def test_add_args_stepwise():
query = Query.region(region="11")
statistic1 = query.add_field("BEV001")
statistic1.add_field("year")
statistic1.add_args({"year": 2017})
query2 = Query.region(
region="11", fields=[Field(name="BEV001", args={"year": 2017}, fields=["year"])]
)
assert query.get_graphql_query() == query2.get_graphql_query()
def test_add_fields_stepwise(patch_return_types):
query = Query.region(region="11", default_fields=False)
statistic1 = query.add_field("BEV001", default_fields=False)
statistic1.add_field("year")
statistic2 = Field(
name="WAHL09",
args={
"year": 2017,
"PART04": "ALL"
},
fields=["value", "PART04"],
default_fields=False,
return_type="WAHL09",
)
query.add_field(statistic2)
query2 = Query.region(
region="11",
fields=[
Field(
name="BEV001",
fields=["year"],
default_fields=False,
return_type="BEV001",
),
Field(
name="WAHL09",
args={
"year": 2017,
"PART04": "ALL"
},
fields=["value", "PART04"],
default_fields=False,
return_type="WAHL09",
),
],
default_fields=False,
)
assert query.get_graphql_query() == query2.get_graphql_query()
graphql_query = query.get_graphql_query()
assert graphql_query[0] == re.sub(
" ",
"",
"""{
region (id: "11"){
BEV001 {year }
WAHL09 (year: 2017, filter:{ PART04: { nin: []}}){value PART04 }
}
}""".replace("\n", ""),
)
def query_with_enum(patch_return_types):
q = Query.region("09", default_fields=False)
stat = q.add_field("WAHL09")
stat.add_field("PART04")
stat.add_field("year")
stat.add_field("value")
return q
def get_chart():
topic = session.get('topic')
myid = session.get('myid')
description = "short_description.str.contains('" + topic + "')"
table = get_statistics().query(description, engine='python')
q = Query.region(myid)
field = table.iloc[0]
field = field.name
f1 = q.add_field(field)
results = q.results()
df = results.set_index('year')
# Save df as csv
df.to_csv('downloads/data.csv', sep='\t')
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
xs = x = df.index
ys = y = df[field]
axis.plot(xs, ys, linestyle='--', marker='o', color='b')
axis.set_xlabel('Time')
axis.set_ylabel("\n".join(wrap(topic + " in " + session.get('city'), 60)))
axis.xaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
return fig
def test_multiple_regions_query():
query = Query.region(region=["01", "02"], fields=["BEV001"])
graphql_query = query.get_graphql_query()
assert len(graphql_query) == 2, "wrong amount of query strings"
assert graphql_query[1].startswith(
'{region (id: "02")'
), "not properly iterated over all regions"
def get_chart_map(): # this is calling the chart
try:
topic = session.get('topic')
regions = get_regions().query("level == 'nuts3'")
cities = regions.query(
'(parent == "091") | (parent == "092") | (parent == "093") | (parent == "094") | (parent == "095") | (parent == "096") | (parent == "097")'
)
# get multiple regions
q = Query.region(list(cities.index))
description = "short_description.str.contains('" + topic + "')"
table = get_statistics().query(description, engine='python')
field = table.iloc[0]
field = field.name
q.add_field(field)
results_nuts3 = q.results()
# read in shps
shp_nuts2 = gpd.read_file("shp/bavaria_nuts2")
max_year = max(results_nuts3["year"])
results_nuts3_lastyear = results_nuts3[results_nuts3["year"] ==
max_year]
# prep for merging
results_nuts3_lastyear = results_nuts3_lastyear.drop_duplicates()
# test if df is empty
row = results_nuts3_lastyear.iloc[4]
emptytest = row.iloc[4]
if (len(emptytest) != 0):
results_nuts3_lastyear.loc[:, "name2"] = results_nuts3_lastyear[
"name"].str.replace(", Landkreis", "")
results_nuts3_lastyear.loc[:, "name2"] = results_nuts3_lastyear[
"name2"].str.replace(", Landeshauptstadt", "")
# merge datenguide data
plot_data = shp_nuts2.merge(results_nuts3_lastyear,
left_on="CC_2",
right_on="id")
# plot
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
axis = plot_data.plot(column=field, legend=True, ax=axis)
fig.suptitle(topic + " in " + str(max_year))
axis.set_axis_off()
# return fig.get_figure()
return fig
except Exception as e:
app.logger.error('an error occurred during the creation of the map:',
e)
def test_get_complex_graphql_string_without_args(patch_return_types):
field = Field(name="WAHL09", fields=["value"], default_fields=False)
no_args_query = Query.region(region="09",
fields=["id", "name", field],
default_fields=False)
