Merge branch 'elastic_format_data' of https://github.com/nkhadka21/slsim

 into elastic_format_data

merged to remote

slsim/LsstSciencePipeline/util_lsst.py

@@ -219,10 +219,11 @@ def opsim_variable_lens_injection(
 def optimized_transient_event_time_mjd(
     mjd_times, lightcurve_range=(-50, 100), min_points=100, optimized_cadence=True
 ):
-    """Finds a transient start time such that at least min_points fall within the light 
-    curve range. If no points meet the requirement for the minimum number of observations,
-    a start point will be randomly selected from the given observation times. Additionally, 
-    if optimized_cadence is set to False, the start point will also be chosen randomly.
+    """Finds a transient start time such that at least min_points fall within
+    the light curve range. If no points meet the requirement for the minimum
+    number of observations, a start point will be randomly selected from the
+    given observation times. Additionally, if optimized_cadence is set to
+    False, the start point will also be chosen randomly.
 
     :param mjd_times: List or array of MJD times.
     :param lightcurve_range: Range of lightcurve in days (e.g., (-50,
@@ -249,11 +250,12 @@ def optimized_transient_event_time_mjd(
             points_in_range = np.sum((mjd_times >= start) & (mjd_times <= end))
 
             if points_in_range >= min_points:
-                start_point=zero_point_mjd  # Return the first valid zero_point_mjd
+                start_point = zero_point_mjd  # Return the first valid zero_point_mjd
             else:
                 warning_msg = (
-                "In the given observations, there is no observation window which contains" 
-                " %s observational points. So, random start point is choosen" % min_points
+                    "In the given observations, there is no observation window which contains"
+                    " %s observational points. So, random start point is choosen"
+                    % min_points
                 )
                 warnings.warn(warning_msg, category=UserWarning, stacklevel=2)
                 start_point = transient_event_time_mjd(min_mjd=min_mjd, max_mjd=max_mjd)
@@ -296,8 +298,8 @@ def transient_data_with_cadence(
     :param transform_pix2angle: Transformation matrix (2x2) of pixels
         into coordinate displacements.
     :param num_pix: number of pixel for the image.
-    :param optimized_cadence: Boolean. If True, search for the time interval where given
-     minimum number of observation points occur.
+    :param optimized_cadence: Boolean. If True, search for the time
+        interval where given minimum number of observation points occur.
     :param min_points: minimum number of observations in a certain time
         window.
     :param noise: Boolean. If True, a gaussian noise is added to the
@@ -314,10 +316,10 @@ def transient_data_with_cadence(
     min_lc_time = min(lens_class.source[0].lightcurve_time)
     max_lc_time = max(lens_class.source[0].lightcurve_time)
     start_point_mjd_time = optimized_transient_event_time_mjd(
-    mjd_times=copied_exposure_data["obs_time"],
-    lightcurve_range=(min_lc_time, max_lc_time),
-    min_points=min_points,
-    optimized_cadence=optimized_cadence
+        mjd_times=copied_exposure_data["obs_time"],
+        lightcurve_range=(min_lc_time, max_lc_time),
+        min_points=min_points,
+        optimized_cadence=optimized_cadence,
     )
     obs_time_in_days = convert_mjd_to_days(
         copied_exposure_data["obs_time"], start_point_mjd_time

tests/test_LsstSciencePipeline/test_opsim_pipeline.py

@@ -8,7 +8,7 @@
     opsim_variable_lens_injection,
     optimized_transient_event_time_mjd,
     transient_data_with_cadence,
-    extract_lightcurves_in_different_bands
+    extract_lightcurves_in_different_bands,
 )
 from slsim.Sources.source import Source
 from slsim.Deflectors.deflector import Deflector
@@ -169,15 +169,15 @@ def test_transient_event_time_no_valid_start():
 
     assert 58000 <= result <= 58100
 
+
 def test_transient_event_time_no_optimized_cadence():
     mjd_times = np.linspace(58000, 58100, 50)
     lightcurve_range = (-50, 100)
-    result = optimized_transient_event_time_mjd(mjd_times, lightcurve_range, 
-                                                optimized_cadence=False)
+    result = optimized_transient_event_time_mjd(
+        mjd_times, lightcurve_range, optimized_cadence=False
+    )
     assert 58000 <= result <= 58100
 
