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Mar 24, 2020
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np.concatenate cleanups. #15679

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4 changes: 2 additions & 2 deletions examples/specialty_plots/radar_chart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,8 +66,8 @@ def _close_line(self, line):
x, y = line.get_data()
# FIXME: markers at x[0], y[0] get doubled-up
if x[0] != x[-1]:
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
x = np.append(x, x[0])
y = np.append(y, y[0])
line.set_data(x, y)

def set_varlabels(self, labels):
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16 changes: 7 additions & 9 deletions lib/matplotlib/bezier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -268,16 +268,14 @@ def split_path_inout(path, inside, tolerance=0.01, reorder_inout=False):

ctl_points_old = ctl_points

concat = np.concatenate

iold = 0
i = 1

for ctl_points, command in path_iter:
iold = i
i += len(ctl_points) // 2
if inside(ctl_points[-2:]) != begin_inside:
bezier_path = concat([ctl_points_old[-2:], ctl_points])
bezier_path = np.concatenate([ctl_points_old[-2:], ctl_points])
break
ctl_points_old = ctl_points
else:
Expand All @@ -302,15 +300,15 @@ def split_path_inout(path, inside, tolerance=0.01, reorder_inout=False):
verts_right = right[:]

if path.codes is None:
path_in = Path(concat([path.vertices[:i], verts_left]))
path_out = Path(concat([verts_right, path.vertices[i:]]))
path_in = Path(np.concatenate([path.vertices[:i], verts_left]))
path_out = Path(np.concatenate([verts_right, path.vertices[i:]]))

else:
path_in = Path(concat([path.vertices[:iold], verts_left]),
concat([path.codes[:iold], codes_left]))
path_in = Path(np.concatenate([path.vertices[:iold], verts_left]),
np.concatenate([path.codes[:iold], codes_left]))

path_out = Path(concat([verts_right, path.vertices[i:]]),
concat([codes_right, path.codes[i:]]))
path_out = Path(np.concatenate([verts_right, path.vertices[i:]]),
np.concatenate([codes_right, path.codes[i:]]))

if reorder_inout and not begin_inside:
path_in, path_out = path_out, path_in
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5 changes: 2 additions & 3 deletions lib/matplotlib/mlab.py
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Expand Up @@ -572,9 +572,8 @@ def _spectral_helper(x, y=None, NFFT=None, Fs=None, detrend_func=None,

if sides == 'twosided':
# center the frequency range at zero
freqs = np.concatenate((freqs[freqcenter:], freqs[:freqcenter]))
result = np.concatenate((result[freqcenter:, :],
result[:freqcenter, :]), 0)
freqs = np.roll(freqs, -freqcenter, axis=0)
result = np.roll(result, -freqcenter, axis=0)
elif not pad_to % 2:
# get the last value correctly, it is negative otherwise
freqs[-1] *= -1
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3 changes: 1 addition & 2 deletions lib/matplotlib/patches.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2342,8 +2342,7 @@ def transmute(self, x0, y0, width, height, mutation_size):
width, height,
mutation_size)
# Add a trailing vertex to allow us to close the polygon correctly
saw_vertices = np.concatenate([np.array(saw_vertices),
[saw_vertices[0]]], axis=0)
saw_vertices = np.concatenate([saw_vertices, [saw_vertices[0]]])
codes = ([Path.MOVETO] +
[Path.CURVE3, Path.CURVE3] * ((len(saw_vertices)-1)//2) +
[Path.CLOSEPOLY])
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4 changes: 1 addition & 3 deletions lib/matplotlib/tests/test_ticker.py
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Expand Up @@ -357,9 +357,7 @@ def test_minor(self, lims, expected_low_ticks):
else:
# subsample
_LogitHelper.assert_almost_equal(
np.sort(np.concatenate((major_ticks, minor_ticks))),
expected_ticks,
)
sorted([*major_ticks, *minor_ticks]), expected_ticks)

def test_minor_attr(self):
loc = mticker.LogitLocator(nbins=100)
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32 changes: 8 additions & 24 deletions lib/mpl_toolkits/axisartist/angle_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,19 +20,11 @@ def select_step_degree(dv):
second_limits_ = np.array(minsec_limits_) / 3600
second_factors = [3600.] * len(second_limits_)

degree_limits = np.concatenate([second_limits_,
minute_limits_,
degree_limits_])
degree_limits = [*second_limits_, *minute_limits_, *degree_limits_]
degree_steps = [*minsec_steps_, *minsec_steps_, *degree_steps_]
degree_factors = [*second_factors, *minute_factors, *degree_factors]

degree_steps = np.concatenate([minsec_steps_,
minsec_steps_,
degree_steps_])

degree_factors = np.concatenate([second_factors,
minute_factors,
degree_factors])

n = degree_limits.searchsorted(dv)
n = np.searchsorted(degree_limits, dv)
step = degree_steps[n]
factor = degree_factors[n]

Expand All @@ -54,19 +46,11 @@ def select_step_hour(dv):
second_limits_ = np.array(minsec_limits_) / 3600
second_factors = [3600.] * len(second_limits_)

hour_limits = np.concatenate([second_limits_,
minute_limits_,
hour_limits_])

hour_steps = np.concatenate([minsec_steps_,
minsec_steps_,
hour_steps_])

hour_factors = np.concatenate([second_factors,
minute_factors,
hour_factors])
hour_limits = [*second_limits_, *minute_limits_, *hour_limits_]
hour_steps = [*minsec_steps_, *minsec_steps_, *hour_steps_]
hour_factors = [*second_factors, *minute_factors, *hour_factors]

n = hour_limits.searchsorted(dv)
n = np.searchsorted(hour_limits, dv)
step = hour_steps[n]
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How does this work? n is an array still, but hour_steps is a list now, and I can't index a list with an array.

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dv is a scalar (this is called by select_step, where it is immediately preceded by if dv > 1 / threshold_factor -- semantically, it is the distance between the axes bounds) so n is a scalar as well and the indexing is fine.

I guess technically it is an API break in that select_step_degree could have been called directly by someone passing dv as an array, but I don't think this is a real issue (and is clearly not how select_step_degree was designed to do).

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s/preceded/succeeded/, but I see what you mean.

factor = hour_factors[n]

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