update predicts parameter handling

packetraven/__main__.py

@@ -26,7 +26,7 @@
 from packetraven.packets import APRSPacket
 from packetraven.packets.tracks import APRSTrack, LocationPacketTrack
 from packetraven.packets.writer import write_packet_tracks
-from packetraven.predicts import get_predictions, PredictionAPIURL, PredictionError
+from packetraven.predicts import packet_track_predictions, PredictionAPIURL, PredictionError
 from packetraven.utilities import (
     ensure_datetime_timezone,
     get_logger,
@@ -429,7 +429,7 @@ def main():
                     if prediction_filename is not None:
                         try:
                             predictions.update(
-                                get_predictions(
+                                packet_track_predictions(
                                     packet_tracks,
                                     **{
                                         key.replace('prediction_', ''): value

packetraven/gui/base.py

@@ -21,7 +21,7 @@
 from packetraven.packets import APRSPacket, LocationPacket
 from packetraven.packets.tracks import LocationPacketTrack, PredictedTrajectory
 from packetraven.packets.writer import write_packet_tracks
-from packetraven.predicts import get_predictions, PredictionError
+from packetraven.predicts import packet_track_predictions, PredictionError
 from packetraven.utilities import get_logger
 
 
@@ -773,7 +773,7 @@ async def retrieve_packets(self):
                 if self.toggles['prediction_file']:
                     try:
                         self.__predictions.update(
-                            get_predictions(
+                            packet_track_predictions(
                                 self.packet_tracks,
                                 **{
                                     key.replace('prediction_', ''): value

packetraven/packets/tracks.py

@@ -1,14 +1,13 @@
 from copy import copy
 from datetime import datetime, timedelta
 from os import PathLike
-from typing import Iterable, List, Union
+from typing import Any, Dict, Iterable, List, Union
 
 from dateutil.parser import parse as parse_date
 import geojson
 import numpy
 from pandas import DataFrame
 from pyproj import CRS
-import typepigeon
 
 from packetraven.model import (
     FREEFALL_DESCENT_RATE,
@@ -425,34 +424,26 @@ class PredictedTrajectory(LocationPacketTrack):
     def __init__(
         self,
         packets: List[LocationPacket],
-        prediction_time: datetime,
-        dataset_time: datetime,
+        parameters: Dict[str, Any],
+        metadata: Dict[str, Any],
         crs: CRS = None,
         **attributes,
     ):
         """
         :param packets: iterable of packets
-        :param prediction_time: time of completed prediction
-        :param dataset_time: time of last dataset update
+        :param parameters: prediction parameters
+        :param metadata: prediction completion metadata
         :param crs: coordinate reference system to use
         """
 
         if 'name' not in attributes:
             attributes['name'] = 'predicted trajectory'
 
-        if not isinstance(prediction_time, datetime):
-            prediction_time = typepigeon.convert_value(prediction_time, datetime)
-
-        if not isinstance(dataset_time, datetime):
-            dataset_time = typepigeon.convert_value(dataset_time, datetime)
+        self.__parameters = parameters
+        self.__metadata = metadata
 
         LocationPacketTrack.__init__(
-            self,
-            packets=packets,
-            prediction_time=prediction_time,
-            dataset_time=dataset_time,
-            crs=crs,
-            **attributes,
+            self, packets=packets, crs=crs, **attributes,
         )
 
     @property
@@ -470,3 +461,11 @@ def from_file(cls, filename: PathLike) -> List['PredictedTrajectory']:
             )
 
         return tracks
+
+    @property
+    def parameters(self) -> Dict[str, Any]:
+        return self.__parameters
+
+    @property
+    def metadata(self) -> Dict[str, Any]:
+        return self.__metadata

packetraven/predicts.py

@@ -278,8 +278,10 @@ def get(self) -> Dict[str, Any]:
             try:
                 error = response.json()['error']['description']
             except:
-                error = ''
-            raise ConnectionError(f'connection raised error {response.status_code} for {response.url} - {error}')
+                error = 'no message'
+            raise ConnectionError(
+                f'connection raised error {response.status_code} for {response.url} - {error}'
+            )
 
     @property
     def predict(self) -> PredictedTrajectory:
@@ -305,8 +307,8 @@ def predict(self) -> PredictedTrajectory:
                     )
                     for point in points
                 ],
-                prediction_time=response['metadata']['complete_datetime'],
-                dataset_time=response['request']['dataset'],
+                parameters=response['request'],
+                metadata=response['metadata'],
             )
         else:
             raise PredictionError(response['error']['description'])
@@ -388,7 +390,7 @@ def query(self) -> Dict[str, Any]:
         return query
 
 
-def get_predictions(
+def packet_track_predictions(
     packet_tracks: Dict[str, LocationPacketTrack],
     start_location: Tuple[float, float, float] = None,
     start_time: datetime = None,
@@ -475,7 +477,7 @@ def get_predictions(
         if float_altitude is not None and not packet_track.falling:
             packets_at_float_altitude = packet_track[
                 numpy.abs(float_altitude - packet_track.altitudes) < float_altitude_uncertainty
-                ]
+            ]
             if (
                 len(packets_at_float_altitude) > 0
                 and packets_at_float_altitude[-1].time == packet_track.times[-1]
@@ -485,7 +487,7 @@ def get_predictions(
             elif packet_track.ascent_rates[-1] >= 0:
                 prediction_float_start_time = prediction_start_time + timedelta(
                     seconds=(float_altitude - prediction_start_location[2])
-                            / prediction_ascent_rate
+                    / prediction_ascent_rate
                 )
                 descent_only = False
             else: