bugs and tests

mooda/__init__.py

@@ -7,4 +7,4 @@
     es_create_indexes, EMSO, widget_qc, widget_emso, widget_emso_qc, \
     widget_save, iplot_line
 
-__version__ = '1.12.3'
+__version__ = '1.12.4'

mooda/waterframe/__init__.py

@@ -15,7 +15,7 @@ class WaterFrame:
     from .iplot import (
         iplot_location, iplot_timeseries, iplot, iplot_scatter, iplot_line,
         iplot_data_intervals, iplot_scatter_mapbox, iplot_bar_polar,
-        iplot_histogram, iplot_candlestick)
+        iplot_histogram, iplot_candlestick, iplot_box, iplot_correlation_headmap)
 
     def __init__(self, df=None, metadata=None, vocabulary=None):
         """ Constructor """

mooda/waterframe/iplot/__init__.py

@@ -8,3 +8,5 @@
 from .iplot_bar_polar import iplot_bar_polar
 from .iplot_histogram import iplot_histogram
 from .iplot_candlestick import iplot_candlestick
+from .iplot_box import iplot_box
+from .iplot_correlation_headmap import iplot_correlation_headmap

mooda/waterframe/iplot/iplot_box.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.validators.scatter.marker import SymbolValidator
+from sklearn.linear_model import LinearRegression
+
+
+def iplot_box(self, parameter="", color=None, **kwds):
+    """
+    It uses plotly.express.box.
+
+    Parameters
+    ----------
+        parameter: str
+        color: str
+            Name of a column in self.data, or a pandas Series or array_like
+            object. Values from this column or array_like are used to assign
+            color to marks.
+            If color == 'auto' and parameter != "", color = 'DEPTH'
+
+    Returns
+    -------
+        fig: plotly.graph_objects.Figure
+    """
+
+    if parameter:
+        if color == 'auto':
+            color = 'DEPTH'
+
+        _df = self.data.copy()
+        _df.reset_index(inplace=True)
+        data_frame = _df[[parameter, 'TIME', 'DEPTH']].copy()
+        # Define x and yof box
+        x = parameter
+
+        fig = px.box(data_frame=data_frame, x=x, color=color, **kwds)
+
+    else:
+        if color == 'auto':
+            color = None
+        fig = px.box(**kwds)
+
+    return fig

mooda/waterframe/iplot/iplot_correlation_headmap.py

@@ -0,0 +1,34 @@
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.validators.scatter.marker import SymbolValidator
+from sklearn.linear_model import LinearRegression
+
+
+def iplot_correlation_headmap(self, parameters=[], **kwds):
+    """
+    It uses plotly.express.imshow.
+
+    Parameters
+    ----------
+        parameter: str
+
+    Returns
+    -------
+        fig: plotly.graph_objects.Figure
+    """
+
+    if parameters:
+
+        _df = self.data.copy()
+        _df.reset_index(inplace=True)
+        data_frame = _df[parameters].copy()
+        corr = data_frame.corr()
+
+        fig = px.imshow(corr, **kwds)
+
+    else:
+        ig = px.imshow(**kwds)
+
+    return fig

mooda/waterframe/iplot/iplot_histogram.py

@@ -1,16 +1,16 @@
 import plotly.express as px
 
 
-def iplot_histogram(self, x='TIME', nbins=None, **kwds):
+def iplot_histogram(self, parameter='', nbins=None, show_median=False, **kwds):
     """
     It uses plotly.express.histogram.
     Each data point is represented as a marker point, whose location is given by the x and y columns
     of self.data.
 
     Parameters
     ----------
-        x: str
-            X axes, column or index of data.
+        parameter: str
+            x axes, column or index of data.
         nbins: int
             By default, the number of bins is chosen so that this number is comparable to the
             typical number of samples in a bin. This number can be customized, as well as the range of values.
@@ -22,8 +22,32 @@ def iplot_histogram(self, x='TIME', nbins=None, **kwds):
         fig: plotly.graph_objects.Figure
     """
 
-    df = self.data.copy()
+    if parameter:
+        _df = self.data.copy()
+        _df.reset_index(inplace=True)
+        df = _df[[parameter, 'TIME', 'DEPTH']].copy()
 
-    fig = px.histogram(df, x=x, nbins=nbins)
+        fig = px.histogram(df, x=parameter, nbins=nbins, **kwds)
+
+        if show_median:
+            mean = df[parameter].mean()
+            print(mean)
+            text = dict(
+                x=mean, y=0.95, xref='x', yref='paper',
+                showarrow=False, xanchor='left', text=f'Mean: {mean:.3f}')
+            shape = dict(
+                x0=mean,
+                x1=mean,
+                y0=0, y1=1,
+                xref='x',
+                yref='paper',
+                line_width=2)
+
+            fig.update_layout(
+                shapes=[shape],
+                annotations=[text])
+    else:
+
+        fig = px.histogram(dnbins=nbins, **kwds)
 
     return fig

setup.py

@@ -6,7 +6,7 @@
 
 
 NAME = 'mooda'
-VERSION = '1.12.3'
+VERSION = '1.12.4'
 DESCRIPTION = 'Module for Ocean Observatory Data Analysis'
 LONG_DESCRIPTION = ("""
 MOODA - Module for Ocean Observatory Data Analysis