Improve argofloat plot

argopy/fetchers.py

@@ -926,7 +926,7 @@ def plot(self, ptype: str = "trajectory", **kwargs):
         Calling this method will automatically trigger a call to the :class:`argopy.DataFetcher.load` method.
 
         """
-        self.load()
+        # self.load()
         if ptype in ["dac", "institution"]:
             if "institution" not in self.index:
                 self.to_index(full=True)

argopy/plot/argo_colors.py

@@ -81,7 +81,7 @@ def __init__(self, name: str = "Set1", N: int = None):
                 "ticklabels": ['PROBABLE', 'CONFIRMED', 'REGISTERED', 'OPERATIONAL', 'INACTIVE', 'CLOSED'],
             },
             "qc": {
-                "name": "Quality control flag scale",
+                "name": "Quality control flag scale for measurements",
                 "aka": ["qc_flag", "quality_control", "quality_control_flag", "quality_control_flag_scale"],
                 "constructor": self._colormap_quality_control_flag,
                 "ticks": np.arange(0, 9 + 1),
@@ -104,6 +104,19 @@ def __init__(self, name: str = "Set1", N: int = None):
                 "ticklabels": ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
                                "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
             },
+            "pqc": {
+                "name": "Quality control flag scale for whole profiles",
+                "aka": ["pqc_flag", "profile_qc", "profile_quality_control_flag", "profile_quality_control_flag", "profile_quality_control_flag_scale"],
+                "constructor": self._colormap_profile_quality_control_flag,
+                "ticks": ["N", "A", "B", "C", "D", "E", "F"],
+                "ticklabels": ["No QC performed",
+                               "100% of profile good",
+                               "75% - 100% of profile good",
+                               "50% - 75% of profile good",
+                               "25% - 50% of profile good",
+                               "0% - 25% of profile good",
+                               "0% of profile good"]
+            },
         }
         self.registered = self.name in self.list_valid_known_colormaps
         self._colormap = self.cmap
@@ -270,6 +283,18 @@ def _colormap_quality_control_flag(self):
                  ]
         return mcolors.LinearSegmentedColormap.from_list(self.definition['name'], clist, 10)
 
+    def _colormap_profile_quality_control_flag(self):
+        """Return a colormap for Profile QC flag"""
+        clist = ['#000000',
+                 '#31FC03',
+                 '#ADFC03',
+                 '#FCBA03',
+                 '#324CA8',
+                 '#B22CC9',
+                 '#FC1C03',
+                 ]
+        return mcolors.LinearSegmentedColormap.from_list(self.definition['name'], clist, 7)
+
     @property
     def cmap(self):
         """Discrete colormap as :class:`matplotlib.colors.LinearSegmentedColormap`

argopy/plot/plot.py

@@ -496,7 +496,10 @@ def guess_cmap(hue):
         if hue.lower() in ArgoColors().list_valid_known_colormaps:
             cmap = hue.lower()
         elif "qc" in hue.lower():
-            cmap = "qc"
+            if "profile_" not in hue.lower():
+                cmap = "qc"
+            else:
+                cmap = "pqc"
         elif "mode" in hue.lower():
             cmap = "data_mode"
         elif "status_code" in hue.lower():
@@ -789,7 +792,7 @@ def get_vlabel(this_ds, this_v):
             # ax.set_facecolor(bgcolor)
 
         if cbar:
-            cbar = fig.colorbar(m, shrink=0.9, extend="both", ax=ax)
+            cbar = fig.colorbar(m, shrink=0.9, ax=ax)
             cbar.ax.set_ylabel(get_vlabel(ds, this_param), rotation=90)
 
         ylim = ax.get_ylim()

argopy/stores/float/argo_float.py

@@ -77,8 +77,9 @@ class ArgoFloat(FloatStore):
         af.plot.trajectory()
         af.plot.trajectory(figsize=(18,18), padding=[1, 5])
         af.plot.map('TEMP', pres=450, cmap='Spectral_r')
-        af.plot.map('DATA_MODE', cbar=False, legend=True)
-        af.plot.scatter('PSAL')
+        af.plot.map('DATA_MODE')
+        af.plot.scatter('TEMP')
+        af.plot.scatter('PSAL_QC')
         af.plot.scatter('DOXY', ds='Sprof')
         af.plot.scatter('MEASUREMENT_CODE', ds='Rtraj')
 

argopy/stores/float/implementations/plot.py

@@ -1,8 +1,8 @@
 import numpy as np
 from typing import Any
 
-from ....plot import scatter_plot, scatter_map
-from ....utils.lists import list_multiprofile_file_variables, list_bgc_s_variables
+from ....plot import scatter_plot, scatter_map, ArgoColors
+from ....utils import list_multiprofile_file_variables, list_bgc_s_variables, to_list
 from ..extensions import ArgoFloatPlotProto
 
