Add information about method

Author: AntSimi

examples/07_cube_manipulation/pet_fsle_med.py

@@ -26,6 +26,10 @@
 # %%
 # ADT in med
 # ----------
+# :py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_cube` method is
+# made for data stores in time cube, you could use also 
+# :py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_list` method to
+# load data-cube from multiple file.
 c = GridCollection.from_netcdf_cube(
     get_demo_path("dt_med_allsat_phy_l4_2005T2.nc"),
     "longitude",

examples/16_network/pet_ioannou_2017_case.py

@@ -21,7 +21,7 @@
 from py_eddy_tracker.gui import GUI_AXES
 from py_eddy_tracker.observations.network import NetworkObservations
 
-from py_eddy_tracker.generic import coordinates_to_local, local_to_coordinates
+from py_eddy_tracker.generic import coordinates_to_local
 from py_eddy_tracker.poly import fit_ellipse
 
 # %%
@@ -217,22 +217,22 @@ def update_axes(ax, mappable=None):
 # Theta
 ax = timeline_axes()
 m = close_to_i3.scatter_timeline(ax, theta_, vmin=-pi / 2, vmax=pi / 2, cmap="hsv")
-cb = update_axes(ax, m["scatter"])
+_ = update_axes(ax, m["scatter"])
 
 # %%
 # a
 ax = timeline_axes()
 m = close_to_i3.scatter_timeline(ax, a_ * 1e-3, vmin=0, vmax=80, cmap="Spectral_r")
-cb = update_axes(ax, m["scatter"])
+_ = update_axes(ax, m["scatter"])
 
 # %%
 # b
 ax = timeline_axes()
 m = close_to_i3.scatter_timeline(ax, b_ * 1e-3, vmin=0, vmax=80, cmap="Spectral_r")
-cb = update_axes(ax, m["scatter"])
+_ = update_axes(ax, m["scatter"])
 
 # %%
 # a/b
 ax = timeline_axes()
 m = close_to_i3.scatter_timeline(ax, a_ / b_, vmin=1, vmax=2, cmap="Spectral_r")
-cb = update_axes(ax, m["scatter"])
+_ = update_axes(ax, m["scatter"])

notebooks/python_module/01_general_things/pet_storage.ipynb

@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Eddies files (zarr or netcdf) could be loaded with ```load_file``` method:\n\n"
+        "Eddies files (zarr or netcdf) can be loaded with ```load_file``` method:\n\n"
       ]
     },
     {

notebooks/python_module/07_cube_manipulation/pet_fsle_med.ipynb

@@ -33,7 +33,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## ADT in med\n\n"
+        "## ADT in med\n:py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_cube` method is\nmade for data stores in time cube, you could use also \n:py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_list` method to\nload data-cube from multiple file.\n\n"
       ]
     },
     {
@@ -172,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.9.2"
     }
   },
   "nbformat": 4,

