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HazyResearch/metal | metal/tuners/hyperband_tuner.py | HyperbandTuner.get_largest_schedule_within_budget | def get_largest_schedule_within_budget(self, budget, proportion_discard):
"""
Gets the largest hyperband schedule within target_budget.
This is required since the original hyperband algorithm uses R,
the maximum number of resources per configuration.
TODO(maxlam): Possibly binary... | python | def get_largest_schedule_within_budget(self, budget, proportion_discard):
"""
Gets the largest hyperband schedule within target_budget.
This is required since the original hyperband algorithm uses R,
the maximum number of resources per configuration.
TODO(maxlam): Possibly binary... | [
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HazyResearch/metal | metal/tuners/hyperband_tuner.py | HyperbandTuner.generate_hyperband_schedule | def generate_hyperband_schedule(self, R, eta):
"""
Generate hyperband schedule according to the paper.
Args:
R: maximum resources per config.
eta: proportion of configruations to discard per
iteration of successive halving.
Returns: hyperband sch... | python | def generate_hyperband_schedule(self, R, eta):
"""
Generate hyperband schedule according to the paper.
Args:
R: maximum resources per config.
eta: proportion of configruations to discard per
iteration of successive halving.
Returns: hyperband sch... | [
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HazyResearch/metal | metal/tuners/hyperband_tuner.py | HyperbandTuner.search | def search(
self,
search_space,
valid_data,
init_args=[],
train_args=[],
init_kwargs={},
train_kwargs={},
module_args={},
module_kwargs={},
max_search=None,
shuffle=True,
verbose=True,
seed=None,
**score_kwar... | python | def search(
self,
search_space,
valid_data,
init_args=[],
train_args=[],
init_kwargs={},
train_kwargs={},
module_args={},
module_kwargs={},
max_search=None,
shuffle=True,
verbose=True,
seed=None,
**score_kwar... | [
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At the beginning of each bracket, we generate a
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for the number of brackets in the schedule.
Args:
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HazyResearch/metal | metal/contrib/info_extraction/utils.py | mark_entities | def mark_entities(tokens, positions, markers=[], style="insert"):
"""Adds special markers around tokens at specific positions (e.g., entities)
Args:
tokens: A list of tokens (the sentence)
positions:
1) A list of inclusive ranges (tuples) corresponding to the
token range... | python | def mark_entities(tokens, positions, markers=[], style="insert"):
"""Adds special markers around tokens at specific positions (e.g., entities)
Args:
tokens: A list of tokens (the sentence)
positions:
1) A list of inclusive ranges (tuples) corresponding to the
token range... | [
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HazyResearch/metal | metal/tuners/tuner.py | ModelTuner._clear_state | def _clear_state(self, seed=None):
"""Clears the state, starts clock"""
self.start_time = time()
self.run_stats = []
self.best_index = -1
self.best_score = -1
self.best_config = None
# Note: These must be set at the start of self.search()
self.search_spac... | python | def _clear_state(self, seed=None):
"""Clears the state, starts clock"""
self.start_time = time()
self.run_stats = []
self.best_index = -1
self.best_score = -1
self.best_config = None
# Note: These must be set at the start of self.search()
self.search_spac... | [
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HazyResearch/metal | metal/tuners/tuner.py | ModelTuner.run_stats_df | def run_stats_df(self):
"""Returns self.run_stats over search params as pandas dataframe."""
run_stats_df = []
for x in self.run_stats:
search_results = {**x["search_params"]}
search_results["score"] = x["score"]
run_stats_df.append(search_results)
re... | python | def run_stats_df(self):
"""Returns self.run_stats over search params as pandas dataframe."""
run_stats_df = []
for x in self.run_stats:
search_results = {**x["search_params"]}
search_results["score"] = x["score"]
run_stats_df.append(search_results)
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HazyResearch/metal | metal/tuners/tuner.py | ModelTuner.search | def search(
self,
search_space,
valid_data,
init_args=[],
train_args=[],
init_kwargs={},
train_kwargs={},
module_args={},
module_kwargs={},
max_search=None,
shuffle=True,
verbose=True,
**score_kwargs,
):
... | python | def search(
self,
search_space,
valid_data,
init_args=[],
train_args=[],
init_kwargs={},
train_kwargs={},
module_args={},
module_kwargs={},
max_search=None,
shuffle=True,
verbose=True,
**score_kwargs,
):
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init_args: (list) positional args for initializing the model
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HazyResearch/metal | metal/tuners/tuner.py | ModelTuner.config_generator | def config_generator(search_space, max_search, rng, shuffle=True):
"""Generates config dicts from the given search space
Args:
search_space: (dict) A dictionary of parameters to search over.
See note below for more details.
max_search: (int) The maximum number of... | python | def config_generator(search_space, max_search, rng, shuffle=True):
"""Generates config dicts from the given search space
Args:
search_space: (dict) A dictionary of parameters to search over.
See note below for more details.
max_search: (int) The maximum number of... | [
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HazyResearch/metal | metal/label_model/utils.py | compute_mu | def compute_mu(L_aug, Y, k, p):
"""Given label matrix L_aug and labels Y, compute the true mu params.
Args:
L: (np.array {0,1}) [n, d] The augmented (indicator) label matrix
Y: (np.array int) [n] The true labels in {1,...,k}
k: (int) Cardinality
p: (np.array float) [k] The class... | python | def compute_mu(L_aug, Y, k, p):
"""Given label matrix L_aug and labels Y, compute the true mu params.
Args:
L: (np.array {0,1}) [n, d] The augmented (indicator) label matrix
Y: (np.array int) [n] The true labels in {1,...,k}
k: (int) Cardinality
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HazyResearch/metal | metal/label_model/utils.py | compute_covariance | def compute_covariance(L_aug, Y, k, p):
"""Given label matrix L_aug and labels Y, compute the covariance.
Args:
L: (np.array {0,1}) [n, d] The augmented (indicator) label matrix
Y: (np.array int) [n] The true labels in {1,...,k}
k: (int) Cardinality
p: (np.array float) [k] The c... | python | def compute_covariance(L_aug, Y, k, p):
"""Given label matrix L_aug and labels Y, compute the covariance.
Args:
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HazyResearch/metal | metal/label_model/utils.py | compute_inv_covariance | def compute_inv_covariance(L_aug, Y, k, p):
"""Given label matrix L and labels Y, compute the covariance.
Args:
L: (np.array) [n, d] The augmented (indicator) label matrix
Y: (np.array int) [n] The true labels in {1,...,k}
"""
return np.linalg.inv(compute_covariance(L_aug, Y, k, p)) | python | def compute_inv_covariance(L_aug, Y, k, p):
"""Given label matrix L and labels Y, compute the covariance.
