// final String inputPcapFile = path + "/2018-07/dlink/dlink.wlan1.local.pcap";
// final String outputPcapFile = path + "/2018-07/dlink/dlink-processed.pcap";
// final String triggerTimesFile = path + "/2018-07/dlink/dlink-july-26-2018.timestamps";
-// final String deviceIp = "192.168.1.199"; // .246 == phone; .199 == dlink plug?
+// final String deviceIp = "192.168.1.246"; // .246 == phone; .199 == dlink plug?
// 2) TP-Link July 25 experiment
// final String inputPcapFile = path + "/2018-07/tplink/tplink.wlan1.local.pcap";
// final String deviceIp = "192.168.1.140"; // .246 == phone; .140 == TP-Link bulb
// 7) Kwikset Doorlock August 6 experiment
-// final String inputPcapFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock.wlan1.local.pcap";
-// final String outputPcapFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock-processed.pcap";
-// final String triggerTimesFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock-aug-6-2018.timestamps";
-// final String deviceIp = "192.168.1.246"; // .246 == phone; .142 == SmartThings Hub (note: use eth0 capture for this!)
+ final String inputPcapFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock.wlan1.local.pcap";
+ final String outputPcapFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock-processed.pcap";
+ final String triggerTimesFile = path + "/2018-08/kwikset-doorlock/kwikset-doorlock-aug-6-2018.timestamps";
+ final String deviceIp = "192.168.1.246"; // .246 == phone; .142 == SmartThings Hub (note: use eth0 capture for this!)
// 8) Hue Bulb August 7 experiment
// final String inputPcapFile = path + "/2018-08/hue-bulb/hue-bulb.wlan1.local.pcap";
// final String triggerTimesFile = path + "/2018-08/blossom/blossom-aug-13-2018.timestamps";
// final String deviceIp = "192.168.1.246"; // .246 == phone; .229 == sprinkler
- // 13) DLink siren August 14 experiment
- final String inputPcapFile = path + "/2018-08/dlink-siren/dlink-siren.wlan1.local.pcap";
- final String outputPcapFile = path + "/2018-08/dlink-siren/dlink-siren-processed.pcap";
- final String triggerTimesFile = path + "/2018-08/dlink-siren/dlink-siren-aug-14-2018.timestamps";
- final String deviceIp = "192.168.1.246"; // .246 == phone; .183 == siren
+// // 13) DLink siren August 14 experiment
+// final String inputPcapFile = path + "/2018-08/dlink-siren/dlink-siren.wlan1.local.pcap";
+// final String outputPcapFile = path + "/2018-08/dlink-siren/dlink-siren-processed.pcap";
+// final String triggerTimesFile = path + "/2018-08/dlink-siren/dlink-siren-aug-14-2018.timestamps";
+// final String deviceIp = "192.168.1.183"; // .246 == phone; .183 == siren
// 14) Nest thermostat August 15 experiment
// final String inputPcapFile = path + "/2018-08/nest/nest.wlan1.local.pcap";
--- /dev/null
+from sklearn.cluster import DBSCAN
+from sklearn import metrics
+import matplotlib.cm as cm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# metric function for clustering
+def metric(x, y):
+ # Compare 2 datapoints in array element 2 and 3 that contains C or S
+ if x[2] != y[2] or x[3] != y[3]:
+ # We are not going to cluster these together since they have different directions
+ return sys.maxsize;
+ else:
+ # Compute Euclidian distance here
+ return math.sqrt((x[0] - y[0])**2 + (x[1] - y[1])**2)
+
+# Create a subplot with 1 row and 2 columns
+fig, (ax2) = plt.subplots(1, 1)
+fig.set_size_inches(20, 20)
+
+# Read from file
+# TODO: Just change the following path and filename
+# when needed to read from a different file
+path = "/scratch/July-2018/Pairs3/"
+# TODO: Change the order of the files below to generate
+# the diff plot reversedly
+device1 = "kwikset-off-phone-side"
+device2 = "kwikset-on-phone-side"
+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()
+ pairsSrcLabels1 = list()
+ for line in pairs:
+ # We will see a pair and we need to split it into xpoint and ypoint
+ xpoint, ypoint, srcHost1, srcHost2, src1, src2 = line.split(", ")
+ # Assign 1000 for client and 0 for server to create distance
+ src1Val = 1000 if src1 == 'C' else 0
+ src2Val = 1000 if src2 == 'C' else 0
+ pair = [int(xpoint), int(ypoint), int(src1Val), int(src2Val)]
+ pairSrc = [int(xpoint), int(ypoint), srcHost1, srcHost2, src1, src2]
