def show_dag(self, expand=set()):
"""Generate and show a DAG for the model
"""
from matplotlib.pyplot import show as pltshow
G = self.make_dag(expand=expand)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
## Plotting
edge_labels = dict([((
u,
v,
), d["label"]) for u, v, d in G.edges(data=True)])
n = G.size()
## Manual layout
# if n == 2:
if False:
pos = {
"(var)": [-0.5, +0.5],
"(out)": [+0.5, -0.5],
}
pos[self.functions[0].name] = [+0.5, +0.5]
## Optimized layout
else:
try:
## Planar, if possible
pos = nx.planar_layout(G)
except nx.NetworkXException:
## Scaled spring layout
pos = nx.spring_layout(
G,
k=0.6 * n,
pos={
"(Inputs)": [-0.5 * n, +0.5 * n],
"(Outputs)": [+0.5 * n, -0.5 * n],
},
fixed=["(var)", "(out)"],
threshold=1e-6,
iterations=100,
)
# Generate colormap
color_map = []
for node in G:
if G.nodes[node]["parent"] == self.name:
color_map.append("blue")
else:
color_map.append("green")
# Draw
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
nx.draw(G,
pos,
node_size=1000,
with_labels=True,
node_color=color_map)
pltshow()
def multiplot(datax, datay, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result is saved\
as a '.png' document.
Parameters
----------
datax : list of floats
x-axis values.
datay : list of floats or list of tuples of list of floats
y-axis values per axis and per curve in each axis, if tuple.
labels : list, optional
list of or tupple of list of curve labels (the default is None).
unitx : str, optional
x-axis label (the default is None).
unity : list of str, optional
y-axis labels per axis (the default is None).
limits= : list of tuples, optional
List of (ymin, ymax) values. If None then (ymin, ymax) is set \
automatically.
If 'extend', the y-axis is extend by 5% on both ends (the default is \
'extend').
linewidth : float, optional
The default is 2.
figsize : tuple of float, optional
The default is (5., 5.).
filelabel : str, optional
Figure is saved in 'filelabel.png' if provided (the default is None).
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : list of str, optional
Log-scale activation for each axis according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is ('-b', '--r', ':g', '-.m').
fontsize : int, optional
the default is figsize[0]*4
legendfontsize : int, optional
Figure legend font size. If None then set to 4/5 of fontsize (the \
default is None).
maintitle : str, optional
Figure main title. If set to None, the desactivated (the default is \
None).
axedef : tuple of floats, optional
Figure geometry (left, botom, right, top, height, width).
The default is (.15, .15, .85, .85, .1, .3).
====== \
===============================================================
Value Description
====== \
===============================================================
left the left side of the subplots of the figure.
bottom the bottom of the subplots of the figure.
right the right side of the subplots of the figure.
top the top of the subplots of the figure.
wspace the amount of width reserved for blank space between subplots.
hspace the amount of height reserved for white space between \
subplots.
====== \
===============================================================
markersize : float
Default is 6.
markeredgewidth : float
Width of line around markers (the default is 0.5).
nbinsx, nbinsy : int, optional
number of x-axis and y-axis ticks (the default is 4).
minor : bool
Activate the minor grid.
xpos_ylabel : float, optional
If provided, set the x-position of the ylabels so that they align (the\
default is None).
You probably want it to be negative.
"""
opts = standard.copy()
opts.update(kwargs)
nplots = len(datay)
if opts['fontsize'] is None:
opts['fontsize'] = int(6*opts['figsize'][0])
if opts['legendfontsize'] is None:
opts['legendfontsize'] = int(0.8*opts['fontsize'])
if opts['unity'] is None:
opts['unity'] = ['', ]*nplots
if opts['limits'] is None:
opts['limits'] = [None, ]*nplots
elif opts['limits'] == 'extend':
opts['limits'] = ['extend', ]*nplots
if opts['labels'] is None:
opts['labels'] = [None, ] * nplots
if opts['log'] is None:
opts['log'] = ['', ] * nplots
from matplotlib.pyplot import subplots, close
close('all')
fig, axs = subplots(nplots, 1, sharex=True, figsize=opts['figsize'])
activate_latex(opts)
# Dic of font properties
from matplotlib.pyplot import rc
rc('font', size=opts['fontsize'], **globalfonts())
x = dec(datax, opts)
for n in range(nplots):
miny = float('Inf')
maxy = -float('Inf')
if type(datay[n]) is list:
y = dec(datay[n], opts)
maxy = max([maxy, max(y)])
miny = min([miny, min(y)])
if nplots > 1:
plotn = whichplot(opts['log'][n], axs[n])
else:
plotn = whichplot(opts['log'][n], axs)
plotn(x, y, opts['linestyles'][0], label=opts['labels'][n],
linewidth=opts['linewidth'],
markeredgewidth=opts['markeredgewidth'],
markersize=opts['markersize'])
elif isinstance(datay[n], tuple):
len_yn = len(datay[n])
if opts['labels'][n] is None:
opts['labels'][n] = (None, )*len_yn
for m in range(len_yn):
if isinstance(opts['labels'][n][m], (list, tuple)):
annotate(x, y, opts['labels'][n][m][0],
opts['labels'][n][m][1],
opts['legendfontsize'])
l = None
else:
l = opts['labels'][n][m]
y = dec(datay[n][m])
maxy = max([maxy, max(y)])
miny = min([miny, min(y)])
plotn = whichplot(opts['log'][n], axs[n])
plotn(x, y, opts['linestyles'][m], label=l,
linewidth=opts['linewidth'],
markersize=opts['markersize'],
markeredgewidth=opts['markeredgewidth'])
setlims(axs[n], x, miny, maxy, opts['limits'][n])
setticks(axs[n], opts, n)
axs[n].legend(loc=opts['loc'], fontsize=opts['legendfontsize'])
if opts['unity'][n] is not None:
axs[n].set_ylabel(opts['unity'][n], fontsize=opts['fontsize'])
if opts['xpos_ylabel'] is not None:
axs[n].yaxis.set_label_coords(opts['xpos_ylabel'], 0.5)
if n == nplots-1:
axs[n].set_xlabel(opts['unitx'], fontsize=opts['fontsize'])
else:
from matplotlib.pyplot import setp
setp(axs[n].get_xticklabels(), visible=False)
if opts['maintitle'] is not None:
from matplotlib.pyplot import suptitle
suptitle(opts['maintitle'])
if opts['axedef'] is not None:
print(opts['axedef'])
left, right = opts['axedef'][0], opts['axedef'][2]
bottom, top = opts['axedef'][1], opts['axedef'][3]
wspace, hspace = opts['axedef'][4], opts['axedef'][5]
fig.subplots_adjust(left=left, right=right,
bottom=bottom, top=top,
wspace=wspace, hspace=hspace)
else:
fig.tight_layout(pad=0.6, w_pad=0.5, h_pad=.0)
if opts['filelabel'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['filelabel'] + '.' + opts['format'])
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
def spectrogram(x, fs, show=False, **kwargs):
"""
Plot spectrogramm of datay.
Parameters
----------
x : lists of values
y-axis values for each curve.
fs : floats
samplerate.
label : list, optional
signal label (the default is None).
xlabel : str, optional
x-axis label (the default is None).
ylabel : list of str, optional
y-axis labels (the default is None).
path : str, optional
Figure is saved in 'filelabel.png' (the default is 'multiplot').
title : str, optional
Figure main title. If set to None, the desactivated (the default is
None).
dynamics : positive value, optional
The dynamics is normalized to 0db and the value below minus 'dynamics'
are set to zeros
cmap : str, optional
Colormap specifier. Default is 'gnuplot2'.
"""
opts = standard.copy()
opts['ylabel'] = opts.pop('ylabels')
opts['label'] = opts.pop('labels')
opts.update(kwargs)
if not 'dynamics' in opts.keys():
opts['dynamics'] = 80.
if not 'nfft' in opts.keys():
opts['nfft'] = int(2**8)
else:
opts['nfft'] = int(opts['nfft'])
if not 'cmap' in opts.keys():
opts['cmap'] = 'BuPu'
from matplotlib.pyplot import figure, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig = figure(i)
from matplotlib.pyplot import axes
ax = axes()
if isinstance(opts['label'], (list, tuple)):
annotate(*opts['label'], ax=ax)
noverlap = int(opts['nfft']/2)
Pxx, fbins, tbins, im = ax.specgram(np.array(x)/(opts['nfft']/2.),
mode='psd', Fs=fs,
NFFT=opts['nfft'], noverlap=noverlap,
cmap=opts['cmap'])
Pxx = Pxx/np.max(Pxx)
extent = [tbins.min(), tbins.max(), 0., fbins.max()]
print(extent)
im = ax.imshow(10*np.log10(Pxx), extent=extent, origin='lower',
aspect='auto', cmap=opts['cmap'],
vmin=-opts['dynamics'], vmax=0)
if opts['xlabel'] is not None:
from matplotlib.pyplot import xlabel
xlabel(opts['xlabel'])
if opts['ylabel'] is not None:
from matplotlib.pyplot import ylabel
ylabel(opts['ylabel'])
if opts['title'] is not None:
from matplotlib.pyplot import title
title(opts['title'])
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
fig.colorbar(im, label='Magnitude (dB)')
if opts['log'] is not None and 'y' in opts['log']:
from matplotlib.pyplot import yscale
yscale('log')
if opts['log'] is not None and 'x' in opts['log']:
from matplotlib.pyplot import xscale
xscale('log')
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
def singleplot(x, y, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result is saved\
as a '.png' document.
