product == 'CFSV2': #convert 6hrly to daily
collection = collection_CFSV2.convert_6hrly_to_daily(
collection, var, dSUTC, dEUTC, logger)
calc_img = get_statistic(collection, statistic, logger=logger)
else:
collection = collection.filterDate(dSUTC, dEUTC.advance(1,'day'))
calc_img = get_statistic(collection, statistic, logger=logger)
elif calculation in ['anom', 'anompercentof', 'anompercentchange', 'clim', 'percentile','zscore']:
if product in ['MACA', 'NASANEX']: #this is historical average
hist_coll = collection.filterMetadata('scenario', 'equals', 'historical')
clim_img, clim_coll, climatologyNotes,num_days = get_climatology(
hist_coll, product, var, dSHist, dEHist, statistic,
calculation, yearStartClim, yearEndClim, logger=logger)
else:
clim_img, clim_coll, climatologyNotes,num_days = get_climatology(
collection, product, var, dS, dE, statistic,
calculation, yearStartClim, yearEndClim, logger=logger)
if calculation in ['clim']:
calc_img = clim_img
else:
# anomaly = future wrt historical
if product in ['MACA', 'NASANEX']:
# This is future average
future_col = collection.filterMetadata('scenario', 'equals', scenario)
calc_img, clim_temp, clim_notes_temp,num_day = get_climatology(
future_col, product, var, dSFut, dEFut, statistic,
calculation, yearStartClimFut, yearEndClimFut, logger=logger)
calc_img = get_anomaly(calc_img, clim_img, clim_coll, calculation,statistic,num_days)
else:
collection = collection.filterDate(dSUTC, dEUTC.advance(1, 'day'))
calc_img = get_statistic(collection, statistic, logger=logger)
calc_img = get_anomaly(calc_img, clim_img, clim_coll, calculation,statistic,1)
TV['climatologyNotes'] = climatologyNotes
#==============
#Units
#==============
calc_img = modify_units(calc_img, var, product, calculation, units)
#==============
#Extra template values
#==============
extra_template_values = {
}
#==============
#Apply Mask
#==============
if mask != 'none':
maskMin = float(maskMin)
maskMax = float(maskMax)
if mask == 'above':
calc_img = ee.Image(calc_img).mask(calc_img.lte(maskMax))
elif mask == 'below':
calc_img = ee.Image(calc_img).mask(calc_img.gte(maskMin))
elif mask == 'exterior':
calc_img = ee.Image(calc_img).mask(calc_img.lte(maskMin).Or(calc_img.gte(maskMax)))
elif mask == 'interior':
calc_img = ee.Image(calc_img).mask(calc_img.gte(maskMin).And(calc_img.lte(maskMax)))
#==============
#Get mapid
#==============
mapid = {'mapid':[], 'token':[]}
## Limit the image values to the min and max before visualizing
##here calc_img = ee.Image()
vis_image = calc_img.clamp(float(minColorbar), float(maxColorbar))
#we could change this to just if var in ['TrueColor', 'FalseColor'] instead of all the products
if (product in ['L_TOA', 'L5_TOA', 'L7_TOA', 'L8_TOA', 'L_SR', 'L5_SR', 'L7_SR', 'L8_SR', 'NVET'] and
var in ['TrueColor', 'FalseColor']):
## Hard code display of multi-band Landsat images (for now)
## Intentionally not using "vis_image" here to avoid clamp (for now)
mapid = map_image(calc_img, TV['opacity'], None, minColorbar, maxColorbar)
## Bands can be set separately by passing a string of comma separate numbers
##mapid = map_image(calc_img, TV['opacity'], None, "0, 0, 0", "0.28, 0.28, 0.32")
#elif ((colorbarmap == 'invUSDM' or colorbarmap == 'USDM') and calculation == 'percentile'):
# value_list = [3,6,10,20,30,40]
# mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType, value_list))
#elif ((colorbarmap == 'invUSDMwWet' or colorbarmap == 'USDMwWet') and
# calculation == 'percentile'):
# value_list = [3,6,10,20,30,70,80,90,94,97,100]
# mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType, value_list))
elif calculation == 'anompercentof':
value_list = [int(i) for i in colorbarTicks.split(",")][1:]
#value_list = [5,25,50,70,90,110,130,150,200,400,800]
mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType,value_list))
elif colorbarType == 'continuous':
mapid = map_image(
vis_image, TV['opacity'], palette, minColorbar, maxColorbar)
elif colorbarType == 'discrete':
palette_list = palette.split(',')
## Intentionally add 1 to x since value needs to be for the max of the interval
value_list = [
float(minColorbar) + float(x + 1) * (float(maxColorbar) - float(minColorbar)) / len(palette_list)
for x in xrange(len(palette_list))]
mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType,value_list))
if mapid and mapid['mapid'] and mapid['token']:
extra_template_values['mapid'] = mapid['mapid']
extra_template_values['token'] = mapid['token']
TV.update(extra_template_values)
#==============
#Get point value
#==============
if toolAction == 'showSingleValueOnMap':
point_value = get_point_value(
calc_img, float(TV['pointLat']), float(TV['pointLong']), var)
extra_template_values['pointValue'] = '{0:0.4f}'.format(point_value)
TV.update(extra_template_values)
#==============
#Region data extraction
#==============
if toolAction == 'downloadRectangleSubset' or toolAction=='downloadFusionTableSubset':
#the rect coordinates here have been set for either rect or bounding box of fusion table
NELat = TV['NELat']
NELong = TV['NELong']
SWLat = TV['SWLat']
SWLong = TV['SWLong']
rectangle = '[[' + SWLong + ',' + NELat + '],' + \
'[' + NELong + ',' + NELat + '],' + \
'[' + NELong + ',' + SWLat + '],' + \
'[' + SWLong + ',' + SWLat + ']]'
downloadMapFormat =TV['downloadMapFormat']
projection = TV['downloadProjection']
if toolAction =='downloadFusionTableSubset':
fusiontable = TV['fusiontabledownload']
fusiontablename = TV['fusiontabledownloadname']
region = ee.FeatureCollection('ft:'+fusiontable)
if fusiontablename:
region = region.filter(ee.Filter.eq('Name', fusiontablename))
calc_img = calc_img.clip(region.geometry())
rectangle = json.dumps(region.geometry().bounds().getInfo()['coordinates'])
downloadOptions = {
'name':downloadFilename,
'scale':scale,
'crs':projection,
'region':rectangle,
'maxPixels': 1e9,
#'format':downloadMapFormat
}
#this is a way to get .tif to be non-blank
#if downloadMapFormat=='png' or downloadMapFormat=='jpg' or downloadMapFormat=='tif':
# vis_image = calc_img.visualize(
# bands=var, min=float(minColorbar), max=float(maxColorbar),
# palette=palette.split(',')) #palette must be array of strings, not a string
# downloadURL = vis_img.getDownloadUrl(downloadOptions)
#elif downloadMapFormat=='tif':
downloadURL = calc_img.getDownloadUrl(downloadOptions)
# getDownloadURL is the preferred "spelling" for this function
# Switch at some point when everyone updates their earthengine-api
#downloadURL = ???Export.Image(,'title',{'region': rectangle})
extra_template_values['downloadURL'] = downloadURL
TV.update(extra_template_values)
#==============
#Update template values
#==============
return TV
def set_initial_template_values(self, applicationName):
import loggerFunctions
logger = loggerFunctions.set_logger('templatevariables')
# Override saved maxDates with memcache values
maxDates =collection_dataStore.defaults_maxDates
for key, value in maxDates.iteritems():
try:
data = memcache.get(key)
if data is not None:
maxDates[key] = data
except:
pass
