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Add method to get first and last
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‎examples/10_tracking_diagnostics/pet_birth_and_death.py‎

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"""
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Birth and death
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===============
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Following figures are based on https://doi.org/10.1016/j.pocean.2011.01.002
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"""
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from matplotlib import pyplot as plt
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from py_eddy_tracker.observations.tracking import TrackEddiesObservations
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import py_eddy_tracker_sample
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# %%
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def start_axes(title):
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fig = plt.figure(figsize=(13, 5))
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ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])
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ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
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ax.set_aspect("equal")
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ax.set_title(title)
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return ax
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def update_axes(ax, mappable=None):
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ax.grid()
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if mappable:
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plt.colorbar(mappable, cax=ax.figure.add_axes([0.95, 0.05, 0.01, 0.9]))
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# %%
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# Load an experimental med atlas over a period of 26 years (1993-2019)
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kwargs_load = dict(
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include_vars=(
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"longitude",
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"latitude",
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"observation_number",
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"track",
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"time",
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"speed_contour_longitude",
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"speed_contour_latitude",
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)
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)
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a = TrackEddiesObservations.load_file(
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py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
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)
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c = TrackEddiesObservations.load_file(
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py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
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)
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# %%
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t0, t1 = a.period
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step = 0.125
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bins = ((-10, 37, step), (30, 46, step))
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kwargs = dict(cmap="terrain_r", factor=100 / (t1 - t0), name="count", vmin=0, vmax=1)
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# %%
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# Cyclonic
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# --------
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ax = start_axes("Birth cyclonic frenquency (%)")
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g_c_first = c.first_obs().grid_count(bins, intern=True)
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m = g_c_first.display(ax, **kwargs)
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update_axes(ax, m)
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# %%
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ax = start_axes("Death cyclonic frenquency (%)")
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g_c_last = c.last_obs().grid_count(bins, intern=True)
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m = g_c_last.display(ax, **kwargs)
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update_axes(ax, m)
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# %%
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# Anticyclonic
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# ------------
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ax = start_axes("Birth anticyclonic frequency (%)")
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g_a_first = a.first_obs().grid_count(bins, intern=True)
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m = g_a_first.display(ax, **kwargs)
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update_axes(ax, m)
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# %%
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ax = start_axes("Death anticyclonic frequency (%)")
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g_a_last = a.last_obs().grid_count(bins, intern=True)
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m = g_a_last.display(ax, **kwargs)
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update_axes(ax, m)

‎examples/10_tracking_diagnostics/pet_center_count.py‎

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"""
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Count center
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======================
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============
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"""
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from matplotlib import pyplot as plt

‎notebooks/python_module/10_tracking_diagnostics/pet_birth_and_death.ipynb‎

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{
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"cells": [
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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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"%matplotlib inline"
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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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"\n# Birth and death\n\nFollowing figures are based on https://doi.org/10.1016/j.pocean.2011.01.002\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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"from matplotlib import pyplot as plt\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\nimport py_eddy_tracker_sample"
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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 start_axes(title):\n fig = plt.figure(figsize=(13, 5))\n ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])\n ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n ax.set_aspect(\"equal\")\n ax.set_title(title)\n return ax\n\n\ndef update_axes(ax, mappable=None):\n ax.grid()\n if mappable:\n plt.colorbar(mappable, cax=ax.figure.add_axes([0.95, 0.05, 0.01, 0.9]))"
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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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"Load an experimental med atlas over a period of 26 years (1993-2019)\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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"kwargs_load = dict(\n include_vars=(\n \"longitude\",\n \"latitude\",\n \"observation_number\",\n \"track\",\n \"time\",\n \"speed_contour_longitude\",\n \"speed_contour_latitude\",\n )\n)\na = TrackEddiesObservations.load_file(\n py_eddy_tracker_sample.get_path(\"eddies_med_adt_allsat_dt2018/Anticyclonic.zarr\")\n)\nc = TrackEddiesObservations.load_file(\n py_eddy_tracker_sample.get_path(\"eddies_med_adt_allsat_dt2018/Cyclonic.zarr\")\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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"t0, t1 = a.period\nstep = 0.125\nbins = ((-10, 37, step), (30, 46, step))\nkwargs = dict(cmap=\"terrain_r\", factor=100 / (t1 - t0), name=\"count\", vmin=0, vmax=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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"## Cyclonic\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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"ax = start_axes(\"Birth cyclonic frenquency (%)\")\ng_c_first = c.first_obs().grid_count(bins, intern=True)\nm = g_c_first.display(ax, **kwargs)\nupdate_axes(ax, m)"
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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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"ax = start_axes(\"Death cyclonic frenquency (%)\")\ng_c_last = c.last_obs().grid_count(bins, intern=True)\nm = g_c_last.display(ax, **kwargs)\nupdate_axes(ax, m)"
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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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"## Anticyclonic\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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"ax = start_axes(\"Birth anticyclonic frequency (%)\")\ng_a_first = a.first_obs().grid_count(bins, intern=True)\nm = g_a_first.display(ax, **kwargs)\nupdate_axes(ax, m)"
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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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"ax = start_axes(\"Death anticyclonic frequency (%)\")\ng_a_last = a.last_obs().grid_count(bins, intern=True)\nm = g_a_last.display(ax, **kwargs)\nupdate_axes(ax, m)"
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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": "3.7.7"
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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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}

