{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example II: Tracking already loaded data\n", "\n", "Sometimes data needs to be processed first. For example if derived variables like density potential temperature are applied to the tracking, or data needs to be remapped first. In this scenario you would create the dataset yourself, rather then letting the code load the data, and apply the tracking on the dataset. In this example we are going to process satellite based rainfall estimates from the [CMORPH product](https://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_description.html) over the Indonesian archipelago.\n", "\n", "Before we get started we import all modules that are needed to apply the tracking:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from IPython.display import HTML\n", "from tintx import RunDirectory, config\n", "import xarray as xr\n", "import pandas as pd\n", "from pathlib import Path\n", "import os\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "os.environ[\"DATA_FILES\"] = \"_static/data\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cmorph is globally available from 60°S - 60°N. In this example we are going to open the dataset and select the sub-region that suits the Indonesian archipelago." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "names0: [['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph'], ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph']]\n", "CO: {'time_bounds', 'lat_bounds', 'cmorph', 'lon_bounds', 'time'}\n", "KEYS: ['time', 'time_bounds', 'lat', 'lat_bounds', 'lon', 'lon_bounds', 'cmorph']\n", "names: {'lon_bounds', 'lon', 'time', 'time_bounds', 'lat_bounds', 'cmorph', 'lat'}\n" ] }, { "data": { "text/html": [ "
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       "Dimensions:      (time: 48, lon: 825, nv: 2, lat: 357)\n",
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       "Attributes: (12/57)\n",
       "    ncei_template_version:      NCEI_NetCDF_Grid_template_V2.0\n",
       "    title:                      NOAA Climate Data Record (CDR) of CPC Morphin...\n",
       "    keywords:                   Precipitation, Satellite, High-Resolution, Gl...\n",
       "    summary:                    The CMORPH CDR is a reprocessed and bias-corr...\n",
       "    references:                 Xie, P., et al. (2017), Reprocessed, Bias-Cor...\n",
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       "    geospatial_lat_resolution:  0.072771376\n",
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" ], "text/plain": [ "\n", "Dimensions: (time: 48, lon: 825, nv: 2, lat: 357)\n", "Coordinates:\n", " * lon (lon) float64 100.0 100.1 100.1 100.2 ... 159.8 159.9 160.0\n", " * time (time) datetime64[ns] 2020-01-25 ... 2020-01-25T23:30:00\n", " * lat (lat) float64 -12.95 -12.88 -12.81 -12.73 ... 12.81 12.88 12.95\n", "Dimensions without coordinates: nv\n", "Data variables:\n", " lon_bounds (time, lon, nv) float64 dask.array\n", " time_bounds (time, nv) datetime64[ns] dask.array\n", " lat_bounds (time, lat, nv) float64 dask.array\n", " cmorph (time, lat, lon) float32 dask.array\n", "Attributes: (12/57)\n", " ncei_template_version: NCEI_NetCDF_Grid_template_V2.0\n", " title: NOAA Climate Data Record (CDR) of CPC Morphin...\n", " keywords: Precipitation, Satellite, High-Resolution, Gl...\n", " summary: The CMORPH CDR is a reprocessed and bias-corr...\n", " references: Xie, P., et al. (2017), Reprocessed, Bias-Cor...\n", " Conventions: CF-1.6, ACDD-1.3\n", " ... ...\n", " geospatial_lat_resolution: 0.072771376\n", " geospatial_lat_units: degrees_north\n", " geospatial_lon_min: 0.0\n", " geospatial_lon_max: 360.0\n", " geospatial_lon_resolution: 0.072756669\n", " geospatial_lon_units: degrees_east" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_files = Path(os.environ[\"DATA_FILES\"])\n", "files = [str(f) for f in data_files.rglob('CMORPH*.nc')]\n", "\n", "dset = xr.open_mfdataset(sorted(files), combine='by_coords').sel(\n", " lon=slice(100, 160), lat=slice(-13, 13))\n", "dset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dset = dset.rename({\"cmorph\": \"precip\"})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now simply create a `RunDirectory` object by directly initialising the class. Since the `time`, `lon` and `lat` dimensions already have the default names we don't have to pass the `x_coord`, `y_coord` or `time_coord` keywords:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Attributes: (12/57)\n",
       "    ncei_template_version:      NCEI_NetCDF_Grid_template_V2.0\n",
       "    title:                      NOAA Climate Data Record (CDR) of CPC Morphin...\n",
       "    keywords:                   Precipitation, Satellite, High-Resolution, Gl...\n",