timeSeriesCalc_default,subDomainTypeTS_default,ftChoice1_default,variable_default,productType_default,product_default,statistic_default,calculation_default,units_default,varUnits_default,colorbar_default,colorbarMin_default,colorbarMax_default,colorbarSize_default,colorbarTicks_default,timeperiod_default,timeperiod_days,scale_default,mapCenterLongLat_default,pointLat_default ,pointLon_default,mapCenterLongLat,mapzoom_default,opacity_default,NELat_default,NELong_default,SWLat_default,SWLong_default,minYear_default,minDate_default,runningMeanYears_default,runningMeanDays_default,basemap_default,CHIRPSvariable_default =application_defaults.get_applicationDefaults(self, applicationName)
#Set month/day/Start/End defaults according to dateStart/End
#Set tempend
variable = get_variable(self, 'variable', variable_default)
product = get_variable(self, 'product', product_default)
if variable in collection_dataStore.names_notdefault:
variablename=variable;
else:
variablename='default'
tempend = maxDates[collection_dataStore.names_memcache[product][variablename]]
#Set tempstart
tempstart = (datetime.datetime(int(tempend[0:4]),int(tempend[5:7]),int(tempend[8:10]))-datetime.timedelta(days=timeperiod_days)).strftime('%Y-%m-%d')
#Set Month/Day defaults
monthStart_default = tempstart[5:7].lstrip('0')
dayStart_default = tempstart[8:10].lstrip('0')
monthEnd_default = tempend[5:7].lstrip('0')
dayEnd_default = tempend[8:10].lstrip('0')
toolAction =get_variable(self,'toolAction','getMap')
if(toolAction=='getTimeSeriesOverDateRange'): #timeseries
variable =get_variable(self,'variableTS',variable_default)
variable2display =get_variable(self,'variable2display','none')
productType =get_variable(self,'productTypeTS',productType_default)
product =get_variable(self,'productTS',product_default)
statistic =get_variable(self,'statisticsTS',statistic_default)
calculation =get_variable(self,'calculation',calculation_default)
units =get_variable(self,'unitsTS',units_default)
varUnits =get_variable(self,'varUnitsTS',varUnits_default)
timeperiod =get_variable(self,'timeperiodTS',timeperiod_default)
if variable2display!='none': #2 variables
productType2 =get_variable(self,'productType2TS',productType_default)
product2 =get_variable(self,'product2TS',product_default)
variable2 =get_variable(self,'variable2TS',variable_default)
statistic2 =get_variable(self,'statistic2TS',statistic_default)
varUnits2 =get_variable(self,'var2UnitsTS',varUnits_default)
timeperiod2 =get_variable(self,'timeperiod2TS',timeperiod_default)
else: #1 variable
productType2=productType
product2 = product
variable2=variable
statistic2=statistic
varUnits2=varUnits
timeperiod2=timeperiod
else: #mapping
variable =get_variable(self,'variable',variable_default)
productType =get_variable(self,'productType',productType_default)
product =get_variable(self,'product',product_default)
statistic =get_variable(self,'statistic',statistic_default)
calculation =get_variable(self,'calculation',calculation_default)
units =get_variable(self,'units',units_default)
varUnits =get_variable(self,'varUnits',varUnits_default)
timeperiod =get_variable(self,'timeperiod',timeperiod_default)
productType2=productType
product2 = product
variable2=variable
statistic2=statistic
varUnits2=varUnits
timeperiod2=timeperiod
template_values={
'applicationName':applicationName,
'form_error': {},
#Sharelink breaks if get_all is used since lists can not be embedded in urls
'layer': self.request.get_all('layer',[])
}
list_baseoptions={
'mask': 'none',
'maskMin': '',
'maskMax': '',
'downloadURL': '',
'downloadFilename': 'climateEngine_download',
'downloadregion': 'noRegion',
'downloadProjection': 'EPSG:4326',
'mapid': '',
'token': '',
'dispEnv': '',
#Variable Options
'toolAction': 'getMap',
'variable': variable,
'variableTS': variable,
'variable2TS': variable2,
'variable2display': 'none',
'productType': productType,
'productTypeTS': productType,
'productType2TS': productType2,
'product': product,
'productTS': product,
'product2TS': product2,
'statistic': statistic,
'statisticTS': statistic,
'statistic2TS': statistic2,
'calculation': calculation,
'units': units,
'unitsTS': units,
'varUnits': varUnits,
'varUnitsTS': varUnits,
'var2UnitsTS': varUnits2,
'timeperiod': timeperiod,
#Map Options
'opacity': opacity_default,
'mapCenterLongLat': mapCenterLongLat_default,
'mapzoom': mapzoom_default,
#Get Map Options
'kmlurl': '',
'kmlurl2': '',
'kmloption': '',
'scale': scale_default,
'NELat': NELat_default,
'NELong': NELong_default,
'SWLat': SWLat_default,
'SWLong': SWLong_default,
#Colorbar Options
'palette': '',
'minColorbar': colorbarMin_default,
'maxColorbar': colorbarMax_default,
'colorbarmap': colorbar_default,
'colorbarsize': colorbarSize_default,
'colorbarLabel': '', #technically this is dependent on variable(should ditch)
'colorbarType': 'continuous',
'colorbarTicks': colorbarTicks_default,
#TimeSeries Options
'timeSeriesCalc': timeSeriesCalc_default,
'chartType': '',
'subDomainTypeTS': subDomainTypeTS_default,
'basemap': basemap_default
}
add_templateVariable(self,list_baseoptions,template_values)
#############################################
## FEWSNET OPTIONS ##
#############################################
if applicationName =='fewsNet' or applicationName=='precisionGrazing' or applicationName=='gddTool':
template_values['CHIRPSvariable']= CHIRPSvariable_default,
#############################################
## MACA OPTIONS ##
#############################################
list_MACA={
'model': 'inmcm4',
'scenario': 'historical',
'modelTS': 'inmcm4',
'scenarioTS': 'historical',
'model2TS': 'inmcm4',
'scenario2TS': 'historical'
}
add_templateVariable(self,list_MACA,template_values)
#############################################
## TEMPLATE TIME OPTIONS ##
#############################################
for p in ['product', 'product2']:
product = self.request.get(p, product_default)
yearStartClim= collection_dataStore.defaults_yearClim[product]['yearStartClim']
yT= collection_dataStore.defaults_yearClim[product]['yearTarget']
yearEndClim=collection_dataStore.defaults_yearClim[product]['yearEndClim']
if yearEndClim=='default':
yearEndClim=tempend[0:4]
yearStartClimFut = collection_dataStore.defaults_yearClim['MACA']['yearStartClimFut']
yearEndClimFut = collection_dataStore.defaults_yearClim['MACA']['yearEndClimFut']
if p == 'product':
yearTarget = yT
if p == 'product2':
yearTarget2 = yT
mon_names = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
maxYear_default = tempend[0:4]
maxDate_default = tempend
list_timeoptions={
'minYear': minYear_default,
'minDate': minDate_default,
'maxDate': maxDate_default,
'maxYear': maxYear_default,
'dateStart': tempstart,
'dateEnd': maxDate_default,
'dateStartTS': tempstart,
'dateEndTS': maxDate_default,
'yearTargetData': yearTarget,
'yearTargetFigure': yearTarget,
'yearTargetForm': yearTarget,
'yearStart': yearStartClim,
'yearEnd': yearEndClim,
'monthStart': monthStart_default,
'monthEnd': monthEnd_default,
'dayStart': dayStart_default,
'dayEnd': dayEnd_default,
'season': 'custom',
'monthStartFut': monthStart_default,
'monthEndFut': monthEnd_default,
'dayStartFut': dayStart_default,
'dayEndFut': dayEnd_default,
'seasonFut': 'custom',
'minYear2': minYear_default,
'minDate2': minDate_default,
'maxDate2': maxDate_default,
'maxYear2': maxYear_default,
'dateStart2TS': tempstart,
'dateEnd2TS': maxDate_default,
'yearTarget2Data': yearTarget2,
'yearStart2': yearStartClim,
'yearEnd2': yearEndClim,
'monthStart2': monthStart_default,
'monthEnd2': monthEnd_default,
'dayStart2': dayStart_default,
'dayEnd2': dayEnd_default,
'season2': 'custom',
'runningMeanYears': runningMeanYears_default,
