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post_process.py
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post_process.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import random
import sys
import os
from pathos.multiprocessing import ThreadingPool
from io import StringIO
import argparse
from shutil import rmtree
def read_arguments():
parser = argparse.ArgumentParser(description="Input data")
parser.add_argument(
"-P",
"--plot",
default="False",
help="True: Produce plots of the solutions. False: Do not produce plots",
)
parser.add_argument(
"-PE",
"--plot_ex_prob",
default="False",
help="True: Produce plots of the specified exceedance probabilities. False: Do not produce plots",
)
parser.add_argument(
"-EX",
"--ex_prob",
default='',
help="List of exceedence probabilities to be used for graphical output",
)
parser.add_argument(
"-T",
"--time_steps",
default='',
help="List of time steps to plot (integer >= 0). Type all to plot all the time steps",
)
parser.add_argument(
"-L",
"--levels",
default='',
help="List of vertical levels (integer >= 1) to plot. Type all to plot all the levels",
)
parser.add_argument(
"-D",
"--days_plot",
default='',
help="List of days to plot (YYYYMMDD). Type all to plot all the days",
)
parser.add_argument(
"-C",
"--convert",
default="False",
help="If True, convert output concentration into other species listed with the command -S (--species)",
)
parser.add_argument(
"-S", "--species", default='', help="List of gas species (e.g. CO2)"
)
parser.add_argument(
"-TS", "--tracking_specie", default=None, help="The original emitted specie that is tracked in the simulation"
)
parser.add_argument(
"-N",
"--nproc",
default=1,
help="Maximum number of allowed simultaneous processes",
)
parser.add_argument(
"-M",
"--models",
default="all",
help="Model outputs to post-process. Options: disgas, twodee, all",
)
parser.add_argument(
"-MO",
"--merge_outputs",
default="False",
help="Merge Twodee and Disgas outputs (true or false)",
)
parser.add_argument(
"-U",
"--units",
default=None,
help="Gas concentration units. Possible options are: ppm, kg/m3",
)
parser.add_argument(
"-PL",
"--plot_limits",
default='',
help="Minimum and maximum value of concentration to display. If unspecified, they are obtained from all "
"the outputs",
)
parser.add_argument(
"-TA",
"--time_av",
default=None,
help="Generate time-averaged outputs. Specify the time-averaging interval (in hours), or 0 for averaging "
"over the whole duration",
)
parser.add_argument(
"-OF",
"--output_format",
default="GRD",
help="Select format of the processed output files. Valid options are: GRD",
)
parser.add_argument(
"-PT",
"--plot_topography",
default="False",
help="Plot topography layer (True or False). Warning, it can be time-consuming!",
)
parser.add_argument(
"-TI",
"--topography_isolines",
default=100,
help="Topography height contour lines spatial resolution (in m a.s.l.). Used only if -PT True",
)
parser.add_argument(
"-PR", "--plot_resolution", default=600, help="Specify plot resolution in dpi"
)
args = parser.parse_args()
plot = args.plot
plot_ex_prob = args.plot_ex_prob
ex_prob_in = args.ex_prob
time_steps_in = args.time_steps
levels_in = args.levels
days_plot_in = args.days_plot
species_in = args.species
original_specie = args.tracking_specie
nproc = args.nproc
convert = args.convert
models = args.models
merge_outputs = args.merge_outputs
units = args.units
plot_limits_in = args.plot_limits
time_av = args.time_av
output_format = args.output_format
plot_topography = args.plot_topography
dz_lines_res = args.topography_isolines
plot_resolution = args.plot_resolution
ex_prob = ex_prob_in.split(',')
time_steps = time_steps_in.split(',')
levels = levels_in.split(',')
days_plot = days_plot_in.split(',')
plot_limits = plot_limits_in.split(',')
species = species_in.split(',')
if plot.lower() == "true":
plot = True
if days_plot_in == '':