Args:
L: (np.array) [n, d] The augmented (indicator) label matrix
Y: (np.array int) [n] The true labels in {1,...,k}
"""
return np.linalg.inv(compute_covariance(L_aug, Y, k, p)) | [
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HazyResearch/metal | metal/label_model/utils.py | print_matrix | def print_matrix(X, decimals=1):
"""Pretty printing for numpy matrix X"""
for row in np.round(X, decimals=decimals):
print(row) | python | def print_matrix(X, decimals=1):
"""Pretty printing for numpy matrix X"""
for row in np.round(X, decimals=decimals):
print(row) | [
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HazyResearch/metal | metal/analysis.py | _conflicted_data_points | def _conflicted_data_points(L):
"""Returns an indicator vector where ith element = 1 if x_i is labeled by
at least two LFs that give it disagreeing labels."""
m = sparse.diags(np.ravel(L.max(axis=1).todense()))
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"""Returns an indicator vector where ith element = 1 if x_i is labeled by
at least two LFs that give it disagreeing labels."""
m = sparse.diags(np.ravel(L.max(axis=1).todense()))
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HazyResearch/metal | metal/analysis.py | lf_polarities | def lf_polarities(L):
"""Return the polarities of each LF based on evidence in a label matrix.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
"""
polarities = [sorted(list(set(L[:, i].data))) for i in range(L.shape[1])]
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"""Return the polarities of each LF based on evidence in a label matrix.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
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polarities = [sorted(list(set(L[:, i].data))) for i in range(L.shape[1])]
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HazyResearch/metal | metal/analysis.py | lf_overlaps | def lf_overlaps(L, normalize_by_coverage=False):
"""Return the **fraction of items each LF labels that are also labeled by at
least one other LF.**
Note that the maximum possible overlap fraction for an LF is the LF's
coverage, unless `normalize_by_coverage=True`, in which case it is 1.
Args:
... | python | def lf_overlaps(L, normalize_by_coverage=False):
"""Return the **fraction of items each LF labels that are also labeled by at
least one other LF.**
Note that the maximum possible overlap fraction for an LF is the LF's
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different (non-abstain) label by at least one other LF.**
Note that the maximum possible conflict fraction for an LF is the LF's
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"""Return the **fraction of items each LF labels that are also given a
different (non-abstain) label by at least one other LF.**
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HazyResearch/metal | metal/analysis.py | lf_empirical_accuracies | def lf_empirical_accuracies(L, Y):
"""Return the **empirical accuracy** against a set of labels Y (e.g. dev
set) for each LF.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
Y: an [n] or [n, 1] np.ndarray of gold labels
... | python | def lf_empirical_accuracies(L, Y):
"""Return the **empirical accuracy** against a set of labels Y (e.g. dev
set) for each LF.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
Y: an [n] or [n, 1] np.ndarray of gold labels
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HazyResearch/metal | metal/analysis.py | lf_summary | def lf_summary(L, Y=None, lf_names=None, est_accs=None):
"""Returns a pandas DataFrame with the various per-LF statistics.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
Y: an [n] or [n, 1] np.ndarray of gold labels.
... | python | def lf_summary(L, Y=None, lf_names=None, est_accs=None):
"""Returns a pandas DataFrame with the various per-LF statistics.
Args:
L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
jth LF to the ith candidate
Y: an [n] or [n, 1] np.ndarray of gold labels.
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HazyResearch/metal | metal/analysis.py | single_lf_summary | def single_lf_summary(Y_p, Y=None):
"""Calculates coverage, overlap, conflicts, and accuracy for a single LF
Args:
Y_p: a np.array or torch.Tensor of predicted labels
Y: a np.array or torch.Tensor of true labels (if known)
"""
L = sparse.csr_matrix(arraylike_to_numpy(Y_p).reshape(-1, 1)... | python | def single_lf_summary(Y_p, Y=None):
"""Calculates coverage, overlap, conflicts, and accuracy for a single LF
Args:
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HazyResearch/metal | metal/analysis.py | error_buckets | def error_buckets(gold, pred, X=None):
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Args:
gold: an array-like of gold labels (ints)
pred: an array-like of predictions (ints)
X: an iterable of items
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Args:
gold: an array-like of gold labels (ints)
pred: an array-like of predictions (ints)
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HazyResearch/metal | metal/analysis.py | confusion_matrix | def confusion_matrix(
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Args:
gold: an array-like of gold labels (ints)
pred: an array-like of predictions (ints)
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gold: an array-like of gold labels (ints)
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HazyResearch/metal | metal/analysis.py | ConfusionMatrix.add | def add(self, gold, pred):
"""
Args:
gold: a np.ndarray of gold labels (ints)
pred: a np.ndarray of predictions (ints)
"""
self.counter.update(zip(gold, pred)) | python | def add(self, gold, pred):
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Args:
gold: a np.ndarray of gold labels (ints)
pred: a np.ndarray of predictions (ints)
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verbose=True,
clean_up=True,
seed=Non... | python | def search(
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valid_data,
init_args=[],
train_args=[],
init_kwargs={},
train_kwargs={},
module_args={},
module_kwargs={},
max_search=None,
shuffle=True,
verbose=True,
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cache_folder=settings.cache_folder,
use_cache=settings.use_cache,
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logs_folder=settings.logs_folder,
imgs_folder=settings.imgs_folder,
cache_folder=settings.cache_folder,
use_cache=settings.use_cache,
log_file=settings.log_file,
log_console=settings.log_console,
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gboeing/osmnx | osmnx/utils.py | log | def log(message, level=None, name=None, filename=None):
"""
Write a message to the log file and/or print to the the console.
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message : string
the content of the message to log
level : int
one of the logger.level constants
name : string
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Write a message to the log file and/or print to the the console.
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message : string
the content of the message to log
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gboeing/osmnx | osmnx/utils.py | get_logger | def get_logger(level=None, name=None, filename=None):
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Create a logger or return the current one if already instantiated.
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level : int
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name : string
name of the logger
filename : string
name of the log file
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"""
Create a logger or return the current one if already instantiated.
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level : int
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name : string
name of the logger
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gboeing/osmnx | osmnx/utils.py | induce_subgraph | def induce_subgraph(G, node_subset):
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Parameters
----------
G : networkx multidigraph
node_subset : list-like
the subset of nodes to induce a subgraph of G
Returns
-------
G2 : networkx multidigraph
the subgraph of G induced by node_subset... | python | def induce_subgraph(G, node_subset):
"""
Induce a subgraph of G.
