Working prototype for the implementation of reference path dynamic time-warping

d15a3625 · Lukas Eller · 4525cf76 · d15a3625 · d15a3625 · d15a3625
Commit d15a3625 authored Jul 06, 2021 by Lukas Eller
8 changed files
--- a/measprocess/__init__.py
+++ b/measprocess/__init__.py
@@ -2,6 +2,11 @@ from .plotting import plot_locations_osm, plot_street_nodes

 from .simulation import TCP_on_lines, ShadowFading

+from .dtw import (
+    warp_geodataframe,
+    raw_rp_dtw
+)
+
 from .rtr.process import (
    process,
    interpolate_signal_loc

--- a/measprocess/dtw.py
+++ b/measprocess/dtw.py
+import geopandas as gpd
+from typing import List, Union
+import pandas as pd
+import numpy as np
+from shapely.geometry import LineString, Point
+from tqdm import tqdm
+
+def raw_rp_dtw(rp, y, dist_func=None, return_path_cost_matrix=False, return_moves_matrices=False, verbose=False, band_width=np.inf, export_path_cost_matrix=None, tqdm_text="Warping..."):
+    '''
+    The raw reference path dtw implementation from Vaclav
+    '''
+
+    if dist_func is None:
+        dist_func = lambda a,b: np.sqrt( np.sum((a-b)**2) )  # Euclidean distance
+
+    if rp.ndim == 1:
+        rp = rp.reshape(-1,1)
+        y = y.reshape(-1,1)
+
+    N = rp.shape[0]
+    M = y.shape[0]
+
+    path_cost_matrix = np.full((N,M), np.inf)
+    best_moves_x = np.full((N,M), np.nan, np.int8)
+    best_moves_y = np.full((N,M), np.nan, np.int8)
+
+    path_cost_matrix[0,0] = dist_func(rp[0,:],y[0,:])
+
+    # Calculate the distance
+    #if verbose: print('Warping...')
+    for ii in tqdm(range(N), desc=tqdm_text, disable=(not verbose)):
+        #if verbose & (np.mod(ii,1000)==0): print('{}/{}'.format(ii,N-1))
+        #for jj in range(M):
+        #for jj in np.arange(start=np.min([np.max([0,ii-band_width]),M-band_width]), stop=np.min([ii+1,M]), step=1):
+        jj_min = np.max([0, ii*(M-1)/(N-1)-np.floor(band_width/2)])
+        jj_max = np.min([M, ii*(M-1)/(N-1)+np.ceil(band_width/2)])
+        for jj in np.arange(start=jj_min, stop=jj_max, step=1, dtype=int):
+            if (ii == 0) and (jj == 0): continue
+            if jj > ii: continue
+
+            # Calculate cost of each allowed action
+            cost_move_right = np.inf
+            cost_move_up    = np.inf
+            cost_move_diag  = np.inf
+            if (ii > 0):
+                cost_move_right = path_cost_matrix[ii-1, jj  ]  # Take next sample from the first signal, keep the second fixed
+
+            # This move is never allowed (we always take next sample from the first signal => the first signal stays unchanged)
+            #if (jj > 0):
+            #    cost_move_up    = path_cost_matrix[ii  , jj-1]  # Take next sample from the second signal, keep the first fixed
+
+            if (ii > 0) and (jj > 0):
+                cost_move_diag  = path_cost_matrix[ii-1, jj-1]  # Take next sample from both signals
+
+            cost_moves = np.array([cost_move_right, cost_move_up, cost_move_diag])
+
+            # Select the best cost (i.e. the best previous position)
+            action_idx = cost_moves.argmin()
+            best_move_cost = cost_moves[action_idx]
+            if action_idx == 0:
+                    best_moves_x[ii,jj] = -1  # We came here from left
+                    best_moves_y[ii,jj] = 0
+            elif action_idx == 1:             # Never happens.
+                    best_moves_x[ii,jj] = 0   # We came here from below
+                    best_moves_y[ii,jj] = -1
+            elif action_idx == 2:
+                    best_moves_x[ii,jj] = -1  # We came here diagonally
+                    best_moves_y[ii,jj] = -1
+
+            path_cost_matrix[ii,jj] = dist_func(rp[ii,:], y[jj,:]) + best_move_cost
+
+    # Calculate the indexes: Go back through the distance matrix, following the best (cheapest) moves
+    #x_idx = np.full(M+N, np.nan)  # Preallocation
+    y_idx = np.full(M+N, np.nan)
+
+    xidx_temp = N-1
+    yidx_temp = M-1
+    ii = 0
+
+    while (xidx_temp > 0) or (yidx_temp > 0):
+        if ii > 0:
+            # Get the previous indexes on the path by a lookup from the "moves" matrixes
+            xidx_prev = xidx_temp + best_moves_x[xidx_temp, yidx_temp]
+            yidx_prev = yidx_temp + best_moves_y[xidx_temp, yidx_temp]
+            xidx_temp = xidx_prev
+            yidx_temp = yidx_prev
+
+        # Store the current x and y indexes
+        #x_idx[ii] = xidx_temp
+        y_idx[ii] = yidx_temp
+
+        ii += 1
+
+    #ix = np.flipud( x_idx[~np.isnan(x_idx)] )
+    iy = np.flipud( y_idx[~np.isnan(y_idx)] ).astype(np.int)
+
+    if export_path_cost_matrix is not None:
+        from scipy.io import savemat
+        savemat(export_path_cost_matrix, {'M':path_cost_matrix, 'idx':iy})
+
+    if return_path_cost_matrix and return_moves_matrices:
+        return iy, path_cost_matrix, best_moves_x, best_moves_y
+    if return_path_cost_matrix and (not return_moves_matrices):
+        return iy, path_cost_matrix
+    if (not return_path_cost_matrix) and return_moves_matrices:
+        return iy, best_moves_x, best_moves_y
+    if (not return_path_cost_matrix) and (not return_moves_matrices):
+        return iy
+
+def warp_geodataframe(
+        reference_path : gpd.GeoSeries,
+        measurement_series : List[gpd.GeoDataFrame],
+        resampling_distance=1, used_crs="EPSG:31287",
+        band_width=np.inf, verbose=True
+    ) -> List[gpd.GeoDataFrame]:
+    '''
+    Reindex a list of measurements to a reference path by applying dynamic timewarping
+
+    :param reference_path: geopandas series with EPSG:4326 crs either consisting of multiple points or a single linestring
+    :param measurement_series: geopandas dataframe with geometry specified by a sequence of points in EPSG:4326
+    :param resampling_distance: distance in meters used for resampling of the reference_path. If None then no resampling will be conducted
+    :param used_crs: the coordinate system used for the distance calculation
+    :param band_width: band_width parameter for the the dynamic time_warping algorithm
+    :param verbose: print a progress animation during warping
+
+    :return:
+        A list of geodadaframes reindexed at the reference path positions
+        the previous measurement sample index is stored under original_index
+    '''
+
+    #Implement the inclusion of a single linestring resampling needs to be set
+    if any(series.crs != "EPSG:4326" for series in [reference_path] + measurement_series):
+        raise ValueError("Pass GeoSeries in EPSG:4326!")
+
+    #Check if reference path is linestring
+    linestring_input = False
+    if any(type(el) == LineString for el in reference_path):
+        linestring_input = True
+        if not len(reference_path) == 1:
+            raise ValueError("Only a single LineString can be used for the reference path")
+        elif resampling_distance is None:
+            raise ValueError("Resampling distance cannot be None for LineString Reference Path")
+
+    #Convert to correct CRS
+    reference_path = reference_path.to_crs(used_crs)
+    measurement_series = [series.to_crs(used_crs) for series in measurement_series]
+
+    #Resample the reference path if a resampling distance is passed
+    if resampling_distance is not None:
+        if linestring_input:
+            reference_path_ls = reference_path.iloc[0]
+        else:
+            reference_path_ls = LineString(reference_path)
+
+        reference_path = gpd.GeoSeries(
+            reference_path_ls.interpolate(dist) for dist in np.arange(0, reference_path_ls.length, resampling_distance)
+        )
+
+    #Basically we should simply summarize the indices here. then append them again.
+    try:
+        index_array = [
+            raw_rp_dtw(
+                np.column_stack((reference_path.x, reference_path.y)),
+                np.column_stack((series.geometry.x, series.geometry.y)),
+                verbose=verbose,
+                tqdm_text=f"Warping {i+1}/{len(measurement_series)}",
+                band_width=band_width
+            ) for i, series in enumerate(measurement_series)
+        ]
+    except IndexError:
+        raise IndexError("Encountered Index Error - Choose smaller resampling distance or remove low speed measurement sequences")
+
+    warped_measurement_series = []
+    for i, indices in enumerate(index_array):
+        #Reindex the warped series
+
+        #Save the previous index under original index
+        old_index_name = measurement_series[i].index.name
+        warped_series = measurement_series[i].loc[indices].reset_index()
+        warped_series = warped_series.rename(columns={old_index_name: 'original_index'})
+
+        #Assign the reference path to the warped series
+        warped_series.geometry = reference_path
+        warped_series = warped_series.to_crs("EPSG:4326")
+
+        warped_measurement_series.append(warped_series)
+
+    return warped_measurement_series
+
+    '''
+    # --> What will we return?
+    # --> we should remove values where we have low velocity.
+    # --> what does the bandwidth parameter actually mean?
+    # --> Out of bounds error might mean that the distance was not correct? resampling should be reset basically
+    # --> Should be run on each of the subexamples --> !!maybe get vaclavs simple example from somewhere!!
+    # --> Would it make sense to pass the reference path as a linestring directly?
+    '''
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@ README = (HERE / "README.md").read_text()
 # This call to setup() does all the work
 setup(
    name="measprocess",
-    version="0.5.15",
+    version="0.6",
    description="Collection of measurement processing tools",
    long_description=README,
    long_description_content_type="text/markdown",

