semvec/pypi-packages huggingface dataset:
anutils 0.4.5
ml and single cell utils.
License: MIT
Keywords: anutils

anutils
=======

ML and single cell analysis utils.

usage
-----

general utils: ``anutils.*``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``anutls.glimpse`` is similar to ``dplyr::glimpse`` in R, but enhanced
in: - display the index - when passing ``show_unique=True``, display the
number of unique values for each column - when passing
``show_unique=True``, display the unique values instead of the first N
values for each column

.. code:: python

   import anutils as anu

   df = pd.DataFrame({
       'name': ['Alice', 'Bob', 'Carol', 'David', 'Eric'],
       'letters': ['a', 'a', 'b', 'b', 'c'],
       'digits': [1, 2, 3, 3, 3],
       'colors': ['r', 'g', 'b', 'k', 'k'],
   })
   df.index = df['name']
   anu.glimpse(df, show_unique=True)

   # output:
   # DataFrame: 5 rows, 4 columns
   # index (name)   <object> (5) ['Alice', 'Bob', 'Carol', 'David', 'Eric']
   # $ name         <object> (5) ['Alice', 'Bob', 'Carol', 'David', 'Eric']
   # $ letters      <object> (3) ['a', 'b', 'c']
   # $ digits       <int64>  (3) [1, 2, 3]
   # $ colors       <object> (4) ['r', 'g', 'b', 'k']

single cell utils: ``anutils.scutils.*``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

plotting
^^^^^^^^

.. code:: python

   from anutils import scutils as scu

   # a series of embeddings grouped by disease status
   scu.pl.embeddings(adata, basis='X_umap', groupby='disease_status', **kwargs) # kwargs for sc.pl.embedding

   # enhanced dotplot with groups in hierarchical order
   scu.pl.dotplot(adata, var_names, groupby, **kwargs) # kwargs for sc.pl.dotplot

cuda-accelerated scanpy functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

NOTE: to use these functions, you need to install rapids first. see
`installation <#installation>`__ for details.

.. code:: python

   from anutils.scutils import sc_cuda as cusc

   # 10-100 times faster than `scanpy.tl.leiden`
   cusc.sc.leiden(adata, resolution=0.5, key_added='leiden_0.5')

   # 10-100 times faster than `scib.metrics.silhouette`
   cusc.sb.silhouette(adata, group_key, embed)

machine learning utils:
-----------------------

.. code:: python

   import anutils.mlutils as ml

   # to be added

installation
------------

::

   pip install anutils

**NOTE**: To use ``anutils.scutils.sc_cuda``, you need to install rapids
first. see `rapids.ai <https://rapids.ai/start.html>`__ for details. For
example, to install rapids on a linux machine with cuda 11, you can run:

.. code:: bash

   pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com
   pip install cuml-cu11 --extra-index-url=https://pypi.nvidia.com
   pip install cugraph-cu11 --extra-index-url=https://pypi.nvidia.com
django-ipghrms-settingsapp 1.1
Django IPG HRMS APP settings_app
License: MIT


============================
Django IPG HRMS settings_app
============================


Quick start
============


1. Add 'settings_app' to your INSTALLED_APPS settings like this::

    INSTALLED_APPS = [
        'settings_app'
    ]

2. Include the settings_app to project URLS like this::

    path('settings_app/', include('settings_app.urls')),

3. Run ``python manage.py migrate`` to create settings_app model

4. Another Apps Need for this Apps::
    4.1. custom::
    4.2. employee::
    4.3. user