def checkfreq(freqlike_, count):
# checks frequency of param, also handles the case where it is specified
# as a bool. does not trigger on 0
if ut.is_int(freqlike_):
return (count % freqlike_) == (freqlike_ - 1)
else:
return freqlike_ is True
def set_sorting(model, col_sort_index=None, order=Qt.DescendingOrder):
model.sortreverse = order == Qt.DescendingOrder
if col_sort_index is not None:
if utool.is_str(col_sort_index):
col_sort_index = model.col_name_list.index(col_sort_index)
assert utool.is_int(col_sort_index), "sort by an index not %r" % type(col_sort_index)
model.sortcolumn = col_sort_index
assert model.sortcolumn < len(model.col_name_list), "outofbounds"
print("sortcolumn: %r" % model.sortcolumn)
def _type_from_data(data):
""" If type is not given make an educated guess """
if utool.is_bool(data) or data == 'True' or data == 'False':
return bool
elif utool.is_int(data):
return int
elif utool.is_float(data):
return float
else:
return str
def _type_from_data(data):
""" If type is not given make an educated guess """
if utool.is_bool(data) or data == 'True' or data == 'False':
return bool
elif utool.is_int(data):
return int
elif utool.is_float(data):
return float
else:
return str
def set_sorting(model, col_sort_index=None, order=Qt.DescendingOrder):
model.sortreverse = order == Qt.DescendingOrder
if col_sort_index is not None:
if utool.is_str(col_sort_index):
col_sort_index = model.col_name_list.index(col_sort_index)
assert utool.is_int(
col_sort_index
), 'sort by an index not %r' % type(col_sort_index)
model.sortcolumn = col_sort_index
assert model.sortcolumn < len(model.col_name_list), 'outofbounds'
print('sortcolumn: %r' % model.sortcolumn)
def cast_into_qt(data):
"""
Casts python data into a representation suitable for QT (usually a string)
"""
if SIMPLE_CASTING:
if ut.is_str(data):
return __STR__(data)
elif ut.is_float(data):
# qnumber = QString.number(float(data), format='g', precision=8)
return locale_float(data)
elif ut.is_bool(data):
return bool(data)
elif ut.is_int(data):
return int(data)
elif isinstance(data, uuid.UUID):
return __STR__(data)
elif ut.isiterable(data):
return ', '.join(map(__STR__, data))
else:
return __STR__(data)
if ut.is_str(data):
return __STR__(data)
elif ut.is_float(data):
# qnumber = QString.number(float(data), format='g', precision=8)
return locale_float(data)
elif ut.is_bool(data):
return bool(data)
elif ut.is_int(data):
return int(data)
elif isinstance(data, uuid.UUID):
return __STR__(data)
elif ut.isiterable(data):
return ', '.join(map(__STR__, data))
elif data is None:
return 'None'
else:
return 'Unknown qtype: %r for data=%r' % (type(data), data)
def cast_into_qt(data):
"""
Casts python data into a representation suitable for QT (usually a string)
"""
if SIMPLE_CASTING:
if ut.is_str(data):
return __STR__(data)
elif ut.is_float(data):
#qnumber = QString.number(float(data), format='g', precision=8)
return locale_float(data)
elif ut.is_bool(data):
return bool(data)
elif ut.is_int(data):
return int(data)
elif isinstance(data, uuid.UUID):
return __STR__(data)
elif ut.isiterable(data):
return ', '.join(map(__STR__, data))
else:
return __STR__(data)
if ut.is_str(data):
return __STR__(data)
elif ut.is_float(data):
#qnumber = QString.number(float(data), format='g', precision=8)
return locale_float(data)
elif ut.is_bool(data):
return bool(data)
elif ut.is_int(data):
return int(data)
elif isinstance(data, uuid.UUID):
return __STR__(data)
elif ut.isiterable(data):
return ', '.join(map(__STR__, data))
elif data is None:
return 'None'
else:
return 'Unknown qtype: %r for data=%r' % (type(data), data)
def testdata_expanded_aids(defaultdb=None,
a=None,
ibs=None,
default_qaids=None,
default_daids=None,
qaid_override=None,
daid_override=None,
