def main():
coverage_hist = pd.read_csv(ffile, sep='\t')
globalreport = pd.Series(globaldepth(coverage_hist))
globalreport = globalreport[[
'mean_DP', 'median_DP', 'std_DP', 'q25_DP', 'q75_DP', 'q95_DP',
'dp>=1', 'dp>=10', 'dp>=20', 'dp>=30', 'dp>=50', 'dp>=100'
]].to_frame()
globalreport.columns = [sample]
globalreport.to_csv(output_global_coverage, header=True, sep='\t')
f = plt.figure(figsize=(14, 6))
ax1 = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax1.plot(coverage_hist.DP, coverage_hist.frequency)
ax1.set_yscale('log')
ax1.set_xscale('symlog')
ax1.set_xlabel('DP')
ax1.set_ylabel('density')
ax1.xaxis.set_minor_locator(LogLocator(subs=np.arange(2, 10)))
ax1.grid(True, which="both", ls="--")
ax2.plot(coverage_hist.DP, coverage_hist['cumsum'].values, '.-')
ax2.set_ylim((-0.02, 1))
ax2.set_xscale('symlog')
ax2.set_ylabel('Prob( bp > DP )')
ax2.set_yticks(np.arange(0, 1., 0.1))
ax2.set_xlabel('DP')
ax2.xaxis.set_minor_locator(LogLocator(subs=np.arange(2, 10)))
ax2.yaxis.set_minor_locator(AutoMinorLocator(2))
ax2.grid(True, which="both", ls="--")
f.savefig(out_plot, format='eps')
def __decorate_axis(axis, ax_type):
'''Configure axis tickers, locators, and labels'''
if ax_type == 'tonnetz':
axis.set_major_formatter(TONNETZ_FORMATTER)
axis.set_major_locator(FixedLocator(0.5 + np.arange(6)))
axis.set_label_text('Tonnetz')
elif ax_type == 'chroma':
axis.set_major_formatter(ChromaFormatter())
axis.set_major_locator(FixedLocator(0.5 +
np.add.outer(12 * np.arange(10),
[0, 2, 4, 5, 7, 9, 11]).ravel()))
axis.set_label_text('Pitch class')
elif ax_type == 'tempo':
axis.set_major_formatter(ScalarFormatter())
axis.set_major_locator(LogLocator(base=2.0))
axis.set_label_text('BPM')
elif ax_type == 'time':
axis.set_major_formatter(TimeFormatter(lag=False))
axis.set_major_locator(MaxNLocator(prune=None, steps=[1,5,10,15,20,30,45,60]))
axis.set_label_text('Time')
elif ax_type == 'lag':
axis.set_major_formatter(TimeFormatter(lag=True))
axis.set_major_locator(MaxNLocator(prune=None, steps=[1,5,10,15,20,30,45,60]))
axis.set_label_text('Lag')
elif ax_type == 'cqt_note':
axis.set_major_formatter(NoteFormatter())
axis.set_major_locator(LogLocator(base=2.0))
axis.set_minor_formatter(NoteFormatter(major=False))
axis.set_minor_locator(LogLocator(base=2.0,
subs=2.0**(np.arange(1, 12)/12.0)))
axis.set_label_text('Note')
elif ax_type in ['cqt_hz']:
axis.set_major_formatter(ScalarFormatter())
axis.set_major_locator(LogLocator(base=2.0))
axis.set_minor_locator(LogLocator(base=2.0,
subs=2.0**(np.arange(1, 12)/12.0)))
axis.set_label_text('Hz')
elif ax_type in ['mel', 'log']:
axis.set_major_formatter(ScalarFormatter())
axis.set_major_locator(SymmetricalLogLocator(axis.get_transform()))
axis.set_label_text('Hz')
elif ax_type in ['linear', 'hz']:
axis.set_major_formatter(ScalarFormatter())
axis.set_label_text('Hz')
elif ax_type in ['frames']:
axis.set_label_text('Frames')
elif ax_type in ['off', 'none', None]:
axis.set_label_text('')
axis.set_ticks([])
def plot_timings(timings,
title=None,
figure=None,
seaborn_style: Optional[dict] = DEFAULT_SEABORN_STYLE):
if seaborn_style is not None:
import seaborn
seaborn.set(**seaborn_style)
from matplotlib.ticker import LogLocator, LogFormatter
if figure is None:
import matplotlib.pyplot as plt
figure: plt.Figure = plt.figure(figsize=(9, 4))
axes = figure.gca(xlabel='n_features', ylabel='time[s]')
if title is not None:
axes.set_title(title)
timings = np.asarray(timings)
# sort by n_features so plot lines make sense
timings = timings[np.argsort(timings[:, 1])]
n_samples = timings.T[0]
n_features = timings.T[1]
tm_min = timings.T[2]
for n in np.unique(n_samples)[::-1]: # reverse so legend is in order
mask = n_samples == n
axes.loglog(n_features[mask], tm_min[mask], '.-', label=str(int(n)))
axes.legend(title='n_samples', ncol=1, handlelength=0)
axes.grid(True, which='both', axis='both')
figure.tight_layout()
# show more ticks
axes.xaxis.set_major_locator(LogLocator(subs='all'))
axes.xaxis.set_major_formatter(LogFormatter(minor_thresholds=(100, 0.4)))
axes.yaxis.set_major_locator(LogLocator(subs='all'))
return figure
def update_colorbar_scale(figure, image, scale, vmin, vmax):
""""
Updates the colorbar to the scale and limits given.