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       "    ...                         ...\n",
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" ], "text/plain": [ "\n", "Dimensions: (time: 48, lon: 825, nv: 2, lat: 357)\n", "Coordinates:\n", " * lon (lon) float64 100.0 100.1 100.1 100.2 ... 159.8 159.9 160.0\n", " * time (time) datetime64[ns] 2020-01-25 ... 2020-01-25T23:30:00\n", " * lat (lat) float64 -12.95 -12.88 -12.81 -12.73 ... 12.81 12.88 12.95\n", "Dimensions without coordinates: nv\n", "Data variables:\n", " lon_bounds (time, lon, nv) float64 dask.array\n", " time_bounds (time, nv) datetime64[ns] dask.array\n", " lat_bounds (time, lat, nv) float64 dask.array\n", " precip (time, lat, lon) float32 dask.array\n", "Attributes: (12/57)\n", " ncei_template_version: NCEI_NetCDF_Grid_template_V2.0\n", " title: NOAA Climate Data Record (CDR) of CPC Morphin...\n", " keywords: Precipitation, Satellite, High-Resolution, Gl...\n", " summary: The CMORPH CDR is a reprocessed and bias-corr...\n", " references: Xie, P., et al. (2017), Reprocessed, Bias-Cor...\n", " Conventions: CF-1.6, ACDD-1.3\n", " ... ...\n", " geospatial_lat_resolution: 0.072771376\n", " geospatial_lat_units: degrees_north\n", " geospatial_lon_min: 0.0\n", " geospatial_lon_max: 360.0\n", " geospatial_lon_resolution: 0.072756669\n", " geospatial_lon_units: degrees_east" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RD = RunDirectory(dset, \"precip\")\n", "RD.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The tuning parameters\n", "To apply the actual tracking algorithm we can use the `get_tracks` method. The algorithm can be tuned by passing the values for the tuning parameters. See also the [tuning parameter section](api.html#tracking-parameter-guide) of the docs:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Tracking tuning parameters\n", "--------------------------\n", "\n", "The following parameter can be set to tune the cell tracking algorithm\n", "\n", "* field_thresh: units of 'field' attribute, default: 32\n", " The threshold used for object detection. Detected objects are connected\n", " pixels above this threshold.\n", "* iso_thresh: units of 'field' attribute, default: 4\n", " Used in isolated cell classification. Isolated cells must not be connected\n", " to any other cell by contiguous pixels above this threshold.\n", "* iso_smooth: pixels, default: 4\n", " Gaussian smoothing parameter in peak detection preprocessing. See\n", " single_max in tint.objects.\n", "* min_size: square kilometers, default: 8\n", " The minimum size threshold in pixels for an object to be detected.\n", "* search_margin: meters, default: 250\n", " The radius of the search box around the predicted object center.\n", "* flow_margin: meters, default: 750\n", " The margin size around the object extent on which to perform phase\n", " correlation.\n", "* max_disparity: float, default: 999\n", " Maximum allowable disparity value. Larger disparity values are sent to a\n", " large number.\n", "* max_flow_mag: meters per second, default: 50\n", " Maximum allowable global shift magnitude. See get_global_shift in\n", " tint.phase_correlation.\n", "* max_shift_disp: meters per second, default: 15\n", " Maximum magnitude of difference in meters per second for two shifts to be\n", " considered in agreement. See correct_shift in tint.matching.\n", "* gs_alt: meters, default: 1500\n", " Altitude in meters at which to perform phase correlation for global shift\n", " calculation. See correct_shift in tint.matching.\n", "\n", "\n", "Setting and getting the tuning parameters\n", "-----------------------------------------\n", "The parameters can the set using the :py:class:`tintx.config.set` class.\n", "Getting the values of the currently set parameters can be done by the\n", ":py:func:`tintx.config.get` method.\n", "\n" ] } ], "source": [ "print(config.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the object has been created the cells can be tracked and plotted like in [Example I](I_Tracking_data_from_files.html) (with a little more tuning):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ac92ad6cebf64334b544652cf31d85a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Tracking: 0%| | 0/47 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
timegrid_xgrid_ylonlatareamaxmeanisolated
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002020-01-25 00:00:00623.3042.087145.3314-12.8078235.074.811738True
12020-01-25 00:00:00819.82613.765159.6645-11.93451156.404.352870True
22020-01-25 00:00:00620.88922.111145.1859-11.352393.413.383333True
32020-01-25 00:00:00617.82729.442144.9677-10.8429525.614.107116True
42020-01-25 00:00:00594.50048.400143.2215-9.4603103.683.420000True
.................................
