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logfiles_analysis
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Initial checkin

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Files changed (5) hide show
  1. .gitignore +3 -0
  2. app.py +91 -0
  3. app_desktop.py +90 -0
  4. cattle_log.json +1004 -0
  5. requirements.txt +3 -0
.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ .DS_Store
2
+ .env
3
+
app.py ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import numpy as np
3
+ from sklearn.ensemble import IsolationForest
4
+ import matplotlib.pyplot as plt
5
+ from mpl_toolkits.mplot3d import Axes3D # This import is necessary for 3D plotting, even if it seems unused
6
+ import json
7
+
8
+ # Title for Streamlit app
9
+ st.title('Isolation Forest Anomaly Detection')
10
+
11
+ # Upload log to parse
12
+ json_content = None
13
+ uploaded_file = st.file_uploader("Upload logfile", type="json")
14
+
15
+ if uploaded_file:
16
+ # Get the JSON content
17
+ log_content = json.loads(uploaded_file.getvalue())['logs']
18
+
19
+ # Select dimensions
20
+ num_dimensions = st.selectbox('Select number of dimensions:', [1, 2, 3], index=2)
21
+
22
+ X = []
23
+
24
+ # Iterate over each log entry in the log_content
25
+ for log_entry in log_content:
26
+ # Extract and convert the necessary attributes
27
+ total_today_str = log_entry['distanceTraveled']['totalToday'].rstrip('m')
28
+ heart_rate = int(log_entry['healthData']['heartRate']) # Assuming heart rate is always an integer
29
+ weight_str = log_entry['healthData']['weight'].rstrip('kg')
30
+
31
+ # Convert the distance and weight to floating-point values
32
+ total_today = float(total_today_str) # Convert distance to float
33
+ weight = float(weight_str) # Convert weight to float
34
+
35
+ # Create a 3D vector for the current log entry and append it to the list of vectors
36
+ vector_3d = [total_today, heart_rate, weight]
37
+ X.append(vector_3d)
38
+
39
+ # Convert X into a NumPy array for easier slicing
40
+ X = np.array(X)
41
+
42
+ # Generating synthetic data
43
+ rng = np.random.RandomState(42)
44
+
45
+ # Fit the model
46
+ clf = IsolationForest(max_samples=100, random_state=rng)
47
+ clf.fit(X)
48
+ y_pred = clf.predict(X)
49
+
50
+ # Plotting based on the selected number of dimensions
51
+ if num_dimensions == 3:
52
+ fig = plt.figure(figsize=(10, 7))
53
+ ax = fig.add_subplot(111, projection='3d')
54
+ ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=['red' if pred == -1 else 'blue' for pred in y_pred], s=50)
55
+ ax.set_title("Isolation Forest Anomaly Detection (3D)")
56
+ ax.set_xlabel("Distance travelled")
57
+ ax.set_ylabel("Heartrate")
58
+ ax.set_zlabel("Weight")
59
+ st.pyplot(fig)
60
+ elif num_dimensions == 1:
61
+ fig, ax = plt.subplots()
62
+ # For 1D, ensure to select one dimension (e.g., X[:, 0] for distance travelled)
63
+ ax.scatter(X[:, 0], np.zeros_like(X[:, 0]), color=['red' if pred == -1 else 'blue' for pred in y_pred], s=50)
64
+ ax.set_title("Isolation Forest Anomaly Detection (1D)")
65
+ ax.set_xlabel("Distance travelled")
66
+ st.pyplot(fig)
67
+ else: # Default to 2D plotting
68
+ fig, ax = plt.subplots()
69
+ ax.scatter(X[:, 0], X[:, 1], color=['red' if pred == -1 else 'blue' for pred in y_pred], s=50)
70
+ ax.set_title("Isolation Forest Anomaly Detection (2D)")
71
+ ax.set_xlabel("Distance travelled")
72
+ ax.set_ylabel("Heartrate")
73
+ st.pyplot(fig)
74
+
75
+
76
+
77
+
78
+ # Random data
79
+ # Generating a dataset with points. 95 points are generated from a Gaussian distribution,
80
+ # and 5 points are anomalies added manually.
