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Add example with correspondance
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‎examples/08_tracking_manipulation/pet_how_to_use_correspondances.py‎

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"""
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Correspondances
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===============
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Correspondances is a mechanism to intend to continue tracking with new detection
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"""
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import logging
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# %%
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from matplotlib import pyplot as plt
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from netCDF4 import Dataset
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from py_eddy_tracker import start_logger
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from py_eddy_tracker.data import get_remote_demo_sample
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from py_eddy_tracker.featured_tracking.area_tracker import AreaTracker
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# In order to hide some warning
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import py_eddy_tracker.observations.observation
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from py_eddy_tracker.tracking import Correspondances
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py_eddy_tracker.observations.observation._display_check_warning = False
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# %%
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def plot_eddy(ed):
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fig = plt.figure(figsize=(10, 5))
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ax = fig.add_axes([0.05, 0.03, 0.90, 0.94])
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ed.plot(ax, ref=-10, marker="x")
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lc = ed.display_color(ax, field=ed.time, ref=-10, intern=True)
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plt.colorbar(lc).set_label("Time in Julian days (from 1950/01/01)")
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ax.set_xlim(4.5, 8), ax.set_ylim(36.8, 38.3)
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ax.set_aspect("equal")
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ax.grid()
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# %%
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# Get remote data, we will keep only 20 first days,
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# `get_remote_demo_sample` function is only to get demo dataset, in your own case give a list of identification filename
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# and don't mix cyclonic and anticyclonic files.
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file_objects = get_remote_demo_sample(
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"eddies_med_adt_allsat_dt2018/Anticyclonic_2010_2011_2012"
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)[:20]
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# %%
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# We run a traking with a tracker which use contour overlap, on 10 first time step
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c_first_run = Correspondances(
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datasets=file_objects[:10], class_method=AreaTracker, virtual=4
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)
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start_logger().setLevel("INFO")
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c_first_run.track()
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start_logger().setLevel("WARNING")
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with Dataset("correspondances.nc", "w") as h:
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c_first_run.to_netcdf(h)
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# Next step are done only to build atlas and display it
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c_first_run.prepare_merging()
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# We have now an eddy object
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eddies_area_tracker = c_first_run.merge(raw_data=False)
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eddies_area_tracker.virtual[:] = eddies_area_tracker.time == 0
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eddies_area_tracker.filled_by_interpolation(eddies_area_tracker.virtual == 1)
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# %%
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# Plot from first ten days
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plot_eddy(eddies_area_tracker)
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# %%
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# Restart from previous run
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# -------------------------
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# We give all filenames, the new one and filename from previous run
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c_second_run = Correspondances(
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datasets=file_objects[:20],
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# This parameter must be identical in each run
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class_method=AreaTracker,
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virtual=4,
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# Previous saved correspondancs
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previous_correspondance="correspondances.nc",
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)
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start_logger().setLevel("INFO")
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c_second_run.track()
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start_logger().setLevel("WARNING")
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c_second_run.prepare_merging()
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# We have now another eddy object
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eddies_area_tracker_extend = c_second_run.merge(raw_data=False)
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eddies_area_tracker_extend.virtual[:] = eddies_area_tracker_extend.time == 0
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eddies_area_tracker_extend.filled_by_interpolation(
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eddies_area_tracker_extend.virtual == 1
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)
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# %%
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# Plot with time extension
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plot_eddy(eddies_area_tracker_extend)

