[MRG] Update tests and documentation (#484)

* remove old macos and windows tets update requirements

* speedup ssw and continuaous ot exmaples

* speedup regpath and variane

* speedup conv 2d example + continuous stick

* speedup regpath

8 files changed, 33 insertions, 33 deletions

diff --git a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
index 07a4926..7cbfd98 100644
--- a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
+++ b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
@@ -40,8 +40,8 @@ import torch
 import ot
 import matplotlib.animation as animation
 
-I1 = pl.imread('../../data/redcross.png').astype(np.float64)[::4, ::4, 2]
-I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::4, ::4, 2]
+I1 = pl.imread('../../data/redcross.png').astype(np.float64)[::5, ::5, 2]
+I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::5, ::5, 2]
 
 sz = I2.shape[0]
 XX, YY = np.meshgrid(np.arange(sz), np.arange(sz))
@@ -67,7 +67,7 @@ x2_torch = torch.tensor(x2).to(device=device)
 
 
 lr = 1e3
-nb_iter_max = 100
+nb_iter_max = 50
 
 x_all = np.zeros((nb_iter_max, x1.shape[0], 2))
 
@@ -129,7 +129,7 @@ xbinit = np.random.randn(500, 2) * 10 + 16
 xbary_torch = torch.tensor(xbinit).to(device=device).requires_grad_(True)
 
 lr = 1e3
-nb_iter_max = 100
+nb_iter_max = 50
 
 x_all = np.zeros((nb_iter_max, xbary_torch.shape[0], 2))
 
diff --git a/examples/backends/plot_ssw_unif_torch.py b/examples/backends/plot_ssw_unif_torch.py
index afe3fa6..7459cf6 100644
--- a/examples/backends/plot_ssw_unif_torch.py
+++ b/examples/backends/plot_ssw_unif_torch.py
@@ -35,7 +35,7 @@ import ot
 
 torch.manual_seed(1)
 
-N = 1000
+N = 500
 x0 = torch.rand(N, 3)
 x0 = F.normalize(x0, dim=-1)
 
@@ -72,8 +72,8 @@ ax.legend()
 x = x0.clone()
 x.requires_grad_(True)
 
-n_iter = 500
-lr = 100
+n_iter = 100
+lr = 150
 
 losses = []
 xvisu = torch.zeros(n_iter, N, 3)
@@ -82,7 +82,7 @@ for i in range(n_iter):
     sw = ot.sliced_wasserstein_sphere_unif(x, n_projections=500)
     grad_x = torch.autograd.grad(sw, x)[0]
 
-    x = x - lr * grad_x
+    x = x - lr * grad_x / np.sqrt(i / 10 + 1)
     x = F.normalize(x, p=2, dim=1)
 
     losses.append(sw.item())
@@ -102,7 +102,7 @@ pl.xlabel("Iterations")
 # Plot trajectories of generated samples along iterations
 # -------------------------------------------------------
 
-ivisu = [0, 25, 50, 75, 100, 150, 200, 350, 499]
+ivisu = [0, 10, 20, 30, 40, 50, 60, 70, 80]
 
 fig = pl.figure(3, (10, 10))
 for i in range(9):
@@ -149,5 +149,5 @@ ax.set_ylim((-1.5, 1.5))
 ax.set_title('Iter. {}'.format(ivisu[i]))
 
 
-ani = animation.FuncAnimation(pl.gcf(), _update_plot, n_iter // 5, interval=100, repeat_delay=2000)
+ani = animation.FuncAnimation(pl.gcf(), _update_plot, n_iter // 5, interval=200, repeat_delay=2000)
 # %%
diff --git a/examples/backends/plot_stoch_continuous_ot_pytorch.py b/examples/backends/plot_stoch_continuous_ot_pytorch.py
index 714a5d3..e642986 100644
--- a/examples/backends/plot_stoch_continuous_ot_pytorch.py
+++ b/examples/backends/plot_stoch_continuous_ot_pytorch.py
@@ -27,8 +27,8 @@ import ot.plot
 torch.manual_seed(42)
 np.random.seed(42)
 
-n_source_samples = 10000
-n_target_samples = 10000
+n_source_samples = 1000
+n_target_samples = 1000
 theta = 2 * np.pi / 20
 noise_level = 0.1
 
@@ -89,7 +89,7 @@ reg = 1
 optimizer = torch.optim.Adam(list(u.parameters()) + list(v.parameters()), lr=.005)
 
 # number of iteration
-n_iter = 1000
+n_iter = 500
 n_batch = 500
 
 
diff --git a/examples/barycenters/plot_convolutional_barycenter.py b/examples/barycenters/plot_convolutional_barycenter.py
index 3721f31..143b3a6 100644
--- a/examples/barycenters/plot_convolutional_barycenter.py
+++ b/examples/barycenters/plot_convolutional_barycenter.py
@@ -29,10 +29,10 @@ import ot
 this_file = os.path.realpath('__file__')
 data_path = os.path.join(Path(this_file).parent.parent.parent, 'data')
 