'runningMeanDays': runningMeanDays_default,
'timeperiodTS': timeperiod,
'timeperiod2TS': timeperiod2,
'yearStartClim': yearStartClim,
'yearEndClim': yearEndClim,
'yearStartClimFut': yearStartClimFut,
'yearEndClimFut': yearEndClimFut
}
add_templateVariable(self,list_timeoptions,template_values)
#add maxValue form elements
for key, value in collection_dataStore.maxDates_lookup.iteritems():
add_templateVariable(self,{value:maxDates[key]},template_values)
d = ' %02d' %int(template_values['dayStart'])
template_values['seasonStart'] = mon_names[int(template_values['monthStart']) -1] + d
d = ' %02d' %int(template_values['dayEnd'])
template_values['seasonEnd'] = mon_names[int(template_values['monthEnd']) -1] + d
d = ' %02d' %int(template_values['dayStart2'])
template_values['seasonStart2'] = mon_names[int(template_values['monthStart2']) -1] + d
d = ' %02d' %int(template_values['dayEnd2'])
template_values['seasonEnd2'] = mon_names[int(template_values['monthEnd2']) -1] + d
#if Time Series of Single Year Daily, override start/end dates if needed
if template_values['timeSeriesCalc'] == 'intraannual':
s_dt = datetime.datetime.strptime(template_values['dateStart'],'%Y-%m-%d')
e_dt = datetime.datetime.strptime(template_values['dateEnd'],'%Y-%m-%d')
if (e_dt - s_dt).days > 366:
y = template_values['yearTargetData']
m = template_values['monthStart']
d = template_values['dayStart']
template_values['dateStartTS'] = y + '-' + m + '-' + d
template_values['dateEndTS'] = str(int(y) + 1) + '-' + m + '-' + d
template_values['dateStart'] = template_values['dateStartTS']
template_values['dateEnd'] = template_values['dateEndTS']
s_dt = datetime.datetime.strptime(template_values['dateStart2TS'],'%Y-%m-%d')
e_dt = datetime.datetime.strptime(template_values['dateEnd2TS'],'%Y-%m-%d')
if (e_dt - s_dt).days > 366:
y = template_values['yearTarget2Data']
m = template_values['monthStart2']
d = template_values['dayStart2']
template_values['dateStart2'] = y + '-' + m + '-' + d
template_values['dateEnd2TS'] = str(int(y) + 1) + '-' + m + '-' + d
#############################################
## TEMPLATE FUSION TABLE MAP DOWNLOAD ##
#############################################
list_fusiontablemapdownload={
'fusiontabledownload': '1fRY18cjsHzDgGiJiS2nnpUU3v9JPDc2HNaR7Xk8',
'fusiontabledownloadname': 'California',
'pointLat': pointLat_default,
'pointLong': pointLon_default,
'pointValue': '',
'downloadMapFormat': 'geotiff',
}
add_templateVariable(self,list_fusiontablemapdownload,template_values)
#############################################
## TEMPLATE POINTS ##
#############################################
pointsLongLat_default=mapCenterLongLat
marker_colors=['blue','green','orange','purple','yellow','pink','red']
mapCenterLongLat = template_values['mapCenterLongLat']
template_values['marker_colors']=marker_colors
add_templateVariable(self,{'pointsLongLat':pointsLongLat_default},template_values)
for s_idx in range(1,max_pointsShapes+1):
add_templateVariable(self,{'p'+str(s_idx)+'check':'checked'},template_values)
add_templateVariable(self,{'p'+str(s_idx)+'altname':''},template_values)
add_templateVariable(self,{'p'+str(s_idx):mapCenterLongLat},template_values)
if s_idx==1:
add_templateVariable(self,{'p'+str(s_idx)+'display':'block'},template_values)
else:
add_templateVariable(self,{'p'+str(s_idx)+'display':'none'},template_values)
#############################################
## TEMPLATE SHAPES-FUSION TABLE FOR TIME SERIES ##
#############################################
template_values['shape_colors']=['#0000FF','#00FF00','#FFA500','#551A8B','#FFFF00','#FF69B4','#FF0000']
for s_idx in range(1,max_pointsShapes+1):
add_templateVariable(self,{'ft'+str(s_idx):''},template_values)
add_templateVariable(self,{'ft'+str(s_idx)+'columnName':''},template_values)
add_templateVariable(self,{'ft'+str(s_idx)+'altname':''},template_values)
add_templateVariable(self,{'ftSubChoice'+str(s_idx):''},template_values)
add_templateVariable(self,{'polygon'+str(s_idx):''},template_values)
if s_idx==1:
add_templateVariable(self,{'ft'+str(s_idx)+'display':'block'},template_values)
add_templateVariable(self,{'ft'+str(s_idx)+'check':'checked'},template_values)
add_templateVariable(self,{'ftChoice'+str(s_idx):ftChoice1_default},template_values)
else:
add_templateVariable(self,{'ft'+str(s_idx)+'display':'none'},template_values)
add_templateVariable(self,{'ft'+str(s_idx)+'check':''},template_values)
add_templateVariable(self,{'ftChoice'+str(s_idx):''},template_values)
#############################################
## TEMPLATE FORMS ##
#############################################
template_forms={
#Forms
'formMask': forms.formMask,
'formVariable2Display': forms.formVariable2Display,
'formTimePeriods': forms.formTimePeriods,
'formHighChartLayers': forms.formHighChartLayers,
'formHighChartLayersIntraannual':forms.formHighChartLayersIntraannual,
'formDownloadMapFormat': forms.formDownloadMapFormat,
'formDownloadProjection': forms.formDownloadProjection,
'formSeasons': forms.formSeasons,
'formChartType': forms.formChartType,
'formMonth': forms.formMonth,
'formDay': forms.formDay,
'formMapZoom': forms.formMapZoom,
'formPaletteCustomMap': forms.formPaletteCustomMap,
'formPaletteDivMap': forms.formPaletteDivMap,
'formPaletteSeqMap': forms.formPaletteSeqMap,
'formPaletteSize': forms.formPaletteSize,
'formColorbarType': forms.formColorbarType,
'formOpacity': forms.formOpacity,
'formUnits': forms.formUnits,
'formTimeSeriesCalc': forms.formTimeSeriesCalc,
'formSubDomainTypeTS': forms.formSubDomainTypeTS,
'formDownloadRegion': forms.formDownloadRegion,
'formFusionTableChoices': forms.formFusionTableChoices,
'formProductType': forms.formProductType,
'formProductType1': forms.formProductType1,
'formLayers': forms.formLayers,
#'formLayers1': forms.formLayers1,
'formBoolean': forms.formBoolean
}
template_values.update(template_forms)
#############################################
## EXTRA FEWS ##
#############################################
if applicationName=='fewsNet':
extra_FEWS ={
'formLayers': formsFEWSNET.formFEWSNETLayers,
'formFusionTableChoices': formsFEWSNET.formFusionTableChoicesFEWSNET,
'formCHIRPSChoicesFEWSNET': formsFEWSNET.formCHIRPSChoicesFEWSNET,
'formMODISChoicesFEWSNET': formsFEWSNET.formMODISChoicesFEWSNET,
'formLandsatChoicesFEWSNET': formsFEWSNET.formLandsatChoicesFEWSNET,
}
template_values.update(extra_FEWS)
elif applicationName=='precisionGrazing' or applicationName=='gddTool':
extra_FEWS ={
'formLayers': formsPG.formPGLayers,
'formFusionTableChoices': formsPG.formFusionTableChoicesPG,
'formCHIRPSChoicesFEWSNET': formsFEWSNET.formCHIRPSChoicesFEWSNET,
'formMODISChoicesFEWSNET': formsFEWSNET.formMODISChoicesFEWSNET,
'formLandsatChoicesFEWSNET': formsFEWSNET.formLandsatChoicesFEWSNET,
'formSubDomainTypeTS' :formsPG.formSubDomainTypeTS
}
template_values.update(extra_FEWS)
#############################################
## SHARE LINK ##
#############################################
#Sharelink depends on most template variables
template_values['logger'] = logger
template_values['shareLink'] = set_share_link(template_values,applicationName)
#############################################
## FORMAT TEMPLATE VARIABLES ##
#############################################
#format template values to allow for different date formats etc...