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G : networkx multidigraph
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the subset of nodes to induce a subgraph of G
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gboeing/osmnx | osmnx/utils.py | get_largest_component | def get_largest_component(G, strongly=False):
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Return a subgraph of the largest weakly or strongly connected component
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Parameters
----------
G : networkx multidigraph
strongly : bool
if True, return the largest strongly instead of weakly connected
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G : networkx multidigraph
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gboeing/osmnx | osmnx/utils.py | great_circle_vec | def great_circle_vec(lat1, lng1, lat2, lng2, earth_radius=6371009):
"""
Vectorized function to calculate the great-circle distance between two
points or between vectors of points, using haversine.
Parameters
----------
lat1 : float or array of float
lng1 : float or array of float
lat2 :... | python | def great_circle_vec(lat1, lng1, lat2, lng2, earth_radius=6371009):
"""
Vectorized function to calculate the great-circle distance between two
points or between vectors of points, using haversine.
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gboeing/osmnx | osmnx/utils.py | euclidean_dist_vec | def euclidean_dist_vec(y1, x1, y2, x2):
"""
Vectorized function to calculate the euclidean distance between two points
or between vectors of points.
Parameters
----------
y1 : float or array of float
x1 : float or array of float
y2 : float or array of float
x2 : float or array of fl... | python | def euclidean_dist_vec(y1, x1, y2, x2):
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Vectorized function to calculate the euclidean distance between two points
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gboeing/osmnx | osmnx/utils.py | get_nearest_node | def get_nearest_node(G, point, method='haversine', return_dist=False):
"""
Return the graph node nearest to some specified (lat, lng) or (y, x) point,
and optionally the distance between the node and the point. This function
can use either a haversine or euclidean distance calculator.
Parameters
... | python | def get_nearest_node(G, point, method='haversine', return_dist=False):
"""
Return the graph node nearest to some specified (lat, lng) or (y, x) point,
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gboeing/osmnx | osmnx/utils.py | get_nearest_edge | def get_nearest_edge(G, point):
"""
Return the nearest edge to a pair of coordinates. Pass in a graph and a tuple
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the euclidean distance from the coordinates to the segments determined by each edge.
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"""
Return the nearest edge to a pair of coordinates. Pass in a graph and a tuple
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gboeing/osmnx | osmnx/utils.py | get_nearest_nodes | def get_nearest_nodes(G, X, Y, method=None):
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"""
Return the graph nodes nearest to a list of points. Pass in points
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gboeing/osmnx | osmnx/utils.py | redistribute_vertices | def redistribute_vertices(geom, dist):
"""
Redistribute the vertices on a projected LineString or MultiLineString. The distance
argument is only approximate since the total distance of the linestring may not be
a multiple of the preferred distance. This function works on only [Multi]LineString
geome... | python | def redistribute_vertices(geom, dist):
"""
Redistribute the vertices on a projected LineString or MultiLineString. The distance
argument is only approximate since the total distance of the linestring may not be
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gboeing/osmnx | osmnx/utils.py | get_bearing | def get_bearing(origin_point, destination_point):
"""
Calculate the bearing between two lat-long points. Each tuple should
represent (lat, lng) as decimal degrees.
Parameters
----------
origin_point : tuple
destination_point : tuple
Returns
-------
bearing : float
the c... | python | def get_bearing(origin_point, destination_point):
"""
Calculate the bearing between two lat-long points. Each tuple should
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Parameters
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origin_point : tuple
destination_point : tuple
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bearing : float
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gboeing/osmnx | osmnx/utils.py | add_edge_bearings | def add_edge_bearings(G):
"""
Calculate the compass bearing from origin node to destination node for each
edge in the directed graph then add each bearing as a new edge attribute.
Parameters
----------
G : networkx multidigraph
Returns
-------
G : networkx multidigraph
"""
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"""
Calculate the compass bearing from origin node to destination node for each
edge in the directed graph then add each bearing as a new edge attribute.
Parameters
----------
G : networkx multidigraph
Returns
-------
G : networkx multidigraph
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gboeing/osmnx | osmnx/utils.py | geocode | def geocode(query):
"""
Geocode a query string to (lat, lon) with the Nominatim geocoder.
Parameters
----------
query : string
the query string to geocode
Returns
-------
point : tuple
the (lat, lon) coordinates returned by the geocoder
"""
# send the query to ... | python | def geocode(query):
"""
Geocode a query string to (lat, lon) with the Nominatim geocoder.
Parameters
----------
query : string
the query string to geocode
Returns
-------
point : tuple
the (lat, lon) coordinates returned by the geocoder
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gboeing/osmnx | osmnx/utils.py | get_route_edge_attributes | def get_route_edge_attributes(G, route, attribute=None, minimize_key='length', retrieve_default=None):
"""
Get a list of attribute values for each edge in a path.
Parameters
----------
G : networkx multidigraph
route : list
list of nodes in the path
attribute : string
the na... | python | def get_route_edge_attributes(G, route, attribute=None, minimize_key='length', retrieve_default=None):
"""
Get a list of attribute values for each edge in a path.
Parameters
----------
G : networkx multidigraph
route : list
list of nodes in the path
attribute : string
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gboeing/osmnx | osmnx/utils.py | count_streets_per_node | def count_streets_per_node(G, nodes=None):
"""
Count how many street segments emanate from each node (i.e., intersections and dead-ends) in this graph.
If nodes is passed, then only count the nodes in the graph with those IDs.
Parameters
----------
G : networkx multidigraph
nodes : iterabl... | python | def count_streets_per_node(G, nodes=None):
"""
Count how many street segments emanate from each node (i.e., intersections and dead-ends) in this graph.
If nodes is passed, then only count the nodes in the graph with those IDs.
Parameters
----------
G : networkx multidigraph
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gboeing/osmnx | osmnx/utils.py | round_polygon_coords | def round_polygon_coords(p, precision):
"""
Round the coordinates of a shapely Polygon to some decimal precision.
Parameters
----------
p : shapely Polygon
the polygon to round the coordinates of
precision : int
decimal precision to round coordinates to
Returns
-------
... | python | def round_polygon_coords(p, precision):
"""
Round the coordinates of a shapely Polygon to some decimal precision.
Parameters
----------
p : shapely Polygon
the polygon to round the coordinates of
precision : int
decimal precision to round coordinates to
Returns
-------
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gboeing/osmnx | osmnx/utils.py | round_point_coords | def round_point_coords(pt, precision):
"""
Round the coordinates of a shapely Point to some decimal precision.