Parameters
----------
x : list of numerics
x-axis values.
y : list of lists of numerics
y-axis values for each curves curve.
labels : list, optional
list of curve labels (the default is None). The number of labels must
be a divisor of the number of plots.
xlabel : str, optional
x-axis label (the default is None).
ylabel : str, optional
y-axis label (the default is None).
path : str, optional
Figure is saved in 'path.ext' if provided (the default is None). See
'format' agument for extension.
format : str, optional
Extension in {'pdf', 'png'} for figure export. If not given, the
extension from pyphs.config.py is used.
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : str, optional
Log-scale activation according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is ('-b', '--r', ':g', '-.m').
title : str, optional
Figure main title. If set to None, the desactivated (the default is \
None).
grid = bool
Indicate whether to show the grid or not (the default is False)
"""
opts = standard.copy()
opts['ylabel'] = opts.pop('ylabels')
opts.update(kwargs)
try:
if (isinstance(y[0], (float, int)) or
(isinstance(y[0], numpy.ndarray) and
len(y[0].shape) == 0)):
y = [y]
except IndexError:
pass
nplots = int(y.__len__())
if opts['labels'] is None:
opts['labels'] = [None, ]*nplots
elif len(opts['labels']) != nplots:
nlabels = len(opts['labels'])
if nplots % nlabels == 0:
N = nplots - nlabels
opts['labels'] = list(opts['labels']) + [None, ]*N
if opts['log'] is None:
opts['log'] = ''
from matplotlib.pyplot import figure, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig = figure(i)
from matplotlib.pyplot import axes
ax = axes()
ax.ticklabel_format(style='sci', scilimits=(-2, 2))
print_legend = False
for n, yn in enumerate(y):
if len(yn) == 0:
print('Skipping empty array: ', opts['labels'][n])
continue
if isinstance(opts['labels'][n], (list, tuple)):
annotate(*opts['labels'][n], ax=ax)
l = None
else:
l = opts['labels'][n]
print_legend = l is not None or print_legend
plot = whichplot(opts['log'], ax)
if opts['linestyles'] is not None:
plot(x, yn, opts['linestyles'][n], label=l)
else:
plot(x, yn, label=l)
if print_legend:
from matplotlib.pyplot import legend
legend(loc=opts['loc'])
if opts['xlabel'] is not None:
from matplotlib.pyplot import xlabel
xlabel(opts['xlabel'])
if opts['ylabel'] is not None:
from matplotlib.pyplot import ylabel
ylabel(opts['ylabel'])
if opts['title'] is not None:
from matplotlib.pyplot import title
title(opts['title'])
# Show grid
ax.grid(opts['grid'])
ax.minorticks_on()
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
return fig, ax
def spectrogram(x, fs, show=False, **kwargs):
"""
Plot spectrogramm of datay.
Parameters
----------
x : lists of values
y-axis values for each curve.
fs : floats
samplerate.
label : list, optional
signal label (the default is None).
xlabel : str, optional
x-axis label (the default is None).
ylabel : list of str, optional
y-axis labels (the default is None).
path : str, optional
Figure is saved in 'filelabel.png' (the default is 'multiplot').
title : str, optional
Figure main title. If set to None, the desactivated (the default is
None).
dynamics : positive value, optional
The dynamics is normalized to 0db and the value below minus 'dynamics'
are set to zeros
cmap : str, optional
Colormap specifier. Default is 'gnuplot2'.
"""
opts = standard.copy()
opts['ylabel'] = opts.pop('ylabels')
opts['label'] = opts.pop('labels')
opts.update(kwargs)
if not 'dynamics' in opts.keys():
opts['dynamics'] = 80.
