precommit

config_files/config_map_to_map.yaml

@@ -24,4 +24,4 @@ analysis:
   normalize:
     do: true
     method: median_zscore
-output: results/map_to_map_distance_matrix_submission_0.pkl
+output: results/map_to_map_distance_matrix_submission_0.pkl

src/cryo_challenge/_map_to_map/map_to_map_distance.py

@@ -215,7 +215,9 @@ def compute_cost_fsc_chunk(self, maps_gt_flat, maps_user_flat, n_pix):
         return cost_matrix, fsc_matrix
 
     @override
-    def get_distance_matrix(self, maps1, maps2, global_store_of_running_results):  # custom method
+    def get_distance_matrix(
+        self, maps1, maps2, global_store_of_running_results
+    ):  # custom method
         maps_gt_flat = maps1
         maps_user_flat = maps2
         n_pix = self.config["data"]["n_pix"]
@@ -245,16 +247,26 @@ def __init__(self, config):
         super().__init__(config)
 
     @override
-    def get_distance_matrix(self, maps1, maps2, global_store_of_running_results): # custom method
+    def get_distance_matrix(
+        self, maps1, maps2, global_store_of_running_results
+    ):  # custom method
         # get fsc matrix
-        fourier_pixel_max = self.config['data']['n_pix'] // 2 # TODO: check for odd psizes if this should be +1
-        psize = self.config['data']['psize']
-        fsc_matrix = global_store_of_running_results['fsc']['computed_assets']['fsc_matrix']
-        units_Angstroms = 2 * psize / (np.arange(1,fourier_pixel_max+1) / fourier_pixel_max)
+        fourier_pixel_max = (
+            self.config["data"]["n_pix"] // 2
+        )  # TODO: check for odd psizes if this should be +1
+        psize = self.config["data"]["psize"]
+        fsc_matrix = global_store_of_running_results["fsc"]["computed_assets"][
+            "fsc_matrix"
+        ]
+        units_Angstroms = (
+            2 * psize / (np.arange(1, fourier_pixel_max + 1) / fourier_pixel_max)
+        )
+
         def res_at_fsc_threshold(fscs, threshold=0.5):
             res_fsc_half = np.argmin(fscs > threshold, axis=-1)
-            fraction_nyquist = 0.5*res_fsc_half / fscs.shape[-1]
+            fraction_nyquist = 0.5 * res_fsc_half / fscs.shape[-1]
             return res_fsc_half, fraction_nyquist
+
         res_fsc_half, fraction_nyquist = res_at_fsc_threshold(fsc_matrix)
-        self.stored_computed_assets = {'fraction_nyquist': fraction_nyquist}
-        return units_Angstroms[res_fsc_half]
+        self.stored_computed_assets = {"fraction_nyquist": fraction_nyquist}
+        return units_Angstroms[res_fsc_half]

src/cryo_challenge/_map_to_map/map_to_map_pipeline.py

@@ -2,7 +2,6 @@
 import pandas as pd
 import pickle
 import torch
-import numpy as np
 
 from ..data._validation.output_validators import MapToMapResultsValidator
 from .._map_to_map.map_to_map_distance import (
@@ -79,10 +78,14 @@ def run(config):
             print("cost matrix", distance_label)
 
             cost_matrix = map_to_map_distance.get_distance_matrix(
-                maps_gt_flat, maps_user_flat, global_store_of_running_results=results_dict,
+                maps_gt_flat,
+                maps_user_flat,
+                global_store_of_running_results=results_dict,
             )
             computed_assets = map_to_map_distance.get_computed_assets(
-                maps_gt_flat, maps_user_flat, global_store_of_running_results=results_dict,
+                maps_gt_flat,
+                maps_user_flat,
+                global_store_of_running_results=results_dict,
             )
             computed_assets.update(computed_assets)