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city_retrofit/venv/lib/python3.7/site-packages/trimesh/util.py

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Add virtual environment to the git repository, this isn't 100% right but it's practical at this development point
2020-06-16 10:34:17 -04:00
"""
util.py
-----------
Standalone functions which require only imports from numpy and the
standard library.
Other libraries may be imported must be wrapped in try/except blocks
or imported inside of a function
"""
import numpy as np
import abc
import sys
import copy
import json
import base64
import shutil
import logging
import hashlib
import zipfile
import tempfile
import collections
if sys.version_info >= (3, 4):
# for newer version of python
ABC = abc.ABC
else:
# an abstract base class that works on older versions
ABC = abc.ABCMeta('ABC', (), {})
# a flag we can check elsewhere for Python 3
PY3 = sys.version_info.major >= 3
if PY3:
# for type checking
basestring = str
# Python 3
from io import BytesIO, StringIO
# will be the highest granularity clock available
from time import perf_counter as now
else:
# Python 2
from StringIO import StringIO
# monkey patch StringIO so `with` statements work
StringIO.__enter__ = lambda a: a
StringIO.__exit__ = lambda a, b, c, d: a.close()
BytesIO = StringIO
# perf_counter not available on python 2
from time import time as now
try:
from collections.abc import Mapping
except ImportError:
from collections import Mapping
# create a default logger
log = logging.getLogger('trimesh')
# include constants here so we don't have to import
# a floating point threshold for 0.0
# we are setting it to 100x the resolution of a float64
# which works out to be 1e-13
TOL_ZERO = np.finfo(np.float64).resolution * 100
# how close to merge vertices
TOL_MERGE = 1e-8
# enable additional potentially slow checks
_STRICT = False
def unitize(vectors,
check_valid=False,
threshold=None):
"""
Unitize a vector or an array or row-vectors.
Parameters
------------
vectors : (n,m) or (j) float
Vector or vectors to be unitized
check_valid : bool
If set, will return mask of nonzero vectors
threshold : float
Cutoff for a value to be considered zero.
Returns
---------
unit : (n,m) or (j) float
Input vectors but unitized
valid : (n,) bool or bool
Mask of nonzero vectors returned if `check_valid`
"""
# make sure we have a numpy array
vectors = np.asanyarray(vectors)
# allow user to set zero threshold
if threshold is None:
threshold = TOL_ZERO
if len(vectors.shape) == 2:
# for (m, d) arrays take the per-row unit vector
# using sqrt and avoiding exponents is slightly faster
# also dot with ones is faser than .sum(axis=1)
norm = np.sqrt(np.dot(vectors * vectors,
[1.0] * vectors.shape[1]))
# non-zero norms
valid = norm > threshold
# in-place reciprocal of nonzero norms
norm[valid] **= -1
# multiply by reciprocal of norm
unit = vectors * norm.reshape((-1, 1))
elif len(vectors.shape) == 1:
# treat 1D arrays as a single vector
norm = np.sqrt(np.dot(vectors, vectors))
valid = norm > threshold
if valid:
unit = vectors / norm
else:
unit = vectors.copy()
else:
raise ValueError('vectors must be (n, ) or (n, d)!')
if check_valid:
return unit[valid], valid
return unit
def euclidean(a, b):
"""
Euclidean distance between vectors a and b.
Parameters
------------
a : (n,) float
First vector
b : (n,) float
Second vector
Returns
------------
distance : float
Euclidean distance between A and B
"""
a = np.asanyarray(a, dtype=np.float64)
b = np.asanyarray(b, dtype=np.float64)
return np.sqrt(((a - b) ** 2).sum())
def is_file(obj):
"""
Check if an object is file-like
Parameters
------------
obj : object
Any object type to be checked
Returns
-----------
is_file : bool
True if object is a file
"""
return hasattr(obj, 'read') or hasattr(obj, 'write')
def is_pathlib(obj):
"""
Check if the object is a `pathlib.Path` or subclass.
Parameters
------------
obj : object
Object to be checked
Returns
------------
is_pathlib : bool
Is the input object a pathlib path
"""
# check class name rather than a pathlib import
name = obj.__class__.__name__
return hasattr(obj, 'absolute') and name.endswith('Path')
def is_string(obj):
"""
Check if an object is a string.
Parameters
------------
obj : object
Any object type to be checked
Returns
------------
is_string : bool
True if obj is a string
"""
return isinstance(obj, basestring)
def is_none(obj):
"""
Check to see if an object is None or not.
Handles the case of np.array(None) as well.
Parameters
-------------
obj : object
Any object type to be checked
Returns
-------------
is_none : bool
True if obj is None or numpy None-like
"""
if obj is None:
return True
if (is_sequence(obj) and
len(obj) == 1 and
obj[0] is None):
return True
return False
def is_sequence(obj):
"""
Check if an object is a sequence or not.
Parameters
-------------
obj : object
Any object type to be checked
Returns
-------------
is_sequence : bool
True if object is sequence
"""
seq = (not hasattr(obj, "strip") and
hasattr(obj, "__getitem__") or
hasattr(obj, "__iter__"))
# check to make sure it is not a set, string, or dictionary
seq = seq and all(not isinstance(obj, i) for i in (dict,
set,
basestring))
# PointCloud objects can look like an array but are not
seq = seq and type(obj).__name__ not in ['PointCloud']
# numpy sometimes returns objects that are single float64 values
# but sure look like sequences, so we check the shape
if hasattr(obj, 'shape'):
seq = seq and obj.shape != ()
return seq
def is_shape(obj, shape, allow_zeros=False):
"""
Compare the shape of a numpy.ndarray to a target shape,
with any value less than zero being considered a wildcard
Note that if a list-like object is passed that is not a numpy
array, this function will not convert it and will return False.