if not 'nfft' in opts.keys():
opts['nfft'] = int(2**8)
else:
opts['nfft'] = int(opts['nfft'])
if not 'cmap' in opts.keys():
opts['cmap'] = 'BuPu'
from matplotlib.pyplot import figure, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig = figure(i)
from matplotlib.pyplot import axes
ax = axes()
if isinstance(opts['label'], (list, tuple)):
annotate(*opts['label'], ax=ax)
noverlap = int(opts['nfft'] / 2)
Pxx, fbins, tbins, im = ax.specgram(np.array(x) / (opts['nfft'] / 2.),
mode='psd',
Fs=fs,
NFFT=opts['nfft'],
noverlap=noverlap,
cmap=opts['cmap'])
Pxx = Pxx / np.max(Pxx)
extent = [tbins.min(), tbins.max(), 0., fbins.max()]
im = ax.imshow(10 * np.log10(Pxx),
extent=extent,
origin='lower',
aspect='auto',
cmap=opts['cmap'],
vmin=-opts['dynamics'],
vmax=0)
if opts['xlabel'] is not None:
from matplotlib.pyplot import xlabel
xlabel(opts['xlabel'])
if opts['ylabel'] is not None:
from matplotlib.pyplot import ylabel
ylabel(opts['ylabel'])
if opts['title'] is not None:
from matplotlib.pyplot import title
title(opts['title'])
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
fig.colorbar(im, label='Magnitude (dB)')
if opts['log'] is not None and 'y' in opts['log']:
from matplotlib.pyplot import yscale
yscale('log')
if opts['log'] is not None and 'x' in opts['log']:
from matplotlib.pyplot import xscale
xscale('log')
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
def multiplot(x, y, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result can be
saved as a '.png' document.
Parameters
----------
x : list of floats
x-axis values.
y : list of floats or of tuples of list of floats
y-axis values per axis and per curve in each axis, if tuple.
xlabel : str, optional
x-axis label (the default is None).
ylabels : list of str, optional
y-axis label for each axe (the default is None).
title : str, optional
Figure main title. Desactivated if set to None (default).
labels : list, optional
list of curve labels (the default is None). Textboxes can be used with_files
the elements syntax ['label', (xrel, yrel)] where xrel and yrel are
relative x and y position.
path : str, optional
Figure is saved in 'path.ext' if provided (the default is None).
Extension from pyphs config.py.
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : list of str, optional
Log-scale activation for each axis according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is None.
xpos_ylabel : float, optional
If provided, set the x-position of the ylabels so that they align (the
default is None). You probably want it to be negative.
grid : bool
Indicate whether to show the grid or not.
"""
# ------------------------------------------------------------
# Number of axes
nplots = len(y)
# ------------------------------------------------------------
# Recover standard options
opts = standard.copy()
# Update with new options
opts.update(kwargs)
# ------------------------------------------------------------
# Check fo options length
if opts['ylabels'] is None:
opts['ylabels'] = [
'',
] * nplots
elif not len(opts['ylabels']) == nplots:
raise AttributeError('wrong number of y labels')
if opts['labels'] is None:
opts['labels'] = [
None,
] * nplots
elif not len(opts['ylabels']) == nplots:
raise AttributeError('wrong number of curves labels')
if opts['log'] is None:
opts['log'] = [
'',
] * nplots
elif not len(opts['log']) == nplots:
raise AttributeError('wrong number of log options')
# ------------------------------------------------------------
# Init figure and axes
from matplotlib.pyplot import subplots, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig, axs = subplots(nplots, 1, sharex=True, num=i)
# ------------------------------------------------------------
# Iterate over y data
for n, yn in enumerate(y):
# --------------------------------------------------------
# get axe
if nplots > 1:
axe = axs[n]
else:
axe = axs
axe.ticklabel_format(style='sci', scilimits=(-2, 2))
# --------------------------------------------------------
# flag
print_legend = False
# --------------------------------------------------------
# if yn is a list of values
if (isinstance(yn[0], (float, int)) or
(isinstance(yn[0], numpy.ndarray) and len(yn[0].shape) == 0)):
# get plot in {plot, semilogx, semilogy, loglog}
plotn = whichplot(opts['log'][n], axe)
# Add label or annotation
if isinstance(opts['labels'][n], (list, tuple)):
annotate(*opts['labels'][n], ax=axe)
l = None
else:
l = opts['labels'][n]
print_legend = l is not None or print_legend
# Plot
if opts['linestyles'] is not None:
plotn(x, yn, opts['linestyles'][n][0], label=l)
else:
plotn(x, yn, label=l)
# --------------------------------------------------------
# if yn is a list of signals
else:
# get number of signals in yn
len_yn = len(yn)
# Set appropriate len of labels
if opts['labels'][n] is None:
opts['labels'][n] = (None, ) * len_yn
elif not len(opts['labels'][n]) == len_yn:
raise AttributeError('wrong number of labels')
# iterate over yn data
for m, ynm in enumerate(yn):
# Add label or annotation
if isinstance(opts['labels'][n][m], (list, tuple)):
annotate(*opts['labels'][n][m], ax=axe)
l = None
else:
l = opts['labels'][n][m]
print_legend = l is not None or print_legend
# get plot in {plot, semilogx, semilogy, loglog}
plotn = whichplot(opts['log'][n], axe)
# Plot
if opts['linestyles'] is not None:
plotn(x, ynm, opts['linestyles'][n][m], label=l)
else:
plotn(x, ynm, label=l)
# --------------------------------------------------------
# Print legend
if print_legend:
axs[n].legend(loc=opts['loc'])
# --------------------------------------------------------
# Set y label
if opts['ylabels'][n] is not None:
axs[n].set_ylabel(opts['ylabels'][n])
if opts['xpos_ylabel'] is not None:
axs[n].yaxis.set_label_coords(opts['xpos_ylabel'], 0.5)
# --------------------------------------------------------
# Set x label if last plot
if n == nplots - 1:
axs[n].set_xlabel(opts['xlabel'])
else:
from matplotlib.pyplot import setp
setp(axs[n].get_xticklabels(), visible=False)
# --------------------------------------------------------
# Show grid
axs[n].grid(opts['grid'])
axs[n].minorticks_on()
# ------------------------------------------------------------
# Set title
if opts['title'] is not None:
from matplotlib.pyplot import suptitle
suptitle(opts['title'])
# ------------------------------------------------------------
# Save file
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
# ------------------------------------------------------------
# Show
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
return fig, axs
def singleplot(x, y, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result is saved\
as a '.png' document.
