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Commit78aff43

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committed
MNT: deprecate mlab
.... except for GaussianKDE, which is needed for violinplots
1 parent1f7151e commit78aff43

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)
@@ -4638,237 +4619,6 @@ def test_subplot_key_hash():
46384619
assertax.get_subplotspec().get_geometry()== (5,1,0,0)
46394620

46404621

4641-
@image_comparison(
4642-
["specgram_freqs.png","specgram_freqs_linear.png",
4643-
"specgram_noise.png","specgram_noise_linear.png"],
4644-
remove_text=True,tol=0.07,style="default")
4645-
deftest_specgram():
4646-
"""Test axes.specgram in default (psd) mode."""
4647-
4648-
# use former defaults to match existing baseline image
4649-
matplotlib.rcParams['image.interpolation']='nearest'
4650-
4651-
n=1000
4652-
Fs=10.
4653-
4654-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4655-
NFFT_freqs=int(10*Fs/np.min(fstims))
4656-
x=np.arange(0,n,1/Fs)
4657-
y_freqs=np.concatenate(
4658-
np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1))
4659-
4660-
NFFT_noise=int(10*Fs/11)
4661-
np.random.seed(0)
4662-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4663-
4664-
all_sides= ["default","onesided","twosided"]
4665-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4666-
noverlap=NFFT//2
4667-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4668-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4669-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4670-
pad_to=pad_to,sides=sides)
4671-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4672-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4673-
pad_to=pad_to,sides=sides,
4674-
scale="linear",norm=matplotlib.colors.LogNorm())
4675-
4676-
4677-
@image_comparison(
4678-
["specgram_magnitude_freqs.png","specgram_magnitude_freqs_linear.png",
4679-
"specgram_magnitude_noise.png","specgram_magnitude_noise_linear.png"],
4680-
remove_text=True,tol=0.07,style="default")
4681-
deftest_specgram_magnitude():
4682-
"""Test axes.specgram in magnitude mode."""
4683-
4684-
# use former defaults to match existing baseline image
4685-
matplotlib.rcParams['image.interpolation']='nearest'
4686-
4687-
n=1000
4688-
Fs=10.
4689-
4690-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4691-
NFFT_freqs=int(100*Fs/np.min(fstims))
4692-
x=np.arange(0,n,1/Fs)
4693-
y=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4694-
y[:,-1]=1
4695-
y_freqs=np.hstack(y)
4696-
4697-
NFFT_noise=int(10*Fs/11)
4698-
np.random.seed(0)
4699-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4700-
4701-
all_sides= ["default","onesided","twosided"]
4702-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4703-
noverlap=NFFT//2
4704-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4705-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4706-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4707-
pad_to=pad_to,sides=sides,mode="magnitude")
4708-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4709-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4710-
pad_to=pad_to,sides=sides,mode="magnitude",
4711-
scale="linear",norm=matplotlib.colors.LogNorm())
4712-
4713-
4714-
@image_comparison(
4715-
["specgram_angle_freqs.png","specgram_phase_freqs.png",
4716-
"specgram_angle_noise.png","specgram_phase_noise.png"],
4717-
remove_text=True,tol=0.07,style="default")
4718-
deftest_specgram_angle():
4719-
"""Test axes.specgram in angle and phase modes."""
4720-
4721-
# use former defaults to match existing baseline image
4722-
matplotlib.rcParams['image.interpolation']='nearest'
4723-
4724-
n=1000
4725-
Fs=10.
4726-
4727-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4728-
NFFT_freqs=int(10*Fs/np.min(fstims))
4729-
x=np.arange(0,n,1/Fs)
4730-
y=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4731-
y[:,-1]=1
4732-
y_freqs=np.hstack(y)
4733-
4734-
NFFT_noise=int(10*Fs/11)
4735-
np.random.seed(0)
4736-
y_noise=np.concatenate([np.random.standard_normal(n),np.random.rand(n)])
4737-
4738-
all_sides= ["default","onesided","twosided"]
4739-
fory,NFFTin [(y_freqs,NFFT_freqs), (y_noise,NFFT_noise)]:
4740-
noverlap=NFFT//2
4741-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4742-
formodein ["angle","phase"]:
4743-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4744-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4745-
pad_to=pad_to,sides=sides,mode=mode)
4746-
withpytest.raises(ValueError):
4747-
ax.specgram(y,NFFT=NFFT,Fs=Fs,noverlap=noverlap,
4748-
pad_to=pad_to,sides=sides,mode=mode,
