def plotimage(imgflux_pl, zminfl, zmaxfl, plmode, row, column,
xdim, ydim, winx, winy, tlabel, cmap):
# pixel limits of the subimage
ymin = row
ymax = ymin + ydim
xmin = column
xmax = xmin + xdim
# plot limits for flux image
ymin = float(ymin) - 0.5
ymax = float(ymax) - 0.5
xmin = float(xmin) - 0.5
xmax = float(xmax) - 0.5
# plot the image window
ax = plt.axes([winx,winy,0.37,0.45])
plt.imshow(imgflux_pl,aspect='auto',interpolation='nearest',origin='lower',
vmin=zminfl,vmax=zmaxfl,extent=(xmin,xmax,ymin,ymax),cmap=cmap)
plt.gca().set_autoscale_on(False)
labels = ax.get_yticklabels()
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
if plmode == 1:
plt.setp(plt.gca(),xticklabels=[])
if plmode == 2:
plt.setp(plt.gca(),xticklabels=[],yticklabels=[])
if plmode == 4:
plt.setp(plt.gca(),yticklabels=[])
if plmode == 3 or plmode == 4:
plt.xlabel('Pixel Column Number', {'color' : 'k'})
if plmode == 1 or plmode == 3:
plt.ylabel('Pixel Row Number', {'color' : 'k'})
plt.text(0.05, 0.93,tlabel,horizontalalignment='left',verticalalignment='center',
fontsize=36,fontweight=500,transform=ax.transAxes)
def _setup_msd_plot(ax):
# Label ticks with plain numbers, not scientific notation:
ax.xaxis.set_major_formatter(plt.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(plt.ScalarFormatter(useMathText=True))
ax.set_ylim(0.001, 100)
logger.info('Limits of y range are manually set to %f - %f.', *plt.ylim())
ax.set_xlabel('lag time [s]')
ax.set_ylabel('msd [um$^2$]')
def __init__(self, configurator, label='', title=''):
self.configurator = configurator
self.label = label
self.title = title
self.xlabel = ""
self.ylabel = ""
self.xscale = "linear"
self.yscale = "linear"
self.xformat = plt.ScalarFormatter()
self.yformat = plt.ScalarFormatter()
def location(shape):
"""shape the window, enforce absolute scaling, rotate the labels"""
# position first axes inside the plotting window
ax = plt.axes(shape)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
ax.yaxis.set_major_locator(plt.MaxNLocator(5))
# rotate y labels by 90 deg
labels = ax.get_yticklabels()
return ax
def setup_axis_labels(self):
""" Function responsible for axies management. """
self.ax.yaxis.set_major_formatter(plt.ScalarFormatter())
self.ax.xaxis.set_major_formatter(plt.ScalarFormatter())
self.ax.set_xticks(range(1, self.amount + 1))
self.ax.set_xlim(0.5, self.amount + 0.5)
self.ax.set_xlabel('Device ID', fontsize=13)
self.ax.set_ylabel(self.ylabel, fontsize=13)
self.ax.tick_params(axis='both', labelsize=12)
self.ax.set_xticklabels(self.ids)
def plot_salt(histogram, name, ymaxlimit=None):
fig, ax = plt.subplots()
fig.set_size_inches(6, 5)
# labels, values = zip(*sorted(hist.items()))
labels, values = zip(*sorted(histogram.items(), key=lambda x: int(x[0])))
indexes = np.arange(len(labels))
width = 1
plt.bar(indexes, values, width, color=color, alpha=opacity)
fmt = plt.ScalarFormatter(useOffset=False)
plt.gca().xaxis.set_major_formatter(fmt)
ax.set_xlabel('LC Step #', fontproperties=prop32)
ax.set_ylabel('# high quality PSMs', fontproperties=prop32)
if ymaxlimit:
ax.set_ylim(0, ymaxlimit)
for label in ax.get_yticklabels():
label.set_fontproperties(prop24)
plt.title(
'Peptides identified per Liquid Chromatography Step\n{}'.format(name),
fontproperties=prop24,
y=1.1)
ax.set_xticks(indexes + width / 2)
ax.set_xticklabels(labels, fontproperties=prop24)
#plt.tight_layout()
plt.show()
def setup_figure(width='regular', height=None, aspect='normal', style='sans-serif',
tex=False, param_kwargs={}, figure_kwargs={}):
"""
Create a standardised figure.
