Numbered the files to exercises 01 to 05

03 hansken_facet_heatmap.py

@@ -1,65 +1,65 @@
-# %% [markdown]
-# Plot searches over time
-
-## Initialize Hansken connection
-import sys
-import pandas as pd
-
-from types import SimpleNamespace
-from matplotlib import pyplot
-import seaborn as sns
-from matplotlib.colors import LogNorm, Normalize
-
-from hansken.connect import connect_project
-from hansken.query import RangeFacet
-# %% [python]
-
-# setup Hansken project context 
-
-# if you want a context in a Hansken with authentication
-#
-# context = connect_project(
-#         # endpoint='https://gatekeeper01.prod.hansken.holmes.nl/gatekeeper/',
-#         # # the keystore REST endpoint when this script was exported, note that
-#         # # this can be overridden with --keystore
-#         # keystore='https://keystore01.prod.hansken.holmes.nl/keystore/',
-#         # # the project id of your  project 
-#         # project='d42bd9c3-63db-474c-a36f-b87e1eb9e2d3',
-#         # interactive=True)
-
-# Hansken SDK running on localhost
-
-context = connect_project(endpoint='http://localhost:9091/gatekeeper/',
-                          project='d42bd9c3-63db-474c-a36f-b87e1eb9e2d3',
-                          keystore='http://localhost:9090/keystore/')
-
-# %% 
-
-# Perform facet search in Hansken accross dates and present results in a heatmap 
-
-start = '2022-7-1T00:00Z'
-end = '2022-7-31T23:59Z'
-#search_query = "type:chatMessage"
-search_query = "type:browserHistory"
-
-# 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('dates', scale='hour', min=start, max=end)
-
-# Create a dataframe with entries per hour for the period indicated by start and end
-df = pd.DataFrame()
-df['Time'] = pd.date_range(start,end,freq='1H')
-df['Count'] = 0
-df.set_index('Time',inplace=True)
-
-# Perform search using the facet
-with context.search(search_query, facets=facet, count=0 ) as searchResult:
-  for _, result in searchResult.facets[0].items():
-    df.loc[pd.to_datetime(result.value),'Count']=result.count
-
-# So that we can pivot and prepare a dataframe for our heatmap
-df_map = pd.pivot_table( df, fill_value=0.0, columns=df.index.date, index=df.index.hour, aggfunc="sum")['Count']
-
-sns.heatmap(df_map, cmap="Greens",norm=LogNorm())
-
-
-# %%
+# %% [markdown]
+# Plot searches over time
+
+## Initialize Hansken connection
+import sys
+import pandas as pd
+
+from types import SimpleNamespace
+from matplotlib import pyplot
+import seaborn as sns
+from matplotlib.colors import LogNorm, Normalize
+
+from hansken.connect import connect_project
+from hansken.query import RangeFacet
+# %% [python]
+
+# setup Hansken project context 
+
+# if you want a context in a Hansken with authentication
+#
+# context = connect_project(
+#         # endpoint='https://gatekeeper01.prod.hansken.holmes.nl/gatekeeper/',
+#         # # the keystore REST endpoint when this script was exported, note that
+#         # # this can be overridden with --keystore
+#         # keystore='https://keystore01.prod.hansken.holmes.nl/keystore/',
+#         # # the project id of your  project 
+#         # project='d42bd9c3-63db-474c-a36f-b87e1eb9e2d3',
+#         # interactive=True)
+
+# Hansken SDK running on localhost
+
+context = connect_project(endpoint='http://localhost:9091/gatekeeper/',
+                          project='d42bd9c3-63db-474c-a36f-b87e1eb9e2d3',
+                          keystore='http://localhost:9090/keystore/')
+
+# %% 
+
+# Perform facet search in Hansken accross dates and present results in a heatmap 
+
+start = '2022-7-1T00:00Z'
+end = '2022-7-31T23:59Z'
+#search_query = "type:chatMessage"
+search_query = "type:browserHistory"
+
+# 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('dates', scale='hour', min=start, max=end)
+
+# Create a dataframe with entries per hour for the period indicated by start and end
+df = pd.DataFrame()
+df['Time'] = pd.date_range(start,end,freq='1H')
+df['Count'] = 0
+df.set_index('Time',inplace=True)
+
+# Perform search using the facet
+with context.search(search_query, facets=facet, count=0 ) as searchResult:
+  for _, result in searchResult.facets[0].items():
+    df.loc[pd.to_datetime(result.value),'Count']=result.count
+
+# So that we can pivot and prepare a dataframe for our heatmap
+df_map = pd.pivot_table( df, fill_value=0.0, columns=df.index.date, index=df.index.hour, aggfunc="sum")['Count']
+
+sns.heatmap(df_map, cmap="Greens",norm=LogNorm())
+
+
+# %%