return_annot_info=False,
verbose=None,
use_cache=None):
r"""
Args:
default_qaids (list): (default = [1])
default_daids (str): (default = 'all')
defaultdb (str): (default = 'testdb1')
ibs (IBEISController): ibeis controller object(default = None)
verbose (bool): verbosity flag(default = False)
return_annot_info (bool): (default = False)
Returns:
ibs, qaid_list, daid_list, annot_info:
CommandLine:
python -m ibeis.init.main_helpers testdata_expanded_aids
python -m ibeis.init.main_helpers testdata_expanded_aids --db PZ_MTEST --acfg default:index=0:25 --verbose-testdata
python -m ibeis.init.main_helpers testdata_expanded_aids --db PZ_MTEST --qaid 3
python -m ibeis.init.main_helpers testdata_expanded_aids --db GZ_ALL --acfg ctrl --verbose-testdata
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.init.main_helpers import * # NOQA
>>> import ibeis
>>> from ibeis.expt import annotation_configs
>>> ibs, qaid_list, daid_list, aidcfg = testdata_expanded_aids(return_annot_info=True)
>>> print('Printing annot config')
>>> annotation_configs.print_acfg(aidcfg)
>>> print('Printing annotconfig stats')
>>> ibs.print_annotconfig_stats(qaid_list, daid_list)
>>> print('Combined annotconfig stats')
>>> ibs.print_annot_stats(qaid_list + daid_list, viewcode_isect=True)
>>> print('qaid_list = %r' % (qaid_list,))
"""
if verbose is None:
verbose = 1
if verbose:
print('[main_helpers] testdata_expanded_aids')
default_qaids = ut.get_argval(('--qaid', '--qaid-override'),
type_=list,
default=default_qaids)
if default_qaids is None:
default_qaids = [1]
if defaultdb is None:
defaultdb = 'testdb1'
import ibeis
if ibs is None:
ibs = ibeis.opendb(defaultdb=defaultdb)
# TODO: rectify command line with function arguments
from ibeis.expt import experiment_helpers
_specified2 = True
if a is None:
_specified2 = False
a = ['default']
if isinstance(a, six.string_types):
a = [a]
aidcfg_name_list, _specified = ut.get_argval(('--aidcfg', '--acfg', '-a'),
type_=list,
default=a,
return_specified=True)
if not _specified:
# Allow a to be specified an explicit default
if len(a) == 2:
qaids, daids = a
if ut.is_int(qaids[0]) and ut.is_int(daids[0]):
if return_annot_info:
return ibs, qaids, daids, None
else:
return ibs, qaids, daids
acfg_list, expanded_aids_list = experiment_helpers.get_annotcfg_list(
ibs,
aidcfg_name_list,
qaid_override=qaid_override,
use_cache=use_cache,
daid_override=daid_override,
verbose=max(0, verbose - 1))
#aidcfg = old_main_helpers.get_commandline_aidcfg()
assert len(acfg_list) == 1, (
('multiple acfgs specified, but this function'
'is built to return only 1. len(acfg_list)=%r') % (len(acfg_list), ))
aidcfg = acfg_list[0]
qaid_list, daid_list = expanded_aids_list[0]
if not (_specified or _specified2):
# hack
if default_qaids is not None and qaid_override is None:
qaid_list = default_qaids
if default_daids is not None and daid_override is None:
daid_list = default_daids
if ut.VERYVERBOSE:
ibs.print_annotconfig_stats(qaid_list, daid_list)
#ibeis.other.dbinfo.print_qd_info(ibs, qaid_list, daid_list, verbose=True)
if return_annot_info:
return ibs, qaid_list, daid_list, aidcfg
else:
return ibs, qaid_list, daid_list
def batch_iterator(model, X, y, randomize_batch_order=False, augment_on=False,
X_is_cv2_native=True, verbose=None,
lbl='verbose batch iteration'):
r"""
Breaks up data into to batches defined by model batch size
CommandLine:
python -m ibeis_cnn --tf batch_iterator:0
python -m ibeis_cnn --tf batch_iterator:1
python -m ibeis_cnn --tf batch_iterator:2
python -m ibeis_cnn --tf batch_iterator:1 --DEBUG_AUGMENTATION
python -m ibeis_cnn --tf batch_iterator:1 --noaugment
# Threaded buffering seems to help a lot
python -m ibeis_cnn --tf batch_iterator:1 --augment