--- a/tests/dynamic_time_warping_test.py
+++ b/tests/dynamic_time_warping_test.py
+import unittest
+import geopandas as gpd
+from context import measprocess as mpc
+import pandas as pd
+import numpy as np
+
+from shapely.geometry import LineString, Point
+
+class TestDTW(unittest.TestCase):
+    def setUp(self):
+        '''
+        Note, that the Dummy Example does not use coordinates but x, y positions
+        Still we transform it here to be consistent with the default use-case
+        '''
+
+        #Prepare Reference Path
+        rp = pd.read_csv("tests/example_files/dtw_example/reference_path.csv", index_col=0)
+        rp = gpd.GeoSeries(
+            gpd.points_from_xy(rp.x_m, rp.y_m)
+        ).set_crs("EPSG:31287").to_crs("EPSG:4326")
+        self._rp = rp.drop(columns=["x_m, y_m"])
+
+        #Prepare Measurements
+        measurement_files = (
+            pd.read_csv(f"tests/example_files/dtw_example/measurement_{i}.csv", index_col=0)
+            for i in range(3)
+        )
+        self._measurements = [
+            gpd.GeoDataFrame(
+                meas,
+                geometry=gpd.points_from_xy(meas.x_m, meas.y_m)
+            ).set_crs("EPSG:31287").to_crs("EPSG:4326").drop(columns=["x_m", "y_m"]) for meas in measurement_files
+        ]
+
+    def test_epsg_exception(self):
+        rp = self._rp
+        rp.crs = None
+
+        measurements = self._measurements
+        for meas in measurements:
+            meas.crs = None
+
+        with self.assertRaises(ValueError) as context:
+            mpc.dtw.warp_geodataframe(
+                rp, measurements
+            )
+
+    def test_original_index(self):
+        #Check the no measurement was lost
+        measurement = self._measurements[0]
+        warped_measurement = mpc.dtw.warp_geodataframe(
+            self._rp, [measurement],
+            resampling_distance=1
+        )[0]
+
+        self.assertTrue(
+            all(i in warped_measurement.original_index for i in measurement.index)
+        )
+
+    def test_basic_example_resampling(self):
+        measurement_list = mpc.dtw.warp_geodataframe(
+            self._rp, self._measurements,
+            resampling_distance=1
+        )
+
+        self.assertTrue(
+            ((measurement_list[0].geometry == measurement_list[1].geometry) & (measurement_list[1].geometry == measurement_list[2].geometry)).all()
+        )
+
+    def test_basic_example(self):
+        measurement_list = mpc.dtw.warp_geodataframe(
+            self._rp, self._measurements,
+            resampling_distance=None
+        )
+
+        self.assertTrue(
+            ((measurement_list[0].geometry == measurement_list[1].geometry) & (measurement_list[1].geometry == measurement_list[2].geometry)).all()
+        )
+
+    def test_linestring_input(self):
+        rp = gpd.GeoSeries(LineString(self._rp)).set_crs("EPSG:4326")
+
+        measurement_list = mpc.dtw.warp_geodataframe(
+            rp, self._measurements,
+            resampling_distance=1
+        )
+
+        self.assertTrue(
+            ((measurement_list[0].geometry == measurement_list[1].geometry) & (measurement_list[1].geometry == measurement_list[2].geometry)).all()
+        )
+
+    def test_linestring_input_warning(self):
+        rp = gpd.GeoSeries(LineString(self._rp)).set_crs("EPSG:4326")
+
+        measurement_list = mpc.dtw.warp_geodataframe(
+            rp, self._measurements,
+            resampling_distance=1
+        )
+
+        self.assertTrue(
+            ((measurement_list[0].geometry == measurement_list[1].geometry) & (measurement_list[1].geometry == measurement_list[2].geometry)).all()
+        )
+
+    def test_index_warning(self):
+        rp = gpd.GeoSeries(LineString(self._rp)).set_crs("EPSG:4326")
+
+        with self.assertRaises(IndexError) as context:
+            measurement_list = mpc.dtw.warp_geodataframe(
+                rp, self._measurements,
+                resampling_distance=5
+            )
--- a/tests/example_files/dtw_example/measurement_0.csv
+++ b/tests/example_files/dtw_example/measurement_0.csv
+time_s,RSRP_dBm,x_m,y_m
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