print("ERROR. Please specify at least one day to plot when --plot==True")
sys.exit()
elif plot.lower() == "false":
plot = False
else:
print("ERROR. Wrong value for variable -P --plot")
sys.exit()
if plot_ex_prob.lower() == "true":
plot_ex_prob = True
if ex_prob_in == '':
print(
"ERROR. Please specify at least one exceedance probability to plot when --plot_ex_prob==True"
)
sys.exit()
elif plot_ex_prob.lower() == "false":
plot_ex_prob = False
else:
print("ERROR. Wrong value for variable -PE --plot_ex_prob")
sys.exit()
if plot or plot_ex_prob:
if time_steps_in == '':
print("ERROR. Please specify at least one time step to plot")
sys.exit()
if levels_in == '':
print("ERROR. Please specify at least one level to plot")
sys.exit()
if original_specie == None:
print('ERROR. Please specify the name of the tracked specie')
sys.exit()
if species_in == '':
print("ERROR. Please specify at least one gas specie name")
sys.exit()
if convert.lower() == "true":
convert = True
elif convert.lower() == "false":
convert = False
else:
print("ERROR. Wrong value for variable -C --convert")
sys.exit()
exceedance_probabilities = []
if ex_prob_in != '':
for prob in ex_prob:
exceedance_probabilities.append(float(prob))
if (
models.lower() != "disgas"
and models.lower() != "twodee"
and models.lower() != "all"
):
print("ERROR. Wrong value for variable -M --models")
sys.exit()
if merge_outputs.lower() == "true":
merge_outputs = True
elif merge_outputs.lower() == "false":
merge_outputs = False
else:
print("ERROR. Wrong value for variable -MO --merge_outputs")
sys.exit()
try:
units = units.lower()
except AttributeError:
print("Please provide an option for -U --units")
sys.exit()
if units != "ppm" and units != "kg/m3":
print("ERROR. Wrong value for variable -U --units")
sys.exit()
try:
time_av = int(time_av)
except TypeError:
time_av = None
except ValueError:
try:
time_av = int(float(time_av))
except ValueError:
print("ERROR. Please specify a valid time-averaging interval")
sys.exit()
min_con = max_con = -1.0
if len(plot_limits) > 0:
try:
min_con = float(plot_limits[0])
max_con = float(plot_limits[1])
except ValueError:
print(
"ERROR. Please specify valid minimum and maximum concentration -PL --plot_limits"
)
sys.exit()
if output_format.lower() != "grd":
print(
"ERROR. Please specify a valid output format. Current valid options are: GRD"
)
sys.exit()
else:
output_format = "grd"
if plot_topography.lower() == "true":
plot_topography_layer = True
elif plot_topography.lower() == "false":
plot_topography_layer = False
else:
print("ERROR. Wrong value for variable -PT --plot_topography")
sys.exit()
if plot_topography_layer:
try:
dz_lines_res = float(dz_lines_res)
except ValueError:
dz_lines_res = 100
try:
plot_resolution = int(plot_resolution)
except ValueError:
print("ERROR. Please provide a valid number for -PR --plot_resolution")
sys.exit()
return (
plot,
plot_ex_prob,
time_steps,
levels,
days_plot,
species,
original_specie,
exceedance_probabilities,
nproc,
convert,
models,
merge_outputs,
units,
time_av,
min_con,
max_con,
output_format,
plot_topography_layer,
dz_lines_res,
plot_resolution,
)
def folder_structure():
original_output_folder_name = "simulations"
post_processing = "post_processing"
processed_output_folder_name = original_output_folder_name + "_processed"
ecdf_folder_name = "output_ecdf"
disgas_outputs = os.path.join(root, post_processing, "disgas")
twodee_outputs = os.path.join(root, post_processing, "twodee")
disgas_original_output_folder = os.path.join(
root, original_output_folder_name, "disgas"
)
twodee_original_output_folder = os.path.join(
root, original_output_folder_name, "twodee"
)
disgas_processed_output_folder = os.path.join(
disgas_outputs, processed_output_folder_name
)
disgas_ecdf = os.path.join(disgas_outputs, ecdf_folder_name)
twodee_processed_output_folder = os.path.join(
twodee_outputs, processed_output_folder_name
)
twodee_ecdf = os.path.join(twodee_outputs, ecdf_folder_name)
try:
os.mkdir(post_processing)
except FileExistsError:
print("Folder post_processing already exists")
if models == "disgas" or models == "all":
try:
os.mkdir(disgas_outputs)
except FileExistsError:
print("Folder " + disgas_outputs + " already exists")
try:
os.mkdir(disgas_processed_output_folder)
except FileExistsError:
print("Folder " + disgas_processed_output_folder + " already exists")
list_temp = os.listdir(disgas_processed_output_folder)
for item in list_temp:
try:
rmtree(
os.path.join(disgas_processed_output_folder, item),
ignore_errors=True,
)
except FileNotFoundError:
print(
"Unable to remove "
+ item
+ " in "
+ disgas_processed_output_folder
)
try:
os.mkdir(disgas_ecdf)
except FileExistsError:
print("Folder " + disgas_ecdf + " already exists")
list_temp = os.listdir(disgas_ecdf)
for item in list_temp:
try:
rmtree(os.path.join(disgas_ecdf, item), ignore_errors=True)
except FileNotFoundError:
print("Unable to remove " + item + " in " + disgas_ecdf)
if models == "twodee" or models == "all":
try:
os.mkdir(twodee_outputs)
except FileExistsError:
print("Folder " + twodee_outputs + " already exists")
try:
os.mkdir(twodee_processed_output_folder)
except FileExistsError:
print("Folder " + twodee_processed_output_folder + " already exists")
list_temp = os.listdir(twodee_processed_output_folder)
for item in list_temp:
try:
rmtree(
os.path.join(twodee_processed_output_folder, item),
ignore_errors=True,
)
except FileNotFoundError:
print(
"Unable to remove "
+ item
+ " in "
+ twodee_processed_output_folder
)
try:
os.mkdir(twodee_ecdf)
except FileExistsError:
print("Folder " + twodee_ecdf + " already exists")
list_temp = os.listdir(twodee_ecdf)
for item in list_temp:
try:
rmtree(os.path.join(twodee_ecdf, item), ignore_errors=True)
except FileNotFoundError:
print("Unable to remove " + item + " in " + twodee_ecdf)
twodee_input_file = os.path.join(root, "twodee.inp")
twodee_output_time_step = 0
if models == "all":
models_to_elaborate = ["disgas", "twodee"]
elif models == "disgas":
models_to_elaborate = ["disgas"]
else:
models_to_elaborate = ["twodee"]
# Read the output time interval from the twodee input file
with open(twodee_input_file, "r") as twodee_file:
for line in twodee_file:
if "OUTPUT_INTERVAL_(SEC)" in line:
twodee_output_time_step = float(line.split("=")[1])
if twodee_output_time_step == 0:
print("Unable to read the Twodee output time step")
sys.exit()
return (
disgas_outputs,
disgas_original_output_folder,
disgas_processed_output_folder,
ecdf_folder_name,
disgas_ecdf,
twodee_outputs,
twodee_original_output_folder,
twodee_processed_output_folder,
twodee_ecdf,
models_to_elaborate,
twodee_output_time_step,
)
def gas_properties():
def extract_gas_properties(specie):
data = pd.read_csv(gas_properties_file, error_bad_lines=False)
molar_ratio = None
molar_weight = None
if convert:
if specie == original_specie:
molar_ratio = 1
else:
try:
x = np.sort(data[specie + '/' + original_specie])
list_x = list(x)
samples = random.sample(list_x, 1)
molar_ratio = samples[0]
except KeyError:
print('ERROR. Molar ratio ' + specie + '/' + original_specie + ' not found in gas_properties.csv')
exit()
try:
y = np.sort(data[specie])
molar_weight = list(y)[0]
if molar_weight != molar_weight:
print('ERROR. Molar weight of ' + specie + ' not found in gas_properties.csv')
sys.exit()
except KeyError:
print('ERROR. Molar weight of ' + specie + ' not found in gas_properties.csv')
sys.exit()
return molar_ratio, molar_weight
gas_properties_file = os.path.join(root, "gas_properties.csv")
try:
open(gas_properties_file, "r")
except FileNotFoundError:
print("File " + gas_properties_file + " not present")
sys.exit()
molar_ratios = []
molar_weights = []
for specie in species:
molar_ratio, molar_weight = extract_gas_properties(specie)
molar_ratios.append(molar_ratio)
molar_weights.append(molar_weight)
molar_ratio, molar_weight = extract_gas_properties(original_specie)
molar_ratios_tracking_specie = molar_ratio
