def get_dataset(ds):
""" Get a dataset from a results dict, a string or a dataset
Args:
ds (dict, str or qcodes.DataSet): the data to be put in the dataset
Returns:
ds (qcodes.DataSet) dataset made from the input data
"""
if isinstance(ds, dict):
ds = ds.get('dataset', None)
if ds is None:
return None
if isinstance(ds, str):
ds = qcodes.load_data(ds)
return ds
def test_new_loop_1D_None_result(self):
arrs = {}
p_measure = Parameter("p_measure", set_cmd=None)
with Measurement("new_loop_1D_None_result") as msmt:
for k, val in enumerate(Sweep(self.p_sweep.sweep(0, 1, 0.1))):
arr = arrs.setdefault(msmt.action_indices, np.zeros(msmt.loop_shape))
if not k % 2:
p_measure(k)
else:
p_measure(None)
arr[k] = msmt.measure(p_measure)
verify_msmt(msmt, arrs, allow_nan=True)
# Verify that the measurement dataset records the correct measurement type
data = load_data(msmt.dataset.location)
self.assertEqual(data.metadata.get("measurement_type"), "Measurement")
def show_num(id, useQT=False, **kwargs):
"""
Show and return plot and data for id in current instrument.
Args:
id(number): id of instrument
useQT: Use pyqtgraph as an alternative to Matplotlib
**kwargs: Are passed to plot function
Returns:
plot, data : returns the plot and the dataset
"""
if not getattr(CURRENT_EXPERIMENT, "init", True):
raise RuntimeError("Experiment not initalized. "
"use qc.Init(mainfolder, samplename)")
str_id = '{0:03d}'.format(id)
t = qc.DataSet.location_provider.fmt.format(counter=str_id)
data = qc.load_data(t)
plots = []
for value in data.arrays.keys():
if "set" not in value:
if useQT:
plot = QtPlot(
getattr(data, value),
fig_x_position=CURRENT_EXPERIMENT['plot_x_position'],
**kwargs)
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
str_id)
plot.subplots[0].setTitle(title)
plot.subplots[0].showGrid(True, True)
else:
plot = MatPlot(getattr(data, value), **kwargs)
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
str_id)
plot.subplots[0].set_title(title)
plot.subplots[0].grid()
plots.append(plot)
return data, plots
def show_num(id, useQT=False, do_plots=True):
"""
Show and return plot and data for id in current instrument.
Args:
id(number): id of instrument
do_plots: Default False: if false no plots are produced.
Returns:
plot, data : returns the plot and the dataset
"""
if not getattr(CURRENT_EXPERIMENT, "init", True):
raise RuntimeError("Experiment not initalized. "
"use qc.Init(mainfolder, samplename)")
str_id = '{0:03d}'.format(id)
t = qc.DataSet.location_provider.fmt.format(counter=str_id)
data = qc.load_data(t)
if do_plots:
plots = []
for value in data.arrays.keys():
if "set" not in value:
if useQT:
plot = QtPlot(
getattr(data, value),
fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
str_id)
plot.subplots[0].setTitle(title)
plot.subplots[0].showGrid(True, True)
else:
plot = MatPlot(getattr(data, value))
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
str_id)
plot.subplots[0].set_title(title)
plot.subplots[0].grid()
plots.append(plot)
else:
plots = None
return data, plots
def logCallback(self, index):
''' Function called when a log entry is selected '''
logging.info('logCallback!')
logging.debug('logCallback: index %s' % str(index))
self.__debug['last'] = index
pp = index.parent()
row = index.row()
tag = pp.child(row, 2).data()
# load data
if tag is not None:
print('logCallback! tag %s' % tag)
try:
logging.debug('load tag %s' % tag)
data = qcodes.load_data(tag)
self.qplot.clear()
infotxt = 'arrays: ' + ', '.join(list(data.arrays.keys()))
q = pp.child(row, 1).model()
q.setData(pp.child(row, 1), infotxt)
param_name = self.plot_parameter(data)
if param_name is not None:
logging.info(
'using parameter %s for plotting' % param_name)
self.qplot.add(getattr(data, param_name))
else:
logging.info('could not find parameter for DataSet')
except Exception as e:
print('logCallback! error ...')
