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Commitbeaa1d0

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MNT: deprecate mlab
.... except for GaussianKDE, which is needed for violinplots
1 parentf8cd2c9 commitbeaa1d0

27 files changed

+12
-1329
lines changed

‎lib/matplotlib/mlab.py

Lines changed: 12 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1,8 +1,6 @@
11
"""
2-
Numerical Python functions written for compatibility with MATLAB
3-
commands with the same names. Most numerical Python functions can be found in
4-
the `NumPy`_ and `SciPy`_ libraries. What remains here is code for performing
5-
spectral computations and kernel density estimations.
2+
This module is deprecated in favour of modules that can be found in
3+
the `NumPy`_ and `SciPy`_ libraries.
64
75
.. _NumPy: https://numpy.org
86
.. _SciPy: https://www.scipy.org
@@ -58,6 +56,7 @@
5856
frommatplotlibimport_api,_docstring,cbook
5957

6058

59+
@_api.deprecated("3.6",alternative="np.hanning")
6160
defwindow_hanning(x):
6261
"""
6362
Return *x* times the Hanning (or Hann) window of len(*x*).
@@ -69,6 +68,7 @@ def window_hanning(x):
6968
returnnp.hanning(len(x))*x
7069

7170

71+
@_api.deprecated("3.6",alternative="")
7272
defwindow_none(x):
7373
"""
7474
No window function; simply return *x*.
@@ -80,6 +80,7 @@ def window_none(x):
8080
returnx
8181

8282

83+
@_api.deprecated("3.6",alternative="scipy.signal.detrend")
8384
defdetrend(x,key=None,axis=None):
8485
"""
8586
Return *x* with its trend removed.
@@ -129,6 +130,7 @@ def detrend(x, key=None, axis=None):
129130
f"'constant', 'mean', 'linear', or a function")
130131

131132

133+
@_api.deprecated("3.6",alternative="scipy.signal.detrend")
132134
defdetrend_mean(x,axis=None):
133135
"""
134136
Return *x* minus the mean(*x*).
@@ -157,6 +159,7 @@ def detrend_mean(x, axis=None):
157159
returnx-x.mean(axis,keepdims=True)
158160

159161

162+
@_api.deprecated("3.6",alternative="scipy.signal.detrend")
160163
defdetrend_none(x,axis=None):
161164
"""
162165
Return *x*: no detrending.
@@ -179,6 +182,7 @@ def detrend_none(x, axis=None):
179182
returnx
180183

181184

185+
@_api.deprecated("3.6",alternative="scipy.signal.detrend")
182186
defdetrend_linear(y):
183187
"""
184188
Return *x* minus best fit line; 'linear' detrending.
@@ -531,6 +535,7 @@ def _single_spectrum_helper(
531535
MATLAB compatibility.""")
532536

533537

538+
@_api.deprecated("3.6",alternative="scipy.signal.psd")
534539
@_docstring.dedent_interpd
535540
defpsd(x,NFFT=None,Fs=None,detrend=None,window=None,
536541
noverlap=None,pad_to=None,sides=None,scale_by_freq=None):
@@ -587,6 +592,7 @@ def psd(x, NFFT=None, Fs=None, detrend=None, window=None,
587592
returnPxx.real,freqs
588593

589594

595+
@_api.deprecated("3.6",alternative="scipy.signal.csd")
590596
@_docstring.dedent_interpd
591597
defcsd(x,y,NFFT=None,Fs=None,detrend=None,window=None,
592598
noverlap=None,pad_to=None,sides=None,scale_by_freq=None):
@@ -688,7 +694,6 @@ def csd(x, y, NFFT=None, Fs=None, detrend=None, window=None,
688694
Can return the complex spectrum of segments within the signal.
689695
"""
690696

691-
692697
complex_spectrum=functools.partial(_single_spectrum_helper,"complex")
693698
complex_spectrum.__doc__=_single_spectrum_docs.format(
694699
quantity="complex-valued frequency spectrum",
@@ -707,6 +712,7 @@ def csd(x, y, NFFT=None, Fs=None, detrend=None, window=None,
707712
**_docstring.interpd.params)
708713