Parameters
------------
obj : np.ndarray
Array to check the shape on
shape : list or tuple
Any negative term will be considered a wildcard
Any tuple term will be evaluated as an OR
allow_zeros: bool
if False, zeros do not match negatives in shape
Returns
---------
shape_ok : bool
True if shape of obj matches query shape
Examples
------------------------
In [1]: a = np.random.random((100, 3))
In [2]: a.shape
Out[2]: (100, 3)
In [3]: trimesh.util.is_shape(a, (-1, 3))
Out[3]: True
In [4]: trimesh.util.is_shape(a, (-1, 3, 5))
Out[4]: False
In [5]: trimesh.util.is_shape(a, (100, -1))
Out[5]: True
In [6]: trimesh.util.is_shape(a, (-1, (3, 4)))
Out[6]: True
In [7]: trimesh.util.is_shape(a, (-1, (4, 5)))
Out[7]: False
"""
# if the obj.shape is different length than
# the goal shape it means they have different number
# of dimensions and thus the obj is not the query shape
if (not hasattr(obj, 'shape') or
len(obj.shape) != len(shape)):
return False
# empty lists with any flexible dimensions match
if len(obj) == 0 and -1 in shape:
return True
# loop through each integer of the two shapes
# multiple values are sequences
# wildcards are less than zero (i.e. -1)
for i, target in zip(obj.shape, shape):
# check if current field has multiple acceptable values
if is_sequence(target):
if i in target:
# obj shape is in the accepted values
continue
else:
return False
# check if current field is a wildcard
if target < 0:
if i == 0 and not allow_zeros:
# if a dimension is 0, we don't allow
# that to match to a wildcard
# it would have to be explicitly called out as 0
return False
else:
continue
# since we have a single target and a single value,
# if they are not equal we have an answer
if target != i:
return False
# since none of the checks failed the obj.shape
# matches the pattern
return True
def make_sequence(obj):
"""
Given an object, if it is a sequence return, otherwise
add it to a length 1 sequence and return.
Useful for wrapping functions which sometimes return single
objects and other times return lists of objects.
Parameters
-------------
obj : object
An object to be made a sequence
Returns
--------------
as_sequence : (n,) sequence
Contains input value
"""
if is_sequence(obj):
return np.array(list(obj))
else:
return np.array([obj])
def vector_hemisphere(vectors, return_sign=False):
"""
For a set of 3D vectors alter the sign so they are all in the
upper hemisphere.
If the vector lies on the plane all vectors with negative Y
will be reversed.
If the vector has a zero Z and Y value vectors with a
negative X value will be reversed.
Parameters
------------
vectors : (n, 3) float
Input vectors
return_sign : bool
Return the sign mask or not
Returns
----------
oriented: (n, 3) float
Vectors with same magnitude as source
but possibly reversed to ensure all vectors
are in the same hemisphere.
sign : (n,) float
[OPTIONAL] sign of original vectors
"""
# vectors as numpy array
vectors = np.asanyarray(vectors, dtype=np.float64)
if is_shape(vectors, (-1, 2)):
# 2D vector case
# check the Y value and reverse vector
# direction if negative.
negative = vectors < -TOL_ZERO
zero = np.logical_not(
np.logical_or(negative, vectors > TOL_ZERO))
signs = np.ones(len(vectors), dtype=np.float64)
# negative Y values are reversed
signs[negative[:, 1]] = -1.0
# zero Y and negative X are reversed
signs[np.logical_and(zero[:, 1], negative[:, 0])] = -1.0
elif is_shape(vectors, (-1, 3)):
# 3D vector case
negative = vectors < -TOL_ZERO
zero = np.logical_not(
np.logical_or(negative, vectors > TOL_ZERO))
# move all negative Z to positive
# then for zero Z vectors, move all negative Y to positive
# then for zero Y vectors, move all negative X to positive
signs = np.ones(len(vectors), dtype=np.float64)
# all vectors with negative Z values
signs[negative[:, 2]] = -1.0
# all on-plane vectors with negative Y values
signs[np.logical_and(zero[:, 2], negative[:, 1])] = -1.0
# all on-plane vectors with zero Y values
# and negative X values
signs[np.logical_and(np.logical_and(zero[:, 2],
zero[:, 1]),
negative[:, 0])] = -1.0
else:
raise ValueError('vectors must be (n, 3)!')
# apply the signs to the vectors
oriented = vectors * signs.reshape((-1, 1))
if return_sign:
return oriented, signs
return oriented
def vector_to_spherical(cartesian):
"""
Convert a set of cartesian points to (n, 2) spherical unit
vectors.
Parameters
------------
cartesian : (n, 3) float
Points in space
Returns
------------
spherical : (n, 2) float
Angles, in radians
"""
cartesian = np.asanyarray(cartesian, dtype=np.float64)
if not is_shape(cartesian, (-1, 3)):
raise ValueError('Cartesian points must be (n, 3)!')
unit, valid = unitize(cartesian, check_valid=True)
unit[np.abs(unit) < TOL_MERGE] = 0.0
x, y, z = unit.T
spherical = np.zeros((len(cartesian), 2), dtype=np.float64)
spherical[valid] = np.column_stack((np.arctan2(y, x),
np.arccos(z)))
return spherical
def spherical_to_vector(spherical):
"""
Convert a set of (n, 2) spherical vectors to (n, 3) vectors
Parameters
------------
spherical : (n , 2) float
Angles, in radians
Returns
-----------
vectors : (n, 3) float
Unit vectors
"""
spherical = np.asanyarray(spherical, dtype=np.float64)
if not is_shape(spherical, (-1, 2)):
raise ValueError('spherical coordinates must be (n, 2)!')
theta, phi = spherical.T
st, ct = np.sin(theta), np.cos(theta)
sp, cp = np.sin(phi), np.cos(phi)
vectors = np.column_stack((ct * sp,
st * sp,
cp))
return vectors
def pairwise(iterable):
"""
For an iterable, group values into pairs.
Parameters
------------
iterable : (m, ) list
A sequence of values
Returns
-----------
pairs: (n, 2)
Pairs of sequential values
Example
-----------
In [1]: data
Out[1]: [0, 1, 2, 3, 4, 5, 6]
In [2]: list(trimesh.util.pairwise(data))
Out[2]: [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]
"""
# looping through a giant numpy array would be dumb
# so special case ndarrays and use numpy operations
if isinstance(iterable, np.ndarray):
iterable = iterable.reshape(-1)
stacked = np.column_stack((iterable, iterable))
pairs = stacked.reshape(-1)[1:-1].reshape((-1, 2))
return pairs
# if we have a normal iterable use itertools
import itertools
a, b = itertools.tee(iterable)
# pop the first element of the second item
next(b)
return zip(a, b)
try:
# prefer the faster numpy version of multi_dot
# only included in recent-ish version of numpy
multi_dot = np.linalg.multi_dot
except AttributeError:
log.warning('np.linalg.multi_dot not available, using fallback')
def multi_dot(arrays):
"""
Compute the dot product of two or more arrays in a single function call.
In most versions of numpy this is included, this slower function is
provided for backwards compatibility with ancient versions of numpy.
