update following conda installation

.gitignore

@@ -1,12 +1,6 @@
 # vscode
 .vscode
 
-# JBD dep
-baredSC/covarianceImportanceSampling.py
-baredSC/acf.py
-baredSC/logpriors.py
-baredSC/scaledAdaptiveMetropolis.py
-
 # pycache
 baredSC/__pycache__
 baredSC/lib/__pycache__

README.md

@@ -1,14 +1,16 @@
 baredSC
-========
-[![PyPI Version](https://img.shields.io/pypi/v/baredsc.svg?style=plastic)](https://pypi.org/project/baredSC/) [![DOI](https://zenodo.org/badge/370966963.svg)](https://zenodo.org/badge/latestdoi/370966963)
+=======
 
+[![PyPI Version](https://img.shields.io/pypi/v/baredsc.svg?style=plastic)](https://pypi.org/project/baredSC/) [![bioconda-badge](https://img.shields.io/conda/vn/bioconda/baredSC.svg?style=plastic)](https://anaconda.org/bioconda/baredsc) [![DOI](https://zenodo.org/badge/370966963.svg)](https://zenodo.org/badge/latestdoi/370966963)
 
 baredSC (Bayesian Approach to Retreive Expression Distribution of Single Cell) is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data. It uses the raw counts and the total number of UMI for each cell. The PDF is approximated by a number of 1d or 2d gaussians provided by the user. The likelihood is estimated using the asumption that the raw counts follow a Poisson distribution of parameter equal to the proportion of mRNA for the gene in the cell multiplied by the total number of UMI identified in this cell.
 
 Documentation
 -------------
+
 Visit [our documentation](https://baredsc.readthedocs.io) to see the possible options and follow the tutorials.
 
 Citation
 --------
+
 If you are using baredSC, please cite our [biorxiv paper](https://doi.org/10.1101/2021.05.26.445740).

baredSC_dev_env.yml

@@ -14,11 +14,10 @@ dependencies:
    - scipy >=1.3.0
    - corner >=2.0.0
    - anndata >=0.7
+   - samsam 
    - ipython # For the doc
    - sphinx-argparse # For the doc
    - twine # For the upload
    - pip
    - pip:
-      - --extra-index-url https://obswww.unige.ch/~delisle
-      - samsam >=0.1.2
       - sphinx-autorun # For the doc

baredSC_env.yml

@@ -3,15 +3,13 @@
 name: baredSC
 channels:
    - conda-forge
+   - bioconda
 dependencies:
    - numpy >=1.16
    - matplotlib >=3.1.1
    - pandas >=0.25.0
    - scipy >=1.3.0
    - corner >=2.0.0
    - anndata >=0.7
-   - pip
-   - pip:
-      - --extra-index-url https://obswww.unige.ch/~delisle
-      - samsam >=0.1.2
-      - baredSC
+   - samsam
+   - baredSC

docs/content/installation.rst

@@ -25,8 +25,14 @@ Dependencies of a python package from Jean-Baptiste Delisle dedicated to mcmc:
 Installation
 ------------
 
-For the moment you can install it with pip:
+You can install it with pip:
 
-pip install --extra-index-url https://obswww.unige.ch/~delisle baredSC
+```
+pip install baredSC
+```
 
-It may be accessible in conda later.
+Or with conda:
+
+```
+conda create -n baredSC -c bioconda -c conda-forge baredsc
+```

docs/index.rst

@@ -22,9 +22,3 @@ baredSC is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence
    content/releases.rst
    content/citation.rst
 
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

requirements.txt

@@ -1,4 +1,3 @@
---extra-index-url https://obswww.unige.ch/~delisle
 numpy >=1.16
 matplotlib >=3.1.1
 pandas >=0.25.0