start work on rally_style

gpsfun/rallystyle.py

@@ -0,0 +1,88 @@
+import numpy as np
+from math import sqrt
+
+# Example
+# results = {"segments": [{"segment": 1, "Timestamp":0, data: {}},]
+# ,"totals": {"total_time": 0, "timed": 0, 'transport': 0}}
+
+results = {"segments": [],
+           "totals": {"total_time": None, "timed": None, 'transport': None}
+           }
+
+class RallyResults(object):
+    """
+    segments is the rally definition
+    dataframe
+    example results:
+    results = {"segments": [],
+           "totals": {"total_time": None, "timed": None, 'transport': None}
+           }
+    """
+    def __init__(self, df, segments):
+        self.df = df
+        self.init_columns = df.columns
+        self.segments = segments
+        self.epsilon = 0.00001 # used to for finding acute triangles
+        self.near = .0001
+        self.results = {"segments": [],"totals": {"total_time": None, "timed": None, 'transport': None}}
+        # self.ck_points = pd.DataFrame([p['location'] for p in segments], columns=['Latitude', 'Longitude'])
+        self.ck_points = [p['location'] for p in segments]
+        if not 'shift_Longitude' in self.df.columns or not 'shift_Longitude' in self.df.columns:
+            self.df['shift_Longitude'] = self.df.shift(-1)['Longitude']
+            self.df['shift_Latitude'] = self.df.shift(-1)['Latitude']
+            self.df['dist_to_next'] = np.linalg.norm(self.df[['Latitude', 'Longitude']].values -
+                                     self.df[['shift_Latitude', 'shift_Longitude']].values, axis=1)
+
+    def match_checkpoints(self):
+        """
+        Identify the activity point the represents the arrival at the checkpoint
+        find near points that form acute triangles
+        :arg
+        """
+        self.df['checkpoint'] = np.nan
+        self.df[f'mark'] = False
+        for i, p in enumerate(self.ck_points.values):
+            self.df[f'p_to_A{i}'] = np.linalg.norm(self.df[['Latitude', 'Longitude']].values - p, axis=1)
+            self.df[f'p_to_B{i}'] = np.linalg.norm(self.df[['shift_Latitude', 'shift_Longitude']].values - p, axis=1)
+            self.df['acute'] = self.df[f'p_to_A{i}'] ** 2 + self.df['to_next'] ** 2 <= self.df[f'p_to_B{i}'] ** 2 + self.epsilon
+
+            self.df['checkpoint'] = np.nan
+            self.df[f'mark_{i}'] = False
+            self.df.loc[
+            self.df[(self.df[f'p_to_A{i}'] <= self.near) & (self.df.acute)].index[0], ['checkpoint']] = i
+            self.df.loc[self.df[(self.df[f'p_to_A{i}'] <= self.near) & (self.df.acute)].index[0], [f'mark_{i}', 'mark']] = True
+
+            seg_result = self.df[self.df[f'mark_{1}']][['Date_Time', 'Latitude', 'Longitude']].iloc[0].to_dict()
+            update_results(seg_result, results)
+
+            print('#####')
+            print(self.df[[f'p_to_A{i}', f'p_to_B{i}', 'to_next', 'acute', f'mark_{i}', 'mark']][self.df.mark == True])
+
+
+
+
+
+class Segment(object):
+    """
+    Work in progress
+    """
+    def point_to_line(self, lp1, lp2, p):
+        """
+        Does a perpendicular line from a point to a line intersect between two points.
+        TODO: Consider using shapely https://pypi.org/project/Shapely/
+        """
+        s1 = (lp2[1] - lp1[1])/(lp2[0] - lp1[0])
+        s2 = 1/s1
+        #y1 = s1(x − lp1[0]) + lp1[1]
+        #y2 = s2(x - p[0]) + p[1]
+        x = ((-s1 * lp1[0]) + lp1[1] + s2 * p[0] - p[1]) / (s2 - s1)
+        y = s1 * (x - lp1[0]) + lp1[1]
+        between = (lp2[0] < x < lp1[0]) or (lp2[0] > x > lp1[0]) and (lp2[1] < y < lp1[1]) or (lp2[1] > y > lp1[1])
+        distance = sqrt((p[0] - x)**2 + (p[1] - y)**2)
+
+    def point_to_point(self):
+        df1 = gpsbabel("../tests/test_data/segment_test_1a.gpx")[['Latitude', 'Longitude']]
+        df2 = gpsbabel("../tests/test_data/segment_test_1b.gpx")[['Latitude', 'Longitude']]
+
+        a = distance.cdist(df1, df2, 'euclidean')
+        # b = a[a < .00001]

gpsfun/readers.py

@@ -5,7 +5,10 @@
 from pathlib import Path
 import xmltodict
 import fitdecode
-from . import col
+try:
+    from . import col
+except:
+    import col
 
 class GPSFunException(Exception):
     pass

gpsfun/tracks.py

@@ -2,8 +2,6 @@
 import pandas as pd
 from haversine import haversine, Unit
 import requests
-from scipy.spatial import distance
-from .readers import gpsbabel
 
 
 class Track(object):
@@ -139,28 +137,3 @@ def export_lat_lon_alt(self, file_type='JSON'):
         elif file_type == 'csv':
             self.df[['Latitude', 'Longitude']].to_csv()
 
-class Segment(object):
-    """
-    Work in progress
-    """
-    def point_to_line(self, lp1, lp2, p):
-        """
-        Does a perpendicular line from a point to a line intersect between two points.
-        TODO: Consider using shapely https://pypi.org/project/Shapely/
-        """
-        s1 = (lp2[1] - lp1[1])/(lp2[0] - lp1[0])
-        s2 = 1/s1
-        #y1 = s1(x − lp1[0]) + lp1[1]
-        #y2 = s2(x - p[0]) + p[1]
-        x = ((-s1 * lp1[0]) + lp1[1] + s2 * p[0] - p[1]) / (s2 - s1)
-        y = s1 * (x - lp1[0]) + lp1[1]
-        between = (lp2[0] < x < lp1[0]) or (lp2[0] > x > lp1[0]) and (lp2[1] < y < lp1[1]) or (lp2[1] > y > lp1[1])
-        distance = sqrt((p[0] - x)**2 + (p[1] - y)**2)
-
-    def point_to_point(self):
-        df1 = gpsbabel("../tests/test_data/segment_test_1a.gpx")[['Latitude', 'Longitude']]
-        df2 = gpsbabel("../tests/test_data/segment_test_1b.gpx")[['Latitude', 'Longitude']]
-
-        a = distance.cdist(df1, df2, 'euclidean')
-        # b = a[a < .00001]
-