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modout.py
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from io import StringIO
import csv
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import pandas as pd
import re
import json
from pathlib import Path
import numpy as np
import attr
import pdb
import json
grabnum = re.compile(r"mod_(\d+)_co2\_(\d+).*") ## change this to reflect adding dataset for altitude
def parse_sound(sound_text,colnames):
first_sound = str.strip(sound_text)
theFile = StringIO(first_sound)
reader = csv.reader(theFile)
line1 = next(reader)
file1 = []
for theLine in reader:
try:
vals = theLine[0].split()
theLine = [float(item) for item in vals]
print(f"{len(theLine)} ++++++ {len(colnames)}")
if len(theLine) == len(colnames):
file1.append(theLine)
except:
print("bad: ", theLine)
profiles_df = pd.DataFrame.from_records(file1, columns=colnames)
return profiles_df
def parse_file(filename):
theProfile = re.compile('ATMOSPHERIC PROFILES')
theRadiance = re.compile(r'RADIANCE(WATTS/CM2-STER-XXX)')
theFile = open(filename, 'r')
theLines = theFile.read()
three_profiles = re.split(theProfile, theLines)
colnames_1 = ('I Z P T N2 '
'CNTMSLF MOL_SCAT N-1 O3 O2')
units_1 = ('(-) (KM) (MB) (K) (MOL/CM2 KM) '
'(-) (-) (ATM_CM/KM) (ATM_CM/KM)')
units_1 = units_1.lower().split()
colnames_1 = colnames_1.lower().split()
units_o3_dict = dict(zip(colnames_1,units_1))
profiles1_df = parse_sound(three_profiles[1],colnames_1)
colnames_2 = (' I Z P T CNTMFRN '
'HNO3 AEROSOL_1 AEROSOL_2 AEROSOL_3 AEROSOL_4 '
'AER1*RH CIRRUS RH')
units_2 = ('(-) (KM) (MB) (K) MOL/CM2 KM ATM_CM/KM'
'(-) (-) (-) (-) (-) (-)'
' (PERCNT)')
units_2 = units_2.lower().split()
colnames_2 = colnames_2.lower().split()
units_aero_dict = dict(zip(colnames_2,units_2))
profiles2_df = parse_sound(three_profiles[2],colnames_2)
colnames_3 = ('I Z P T H2O '
'O3 CO2 CO CH4 N2O '
'O2 NH3 NO NO2 SO2')
colnames_3 = colnames_3.lower().split()
units_3 = ('(-) (KM) (MB) (K) ATM_CM/KM ATM_CM/KM ATM_CM/KM '
'ATM_CM/KM ATM_CM/KM ATM_CM/KM ATM_CM/KM ATM_CM/KM ATM_CM/KM '
'ATM_CM/KM ATM_CM/KM')
units_3 = units_3.lower().split()
units_mol_dict = dict(zip(colnames_3,units_3))
profiles3_df = parse_sound(three_profiles[3], colnames_3)
#pdb.set_trace()
rad_parts = re.split(re.compile(r'RADIANCE\(WATTS\/CM2-STER-XXX\)'),
theLines)
print(len(rad_parts))
spectrum = []
colnames = (
'FREQ_invcm WAVLEN_um PATH_THERMAL_cm PATH_THERMAL_mum SURFACE_EMISSION_cm '
'SURFACE_EMISSION_umu SURFACE_REFLECTED_cm SURFACE_REFLECTED_mum '
'TOTAL_RADIANCE_cm TOTAL_RADIANCE_mum INTEGRAL_cm TOTAL_trans')
colnames = colnames.lower().split()
for segment in rad_parts:
theLines = segment.split('\n')
for item in theLines:
try:
theNums = [float(aNum) for aNum in item.split()]
if len(theNums) == 12:
spectrum.append(theNums)
except:
pass
spectrum_df = pd.DataFrame.from_records(spectrum, columns=colnames)
df_class = attr.make_class("attr_df_out", ["o3_prof", "aero_prof","mol_prof",
"rad_spectrum"])
df_out=df_class(profiles1_df,profiles2_df,profiles3_df,spectrum_df)
units_class = attr.make_class("attr_units_out", ["o3_units", "aero_units","mol_units"])
units_out = units_class(units_o3_dict,units_aero_dict,units_mol_dict)
return df_out, units_out
def make_dir(filename):
filename = str(filename)
new_name = filename.replace('.txt', '_dir')
new_dir = Path(new_name)
new_dir.mkdir(parents=True, exist_ok=True)
return new_dir
def make_planck(column, df, the_dir):
co2 = grabnum.search(str(the_dir)).group(1)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
the_rad = df['total_radiance_mum']*np.pi*1.e4
the_wave= df['wavlen_um']
ax.plot(the_wave,the_rad)
ax.set_xlim([4, 40])
ax.set_title(f"CO2 = {co2} ppm")
ax.set_ylabel("flux in W/m^2/micron")
ax.set_xlabel("wavelength (microns)")
ax.grid(True)
filename = the_dir / f"{column}.png"
print(f"saving fig in {filename}")
fig.savefig(filename)
def make_trans_plot(column, df, the_dir):
