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Create_Excel.py
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Create_Excel.py
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from telnetlib import DO
import numpy as np
import pandas as pd
import glob
import matplotlib.pyplot as plt
import plotly.express as px
from plotly.subplots import make_subplots
from dash import Dash, dcc, html, Input, Output
R = str(15000)
Double_File = True
PSG_Directory = "/Users/elitzer/Desktop/PSG/NASA_PSG-main/Final_Data/"
# atm_1ppb_failed = PSG_Directory + '1ppb_atmosphere/try/'
Atmosphere = PSG_Directory + 'Atmosphere'
Lakes = PSG_Directory + 'Lakes/'
Ices = PSG_Directory + 'Ice/'
Full_File1a = PSG_Directory + 'Basic_Titan_Configuration/' + 'Atmosphere/' + 'Base_Titan_Atmosphere_SR1' + '.txt'
Full_File2a = PSG_Directory + 'Basic_Titan_Configuration/' + 'Atmosphere/'+ 'Base_Titan_Atmosphere_SR2' + '.txt'
Full_File1l = PSG_Directory + 'Basic_Titan_Configuration/' + 'LakesIce/' + 'Base_Titan_Atmosphere_SR1' + '.txt'
Full_File2l = PSG_Directory + 'Basic_Titan_Configuration/' + 'LakesIce/' + 'Base_Titan_Atmosphere_SR2' + '.txt'
Full_File = PSG_Directory + 'Basic_Titan_Configuration/' + 'Base_Titan_Atmosphere_SR' + '.txt'
def Load_Spec(file, data_type):
# print(file[-:-4])
(Data_array) = np.genfromtxt(file, comments="#", dtype=np.float64, names=['Wavelength', 'Spectral_Radiance','Noise', 'Titan'])
Data_array = np.array(Data_array)
Data_array = Data_array[data_type]
return Data_array
# Titan_Full = np.genfromtxt(Full_File, comments="#", dtype=np.float64, names=['Wavelength', 'Spectral_Radiance','Noise', 'Titan'])
if Double_File == True:
Wavelength1 = Load_Spec(Full_File1a, 'Wavelength')
Wavelength2 = Load_Spec(Full_File2a, 'Wavelength')
Wavelength = np.concatenate((Wavelength1, Wavelength2))
Titan_SR1 = Load_Spec(Full_File1a, 'Spectral_Radiance')
Titan_SR2 = Load_Spec(Full_File2a, 'Spectral_Radiance')
Titan_SRa = np.concatenate((Titan_SR1, Titan_SR2))
Titan_SR1 = Load_Spec(Full_File1l, 'Spectral_Radiance')
Titan_SR2 = Load_Spec(Full_File2l, 'Spectral_Radiance')
Titan_SRl = np.concatenate((Titan_SR1, Titan_SR2))
else:
Wavelength = Load_Spec(Full_File, 'Wavelength')
Titan_SR=Load_Spec(Full_File, 'Spectral_Radiance')
def files_from_dir(dir_PATH, molecule_type, data_dict_diff, data_dict, Ratio, types):
# Pull files from directory specified from dir_PATH and add molecule name to Ratio,
# Spectral radience data to data_dict and molecule type (atmosphere, surface, lakes, ices) to types
if molecule_type == 'Atmosphere':
Titan_SR = Titan_SRa
else:
Titan_SR = Titan_SRl
files = sorted(glob.glob(dir_PATH + '*.txt'))
# print(files)
for i, filename in enumerate(files):
if Double_File==False:
species = filename[len(dir_PATH):-5]
Ratio.append(species)
data1 = Load_Spec(filename, 'Spectral_Radiance')
# data2 = Load_Spec(files[i+1], 'Spectral_Radiance')
# data = np.concatenate((data1, data2))
# data_dict_diff[species] = data - Titan_SR
# data_dict[species] = data
data1 = np.array(data1)
data_dict_diff.append(data1 - Titan_SR)
data_dict.append(data1)
types[species] = molecule_type
elif ((i % 2) ==0) & (i !=len(files)): #if i odd:
species = filename[len(dir_PATH):-5]
Ratio.append(species)
data1 = Load_Spec(filename, 'Spectral_Radiance')
data2 = Load_Spec(files[i+1], 'Spectral_Radiance')
data = np.concatenate((data1, data2))
# data_dict_diff[species] = data - Titan_SR
# data_dict[species] = data
data_dict_diff.append(np.absolute(data - Titan_SR))
data_dict.append(data)
types[species] = molecule_type
return data_dict_diff, data_dict, Ratio, types #, data
# Initiallize empty dicts and lists to be filled when loading in data
Ratio = []
data_dict_diff = [] #{}
data_dict = [] #{}
types = {}
