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https://github.com/NetherlandsForensicInstitute/hansken-python-workshop.git
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51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# %% [python]
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import io
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from wordcloud import WordCloud, STOPWORDS
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import matplotlib.pyplot as plt
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from hansken.connect import connect_project
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hansken_host = ''
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hansken_project = '5ee273fd-0978-4a0a-b8b0-2af2f8479214'
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context = connect_project(endpoint=f'http://{hansken_host}:9091/gatekeeper/',
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project=hansken_project,
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keystore=f'http://{hansken_host}:9090/keystore/',
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interactive=True)
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# Hansken SDK running on localhost
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# context = connect_project(endpoint='http://localhost:9091/gatekeeper/',
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# project='d42bd9c3-63db-474c-a36f-b87e1eb9e2d3',
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# keystore='http://localhost:9090/keystore/')
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# %% [markdown]
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### Collect words
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# The cell below searches for all `document` traces in the current project. Most documents contain a 'text' data stream which contains text extracted from the document.
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# If this data is available, the words are added to the wordcloud.
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# %% [python]
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words = ""
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with context.search("type:document") as search_result:
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for trace in search_result:
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# verify text data stream is available
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if "text" in trace.data_types:
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with io.TextIOWrapper(trace.open(stream='text'), encoding="utf-8", errors="ignore") as content:
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words += content.read()
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words
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# %% [markdown]
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### Draw Wordcloud
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# The cell below draws a wordcloud using the words occurring in the messages. `STOPWORDS` is used to ignore common english words.
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# %% [python]
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# draw word cloud
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wc = WordCloud(stopwords=STOPWORDS, width=600, height=400).generate(words)
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plt.figure(figsize=(20, 6))
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plt.imshow(wc, interpolation="bilinear")
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plt.axis("off")
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plt.show()
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# %%
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