def transform_frame(frame,
transform,
columns=None,
direction='forward',
return_all=True,
args=(),
**kwargs):
'''
Apply transform to specified columns.
direction: 'forward' | 'inverse'
return_all: bool
True - return all columns, with specified ones transformed.
False - return only specified columns.
.. warning:: deprecated
'''
tfun, tname = parse_transform(transform, direction)
columns = to_list(columns)
if columns is None:
columns = frame.columns
if return_all:
transformed = frame.copy()
for c in columns:
transformed[c] = tfun(frame[c], *args, **kwargs)
else:
transformed = frame.filter(columns).apply(tfun, *args, **kwargs)
return transformed
def apply(self, func, ids=None, applyto='measurement', noneval=nan,
setdata=False, output_format='dict', ID=None,
**kwargs):
'''
Apply func to each of the specified measurements.
Parameters
----------
func : callable
Accepts a Measurement object or a DataFrame.
ids : hashable| iterable of hashables | None
Keys of measurements to which func will be applied.
If None is given apply to all measurements.
applyto : 'measurement' | 'data'
* 'measurement' : apply to measurements objects themselves.
* 'data' : apply to measurement associated data
noneval : obj
Value returned if applyto is 'data' but no data is available.
setdata : bool
Whether to set the data in the Measurement object.
Used only if data is not already set.
output_format : ['dict' | 'collection']
* collection : keeps result as collection
WARNING: For collection, func should return a copy of the measurement instance rather
than the original measurement instance.
Returns
-------
Dictionary keyed by measurement keys containing the corresponding output of func
or returns a collection (if output_format='collection').
'''
if ids is None:
ids = self.keys()
else:
ids = to_list(ids)
result = dict((i, self[i].apply(func, applyto, noneval, setdata)) for i in ids)
if output_format == 'collection':
can_keep_as_collection = all(
[isinstance(r, self._measurement_class) for r in result.values()])
if not can_keep_as_collection:
raise TypeError(
'Cannot turn output into a collection. The provided func must return results of type {}'.format(
self._measurement_class))
new_collection = self.copy()
# Locate IDs to remove
ids_to_remove = [x for x in self.keys() if x not in ids]
# Remove data for these IDs
for ids in ids_to_remove:
new_collection.pop(ids)
# Update keys with new values
for k, v in new_collection.iteritems():
new_collection[k] = result[k]
if ID is not None:
new_collection.ID = ID
return new_collection
else:
# Return a dictionary
return result
def add_callback(self, func):
""" Registers a call back function """
if func is None: return
func_list = to_list(func)
if not hasattr(self, 'callback_list'):
self.callback_list = func_list
else:
self.callback_list.extend(func_list)
def filter_by_key(self, keys, ID=None):
"""
Keep only Measurements with given keys.
"""
keys = to_list(keys)
fil = lambda x: x in keys
if ID is None:
ID = self.ID
return self.filter(fil, applyto='keys', ID=ID)
def add_callback(self, func):
""" Registers a call back function """
if func is None: return
func_list = to_list(func)
if not hasattr(self, 'callback_list'):
self.callback_list = func_list
else:
self.callback_list.extend(func_list)
def filter_by_key(self, keys, ID=None):
"""
Keep only Measurements with given keys.
"""
keys = to_list(keys)
fil = lambda x: x in keys
if ID is None:
ID = self.ID
return self.filter(fil, applyto='keys', ID=ID)
def filter_by_rows(self, rows, ID=None):
"""
Keep only Measurements in corresponding rows.
"""
rows = to_list(rows)
fil = lambda x: x in rows
applyto = {k: self._positions[k][0] for k in self.iterkeys()}
if ID is None:
ID = self.ID
return self.filter(fil, applyto=applyto, ID=ID)
def filter_by_cols(self, cols, ID=None):
"""
Keep only Measurements in corresponding columns.
"""
rows = to_list(cols)
fil = lambda x: x in rows
applyto = {k: self._positions[k][1] for k in self.iterkeys()}
if ID is None:
ID = self.ID + '.filtered_by_cols'
return self.filter(fil, applyto=applyto, ID=ID)
def filter_by_rows(self, rows, ID=None):
"""
Keep only Measurements in corresponding rows.
"""
rows = to_list(rows)
fil = lambda x: x in rows
applyto = {k: self._positions[k][0] for k in self.iterkeys()}
if ID is None:
ID = self.ID
return self.filter(fil, applyto=applyto, ID=ID)
def filter_by_cols(self, cols, ID=None):
"""
Keep only Measurements in corresponding columns.
