def test_limit_range(self):
assert list(mm.limit_range(range(10), rng=(25, 75))) == [2, 3, 4, 5, 6, 7]
assert list(mm.limit_range(range(10), rng=(10, 90))) == [1, 2, 3, 4, 5, 6, 7, 8]
assert list(mm.limit_range([0, 1], rng=(25, 75))) == [0, 1]
assert list(
mm.limit_range(np.array([0, 1, 2, 3, 4, np.nan]), rng=(25, 75))
) == [1, 2, 3]
def __init__(self,
gdf,
values,
spatial_weights,
unique_id,
rng=None,
verbose=True):
try:
from inequality.gini import Gini
except ImportError:
raise ImportError("The 'inequality' package is required.")
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
data = gdf.copy()
if values is not None:
if not isinstance(values, str):
data["mm_v"] = values
values = "mm_v"
self.values = data[values]
if self.values.min() < 0:
raise ValueError(
"Values contain negative numbers. Normalise data before"
"using momepy.Gini.")
data = data.set_index(unique_id)[values]
if rng:
from momepy import limit_range
results_list = []
for index in tqdm(data.index, total=data.shape[0],
disable=not verbose):
if index in spatial_weights.neighbors.keys():
neighbours = spatial_weights.neighbors[index].copy()
if neighbours:
neighbours.append(index)
values_list = data.loc[neighbours].values
if rng:
values_list = limit_range(values_list, rng=rng)
results_list.append(Gini(values_list).g)
else:
results_list.append(0)
else:
results_list.append(np.nan)
self.series = pd.Series(results_list, index=gdf.index)
def __init__(self,
gdf,
values,
spatial_weights,
unique_id,
rng=None,
mode="mean"):
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
self.mode = mode
data = gdf.copy()
if values is not None:
if not isinstance(values, str):
data["mm_v"] = values
values = "mm_v"
self.values = data[values]
data = data.set_index(unique_id)
results_list = []
for index, row in tqdm(data.iterrows(), total=data.shape[0]):
neighbours = spatial_weights.neighbors[index].copy()
if neighbours:
neighbours.append(index)
else:
neighbours = [index]
values_list = data.loc[neighbours][values]
if rng:
from momepy import limit_range
values_list = limit_range(values_list, rng=rng)
if mode == "mean":
results_list.append(np.mean(values_list))
elif mode == "median":
results_list.append(np.median(values_list))
elif mode == "mode":
results_list.append(sp.stats.mode(values_list)[0][0])
else:
raise ValueError("{} is not supported as mode.".format(mode))
self.series = pd.Series(results_list, index=gdf.index)
def __init__(self,
gdf,
values,
spatial_weights,
unique_id,
rng=None,
verbose=True):
try:
from inequality.theil import Theil
except ImportError:
raise ImportError("The 'inequality' package is required.")
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
data = gdf.copy()
if values is not None:
if not isinstance(values, str):
data["mm_v"] = values
values = "mm_v"
self.values = data[values]
data = data.set_index(unique_id)[values]
if rng:
from momepy import limit_range
results_list = []
for index in tqdm(data.index, total=data.shape[0],
disable=not verbose):
if index in spatial_weights.neighbors.keys():
neighbours = [index]
neighbours += spatial_weights.neighbors[index]
values_list = data.loc[neighbours]
if rng:
values_list = limit_range(values_list, rng=rng)
results_list.append(Theil(values_list).T)
else:
results_list.append(np.nan)
self.series = pd.Series(results_list, index=gdf.index)
def __init__(self, gdf, values, spatial_weights, unique_id, rng=None):
try:
from inequality.theil import Theil
except ImportError:
try:
from pysal.explore.inequality.theil import Theil
except ImportError:
raise ImportError("The 'inequality' or 'pysal' package is required.")
