| import networkx as nx | |
| def draw_networkx_nodes_ellipses(G, pos, | |
| nodelist=None, | |
| node_height=1, | |
| node_width=2, | |
| node_angle=0, | |
| node_color='r', | |
| node_shape='o', | |
| alpha=1.0, |
Python function that will rerun a function until its output convergences. The output must be numerical values. It can been an int, float, list, or array. Requires numpy.
This method runs the function in chunks (N=sample_size). It calculates the mean and standard deviation of the total set of samples after each chunk. If the mean and standard deviation of the total set is relatively close (see rtol) the last iteration, the sampling is assumed to have converged.
| # By Jake VanderPlas | |
| # License: BSD-style | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def discrete_cmap(N, base_cmap=None): | |
| """Create an N-bin discrete colormap from the specified input map""" |
A proposed JSON format for Dendropy's Tree class. This is D3 inspired, and easily portable to all of D3's hierarchical visualization examples.
For the following example, I'll use the simple string (taken from wikipedia) converted to a JSON format
(A:0.1,B:0.2,(C:0.3,D:0.4)E:0.5)F;
After a fair bit of digging, I was unable to find a single, defined JSON structure for phylogenetic data. With awesome visualization libraries (like D3) emerging, I think it is necessary to standardize a JSON grammar to allow libraries like DendroPy to access such visualizations.
In this document, I attempt to define this set of rules and grammar.
| from IPython.display import HTML | |
| from IPython.display import display | |
| # Taken from https://stackoverflow.com/questions/31517194/how-to-hide-one-specific-cell-input-or-output-in-ipython-notebook | |
| tag = HTML('''<script> | |
| code_show=true; | |
| function code_toggle() { | |
| if (code_show){ | |
| $('div.cell.code_cell.rendered.selected div.input').hide(); | |
| } else { |
| import pandas as pd | |
| import pandas_flavor as pf | |
| @pf.registor_dataframe_method | |
| def check_columns(df): | |
| # Required columns | |
| required_cols = {'x', 'y', 'z'} | |
| # Find columns missing from | |
| if required_cols.issubset(df.columns) is False: |
Create conda environment.
conda create -n <kernel name> pip jupyter
Activate environment.
source activate <kernel name>
| import phylovega as phy | |
| tree = phy.Tree(data).encode( | |
| nodes=[], | |
| edges=[], | |
| node_color=[], | |
| edge_color=[], |