81
+ # X = 0.3 * rng.randn(95, num_dimensions)
82
+ # X = np.r_[X + 2, X - 2]
83
+ # X_outliers = rng.uniform(low=-4, high=4, size=(5, num_dimensions))
84
+ # X = np.r_[X, X_outliers]
85
+
86
+ # # # Fit the model
87
+ # clf = IsolationForest(max_samples=100, random_state=rng)
88
+ # clf.fit(X)
89
+
90
+ # Predictions
91
+ # y_pred = clf.predict(X)
app_desktop.py ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ from sklearn.ensemble import IsolationForest
3
+ import matplotlib.pyplot as plt
4
+ from mpl_toolkits.mplot3d import Axes3D
5
+ import json
6
+
7
+ def get_data_from_json():
8
+ with open('cattle_log.json', 'r') as file:
9
+ log_content = json.load(file)['logs']
10
+
11
+ X = []
12
+
13
+ # Iterate over each log entry in the log_content
14
+ for log_entry in log_content:
15
+ # Extract and convert the necessary attributes
16
+ total_today_str = log_entry['distanceTraveled']['totalToday'].rstrip('m')
17
+ heart_rate = int(log_entry['healthData']['heartRate']) # Assuming heart rate is always an integer
18
+ weight_str = log_entry['healthData']['weight'].rstrip('kg')
19
+
20
+ # Convert the distance and weight to floating-point values
21
+ total_today = float(total_today_str) # Convert distance to float
22
+ weight = float(weight_str) # Convert weight to float
23
+
24
+ # Create a 3D vector for the current log entry and append it to the list of vectors
25
+ vector_3d = [total_today, heart_rate, weight]
26
+ X.append(vector_3d)
27
+
28
+ # Convert X into a NumPy array for easier slicing
29
+ X = np.array(X)
30
+ return X
31
+
32
+
33
+ def generate_random_data(num_dimensions, rng):
34
+ # Generating a dataset with 100 points. 95 points are generated from a Gaussian distribution,
35
+ # and 5 points are anomalies added manually.
36
+ X = 0.3 * rng.randn(95, num_dimensions)
37
+ X = np.r_[X + 2, X - 2]
38
+ X_outliers = rng.uniform(low=-4, high=4, size=(5, num_dimensions))
39
+ X = np.r_[X, X_outliers]
40
+ return X
41
+
42
+
43
+
44
+ # Generating synthetic data
45
+ rng = np.random.RandomState(42)
46
+
47
+ # Ask the user for the number of dimensions
48
+ num_dimensions = int(input("Select number of dimensions (1, 2, or 3): "))
49
+
50
+ # Input data
51
+ X = get_data_from_json()
52
+ # X = generate_random_data(num_dimensions, rng)
53
+
54
+ # Fit the model
55
+ clf = IsolationForest(max_samples=100, random_state=rng)
56
+ clf.fit(X)
57
+
58
+ # # Predictions
59
+ y_pred = clf.predict(X)
60
+
61
+ if num_dimensions == 3:
62
+ # Plotting in 3D
63
+ fig = plt.figure(figsize=(10, 7))
64
+ ax = fig.add_subplot(111, projection='3d') # Create a 3D subplot
65
+
66
+ # Extracting the three dimensions for plotting
67
+ x_axis = X[:, 0]
68
+ y_axis = X[:, 1]
69
+ z_axis = X[:, 2]
70
+
71
+ # Scatter plot for 3D data
72
+ ax.scatter(x_axis, y_axis, z_axis, color=['red' if pred == -1 else 'blue' for pred in y_pred], s=50)
73
+
74