print(e)
logging.warning(e)
pass
def load_dataset(location, io=None, verbose=0):
""" Load a dataset from storage
An attempt is made to automatically detect the formatter. Supported are currently GNUPlotFormat and HDF5Format
Args:
location (str): either the relative or full location
io (None or qcodes.DiskIO):
Returns:
dataset (DataSet or None)
"""
if io is None:
io = qcodes.DataSet.default_io
formatters = [qcodes.DataSet.default_formatter]
try:
from qcodes.data.hdf5_format_hickle import HDF5FormatHickle
formatters += [HDF5FormatHickle()]
except:
pass
from qcodes.data.hdf5_format import HDF5Format
formatters += [HDF5Format()]
from qcodes.data.gnuplot_format import GNUPlotFormat
formatters += [GNUPlotFormat()]
data = None
for ii, hformatter in enumerate(formatters):
try:
if verbose:
print('%d: %s' % (ii, hformatter))
data = qcodes.load_data(location, formatter=hformatter, io=io)
if len(data.arrays) == 0:
data = None
raise Exception(
'empty dataset, probably a HDF5 format misread by GNUPlotFormat'
)
logging.debug('load_data: loaded %s with %s' %
(location, hformatter))
except Exception as ex:
logging.info(
'load_data: location %s: failed for formatter %d: %s' %
(location, ii, hformatter))
if verbose:
print(ex)
pass
finally:
if data is not None:
if isinstance(hformatter, GNUPlotFormat):
# workaround for bug in GNUPlotFormat not saving the units
if '__dataset_metadata' in data.metadata:
dataset_meta = data.metadata['__dataset_metadata']
for key, array_metadata in dataset_meta[
'arrays'].items():
if key in data.arrays:
if data.arrays[key].unit is None:
if verbose:
print(
'load_dataset: updating unit for %s'
% key)
data.arrays[key].unit = array_metadata[
'unit']
if verbose:
print('succes with formatter %s' % hformatter)
break
if verbose:
if data is None:
print('could not load data from %s, returning None' % location)
return data
def show_num(ids,
samplefolder=None,
useQT=False,
avg_sub='',
do_plots=True,
savepng=True,
fig_size=[6, 4],
clim=None,
dataname=None,
xlim=None,
ylim=None,
transpose=False,
auto_color_scale: Optional[bool] = None,
cutoff_percentile: Optional[Union[Tuple[Number, Number],
Number]] = None,
**kwargs):
"""
Show and return plot and data.
Args:
ids (number, list): id or list of ids of dataset(s)
samplefolder (str): Sample folder if loading data from different sample than the initialized.
useQT (boolean): If true plots with QTplot instead of matplotlib
avg_sub (str: 'col' or 'row'): Subtracts average from either each collumn ('col') or each row ('row')
do_plots: (boolean): if false no plots are produced.
dataname (str): If given only plots dataset with that name
savepng (boolean): If true saves matplotlib figure as png
fig_size [6,4]: Figure size in inches
clim [cmin,cmax]: Set min and max of colorbar to cmin and cmax respectrively
xlim [xmin,xmax]: Set limits on x axis
ylim [ymin,ymax]: set limits on y axis
transpose (boolean): Transpose data to be plotted (only works for 2D scans and qc.MatPlot)
auto_color_scale: if True, the colorscale of heatmap plots will be
automatically adjusted to disregard outliers.
cutoff_percentile: percentile of data that may maximally be clipped
on both sides of the distribution.
If given a tuple (a,b) the percentile limits will be a and 100-b.
See also the plotting tuorial notebook.
**kwargs: Are passed to plot function
Returns:
data, plots : returns the plots and the datasets
"""
# default values
if auto_color_scale is None:
auto_color_scale = qcodes.config.plotting.auto_color_scale.enabled
if cutoff_percentile is None:
cutoff_percentile = cast(
Tuple[Number, Number],
tuple(qcodes.config.plotting.auto_color_scale.cutoff_percentile))
if not isinstance(ids, collections.Iterable):
ids = (ids, )
data_list = []
keys_list = []
# Define samplefolder
if samplefolder == None:
check_experiment_is_initialized()
samplefolder = qc.DataSet.location_provider.fmt.format(counter='')
# Load all datasets into list
for id in ids:
path = samplefolder + '{0:03d}'.format(id)
data = qc.load_data(path)
data_list.append(data)
# find datanames to be plotted
if do_plots:
if useQT and len(ids) is not 1:
raise ValueError(
'qcodes.QtPlot does not support multigraph plotting. Set useQT=False to plot multiple datasets.'