709714

715+
@_api.deprecated("3.6",alternative="scipy.signal.welch")
710716
@_docstring.dedent_interpd
711717
defspecgram(x,NFFT=None,Fs=None,detrend=None,window=None,
712718
noverlap=None,pad_to=None,sides=None,scale_by_freq=None,
@@ -790,6 +796,7 @@ def specgram(x, NFFT=None, Fs=None, detrend=None, window=None,
790796
returnspec,freqs,t
791797

792798

799+
@_api.deprecated("3.6",alternative="scipy.signal.coherence")
793800
@_docstring.dedent_interpd
794801
defcohere(x,y,NFFT=256,Fs=2,detrend=detrend_none,window=window_hanning,
795802
noverlap=0,pad_to=None,sides='default',scale_by_freq=None):
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‎lib/matplotlib/tests/test_axes.py

Lines changed: 0 additions & 250 deletions
Original file line numberDiff line numberDiff line change
@@ -134,25 +134,6 @@ def test_label_shift():
134134
assertax.yaxis.get_label().get_horizontalalignment()=="center"
135135

136136

137-
@check_figures_equal(extensions=["png"])
138-
deftest_acorr(fig_test,fig_ref):
139-
np.random.seed(19680801)
140-
Nx=512
141-
x=np.random.normal(0,1,Nx).cumsum()
142-
maxlags=Nx-1
143-
144-
ax_test=fig_test.subplots()
145-
ax_test.acorr(x,maxlags=maxlags)
146-
147-
ax_ref=fig_ref.subplots()
148-
# Normalized autocorrelation
149-
norm_auto_corr=np.correlate(x,x,mode="full")/np.dot(x,x)
150-
lags=np.arange(-maxlags,maxlags+1)
151-
norm_auto_corr=norm_auto_corr[Nx-1-maxlags:Nx+maxlags]
152-
ax_ref.vlines(lags, [0],norm_auto_corr)
153-
ax_ref.axhline(y=0,xmin=0,xmax=1)
154-
155-
156137
@check_figures_equal(extensions=["png"])
157138
deftest_spy(fig_test,fig_ref):
158139
np.random.seed(19680801)
@@ -4623,237 +4604,6 @@ def test_subplot_key_hash():
46234604
assertax.get_subplotspec().get_geometry()== (5,1,0,0)
46244605