+ ax.set_title("Isolation Forest Anomaly Detection (3D)")
75
+ ax.set_xlabel("Distance travelled")
76
+ ax.set_ylabel("Heartrate")
77
+ ax.set_zlabel("Weight")
78
+ plt.show()
79
+
80
+ else:
81
+ # Plotting
82
+ plt.figure(figsize=(10, 7))
83
+ x_axis = X if num_dimensions == 1 else X[:, 0]
84
+ y_axis = np.zeros_like(X) if num_dimensions == 1 else X[:, 1]
85
+ plt.scatter(x_axis,y_axis, color=['red' if pred == -1 else 'blue' for pred in y_pred], s=50)
86
+ plt.title("Isolation Forest Anomaly Detection")
87
+ plt.xlabel("Distance travelled")
88
+ plt.ylabel("Heartrate")
89
+ plt.grid(True)
90
+ plt.show()
cattle_log.json ADDED
@@ -0,0 +1,1004 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "logs": [
3
+ {
4
+ "timestamp": "2024-04-10T08:30:00Z",
5
+ "cattleID": "A123",
6
+ "event": "dailyLog",
7
+ "location": {
8
+ "latitude": 35.6895,
9
+ "longitude": 139.6917
10
+ },
11
+ "distanceTraveled": {
12
+ "sinceLastUpdate": "200m",
13
+ "totalToday": "2000m"
14
+ },
15
+ "healthData": {
16
+ "heartRate": 60,
17
+ "temperature": 38.5,
18
+ "activityLevel": "normal",
19
+ "weight": "502.0kg",
20
+ "age": "4 years"
21
+ }
22
+ },
23
+ {
24
+ "timestamp": "2024-04-11T08:30:00Z",
25
+ "cattleID": "A123",
26
+ "event": "dailyLog",
27
+ "location": {
28
+ "latitude": 35.689600000000006,
29
+ "longitude": 139.6918
30
+ },
31
+ "distanceTraveled": {
32
+ "sinceLastUpdate": "200m",
33
+ "totalToday": "2008m"
34
+ },
35
+ "healthData": {
36
+ "heartRate": 60,
37
+ "temperature": 38.5,
38
+ "activityLevel": "normal",
39
+ "weight": "500.6kg",
40
+ "age": "4 years"
41
+ }
42
+ },
43
+ {
44
+ "timestamp": "2024-04-12T08:30:00Z",
45
+ "cattleID": "A123",
46
+ "event": "dailyLog",
47
+ "location": {
48
+ "latitude": 35.6897,
49
+ "longitude": 139.6919
50
+ },
51
+ "distanceTraveled": {
52
+ "sinceLastUpdate": "200m",
53
+ "totalToday": "2006m"
54
+ },
55
+ "healthData": {
56
+ "heartRate": 60,
57
+ "temperature": 38.3,
58
+ "activityLevel": "normal",
59
+ "weight": "503.3kg",
60
+ "age": "4 years"
61
+ }
62
+ },
63
+ {
64
+ "timestamp": "2024-04-13T08:30:00Z",
65
+ "cattleID": "A123",
66
+ "event": "dailyLog",
67
+ "location": {
68
+ "latitude": 35.689800000000005,
69
+ "longitude": 139.692
70
+ },
71
+ "distanceTraveled": {
72
+ "sinceLastUpdate": "200m",
73
+ "totalToday": "1996m"
74
+ },
75
+ "healthData": {
76
+ "heartRate": 60,
77
+ "temperature": 38.8,
78
+ "activityLevel": "normal",
79
+ "weight": "500.8kg",
80
+ "age": "4 years"
81
+ }
82
+ },
83
+ {
84
+ "timestamp": "2024-04-14T08:30:00Z",
85
+ "cattleID": "A123",
86
+ "event": "dailyLog",
87
+ "location": {
88
+ "latitude": 35.6899,
89
+ "longitude": 139.6921
90
+ },
91
+ "distanceTraveled": {
92
+ "sinceLastUpdate": "200m",
93
+ "totalToday": "1994m"
94
+ },
95
+ "healthData": {
96
+ "heartRate": 60,
97
+ "temperature": 38.5,
98
+ "activityLevel": "normal",
99
+ "weight": "500.9kg",
100
