Update README file

README.md

@@ -1,2 +1,44 @@
-# extragalactic_hostless
-Repository for the extragalactic hostless project from CRP7
+# ELEPHANT: ExtragaLactic Pipeline for Hostless AstroNomical Transients
+This repository contains the pipeline for potential hostless transients detection discussed in the paper(link).
+
+
+Install the requirements with the command below 
+
+    pip install -r requirements.txt
+
+The pipeline parameters can be configured in pipeline_config.json file
+
+    {
+        "parquet_files_list": "data/*.parquet",
+        "save_directory": "/path/to/save/results/",
+        ...
+    }
+
+To start the pipeline use the command below
+
+    python run_pipeline.py
+
+The pipeline generates a result parquet file with the following columns for each input parquet file
+
+- **b:cutoutScience_stampData_stacked:** stacked science images 
+- **b:cutoutTemplate_stampData_stacked:** stacked template images
+- **b:cutoutDifference_stampData_stacked:** stacked template images
+- **science_clipped:** stacked sigma clipped science image
+- **template_clipped:** stacked sigma clipped template image
+- **number_of_stamps_in_stacking:** number of images used for stacking after FWHM stamp preprocessing
+- **is_hostless_candidate_clipping:** True if the candidate flagged as hostless by sigma clipping approach
+- **distance_science:** distance from transient to nearest mask in pixels
+- **anderson-darling_SCIENCE_N_statistic:** Anderson darling test statistic value for N x N cutout science image
+- **anderson-darling_SCIENCE_N_pvalue:** Anderson darling test p-value for N x N cutout science image
+- **anderson-darling_TEMPLATE_N_statistic:** Anderson darling test statistic value for N x N cutout template image
+- **anderson-darling_TEMPLATE_N_pvalue:** Anderson darling test p-value for N x N cutout template image
+- **kstest_SCIENCE_N_statistic:** Kolmogorov-Smirnov test statistic value for N x N cutout science image
+- **kstest_SCIENCE_N_pvalue:** Kolmogorov-Smirnov test p-value for N x N cutout science image
+- **kstest_TEMPLATE_N_statistic:** Kolmogorov-Smirnov test statistic value for N x N cutout template image
+- **kstest_TEMPLATE_N_pvalue:** Kolmogorov-Smirnov test p-value for N x N cutout template image
+
+The project is part of [COIN Residence Program #7, Portugal, 2023](https://cosmostatistics-initiative.org/residence-programs/crp7/)
+
+
+
+

src/pipeline_config.json

@@ -13,5 +13,5 @@
     "max_number_of_pixels_clipped": 5,
     "min_number_of_pixels_clipped": 3
   },
-  "number_of_processes": 20
+  "number_of_processes": 1
 }

src/pipeline_utils.py

@@ -458,10 +458,10 @@ def run_powerspectrum_analysis(
     template_data = create_noise_filled_mask(
         template_image, template_mask, image_size)
     _, anderson_results_dict = ps.detect_host_with_powerspectrum(
-        science_data, template_data, N_iter=number_of_iterations,
+        science_data, template_data, number_of_iterations=number_of_iterations,
         metric="anderson-darling")
     _, kstest_results_dict = ps.detect_host_with_powerspectrum(
-        science_data, template_data, N_iter=number_of_iterations,
+        science_data, template_data, number_of_iterations=number_of_iterations,
         metric="kstest")
     out_results = {**anderson_results_dict, **kstest_results_dict}
     return out_results

src/powerspectrum.py

@@ -3,15 +3,15 @@
 from scipy.stats import binned_statistic, wasserstein_distance, anderson_ksamp, kstest
 
 
-def detect_host_with_powerspectrum(sci_image=None, tpl_image=None, N_iter=1000, cutout_sizes=[7, 15, 29],
+def detect_host_with_powerspectrum(sci_image=None, tpl_image=None, number_of_iterations=1000, cutout_sizes=[7, 15, 29],
                                    metric='anderson-darling'):
     """
     Function to detect host with power spectrum analysis.
 
     Parameters:
     - sci_image: Science image (default: None)
     - tpl_image: Template image (default: None)
-    - N_iter: Number of iterations for shuffling (default: 1000)
+    - number_of_iterations: Number of iterations for shuffling (default: 1000)
     - cutout_sizes: List of cutout sizes for analysis (default: [7, 15, 29])
     - metric: Metric for comparison ('anderson-darling' or 'kstest') (default: 'anderson-darling')
 
@@ -63,7 +63,7 @@ def get_powerspectrum(data, size):
         shuffled_Abins_dict = {}  # Dictionary to store shuffled Abins
 
         # Iterate through shuffling process
-        for n in range(N_iter):
+        for n in range(number_of_iterations):
             copy = np.copy(image)
             copy = copy.reshape(full_len * full_len)
             np.random.shuffle(copy)
@@ -76,7 +76,7 @@ def get_powerspectrum(data, size):
                 N_bins = len(np.arange(0.5, size // 2 + 1, 1.)) - 1
 
                 if n == 0:
-                    shuffled_Abins_dict[size] = np.zeros((N_iter, N_bins))
+                    shuffled_Abins_dict[size] = np.zeros((number_of_iterations, N_bins))
 
                     image_resized = image[start: stop, start: stop]
                     Abins = get_powerspectrum(image_resized, size)
@@ -92,13 +92,13 @@ def get_powerspectrum(data, size):
             WD_dist_real_to_shuffled = []
             WD_dist_shuffled_to_shuffled = []
 
-            for n in range(N_iter):
+            for n in range(number_of_iterations):
 
                 iter1 = shuffled_Abins_dict[size][n]
                 wd = wasserstein_distance(iter1, real_Abins_dict[size])
                 WD_dist_real_to_shuffled.append(wd)
 
-                for m in range(N_iter):
+                for m in range(number_of_iterations):
                     if m >= n:
                         continue
                     iter2 = shuffled_Abins_dict[size][m]