#See format_ functions in formchecks.py
formatted_template_values = {}
for key, val in template_values.iteritems():
format_function_name = 'format_' + key
try:
format_function = getattr(forms,format_function_name)
except:
format_function = None
if format_function:
formatted_template_values[key] = format_function(val)
else:
formatted_template_values[key] = val
return formatted_template_values
def processPointData(template_values, extra_template_values, time_vars,
point_info, logger, timeSeriesTextData,
timeSeriesGraphData, varnum):
logger = loggerFunctions.set_logger('info')
name = point_info['name']
altname = point_info['altname']
marker_color = point_info['marker_color']
#Initialize the data dicts
data_dict_ts = formatData.initialize_timeSeriesTextDataDict(
name, altname=altname)
data_dict_graph = formatData.initialize_timeSeriesGraphDataDict(
name, marker_color, altname=altname)
#logger.info('**********timeSeriesCalc************'+template_values['timeSeriesCalc'])
climoData = []
percentileData = []
#process data according to what timeSereisCalc is
if template_values['timeSeriesCalc'] == 'days':
data_dict_ts['Data'], data_dict_graph[
'Data'] = process_daily_threadData(point_info['data'],
template_values, varnum)
elif template_values['timeSeriesCalc'] == 'interannual':
sS_doy = time_vars['seasonStart_doy']
sE_doy = time_vars['seasonEnd_doy']
data_dict_ts['Data'], data_dict_graph[
'Data'] = process_interannual_threadData(point_info['data'],
template_values, sS_doy,
sE_doy, varnum, logger)
elif template_values['timeSeriesCalc'] == 'intraannual':
#logger.info('**********inside intrannaul************')
#get data for all years stored in dict with keys year
doyS = time_vars['doyStart']
doyE = time_vars['doyEnd']
yS = time_vars['yearStart']
yE = time_vars['yearEnd']
year_dict_ts, year_dict_graph, climoData, percentileData = process_intraannual_threadData(
point_info['data'], template_values, doyS, doyE, yS, yE, varnum,
logger)
for year in range(int(yS), int(yE) + 1):
'''
if year in year_dict_ts.keys() and year_dict_ts[year]:
data_dict_ts['Data'].append(year_dict_ts[year])
if year in year_dict_graph.keys() and year_dict_graph[year]:
data_dict_graph['Data'].append(year_dict_graph[year])
'''
data_dict_ts['Data'].append(year_dict_ts[year])
data_dict_graph['Data'].append(year_dict_graph[year])
timeSeriesTextData.append(data_dict_ts)
timeSeriesGraphData.append(data_dict_graph)
if climoData:
if varnum == 1:
extra_template_values['climoData'] = json.dumps(climoData)
if varnum == 2:
extra_template_values['climoData2'] = json.dumps(climoData)
if percentileData:
if varnum == 1:
extra_template_values['percentileData'] = json.dumps(
percentileData)
if varnum == 2:
extra_template_values['percentileData2'] = json.dumps(
percentileData)
return extra_template_values, timeSeriesTextData, timeSeriesGraphData
def get_climatology(collection,
product,
variable,
dateStart,
dateEnd,
statistic,
calculation,
yearStartClim,
yearEndClim,
logger=None):
"""Return the climatology image
Args:
collection: EarthEngine collection to process (has already selected variable)
product: string of the product ()
variable: string of the variable ()
dateStart: string of the start date isoformat (YYYY-MM-DD)
dateEnd: string of the end date isoformat (YYYY-MM-DD)
statistic: string of the statistic (Mean, Median, Total, etc.)
calculation: string of the calculation type
(anom, value, anompercentof,anompercentchange,clim)
yearStartClim: string of the climatology start year
yearEndClim: string of the climatology end year
logger:
Returns:
EarthEngine image object
String of additional notes about the collection
"""
#==============
logger = loggerFunctions.set_logger('get_images_debug')
yearStartClim = int(yearStartClim)
yearEndClim = int(yearEndClim)
#Build python datetime objects from the date string
dateStart_dt = dt.datetime.strptime(dateStart, '%Y-%m-%d')
dateEnd_dt = dt.datetime.strptime(dateEnd, '%Y-%m-%d')
#==============
#Check timedelta between start and end is greater than 1 year
def yearsahead(years, start_date):
try:
return start_date.replace(year=start_date.year + years)
except: # Must be 2/29!
assert from_date.month == 2 and from_date.day == 29 # can be removed
return from_date.replace(month=2,
day=28,
year=start_date.year + years)
#Climo products will have less than a year to avg over too
if dateEnd_dt <= yearsahead(1, dateStart_dt) or product in [
'MACA', 'NASANEX'
]:
sub_year_flag = False
doyStart = dateStart_dt.timetuple().tm_yday
doyEnd = dateEnd_dt.timetuple().tm_yday
dayStart = dateStart[5:]
dayEnd = dateEnd[5:]
if (doyStart < doyEnd):
num_days = len(range(doyStart, doyEnd))
else:
num_days = len(range(doyStart, 366)) + len(range(1, doyEnd))
else:
sub_year_flag = True
doyStart = 1
doyEnd = 366
num_days = 366
#==============
if sub_year_flag:
#List sequence is inclusive (i.e. don't advance yearEnd)
yearListClim = ee.List.sequence(yearStartClim,
yearEndClim) #list inclusive
num_years = yearEndClim - yearStartClim + 1
else:
yearListClim = ee.List.sequence(yearStartClim,
yearEndClim - 1) #list inclusive
num_years = yearEndClim - 1 - yearStartClim + 1
#==============Not technically correct.. takes min over a single year.. not min over the time period
# these are both resulting in 429 errors right now and 500 server errors too
doy_filter = ee.Filter.calendarRange(doyStart, doyEnd, 'day_of_year')
if statistic == 'Min':
def min_climatology_func(year):
"""For each year, return an image of the minimum value over the DOY range"""
year_filter = ee.Filter.calendarRange(year, year, 'year')
return ee.Image(
collection.filter(year_filter).filter(doy_filter).min())
climatology_coll = ee.ImageCollection.fromImages(
yearListClim.map(min_climatology_func))
climatology_img = get_statistic(climatology_coll,
'Mean',
logger=logger)
#==============Not technically correct.. takes max over a single year.. not max over the time period
# these are both resulting in 429 errors right now... too many requests. and 500 server errors too
elif statistic == 'Max':
def max_climatology_func(year):
"""For each year, return an image of the minimum value over the DOY range"""
year_filter = ee.Filter.calendarRange(year, year, 'year')
return ee.Image(
collection.filter(year_filter).filter(doy_filter).max())
climatology_coll = ee.ImageCollection.fromImages(
yearListClim.map(max_climatology_func))
climatology_img = get_statistic(climatology_coll,
'Mean',
logger=logger)
#==============
elif (statistic == 'Mean' or statistic == 'Total'
or statistic == 'Median'):
#FilterDate needs an extra day on the high end,Set yearEnd to Jan 1st of next year
yearStartClimUTC = dt.datetime(yearStartClim, 1, 1)
yearEndClimUTC = dt.datetime(yearEndClim + 1, 1, 1)
#===================
climatology_coll = collectionMethods.get_climatology_collection(
product, variable)
if climatology_coll == 'None' or (