 
@@ -24,7 +24,7 @@ class ArgoFloatPlot(ArgoFloatPlotProto):
 
         af.plot.map('TEMP', pres=450, cmap='Spectral_r')
 
-        af.plot.map('DATA_MODE', cbar=False, legend=True)
+        af.plot.map('DATA_MODE')
 
         af.plot.scatter('PSAL')
 
@@ -143,8 +143,11 @@ def map(
 
             from argopy import ArgoFloat
             af = ArgoFloat(wmo)
-            af.plot.map('TEMP', pres=450, cmap='Spectral_r')
-            af.plot.map('DATA_MODE', cbar=False, legend=True)
+
+            af.plot.map('TEMP')  # Plot pressure level closest to 0 by default
+            af.plot.map('PSAL', pres=450.)
+            af.plot.map('PROFILE_TEMP_QC')
+            af.plot.map('DATA_MODE')
         """
 
         if ds == "prof" and param not in list_multiprofile_file_variables():
@@ -175,12 +178,29 @@ def map(
             N_LEVELS=0
         )
 
+        # Check if param will be plotted using a discrete and known Argo colormap
+        discrete, cmap = False, 'Spectral_r'
+        if "qc" in param.lower() or "mode" in param.lower():
+            discrete, cmap = True, None  # Let scatter_map guess cmap
+
+        if "N_LEVELS" in self._obj.dataset(ds)[param].dims:
+            legend_title = "%s @ %s PRES level in [%0.1f-%0.1f] db" % (
+                param,
+                select,
+                bins[0],
+                bins[1],
+            )
+        else:
+            legend_title = param
+
         default_kwargs = {
             "x": "LONGITUDE",
             "y": "LATITUDE",
             "hue": param,
-            "legend": False,
-            "cbar": True,
+            "cmap": cmap,
+            "legend": True if discrete else False,
+            "cbar": False if discrete else True,
+            "legend_title": legend_title,
         }
         this_kwargs = {**default_kwargs, **kwargs}
 
@@ -189,7 +209,7 @@ def map(
         return fig, ax, hdl
 
     def scatter(self, param, ds="prof", **kwargs) -> Any:
-        """Scatter plot for one dataset parameter
+        """Scatter plot for a 2-dimensional dataset parameter
 
         This method creates a 2D scatter plot with the :meth:`argopy.plot.scatter_plot` method.
 
@@ -207,14 +227,16 @@ def scatter(self, param, ds="prof", **kwargs) -> Any:
                 - :class:`matplotlib.figure.Figure`
                 - :class:`matplotlib.axes.Axes`
                 - list of patches
+                - :class:`matplotlib.colorbar.Colorbar`
 
         Examples
         --------
         .. code-block:: python
 
             from argopy import ArgoFloat
             af = ArgoFloat(wmo)
-            af.plot.scatter('PSAL')
+            af.plot.scatter('TEMP')
+            af.plot.scatter('PSAL_QC')  # Appropriate colormap automatically selected
             af.plot.scatter('DOXY', ds='Sprof')
             af.plot.scatter('MEASUREMENT_CODE', ds='Rtraj')
 
@@ -238,9 +260,22 @@ def scatter(self, param, ds="prof", **kwargs) -> Any:
         default_kwargs = {"this_x": "JULD", "cbar": True}
         this_kwargs = {**default_kwargs, **kwargs}
 
+        if "_QC" in param:
+            mycolors = ArgoColors('qc', 9)
+            this_kwargs.update({
+                "cmap": mycolors.cmap,
+                "vmin": 0,
+                "vmax": 9+1,
+            })
+
         if this_kwargs["cbar"]:
             fig, ax, m, cbar = scatter_plot(this_ds, param, **this_kwargs)
             ax.set_title(self._default_title)
+
+            if "_QC" in param:
+                cbar.set_ticks(to_list([k + 0.5 for k in mycolors.ticklabels.keys()]))
+                cbar.set_ticklabels(to_list([k for k in mycolors.ticklabels.values()]))
+
             return fig, ax, m, cbar
         else:
             fig, ax, m = scatter_plot(this_ds, param, **this_kwargs)

argopy/stores/index/implementations/plot.py

@@ -46,6 +46,19 @@ class ArgoIndexPlot(ArgoIndexPlotProto):
 
         idx.plot.bar(by='dac')
 
+    .. code-block:: python
+        :caption: Full index vs query results
+
+        from argopy import ArgoIndex
+        idx = ArgoIndex(index_file='bgc-s')
+        idx.query.params('CHLA')
+
+        idx.plot.bar(by='profiler')  # Plot query results (default)
+
+        idx.plot.bar(by='profiler', index=True)  # Force plot of full index
+
+
+
     See Also
     --------
     :class:`ArgoIndex.plot.trajectory`, :class:`ArgoIndex.plot.bar`

argopy/tests/test_stores_float_plot.py

@@ -61,15 +61,18 @@ def test_plot_map_errors(self):
         with pytest.raises(ValueError):
             af.plot.map("NOT_A_BGC_PARAM", ds="Sprof")
         with pytest.raises(ValueError):
-            af.plot.map("DOXY", ds="prof")
+            af.plot.map("NOT_A_TRAJ_PARAM", ds="Rtraj")
 
