Integrate coverage badges and FAµST logo on pypi, readme and anaconda descriptions.

README.md

 [![pipeline status](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/pipeline.svg)](https://gitlab.inria.fr/faustgrp/faust/commits/hakim_branch)
+![pyfaust test coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=pyfaustcov)
+![matfaust test coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=matfaust_test_code_coverage&key_text=matfaustcov)
 ![FAµST Logo](./gen_doc/images/logo.png)
 
 # FAuST Toolbox -- Flexible Approximate Multi-Layer Sparse Transform

misc/continuous_integration/conda_pkg/meta.yaml.in

@@ -30,6 +30,10 @@ about:
   license_file: @LICENSE_FILEPATH@
   summary: 'FAµST python toolbox'
   description: |
+    .. image:: https://faust.inria.fr/files/2016/11/cropped-FAUST.jpeg
+    .. image:: https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/pipeline.svg
+    .. image:: https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=coverage
+
     The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).
 
     FaµST can be used to:
@@ -37,4 +41,3 @@ about:
         - learn dictionaries with an intrinsically efficient implementation,
         - compute (approximate) fast Fourier transforms on graphs.
 
-

wrapper/python/setup.py.in

@@ -71,7 +71,10 @@ setup(
     author = 'INRIA',
     author_email = 'remi.gribonval@inria.fr',
     description = 'FAµST python toolbox',
-    long_description = """The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).  FaµST can be used to:
+    long_description = """![FAµST logo](https://faust.inria.fr/files/2016/11/cropped-FAUST.jpeg)
+    ![pipeline status](https://gitlab.inria.fr/faustgrp/faust/badges/main/pipeline.svg) ![coverage](https://gitlab.inria.fr/faustgrp/faust/badges/hakim_branch/coverage.svg?job=pyfaust_test_code_coverage&key_text=coverage)
+
+    The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).  FaµST can be used to:
 
         - speedup / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
 
@@ -80,6 +83,7 @@ setup(
         - compute (approximate) fast Fourier transforms on graphs.
 
     """,
+    long_description_content_type='text/markdown',
     classifiers = [ 'License :: OSI Approved :: BSD License',
                    'Programming Language :: Python :: 3',
                   @GPU_CUDA_VERSION_PYPI_CLASSIFIER@],