:param figure: A matplotlib figure instance
:param image: The matplotlib image containing the colorbar
:param scale: The norm scale of the colorbar, this should be a matplotlib colormap norm type
:param vmin: the minimum value on the colorbar
:param vmax: the maximum value on the colorbar
"""
if vmin <= 0 and scale == LogNorm:
vmin = 0.0001 # Avoid 0 log scale error
mantid.kernel.logger.warning(
"Scale is set to logarithmic so non-positive min value has been changed to 0.0001."
)
if vmax <= 0 and scale == LogNorm:
vmax = 1 # Avoid 0 log scale error
mantid.kernel.logger.warning(
"Scale is set to logarithmic so non-positive max value has been changed to 1."
)
image.set_norm(scale(vmin=vmin, vmax=vmax))
if image.colorbar:
image.colorbar.remove()
locator = None
if scale == LogNorm:
locator = LogLocator(subs=np.arange(1, 10))
if locator.tick_values(vmin=vmin, vmax=vmax).size == 0:
locator = LogLocator()
mantid.kernel.logger.warning(
"Minor ticks on colorbar scale cannot be shown "
"as the range between min value and max value is too large"
)
figure.colorbar(image, ticks=locator)
def colorbar(self, clev):
""""""
cb = plt.colorbar(clev, orientation='horizontal', extend='both', pad=.1, format=self.cb_fmt)
# Adjust colorbar ticks for log scale
if isinstance(clev.norm, (LogNorm, SymLogNorm)):
clim = list(clev.get_clim())
# For symmetric log scales, the central tick is the linear threshold
if self.pm:
clim[0] = clev.norm.linthresh
# Determine major (every decade) and minor (every tenth of a decade) ticks
minor_ticks = LogLocator(subs=range(2, 10)).tick_values(*clim)
major_ticks = LogLocator().tick_values(*clim)
minor_ticks = minor_ticks[(clim[0] <= minor_ticks) & (clim[1] >= minor_ticks)]
major_ticks = major_ticks[(clim[0] <= major_ticks) & (clim[1] >= major_ticks)]
# For symmetric log scales, ticks are mirrored around the central tick
if self.pm:
minor_ticks = np.concatenate([-minor_ticks, minor_ticks])
major_ticks = np.concatenate([-major_ticks[1:], major_ticks])
cb.set_ticks(major_ticks)
# TODO: When self.cb_fmt = None, this drops the offset label (no longer a ScalarFormatter)- need to add
# it back manually or use a FuncFormatter For symmetric log scales, update the central tick label to
# indicate that it represents the linear threshold
if self.pm:
major_ticklabels = [i.get_text() for i in cb.ax.xaxis.get_ticklabels()]
major_ticklabels[len(major_ticks) / 2] = '< |{}|'.format(major_ticklabels[len(major_ticks) / 2])
cb.set_ticklabels(major_ticklabels)
cb.ax.xaxis.set_ticks(clev.norm(minor_ticks), minor=True)
def _polish(self, f):
# Handle properties of axes directly
#a = plt.gca() # Current set of axes
formatter_scalar = ScalarFormatter(useOffset=True, useMathText=False)
formatter_scalar.set_powerlimits((-3, 3))
formatter_log = LogFormatterMathtext(base=10.0, labelOnlyBase=False)
# Neaten axes formatters
for ax in f.get_axes():
if not isinstance(ax.xaxis.get_major_formatter(), NullFormatter):
if ax.xaxis.get_scale() == "log":
ax.xaxis.set_major_locator(
LogLocator(base=10.0, subs=[1.0], numdecs=1))
ax.xaxis.set_major_formatter(formatter_log)
else:
ax.xaxis.set_major_formatter(formatter_scalar)
if not isinstance(ax.yaxis.get_major_formatter(), NullFormatter):
if ax.yaxis.get_scale() == "log":
ax.yaxis.set_major_locator(
LogLocator(base=10.0, subs=[1.0], numdecs=1))
ax.yaxis.set_major_formatter(formatter_log)
#ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=[10], numdecs=1)) # why is this necessary?
else:
ax.yaxis.set_major_formatter(formatter_scalar)
def set_default_locators_and_formatters(self, axis):
# docstring inherited
axis.set_major_locator(LogLocator(self.base))
axis.set_major_formatter(LogFormatterSciNotation(self.base))
axis.set_minor_locator(LogLocator(self.base, self.subs))
axis.set_minor_formatter(
LogFormatterSciNotation(self.base,
labelOnlyBase=(self.subs is not None)))
def set_default_locators_and_formatters(self, axis):
if isinstance(axis, XAxis):
axis.set_tick_params(which='both', pad=7)
axis.labelpad = 8
axis.set_major_locator(LogLocator(self.base))
axis.set_major_formatter(GWpyLogFormatterMathtext(self.base))
axis.set_minor_locator(LogLocator(self.base, self.subs))
axis.set_minor_formatter(MinorLogFormatterMathtext(self.base))
def set_default_locators_and_formatters(self, axis):
"""
Set the locators and formatters to specialized versions for
log scaling.
