scPhyloX.tools
==============

.. py:module:: scPhyloX.tools


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/scPhyloX/tools/PhyloCircular/index
   /autoapi/scPhyloX/tools/data_factory/index
   /autoapi/scPhyloX/tools/utils/index


Attributes
----------

.. autoapisummary::

   scPhyloX.tools.population_size
   scPhyloX.tools.tumor_data
   scPhyloX.tools.tumor_size


Functions
---------

.. autoapisummary::

   scPhyloX.tools.corr_plot
   scPhyloX.tools.colless_index
   scPhyloX.tools.colless_index_corrected
   scPhyloX.tools.ext_gen
   scPhyloX.tools.ext_cid
   scPhyloX.tools.reconstruct
   scPhyloX.tools.cell_number_calc
   scPhyloX.tools.get_branchlen
   scPhyloX.tools.get_mutnum
   scPhyloX.tools.mutnum_fly
   scPhyloX.tools.branchlen_fly
   scPhyloX.tools.polar_plot


Package Contents
----------------

.. py:function:: corr_plot(x, y, ax, stats='pearson', r0_x=None, r0_y=None, r1_x=None, r1_y=None, line='fit', alternative='two-sided', fontsize=10)

   Draw a scatter plot of the two sets of data and show their correlation coefficients

   Args:
       x: 
           data1
       y: 
           data2
       ax: 
           axes to draw scatter on
       stats: 
           pearson or spearman
       r0_x, r0_y, r1_x, r1_y: 
           locations to label the correlation coefficient and the p-value
       line:
           fit or diag, fit for linear regression
       alternative:
           greater, less or two-sided
       fontsize:
           fontsize
   Return:
       matplotlib.axes


.. py:function:: colless_index(tree)

   calculate Colless' index

   Args:
       tree:
           Phylogenetic tree
   Return:
       float:
           Colless' index


.. py:function:: colless_index_corrected(tree)

   calculate corrected Colless' index

   Args:
       tree:
           Phylogenetic tree
   Return:
       float:
           corrected Colless' index


.. py:function:: ext_gen(cell)

   Extract generation with given cell name


.. py:function:: ext_cid(cell)

   Extract cell id with given cell name


.. py:function:: reconstruct(lineage_info, sel_cells, file_name=None)

   Reconstruct phylogenetic tree of simulation data

   Args:
       lineage_info:
           system.lineage_info
       sel_cells:
           cells to build phylogenetic tree
       file_name:
           path to save tree. if None, just return
   Return:
       text newick tree


.. py:data:: population_size

.. py:function:: cell_number_calc(w, l, r, coef=100000000.0)

   Calculate tumor cell number with given width, height and scale

   Args:
       w: 
           width
       l:
           heigth
       r:
           scale
       coef:
           cell density
   Return:
       float:
           cell number


.. py:data:: tumor_data

.. py:data:: tumor_size

.. py:function:: get_branchlen(seqtab: numpy.ndarray = None, sys=None)

   Calculate LP distance with given mutation character matrix

   Args:
       seqtab:
           mutation character matrix
       sys:
           Gillespie simulator with results
   Return:
       np.array:
           LP distance 


.. py:function:: get_mutnum(seqtab=None, sys=None, filter_trunk=True)

   Calculate LR distance with given mutation character matrix

   Args:
       seqtab:
           mutation character matrix
       sys:
           Gillespie simulator with results
       filter_trunk:
           Filter trunk mutations
   Return:
       np.array:
           LR distance 


.. py:function:: mutnum_fly(seqs, ref)

   calculate LR distance for SMALT-fly dataset

   Args:
       seqs:
           DNA muataion character matrix
       ref:
           reference seuence

   Return:
       np.array:
           LR distance 


.. py:function:: branchlen_fly(seqs, ref, rs=1)

   calculate LP distance for SMALT-fly dataset

   Args:
       seqs:
           DNA muataion character matrix
       ref:
           reference seuence
       rs:
           resampling ratio

   Return:
       np.array:
           LP distance 


.. py:function:: polar_plot(tree, ax=None, arc=350, start=0, root_distance=0.1, label_leaf=True, patch_leaf=True, wedge=True, pad_label=0, pad_patch=0, pad_wedge=0, externals=[], lw=1)