Skip to content

Add files via upload #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
afullhart wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Add files via upload #1

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
197 changes: 197 additions & 0 deletions precip_daily_variables_time_batches.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,197 @@

'at least two stations and one year needed for output to be produced?'
from dateutil.relativedelta import relativedelta
import ee
import datetime as dt
import time
now = dt.datetime.now()

#ee.Authenticate()
ee.Initialize()


product_name = 'ECMWF/ERA5/DAILY'
band_labels = ['total_precipitation']
asset = ee.FeatureCollection( 'users/andrewfullhart/PimaSantaCruz_ERA_Grid' )
start = '2000-01-01'
end = '2020-01-01'

length_unit_factor_of_mm = (1./1000.) #1 indicates length unit is in mm 1/1000 indicates length unit is in m

stations_ = ee.FeatureCollection( asset )
'slice stations in case of restart'
#station_feats = stations_.toList( 10000000 )
#stations_ = ee.FeatureCollection( station_feats.slice( 0, 3 ) )
stationIDs = ee.List( stations_.reduceColumns( ee.Reducer.toList(), ['stationID'] ).get( 'list' ) )

start_dt = dt.datetime.strptime( start, '%Y-%m-%d' )
end_dt = dt.datetime.strptime( end, '%Y-%m-%d' )
years_per_batch = 2
years = int(((end_dt - start_dt) / dt.timedelta( days=1 )) / 365.)

starts_endings = []
batch_start = start_dt
batch_ct = int(years / years_per_batch)
for i in range(batch_ct):
batch_s = batch_start
batch_e = batch_start + relativedelta( years=years_per_batch )
starts_endings.append( [dt.datetime.strftime( batch_s, '%Y-%m-%d' ),
dt.datetime.strftime( batch_e, '%Y-%m-%d' )] )
batch_start += relativedelta( years=years_per_batch )

def dates_list_to_feats( date ):
dct = {'system:time_start': ee.Date( date ).millis()}
return ee.Feature( None, dct )

def region_array_to_feats( img_vals ):
val_list = ee.List( img_vals )
dct = {'system:time_start':ee.Date( val_list.get( 3 ) ).millis(),
'precip': ee.Number( val_list.get( 4 ) )}
return ee.Feature( None, dct )

def dict_list_unpacker( dct_obj ):
dct = ee.Dictionary( dct_obj )
return dct.values()

for batch_i in range(batch_ct):
start_ = starts_endings[batch_i][0]
end_ = starts_endings[batch_i][1]

def main_funcs_caller( mo ):
mo = ee.Number( mo ).int()
month_filter = ee.Filter.calendarRange( mo, mo.add( 1 ), 'month' )
ic = ee.ImageCollection( product_name ) \
.filterDate( start_, end_ ) \
.filter( month_filter ) \
.select( band_labels[0] )

def station_funcs( station ):
ic_prop_array = ic.getRegion( ee.FeatureCollection( station ), 1000 ).slice( 1 )
raw_data_fc = ee.FeatureCollection( ic_prop_array.map( region_array_to_feats ) )

def daily_sorter( day ):
start_day = ee.Date( day.get( 'system:time_start' ) )
end_day = start_day.advance( 1, 'day' )
day_filter = ee.Filter.date( start_day, end_day )
fc = raw_data_fc.filter( day_filter )
total = fc.reduceColumns( ee.Reducer.sum(), ee.List( ['precip'] ) )
acc = ee.Number( total.get( 'sum' ) ).divide( length_to_mm_factor_ )
return ee.Feature( None, {'precip': acc} )

day_dates_fc = dates_fc_.filter( month_filter )
day_vals_fc = ee.FeatureCollection( day_dates_fc.map( daily_sorter ) )
day_vals = day_vals_fc.reduceColumns( ee.Reducer.toList(), ['precip'] ).get( 'list' )

def prob_counter( precip_list_obj ):
precip_list = ee.List( precip_list_obj )
first = precip_list.get( 0 )
counts = ee.List( [0, 0, 0, 0] )
first_data = ee.List( [first, counts] )

def begin_counter( curr, prev_data ):
curr_num = ee.Number( curr ).float()
data = ee.List( prev_data )
prev_num = ee.Number( data.get( 0 ) ).float()
prev_counts = ee.List( data.get( 1 ) )
ww_ct = ee.Number( prev_counts.get( 0 ) ).int()
wd_ct = ee.Number( prev_counts.get( 1 ) ).int()
dw_ct = ee.Number( prev_counts.get( 2 ) ).int()
dd_ct = ee.Number( prev_counts.get( 3 ) ).int()
out = ee.Algorithms.If( curr_num.gt( 0. ),
ee.Algorithms.If( prev_num.gt( 0. ),
ee.List( [curr_num, ee.List( [ww_ct.add( 1 ), wd_ct, dw_ct, dd_ct] ) ] ),
ee.List( [curr_num, ee.List( [ww_ct, wd_ct.add( 1 ), dw_ct, dd_ct] ) ] ) ),
ee.Algorithms.If( prev_num.gt( 0. ),
ee.List( [curr_num, ee.List( [ww_ct, wd_ct, dw_ct.add( 1 ), dd_ct] ) ] ),
ee.List( [curr_num, ee.List( [ww_ct, wd_ct, dw_ct, dd_ct.add( 1 )] ) ] ) ) )
return ee.List( out )