-
-
 
 @pytest.fixture
 def lens_class_instance():
@@ -271,7 +271,7 @@ def test_transient_data_with_cadence(lens_class_instance, exposure_data):
         num_pix=61,
         min_points=5,
     )
-    lightcurves=extract_lightcurves_in_different_bands(result)
+    lightcurves = extract_lightcurves_in_different_bands(result)
     expected_keys = lightcurves.keys()
     colname = result.colnames
     assert isinstance(result, Table)
@@ -299,18 +299,21 @@ def test_transient_data_with_cadence(lens_class_instance, exposure_data):
     assert "obs_time" in expected_keys
     assert "image_lists" in expected_keys
 
-    results_i = result[result["band"]=="i"]
+    results_i = result[result["band"] == "i"]
     mag_i = results_i["mag_image_1"]
-    final_lightcurve_i=lightcurves["magnitudes"]["mag_image_1"]["i"]
+    final_lightcurve_i = lightcurves["magnitudes"]["mag_image_1"]["i"]
     assert np.all(np.array(list(mag_i))) == np.all(np.array(final_lightcurve_i))
     error_i_low = results_i["mag_error_image_1_low"]
     error_i_high = results_i["mag_error_image_1_high"]
-    lightcurve_error_i_low=lightcurves["errors_low"]["mag_error_image_1_low"]["i"]
-    lightcurve_error_i_high=lightcurves["errors_high"]["mag_error_image_1_high"]["i"]
-    assert np.all(np.array(list(error_i_low))) == np.all(np.array(
-        lightcurve_error_i_low))
-    assert np.all(np.array(list(error_i_high))) == np.all(np.array(
-        lightcurve_error_i_high))
+    lightcurve_error_i_low = lightcurves["errors_low"]["mag_error_image_1_low"]["i"]
+    lightcurve_error_i_high = lightcurves["errors_high"]["mag_error_image_1_high"]["i"]
+    assert np.all(np.array(list(error_i_low))) == np.all(
+        np.array(lightcurve_error_i_low)
+    )
+    assert np.all(np.array(list(error_i_high))) == np.all(
+        np.array(lightcurve_error_i_high)
+    )
+
 
 if __name__ == "__main__":
     pytest.main()

tests/test_Plots/test_plot_functions.py

@@ -7,7 +7,8 @@
 from slsim.Plots.plot_functions import (
     create_image_montage_from_image_list,
     plot_montage_of_random_injected_lens,
-    create_montage, plot_lightcurves
+    create_montage,
+    plot_lightcurves,
 )
 from slsim.image_simulation import sharp_image
 from slsim.Sources.source import Source
@@ -123,6 +124,7 @@ def test_plot_montage_of_random_injected_lens(quasar_lens_pop_instance):
     assert isinstance(fig, plt.Figure)
     assert fig.get_size_inches()[0] == np.array([num_cols * 3, num_rows * 3])[0]
 
+
 def test_create_montage_basics():
     images = [
         np.random.rand(5, 5),
@@ -131,14 +133,15 @@ def test_create_montage_basics():
         np.random.rand(5, 5),
     ]
     montage = create_montage(images)
-    
+
     # Check shape
     assert montage.shape == (5, 15)  # 1 row, 3 images wide
 
     # Check normalization range
     assert np.min(montage) >= 0
     assert np.max(montage) <= 1
 
+
 def test_create_montage_specified_grid():
     images = [
         np.random.rand(5, 5),
@@ -151,6 +154,7 @@ def test_create_montage_specified_grid():
     # Check shape
     assert montage.shape == (5, 15)  # 1 row, 3 images wide
 
+
 def test_plot_lightcurves():
     data = {
         "magnitudes": {
@@ -174,13 +178,14 @@ def test_plot_lightcurves():
 
     fig = plot_lightcurves(data, images=True)
     fig2 = plot_lightcurves(data, images=False)
-    ax1=fig.get_axes()
-    ax2=fig2.get_axes()
+    ax1 = fig.get_axes()
+    ax2 = fig2.get_axes()
     assert fig is not None
     assert isinstance(fig, plt.Figure)
     assert fig2 is not None
     assert isinstance(fig2, plt.Figure)
-    assert len(ax1) == len(ax2)+2
+    assert len(ax1) == len(ax2) + 2
+
 
 if __name__ == "__main__":
     pytest.main()