remove private method

Author: AntSimi

examples/01_general_things/pet_storage.py

@@ -10,9 +10,9 @@
 
 - **Eddies collections** : contain a list of eddies without link between them
 - **Track eddies collections** :
-   manage eddies associated in trajectories, the ```track``` field allows to separate each trajectory
+  manage eddies associated in trajectories, the ```track``` field allows to separate each trajectory
 - **Network eddies collections** :
-   manage eddies associated in networks, the ```track``` and ```segment``` fields allow to separate observations
+  manage eddies associated in networks, the ```track``` and ```segment``` fields allow to separate observations
 """
 
 import py_eddy_tracker_sample
@@ -65,7 +65,7 @@
 #
 # - **track** : Trajectory number
 # - **observation_flag** : Flag indicating if the value is interpolated between two observations or not
-#                          (0: observed eddy, 1: interpolated eddy)"
+#   (0: observed eddy, 1: interpolated eddy)"
 # - **observation_number** : Eddy temporal index in a trajectory, days starting at the eddy first detection
 # - **cost_association** : result of the cost function to associate the eddy with the next observation
 eddies_tracks = TrackEddiesObservations.load_file(
@@ -82,7 +82,7 @@
 # - track :  ID of network (ID 0 correspond to lonely eddies)
 # - segment :  ID of a segment within a network (from 1 to N)
 # - previous_obs : Index of the previous observation in the full dataset,
-#                  if -1 there are no previous observation (the segment starts)
+#   if -1 there are no previous observation (the segment starts)
 # - next_obs : Index of the next observation in the full dataset, if -1 there are no next observation (the segment ends)
 # - previous_cost : Result of the cost function (1 is a good association, 0 is bad) with previous observation
 # - next_cost : Result of the cost function (1 is a good association, 0 is bad) with next observation

examples/16_network/pet_atlas.py

@@ -35,7 +35,6 @@ def start_axes(title):
 
 def update_axes(ax, mappable=None):
     ax.grid()
-    ax.update_env()
     if mappable:
         return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))
 

examples/16_network/pet_ioannou_2017_case.py

@@ -52,7 +52,6 @@ def start_axes(title=""):
     ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)
     ax.set_aspect("equal")
     ax.set_title(title, weight="bold")
-    ax.update_env()
     return ax
 
 
@@ -122,7 +121,6 @@ def update_axes(ax, mappable=None):
     field_txt="segment",
 )
 a.ax.set_xlim(19, 30), a.ax.set_ylim(32, 35.25)
-a.ax.update_env()
 a.txt.set_position((21.5, 32.7))
 # We display in video only from the 100th day to the 500th
 kwargs = dict(frames=arange(*a.period)[100:501], interval=100)

examples/16_network/pet_replay_segmentation.py

@@ -28,7 +28,6 @@ def start_axes(title=""):
     ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)
     ax.set_aspect("equal")
     ax.set_title(title, weight="bold")
-    ax.update_env()
     return ax
 
 

notebooks/python_module/01_general_things/pet_storage.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# How data is stored\n\nGeneral information about eddies storage.\n\nAll files have the same structure, with more or less fields and possible different order.\n\nThere are 3 class of files:\n\n- **Eddies collections** : contain a list of eddies without link between them\n- **Track eddies collections** :\n   manage eddies associated in trajectories, the ```track``` field allows to separate each trajectory\n- **Network eddies collections** :\n   manage eddies associated in networks, the ```track``` and ```segment``` fields allow to separate observations\n"
+        "\n# How data is stored\n\nGeneral information about eddies storage.\n\nAll files have the same structure, with more or less fields and possible different order.\n\nThere are 3 class of files:\n\n- **Eddies collections** : contain a list of eddies without link between them\n- **Track eddies collections** :\n  manage eddies associated in trajectories, the ```track``` field allows to separate each trajectory\n- **Network eddies collections** :\n  manage eddies associated in networks, the ```track``` and ```segment``` fields allow to separate observations\n"
       ]
     },
     {
@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Trajectories\nTracks eddies collections add several fields :\n\n- **track** : Trajectory number\n- **observation_flag** : Flag indicating if the value is interpolated between two observations or not\n                         (0: observed eddy, 1: interpolated eddy)\"\n- **observation_number** : Eddy temporal index in a trajectory, days starting at the eddy first detection\n- **cost_association** : result of the cost function to associate the eddy with the next observation\n\n"
+        "## Trajectories\nTracks eddies collections add several fields :\n\n- **track** : Trajectory number\n- **observation_flag** : Flag indicating if the value is interpolated between two observations or not\n  (0: observed eddy, 1: interpolated eddy)\"\n- **observation_number** : Eddy temporal index in a trajectory, days starting at the eddy first detection\n- **cost_association** : result of the cost function to associate the eddy with the next observation\n\n"
       ]
     },
     {
@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Networks\nNetwork files use some specific fields :\n\n- track :  ID of network (ID 0 correspond to lonely eddies)\n- segment :  ID of a segment within a network (from 1 to N)\n- previous_obs : Index of the previous observation in the full dataset,\n                 if -1 there are no previous observation (the segment starts)\n- next_obs : Index of the next observation in the full dataset, if -1 there are no next observation (the segment ends)\n- previous_cost : Result of the cost function (1 is a good association, 0 is bad) with previous observation\n- next_cost : Result of the cost function (1 is a good association, 0 is bad) with next observation\n\n"
+        "## Networks\nNetwork files use some specific fields :\n\n- track :  ID of network (ID 0 correspond to lonely eddies)\n- segment :  ID of a segment within a network (from 1 to N)\n- previous_obs : Index of the previous observation in the full dataset,\n  if -1 there are no previous observation (the segment starts)\n- next_obs : Index of the next observation in the full dataset, if -1 there are no next observation (the segment ends)\n- previous_cost : Result of the cost function (1 is a good association, 0 is bad) with previous observation\n- next_cost : Result of the cost function (1 is a good association, 0 is bad) with next observation\n\n"
       ]
     },
     {