Parameters
----------
pt : shapely Point
the Point to round the coordinates of
precision : int
decimal precision to round coordinates to
Returns
-------
Po... | python | def round_point_coords(pt, precision):
"""
Round the coordinates of a shapely Point to some decimal precision.
Parameters
----------
pt : shapely Point
the Point to round the coordinates of
precision : int
decimal precision to round coordinates to
Returns
-------
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gboeing/osmnx | osmnx/utils.py | round_linestring_coords | def round_linestring_coords(ls, precision):
"""
Round the coordinates of a shapely LineString to some decimal precision.
Parameters
----------
ls : shapely LineString
the LineString to round the coordinates of
precision : int
decimal precision to round coordinates to
Return... | python | def round_linestring_coords(ls, precision):
"""
Round the coordinates of a shapely LineString to some decimal precision.
Parameters
----------
ls : shapely LineString
the LineString to round the coordinates of
precision : int
decimal precision to round coordinates to
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gboeing/osmnx | osmnx/utils.py | round_shape_coords | def round_shape_coords(shape, precision):
"""
Round the coordinates of a shapely geometry to some decimal precision.
Parameters
----------
shape : shapely geometry, one of Point, MultiPoint, LineString,
MultiLineString, Polygon, or MultiPolygon
the geometry to round the coordina... | python | def round_shape_coords(shape, precision):
"""
Round the coordinates of a shapely geometry to some decimal precision.
Parameters
----------
shape : shapely geometry, one of Point, MultiPoint, LineString,
MultiLineString, Polygon, or MultiPolygon
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gboeing/osmnx | osmnx/utils.py | overpass_json_from_file | def overpass_json_from_file(filename):
"""
Read OSM XML from input filename and return Overpass-like JSON.
Parameters
----------
filename : string
name of file containing OSM XML data
Returns
-------
OSMContentHandler object
"""
_, ext = os.path.splitext(filename)
... | python | def overpass_json_from_file(filename):
"""
Read OSM XML from input filename and return Overpass-like JSON.
Parameters
----------
filename : string
name of file containing OSM XML data
Returns
-------
OSMContentHandler object
"""
_, ext = os.path.splitext(filename)
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gboeing/osmnx | osmnx/utils.py | bbox_to_poly | def bbox_to_poly(north, south, east, west):
"""
Convenience function to parse bbox -> poly
"""
return Polygon([(west, south), (east, south), (east, north), (west, north)]) | python | def bbox_to_poly(north, south, east, west):
"""
Convenience function to parse bbox -> poly
"""
return Polygon([(west, south), (east, south), (east, north), (west, north)]) | [
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gboeing/osmnx | osmnx/pois.py | parse_poi_query | def parse_poi_query(north, south, east, west, amenities=None, timeout=180, maxsize=''):
"""
Parse the Overpass QL query based on the list of amenities.
Parameters
----------
north : float
Northernmost coordinate from bounding box of the search area.
south : float
Southernmo... | python | def parse_poi_query(north, south, east, west, amenities=None, timeout=180, maxsize=''):
"""
Parse the Overpass QL query based on the list of amenities.
Parameters
----------
north : float
Northernmost coordinate from bounding box of the search area.
south : float
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gboeing/osmnx | osmnx/pois.py | osm_poi_download | def osm_poi_download(polygon=None, amenities=None, north=None, south=None, east=None, west=None,
timeout=180, max_query_area_size=50*1000*50*1000):
"""
Get points of interests (POIs) from OpenStreetMap based on selected amenity types.
Parameters
----------
poly : shapely.geomet... | python | def osm_poi_download(polygon=None, amenities=None, north=None, south=None, east=None, west=None,
timeout=180, max_query_area_size=50*1000*50*1000):
"""
Get points of interests (POIs) from OpenStreetMap based on selected amenity types.
Parameters
----------
poly : shapely.geomet... | [
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gboeing/osmnx | osmnx/pois.py | parse_nodes_coords | def parse_nodes_coords(osm_response):
"""
Parse node coordinates from OSM response. Some nodes are
standalone points of interest, others are vertices in
polygonal (areal) POIs.
Parameters
----------
osm_response : string
OSM response JSON string
Returns
-------
... | python | def parse_nodes_coords(osm_response):
"""
Parse node coordinates from OSM response. Some nodes are
standalone points of interest, others are vertices in
polygonal (areal) POIs.
Parameters
----------
osm_response : string
OSM response JSON string
Returns
-------
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gboeing/osmnx | osmnx/pois.py | parse_polygonal_poi | def parse_polygonal_poi(coords, response):
"""
Parse areal POI way polygons from OSM node coords.
Parameters
----------
coords : dict
dict of node IDs and their lat, lon coordinates
Returns
-------
dict of POIs containing each's nodes, polygon geometry, and osmid
"""
i... | python | def parse_polygonal_poi(coords, response):
"""
Parse areal POI way polygons from OSM node coords.
Parameters
----------
coords : dict
dict of node IDs and their lat, lon coordinates
Returns
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dict of POIs containing each's nodes, polygon geometry, and osmid
"""
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gboeing/osmnx | osmnx/pois.py | parse_osm_node | def parse_osm_node(response):
"""
Parse points from OSM nodes.
Parameters
----------
response : JSON
Nodes from OSM response.
Returns
-------
Dict of vertex IDs and their lat, lon coordinates.
"""
try:
point = Point(response['lon'], response['lat'])
... | python | def parse_osm_node(response):
"""
Parse points from OSM nodes.
Parameters
----------
response : JSON
Nodes from OSM response.
Returns
-------
Dict of vertex IDs and their lat, lon coordinates.
"""
try:
point = Point(response['lon'], response['lat'])
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gboeing/osmnx | osmnx/pois.py | invalid_multipoly_handler | def invalid_multipoly_handler(gdf, relation, way_ids):
"""
Handles invalid multipolygon geometries when there exists e.g. a feature without
geometry (geometry == NaN)
Parameters
----------
gdf : gpd.GeoDataFrame
GeoDataFrame with Polygon geometries that should be converted into a Mult... | python | def invalid_multipoly_handler(gdf, relation, way_ids):
"""
Handles invalid multipolygon geometries when there exists e.g. a feature without
geometry (geometry == NaN)
Parameters
----------
gdf : gpd.GeoDataFrame
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gboeing/osmnx | osmnx/pois.py | parse_osm_relations | def parse_osm_relations(relations, osm_way_df):
"""
Parses the osm relations (multipolygons) from osm
ways and nodes. See more information about relations
from OSM documentation: http://wiki.openstreetmap.org/wiki/Relation
Parameters
----------
relations : list
OSM 'rela... | python | def parse_osm_relations(relations, osm_way_df):
"""
Parses the osm relations (multipolygons) from osm
ways and nodes. See more information about relations
from OSM documentation: http://wiki.openstreetmap.org/wiki/Relation
Parameters
----------
relations : list
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gboeing/osmnx | osmnx/pois.py | create_poi_gdf | def create_poi_gdf(polygon=None, amenities=None, north=None, south=None, east=None, west=None):
"""
Parse GeoDataFrames from POI json that was returned by Overpass API.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
geographic shape to fetch the POIs within
amenities: l... | python | def create_poi_gdf(polygon=None, amenities=None, north=None, south=None, east=None, west=None):
"""
Parse GeoDataFrames from POI json that was returned by Overpass API.
