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update func in StatVisAnalyzer.py
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@EhsanGharibNezhad
EhsanGharibNezhad committed Apr 11, 2024
1 parent ba9abef commit 4f3a26a
Showing 1 changed file with 14 additions and 57 deletions.
71 changes: 14 additions & 57 deletions TelescopeML/StatVisAnalyzer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -544,9 +544,9 @@ def plot_pred_vs_obs_errorbar(object_name,
source = ColumnDataSource(data=dict(x=x_obs, y=y_obs, upper=upper, lower=lower))

# Create the Observational figure
p = figure(title=f"{object_name}: Calibrated Observational VS. Predicted Spectra",
p = figure(title=f"{object_name}: Observational vs. ML Predicted Spectra",
x_axis_label="Wavelength [𝜇m]",
y_axis_label="Flux (F𝜈) [erg/s/cm2/Hz]",
y_axis_label="TOA Flux (F𝜈) [erg/s/cm2/Hz]",
width=800, height=300,
y_axis_type="log",
tools="pan,wheel_zoom,box_zoom,reset")
Expand Down Expand Up @@ -1109,14 +1109,6 @@ def find_closest_chi_square(df, chi_square_statistic_list):

return closest_chi_square, closest_p_value

# Example usage with df = 103 and chi_square_list containing chi-square statistics
# df_value = 103
# chi_square_list = [93, 32, 150.456789123, 120.789123456] # Replace with actual chi-square statistics



# check_chi_square(103, chi_square_list)


def plot_scatter_x_y (x, y,
plot_title="Scatter Plot",
Expand Down Expand Up @@ -1279,16 +1271,14 @@ def plot_filtered_dataframe_notUsed(dataset, filter_bounds, feature_to_plot, tit
plt.show()


def plot_model_loss(history=None, title=None):
def plot_ML_model_loss(trained_ML_model_history=None, title=None):
"""
Plot the trained model history for all individual target features
"""
# from bokeh.plotting import figure, show
# from bokeh.models import Legend

# history = self.trained_model_history if history is None else history
# Define the epochs as a list
epochs = list(range(len(history['loss'])))
epochs = list(range(len(trained_ML_model_history['loss'])))

# Define colorblind-friendly colors
colors = ['#d62728', '#ff7f0e', '#2ca02c', '#9467bd', '#8c564b']
Expand All @@ -1297,25 +1287,25 @@ def plot_model_loss(history=None, title=None):
p = figure(title=title, width=1000, height=300, y_axis_type='log', x_axis_label='Epochs', y_axis_label='Loss')

# Add the data lines to the figure with colorblind-friendly colors and increased line width
p.line(epochs, history['loss'], line_color=colors[0], line_dash='solid', line_width=2,
p.line(epochs, trained_ML_model_history['loss'], line_color=colors[0], line_dash='solid', line_width=2,
legend_label='Total loss')
p.line(epochs, history['val_loss'], line_color=colors[0], line_dash='dotted', line_width=2)
p.line(epochs, trained_ML_model_history['val_loss'], line_color=colors[0], line_dash='dotted', line_width=2)

p.line(epochs, history['output__gravity_loss'], line_color=colors[1], line_dash='solid', line_width=2,
p.line(epochs, trained_ML_model_history['output__gravity_loss'], line_color=colors[1], line_dash='solid', line_width=2,
legend_label='gravity')
p.line(epochs, history['val_output__gravity_loss'], line_color=colors[1], line_dash='dotted', line_width=2)
p.line(epochs, trained_ML_model_history['val_output__gravity_loss'], line_color=colors[1], line_dash='dotted', line_width=2)

p.line(epochs, history['output__c_o_ratio_loss'], line_color=colors[2], line_dash='solid', line_width=2,
p.line(epochs, trained_ML_model_history['output__c_o_ratio_loss'], line_color=colors[2], line_dash='solid', line_width=2,
legend_label='c_o_ratio')
p.line(epochs, history['val_output__c_o_ratio_loss'], line_color=colors[2], line_dash='dotted', line_width=2)
p.line(epochs, trained_ML_model_history['val_output__c_o_ratio_loss'], line_color=colors[2], line_dash='dotted', line_width=2)

