def custom_colormap(name):
""" returns one of the custom colormaps as a colormap object if input is string.
If input is a colormap, simply returns the object right back.
>>> type(custom_colormap('rainbow_black_violet'))
<class 'mpltools.color.LinearColormap'>
>>> type(custom_colormap('gist_rainbow_r'))
<class 'matplotlib.colors.LinearSegmentedColormap'>
>>> type(custom_colormap('Greens'))
<class 'matplotlib.colors.LinearSegmentedColormap'>
>>> custom_colormap('not_here')
Traceback (most recent call last):
Exception: Colormap not present in custom colormaps: not_here
"""
if isinstance(name, C.LinearSegmentedColormap):
return name
if name in [str(x) for x in P.colormaps()]:
return P.cm.get_cmap(name)
if name in CUSTOM_LINEAR_SEGMENTED_COLORMAPS:
return quick_colormap(CUSTOM_LINEAR_SEGMENTED_COLORMAPS[name], name)
if name in CUSTOM_CONSTRUCTED_COLORMAPS:
return custom_constructed_colormaps(name)
raise Exception("Colormap not present in custom colormaps: " + name)
def __init__(self, app):
self.dialog = QWidget.__init__(self)
self.app = app
# Set up the user interface from Designer.
self.ui = Ui_ranges()
self.ui.setupUi(self)
self.cmapList = [colormap for colormap in py.colormaps() if not colormap.endswith("_r")]
self.ui.colorMap.addItems(self.cmapList)
self.ui.colorMap.setCurrentIndex(60)
# flags
self.f_mapOpen = False
self.f_spOpen = False
self.f_fileOpen = False
self.f_firstOpening = False
self.f_cacheRecovered = False
self.measParams = {}
self.plotParams = {}
self.plotSpPos = None
self.spMapPos = None
self.spVerticalBarCounter = 0
self.spWaveLimits = []
# Connect up the buttons.
self.ui.squareMeas.toggled.connect(self.squareMeasClicked)
self.ui.xMeasMin.valueChanged.connect(self.measRangeUpdate)
self.ui.xMeasMax.valueChanged.connect(self.measRangeUpdate)
self.ui.xMeasStep.valueChanged.connect(self.measRangeUpdate)
self.ui.colorPlotFullscale.clicked.connect(self.colorPlotFullscale)
self.ui.xPlotFullscale.clicked.connect(self.xPlotFullscale)
self.ui.yPlotFullscale.clicked.connect(self.yPlotFullscale)
self.ui.wavePlotFullscale.clicked.connect(self.wavePlotFullscale)
self.ui.plotUpdate.clicked.connect(self.update)
self.ui.selectFileButton.clicked.connect(self.selectFile)
QTimer.singleShot(100, self.selectFile) # select file at start
"""
Example script computing the battery size as a function of the
self-sufficiency and self-consumption rates
Created on Fri Mar 4 01:27:31 2016
@author: Sylvain Quoilin (JRC)
"""
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pylab import colormaps
cmaps = colormaps()
from scipy.optimize import fsolve
from SC_functions import fSSR
# Version of the function with only r_bat as input variable (r_PV is provided as a parameter) for fsolve:
def SSR_fsolve(r_bat, *param):
a, r_PV, SSR = param
if r_PV == 0:
return r_bat
else:
return SSR - fSSR(r_PV, r_bat, a)
# test the coefficients of the 16 parameters function:
coef = [
def BenchmarkSummaryFigure(models,variables,data,figname,vcolor=None,rel_only=False):
"""Creates a summary figure for the benchmark results contained in the
data array.
Parameters
----------
models : list
a list of the model names
variables : list
a list of the variable names
data : numpy.ndarray or numpy.ma.ndarray
data scores whose shape is ( len(variables), len(models) )
figname : str
the full path of the output file to write
vcolor : list, optional
an array parallel to the variables array containing background
colors for the labels to be displayed on the y-axis.