Parameters
----------
polygon : shapely Polygon or MultiPolygon
geographic shape to fetch the POIs within
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gboeing/osmnx | osmnx/pois.py | pois_from_point | def pois_from_point(point, distance=None, amenities=None):
"""
Get point of interests (POIs) within some distance north, south, east, and west of
a lat-long point.
Parameters
----------
point : tuple
a lat-long point
distance : numeric
distance in meters
amenities : list... | python | def pois_from_point(point, distance=None, amenities=None):
"""
Get point of interests (POIs) within some distance north, south, east, and west of
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----------
point : tuple
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distance in meters
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gboeing/osmnx | osmnx/pois.py | pois_from_address | def pois_from_address(address, distance, amenities=None):
"""
Get OSM points of Interests within some distance north, south, east, and west of
an address.
Parameters
----------
address : string
the address to geocode to a lat-long point
distance : numeric
distance in meters
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"""
Get OSM points of Interests within some distance north, south, east, and west of
an address.
Parameters
----------
address : string
the address to geocode to a lat-long point
distance : numeric
distance in meters
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gboeing/osmnx | osmnx/pois.py | pois_from_place | def pois_from_place(place, amenities=None):
"""
Get points of interest (POIs) within the boundaries of some place.
Parameters
----------
place : string
the query to geocode to get geojson boundary polygon.
amenities : list
List of amenities that will be used for finding the POIs... | python | def pois_from_place(place, amenities=None):
"""
Get points of interest (POIs) within the boundaries of some place.
Parameters
----------
place : string
the query to geocode to get geojson boundary polygon.
amenities : list
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gboeing/osmnx | osmnx/simplify.py | is_endpoint | def is_endpoint(G, node, strict=True):
"""
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otherwise False. OSM data includes lots of nodes that exist only as points \
to help streets bend around curves. An end point is a node that either: \
1) is its own neighbor, ie, it s... | python | def is_endpoint(G, node, strict=True):
"""
Return True if the node is a "real" endpoint of an edge in the network, \
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gboeing/osmnx | osmnx/simplify.py | build_path | def build_path(G, node, endpoints, path):
"""
Recursively build a path of nodes until you hit an endpoint node.
Parameters
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G : networkx multidigraph
node : int
the current node to start from
endpoints : set
the set of all nodes in the graph that are endpoints
... | python | def build_path(G, node, endpoints, path):
"""
Recursively build a path of nodes until you hit an endpoint node.
Parameters
----------
G : networkx multidigraph
node : int
the current node to start from
endpoints : set
the set of all nodes in the graph that are endpoints
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gboeing/osmnx | osmnx/simplify.py | get_paths_to_simplify | def get_paths_to_simplify(G, strict=True):
"""
Create a list of all the paths to be simplified between endpoint nodes.
The path is ordered from the first endpoint, through the interstitial nodes,
to the second endpoint. If your street network is in a rural area with many
interstitial nodes between ... | python | def get_paths_to_simplify(G, strict=True):
"""
Create a list of all the paths to be simplified between endpoint nodes.
The path is ordered from the first endpoint, through the interstitial nodes,
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gboeing/osmnx | osmnx/simplify.py | simplify_graph | def simplify_graph(G, strict=True):
"""
Simplify a graph's topology by removing all nodes that are not intersections
or dead-ends.
Create an edge directly between the end points that encapsulate them,
but retain the geometry of the original edges, saved as attribute in new
edge.
Parameters... | python | def simplify_graph(G, strict=True):
"""
Simplify a graph's topology by removing all nodes that are not intersections
or dead-ends.
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but retain the geometry of the original edges, saved as attribute in new
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gboeing/osmnx | osmnx/simplify.py | clean_intersections | def clean_intersections(G, tolerance=15, dead_ends=False):
"""
Clean-up intersections comprising clusters of nodes by merging them and
returning their centroids.
Divided roads are represented by separate centerline edges. The intersection
of two divided roads thus creates 4 nodes, representing wher... | python | def clean_intersections(G, tolerance=15, dead_ends=False):
"""
Clean-up intersections comprising clusters of nodes by merging them and
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gboeing/osmnx | osmnx/projection.py | project_geometry | def project_geometry(geometry, crs=None, to_crs=None, to_latlong=False):
"""
Project a shapely Polygon or MultiPolygon from lat-long to UTM, or
vice-versa
Parameters
----------
geometry : shapely Polygon or MultiPolygon
the geometry to project
crs : dict
the starting coordin... | python | def project_geometry(geometry, crs=None, to_crs=None, to_latlong=False):
"""
Project a shapely Polygon or MultiPolygon from lat-long to UTM, or
vice-versa
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geometry : shapely Polygon or MultiPolygon
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gboeing/osmnx | osmnx/projection.py | project_gdf | def project_gdf(gdf, to_crs=None, to_latlong=False):
"""
Project a GeoDataFrame to the UTM zone appropriate for its geometries'
centroid.
The simple calculation in this function works well for most latitudes, but
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northern... | python | def project_gdf(gdf, to_crs=None, to_latlong=False):
"""
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Parameters
----------
G : networkx multidigraph
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to_crs : dict
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"""
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gboeing/osmnx | osmnx/plot.py | plot_shape | def plot_shape(gdf, fc='#cbe0f0', ec='#999999', linewidth=1, alpha=1,
figsize=(6,6), margin=0.02, axis_off=True):
"""
Plot a GeoDataFrame of place boundary geometries.
Parameters
----------
gdf : GeoDataFrame
the gdf containing the geometries to plot
fc : string or list
... | python | def plot_shape(gdf, fc='#cbe0f0', ec='#999999', linewidth=1, alpha=1,
figsize=(6,6), margin=0.02, axis_off=True):
"""
Plot a GeoDataFrame of place boundary geometries.
Parameters
----------
gdf : GeoDataFrame
the gdf containing the geometries to plot
fc : string or list
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gboeing/osmnx | osmnx/plot.py | rgb_color_list_to_hex | def rgb_color_list_to_hex(color_list):
"""
Convert a list of RGBa colors to a list of hexadecimal color codes.