)
if dataname is not None:
if dataname not in [
key for key in data.arrays.keys() if "_set" not in key
]:
raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\''.format(dataname), \
'while dataname(s) in dataset are: \'{}\'.'.format('\', \''.join(data.arrays.keys())))
keys = [dataname]
else:
keys = [key for key in data.arrays.keys() if "_set" not in key]
keys_list.append(keys)
if do_plots:
unique_keys = list(
set([item for sublist in keys_list for item in sublist]))
plots = []
num = ''
l = len(unique_keys)
for j, key in enumerate(unique_keys):
array_list = []
xlims = [[], []]
ylims = [[], []]
clims = [[], []]
# Find datasets containing data with dataname == key
for data, keys in zip(data_list, keys_list):
if key in keys:
arrays = getattr(data, key)
if transpose and len(arrays.set_arrays) == 2:
if useQT:
raise AttributeError(
'Transpose only works for qc.MatPlot.')
if dataname is None and l != 1:
raise ValueError(
'Dataname has to be provided to plot data transposed for dataset with more '
'than 1 measurement. Datanames in dataset are: \'{}\'.'
.format('\', \''.join(unique_keys)))
arrays.ndarray = arrays.ndarray.T
set0_temp = arrays.set_arrays[0]
set1_temp = arrays.set_arrays[1]
set0_temp.ndarray = set0_temp.ndarray.T
set1_temp.ndarray = set1_temp.ndarray.T
arrays.set_arrays = (
set1_temp,
set0_temp,
)
if avg_sub == 'row':
for i in range(np.shape(arrays.ndarray)[0]):
arrays.ndarray[i, :] -= np.nanmean(
arrays.ndarray[i, :])
if avg_sub == 'col':
for i in range(np.shape(arrays.ndarray)[1]):
arrays.ndarray[:,
i] -= np.nanmean(arrays.ndarray[:,
i])
array_list.append(arrays)
# Find axis limits for dataset
if len(arrays.set_arrays) == 2:
xlims[0].append(np.nanmin(arrays.set_arrays[1]))
xlims[1].append(np.nanmax(arrays.set_arrays[1]))
ylims[0].append(np.nanmin(arrays.set_arrays[0]))
ylims[1].append(np.nanmax(arrays.set_arrays[0]))
if auto_color_scale:
vlims = auto_range_iqr(arrays.ndarray)
else:
vlims = (np.nanmin(arrays.ndarray),
np.nanmax(arrays.ndarray))
clims[0].append(vlims[0])
clims[1].append(vlims[1])
else:
xlims[0].append(np.nanmin(arrays.set_arrays[0]))
xlims[1].append(np.nanmax(arrays.set_arrays[0]))
ylims[0].append(np.nanmin(arrays.ndarray))
ylims[1].append(np.nanmax(arrays.ndarray))
if useQT:
plot = QtPlot(
array_list[0],
fig_x_position=CURRENT_EXPERIMENT['plot_x_position'],
**kwargs)
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
'{}'.format(ids[0]))
plot.subplots[0].setTitle(title)
plot.subplots[0].showGrid(True, True)
if savepng:
print('Save plot only working for matplotlib figure.', \
'Set useQT=False to save png.')
