Merge pull request #88 from rflamary/doc_modules

[MRG] Update documentation and add quick start guide

Author: rflamary

Makefile

@@ -42,10 +42,10 @@ pep8 :
 	flake8 examples/ ot/ test/
 
 test : FORCE pep8
-	$(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html
+	$(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/  --cov=ot --cov-report html:cov_html
 
 pytest : FORCE 
-	$(PYTHON) -m pytest -v test/ --cov=ot
+	$(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/  --cov=ot
 
 uploadpypi :
 	#python setup.py register

README.md

@@ -54,6 +54,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp
 
 #### Pip installation
 
+Note that due to a limitation of pip, `cython` and `numpy` need to be installed
+prior to installing POT. This can be done easily with
+```
+pip install numpy cython
+```
+
 You can install the toolbox through PyPI with:
 ```
 pip install POT
@@ -63,6 +69,8 @@ or get the very latest version by downloading it and then running:
 python setup.py install --user # for user install (no root)
 ```
 
+
+
 #### Anaconda installation with conda-forge
 
 If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies:
@@ -151,7 +159,12 @@ You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.
 
 ## Acknowledgements
 
-The contributors to this library are:
+This toolbox has been created and is maintained by
+
+* [Rémi Flamary](http://remi.flamary.com/)
+* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
+
+The contributors to this library are 
 
 * [Rémi Flamary](http://remi.flamary.com/)
 * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)

docs/cache_nbrun

@@ -1 +1 @@
-{"plot_otda_mapping_colors_images.ipynb": "cc8bf9a857f52e4a159fe71dfda19018", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_stochastic.ipynb": "e18253354c8c1d72567a4259eb1094f7", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
+{"plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_UOT_1D.ipynb": "fc7dd383e625597bd59fff03a8430c91", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_fgw.ipynb": "2ba3e100e92ecf4dfbeb605de20b40ab", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_barycenter_fgw.ipynb": "e14100dd276bff3ffdfdf176f1b6b070", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_UOT_barycenter_1D.ipynb": "c72f0bfb6e1a79710dad3fef9f5c557c", "plot_otda_mapping_colors_images.ipynb": "cc8bf9a857f52e4a159fe71dfda19018", "plot_stochastic.ipynb": "e18253354c8c1d72567a4259eb1094f7", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_OT_2D_samples.ipynb": "912a77c5dd0fc0fafa03fac3d86f1502"}