initial commit

searches_time.py

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+# %% [markdown]
+## Plot searches over time
+
+### Initialize Hansken connection
+# Replace `hansken_host` with the ip of a Hansken instance.
+
+# %% [python]
+import sys
+import pandas as pd
+
+from types import SimpleNamespace
+from matplotlib import pyplot
+
+from hansken.connect import connect_project
+from hansken.query import RangeFacet
+
+# The line below finds out if we run in the browser by checking for the js module
+in_browser = 'js' in sys.modules
+hansken_host = ''
+context = connect_project(endpoint=f'http://{hansken_host}:9091/gatekeeper/',
+                          project='5ee273fd-0978-4a0a-b8b0-2af2f8479214',
+                          keystore=f'http://{hansken_host}:9091/keystore/',
+                          # Authentication is faked if we run in the browser,
+                          # because an authenticated session should already be present
+                          auth=SimpleNamespace() if in_browser else None,
+                          interactive=True)
+
+# %% [markdown]
+### Aggregate browser history data
+# The cell below retrieves the browser activity from Hansken. We use a `Facet` to count the number of traces where the `accessedOn` property is within a specific day.
+# %% [python]
+# Group the number of searches by the accessedOn property on a scale of a day. A Facet on a date requires a min and max
+facet = RangeFacet('browserHistory.accessedOn', scale='day', min="2022-01-01", max="2023-01-01")
+# Perform search using the facet, set count=0 to prevent hansken returning traces
+with context.search("browserHistory.accessedOn=2022", facets=facet, count=0) as searchResult:
+  # Convert to dataframe
+  dateFacetResult = searchResult.facets[0]
+  df = pd.DataFrame([[counter.value, counter.count] for _, counter in searchResult.facets[0].items()], columns=['Day', 'Count'])
+# make sure pandas knows this is a timestamp
+df['Day'] = pd.to_datetime(df['Day'])
+df
+
+# %% [markdown]
+### Plot the results
+# The cell below uses `pyplot` to create a bar chart using the previous information, plotting the number of traces/day.
+
+# %% [python]
+# Plot results
+fig, ax = pyplot.subplots(figsize=(10, 6))
+ax.bar(df['Day'], df['Count'])
+ax.set_xlabel("day")
+ax.set_ylabel("count")
+ax.set_title('')
+pyplot.show()