else:
plot = MatPlot(array_list, **kwargs)
plot.rescale_axis()
plot.fig.tight_layout(pad=3)
plot.fig.set_size_inches(fig_size)
# Set axis limits
if xlim is None:
plot[0].axes.set_xlim(
[np.nanmin(xlims[0]),
np.nanmax(xlims[1])])
else:
plot[0].axes.set_xlim(xlim)
if ylim is None:
plot[0].axes.set_ylim(
[np.nanmin(ylims[0]),
np.nanmax(ylims[1])])
else:
plot[0].axes.set_ylim(ylim)
if len(arrays.set_arrays) == 2:
total_clim = [None, None]
for i in range(len(array_list)):
# TODO(DV): get colorbar from plot children (should be ax.qcodes_colorbar)
if clim is None:
internal_clim = np.nanmin(clims[0]), np.nanmax(
clims[1])
else:
internal_clim = clim
if total_clim[0] is None or internal_clim[
0] < total_clim[0]:
total_clim[0] = internal_clim[0]
if total_clim[1] is None or internal_clim[
1] > total_clim[1]:
total_clim[1] = internal_clim[1]
colorbar = plot[0].qcodes_colorbar
apply_color_scale_limits(colorbar,
new_lim=tuple(total_clim))
# Set figure titles
plot.fig.suptitle(samplefolder)
if len(ids) < 6:
plot.subplots[0].set_title(', '.join(map(str, ids)))
else:
plot.subplots[0].set_title(' - '.join(
map(str, [ids[0], ids[-1]])))
plt.draw()
# Save figure
if savepng:
if len(ids) == 1:
title_png = samplefolder + CURRENT_EXPERIMENT[
'png_subfolder'] + sep + '{}'.format(ids[0])
else:
title_png = samplefolder + CURRENT_EXPERIMENT[
'png_subfolder'] + sep + '{}-{}'.format(
ids[0], ids[-1])
if l > 1:
num = '{}'.format(j + 1)
plt.savefig(title_png + '_{}_{}.png'.format(num, avg_sub),
dpi=500)
plots.append(plot)
else:
plots = None
return data_list, plots
def fit(self,
id,
fitclass,
do_plots=True,
dataname=None,
samplefolder=None,
p0=None,
**kwargs):
str_id = '{0:03d}'.format(id)
if samplefolder == None:
check_experiment_is_initialized()
path = qc.DataSet.location_provider.fmt.format(counter=str_id)
data = qc.load_data(path)
else:
path = '{}{}{}'.format(samplefolder, sep, str_id)
data = qc.load_data(path)
# Get dataname to be fitted
if dataname == None:
# Function only made for a single dataname. Check there isnt more datanames in the dataset.
key_data = [
key for key in data.arrays.keys() if "_set" not in key[-4:]
]
if len(key_data) != 1:
raise RuntimeError(
'Dataset has more than one dataname. Choose dataname to be fitted from: {}.'
.format(', '.join(key_data)))
else:
# Check input dataname is in dataset.
if dataname not in [
key for key in data.arrays.keys() if "_set" not in key[-4:]
]:
raise RuntimeError(
'Dataname not in dataset. Input dataname was: \'{}\' while dataname(s) in dataset are: {}.'
.format(dataname, ', '.join(key_data)))
key_data = [dataname]
# Do fits separately for 1D and 2D datasets
keys_set = [key for key in data.arrays.keys() if "_set" in key[-4:]]
# Do fit and plot for 1D data
if len(keys_set) == 1:
qcxdata = getattr(data, keys_set[0])
qcydata = getattr(data, key_data[0])
# Get initial guess on parameter is guess function is defined
if (p0 == None and hasattr(fitclass, 'guess')):
p0 = getattr(fitclass, 'guess')(qcxdata.ndarray,
qcydata.ndarray)
popt, pcov = curve_fit(fitclass.fun,
qcxdata.ndarray,
qcydata.ndarray,
p0=p0,
**kwargs)
if do_plots:
plot = self.plot_1D(qcxdata, qcydata, fitclass, popt)
title_list = plot.get_default_title().split(sep)
title_list.insert(-1, 'analysis')
title = sep.join(title_list)
plt.savefig("{}_{}.png".format(title, fitclass.name), dpi=500)
return popt, pcov, plot
else:
return popt, pcov
if len(keys_set) == 2:
print('Fitting for 2D datasets not implemented.')
from qtt.parameterviewer import createParameterWidget
if __name__ == '__main__' and not remote:
p = createParameterWidget([
gates,
])
if __name__ == '__main__' and remote:
p = createParameterWidgetRemote([
gates,
])
#%% load data and plot results
if __name__ == '__main__' and 1:
olddatadir = r'K:\ns\qt\spin-qubits\data\b057_data\2016 3dot experiment\data\2016-11-11\18-20-44_P2_P3'
dataset_old = load_data(location=olddatadir)
# qcodes.plots.pyqtgraph.QtPlot(dataset_old.measured, interval=0)
plotje = qcodes.plots.qcmatplotlib.MatPlot(dataset_old.measured,
interval=0)
#%% delta
x = 2
gates.P1.set(gates.P1.get() + Dot_delta['P1'] * x)
gates.P2.set(gates.P2.get() + Dot_delta['P2'] * x)
gates.P3.set(gates.P3.get() + Dot_delta['P3'] * x)
#%% epsilon
x = 2
gates.P1.set(gates.P1.get() + Dot_epsilon['P1'] * x)
gates.P2.set(gates.P2.get() + Dot_epsilon['P2'] * x)
gates.P3.set(gates.P3.get() + Dot_epsilon['P3'] * x)
def show_num(ids, samplefolder=None,useQT=False,avg_sub='',do_plots=True,savepng=True,
fig_size=[6,4],clim=None,dataname=None,xlim=None,ylim=None,**kwargs):
"""
Show and return plot and data.