‎notebooks/python_module/08_tracking_manipulation/pet_how_to_use_correspondances.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Correspondances\n\nCorrespondances is a mechanism to intend to continue tracking with new detection\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import logging"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\nfrom netCDF4 import Dataset\n\nfrom py_eddy_tracker import start_logger\nfrom py_eddy_tracker.data import get_remote_demo_sample\nfrom py_eddy_tracker.featured_tracking.area_tracker import AreaTracker\n\n# In order to hide some warning\nimport py_eddy_tracker.observations.observation\nfrom py_eddy_tracker.tracking import Correspondances\n\npy_eddy_tracker.observations.observation._display_check_warning = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def plot_eddy(ed):\n fig = plt.figure(figsize=(10, 5))\n ax = fig.add_axes([0.05, 0.03, 0.90, 0.94])\n ed.plot(ax, ref=-10, marker=\"x\")\n lc = ed.display_color(ax, field=ed.time, ref=-10, intern=True)\n plt.colorbar(lc).set_label(\"Time in Julian days (from 1950/01/01)\")\n ax.set_xlim(4.5, 8), ax.set_ylim(36.8, 38.3)\n ax.set_aspect(\"equal\")\n ax.grid()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Get remote data, we will keep only 20 first days,\n`get_remote_demo_sample` function is only to get demo dataset, in your own case give a list of identification filename\nand don't mix cyclonic and anticyclonic files.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"file_objects = get_remote_demo_sample(\n \"eddies_med_adt_allsat_dt2018/Anticyclonic_2010_2011_2012\"\n)[:20]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We run a traking with a tracker which use contour overlap, on 10 first time step\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"c_first_run = Correspondances(\n datasets=file_objects[:10], class_method=AreaTracker, virtual=4\n)\nstart_logger().setLevel(\"INFO\")\nc_first_run.track()\nstart_logger().setLevel(\"WARNING\")\nwith Dataset(\"correspondances.nc\", \"w\") as h:\n c_first_run.to_netcdf(h)\n# Next step are done only to build atlas and display it\nc_first_run.prepare_merging()\n\n# We have now an eddy object\neddies_area_tracker = c_first_run.merge(raw_data=False)\neddies_area_tracker.virtual[:] = eddies_area_tracker.time == 0\neddies_area_tracker.filled_by_interpolation(eddies_area_tracker.virtual == 1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Plot from first ten days\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plot_eddy(eddies_area_tracker)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Restart from previous run\nWe give all filenames, the new one and filename from previous run\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"c_second_run = Correspondances(\n datasets=file_objects[:20],\n # This parameter must be identical in each run\n class_method=AreaTracker,\n virtual=4,\n # Previous saved correspondancs\n previous_correspondance=\"correspondances.nc\",\n)\nstart_logger().setLevel(\"INFO\")\nc_second_run.track()\nstart_logger().setLevel(\"WARNING\")\nc_second_run.prepare_merging()\n# We have now another eddy object\neddies_area_tracker_extend = c_second_run.merge(raw_data=False)\neddies_area_tracker_extend.virtual[:] = eddies_area_tracker_extend.time == 0\neddies_area_tracker_extend.filled_by_interpolation(\n eddies_area_tracker_extend.virtual == 1\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Plot with time extension\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plot_eddy(eddies_area_tracker_extend)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎src/py_eddy_tracker/observations/observation.py‎

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_software_version_reduced = packaging.version.Version(
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"{v.major}.{v.minor}".format(v=packaging.version.parse(__version__))
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)
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_display_check_warning = True
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def _check_versions(version):
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:param version: string version of software used to create the file. If None, version was not provided
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:type version: str, None
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"""
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if not _display_check_warning:
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return
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file_version = packaging.version.parse(version) if version is not None else None
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if file_version is None or file_version < _software_version_reduced:
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logger.warning(
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zarr_file = filename_.endswith(end)
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else:
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zarr_file = False
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logger.info(f"loading file '{filename}'")
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logger.info(f"loading file '{filename_}'")
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if zarr_file:
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return cls.load_from_zarr(filename, **kwargs)
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else:

‎src/py_eddy_tracker/tracking.py‎

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"""
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Class to store link between observations
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"""
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from datetime import datetime, timedelta
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import json
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import logging
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import platform
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from tarfile import ExFileObject
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from netCDF4 import Dataset, default_fillvals
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from numba import njit, types as numba_types
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# We begin with second file, first one is in previous
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for file_name in self.datasets[first_dataset:]:
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self.swap_dataset(file_name, **kwargs)
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logger.info("%s match with previous state", file_name)
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filename_ = (
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file_name.filename if isinstance(file_name, ExFileObject) else file_name
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)
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logger.info("%s match with previous state", filename_)
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logger.debug("%d obs to match", len(self.current_obs))
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nb_real_obs = len(self.previous_obs)

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