"""
rows = to_list(cols)
fil = lambda x: x in rows
applyto = {k: self._positions[k][1] for k in self.iterkeys()}
if ID is None:
ID = self.ID + '.filtered_by_cols'
return self.filter(fil, applyto=applyto, ID=ID)
def __init__(self, vert, channels, region, name=None):
self.vert = vert
channels = to_list(channels)
self.channels = channels
if name == None:
self.name = "Unnamed Gate {0}".format(Gate.unnamed_gate_num)
Gate.unnamed_gate_num += 1
else:
self.name = name
self.region = region
self.validiate_input()
def __init__(self, vert, channels, region, name=None):
self.vert = vert
channels = to_list(channels)
self.channels = channels
if name == None:
self.name = "Unnamed Gate {0}".format(Gate.unnamed_gate_num)
Gate.unnamed_gate_num += 1
else:
self.name = name
self.region = region
self.validate_input()
def _find_orientation(self, ax_channels):
ax_channels = to_list(ax_channels)
c = self.channels[0]
if ax_channels is not None:
try:
i = ax_channels.index(c)
if i == 0:
flip = False
else:
flip = True
except ValueError:
raise Exception("""Trying to plot gate that is defined on channel {0},
but figure axis correspond to channels {1}""".format(c, ax_channels))
if len(self.channels) == 2:
c = self.channels[1]
if c not in ax_channels:
raise Exception("""Trying to plot gate that is defined on channel {0},
but figure axis correspond to channels {1}""".format(c, ax_channels))
return flip
def get_measurement_metadata(self,
fields,
ids=None,
noneval=nan,
output_format='DataFrame'):
"""
Get the metadata fields of specified measurements (all if None given).
Parameters
----------
fields : str | iterable of str
Names of metadata fields to be returned.
ids : hashable| iterable of hashables | None
Keys of measurements for which metadata will be returned.
If None is given return metadata of all measurements.
noneval : obj
Value returned if applyto is 'data' but no data is available.
output_format : 'DataFrame' | 'dict'
'DataFrame' : return DataFrame,
'dict' : return dictionary.
Returns
-------
Measurement metadata in specified output_format.
"""
fields = to_list(fields)
func = lambda x: x.get_meta_fields(fields)
meta_d = self.apply(func,
ids=ids,
applyto='measurement',
noneval=noneval,
output_format='dict')
if output_format is 'dict':
return meta_d
elif output_format is 'DataFrame':
from pandas import DataFrame as DF
meta_df = DF(meta_d, index=fields)
return meta_df
else:
msg = ("The output_format must be either 'dict' or 'DataFrame'. " +
"Encountered unsupported value %s." % repr(output_format))
raise Exception(msg)
def set_positions(self, positions=None, position_mapper='name', ids=None):
'''
checks for position validity & collisions,
but not that all measurements are assigned.
Parameters
-----------
positions : is dict-like of measurement_key:(row,col)
parser :
callable - gets key and returns position
mapping - key:pos
'name' - parses things like 'A1', 'G12'
'number' - converts number to positions, going over rows first.
ids :
parser will be applied to specified ids only.
If None is given, parser will be applied to all measurements.
TODO: output a more informative message for position collisions
'''
if positions is None:
if ids is None:
ids = self.keys()
else:
ids = to_list(ids)
mapper = self._get_ID2position_mapper(position_mapper)
positions = dict((ID, mapper(ID)) for ID in ids)
else:
pass
# check that resulting assignment is unique (one measurement per position)
temp = self._positions.copy()
temp.update(positions)
if not len(temp.values()) == len(set(temp.values())):
msg = 'A position can only be occupied by a single measurement'
raise Exception(msg)
for k, pos in positions.iteritems():
if not self._is_valid_position(pos):
msg = 'Position {} is not supported for this collection'.format(
pos)
raise ValueError(msg)
self._positions[k] = pos
self[k]._set_position(self.ID, pos)
def set_positions(self, positions=None, position_mapper='name', ids=None):
'''
checks for position validity & collisions,
but not that all measurements are assigned.
Parameters
-----------
positions : is dict-like of measurement_key:(row,col)
parser :
callable - gets key and returns position
mapping - key:pos
'name' - parses things like 'A1', 'G12'
'number' - converts number to positions, going over rows first.
ids :
parser will be applied to specified ids only.