"""
axis.set_major_locator(LogLocator(self.base))
axis.set_major_formatter(LogFormatterMathtext(self.base))
axis.set_minor_locator(LogLocator(self.base, self.subs))
axis.set_minor_formatter(NullFormatter())
def visualize_periodic():
f = h5py.File('periodic_output.hdf5', 'r',
driver='mpio', comm=mpi4py.MPI.COMM_WORLD)
iterations = f.get("its")[:]
dofs = f.get("num_dofs")[:]
slaves = np.sum(f.get("num_slaves")[:], axis=1)
solver = f.get("solve_time").attrs["solver"].decode("utf-8")
ct = f.get("solve_time").attrs["ct"].decode("utf-8")
degree = f.get("solve_time").attrs["degree"].decode("utf-8")
f.close()
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.plot(dofs, iterations, "-ro", label="MPC", markersize=12)
f_ref = h5py.File('periodic_ref_output.hdf5', 'r',
driver='mpio', comm=mpi4py.MPI.COMM_WORLD)
iterations_ref = f_ref.get("its")[:]
dofs_ref = f_ref.get("num_dofs")[:]
f_ref.close()
plt.plot(dofs_ref, iterations_ref, "-bs", label="Unconstrained")
ax.tick_params(axis='both', which='major', labelsize=20)
ax.set_xscale("log")
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_ylim([0, max(iterations) + 1])
ax.set_xlim([1e2, max(dofs) + 1])
plt.xlabel("# DOFS", fontsize=20)
plt.ylabel("# Iterations", fontsize=20)
trans_offset = mtransforms.offset_copy(ax.transData, fig=fig,
x=0.025, y=0.025, units='inches')
for i in range(len(iterations)):
plt.text(dofs[i], iterations[i], slaves[i],
transform=trans_offset, fontsize=20)
plt.title("Periodic Poisson (CG {0:s}, {1:s}) with {2:s}".format(
degree, ct, solver), fontsize=25)
plt.legend(fontsize=15)
ax.minorticks_on()
ax.set_ylim([0, max(iterations) + 1])
ax.set_xlim([1e2, 1e8])
locmax = LogLocator(
base=10.0,
numticks=8)
ax.xaxis.set_major_locator(locmax)
locmin = LogLocator(
base=10.0, subs=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
numticks=9)
ax.xaxis.set_minor_locator(locmin)
ax.xaxis.set_minor_formatter(NullFormatter())
plt.grid(True, which="both", axis="both")
plt.savefig("periodic_iterations_CG{0:s}_{1:s}.png".format(
degree, ct), bbox_inches='tight')
def set_default_locators_and_formatters(self, axis):
"""
Set the locators and formatters to specialized versions for
log scaling.
"""
axis.set_major_locator(LogLocator(self.base))
axis.set_major_formatter(LogFormatterSciNotation(self.base))
axis.set_minor_locator(LogLocator(self.base, self.subs))
axis.set_minor_formatter(
LogFormatterSciNotation(self.base, labelOnlyBase=self.subs))
def _get_locators(self, locator, at, upto, count, every, between, minor):
log_base, symlog_thresh = self._parse_for_log_params(self.trans)
if locator is not None:
major_locator = locator
elif upto is not None:
if log_base:
major_locator = LogLocator(base=log_base, numticks=upto)
else:
major_locator = MaxNLocator(upto, steps=[1, 1.5, 2, 2.5, 3, 5, 10])
elif count is not None:
if between is None:
# This is rarely useful (unless you are setting limits)
major_locator = LinearLocator(count)
else:
if log_base or symlog_thresh:
forward, inverse = self._get_transform()
lo, hi = forward(between)
ticks = inverse(np.linspace(lo, hi, num=count))
else:
ticks = np.linspace(*between, num=count)
major_locator = FixedLocator(ticks)
elif every is not None:
if between is None:
major_locator = MultipleLocator(every)
else:
lo, hi = between
ticks = np.arange(lo, hi + every, every)
major_locator = FixedLocator(ticks)
elif at is not None:
major_locator = FixedLocator(at)
else:
if log_base:
major_locator = LogLocator(log_base)
elif symlog_thresh:
major_locator = SymmetricalLogLocator(linthresh=symlog_thresh, base=10)
else:
major_locator = AutoLocator()
if minor is None:
minor_locator = LogLocator(log_base, subs=None) if log_base else None
else:
if log_base:
subs = np.linspace(0, log_base, minor + 2)[1:-1]
minor_locator = LogLocator(log_base, subs=subs)
else:
minor_locator = AutoMinorLocator(minor + 1)
return major_locator, minor_locator
def build_chart(x_data, y_data, label_req, dur, scatter = False ):
matplotlib.rcParams.update({'font.family': 'monospace'})
label = labels[label_req]
dates = mdate.epoch2num(x_data)
fig, ax = plt.subplots()
if scatter:
plt.scatter(dates, y_data,s=.01)
else:
ax.plot(dates, y_data,linewidth=.5,color = 'black')
if dur !='1M':
label['x-axis'] ='Year'
if label['scale']:
if 'ℳ' in list(label['y-axis']) :
plt.ylabel('ℳ', fontdict = {'family': 'sans'})