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
data = gdf.copy()
if values is not None:
if not isinstance(values, str):
data["mm_v"] = values
values = "mm_v"
self.values = data[values]
data = data.set_index(unique_id)
results_list = []
for index, row in tqdm(data.iterrows(), total=data.shape[0]):
neighbours = spatial_weights.neighbors[index].copy()
if neighbours:
neighbours.append(index)
else:
neighbours = [index]
values_list = data.loc[neighbours][values]
if rng:
from momepy import limit_range
values_list = limit_range(values_list, rng=rng)
results_list.append(Theil(values_list).T)
self.series = pd.Series(results_list, index=gdf.index)
def __init__(
self,
gdf,
values,
spatial_weights,
unique_id,
rng=None,
mode="all",
verbose=True,
):
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
self.modes = mode
if rng:
from momepy import limit_range
data = gdf.copy()
if values is not None:
if not isinstance(values, str):
data["mm_v"] = values
values = "mm_v"
self.values = data[values]
data = data.set_index(unique_id)[values]
means = []
medians = []
modes = []
allowed = ["mean", "median", "mode"]
if mode == "all":
mode = allowed
elif isinstance(mode, list):
for m in mode:
if m not in allowed:
raise ValueError(
"{} is not supported as mode.".format(mode))
elif isinstance(mode, str):
if mode not in allowed:
raise ValueError("{} is not supported as mode.".format(mode))
mode = [mode]
for index in tqdm(data.index, total=data.shape[0],
disable=not verbose):
if index in spatial_weights.neighbors.keys():
neighbours = [index]
neighbours += spatial_weights.neighbors[index]
values_list = data.loc[neighbours]
if rng:
values_list = limit_range(values_list, rng=rng)
if "mean" in mode:
means.append(np.mean(values_list))
if "median" in mode:
medians.append(np.median(values_list))
if "mode" in mode:
modes.append(sp.stats.mode(values_list)[0][0])
else:
if "mean" in mode:
means.append(np.nan)
if "median" in mode:
medians.append(np.nan)
if "mode" in mode:
modes.append(np.nan)
if "mean" in mode:
self.series = self.mean = pd.Series(means, index=gdf.index)
if "median" in mode:
self.median = pd.Series(medians, index=gdf.index)
if "mode" in mode:
self.mode = pd.Series(modes, index=gdf.index)
def test_limit_range(self):
assert mm.limit_range(range(10), rng=(25, 75)) == [3, 4, 5, 6]
assert mm.limit_range(range(10),
rng=(10, 90)) == [1, 2, 3, 4, 5, 6, 7, 8]
def theil(objects, values, spatial_weights=None, order=None, rng=(0, 100)):
"""
Calculates the Theil measure of inequality of values within k steps of morphological tessellation
Uses `pysal.explore.inequality.theil.Theil` under the hood.
.. math::
Parameters
----------
objects : GeoDataFrame
GeoDataFrame containing morphological tessellation
values : str, list, np.array, pd.Series
the name of the dataframe column, np.array, or pd.Series where is stored character value.
spatial_weights : libpysal.weights, optional
spatial weights matrix - If None, Queen contiguity matrix of set order will be calculated
based on objects.
order : int
order of Queen contiguity
rng : Two-element sequence containing floats in range of [0,100], optional
Percentiles over which to compute the range. Each must be
between 0 and 100, inclusive. The order of the elements is not important.
Returns
-------
Series
Series containing resulting values.
References
----------
Examples
--------
"""
from pysal.explore.inequality.theil import Theil
# define empty list for results
results_list = []
print('Calculating Theil index...')
if spatial_weights is None:
print('Generating weights matrix (Queen) of {} topological steps...'.format(order))
from momepy import Queen_higher
# matrix to define area of analysis (more steps)
spatial_weights = Queen_higher(k=order, geodataframe=objects)
else:
if not all(objects.index == range(len(objects))):
raise ValueError('Index is not consecutive range 0:x, spatial weights will not match objects.')
if values is not None:
if not isinstance(values, str):
objects['mm_v'] = values
values = 'mm_v'
for index, row in tqdm(objects.iterrows(), total=objects.shape[0]):
neighbours = spatial_weights.neighbors[index]
values_list = objects.iloc[neighbours][values].tolist()
if values_list:
values_list.append(row[values])
else:
values_list = [row[values]]
if rng:
from momepy import limit_range
values_list = limit_range(values_list, rng=rng)
results_list.append(Theil(values_list).T)
series = pd.Series(results_list)
if 'mm_v' in objects.columns:
objects.drop(columns=['mm_v'], inplace=True)
print('Theil index calculated.')
return series