Parameters
----------
x : list of numerics
x-axis values.
y : list of lists of numerics
y-axis values for each curves curve.
labels : list, optional
list of curve labels (the default is None).
xlabel : str, optional
x-axis label (the default is None).
ylabel : str, optional
y-axis label (the default is None).
path : str, optional
Figure is saved in 'path.ext' if provided (the default is None).
Extension from pyphs config.py.
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : str, optional
Log-scale activation according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is ('-b', '--r', ':g', '-.m').
title : str, optional
Figure main title. If set to None, the desactivated (the default is \
None).
grid = bool
Indicate whether to show the grid or not (the default is False)
"""
opts = standard.copy()
opts['ylabel'] = opts.pop('ylabels')
opts.update(kwargs)
if (isinstance(y[0], (float, int)) or
(isinstance(y[0], numpy.ndarray) and len(y[0].shape) == 0)):
y = [y]
nplots = int(y.__len__())
if opts['labels'] is None:
opts['labels'] = [None, ]*nplots
if opts['log'] is None:
opts['log'] = ''
from matplotlib.pyplot import figure, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig = figure(i)
from matplotlib.pyplot import axes
ax = axes()
ax.ticklabel_format(style='sci', scilimits=(-2, 2))
print_legend = False
for n, yn in enumerate(y):
if len(yn) == 0:
print('Skipping empty array: ', opts['labels'][n])
continue
if isinstance(opts['labels'][n], (list, tuple)):
annotate(*opts['labels'][n], ax=ax)
l = None
else:
l = opts['labels'][n]
print_legend = l is not None or print_legend
plot = whichplot(opts['log'], ax)
if opts['linestyles'] is not None:
plot(x, yn, opts['linestyles'][n], label=l)
else:
plot(x, yn, label=l)
if print_legend:
from matplotlib.pyplot import legend
legend(loc=opts['loc'])
if opts['xlabel'] is not None:
from matplotlib.pyplot import xlabel
xlabel(opts['xlabel'])
if opts['ylabel'] is not None:
from matplotlib.pyplot import ylabel
ylabel(opts['ylabel'])
if opts['title'] is not None:
from matplotlib.pyplot import title
title(opts['title'])
# Show grid
ax.grid(opts['grid'])
ax.minorticks_on()
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
return fig, ax
def singleplot(datax, datay, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result is saved\
as a '.png' document.
Parameters
----------
datax : list of numerics
x-axis values.
datay : list of lists of numerics
y-axis values for each curves curve.
labels : list, optional
list of curve labels (the default is None).
unitx : str, optional
x-axis label (the default is None).
unity : list of str, optional
y-axis labels (the default is None).
ylims : tuple of float, optional
(ymin, ymax) values. If None then (ymin, ymax) is set automatically \
(the default is None).
If extend the y-axis is extended by 5% on both sides (the default is \
'extend').
linewidth : float, optional
The default is 2.
figsize : tuple of float, optional
The default is (5., 5.).
filelabel : str, optional
Figure is saved in 'filelabel.png' (the default is 'multiplot').
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : str, optional
Log-scale activation according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is ('-b', '--r', ':g', '-.m').
fontsize : int, optional
the default is figsize[0]*4.
legendfontsize : int, optional
Figure legend font size. If None then set to 4/5 of fontsize (the \
default is None).
maintitle : str, optional
Figure main title. If set to None, the desactivated (the default is \
None).
axedef : tuple of floats, optional
Figure geometry (left, botom, right, top).