product == 'G' and
(yearEndClim != 1981
or yearStartClim != 2010)): #no pre-calculated climos
climatology_coll_temp = 'None'
climatology_coll = collection.filterDate(
yearStartClimUTC, yearEndClimUTC).filter(doy_filter)
else: #yes pre-calculated climos
climatology_coll_temp = 'Some'
#climatology_coll = climatology_coll.filterMetadata('doy','greater_than',doyStart) \
# .filterMetadata('doy','not_greater_than',doyEnd)
if doyStart <= doyEnd:
# Date range is in same year
climatology_coll = climatology_coll.filter(
ee.Filter.And(ee.Filter.gte('doy', doyStart),
ee.Filter.lte('doy', doyEnd)))
else:
# Date range wraps around to next year
climatology_coll = climatology_coll.filter(
ee.Filter.Or(ee.Filter.gte('doy', doyStart),
ee.Filter.lte('doy', doyEnd)))
#===================
if not sub_year_flag:
climatology_img = get_statistic(climatology_coll,
statistic,
logger=logger)
if statistic == 'Total' and climatology_coll_temp == 'None':
climatology_img = climatology_img.divide(num_years)
else:
climatology_img = get_statistic(climatology_coll,
statistic,
logger=logger)
climatologyNote = 'Average calculated from {0}-{1}'.format(
str(yearStartClim), str(yearEndClim))
return climatology_img, climatology_coll, climatologyNote, num_days
def get_images(template_values):
""""""
#from forms import stateLat, stateLong
#set up logger
logger = loggerFunctions.set_logger('get_images_debug')
TV = {}
for key, val in template_values.iteritems():
TV[key] = val
product = TV['product']
var = TV['variable']
model = TV['model']
scenario = TV['scenario']
calculation = TV['calculation']
toolAction = TV['toolAction']
yearStartClim = TV['yearStartClim']
yearEndClim = TV['yearEndClim']
statistic = TV['statistic']
units = TV['units']
palette = TV['palette']
minColorbar = template_values['minColorbar']
maxColorbar = template_values['maxColorbar']
colorbarType = template_values['colorbarType']
colorbarmap = template_values['colorbarmap']
colorbarTicks = template_values['colorbarTicks']
scale = TV['scale']
downloadFilename = TV['downloadFilename']
mask = TV['mask']
if mask != 'none':
maskMin = TV['maskMin']
maskMax = TV['maskMax']
# Build EarthEngine date objects from date strings and explicitly set GMT
# Note, by default EarthEngine date objects are already GMT
if product in ['MACA', 'NASANEX']:
monthList = [
'', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept',
'Oct', 'Nov', 'Dec'
]
yearStartClimFut = TV['yearStartClimFut']
yearEndClimFut = TV['yearEndClimFut']
yearStartClim = TV['yearStartClim']
yearEndClim = TV['yearEndClim']
#these are for calculating the source title
if calculation in [
'anom', 'anompercentof', 'anompercentchange', 'percentile',
'value'
]:
dS = yearStartClimFut + '-' + TV['monthStartFut'] + '-' + TV[
'dayStartFut']
dE = yearEndClimFut + '-' + TV['monthEndFut'] + '-' + TV[
'dayEndFut']
else:
dS = yearStartClim + '-' + TV['monthStartFut'] + '-' + TV[
'dayStartFut']
dE = yearEndClim + '-' + TV['monthEndFut'] + '-' + TV['dayEndFut']
#just need to get the doyfilter right with these
dSFut = yearStartClimFut + '-' + TV['monthStartFut'] + '-' + TV[
'dayStartFut']
dSHist = yearStartClim + '-' + TV['monthStartFut'] + '-' + TV[
'dayStartFut']
if int(TV['monthEndFut']) > int(TV['monthStartFut']):
dEFut = yearStartClimFut + '-' + TV['monthEndFut'] + '-' + TV[
'dayEndFut']
dEHist = yearStartClim + '-' + TV['monthEndFut'] + '-' + TV[
'dayEndFut']
else:
dEFut = str(int(yearStartClimFut) +
1) + '-' + TV['monthEndFut'] + '-' + TV['dayEndFut']
dEHist = str(int(yearStartClim) +
1) + '-' + TV['monthEndFut'] + '-' + TV['dayEndFut']
dSUTC = ee.Date(dSHist, 'GMT')
dEUTC = ee.Date(dEHist, 'GMT')
else:
dS = TV['dateStart']
dE = TV['dateEnd']
dSUTC = ee.Date(dS, 'GMT')
dEUTC = ee.Date(dE, 'GMT')
#==============
#Initial Collection
#==============
frequency = 'daily'
if product == 'MACA' or product == 'NASANEX':
frequency = 'monthly'
collection, coll_name, coll_desc, var_desc, notes = collectionMethods.get_collection(
product, var, model, scenario, frequency, logger=logger)
#==============
#Calculation:Values,Climatology,Anomalies
#==============
#get_statistic returns ee.ImageCollection
if calculation in ['value']:
if product in ['MACA', 'NASANEX']:
# This is future average
# FilterDate is exclusive on the high end, include an extra day on dEUTC
collection = collection.filterMetadata('scenario', 'equals',
scenario)
calc_img, clim_temp, clim_notes_temp, num_days = get_climatology(
collection,
product,
var,
dSFut,
dEFut,
statistic,
calculation,
yearStartClimFut,
yearEndClimFut,
logger=logger)
elif product == 'CFSV2': #convert 6hrly to daily
collection = collection_CFSV2.convert_6hrly_to_daily(
collection, var, dSUTC, dEUTC, logger)
calc_img = get_statistic(collection, statistic, logger=logger)
else:
collection = collection.filterDate(dSUTC, dEUTC.advance(1, 'day'))
calc_img = get_statistic(collection, statistic, logger=logger)
elif calculation in [
'anom', 'anompercentof', 'anompercentchange', 'clim', 'percentile',
'zscore'
]:
if product in ['MACA', 'NASANEX']: #this is historical average
hist_coll = collection.filterMetadata('scenario', 'equals',
'historical')
clim_img, clim_coll, climatologyNotes, num_days = get_climatology(
hist_coll,
product,
var,
dSHist,
dEHist,
statistic,
calculation,
yearStartClim,
yearEndClim,
logger=logger)
else:
clim_img, clim_coll, climatologyNotes, num_days = get_climatology(
collection,
product,
var,
dS,
dE,
statistic,
calculation,
yearStartClim,
yearEndClim,
logger=logger)
if calculation in ['clim']:
calc_img = clim_img
else:
# anomaly = future wrt historical
if product in ['MACA', 'NASANEX']:
# This is future average
future_col = collection.filterMetadata('scenario', 'equals',
scenario)
calc_img, clim_temp, clim_notes_temp, num_day = get_climatology(
future_col,
product,
var,
dSFut,
dEFut,
statistic,
calculation,
yearStartClimFut,
yearEndClimFut,
logger=logger)
calc_img = get_anomaly(calc_img, clim_img, clim_coll,
calculation, statistic, num_days)
else:
collection = collection.filterDate(dSUTC,
dEUTC.advance(1, 'day'))
calc_img = get_statistic(collection, statistic, logger=logger)
calc_img = get_anomaly(calc_img, clim_img, clim_coll,
calculation, statistic, 1)
TV['climatologyNotes'] = climatologyNotes
#==============
#Units
#==============
calc_img = modify_units(calc_img, var, product, calculation, units)
#==============
#Extra template values
#==============
extra_template_values = {}
#==============
#Apply Mask
#==============
if mask != 'none':
maskMin = float(maskMin)
maskMax = float(maskMax)
if mask == 'above':
calc_img = ee.Image(calc_img).mask(calc_img.lte(maskMax))
elif mask == 'below':
calc_img = ee.Image(calc_img).mask(calc_img.gte(maskMin))
elif mask == 'exterior':
calc_img = ee.Image(calc_img).mask(
calc_img.lte(maskMin).Or(calc_img.gte(maskMax)))
elif mask == 'interior':
calc_img = ee.Image(calc_img).mask(
