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Fix deprecation warnings and diagnostic plotting of regression.
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@m-samland
m-samland committed Nov 20, 2023
1 parent a2a0659 commit ba7474b
Showing 1 changed file with 57 additions and 56 deletions.
113 changes: 57 additions & 56 deletions trap/regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,30 +4,30 @@
@author: Matthias Samland
MPIA Heidelberg
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from __future__ import absolute_import, division, print_function, unicode_literals

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from tqdm import tqdm
from astropy.stats import mad_std, sigma_clip
from scipy import spatial
from scipy.linalg import inv, pinv
from scipy.optimize import curve_fit

from astropy.stats import mad_std, sigma_clip
from sklearn.model_selection import train_test_split
from tqdm import tqdm

from trap.utils import (compute_empirical_correlation_matrix,
det_max_ncomp_specific, exponential_kernel,
matern32_kernel, matern52_kernel)
from trap.utils import (
compute_empirical_correlation_matrix,
det_max_ncomp_specific,
exponential_kernel,
matern32_kernel,
matern52_kernel,
)

from . import (image_coordinates, pca_regression, plotting_tools,
regressor_selection)
from . import image_coordinates, pca_regression, plotting_tools, regressor_selection
from .embed_shell import ipsh


Expand Down Expand Up @@ -70,7 +70,7 @@ def __init__(
Description of parameter `residuals`.
reduction_mask : array_like, optional
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
data : array_like, optional
Image cube used as input for the reduction.
Not neccessary other than for diagnostics.
Expand Down Expand Up @@ -513,7 +513,7 @@ def run_trap_with_model_temporal(
or `yx_center_injection` if provided.
reduction_mask : array_like
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
yx_center : array_like, optional
The center position of the image as used in reduction.
yx_center_injection : array_like
Expand Down Expand Up @@ -886,7 +886,7 @@ def run_trap_with_model_temporal(
'data_for_pixel_long_test.jpg'), dpi=300)

plt.close()
plt.style.use("paper")
# plt.style.use("paper")
fig = plt.figure()
ax = plt.subplot(111)
alpha = 0.7
Expand All @@ -910,44 +910,45 @@ def run_trap_with_model_temporal(
plt.tight_layout()
plt.savefig(os.path.join(diagnostic_image_folder, 'residuals_test.jpg'), dpi=300)

plt.close()
number_of_comp_plotted = 10
fig = plt.figure()
ax = plt.subplot(111)
for i in range(number_of_comp_plotted):
# ax.plot(B[:, i] * lambdas_full[i] / np.cumsum(lambdas_full)[-1] + 1 * i, label=i) # + 1 * i, label=i)
ax.plot(B[:, i] + 1 * i, label=i) # + 1 * i, label=i)

ax.plot(model_for_pixel / np.max(model_for_pixel) +
number_of_comp_plotted, color='black', label='model')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc='upper right')
plt.title('Principal component lightcurves')
# plt.ylim(-1, 5)
# plt.xlim(0, 300)
# plt.show()
plt.savefig(os.path.join(diagnostic_image_folder,
'principal_component_lightcurves_normalized_to_overall_variance.png'), dpi=300)

plt.close()
number_of_comp_plotted = 10
fig = plt.figure()
ax = plt.subplot(111)
for i in range(number_of_comp_plotted):
ax.plot(B[:, i] * lambdas_full[i] / np.cumsum(lambdas_full)
[-1] + 1 * i, label=i) # + 1 * i, label=i)
# ax.plot(B[:, i] + 1 * i, label=i) # + 1 * i, label=i)
if number_of_pca_regressors != 0:
plt.close()
number_of_comp_plotted = 10
fig = plt.figure()
ax = plt.subplot(111)
for i in range(number_of_comp_plotted):
# ax.plot(B[:, i] * lambdas_full[i] / np.cumsum(lambdas_full)[-1] + 1 * i, label=i) # + 1 * i, label=i)
ax.plot(B[:, i] + 1 * i, label=i) # + 1 * i, label=i)

ax.plot(model_for_pixel / np.max(model_for_pixel) +
number_of_comp_plotted, color='black', label='model')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc='upper right')
plt.title('Principal component lightcurves')
# plt.ylim(-1, 5)
# plt.xlim(0, 300)
# plt.show()
plt.savefig(os.path.join(diagnostic_image_folder,
'principal_component_lightcurves_normalized_to_overall_variance.png'), dpi=300)

ax.plot(model_for_pixel / np.max(model_for_pixel) +
number_of_comp_plotted, color='black', label='model')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc='upper right')
plt.title('Principal component lightcurves')
# plt.ylim(-1, 5)
# plt.xlim(0, 300)
# plt.show()
plt.savefig(os.path.join(diagnostic_image_folder,
'principal_component_lightcurves.png'), dpi=300)
plt.close()
number_of_comp_plotted = 10
fig = plt.figure()
ax = plt.subplot(111)
for i in range(number_of_comp_plotted):
ax.plot(B[:, i] * lambdas_full[i] / np.cumsum(lambdas_full)
[-1] + 1 * i, label=i) # + 1 * i, label=i)
# ax.plot(B[:, i] + 1 * i, label=i) # + 1 * i, label=i)

ax.plot(model_for_pixel / np.max(model_for_pixel) +
number_of_comp_plotted, color='black', label='model')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc='upper right')
plt.title('Principal component lightcurves')
# plt.ylim(-1, 5)
# plt.xlim(0, 300)
# plt.show()
plt.savefig(os.path.join(diagnostic_image_folder,
'principal_component_lightcurves.png'), dpi=300)

if model is not None:
diagnostic_image[:, reduction_pix_indeces[idx, 0], reduction_pix_indeces[idx, 1]] = (
Expand Down Expand Up @@ -1020,7 +1021,7 @@ def run_trap_with_model_spatial(
or `yx_center_injection` if provided.
reduction_mask : array_like
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
yx_center : array_like, optional
The center position of the image as used in reduction.
yx_center_injection : array_like
Expand Down Expand Up @@ -1281,7 +1282,7 @@ def run_trap_with_model_wavelength(
or `yx_center_injection` if provided.
reduction_mask : array_like
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
yx_center : array_like, optional
The center position of the image as used in reduction.
yx_center_injection : array_like
Expand Down Expand Up @@ -1555,7 +1556,7 @@ def run_trap_with_model_wavelength(
plt.savefig('diagnostic_plots/data_for_pixel_long_test.jpg', dpi=300)

plt.close()
plt.style.use("paper")
# plt.style.use("paper")
fig = plt.figure()
ax = plt.subplot(111)
alpha = 0.7
Expand Down Expand Up @@ -1661,7 +1662,7 @@ def run_trap_with_model_temporal_optimized(
necessary for the TRAP pipeline.
reduction_mask : array_like
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
inverse_variance_reduction_area : array_like, optional
Inverse variance for pixels in `reduction_mask`. Use if `include_noise`
in `reduction_parameters` is True.
Expand Down Expand Up @@ -1797,7 +1798,7 @@ def temporal_pca_cross_validation(
necessary for the TRAP pipeline.
reduction_mask : array_like
Boolean mask of data included in the reduction
(\mathcal{P}_\mathcal{Y} in Samland et al. 2020)
(\\mathcal{P}_\\mathcal{Y} in Samland et al. 2020)
inverse_variance_reduction_area : array_like, optional
Inverse variance for pixels in `reduction_mask`. Use if `include_noise`
in `reduction_parameters` is True.
Expand Down

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