Args:
width (float, str): width of the figure in cm or a preset string
height (float): height of the figure in cm
aspect (float): aspect ratio of the figure (height/width)
style (str): The font style to use in the figure
tex (bool): Whether to use LaTeX to render the figure
**param_kwargs: Arbitrary keyword arguments that are added to or override existing figure
parameters.
**figure_kwargs: Arbitrary keyword arguments that are passed to figure creation.
Returns:
Figure: a matplotlib figure instance
"""
# set the figure size
fig_size = get_figure_size(width, height, aspect)
# set the figure parameters
fig_params = get_fig_params(style, tex, **param_kwargs)
plt.rcParams.update(fig_params)
formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
formatter.set_scientific(True)
fig = plt.figure(figsize=fig_size, tight_layout=True, **figure_kwargs)
return fig
def print_performance(cm,fignum):
if fignum == 20200223:
fignum = '_DeepSleepNet'
tp = np.diagonal(cm).astype(np.float)
tpfp = np.sum(cm, axis=0).astype(np.float) # sum of each col
tpfn = np.sum(cm, axis=1).astype(np.float) # sum of each row
acc = np.sum(tp) / np.sum(cm)
precision = tp / tpfp
recall = tp / tpfn
f1 = (2 * precision * recall) / (precision + recall)
mf1 = np.mean(f1)
y_formatter = plt.ScalarFormatter(useOffset=False)
FL_acc.append(acc)
FL_F1acc.append(mf1)
sn.heatmap(cm, annot=True, annot_kws={"size": 10}, fmt='d')
plt.savefig('./results/ConfusionMatrix/subject'+str(fignum)+'.png', dpi=300)
plt.clf()
print("Sample: {}".format(np.sum(cm)))
print("W: {}".format(tpfn[W]))
print("N1: {}".format(tpfn[N1]))
print("N2: {}".format(tpfn[N2]))
print("N3: {}".format(tpfn[N3]))
print("REM: {}".format(tpfn[REM]))
print("Confusion matrix:")
print(cm)
print("Precision: {}".format(precision))
print("Recall: {}".format(recall))
print("F1: {}".format(f1))
print("Overall accuracy: {}".format(acc))
print("Macro-F1 accuracy: {}".format(mf1))
def set_scientific(low, high, axis=None, *axes):
"""Set the axes or axis specified by `axis` to use scientific notation for
ticklabels, if the value is <10**low or >10**high.
Parameters
----------
low : int
Lower exponent bound for non-scientific notation
high : int
Upper exponent bound for non-scientific notation
axis : str (default=None)
Which axis to format ('x', 'y', or None for both)
ax : axis object (default=pyplot.gca())
The matplotlib axis object to use
"""
# get the axis
if len(axes) == 0:
axes = [plt.gca()]
# create the tick label formatter
fmt = plt.ScalarFormatter()
fmt.set_scientific(True)
fmt.set_powerlimits((low, high))
# format the axis/axes
for ax in axes:
if axis is None or axis == 'x':
ax.get_yaxis().set_major_formatter(fmt)
if axis is None or axis == 'y':
ax.get_yaxis().set_major_formatter(fmt)
def set_pressure_axis(ax, **kargs):
'''
Adjust the y-axis of a plot to represent pressure levels.
Parameters
----------
ax : axis object
Keyword arguments
-----------------
limits : (float, float)
A tuple of float to indicate the lower and upper limit.
ticks : list of int
A list of int to replace the standard ticks. (20, 50, 100, 200, 500, 1000)
label : string
Label text that shall be shown.