ax.set(xlabel = label['x-axis'], ylabel = label['y-axis'], title = label['title'], yscale = label['scale'])
else:
ax.set(xlabel = label['x-axis'], ylabel = label['y-axis'], title = label['title'])
if label['scale']=='log':
ax.yaxis.set_major_locator(LogLocator(base=10))
ax.yaxis.set_minor_locator(LogLocator(base=10,subs=[0.0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]))
ax.yaxis.set_major_formatter(LogFormatterMathtext(labelOnlyBase=True))
if dur == '1M':
date_formatter = mdate.DateFormatter('%m/%d/%y')
ax.xaxis.set_minor_locator(mdate.DayLocator())
ax.xaxis.set_major_locator(mdate.WeekdayLocator(byweekday = mdate.SU))
ax.xaxis.set_major_formatter(date_formatter)
elif dur == '1Y':
date_formatter = mdate.DateFormatter("%b.%y")
ax.xaxis.set_minor_locator(mdate.MonthLocator())
ax.xaxis.set_major_locator(mdate.MonthLocator(bymonth=[1,4,7,10]))
ax.xaxis.set_major_formatter(date_formatter)
else:
date_formatter = mdate.DateFormatter('%y')
ax.xaxis.set_minor_locator(mdate.MonthLocator(bymonth=[1,4,7,10]))
ax.xaxis.set_major_locator(mdate.YearLocator())
ax.xaxis.set_major_formatter(date_formatter)
ax.grid(which='major',linestyle = '--')
ax.grid(which='minor',linestyle = ':')
ax.grid(True)
fig.set_size_inches(8,5)
fig.savefig("static/data/"+ label['file-name'] +"_" + dur + ".png",dpi=150)
plt.cla()
plt.clf()
plt.close('all')
return True
def addAEPGrid(axes):
"""
Add a logarithmic graticuyle to the subplot axes
:param axes: :class:`axes` instance
"""
axes.yaxis.set_major_locator(LogLocator())
axes.yaxis.set_minor_locator(
LogLocator(subs=[.1, .2, .3, .4, .5, .6, .7, .8, .9]))
axes.autoscale(True, axis='y', tight=True)
axes.grid(True, which='major', linestyle='-')
axes.grid(True, which='minor', linestyle='--', linewidth=0.5)
def format_log_axis(ax, num_ticks):
"""
change ax tick to 1, 10, 100, 1000, ...
add proper minor ticks
"""
ax.set_major_locator(LogLocator(base=10, numticks=num_ticks))
ax.set_minor_locator(
LogLocator(base=10,
subs=(0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
numticks=num_ticks), )
ax.set_minor_formatter(NullFormatter())
def make_single_plots(qArray, quantity, layerOverlap, hitOverlap,
layer_binary):
minValue = min(qArray[qArray > -999])
maxValue = max(qArray)
histMinLimit = 9e-6
histMaxLimit = 1.1e2
if abs(histMaxLimit -
histMinLimit) > 10 and histMinLimit > 0 or "/" in quantity:
binning = np.logspace(np.log10(histMinLimit), np.log10(histMaxLimit),
500)
else:
binning = np.linspace(histMinLimit, histMaxLimit, 500)
allHist = Hist1D(ak.to_numpy(qArray[qArray > -999]),
bins=binning,
label="{}".format(quantity))
fig, ax = plt.subplots()
ax.set_yscale("log")
if abs(histMaxLimit -
histMinLimit) > 10 and histMinLimit > 0 or "/" in quantity:
x_major = LogLocator(base=10.0, numticks=5)
x_minor = LogLocator(base=10.0,
subs=np.arange(1.0, 10.0) * 0.1,
numticks=10)
ax.xaxis.set_major_locator(x_major)
ax.xaxis.set_minor_locator(x_minor)
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_xscale("log")
#find the 99%
print("99% limit at {}".format(allHist.quantile(0.99)))
allHist.plot(alpha=0.8, color="C0", label="all", histtype="stepfilled")
layers = parseLayers(layer_binary)
plt.title("{}, layer overlap = {}, hit overlap = {}".format(
(",").join(layers), layerOverlap, hitOverlap))
plt.xlabel(quantity)
plt.suptitle("Sample = {}".format(sys.argv[3]))
if "inner" in quantity:
plt.savefig(
"{}/inner_v_sim_layerOverlap_{}_hitOverlap_{}_{}.pdf".format(
sys.argv[2], layerOverlap, hitOverlap, ("").join(layers)))
else:
plt.savefig(
"{}/outer_v_sim_layerOverlap_{}_hitOverlap_{}_{}.pdf".format(
sys.argv[2], layerOverlap, hitOverlap, ("").join(layers)))
plt.close()
def addGrid(self, axes):
"""
Add a logarithmic graticule to the subplot axes.
:param axes: :class:`axes` instance.
"""
axes.xaxis.set_major_locator(LogLocator())
axes.xaxis.set_major_formatter(FormatStrFormatter('%d'))
axes.xaxis.set_minor_locator(LogLocator(subs=[.1, .2, .3, .4, .5, .6, .7, .8, .9]))
axes.autoscale(True, axis='x', tight=True)
axes.grid(True, which='major', linestyle='-', linewidth=0.5)
axes.grid(True, which='minor', linestyle='-', linewidth=0.5)
def plot_window_function_2d(path_window,key,scale='slinlin',title='$\\mathcal{W}_{\ell}(s)$',path='window_2d.png'):
window = WindowFunction.load(path_window)
s,d = window.s[0][window.s[0]>0.],window.s[1][window.s[1]>0.]
