def parse_tableau(file_contents):
"""
This is a weird blob containing *two* JSON encoded dictionaries.
Each is preceded by its length in bytes, but using a regex (instead of counting) is simple enough.
Follows the approach in tableau-scraping. See the links below:
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/TableauScraper.py#L77-L84
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/dashboard.py#L35
"""
info_and_data = re.search(r"\d+;({.*})\d+;({.*})", file_contents,
re.MULTILINE)
data = json.loads(info_and_data.group(2))
presModelMap = data["secondaryInfo"]["presModelMap"]
dataSegments = presModelMap["dataDictionary"]["presModelHolder"][
"genDataDictionaryPresModel"]["dataSegments"]
full_data = utils.getDataFull(presModelMap, dataSegments)
indices_info = utils.getIndicesInfo(presModelMap, "Vaccination Sites")
data_dict = utils.getData(full_data, indices_info)
num_entries = len(data_dict["Site-value"])
# Transpose columns to rows (tableau-scraping uses pandas, but we don't strictly need to do that)
# Rows are actually duplicated; some have map, some have website.
entries = []
for i in range(0, num_entries, 2):
main_data = {k: v[i] for (k, v) in data_dict.items()}
extra_data = {
"Dimension-value": data_dict["Dimension-value"][i + 1],
"Value-alias": data_dict["Value-alias"][i + 1],
}
entries.append((main_data, extra_data))
return [tableau_item_to_parsed_site(entry) for entry in entries]
def parse_tableau(file_contents):
"""
This is a weird blob containing *two* JSON encoded dictionaries.
Each is preceded by its length in bytes, but using a regex (instead of counting) is simple enough.
Follows the approach in tableau-scraping. See the links below:
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/TableauScraper.py#L77-L84
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/dashboard.py#L35
"""
info_and_data = re.search(r"\d+;({.*})\d+;({.*})", file_contents,
re.MULTILINE)
data = json.loads(info_and_data.group(2))
presModelMap = data["secondaryInfo"]["presModelMap"]
dataSegments = presModelMap["dataDictionary"]["presModelHolder"][
"genDataDictionaryPresModel"]["dataSegments"]
full_data = utils.getDataFull(presModelMap, dataSegments)
indices_info = utils.getIndicesInfo(presModelMap, "List")
data_dict = utils.getData(full_data, indices_info)
# Transpose columns to rows (tableau-scraping uses pandas, but we don't strictly need to do that)
# i.e. {'a': [a1, a2, a3], 'b': [b1, b2, b3]} --> [{'a': a1, 'b': b1}, {'a': a2, 'b': b2}, {'a': a3, 'b': b3}]
transposed_data = map(
dict,
itertools.starmap(
zip,
zip(itertools.repeat(data_dict.keys()), zip(*data_dict.values()))),
)
return (tableau_item_to_parsed_site(entry) for entry in transposed_data)
def parse_tableau(file_contents):
"""
This is a weird blob containing *two* JSON encoded dictionaries.
Each is preceded by its length in bytes, but using a regex (instead of counting) is simple enough.
Follows the approach in tableau-scraping. See the links below:
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/TableauScraper.py#L77-L84
https://github.com/bertrandmartel/tableau-scraping/blob/9dba25af057ac29f921a75df374943060ab79b0a/tableauscraper/dashboard.py#L35
"""
info_and_data = re.search(r"\d+;({.*})\d+;({.*})", file_contents,
re.MULTILINE)
data = json.loads(info_and_data.group(2))
presModelMap = data["secondaryInfo"]["presModelMap"]
dataSegments = presModelMap["dataDictionary"]["presModelHolder"][
"genDataDictionaryPresModel"]["dataSegments"]
full_data = utils.getDataFull(presModelMap, dataSegments)
indices_info = utils.getIndicesInfo(presModelMap, "Vaccination Sites")
data_dict = utils.getData(full_data, indices_info)
# Transpose columns to rows (tableau-scraping uses pandas, but we don't strictly need to do that)
# i.e. {'a': [a1, a2, a3], 'b': [b1, b2, b3]} --> [{'a': a1, 'b': b1}, {'a': a2, 'b': b2}, {'a': a3, 'b': b3}]
transposed_data = map(
dict,
itertools.starmap(
zip,
zip(itertools.repeat(data_dict.keys()), zip(*data_dict.values()))),
)
# Data contains at least one bad value; filter it out. See https://github.com/CAVaccineInventory/vaccine-feed-ingest/issues/621
filtered_transposed_data = (row for row in transposed_data
if row["Site-value"] != "%null%")