Parameters
----------
color_list : list
the list of RGBa colors
Returns
-------
color_list_hex : list
"""
color_list_rgb = [[int(x*255) for x in c[0:3]] for c ... | python | def rgb_color_list_to_hex(color_list):
"""
Convert a list of RGBa colors to a list of hexadecimal color codes.
Parameters
----------
color_list : list
the list of RGBa colors
Returns
-------
color_list_hex : list
"""
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gboeing/osmnx | osmnx/plot.py | get_colors | def get_colors(n, cmap='viridis', start=0., stop=1., alpha=1., return_hex=False):
"""
Return n-length list of RGBa colors from the passed colormap name and alpha.
Parameters
----------
n : int
number of colors
cmap : string
name of a colormap
start : float
where to s... | python | def get_colors(n, cmap='viridis', start=0., stop=1., alpha=1., return_hex=False):
"""
Return n-length list of RGBa colors from the passed colormap name and alpha.
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gboeing/osmnx | osmnx/plot.py | get_node_colors_by_attr | def get_node_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none'):
"""
Get a list of node colors by binning some continuous-variable attribute into
quantiles.
Parameters
----------
G : networkx multidigraph
attr : string
the name of the attribute
... | python | def get_node_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none'):
"""
Get a list of node colors by binning some continuous-variable attribute into
quantiles.
Parameters
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G : networkx multidigraph
attr : string
the name of the attribute
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gboeing/osmnx | osmnx/plot.py | get_edge_colors_by_attr | def get_edge_colors_by_attr(G, attr, num_bins=5, cmap='viridis', start=0, stop=1, na_color='none'):
"""
Get a list of edge colors by binning some continuous-variable attribute into
quantiles.
Parameters
----------
G : networkx multidigraph
attr : string
the name of the continuous-va... | python | def get_edge_colors_by_attr(G, attr, num_bins=5, cmap='viridis', start=0, stop=1, na_color='none'):
"""
Get a list of edge colors by binning some continuous-variable attribute into
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G : networkx multidigraph
attr : string
the name of the continuous-va... | [
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gboeing/osmnx | osmnx/plot.py | save_and_show | def save_and_show(fig, ax, save, show, close, filename, file_format, dpi, axis_off):
"""
Save a figure to disk and show it, as specified.
Parameters
----------
fig : figure
ax : axis
save : bool
whether to save the figure to disk or not
show : bool
whether to display the... | python | def save_and_show(fig, ax, save, show, close, filename, file_format, dpi, axis_off):
"""
Save a figure to disk and show it, as specified.
Parameters
----------
fig : figure
ax : axis
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whether to save the figure to disk or not
show : bool
whether to display the... | [
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gboeing/osmnx | osmnx/plot.py | plot_graph | def plot_graph(G, bbox=None, fig_height=6, fig_width=None, margin=0.02,
axis_off=True, equal_aspect=False, bgcolor='w', show=True,
save=False, close=True, file_format='png', filename='temp',
dpi=300, annotate=False, node_color='#66ccff', node_size=15,
node_alp... | python | def plot_graph(G, bbox=None, fig_height=6, fig_width=None, margin=0.02,
axis_off=True, equal_aspect=False, bgcolor='w', show=True,
save=False, close=True, file_format='png', filename='temp',
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node_alp... | [
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gboeing/osmnx | osmnx/plot.py | node_list_to_coordinate_lines | def node_list_to_coordinate_lines(G, node_list, use_geom=True):
"""
Given a list of nodes, return a list of lines that together follow the path
defined by the list of nodes.
Parameters
----------
G : networkx multidigraph
route : list
the route as a list of nodes
use_geom : bool... | python | def node_list_to_coordinate_lines(G, node_list, use_geom=True):
"""
Given a list of nodes, return a list of lines that together follow the path
defined by the list of nodes.
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----------
G : networkx multidigraph
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the route as a list of nodes
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gboeing/osmnx | osmnx/plot.py | plot_graph_route | def plot_graph_route(G, route, bbox=None, fig_height=6, fig_width=None,
margin=0.02, bgcolor='w', axis_off=True, show=True,
save=False, close=True, file_format='png', filename='temp',
dpi=300, annotate=False, node_color='#999999',
node_... | python | def plot_graph_route(G, route, bbox=None, fig_height=6, fig_width=None,
margin=0.02, bgcolor='w', axis_off=True, show=True,
save=False, close=True, file_format='png', filename='temp',
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gboeing/osmnx | osmnx/plot.py | make_folium_polyline | def make_folium_polyline(edge, edge_color, edge_width, edge_opacity, popup_attribute=None):
"""
Turn a row from the gdf_edges GeoDataFrame into a folium PolyLine with
attributes.
Parameters
----------
edge : GeoSeries
a row from the gdf_edges GeoDataFrame
edge_color : string
... | python | def make_folium_polyline(edge, edge_color, edge_width, edge_opacity, popup_attribute=None):
"""
Turn a row from the gdf_edges GeoDataFrame into a folium PolyLine with
attributes.
Parameters
----------
edge : GeoSeries
a row from the gdf_edges GeoDataFrame
edge_color : string
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gboeing/osmnx | osmnx/plot.py | plot_graph_folium | def plot_graph_folium(G, graph_map=None, popup_attribute=None,
tiles='cartodbpositron', zoom=1, fit_bounds=True,
edge_color='#333333', edge_width=5, edge_opacity=1):
"""
Plot a graph on an interactive folium web map.
Note that anything larger than a small city ca... | python | def plot_graph_folium(G, graph_map=None, popup_attribute=None,
tiles='cartodbpositron', zoom=1, fit_bounds=True,
edge_color='#333333', edge_width=5, edge_opacity=1):
"""
Plot a graph on an interactive folium web map.
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gboeing/osmnx | osmnx/plot.py | plot_route_folium | def plot_route_folium(G, route, route_map=None, popup_attribute=None,
tiles='cartodbpositron', zoom=1, fit_bounds=True,
route_color='#cc0000', route_width=5, route_opacity=1):
"""
Plot a route on an interactive folium web map.
Parameters
----------
G : ne... | python | def plot_route_folium(G, route, route_map=None, popup_attribute=None,
tiles='cartodbpositron', zoom=1, fit_bounds=True,
route_color='#cc0000', route_width=5, route_opacity=1):
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Plot a route on an interactive folium web map.
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gboeing/osmnx | osmnx/plot.py | plot_figure_ground | def plot_figure_ground(G=None, address=None, point=None, dist=805,
network_type='drive_service', street_widths=None,
default_width=4, fig_length=8, edge_color='w',
bgcolor='#333333', smooth_joints=True, filename=None,
file_forma... | python | def plot_figure_ground(G=None, address=None, point=None, dist=805,
network_type='drive_service', street_widths=None,
default_width=4, fig_length=8, edge_color='w',
bgcolor='#333333', smooth_joints=True, filename=None,
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gboeing/osmnx | osmnx/core.py | save_to_cache | def save_to_cache(url, response_json):
"""
Save an HTTP response json object to the cache.
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be a GET-style representation of request. Users should always pass
OrderedDicts instead of dicts of parameters into request functions... | python | def save_to_cache(url, response_json):
"""
Save an HTTP response json object to the cache.
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gboeing/osmnx | osmnx/core.py | get_from_cache | def get_from_cache(url):
"""
Retrieve a HTTP response json object from the cache.
Parameters
----------
url : string
the url of the request
Returns
-------
response_json : dict
"""
# if the tool is configured to use the cache
if settings.use_cache:
# determi... | python | def get_from_cache(url):
"""
Retrieve a HTTP response json object from the cache.
Parameters
----------
url : string
the url of the request
Returns
-------
response_json : dict
"""
# if the tool is configured to use the cache
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gboeing/osmnx | osmnx/core.py | get_http_headers | def get_http_headers(user_agent=None, referer=None, accept_language=None):
"""
Update the default requests HTTP headers with OSMnx info.
Parameters
----------
user_agent : str
the user agent string, if None will set with OSMnx default
referer : str
the referer string, if None wi... | python | def get_http_headers(user_agent=None, referer=None, accept_language=None):
"""
Update the default requests HTTP headers with OSMnx info.
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----------
user_agent : str
the user agent string, if None will set with OSMnx default
referer : str
the referer string, if None wi... | [
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gboeing/osmnx | osmnx/core.py | nominatim_request | def nominatim_request(params, type = "search", pause_duration=1, timeout=30, error_pause_duration=180):
"""
Send a request to the Nominatim API via HTTP GET and return the JSON
response.
Parameters
----------
params : dict or OrderedDict
key-value pairs of parameters
type : string
... | python | def nominatim_request(params, type = "search", pause_duration=1, timeout=30, error_pause_duration=180):
"""
Send a request to the Nominatim API via HTTP GET and return the JSON
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Parameters
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params : dict or OrderedDict
key-value pairs of parameters
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gboeing/osmnx | osmnx/core.py | overpass_request | def overpass_request(data, pause_duration=None, timeout=180, error_pause_duration=None):
"""
Send a request to the Overpass API via HTTP POST and return the JSON
response.
Parameters
----------
data : dict or OrderedDict
key-value pairs of parameters to post to the API
pause_duratio... | python | def overpass_request(data, pause_duration=None, timeout=180, error_pause_duration=None):
"""
Send a request to the Overpass API via HTTP POST and return the JSON
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----------
data : dict or OrderedDict
key-value pairs of parameters to post to the API
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gboeing/osmnx | osmnx/core.py | osm_polygon_download | def osm_polygon_download(query, limit=1, polygon_geojson=1):
"""
Geocode a place and download its boundary geometry from OSM's Nominatim API.
Parameters
----------
query : string or dict
query string or structured query dict to geocode/download
limit : int
max number of results ... | python | def osm_polygon_download(query, limit=1, polygon_geojson=1):
"""
Geocode a place and download its boundary geometry from OSM's Nominatim API.
Parameters
----------
query : string or dict
query string or structured query dict to geocode/download
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gboeing/osmnx | osmnx/core.py | gdf_from_place | def gdf_from_place(query, gdf_name=None, which_result=1, buffer_dist=None):
"""
Create a GeoDataFrame from a single place name query.
Parameters
----------
query : string or dict
query string or structured query dict to geocode/download
gdf_name : string
name attribute metadata ... | python | def gdf_from_place(query, gdf_name=None, which_result=1, buffer_dist=None):
"""
Create a GeoDataFrame from a single place name query.
Parameters
----------
query : string or dict
query string or structured query dict to geocode/download
gdf_name : string
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gboeing/osmnx | osmnx/core.py | gdf_from_places | def gdf_from_places(queries, gdf_name='unnamed', buffer_dist=None):
"""
Create a GeoDataFrame from a list of place names to query.
Parameters
----------
queries : list
list of query strings or structured query dicts to geocode/download, one
at a time
gdf_name : string
na... | python | def gdf_from_places(queries, gdf_name='unnamed', buffer_dist=None):
"""
Create a GeoDataFrame from a list of place names to query.
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queries : list
list of query strings or structured query dicts to geocode/download, one
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gdf_name : string
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gboeing/osmnx | osmnx/core.py | get_osm_filter | def get_osm_filter(network_type):
"""
Create a filter to query OSM for the specified network type.
Parameters
----------
network_type : string
{'walk', 'bike', 'drive', 'drive_service', 'all', 'all_private', 'none'}
what type of street or other network to get
Returns
------... | python | def get_osm_filter(network_type):
"""
Create a filter to query OSM for the specified network type.
Parameters
----------
network_type : string
{'walk', 'bike', 'drive', 'drive_service', 'all', 'all_private', 'none'}
what type of street or other network to get
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gboeing/osmnx | osmnx/core.py | osm_net_download | def osm_net_download(polygon=None, north=None, south=None, east=None, west=None,
network_type='all_private', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, infrastructure='way["highway"]',
custom_filter=None):
"""
Download OSM ways a... | python | def osm_net_download(polygon=None, north=None, south=None, east=None, west=None,
network_type='all_private', timeout=180, memory=None,
max_query_area_size=50*1000*50*1000, infrastructure='way["highway"]',
custom_filter=None):
"""
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gboeing/osmnx | osmnx/core.py | get_polygons_coordinates | def get_polygons_coordinates(geometry):
"""
Extract exterior coordinates from polygon(s) to pass to OSM in a query by
polygon. Ignore the interior ("holes") coordinates.
Parameters
----------
geometry : shapely Polygon or MultiPolygon
the geometry to extract exterior coordinates from
... | python | def get_polygons_coordinates(geometry):
"""
Extract exterior coordinates from polygon(s) to pass to OSM in a query by
polygon. Ignore the interior ("holes") coordinates.
Parameters
----------
geometry : shapely Polygon or MultiPolygon
the geometry to extract exterior coordinates from
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gboeing/osmnx | osmnx/core.py | get_node | def get_node(element):
"""
Convert an OSM node element into the format for a networkx node.
