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runscript_b003.py
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runscript_b003.py
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#
# runscript.py
#
# runtime estimation formula (in minutes)
#
# init_time + mean_surface_time + ( avg_iteration_time * ( iterations / processes ) ) / 60
#
# where:
# init_time = 2 (minutes)
# mean_surface_time = 12 (minutes)
# avg_iteration_time = 24 (seconds)
# iterations = number of iterations being run
# processes = (number of nodes * cores per node) - 1
#
# example:
# for a 1000 iteration job running on 4 c11 nodes which have 8 cores each
#
# runtime = 2 + 12 + ( 24 ( 1000 / 31 ) ) / 60
# runtime = 26.9 minutes
#
# actual recorded runtime for same conditions = 26.2 minutes
#
# small difference is due to rounding up the estimated
# init_time and mean_surface_time which adds 30-45 seconds
# ====================================================================================================
from __future__ import print_function
from mpi4py import MPI
import os
import sys
import errno
from copy import deepcopy
import time
import random
import math
import numpy as np
import pandas as pd
from shapely.geometry import Polygon, Point, shape, box
import shapefile
# ====================================================================================================
# general init
# mpi info
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
status = MPI.Status()
# absolute path to script directory
dir_base = os.path.dirname(os.path.abspath(__file__))
# python /path/to/runscript.py nepal NPL 0.1 10
arg = sys.argv
try:
country = sys.argv[1]
abbr = sys.argv[2]
pixel_size = float(sys.argv[3])
# maximum number of iterations to run
# iterations = int(sys.argv[4])
# raw_filter = sys.argv[5]
# Ts = int(sys.argv[6])
# Rid = int(sys.argv[7])
Ts = int(time.time())
Rid = "12346"
# run_mean_surf = int(sys.argv[8])
run_mean_surf = 1
# if run_mean_surf == 2:
# path_mean_surf = sys.argv[9]
# data_version = sys.argv[10]
# run_version = sys.argv[11]
data_version = 1.1
run_version = "b003"
# log_mean_surf = int(sys.argv[12])
log_mean_surf = 0
except:
sys.exit("invalid inputs")
# maximum number of iterations to run
# iter_max = 100
iterations = 100
# iteration intervals at which to check error val
iter_interval = [10, 100, 1000, 5000, 10000, 50000, 100000]
iter_step = 0
# difference from true mean (decimal percentage)
iter_thresh = 0.05
# minimum improvement over previous iteration interval required to continue (decimal percentage)
iter_improvement = 0.01
# check for valid pixel size
# examples of valid pixel sizes: 1.0, 0.5, 0.25, 0.2, 0.1, 0.05, 0.025, ...
if (1/pixel_size) != int(1/pixel_size):
sys.exit("invalid pixel size: "+str(pixel_size))
# pixel size inverse
psi = 1/pixel_size
# iterations range
i_control = range(int(iterations))
# --------------------------------------------------
# filter options
# filter_type = "all"
filter_type = "specfic"
filters = {
"ad_sector_names": {
"Agriculture": 0
}
}
# --------------------------------------------------
# vars to be added as inputs
# not used by mcr function
# nodata value for output raster
nodata = -9999
# field name for aid values
aid_field = "total_commitments"
# boolean field identifying if project is geocoded
is_geocoded = "is_geocoded"
# when True, only use geocoded data
only_geocoded = False
# --------------------------------------------------
# static vars that may be added as some type of input
# used by mcr functions
# type definition for non geocoded projects
# either allocated at country level ("country") or ignored ("None")
not_geocoded = "country"
if only_geocoded:
not_geocoded = "None"
# fields name associated with values in lookup dict
code_field = "precision_code"
# aggregation types used in lookup dict
agg_types = ["point", "buffer", "adm"]
# code field values
lookup = {
"1": {"type":"point","data":0},
"2": {"type":"buffer","data":1},
"3": {"type":"adm","data":"2"},
"4": {"type":"adm","data":"2"},
"5": {"type":"buffer","data":1},