The default is (.15, .15, .85, .85).
nbinsx, nbinsy : int, optional
number of x-axis and y-axis ticks (the default is 4).
minor : bool
Activate the minor grid.
markersize : float
Default is 6.
markeredgewidth : float
Width of line around markers (the default is 0.5).
"""
opts = standard.copy()
opts.update(kwargs)
if opts['axedef'] is None:
opts['axedef'] = [.15, .15, .75, .75]
if opts['linestyles'] is None:
opts['linestyles'] = ['-b', '--r', '-.g', ':m']
nplots = int(datay.__len__())
if opts['fontsize'] is None:
opts['fontsize'] = int(4 * opts['figsize'][0])
if opts['legendfontsize'] is None:
opts['legendfontsize'] = int(0.8 * opts['fontsize'])
if opts['labels'] is None:
opts['labels'] = [
None,
] * nplots
if opts['log'] is None:
opts['log'] = ''
activate_latex(opts)
from matplotlib.pyplot import rc
rc('font', size=opts['fontsize'], **globalfonts())
from matplotlib.pyplot import close
close('all')
from matplotlib.pyplot import figure
fig = figure(1, figsize=opts['figsize'])
nplots = datay.__len__()
if isinstance(datax[0], (float, int)):
datax = [
datax,
] * nplots
from matplotlib.pyplot import axes
if opts['axedef'] is not None:
geometry = opts['axedef'][:4]
else:
geometry = None
ax = axes(geometry)
miny = float('Inf')
maxy = -float('Inf')
for n in range(nplots):
x = dec(datax[n], opts)
y = dec(datay[n], opts)
maxy = max([maxy, max(y)])
miny = min([miny, min(y)])
if isinstance(opts['labels'][n], (list, tuple)):
annotate(x, y, opts['labels'][n][0], opts['labels'][n][1],
opts['legendfontsize'])
l = None
else:
l = opts['labels'][n]
plotn = whichplot(opts['log'], ax)
plotn(x,
y,
opts['linestyles'][n],
linewidth=opts['linewidth'],
markeredgewidth=opts['markeredgewidth'],
markersize=opts['markersize'],
label=l)
setlims(ax, x, miny, maxy, opts['ylims'])
setticks(ax, opts)
from matplotlib.pyplot import legend
legend(loc=opts['loc'], fontsize=opts['legendfontsize'])
if opts['unitx'] is not None:
from matplotlib.pyplot import xlabel
xlabel(opts['unitx'])
if opts['unity'] is not None:
from matplotlib.pyplot import ylabel
ylabel(opts['unity'])
if opts['maintitle'] is not None:
from matplotlib.pyplot import title
title(opts['maintitle'])
if opts['axedef'] is None:
fig.tight_layout()
if opts['filelabel'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['filelabel'] + '.' + opts['format'])
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
def multiplot(x, y, show=True, **kwargs):
"""
Plot multiple y-axis and possible multiple curves in each. Result can be
saved as a '.png' document.
Parameters
----------
x : list of floats
x-axis values.
y : list of floats or of tuples of list of floats
y-axis values per axis and per curve in each axis, if tuple.
xlabel : str, optional
x-axis label (the default is None).
ylabels : list of str, optional
y-axis label for each axe (the default is None).
title : str, optional
Figure main title. Desactivated if set to None (default).
labels : list, optional
list of curve labels (the default is None). Textboxes can be used with_files
the elements syntax ['label', (xrel, yrel)] where xrel and yrel are
relative x and y position.
path : str, optional
Figure is saved in 'path.ext' if provided (the default is None).
Extension from pyphs config.py.
loc : int or string or pair of floats, default: 0
The location of the legend. Possible codes are:
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
log : list of str, optional
Log-scale activation for each axis according to
=============== =============
Plot type Code
=============== =============
'plot' ''
'semilogx' 'x'
'semilogy' 'y'
'loglog' 'xy'
=============== =============
linestyles : iterable, otpional
List of line styles; the default is None.
xpos_ylabel : float, optional
If provided, set the x-position of the ylabels so that they align (the
default is None). You probably want it to be negative.
grid : bool
Indicate whether to show the grid or not.