calc_img.gte(maskMin).And(calc_img.lte(maskMax)))
#==============
#Get mapid
#==============
mapid = {'mapid': [], 'token': []}
## Limit the image values to the min and max before visualizing
##here calc_img = ee.Image()
vis_image = calc_img.clamp(float(minColorbar), float(maxColorbar))
#we could change this to just if var in ['TrueColor', 'FalseColor'] instead of all the products
if (product in [
'L_TOA', 'L5_TOA', 'L7_TOA', 'L8_TOA', 'L_SR', 'L5_SR', 'L7_SR',
'L8_SR'
] and var in ['TrueColor', 'FalseColor']):
## Hard code display of multi-band Landsat images (for now)
## Intentionally not using "vis_image" here to avoid clamp (for now)
mapid = map_image(calc_img, TV['opacity'], None, minColorbar,
maxColorbar)
## Bands can be set separately by passing a string of comma separate numbers
##mapid = map_image(calc_img, TV['opacity'], None, "0, 0, 0", "0.28, 0.28, 0.32")
#elif ((colorbarmap == 'invUSDM' or colorbarmap == 'USDM') and calculation == 'percentile'):
# value_list = [3,6,10,20,30,40]
# mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType, value_list))
#elif ((colorbarmap == 'invUSDMwWet' or colorbarmap == 'USDMwWet') and
# calculation == 'percentile'):
# value_list = [3,6,10,20,30,70,80,90,94,97,100]
# mapid = map_image(apply_sld_colormap_styling_image(vis_image, palette, colorbarType, value_list))
elif calculation == 'anompercentof':
value_list = [int(i) for i in colorbarTicks.split(",")][1:]
#value_list = [5,25,50,70,90,110,130,150,200,400,800]
mapid = map_image(
apply_sld_colormap_styling_image(vis_image, palette, colorbarType,
value_list))
elif colorbarType == 'continuous':
mapid = map_image(vis_image, TV['opacity'], palette, minColorbar,
maxColorbar)
elif colorbarType == 'discrete':
palette_list = palette.split(',')
## Intentionally add 1 to x since value needs to be for the max of the interval
value_list = [
float(minColorbar) + float(x + 1) *
(float(maxColorbar) - float(minColorbar)) / len(palette_list)
for x in xrange(len(palette_list))
]
mapid = map_image(
apply_sld_colormap_styling_image(vis_image, palette, colorbarType,
value_list))
if mapid and mapid['mapid'] and mapid['token']:
extra_template_values['mapid'] = mapid['mapid']
extra_template_values['token'] = mapid['token']
TV.update(extra_template_values)
#==============
#Get point value
#==============
if toolAction == 'showSingleValueOnMap':
point_value = get_point_value(calc_img, float(TV['pointLat']),
float(TV['pointLong']), var)
extra_template_values['pointValue'] = '{0:0.4f}'.format(point_value)
TV.update(extra_template_values)
#==============
#Region data extraction
#==============
if toolAction == 'downloadRectangleSubset' or toolAction == 'downloadFusionTableSubset':
#the rect coordinates here have been set for either rect or bounding box of fusion table
NELat = TV['NELat']
NELong = TV['NELong']
SWLat = TV['SWLat']
SWLong = TV['SWLong']
rectangle = '[[' + SWLong + ',' + NELat + '],' + \
'[' + NELong + ',' + NELat + '],' + \
'[' + NELong + ',' + SWLat + '],' + \
'[' + SWLong + ',' + SWLat + ']]'
downloadMapFormat = TV['downloadMapFormat']
projection = TV['downloadProjection']
if toolAction == 'downloadFusionTableSubset':
fusiontable = TV['fusiontabledownload']
fusiontablename = TV['fusiontabledownloadname']
region = ee.FeatureCollection('ft:' + fusiontable)
if fusiontablename:
region = region.filter(ee.Filter.eq('Name', fusiontablename))
calc_img = calc_img.clip(region.geometry())
rectangle = json.dumps(
region.geometry().bounds().getInfo()['coordinates'])
downloadOptions = {
'name': downloadFilename,
'scale': scale,
'crs': projection,
'region': rectangle,
'maxPixels': 1e9,
#'format':downloadMapFormat
}
#this is a way to get .tif to be non-blank
#if downloadMapFormat=='png' or downloadMapFormat=='jpg' or downloadMapFormat=='tif':
# vis_image = calc_img.visualize(
# bands=var, min=float(minColorbar), max=float(maxColorbar),
# palette=palette.split(',')) #palette must be array of strings, not a string
# downloadURL = vis_img.getDownloadUrl(downloadOptions)
#elif downloadMapFormat=='tif':
downloadURL = calc_img.getDownloadUrl(downloadOptions)
# getDownloadURL is the preferred "spelling" for this function
# Switch at some point when everyone updates their earthengine-api
#downloadURL = ???Export.Image(,'title',{'region': rectangle})
extra_template_values['downloadURL'] = downloadURL
TV.update(extra_template_values)
#==============
#Update template values
#==============
return TV
def set_initial_template_values(self, applicationName):
import loggerFunctions
logger = loggerFunctions.set_logger('templatevariables')
# Override saved maxDates with memcache values
maxDates = collection_dataStore.defaults_maxDates
for key, value in maxDates.iteritems():
try:
data = memcache.get(key)
if data is not None:
maxDates[key] = data
except:
pass
timeSeriesCalc_default, subDomainTypeTS_default, ftChoice1_default, variable_default, productType_default, product_default, statistic_default, calculation_default, units_default, varUnits_default, colorbar_default, colorbarMin_default, colorbarMax_default, colorbarSize_default, colorbarTicks_default, timeperiod_default, timeperiod_days, scale_default, mapCenterLongLat_default, pointLat_default, pointLon_default, mapCenterLongLat, mapzoom_default, opacity_default, NELat_default, NELong_default, SWLat_default, SWLong_default, minYear_default, minDate_default, runningMeanYears_default, runningMeanDays_default, basemap_default, CHIRPSvariable_default = application_defaults.get_applicationDefaults(
self, applicationName)
#Set month/day/Start/End defaults according to dateStart/End
#Set tempend
variable = get_variable(self, 'variable', variable_default)
product = get_variable(self, 'product', product_default)
if variable in collection_dataStore.names_notdefault:
variablename = variable
else:
variablename = 'default'
tempend = maxDates[collection_dataStore.names_memcache[product]
[variablename]]
#Set tempstart
tempstart = (datetime.datetime(int(tempend[0:4]), int(tempend[5:7]),
int(tempend[8:10])) -
datetime.timedelta(days=timeperiod_days)).strftime('%Y-%m-%d')
#Set Month/Day defaults
monthStart_default = tempstart[5:7].lstrip('0')
dayStart_default = tempstart[8:10].lstrip('0')
monthEnd_default = tempend[5:7].lstrip('0')
dayEnd_default = tempend[8:10].lstrip('0')
toolAction = get_variable(self, 'toolAction', 'getMap')
if (toolAction == 'getTimeSeriesOverDateRange'): #timeseries
variable = get_variable(self, 'variableTS', variable_default)
variable2display = get_variable(self, 'variable2display', 'none')
productType = get_variable(self, 'productTypeTS', productType_default)
product = get_variable(self, 'productTS', product_default)
statistic = get_variable(self, 'statisticsTS', statistic_default)
calculation = get_variable(self, 'calculation', calculation_default)