If None is given, parser will be applied to all measurements.
TODO: output a more informative message for position collisions
'''
if positions is None:
if ids is None:
ids = self.keys()
else:
ids = to_list(ids)
mapper = self._get_ID2position_mapper(position_mapper)
positions = dict((ID, mapper(ID)) for ID in ids)
else:
pass
# check that resulting assignment is unique (one measurement per position)
temp = self._positions.copy()
temp.update(positions)
if not len(temp.values()) == len(set(temp.values())):
msg = 'A position can only be occupied by a single measurement'
raise Exception(msg)
for k, pos in positions.iteritems():
if not self._is_valid_position(pos):
msg = 'Position {} is not supported for this collection'.format(pos)
raise ValueError(msg)
self._positions[k] = pos
self[k]._set_position(self.ID, pos)
def _find_orientation(self, ax_channels):
ax_channels = to_list(ax_channels)
c = self.channels[0]
if ax_channels is not None:
try:
i = ax_channels.index(c)
if i == 0:
flip = False
else:
flip = True
except ValueError:
raise Exception("""Trying to plot gate that is defined on channel {0},
but figure axis correspond to channels {1}""".format(c,
ax_channels))
if len(self.channels) == 2:
c = self.channels[1]
if c not in ax_channels:
raise Exception("""Trying to plot gate that is defined on channel {0},
but figure axis correspond to channels {1}""".format(c,
ax_channels))
return flip
def transform_frame(frame, transform, columns=None, direction='forward',
return_all=True, args=(), **kwargs):
'''
Apply transform to specified columns.
direction: 'forward' | 'inverse'
return_all: bool
True - return all columns, with specified ones transformed.
False - return only specified columns.
.. warning:: deprecated
'''
tfun, tname = parse_transform(transform, direction)
columns = to_list(columns)
if columns is None:
columns = frame.columns
if return_all:
transformed = frame.copy()
for c in columns:
transformed[c] = tfun(frame[c], *args, **kwargs)
else:
transformed = frame.filter(columns).apply(tfun, *args, **kwargs)
return transformed
def get_measurement_metadata(self, fields, ids=None, noneval=nan,
output_format='DataFrame'):
"""
Get the metadata fields of specified measurements (all if None given).
Parameters
----------
fields : str | iterable of str
Names of metadata fields to be returned.
ids : hashable| iterable of hashables | None
Keys of measurements for which metadata will be returned.
If None is given return metadata of all measurements.
noneval : obj
Value returned if applyto is 'data' but no data is available.
output_format : 'DataFrame' | 'dict'
'DataFrame' : return DataFrame,
'dict' : return dictionary.
Returns
-------
Measurement metadata in specified output_format.
"""
fields = to_list(fields)
func = lambda x: x.get_meta_fields(fields)
meta_d = self.apply(func, ids=ids, applyto='measurement',
noneval=noneval, output_format='dict')
if output_format is 'dict':
return meta_d
elif output_format is 'DataFrame':
from pandas import DataFrame as DF
meta_df = DF(meta_d, index=fields)
return meta_df
else:
msg = ("The output_format must be either 'dict' or 'DataFrame'. " +
"Encountered unsupported value %s." % repr(output_format))
raise Exception(msg)
def set_visible(self, visible=True):
for artist in to_list(self.artist):
artist.set_visible(visible)
def plotFCM(data,
channel_names,
kind='histogram',
ax=None,
autolabel=True,
xlabel_kwargs={},
ylabel_kwargs={},
colorbar=False,
grid=False,
**kwargs):
"""
Plots the sample on the current axis.
Follow with a call to matplotlibs show() in order to see the plot.
