From 4b1565ac7b5b0e61fa028c398650cc9162db4a53 Mon Sep 17 00:00:00 2001
From: glia <glia@dtu.dk>
Date: Tue, 13 Aug 2024 15:10:03 +0200
Subject: [PATCH] Upload helper.py
---
helper.py | 180 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 180 insertions(+)
create mode 100644 helper.py
diff --git a/helper.py b/helper.py
new file mode 100644
index 0000000..ac6606f
--- /dev/null
+++ b/helper.py
@@ -0,0 +1,180 @@
+# -*- 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
--
GitLab