"6": {"type":"adm","data":"0"},
"7": {"type":"adm","data":"0"},
"8": {"type":"adm","data":"0"}
}
# ====================================================================================================
# functions
# check csv delim and return if valid type
def getCSV(path):
if path.endswith('.tsv'):
return pd.read_csv(path, sep='\t', quotechar='\"', na_values='', keep_default_na=False)
elif path.endswith('.csv'):
return pd.read_csv(path, quotechar='\"', na_values='', keep_default_na=False)
else:
sys.exit('getCSV - file extension not recognized.\n')
# get project and location data in path directory
# requires a field name to merge on and list of required fields
def getData(path, merge_id, field_ids, only_geo):
amp_path = path+"/projects.tsv"
loc_path = path+"/locations.tsv"
# make sure files exist
#
# read input csv files into memory
amp = getCSV(amp_path)
loc = getCSV(loc_path)
if not merge_id in amp or not merge_id in loc:
sys.exit("getData - merge field not found in amp or loc files")
amp[merge_id] = amp[merge_id].astype(str)
loc[merge_id] = loc[merge_id].astype(str)
# create projectdata by merging amp and location files by project_id
if only_geo:
tmp_merged = amp.merge(loc, on=merge_id)
else:
tmp_merged = amp.merge(loc, on=merge_id, how="left")
if not "longitude" in tmp_merged or not "latitude" in tmp_merged:
sys.exit("getData - latitude and longitude fields not found")
for field_id in field_ids:
if not field_id in tmp_merged:
sys.exit("getData - required code field not found")
return tmp_merged
# --------------------------------------------------
# defin tags enum
def enum(*sequential, **named):
# source: http://stackoverflow.com/questions/36932/how-can-i-represent-an-enum-in-python
enums = dict(zip(sequential, range(len(sequential))), **named)
return type('Enum', (), enums)
# gets geometry type based on lookup table
# depends on lookup and not_geocoded
def geomType(is_geo, code):
try:
is_geo = int(is_geo)
code = str(int(code))
if is_geo == 1:
if code in lookup:
tmp_type = lookup[code]["type"]
return tmp_type
else:
print("lookup code not recognized: " + code)
return "None"
elif is_geo == 0:
return not_geocoded
else:
print("is_geocoded integer code not recognized: " + str(is_geo))
return "None"
except:
return not_geocoded
# finds shape in set of polygons which arbitrary polygon is within
# returns 0 if item is not within any of the shapes
def getPolyWithin(item, polys):
c = 0
for shp in polys:
tmp_shp = shape(shp)
if item.within(tmp_shp):
return tmp_shp
return c
# checks if arbitrary polygon is within country (adm0) polygon
# depends on adm0
def inCountry(shp):
return shp.within(adm0)
# build geometry for point based on code
# depends on lookup and adm0
def getGeom(code, lon, lat):
tmp_pnt = Point(lon, lat)
if not inCountry(tmp_pnt):
print("point not in country")
return 0
elif lookup[code]["type"] == "point":
return tmp_pnt
elif lookup[code]["type"] == "buffer":
try:
tmp_int = float(lookup[code]["data"])
tmp_buffer = tmp_pnt.buffer(tmp_int)
if inCountry(tmp_buffer):
return tmp_buffer
else:
return tmp_buffer.intersection(adm0)
except:
print("buffer value could not be converted to float")
return 0
elif lookup[code]["type"] == "adm":
try:
tmp_int = int(lookup[code]["data"])
return getPolyWithin(tmp_pnt, adm_shps[tmp_int])
except:
print("adm value could not be converted to int")
return 0
else:
print("code type not recognized")
return 0
# returns geometry for point
# depends on agg_types and adm0
def geomVal(agg_type, code, lon, lat):
if agg_type in agg_types:
code = str(int(code))
tmp_geom = getGeom(code, lon, lat)
if tmp_geom != 0:
return tmp_geom
return "None"
elif agg_type == "country":
return adm0
else:
print("agg_type not recognized: " + str(agg_type))
return "None"
# --------------------------------------------------
# random point gen function
def get_random_point_in_polygon(poly):
INVALID_X = -9999
INVALID_Y = -9999
(minx, miny, maxx, maxy) = poly.bounds
p = Point(INVALID_X, INVALID_Y)
px = 0
while not poly.contains(p):
p_x = random.uniform(minx, maxx)
