def county2code(county_names):
"""Convert county_name to abbrevation code.
Parameters
----------
county_names : list of str
list containing county_names.
Returns
-------
list of str containing abbrevations.
"""
[DIROK, DIRMAP] = download_resources()
df=pd.read_csv(CITYCODE, sep=';', encoding='latin1')
try:
[dfmatch,_] = deepStringMatching(df.Country, county_names, methodtype='FUZZY', verbose=0)
citymatch = dfmatch.idxmax(axis=0).values
dfmatch.index = list(df.code)
citycode = list(dfmatch.idxmax(axis=0).values)
except:
idx = np.where(np.isin(df.Country.str.lower(), county_names.lower()))[0]
if len(idx)!=0:
citycode = df.code.iloc[idx].values[0]
citymatch = df.Country.iloc[idx].values[0]
return(citycode, citymatch)
def _matchmap(map_name):
out=''
# Get all map names from directory
[getcounty_names,getfiles] = list_map_names()
# Match name
try:
[dfmatch,_]=deepStringMatching(getcounty_names, map_name, methodtype='FUZZY', verbose=0)
getfiles=np.array(getfiles)
out=getfiles[np.isin(getcounty_names, dfmatch.idxmax(axis=0)[0])][0]
except:
idx = np.where(np.isin(getcounty_names, map_name.lower()))[0]
if len(idx)!=0:
out = np.array(getfiles)[idx][0]
return(out)
def plot(county_names, map_name='world', opacity=[], cmap='Set1', filename='custom_map.svg', showfig=True, verbose=True):
"""Color countries.
Parameters
----------
county_names : list of str
Names of countries or regions within a county to be colored.
map_name : str, optional
Name of the county to be colored. The default is 'world'.
opacity : list of float [0-1], optional
Set the opacity for each of the map_name(s). The default is [].
If values are >1, it is scaled between [0-1]
0 = full transparancy, 1= no transparancy
cmap : str, optional
Colormap to be used. Colors is set on the unique county_names. The default is 'Set1'. All colormaps can be reverted using the "_r": e.g., binary_r
['#ffffff','#1f1f1f']: Each map with a specified color
['#ff0000']: All maps have a red color
'Set1' : Discrete colors (default)
'Pastel1' : Discrete colors
'Paired' : Discrete colors
'bwr' : Blue-white-red
'RdBu' : Red-white-Blue
'binary' : black-white
'seismic' : Blue-white-red
'rainbow' : all coors
'Blues' : white-to-blue
filename : str, optional
filepath to write the output file. The default is 'custom_map.svg'.
showfig : bool, optional
Open figure in browser. The default is False.
verbose : int, optional
print message to screen. The default is True.
Returns
-------
tuple containing dict (out) with results and the filename (out, filename).
"""
Param = {}
Param['county_names'] = county_names
Param['opacity'] = opacity
Param['verbose'] = verbose
Param['map_name'] = map_name.lower()
Param['filename'] = filename
Param['showfig'] = showfig
Param['cmap'] = cmap
# FILES
[DIROK, DIRMAP] = download_resources()
if not DIROK: return
# SETUP COLORS SCHEMES
if Param['opacity']==[]:
Param['opacity']=np.ones(len(Param['county_names']))
else:
if np.any(np.array(Param['opacity'])>1):
if Param['verbose']: print('[worldmap] Scaling opacity between [0-1]')
Param['opacity'] = minmax_scale(np.append([0,np.max(Param['opacity']) * 1.5],Param['opacity']))
Param['opacity'] = Param['opacity'][2:]
# Get color schemes
if 'str' in str(type(Param['cmap'])):
import colourmap
getcolors=np.array(sns.color_palette(Param['cmap'],len(Param['county_names'])).as_hex())
# getcolors=colourmap.generate(len(Param['county_names']), cmap=Param['cmap'])
elif 'list' in str(type(Param['cmap'])) and len(Param['cmap'])==1:
getcolors=np.repeat(Param['cmap'], len(Param['county_names']))
else:
getcolors=Param['cmap']
# READ MAP FROM DIRECTORY AND MATCH WITH DESIRED MAP
getsvg = _matchmap(Param['map_name'])
# PARSE SVG
[paths, attributes] = svg2paths(os.path.join(DIRMAP,getsvg))
if Param['verbose']: print('[worldmap] Map loaded and parsed: %s' %(getsvg))
# Extract city names to color them according the scores
d = {'county_names':Param['county_names'], 'SVGcity':np.nan, 'opacity': Param['opacity'], 'fill':getcolors, 'attr':np.nan}
df = pd.DataFrame(d)
# Retrieve all city/county names
SVGcounty_names=[]
for i in range(0,len(attributes)):
SVGcounty_names = np.append(SVGcounty_names, attributes[i]['title'])
# Match with best input-names
[dfmatch,_] = deepStringMatching(SVGcounty_names,Param['county_names'], methodtype='FUZZY', verbose=0)
# IN
SVGcolorCity = dfmatch.idxmax(axis=0).index.values
dfmatch.reset_index(inplace=True, drop=True)
idx = dfmatch.idxmax(axis=0).values
attributesIN = np.array(attributes)[idx]
# OUT
idxOUT=np.setdiff1d(np.arange(0,dfmatch.shape[0]),idx)
attributesOUT = np.array(attributes)[idxOUT]
# STORE
df['SVGcity'] = SVGcolorCity
df['SVGcity'] = df['SVGcity'].str.replace(' ','')
df['attr'] = attributesIN
if Param['verbose']: print('[worldmap] %.0f out of %.0f cities/counties detected and processed.' %(df.shape[0], len(Param['county_names'])))
# DFOUT
SVGcounty_namesOUT=[]
for i in range(0,len(attributesOUT)):
SVGcounty_namesOUT = np.append(SVGcounty_namesOUT, attributesOUT[i]['title'])
d = {'county_names':SVGcounty_namesOUT, 'SVGcity':SVGcounty_namesOUT, 'opacity': 1, 'fill':'#CCCCCC', 'attr':attributesOUT}
dfout = pd.DataFrame(d)
dfout=pd.concat((df,dfout), axis=0)
# WRITE TO FILE
_to_svg(df, attributesOUT, Param)
# Open figures
if Param['showfig']: webbrowser.open(os.path.abspath(Param['filename']), new=2)
del dfout['attr']
return(dfout, filename)