473222020-01-25 23:30:00463.125257.875133.69045.821785.975.950000True
3232020-01-25 23:30:00363.900271.100126.48756.76771014.006.623000True
2722020-01-25 23:30:00364.314279.451126.48757.34995191.0021.292744True
3242020-01-25 23:30:00375.667292.216127.36058.2959519.544.426471True
3252020-01-25 23:30:00363.475294.220126.41478.44155916.906.375593True
\n", "

2732 rows × 9 columns

\n", "" ], "text/plain": [ " time grid_x grid_y lon lat area \\\n", "scan uid \n", "0 0 2020-01-25 00:00:00 623.304 2.087 145.3314 -12.8078 23 \n", " 1 2020-01-25 00:00:00 819.826 13.765 159.6645 -11.9345 115 \n", " 2 2020-01-25 00:00:00 620.889 22.111 145.1859 -11.3523 9 \n", " 3 2020-01-25 00:00:00 617.827 29.442 144.9677 -10.8429 52 \n", " 4 2020-01-25 00:00:00 594.500 48.400 143.2215 -9.4603 10 \n", "... ... ... ... ... ... ... \n", "47 322 2020-01-25 23:30:00 463.125 257.875 133.6904 5.8217 8 \n", " 323 2020-01-25 23:30:00 363.900 271.100 126.4875 6.7677 10 \n", " 272 2020-01-25 23:30:00 364.314 279.451 126.4875 7.3499 51 \n", " 324 2020-01-25 23:30:00 375.667 292.216 127.3605 8.2959 51 \n", " 325 2020-01-25 23:30:00 363.475 294.220 126.4147 8.4415 59 \n", "\n", " max mean isolated \n", "scan uid \n", "0 0 5.07 4.811738 True \n", " 1 6.40 4.352870 True \n", " 2 3.41 3.383333 True \n", " 3 5.61 4.107116 True \n", " 4 3.68 3.420000 True \n", "... ... ... ... \n", "47 322 5.97 5.950000 True \n", " 323 14.00 6.623000 True \n", " 272 91.00 21.292744 True \n", " 324 9.54 4.426471 True \n", " 325 16.90 6.375593 True \n", "\n", "[2732 rows x 9 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RD.tracks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After each tracking the `RunDirectory` class creates a so called hash of the resulting `DataFrame` and saves the tuning parameters for this specific tracking. The next time you call the tracking method the previous tuning parameters are noted and set automatically. This means that in the above example the `filed_thresh` and `min_size` parameters are automatically set for the next tracking." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying the results\n", "Data visualisation works just like in the previous example. For example we can plot the storm trajectories:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = RD.plot_trajectories(plot_style={\"resolution\": \"10m\", \"ms\": 15, \"lw\": 1.5},\n", " label=False,\n", " mintrace=10,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the tracks for later analysis\n", "Again, for a later analysis the results can be saved using the `save_tracks` method. This will save the tracks `DataFrame` to a hdf5 table format. hdf5 is the format of choice as it can save additional metadata to like the tracking parameters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "RD.save_tracks(\"/tmp/tint_tracks_sat.h5\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the tracked files\n", "\n", "Loading the files is somewhat more tricky since we haven't used the `from_files` class method to load the data set and the `RunDirectory` class has no information about the data files that were opened to create the dataset.\n", "\n", "Still we can load a tracked dataset from a hdf5 tracking file by providing the dataset the tracking is based on. In our case this is the cmorph dataset we opened:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:      (time: 48, nv: 2, lat: 357, lon: 825)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2020-01-25 ... 2020-01-25T23:30:00\n",