graphql_query = no_args_query.get_graphql_query()
assert graphql_query[0] == re.sub(
" ",
"",
"""{
region (id: "09"){
id name WAHL09 {value }}
}""".replace("\n", ""),
)
def test_multiple_filter_args(patch_return_types):
statistic1 = Field(
name="BETR09",
args={"FRUNW2": ["FRUART0111", "FRUART0112"]},
fields=["FRUNW2", "value", "year"],
default_fields=False,
)
multiple_args_query = Query.region(region="02",
fields=[statistic1],
default_fields=False)
graphql_query = multiple_args_query.get_graphql_query()
assert graphql_query[0] == re.sub(
" ",
"",
"""{
region (id: "02"){
BETR09 (FRUNW2: [FRUART0111, FRUART0112]){FRUNW2 value year }
}
}""".replace("\n", ""),
)
def test_filter_for_all(query):
field = Field(
name="WAHL09",
args={"year": 2017, "PART04": "ALL"},
fields=["value", "PART04"],
default_fields=False,
return_type="WAHL09",
)
query = Query.region(region="09", fields=["id", "name", field])
graphql_query = query.get_graphql_query()
assert graphql_query[0] == re.sub(
" ",
"",
"""{
region (id: "09"){
id name WAHL09 (
year: 2017, filter:{ PART04: { nin: []}}){
value PART04 }
}
}""".replace(
"\n", ""
),
)
def query():
return Query.region(region="09", fields=["BEV001"], default_fields=False)
def query_default():
return Query.region(region="09", fields=["BEV001"])
def test_missing_statistic_field_raises_exception():
q = Query.region("09162000")
with pytest.raises(Exception, match=r"add .* field"):
q.results()
def test_invalid_region_name_raises_exception():
q = Query.region("jodelverein")
q.add_field("BEV001")
with pytest.raises(ValueError, match=r"region is invalid."):
q.results()
#from datenguidepy.query_helper import get_regions, get_statistics, get_availability_summary
from datenguidepy import Query
import pandas as pd
import matplotlib.pyplot as plt
## python info-stats
#print(r[(r['name']=='Berlin')])
#print(r[r['name'].str.contains("Berlin|Hamburg|München|Köln|Frankfurt|Stuttgart|Düsseldorf|Dortmund|Essen|Leipzig|Bremen|Dresden")])
#stats_names = get_statistics()
# Availability
#availability = get_availability_summary()
#print(availability.shape)
## Query Statistics Accidents + some info on cities (pop, dens, cars)
q = Query.region(['11000', '14612', '14713', '02000', '04011', '05111', '05113', '05315', '05913', '06412', '08111', '09162'])
#stat = q.add_field("VER056")
#stat.get_info()
q.add_field('BEV016') ## Einwohner
q.add_field('AI0201') ## Bevölkerungsdichte (Einwohner je qkm)
q.add_field('AI0113') ## Siedlungs- und Verkehrsfläche je Einwohner
q.add_field('AI1301') ## Pkw je 1.000 Einwohner
q.add_field('AI1302') ## Straßenverkehrsunfälle je 10.000 Einwohner
q.add_field('AI1303') ## Straßenverkehrsunfälle je 10.000 Kfz
q.add_field('AI1304') ## Getötete bei Straßenverkehrsunfällen je 100.000 EW
q.add_field('AI1305') ## Verletzte bei Straßenverkehrsunfällen je 100.000EW
q.add_field('VER001') ## Unfälle (insgesamt)
q.add_field('VER002') ## Unfälle mit Personenschaden
q.add_field('VER005') ## Schwerwiegende Unfälle mit Sachschäden i.e.S.
def query_default(patch_return_types):
return Query.region(region="09", fields=["BEV001"])
def query(patch_return_types):
return Query.region(region="09", fields=["BEV001"], default_fields=False)
def complex_query(field):
return Query.region(region="09", fields=["id", "name", field], default_fields=False)
# get short names for statistics
stats_names = get_statistics()
print(stats_names.head(10))
# Availability
availability = get_availability_summary()
print(availability.shape)
# Availability for Berlin
availability_berlin = availability[availability['region_name'] == 'Berlin']
print(availability_berlin.shape) # 1133 statistics
print(availability_berlin.head(20))
# query region Berlin (id == '11')
q = Query.region('11')
# add fields
#q.add_field('BEV001') # Statistik der Geburten
#q.add_field('BEV002') # Statistik der Sterbefaelle
#q.add_field('ERWP06') # Arbeitsmarktstatistik der Bundesagentur für Arbeit
#q.add_field('FLC047') # Agrarstrukturerhebung / Landwirtschaftszählung
#q.add_field('PERS01') # Statistik der öffentlich geförderten Kindertagespflege
#q.add_field('FLCX05') # Flächenerhebung nach Art der tatsächlichen Nutzung
q.add_field('BEV081') # Wanderungsstatistik Zuzuege
q.add_field('BEV082') # Wanderungsstatistik Fortzuege
# get the results as a Pandas DataFrame
df_results = q.results(add_units=True)
# print column names