Parameters
----------
element : dict
an OSM node element
Returns
-------
dict
"""
node = {}
node['y'] = element['lat']
node['x'] = element['lon']
node['osmid'] = ele... | python | def get_node(element):
"""
Convert an OSM node element into the format for a networkx node.
Parameters
----------
element : dict
an OSM node element
Returns
-------
dict
"""
node = {}
node['y'] = element['lat']
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gboeing/osmnx | osmnx/core.py | get_path | def get_path(element):
"""
Convert an OSM way element into the format for a networkx graph path.
Parameters
----------
element : dict
an OSM way element
Returns
-------
dict
"""
path = {}
path['osmid'] = element['id']
# remove any consecutive duplicate element... | python | def get_path(element):
"""
Convert an OSM way element into the format for a networkx graph path.
Parameters
----------
element : dict
an OSM way element
Returns
-------
dict
"""
path = {}
path['osmid'] = element['id']
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gboeing/osmnx | osmnx/core.py | parse_osm_nodes_paths | def parse_osm_nodes_paths(osm_data):
"""
Construct dicts of nodes and paths with key=osmid and value=dict of
attributes.
Parameters
----------
osm_data : dict
JSON response from from the Overpass API
Returns
-------
nodes, paths : tuple
"""
nodes = {}
paths = {... | python | def parse_osm_nodes_paths(osm_data):
"""
Construct dicts of nodes and paths with key=osmid and value=dict of
attributes.
Parameters
----------
osm_data : dict
JSON response from from the Overpass API
Returns
-------
nodes, paths : tuple
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nodes = {}
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gboeing/osmnx | osmnx/core.py | remove_isolated_nodes | def remove_isolated_nodes(G):
"""
Remove from a graph all the nodes that have no incident edges (ie, node
degree = 0).
Parameters
----------
G : networkx multidigraph
the graph from which to remove nodes
Returns
-------
networkx multidigraph
"""
isolated_nodes = [n... | python | def remove_isolated_nodes(G):
"""
Remove from a graph all the nodes that have no incident edges (ie, node
degree = 0).
Parameters
----------
G : networkx multidigraph
the graph from which to remove nodes
Returns
-------
networkx multidigraph
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gboeing/osmnx | osmnx/core.py | truncate_graph_dist | def truncate_graph_dist(G, source_node, max_distance=1000, weight='length', retain_all=False):
"""
Remove everything further than some network distance from a specified node
in graph.
Parameters
----------
G : networkx multidigraph
source_node : int
the node in the graph from which ... | python | def truncate_graph_dist(G, source_node, max_distance=1000, weight='length', retain_all=False):
"""
Remove everything further than some network distance from a specified node
in graph.
Parameters
----------
G : networkx multidigraph
source_node : int
the node in the graph from which ... | [
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gboeing/osmnx | osmnx/core.py | truncate_graph_bbox | def truncate_graph_bbox(G, north, south, east, west, truncate_by_edge=False, retain_all=False):
"""
Remove every node in graph that falls outside a bounding box.
Needed because overpass returns entire ways that also include nodes outside
the bbox if the way (that is, a way with a single OSM ID) has a n... | python | def truncate_graph_bbox(G, north, south, east, west, truncate_by_edge=False, retain_all=False):
"""
Remove every node in graph that falls outside a bounding box.
Needed because overpass returns entire ways that also include nodes outside
the bbox if the way (that is, a way with a single OSM ID) has a n... | [
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gboeing/osmnx | osmnx/core.py | quadrat_cut_geometry | def quadrat_cut_geometry(geometry, quadrat_width, min_num=3, buffer_amount=1e-9):
"""
Split a Polygon or MultiPolygon up into sub-polygons of a specified size,
using quadrats.
Parameters
----------
geometry : shapely Polygon or MultiPolygon
the geometry to split up into smaller sub-poly... | python | def quadrat_cut_geometry(geometry, quadrat_width, min_num=3, buffer_amount=1e-9):
"""
Split a Polygon or MultiPolygon up into sub-polygons of a specified size,
using quadrats.
Parameters
----------
geometry : shapely Polygon or MultiPolygon
the geometry to split up into smaller sub-poly... | [
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gboeing/osmnx | osmnx/core.py | intersect_index_quadrats | def intersect_index_quadrats(gdf, geometry, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):
"""
Intersect points with a polygon, using an r-tree spatial index and cutting
the polygon up into smaller sub-polygons for r-tree acceleration.
Parameters
----------
gdf : GeoDataFrame
the s... | python | def intersect_index_quadrats(gdf, geometry, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):
"""
Intersect points with a polygon, using an r-tree spatial index and cutting
the polygon up into smaller sub-polygons for r-tree acceleration.
Parameters
----------
gdf : GeoDataFrame
the s... | [
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gboeing/osmnx | osmnx/core.py | truncate_graph_polygon | def truncate_graph_polygon(G, polygon, retain_all=False, truncate_by_edge=False, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):
"""
Remove every node in graph that falls outside some shapely Polygon or
MultiPolygon.
Parameters
----------
G : networkx multidigraph
polygon : Polygon or M... | python | def truncate_graph_polygon(G, polygon, retain_all=False, truncate_by_edge=False, quadrat_width=0.05, min_num=3, buffer_amount=1e-9):
"""
Remove every node in graph that falls outside some shapely Polygon or
MultiPolygon.
Parameters
----------
G : networkx multidigraph
polygon : Polygon or M... | [
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gboeing/osmnx | osmnx/core.py | add_edge_lengths | def add_edge_lengths(G):
"""
Add length (meters) attribute to each edge by great circle distance between
nodes u and v.
Parameters
----------
G : networkx multidigraph
Returns
-------
G : networkx multidigraph
"""
start_time = time.time()
# first load all the edges' o... | python | def add_edge_lengths(G):
"""
Add length (meters) attribute to each edge by great circle distance between
nodes u and v.
Parameters
----------
G : networkx multidigraph
Returns
-------
G : networkx multidigraph
"""
start_time = time.time()
# first load all the edges' o... | [
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gboeing/osmnx | osmnx/core.py | add_path | def add_path(G, data, one_way):
"""
Add a path to the graph.
Parameters
----------
G : networkx multidigraph
data : dict
the attributes of the path
one_way : bool
if this path is one-way or if it is bi-directional
Returns
-------
None
"""
# extract the ... | python | def add_path(G, data, one_way):
"""
Add a path to the graph.
Parameters
----------
G : networkx multidigraph
data : dict
the attributes of the path
one_way : bool
if this path is one-way or if it is bi-directional
Returns
-------
None
"""
# extract the ... | [
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