p_y = random.uniform(miny, maxy)
p = Point(p_x, p_y)
return p
# generate random point geom or use actual point
def addPt(agg_type, agg_geom):
if agg_type == "point":
return agg_geom
else:
tmp_rnd = get_random_point_in_polygon(agg_geom)
return tmp_rnd
# ====================================================================================================
# --------------------------------------------------
# load shapefiles
# must start at and inlcude ADM0
# all additional ADM shps must be included so that adm_path index corresponds to adm level
adm_paths = []
adm_paths.append(dir_base+"/countries/"+country+"/shapefiles/ADM0/"+abbr+"_adm0.shp")
adm_paths.append(dir_base+"/countries/"+country+"/shapefiles/ADM1/"+abbr+"_adm1.shp")
adm_paths.append(dir_base+"/countries/"+country+"/shapefiles/ADM2/"+abbr+"_adm2.shp")
# get adm0 bounding box
adm_shps = [shapefile.Reader(adm_path).shapes() for adm_path in adm_paths]
# define country shape
adm0 = shape(adm_shps[0][0])
# --------------------------------------------------
# create point grid for country
# country bounding box
(adm0_minx, adm0_miny, adm0_maxx, adm0_maxy) = adm0.bounds
# print( (adm0_minx, adm0_miny, adm0_maxx, adm0_maxy) )
# grid_buffer
gb = 0.5
# bounding box rounded to pixel size (always increases bounding box size, never decreases)
(adm0_minx, adm0_miny, adm0_maxx, adm0_maxy) = (math.floor(adm0_minx*gb)/gb, math.floor(adm0_miny*gb)/gb, math.ceil(adm0_maxx*gb)/gb, math.ceil(adm0_maxy*gb)/gb)
# print( (adm0_minx, adm0_miny, adm0_maxx, adm0_maxy) )
# generate arrays of new grid x and y values
cols = np.arange(adm0_minx, adm0_maxx+pixel_size*0.5, pixel_size)
rows = np.arange(adm0_maxy, adm0_miny-pixel_size*0.5, -1*pixel_size)
# print cols
# print rows
# init grid reference object
gref = {}
idx = 0
for r in rows:
gref[str(r)] = {}
for c in cols:
# build grid reference object
gref[str(r)][str(c)] = idx
idx += 1
# --------------------------------------------------
# load project data
dir_data = dir_base+"/countries/"+country+"/data/"+country+"_"+str(data_version)+"/data"
merged = getData(dir_data, "project_id", (code_field, "project_location_id"), only_geocoded)
# --------------------------------------------------
# filters
# apply filters to project data
# filtered = merged.loc[merged.ad_sector_names == "Agriculture"]
# --------------------------------------------------
# generate random dollars
# create dataframe for use in iterations
column_list = ['iter', 'id', 'ran_dollars']
dollar_table = pd.DataFrame(columns=column_list)
# create copy of merged project data
i_m = deepcopy(merged)
# i_m = deepcopy(filtered)
# add new column of random numbers (0-1)
i_m['ran_num'] = (pd.Series(np.random.random(len(i_m)))).values
# group merged table by project ID for the sum of each project ID's random numbers
grouped_random_series = i_m.groupby('project_id')['ran_num'].sum()
# get location count for each project
i_m['ones'] = (pd.Series(np.ones(len(i_m)))).values
grouped_location_count = i_m.groupby('project_id')['ones'].sum()
# create new empty dataframe
df_group_random = pd.DataFrame()
# add grouped random 'Series' to the newly created 'Dataframe' under a new grouped_random column
df_group_random['grouped_random'] = grouped_random_series
# add location count series to dataframe
df_group_random['location_count'] = grouped_location_count
# add the series index, composed of project IDs, as a new column called project_ID
df_group_random['project_id'] = df_group_random.index
# now that we have project_ID in both the original merged 'Dataframe' and the new 'Dataframe' they can be merged
i_m = i_m.merge(df_group_random, on='project_id')
# calculate the random dollar ammount per point for each entry
i_m['random_dollars_pp'] = (i_m.ran_num / i_m.grouped_random) * i_m[aid_field]
# aid field value split evenly across all project locations based on location count
i_m[aid_field].fillna(0, inplace=True)
i_m['split_dollars_pp'] = (i_m[aid_field] / i_m.location_count)