‎notebooks/python_module/10_tracking_diagnostics/pet_center_count.ipynb‎

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@@ -15,7 +15,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\nCount center\n======================\n"
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"\n# Count center\n"
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]
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},
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{
@@ -62,7 +62,7 @@
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},
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"outputs": [],
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"source": [
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"fig = plt.figure(figsize=(12, 18.5))\nax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])\nax_a.set_title(\"Anticyclonic center frequency\")\nax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])\nax_c.set_title(\"Cyclonic center frequency\")\nax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])\nax_all.set_title(\"All eddies center frequency\")\nax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])\nax_ratio.set_title(\"Ratio cyclonic / Anticyclonic\")\n\n# Count pixel used for each center\ng_a = a.grid_count(bins, intern=True, center=True)\ng_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True, center=True)\ng_c.display(ax_c, **kwargs_pcolormesh)\n# Compute a ratio Cyclonic / Anticyclonic\nratio = g_c.vars[\"count\"] / g_a.vars[\"count\"]\n\n# Mask manipulation to be able to sum the 2 grids\nm_c = g_c.vars[\"count\"].mask\nm = m_c & g_a.vars[\"count\"].mask\ng_c.vars[\"count\"][m_c] = 0\ng_c.vars[\"count\"] += g_a.vars[\"count\"]\ng_c.vars[\"count\"].mask = m\n\nm = g_c.display(ax_all, **kwargs_pcolormesh)\ncb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.27, 0.01, 0.7]))\ncb.set_label(\"Eddies by 1\u00b0^2 by day\")\n\ng_c.vars[\"count\"] = ratio\nm = g_c.display(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm())\nplt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.2]))\n\nfor ax in (ax_a, ax_c, ax_all, ax_ratio):\n ax.set_aspect(\"equal\")\n ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n ax.grid()"
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"fig = plt.figure(figsize=(12, 18.5))\nax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])\nax_a.set_title(\"Anticyclonic center frequency\")\nax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])\nax_c.set_title(\"Cyclonic center frequency\")\nax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])\nax_all.set_title(\"All eddies center frequency\")\nax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])\nax_ratio.set_title(\"Ratio cyclonic / Anticyclonic\")\n\n# Count pixel used for each center\ng_a = a.grid_count(bins, intern=True, center=True)\ng_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True, center=True)\ng_c.display(ax_c, **kwargs_pcolormesh)\n# Compute a ratio Cyclonic / Anticyclonic\nratio = g_c.vars[\"count\"] / g_a.vars[\"count\"]\n\n# Mask manipulation to be able to sum the 2 grids\nm_c = g_c.vars[\"count\"].mask\nm = m_c & g_a.vars[\"count\"].mask\ng_c.vars[\"count\"][m_c] = 0\ng_c.vars[\"count\"] += g_a.vars[\"count\"]\ng_c.vars[\"count\"].mask = m\n\nm = g_c.display(ax_all, **kwargs_pcolormesh)\ncb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.27, 0.01, 0.7]))\ncb.set_label(\"Eddies by 1\u00b0^2 by day\")\n\ng_c.vars[\"count\"] = ratio\nm = g_c.display(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm(), cmap='coolwarm_r')\nplt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.2]))\n\nfor ax in (ax_a, ax_c, ax_all, ax_ratio):\n ax.set_aspect(\"equal\")\n ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n ax.grid()"
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]
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}
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],

‎src/py_eddy_tracker/observations/observation.py‎

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Original file line numberDiff line numberDiff line change
@@ -1353,6 +1353,43 @@ def set_global_attr_netcdf(self, h_nc):
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for key, item in self.global_attr.items():
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h_nc.setncattr(key, item)
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def extract_with_area(self, area, **kwargs):
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"""
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Extract with a bounding box
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:param dict area: 4 coordinates in a dictionary to specify bounding box (lower left corner and upper right corner)
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:param dict kwargs: look at :py:meth:`extract_with_mask`
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:return: Return all eddy tracks which are in bounds
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:rtype: EddiesObservations
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.. minigallery:: py_eddy_tracker.EddiesObservations.extract_with_area
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"""
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mask = (self.latitude > area["llcrnrlat"]) * (self.latitude < area["urcrnrlat"])
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lon0 = area["llcrnrlon"]
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lon = (self.longitude - lon0) % 360 + lon0
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mask *= (lon > lon0) * (lon < area["urcrnrlon"])
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return self.extract_with_mask(mask, **kwargs)
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def extract_with_mask(self, mask):
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"""
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Extract a subset of observations
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:param array(bool) mask: mask to select observations
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:return: same object with selected observations
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:rtype: self
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"""
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nb_obs = mask.sum()
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new = self.__class__.new_like(self, nb_obs)
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new.sign_type = self.sign_type
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if nb_obs == 0:
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logger.warning("Empty dataset will be created")
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else:
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for field in self.obs.dtype.descr:
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logger.debug("Copy of field %s ...", field)
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var = field[0]
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new.obs[var] = self.obs[var][mask]
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return new
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def scatter(self, ax, name, ref=None, factor=1, **kwargs):
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"""
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:param matplotlib.axes.Axes ax: matplotlib axes use to draw
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lon_e, lat_e = wrap_longitude(lon_e, lat_e, ref, cut=True)
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ax.plot(lon_e, lat_e, linestyle="-.", **kwargs_e)
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def first_obs(self):
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"""
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Get first obs of each tracks.
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:rtype: __class__
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.. minigallery:: py_eddy_tracker.EddiesObservations.first_obs
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"""
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return self.extract_with_mask(self["n"] == 0)
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def last_obs(self):
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"""
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Get Last obs of each tracks.
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:rtype: __class__
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.. minigallery:: py_eddy_tracker.EddiesObservations.last_obs
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"""
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m = zeros(len(self), dtype="bool")
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m[-1] = True
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m[:-1][self["n"][1:] == 0] = True
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return self.extract_with_mask(m)
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def grid_count(self, bins, intern=False, center=False):
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"""
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Compute count of eddies in each bin (use of all pixel in each contour)

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