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.batch_processing import * # NOQA
>>> from ibeis_cnn import models
>>> model = models.DummyModel(batch_size=16)
>>> X, y = model.make_random_testdata(num=99, seed=None, cv2_format=True)
>>> model.ensure_data_params(X, y)
>>> y = None
>>> encoder = None
>>> randomize_batch_order = True
>>> result_list = [(Xb, Yb) for Xb, Yb in batch_iterator(model, X, y,
... randomize_batch_order)]
>>> result = ut.depth_profile(result_list, compress_consecutive=True)
>>> print(result)
[[(16, 1, 4, 4), 16]] * 6 + [[(3, 1, 4, 4), 3]]
Example1:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.batch_processing import * # NOQA
>>> from ibeis_cnn import models
>>> import time
>>> model = models.SiameseL2(batch_size=128, data_shape=(8, 8, 1))
>>> X, y = model.make_random_testdata(num=1000, seed=None, cv2_format=True)
>>> model.ensure_data_params(X, y)
>>> encoder = None
>>> result_list1 = []
>>> result_list2 = []
>>> augment_on=not ut.get_argflag('--noaugment')
>>> iterkw = dict(randomize_batch_order=True,
>>> augment_on=augment_on,
>>> showprog=True, verbose=ut.VERBOSE)
>>> sleep_time = .05
>>> inside_time1 = 0
>>> inside_time2 = 0
>>> with ut.Timer('buffered') as t2:
>>> generator = batch_iterator(model, X, y, **iterkw)
>>> for Xb, Yb in ut.buffered_generator(generator, buffer_size=3):
>>> with ut.Timer('Inside', verbose=False) as t:
>>> time.sleep(sleep_time)
>>> result_list2.append(Xb.shape)
>>> inside_time2 += t.ellapsed
>>> with ut.Timer('unbuffered') as t1:
>>> generator = batch_iterator(model, X, y, **iterkw)
>>> for Xb, Yb in generator:
>>> with ut.Timer('Inside', verbose=False) as t:
>>> time.sleep(sleep_time)
>>> result_list1.append(Xb.shape)
>>> inside_time1 += t.ellapsed
>>> print('\nInside times should be the same')
>>> print('inside_time1 = %r' % (inside_time1,))
>>> print('inside_time2 = %r' % (inside_time2,))
>>> print('Outside times show the overhead of data augmentation')
>>> print('Overhead Unbuffered = %r' % (t1.ellapsed - inside_time1,))
>>> print('Overhead Buffered = %r' % (t2.ellapsed - inside_time2,))
>>> print('Efficiency Unbuffered = %.2f' % (100 * inside_time1 / t1.ellapsed,))
>>> print('Efficiency Buffered = %.2f' % (100 * inside_time2 / t2.ellapsed,))
>>> assert result_list1 == result_list2
>>> print(len(result_list2))
Example2:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.batch_processing import * # NOQA
>>> from ibeis_cnn.models.mnist import MNISTModel
>>> from ibeis_cnn import ingest_data
>>> # should yield float32 regardlesss of original format
>>> ut.exec_funckw(batch_iterator, globals())
>>> randomize_batch_order = False
>>> # ---
>>> dataset1 = ingest_data.grab_mnist_category_dataset_float()
>>> model1 = MNISTModel(batch_size=8, data_shape=dataset1.data_shape,
>>> output_dims=dataset1.output_dims,
>>> arch_tag=dataset1.alias_key,
>>> training_dpath=dataset1.training_dpath)
>>> X1, y1 = dataset1.subset('train')
>>> model1.ensure_data_params(X1, y1)
>>> _iter1 = batch_iterator(model1, X1, y1, randomize_batch_order)
>>> Xb1, yb1 = six.next(_iter1)
>>> # ---
>>> dataset2 = ingest_data.grab_mnist_category_dataset()
>>> model2 = MNISTModel(batch_size=8, data_shape=dataset2.data_shape,
>>> output_dims=dataset2.output_dims,
>>> arch_tag=dataset2.alias_key,
>>> training_dpath=dataset2.training_dpath)
>>> X2, y2 = dataset2.subset('train')
>>> model2.ensure_data_params(X2, y2)
>>> _iter2 = batch_iterator(model2, X2, y2, randomize_batch_order)
>>> Xb2, yb2 = six.next(_iter2)
>>> # ---
>>> X, y, model = X1, y1, model1
>>> assert np.all(yb2 == yb2)
>>> # The uint8 and float32 data should produce similar values
>>> # For this mnist set it will be a bit more off because the
>>> # original uint8 scaling value was 256 not 255.
