Fixing choropleth

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'

plotly/figure_factory/_county_choropleth.py

@@ -4,6 +4,7 @@
 
 import io
 import numpy as np
+import os
 import pandas as pd
 import warnings
 
@@ -19,30 +20,33 @@
 
 def _create_us_counties_df(st_to_state_name_dict, state_to_st_dict):
  # URLS
- data_url = 'plotly/package_data/data/'
+ abs_file_path = os.path.realpath(__file__)
+ abs_dir_path = os.path.dirname(abs_file_path)
 
- shape_pre2010 = 'gz_2010_us_050_00_500k/gz_2010_us_050_00_500k.shp'
- shape_pre2010 = data_url + shape_pre2010
+ abs_plotly_dir_path = abs_dir_path[:abs_dir_path.find('/figure_factory')]
+ abs_package_data_dir_path = abs_plotly_dir_path + '/package_data/'
+
+ shape_pre2010 = 'gz_2010_us_050_00_500k.shp'
+ shape_pre2010 = abs_package_data_dir_path + shape_pre2010
  df_shape_pre2010 = gp.read_file(shape_pre2010)
  df_shape_pre2010['FIPS'] = (df_shape_pre2010['STATE'] +
  df_shape_pre2010['COUNTY'])
  df_shape_pre2010['FIPS'] = pd.to_numeric(df_shape_pre2010['FIPS'])
 
- states_path = 'cb_2016_us_state_500k/cb_2016_us_state_500k.shp'
- states_path = data_url + states_path
+ states_path = 'cb_2016_us_state_500k.shp'
+ states_path = abs_package_data_dir_path + states_path
 
  # state df
  df_state = gp.read_file(states_path)
  df_state = df_state[['STATEFP', 'NAME', 'geometry']]
  df_state = df_state.rename(columns={'NAME': 'STATE_NAME'})
 
- county_url = 'plotly/package_data/data/cb_2016_us_county_500k/'
  filenames = ['cb_2016_us_county_500k.dbf',
  'cb_2016_us_county_500k.shp',
  'cb_2016_us_county_500k.shx']
 
  for j in range(len(filenames)):
- filenames[j] = county_url + filenames[j]
+ filenames[j] = abs_package_data_dir_path + filenames[j]
 
  dbf = io.open(filenames[0], 'rb')
  shp = io.open(filenames[1], 'rb')
@@ -638,14 +642,14 @@ def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
  list(np.linspace(0, 1, viri_len))
  )[1:-1]
 
- for l in np.linspace(0, 1, len(LEVELS)):
+ for L in np.linspace(0, 1, len(LEVELS)):
  for idx, inter in enumerate(viri_intervals):
- if l == 0:
+ if L == 0:
  break
- elif inter[0] < l <= inter[1]:
+ elif inter[0] < L <= inter[1]:
  break
 
- intermed = ((l - viri_intervals[idx][0]) /
+ intermed = ((L - viri_intervals[idx][0]) /
  (viri_intervals[idx][1] - viri_intervals[idx][0]))
 
  float_color = colors.find_intermediate_color(

plotly/version.py

@@ -1 +1 @@
-__version__ = '2.4.0'
+__version__ = '2.4.1'