molar_weights_tracking_specie = molar_weight
species_properties = []
for i in range(0, len(species)):
gas_specie = {}
gas_specie["specie_name"] = species[i]
gas_specie["molar_ratio"] = molar_ratios[i]
gas_specie["molar_weight"] = molar_weights[i]
species_properties.append(gas_specie)
gas_specie = {}
gas_specie["specie_name"] = original_specie
gas_specie["molar_ratio"] = molar_ratios_tracking_specie
gas_specie["molar_weight"] = molar_weights_tracking_specie
species_properties.append(gas_specie)
return species_properties
def domain(model):
import re
output_levels = []
if model == "disgas":
with open(file="disgas.inp") as input_file:
for record in input_file:
try:
record_splitted = record.split("=")
temp = record_splitted[1].split("(")
if "SIMULATION_INTERVAL_(SEC)" in record_splitted[0]:
tot_time = int(temp[0])
elif "NX" in record_splitted[0]:
nx = int(temp[0])
elif "NY" in record_splitted[0]:
ny = int(temp[0])
elif "NZ" in record_splitted[0]:
nz = int(temp[0])
elif "OUTPUT_INTERVAL_(SEC)" in record_splitted[0]:
dt = int(temp[0])
elif "DX_(M)" in record_splitted[0]:
dx = float(temp[0])
elif "DY_(M)" in record_splitted[0]:
dy = float(temp[0])
elif "X_ORIGIN_(UTM_M)" in record_splitted[0]:
x0 = float(temp[0])
elif "Y_ORIGIN_(UTM_M)" in record_splitted[0]:
y0 = float(temp[0])
elif "OUTPUT_INTERVAL_(SEC)" in record_splitted[0]:
dt = float(temp[0])
except (IndexError, ValueError):
continue
else:
with open(file="twodee.inp") as input_file:
for record in input_file:
try:
record_splitted = record.split("=")
temp = record_splitted[1].split("(")
if "SIMULATION_INTERVAL_(SEC)" in record_splitted[0]:
tot_time = int(temp[0])
elif "NX" in record_splitted[0]:
nx = int(temp[0])
elif "NY" in record_splitted[0]:
ny = int(temp[0])
elif "OUTPUT_INTERVAL_(SEC)" in record_splitted[0]:
dt = int(temp[0])
elif "DX_(M)" in record_splitted[0]:
dx = float(temp[0])
elif "DY_(M)" in record_splitted[0]:
dy = float(temp[0])
elif "X_ORIGIN_(UTM_M)" in record_splitted[0]:
x0 = float(temp[0])
elif "Y_ORIGIN_(UTM_M)" in record_splitted[0]:
y0 = float(temp[0])
elif "HEIGHTS_(M)" in record_splitted[0]:
heights = temp[0]
extracted_heights = re.findall(
"\d+\.\d+", heights
) # This extracts decimal numbers only!
nz = len(extracted_heights)
for height in extracted_heights:
output_levels.append(float(height))
output_levels = sorted(output_levels)
elif "OUTPUT_INTERVAL_(SEC)" in record_splitted[0]:
dt = float(temp[0])
except (IndexError, ValueError):
continue
yf = y0 + ny * dy
xf = x0 + nx * dx
n_time_steps = int(tot_time / dt)
return x0, xf, y0, yf, nx, ny, nz, dx, dy, n_time_steps, dt, output_levels
def extract_days():
days = []
days_list_path = os.path.join(root, "days_list.txt")
days_to_plot = []
with open(days_list_path, "r") as days_list:
for line in days_list:
day_temp = line.split(" ")[0]
day_temp = day_temp.split("-")
day = day_temp[0] + day_temp[1] + day_temp[2]
days.append(day)
if days_plot[0] == "all":
days_to_plot.append(day)
else:
for day_to_plot in days_plot:
if day_to_plot == day:
days_to_plot.append(day_to_plot)
return days, days_to_plot
def converter(input_file, processed_file, specie_input, model):
Z = np.loadtxt(input_file, skiprows=5)
Z[Z < 0] = 0
if units == "ppm":
if model == "disgas":
file_time_step = os.path.split(processed_file)[1]
file_time_step = file_time_step.split("_")[2]
file_time_step = int(file_time_step.split(".grd")[0])
file_time_s = file_time_step * dt
hours, remainder = divmod(file_time_s, 3600)
minutes, seconds = divmod(remainder, 60)
hours_int = int(hours)
minutes_int = int(minutes)
# Round to the next hours. It can be improved with a linear interpolation
if minutes_int > 30:
minutes_int = 0
hours_int += 1
if (
hours_int > 23
):
# First order approximation for the last time step. To be modified if simulations > 24 hours are to be
# implemented
hours_int = 23
file_time_h = "{:02}{:02}".format(hours_int, minutes_int)
file_name = input_file.split(os.sep)[-1]
