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benchmarks: pin dependencies of rw-heatmaps

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Signed-off-by: Cenk Alti <cenkalti@gmail.com>
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Cenk Alti 2023-05-23 14:24:41 -04:00
parent 8c715f8a40
commit 2efecd1978
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2 changed files with 4 additions and 101 deletions
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102
tools/rw-heatmaps/plot_data.py
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@ -79,106 +79,6 @@ def load_data_files(*args):
return res
# This is copied directly from matplotlib source code. Some early versions of matplotlib
# do not have CenteredNorm class
class CenteredNorm(colors.Normalize):
def __init__(self, vcenter=0, halfrange=None, clip=False):
"""
Normalize symmetrical data around a center (0 by default).
Unlike `TwoSlopeNorm`, `CenteredNorm` applies an equal rate of change
around the center.
Useful when mapping symmetrical data around a conceptual center
e.g., data that range from -2 to 4, with 0 as the midpoint, and
with equal rates of change around that midpoint.
Parameters
----------
vcenter : float, default: 0
The data value that defines ``0.5`` in the normalization.
halfrange : float, optional
The range of data values that defines a range of ``0.5`` in the
normalization, so that *vcenter* - *halfrange* is ``0.0`` and
*vcenter* + *halfrange* is ``1.0`` in the normalization.
Defaults to the largest absolute difference to *vcenter* for
the values in the dataset.
Examples
--------
This maps data values -2 to 0.25, 0 to 0.5, and 4 to 1.0
(assuming equal rates of change above and below 0.0):
>>> import matplotlib.colors as mcolors
>>> norm = mcolors.CenteredNorm(halfrange=4.0)
>>> data = [-2., 0., 4.]
>>> norm(data)
array([0.25, 0.5 , 1. ])
"""
self._vcenter = vcenter
self.vmin = None
self.vmax = None
# calling the halfrange setter to set vmin and vmax
self.halfrange = halfrange
self.clip = clip
def _set_vmin_vmax(self):
"""
Set *vmin* and *vmax* based on *vcenter* and *halfrange*.
"""
self.vmax = self._vcenter + self._halfrange
self.vmin = self._vcenter - self._halfrange
def autoscale(self, A):
"""
Set *halfrange* to ``max(abs(A-vcenter))``, then set *vmin* and *vmax*.
"""
A = np.asanyarray(A)
self._halfrange = max(self._vcenter-A.min(),
A.max()-self._vcenter)
self._set_vmin_vmax()
def autoscale_None(self, A):
"""Set *vmin* and *vmax*."""
A = np.asanyarray(A)
if self._halfrange is None and A.size:
self.autoscale(A)
@property
def vcenter(self):
return self._vcenter
@vcenter.setter
def vcenter(self, vcenter):
self._vcenter = vcenter
if self.vmax is not None:
# recompute halfrange assuming vmin and vmax represent
# min and max of data
self._halfrange = max(self._vcenter-self.vmin,
self.vmax-self._vcenter)
self._set_vmin_vmax()
@property
def halfrange(self):
return self._halfrange
@halfrange.setter
def halfrange(self, halfrange):
if halfrange is None:
self._halfrange = None
self.vmin = None
self.vmax = None
else:
self._halfrange = abs(halfrange)
def __call__(self, value, clip=None):
if self._halfrange is not None:
# enforce symmetry, reset vmin and vmax
self._set_vmin_vmax()
return super().__call__(value, clip=clip)
# plot type is the type of the data to plot. Either 'read' or 'write'
def plot_data(title, plot_type, cmap_name_default, *args):
if len(args) == 1:
@ -220,7 +120,7 @@ def plot_data(title, plot_type, cmap_name_default, *args):
if col == 2:
cmap_name = 'bwr'
if params.zero:
norm = CenteredNorm()
norm = colors.CenteredNorm()
else:
cmap_name = cmap_name_default
plt.tripcolor(df['conn_size'], df['value_size'], df[plot_type],

3
tools/rw-heatmaps/requirements.txt Normal file
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@ -0,0 +1,3 @@
matplotlib==3.7.1
numpy==1.24.3
pandas==2.0.1