Adding plotting scriptto plot both on and off plots at once.

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

diff --git a/python_ml/plotting-dbscan-complete.py b/python_ml/plotting-dbscan-complete.py
new file mode 100644 (file)
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+++ b/python_ml/plotting-dbscan-complete.py
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+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/Pairs/"
+device1 = "tplink-bulb-on"
+device2 = "tplink-bulb-off"
+filename1 = device1 + ".txt"
+filename2 = device2 + ".txt"
+
+# Number of triggers
+trig = 50
+
+# PLOTTING FOR DEVICE ON EVENT
+# Read and create an array of pairs
+with open(path + filename1, "r") as pairs:
+       pairsArr = []
+       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)]
+               pairsArr.append(pair)
+
+# Formed array of pairs                
+#print(pairsArr)
+X = np.array(pairsArr);
+
+# Compute DBSCAN
+# eps = distances
+# min_samples = minimum number of members of a cluster
+db = DBSCAN(eps=30, min_samples=trig - 5).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)
+#print('Estimated number of clusters: %d' % n_clusters_)
+
+# Black removed and is used for noise instead.
+unique_labels = set(labels)
+#print("Labels: " + str(labels))
+
+colors = [plt.cm.Spectral(each)
+             for each in np.linspace(0, 1, len(unique_labels))]
+for k, col in zip(unique_labels, colors):
+       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',
+                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 pairsArr:
+       plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) + 
+               "\nFreq: " + str(labels.tolist().count(labels[count])), fontsize=10)
+       count = count + 1
+
+#====================================================================================================
+
+# PLOTTING FOR DEVICE ON EVENT
+# Read and create an array of pairs
+with open(path + filename2, "r") as pairs:
+       pairsArr = []
+       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)]
+               pairsArr.append(pair)
+
+# Formed array of pairs                
+#print(pairsArr)
+X = np.array(pairsArr);
+
+# Compute DBSCAN
+# eps = distances
+# min_samples = minimum number of members of a cluster
+db = DBSCAN(eps=10, min_samples=trig - 5).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)
+#print('Estimated number of clusters: %d' % n_clusters_)
+
+import matplotlib.pyplot as plt
+
+# Black removed and is used for noise instead.
+unique_labels = set(labels)
+#print("Labels: " + str(labels))
+
+colors = [plt.cm.Spectral(each)
+             for each in np.linspace(0, 1, len(unique_labels))]
+for k, col in zip(unique_labels, colors):
+       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',
+                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 pairsArr:
+       plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) + 
+               "\nFreq: " + str(labels.tolist().count(labels[count])), fontsize=10)
+       count = count + 1
+
+
+       
+plt.title(device1 + ' & ' + device2 + ' - Estimated number of clusters: %d' % n_clusters_)
+plt.show()
+
+