#s,d = window.s[0][window.s[0]>0.],window.s[1][window.s[1]>1.e3]
scaling = scalings[scale]
s = s[(s>=scaling['xlim'][0]) & (s<scaling['xlim'][-1])]
d = d[(d>=scaling['xlim'][0]) & (d<scaling['xlim'][-1])]
figsize = 7; xextend = 0.8
xlabel,ylabel = ('$s$ [$\\mathrm{Mpc} \ h^{-1}$]','$\\Delta$ [$\\mathrm{Mpc} \ h^{-1}$]')
cmap = pyplot.get_cmap('Spectral')
ells1,ells2 = window.ells
#ells1,ells2 = ells1[:3],ells2[:3]
ells1,ells2 = [2],[0]
tmp = [window((s,d),(ell1,ell2)) for ell1 in ells1 for ell2 in ells2]
norm = Normalize(scipy.amin(tmp),scipy.amax(tmp))
#norm = Normalize(0,1)
ncols = len(ells1); nrows = len(ells2)
fig = pyplot.figure(figsize=(figsize/xextend,figsize))
if title is not None: fig.suptitle(title,fontsize=fontsize)
gs = gridspec.GridSpec(nrows,ncols,wspace=0.1,hspace=0.1)
for ill1,ell1 in enumerate(ells1):
for ill2,ell2 in enumerate(ells2):
ax = pyplot.subplot(gs[nrows-1-ill2,ill1])
im = ax.pcolormesh(s,d,window([s,d],(ell1,ell2)).T,norm=norm,cmap=cmap)
#im = ax.pcolormesh(s,d,scipy.log(scipy.absolute(window([s,d],(ell1,ell2)).T)),norm=norm,cmap=cmap)
ax.set_xscale(scaling['xscale'])
ax.set_yscale(scaling['xscale'])
if ill1>0: ax.get_yaxis().set_visible(False)
elif 'log' in scale:
ax.yaxis.set_major_locator(LogLocator(base=10.,subs=(1,),numticks=3))
ax.yaxis.set_minor_locator(LogLocator(base=10.,subs=range(1,11),numticks=3))
ax.yaxis.set_minor_formatter(NullFormatter())
if ill2>0: ax.get_xaxis().set_visible(False)
elif 'log' in scale:
ax.xaxis.set_major_locator(LogLocator(base=10.,subs=(1,),numticks=3))
ax.xaxis.set_minor_locator(LogLocator(base=10.,subs=range(1,11),numticks=3))
ax.xaxis.set_minor_formatter(NullFormatter())
ax.tick_params(labelsize=labelsize)
text = '$\\mathcal{{W}}_{{{:d},{:d}}}$'.format(ell1,ell2)
ax.text(0.05,0.95,text,horizontalalignment='left',verticalalignment='top',transform=ax.transAxes,color='black',fontsize=labelsize)
utils.suplabel('x',xlabel,shift=-0.04,labelpad=7,size=labelsize)
utils.suplabel('y',ylabel,shift=0,labelpad=8,size=labelsize)
fig.subplots_adjust(right=xextend)
cbar_ax = fig.add_axes([xextend+0.05,0.15,0.03,0.7])
cbar_ax.tick_params(labelsize=labelsize)
cbar = fig.colorbar(im,cax=cbar_ax,format='%.1e')
utils.savefig(path,dpi=dpi,bbox_inches='tight',pad_inches=0.1)
def setup(ax, indx, num_cc, point_boundary):
data_tail = [[], []]
data_tail_targets = [[], []]
data_targets = [[], []]
for line in open(indx + '_tail'):
data = line.split()
data_tail[0].append(float(data[0]))
data_tail[1].append(float(data[1]))
for line in open(indx + '_tail_other_targets'):
data = line.split()
data_tail_targets[0].append(float(data[0]))
data_tail_targets[1].append(float(data[1]))
for line in open(indx + '_other_targets'):
data = line.split()
data_targets[0].append(float(data[0]))
data_targets[1].append(float(data[1]))
ax.semilogy(data_tail[0][:point_boundary],
data_tail[1][:point_boundary],
'b',
label=str(num_cc) + 'CC BPRM')
ax.semilogy(data_tail[0][point_boundary:],
data_tail[1][point_boundary:],
'-.g',
label='Tail')
ax.semilogy(data_targets[0], data_targets[1], 'k', label='Other cores')
ax.semilogy(data_tail_targets[0],
data_tail_targets[1],
'r',
label='Total (including other cores)')
ax.legend(prop={'size': 8})
ax.set_xlim(0, 530)
ax.yaxis.set_major_locator(LogLocator(base=10, numticks=6))
ax.set_yticklabels(ax.get_yticks())
labels = [
str(int(log10(float(label.get_text()))))
for label in ax.get_yticklabels()
]
ax.set_yticklabels(labels)
ax.yaxis.set_minor_locator(
LogLocator(base=10, subs=(0.2, 0, 4, 0.6, 0.8), numticks=24))
ax.xaxis.set_minor_locator(AutoMinorLocator(5))
def set_log_chart_params(ax):
for a in np.ndindex(ax.shape):
ax[a].set_yscale('log')
ax[a].set_xlabel('Pipe Diameter (in)')
ax[a].set_ylabel('Cost ($)')
ax[a].legend()
# x ticks
ax[a].xaxis.set_major_locator(MultipleLocator(4))
ax[a].xaxis.set_minor_locator(MultipleLocator(2))
ax[a].yaxis.set_major_locator(LogLocator(base=10))
subs = (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
locmin = LogLocator(base=10.0, subs=subs, numticks=12)
ax[a].yaxis.set_minor_locator(locmin)
ax[a].grid(which='minor', color='#f0f0f0', zorder=0)
ax[a].grid(which='major', color='#ccc', zorder=0)
def PlotFlowRateVsTime(Data):
fig = plt.figure(figsize=(10, 7))
ax = fig.gca()
plt.xlabel('Time [Hours]', fontsize=16)
plt.ylabel(r'Outgassing Rate [mBar$\,\cdot\,$Liter/s]', fontsize=16)
plt.yscale('log')
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
ax.grid(b=True, which='major', color='grey', linestyle='--')
ax.grid(b=True, which='minor', color='grey', linestyle=':')
ax.xaxis.set_minor_locator(AutoMinorLocator(5))
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.yaxis.set_major_locator(LogLocator(
base=10,
numticks=12,
))
for jj, data in enumerate(Data):
for ii, (X, Y) in enumerate(zip(data.Time, data.FlowRate)):
plt.plot(X,
Y,
label=data.Labels[ii],
color=colors[jj],
linewidth=2.)