"""
arrays = np.asanyarray(arrays)
result = arrays[0].copy()
for i in arrays[1:]:
result = np.dot(result, i)
return result
def diagonal_dot(a, b):
"""
Dot product by row of a and b.
There are a lot of ways to do this though
performance varies very widely. This method
uses a dot product to sum the row and avoids
function calls if at all possible.
Comparing performance of some equivalent versions:
```
In [1]: import numpy as np; import trimesh
In [2]: a = np.random.random((10000, 3))
In [3]: b = np.random.random((10000, 3))
In [4]: %timeit (a * b).sum(axis=1)
1000 loops, best of 3: 181 us per loop
In [5]: %timeit np.einsum('ij,ij->i', a, b)
10000 loops, best of 3: 62.7 us per loop
In [6]: %timeit np.diag(np.dot(a, b.T))
1 loop, best of 3: 429 ms per loop
In [7]: %timeit np.dot(a * b, np.ones(a.shape[1]))
10000 loops, best of 3: 61.3 us per loop
In [8]: %timeit trimesh.util.diagonal_dot(a, b)
10000 loops, best of 3: 55.2 us per loop
```
Parameters
------------
a : (m, d) float
First array
b : (m, d) float
Second array
Returns
-------------
result : (m,) float
Dot product of each row
"""
# make sure `a` is numpy array
# doing it for `a` will force the multiplication to
# convert `b` if necessary and avoid function call otherwise
a = np.asanyarray(a)
# 3x faster than (a * b).sum(axis=1)
# avoiding np.ones saves 5-10% sometimes
return np.dot(a * b, [1.0] * a.shape[1])
def row_norm(data):
"""
Compute the norm per-row of a numpy array.
This is identical to np.linalg.norm(data, axis=1) but roughly
three times faster due to being less general.
In [3]: %timeit trimesh.util.row_norm(a)
76.3 us +/- 651 ns per loop
In [4]: %timeit np.linalg.norm(a, axis=1)
220 us +/- 5.41 us per loop
Parameters
-------------
data : (n, d) float
Input 2D data to calculate per-row norm of
Returns
-------------
norm : (n,) float
Norm of each row of input array
"""
return np.sqrt(np.dot(data ** 2, [1] * data.shape[1]))
def stack_3D(points, return_2D=False):
"""
For a list of (n, 2) or (n, 3) points return them
as (n, 3) 3D points, 2D points on the XY plane.
Parameters
------------
points : (n, 2) or (n, 3) float
Points in either 2D or 3D space
return_2D : bool
Were the original points 2D?
Returns
----------
points : (n, 3) float
Points in space
is_2D : bool
[OPTIONAL] if source points were (n, 2)
"""
points = np.asanyarray(points, dtype=np.float64)
shape = points.shape
if len(shape) != 2:
raise ValueError('Points must be 2D array!')
if shape[1] == 2:
points = np.column_stack((points,
np.zeros(len(points))))
is_2D = True
elif shape[1] == 3:
is_2D = False
else:
raise ValueError('Points must be (n, 2) or (n, 3)!')
if return_2D:
return points, is_2D
return points
def grid_arange(bounds, step):
"""
Return a grid from an (2,dimension) bounds with samples step distance apart.
Parameters
------------
bounds: (2,dimension) list of [[min x, min y, etc], [max x, max y, etc]]
step: float, or (dimension) floats, separation between points
Returns
---------
grid: (n, dimension), points inside the specified bounds
"""
bounds = np.asanyarray(bounds, dtype=np.float64)
if len(bounds) != 2:
raise ValueError('bounds must be (2, dimension!')
# allow single float or per-dimension spacing
step = np.asanyarray(step, dtype=np.float64)
if step.shape == ():
step = np.tile(step, bounds.shape[1])
grid_elements = [np.arange(*b, step=s) for b, s in zip(bounds.T, step)]
grid = np.vstack(np.meshgrid(*grid_elements, indexing='ij')
).reshape(bounds.shape[1], -1).T
return grid
def grid_linspace(bounds, count):
"""
Return a grid spaced inside a bounding box with edges spaced using np.linspace.
Parameters
------------
bounds: (2,dimension) list of [[min x, min y, etc], [max x, max y, etc]]
count: int, or (dimension,) int, number of samples per side
Returns
---------
grid: (n, dimension) float, points in the specified bounds
"""
bounds = np.asanyarray(bounds, dtype=np.float64)
if len(bounds) != 2:
raise ValueError('bounds must be (2, dimension!')
count = np.asanyarray(count, dtype=np.int)
if count.shape == ():
count = np.tile(count, bounds.shape[1])
grid_elements = [np.linspace(*b, num=c) for b, c in zip(bounds.T, count)]
grid = np.vstack(np.meshgrid(*grid_elements, indexing='ij')
).reshape(bounds.shape[1], -1).T
return grid
def multi_dict(pairs):
"""
Given a set of key value pairs, create a dictionary.
If a key occurs multiple times, stack the values into an array.
Can be called like the regular dict(pairs) constructor
Parameters
------------
pairs: (n, 2) array of key, value pairs
Returns
----------
result: dict, with all values stored (rather than last with regular dict)
"""
result = collections.defaultdict(list)
for k, v in pairs:
result[k].append(v)
return result
def tolist(data):
"""
Ensure that any arrays or dicts passed containing
numpy arrays are properly converted to lists
Parameters
-------------
data : any
Usually a dict with some numpy arrays as values
Returns
----------
result : any
JSON-serializable version of data
"""
result = json.loads(jsonify(data))
return result
def is_binary_file(file_obj):
"""
Returns True if file has non-ASCII characters (> 0x7F, or 127)
Should work in both Python 2 and 3
"""
start = file_obj.tell()
fbytes = file_obj.read(1024)
file_obj.seek(start)
is_str = isinstance(fbytes, str)
for fbyte in fbytes:
if is_str:
code = ord(fbyte)
else:
code = fbyte
if code > 127:
return True
return False
def distance_to_end(file_obj):
"""
For an open file object how far is it to the end
Parameters
------------
file_obj: open file-like object
Returns
----------
distance: int, bytes to end of file
"""
position_current = file_obj.tell()
file_obj.seek(0, 2)
position_end = file_obj.tell()
file_obj.seek(position_current)
distance = position_end - position_current
return distance
def decimal_to_digits(decimal, min_digits=None):
"""
Return the number of digits to the first nonzero decimal.