file_folder = input_file.split(file_name)[0]
file_folder_daily = file_folder.split("outfiles")[0]
surface_data = os.path.join(file_folder_daily, "surface_data.txt")
with open(surface_data) as surface_data_file:
for line in surface_data_file:
try:
records = line.split("\t")
except BaseException:
continue
if file_time_h == records[0]:
t2m = float(records[2])
p2m = float(records[3]) / 100 # in hPa for this conversion
break
for specie in species_properties:
if specie["specie_name"] == specie_input:
molar_weight = specie["molar_weight"]
conversion_factor = (
(22.4 / molar_weight) * ((273 + t2m) / 273) * (1013 / p2m)
) * 1000000
Z_converted = np.multiply(Z, conversion_factor) # convert kg/m3 to ppm
else:
Z_converted = Z
else:
if model == "twodee":
file_time_step = os.path.split(processed_file)[1]
file_time_step = file_time_step.split("_")[2]
file_time_step = int(file_time_step.split(".grd")[0])
file_time_s = file_time_step * dt
hours, remainder = divmod(file_time_s, 3600)
minutes, seconds = divmod(remainder, 60)
hours_int = int(hours)
minutes_int = int(minutes)
# Round to the next hours. It can be improved with a linear interpolation
if minutes_int > 30:
minutes_int = 0
hours_int += 1
if (
hours_int > 23
):
# First order approximation for the last time step. To be modified if simulations > 24 hours are to be
# implemented
hours_int = 23
file_time_h = "{:02}{:02}".format(hours_int, minutes_int)
file_name = input_file.split(os.sep)[-1]
file_folder = input_file.split(file_name)[0]
file_folder_daily = file_folder.split("outfiles")[
0
] # To be changed when folder structures will change
surface_data = os.path.join(file_folder_daily, "surface_data.txt")
with open(surface_data) as surface_data_file:
for line in surface_data_file:
try:
records = line.split("\t")
except BaseException:
continue
if file_time_h == records[0]:
t2m = float(records[2])
p2m = float(records[3]) / 100 # in hPa for this conversion
break
for specie in species_properties:
if specie["specie_name"] == specie_input:
molar_weight = specie["molar_weight"]
conversion_factor = (
(molar_weight / 22.4) * (273 / (273 + t2m)) * (p2m / 1013)
) / 1000000
Z_converted = np.multiply(Z, conversion_factor) # convert ppm to kg/m3
else:
Z_converted = Z
# Create header of the processed file
with open(processed_file, "a") as processed_file:
if output_format == "grd":
processed_file.write("DSAA\n")
processed_file.write(str(nx) + " " + str(ny) + "\n")
processed_file.write(str(x0) + " " + str(xf) + "\n")
processed_file.write(str(y0) + " " + str(yf) + "\n")
if not convert:
processed_file.write(
str(np.amin(Z_converted)) + " " + str(np.amax(Z_converted)) + "\n"
)
np.savetxt(processed_file, Z_converted, fmt="%.2e")
else:
for specie in species_properties:
if specie["specie_name"] == specie_input:
molar_ratio = specie["molar_ratio"]
if units == 'ppm':
species_conversion_factor = molar_ratio
elif units == 'kg/m3':
for specie in species_properties:
if specie["specie_name"] == specie_input:
molar_weight = specie["molar_weight"]
if specie["specie_name"] == original_specie:
molar_weight_tracking_specie = specie["molar_weight"]
species_conversion_factor = molar_ratio * (molar_weight / molar_weight_tracking_specie)
Z_converted = np.multiply(Z_converted, species_conversion_factor)
processed_file.write(
str(np.amin(Z_converted)) + " " + str(np.amax(Z_converted)) + "\n"
)
np.savetxt(processed_file, Z_converted, fmt="%.2e")
processed_file.close()
def time_average(files_to_average, outfile):
Z_sum = 0
for file in files_to_average:
if output_format == "grd":
Z = np.loadtxt(file, skiprows=5)
Z_sum += Z
Z_avg = np.divide(Z_sum, len(files_to_average))
# Create header of the processed file
with open(outfile, "a") as processed_file:
if output_format == "grd":
processed_file.write("DSAA\n")
processed_file.write(str(nx) + " " + str(ny) + "\n")
processed_file.write(str(x0) + " " + str(xf) + "\n")
processed_file.write(str(y0) + " " + str(yf) + "\n")