Parameters
----------
data : DataFrame
{graph_plotFCM_pars}
{common_plot_ax}
Returns
-------
The output of the plot command used
"""
if ax == None: ax = pl.gca()
xlabel_kwargs.setdefault('size', 16)
ylabel_kwargs.setdefault('size', 16)
channel_names = to_list(channel_names)
if len(channel_names) == 1:
# 1D so histogram plot
kwargs.setdefault('color', 'gray')
kwargs.setdefault('histtype', 'stepfilled')
kwargs.setdefault('bins', 200) # Do not move above
x = data[channel_names[0]].values
if len(x) >= 1:
if (len(x) == 1) and isinstance(kwargs['bins'], int):
# Only needed for hist (not hist2d) due to hist function doing
# excessive input checking
warnings.warn("One of the data sets only has a single event. " \
"This event won't be plotted unless the bin locations" \
" are explicitely provided to the plotting function. ")
return None
plot_output = ax.hist(x, **kwargs)
else:
return None
elif len(channel_names) == 2:
x = data[channel_names[0]].values # value of first channel
y = data[channel_names[1]].values # value of second channel
if len(x) == 0:
# Don't draw a plot if there's no data
return None
if kind == 'scatter':
kwargs.setdefault('edgecolor', 'none')
plot_output = ax.scatter(x, y, **kwargs)
elif kind == 'histogram':
kwargs.setdefault('bins', 200) # Do not move above
kwargs.setdefault('cmin', 1)
kwargs.setdefault('cmap', pl.cm.copper)
kwargs.setdefault('norm', matplotlib.colors.LogNorm())
plot_output = ax.hist2d(x, y, **kwargs)
mappable = plot_output[-1]
if colorbar:
pl.colorbar(mappable, ax=ax)
else:
raise ValueError(
"Not a valid plot type. Must be 'scatter', 'histogram'")
pl.grid(grid)
if autolabel:
y_label_text = 'Counts' if len(
channel_names) == 1 else channel_names[1]
ax.set_xlabel(channel_names[0], **xlabel_kwargs)
ax.set_ylabel(y_label_text, **ylabel_kwargs)
return plot_output
def set_visible(self, visible=True):
for artist in to_list(self.artist):
artist.set_visible(visible)
def create_artist(self):
self.poly = pl.Polygon(self.coordinates, color='k', fill=False)
self.artist_list = to_list(self.poly)
self.ax.add_artist(self.poly)
def grid_plot(self, func, applyto='measurement', ids=None,
row_labels=None, col_labels=None,
xlim='auto', ylim='auto',
xlabel=None, ylabel=None,
colorbar=True,
row_label_xoffset=None, col_label_yoffset=None,
hide_tick_labels=True, hide_tick_lines=True,
hspace=0, wspace=0,
row_labels_kwargs={}, col_labels_kwargs={}):
"""
Creates subplots for each well in the plate. Uses func to plot on each axis.
Follow with a call to matplotlibs show() in order to see the plot.
Parameters
----------
func : callable
func is a callable that accepts a measurement
object (with an optional axis reference) and plots on the current axis.
Return values from func are ignored.
.. note: if using applyto='measurement', the function
when querying for data should make sure that the data
actually exists
applyto : 'measurement' | 'data'
{_graph_grid_layout}
{bases_OrderedCollection_grid_plot_pars}
Returns
-------
{_graph_grid_layout_returns}
Examples
---------
>>> def y(well, ax):
>>> data = well.get_data()
>>> if data is None:
>>> return None
>>> graph.plotFCM(data, 'Y2-A')
>>> def z(data, ax):
>>> plot(data[0:100, 1], data[0:100, 2])
>>> plate.plot(y, applyto='measurement');
>>> plate.plot(z, applyto='data');
"""
# Acquire call arguments to be passed to create plate layout
callArgs = locals().copy() # This statement must remain first. The copy is just defensive.
[callArgs.pop(varname) for varname in
['self', 'func', 'applyto', 'ids', 'colorbar', 'xlim', 'ylim']] # pop args
callArgs['rowNum'] = self.shape[0]
callArgs['colNum'] = self.shape[1]
subplots_adjust_args = {}
subplots_adjust_args.setdefault('right', 0.85)
subplots_adjust_args.setdefault('top', 0.85)
pl.subplots_adjust(**subplots_adjust_args)
# Uses plate default row/col labels if user does not override them by specifying row/col labels
if row_labels == None: callArgs['row_labels'] = self.row_labels
if col_labels == None: callArgs['col_labels'] = self.col_labels
ax_main, ax_subplots = graph.create_grid_layout(**callArgs)
subplots_ax = DF(ax_subplots, index=self.row_labels, columns=self.col_labels)
if ids is None:
ids = self.keys()
ids = to_list(ids)
for ID in ids:
measurement = self[ID]
if not hasattr(measurement, 'data'):
continue
row, col = self._positions[ID]
ax = subplots_ax[col][row]
pl.sca(ax) # sets the current axis
if applyto == 'measurement':
func(measurement, ax) # reminder: pandas row/col order is reversed
elif applyto == 'data':
data = measurement.get_data()
if data is not None:
if func.func_code.co_argcount == 1:
func(data)
else:
func(data, ax)
else:
raise ValueError('Encountered unsupported value {} for applyto parameter.'.format(
applyto))
# Autoscaling axes
graph.scale_subplots(ax_subplots, xlim=xlim, ylim=ylim)
#####
# Placing ticks on the top left subplot
ax_label = ax_subplots[0, -1]
pl.sca(ax_label)
if xlabel:
xlim = ax_label.get_xlim()
pl.xticks([xlim[0], xlim[1]], rotation=90)
if ylabel:
ylim = ax_label.get_ylim()
pl.yticks([ylim[0], ylim[1]], rotation=0)
pl.sca(ax_main) # sets to the main axis -- more intuitive
return ax_main, ax_subplots
def plotFCM(data, channel_names, kind='histogram', ax=None,
autolabel=True, xlabel_kwargs={}, ylabel_kwargs={},
colorbar=False, grid=False,
**kwargs):
"""
Plots the sample on the current axis.