plt.xlim(np.min(Data[0].Time[0]), np.max(Data[0].Time[-1]))
plt.ylim(ymin=1E-12)
ax.legend(loc='upper right', fontsize=14)
fig.tight_layout()
def plot_variable(filename,
varname,
rmin=0,
log=None,
plot_fit=None,
p0=[-1, -1, 20]):
var = ExoReader(filename).read(varname)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(var.r_label, labelpad=10, fontsize=20)
ax.set_ylabel(var.label, labelpad=10, fontsize=20)
ax.tick_params(labelsize=20)
r_data = var.r[var.r >= rmin]
var_data = var.data[var.r >= rmin]
ax.set_xlim(rmin, max(r_data))
if not not plot_fit:
r_data /= r_data[0]
var_data /= var_data[0]
ax.set_xlim(1, max(r_data))
ax.plot(r_data, var_data, "x")
if not not plot_fit:
fpars = fit_variable(filename,
varname,
algorithm=plot_fit,
rmin=rmin,
p0=p0)
p = fpars[2]
cov = fpars[3]
if plot_fit == "power":
y_data = fit_powerlaw(r_data, p[0])
elif plot_fit == "curved":
y_data = fit_curved_powerlaw(r_data, p[0], p[1])
elif plot_fit == "double":
y_data = fit_double_powerlaw(r_data, p[0], p[1])
elif plot_fit == "broken":
y_data = fit_broken_powerlaw(r_data, p[0], p[1], p[2])
elif plot_fit == "exp":
y_data = fit_exp_powerlaw(r_data, p[0], p[1])
ax.plot(r_data, y_data, color="r")
if not not log:
plt.yscale("log")
ax.yaxis.set_major_locator(LogLocator())
ax.xaxis.set_major_locator(AutoLocator())
if not log:
ax.yaxis.set_major_locator(AutoLocator())
plt.tight_layout()
plt.grid(which="major")
plt.grid(which="minor")
plt.show()
return [p, cov]
def fitAndPlotLinear(x, y, rngt, ax, alfa, showPlot, labelAlfa, p):
markers=["o", "s","D","^","d","h","p","o"]
labelRange = ['low', 'med', 'high']
labelRange = labelRange+list([str(i) for i in rngt])
cm = plt.get_cmap('Set1')
cm1 = plt.get_cmap('Set3')
slopes = []
if showPlot:
inf.smallerFont(ax, 8)
ax.scatter(x, y, color=cm(abs(alfa/9)), alpha=0.75, edgecolors='none', marker=markers[alfa])#, "o", lw=0.5)
arrow = dict(arrowstyle="-", connectionstyle="arc3", ls="--", color="gray")
putLabels(ax, p.calc, alfa)
for i in range(0,len(rngt)-1):
a, b = fun.linearFit(x, y, rngt[i], rngt[i+1])
slopes.append(b)
if showPlot:
ax.semilogy(x[rngt[i]:rngt[i+1]+1], np.exp(fun.linear(x[rngt[i]:rngt[i+1]+1], a, b)), ls="-", color=cm1((i+abs(alfa)*3)/12))
xHalf = (x[rngt[i]]+x[rngt[i+1]]+1)/2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b))
if alfa == -1:
ax.text(xHalf, 2e1, r"$"+roman.toRoman(i+1)+r"$", color="gray", ha="right", va="center")#, transform=axarr[i].transAxes)
xHalf *= 1.15
yHalf *= 5
text = r"$E_a="+text+r"$"
bbox_props = dict(boxstyle="round", fc="w", ec="1", alpha=0.6)
ax.text(xHalf,yHalf, text, color=cm(abs(alfa/9)), bbox=bbox_props, ha="center", va="center", size=6)
if showPlot and alfa > -1:
locator = LogLocator(100,[1e-1])
ax.yaxis.set_major_locator(locator)
return slopes
def PlotImpuritiesVsTime(Data):
fig = plt.figure(figsize=(10, 7))
ax = fig.gca()
plt.xlabel('Time [hours]', fontsize=16)
plt.ylabel(r'Total Number of Impurities', fontsize=16)
plt.yscale('log')
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
ax.grid(b=True, which='major', color='grey', linestyle='--')
ax.grid(b=True, which='minor', color='grey', linestyle=':')
ax.xaxis.set_minor_locator(AutoMinorLocator(5))
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.yaxis.set_major_locator(LogLocator(
base=10,
numticks=12,
))
for jj, data in enumerate(Data):
for ii, (X, Y) in enumerate(zip(data.Time, data.Impurities)):
plt.plot(X,
Y,
label=data.Labels[ii],
color=colors[jj],
linewidth=2.)
