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# -*- coding: utf-8 -*-
"""
Generic helper functions
@author: Qianliang Li (glia@dtu.dk)
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import time
# Function to get the current time in my preferred format
def time_now():
c_time = time.localtime()
c_time = time.strftime("%a %d %b %Y %H:%M:%S", c_time)
return c_time
def numpy_arr_to_pandas_df(array, col_names: list, col_values: list, dtypes: list):
""" Convert array to 2D Dataframe """
# The dimensions of the array will each be a column with numbers
# and the last column will be the actual values
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, array.shape),
indexing="ij"))) + [array.ravel()])
# Initialize the dataframe
df = pd.DataFrame(arr, columns = col_names)
# Change of the numerical coding to the actual values
temp_df = df.copy() # make temp df to not sequentially overwrite when modifying
for col in range(len(col_values)):
col_name = df.columns[col]
# Fix dtype
temp_df[col_name] = temp_df[col_name].astype(dtypes[col])
# Insert col values
for shape in range(array.shape[col]):
temp_df.loc[df.iloc[:,col] == shape,col_name]\
= col_values[col][shape]
df = temp_df # replace original df
return df
def get_feature_indices(X, feature_name_list):
col_idx = np.zeros(len(X.columns), dtype=bool)
for fset in range(len(feature_name_list)):
temp_feat = feature_name_list[fset]
col_idx0 = X.columns.str.contains(temp_feat)
col_idx = np.logical_or(col_idx,col_idx0) # append all trues
return col_idx
def heatmap(data, row_labels, col_labels, ax=None, rotate_x_labels=True,
cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and two lists of labels.
Parameters
----------
data
A 2D numpy array of shape (N, M).
row_labels
A list or array of length N with the labels for the rows.
col_labels
A list or array of length M with the labels for the columns.
ax
A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If
not provided, use current axes or create a new one. Optional.
rotate_x_labels
Boolean variable for rotating the x labels on top of the figure.
cbar_kw
A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional.
cbarlabel
The label for the colorbar. Optional.
**kwargs
All other arguments are forwarded to `imshow`.
"""
if not ax:
ax = plt.gca()
# Plot the heatmap
im = ax.imshow(data, **kwargs)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks...
ax.set_xticks(np.arange(data.shape[1]))
ax.set_yticks(np.arange(data.shape[0]))
# ... and label them with the respective list entries.
ax.set_xticklabels(col_labels)
ax.set_yticklabels(row_labels)
# Let the horizontal axes labeling appear on top.
ax.tick_params(top=True, bottom=False,
labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment.
if rotate_x_labels == True:
plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
rotation_mode="anchor")
# Turn spines off and create white grid.
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
ax.tick_params(which="minor", bottom=False, left=False)
return im, cbar
def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
sem=None, p_value=None,
textcolors=["black", "white"],
threshold=None, **textkw):
"""
A function to annotate a heatmap.
Parameters
----------
im
The AxesImage to be labeled.
data
Data used to annotate. If None, the image's data is used. Optional.
sem
Standard error or mean. In the same shape as Data. Optional.
SD can also be used, this input just insert the number as text.
valfmt
The format of the annotations inside the heatmap. This should either
use the string format method, e.g. "$ {x:.2f}", or be a
`matplotlib.ticker.Formatter`. Optional.
textcolors
A list or array of two color specifications. The first is used for
values below a threshold, the second for those above. Optional.
threshold
Value in data units according to which the colors from textcolors are
applied. If None (the default) uses the middle of the colormap as
separation. Optional.
**kwargs
All other arguments are forwarded to each call to `text` used to create
the text labels.
"""
if not isinstance(data, (list, np.ndarray)):
data = im.get_array()
# Normalize the threshold to the images color range.
if threshold is not None:
threshold = im.norm(threshold)
else:
threshold = im.norm(data.max())/2.
# Set default alignment to center, but allow it to be
# overwritten by textkw.
kw = dict(horizontalalignment="center",
verticalalignment="center")
kw.update(textkw)
# Get the formatter in case a string is supplied
if isinstance(valfmt, str):
valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
# Loop over the data and create a `Text` for each "pixel".
# Change the text's color depending on the data.
texts = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
# Add text and SEM if not none
if sem is not None:
text = im.axes.text(j, i, (valfmt(data[i, j], None)+"\n"+u"\u00B1"+
valfmt(sem[i, j], None)), **kw)
elif p_value is not None:
text = im.axes.text(j, i, (valfmt(data[i, j], None)+"\n"+"p = "+
valfmt(p_value[i, j], None)), **kw)
else:
text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
texts.append(text)
return texts