units = get_variable(self, 'unitsTS', units_default)
varUnits = get_variable(self, 'varUnitsTS', varUnits_default)
timeperiod = get_variable(self, 'timeperiodTS', timeperiod_default)
if variable2display != 'none': #2 variables
productType2 = get_variable(self, 'productType2TS',
productType_default)
product2 = get_variable(self, 'product2TS', product_default)
variable2 = get_variable(self, 'variable2TS', variable_default)
statistic2 = get_variable(self, 'statistic2TS', statistic_default)
varUnits2 = get_variable(self, 'var2UnitsTS', varUnits_default)
timeperiod2 = get_variable(self, 'timeperiod2TS',
timeperiod_default)
else: #1 variable
productType2 = productType
product2 = product
variable2 = variable
statistic2 = statistic
varUnits2 = varUnits
timeperiod2 = timeperiod
else: #mapping
variable = get_variable(self, 'variable', variable_default)
productType = get_variable(self, 'productType', productType_default)
product = get_variable(self, 'product', product_default)
statistic = get_variable(self, 'statistic', statistic_default)
calculation = get_variable(self, 'calculation', calculation_default)
units = get_variable(self, 'units', units_default)
varUnits = get_variable(self, 'varUnits', varUnits_default)
timeperiod = get_variable(self, 'timeperiod', timeperiod_default)
productType2 = productType
product2 = product
variable2 = variable
statistic2 = statistic
varUnits2 = varUnits
timeperiod2 = timeperiod
template_values = {
'applicationName': applicationName,
'form_error': {},
#Sharelink breaks if get_all is used since lists can not be embedded in urls
'layer': self.request.get_all('layer', [])
}
list_baseoptions = {
'mask': 'none',
'maskMin': '',
'maskMax': '',
'downloadURL': '',
'downloadFilename': 'climateEngine_download',
'downloadregion': 'noRegion',
'downloadProjection': 'EPSG:4326',
'mapid': '',
'token': '',
'dispEnv': '',
#Variable Options
'toolAction': 'getMap',
'variable': variable,
'variableTS': variable,
'variable2TS': variable2,
'variable2display': 'none',
'productType': productType,
'productTypeTS': productType,
'productType2TS': productType2,
'product': product,
'productTS': product,
'product2TS': product2,
'statistic': statistic,
'statisticTS': statistic,
'statistic2TS': statistic2,
'calculation': calculation,
'units': units,
'unitsTS': units,
'varUnits': varUnits,
'varUnitsTS': varUnits,
'var2UnitsTS': varUnits2,
'timeperiod': timeperiod,
#Map Options
'opacity': opacity_default,
'mapCenterLongLat': mapCenterLongLat_default,
'mapzoom': mapzoom_default,
#Get Map Options
'kmlurl': '',
'kmlurl2': '',
'kmloption': '',
'scale': scale_default,
'NELat': NELat_default,
'NELong': NELong_default,
'SWLat': SWLat_default,
'SWLong': SWLong_default,
#Colorbar Options
'palette': '',
'minColorbar': colorbarMin_default,
'maxColorbar': colorbarMax_default,
'colorbarmap': colorbar_default,
'colorbarsize': colorbarSize_default,
'colorbarLabel':
'', #technically this is dependent on variable(should ditch)
'colorbarType': 'continuous',
'colorbarTicks': colorbarTicks_default,
#TimeSeries Options
'timeSeriesCalc': timeSeriesCalc_default,
'chartType': '',
'subDomainTypeTS': subDomainTypeTS_default,
'basemap': basemap_default
}
add_templateVariable(self, list_baseoptions, template_values)
#############################################
## FEWSNET OPTIONS ##
#############################################
if applicationName == 'fewsNet' or applicationName == 'precisionGrazing' or applicationName == 'gddTool':
template_values['CHIRPSvariable'] = CHIRPSvariable_default,
#############################################
## MACA OPTIONS ##
#############################################
list_MACA = {
'model': 'inmcm4',
'scenario': 'historical',
'modelTS': 'inmcm4',
'scenarioTS': 'historical',
'model2TS': 'inmcm4',
'scenario2TS': 'historical'
}
add_templateVariable(self, list_MACA, template_values)
#############################################
## TEMPLATE TIME OPTIONS ##
#############################################
for p in ['product', 'product2']:
product = self.request.get(p, product_default)
yearStartClim = collection_dataStore.defaults_yearClim[product][
'yearStartClim']
yT = collection_dataStore.defaults_yearClim[product]['yearTarget']
yearEndClim = collection_dataStore.defaults_yearClim[product][
'yearEndClim']
if yearEndClim == 'default':
yearEndClim = tempend[0:4]
yearStartClimFut = collection_dataStore.defaults_yearClim['MACA'][
'yearStartClimFut']
yearEndClimFut = collection_dataStore.defaults_yearClim['MACA'][
'yearEndClimFut']
if p == 'product':
yearTarget = yT
if p == 'product2':
yearTarget2 = yT
mon_names = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
maxYear_default = tempend[0:4]
maxDate_default = tempend
list_timeoptions = {
'minYear': minYear_default,
'minDate': minDate_default,
'maxDate': maxDate_default,
'maxYear': maxYear_default,
'dateStart': tempstart,
'dateEnd': maxDate_default,
'dateStartTS': tempstart,
'dateEndTS': maxDate_default,
'yearTargetData': yearTarget,
'yearTargetFigure': yearTarget,
'yearTargetForm': yearTarget,
'yearStart': yearStartClim,
'yearEnd': yearEndClim,
'monthStart': monthStart_default,
'monthEnd': monthEnd_default,
'dayStart': dayStart_default,
'dayEnd': dayEnd_default,
'season': 'custom',
'monthStartFut': monthStart_default,
'monthEndFut': monthEnd_default,
'dayStartFut': dayStart_default,
'dayEndFut': dayEnd_default,
'seasonFut': 'custom',
'minYear2': minYear_default,
'minDate2': minDate_default,
'maxDate2': maxDate_default,
'maxYear2': maxYear_default,
'dateStart2TS': tempstart,
'dateEnd2TS': maxDate_default,
'yearTarget2Data': yearTarget2,
'yearStart2': yearStartClim,
'yearEnd2': yearEndClim,
'monthStart2': monthStart_default,
'monthEnd2': monthEnd_default,
'dayStart2': dayStart_default,
'dayEnd2': dayEnd_default,
'season2': 'custom',
'runningMeanYears': runningMeanYears_default,
'runningMeanDays': runningMeanDays_default,
'timeperiodTS': timeperiod,
'timeperiod2TS': timeperiod2,
'yearStartClim': yearStartClim,
'yearEndClim': yearEndClim,
'yearStartClimFut': yearStartClimFut,
'yearEndClimFut': yearEndClimFut
}
add_templateVariable(self, list_timeoptions, template_values)
#add maxValue form elements
for key, value in collection_dataStore.maxDates_lookup.iteritems():
add_templateVariable(self, {value: maxDates[key]}, template_values)
d = ' %02d' % int(template_values['dayStart'])
template_values['seasonStart'] = mon_names[
int(template_values['monthStart']) - 1] + d
d = ' %02d' % int(template_values['dayEnd'])
template_values['seasonEnd'] = mon_names[int(template_values['monthEnd']) -
1] + d
d = ' %02d' % int(template_values['dayStart2'])
template_values['seasonStart2'] = mon_names[
int(template_values['monthStart2']) - 1] + d
d = ' %02d' % int(template_values['dayEnd2'])
template_values['seasonEnd2'] = mon_names[int(template_values['monthEnd2'])
- 1] + d
#if Time Series of Single Year Daily, override start/end dates if needed