     @requires_cartopy
     @pytest.mark.parametrize(
         "wmo", [VALID_WMO[0]], indirect=False, ids=[f"wmo={w}" for w in [VALID_WMO[0]]]
     )
-    def test_plot_map(self, wmo):
+    @pytest.mark.parametrize(
+        "pres", [0., 200.], indirect=False, ids=[f"pres={p}" for p in [0., 200.]]
+    )
+    def test_plot_map(self, wmo, pres):
         af = ArgoFloat(wmo, host=VALID_HOST, cache=True)
-        fig, ax, hdl = af.plot.map("TEMP", ds="prof")
+        fig, ax, hdl = af.plot.map("TEMP", pres=pres, ds="prof")
         assert isinstance(fig, mpl.figure.Figure)
         assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxesSubplot)
         mpl.pyplot.close(fig)
@@ -89,13 +92,16 @@ def test_plot_scatter_errors(self):
     @pytest.mark.parametrize(
         "cbar", [True, False], indirect=False, ids=[f"cbar={c}" for c in [True, False]]
     )
-    def test_plot_scatter(self, wmo, cbar):
+    @pytest.mark.parametrize(
+        "param", ["TEMP", "TEMP_QC"], indirect=False, ids=[f"param={p}" for p in ["TEMP", "TEMP_QC"]]
+    )
+    def test_plot_scatter(self, wmo, cbar, param):
         af = ArgoFloat(wmo, host=VALID_HOST, cache=True)
 
         if cbar:
-            fig, ax, m, cbar = af.plot.scatter("TEMP", ds="prof", cbar=cbar)
+            fig, ax, m, cbar = af.plot.scatter(param, ds="prof", cbar=cbar)
         else:
-            fig, ax, m = af.plot.scatter("TEMP", ds="prof", cbar=cbar)
+            fig, ax, m = af.plot.scatter(param, ds="prof", cbar=cbar)
 
         assert isinstance(fig, mpl.figure.Figure)
         assert isinstance(ax, mpl.axes.Axes)

docs/advanced-tools/stores/argofloat.rst

@@ -101,3 +101,77 @@ The :class:`ArgoFloat` class is further used in **argopy** in the :class:`ArgoIn
     for a_float in idx.iterfloats():
         ds = a_float.open_dataset('meta')
         print(a_float.WMO, ds['LAUNCH_DATE'].data)
+
+.. _argofloat-visu:
+
+Plotting features
+-----------------
+.. currentmodule:: argopy
+
+The :class:`ArgoFloat` class come with a :class:`ArgoFloat.plot` accessor than can take several methods to quickly visualize data from the float:
+
+Check all the detailed arguments on the API reference :class:`ArgoFloat.plot`.
+
+.. tabs::
+
+    .. tab:: Simple trajectory
+
+        .. code-block:: python
+
+                from argopy import ArgoFloat
+                af = ArgoFloat(6903262)
+
+                af.plot.trajectory()
+                # af.plot.trajectory(figsize=(18,18), padding=[1, 5])
+
+        .. image:: ../../_static/ArgoFloat_trajectory.png
+
+    .. tab:: Data along trajectory
+
+        .. code-block:: python
+
+                from argopy import ArgoFloat
+                af = ArgoFloat(6903262)
+
+                af.plot.map('TEMP', pres=450, cmap='Spectral_r')
+
+        .. image:: ../../_static/ArgoFloat_TEMP.png
+
+        .. code-block:: python
+
+                from argopy import ArgoFloat
+                af = ArgoFloat(6903262)
+
+                af.plot.map('PROFILE_PSAL_QC')
+
+        .. image:: ../../_static/ArgoFloat_PROFILE_PSAL_QC.png
+
+    .. tab:: Data as a function of pressure
+
+        .. code-block:: python
+
+                from argopy import ArgoFloat
+                af = ArgoFloat(6903262)
+
+                af.plot.scatter('TEMP')
+
+        .. image:: ../../_static/ArgoFloat_TEMPscatter.png
+
+        Plotting QC will automatically select the appropriate colormap:
+
+        .. code-block:: python
+
+                af.plot.scatter('PSAL_QC')
+
+        .. image:: ../../_static/ArgoFloat_PSAL_QC.png
+
+        Note that by default, variables are loaded from the `prof` netcdf dataset, but variables from other netcdf dataset can also be plotted if the appropriate dataset is indicated with the `ds` argument:
+
+        .. code-block:: python
+
+                from argopy import ArgoFloat
+                af = ArgoFloat(6903262)
+
+                af.plot.scatter('MEASUREMENT_CODE', ds='Rtraj')
+
+        .. image:: ../../_static/ArgoFloat_MEASUREMENT_CODE.png