Add Plotly/Mapbox plotting with temperature coloring

src/pandapipes/plotting/colormaps.py

@@ -0,0 +1,139 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2023 by University of Kassel and Fraunhofer Institute for Energy Economics
+# and Energy System Technology (IEE), Kassel. All rights reserved.
+
+
+try:
+    from matplotlib.colors import ListedColormap, BoundaryNorm, LinearSegmentedColormap, Normalize, LogNorm
+    MATPLOTLIB_INSTALLED = True
+except ImportError:
+    MATPLOTLIB_INSTALLED = False
+
+import numpy as np
+
+
+def cmap_discrete(cmap_list):
+    """
+    Can be used to create a discrete colormap.
+
+    INPUT:
+        - cmap_list (list) - list of tuples, where each tuple represents one range. Each tuple has
+                             the form of ((from, to), color).
+
+    OUTPUT:
+        - cmap - matplotlib colormap
+
+        - norm - matplotlib norm object
+
+    EXAMPLE:
+        >>> from pandapower.plotting import cmap_discrete, create_line_collection, draw_collections
+        >>> from pandapower.networks import mv_oberrhein
+        >>> net = mv_oberrhein("generation")
+        >>> cmap_list = [((0, 10), "green"), ((10, 30), "yellow"), ((30, 100), "red")]
+        >>> cmap, norm = cmap_discrete(cmap_list)
+        >>> lc = create_line_collection(net, cmap=cmap, norm=norm)
+        >>> draw_collections([lc])
+    """
+    if not MATPLOTLIB_INSTALLED:
+        raise UserWarning("install matplotlib to use this function")
+    cmap_colors = []
+    boundaries = []
+    last_upper = None
+    for (lower, upper), color in cmap_list:
+        if last_upper is not None and lower != last_upper:
+            raise ValueError("Ranges for colormap must be continuous")
+        cmap_colors.append(color)
+        boundaries.append(lower)
+        last_upper = upper
+    boundaries.append(upper)
+    cmap = ListedColormap(cmap_colors)
+    norm = BoundaryNorm(boundaries, cmap.N)
+    return cmap, norm
+
+
+def cmap_continuous(cmap_list):
+    """
+    Can be used to create a continuous colormap.
+
+    INPUT:
+        - cmap_list (list) - list of tuples, where each tuple represents one color. Each tuple has
+                             the form of (center, color). The colorbar is a linear segmentation of
+                             the colors between the centers.
+
+    OUTPUT:
+        - cmap - matplotlib colormap
+
+        - norm - matplotlib norm object
+
+    EXAMPLE:
+        >>> from pandapower.plotting import cmap_continuous, create_bus_collection, draw_collections
+        >>> from pandapower.networks import mv_oberrhein
+        >>> net = mv_oberrhein("generation")
+        >>> cmap_list = [(0.97, "blue"), (1.0, "green"), (1.03, "red")]
+        >>> cmap, norm = cmap_continuous(cmap_list)
+        >>> bc = create_bus_collection(net, size=70, cmap=cmap, norm=norm)
+        >>> draw_collections([bc])
+    """
+    if not MATPLOTLIB_INSTALLED:
+        raise UserWarning("install matplotlib to use this function")
+    min_loading = cmap_list[0][0]
+    max_loading = cmap_list[-1][0]
+    cmap_colors = [((loading-min_loading)/(max_loading - min_loading), color) for
+                 (loading, color) in cmap_list]
+    cmap = LinearSegmentedColormap.from_list('name', cmap_colors)
+    norm = Normalize(min_loading, max_loading)
+    return cmap, norm
+
+
+def cmap_logarithmic(min_value, max_value, colors):
+    """
+        Can be used to create a logarithmic colormap. The colormap itself has a linear segmentation of
+        the given colors. The values however will be matched to the colors based on a logarithmic
+        normalization (c.f. matplotlib.colors.LogNorm for more information on how the logarithmic
+        normalization works).
+
+        \nPlease note: {There are numerous ways of how a logarithmic scale might
+                        be created, the intermediate values on the scale are created automatically based on the minimum
+                        and maximum given values in analogy to the LogNorm. Also, the logarithmic colormap can only be
+                        used with at least 3 colors and increasing values which all have to be above 0.}
+
+        INPUT:
+            **min_value** (float) - the minimum value of the colorbar
+
+            **max_value** (float) - the maximum value for the colorbar
+
+            **colors** (list) - list of colors to be used for the colormap
+
+        OUTPUT:
+            **cmap** - matplotlib colormap
+
+            **norm** - matplotlib norm object
+
+        EXAMPLE:
+
+        >>> from pandapower.plotting import cmap_logarithmic, create_bus_collection, draw_collections
+        >>> from pandapower.networks import mv_oberrhein
+        >>> net = mv_oberrhein("generation")
+        >>> min_value, max_value = 1.0, 1.03
+        >>> colors = ["blue", "green", "red"]
+        >>> cmap, norm = cmap_logarithmic(min_value, max_value, colors)
+        >>> bc = create_bus_collection(net, size=70, cmap=cmap, norm=norm)
+        >>> draw_collections([bc])
+
+    """
+
+    num_values = len(colors)
+    if num_values < 2:
+        raise UserWarning("Cannot create a logarithmic colormap less than 2 colors.")
+    if min_value <= 0:
+        raise UserWarning("The minimum value must be above 0.")
+    if max_value <= min_value:
+        raise UserWarning("The upper bound must be larger than the lower bound.")
+    values = np.arange(num_values + 1)
+    diff = (max_value - min_value) / (num_values - 1)
+    values = (np.log(min_value + values * diff) - np.log(min_value)) \
+             / (np.log(max_value) - np.log(min_value))
+    cmap = LinearSegmentedColormap.from_list("name", list(zip(values, colors)))
+    norm = LogNorm(min_value, max_value)
+    return cmap, norm