       "  * lat          (lat) float64 -12.95 -12.88 -12.81 -12.73 ... 12.81 12.88 12.95\n",
       "  * lon          (lon) float64 100.0 100.1 100.1 100.2 ... 159.8 159.9 160.0\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bounds  (time, nv) datetime64[ns] dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "    lat_bounds   (time, lat, nv) float64 dask.array<chunksize=(2, 357, 2), meta=np.ndarray>\n",
       "    lon_bounds   (time, lon, nv) float64 dask.array<chunksize=(2, 825, 2), meta=np.ndarray>\n",
       "    precip       (time, lat, lon) float32 dask.array<chunksize=(2, 357, 825), meta=np.ndarray>\n",
       "Attributes: (12/57)\n",
       "    ncei_template_version:      NCEI_NetCDF_Grid_template_V2.0\n",
       "    title:                      NOAA Climate Data Record (CDR) of CPC Morphin...\n",
       "    keywords:                   Precipitation, Satellite, High-Resolution, Gl...\n",
       "    summary:                    The CMORPH CDR is a reprocessed and bias-corr...\n",
       "    references:                 Xie, P., et al. (2017), Reprocessed, Bias-Cor...\n",
       "    Conventions:                CF-1.6, ACDD-1.3\n",
       "    ...                         ...\n",
       "    geospatial_lat_resolution:  0.072771376\n",
       "    geospatial_lat_units:       degrees_north\n",
       "    geospatial_lon_min:         0.0\n",
       "    geospatial_lon_max:         360.0\n",
       "    geospatial_lon_resolution:  0.072756669\n",
       "    geospatial_lon_units:       degrees_east
" ], "text/plain": [ "\n", "Dimensions: (time: 48, nv: 2, lat: 357, lon: 825)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2020-01-25 ... 2020-01-25T23:30:00\n", " * lat (lat) float64 -12.95 -12.88 -12.81 -12.73 ... 12.81 12.88 12.95\n", " * lon (lon) float64 100.0 100.1 100.1 100.2 ... 159.8 159.9 160.0\n", "Dimensions without coordinates: nv\n", "Data variables:\n", " time_bounds (time, nv) datetime64[ns] dask.array\n", " lat_bounds (time, lat, nv) float64 dask.array\n", " lon_bounds (time, lon, nv) float64 dask.array\n", " precip (time, lat, lon) float32 dask.array\n", "Attributes: (12/57)\n", " ncei_template_version: NCEI_NetCDF_Grid_template_V2.0\n", " title: NOAA Climate Data Record (CDR) of CPC Morphin...\n", " keywords: Precipitation, Satellite, High-Resolution, Gl...\n", " summary: The CMORPH CDR is a reprocessed and bias-corr...\n", " references: Xie, P., et al. (2017), Reprocessed, Bias-Cor...\n", " Conventions: CF-1.6, ACDD-1.3\n", " ... ...\n", " geospatial_lat_resolution: 0.072771376\n", " geospatial_lat_units: degrees_north\n", " geospatial_lon_min: 0.0\n", " geospatial_lon_max: 360.0\n", " geospatial_lon_resolution: 0.072756669\n", " geospatial_lon_units: degrees_east" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RD = RunDirectory.from_dataframe(\"/tmp/tint_tracks_sat.h5\", dataset=dset)\n", "RD.data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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scanuid
002020-01-25 00:00:00623.3042.087145.3314-12.8078235.074.811738True
12020-01-25 00:00:00819.82613.765159.6645-11.93451156.404.352870True
22020-01-25 00:00:00620.88922.111145.1859-11.352393.413.383333True
32020-01-25 00:00:00617.82729.442144.9677-10.8429525.614.107116True
42020-01-25 00:00:00594.50048.400143.2215-9.4603103.683.420000True
.................................
