phylovelo.ana_utils
===================

.. py:module:: phylovelo.ana_utils


Functions
---------

.. autoapisummary::

   phylovelo.ana_utils.loadtree
   phylovelo.ana_utils.logNormalize
   phylovelo.ana_utils.plot_tree
   phylovelo.ana_utils.get_weight
   phylovelo.ana_utils.generate_grid
   phylovelo.ana_utils.velocity_embedding_to_grid
   phylovelo.ana_utils.velocity_plot
   phylovelo.ana_utils.plot_tree
   phylovelo.ana_utils.mullerplot
   phylovelo.ana_utils.label_name
   phylovelo.ana_utils.corr_plot


Module Contents
---------------

.. py:function:: loadtree(file)

   Reformat tree file from simulation data

   Args:
       file(str): 
           File path generated by simulation code
       
   Returns:
       Bio.Phylo.Tree: 
           biopython's phylo tree
       list[str]: 
           cell types of leave nodes


.. py:function:: logNormalize(data, scaling=1)

   Log normalize data

   Arg:
       data(pandas.DataFrame, numpy.array): 
           expression data
       scaling(int): 
           Normalization scale

   Return:
       normalized data


.. py:function:: plot_tree(tree: Bio.Phylo.tree, colors, ax, stain='all')

   Plot the simulated phylogenetic tree.

   Args:
       tree (Bio.Phylo.tree): 
           Loaded from loadtree function
       ax (matplotlib.axes): 
           Axes to return tree plot
       colors (list): 
           Loaded from loadtree
       is_show (bool): 
           Show the figure or return an axes
       stain ('all', 'terminals'): 
           'all': color all branches; 'terminals': color leave only

   Return:
       matplotlib.axes


.. py:function:: get_weight(x: list, distance: list, scale, length: int)

   Weight sum the velocity to grid

   Args:
       x: 
           neighbors
       distance: 
           List of distance to neighbors
       scale: 
           Scale factor
       length: 
           Length of neighbors

   Return:
       Weighted velocities



.. py:function:: generate_grid(xlim=(-1, 1), ylim=(-1, 1), density: int = 20)

   Generate grid to project velocities.

   Args:
       xlim: 
           Grid bound on x axis
       ylim: 
           Grid bound on y axis
       density: 
           How much grid to split
       
   Return:
       grid_X, grid_Y, grid_XY



.. py:function:: velocity_embedding_to_grid(pts: numpy.array, vel: numpy.array, nn: str:knn, radius = 'radius', grid_density: int = 20, n_neighbors: int = 4, radius: float = 2, xlim=(None, None), ylim=(None, None))

   Project velocities to grid

   Args:
       pts: 
           UMAP/tSNE coordinates
       vel: 
           Velocity vector
       nn: 
           knn or radius neighbors to use
       grid_density: 
           density of the grid
       n_neighbors: 
           How much neighbors, works when nn=='knn'
       radius: 
           How large radius, works when nn='radius'
       xlim: 
           Grid bound on x axis
       ylim: 
           Grid bound on y axis

   Return:
       


.. py:function:: velocity_plot(pts, vel, ax, figtype: str:stream, grid, point = 'grid', nn: str:knn, radius = 'radius', grid_density: int = 20, n_neighbors: int = 4, radius: float = 2, streamdensity: float = 1.5, xlim=(None, None), ylim=(None, None), **kwargs)

   Project velocities into embedding

   Args:
       pts: 
           UMAP/tSNE coordinates
       vel: 
           Velocity vector
       ax: 
           matplotlib.axes
       figtype: 
           'stream', 'grid' or 'point'(single cell)
       nn: 
           knn or radius neighbors to use
       grid_density: 
           density of the grid
       n_neighbors: 
           How much neighbors, works when nn=='knn'
       radius: 
           How large radius, works when nn='radius'
       streamdensity: 
           Density of streamplot, works when figtype==stream
       xlim: 
           Grid bound on x axis
       ylim: 
           Grid bound on y axis
       
   Return:
       matplotlib.axes



.. py:function:: plot_tree(tree, colors, ax: matplotlib.axes, colortab: list = ['gray', 'blue', 'green', 'orange', 'purple'], stain: str: 'all' or 'terminals' = 'all')

   Draw phylogenetic tree

   Args:
       tree: 
           Load from loadtree
       colors:  
           Load from loadtree
       ax: 
           matplotlib axes to draw on
       colortab: 
           A list of colors to paint different cell types
       stain: 
           'all' for color all branches, 'terminals' for color only terminals branches

   Return:
       matplotlib.axes


.. py:function:: mullerplot(data: numpy.ndarray, label: list, color: list, absolute: bool = 0, alpha: float = 0.8, ax: matplotlib.axes = None)

   Draw mullerplot

   Args:
       data: 
           Population size array. rows for cell type, columns for time point
       label: 
           Cell type names
       color: 
           Colors list
       absolute: 
           False: show frequency; True: show cell number
       alpha: 
           [0-1], transparent 
       ax: 
           axes to draw mullerplot
       
   Return:
       matplotlib.axes


.. py:function:: label_name(loc, cell_types, ax, fontsize=12, font='DejaVu Sans')

   Label cell type names on figures.

   Args:
       loc: 
           x, y locations in embedding
       cell_types: 
           Cell type names
       ax: 
           axes to label cell type name
       fontsize: 
           fontsize
       font: 
           font

   Return:
       matplotlib.axes


.. py:function:: corr_plot(x, y, ax, stats='pearson', r0_x=None, r0_y=None, r1_x=None, r1_y=None, 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
       fontsize:
           fontsize
   Return:
       matplotlib.axes