notebooks/python_module/16_network/pet_atlas.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "def start_axes(title):\n    fig = plt.figure(figsize=(13, 5))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid()\n    ax.update_env()\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
+        "def start_axes(title):\n    fig = plt.figure(figsize=(13, 5))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid()\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
       ]
     },
     {

notebooks/python_module/16_network/pet_ioannou_2017_case.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)\n\n\n@FuncFormatter\ndef formatter(x, pos):\n    return (timedelta(x) + datetime(1950, 1, 1)).strftime(\"%d/%m/%Y\")\n\n\ndef start_axes(title=\"\"):\n    fig = plt.figure(figsize=(13, 6))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    ax.update_env()\n    return ax\n\n\ndef timeline_axes(title=\"\"):\n    fig = plt.figure(figsize=(15, 5))\n    ax = fig.add_axes([0.03, 0.06, 0.90, 0.88])\n    ax.set_title(title, weight=\"bold\")\n    ax.xaxis.set_major_formatter(formatter), ax.grid()\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid(True)\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
+        "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)\n\n\n@FuncFormatter\ndef formatter(x, pos):\n    return (timedelta(x) + datetime(1950, 1, 1)).strftime(\"%d/%m/%Y\")\n\n\ndef start_axes(title=\"\"):\n    fig = plt.figure(figsize=(13, 6))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    return ax\n\n\ndef timeline_axes(title=\"\"):\n    fig = plt.figure(figsize=(15, 5))\n    ax = fig.add_axes([0.03, 0.06, 0.90, 0.88])\n    ax.set_title(title, weight=\"bold\")\n    ax.xaxis.set_major_formatter(formatter), ax.grid()\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid(True)\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
       ]
     },
     {
@@ -127,7 +127,7 @@
       },
       "outputs": [],
       "source": [
-        "a = Anim(\n    close_to_i3,\n    figsize=(12, 4),\n    cmap=colors.ListedColormap(\n        list(close_to_i3.COLORS), name=\"from_list\", N=close_to_i3.segment.max() + 1\n    ),\n    nb_step=7,\n    dpi=70,\n    field_color=\"segment\",\n    field_txt=\"segment\",\n)\na.ax.set_xlim(19, 30), a.ax.set_ylim(32, 35.25)\na.ax.update_env()\na.txt.set_position((21.5, 32.7))\n# We display in video only from the 100th day to the 500th\nkwargs = dict(frames=arange(*a.period)[100:501], interval=100)\nani = VideoAnimation(a.fig, a.func_animation, **kwargs)"
+        "a = Anim(\n    close_to_i3,\n    figsize=(12, 4),\n    cmap=colors.ListedColormap(\n        list(close_to_i3.COLORS), name=\"from_list\", N=close_to_i3.segment.max() + 1\n    ),\n    nb_step=7,\n    dpi=70,\n    field_color=\"segment\",\n    field_txt=\"segment\",\n)\na.ax.set_xlim(19, 30), a.ax.set_ylim(32, 35.25)\na.txt.set_position((21.5, 32.7))\n# We display in video only from the 100th day to the 500th\nkwargs = dict(frames=arange(*a.period)[100:501], interval=100)\nani = VideoAnimation(a.fig, a.func_animation, **kwargs)"
       ]
     },
     {

notebooks/python_module/16_network/pet_replay_segmentation.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime, timedelta\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import FuncFormatter\nfrom numpy import where\n\nimport py_eddy_tracker.gui\nfrom py_eddy_tracker.data import get_path\nfrom py_eddy_tracker.observations.network import NetworkObservations\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\n\n\n@FuncFormatter\ndef formatter(x, pos):\n    return (timedelta(x) + datetime(1950, 1, 1)).strftime(\"%d/%m/%Y\")\n\n\ndef start_axes(title=\"\"):\n    fig = plt.figure(figsize=(13, 6))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    ax.update_env()\n    return ax\n\n\ndef timeline_axes(title=\"\"):\n    fig = plt.figure(figsize=(15, 5))\n    ax = fig.add_axes([0.04, 0.06, 0.89, 0.88])\n    ax.set_title(title, weight=\"bold\")\n    ax.xaxis.set_major_formatter(formatter), ax.grid()\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid(True)\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
+        "from datetime import datetime, timedelta\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import FuncFormatter\nfrom numpy import where\n\nimport py_eddy_tracker.gui\nfrom py_eddy_tracker.data import get_path\nfrom py_eddy_tracker.observations.network import NetworkObservations\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\n\n\n@FuncFormatter\ndef formatter(x, pos):\n    return (timedelta(x) + datetime(1950, 1, 1)).strftime(\"%d/%m/%Y\")\n\n\ndef start_axes(title=\"\"):\n    fig = plt.figure(figsize=(13, 6))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94], projection=\"full_axes\")\n    ax.set_xlim(19, 29), ax.set_ylim(31, 35.5)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    return ax\n\n\ndef timeline_axes(title=\"\"):\n    fig = plt.figure(figsize=(15, 5))\n    ax = fig.add_axes([0.04, 0.06, 0.89, 0.88])\n    ax.set_title(title, weight=\"bold\")\n    ax.xaxis.set_major_formatter(formatter), ax.grid()\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid(True)\n    if mappable:\n        return plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
       ]
     },
     {