Args:
ids (number, list): id or list of ids of dataset(s)
samplefolder (str): Sample folder if loading data from different sample than the initialized.
useQT (boolean): If true plots with QTplot instead of matplotlib
avg_sub (str: 'col' or 'row'): Subtracts average from either each collumn ('col') or each row ('row')
do_plots: (boolean): if false no plots are produced.
dataname (str): If given only plots dataset with that name
savepng (boolean): If true saves matplotlib figure as png
fig_size [6,4]: Figure size in inches
clim [cmin,cmax]: Set min and max of colorbar to cmin and cmax respectrively
xlim [xmin,xmax]: Set limits on x axis
ylim [ymin,ymax]: set limits on y axis
**kwargs: Are passed to plot function
Returns:
data, plots : returns the plots and the datasets
"""
if not isinstance(ids, collections.Iterable):
ids = (ids,)
data_list = []
keys_list = []
# Define samplefolder
if samplefolder==None:
check_experiment_is_initialized()
samplefolder = qc.DataSet.location_provider.fmt.format(counter='')
# Load all datasets into list
for id in ids:
path = samplefolder + '{0:03d}'.format(id)
data = qc.load_data(path)
data_list.append(data)
# find datanames to be plotted
if do_plots:
if useQT and len(ids) is not 1:
raise ValueError('qcodes.QtPlot does not support multigraph plotting. Set useQT=False to plot multiple datasets.')
if dataname is not None:
if dataname not in [key for key in data.arrays.keys() if "_set" not in key]:
raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\'', \
'while dataname(s) in dataset are: {}.'.format(dataname,', '.join(data.arrays.keys())))
keys = [dataname]
else:
keys = [key for key in data.arrays.keys() if "_set" not in key]
keys_list.append(keys)
if do_plots:
unique_keys = list(set([item for sublist in keys_list for item in sublist]))
plots = []
num = ''
l = len(unique_keys)
for j, key in enumerate(unique_keys):
array_list = []
xlims = [[],[]]
ylims = [[],[]]
clims = [[],[]]
# Find datasets containing data with dataname == key
for data, keys in zip(data_list,keys_list):
if key in keys:
arrays = getattr(data, key)
if avg_sub == 'row':
for i in range(np.shape(arrays)[0]):
arrays[i,:] -= np.nanmean(arrays[i,:])
if avg_sub == 'col':
for i in range(np.shape(arrays)[1]):
arrays[:,i] -= np.nanmean(arrays[:,i])
array_list.append(arrays)
# Find axis limits for dataset
if len(arrays.set_arrays)==2:
xlims[0].append(np.nanmin(arrays.set_arrays[1]))
xlims[1].append(np.nanmax(arrays.set_arrays[1]))
ylims[0].append(np.nanmin(arrays.set_arrays[0]))
ylims[1].append(np.nanmax(arrays.set_arrays[0]))
clims[0].append(np.nanmin(arrays.ndarray))
clims[1].append(np.nanmax(arrays.ndarray))
else:
xlims[0].append(np.nanmin(arrays.set_arrays[0]))
xlims[1].append(np.nanmax(arrays.set_arrays[0]))
ylims[0].append(np.nanmin(arrays.ndarray))
ylims[1].append(np.nanmax(arrays.ndarray))
if useQT:
plot = QtPlot(array_list[0],
fig_x_position=CURRENT_EXPERIMENT['plot_x_position'],
**kwargs)
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
'{}'.format(ids[0]))
plot.subplots[0].setTitle(title)
plot.subplots[0].showGrid(True, True)
if savepng:
print('Save plot only working for matplotlib figure.', \
'Set useQT=False to save png.')