co2 = grabnum.search(str(the_dir)).group(1)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.plot('wavlen_um', 'total_trans', data=df)
ax.set_xlim([4, 25])
ax.set_title(f"CO2 = {co2} ppm")
ax.set_xlabel("wavelength (microns)")
ax.set_ylabel("transmission (height=70 km)")
ax.grid(True)
filename = the_dir / f"{column}.png"
print(f"saving fig in {filename}")
fig.savefig(filename)
if __name__ == "__main__":
keep_dict = {}
files = list(Path().glob("*co2*.txt"))
print(files)
co2 = [grabnum.search(str(item)).group(1) for item in files]
altitude = [grabnum.search(str(item)).group(2) for item in files]
print(co2)
out_dirs = [make_dir(item) for item in files]
toc_dict = {}
for i in altitude:
toc_dict[i] = {}
for j in co2:
toc_dict[i][j]=''
for the_file, the_dir in zip(files, out_dirs):
co2 = grabnum.search(str(the_file)).group(1)
altitude = grabnum.search(str(the_file)).group(2)
toc_dict[altitude][co2] = str(the_dir)
f"created {the_dir}"
df_out, units_out = parse_file(the_file)
#pdb.set_trace()
the_dfs = [df_out.o3_prof,df_out.aero_prof,df_out.mol_prof, df_out.rad_spectrum]
the_pqs = ['o3_prof.pq','aero_prof.pq','mol_prof.pq','rad_spectrum.pq']
for pqfile, df in zip(the_pqs, the_dfs):
parquet_file = the_dir / pqfile
csv_file = parquet_file.with_suffix('.csv')
df.to_parquet(parquet_file)
df.to_csv(csv_file, index=False)
units_dict=dict()
for unitname in ['aero_units', 'mol_units', 'o3_units']:
the_dict = getattr(units_out,unitname)
units_dict[unitname]=the_dict
json_file = the_dir / 'units.json'
with open(json_file,'w') as outfile:
json.dump(units_dict,outfile,indent=4)
#pdb.set_trace()
make_trans_plot('total_trans', df_out.rad_spectrum, the_dir)
make_planck('total_radiance_mum', df_out.rad_spectrum, the_dir)
with open('toc_files.json', 'w') as outfile:
json.dump(toc_dict, outfile, indent=4)
# theFig = plt.figure(1)
# theFig.clf()
# theAx = theFig.add_subplot(111)
# theAx.plot(out270['wavlen'], out270['total_radiance_mum'] * np.pi)
# theAx.set_xlim([5, 40])
# #convert from cm^2 to m^2
# theIntens270 = np.sum(
# np.diff(out270['wavlen']) * out270['total_radiance_mum'][1:] *
# np.pi) * 1.e4
# out540 = parse_file('mod_540.txt')
# theAx.plot(out540['wavlen'], out540['total_radiance_mum'] * np.pi, 'r-')
# theIntens540 = np.sum(
# np.diff(out540['wavlen']) * out540['total_radiance_mum'][1:] *
# np.pi) * 1.e4
# theFig = plt.figure(2)
# theFig.clf()
# theAx = theFig.add_subplot(111)
# theAx.semilogy(out0['freq'], out0['total_trans'])
# theAx.semilogy(out270['freq'], out270['total_trans'])
# theAx.semilogy(out540['freq'], out540['total_trans'], 'r-')
# theAx.set_title("total trans 540")
# theAx.set_xlim([100, 1600])
## theDict=csv.DictReader(theFile,colnames,delimiter=' ')
## for item in theDict:
## print item
#from Bio.Statistics import lowess
## co2index=np.where(np.logical_and(out270['wavlen'] > 11,out270['wavlen'] < 18))
## newout0=lowess.lowess(out0['wavlen'][co2index],out0['total_radiance_mum'][co2index]*np.pi,f=0.05)
## newout270=lowess.lowess(out270['wavlen'][co2index],out270['total_radiance_mum'][co2index]*np.pi,f=0.05)
## newout540=lowess.lowess(out540['wavlen'][co2index],out540['total_radiance_mum'][co2index]*np.pi,f=0.05)
## theFig=plt.figure(3)
## theFig.clf()
## theAx=theFig.add_subplot(111)
## #theAx.plot(out270['wavlen'],out270['total_radiance_mum']*np.pi)
## theAx.plot(out270['wavlen'][co2index],newout270,'b-')
## theAx.plot(out0['wavlen'][co2index],newout0,'g-')
## theAx.plot(out540['wavlen'][co2index],newout540,'k-')
## theAx.set_xlim([3,40])
# theFig = plt.figure(4)
# theFig.clf()
# theAx = theFig.add_subplot(111)
# #theAx.plot(out270['wavlen'],out270['total_radiance_mum']*np.pi)
# theAx.plot(out270['freq'], out270['total_radiance_cm'] * np.pi * 1.e4,
# 'b-')
# theAx.plot(out0['freq'], out0['total_radiance_cm'] * np.pi * 1.e4, 'g-')
# theAx.plot(out540['freq'], out540['total_radiance_cm'] * np.pi * 1.e4,
# 'k-')
# theAx.set_xlim([100, 1400])
# plt.show()