From dd70ff215f145ae2db0a7add57a664fc3ab77a45 Mon Sep 17 00:00:00 2001
From: Jun Wang <140130585+AugustJW@users.noreply.github.com>
Date: Tue, 12 Dec 2023 11:48:27 +0800
Subject: [PATCH] Add files via upload

add two visualisation functions in utils
---
 pypots/utils/__init__.py |   1 +
 pypots/utils/visual.py   | 102 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 103 insertions(+)
 create mode 100644 pypots/utils/visual.py

diff --git a/pypots/utils/__init__.py b/pypots/utils/__init__.py
index 6c5074ee..fdf5413c 100644
--- a/pypots/utils/__init__.py
+++ b/pypots/utils/__init__.py
@@ -12,4 +12,5 @@
     "logging",
     "metrics",
     "random",
+    "visual",
 ]
diff --git a/pypots/utils/visual.py b/pypots/utils/visual.py
new file mode 100644
index 00000000..835523ba
--- /dev/null
+++ b/pypots/utils/visual.py
@@ -0,0 +1,102 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def plot_data(vals_obs, vals_eval, vals_imputed, dataidx = None, nrows = 10, ncols = 4, figsize=[24.0, 36.0]):
+    """ Plot the imputed values, the observed values, and the evaluated values of one multivariate timeseries. The observed values are marked with red 'x',  the evaluated values are marked with blue 'o', and the imputed values are marked with solid green line.
+    Parameters
+    ----------
+    vals_obs : ndarray,
+        The observed values
+
+    vals_eval : ndarray,
+        The evaluated values
+
+    vals_imputed : ndarray,
+        The imputed values
+
+    dataidx : int,
+        The index of the sample to be plotted
+
+    nrows : int,
+        The number of rows in the plot
+
+    ncols : int,
+        The number of columns in the plot
+    
+    figsize : list,
+        The size of the figure
+    """
+    n_s, n_l, n_c = vals_obs.shape
+    if dataidx == None:
+        dataidx = np.random.randint(low=0, high=n_s)
+
+    n_k = nrows*ncols
+    K = np.min([n_c, n_k])
+    L = n_l
+    plt.rcParams["font.size"] = 16
+    fig, axes = plt.subplots(nrows=nrows, ncols=ncols,figsize=(figsize[0], figsize[1]))
+    # fig.delaxes(axes[-1][-1])
+
+    for k in range(K):
+        df = pd.DataFrame({"x":np.arange(0,L), "val":vals_imputed[dataidx,:,k]})
+        df1 = pd.DataFrame({"x":np.arange(0,L), "val":vals_obs[dataidx,:,k]})
+        df2 = pd.DataFrame({"x":np.arange(0,L), "val":vals_eval[dataidx,:,k]})
+        row = k // ncols
+        col = k % ncols
+        axes[row][col].plot(df1.x,df1.val, color = 'r',  marker = 'x', linestyle='None')
+        axes[row][col].plot(df2.x,df2.val, color = 'b',  marker = 'o', linestyle='None')
+        axes[row][col].plot(df.x,df.val, color = 'g', linestyle='solid')
+        if col == 0:
+            plt.setp(axes[row, 0], ylabel='value')
+        if row == -1:
+            plt.setp(axes[-1, col], xlabel='time')
+
+def plot_missingness(mask, t_max = 1, t_min = 0, dataidx = None):
+    """ Plot the missingness pattern of one multivariate timeseries. For each feature, the observed timestamp is marked with blue '|'. The distribution of sequence lengths is also plotted. Hereby, the sequence length is defined as the number of observed timestamps in one feature.
+    Parameters
+    ----------
+    mask : ndarray,
+        The mask matrix of one multivariate timeseries
+
+    t_max : int,
+        The maximum time
+
+    t_min : int,
+        The minimum time
+
+    dataidx : int,
+        The index of the sample to be plotted
+    """
+    n_s,n_l,n_c = mask.shape
+    time = np.repeat(np.repeat(np.linspace(0,t_max, n_l).reshape(1, n_l, 1), axis=2, repeats=n_c), axis=0, repeats=n_s)
+    if dataidx == None:
+        dataidx = np.random.randint(low=0, high=n_s)
+    fig, axes = plt.subplots(figsize=[12,3.5], dpi = 200, nrows=1, ncols=2)
+    plt.subplots_adjust(hspace=0.1)
+    seq_len = []
+    sample = np.transpose(time[dataidx], (1, 0))
+    mask_s = np.transpose(mask[dataidx], (1, 0))
+    for feature_idx in range(n_c):
+        t = sample[feature_idx][mask_s[feature_idx]==1]
+        axes[0].scatter(t, np.ones_like(t)*(feature_idx), alpha=1, c='C0', marker="|")
+        seq_len.append(len(t)) 
+    axes[0].set_title("Visualization of arrival times", fontsize=9)
+    axes[0].set_xlabel("Time", fontsize=7)
+    axes[0].set_ylabel("Features #", fontsize=7)
+    axes[0].set_xlim(-1, n_l)
+    # axes[0].set_ylim(0, n_c-1)
+    axes[0].tick_params(axis="both", labelsize=7)  
+
+    axes[1].set_title("Distribution of sequence lengths", fontsize=9)
+    axes[1].hist(seq_len, bins = n_l, color="C1", range=(t_min, t_max),) 
+    axes[1].set_xlabel(r"Sequence length", fontsize=7)
+    axes[1].set_ylabel("Frequency", fontsize=7)
+    axes[1].tick_params(axis="both", labelsize=7)
+    plt.show()