cS = np.sqrt(Te0/mi)
rhoS = cS/omCI
# From normalization of parallel current equation
factor = (omCI**2)*n0
# Obtain the suptitle
suptitle = fig.texts[0].get_text()
# Close the figure
plt.close(fig)
#}}}
# Make new ax to plot to
fig, (normalAx ,nnAx) = plt.subplots(ncols=2,\
figsize = SizeMaker.array(2,1, aSingle=0.5))
size = "large"
#{{{Extract and plot nn
# Find the min and the max
maxmin = []
for line in oldNnAx.get_lines():
x, y = line.get_data()
x *= rhoS
y *= factor
maxmin.append((np.max(y), np.min(y)))
nnAx.plot(x,y, color=line.get_color())
# Set the texts
maxInd = np.argmax(tuple(curVal[0] for curVal in maxmin))
minInd = np.argmin(tuple(curVal[1] for curVal in maxmin))
mi = 39.948 * cst.u
e = cst.e
omCI = e * B0 / mi
cS = np.sqrt(Te0 / mi)
rhoS = cS / omCI
# From normalization of parallel current equation
factor = omCI * n0 * e * cS
# Obtain the suptitle
suptitle = fig.texts[0].get_text()
# Make new ax to plot to
fig, newAx = plt.subplots(figsize=SizeMaker.standard(w=4, a=0.5))
maxmin = []
for line in oldAx.get_lines():
x, y = line.get_data()
x *= rhoS
y *= factor
ny = len(x)
maxmin.append((np.max(y), np.min(y)))
newAx.plot(x, y, color=line.get_color())
# Set the texts
maxInd = np.argmax(tuple(curVal[0] for curVal in maxmin))
minInd = np.argmin(tuple(curVal[1] for curVal in maxmin))
boltzmann = oldAx.get_lines()[minInd]
# Make segments
# +3 as we would like some space between the bars
lenKeys = len(keys) + 5
nBars = len(modes) * lenKeys
xBarVals = tuple(range(nBars))
data["Arithmetics"]["x"] = xBarVals[0::lenKeys]
data["Communication"]["x"] = xBarVals[1::lenKeys]
data["Input/output"]["x"] = xBarVals[2::lenKeys]
data["Laplace inversions"]["x"] = xBarVals[3::lenKeys]
data["Time solver"]["x"] = xBarVals[4::lenKeys]
tickVals = data["Input/output"]["x"]
# Create the figure
fig, ax = plt.subplots(figsize=SizeMaker.standard(a=0.5, s=0.5))
for key in keys:
d = data[key]
ax.bar(d["x"], d["mean"], yerr=d["std"], label=key)
PlotHelper.makePlotPretty(ax)
ax.xaxis.grid(False)
ax.xaxis.set_ticks(tickVals)
ax.xaxis.set_ticklabels(
("Initial\nphase", "Expand\nphase", "Linear\nphase", "Turbulent\nphase"))
ax.set_ylabel("$\%$")
# Move legend outside
handles, labels = ax.get_legend_handles_labels()
ax = fig.get_axes()[0]
# Get the legends, and make keys out of these
handles, labels = ax.get_legend_handles_labels()
# Obtain the lines
for nr, label in enumerate(labels):
sD[scan][label] = ax.get_lines()[nr].get_data()[1]
xAxis = ax.get_lines()[0].get_data()[0]
plt.close(fig)
# Make a new figure
fig, (sAx, kAx) = plt.subplots(nrows=2, figsize = SizeMaker.standard(w=3, a=1.5),\
sharex=True)
for scan in scans:
curScan = float(scan[4:])
for key in sD[scan].keys():
if "skew" in key.lower():
sAx.plot(xAxis, sD[scan][key],\
ls = sD[scan]["ls"],\
color = sD[scan]["color"],\
marker = sD[scan]["marker"],\
ms = 7,\
alpha = 0.7,\
label = "$B_0 = {} \mathrm{{T}}$".format(curScan)\
)
sAx.set_ylabel(key.replace("Skewness", r"Skewness \;"))