notebooks/python_module/14_generic_tools/pet_fit_contour.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib import pyplot as plt\nfrom numpy import cos, linspace, radians, sin\n\nfrom py_eddy_tracker import data\nfrom py_eddy_tracker.generic import coordinates_to_local, local_to_coordinates\nfrom py_eddy_tracker.observations.observation import EddiesObservations\nfrom py_eddy_tracker.poly import fit_circle_, fit_ellips"
+        "from matplotlib import pyplot as plt\nfrom numpy import cos, linspace, radians, sin\n\nfrom py_eddy_tracker import data\nfrom py_eddy_tracker.generic import coordinates_to_local, local_to_coordinates\nfrom py_eddy_tracker.observations.observation import EddiesObservations\nfrom py_eddy_tracker.poly import fit_circle_, fit_ellipse"
       ]
     },
     {
@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Function to draw circle or ellips from parameter\n\n"
+        "Function to draw circle or ellipse from parameter\n\n"
       ]
     },
     {
@@ -62,14 +62,14 @@
       },
       "outputs": [],
       "source": [
-        "def build_circle(x0, y0, r):\n    angle = radians(linspace(0, 360, 50))\n    x_norm, y_norm = cos(angle), sin(angle)\n    return local_to_coordinates(x_norm * r, y_norm * r, x0, y0)\n\n\ndef build_ellips(x0, y0, a, b, theta):\n    angle = radians(linspace(0, 360, 50))\n    x = a * cos(theta) * cos(angle) - b * sin(theta) * sin(angle)\n    y = a * sin(theta) * cos(angle) + b * cos(theta) * sin(angle)\n    return local_to_coordinates(x, y, x0, y0)"
+        "def build_circle(x0, y0, r):\n    angle = radians(linspace(0, 360, 50))\n    x_norm, y_norm = cos(angle), sin(angle)\n    return local_to_coordinates(x_norm * r, y_norm * r, x0, y0)\n\n\ndef build_ellipse(x0, y0, a, b, theta):\n    angle = radians(linspace(0, 360, 50))\n    x = a * cos(theta) * cos(angle) - b * sin(theta) * sin(angle)\n    y = a * sin(theta) * cos(angle) + b * cos(theta) * sin(angle)\n    return local_to_coordinates(x, y, x0, y0)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Plot fitted circle or ellips on stored contour\n\n"
+        "Plot fitted circle or ellipse on stored contour\n\n"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "xs, ys = a.contour_lon_s, a.contour_lat_s\n\nfig = plt.figure(figsize=(15, 15))\n\nj = 1\nfor i in range(0, 800, 30):\n    x, y = xs[i], ys[i]\n    x0_, y0_ = x.mean(), y.mean()\n    x_, y_ = coordinates_to_local(x, y, x0_, y0_)\n    ax = fig.add_subplot(4, 4, j)\n    ax.grid(), ax.set_aspect(\"equal\")\n    ax.plot(x, y, label=\"store\", color=\"black\")\n    x0, y0, a, b, theta = fit_ellips(x_, y_)\n    x0, y0 = local_to_coordinates(x0, y0, x0_, y0_)\n    ax.plot(*build_ellips(x0, y0, a, b, theta), label=\"ellips\", color=\"green\")\n    x0, y0, radius, shape_error = fit_circle_(x_, y_)\n    x0, y0 = local_to_coordinates(x0, y0, x0_, y0_)\n    ax.plot(*build_circle(x0, y0, radius), label=\"circle\", color=\"red\", lw=0.5)\n    if j == 16:\n        break\n    j += 1"
+        "xs, ys = a.contour_lon_s, a.contour_lat_s\n\nfig = plt.figure(figsize=(15, 15))\n\nj = 1\nfor i in range(0, 800, 30):\n    x, y = xs[i], ys[i]\n    x0_, y0_ = x.mean(), y.mean()\n    x_, y_ = coordinates_to_local(x, y, x0_, y0_)\n    ax = fig.add_subplot(4, 4, j)\n    ax.grid(), ax.set_aspect(\"equal\")\n    ax.plot(x, y, label=\"store\", color=\"black\")\n    x0, y0, a, b, theta = fit_ellipse(x_, y_)\n    x0, y0 = local_to_coordinates(x0, y0, x0_, y0_)\n    ax.plot(*build_ellipse(x0, y0, a, b, theta), label=\"ellipse\", color=\"green\")\n    x0, y0, radius, shape_error = fit_circle_(x_, y_)\n    x0, y0 = local_to_coordinates(x0, y0, x0_, y0_)\n    ax.plot(*build_circle(x0, y0, radius), label=\"circle\", color=\"red\", lw=0.5)\n    if j == 16:\n        break\n    j += 1"
       ]
     }
   ],
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.9.2"
     }
   },
   "nbformat": 4,