'''
from met_tools import USstdP
import matplotlib.pyplot as plt
import calendar
limits = sorted(kargs.pop(
"limits", (1000., USstdP(30 * kilo) / hecto)))[::-1] # reverse sorted
ticks = kargs.pop("ticks", [20, 50, 100, 200, 500, 1000])
label = kargs.pop("label", "Pressure (hPa)")
ax.set_ylabel(label)
ax.set_yscale('log')
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(ticks)
ax.invert_yaxis()
ax.set_ylim(limits)
def display_data(data, x_ax_data, y_ax_data):
formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-3, 3))
fig = plt.figure()
ax = fig.add_subplot(111)
Z = np.nan_to_num(data)
Xi = x_ax_data
Yi = np.nan_to_num(y_ax_data)
minimum = Z.min()
maximum = Z.max()
# set colour mapping for all axes #
c_levels = np.linspace(minimum, 1.05 * maximum, 100)
# create longitudinal spectral image subplot #
X, Y = np.meshgrid(Xi, Yi)
cfax = ax.contourf(X,
Y,
Z,
c_levels,
alpha=1.0,
cmap=cm.get_cmap(cm.Spectral_r,
len(c_levels) - 1))
# add colour bar to far right of figure #
cb = plt.colorbar(cfax, shrink=0.75, extend='both')
cb.set_label('optical scattering (a.u.)')
#ax.set_yscale('log')
ax.xaxis.set_major_formatter(formatter)
def __init__(self, temporal_data):
self.data = temporal_data
# figure setup #
self.setup_figure()
self.fig = plt.figure()
# setup subplots #
self.gs0 = gridspec.GridSpec(1, 2)
self.spectra_ax = plt.Subplot(self.fig, self.gs0[0, 1])
self.gs00 = gridspec.GridSpecFromSubplotSpec(1,
3,
subplot_spec=self.gs0[0,
0])
self.conductance_ax = plt.Subplot(self.fig,
self.gs00[0, 1],
sharey=self.spectra_ax)
self.current_ax = 0
self.force_ax = plt.Subplot(self.fig,
self.gs00[0, 0],
sharey=self.spectra_ax)
# set axis formatter #
self.formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
self.formatter.set_scientific(True)
self.formatter.set_powerlimits((-3, 3))
# add subplots #
self.add_spectra_subplot()
self.add_conductance_subplot()
self.add_force_subplot()
#plt.tight_layout()
self.gs0.tight_layout(self.fig, pad=0.25) #, w_pad=0.1, h_pad=0.1)
self.save_figure()
def imshow_logpolars(fig, spectra, log_base, im_shape):
import matplotlib.pyplot as plt
import mpl_toolkits.axes_grid1 as axg
low = 1.0
high = log_base ** spectra[0].shape[1]
logpolars_extent = (low, high,
0, 180)
grid = axg.ImageGrid(
fig, 111, nrows_ncols=(2, 1),
add_all=True,
aspect=False,
axes_pad=0.4, label_mode="L",
)
ims = [np.log(np.abs(im)) for im in spectra]
for ii, im in enumerate(ims):
vmin = np.percentile(im, 1)
vmax = np.percentile(im, 99)
grid[ii].set_xscale("log", basex=log_base)
grid[ii].get_xaxis().set_major_formatter(plt.ScalarFormatter())
im = grid[ii].imshow(im, cmap=plt.cm.viridis, vmin=vmin, vmax=vmax,
aspect="auto", extent=logpolars_extent)
grid[ii].set_xlabel(_t("log radius"))
grid[ii].set_ylabel(_t("azimuth / degrees"))
xticklabels = ["{:.3g}".format(tick * 2 / im_shape[0])
for tick in grid[ii].get_xticks()]
grid[ii].set_xticklabels(
xticklabels,
rotation=40, rotation_mode="anchor", ha="right"
)
return fig
def barplot(method,new_df):
plt.figure()