# --------------------------------------------------
# assign geometries
# add geom columns
i_m["agg_type"] = ["None"] * len(i_m)
i_m["agg_geom"] = ["None"] * len(i_m)
i_m.agg_type = i_m.apply(lambda x: geomType(x[is_geocoded], x[code_field]), axis=1)
i_m.agg_geom = i_m.apply(lambda x: geomVal(x.agg_type, x[code_field], x.longitude, x.latitude), axis=1)
i_mx = i_m.loc[i_m.agg_geom != "None"].copy(deep=True)
# i_mx['index'] = i_mx['project_location_id']
i_mx['unique'] = range(0, len(i_mx))
i_mx['index'] = range(0, len(i_mx))
i_mx = i_mx.set_index('index')
# ====================================================================================================
# master init
if rank == 0:
# --------------------------------------------------
# initialize results file output
results_str = "Monte Carlo Rasterization Output File\t "
results_str += "\nstart time\t" + str(Ts)
results_str += "\ncountry\t" + str(country)
results_str += "\nabbr\t" + str(abbr)
results_str += "\npixel_size\t" + str(pixel_size)
results_str += "\niterations\t" + str(iterations)
results_str += "\nnodata\t" + str(nodata)
results_str += "\naid_field\t" + str(aid_field)
results_str += "\ncode_field\t" + str(code_field)
results_str += "\ncountry bounds\t" + str((adm0_minx, adm0_miny, adm0_maxx, adm0_maxy))
results_str += "\nrows\t" + str(len(rows))
results_str += "\ncolumns\t" + str(len(cols))
results_str += "\nlocations\t" + str(len(i_mx))
# results_str += "\nfilters\t" + str(filters)
# --------------------------------------------------
# initialize asc file output
asc = ""
asc += "NCOLS " + str(len(cols)) + "\n"
asc += "NROWS " + str(len(rows)) + "\n"
# asc += "XLLCORNER " + str(adm0_minx-pixel_size*0.5) + "\n"
# asc += "YLLCORNER " + str(adm0_miny-pixel_size*0.5) + "\n"
asc += "XLLCENTER " + str(adm0_minx) + "\n"
asc += "YLLCENTER " + str(adm0_miny) + "\n"
asc += "CELLSIZE " + str(pixel_size) + "\n"
asc += "NODATA_VALUE " + str(nodata) + "\n"
# --------------------------------------------------
# build output directories
# creates directories
def make_dir(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
# dir_country = dir_base+"/outputs/"+country
# dir_working = dir_country+"/"+country+"_"+str(pixel_size)+"_"+str(iterations)+"_"+str(int(Ts))
dir_country = dir_base+"/chains/"+country
dir_chain = dir_country+"/"+country+"_"+str(pixel_size)+"_"+str(Ts)+"_"+str(Rid)
dir_outputs = dir_chain+"/outputs"
dir_working = dir_outputs+"/"+country+"_"+str(pixel_size)+"_"+str(iterations)
make_dir(dir_working)
# --------------------------------------------------
# record init runtime
T_init = time.time()
Tloc = int(T_init - Ts)
results_str += "\nInit Runtime\t" + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s'
print('\t\Init Runtime: ' + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s')
# ====================================================================================================
# ====================================================================================================
comm.Barrier()
# sys.exit("! - init only")
# ====================================================================================================
# ====================================================================================================
# mpi prep
# terminate if master init fails
#
# Define MPI message tags
tags = enum('READY', 'DONE', 'EXIT', 'START', 'ERROR')
# init for later
sum_mean_surf = 0
# ====================================================================================================
# generate mean surface raster
if run_mean_surf == 1 and rank == 0:
# ==================================================
# MASTER START STUFF
all_mean_surf = []
unique_ids = i_mx['unique']
# ==================================================
task_index = 0
num_workers = size - 1
closed_workers = 0
err_status = 0
print("Mean Surf Master starting with %d workers" % num_workers)
# distribute work
while closed_workers < num_workers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY:
# Worker is ready, so send it a task
if task_index < len(unique_ids):