>>> assert (Xb1[0] - Xb2[0]).max() < .1
>>> assert np.isclose(Xb1.mean(), 0, atol=.01)
>>> assert np.isclose(Xb2.mean(), 0, atol=.01)
>>> assert Xb1.max() < 1.0 and Xb2.max() < 1.0, 'out of (-1, 1)'
>>> assert Xb1.min() > -1.0 and Xb1.min() > -1.0, 'out of (-1, 1)'
>>> assert Xb1.min() < 0, 'should have some negative values'
>>> assert Xb2.min() < 0, 'should have some negative values'
>>> assert Xb1.max() > 0, 'should have some positive values'
>>> assert Xb2.max() > 0, 'should have some positive values'
"""
if verbose:
verbose = VERBOSE_BATCH
# need to be careful with batchsizes if directly specified to theano
wraparound = model.input_shape[0] is not None
augment_on = augment_on and hasattr(model, 'augment')
encoder = getattr(model, 'encoder', None)
needs_convert = ut.is_int(X)
if y is not None:
assert X.shape[0] == (y.shape[0] * model.data_per_label_input), (
'bad data / label alignment')
num_batches = (X.shape[0] + model.batch_size - 1) // model.batch_size
if randomize_batch_order:
# Randomly shuffle data
# 0.079 mnist time fraction
X, y = utils.data_label_shuffle(X, y, model.data_per_label_input)
if verbose:
print('[batchiter] BEGIN')
print('[batchiter] X.shape %r' % (X.shape, ))
if y is not None:
print('[batchiter] y.shape %r' % (y.shape, ))
print('[batchiter] augment_on %r' % (augment_on, ))
print('[batchiter] encoder %r' % (encoder, ))
print('[batchiter] wraparound %r' % (wraparound, ))
print('[batchiter] model.data_per_label_input %r' % (model.data_per_label_input, ))
print('[batchiter] needs_convert = %r' % (needs_convert,))
# FIXME: put in a layer?
center_mean = None
center_std = None
# Load precomputed whitening parameters
if model.data_params is not None:
center_mean = np.array(model.data_params['center_mean'], dtype=np.float32)
center_std = np.array(model.data_params['center_std'], dtype=np.float32)
do_whitening = (center_mean is not None and
center_std is not None and
center_std != 0.0)
if needs_convert:
ceneter_mean01 = center_mean / np.array(255.0, dtype=np.float32)
center_std01 = center_std / np.array(255.0, dtype=np.float32)
else:
ceneter_mean01 = center_mean
center_std01 = center_std
# Slice and preprocess data in batch
for batch_index in range(num_batches):
# Take a slice from the data
Xb_orig, yb_orig = utils.slice_data_labels(
X, y, model.batch_size, batch_index, model.data_per_label_input,
wraparound=wraparound)
# Ensure correct format for the GPU
Xb = Xb_orig.astype(np.float32)
yb = None if yb_orig is None else yb_orig.astype(np.int32)
if needs_convert:
# Rescale the batch data to the range 0 to 1
Xb = Xb / 255.0
if augment_on:
# Apply defined transformations
Xb, yb, = augment_batch(model, Xb, yb, batch_index, verbose, num_batches)
if do_whitening:
# Center the batch data in the range (-1.0, 1.0)
Xb = (Xb - ceneter_mean01) / (center_std01)
if X_is_cv2_native:
# Convert from cv2 to lasange format
Xb = Xb.transpose((0, 3, 1, 2))
if yb is not None:
if encoder is not None:
# Apply an encoding if applicable
yb = encoder.transform(yb).astype(np.int32)
if model.data_per_label_input > 1 and getattr(model, 'needs_padding', False):
# Pad data for siamese networks
yb = pad_labels(model, yb)
if verbose:
# Print info if requested
print_batch_info(Xb, yb, verbose, batch_index, num_batches)
yield Xb, yb
if verbose:
print('[batch] END')
def figure(fnum=None,
pnum=(1, 1, 1),
docla=False,
title=None,
figtitle=None,
doclf=False,
projection=None,
**kwargs):
"""
TODO: gridspec
http://matplotlib.org/users/gridspec.html
Args:
fnum (int): fignum = figure number
pnum (int, str, or tuple(int, int, int)): plotnum = plot tuple
Args:
fnum (int): fignum = figure number
pnum (int, str, or tuple(int, int, int)): plotnum = plot tuple
docla (bool): (default = False)
title (str): (default = None)
figtitle (None): (default = None)
doclf (bool): (default = False)
projection (None): (default = None)
Returns:
?: fig
CommandLine:
python -m plottool.custom_figure --exec-figure:0 --show
python -m plottool.custom_figure --exec-figure:1 --show
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> ut.show_if_requested()
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> fig = figure(fnum, (2, 4, (1, slice(1, None))))
>>> gca().text(0.5, 0.5, "ax3", va="center", ha="center")
>>> ut.show_if_requested()
"""
#mpl.pyplot.xkcd()
fig = get_fig(fnum)
axes_list = fig.get_axes()
# Ensure my customized settings
customize_figure(fig, docla)
if ut.is_int(pnum):
pnum = _convert_pnum_int_to_tup(pnum)
def pnum_to_subspec(pnum):
if isinstance(pnum, six.string_types):
pnum = list(pnum)
#if isinstance(pnum, (list, tuple)):
nrow, ncols, plotnum = pnum
if kwargs.get('use_gridspec', True):
# Convert old pnums to gridspec
gs = gridspec.GridSpec(nrow, ncols)
if isinstance(plotnum, (tuple, slice, list)):
subspec = gs[plotnum]
else:
subspec = gs[plotnum - 1]
return (subspec, )
else:
return (nrow, ncols, plotnum)
if doclf: # a bit hacky. Need to rectify docla and doclf
fig.clf()
# <HACK TO CLEAR AXES>
#for ax in axes_list:
# ax.clear()
#for ax in fig.get_axes:
# fig.delaxes(ax)
#axes_list = []
# </HACK TO CLEAR AXES>
# Get the subplot
if docla or len(axes_list) == 0:
#printDBG('[df2] *** NEW FIGURE %r.%r ***' % (fnum, pnum))
if pnum is not None:
assert pnum[0] > 0, 'nRows must be > 0: pnum=%r' % (pnum, )
assert pnum[1] > 0, 'nCols must be > 0: pnum=%r' % (pnum, )
#ax = plt.subplot(*pnum)
subspec = pnum_to_subspec(pnum)
ax = fig.add_subplot(*subspec, projection=projection)
#ax = fig.add_subplot(*pnum, projection=projection)
ax.cla()
else:
ax = gca()
else:
#printDBG('[df2] *** OLD FIGURE %r.%r ***' % (fnum, pnum))
if pnum is not None:
subspec = pnum_to_subspec(pnum)
ax = plt.subplot(*subspec)
#ax = plt.subplot(nrow, ncols, plotnum)
#ax = plt.subplot(*pnum) # fig.add_subplot fails here
#ax = fig.add_subplot(*pnum)
else:
ax = gca()
#ax = axes_list[0]
# Set the title
if title is not None:
ax = gca()
set_title(title, ax=ax)
# Add title to figure
# HACK HACK HACK
if figtitle is None and pnum == (1, 1, 1):
figtitle = title
if figtitle is not None:
set_figtitle(figtitle, incanvas=False)
return fig
def figure(fnum=None, pnum=(1, 1, 1), docla=False, title=None, figtitle=None,
doclf=False, projection=None, **kwargs):
"""
TODO: gridspec
http://matplotlib.org/users/gridspec.html
Args:
fnum (int): fignum = figure number
pnum (int, str, or tuple(int, int, int)): plotnum = plot tuple
Args:
fnum (int): fignum = figure number
pnum (int, str, or tuple(int, int, int)): plotnum = plot tuple
docla (bool): (default = False)
title (str): (default = None)
figtitle (None): (default = None)
doclf (bool): (default = False)
projection (None): (default = None)
Returns:
?: fig
CommandLine:
python -m plottool.custom_figure --exec-figure:0 --show
python -m plottool.custom_figure --exec-figure:1 --show
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> ut.show_if_requested()
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> fig = figure(fnum, (2, 4, (1, slice(1, None))))
>>> gca().text(0.5, 0.5, "ax3", va="center", ha="center")
>>> ut.show_if_requested()
"""
#mpl.pyplot.xkcd()
fig = get_fig(fnum)
axes_list = fig.get_axes()
# Ensure my customized settings
customize_figure(fig, docla)
if ut.is_int(pnum):