-f1 = 1 - plt.imread(os.path.join(data_path, 'redcross.png'))[:, :, 2]
-f2 = 1 - plt.imread(os.path.join(data_path, 'tooth.png'))[:, :, 2]
-f3 = 1 - plt.imread(os.path.join(data_path, 'heart.png'))[:, :, 2]
-f4 = 1 - plt.imread(os.path.join(data_path, 'duck.png'))[:, :, 2]
+f1 = 1 - plt.imread(os.path.join(data_path, 'redcross.png'))[::2, ::2, 2]
+f2 = 1 - plt.imread(os.path.join(data_path, 'tooth.png'))[::2, ::2, 2]
+f3 = 1 - plt.imread(os.path.join(data_path, 'heart.png'))[::2, ::2, 2]
+f4 = 1 - plt.imread(os.path.join(data_path, 'duck.png'))[::2, ::2, 2]
 
 f1 = f1 / np.sum(f1)
 f2 = f2 / np.sum(f2)
diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py
index 626938c..4f233fe 100644
--- a/examples/plot_OT_1D_smooth.py
+++ b/examples/plot_OT_1D_smooth.py
@@ -14,7 +14,7 @@ sparsity-constrained OT, together with their visualizations.
 #
 # License: MIT License
 
-# sphinx_gallery_thumbnail_number = 6
+# sphinx_gallery_thumbnail_number = 5
 
 import numpy as np
 import matplotlib.pylab as pl
diff --git a/examples/sliced-wasserstein/plot_variance.py b/examples/sliced-wasserstein/plot_variance.py
index 2293247..77df2f5 100644
--- a/examples/sliced-wasserstein/plot_variance.py
+++ b/examples/sliced-wasserstein/plot_variance.py
@@ -29,7 +29,7 @@ import ot
 
 # %% parameters and data generation
 
-n = 500  # nb samples
+n = 200  # nb samples
 
 mu_s = np.array([0, 0])
 cov_s = np.array([[1, 0], [0, 1]])
@@ -58,9 +58,9 @@ pl.title('Source and target distributions')
 # Sliced Wasserstein distance for different seeds and number of projections
 # -------------------------------------------------------------------------
 
-n_seed = 50
-n_projections_arr = np.logspace(0, 3, 25, dtype=int)
-res = np.empty((n_seed, 25))
+n_seed = 20
+n_projections_arr = np.logspace(0, 3, 10, dtype=int)
+res = np.empty((n_seed, 10))
 
 # %% Compute statistics
 for seed in range(n_seed):
diff --git a/examples/sliced-wasserstein/plot_variance_ssw.py b/examples/sliced-wasserstein/plot_variance_ssw.py
index f5fc35f..246b2a8 100644
--- a/examples/sliced-wasserstein/plot_variance_ssw.py
+++ b/examples/sliced-wasserstein/plot_variance_ssw.py
@@ -28,7 +28,7 @@ import ot
 
 # %% parameters and data generation
 
-n = 500  # nb samples
+n = 200  # nb samples
 
 xs = np.random.randn(n, 3)
 xt = np.random.randn(n, 3)
@@ -81,9 +81,9 @@ pl.title("Source and Target distribution")
 # Spherical Sliced Wasserstein for different seeds and number of projections
 # --------------------------------------------------------------------------
 
-n_seed = 50
-n_projections_arr = np.logspace(0, 3, 25, dtype=int)
-res = np.empty((n_seed, 25))
+n_seed = 20
+n_projections_arr = np.logspace(0, 3, 10, dtype=int)
+res = np.empty((n_seed, 10))
 
 # %% Compute statistics
 for seed in range(n_seed):
diff --git a/examples/unbalanced-partial/plot_regpath.py b/examples/unbalanced-partial/plot_regpath.py
index d1f2042..ffedc6e 100644
--- a/examples/unbalanced-partial/plot_regpath.py
+++ b/examples/unbalanced-partial/plot_regpath.py
@@ -27,7 +27,7 @@ import matplotlib.animation as animation
 
 #%% parameters and data generation
 
-n = 50  # nb samples
+n = 20  # nb samples
 
 mu_s = np.array([-1, -1])
 cov_s = np.array([[1, 0], [0, 1]])
@@ -63,7 +63,7 @@ pl.show()
 # -----------------------------------------------------------
 
 #%%
-final_gamma = 1e-8
+final_gamma = 1e-6
 t, t_list, g_list = ot.regpath.regularization_path(a, b, M, reg=final_gamma,
                                                    semi_relaxed=False)
 t2, t_list2, g_list2 = ot.regpath.regularization_path(a, b, M, reg=final_gamma,
@@ -111,7 +111,7 @@ pl.show()
 # Animation of the regpath for UOT l2
 # -----------------------------------
 
-nv = 100
+nv = 50
 g_list_v = np.logspace(-.5, -2.5, nv)
 
 pl.figure(3)
@@ -144,7 +144,7 @@ def _update_plot(iv):
 i = 0
 _update_plot(i)
 
-ani = animation.FuncAnimation(pl.gcf(), _update_plot, nv, interval=50, repeat_delay=2000)
+ani = animation.FuncAnimation(pl.gcf(), _update_plot, nv, interval=100, repeat_delay=2000)
 
 
 ##############################################################################
@@ -183,8 +183,8 @@ pl.show()
 # Animation of the regpath for semi-relaxed UOT l2
 # ------------------------------------------------
 
-nv = 100
-g_list_v = np.logspace(2.5, -2, nv)
+nv = 50
+g_list_v = np.logspace(2, -2, nv)
 
 pl.figure(5)
 
@@ -216,4 +216,4 @@ def _update_plot(iv):
 i = 0
 _update_plot(i)
 
-ani = animation.FuncAnimation(pl.gcf(), _update_plot, nv, interval=50, repeat_delay=2000)
+ani = animation.FuncAnimation(pl.gcf(), _update_plot, nv, interval=100, repeat_delay=2000)