if template_values['timeSeriesCalc'] == 'intraannual':
s_dt = datetime.datetime.strptime(template_values['dateStart'],
'%Y-%m-%d')
e_dt = datetime.datetime.strptime(template_values['dateEnd'],
'%Y-%m-%d')
if (e_dt - s_dt).days > 366:
y = template_values['yearTargetData']
m = template_values['monthStart']
d = template_values['dayStart']
template_values['dateStartTS'] = y + '-' + m + '-' + d
template_values['dateEndTS'] = str(int(y) + 1) + '-' + m + '-' + d
template_values['dateStart'] = template_values['dateStartTS']
template_values['dateEnd'] = template_values['dateEndTS']
s_dt = datetime.datetime.strptime(template_values['dateStart2TS'],
'%Y-%m-%d')
e_dt = datetime.datetime.strptime(template_values['dateEnd2TS'],
'%Y-%m-%d')
if (e_dt - s_dt).days > 366:
y = template_values['yearTarget2Data']
m = template_values['monthStart2']
d = template_values['dayStart2']
template_values['dateStart2'] = y + '-' + m + '-' + d
template_values['dateEnd2TS'] = str(int(y) + 1) + '-' + m + '-' + d
#############################################
## TEMPLATE FUSION TABLE MAP DOWNLOAD ##
#############################################
list_fusiontablemapdownload = {
'fusiontabledownload': '1fRY18cjsHzDgGiJiS2nnpUU3v9JPDc2HNaR7Xk8',
'fusiontabledownloadname': 'California',
'pointLat': pointLat_default,
'pointLong': pointLon_default,
'pointValue': '',
'downloadMapFormat': 'geotiff',
}
add_templateVariable(self, list_fusiontablemapdownload, template_values)
#############################################
## TEMPLATE POINTS ##
#############################################
pointsLongLat_default = mapCenterLongLat
marker_colors = [
'blue', 'green', 'orange', 'purple', 'yellow', 'pink', 'red'
]
mapCenterLongLat = template_values['mapCenterLongLat']
template_values['marker_colors'] = marker_colors
add_templateVariable(self, {'pointsLongLat': pointsLongLat_default},
template_values)
for s_idx in range(1, max_pointsShapes + 1):
add_templateVariable(self, {'p' + str(s_idx) + 'check': 'checked'},
template_values)
add_templateVariable(self, {'p' + str(s_idx) + 'altname': ''},
template_values)
add_templateVariable(self, {'p' + str(s_idx): mapCenterLongLat},
template_values)
if s_idx == 1:
add_templateVariable(self, {'p' + str(s_idx) + 'display': 'block'},
template_values)
else:
add_templateVariable(self, {'p' + str(s_idx) + 'display': 'none'},
template_values)
#############################################
## TEMPLATE SHAPES-FUSION TABLE FOR TIME SERIES ##
#############################################
template_values['shape_colors'] = [
'#0000FF', '#00FF00', '#FFA500', '#551A8B', '#FFFF00', '#FF69B4',
'#FF0000'
]
for s_idx in range(1, max_pointsShapes + 1):
add_templateVariable(self, {'ft' + str(s_idx): ''}, template_values)
add_templateVariable(self, {'ft' + str(s_idx) + 'columnName': ''},
template_values)
add_templateVariable(self, {'ft' + str(s_idx) + 'altname': ''},
template_values)
add_templateVariable(self, {'ftSubChoice' + str(s_idx): ''},
template_values)
add_templateVariable(self, {'polygon' + str(s_idx): ''},
template_values)
if s_idx == 1:
add_templateVariable(self,
{'ft' + str(s_idx) + 'display': 'block'},
template_values)
add_templateVariable(self,
{'ft' + str(s_idx) + 'check': 'checked'},
template_values)
add_templateVariable(self,
{'ftChoice' + str(s_idx): ftChoice1_default},
template_values)
else:
add_templateVariable(self, {'ft' + str(s_idx) + 'display': 'none'},
template_values)
add_templateVariable(self, {'ft' + str(s_idx) + 'check': ''},
template_values)
add_templateVariable(self, {'ftChoice' + str(s_idx): ''},
template_values)
#############################################
## TEMPLATE FORMS ##
#############################################
template_forms = {
#Forms
'formMask': forms.formMask,
'formVariable2Display': forms.formVariable2Display,
'formTimePeriods': forms.formTimePeriods,
'formHighChartLayers': forms.formHighChartLayers,
'formHighChartLayersIntraannual': forms.formHighChartLayersIntraannual,
'formDownloadMapFormat': forms.formDownloadMapFormat,
'formDownloadProjection': forms.formDownloadProjection,
'formSeasons': forms.formSeasons,
'formChartType': forms.formChartType,
'formMonth': forms.formMonth,
'formDay': forms.formDay,
'formMapZoom': forms.formMapZoom,
'formPaletteCustomMap': forms.formPaletteCustomMap,
'formPaletteDivMap': forms.formPaletteDivMap,
'formPaletteSeqMap': forms.formPaletteSeqMap,
'formPaletteSize': forms.formPaletteSize,
'formColorbarType': forms.formColorbarType,
'formOpacity': forms.formOpacity,
'formUnits': forms.formUnits,
'formTimeSeriesCalc': forms.formTimeSeriesCalc,
'formSubDomainTypeTS': forms.formSubDomainTypeTS,
'formDownloadRegion': forms.formDownloadRegion,
'formFusionTableChoices': forms.formFusionTableChoices,
'formProductType': forms.formProductType,
'formProductType1': forms.formProductType1,
'formLayers': forms.formLayers,
#'formLayers1': forms.formLayers1,
'formBoolean': forms.formBoolean
}
template_values.update(template_forms)
#############################################
## EXTRA FEWS ##
#############################################
if applicationName == 'fewsNet':
extra_FEWS = {
'formLayers': formsFEWSNET.formFEWSNETLayers,
'formFusionTableChoices':
formsFEWSNET.formFusionTableChoicesFEWSNET,
'formCHIRPSChoicesFEWSNET': formsFEWSNET.formCHIRPSChoicesFEWSNET,
'formMODISChoicesFEWSNET': formsFEWSNET.formMODISChoicesFEWSNET,
'formLandsatChoicesFEWSNET':
formsFEWSNET.formLandsatChoicesFEWSNET,
}
template_values.update(extra_FEWS)
elif applicationName == 'precisionGrazing' or applicationName == 'gddTool':
extra_FEWS = {
'formLayers': formsPG.formPGLayers,
'formFusionTableChoices': formsPG.formFusionTableChoicesPG,
'formCHIRPSChoicesFEWSNET': formsFEWSNET.formCHIRPSChoicesFEWSNET,
'formMODISChoicesFEWSNET': formsFEWSNET.formMODISChoicesFEWSNET,
'formLandsatChoicesFEWSNET':
formsFEWSNET.formLandsatChoicesFEWSNET,
'formSubDomainTypeTS': formsPG.formSubDomainTypeTS
}
template_values.update(extra_FEWS)
#############################################
## SHARE LINK ##
#############################################
#Sharelink depends on most template variables
template_values['logger'] = logger
template_values['shareLink'] = set_share_link(template_values,
applicationName)
#############################################
## FORMAT TEMPLATE VARIABLES ##
#############################################
#format template values to allow for different date formats etc...
#See format_ functions in formchecks.py
formatted_template_values = {}
for key, val in template_values.iteritems():
format_function_name = 'format_' + key
try:
format_function = getattr(forms, format_function_name)
except:
format_function = None
if format_function:
formatted_template_values[key] = format_function(val)
else:
formatted_template_values[key] = val
return formatted_template_values