473222020-01-25 23:30:00463.125257.875133.69045.821785.975.950000True
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3242020-01-25 23:30:00375.667292.216127.36058.2959519.544.426471True
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2732 rows × 9 columns

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" ], "text/plain": [ " time grid_x grid_y lon lat area \\\n", "scan uid \n", "0 0 2020-01-25 00:00:00 623.304 2.087 145.3314 -12.8078 23 \n", " 1 2020-01-25 00:00:00 819.826 13.765 159.6645 -11.9345 115 \n", " 2 2020-01-25 00:00:00 620.889 22.111 145.1859 -11.3523 9 \n", " 3 2020-01-25 00:00:00 617.827 29.442 144.9677 -10.8429 52 \n", " 4 2020-01-25 00:00:00 594.500 48.400 143.2215 -9.4603 10 \n", "... ... ... ... ... ... ... \n", "47 322 2020-01-25 23:30:00 463.125 257.875 133.6904 5.8217 8 \n", " 323 2020-01-25 23:30:00 363.900 271.100 126.4875 6.7677 10 \n", " 272 2020-01-25 23:30:00 364.314 279.451 126.4875 7.3499 51 \n", " 324 2020-01-25 23:30:00 375.667 292.216 127.3605 8.2959 51 \n", " 325 2020-01-25 23:30:00 363.475 294.220 126.4147 8.4415 59 \n", "\n", " max mean isolated \n", "scan uid \n", "0 0 5.07 4.811738 True \n", " 1 6.40 4.352870 True \n", " 2 3.41 3.383333 True \n", " 3 5.61 4.107116 True \n", " 4 3.68 3.420000 True \n", "... ... ... ... \n", "47 322 5.97 5.950000 True \n", " 323 14.00 6.623000 True \n", " 272 91.00 21.292744 True \n", " 324 9.54 4.426471 True \n", " 325 16.90 6.375593 True \n", "\n", "[2732 rows x 9 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RD.tracks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After loading the track data we can inquire the tuning parameters that had been applied to create the tracks:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ISO_THRESH': 10.0,\n", " 'FIELD_THRESH': 3.0,\n", " 'ISO_SMOOTH': 4.0,\n", " 'MIN_SIZE': 8.0,\n", " 'SEARCH_MARGIN': 8750.0,\n", " 'FLOW_MARGIN': 1750.0,\n", " 'MAX_DISPARITY': 999.0,\n", " 'MAX_FLOW_MAG': 5000.0,\n", " 'MAX_SHIFT_DISP': 1000.0,\n", " 'GS_ALT': 1500.0,\n", " 'ISO_SMOTH': 10.0}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RD.get_parameters()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This concludes the second tutorial, in [the next one](III_Using_the_command_line_interface.html) we will explore the application of the tracking algorithm via the Command Line Interface." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "46473610eeec4029a3472397e70dbdbd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "46670607ecf14fdfad42fbcdb05f6bc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_652e7376676d41ee875ef7e2615cd853", "max": 47, "style": "IPY_MODEL_78932331dd7744899848b9e62b84b0d5", "value": 47 } }, "652e7376676d41ee875ef7e2615cd853": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "78932331dd7744899848b9e62b84b0d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "8d45b61e3bd943b6a09c17f5c5ef28fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "922f57dc169346eb9223b044e2beea35": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "9ef533dfe5ae4139a936790292115d9d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_cc674881c161436d8eef3237126ef8b1", "style": "IPY_MODEL_8d45b61e3bd943b6a09c17f5c5ef28fe", "value": "Tracking: " } }, "ac92ad6cebf64334b544652cf31d85a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_9ef533dfe5ae4139a936790292115d9d", "IPY_MODEL_46670607ecf14fdfad42fbcdb05f6bc5", "IPY_MODEL_ad6583fde43847d484852f82ab421675" ], "layout": "IPY_MODEL_46473610eeec4029a3472397e70dbdbd" } }, "ad6583fde43847d484852f82ab421675": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_922f57dc169346eb9223b044e2beea35", "style": "IPY_MODEL_dc95af1714014a70ac4fc9b62493f4ec", "value": " 48/? 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