def _fetch_data(self, dataset, query=None):
yield Result(127, {
"municipality": "Robertsfors kommun",
})
yield Result(17, {
"municipality": "Region Gotland",
})
def _fetch_data(self, dataset, query=None):
if dataset.id == "Dataset_1":
yield Result(127, {
"date": "2017-08-10",
"municipality": "Robertsfors kommun",
})
elif dataset.id == "Dataset_2":
yield Result(12, {
"date": "2017-02-06",
"municipality": "Umeå kommun",
})
yield Result(130, {
"date": "2017-02-07",
"municipality": "Robertsfors kommun",
})
def _fetch_data(self, dataset, query):
"""Make the actual query.
:param query:
"""
# get latest month by default
if query is None:
query = "latest_month"
months = []
if query == "latest_month":
months.append(dataset.latest_month)
elif isinstance(query, dict):
since = query.get("from", "2000-01")
to = query.get("to", datetime.now().strftime("%Y-%m"))
months = [
x for x in dataset.dimensions["month"].allowed_values
if since <= x.label and x.label <= to
]
for month in months:
self.log.info("Fecthing data from {}".format(month.label))
url = BASE_URL + "/BankruptcyStatisticsCategoryPage/GetStatistics"
html = self._post_request(url, {"selectedvalue": month.value})
for res in parse_result_page(html):
value = res["value"]
res.pop("value")
res["month"] = month.label
yield Result(value, res)
def _parse_result_page(self, url, payload, only_region=False, region=None):
""" Get data from a result page
:param url: url to query
:param payload: payload to pass
:return: a dictlist with data
"""
data = []
if only_region:
html = self.scraper._get_html(url)
else:
try:
html = self.scraper._post_html(url, payload=payload)
except HTTPError as e:
if e.response.status_code == 500:
self.scraper.log.warning(
u"Unable to get {} with {}".format(url, payload))
return []
current_selection = self._get_current_selection(html)
table = Datatable(html)
data = []
_region = None
for row in table.data:
region_or_unit_id, region_or_unit_label = row["region_or_unit"]
region = self.regions.get_by_label(region_or_unit_label)
if region:
row["region"] = region.label
row["unit"] = None
row["unit_id"] = None
_region = region.label
else:
assert region_or_unit_label is not None
assert region_or_unit_id is not None
row["region"] = _region
row["unit"] = region_or_unit_label
row["unit_id"] = region_or_unit_id
value = row["value"]
row.pop("value", None)
row.pop("region_or_unit", None)
for dim in self.dimensions:
if dim.id not in row:
row[dim.id] = current_selection[dim.id][1] # gets label
data.append(Result(value, row))
return data
def _fetch_data(self, dataset, query):
"""Make the actual query.
The only queryable dimensions are period.
>>> dataset.fetch({"period": "2016"})
>>> dataset.fetch({"period": ["2015", "2016"]})
>>> dataset.fetch({"period": "*"}) # Get all periods
"""
default_query = {
"period": dataset.latest_period[1],
}
if query is None:
query = {}
default_query.update(query)
query = default_query
allowed_query_dims = ["period"]
for dim in query.keys():
if dim not in allowed_query_dims:
msg = "Querying on {} is not implemented yet".format(dim)
raise NotImplementedError(msg)
if query["period"] == "*":
periods = [x[1] for x in dataset.periods]
else:
if not isinstance(query["period"], list):
periods = [query["period"]]
else:
periods = query["period"]
# Get the period id's needed to build url
periods = [dataset._get_period_id(x) for x in periods]
for period in periods:
# Hack: For datasets with multiple uttag we get the latest
# This should rather be a part of query
if dataset.has_uttag:
uttag = dataset.get_latest_uttag(period)[0]
else:
uttag = None
url = dataset.get_xml_url(period, uttag)
xml_data = self._get_html(url)
for datapoint in get_data_from_xml(xml_data):
value = datapoint["value"]
del datapoint["value"]
yield Result(value, datapoint)
def _fetch_data(self, dataset, query=None):
files = [(y, m) for y in query['years'] for m in query['months']]
frames = []
# Download and clean every monthly Excel file
for file in files:
year, month = file
url = self.BASE_URL.format(year=year, month=MONTHS[month])
frame = self._clean_data(pd.read_excel(url), year, month)
frames.append(frame)
# Yield individual rows of type Result from the dataframe
raw_data = pd.concat(frames)
for i, row in raw_data.iterrows():
val = row.pop('value')
yield Result(val, json.loads(row.to_json()))
def _fetch_data(self, dataset, query):
if query is None:
query = {}
body = {
'query': [{
'code': key,
'selection': {
'filter': filtertype,
# value can be a list or a value
'values': value if isinstance(value, list) else [value]
}
} for key, (filtertype, value) in query.items()],
'response': {
'format': "json"
}
}
try:
raw = requests.post(self._api_path(dataset), json=body)
if raw.headers["content-type"] == "text/html":
# This is an error message
raise(InvalidData(f"""Error message from PX Web:
{raw.content}
Check your query for spelling errors, or try reducing the size.