notebooks/python_module/16_network/pet_follow_particle.ipynb

@@ -37,7 +37,7 @@
       },
       "outputs": [],
       "source": [
-        "class VideoAnimation(FuncAnimation):\n    def _repr_html_(self, *args, **kwargs):\n        \"\"\"To get video in html and have a player\"\"\"\n        content = self.to_html5_video()\n        return re.sub(\n            r'width=\"[0-9]*\"\\sheight=\"[0-9]*\"', 'width=\"100%\" height=\"100%\"', content\n        )\n\n    def save(self, *args, **kwargs):\n        if args[0].endswith(\"gif\"):\n            # In this case gif is use to create thumbnail which are not use but consume same time than video\n            # So we create an empty file, to save time\n            with open(args[0], \"w\") as _:\n                pass\n            return\n        return super().save(*args, **kwargs)"
+        "class VideoAnimation(FuncAnimation):\n    def _repr_html_(self, *args, **kwargs):\n        \"\"\"To get video in html and have a player\"\"\"\n        content = self.to_html5_video()\n        return re.sub(\n            r'width=\"[0-9]*\"\\sheight=\"[0-9]*\"', 'width=\"100%\" height=\"100%\"', content\n        )\n\n    def save(self, *args, **kwargs):\n        if args[0].endswith(\"gif\"):\n            # In this case gif is used to create thumbnail which are not used but consumes same time than video\n            # So we create an empty file, to save time\n            with open(args[0], \"w\") as _:\n                pass\n            return\n        return super().save(*args, **kwargs)"
       ]
     },
     {
@@ -120,7 +120,7 @@
       },
       "outputs": [],
       "source": [
-        "def advect(x, y, c, t0, delta_t):\n    \"\"\"\n    Advect particle from t0 to t0 + delta_t, with data cube.\n    \"\"\"\n    kw = dict(nb_step=6, time_step=86400 / 6)\n    if delta_t < 0:\n        kw[\"backward\"] = True\n        delta_t = -delta_t\n    p = c.advect(x, y, \"u\", \"v\", t_init=t0, **kw)\n    for _ in range(delta_t):\n        t, x, y = p.__next__()\n    return t, x, y\n\n\ndef particle_candidate(x, y, c, eddies, t_start, i_target, pct, **kwargs):\n    # Obs from initial time\n    m_start = eddies.time == t_start\n    e = eddies.extract_with_mask(m_start)\n    # to be able to get global index\n    translate_start = where(m_start)[0]\n    # Identify particle in eddies(only in core)\n    i_start = e.contains(x, y, intern=True)\n    m = i_start != -1\n    x, y, i_start = x[m], y[m], i_start[m]\n    # Advect\n    t_end, x, y = advect(x, y, c, t_start, **kwargs)\n    # eddies at last date\n    m_end = eddies.time == t_end / 86400\n    e_end = eddies.extract_with_mask(m_end)\n    # to be able to get global index\n    translate_end = where(m_end)[0]\n    # Id eddies for each alive particle(in core and extern)\n    i_end = e_end.contains(x, y)\n    # compute matrix and filled target array\n    get_matrix(i_start, i_end, translate_start, translate_end, i_target, pct)\n\n\n@njit(cache=True)\ndef get_matrix(i_start, i_end, translate_start, translate_end, i_target, pct):\n    nb_start, nb_end = translate_start.size, translate_end.size\n    # Matrix which will store count for every couple\n    count = zeros((nb_start, nb_end), dtype=nb_types.int32)\n    # Number of particle in each origin observation\n    ref = zeros(nb_start, dtype=nb_types.int32)\n    # For each particle\n    for i in range(i_start.size):\n        i_end_ = i_end[i]\n        i_start_ = i_start[i]\n        if i_end_ != -1:\n            count[i_start_, i_end_] += 1\n        ref[i_start_] += 1\n    for i in range(nb_start):\n        for j in range(nb_end):\n            pct_ = count[i, j]\n            # If there are particle from i to j\n            if pct_ != 0:\n                # Get percent\n                pct_ = pct_ / ref[i] * 100.0\n                # Get indices in full dataset\n                i_, j_ = translate_start[i], translate_end[j]\n                pct_0 = pct[i_, 0]\n                if pct_ > pct_0:\n                    pct[i_, 1] = pct_0\n                    pct[i_, 0] = pct_\n                    i_target[i_, 1] = i_target[i_, 0]\n                    i_target[i_, 0] = j_\n                elif pct_ > pct[i_, 1]:\n                    pct[i_, 1] = pct_\n                    i_target[i_, 1] = j_\n    return i_target, pct"
+        "def advect(x, y, c, t0, delta_t):\n    \"\"\"\n    Advect particle from t0 to t0 + delta_t, with data cube.\n    \"\"\"\n    kw = dict(nb_step=6, time_step=86400 / 6)\n    if delta_t < 0:\n        kw[\"backward\"] = True\n        delta_t = -delta_t\n    p = c.advect(x, y, \"u\", \"v\", t_init=t0, **kw)\n    for _ in range(delta_t):\n        t, x, y = p.__next__()\n    return t, x, y\n\n\ndef particle_candidate(x, y, c, eddies, t_start, i_target, pct, **kwargs):\n    # Obs from initial time\n    m_start = eddies.time == t_start\n    e = eddies.extract_with_mask(m_start)\n    # to be able to get global index\n    translate_start = where(m_start)[0]\n    # Identify particle in eddies (only in core)\n    i_start = e.contains(x, y, intern=True)\n    m = i_start != -1\n    x, y, i_start = x[m], y[m], i_start[m]\n    # Advect\n    t_end, x, y = advect(x, y, c, t_start, **kwargs)\n    # eddies at last date\n    m_end = eddies.time == t_end / 86400\n    e_end = eddies.extract_with_mask(m_end)\n    # to be able to get global index\n    translate_end = where(m_end)[0]\n    # Id eddies for each alive particle (in core and extern)\n    i_end = e_end.contains(x, y)\n    # compute matrix and fill target array\n    get_matrix(i_start, i_end, translate_start, translate_end, i_target, pct)\n\n\n@njit(cache=True)\ndef get_matrix(i_start, i_end, translate_start, translate_end, i_target, pct):\n    nb_start, nb_end = translate_start.size, translate_end.size\n    # Matrix which will store count for every couple\n    count = zeros((nb_start, nb_end), dtype=nb_types.int32)\n    # Number of particles in each origin observation\n    ref = zeros(nb_start, dtype=nb_types.int32)\n    # For each particle\n    for i in range(i_start.size):\n        i_end_ = i_end[i]\n        i_start_ = i_start[i]\n        if i_end_ != -1:\n            count[i_start_, i_end_] += 1\n        ref[i_start_] += 1\n    for i in range(nb_start):\n        for j in range(nb_end):\n            pct_ = count[i, j]\n            # If there are particles from i to j\n            if pct_ != 0:\n                # Get percent\n                pct_ = pct_ / ref[i] * 100.0\n                # Get indices in full dataset\n                i_, j_ = translate_start[i], translate_end[j]\n                pct_0 = pct[i_, 0]\n                if pct_ > pct_0:\n                    pct[i_, 1] = pct_0\n                    pct[i_, 0] = pct_\n                    i_target[i_, 1] = i_target[i_, 0]\n                    i_target[i_, 0] = j_\n                elif pct_ > pct[i_, 1]:\n                    pct[i_, 1] = pct_\n                    i_target[i_, 1] = j_\n    return i_target, pct"
       ]
     },
     {
@@ -169,7 +169,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.9.2"
     }
   },
   "nbformat": 4,