#
# !!!
# if task if for a point (not point with small buffer, etc.)
# then let master do work
# run tests to see if this actually improves runtimes
# !!!
#
comm.send(unique_ids[task_index], dest=source, tag=tags.START)
print("Sending task %d to worker %d" % (task_index, source))
task_index += 1
else:
comm.send(None, dest=source, tag=tags.EXIT)
elif tag == tags.DONE:
# ==================================================
# MASTER MID STUFF
all_mean_surf.append(data)
print("Got data from worker %d" % source)
# ==================================================
elif tag == tags.EXIT:
print("Worker %d exited." % source)
closed_workers += 1
elif tag == tags.ERROR:
print("Error reported by worker %d ." % source)
# broadcast error to all workers
#
# make sure they all get message and terminate
#
err_status = 1
break
# ==================================================
# MASTER END STUFF
if err_status == 0:
# calc results
print("Mean Surf Master calcing")
stack_mean_surf = np.vstack(all_mean_surf)
sum_mean_surf = np.sum(stack_mean_surf, axis=0)
save_mean_surf = dir_outputs+"/output_"+country+"_"+str(pixel_size)+"_surf.npy"
np.save(save_mean_surf, sum_mean_surf)
# write asc file
sum_mean_surf_str = ' '.join(np.char.mod('%f', sum_mean_surf))
asc_sum_mean_surf_str = asc + sum_mean_surf_str
fout_sum_mean_surf = open(dir_outputs+"/output_"+country+"_"+str(pixel_size)+"_surf.asc", "w")
fout_sum_mean_surf.write(asc_sum_mean_surf_str)
print("Mean Surf Master finishing")
T_surf = time.time()
Tloc = int(T_surf - T_init)
results_str += "\nMean Surf Runtime\t" + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s'
print('\t\tMean Surf Runtime: ' + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s')
print('\n')
else:
print("Mean Surf Master terminating due to worker error.")