4749-
scale="dB")
4750-
4751-
4752-
deftest_specgram_fs_none():
4753-
"""Test axes.specgram when Fs is None, should not throw error."""
4754-
spec,freqs,t,im=plt.specgram(np.ones(300),Fs=None,scale='linear')
4755-
xmin,xmax,freq0,freq1=im.get_extent()
4756-
assertxmin==32andxmax==96
4757-
4758-
4759-
@check_figures_equal(extensions=["png"])
4760-
deftest_specgram_origin_rcparam(fig_test,fig_ref):
4761-
"""Test specgram ignores image.origin rcParam and uses origin 'upper'."""
4762-
t=np.arange(500)
4763-
signal=np.sin(t)
4764-
4765-
plt.rcParams["image.origin"]='upper'
4766-
4767-
# Reference: First graph using default origin in imshow (upper),
4768-
fig_ref.subplots().specgram(signal)
4769-
4770-
# Try to overwrite the setting trying to flip the specgram
4771-
plt.rcParams["image.origin"]='lower'
4772-
4773-
# Test: origin='lower' should be ignored
4774-
fig_test.subplots().specgram(signal)
4775-
4776-
4777-
deftest_specgram_origin_kwarg():
4778-
"""Ensure passing origin as a kwarg raises a TypeError."""
4779-
t=np.arange(500)
4780-
signal=np.sin(t)
4781-
4782-
withpytest.raises(TypeError):
4783-
plt.specgram(signal,origin='lower')
4784-
4785-
4786-
@image_comparison(
4787-
["psd_freqs.png","csd_freqs.png","psd_noise.png","csd_noise.png"],
4788-
remove_text=True,tol=0.002)
4789-
deftest_psd_csd():
4790-
n=10000
4791-
Fs=100.
4792-
4793-
fstims= [[Fs/4,Fs/5,Fs/11], [Fs/4.7,Fs/5.6,Fs/11.9]]
4794-
NFFT_freqs=int(1000*Fs/np.min(fstims))
4795-
x=np.arange(0,n,1/Fs)
4796-
ys_freqs=np.sin(2*np.pi*np.multiply.outer(fstims,x)).sum(axis=1)
4797-
4798-
NFFT_noise=int(1000*Fs/11)
4799-
np.random.seed(0)
4800-
ys_noise= [np.random.standard_normal(n),np.random.rand(n)]
4801-
4802-
all_kwargs= [{"sides":"default"},
4803-
{"sides":"onesided","return_line":False},
4804-
{"sides":"twosided","return_line":True}]
4805-
forys,NFFTin [(ys_freqs,NFFT_freqs), (ys_noise,NFFT_noise)]:
4806-
noverlap=NFFT//2
4807-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4808-
forax,kwargsinzip(plt.figure().subplots(3),all_kwargs):
4809-
ret=ax.psd(np.concatenate(ys),NFFT=NFFT,Fs=Fs,
4810-
noverlap=noverlap,pad_to=pad_to,**kwargs)
4811-
assertlen(ret)==2+kwargs.get("return_line",False)
4812-
ax.set(xlabel="",ylabel="")
4813-
forax,kwargsinzip(plt.figure().subplots(3),all_kwargs):
4814-
ret=ax.csd(*ys,NFFT=NFFT,Fs=Fs,
4815-
noverlap=noverlap,pad_to=pad_to,**kwargs)
4816-
assertlen(ret)==2+kwargs.get("return_line",False)
4817-
ax.set(xlabel="",ylabel="")
4818-
4819-
4820-
@image_comparison(
4821-
["magnitude_spectrum_freqs_linear.png",
4822-
"magnitude_spectrum_freqs_dB.png",
4823-
"angle_spectrum_freqs.png",
4824-
"phase_spectrum_freqs.png",
4825-
"magnitude_spectrum_noise_linear.png",
4826-
"magnitude_spectrum_noise_dB.png",
4827-
"angle_spectrum_noise.png",
4828-
"phase_spectrum_noise.png"],
4829-
remove_text=True)
4830-
deftest_spectrum():
4831-
n=10000
4832-
Fs=100.
4833-
4834-
fstims1= [Fs/4,Fs/5,Fs/11]
4835-
NFFT=int(1000*Fs/min(fstims1))
4836-
pad_to=int(2**np.ceil(np.log2(NFFT)))
4837-
4838-
x=np.arange(0,n,1/Fs)
4839-
y_freqs= ((np.sin(2*np.pi*np.outer(x,fstims1))*10**np.arange(3))
4840-
.sum(axis=1))
4841-
np.random.seed(0)
4842-
y_noise=np.hstack([np.random.standard_normal(n),np.random.rand(n)])-.5
4843-
4844-
all_sides= ["default","onesided","twosided"]
4845-
kwargs= {"Fs":Fs,"pad_to":pad_to}
4846-
foryin [y_freqs,y_noise]:
4847-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4848-
spec,freqs,line=ax.magnitude_spectrum(y,sides=sides,**kwargs)
4849-
ax.set(xlabel="",ylabel="")
4850-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4851-
spec,freqs,line=ax.magnitude_spectrum(y,sides=sides,**kwargs,
4852-
scale="dB")
4853-
ax.set(xlabel="",ylabel="")
4854-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4855-
spec,freqs,line=ax.angle_spectrum(y,sides=sides,**kwargs)
4856-
ax.set(xlabel="",ylabel="")
4857-
forax,sidesinzip(plt.figure().subplots(3),all_sides):
4858-
spec,freqs,line=ax.phase_spectrum(y,sides=sides,**kwargs)
4859-
ax.set(xlabel="",ylabel="")
4860-
4861-
4862-
deftest_psd_csd_edge_cases():
4863-
# Inverted yaxis or fully zero inputs used to throw exceptions.
4864-
axs=plt.figure().subplots(2)
4865-
foraxinaxs:
4866-
ax.yaxis.set(inverted=True)
4867-
withnp.errstate(divide="ignore"):
4868-
axs[0].psd(np.zeros(5))
4869-
axs[1].csd(np.zeros(5),np.zeros(5))
4870-
4871-
48724622
@check_figures_equal(extensions=['png'])
48734623
deftest_twin_remove(fig_test,fig_ref):
48744624
ax_test=fig_test.add_subplot()

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