processed_file.write(
str(np.amin(Z_avg)) + " " + str(np.amax(Z_avg)) + "\n"
)
np.savetxt(processed_file, Z_avg, fmt="%.2e")
def elaborate_day(day_input, model):
if model == "disgas":
model_output_folder = os.path.join(
disgas_original_output_folder, day_input, "outfiles"
)
model_processed_output_folder_daily = os.path.join(
disgas_processed_output_folder, day_input
)
else:
model_output_folder = os.path.join(
twodee_original_output_folder, day_input, "outfiles"
)
model_processed_output_folder_daily = os.path.join(
twodee_processed_output_folder, day_input
)
try:
os.mkdir(model_processed_output_folder_daily)
except FileExistsError:
print("Folder " + model_processed_output_folder_daily + " already exists")
for specie in species:
model_processed_output_folder_specie = os.path.join(
model_processed_output_folder_daily, specie
)
try:
os.mkdir(model_processed_output_folder_specie)
except FileExistsError:
print("Folder " + model_processed_output_folder_specie + " already exists")
except PermissionError: # retry
try:
os.mkdir(model_processed_output_folder_specie)
except FileExistsError:
print(
"Folder " + model_processed_output_folder_specie + " already exists"
)
files_list_temp = os.listdir(model_output_folder)
files_list_path = []
files_list = []
models = []
for file in files_list_temp:
if file[0:2] == "c_":
files_list.append(file)
for specie in species:
files_list_path.append(os.path.join(model_output_folder, file)) #
models.append(model)
converted_files = []
processed_files = []
species_list = []
processed_files_species = []
levels = []
time_steps = []
tavg_intervals = []
for specie in species:
processed_files_specie = []
for file in files_list:
species_list.append(specie)
if model == "twodee":
file_name_splitted = file.split("_")
file_level = file_name_splitted[1]
file_time_step = file_name_splitted[2].split(".")[0]
file_level = float(file_level.split("cm")[0]) / 100
try:
file_level_index = output_levels.index(file_level)
except BaseException:
print(
"Cannot find the expected TWODEE output file at the level "
+ str(file_level)
)
sys.exit()
file_level = "{:03d}".format(file_level_index + 1)
file_time_step = "{:06d}".format(
int((int(file_time_step) / twodee_output_time_step))
)
file = "c_" + file_level + "_" + file_time_step + ".grd"
else:
file_name_splitted = file.split("_")
file_level = file_name_splitted[1]
file_time_step = file_name_splitted[2]
file_time_step = file_time_step.split(".")[0]
if file_level not in levels:
levels.append(file_level)
if file_time_step not in time_steps:
time_steps.append(int(file_time_step))
converted_file = file
converted_files.append(converted_file)
processed_files.append(
os.path.join(
os.path.join(model_processed_output_folder_daily, specie),
converted_file,
)
)
processed_files_specie.append(
os.path.join(
os.path.join(model_processed_output_folder_daily, specie),
converted_file,
)
)
processed_files_species.append(processed_files_specie)
n_elaborated_files = 0
while n_elaborated_files < len(files_list_path):
start = n_elaborated_files
end = n_elaborated_files + max_number_processes
if end > len(files_list_path):
end = len(files_list_path)
try:
pool_files = ThreadingPool(max_number_processes)
pool_files.map(
converter,
files_list_path[start:end],
processed_files[start:end],
species_list[start:end],
models[start:end],
)
except BaseException:
print("Unable to convert files")
sys.exit()
n_elaborated_files = end
if n_elaborated_files == len(files_list_path):
break
if time_av is not None:
averaged_files = []
time_min = min(time_steps)
if time_av == 0:
time_max = max(time_steps)
tavg_intervals.append(str(time_min) + "-" + str(time_max) + "-tavg")
else:
time_max = time_min + time_av - 1
while time_max <= max(time_steps):
# tavg_intervals.append(str(time_min) + '-' + str(time_max) + '-tavg')
for i in range(0, len(species)):
files_to_average = []
for level in levels:
files_in_level = []
for file in processed_files_species[i]:
file_level = os.path.split(file)[1]
file_level = file_level.split("_")[1]
if file_level == level:
files_in_level.append(file)
time_averaged_file = os.path.join(
os.path.join(model_processed_output_folder_daily, species[i]),
"c_"
+ level
+ "_"
+ str(time_min)
+ "-"
+ str(time_max)
+ "-tavg.grd",
)
for file in processed_files_species[i]:
file_name = os.path.split(file)[1]
file_level = file_name.split("_")[1]
file_time_step = file_name.split("_")[2]
file_time_step = file_time_step.split(".")[0]
if file_level == level:
if time_min <= int(file_time_step) <= time_max:
files_to_average.append(file)
averaged_files.append(file)
if (
max(time_steps) - time_max < time_av
and len(files_in_level) != 0
):
for file in files_in_level:
if file not in averaged_files:
files_to_average.append(file)
averaged_files.append(file)
time_averaged_file = os.path.join(
os.path.join(
model_processed_output_folder_daily, species[i]
),
"c_"
+ level
+ "_"
+ str(time_min)
+ "-"
+ str(max(time_steps))
+ "-tavg.grd",
)
tavg_intervals[-1] = (
str(time_min) + "-" + str(max(time_steps)) + "-tavg"
)
time_average(files_to_average, time_averaged_file)
files_to_average = []
if time_av == 0:
break
else:
time_min = time_max + 1
time_max = time_min + time_av - 1
continue
return tavg_intervals
def probabilistic_output(model):
def ecdf(index):
specie = index[1]
level = index[2]
time_step = index[3]
ex_prob = index[0]
quantile = 1 - ex_prob
output_files = []
for day in days:
try:
file_name = (
"c_"
+ "{:03d}".format(int(level))
+ "_"
+ "{:06d}".format(int(time_step))
+ ".grd"
)
except ValueError:
file_name = (
"c_" + "{:03d}".format(int(level)) + "_" + time_step + ".grd"
)
output_folder = os.path.join(model_processed_output_folder, day, specie)
output_files.append(os.path.join(output_folder, file_name))
try:
ecdf_output_file = os.path.join(
ecdf_folder,
str(ex_prob),
specie,
"c_"
+ "{:03d}".format(int(level))
+ "_"
+ "{:06d}".format(int(time_step))
+ ".grd",
)
except ValueError:
ecdf_output_file = os.path.join(
ecdf_folder,
str(ex_prob),
specie,
"c_" + "{:03d}".format(int(level)) + "_" + time_step + ".grd",
)
output_quantile = np.zeros((ny, nx))
c_arrays = []
files_not_available = []
for file in output_files:
try:
input_file = open(file)
except FileNotFoundError:
print("File " + file + " not found")
files_not_available.append(file)
continue
records = []
nline = 1
for line in input_file:
if nline > 5:
records.append(line.split(" "))
nline += 1
c_arrays.append(records)
for file in files_not_available:
output_files.remove(file)
for j in range(0, ny):
for i in range(0, nx):
c_list = []
for k in range(0, len(output_files)):
c_list.append(float(c_arrays[k][j][i]))
output_quantile[j, i] = np.quantile(c_list, q=quantile)
# Create header of the processed file
with open(ecdf_output_file, "a") as processed_file:
if output_format == "grd":
processed_file.write("DSAA\n")
processed_file.write(str(nx) + " " + str(ny) + "\n")
processed_file.write(str(x0) + " " + str(xf) + "\n")
processed_file.write(str(y0) + " " + str(yf) + "\n")
processed_file.write(
str(np.amin(output_quantile))
+ " "
+ str(np.amax(output_quantile))
+ "\n"
)
np.savetxt(processed_file, output_quantile, fmt="%.2e")
if model == "disgas":
ecdf_folder = disgas_ecdf
model_processed_output_folder = disgas_processed_output_folder
else:
ecdf_folder = twodee_ecdf
model_processed_output_folder = twodee_processed_output_folder
for probability in exceedance_probabilities:
prob_folder = os.path.join(ecdf_folder, str(probability))
try:
os.mkdir(prob_folder)
except FileExistsError:
print("Folder " + prob_folder + " already exists")
for specie in species:
specie_folder = os.path.join(ecdf_folder, prob_folder, specie)
try:
os.mkdir(specie_folder)
except FileExistsError:
print("Folder " + specie_folder + " already exists")
indexes = []
pools_ecdfs = []
indexes_tavg = []
pools_ecdfs_tavg = []
n_pool = 0
n_pool_tavg = 0
for probability in exceedance_probabilities: