@@ -134,25 +134,6 @@ def test_label_shift():
134134 assert ax .yaxis .get_label ().get_horizontalalignment () == "center"
135135
136136
137- @check_figures_equal (extensions = ["png" ])
138- def test_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" ])
157138def test_spy (fig_test , fig_ref ):
158139 np .random .seed (19680801 )
@@ -4638,237 +4619,6 @@ def test_subplot_key_hash():
46384619 assert ax .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- def test_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- for y , NFFT in [(y_freqs , NFFT_freqs ), (y_noise , NFFT_noise )]:
4666- noverlap = NFFT // 2
4667- pad_to = int (2 ** np .ceil (np .log2 (NFFT )))
4668- for ax , sides in zip (plt .figure ().subplots (3 ), all_sides ):
4669- ax .specgram (y , NFFT = NFFT , Fs = Fs , noverlap = noverlap ,
4670- pad_to = pad_to , sides = sides )
4671- for ax , sides in zip (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- def test_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- for y , NFFT in [(y_freqs , NFFT_freqs ), (y_noise , NFFT_noise )]:
4703- noverlap = NFFT // 2
4704- pad_to = int (2 ** np .ceil (np .log2 (NFFT )))
4705- for ax , sides in zip (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- for ax , sides in zip (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- def test_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- for y , NFFT in [(y_freqs , NFFT_freqs ), (y_noise , NFFT_noise )]:
4740- noverlap = NFFT // 2
4741- pad_to = int (2 ** np .ceil (np .log2 (NFFT )))
4742- for mode in ["angle" , "phase" ]:
4743- for ax , sides in zip (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- with pytest .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- def test_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- assert xmin == 32 and xmax == 96
4757-
4758-
4759- @check_figures_equal (extensions = ["png" ])
4760- def test_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- def test_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- with pytest .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- def test_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- for ys , NFFT in [(ys_freqs , NFFT_freqs ), (ys_noise , NFFT_noise )]:
4806- noverlap = NFFT // 2
4807- pad_to = int (2 ** np .ceil (np .log2 (NFFT )))
4808- for ax , kwargs in zip (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- assert len (ret ) == 2 + kwargs .get ("return_line" , False )
4812- ax .set (xlabel = "" , ylabel = "" )
4813- for ax , kwargs in zip (plt .figure ().subplots (3 ), all_kwargs ):
4814- ret = ax .csd (* ys , NFFT = NFFT , Fs = Fs ,
4815- noverlap = noverlap , pad_to = pad_to , ** kwargs )
4816- assert len (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- def test_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- for y in [y_freqs , y_noise ]:
4847- for ax , sides in zip (plt .figure ().subplots (3 ), all_sides ):
4848- spec , freqs , line = ax .magnitude_spectrum (y , sides = sides , ** kwargs )
4849- ax .set (xlabel = "" , ylabel = "" )
4850- for ax , sides in zip (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- for ax , sides in zip (plt .figure ().subplots (3 ), all_sides ):
4855- spec , freqs , line = ax .angle_spectrum (y , sides = sides , ** kwargs )
4856- ax .set (xlabel = "" , ylabel = "" )
4857- for ax , sides in zip (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- def test_psd_csd_edge_cases ():
4863- # Inverted yaxis or fully zero inputs used to throw exceptions.
4864- axs = plt .figure ().subplots (2 )
4865- for ax in axs :
4866- ax .yaxis .set (inverted = True )
4867- with np .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' ])
48734623def test_twin_remove (fig_test , fig_ref ):
48744624 ax_test = fig_test .add_subplot ()
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