import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.markers as mk
import matplotlib.pylab as plt
[docs]def sp_plot(df, x_col, y_col, color_col,ci = None,domain_range=[0, 20, 0 , 20],
ax=None,aggplot=True,x_jitter=0,height=3,legend=True):
"""
create SP vizualization plot from 2 columns of a df
"""
# # create axes if not passed
# if ax is None:
# fig = plt.figure()
# ax = fig.add_subplot(111)
all_markers = list(mk.MarkerStyle.markers.keys())
n_markers = df[color_col].unique().shape[0] # number unique
cur_markers = all_markers[:n_markers]
sns.lmplot(x_col, y_col, data=df, hue=color_col, ci=ci,
markers =cur_markers, palette="Set1",x_jitter=x_jitter,
height=height,legend=legend)
if aggplot:
# adda whole data regression line, but don't cover the scatter data
sns.regplot(x_col, y_col, data=df, color='black', scatter=False, ci=ci,)
plt.axis(domain_range)
[docs]def plot_clustermat(z,fmt=None):
"""
black and white matshow for clustering and feat allocation matrices
Parameters
-----------
z : nparray, square to be plotted
fmt : if z is not a square, then str of what it is
fmt options:
'crplist' : a list of values from zero to k
'ibplist' : a list of lists of varying lengths
'list' : a list, but not nparray otherwise ready to plot
"""
processing = {'crplist': lambda x: list_to_mat(x),
'ibplist': lambda x: make_square(x),
'list': lambda x: np.asarray(x),
None: lambda x: x}
z_mat = processing[fmt](z)
# print(z_mat)
N,K = z_mat.shape
# no white grid
sns.set_style("whitegrid", {'axes.grid' : False})
# plot the data
plt.matshow(z_mat,cmap=plt.cm.gray_r)
# make the tick marks at the ints
ax = plt.gca()
ax.set_xticks(np.arange(0, K, 1))
ax.set_yticks(np.arange(0, N, 1))
# Labels for major ticks
ax.set_xticklabels(np.arange(0, K, 1))
ax.set_yticklabels(np.arange(0, N, 1))
# Minor ticks at 1/2 marks
ax.set_xticks(np.arange(-.5, K, 1), minor=True)
ax.set_yticks(np.arange(-.5, N, 1), minor=True)
# Gridlines based on minor ticks
plt.grid(which='minor', color='k', linestyle='-', linewidth=3)
def make_square(z):
"""
convert a list of lists of varying sizes to a square matrix
"""
D = len(z[-1])
return np.asarray([np.concatenate((z_i,np.zeros([D-len(z_i)]))) for z_i in z])
def list_to_mat(z):
"""
make a list of length N with values 1 to K into an NxK binanry matrix
"""
K = np.max(z)
tmp = np.eye(K+1)
return np.asarray([tmp[z_i] for z_i in z])