+ # Array of actual points
+ pairsArr1.append(pair)
+ # Array of source labels
+ pairsSrcLabels1.append(pairSrc)
+
+# PLOTTING FOR DEVICE ON EVENT
+# Read and create an array of pairs
+with open(path + filename2, "r") as pairs:
+ pairsArr2 = list()
+ pairsSrcLabels2 = list()
+ for line in pairs:
+ # We will see a pair and we need to split it into xpoint and ypoint
+ xpoint, ypoint, srcHost1, srcHost2, src1, src2 = line.split(", ")
+ # Assign 1000 for client and 0 for server to create distance
+ src1Val = 1000 if src1 == 'C' else 0
+ src2Val = 1000 if src2 == 'C' else 0
+ pair = [int(xpoint), int(ypoint), int(src1Val), int(src2Val)]
+ pairSrc = [int(xpoint), int(ypoint), srcHost1, srcHost2, src1, src2]
+ # Array of actual points
+ pairsArr2.append(pair)
+ # Array of source labels
+ pairsSrcLabels2.append(pairSrc)
+
+diff12 = [i for i in pairsArr1 if i not in pairsArr2]
+diff12SrcLabels = [i for i in pairsSrcLabels1 if i not in pairsSrcLabels2]
+
+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)
+
+# Print lengths
+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
+
+# Print source-destination labels
+count = 0
+for pair in diff12SrcLabels:
+ # Only print the frequency when this is a real cluster
+ plt.text(pair[0], pair[1], str(pair[4]) + "->" + str(pair[5]))
+ count = count + 1
+
+plt.title(device1 + ' - diff - ' + device2)
+plt.show()
+
+
--- /dev/null
+from sklearn.cluster import DBSCAN
+from sklearn import metrics
+import sys
+import math
+import matplotlib.cm as cm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# metric function for clustering
+def metric(x, y):
+ # Compare 2 datapoints in array element 2 and 3 that contains C or S
+ if x[2] != y[2] or x[3] != y[3]:
+ # We are not going to cluster these together since they have different directions
+ return sys.maxsize;
+ else:
+ # Compute Euclidian distance here
+ return math.sqrt((x[0] - y[0])**2 + (x[1] - y[1])**2)
+
+# Create a subplot with 1 row and 2 columns
+fig, (ax2) = plt.subplots(1, 1)
+fig.set_size_inches(20, 20)
+
+
+# Read from file
+# TODO: Just change the following path and filename
+# when needed to read from a different file
+path = "/scratch/July-2018/Pairs3/"
+device = "kwikset-off-phone-side"
+filename = device + ".txt"
+plt.ylim(0, 2000)
+plt.xlim(0, 2000)
+
+# Number of triggers
+trig = 50
+
+# Read and create an array of pairs
+with open(path + filename, "r") as pairs:
+ pairsArr = []
+ pairsSrcLabels = []
+ for line in pairs:
+ # We will see a pair and we need to split it into xpoint and ypoint
+ xpoint, ypoint, srcHost1, srcHost2, src1, src2 = line.split(", ")
+ # Assign 1000 for client and 0 for server to create distance
+ src1Val = 1000 if src1 == 'C' else 0
+ src2Val = 1000 if src2 == 'C' else 0
+ pair = [int(xpoint), int(ypoint), int(src1Val), int(src2Val)]
+ pairSrc = [int(xpoint), int(ypoint), srcHost1, srcHost2, src1, src2]
+ # Array of actual points
+ pairsArr.append(pair)
+ # Array of source labels
+ pairsSrcLabels.append(pairSrc)
+
+# 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=20, min_samples=trig - 5).fit(X)
+# TODO: This is just for seeing more clusters
+db = DBSCAN(eps=20, min_samples=trig - 45, metric=metric).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):
+ cluster_col = [1, 0, 0, 1]
+ if k == -1:
+ # Black used for noise.
+ col = [0, 0, 0, 1]
+
+ class_member_mask = (labels == 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)
+
+# Print lengths
+count = 0
+for pair in pairsArr:
+ 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]) +
+ " f: " + str(labels.tolist().count(labels[count])), fontsize=10)
+ count = count + 1
+
+# Print source-destination labels
+count = 0
+for pair in pairsSrcLabels:
+ # Only print the frequency when this is a real cluster
+ plt.text(pair[0], pair[1], str(pair[4]) + "->" + str(pair[5]))
+ count = count + 1
+
+plt.title(device + ' - Clusters: %d' % n_clusters_)
+plt.show()
+
else:
# Only print the frequency when this is a real cluster
plt.text(pair[0], pair[1], str(pair[0]) + ", " + str(pair[1]) +
- " f: " + str(labels.tolist().count(labels[count])), fontsize=10)
+ " : " + str(labels.tolist().count(labels[count])), fontsize=10)
count = count + 1
projects / pingpong.git / commitdiff