Parameters
-----------
decimal: float
min_digits: int, minimum number of digits to return
Returns
-----------
digits: int, number of digits to the first nonzero decimal
"""
digits = abs(int(np.log10(decimal)))
if min_digits is not None:
digits = np.clip(digits, min_digits, 20)
return digits
def hash_file(file_obj,
hash_function=hashlib.md5):
"""
Get the hash of an open file-like object.
Parameters
------------
file_obj: file like object
hash_function: function to use to hash data
Returns
---------
hashed: str, hex version of result
"""
# before we read the file data save the current position
# in the file (which is probably 0)
file_position = file_obj.tell()
# create an instance of the hash object
hasher = hash_function()
# read all data from the file into the hasher
hasher.update(file_obj.read())
# get a hex version of the result
hashed = hasher.hexdigest()
# return the file object to its original position
file_obj.seek(file_position)
return hashed
def md5_object(obj):
"""
If an object is hashable, return the string of the MD5.
Parameters
------------
obj: object
Returns
----------
md5: str, MD5 hash
"""
hasher = hashlib.md5()
if isinstance(obj, basestring) and PY3:
# in python3 convert strings to bytes before hashing
hasher.update(obj.encode('utf-8'))
else:
hasher.update(obj)
md5 = hasher.hexdigest()
return md5
def attach_to_log(level=logging.DEBUG,
handler=None,
loggers=None,
colors=True,
capture_warnings=True,
blacklist=None):
"""
Attach a stream handler to all loggers.
Parameters
------------
level : enum
Logging level, like logging.INFO
handler : None or logging.Handler
Handler to attach
loggers : None or (n,) logging.Logger
If None, will try to attach to all available
colors : bool
If True try to use colorlog formatter
blacklist : (n,) str
Names of loggers NOT to attach to
"""
# default blacklist includes ipython debugging stuff
if blacklist is None:
blacklist = ['TerminalIPythonApp',
'PYREADLINE',
'pyembree',
'shapely.geos',
'shapely.speedups._speedups',
'parso.cache',
'parso.python.diff']
# make sure we log warnings from the warnings module
logging.captureWarnings(capture_warnings)
# create a basic formatter
formatter = logging.Formatter(
"[%(asctime)s] %(levelname)-7s (%(filename)s:%(lineno)3s) %(message)s",
"%Y-%m-%d %H:%M:%S")
if colors:
try:
from colorlog import ColoredFormatter
formatter = ColoredFormatter(
("%(log_color)s%(levelname)-8s%(reset)s " +
"%(filename)17s:%(lineno)-4s %(blue)4s%(message)s"),
datefmt=None,
reset=True,
log_colors={'DEBUG': 'cyan',
'INFO': 'green',
'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red'})
except ImportError:
pass
# if no handler was passed use a StreamHandler
if handler is None:
handler = logging.StreamHandler()
# add the formatters and set the level
handler.setFormatter(formatter)
handler.setLevel(level)
# if nothing passed use all available loggers
if loggers is None:
# de-duplicate loggers using a set
loggers = set(logging.Logger.manager.loggerDict.values())
# add the warnings logging
loggers.add(logging.getLogger('py.warnings'))
# disable pyembree warnings
logging.getLogger('pyembree').disabled = True
# loop through all available loggers
for logger in loggers:
# skip loggers on the blacklist
if (logger.__class__.__name__ != 'Logger' or
logger.name in blacklist):
continue
logger.addHandler(handler)
logger.setLevel(level)
# set nicer numpy print options
np.set_printoptions(precision=5, suppress=True)
def stack_lines(indices):
"""
Stack a list of values that represent a polyline into
individual line segments with duplicated consecutive values.
Parameters
------------
indices : (m,) any
List of items to be stacked
Returns
---------
stacked : (n, 2) any
Stacked items
Examples
----------
In [1]: trimesh.util.stack_lines([0, 1, 2])
Out[1]:
array([[0, 1],
[1, 2]])
In [2]: trimesh.util.stack_lines([0, 1, 2, 4, 5])
Out[2]:
array([[0, 1],
[1, 2],
[2, 4],
[4, 5]])
In [3]: trimesh.util.stack_lines([[0, 0], [1, 1], [2, 2], [3, 3]])
Out[3]:
array([[0, 0],
[1, 1],
[1, 1],
[2, 2],
[2, 2],
[3, 3]])
"""
indices = np.asanyarray(indices)
if len(indices) == 0:
return np.array([])
elif is_sequence(indices[0]):
shape = (-1, len(indices[0]))
else:
shape = (-1, 2)
return np.column_stack((indices[:-1],
indices[1:])).reshape(shape)
def append_faces(vertices_seq, faces_seq):
"""
Given a sequence of zero-indexed faces and vertices
combine them into a single array of faces and
a single array of vertices.
Parameters
-----------
vertices_seq : (n, ) sequence of (m, d) float
Multiple arrays of verticesvertex arrays
faces_seq : (n, ) sequence of (p, j) int
Zero indexed faces for matching vertices
Returns
----------
vertices : (i, d) float
Points in space
faces : (j, 3) int
Reference vertex indices
"""
# the length of each vertex array
vertices_len = np.array([len(i) for i in vertices_seq])
# how much each group of faces needs to be offset
face_offset = np.append(0, np.cumsum(vertices_len)[:-1])
new_faces = []
for offset, faces in zip(face_offset, faces_seq):
if len(faces) == 0:
continue
# apply the index offset
new_faces.append(faces + offset)
# stack to clean (n, 3) float
vertices = vstack_empty(vertices_seq)
# stack to clean (n, 3) int
faces = vstack_empty(new_faces)
return vertices, faces
def array_to_string(array,
col_delim=' ',
row_delim='\n',
digits=8,
value_format='{}'):
"""
Convert a 1 or 2D array into a string with a specified number
of digits and delimiter. The reason this exists is that the
basic numpy array to string conversions are surprisingly bad.
Parameters
------------
array : (n,) or (n, d) float or int
Data to be converted
If shape is (n,) only column delimiter will be used
col_delim : str
What string should separate values in a column
row_delim : str
What string should separate values in a row
digits : int
How many digits should floating point numbers include
value_format : str
Format string for each value or sequence of values
If multiple values per value_format it must divide
into array evenly.
Returns
----------
formatted : str
String representation of original array
"""
# convert inputs to correct types
array = np.asanyarray(array)
digits = int(digits)
row_delim = str(row_delim)
col_delim = str(col_delim)
value_format = str(value_format)
# abort for non-flat arrays
if len(array.shape) > 2:
raise ValueError('conversion only works on 1D/2D arrays not %s!',
str(array.shape))
# allow a value to be repeated in a value format
repeats = value_format.count('{}')
if array.dtype.kind == 'i':
# integer types don't need a specified precision
format_str = value_format + col_delim
elif array.dtype.kind == 'f':
# add the digits formatting to floats
format_str = value_format.replace(
'{}', '{:.' + str(digits) + 'f}') + col_delim
else:
raise ValueError('dtype %s not convertible!',
array.dtype.name)
# length of extra delimiters at the end
end_junk = len(col_delim)
# if we have a 2D array add a row delimiter
if len(array.shape) == 2:
format_str *= array.shape[1]
# cut off the last column delimiter and add a row delimiter
format_str = format_str[:-len(col_delim)] + row_delim
end_junk = len(row_delim)
# expand format string to whole array
format_str *= len(array)
# if an array is repeated in the value format
# do the shaping here so we don't need to specify indexes
shaped = np.tile(array.reshape((-1, 1)),
(1, repeats)).reshape(-1)
# run the format operation and remove the extra delimiters
formatted = format_str.format(*shaped)[:-end_junk]
return formatted
def array_to_encoded(array, dtype=None, encoding='base64'):
"""
Export a numpy array to a compact serializable dictionary.
Parameters
------------
array : array
Any numpy array
dtype : str or None
Optional dtype to encode array
encoding : str
'base64' or 'binary'
Returns
---------
encoded : dict
Has keys:
'dtype': str, of dtype
'shape': tuple of shape
'base64': str, base64 encoded string
"""
array = np.asanyarray(array)
shape = array.shape
# ravel also forces contiguous
flat = np.ravel(array)
if dtype is None:
dtype = array.dtype
encoded = {'dtype': np.dtype(dtype).str,
'shape': shape}
if encoding in ['base64', 'dict64']:
packed = base64.b64encode(flat.astype(dtype).tostring())
if hasattr(packed, 'decode'):
packed = packed.decode('utf-8')
encoded['base64'] = packed
elif encoding == 'binary':
encoded['binary'] = array.tostring(order='C')
else:
raise ValueError('encoding {} is not available!'.format(encoding))
return encoded
def decode_keys(store, encoding='utf-8'):
"""
If a dictionary has keys that are bytes decode them to a str.
Parameters
------------
store : dict
Dictionary with data
Returns
---------
result : dict
Values are untouched but keys that were bytes
are converted to ASCII strings.
Example
-----------
In [1]: d
Out[1]: {1020: 'nah', b'hi': 'stuff'}
In [2]: trimesh.util.decode_keys(d)
Out[2]: {1020: 'nah', 'hi': 'stuff'}
"""
keys = store.keys()
for key in keys:
if hasattr(key, 'decode'):
decoded = key.decode(encoding)
if key != decoded:
store[key.decode(encoding)] = store[key]
store.pop(key)
return store
def comment_strip(text, starts_with='#', new_line='\n'):
"""
Strip comments from a text block.
Parameters
-----------
text : str
Text to remove comments from
starts_with : str
Character or substring that starts a comment
new_line : str
Character or substring that ends a comment
Returns
-----------
stripped : str
Text with comments stripped
"""
# if not contained exit immediately
if starts_with not in text:
return text
# start by splitting into chunks by the comment indicator
split = (text + new_line).split(starts_with)
# special case files that start with a comment
if text.startswith(starts_with):
lead = ''
else:
lead = split[0]
# take each comment up until the newline
removed = [i.split(new_line, 1) for i in split]
# add the leading string back on
result = lead + new_line + new_line.join(
i[1] for i in removed
if len(i) > 1 and len(i[1]) > 0)
# strip leading and trailing whitespace
result = result.strip()
return result
def encoded_to_array(encoded):
"""
Turn a dictionary with base64 encoded strings back into a numpy array.
Parameters
------------
encoded : dict
Has keys:
dtype: string of dtype
shape: int tuple of shape
base64: base64 encoded string of flat array
binary: decode result coming from numpy.tostring
Returns
----------
array: numpy array
"""
if not isinstance(encoded, dict):
if is_sequence(encoded):
as_array = np.asanyarray(encoded)
return as_array
else:
raise ValueError('Unable to extract numpy array from input')
encoded = decode_keys(encoded)
dtype = np.dtype(encoded['dtype'])
if 'base64' in encoded:
array = np.frombuffer(base64.b64decode(encoded['base64']),
dtype)
elif 'binary' in encoded:
array = np.frombuffer(encoded['binary'],
dtype=dtype)
if 'shape' in encoded:
array = array.reshape(encoded['shape'])
return array
def is_instance_named(obj, name):
"""
Given an object, if it is a member of the class 'name',
or a subclass of 'name', return True.
Parameters
------------
obj : instance
Some object of some class
name: str
The name of the class we want to check for
Returns
---------
is_instance : bool
Whether the object is a member of the named class
"""
try:
type_named(obj, name)
return True
except ValueError:
return False
def type_bases(obj, depth=4):
"""
Return the bases of the object passed.
"""
bases = collections.deque([list(obj.__class__.__bases__)])
for i in range(depth):
bases.append([i.__base__ for i in bases[-1] if i is not None])
try:
bases = np.hstack(bases)
except IndexError:
bases = []
# we do the hasattr as None/NoneType can be in the list of bases
bases = [i for i in bases if hasattr(i, '__name__')]
return np.array(bases)
def type_named(obj, name):
"""
Similar to the type() builtin, but looks in class bases
for named instance.
Parameters
------------
obj: object to look for class of
name : str, name of class
Returns
----------
named class, or None
"""
# if obj is a member of the named class, return True
name = str(name)
if obj.__class__.__name__ == name:
return obj.__class__
for base in type_bases(obj):
if base.__name__ == name:
return base
raise ValueError('Unable to extract class of name ' + name)
def concatenate(a, b=None):
"""
Concatenate two or more meshes.
