added a convenience function for creating logarithmic colormaps

doc/plotting/matplotlib/create_colormaps.rst

@@ -8,4 +8,8 @@ Discrete
 
 Continous
 ==============================
-.. autofunction:: pandapower.plotting.cmap_continous
+.. autofunction:: pandapower.plotting.cmap_continous
+
+Logarithmic
+==============================
+.. autofunction:: pandapower.plotting.cmap_logarithmic

pandapower/plotting/colormaps.py

@@ -4,27 +4,32 @@
 # and Energy System Technology (IEE), Kassel. All rights reserved.
 
 
-from matplotlib.colors import ListedColormap, BoundaryNorm, LinearSegmentedColormap, Normalize
+from matplotlib.colors import ListedColormap, BoundaryNorm, LinearSegmentedColormap, Normalize, \
+    LogNorm
+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).
+        - 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_trace, draw_traces
-        >>> cmap_list = [((20, 50), "green"), ((50, 70), "yellow"), ((70, 100), "red")]
+        >>> 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_trace(net, cmap=cmap, norm=norm)
-        >>> draw_traces([lc])
+        >>> lc = create_line_collection(net, cmap=cmap, norm=norm)
+        >>> draw_collections([lc])
     """
     cmap_colors = []
     boundaries = []
@@ -46,19 +51,23 @@ def cmap_continous(cmap_list):
     Can be used to create a continous 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.
+        - 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_continous, create_bus_trace, draw_traces
+        >>> from pandapower.plotting import cmap_continous, 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_continous(cmap_list)
-        >>> bc = create_bus_trace(net, cmap=cmap, norm=norm)
-        >>> draw_traces([bc])
+        >>> bc = create_bus_collection(net, size=70, cmap=cmap, norm=norm)
+        >>> draw_collections([bc])
     """
     min_loading = cmap_list[0][0]
     max_loading = cmap_list[-1][0]
@@ -67,3 +76,51 @@ def cmap_continous(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

pandapower/test/plotting/test_create_colormaps.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2019 by University of Kassel and Fraunhofer Institute for Energy Economics
+# and Energy System Technology (IEE), Kassel. All rights reserved.
+
+
+import numpy as np
+import pandapower.plotting as plot
+import pytest
+
+
+def test_cmap_discrete():
+    cmap_list = [((0, 10), "green"), ((10, 30), "yellow"), ((30, 100), "red")]
+    cmap, norm = plot.cmap_discrete(cmap_list)
+    assert cmap.N == 3
+    assert norm.N == 4
+    assert norm(0) == 0
+    assert norm(9.999) == 0
+    assert norm(10.01) == 1
+    assert norm(29.999) == 1
+    assert norm(30.01) == 2
+    assert norm(99.999) == 2
+
+
+def test_cmap_continuous():
+    cmap_list = [(0.97, "blue"), (1.0, "green"), (1.03, "red")]
+    cmap, norm = plot.cmap_continous(cmap_list)
+
+    assert np.allclose(cmap(0.99), (0.984313725490196, 0.007873894655901603, 0.0, 1.0))
+    assert np.allclose(cmap(1.02), (1.0, 0.0, 0.0, 1.0))
+    assert np.allclose([norm(0.97 + 0.01 * n) for n in range(7)],
+                       [0.16666666666666666 * n for n in range(7)])
+
+
+def test_cmap_logarithmic():
+    min_value, max_value = 1.0, 1.03
+    colors = ["blue", "green", "red"]
+    cmap, norm = plot.cmap_logarithmic(min_value, max_value, colors)
+    assert np.allclose(cmap(0.1), (0.0, 0.09770172300400634, 0.8053598487029561, 1.0))
+    assert np.allclose(cmap(0.5), (0.0, 0.5002328217805124, 0.003442425359135415, 1.0))
+    assert np.allclose(cmap(0.8), (0.5970222233016451, 0.20227904085250753, 0.0, 1.0))
+    assert norm(1.) == 0
+    assert norm(1.03) == 1
+    assert np.isclose(norm(1.01), 0.3366283509006011)
+    assert np.isclose(norm(1.02), 0.669940112402371)
+
+
+if __name__ == "__main__":
+    pytest.main(["test_create_colormaps.py"])

pandapower/test/plotting/test_to_html.py

@@ -10,9 +10,11 @@
 import pandapower.plotting as plot
 from pandapower.test.toolbox import assert_net_equal, create_test_network, tempdir, net_in
 
+
 def test_html(net_in, tempdir):
     filename = os.path.join(tempdir, "testfile.html")
     plot.to_html(net_in, filename)
 
+
 if __name__ == "__main__":
     pytest.main(["test_to_html.py"])