Merge pull request #35 from rahil-makadia/dev

added grss.prop.prop_parallel module, ca/impact sliding window filter

grss/prop/__init__.py

@@ -1,4 +1,5 @@
 """GRSS orbit propagation subpackage"""
+from .prop_parallel import *
 from .prop_simulation import *
 from .prop_unscented import *
 from .prop_utils import *

grss/prop/prop_parallel.py

@@ -0,0 +1,55 @@
+"""Parallel computing utilities for the GRSS orbit propagation code"""
+import pandas as pd
+
+__all__ = [ 'cluster_ca_or_impacts',
+]
+
+def cluster_ca_or_impacts(full_list, max_duration=45, central_body=399):
+    """
+    Cluster a list of close approaches by time and uniqueness.
+
+    Parameters
+    ----------
+    full_list : list of prop_simulation.CloseApproachParameters objects
+        List of close approaches to cluster.
+    max_duration : float
+        Maximum duration (in days) between close approaches in a cluster.
+    central_body : int
+        SPICE ID of the central body.
+
+    Returns
+    -------
+    all_clusters : tuple of list of prop_simulation.CloseApproachParameters objects
+        A tuple of close approach clusters (each cluster is a list of
+        close approaches).
+    """
+    all_clusters = []
+    full_list = [ca_or_impact for ca_or_impact in full_list
+                    if ca_or_impact.centralBodySpiceId == central_body]
+    if not full_list:
+        return tuple(all_clusters)
+    times = [ca_or_impact.t for ca_or_impact in full_list]
+    bodies = [ca_or_impact.flybyBody for ca_or_impact in full_list]
+    idx_list = list(range(len(full_list)))
+
+    df = pd.DataFrame({'time': times, 'body': bodies, 'idx': idx_list})
+    df = df.sort_values(by=['time'])
+
+    # create a new cluster if one of two conditions is met:
+    # 1. the time between the current close approach and the last close approach
+    #       is greater than max_duration
+    # 2. the current close approach body is already in the current cluster
+    cluster = [full_list[df.iloc[0]['idx']]]
+    cluster_bodies = [df.iloc[0]['body']]
+    for i in range(1, len(df)):
+        time_condition = df.iloc[i]['time'] - df.iloc[i-1]['time'] > max_duration
+        body_condition = df.iloc[i]['body'] in cluster_bodies
+        if time_condition or body_condition:
+            all_clusters.append(cluster)
+            cluster = [full_list[df.iloc[i]['idx']]]
+            cluster_bodies = [df.iloc[i]['body']]
+        else:
+            cluster.append(full_list[df.iloc[i]['idx']])
+            cluster_bodies.append(df.iloc[i]['body'])
+    all_clusters.append(cluster)
+    return tuple(all_clusters)

grss/version.txt

@@ -1 +1 @@
-2.4.1
+2.5.0