plt.xlim(np.min(Data[0].Time[0]), np.max(Data[0].Time[-1]))
# plt.ylim(ymin=1E12,ymax=1E20)
ax.legend(loc='upper right', fontsize=14)
fig.tight_layout()
def establishTicks(axMain, axCrazy, axBlowUp, options, data):
#data.axDict['main'].set_xticks( data.xData )
#data.axDict['main'].set_xticklabels( prettyList( data.valuesDict['columnLength'] ))
if options.mode in set(
['blocks', 'contigs', 'contigPaths', 'scaffPaths', 'scaffolds']):
if not options.SMM:
if options.mode != 'contigPaths' and options.mode != 'scaffPaths':
axCrazy.set_yticks([0, 1])
axCrazy.set_yticklabels([0, '%d' % data.crazyMax])
minorLocator = MultipleLocator(5)
minorLocator = LogLocator(base=10, subs=range(1, 10))
if options.SMM:
axBlowUp.set_yticklabels([])
axBlowUp.set_xticklabels([])
axMain.set_yticklabels([])
axMain.set_xticklabels([])
for i in xrange(1, 8):
axMain.add_line(
lines.Line2D(xdata=[10**i, 10**i],
ydata=[0, 0.1],
color=(0.8, 0.8, 0.8),
linewidth=0.5))
else:
axBlowUp.set_yticks([0.9, 0.92, 0.94, 0.96, 0.98, 1.0], minor=False)
axBlowUp.set_yticks(
[0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0],
minor=True)
axMain.xaxis.set_minor_locator(minorLocator)
axBlowUp.xaxis.set_minor_locator(minorLocator)
if axCrazy:
axCrazy.xaxis.set_minor_locator(minorLocator)
def plot_prpl_ratio(interp_pkl_num, interp_pkl_den, include_cbar=True):
emui_edges, l_ice_edges, prpls_num = plot_prpl(interp_pkl_num, False,
False)
emui_edges, l_ice_edges, prpls_den = plot_prpl(interp_pkl_den, False,
False)
plt.figure()
plt.pcolormesh(emui_edges,
l_ice_edges / 1e3,
np.ma.masked_invalid(prpls_num / prpls_den),
norm=colors.LogNorm(vmin=1e-2, vmax=1e2),
cmap='coolwarm')
if include_cbar:
plt.colorbar()
plt.xlabel(r'$E_\mu^{\rm i}$ [GeV]')
plt.ylabel(r'$l_{\rm ice}$ [km]')
# plt.yscale('log')
# plt.gca().yaxis.set_major_formatter(ScalarFormatter())
plt.xscale('log')
xlocmaj = LogLocator(base=10, numticks=12)
plt.gca().xaxis.set_major_locator(xlocmaj)
plt.gca().minorticks_off()
plt.ticklabel_format(style='plain', axis='y')
plt.xlim(1e2, 1e8)
plt.ylim(1, 40)
plt.title('{}/{}'.format(
os.path.splitext(os.path.basename(interp_pkl_num))[0],
os.path.splitext(os.path.basename(interp_pkl_den))[0]))
def process_command(self, command):
from matplotlib.ticker import LogLocator, AutoLocator
self.output(command[0])
if command[0] == 'ig':
self.ig = command[1]
elif command[0] == 'plot':
xdata, ydata = command[1:]
ig = self.ig
ax = self.ax[ig]
ax.set_yscale(self.yscales[ig])
ax.yaxis.grid(True)
ax.plot(xdata, ydata)
if self.yscales[ig] == 'log':
ymajor_formatter = ax.yaxis.get_major_formatter()
ymajor_formatter.label_minor(True)
yminor_locator = LogLocator()
else:
yminor_locator = AutoLocator()
self.ax[ig].yaxis.set_minor_locator(yminor_locator)
elif command[0] == 'vline':
x, kwargs = command[1:]
self.vlines[self.ig].append((x, kwargs))
elif command[0] == 'clear':
self.ax[self.ig].cla()
elif command[0] == 'legends':
for ig, ax in enumerate(self.ax):
try:
ax.legend(self.data_names[ig])
except:
pass
if self.xlabels[ig]:
ax.set_xlabel(self.xlabels[ig])
if self.ylabels[ig]:
ax.set_ylabel(self.ylabels[ig])
for x, kwargs in self.vlines[ig]:
ax.axvline(x, **kwargs)
elif command[0] == 'add_axis':
ig, names, yscale, xlabel, ylabel = command[1:]
self.data_names[ig] = names
self.yscales[ig] = yscale
self.xlabels[ig] = xlabel
self.ylabels[ig] = ylabel
self.n_gr = len(self.data_names)
self.make_axes()
elif command[0] == 'save':
self.fig.savefig(command[1])
self.pipe.send(True) # Acknowledge save.