iter_out = ee.List( precip_list.iterate( begin_counter, first_data ) )
return ee.List( iter_out )

nonzeros = day_vals_fc.filter( ee.Filter.gt( 'precip', ee.Number( 0 ) ) )
mean = nonzeros.reduceColumns( ee.Reducer.mean(), ['precip'] )
stdDev = nonzeros.reduceColumns( ee.Reducer.sampleStdDev(), ['precip'] )
skew = nonzeros.reduceColumns( ee.Reducer.skew(), ['precip'] )
counts = ee.List( prob_counter( day_vals ).get( 1 ) )
ww_ct = ee.Number( counts.get( 0 ) ).float()
wd_ct = ee.Number( counts.get( 1 ) ).float()
dw_ct = ee.Number( counts.get( 2 ) ).float()
dd_ct = ee.Number( counts.get( 3 ) ).float()
wd = wd_ct.divide( wd_ct.add( dd_ct ) )
ww = ww_ct.divide( dw_ct.add( ww_ct ) )
dct = {'mean': mean, 'stdDev': stdDev, 'skew': skew,
'pWD': {'pWD': wd}, 'pWW': {'pWW': ww}}
return ee.Feature( None, dct )

out_fc = ee.FeatureCollection( stations_.map( station_funcs ) )
mean = out_fc.reduceColumns( ee.Reducer.toList(), ['mean'] ).get( 'list' )
stdDev = out_fc.reduceColumns( ee.Reducer.toList(), ['stdDev'] ).get( 'list' )
skew = out_fc.reduceColumns( ee.Reducer.toList(), ['skew'] ).get( 'list' )
pWD = out_fc.reduceColumns( ee.Reducer.toList(), ['pWD'] ).get( 'list' )
pWW = out_fc.reduceColumns( ee.Reducer.toList(), ['pWW'] ).get( 'list' )
out_list = ee.List( [] )
out_list = out_list.add( ee.List( mean ).map( dict_list_unpacker ) )
out_list = out_list.add( ee.List( stdDev ).map( dict_list_unpacker ) )
out_list = out_list.add( ee.List( skew ).map( dict_list_unpacker ) )
out_list = out_list.add( ee.List( pWD ).map( dict_list_unpacker ) )
out_list = out_list.add( ee.List( pWW ).map( dict_list_unpacker ) )
return out_list

def list_flatten( list_obj ):
return ee.List( list_obj ).flatten()

def out_list_unpacker( station_id ):
station_str = ee.String( station_id )
station_i = station_ids_strs_.indexOf( station_str )
def stat_features( nested_i ):
nested_i = ee.Number( nested_i ).int()
nested_i_list = ee.Array( out_list_.get( nested_i ) ).transpose().toList()
nested_i_list = nested_i_list.map( list_flatten ).get( station_i )
station_stat_str = station_str.cat( '_' ).cat( strs_of_stats_.get( nested_i ) )
dct = {'station_ID': station_stat_str,
'statistic': nested_i_list}
return ee.Feature( None, dct )
fc = ee.FeatureCollection( number_of_stats_seq_.map( stat_features ) )
return fc

dates = []
start_date = ee.Date( start_ )
end_date = ee.Date( end_ )
n = end_date.difference( start_date, 'day' )
for i in range(n.getInfo()):
date = start_date.advance( ee.Number( i ), 'day' )
dates.append( date )
dates_ee = ee.List( dates )
dates_fc_ = ee.FeatureCollection( dates_ee.map( dates_list_to_feats ) )

station_ids = ee.List( stations_.reduceColumns( ee.Reducer.toList(), ['stationID'] ).get( 'list' ) )
station_ids_strs_ = ee.List( [str(elem) for elem in station_ids.getInfo()] )

length_to_mm_factor_ = ee.Number( length_unit_factor_of_mm )
months_seq = ee.List.sequence( 1, 12 )

strs_of_stats_ = ee.List( ['mean', 'stdDev', 'skew', 'pWD', 'pWW'] )
number_of_stats_seq_ = ee.List.sequence( 0, strs_of_stats_.length().add( -1 ) )

out_list = months_seq.map( main_funcs_caller )
out_array = ee.Array( out_list ).transpose()
out_list_ = out_array.toList()

out_fc = ee.FeatureCollection( station_ids_strs_.map( out_list_unpacker ) ).flatten()

task = ee.batch.Export.table.toDrive( collection=out_fc,
description='ERA_Demo_2000_2020_{}'.format( str(batch_i) ),
folder='GEE_Downloads' )

task.start()

while task.active():
time.sleep( 1 )
if task.status()['state'] != 'COMPLETED':
pass
else:
pass

later = dt.datetime.now()
print(str(later - now))