[DOC] Corrected spelling errors (#467)

* Fix typos in docstrings and examples

* A few more fixes

* Fix ref for `center_ot_dual` function

* Another typo

* Fix titles formatting

* Explicit empty line after math blocks

* Typo: asymmetric

* Fix code cell formatting for 1D barycenters

* Empirical

* Fix indentation for references

* Fixed all WARNINGs about title formatting

* Fix empty lines after math blocks

* Fix whitespace line

* Update changelog

* Consistent Gromov-Wasserstein

* More Gromov-Wasserstein consistency

---------

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

5 files changed, 10 insertions, 10 deletions

diff --git a/examples/others/plot_WeakOT_VS_OT.py b/examples/others/plot_WeakOT_VS_OT.py
index a29c875..e3164ba 100644
--- a/examples/others/plot_WeakOT_VS_OT.py
+++ b/examples/others/plot_WeakOT_VS_OT.py
@@ -5,7 +5,7 @@ Weak Optimal Transport VS exact Optimal Transport
 ====================================================
 
 Illustration of 2D optimal transport between distributions that are weighted
-sum of diracs. The OT matrix is plotted with the samples.
+sum of Diracs. The OT matrix is plotted with the samples.
 
 """
 
diff --git a/examples/others/plot_factored_coupling.py b/examples/others/plot_factored_coupling.py
index b5b1c9f..02074d7 100644
--- a/examples/others/plot_factored_coupling.py
+++ b/examples/others/plot_factored_coupling.py
@@ -47,8 +47,8 @@ pl.title('Source and target distributions')
 
 
 # %%
-# Compute Factore OT and exact OT solutions
-# --------------------------------------
+# Compute Factored OT and exact OT solutions
+# ------------------------------------------
 
 #%% EMD
 M = ot.dist(xs, xt)
@@ -61,7 +61,7 @@ Ga, Gb, xb = ot.factored_optimal_transport(xs, xt, a, b, r=4)
 
 # %%
 # Plot factored OT and exact OT solutions
-# --------------------------------------
+# ---------------------------------------
 
 pl.figure(2, (14, 4))
 
diff --git a/examples/others/plot_logo.py b/examples/others/plot_logo.py
index bb4f640..b032801 100644
--- a/examples/others/plot_logo.py
+++ b/examples/others/plot_logo.py
@@ -8,7 +8,7 @@ Logo of the POT toolbox
 In this example we plot the logo of the POT toolbox.
 
 This logo is that it is done 100% in Python and generated using
-matplotlib and ploting teh solution of the EMD solver from POT.
+matplotlib and plotting the solution of the EMD solver from POT.
 
 """
 
diff --git a/examples/others/plot_screenkhorn_1D.py b/examples/others/plot_screenkhorn_1D.py
index 2023649..3640b88 100644
--- a/examples/others/plot_screenkhorn_1D.py
+++ b/examples/others/plot_screenkhorn_1D.py
@@ -62,8 +62,8 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
 
 # Screenkhorn
 lambd = 2e-03  # entropy parameter
-ns_budget = 30  # budget number of points to be keeped in the source distribution
-nt_budget = 30  # budget number of points to be keeped in the target distribution
+ns_budget = 30  # budget number of points to be kept in the source distribution
+nt_budget = 30  # budget number of points to be kept in the target distribution
 
 G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)
 pl.figure(4, figsize=(5, 5))
diff --git a/examples/others/plot_stochastic.py b/examples/others/plot_stochastic.py
index 3a1ef31..f3afb0b 100644
--- a/examples/others/plot_stochastic.py
+++ b/examples/others/plot_stochastic.py
@@ -3,7 +3,7 @@
 Stochastic examples
 ===================
 
-This example is designed to show how to use the stochatic optimization
+This example is designed to show how to use the stochastic optimization
 algorithms for discrete and semi-continuous measures from the POT library.
 
 [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F.
@@ -61,7 +61,7 @@ print(sag_pi)
 # Semi-Continuous Case
 # ````````````````````
 #
-# Sample one general measure a, one discrete measures b for the semicontinous
+# Sample one general measure a, one discrete measures b for the semicontinuous
 # case, the points where source and target measures are defined and compute the
 # cost matrix.
 
@@ -80,7 +80,7 @@ Y_target = rng.randn(n_target, 2)
 M = ot.dist(X_source, Y_target)
 
 #############################################################################
-# Call the "ASGD" method to find the transportation matrix in the semicontinous
+# Call the "ASGD" method to find the transportation matrix in the semicontinuous
 # case.
 
 method = "ASGD"