g=sns.factorplot(var,data=new_df,aspect=2,kind="count",color="steelblue")
if len(np.unique(new_df[var]))>=20:
g.set_xticklabels(step=10,rotation=35)
plt.title(enmethoden(method),fontsize=24)
plt.ScalarFormatter()
plt.savefig('./imputation_photo/'+name[0]+'/'+count+var+'_'+method+'_factor.png',bbox_inches='tight',facecolor="w" )
def main():
kepler_response = retrieve_kepler_response()
vmag_response = retrieve_vmag_response()
planets = build_planet_list()
xdata, ydata = [], []
label_point, label_value = [], []
for spectral_type, temperature in spectral_types():
spectra = spectra_pickles(spectral_type)
vmag = spectra.custom_mag(vmag_response)[0]
kepmag = spectra.custom_mag(kepler_response)[0]
correction_factor = vmag - kepmag
print "{spectype}: {teff} - {cf:5.3f}".format(
spectype=spectral_type.upper(),
cf=correction_factor,
teff=temperature)
xdata.append(temperature)
ydata.append(correction_factor)
if '0v' in spectral_type.lower():
label_point.append(temperature)
label_value.append(spectral_type.replace('0v', '').upper())
with open('results/mapping.json', 'w') as outfile:
json.dump([{
'teff': teff,
'correction': correction
} for (teff, correction) in zip(xdata, ydata)],
outfile,
indent=2)
plt.plot(xdata, ydata, 'r.')
plt.xscale('log')
errs = np.vstack([planets.TEFFUPPER, planets.TEFFLOWER])
plt.errorbar(planets.TEFF,
planets.correction_factor,
xerr=errs,
ls='None',
color='b')
plt.scatter(planets.TEFF, planets.correction_factor, color='b')
plt.xlabel(r'Stellar effective temperature / K')
plt.ylabel(r'$V - K_{p}$')
ticks = sorted([base * 10**exp for base in [1, 5] for exp in [3, 4, 5]])
plt.xticks(ticks, ticks)
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter())
lims = plt.xlim(2000, 50000)
plt.twiny()
plt.xscale('log')
plt.xticks(label_point, label_value)
plt.xlim(*lims)
plt.xlabel(r'Approximate spectral type')
plt.tight_layout()
plt.savefig('results/plot.png')
def plot_1d(test, argname, ax, label=None):
x_axis = test.args[argname]
y_axis = test.times
ax.plot(x_axis, y_axis, label=label)
if is_multiplicative(x_axis):
ax.set_xscale('log', base=(x_axis[1] / x_axis[0]))
ax.xaxis.set_major_formatter(plt.ScalarFormatter())
ax.legend()
ax.set_xlabel(argname)
ax.set_ylabel('rows/sec')
def plot(flist, sweepnr=None):
extr = extrapolate_base.energy2y(flist, sweepnr)
xdata = extr.xdata()
ydata = extr.ydata()
fit = extr.linfit()
xf = np.linspace(1e-16, max(xdata), 100)
yf = fit(xf)
print "{:30s} {:13.9f} {:12.1f} {:5s} {:3s}".format(
"extrapolation", fit(0), 0, "infty", "")
print "{:30s} {:14.9f}".format("extrapolation error", fit(0) - ydata[0])
fig = plt.figure()
ax = fig.add_subplot(111)
dots = plt.plot(xdata, ydata, 'o', xf, yf, '-')
plt.ticklabel_format(style='sci',
axis='x',
scilimits=(3, 4),
useOffset=False)
xfmt = plt.ScalarFormatter(useOffset=False, useMathText=True)
xfmt.set_scientific(True)
ax.yaxis.set_major_formatter(xfmt)
plt.xlabel(r'\textbf{truncation error} $\varepsilon$')
plt.ylabel(r'\textbf{Energy [Hartree]}')
#plt.xlabel('truncation error $\\varepsilon$')
#plt.ylabel('Energy [Hartree]')