pnum = _convert_pnum_int_to_tup(pnum)
def pnum_to_subspec(pnum):
if isinstance(pnum, six.string_types):
pnum = list(pnum)
#if isinstance(pnum, (list, tuple)):
nrow, ncols, plotnum = pnum
if kwargs.get('use_gridspec', True):
# Convert old pnums to gridspec
gs = gridspec.GridSpec(nrow, ncols)
if isinstance(plotnum, (tuple, slice, list)):
subspec = gs[plotnum]
else:
subspec = gs[plotnum - 1]
return (subspec,)
else:
return (nrow, ncols, plotnum)
if doclf: # a bit hacky. Need to rectify docla and doclf
fig.clf()
# <HACK TO CLEAR AXES>
#for ax in axes_list:
# ax.clear()
#for ax in fig.get_axes:
# fig.delaxes(ax)
#axes_list = []
# </HACK TO CLEAR AXES>
# Get the subplot
if docla or len(axes_list) == 0:
#printDBG('[df2] *** NEW FIGURE %r.%r ***' % (fnum, pnum))
if pnum is not None:
assert pnum[0] > 0, 'nRows must be > 0: pnum=%r' % (pnum,)
assert pnum[1] > 0, 'nCols must be > 0: pnum=%r' % (pnum,)
#ax = plt.subplot(*pnum)
subspec = pnum_to_subspec(pnum)
ax = fig.add_subplot(*subspec, projection=projection)
#ax = fig.add_subplot(*pnum, projection=projection)
ax.cla()
else:
ax = gca()
else:
#printDBG('[df2] *** OLD FIGURE %r.%r ***' % (fnum, pnum))
if pnum is not None:
subspec = pnum_to_subspec(pnum)
ax = plt.subplot(*subspec)
#ax = plt.subplot(nrow, ncols, plotnum)
#ax = plt.subplot(*pnum) # fig.add_subplot fails here
#ax = fig.add_subplot(*pnum)
else:
ax = gca()
#ax = axes_list[0]
# Set the title
if title is not None:
ax = gca()
set_title(title, ax=ax)
# Add title to figure
# HACK HACK HACK
if figtitle is None and pnum == (1, 1, 1):
figtitle = title
if figtitle is not None:
set_figtitle(figtitle, incanvas=False)
return fig
def figure(
fnum=None,
pnum=(1, 1, 1),
docla=False,
title=None,
figtitle=None,
doclf=False,
projection=None,
**kwargs,
):
"""
http://matplotlib.org/users/gridspec.html
Args:
fnum (int): fignum = figure number
pnum (int, str, or tuple(int, int, int)): plotnum = plot tuple
docla (bool): (default = False)
title (str): (default = None)
figtitle (None): (default = None)
doclf (bool): (default = False)
projection (None): (default = None)
Returns:
?: fig
CommandLine:
python -m wbia.plottool.custom_figure --exec-figure:0 --show
python -m wbia.plottool.custom_figure --exec-figure:1 --show
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> import wbia.plottool as pt
>>> pt.show_if_requested()
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.plottool.custom_figure import * # NOQA
>>> fnum = 1
>>> fig = figure(fnum, (2, 2, 1))
>>> gca().text(0.5, 0.5, "ax1", va="center", ha="center")
>>> fig = figure(fnum, (2, 2, 2))
>>> gca().text(0.5, 0.5, "ax2", va="center", ha="center")
>>> fig = figure(fnum, (2, 4, (1, slice(1, None))))
>>> gca().text(0.5, 0.5, "ax3", va="center", ha="center")
>>> import wbia.plottool as pt
>>> pt.show_if_requested()
"""
# mpl.pyplot.xkcd()
fig = ensure_fig(fnum)
axes_list = fig.get_axes()
# Ensure my customized settings
customize_figure(fig, docla)
if pnum is None:
return fig
if ut.is_int(pnum):
pnum = _convert_pnum_int_to_tup(pnum)
if doclf:
fig.clf()
if docla or len(axes_list) == 0:
if pnum is not None:
assert pnum[0] > 0, 'nRows must be > 0: pnum=%r' % (pnum, )
assert pnum[1] > 0, 'nCols must be > 0: pnum=%r' % (pnum, )
subspec = _pnum_to_subspec(pnum)
ax = fig.add_subplot(*subspec, projection=projection)
if docla:
ax.cla()
else:
ax = gca()
else:
if pnum is not None:
subspec = _pnum_to_subspec(pnum)
ax = plt.subplot(*subspec)
else:
ax = gca()
# Set the title / figtitle
if title is not None:
ax = gca()
set_title(title, ax=ax)
if figtitle is not None:
set_figtitle(figtitle, incanvas=False, fig=fig)
return fig