"""))
data = raw.json()
except JSONDecodeError:
raise InvalidData("""No valid response from PX Web.
Check your query for spelling errors, or try reducing the size.
This error is frequently due to a too large result being requested.""")
# All available dimensions are not always returned.
# What is returned depends on the query
raw_return_dimension = data["columns"]
# Filter out dimensions only
raw_return_dimension = [x for x in raw_return_dimension if x["type"] != "c"]
for row in data[u"data"]:
for value in row[u"values"]:
dimensions = {}
# 'key' contains one value for each dimension,
# always preserving order.
for d, v in zip(raw_return_dimension, row[u"key"]):
dimensions[d["code"]] = v
yield Result(value, dimensions=dimensions)
def _fetch_data(self, dataset, query=None):
(c, r, p) = dataset.blob
self.browser\
.find_element_by_xpath("//div[@title='Skicka till Excel']")\
.click()
# Press enter trice in case of any prompts
actions = ActionChains(self.browser)
actions.send_keys(Keys.RETURN)
actions.send_keys(Keys.RETURN)
actions.send_keys(Keys.RETURN)
actions.perform()
# Wait for download
i = 0
while not os.listdir(self.tempdir):
sleep(1)
i += 1
if i > PAGELOAD_TIMEOUT:
# TODO: Use a suitable basescraper exception
raise Exception("Download timed out")
sleep(20) # TODO: We need to check that the file is complete.
# Something like this:
# https://stackoverflow.com/questions/35891393/how-to-get-file-download-complete-status-using-selenium-web-driver-c-sharp#35892347
# WARNING: Assuming the latest downloaded xls to be our file.
# This is obviously not 100 % water proof.
latest_download = max(iglob(os.path.join(self.tempdir, "*.xls")),
key=os.path.getctime)
workbook = open_workbook(latest_download)
sheet = workbook.sheet_by_index(0)
periods = sheet.row_values(0)[2:-1]
periods = [int(x) for x in periods]
for n in range(1, sheet.nrows):
row = sheet.row_values(n)
region = row.pop(0)
row.pop(0) # empty due to merged cells
if region == "Total":
break
i = 0
for col in row[:-1]:
yield Result(int(col), {
"region": region,
"period": periods[i],
})
def _fetch_data(self, dataset, query):
"""Make the actual query.
"""
if query is None:
query = {}
# default query
_query = {
"year": [dataset.latest_year],
"region": "Hela landet"
}
_query.update(query)
allowed_query_dims = ["year", "region"]
# Validate query
for dim in query.keys():
if dim not in allowed_query_dims:
msg = "Querying on {} is not implemented yet".format(dim)
raise NotImplementedError(msg)
for dim, value in _query.iteritems():
if value == "*":
_query[dim] = [x.value for x in dataset.dimensions[dim].allowed_values]
elif not isinstance(value, list):
_query[dim] = [value]
# get all input elem values
payload = {}
for input_elem in dataset.soup.select("input"):
payload[input_elem["name"]] = input_elem.get("value")
for region in _query["region"]:
region_id = dataset._get_region_id(region)
for year in _query["year"]:
payload.update({
"ctl01$ctl11$lstCounties": region_id,
"ctl01$ctl11$lstYearInterval": year,
})
result_page = self._post_html(URL, payload)
for datapoint in parse_result_page(result_page):
value = datapoint["value"]
del datapoint["value"]
yield Result(value, datapoint)
def _fetch_data(self, dataset, query=None):
html = requests.get(
"http://web05.lansstyrelsen.se/transtat_O/transtat.asp").text
soup = BeautifulSoup(html, 'html.parser')
table = soup.find("table",
"line").find_all("table")[2].findNext("table")
rows = table.find_all("tr")
column_headers = rows.pop(0).find_all("td", recursive=False)
years = [x.text for x in column_headers[2:]]
for row in rows:
cells = row.find_all("td")
date = cells.pop(0).text
month = cells.pop(0).text
i = 0
for value in cells:
# Each column from here is a year.
if value.text:
yield Result(value.text.encode("utf-8"), {
"date": date,
"month": month,
"year": years[i],
})
i += 1
def _fetch_data(self, dataset, query):
url = "http://statistik.uka.se/4.5d85793915901d205f935d0f.12.5d85793915901d205f965eab.portlet?action=resultat&view=resultTable&frageTyp=3&frageNr=240&tid=%s&grupp1=%s&grupp2=%s"
thenmap_url = "http://api.thenmap.net/v1/se-7/data/%s?data_props=name|kommunkod"
# 6 is 1993, the first year in the db
if query is None:
query = {}
if "from" not in query:
query['from'] = 1993
if "semesters" not in query:
query['semesters'] = (2016 - query["from"]) * 2
start = (query["from"] - 1993) * 2 + 5
terms = range(start,
start + query["semesters"] + 2)
for t in terms:
# Get all municipalities, and their codes, from this year
year = ((t - 5) / 2) + 1993
semester = ["HT", "VT"][t % 2]
municipalities = requests.get(thenmap_url % year).json()
for id_, municipality_ in municipalities["data"].items():
municipality = municipality_.pop()
code = municipality["kommunkod"].zfill(4)
c, m = code[:2], code[2:]
html = requests.get(url % (t, c, m)).text
soup = BeautifulSoup(html, 'html.parser')
table = soup.find("table")
# The first rows are headers, the last are empty
rows = table.find_all("tr")[5:-2]
for row in rows:
cells = row.find_all("td")
yield Result(cells[2].text.strip(), {
"municipality": municipality["name"],
"school": cells[0].text.strip(),
"semester": semester,
"year": year,
})
def _fetch_data(self, dataset, query):
"""
"""
url = dataset.url.replace("val", "resultat")
# Start building the POST payload from values of hidden inputs
payload = dataset.hidden_inputs
query_size = 1
for dim_key, values in query.items():
# Get dimensions from query by id
# Will error on bad query
try:
dim = dataset.dimensions[dim_key]
except NoSuchItem:
dim_ids = [x.id for x in dataset.dimensions]
msg = "{} is not a valid dimension id. Try: ".format(
dim_key, dim_ids)
raise InvalidQuery(msg)
if values == "*":
values = [x.value for x in dim.allowed_values]
elif not isinstance(values, list):
values = [values]
query_values = []
for val in values:
# validate the value passed used in query
# will error if invalid value
try:
dim_value = dim.allowed_values[val]
except StopIteration: # Odd exception from statscraper
dim_value = dim.allowed_values.get_by_label(val)
if dim_value is None:
msg = "{} is not an allowed value or label for {}. Try: {}"\
.format(val, dim_key, dim.allowed_values)
raise InvalidQuery(msg)
query_values.append(dim_value.in_query)
query_size *= len(query_values)
query_value = "".join(query_values) # ";01;;02;;03;"
payload[dim.query_key] = query_value
# Socialstyrelsen has a limit of 50 000 datapoints per query
# TODO: Split into multiple queries automatically
MAX_QUERY_SIZE = 50000
if query_size > MAX_QUERY_SIZE:
msg = ("Your query was too large: {}, threshold is {}. "
"Try splitting it into multiple queries.")\
.format(query_size, MAX_QUERY_SIZE)
raise TooLargeQuery(msg)
html = self._post_html(url, payload)
# Check if the result is an error page
error_msg = re.search("Fel med nummer (-?\d+)", html)
if error_msg:
# TODO: Find out what these error codes mean.
# Known codes are -1 and 10.
error_code = error_msg.group(1)
msg = "Result page didn't render. Socialstyrelsen error code: {}".format(
error_code)
raise InvalidQuery(msg)
for value, index in parse_result_table(html):
yield Result(value, index)
def _fetch_data(self, dataset, query={}, include_inactive_stations=False):
""" Should yield dataset rows
"""
data = []
parameter = dataset
station_dim = dataset.dimensions["station"]
all_stations = station_dim.allowed_values
# Step 1: Prepare query
if "station" not in query:
if include_inactive_stations:
# Get all stations
query["station"] = list(all_stations)
else:
# Get only active stations
query["station"] = list(station_dim.active_stations())
else:
if not isinstance(query["station"], list):
query["station"] = [query["station"]]
# Make sure that the queried stations actually exist
query["station"] = [
all_stations.get_by_label(x) for x in query["station"]
]
if "period" not in query:
# TODO: I'd prepare to do dataset.get("period").allowed_values here
query["period"] = PERIODS
elif not isinstance(query["period"], list):
query["period"] = [query["period"]]
for period in query["period"]:
if period not in PERIODS:
msg = u"{} is not an allowed period".format(period)
raise Exception(msg)
# Step 3: Get data
n_queries = len(query["station"]) * len(query["period"])
counter = 0
print("Fetching data with {} queries.".format(n_queries))
for station in query["station"]:
for period in query["period"]:
url = dataset.url\
.replace(".json", "/station/{}/period/{}/data.csv"\
.format(station.key, period))
print("/GET {} ".format(url))
r = requests.get(url)
if r.status_code == 200:
raw_data = DataCsv().from_string(r.content).to_dictlist()
# TODO: This is a very hard coded parse function
# Expects fixed start row and number of cols
for row in raw_data:
#timepoint = datetime.strptime(timepoint_str, "%Y-%m-%d %H:%M:%S")
value_col = parameter.id.split(",")[0]
value = float(row[value_col])
row["parameter"] = parameter.id
row["station"] = station.label
row["station_key"] = station.key
row["period"] = period
row.pop(value_col, None)
datapoint = Result(value, row)
yield datapoint
elif r.status_code == 404:
print("Warning no data at {}".format(url))
else:
raise Exception("Connection error for {}".format(url))
def test_pandas_export(self):
"""Get results as pandas dataframe."""
result = ResultSet()
result.append(Result(45483, {'city': "Voi"}))
df = result.pandas
self.assertTrue(ptypes.is_numeric_dtype(df.value))
def _fetch_data(self, dataset, query):
"""Make query for actual data.
Get all regions and years by default.
`period` (year), `municipality` and `municipality_groups` are the only
implemented queryable dimensions.
:param query: a dict with dimensions and values to query by.
Examples:
{"municipality": ["0180"]}
{"period": 2016 }
"""
# Make query a dict if it already isn't
if isinstance(query, dict) == False:
query = {}
# If nothing is set, default to all allowed municipalities
queryable_dims = ['municipality', 'period', 'municipality_groups']
if all([x not in query for x in queryable_dims]):
query['municipality'] = []
for x in dataset.dimensions['municipality'].allowed_values:
query['municipality'].append(x.value)
# Listify queried values (to allow single values in query, like {"year": 2016})
for key, values in query.items():
if not isinstance(values, list):
query[key] = [values]
# Format all values as strings for url creation
query[key] = [str(x) for x in query[key]]
# Validate query
for dim in query.keys():
if dim not in queryable_dims:
raise Exception(
"You cannot query on dimension '{}'".format(dim))
# Check if the values are allowed
if dim in ('municipality', 'municipality_groups'):
allowed = [
x.value for x in dataset.dimensions[dim].allowed_values
]
for dimVal in query[dim]:
if dimVal not in allowed:
raise Exception(
"You cannot query on dimension '{}' with '{}'".
format(dim, dimVal))
# base url for query
next_url = '{}data/kpi/{}'.format(self.base_url, dataset.id)
# Merge `municipality` and `municipality_groups`
municipalities = []
if 'municipality' in query:
municipalities = municipalities + query['municipality']
if 'municipality_groups' in query:
municipalities = municipalities + query['municipality_groups']
if len(municipalities) > 0:
next_url += '/municipality/{}'.format(','.join(municipalities))
if 'period' in query:
next_url += '/year/{}'.format(','.join(query['period']))
while next_url:
print('/GET {}'.format(next_url))
r = requests.get(next_url)
r.raise_for_status()
json_data = r.json()
for row in json_data['values']:
for d in row['values']:
yield Result(
d['value'], {
'kpi': dataset.id,
'kpi_label': dataset.label,
'municipality': row['municipality'],
'period': row['period'],
'gender': d['gender'],
'status': d['status'],
})
#
if 'next_page' in json_data:
next_url = json_data['next_page']
else:
next_url = False
def _fetch_data(self, dataset, query={}):
"""Make query for actual data.
Get all regions and years by default.
`period` (year) and `municipality` are the only implemented queryable
dimensions.
:param query: a dict with dimensions and values to query by.
Examples:
{"municipality": ["0180"]}
{"period": 2016 }
"""
#
if "municipality" not in query and "period" not in query:
query = {
"municipality":
[x.id for x in self.dimension["municipality"].allowed_values]
}
# Listify queried values (to allow single values in query, like {"year": 2016})
for key, values in query.items():
if not isinstance(values, list):
query[key] = [values]
# Format all values as strings for url creation
query[key] = [str(x) for x in query[key]]
# Validate query
queryable_dims = ["municipality", "period"]
for dim in query.keys():
if dim not in queryable_dims:
raise Exception(
"You cannot query on dimension '{}'".format(dim))
#TODO: Make sure tha values passed in query are allowed.
# base url for query
next_url = '{}data/kpi/{}'.format(self.base_url, dataset.id)
if "municipality" in query:
next_url += "/municipality/{}".format(",".join(
query["municipality"]))
elif "period" in query:
next_url += "/year/{}".format(",".join(query["period"]))
while next_url:
print("/GET {}".format(next_url))
r = requests.get(next_url)
r.raise_for_status()
json_data = r.json()
for row in json_data["values"]:
for d in row["values"]:
yield Result(
d['value'], {
'kpi': dataset.id,
'kpi_label': dataset.label,
'municipality': row['municipality'],
'period': row['period'],
'gender': d['gender'],
'status': d['status'],
})
#
if "next_page" in json_data:
next_url = json_data["next_page"]
else:
next_url = False
class VantetiderDataset(Dataset):
def get_url(self, region="Sverige"):
if region=="Sverige":
# Hack: _get_region_slug expects the page to be loaded, but to be
# able to load the page we first have to show it on national levle
region_slug = "Sveriges"
else:
region_slug = self._get_region_slug(region)
return BASE_URL + region_slug + "/" + self.id + "/"
@property
def html(self):
if not hasattr(self, "_html"):
url = self.get_url()
self._html = self.scraper._get_html(url)
return self._html
@property
def soup(self):
return BeautifulSoup(self.html, 'html.parser')
@property
def regions(self):
""" Get a list of all regions
"""
return self.dimensions["region"].allowed_values
@property
def years(self):
""" Get a list of all available years
"""
return self.dimensions["year"].allowed_values
@property
def latest_timepoint(self):
"""Get the latest available year and period.
This method will have to be re-written to support pages with ajax load
"""
return {
"year": self.dimensions["year"].default_value,
"period": self.dimensions["period"].default_value,
}
def _get_region_slug(self, id_or_label):
""" Get the regional slug to be used in url
"Norrbotten" => "Norrbottens"
:param id_or_label: Id or label of region
"""
region = self.regions.get_by_label(id_or_label)
if region is None:
try:
region = self.regions[id_or_label]
except StopIteration:
# this is a strange error thrown by statscraper
raise KeyError(u"{} is not a valid region id or label".format(id_or_label))
slug = region.label\
.replace(u" ","-")\
.replace(u"ö","o")\
.replace(u"Ö","O")\
.replace(u"ä","a")\
.replace(u"å","a")
if not "region" in slug:
slug = slug + "s"
EXCEPTIONS = {
"Jamtland-Harjedalens": "Jamtlands",
"Rikets": "Sveriges",
"Alla-landstings": "Sveriges",
}
if slug in EXCEPTIONS:
slug = EXCEPTIONS[slug]
return slug
def _parse_result_page(self, url, payload, only_region=False, region=None):
""" Get data from a result page
:param url: url to query
:param payload: payload to pass
:return: a dictlist with data
"""
data = []
if only_region:
html = self.scraper._get_html(url)
else:
try:
html = self.scraper._post_html(url, payload=payload)
except HTTPError, e:
if e.response.status_code == 500:
self.scraper.log.warning(u"Unable to get {} with {}".format(url, payload))
return []
current_selection = self._get_current_selection(html)
table = Datatable(html)
data = []
_region = None
for row in table.data:
region_or_unit_id, region_or_unit_label = row["region_or_unit"]
region = self.regions.get_by_label(region_or_unit_label)
if region:
row["region"] = region.label
row["unit"] = None
row["unit_id"] = None
_region = region.label
else:
assert region_or_unit_label is not None
assert region_or_unit_id is not None
row["region"] = _region
row["unit"] = region_or_unit_label
row["unit_id"] = region_or_unit_id
value = row["value"]
row.pop("value", None)
row.pop("region_or_unit", None)
for dim in self.dimensions:
if dim.id not in row:
row[dim.id] = current_selection[dim.id][1] # gets label
data.append(Result(value, row))
return data