Parameters
------------
a : trimesh.Trimesh
Mesh or list of meshes to be concatenated
object, or list of such
b : trimesh.Trimesh
Mesh or list of meshes to be concatenated
Returns
----------
result : trimesh.Trimesh
Concatenated mesh
"""
if b is None:
b = []
# stack meshes into flat list
meshes = np.append(a, b)
# extract the trimesh type to avoid a circular import
# and assert that both inputs are Trimesh objects
trimesh_type = type_named(meshes[0], 'Trimesh')
# append faces and vertices of meshes
vertices, faces = append_faces(
[m.vertices.copy() for m in meshes],
[m.faces.copy() for m in meshes])
# only save face normals if already calculated
face_normals = None
if all('face_normals' in m._cache for m in meshes):
face_normals = np.vstack([m.face_normals
for m in meshes])
try:
# concatenate visuals
visual = meshes[0].visual.concatenate(
[m.visual for m in meshes[1:]])
except BaseException:
log.warning('failed to combine visuals',
exc_info=True)
visual = None
# create the mesh object
mesh = trimesh_type(vertices=vertices,
faces=faces,
face_normals=face_normals,
visual=visual,
process=False)
return mesh
def submesh(mesh,
faces_sequence,
repair=True,
only_watertight=False,
min_faces=None,
append=False):
"""
Return a subset of a mesh.
Parameters
------------
mesh : Trimesh
Source mesh to take geometry from
faces_sequence : sequence (p,) int
Indexes of mesh.faces
only_watertight : bool
Only return submeshes which are watertight.
append : bool
Return a single mesh which has the faces appended,
if this flag is set, only_watertight is ignored
Returns
---------
if append : Trimesh object
else list of Trimesh objects
"""
# evaluate generators so we can escape early
faces_sequence = list(faces_sequence)
if len(faces_sequence) == 0:
return []
# avoid nuking the cache on the original mesh
original_faces = mesh.faces.view(np.ndarray)
original_vertices = mesh.vertices.view(np.ndarray)
faces = []
vertices = []
normals = []
visuals = []
# for reindexing faces
mask = np.arange(len(original_vertices))
for index in faces_sequence:
# sanitize indices in case they are coming in as a set or tuple
index = np.asanyarray(index)
if len(index) == 0:
# regardless of type empty arrays are useless
continue
if index.dtype.kind == 'b':
# if passed a bool with no true continue
if not index.any():
continue
# if fewer faces than minimum
if min_faces is not None and index.sum() < min_faces:
continue
elif min_faces is not None and len(index) < min_faces:
continue
current = original_faces[index]
unique = np.unique(current.reshape(-1))
# redefine face indices from zero
mask[unique] = np.arange(len(unique))
normals.append(mesh.face_normals[index])
faces.append(mask[current])
vertices.append(original_vertices[unique])
visuals.append(mesh.visual.face_subset(index))
if len(vertices) == 0:
return np.array([])
# we use type(mesh) rather than importing Trimesh from base
# to avoid a circular import
trimesh_type = type_named(mesh, 'Trimesh')
if append:
if all(hasattr(i, 'concatenate') for i in visuals):
visuals = np.array(visuals)
visual = visuals[0].concatenate(visuals[1:])
else:
visual = None
vertices, faces = append_faces(vertices, faces)
appended = trimesh_type(
vertices=vertices,
faces=faces,
face_normals=np.vstack(normals),
visual=visual,
process=False)
return appended
# generate a list of Trimesh objects
result = [trimesh_type(
vertices=v,
faces=f,
face_normals=n,
visual=c,
metadata=copy.deepcopy(mesh.metadata),
process=False) for v, f, n, c in zip(vertices,
faces,
normals,
visuals)]
result = np.array(result)
if only_watertight or repair:
# fill_holes will attempt a repair and returns the
# watertight status at the end of the repair attempt
watertight = np.array([i.fill_holes() and len(i.faces) >= 4
for i in result])
if only_watertight:
# remove unrepairable meshes
result = result[watertight]
return result
def zero_pad(data, count, right=True):
"""
Parameters
------------
data : (n,)
1D array
count : int
Minimum length of result array
Returns
---------
padded : (m,)
1D array where m >= count
"""
if len(data) == 0:
return np.zeros(count)
elif len(data) < count:
padded = np.zeros(count)
if right:
padded[-len(data):] = data
else:
padded[:len(data)] = data
return padded
else:
return np.asanyarray(data)
def jsonify(obj, **kwargs):
"""
A version of json.dumps that can handle numpy arrays
by creating a custom encoder for numpy dtypes.
Parameters
--------------
obj : JSON-serializable blob
**kwargs :
Passed to json.dumps
Returns
--------------
dumped : str
JSON dump of obj
"""
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
# will work for numpy.ndarrays
# as well as their int64/etc objects
if hasattr(obj, 'tolist'):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
# run the dumps using our encoder
dumped = json.dumps(obj, cls=NumpyEncoder, **kwargs)
return dumped
def convert_like(item, like):
"""
Convert an item to have the dtype of another item
Parameters
------------
item : any
Item to be converted
like : any
Object with target dtype
If None, item is returned unmodified
Returns
----------
result: item, but in dtype of like
"""
# if it's a numpy array
if isinstance(like, np.ndarray):
return np.asanyarray(item, dtype=like.dtype)
# if it's already the desired type just return it
if isinstance(item, like.__class__) or is_none(like):
return item
# if it's an array with one item return it
if (is_sequence(item) and len(item) == 1 and
isinstance(item[0], like.__class__)):
return item[0]
# otherwise just run the conversion
item = like.__class__(item)
return item
def bounds_tree(bounds):
"""
Given a set of axis aligned bounds create an r-tree for
broad-phase collision detection.
Parameters
------------
bounds : (n, 2D) or (n, 2, D) float
Non-interleaved bounds where D=dimension
E.G a 2D bounds tree:
[(minx, miny, maxx, maxy), ...]
Returns
---------
tree : Rtree
Tree containing bounds by index
"""
# rtree is a soft dependency
import rtree
# make sure we've copied bounds
bounds = np.array(bounds, dtype=np.float64, copy=True)
if len(bounds.shape) == 3:
# should be min-max per bound
if bounds.shape[1] != 2:
raise ValueError('bounds not (n, 2, dimension)!')
# reshape to one-row-per-hyperrectangle
bounds = bounds.reshape((len(bounds), -1))
elif len(bounds.shape) != 2 or bounds.size == 0:
raise ValueError('Bounds must be (n, dimension * 2)!')
