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Float division patches #3289

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d174810
misc / -> //
alexbrc Jul 23, 2014
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alexbrc Jul 23, 2014
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3 changes: 1 addition & 2 deletions lib/matplotlib/cbook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1734,10 +1734,10 @@ def get_siblings(self, a):


def simple_linear_interpolation(a, steps):
steps = int(np.floor(steps))
if steps == 1:
return a

steps = np.floor(steps)
new_length = ((len(a) - 1) * steps) + 1
new_shape = list(a.shape)
new_shape[0] = new_length
Expand All @@ -1747,7 +1747,6 @@ def simple_linear_interpolation(a, steps):
a0 = a[0:-1]
a1 = a[1:]
delta = ((a1 - a0) / steps)
steps = int(steps)
for i in range(1, steps):
result[i::steps] = delta * i + a0
result[steps::steps] = a1
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8 changes: 4 additions & 4 deletions lib/matplotlib/contour.py
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Expand Up @@ -474,20 +474,20 @@ def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
xy2 = mlab.less_simple_linear_interpolation(
pl, lc, [xi[1]])

# Make integer
# Make floating point representations of integers, keeping nans.
I = [np.floor(I[0]), np.ceil(I[1])]

# Actually break contours
if closed:
# This will remove contour if shorter than label
if np.all(~np.isnan(I)):
nlc.append(np.r_[xy2, lc[I[1]:I[0] + 1], xy1])
nlc.append(np.r_[xy2, lc[int(I[1]):int(I[0]) + 1], xy1])
else:
# These will remove pieces of contour if they have length zero
if not np.isnan(I[0]):
nlc.append(np.r_[lc[:I[0] + 1], xy1])
nlc.append(np.r_[lc[:int(I[0]) + 1], xy1])
if not np.isnan(I[1]):
nlc.append(np.r_[xy2, lc[I[1]:]])
nlc.append(np.r_[xy2, lc[int(I[1]):]])

# The current implementation removes contours completely
# covered by labels. Uncomment line below to keep
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2 changes: 1 addition & 1 deletion lib/matplotlib/tests/test_axes.py
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Expand Up @@ -523,7 +523,7 @@ def test_nonfinite_limits():
y = np.log(x)
finally:
np.seterr(**olderr)
x[len(x)/2] = np.nan
x[len(x) // 2] = np.nan
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
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4 changes: 2 additions & 2 deletions lib/matplotlib/tests/test_mlab.py
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Expand Up @@ -1325,8 +1325,8 @@ def createStim(self, fstims, iscomplex, sides, nsides, len_x=None,

freqs_specgram = freqs_density
# time points for specgram
t_start = NFFT_specgram_real/2
t_stop = len(x) - NFFT_specgram_real/2+1
t_start = NFFT_specgram_real // 2
t_stop = len(x) - NFFT_specgram_real // 2 + 1
t_step = NFFT_specgram_real - nover_specgram_real
t_specgram = x[t_start:t_stop:t_step]
if NFFT_specgram_real % 2:
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2 changes: 1 addition & 1 deletion lib/matplotlib/tests/test_simplification.py
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Expand Up @@ -153,7 +153,7 @@ def test_start_with_moveto():
decodebytes = base64.decodestring

verts = np.fromstring(decodebytes(data), dtype='<i4')
verts = verts.reshape((len(verts) / 2, 2))
verts = verts.reshape((len(verts) // 2, 2))
path = Path(verts)
segs = path.iter_segments(transforms.IdentityTransform(), clip=(0.0, 0.0, 100.0, 100.0))
segs = list(segs)
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2 changes: 1 addition & 1 deletion lib/matplotlib/tri/trirefine.py
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Expand Up @@ -231,7 +231,7 @@ def _refine_triangulation_once(triangulation, ancestors=None):
# points
# hint: each apex is shared by 2 masked_triangles except the borders.
borders = np.sum(neighbors == -1)
added_pts = (3*ntri + borders) / 2
added_pts = (3*ntri + borders) // 2
refi_npts = npts + added_pts
refi_x = np.zeros(refi_npts)
refi_y = np.zeros(refi_npts)
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