"""
from mpl_toolkits.axes_grid1 import make_axes_locatable
# data checks
assert type(models) is type(list())
assert type(variables) is type(list())
assert (type(data) is type(np .empty(1)) or
type(data) is type(np.ma.empty(1)))
assert data.shape[0] == len(variables)
assert data.shape[1] == len(models )
assert type(figname) is type("")
if vcolor is not None:
assert type(vcolor) is type(list())
assert len(vcolor) == len(variables)
rel_cmap = "PuOr"
# define some parameters
nmodels = len(models)
nvariables = len(variables)
maxV = max([len(v) for v in variables])
maxM = max([len(m) for m in models])
wpchar = 0.15
wpcell = 0.19
hpcell = 0.25
w = maxV*wpchar + max(4,nmodels)*wpcell
if not rel_only: w += (max(4,nmodels)+1)*wpcell
h = maxM*wpchar + nvariables*hpcell + 1.0
bad = 0.5
if "stoplight" not in plt.colormaps(): RegisterCustomColormaps()
# plot the variable scores
if rel_only:
fig,ax = plt.subplots(figsize=(w,h),ncols=1,tight_layout=True)
ax = [ax]
else:
fig,ax = plt.subplots(figsize=(w,h),ncols=2,tight_layout=True)
# absolute score
if not rel_only:
cmap = plt.get_cmap('stoplight')
cmap.set_bad('k',bad)
qc = ax[0].pcolormesh(np.ma.masked_invalid(data[::-1,:]),cmap=cmap,vmin=0,vmax=1,linewidth=0)
div = make_axes_locatable(ax[0])
fig.colorbar(qc,
ticks=(0,0.25,0.5,0.75,1.0),
format="%g",
cax=div.append_axes("bottom", size="5%", pad=0.05),
orientation="horizontal",
label="Absolute Score")
plt.tick_params(which='both', length=0)
ax[0].xaxis.tick_top()
ax[0].set_xticks (np.arange(nmodels )+0.5)
ax[0].set_xticklabels(models,rotation=90)
ax[0].set_yticks (np.arange(nvariables)+0.5)
ax[0].set_yticklabels(variables[::-1])
ax[0].tick_params('both',length=0,width=0,which='major')
ax[0].tick_params(axis='y',pad=10)
ax[0].set_xlim(0,nmodels)
ax[0].set_ylim(0,nvariables)
if vcolor is not None:
for i,t in enumerate(ax[0].yaxis.get_ticklabels()):
t.set_backgroundcolor(vcolor[::-1][i])
# relative score
i = 0 if rel_only else 1
np.seterr(invalid='ignore',under='ignore')
data = np.ma.masked_invalid(data)
data.data[data.mask] = 1.
data = np.ma.masked_values(data,1.)
mean = data.mean(axis=1)
std = data.std (axis=1).clip(0.02)
np.seterr(invalid='ignore',under='ignore')
Z = (data-mean[:,np.newaxis])/std[:,np.newaxis]
Z = np.ma.masked_invalid(Z)
np.seterr(invalid='warn',under='raise')
cmap = plt.get_cmap(rel_cmap)
cmap.set_bad('k',bad)
qc = ax[i].pcolormesh(Z[::-1],cmap=cmap,vmin=-2,vmax=2,linewidth=0)
div = make_axes_locatable(ax[i])
fig.colorbar(qc,
ticks=(-2,-1,0,1,2),
format="%+d",
cax=div.append_axes("bottom", size="5%", pad=0.05),
orientation="horizontal",
label="Relative Score")
plt.tick_params(which='both', length=0)
ax[i].xaxis.tick_top()
ax[i].set_xticks(np.arange(nmodels)+0.5)
ax[i].set_xticklabels(models,rotation=90)
ax[i].tick_params('both',length=0,width=0,which='major')
ax[i].set_yticks([])
ax[i].set_xlim(0,nmodels)
ax[i].set_ylim(0,nvariables)
if rel_only:
ax[i].set_yticks (np.arange(nvariables)+0.5)
ax[i].set_yticklabels(variables[::-1])
if vcolor is not None:
for i,t in enumerate(ax[i].yaxis.get_ticklabels()):
t.set_backgroundcolor(vcolor[::-1][i])
# save figure
fig.savefig(figname)
pylab.axis('off')
if __name__ == '__main__':
dims = numpy.array([50, 50])
sesName = 'a98nm5'
grpName = 'movie'
chan = 1
movtregion = [10.0, 40.0]
dt = 0.049584
mov = getRFMovieStm(dims, movtregion, sesName, grpName, chan)
pylab.colormaps()
plotMovieFrames(mov, npic=100, cmap=pylab.cm.gray, tstart=movtregion[0])
dim_t = 300
Tsim = 1.2
nTimeSteps = 300
stmframes = numpy.r_[numpy.zeros(125),
numpy.ones(25),
numpy.zeros(25),
numpy.ones(25),
numpy.zeros(25),
numpy.ones(25),
numpy.zeros(50)]
letters = ['A', 'B', 'E']