46254606

4626-
@image_comparison(
4627-
["specgram_freqs.png","specgram_freqs_linear.png",
4628-
"specgram_noise.png","specgram_noise_linear.png"],
4629-
remove_text=True,tol=0.07,style="default")
4630-
deftest_specgram():
4631-
"""Test axes.specgram in default (psd) mode."""
4632-
4633-
# use former defaults to match existing baseline image
4634-
matplotlib.rcParams['image.interpolation']='nearest'
4635-
4636-
n=1000
4637-
Fs=10.
4638-
4639-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4640-
NFFT_freqs=int(10*Fs/np.min(fstims))
4641-
x=np.arange(0,n,1/Fs)
4642-
y_freqs=np.concatenate(
4643-
np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1))
4644-
4645-
NFFT_noise=int(10*Fs/11)
4646-
np.random.seed(0)
4647-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4648-
4649-
all_sides= ["default","onesided","twosided"]
4650-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4651-
noverlap=NFFT//2
4652-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4653-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4654-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4655-
pad_to=pad_to,sides=sides)
4656-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4657-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4658-
pad_to=pad_to,sides=sides,
4659-
scale="linear",norm=matplotlib.colors.LogNorm())
4660-
4661-
4662-
@image_comparison(
4663-
["specgram_magnitude_freqs.png","specgram_magnitude_freqs_linear.png",
4664-
"specgram_magnitude_noise.png","specgram_magnitude_noise_linear.png"],
4665-
remove_text=True,tol=0.07,style="default")
4666-
deftest_specgram_magnitude():
4667-
"""Test axes.specgram in magnitude mode."""
4668-
4669-
# use former defaults to match existing baseline image
4670-
matplotlib.rcParams['image.interpolation']='nearest'
4671-
4672-
n=1000
4673-
Fs=10.
4674-
4675-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4676-
NFFT_freqs=int(100*Fs/np.min(fstims))
4677-
x=np.arange(0,n,1/Fs)
4678-
y=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4679-
y[:,-1]=1
4680-
y_freqs=np.hstack(y)
4681-
4682-
NFFT_noise=int(10*Fs/11)
4683-
np.random.seed(0)
4684-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4685-
4686-
all_sides= ["default","onesided","twosided"]
4687-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4688-
noverlap=NFFT//2
4689-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4690-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4691-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4692-
pad_to=pad_to,sides=sides,mode="magnitude")
4693-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4694-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4695-
pad_to=pad_to,sides=sides,mode="magnitude",
4696-
scale="linear",norm=matplotlib.colors.LogNorm())
4697-
4698-
4699-
@image_comparison(
4700-
["specgram_angle_freqs.png","specgram_phase_freqs.png",
4701-
"specgram_angle_noise.png","specgram_phase_noise.png"],
4702-
remove_text=True,tol=0.07,style="default")
4703-
deftest_specgram_angle():
4704-
"""Test axes.specgram in angle and phase modes."""
4705-
4706-
# use former defaults to match existing baseline image
4707-
matplotlib.rcParams['image.interpolation']='nearest'
4708-
4709-
n=1000
4710-
Fs=10.
4711-
4712-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4713-
NFFT_freqs=int(10*Fs/np.min(fstims))
4714-
x=np.arange(0,n,1/Fs)
4715-
y=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4716-
y[:,-1]=1
4717-
y_freqs=np.hstack(y)
4718-
4719-
NFFT_noise=int(10*Fs/11)
4720-
np.random.seed(0)
4721-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4722-
4723-
all_sides= ["default","onesided","twosided"]
4724-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4725-
noverlap=NFFT//2
4726-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4727-
formodein ["angle","phase"]:
4728-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4729-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4730-
pad_to=pad_to,sides=sides,mode=mode)
4731-
withpytest.raises(ValueError):
4732-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4733-
pad_to=pad_to,sides=sides,mode=mode,
4734-
scale="dB")
4735-
4736-
4737-
deftest_specgram_fs_none():
4738-
"""Test axes.specgram when Fs is None, should not throw error."""
4739-
spec,freqs,t,im=plt.specgram(np.ones(300),Fs=None,scale='linear')
4740-