fig.subplots_adjust(left=0.2)
def autolabel(xd, yd, labels):
# attach some text labels
yshift = (max(yd) - min(yd)) * 0.05
xshift = (max(xd) - min(xd)) * 0.2
for x, y, l in zip(xd, yd, labels):
#item = ax.text(x - xshift, y, '$m=%d$'%l,
# ha='right', va='center')
item = ax.text(x, y, '$m=%d$' % l, ha='center', va='bottom')
item.set_fontsize(18)
autolabel(xdata, ydata, extr.m())
# add the extrapolated energy to the plot"
ext_note = ax.text(max(xf) / 2.0 + (max(xf) - min(xf)) * 0.05,
fit(0),
'extrapolation:\n$%.6f$' % fit(0),
ha='left',
va='center')
# position below is yaxis
#ext_note = ax.text(0, fit(0), '$%.6f$'%fit(0), ha='right', va='top')
plt.savefig('e.svg')
def setup_axes(self):
self.ax.autoscale(True)
self.formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
self.ax.yaxis.set_major_formatter(self.formatter)
self.rax.yaxis.set_major_formatter(self.formatter)
self.ax.minorticks_on()
self.rax.minorticks_on()
self.ax.set_xlabel("Time")
self.ax.set_ylabel("Temperature ($^\circ$C)")
self.rax.set_ylabel("Humidity (% RH)")
labels = self.ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
def baseZero(self): """ Set baseline of seismograms as zeros (stack all). Do not normalize seismogram by setting opts.ynorm<0. """ self.getZero() anorm = 1./np.clip(self.nseis/50, 2, 10) self.alphas *= anorm self.opts.ynorm = -1 #self.axss.ticklabel_format(style='sci', scilimits=(0,0), axis='y') formatter = plt.ScalarFormatter(useMathText=True) formatter.set_powerlimits((0, 0)) self.axss.yaxis.set_major_formatter(formatter)
def setup_axis_labels(self, runtime, time, ymin, ymax, xtick_amount=5):
"""
Setup axis labels based on runtime, time, and ylims. This functions is responsible for dynamically managing
axises and axises labels.
"""
xtick_step = round(len(runtime) / xtick_amount)
self.ax.set_xlim(runtime[0], runtime[-1])
self.ax.set_xticks(runtime[::-xtick_step])
self.ax.set_xticklabels(time[::-xtick_step])
self.ax.yaxis.set_major_formatter(plt.ScalarFormatter())
self.ax.set_ylim(ymin, ymax)
self.ax.set_xlabel(self.xlabel, fontsize=13)
self.ax.set_ylabel(self.ylabel, fontsize=13)
self.ax.tick_params(axis='both', labelsize=12)
def symticks(ax, linthresh=1, linstep=0.2, axis='x'):
l, r = ax.get_xlim() if axis == 'x' else ax.get_ylim()
axis = ax.xaxis if axis == 'x' else ax.yaxis
m = max(l, r)
k = min(l, r)
major = int(np.floor(np.log10(m)))
lin_pos = np.arange(-linthresh, 0, linstep)[1:]
major_pos = 10**np.arange(major + 1)
minor_pos = [np.arange(2, 10) * 10**i for i in range(major)]
rest = np.arange(np.ceil(m / 10**major)) * 10**major
minor_pos = np.array(minor_pos).flat
axis.set_ticks(np.hstack((-lin_pos, lin_pos[lin_pos > k], minor_pos, rest)),
minor=True)
axis.set_ticks(np.hstack((0, major_pos)))
axis.set_major_formatter(plt.ScalarFormatter())
def plotimage(imgsum_pl, imgvar_pl, imgdev_pl, zminsum, zminvar, zmindev,
zmaxsum, zmaxvar, zmaxdev, xmin ,xmax, ymin, ymax, colmap,
plotfile):
plt.figure(figsize=[15, 6])
plt.clf()
# plot the image window
ax = plt.axes([0.04, 0.11, 0.31, 0.78])