# Load in files from directories. Identify where the molecule is on Titan
# data_dict_diff, data_dict, Ratio, types = files_from_dir(Atmosphere, 'Atmosphere', data_dict_diff, data_dict, Ratio, types)
# data_dict_diff, data_dict, Ratio, types = files_from_dir(Lakes, 'Lakes', data_dict_diff, data_dict, Ratio, types)
data_dict_diff, data_dict, Ratio, types = files_from_dir(Ices, 'Ice', data_dict_diff, data_dict, Ratio, types)
Ratio = np.array(Ratio)
print(data_dict_diff)
# df_diff = pd.DataFrame(data_array_diff.T, index=Wavelength, columns=Ratio[np_mask])
data_dict = np.array(data_dict)
data_dict_diff = np.array(data_dict_diff)
Titan_SR = np.array(Titan_SRa)
pddf_diff = pd.DataFrame(data= data_dict_diff.T,
columns = Ratio,
index = Wavelength)
pddf = pd.DataFrame(data= data_dict.T,
columns = Ratio,
index = Wavelength)
titan_pf = pd.DataFrame(data= Titan_SR.T,
columns = ['Titan_SR'],
index = Wavelength)
with pd.ExcelWriter('Excel/' + 'Atmosphere_diff_R15000' + '.xlsx') as writer:
pddf.to_excel(writer, sheet_name='Atmosphere')
pddf_diff.to_excel(writer, sheet_name='Atmosphere_Diff')
titan_pf.to_excel(writer, sheet_name='Titan_SR')
print("done")
"""
fig = plt.figure(dpi=300) #, figsize=(10, 2))
N = len(data_dict) + 1
colors = plt.cm.jet(np.linspace(0,1,N))
for i, Isotope in enumerate(data_dict):
r = Ratio[i]
plt.plot(Wavelength, data_dict[i], label=( r), color=colors[i+1], linewidth=.75, alpha=1)
# plt.plot(Wavelength, Titan_SR, label='Titan SR', color=colors[1], linewidth=.75, alpha=1)
plt.title("Base Titan Configuration Spectral Radiance")
plt.ylabel("Spectral Radiance"+ r' ($\frac{W}{sr *m^2 *µm}$)')
plt.xlabel("Wavelength" + ' (µm)')
plt.yscale('log')
plt.xlim(4.5,5)
plt.ylim(1e-3,5e-3)
plt.legend()
plt.tight_layout()
# fig.savefig("Figures/" + 'Lakes_Log' + ".png", dpi=300)
plt.show()
# ----
# Create dash app with interactive plot of data
app = Dash(__name__)
app.layout = html.Div(children=[
html.Div([
html.H4('Molecules on Titan: A Comparison with a Standard Titan Atmosphere'),
dcc.Graph(id='Diff'),
dcc.Graph(id='Full'),
dcc.Interval(
id='interval-component',
interval=15000,
),
dcc.Checklist(
id='checklist',
options=['Atmosphere', 'Lakes', 'Ice'],
value=['Atmosphere', 'Lakes', 'Ice'],
inline=True
)]),
])
@app.callback(
Output("Diff", "figure"),
Input('checklist', 'value')
)
def update_line_chart1(pick_type):
np_mask = np.isin(list(types.values()), pick_type)
data_array_diff = np.array(data_dict_diff)
# data_array_diff = np.array(list(data_dict_diff.values()), dtype=object)
data_array_diff = data_array_diff[np_mask]
# print(data_array_diff.shape)
df_diff = pd.DataFrame(data_array_diff.T, index=Wavelength, columns=Ratio[np_mask])
# fig1 = px.line(data_array_diff, x = Wavelength, y=data_array_diff, title='Titan Molecule Spectra Comparison with Basic Titan Model', log_y=False)
fig1 = px.line(df_diff, title='Titan Molecule Spectra Comparison with Basic Titan Model', log_y=True, labels=dict(index=('Wavelength (µm)'), value=("Spectral Radiance W/(sr*m^2*µm)"), variable='Molecule'))
# for i, newname in enumerate(Ratio[np_mask]):
# fig1.data[i].name = newname
return fig1#.write_html("SR_dif.html")
@app.callback(
Output("Full", "figure"),
Input('checklist', 'value')
)
def update_line_chart2(pick_type):
np_mask = np.isin(list(types.values()), pick_type)
data_array = np.array(data_dict)
# data_array = np.array(list(data_dict.values()), dtype=object)
data_array = data_array[np_mask]
# print(np_mask)
# Wavelength_rep= np.tile(Wavelength, (12, 1))
# print("wav",Wavelength_rep.shape, np_mask)
# for i in range(0, len(np_mask)):
df = pd.DataFrame(data_array.T, index=Wavelength, columns=Ratio[np_mask])
# fig2 = px.line(data_array, x = Wavelength, y=data_array, title='Titan Molecule Spectral Radience', log_y=False)
fig2 = px.line(df, title='Titan Molecule Spectral Radience', log_y=False, labels=dict(index=('Wavelength (µm)'), value=("Spectral Radiance W/(sr*m^2*µm)"), variable='Molecule'))
# for i, newname in enumerate(Ratio[np_mask]):
# fig2.data[i].name = newname
return fig2#.write_html("SR.html")
app.run_server(debug=True)
# """