Updates for 0.18.1

Author: mar3021

1-tutorials/1-getting-started.ipynb

@@ -287,6 +287,13 @@
     "tc.hvplot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can filter out trajectories based on their duration:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -322,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/10-smoothing-trajectories.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "To smooth trajectories, we can use a Kalman filter. The implemented KalmanSmootherCV is based on the assumption of a nearly-constant velocity (CV) model. To use KalmanSmootherCV, the optional dependency `StoneSoup` needs to be installed.\n",
     "\n",
-    "[Documentation](https://movingpandas.readthedocs.io/en/main/trajectorysmoother.html)\n",
+    "[Documentation](https://movingpandas.readthedocs.io/en/main/api/trajectorysmoother.html)\n",
     "\n",
     "A closely related type of operation is [trajectory generalization which is coverd in a separate notebook](./7-generalizing-trajectories.ipynb). "
    ]
@@ -171,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/11-measuring-distances.ipynb

@@ -16,8 +16,8 @@
     "Distances can be computed between trajectories as well as between trajectories and other geometry objects. \n",
     "The implemented distance measures are:\n",
     "\n",
-    "* [Shortest distance](https://movingpandas.readthedocs.io/en/main/trajectory.html#movingpandas.Trajectory.distance)\n",
-    "* [Hausdorff distance](https://movingpandas.readthedocs.io/en/main/trajectory.html#movingpandas.Trajectory.hausdorff_distance)\n"
+    "* [Shortest distance](https://movingpandas.readthedocs.io/en/main/api/trajectory.html#movingpandas.Trajectory.distance)\n",
+    "* [Hausdorff distance](https://movingpandas.readthedocs.io/en/main/api/trajectory.html#movingpandas.Trajectory.hausdorff_distance)\n"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/2-computing-speed.ipynb

@@ -232,7 +232,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/3-extracting-mover-positions.ipynb

@@ -259,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/4-exporting-trajectories.ipynb

@@ -182,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/5-intersecting-with-polygons.ipynb

@@ -125,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Computing intersections"
+    "## Computing intersections for a Trajectory"
    ]
   },
   {
@@ -151,6 +151,50 @@
     "intersections"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersections.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersections.to_point_gdf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing intersections for a TrajectoryCollection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersections = tc.intersection(polygon_feature)\n",
+    "intersections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersections.plot()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -184,7 +228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/6-splitting-trajectories.ipynb

@@ -236,7 +236,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/7-generalizing-trajectories.ipynb

@@ -19,9 +19,9 @@
     "- Temporal generalization by down-sampling, i.e. increasing the time interval between records\n",
     "- Spatiotemporal generalization, e.g. using Top-Down Time Ratio algorithm\n",
     "\n",
-    "[Documentation](https://movingpandas.readthedocs.io/en/main/trajectorygeneralizer.html)\n",
+    "[Documentation](https://movingpandas.readthedocs.io/en/main/api/trajectorygeneralizer.html)\n",
     "\n",
-    "A closely related type of operation is [trajectory smoothing which is coverd in a separate notebook](./10-smoothing-trajectories.ipynb). "
+    "A closely related type of operation is [trajectory smoothing which is covered in a separate notebook](./10-smoothing-trajectories.ipynb). "
    ]
   },
   {
@@ -199,6 +199,24 @@
     "dp_generalized.plot(ax=axes[1], column='speed', vmax=20, **plot_defaults)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare this to the MinTimeDelta result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(19,4))\n",
+    "tdtr_generalized.plot(ax=axes[0], column='speed', vmax=20, **plot_defaults)\n",
+    "time_generalized.plot(ax=axes[1], column='speed', vmax=20, **plot_defaults)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -223,7 +241,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

1-tutorials/8-detecting-stops.ipynb

@@ -174,7 +174,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "stops = detector.get_stop_segments(min_duration=timedelta(seconds=60), max_diameter=100)"
+    "stop_segments = detector.get_stop_segments(min_duration=timedelta(seconds=60), max_diameter=100)"
    ]
   },
   {
@@ -183,7 +183,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stops"
+    "stop_segments"
    ]
   },
   {
@@ -192,7 +192,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stop_segment_plot = stop_point_plot * stops.hvplot(line_width=7.0, tiles=None, color='orange') \n",
+    "stop_segment_plot = stop_point_plot * stop_segments.hvplot(line_width=7.0, tiles=None, color='orange') \n",
     "stop_segment_plot"
    ]
   },
@@ -297,7 +297,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,