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[READ-ONLY] Mirror of https://github.com/emilyhunt/masters_project. My Master's project code, using a mixture density network to infer photometric redshifts to galaxies with spectroscopic redshifts as training data.
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masters_project plotter_photspecs.py
4.6 kB · 85 lines
Python
at master
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import numpy as npimport pandas as pdimport matplotlib.pyplot as plt
from scripts import z_plot
# Grab some datadata_train = pd.read_csv('./final_run_data/data_training.csv')data_valid = pd.read_csv('./final_run_data/data_validation.csv')data_test = pd.read_csv('./final_run_data/data_no_spec_z.csv')
results_valid = pd.read_csv('./final_run_data/validation_results.csv')results_test = pd.read_csv('./final_run_data/validation_results_no_spec_z.csv')
# FOR THE PRESENTATION# VALIDATION DATA PLOTS# For MLsigma_3, sigma_5 = z_plot.error_evaluator(data_valid['z_spec'], results_valid['map'], results_valid['lower'], results_valid['upper'])
z_plot.phot_vs_spec(data_valid['z_spec'], results_valid['map'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/pres_ML_valid_phot_spec.png', show_fig=True, set_nmad_old_value=True, figsize=(3, 3), x_label=r'$z_{spectroscopic}$', y_label=r'$z_{ML}$', plot_extra_axes=False)
# For EAZYsigma_3, sigma_5 = z_plot.error_evaluator(data_valid['z_spec'], data_valid['z_phot_lit'], data_valid['z_phot_lit_l68'], data_valid['z_phot_lit_u68'])
z_plot.phot_vs_spec(data_valid['z_spec'], data_valid['z_phot_lit'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/pres_EAZY_valid_phot_spec.png', show_fig=True, point_color='b', figsize=(3, 3), x_label=r'$z_{spectroscopic}$', y_label=r'$z_{EAZY}$', plot_extra_axes=False)
# For test datasigma_3, sigma_5 = z_plot.error_evaluator(data_test['z_phot_lit'], results_test['map'], results_test['lower'], results_test['upper'])
z_plot.phot_vs_spec(data_test['z_phot_lit'], results_test['map'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/pres_ML_EAZY_test_phot_spec.png', show_fig=True, point_color='b', point_alpha=0.02, plot_extra_axes=False, figsize=(3, 3), x_label=r'$z_{EAZY}$', y_label=r'$z_{ML}$')
# For data that hasn't been preprocesseddata_valid = pd.read_csv('./final_run_data/different_sn_no_pp/data_validation_sn=0.0.csv')results_valid = pd.read_csv('./final_run_data/different_sn_no_pp/validation_results_sn=0.0.csv')
sigma_3, sigma_5 = z_plot.error_evaluator(data_valid['z_spec'], results_valid['map'], results_valid['lower'], results_valid['upper'])
z_plot.phot_vs_spec(data_valid['z_spec'], results_valid['map'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/pres_ML_valid_phot_spec_no_pp.png', show_fig=True, set_nmad_old_value=False, figsize=(3, 3), x_label=r'$z_{spectroscopic}$', y_label=r'$z_{ML}$', plot_extra_axes=False)
"""
# VALIDATION DATA PLOTS# For MLsigma_3, sigma_5 = z_plot.error_evaluator(data_valid['z_spec'], results_valid['map'], results_valid['lower'], results_valid['upper'])
z_plot.phot_vs_spec(data_valid['z_spec'], results_valid['map'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/ML_valid_phot_spec.png', show_fig=True, set_nmad_old_value=True)
# For EAZYsigma_3, sigma_5 = z_plot.error_evaluator(data_valid['z_spec'], data_valid['z_phot_lit'], data_valid['z_phot_lit_l68'], data_valid['z_phot_lit_u68'])
z_plot.phot_vs_spec(data_valid['z_spec'], data_valid['z_phot_lit'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/EAZY_valid_phot_spec.png', show_fig=True, point_color='b', x_label=r'$z_{spec}$', y_label=r'$z_{EAZY}$')
# For test datasigma_3, sigma_5 = z_plot.error_evaluator(data_test['z_phot_lit'], results_test['map'], results_test['lower'], results_test['upper'])
z_plot.phot_vs_spec(data_test['z_phot_lit'], results_test['map'], nmad_bins=10, limits=[0, 7], show_5_sigma=sigma_5/100, save_name='./final_plots/ML_EAZY_test_phot_spec.png', show_fig=True, point_color='b', point_alpha=0.02, plot_extra_axes=False, figsize=(3, 3), x_label=r'$z_{EAZY}$', y_label=r'$z_{ML}$')"""