plt.imshow(imgsum_pl,aspect='auto', interpolation='nearest',
origin='lower', vmin=zminsum, vmax=zmaxsum,
extent=(xmin, xmax, ymin, ymax), cmap=colmap)
plt.gca().set_autoscale_on(False)
labels = ax.get_yticklabels()
plt.setp(labels, 'rotation', 90)
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.xlabel('Pixel Column Number', {'color' : 'k'})
plt.ylabel('Pixel Row Number', {'color' : 'k'})
plt.title('Flux', {'color' : 'k', 'fontsize' : '16'})
# plot the variance window
plt.axes([0.36, 0.11, 0.31, 0.78])
plt.imshow(imgvar_pl, aspect='auto', interpolation='nearest',
origin='lower', vmin=zminvar, vmax=zmaxvar,
extent=(xmin, xmax, ymin, ymax), cmap=colmap)
plt.gca().set_autoscale_on(False)
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),yticklabels=[])
plt.xlabel('Pixel Column Number', {'color' : 'k'})
try:
plt.title(r'$\chi$ Distribution', {'color' : 'k', 'fontsize' : '16'})
except:
plt.title('Chi Distribution', {'color' : 'k', 'fontsize' : '16'})
# plot the normalized standard deviation window
plt.axes([0.68, 0.11, 0.31, 0.78])
plt.imshow(imgdev_pl, aspect='auto', interpolation='nearest',
origin='lower', vmin=zmindev, vmax=zmaxdev,
extent=(xmin, xmax, ymin, ymax), cmap=colmap)
plt.gca().set_autoscale_on(False)
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),yticklabels=[])
plt.xlabel('Pixel Column Number', {'color' : 'k'})
plt.title('Normalized Standard Deviation', {'color' : 'k', 'fontsize' : '16'})
# render plot
plt.show()
if plotfile is not None:
plt.savefig(plotfile)
def __init__(self, spatial_data):
self.data = spatial_data
# figure setup #
self.setup_figure()
self.fig = plt.figure()
# setup subplots #
if self.data.dual_pol:
self.gs0 = gridspec.GridSpec(1, 3, width_ratios=[0.75, 1, 1])
self.spectra_long_ax = plt.Subplot(self.fig, self.gs0[0, 1])
self.spectra_trans_ax = plt.Subplot(self.fig,
self.gs0[0, 2],
sharex=self.spectra_long_ax,
sharey=self.spectra_long_ax)
else:
self.gs0 = gridspec.GridSpec(1, 2, width_ratios=[0.75, 1])
self.spectra_long_ax = plt.Subplot(self.fig, self.gs0[0, 1])
self.gs00 = gridspec.GridSpecFromSubplotSpec(1,
3,
subplot_spec=self.gs0[0,
0])
self.conductance_ax = plt.Subplot(self.fig,
self.gs00[0, 2],
sharey=self.spectra_long_ax)
self.current_ax = 0
self.force_y_ax = plt.Subplot(self.fig,
self.gs00[0, 1],
sharey=self.spectra_long_ax)
self.force_x_ax = 0
self.displacement_ax = plt.Subplot(self.fig,
self.gs00[0, 0],
sharey=self.spectra_long_ax)
# set axis formatter #
self.formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
self.formatter.set_scientific(True)
self.formatter.set_powerlimits((-3, 3))
# add subplots #
self.add_spectra_subplot()
self.add_conductance_subplot()
self.add_force_subplot()
self.add_displacement_subplot()
#plt.tight_layout()
self.gs0.tight_layout(self.fig, pad=0.25) #, w_pad=0.1, h_pad=0.1)
if self.data.analysed: self.annotate_analysis() # annotate analysis
self.save_figure() # save figure
def on_units_change(self, evnt):
"""
Event handler for change of x axis units events.