notebooks/python_module/16_network/pet_ioannou_2017_case.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import re\nfrom datetime import datetime, timedelta\n\nfrom matplotlib import colors\nfrom matplotlib import pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.ticker import FuncFormatter\nfrom numpy import arange, where\n\nfrom py_eddy_tracker.appli.gui import Anim\nfrom py_eddy_tracker.data import get_demo_path\nfrom py_eddy_tracker.gui import GUI_AXES\nfrom py_eddy_tracker.observations.network import NetworkObservations"
+        "import re\nfrom datetime import datetime, timedelta\n\nfrom matplotlib import colors\nfrom matplotlib import pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.ticker import FuncFormatter\nfrom numpy import arange, where, array, pi\n\nfrom py_eddy_tracker.appli.gui import Anim\nfrom py_eddy_tracker.data import get_demo_path\nfrom py_eddy_tracker.gui import GUI_AXES\nfrom py_eddy_tracker.observations.network import NetworkObservations\n\nfrom py_eddy_tracker.generic import coordinates_to_local\nfrom py_eddy_tracker.poly import fit_ellipse"
       ]
     },
     {
@@ -230,6 +230,96 @@
       "source": [
         "ax = timeline_axes()\nm = close_to_i3.scatter_timeline(ax, \"shape_error_e\", vmin=14, vmax=70, **kw)\ncb = update_axes(ax, m[\"scatter\"])\ncb.set_label(\"Effective shape error\")"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Rotation angle\nFor each obs, fit an ellipse to the contour, with theta the angle from the x-axis,\na the semi ax in x direction and b the semi ax in y dimension\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "theta_ = list()\na_ = list()\nb_ = list()\nfor obs in close_to_i3:\n    x, y = obs[\"contour_lon_s\"], obs[\"contour_lat_s\"]\n    x0_, y0_ = x.mean(), y.mean()\n    x_, y_ = coordinates_to_local(x, y, x0_, y0_)\n    x0, y0, a, b, theta = fit_ellipse(x_, y_)\n    theta_.append(theta)\n    a_.append(a)\n    b_.append(b)\na_ = array(a_)\nb_ = array(b_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Theta\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ax = timeline_axes()\nm = close_to_i3.scatter_timeline(ax, theta_, vmin=-pi / 2, vmax=pi / 2, cmap=\"hsv\")\n_ = update_axes(ax, m[\"scatter\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "a\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ax = timeline_axes()\nm = close_to_i3.scatter_timeline(ax, a_ * 1e-3, vmin=0, vmax=80, cmap=\"Spectral_r\")\n_ = update_axes(ax, m[\"scatter\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "b\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ax = timeline_axes()\nm = close_to_i3.scatter_timeline(ax, b_ * 1e-3, vmin=0, vmax=80, cmap=\"Spectral_r\")\n_ = update_axes(ax, m[\"scatter\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "a/b\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ax = timeline_axes()\nm = close_to_i3.scatter_timeline(ax, a_ / b_, vmin=1, vmax=2, cmap=\"Spectral_r\")\n_ = update_axes(ax, m[\"scatter\"])"
+      ]
     }
   ],
   "metadata": {