Follow with a call to matplotlibs show() in order to see the plot.
Parameters
----------
data : DataFrame
{graph_plotFCM_pars}
{common_plot_ax}
Returns
-------
The output of the plot command used
"""
if ax == None: ax = pl.gca()
xlabel_kwargs.setdefault('size', 16)
ylabel_kwargs.setdefault('size', 16)
channel_names = to_list(channel_names)
if len(channel_names) == 1:
# 1D so histogram plot
kwargs.setdefault('color', 'gray')
kwargs.setdefault('histtype', 'stepfilled')
kwargs.setdefault('bins', 200) # Do not move above
x = data[channel_names[0]].values
if len(x) >= 1:
if (len(x) == 1) and isinstance(kwargs['bins'], int):
# Only needed for hist (not hist2d) due to hist function doing
# excessive input checking
warnings.warn("One of the data sets only has a single event. " \
"This event won't be plotted unless the bin locations" \
" are explicitely provided to the plotting function. ")
return None
plot_output = ax.hist(x, **kwargs)
else:
return None
elif len(channel_names) == 2:
x = data[channel_names[0]].values # value of first channel
y = data[channel_names[1]].values # value of second channel
if len(x) == 0:
# Don't draw a plot if there's no data
return None
if kind == 'scatter':
kwargs.setdefault('edgecolor', 'none')
plot_output = ax.scatter(x, y, **kwargs)
elif kind == 'histogram':
kwargs.setdefault('bins', 200) # Do not move above
kwargs.setdefault('cmin', 1)
kwargs.setdefault('cmap', pl.cm.copper)
kwargs.setdefault('norm', matplotlib.colors.LogNorm())
plot_output = ax.hist2d(x, y, **kwargs)
mappable = plot_output[-1]
if colorbar:
pl.colorbar(mappable, ax=ax)
else:
raise ValueError("Not a valid plot type. Must be 'scatter', 'histogram'")
pl.grid(grid)
if autolabel:
y_label_text = 'Counts' if len(channel_names) == 1 else channel_names[1]
ax.set_xlabel(channel_names[0], **xlabel_kwargs)
ax.set_ylabel(y_label_text, **ylabel_kwargs)
return plot_output
def grid_plot(self,
func,
applyto='measurement',
ids=None,
row_labels=None,
col_labels=None,
xlim='auto',
ylim='auto',
xlabel=None,
ylabel=None,
colorbar=True,
row_label_xoffset=None,
col_label_yoffset=None,
hide_tick_labels=True,
hide_tick_lines=True,
hspace=0,
wspace=0,
row_labels_kwargs={},
col_labels_kwargs={}):
"""
Creates subplots for each well in the plate. Uses func to plot on each axis.
Follow with a call to matplotlibs show() in order to see the plot.
Parameters
----------
func : callable
func is a callable that accepts a measurement
object (with an optional axis reference) and plots on the current axis.
Return values from func are ignored.
.. note: if using applyto='measurement', the function
when querying for data should make sure that the data
actually exists
applyto : 'measurement' | 'data'
{_graph_grid_layout}
{bases_OrderedCollection_grid_plot_pars}
Returns
-------
{_graph_grid_layout_returns}
Examples
---------
>>> def y(well, ax):
>>> data = well.get_data()
>>> if data is None:
>>> return None
>>> graph.plotFCM(data, 'Y2-A')
>>> def z(data, ax):
>>> plot(data[0:100, 1], data[0:100, 2])
>>> plate.plot(y, applyto='measurement');
>>> plate.plot(z, applyto='data');
"""
# Acquire call arguments to be passed to create plate layout
callArgs = locals().copy(
) # This statement must remain first. The copy is just defensive.