# Adjacent rows are actually duplicates; some have map, some have website. Combine into one.
doubled_filtered_transposed_data = zip(filtered_transposed_data,
filtered_transposed_data)
return (tableau_item_to_parsed_site(entry)
for entry in doubled_filtered_transposed_data)
def test_getData():
presModel = utils.getPresModelVizData(data)
dataFull = utils.getDataFull(presModel, {})
indicesInfo = utils.getIndicesInfo(presModel, "[WORKSHEET1]")
frameData = utils.getData(dataFull, indicesInfo)
assert len(frameData.keys()) == 2
assert "[FIELD1]-value" in frameData
assert "[FIELD2]-alias" in frameData
assert len(frameData["[FIELD1]-value"]) == 4
assert len(frameData["[FIELD2]-alias"]) == 4
assert frameData["[FIELD1]-value"] == ["2", "3", "4", "5"]
assert frameData["[FIELD2]-alias"] == ["6", "7", "8", "9"]
def getWorksheet(TS, data, info, worksheet) -> TableauWorksheet:
presModelMap = data["secondaryInfo"]["presModelMap"]
indicesInfo = utils.getIndicesInfo(presModelMap, worksheet)
dataFull = utils.getDataFull(presModelMap)
frameData = utils.getData(dataFull, indicesInfo)
df = pd.DataFrame.from_dict(frameData, orient="index").fillna(0).T
return TableauWorksheet(
scraper=TS,
originalData=data,
originalInfo=info,
worksheetName=worksheet,
dataFrame=df,
)
def get(TS, data, info, logger):
worksheets = utils.selectWorksheet(data, logger, single=True)
if len(worksheets) == 0:
return TableauDashboard(scraper=TS,
originalData=data,
originalInfo=info,
data=[])
selectedWorksheet = worksheets[0]
presModel = utils.getPresModelVizData(data)
result = utils.getIndicesInfo(presModel,
selectedWorksheet,
noSelectFilter=False)
for idx, t in enumerate(result):
logger.info(f"[{idx}] {t['fieldCaption']}")
selected = input(f"select field by index : ")
if (selected is None) or (selected == ""):
raise (Exception("you must select at least one field"))
field = result[int(selected)]
logger.info(f"you have selected {field['fieldCaption']}")
dataFull = utils.getDataFull(presModel)
frameData = utils.getData(dataFull, [field])
frameDataKeys = list(frameData.keys())
if len(frameDataKeys) == 0:
raise (Exception("no data extracted"))
data = frameData[frameDataKeys[0]]
for idx, t in enumerate(data):
logger.info(f"[{idx}] {t}")
selected = input(f"select value by index : ")
if (selected is None) or (selected == ""):
raise (Exception("you must select at least one value"))
value = data[int(selected)]
logger.info(f"you have selected {value}")
r = api.select(TS, selectedWorksheet, [int(selected) + 1])
return dashboard.getCmdResponse(TS, r, logger)
def getWorksheet(TS, data, info, worksheet) -> TableauWorksheet:
presModelMap = utils.getPresModelVizData(data)
if presModelMap is None:
presModelMap = utils.getPresModelVizInfo(info)
indicesInfo = utils.getIndicesInfoStoryPoint(presModelMap, worksheet)
if "dataDictionary" not in presModelMap:
presModelMap = utils.getPresModelVizDataWithoutViz(data)
dataFull = utils.getDataFull(presModelMap, TS.dataSegments)
else:
indicesInfo = utils.getIndicesInfo(presModelMap, worksheet)
dataFull = utils.getDataFull(presModelMap, TS.dataSegments)
frameData = utils.getData(dataFull, indicesInfo)
df = pd.DataFrame.from_dict(frameData, orient="index").fillna(0).T
return TableauWorksheet(scraper=TS,
originalData=data,
originalInfo=info,
worksheetName=worksheet,
dataFull=dataFull,
dataFrame=df)