else:
plot = MatPlot(array_list, **kwargs)
plot.rescale_axis()
plot.fig.tight_layout(pad=3)
plot.fig.set_size_inches(fig_size)
# Set axis limits
if xlim is None:
plot[0].axes.set_xlim([np.nanmin(xlims[0]),np.nanmax(xlims[1])])
else:
plot[0].axes.set_xlim(xlim)
if ylim is None:
plot[0].axes.set_ylim([np.nanmin(ylims[0]),np.nanmax(ylims[1])])
else:
plot[0].axes.set_ylim(ylim)
if len(arrays.set_arrays)==2:
for i in range(len(array_list)):
if clim is None:
plot[0].get_children()[i].set_clim(np.nanmin(clims[0]),np.nanmax(clims[1]))
else:
plot[0].get_children()[i].set_clim(clim)
# Set figure titles
plot.fig.suptitle(samplefolder)
if len(ids)<6:
plot.subplots[0].set_title(', '.join(map(str,ids)))
else:
plot.subplots[0].set_title(' - '.join(map(str,[ids[0],ids[-1]])))
plt.draw()
# Save figure
if savepng:
if len(ids) == 1:
title_png = samplefolder+CURRENT_EXPERIMENT['png_subfolder']+sep+'{}'.format(ids[0])
else:
title_png = samplefolder+CURRENT_EXPERIMENT['png_subfolder']+sep+'{}-{}'.format(ids[0],ids[-1])
if l>1:
num = '{}'.format(j+1)
plt.savefig(title_png+'_{}_{}.png'.format(num,avg_sub),dpi=500)
plots.append(plot)
else:
plots = None
return data_list, plots
def show_num(id, samplefolder=None, useQT=False, ave_col=False, ave_row=False, do_plots=True, savepng=False, clim=None, **kwargs):
"""
Show and return plot and data for id in current instrument.
Args:
id(number): id of instrument
useQT (boolean): If true plots with QTplot instead of matplotlib
ave_col (boolean): If true subtracts average from each collumn
ave_row (boolean): If true subtracts average from each row
do_plots: Default False: if false no plots are produced.
savepng (boolean): If true saves matplotlib figure as png
clim [cmin,cmax]: Set min and max of colorbar to cmin and cmax respectrively
**kwargs: Are passed to plot function
Returns:
data, plots : returns the plot and the dataset
"""
str_id = '{0:03d}'.format(id)
if samplefolder==None:
check_experiment_is_initialized()
path = qc.DataSet.location_provider.fmt.format(counter=str_id)
data = qc.load_data(path)
else:
path = '{}{}{}'.format(samplefolder,sep,str_id)
data = qc.load_data(path)
keys = [key for key in data.arrays.keys() if "_set" not in key[-4:]]
if do_plots:
plots = []
avestr = ''
num = ''
l = len(keys)
for j, value in enumerate(keys):
arrays = getattr(data, value)
if ave_row:
for i in range(np.shape(arrays)[0]):
arrays[i,:] -= arrays[i,:].mean()
avestr = '_row'
if ave_col:
for i in range(np.shape(arrays)[1]):
arrays[:,i] -= arrays[:,i].mean()
avestr = '_col'
if useQT:
plot = QtPlot(
getattr(data, value),
fig_x_position=CURRENT_EXPERIMENT['plot_x_position'],
**kwargs)
title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
str_id)
plot.subplots[0].setTitle(title)
plot.subplots[0].showGrid(True, True)
if savepng:
print('Save plot only working for matplotlib figure. Set useQT=False to save png.')
else:
plot = MatPlot(getattr(data, value), **kwargs)
plot.rescale_axis()
plot.fig.tight_layout(pad=3)
if clim is not None:
mesh = plot[0].get_children()[0]
mesh.set_clim(clim[0],clim[1])
plt.draw()
if savepng:
title_list_png = plot.get_default_title().split(sep)
title_list_png.insert(-1, CURRENT_EXPERIMENT['png_subfolder'])
title_png = sep.join(title_list_png)
if l>1:
num = '_{}'.format(j+1)
plt.savefig("{}{}{}.png".format(title_png,num,avestr),dpi=500)
plots.append(plot)
else:
plots = None
return data, plots