xmin,xmax,freq0,freq1=im.get_extent()
4741-
assertxmin==32andxmax==96
4742-
4743-
4744-
@check_figures_equal(extensions=["png"])
4745-
deftest_specgram_origin_rcparam(fig_test,fig_ref):
4746-
"""Test specgram ignores image.origin rcParam and uses origin 'upper'."""
4747-
t=np.arange(500)
4748-
signal=np.sin(t)
4749-
4750-
plt.rcParams["image.origin"]='upper'
4751-
4752-
# Reference: First graph using default origin in imshow (upper),
4753-
fig_ref.subplots().specgram(signal)
4754-
4755-
# Try to overwrite the setting trying to flip the specgram
4756-
plt.rcParams["image.origin"]='lower'
4757-
4758-
# Test: origin='lower' should be ignored
4759-
fig_test.subplots().specgram(signal)
4760-
4761-
4762-
deftest_specgram_origin_kwarg():
4763-
"""Ensure passing origin as a kwarg raises a TypeError."""
4764-
t=np.arange(500)
4765-
signal=np.sin(t)
4766-
4767-
withpytest.raises(TypeError):
4768-
plt.specgram(signal,origin='lower')
4769-
4770-
4771-
@image_comparison(
4772-
["psd_freqs.png","csd_freqs.png","psd_noise.png","csd_noise.png"],
4773-
remove_text=True,tol=0.002)
4774-
deftest_psd_csd():
4775-
n=10000
4776-
Fs=100.
4777-
4778-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4779-
NFFT_freqs=int(1000*Fs/np.min(fstims))
4780-
x=np.arange(0,n,1/Fs)
4781-
ys_freqs=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4782-
4783-
NFFT_noise=int(1000*Fs/11)
4784-
np.random.seed(0)
4785-
ys_noise= [np.random.standard_normal(n),np.random.rand(n)]
4786-
4787-
all_kwargs= [{"sides":"default"},
4788-
{"sides":"onesided","return_line":False},
4789-
{"sides":"twosided","return_line":True}]
4790-
forys,NFFTin [(ys_freqs,NFFT_freqs), (ys_noise,NFFT_noise)]:
4791-
noverlap=NFFT//2
4792-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4793-
forax,kwargsinzip(plt.figure().subplots(3),all_kwargs):
4794-
ret=ax.psd(np.concatenate(ys),NFFT=NFFT,Fs=Fs,
4795-
noverlap=noverlap,pad_to=pad_to,**kwargs)
4796-
assertlen(ret)==2+kwargs.get("return_line",False)
4797-
ax.set(xlabel="",ylabel="")
4798-
forax,kwargsinzip(plt.figure().subplots(3),all_kwargs):
4799-
ret=ax.csd(*ys,NFFT=NFFT,Fs=Fs,
4800-
noverlap=noverlap,pad_to=pad_to,**kwargs)
4801-
assertlen(ret)==2+kwargs.get("return_line",False)
4802-
ax.set(xlabel="",ylabel="")
4803-
4804-
4805-
@image_comparison(
4806-
["magnitude_spectrum_freqs_linear.png",
4807-
"magnitude_spectrum_freqs_dB.png",
4808-
"angle_spectrum_freqs.png",
4809-
"phase_spectrum_freqs.png",
4810-
"magnitude_spectrum_noise_linear.png",
4811-
"magnitude_spectrum_noise_dB.png",
4812-
"angle_spectrum_noise.png",
4813-
"phase_spectrum_noise.png"],
4814-
remove_text=True)
4815-
deftest_spectrum():
4816-
n=10000
4817-
Fs=100.
4818-
4819-
fstims1= [Fs/4,Fs/5,Fs/11]
4820-
NFFT=int(1000*Fs/min(fstims1))
4821-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4822-
4823-
x=np.arange(0,n,1/Fs)
4824-
y_freqs= ((np.sin(2*np.pi*np.outer(x,fstims1))*10**np.arange(3))
4825-
.sum(axis=1))
4826-
np.random.seed(0)
4827-
y_noise=np.hstack([np.random.standard_normal(n),np.random.rand(n)])-.5
4828-
4829-
all_sides= ["default","onesided","twosided"]
4830-
kwargs= {"Fs":Fs,"pad_to":pad_to}
4831-
foryin [y_freqs,y_noise]:
4832-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4833-
spec,freqs,line=ax.magnitude_spectrum(y,sides=sides,**kwargs)
4834-
ax.set(xlabel="",ylabel="")
4835-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4836-
spec,freqs,line=ax.magnitude_spectrum(y,sides=sides,**kwargs,
4837-
scale="dB")
4838-
ax.set(xlabel="",ylabel="")
4839-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4840-
spec,freqs,line=ax.angle_spectrum(y,sides=sides,**kwargs)
4841-
ax.set(xlabel="",ylabel="")
4842-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4843-
spec,freqs,line=ax.phase_spectrum(y,sides=sides,**kwargs)
4844-
ax.set(xlabel="",ylabel="")
4845-
4846-
4847-
deftest_psd_csd_edge_cases():
4848-
# Inverted yaxis or fully zero inputs used to throw exceptions.
4849-
axs=plt.figure().subplots(2)
4850-
foraxinaxs:
4851-
ax.yaxis.set(inverted=True)
4852-
withnp.errstate(divide="ignore"):
4853-
axs[0].psd(np.zeros(5))
4854-
axs[1].csd(np.zeros(5),np.zeros(5))
4855-
4856-
48574607
@check_figures_equal(extensions=['png'])
48584608
deftest_twin_remove(fig_test,fig_ref):
48594609
ax_test=fig_test.add_subplot()

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