+ "age": "4 years"
101
+ }
102
+ },
103
+ {
104
+ "timestamp": "2024-04-15T08:30:00Z",
105
+ "cattleID": "A123",
106
+ "event": "dailyLog",
107
+ "location": {
108
+ "latitude": 35.690000000000005,
109
+ "longitude": 139.69219999999999
110
+ },
111
+ "distanceTraveled": {
112
+ "sinceLastUpdate": "200m",
113
+ "totalToday": "2006m"
114
+ },
115
+ "healthData": {
116
+ "heartRate": 60,
117
+ "temperature": 38.2,
118
+ "activityLevel": "normal",
119
+ "weight": "498.3kg",
120
+ "age": "4 years"
121
+ }
122
+ },
123
+ {
124
+ "timestamp": "2024-04-16T08:30:00Z",
125
+ "cattleID": "A123",
126
+ "event": "dailyLog",
127
+ "location": {
128
+ "latitude": 35.6901,
129
+ "longitude": 139.6923
130
+ },
131
+ "distanceTraveled": {
132
+ "sinceLastUpdate": "200m",
133
+ "totalToday": "1985m"
134
+ },
135
+ "healthData": {
136
+ "heartRate": 60,
137
+ "temperature": 38.3,
138
+ "activityLevel": "normal",
139
+ "weight": "504.9kg",
140
+ "age": "4 years"
141
+ }
142
+ },
143
+ {
144
+ "timestamp": "2024-04-17T08:30:00Z",
145
+ "cattleID": "A123",
146
+ "event": "dailyLog",
147
+ "location": {
148
+ "latitude": 35.690200000000004,
149
+ "longitude": 139.6924
150
+ },
151
+ "distanceTraveled": {
152
+ "sinceLastUpdate": "200m",
153
+ "totalToday": "1995m"
154
+ },
155
+ "healthData": {
156
+ "heartRate": 60,
157
+ "temperature": 38.2,
158
+ "activityLevel": "normal",
159
+ "weight": "503.1kg",
160
+ "age": "4 years"
161
+ }
162
+ },
163
+ {
164
+ "timestamp": "2024-04-18T08:30:00Z",
165
+ "cattleID": "A123",
166
+ "event": "dailyLog",
167
+ "location": {
168
+ "latitude": 35.6903,
169
+ "longitude": 139.6925
170
+ },
171
+ "distanceTraveled": {
172
+ "sinceLastUpdate": "200m",
173
+ "totalToday": "1983m"
174
+ },
175
+ "healthData": {
176
+ "heartRate": 60,
177
+ "temperature": 38.6,
178
+ "activityLevel": "normal",
179
+ "weight": "505.6kg",
180
+ "age": "4 years"
181
+ }
182
+ },
183
+ {
184
+ "timestamp": "2024-04-19T08:30:00Z",
185
+ "cattleID": "A123",
186
+ "event": "dailyLog",
187
+ "location": {
188
+ "latitude": 35.690400000000004,
189
+ "longitude": 139.6926
190
+ },
191
+ "distanceTraveled": {
192
+ "sinceLastUpdate": "200m",
193
+ "totalToday": "1990m"
194
+ },
195
+ "healthData": {
196
+ "heartRate": 60,
197
+ "temperature": 38.6,
198
+ "activityLevel": "normal",
199
+ "weight": "503.1kg",
200
+ "age": "4 years"
201
+ }
202
+ },
203
+ {
204
+ "timestamp": "2024-04-20T08:30:00Z",
205
+ "cattleID": "A123",
206
+ "event": "dailyLog",
207
+ "location": {
208
+ "latitude": 35.6905,
209
+ "longitude": 139.6927
210
+ },
211
+ "distanceTraveled": {
212
+ "sinceLastUpdate": "200m",
213
+ "totalToday": "2002m"
214
+ },
215
+ "healthData": {
216
+ "heartRate": 60,
217
+ "temperature": 38.4,
218
+ "activityLevel": "normal",
219
+ "weight": "501.9kg",
220
+ "age": "4 years"
221
+ }
222
+ },
223
+ {
224
+ "timestamp": "2024-04-21T08:30:00Z",
225
+ "cattleID": "A123",
