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z45=^v%Ln}9FaLdDe;<{9AK2e#_umut=WhLbCI1c{|HiPt6T$z_L4Rnf@ox?e+KtUx z>L$nx|0aNc6TtsjA^e*F{!IXXR|9{_#eWmP|JwvGs@A&SUL9C?GY9^&d6R)wmWJ(- F{{<|h5!(O& literal 0 HcmV?d00001 diff --git a/geosnap/data/data.py b/geosnap/data/data.py index 21e7712a..cd768d02 100644 --- a/geosnap/data/data.py +++ b/geosnap/data/data.py @@ -9,11 +9,11 @@ import quilt import sys -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +sys.path.insert(0, + os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from util import adjust_inflation, convert_gdf - try: from quilt.data.spatialucr import census except ImportError: @@ -46,13 +46,20 @@ def __dir__(self): _package_directory = os.path.dirname(os.path.abspath(__file__)) _cbsa = pd.read_parquet(os.path.join(_package_directory, 'cbsas.parquet')) - dictionary = pd.read_csv(os.path.join(_package_directory, "variables.csv")) -states = census.states() -counties = census.counties() -tracts = census.tracts_2010 -metros = convert_gdf(census.msas()) +try: # if any of these aren't found, the user needs to refresh the quilt data package + states = census.states() + counties = census.counties() + tracts = census.tracts_2010 + metros = convert_gdf(census.msas()) +except AttributeError: + warn( + 'Quilt data is outdated... rebuilding\n' + ' You will need to restart your Python kernel once downloading has completed' + ) + quilt.install("spatialucr/census", force=True) + quilt.install("spatialucr/census_cartographic", force=True) def _db_checker(database): @@ -69,12 +76,11 @@ def _db_checker(database): #: A dict containing tabular data available to geosnap -db = Bunch(census_90=census.variables_1990(), - census_00=census.variables_2000(), - ltdb=_db_checker('ltdb'), - ncdb=_db_checker('ncdb') - ) - +db = Bunch( + census_90=census.variables_1990(), + census_00=census.variables_2000(), + ltdb=_db_checker('ltdb'), + ncdb=_db_checker('ncdb')) # LTDB importer @@ -135,14 +141,16 @@ def _ltdb_reader(path, file, year, dropcols=None): df["year"] = year - inflate_cols = ["mhmval", "mrent", "incpc", - "hinc", "hincw", "hincb", "hinch", "hinca"] + inflate_cols = [ + "mhmval", "mrent", "incpc", "hinc", "hincw", "hincb", "hinch", + "hinca" + ] - inflate_available = list(set(df.columns).intersection(set( - inflate_cols))) + inflate_available = list( + set(df.columns).intersection(set(inflate_cols))) if len(inflate_available): - # try: + # try: df = adjust_inflation(df, inflate_available, year) # except KeyError: # half the dfs don't have these variables # pass @@ -206,8 +214,8 @@ def _ltdb_reader(path, file, year, dropcols=None): fullcount00.iloc[:, 7:], how="left") ltdb_2010 = sample10 - df = pd.concat( - [ltdb_1970, ltdb_1980, ltdb_1990, ltdb_2000, ltdb_2010], sort=True) + df = pd.concat([ltdb_1970, ltdb_1980, ltdb_1990, ltdb_2000, ltdb_2010], + sort=True) renamer = dict( zip(dictionary['ltdb'].tolist(), dictionary['variable'].tolist())) @@ -218,7 +226,8 @@ def _ltdb_reader(path, file, year, dropcols=None): for row in dictionary['formula'].dropna().tolist(): df.eval(row, inplace=True) - keeps = df.columns[df.columns.isin(dictionary['variable'].tolist() + ['year'])] + keeps = df.columns[df.columns.isin(dictionary['variable'].tolist() + + ['year'])] df = df[keeps] data_store._set(['ltdb'], df) @@ -325,7 +334,8 @@ def read_ncdb(filepath): df = df.round(0) - keeps = df.columns[df.columns.isin(dictionary['variable'].tolist() + ['year'])] + keeps = df.columns[df.columns.isin(dictionary['variable'].tolist() + + ['year'])] df = df[keeps] @@ -416,12 +426,13 @@ def __init__(self, self.boundary = boundary if boundary.crs != self.tracts.crs: if not boundary.crs: - raise('Boundary must have a CRS to ensure valid spatial \ + raise ('Boundary must have a CRS to ensure valid spatial \ selection') self.tracts = self.tracts.to_crs(boundary.crs) - self.tracts = self.tracts[self.tracts.representative_point() - .within(self.boundary.unary_union)] + self.tracts = self.tracts[ + self.tracts.representative_point().within( + self.boundary.unary_union)] self.counties = convert_gdf(self.counties[counties.geoid.isin( self.tracts.geoid.str[0:5])]) self.states = convert_gdf(self.states[states.geoid.isin( @@ -471,7 +482,9 @@ def __init__(self, if source in ['ltdb', 'ncdb']: _df = _db_checker(source) if len(_df) == 0: - raise ValueError("Unable to locate {source} data. Please import the database with the `read_{source}` function".format(source=source)) + raise ValueError( + "Unable to locate {source} data. Please import the database with the `read_{source}` function" + .format(source=source)) elif source == "external": _df = data else: @@ -481,8 +494,8 @@ def __init__(self, if cbsafips: if not add_indices: add_indices = [] - add_indices += _cbsa[_cbsa['CBSA Code'] == cbsafips][ - 'stcofips'].tolist() + add_indices += _cbsa[_cbsa['CBSA Code'] == + cbsafips]['stcofips'].tolist() if add_indices: for index in add_indices: @@ -492,8 +505,8 @@ def __init__(self, convert_gdf(counties[counties.geoid.str.startswith( index[0:5])])) self.tracts = self.tracts[~self.tracts.geoid.duplicated(keep='first')] - self.counties = self.counties[ - ~self.counties.geoid.duplicated(keep='first')] + self.counties = self.counties[~self.counties.geoid.duplicated( + keep='first')] self.census = _df[_df.index.isin(self.tracts.geoid)] def plot(self, @@ -545,8 +558,8 @@ def plot(self, else: if self.name: plt.title( - self.name + " " + str(year) + '\n' + - colname, fontsize=20) + self.name + " " + str(year) + '\n' + colname, + fontsize=20) else: plt.title(colname + " " + str(year), fontsize=20) plt.axis("off") @@ -561,10 +574,7 @@ def plot(self, if plot_counties is True: self.counties.plot( - edgecolor="#5c5353", - linewidth=0.8, - facecolor="none", - ax=ax) + edgecolor="#5c5353", linewidth=0.8, facecolor="none", ax=ax) return ax diff --git a/geosnap/tests/baseline_images/san_diego_kmeans.png b/geosnap/tests/baseline_images/san_diego_kmeans.png new file mode 100644 index 0000000000000000000000000000000000000000..eb0c53851135af551ebc3d2b2a9c8e37446e3ccd GIT binary patch literal 41878 zcmd>lWmgJ+nRCwN+hJPdyW*sw|6+LWBYZ1%)mzC#4Pr1zink6i5h=o&^RvTgVrjn}ob3 z66E8DWDyBDM|PIeb%TOJ#rdy6&u#>~LOKEN(mL)MPFC)ormmJy4yNwT_D=5hwq_LH zEnVGgog68+SlL-QnJH}C-JJ#4*#7SgtWK`hZ2Uoj|Dd2KpyZ{*HNA4qHoS8Tv~sUL zrdkxw7YsWtkCo{ysC^}HkW8_$u*KEj2H>$gG{wa6?q>3Tg$fk$3s4tLvB!-){wfRw zMHVs_77Z6d!@$YO(RWFSBRpa|`foq8Np8zdS7yr?>A`xl;Bclr$ 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image_comparison +import matplotlib.pyplot as plt + +@pytest.mark.mpl_image_compare +def test_plot(): + fig, ax = plt.subplots() + sd = data.Community(name='sd', source='ltdb', cbsafips='41740') + sd_clusters = analyze.cluster(sd, columns=['median_household_income', 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'], method='kmeans') + sd_clusters.plot(column='kmeans', ax=ax) + return fig diff --git a/requirements_tests.txt b/requirements_tests.txt index f9d2ad6d..885f8136 100644 --- a/requirements_tests.txt +++ b/requirements_tests.txt @@ -4,3 +4,4 @@ nose-exclude coverage coveralls matplotlib +pytest-mpl