Cleaning up the software folder.

[pingpong.git] / python_ml / plotting-dbscan-diff.py

diff --git a/python_ml/plotting-dbscan-diff.py b/python_ml/plotting-dbscan-diff.py
deleted file mode 100644 (file)
index 15d7728..0000000
--- a/python_ml/plotting-dbscan-diff.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from sklearn.cluster import DBSCAN
-from sklearn import metrics
-import matplotlib.cm as cm
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Create a subplot with 1 row and 2 columns
-fig, (ax2) = plt.subplots(1, 1)
-fig.set_size_inches(7, 7)
-
-# Read from file
-# TODO: Just change the following path and filename 
-#      when needed to read from a different file
-path = "/scratch/July-2018/Pairs2/"
-# TODO: Change the order of the files below to generate
-#              the diff plot reversedly
-device1 = "dlink-siren-device-off"
-device2 = "dlink-siren-device-on"
-filename1 = device1 + ".txt"
-filename2 = device2 + ".txt"
-plt.ylim(0, 2000)
-plt.xlim(0, 2000)
-
-# Number of triggers
-trig = 50
-
-# PLOTTING FOR DEVICE ON EVENT
-# Read and create an array of pairs
-with open(path + filename1, "r") as pairs:
-       pairsArr1 = list()
-       for line in pairs:
-               # We will see a pair and we need to split it into xpoint and ypoint
-               xpoint, ypoint = line.split(", ")
-               pair = [int(xpoint), int(ypoint)]
-               pairsArr1.append(pair)
-
-# PLOTTING FOR DEVICE ON EVENT
-# Read and create an array of pairs
-with open(path + filename2, "r") as pairs:
-       pairsArr2 = list()
-       for line in pairs:
-               # We will see a pair and we need to split it into xpoint and ypoint
-               xpoint, ypoint = line.split(", ")
-               pair = [int(xpoint), int(ypoint)]
-               pairsArr2.append(pair)
-
-diff12 = [i for i in pairsArr1 if i not in pairsArr2]
-
-X = np.array(diff12);
-
-# Compute DBSCAN
-# eps = distances
-# min_samples = minimum number of members of a cluster
-db = DBSCAN(eps=10, min_samples=trig - 45).fit(X)
-core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
-core_samples_mask[db.core_sample_indices_] = True
-labels = db.labels_
-
-# Number of clusters in labels, ignoring noise if present.
-n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
-
-# Black removed and is used for noise instead.
-unique_labels = set(labels)
-
-colors = [plt.cm.Spectral(each)
-             for each in np.linspace(0, 1, len(unique_labels))]
-for k, col in zip(unique_labels, colors):
-       cluster_col = [1, 0, 0, 1]
-       if k == -1:
-           # Black used for noise.
-           col = [0, 0, 0, 1]
-
-       class_member_mask = (labels == k)
-
-       # print("Unique label: " + str(k) + " with freq: " + str(labels.tolist().count(k)))
-       xy = X[class_member_mask & core_samples_mask]
-       plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(cluster_col),
-                markeredgecolor='k', markersize=10)
-
-       xy = X[class_member_mask & ~core_samples_mask]
-       plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
-                markeredgecolor='k', markersize=6)
-
-count = 0
-for pair in diff12:
-       if labels[count] == -1:
-               plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]), fontsize=10)
-       else:
-       # Only print the frequency when this is a real cluster
-               plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) + 
-                       " - Freq:" + str(labels.tolist().count(labels[count])), fontsize=10)
-       count = count + 1
-
-plt.title(device1 + ' - diff - ' + device2)
-plt.show()
-
-