p.line(epochs, history['output__metallicity_loss'], line_color=colors[3], line_dash='solid', line_width=2,
p.line(epochs, trained_ML_model_history['output__metallicity_loss'], line_color=colors[3], line_dash='solid', line_width=2,
legend_label='metallicity')
p.line(epochs, history['val_output__metallicity_loss'], line_color=colors[3], line_dash='dotted', line_width=2)
p.line(epochs, trained_ML_model_history['val_output__metallicity_loss'], line_color=colors[3], line_dash='dotted', line_width=2)

p.line(epochs, history['output__temperature_loss'], line_color=colors[4], line_dash='solid', line_width=2,
p.line(epochs, trained_ML_model_history['output__temperature_loss'], line_color=colors[4], line_dash='solid', line_width=2,
legend_label='temperature')
p.line(epochs, history['val_output__temperature_loss'], line_color=colors[4], line_dash='dotted', line_width=2)
p.line(epochs, trained_ML_model_history['val_output__temperature_loss'], line_color=colors[4], line_dash='dotted', line_width=2)

# Increase size of x and y ticks
p.title.text_font_size = '14pt'
Expand All @@ -1326,25 +1316,12 @@ def plot_model_loss(history=None, title=None):

# display legend in top left corner (default is top right corner)
p.legend.location = "bottom_left"

# change appearance of legend text
# p.legend.label_text_font = "times"
# p.legend.label_text_font_style = "italic"
# p.legend.label_text_color = "navy"

# change border and background of legend
# p.legend.border_line_width = 3
# p.legend.border_line_color = "navy"
# p.legend.border_line_alpha = 0.8
p.legend.background_fill_color = 'white'
p.legend.background_fill_alpha = 0.5

# Show the plot
show(p)

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

def plot_boxplot(data,
title=None, xlabel='Wavelength [$\mu$m]', ylabel='Scaled Values',
Expand Down Expand Up @@ -1532,38 +1509,18 @@ def plot_pred_vs_obs_errorbar_stat_matplotlib( stat_df,
Y = stat_df['mean']
std = stat_df['std_values']

# Create a figure

# Create the figure and axis
# Create the figure and axis
plt.figure(figsize=(12, 6))
ax = plt.gca()

# Plot observational data with error bars
# ax.scatter(x_obs, y_obs, color='blue', label=f"Observational data",s=6, marker='o' )
# ax.errorbar(x_obs, y_obs, yerr=y_obs_err, color='gray', linestyle='', alpha=0.5, markersize=1)
ax.errorbar(x_obs, y_obs, yerr=y_obs_err,
fmt='o', color='blue', alpha=0.8, markersize=2, capsize=3, elinewidth=1, ecolor='gray',label=f"Observational data")

# # Plot predicted data
# ax.plot(stat_df['wl'][::-1], stat_df['mean'], color='blue', linewidth=2, label='ML Predicted')
#
# # Plot shaded regions for confidence intervals
# ax.fill_between(stat_df['wl'][::-1], stat_df['confidence_level_lower'], stat_df['confidence_level_upper'], color='red', alpha=0.5, label=f'Confidence Level: {confidence_level}%')
#
# # Plot shaded regions for 1 sigma
# ax.fill_between(stat_df['wl'][::-1], stat_df['mean'] - stat_df['std_values'], stat_df['mean'] + stat_df['std_values'], color='green', alpha=0.4, label='1σ')

# Plot data points
# ax.errorbar(X, Y, yerr=std, fmt='-', markersize=5, capsize=3, elinewidth=1, label='Data with Error Bars')
# ax.errorbar(stat_df['wl'][::-1], stat_df['mean'], yerr=std, fmt='-', markersize=5, capsize=3, elinewidth=1, label='Data with Error Bars')
ax.plot(stat_df['wl'][::-1], stat_df['mean'], color='red', label='ML predicted', linewidth=2)

# Shade the region representing standard deviation
# ax.fill_between(stat_df['wl'][::-1], Y - stat_df['confidence_level_lower'], Y + stat_df['confidence_level_lower'],
# alpha=0.6, color='red', label='Confidence Level: 95%')
ax.fill_between(X, Y - std, Y + std, alpha=0.4, color='green', label='1$\sigma$')
# ax.fill_between(X, Y - 2*std, Y + 2*std, alpha=0.4, color='green', label='2$\sigma$')

# Set logarithmic scale for y-axis
ax.set_yscale('log')
Expand Down

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