def PlotPatchBins(Sc, PatchData, NumBins, color, MinimumBinSize=7, ErrorBars=True):
"""
Plot E*R* data binned from the hilltop pacth data.
"""
E_s = E_Star(Sc, PatchData[6], PatchData[2])
R_s = R_Star(Sc, PatchData[10], PatchData[2])
bin_x, bin_std_x, bin_y, bin_std_y, std_err_x, std_err_y, count = Bin.bin_data_log10(E_s, R_s, NumBins)
# filter bins based on the number of data points used in their calculation
bin_x = np.ma.masked_where(count < MinimumBinSize, bin_x)
bin_y = np.ma.masked_where(count < MinimumBinSize, bin_y)
# these lines produce a meaningless warning - don't know how to solve it yet.
if ErrorBars:
# only plot errorbars for y as std dev of x is just the bin width == meaningless
plt.scatter(
bin_x,
bin_y,
c=count,
s=50,
edgecolor="",
cmap=plt.get_cmap("autumn_r"),
label="Binned Patch Data",
zorder=100,
)
plt.errorbar(bin_x, bin_y, yerr=std_err_y, fmt=None, ecolor="k", elinewidth=2, capsize=3, zorder=0)
cbar = plt.colorbar()
cbar.set_label("Number of values per bin")
else:
plt.errorbar(bin_x, bin_y, fmt="o", color=color, label="No. of Bins = " + str(NumBins))
def PlotRawBins(Sc, RawData, NumBins, MinimumBinSize=100, ErrorBars=True):
"""
Plot E*R* data binned from the raw data.
"""
E_s = E_Star(Sc, RawData[3], RawData[2])
R_s = R_Star(Sc, RawData[4], RawData[2])
(bin_x, bin_std_x, bin_y, bin_std_y, std_err_x, std_err_y,
count) = Bin.bin_data_log10(E_s, R_s, NumBins)
# filter bins based on the number of data points used in their calculation
bin_x = np.ma.masked_where(count < MinimumBinSize, bin_x)
bin_y = np.ma.masked_where(count < MinimumBinSize, bin_y)
# these lines produce a meaningless warning - don't know how to solve it yet
if ErrorBars:
# only plot errorbars for y as std dev of x is the bin width
plt.scatter(bin_x,
bin_y,
c=count,
s=50,
edgecolor='',
cmap=plt.get_cmap("autumn_r"),
label='Binned Raw Data',
zorder=100)
plt.errorbar(bin_x,
bin_y,
yerr=std_err_y,
fmt=None,
ecolor='k',
zorder=0)
cbar = plt.colorbar()
cbar.set_label('Number of values per bin')
else:
plt.errorbar(bin_x, bin_y, fmt='bo', label='Binned Raw Data')
def PlotPatchBins(Sc, PatchData, NumBins, MinimumBinSize=10, ErrorBars=True):
"""
Plot E*R* data binned from the hilltop pacth data.
"""
E_s = E_Star(Sc, PatchData[6], PatchData[2])
R_s = R_Star(Sc, PatchData[10], PatchData[2])
(bin_x, bin_std_x, bin_y, bin_std_y,
std_err_x, std_err_y, count) = Bin.bin_data_log10(E_s, R_s, NumBins)
# filter bins based on the number of data points used in their calculation
bin_x = np.ma.masked_where(count < MinimumBinSize, bin_x)
bin_y = np.ma.masked_where(count < MinimumBinSize, bin_y)
# these lines produce a meaningless warning - don't know how to solve it yet
if ErrorBars:
# only plot errorbars for y as std dev of x is the bin width
plt.scatter(bin_x, bin_y, c=count, s=50, edgecolor='',
cmap=plt.get_cmap("autumn_r"), label='Binned Patch Data',
zorder=100)
plt.errorbar(bin_x, bin_y, yerr=std_err_y, fmt=None, ecolor='k',
zorder=0)
cbar = plt.colorbar()
cbar.set_label('Number of values per bin')
else:
plt.errorbar(bin_x, bin_y, fmt='bo', label='Binned Patch Data')