226
+ "event": "dailyLog",
227
+ "location": {
228
+ "latitude": 35.6906,
229
+ "longitude": 139.6928
230
+ },
231
+ "distanceTraveled": {
232
+ "sinceLastUpdate": "200m",
233
+ "totalToday": "1996m"
234
+ },
235
+ "healthData": {
236
+ "heartRate": 61,
237
+ "temperature": 38.8,
238
+ "activityLevel": "normal",
239
+ "weight": "506.7kg",
240
+ "age": "4 years"
241
+ }
242
+ },
243
+ {
244
+ "timestamp": "2024-04-22T08:30:00Z",
245
+ "cattleID": "A123",
246
+ "event": "dailyLog",
247
+ "location": {
248
+ "latitude": 35.6907,
249
+ "longitude": 139.6929
250
+ },
251
+ "distanceTraveled": {
252
+ "sinceLastUpdate": "200m",
253
+ "totalToday": "1989m"
254
+ },
255
+ "healthData": {
256
+ "heartRate": 63,
257
+ "temperature": 38.5,
258
+ "activityLevel": "normal",
259
+ "weight": "499.1kg",
260
+ "age": "4 years"
261
+ }
262
+ },
263
+ {
264
+ "timestamp": "2024-04-23T08:30:00Z",
265
+ "cattleID": "A123",
266
+ "event": "dailyLog",
267
+ "location": {
268
+ "latitude": 35.6908,
269
+ "longitude": 139.69299999999998
270
+ },
271
+ "distanceTraveled": {
272
+ "sinceLastUpdate": "200m",
273
+ "totalToday": "1981m"
274
+ },
275
+ "healthData": {
276
+ "heartRate": 61,
277
+ "temperature": 38.6,
278
+ "activityLevel": "normal",
279
+ "weight": "505.0kg",
280
+ "age": "4 years"
281
+ }
282
+ },
283
+ {
284
+ "timestamp": "2024-04-24T08:30:00Z",
285
+ "cattleID": "A123",
286
+ "event": "dailyLog",
287
+ "location": {
288
+ "latitude": 35.6909,
289
+ "longitude": 139.6931
290
+ },
291
+ "distanceTraveled": {
292
+ "sinceLastUpdate": "200m",
293
+ "totalToday": "1992m"
294
+ },
295
+ "healthData": {
296
+ "heartRate": 60,
297
+ "temperature": 38.4,
298
+ "activityLevel": "normal",
299
+ "weight": "499.5kg",
300
+ "age": "4 years"
301
+ }
302
+ },
303
+ {
304
+ "timestamp": "2024-04-25T08:30:00Z",
305
+ "cattleID": "A123",
306
+ "event": "dailyLog",
307
+ "location": {
308
+ "latitude": 35.691,
309
+ "longitude": 139.6932
310
+ },
311
+ "distanceTraveled": {
312
+ "sinceLastUpdate": "200m",
313
+ "totalToday": "1990m"
314
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973
+ "totalToday": "2068m"
974
+ },
975
+ "healthData": {
976
+ "heartRate": 60,
977
+ "temperature": 37.3,
978
+ "activityLevel": "normal",
979
+ "weight": "518.2kg",
980
+ "age": "4 years"
981
+ }
982
+ },
983
+ {
984
+ "timestamp": "2024-05-29T08:30:00Z",
985
+ "cattleID": "A123",
986
+ "event": "dailyLog",
987
+ "location": {
988
+ "latitude": 35.6944,
989
+ "longitude": 139.6966
990
+ },
991
+ "distanceTraveled": {
992
+ "sinceLastUpdate": "200m",
993
+ "totalToday": "1917m"
994
+ },
995
+ "healthData": {
996
+ "heartRate": 60,
997
+ "temperature": 36.8,
998
+ "activityLevel": "normal",
999
+ "weight": "518.8kg",
1000
+ "age": "4 years"
1001
+ }
1002
+ }
1003
+ ]
1004
+ }
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ streamlit
2
+ matplotlib
3
+ scikit-learn