def plot_sumfile(handle,v,clim=(10,100000)):
""" Plot UHEL's sum-formatted aerosol number-size distribution """
time = v[1:,0] # This is datenum
dp = v[0,2:]
data = v[1:,2:]
data[data<=0]=1e-30 # No holes in plots
mesh_dp, mesh_time = np.meshgrid(dp,time)
pcolorplot = handle.pcolormesh(mesh_time,mesh_dp,data,
norm=colors.LogNorm(),
linewidth=0,rasterized=True,cmap='jet')
handle.set_yscale('log')
pcolorplot.set_clim(clim)
pcolorplot.set_edgecolor('face')
handle.autoscale(tight='true')
#handle.set_xlim((np.floor(time[0]),np.floor(time[0])+1)) # makes 24-h axis
handle.grid('on',which='both',linestyle='--',color='w',lw=0.5)
handle.xaxis.set_major_locator(dts.HourLocator(interval=1))
handle.xaxis.set_major_formatter(dts.DateFormatter('%H'))
#handle.set_xticks(np.floor(time[0])+np.arange(0,25)/24.0)
#handle.set_xticklabels(["%2.2i" % x for x in np.arange(0,25)])
plt.setp(handle.get_xticklabels(), rotation=80)
handle.set_ylabel('Dp, [m]')
handle.set_xlabel('UTC'+'%+d'%v[0,0]+', [h]')
cbar = plt.colorbar(pcolorplot, ax=handle,
ticks=LogLocator(subs=range(10)))
cbar.set_label('dN/dlogDp, [cm-3]')
return pcolorplot
def weight_dist(w_, save_path=None, yscale='log'):
"""
Plots log distributions of continuous weights in a binary neural network;
saves output
Parameters
----------
w_ : list of arrays
arrays are continous weights
data_set : str
data set being used; used for the save file
"""
x_grid = np.arange(-1, 1, 0.02)
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(3, 2),
sharex=True, sharey=True)
for i, w in enumerate(w_):
pdf = kde_scipy(w.ravel(), x_grid, bandwidth=.1)
ax = axes
ax.plot(x_grid, pdf, lw=1, alpha=1.0, label='Layer {}'.format(i+1))
ax.set_ylim([0.00001, 3])
ax.xaxis.set_major_locator(MaxNLocator(3))
if yscale == 'log':
ax.yaxis.set_major_locator(LogLocator(base=10, numticks=6))
elif yscale == 'linear':
ax.yaxis.set_major_locator(MaxNLocator(3))
else:
raise ValueError('Invalid yscale')
ax.set_yscale(yscale)
plt.legend(loc=8, labelspacing=0.1, prop={'size': 8})
plt.title('Weight Histogram')
plt.tight_layout()
if save_path is not None:
plt.savefig(save_path)
return fig, ax
def DrawFigure(x_values, y_values, legend_labels, x_label, y_label, y_min, y_max, filename, allow_legend):
# you may change the figure size on your own.
fig = plt.figure(figsize=(10, 3))
figure = fig.add_subplot(111)
FIGURE_LABEL = legend_labels
if not os.path.exists(FIGURE_FOLDER):
os.makedirs(FIGURE_FOLDER)
# values in the x_xis
index = np.arange(len(x_values))
# the bar width.
# you may need to tune it to get the best figure.
width = 0.08
# draw the bars
bars = [None] * (len(FIGURE_LABEL))
for i in range(len(y_values)):
bars[i] = plt.bar(index + i * width + width / 2,
y_values[i], width,
hatch=PATTERNS[i],
color=LINE_COLORS[i],
label=FIGURE_LABEL[i],edgecolor='black', linewidth=3)
# sometimes you may not want to draw legends.
if allow_legend == True:
plt.legend(bars, FIGURE_LABEL,
prop=LEGEND_FP,
ncol=4,
loc='upper center',
# mode='expand',
shadow=False,
bbox_to_anchor=(0.45, 1.6),
columnspacing=0.1,
handletextpad=0.2,
# bbox_transform=ax.transAxes,
# frameon=True,
# columnspacing=5.5,
# handlelength=2,
)
# you may need to tune the xticks position to get the best figure.
plt.xticks(index + 5 * width, x_values)
# plt.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
# plt.grid(axis='y', color='gray')
# figure.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
# you may need to tune the xticks position to get the best figure.
plt.yscale('log')
#
# plt.grid(axis='y', color='gray')
figure.yaxis.set_major_locator(LogLocator(base=10))
# figure.xaxis.set_major_locator(LinearLocator(5))
figure.get_xaxis().set_tick_params(direction='in', pad=10)
figure.get_yaxis().set_tick_params(direction='in', pad=10)
plt.xlabel(x_label, fontproperties=LABEL_FP)
plt.ylabel(y_label, fontproperties=LABEL_FP)
plt.savefig(FIGURE_FOLDER + "/" + filename + ".pdf", bbox_inches='tight')