"""
#get the matplotlib axes object from the subplot
ax = self.subplot.get_mpl_axes()
#change the axis labels and label formatting based on the choice of
#units
if evnt.GetString() == 'Degrees':
ax.set_xlabel(r'$\theta$ (degrees)')
ax.xaxis.set_major_formatter(plt.FuncFormatter(rad2deg))
else:
ax.set_xlabel(r'$\theta$ (radians)')
ax.xaxis.set_major_formatter(plt.ScalarFormatter())
#draw our changes in the display
self.subplot.update()
def plot_monthly_timeseries(spinup_run, var, location):
"""12-panel plot: timeseries of each month from all spinup years
ARGS:
spinup_run (CLM_spinup_analyzer): object of class
CLM_spinup_analyzer describing the spinup run
var (CLM_var): object of class CLM_var containing the data and
metadata to be plotted
location (Location): object of class Location describing the
location of the data to be plotted
"""
months = range(1, 13)
nrows = np.int(np.ceil(len(months) / 2))
fig, ax = plt.subplots(ncols=2, nrows=nrows, figsize=(8.5, 11))
if var.depth is not None:
depth_str = "{:0.2f} m".format(var.depth)
else:
depth_str = ""
fig.text(0.5,
0.95,
'{} {} {} ({})'.format(location.name, var.varname_long, depth_str,
var.units),
verticalalignment='top',
horizontalalignment='center',
size='large')
for i, this_month in enumerate(months):
ax_idx = np.unravel_index(i, ax.shape)
idx = np.array([
this_file.find('{:02d}.nc'.format(this_month))
for this_file in spinup_run.all_files
]) > 0
ax[ax_idx].plot(var.data[idx, ...].squeeze())
ax[ax_idx].set_ylabel(var.varname)
ax[ax_idx].xaxis.set_major_formatter(plt.NullFormatter())
ax[ax_idx].annotate(calendar.month_abbr[this_month],
xycoords='axes fraction',
xy=(0, 0),
bbox=dict(boxstyle="round", fc="0.8"))
for this_col in range(2):
ax[nrows - 1,
this_col].xaxis.set_major_formatter(plt.ScalarFormatter())
ax[nrows - 1, this_col].set_xlabel('year of spinup')
return (fig, ax)
def plot_nullclines(x,
y,
params=None,
which=["h", "i", "t"],
fit=False,
err=0,
legend=0,
title=None,
legLoc="best"):
if (params == None and not fit):
params = np.array(
(defaultParams("h"), defaultParams("i"), defaultParams("t")))
if (legend == 0 and not fit):
legend = "Holling", "Ivlev", "Trigonometric"
if (title == None and not fit):
title = "Nullclines of the different systems for K = " + str(K)
if (fit):
fparams = params[0]
dparams = params[1]
title = "Nullclines: " + which[0] + '-Model fitted to ' + which[1] + "-Model K = " + str(K)\
+ "\n Data-Param: " + str(dparams) + "\n Fitted-Param: " + str(fparams) + " Error: " + str(err)
ax = plt.subplot()
ax.set_xscale('log')
for axis in [ax.xaxis]:
axis.set_major_formatter(plt.ScalarFormatter())
i = 0
for s in which:
plt.plot(x, nullcl_x(x, params[i], s), color=color(s), zorder=1)
plt.axvline(nullcl_y(x, params[i], s), color=color(s), zorder=2)
equilibrium(x, params[i], s)
i = i + 1
if (legend != None):
plt.legend(legend, loc=legLoc)
plt.xticks([0.01, 0.05, 0.25])
# plt.xticks([0.01,0.1,1,5,20],[0.01,0.1,1,5,20])
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2],
[0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2])
plt.ylim(0, 1.1)
plt.xlim(0.01, 0.25)
plt.title(title, size=13)
plt.ylabel("predator concentration, y ", fontsize=12)