# check to make sure we have correct shape
dimension = bounds.shape[1]
if (dimension % 2) != 0:
raise ValueError('Bounds must be (n,dimension*2)!')
dimension = int(dimension / 2)
# some versions of rtree screw up indexes on stream loading
# do a test here so we know if we are free to use stream loading
# or if we have to do a loop to insert things which is 5x slower
rtree_test = rtree.index.Index(
[(1564, [0, 0, 0, 10, 10, 10], None)],
properties=rtree.index.Property(dimension=3))
rtree_stream_ok = next(rtree_test.intersection(
[1, 1, 1, 2, 2, 2])) == 1564
properties = rtree.index.Property(dimension=dimension)
if rtree_stream_ok:
# stream load was verified working on import above
tree = rtree.index.Index(zip(np.arange(len(bounds)),
bounds,
[None] * len(bounds)),
properties=properties)
else:
# in some rtree versions stream loading goofs the index
log.warning('rtree stream loading broken! Try upgrading rtree!')
tree = rtree.index.Index(properties=properties)
for i, b in enumerate(bounds):
tree.insert(i, b)
return tree
def wrap_as_stream(item):
"""
Wrap a string or bytes object as a file object.
Parameters
------------
item: str or bytes
Item to be wrapped
Returns
---------
wrapped : file-like object
Contains data from item
"""
if not PY3:
# in python 2 StringIO handles bytes and str
return StringIO(item)
if isinstance(item, str):
return StringIO(item)
elif isinstance(item, bytes):
return BytesIO(item)
raise ValueError('{} is not wrappable!'.format(type(item).__name__))
def sigfig_round(values, sigfig=1):
"""
Round a single value to a specified number of significant figures.
Parameters
------------
values : float
Value to be rounded
sigfig : int
Number of significant figures to reduce to
Returns
----------
rounded : float
Value rounded to the specified number of significant figures
Examples
----------
In [1]: trimesh.util.round_sigfig(-232453.00014045456, 1)
Out[1]: -200000.0
In [2]: trimesh.util.round_sigfig(.00014045456, 1)
Out[2]: 0.0001
In [3]: trimesh.util.round_sigfig(.00014045456, 4)
Out[3]: 0.0001405
"""
as_int, multiplier = sigfig_int(values, sigfig)
rounded = as_int * (10 ** multiplier)
return rounded
def sigfig_int(values, sigfig):
"""
Convert a set of floating point values into integers with a specified number
of significant figures and an exponent.
Parameters
------------
values : (n,) float or int
Array of values
sigfig : (n,) int
Number of significant figures to keep
Returns
------------
as_int : (n,) int
Every value[i] has sigfig[i] digits
multiplier : (n, int)
Exponent, so as_int * 10 ** multiplier is
the same order of magnitude as the input
"""
values = np.asanyarray(values).reshape(-1)
sigfig = np.asanyarray(sigfig, dtype=np.int).reshape(-1)
if sigfig.shape != values.shape:
raise ValueError('sigfig must match identifier')
exponent = np.zeros(len(values))
nonzero = np.abs(values) > TOL_ZERO
exponent[nonzero] = np.floor(np.log10(np.abs(values[nonzero])))
multiplier = exponent - sigfig + 1
as_int = np.round(values / (10**multiplier)).astype(np.int32)
return as_int, multiplier
def decompress(file_obj, file_type):
"""
Given an open file object and a file type, return all components
of the archive as open file objects in a dict.
Parameters
------------
file_obj : file-like
Containing compressed data
file_type : str
File extension, 'zip', 'tar.gz', etc
Returns
---------
decompressed : dict
Data from archive in format {file name : file-like}
"""
def is_zip():
archive = zipfile.ZipFile(file_obj)
result = {name: wrap_as_stream(archive.read(name))
for name in archive.namelist()}
return result
def is_tar():
import tarfile
archive = tarfile.open(fileobj=file_obj, mode='r')
result = {name: archive.extractfile(name)
for name in archive.getnames()}
return result
file_type = str(file_type).lower()
if isinstance(file_obj, bytes):
file_obj = wrap_as_stream(file_obj)
if file_type[-3:] == 'zip':
return is_zip()
if 'tar' in file_type[-6:]:
return is_tar()
raise ValueError('Unsupported type passed!')
def compress(info):
"""
Compress data stored in a dict.
Parameters
-----------
info : dict
Data to compress in form:
{file name in archive: bytes or file-like object}
Returns
-----------
compressed : bytes
Compressed file data
"""
if PY3:
file_obj = BytesIO()
else:
file_obj = StringIO()
with zipfile.ZipFile(
file_obj,
mode='w',
compression=zipfile.ZIP_DEFLATED) as zipper:
for name, data in info.items():
if hasattr(data, 'read'):
# if we were passed a file object, read it
data = data.read()
zipper.writestr(name, data)
file_obj.seek(0)
compressed = file_obj.read()
return compressed
def split_extension(file_name, special=['tar.bz2', 'tar.gz']):
"""
Find the file extension of a file name, including support for
special case multipart file extensions (like .tar.gz)
Parameters
------------
file_name : str
File name
special : list of str
Multipart extensions
eg: ['tar.bz2', 'tar.gz']
Returns
----------
extension : str
Last characters after a period, or
a value from 'special'
"""
file_name = str(file_name)
if file_name.endswith(tuple(special)):
for end in special:
if file_name.endswith(end):
return end
return file_name.split('.')[-1]
def triangle_strips_to_faces(strips):
"""
Convert a sequence of triangle strips to (n, 3) faces.
Processes all strips at once using np.concatenate and is significantly
faster than loop-based methods.
From the OpenGL programming guide describing a single triangle
strip [v0, v1, v2, v3, v4]:
Draws a series of triangles (three-sided polygons) using vertices
v0, v1, v2, then v2, v1, v3 (note the order), then v2, v3, v4,
and so on. The ordering is to ensure that the triangles are all
drawn with the same orientation so that the strip can correctly form
part of a surface.
Parameters
------------
strips: (n,) list of (m,) int
Vertex indices
Returns
------------
faces : (m, 3) int
Vertex indices representing triangles
"""
# save the length of each list in the list of lists
lengths = np.array([len(i) for i in strips])
# looping through a list of lists is extremely slow
# combine all the sequences into a blob we can manipulate
blob = np.concatenate(strips)
# preallocate and slice the blob into rough triangles
tri = np.zeros((len(blob) - 2, 3), dtype=np.int)
for i in range(3):
tri[:len(blob) - 3, i] = blob[i:-3 + i]
# the last triangle is left off from the slicing, add it back
tri[-1] = blob[-3:]
# remove the triangles which were implicit but not actually there
# because we combined everything into one big array for speed
length_index = np.cumsum(lengths)[:-1]
keep = np.ones(len(tri), dtype=np.bool)
keep[length_index - 2] = False
keep[length_index - 1] = False
tri = tri[keep]
# flip every other triangle so they generate correct normals/winding
length_index = np.append(0, np.cumsum(lengths - 2))
flip = np.zeros(length_index[-1], dtype=np.bool)
for i in range(len(length_index) - 1):
flip[length_index[i] + 1:length_index[i + 1]][::2] = True
tri[flip] = np.fliplr(tri[flip])
return tri
def vstack_empty(tup):
"""
A thin wrapper for numpy.vstack that ignores empty lists.