"""
# ------------------------------------------------------------
# Number of axes
nplots = len(y)
# ------------------------------------------------------------
# Recover standard options
opts = standard.copy()
# Update with new options
opts.update(kwargs)
# ------------------------------------------------------------
# Check fo options length
if opts['ylabels'] is None:
opts['ylabels'] = ['', ]*nplots
elif not len(opts['ylabels']) == nplots:
raise AttributeError('wrong number of y labels')
if opts['labels'] is None:
opts['labels'] = [None, ]*nplots
elif not len(opts['ylabels']) == nplots:
raise AttributeError('wrong number of curves labels')
if opts['log'] is None:
opts['log'] = ['', ] * nplots
elif not len(opts['log']) == nplots:
raise AttributeError('wrong number of log options')
# ------------------------------------------------------------
# Init figure and axes
from matplotlib.pyplot import subplots, fignum_exists
i = 1
while fignum_exists(i):
i += 1
fig, axs = subplots(nplots, 1, sharex=True, num=i)
# ------------------------------------------------------------
# Iterate over y data
for n, yn in enumerate(y):
# --------------------------------------------------------
# get axe
if nplots > 1:
axe = axs[n]
else:
axe = axs
axe.ticklabel_format(style='sci', scilimits=(-2, 2))
# --------------------------------------------------------
# flag
print_legend = False
# --------------------------------------------------------
# if yn is a list of values
if (isinstance(yn[0], (float, int)) or
(isinstance(yn[0], numpy.ndarray) and len(yn[0].shape) == 0)):
# get plot in {plot, semilogx, semilogy, loglog}
plotn = whichplot(opts['log'][n], axe)
# Add label or annotation
if isinstance(opts['labels'][n], (list, tuple)):
annotate(*opts['labels'][n], ax=axe)
l = None
else:
l = opts['labels'][n]
print_legend = l is not None or print_legend
# Plot
if opts['linestyles'] is not None:
plotn(x, yn, opts['linestyles'][n][0],
label=l)
else:
plotn(x, yn, label=l)
# --------------------------------------------------------
# if yn is a list of signals
else:
# get number of signals in yn
len_yn = len(yn)
# Set appropriate
if opts['labels'][n] is None:
opts['labels'][n] = (None, )*len_yn
elif not len(opts['labels'][n]) == len_yn:
raise AttributeError('wrong number of labels')
# iterate over yn data
for m, ynm in enumerate(yn):
# Add label or annotation
if isinstance(opts['labels'][n][m], (list, tuple)):
annotate(*opts['labels'][n][m], ax=axe)
l = None
else:
l = opts['labels'][n][m]
print_legend = l is not None or print_legend
# get plot in {plot, semilogx, semilogy, loglog}
plotn = whichplot(opts['log'][n], axe)
# Plot
if opts['linestyles'] is not None:
plotn(x, ynm, opts['linestyles'][n][m], label=l)
else:
plotn(x, ynm, label=l)
# --------------------------------------------------------
# Print legend
if print_legend:
axs[n].legend(loc=opts['loc'])
# --------------------------------------------------------
# Set y label
if opts['ylabels'][n] is not None:
axs[n].set_ylabel(opts['ylabels'][n])
if opts['xpos_ylabel'] is not None:
axs[n].yaxis.set_label_coords(opts['xpos_ylabel'], 0.5)
# --------------------------------------------------------
# Set x label if last plot
if n == nplots-1:
axs[n].set_xlabel(opts['xlabel'])
else:
from matplotlib.pyplot import setp
setp(axs[n].get_xticklabels(), visible=False)
# --------------------------------------------------------
# Show grid
axs[n].grid(opts['grid'])
axs[n].minorticks_on()
# ------------------------------------------------------------
# Set title
if opts['title'] is not None:
from matplotlib.pyplot import suptitle
suptitle(opts['title'])
# ------------------------------------------------------------
# Save file
if opts['path'] is not None:
from matplotlib.pyplot import savefig
savefig(opts['path'] + '.' + opts['format'])
# ------------------------------------------------------------
# Show
if show:
from matplotlib.pyplot import show as pltshow
pltshow()
return fig, axs
def max_ocean(pl,
n_stats=10,
at_age=None,
name_rms='dyn_top_aspect_prime',
phi0=None,
plot=False,
verbose=False,
spectrum_fname='base_spectrum_l1.pkl',
spectrum_fpath='/home/claire/Works/exo-top/exotop/top_spectra/',
rho_w=1000,
**kwargs):
water_load_ratio = pl.rho_m / (pl.rho_m - rho_w)
if verbose:
print('including water loading')
if phi0 is None:
degree, phi0 = sh.load_model_spectrum_pkl(fname=spectrum_fname,
path=spectrum_fpath)
h_rms1 = eval('pl.' + name_rms)
if verbose:
h_rms1_dim = eval('pl.' + 'dyn_top_rms')
print('nondimensional h rms', h_rms1[-1])
print('dimensional h rms', h_rms1_dim[-1])
l = np.arange(len(phi0))
k = sh.l_to_k(l, R=2) # original model spectrum uses R_b = 2d = 2
h_rms0 = sh.parseval_rms(phi0, k)
if np.isnan(h_rms0):
print('phi0', phi0)
print('k', k)
raise Exception('rms of model spectrum is nan!')