plt.xlabel("prey concentration, x", fontsize=12)
plt.show()
def classical_plot_th(cls,Q,fff,tt,acc,yb,rd,n_scale):
title_string_acc='Acceleration SDOF Response fn= '+ fff +' Hz Q='+str(Q)
title_string_rd='Rel Disp SDOF Response fn= '+ fff +' Hz Q='+str(Q)
#
for i in range(1,200):
if(Q==float(i)):
title_string_acc='Acceleration SDOF Response fn= '+ fff +' Hz Q='+str(i)
title_string_rd='Rel Disp SDOF Response fn= '+ fff +' Hz Q='+str(i)
break
#
plt.close(1)
plt.figure(1)
plt.plot(tt, acc, label="response")
plt.plot(tt, yb, label="input")
plt.xlabel('Time (sec)')
plt.ylabel('Accel (G)')
plt.grid(True)
plt.title(title_string_acc)
plt.legend(loc="upper right")
plt.draw()
#
plt.close(2)
plt.figure(2)
plt.plot(tt, rd)
plt.xlabel('Time (sec)')
if(n_scale==0):
plt.ylabel('Rel Disp (in)')
else:
plt.ylabel('Rel Disp (mm)')
plt.grid(True)
plt.title(title_string_rd)
axes = plt.gca()
myformatter = plt.ScalarFormatter(useMathText=True)
myformatter.set_powerlimits((-3,3))
axes.yaxis.set_major_formatter(myformatter)
plt.draw()
plt.show()
def setup_figure_rc(width='regular', height=None, aspect=None, style='sans-serif', tex=False,
**kwargs):
"""
Sets the default configuration for all future figures.
Args:
width (float, str): width of the figure in cm or a preset string
height (float): height of the figure in cm
aspect (float): aspect ratio of the figure (height/width)
style (str): The font style to use in the figure
tex (bool): Whether to use LaTeX to render the figure
**kwargs: Arbitrary keyword arguments that are added to or override existing figure
parameters.
"""
fig_size = get_figure_size(width, height, aspect)
fig_params = get_fig_params(style, tex, **kwargs)
fig_params['figure.figsize'] = fig_size
plt.rcParams.update(fig_params)
formatter = plt.ScalarFormatter(useOffset=False, useMathText=True)
formatter.set_scientific(True)
def plot_energy_comparison(args):
"""Plot energy trajectories for comparison"""
outfile = (args.out_params + '.png').format(param='cmp_free_energy',
**args.__dict__)
pyplot.figure()
names = args.cmp_energy.keys()
vals = -sp.array([args.cmp_energy[n] for n in names]).T
print names
print vals
lines = pyplot.plot(vals)
line_types = ['--', '-.', ':', '-', 'steps'] * 3
[pyplot.setp(l, linestyle=t) for l, t in zip(lines, line_types)]
pyplot.legend(names, loc='lower right')
#pyplot.title('Free energy learning with %s' % args.approx)
formatter = pyplot.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-3, 3))
pyplot.gca().yaxis.set_major_formatter(formatter)
pyplot.xlabel("Iteration")
pyplot.ylabel('-Free Energy')
pyplot.savefig(os.path.join(args.out_dir, outfile))
pyplot.close('all')
def make_plot(ax, x1, x2, rng, nbins, ticks):
bins = np.exp(np.linspace(np.log(rng[0]), np.log(rng[1]), nbins))
ax.hist([x2, x1],
bins,
histtype="stepfilled",
stacked=False,
color=[COLORS["MODEL_1"], COLORS["MODEL_2"]],
alpha=0.1,
lw=0)
n, _, _ = ax.hist(x1, bins, histtype="step", color=COLORS["MODEL_2"], lw=2)
ax.hist(x2,
bins,
histtype="step",
color=COLORS["MODEL_1"],
lw=2,
hatch="/")
ax.set_ylim(0, np.max(n) + 0.5)
ax.set_xscale("log")
ax.set_xticks(ticks)
ax.get_xaxis().set_major_formatter(pl.ScalarFormatter())
ax.set_xlim(rng)