[
callArgs.pop(varname) for varname in
['self', 'func', 'applyto', 'ids', 'colorbar', 'xlim', 'ylim']
] # pop args
callArgs['rowNum'] = self.shape[0]
callArgs['colNum'] = self.shape[1]
subplots_adjust_args = {}
subplots_adjust_args.setdefault('right', 0.85)
subplots_adjust_args.setdefault('top', 0.85)
pl.subplots_adjust(**subplots_adjust_args)
# Uses plate default row/col labels if user does not override them by specifying row/col labels
if row_labels == None: callArgs['row_labels'] = self.row_labels
if col_labels == None: callArgs['col_labels'] = self.col_labels
ax_main, ax_subplots = graph.create_grid_layout(**callArgs)
subplots_ax = DF(ax_subplots,
index=self.row_labels,
columns=self.col_labels)
if ids is None:
ids = self.keys()
ids = to_list(ids)
for ID in ids:
measurement = self[ID]
if not hasattr(measurement, 'data'):
continue
row, col = self._positions[ID]
ax = subplots_ax[col][row]
pl.sca(ax) # sets the current axis
if applyto == 'measurement':
func(measurement,
ax) # reminder: pandas row/col order is reversed
elif applyto == 'data':
data = measurement.get_data()
if data is not None:
if func.func_code.co_argcount == 1:
func(data)
else:
func(data, ax)
else:
raise ValueError(
'Encountered unsupported value {} for applyto parameter.'.
format(applyto))
# Autoscaling axes
graph.scale_subplots(ax_subplots, xlim=xlim, ylim=ylim)
#####
# Placing ticks on the top left subplot
ax_label = ax_subplots[0, -1]
pl.sca(ax_label)
if xlabel:
xlim = ax_label.get_xlim()
pl.xticks([xlim[0], xlim[1]], rotation=90)
if ylabel:
ylim = ax_label.get_ylim()
pl.yticks([ylim[0], ylim[1]], rotation=0)
pl.sca(ax_main) # sets to the main axis -- more intuitive
return ax_main, ax_subplots
def apply(self,
func,
ids=None,
applyto='measurement',
noneval=nan,
setdata=False,
output_format='dict',
ID=None,
**kwargs):
'''
Apply func to each of the specified measurements.
Parameters
----------
func : callable
Accepts a Measurement object or a DataFrame.
ids : hashable| iterable of hashables | None
Keys of measurements to which func will be applied.
If None is given apply to all measurements.
applyto : 'measurement' | 'data'
* 'measurement' : apply to measurements objects themselves.
* 'data' : apply to measurement associated data
noneval : obj
Value returned if applyto is 'data' but no data is available.
setdata : bool
Whether to set the data in the Measurement object.
Used only if data is not already set.
output_format : ['dict' | 'collection']
* collection : keeps result as collection
WARNING: For collection, func should return a copy of the measurement instance rather
than the original measurement instance.
Returns
-------
Dictionary keyed by measurement keys containing the corresponding output of func
or returns a collection (if output_format='collection').
'''
if ids is None:
ids = self.keys()
else:
ids = to_list(ids)
result = dict(
(i, self[i].apply(func, applyto, noneval, setdata)) for i in ids)
if output_format == 'collection':
can_keep_as_collection = all([
isinstance(r, self._measurement_class)
for r in result.values()
])
if not can_keep_as_collection:
raise TypeError(
'Cannot turn output into a collection. The provided func must return results of type {}'
.format(self._measurement_class))
new_collection = self.copy()
# Locate IDs to remove
ids_to_remove = [x for x in self.keys() if x not in ids]
# Remove data for these IDs
for ids in ids_to_remove:
new_collection.pop(ids)
# Update keys with new values
for k, v in new_collection.iteritems():
new_collection[k] = result[k]
if ID is not None:
new_collection.ID = ID
return new_collection
else:
# Return a dictionary
return result
def create_artist(self):
self.poly = pl.Polygon(self.coordinates, color='k', fill=False)
self.artist_list = to_list(self.poly)
self.ax.add_artist(self.poly)