if at_age is not None:
it = age_index(pl.t, at_age, parameters.sec2Gyr
) # get time index nearest to desired snap given in Gyr
it = range(it, it + 1)
else: # all times
it = range(len(pl.t))
pl.max_ocean = np.zeros_like(it, dtype=np.float64)
pl.h_peak_spectral = np.zeros_like(it, dtype=np.float64)
for ii in it:
h_rms = h_rms1[ii]
h_ratio = h_rms / h_rms0
nn = 0
vols = []
peaks = []
rms_dims = []
rms_nondims = []
while nn < n_stats:
clm = sh.random_harms_from_psd(phi0,
l,
R=2,
h_ratio=h_ratio,
plot=plot,
verbose=verbose)
# shgrid = sh.coeffs_to_grid(clm, R_b=2, plot_grid=False, plot_spectrum=False, verbose=verbose,)
# lmax = shgrid.lmax
# data = shgrid.data
# lats = shgrid.lats()
# lons = shgrid.lons()
# vol = sh.integrate_to_peak(grid_dim, lats, lons, R_b=pl.R_p, lmax=shgrid.lmax, verbose=verbose)
data = clm.expand(grid='GLQ', extend=False).to_array()
lmax = clm.lmax
rms_nondim = np.sqrt(np.mean(data**2))
# if verbose:
# print('RMS of map nondimensional', rms_nondim)
grid_dim = dimensionalise(data, pl, i=ii) * water_load_ratio
vol = sh.integrate_to_peak_GLQ(grid_dim,
R=pl.R_p,
lmax=lmax,
verbose=verbose)
if np.isnan(vol):
print('phi0', phi0, 'h_ratio', h_ratio)
raise Exception('ocean vol nan!')
vols.append(vol)
rms_dim = np.sqrt(np.mean(grid_dim**2))
peaks.append(np.max(grid_dim))
rms_nondims.append(rms_nondim)
rms_dims.append(rms_dim)
nn = nn + 1
if verbose:
# print('RMS of map dimensionalised', rms_dim, 'm')
if nn > 0:
verbose = False # don't repeat output
basin_capacity = np.mean(vols)
# phi = sh.scale_spectrum(h_rms=h_rms1[ii], **kwargs) # old verj
# h_peak_grid = sh.hpeak_from_spectrum(phi, n=n_stats, **kwargs)
# basin_capacity = sh.vol_from_peak(r0=pl.R_p, h_peak=h_peak_grid, **kwargs)
if at_age is None:
pl.max_ocean[ii] = basin_capacity
pl.h_peak_spectral[ii] = np.mean(peaks)
else:
pl.max_ocean = basin_capacity
pl.h_peak_spectral = np.mean(peaks)
if verbose:
print('\n')
print(' scaled h_rms', name_rms, '=',
dimensionalise(h_rms1[-1], pl=pl, i=-1),
'm without water loading')
print(' water load ratio', water_load_ratio)
print(
"%.2f" % (pl.M_p / parameters.M_E), 'M_E |',
"%.2f" % (pl.R_p * 1e-3), 'km radius | ocean vol:',
"%.2f" % (basin_capacity / 1.4e18), 'EO | V_shell =',
"%.2f" %
(4 / 3 * np.pi *
((pl.R_p + np.mean(peaks))**3 - pl.R_p**3) / 1.4e18),
'EO | h_peak =', "%.2f" % (np.mean(peaks) * 1e-3),
'km | h_rms =', "%.2f" % (np.mean(rms_dims) * 1e-3), 'km')
print('h_rms_prime =', "%.5f" % np.mean(rms_nondims), '| b =',
"%.2f" % pl.b[-1], '| log Ra_i =',
"%.2f" % np.log10(pl.Ra_i[-1]), '| d =',
"%.2f" % (pl.d * 1e-3), '| dT =',
"%.2f" % pl.delta_T[-1], '| alpha_m =',
"%.2f" % pl.alpha_m)
# print('t', ii, 'R_p:', pl.R_p * 1e-3, 'km, h_rms', h_rms1[ii]*1e-3, 'km, h_peak', h_spectrum_max * 1e-3, 'km, ocn vol:', basin_capacity / parameters.TO, 'TO')
# print('final h_ratio', h_ratio)
if plot:
from matplotlib.pyplot import show as pltshow
pltshow()
return pl