# ==================================================
elif run_mean_surf == 1:
# Worker processes execute code below
name = MPI.Get_processor_name()
print("I am a worker with rank %d on %s." % (rank, name))
while True:
comm.send(None, dest=0, tag=tags.READY)
task = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START:
# ==================================================
# WORKER STUFF
mean_surf = np.zeros((int(idx+1),), dtype=np.int)
# poly grid pixel size and poly grid pixel size inverse
# poly grid pixel size is 1 order of magnitude higher resolution than output pixel_size
pg_pixel_size = pixel_size * 0.1
pg_psi = 1/pg_pixel_size
pg_data = i_mx.loc[task]
pg_type = pg_data.agg_type
if (pg_type != "point" and pg_type in agg_types) or pg_type == "country":
# for each row generate grid based on bounding box of geometry
pg_geom = pg_data.agg_geom
(pg_minx, pg_miny, pg_maxx, pg_maxy) = pg_geom.bounds
# print( (pg_minx, pg_miny, pg_maxx, pg_maxy) )
(pg_minx, pg_miny, pg_maxx, pg_maxy) = (math.floor(pg_minx*pg_psi)/pg_psi, math.floor(pg_miny*pg_psi)/pg_psi, math.ceil(pg_maxx*pg_psi)/pg_psi, math.ceil(pg_maxy*pg_psi)/pg_psi)
# print( (pg_minx, pg_miny, pg_maxx, pg_maxy) )
pg_cols = np.arange(pg_minx, pg_maxx+pg_pixel_size*0.5, pg_pixel_size)
pg_rows = np.arange(pg_maxy, pg_miny-pg_pixel_size*0.5, -1*pg_pixel_size)
# evenly split the aid for that row (i_mx['split_dollars_pp'] field) among new grid points
# full poly grid reference object and count
pg_gref = {}
pg_idx = 0
# poly grid points within actual geom and count
# pg_in = {}
pg_count = 0
for r in pg_rows:
pg_gref[str(r)] = {}
for c in pg_cols:
pg_idx += 1
# check if point is within geom
pg_point = Point(c,r)
pg_within = pg_point.within(pg_geom)
if pg_within:
pg_gref[str(r)][str(c)] = pg_idx
pg_count += 1
else:
pg_gref[str(r)][str(c)] = "None"
# init grid reference object
for r in pg_rows:
for c in pg_cols:
if pg_gref[str(r)][str(c)] != "None":
# round new grid points to old grid points and update old grid
gref_id = gref[str(round(r * psi) / psi)][str(round(c * psi) / psi)]
mean_surf[gref_id] += pg_data['split_dollars_pp'] / pg_count
elif pg_type == "point":
# round new grid points to old grid points and update old grid
gref_id = gref[str(round(pg_data.latitude * psi) / psi)][str(round(pg_data.longitude * psi) / psi)]
mean_surf[gref_id] += pg_data['split_dollars_pp']
# --------------------------------------------------
# send np arrays back to master
comm.send(mean_surf, dest=0, tag=tags.DONE)
# ==================================================
elif tag == tags.EXIT:
comm.send(None, dest=0, tag=tags.EXIT)
break
elif tag == tags.ERROR:
print("Error message from master. Shutting down." % source)
# confirm error message received
#
# terminate process
#
break
elif run_mean_surf == 0 and rank == 0:
load_mean_surf = dir_outputs+"/output_"+country+"_"+str(pixel_size)+"_surf.npy"
sum_mean_surf = np.load(load_mean_surf)
elif run_mean_surf == 2 and rank == 0:
load_mean_surf = dir_base+"/surf_log/"+country+"_"+str(pixel_size)+"_"+str(run_version)+"_"+str(data_version)+"_surf.npy"
sum_mean_surf = np.load(load_mean_surf)
elif run_mean_surf == 3 and rank == 0:
load_mean_surf = dir_base+"/"+path_mean_surf
sum_mean_surf = np.load(load_mean_surf)
if log_mean_surf == 1 and rank == 0:
save_mean_surf = dir_base+"/surf_log/"+country+"_"+str(pixel_size)+"_"+str(run_version)+"_"+str(data_version)+"_surf.npy"
np.save(save_mean_surf, sum_mean_surf)
# ====================================================================================================
# ====================================================================================================
comm.Barrier()
# sys.exit("! - mean surf only")
# ====================================================================================================
# ====================================================================================================
# mpi stuff
# structured based on https://github.com/jbornschein/mpi4py-examples/blob/master/09-task-pull.py
if rank == 0:
# ==================================================
# MASTER START STUFF
print("starting iterations - %d to be run" % iterations)
total_aid = [] # [0] * iterations
total_count = [] # [0] * iterations
# ==================================================
task_index = 0
num_workers = size - 1
closed_workers = 0
err_status = 0
print("Master starting with %d workers" % num_workers)
# distribute work
while closed_workers < num_workers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY:
# Worker is ready, so send it a task
if task_index < len(i_control):
comm.send(i_control[task_index], dest=source, tag=tags.START)
print("Sending task %d to worker %d" % (task_index, source))
task_index += 1
else:
comm.send(None, dest=source, tag=tags.EXIT)
elif tag == tags.DONE:
# ==================================================
# MASTER MID STUFF
total_aid.append(data[0])
total_count.append(data[1])
print("Got data from worker %d" % source)
# ==================================================
elif tag == tags.EXIT:
print("Worker %d exited." % source)
closed_workers += 1
elif tag == tags.ERROR:
print("Error reported by worker %d ." % source)
# broadcast error to all workers
#
# make sure they all get message and terminate
#
err_status = 1
break
# ==================================================
# MASTER END STUFF
if err_status == 0:
# calc results
print("Master calcing")
stack_aid = np.vstack(total_aid)
std_aid = np.std(stack_aid, axis=0)
mean_aid = np.mean(stack_aid, axis=0)
sum_aid = np.sum(mean_aid)
stack_count = np.vstack(total_count)
std_count = np.std(stack_count, axis=0)
mean_count = np.mean(stack_count, axis=0)
error_log = 0
if type(sum_mean_surf) != type(0):
error_surf = np.absolute(np.subtract(sum_mean_surf, mean_aid))
error_surf_str = ' '.join(np.char.mod('%f', error_surf))
asc_error_surf_str = asc + error_surf_str
fout_error_surf = open(dir_working+"/"+country+"_output_"+str(pixel_size)+"_"+str(iterations)+"_error_surf.asc", "w")
fout_error_surf.write(asc_error_surf_str)
error_log_mean = np.mean(np.absolute(error_surf))
error_log_sum = np.sum(np.absolute(error_surf))
error_log_percent = error_log_sum / sum_aid
results_str += "\nerror mean\t" + str(error_log_mean)
results_str += "\nerror sum\t" + str(error_log_sum)
results_str += "\nerror percent\t" + str(error_log_percent)
# write asc files
std_aid_str = ' '.join(np.char.mod('%f', std_aid))
asc_std_aid_str = asc + std_aid_str
fout_std_aid = open(dir_working+"/"+country+"_output_"+str(pixel_size)+"_"+str(iterations)+"_std_aid.asc", "w")
fout_std_aid.write(asc_std_aid_str)
mean_aid_str = ' '.join(np.char.mod('%f', mean_aid))
asc_mean_aid_str = asc + mean_aid_str
fout_mean_aid = open(dir_working+"/"+country+"_output_"+str(pixel_size)+"_"+str(iterations)+"_mean_aid.asc", "w")
fout_mean_aid.write(asc_mean_aid_str)
std_count_str = ' '.join(np.char.mod('%f', std_count))
asc_std_count_str = asc + std_count_str
fout_std_count = open(dir_working+"/"+country+"_output_"+str(pixel_size)+"_"+str(iterations)+"_std_count.asc", "w")
fout_std_count.write(asc_std_count_str)
mean_count_str = ' '.join(np.char.mod('%f', mean_count))
asc_mean_count_str = asc + mean_count_str
fout_mean_count = open(dir_working+"/"+country+"_output_"+str(pixel_size)+"_"+str(iterations)+"_mean_count.asc", "w")
fout_mean_count.write(asc_mean_count_str)
print("Master finishing")
T_iter = int(time.time() - T_surf)
Tloc = int(time.time() - Ts)
results_str += "\nIterations Runtime\t" + str(T_iter//60) +'m '+ str(int(T_iter%60)) +'s'
results_str += "\nTotal Runtime\t" + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s'
print('\n\tRun Results:')
print('\t\tError Value for ' + str(iterations) + ' iterations: ' + str(error_log))
print('\t\tIterations Runtime: ' + str(T_iter//60) +'m '+ str(int(T_iter%60)) +'s')
print('\t\tTotal Runtime: ' + str(Tloc//60) +'m '+ str(int(Tloc%60)) +'s')