Parameters
------------
tup : tuple or list of arrays
With the same number of columns
Returns
------------
stacked : (n, d) array
With same number of columns as
constituent arrays.
"""
# filter out empty arrays
stackable = [i for i in tup if len(i) > 0]
# if we only have one array just return it
if len(stackable) == 1:
return np.asanyarray(stackable[0])
# if we have nothing return an empty numpy array
elif len(stackable) == 0:
return np.array([])
# otherwise just use vstack as normal
return np.vstack(stackable)
def write_encoded(file_obj,
stuff,
encoding='utf-8'):
"""
If a file is open in binary mode and a
string is passed, encode and write.
If a file is open in text mode and bytes are
passed decode bytes to str and write.
Parameters
-----------
file_obj : file object
With 'write' and 'mode'
stuff : str or bytes
Stuff to be written
encoding : str
Encoding of text
"""
binary_file = 'b' in file_obj.mode
string_stuff = isinstance(stuff, basestring)
binary_stuff = isinstance(stuff, bytes)
if not PY3:
file_obj.write(stuff)
elif binary_file and string_stuff:
file_obj.write(stuff.encode(encoding))
elif not binary_file and binary_stuff:
file_obj.write(stuff.decode(encoding))
else:
file_obj.write(stuff)
file_obj.flush()
return stuff
def unique_id(length=12, increment=0):
"""
Generate a decent looking alphanumeric unique identifier.
First 16 bits are time-incrementing, followed by randomness.
This function is used as a nicer looking alternative to:
>>> uuid.uuid4().hex
Follows the advice in:
https://eager.io/blog/how-long-does-an-id-need-to-be/
Parameters
------------
length : int
Length of desired identifier
increment : int
Number to add to header uint16
useful if calling this function repeatedly
in a tight loop executing faster than time
can increment the header
Returns
------------
unique : str
Unique alphanumeric identifier
"""
# head the identifier with 16 bits of time information
# this provides locality and reduces collision chances
head = np.array((increment + now() * 10) % 2**16,
dtype=np.uint16).tostring()
# get a bunch of random bytes
random = np.random.random(int(np.ceil(length / 5))).tostring()
# encode the time header and random information as base64
# replace + and / with spaces
unique = base64.b64encode(head + random,
b' ').decode('utf-8')
# remove spaces and cut to length
unique = unique.replace(' ', '')[:length]
return unique
def generate_basis(z):
"""
Generate an arbitrary basis (also known as a coordinate frame)
from a given z-axis vector.
Parameters
------------
z: (3,) float
A vector along the positive z-axis
Returns
---------
x : (3,) float
Vector along x axis
y : (3,) float
Vector along y axis
z : (3,) float
Vector along z axis
"""
# get a copy of input vector
z = np.array(z, dtype=np.float64, copy=True)
# must be a 3D vector
if z.shape != (3,):
raise ValueError('z must be (3,) float!')
# normalize vector in-place
z /= np.linalg.norm(z)
# X as arbitrary perpendicular vector
x = np.array([-z[1], z[0], 0.0])
# avoid degenerate case
x_norm = np.linalg.norm(x)
if x_norm < 1e-12:
# this means that
# so a perpendicular X is just X
x = np.array([-z[2], z[1], 0.0])
x /= np.linalg.norm(x)
else:
# otherwise normalize X in-place
x /= x_norm
# get perpendicular Y with cross product
y = np.cross(z, x)
# append result values into (3, 3) vector
result = np.array([x, y, z], dtype=np.float64)
if _STRICT:
# run checks to make sure axis are perpendicular
assert np.abs(np.dot(x, z)) < 1e-8
assert np.abs(np.dot(y, z)) < 1e-8
assert np.abs(np.dot(x, y)) < 1e-8
# all vectors should be unit vector
assert np.allclose(np.linalg.norm(result, axis=1), 1.0)
return result
def isclose(a, b, atol):
"""
A replacement for np.isclose that does fewer checks
and validation and as a result is roughly 4x faster.
Note that this is used in tight loops, and as such
a and b MUST be np.ndarray, not list or "array-like"
Parameters
------------
a : np.ndarray
To be compared
b : np.ndarray
To be compared
atol : float
Acceptable distance between `a` and `b` to be "close"
Returns
-----------
close : np.ndarray, bool
Per-element closeness
"""
diff = a - b
close = np.logical_and(diff > -atol, diff < atol)
return close
def allclose(a, b, atol):
"""
A replacement for np.allclose that does few checks
and validation and as a result is faster.
Parameters
------------
a : np.ndarray
To be compared
b : np.ndarray
To be compared
atol : float
Acceptable distance between `a` and `b` to be "close"
Returns
-----------
bool indicating if all elements are within `atol`.
"""
return np.all(np.abs(a - b).max() < atol)
class FunctionRegistry(Mapping):
"""
Non-overwritable mapping of string keys to functions.
This allows external packages to register additional implementations
of common functionality without risk of breaking implementations provided
by trimesh.
See trimesh.voxel.morphology for example usage.
"""
def __init__(self, **kwargs):
self._dict = {}
for k, v in kwargs.items():
self[k] = v
def __getitem__(self, key):
return self._dict[key]
def __setitem__(self, key, value):
if not isinstance(key, str):
raise ValueError('key must be a string, got %s' % str(key))
if key in self:
raise KeyError('Cannot set new value to existing key %s' % key)
if not callable(value):
raise ValueError('Cannot set value which is not callable.')
self._dict[key] = value
def __iter__(self):
return iter(self._dict)
def __len__(self):
return len(self._dict)
def __contains__(self, key):
return key in self._dict
def __call__(self, key, *args, **kwargs):
return self[key](*args, **kwargs)
class TemporaryDirectory(object):
"""
Same basic usage as tempfile.TemporaryDirectory
but functional in Python 2.7+.
Example
---------
```
with trimesh.util.TemporaryDirectory() as path:
writable = os.path.join(path, 'hi.txt')
```
"""
def __enter__(self):
self.path = tempfile.mkdtemp()
return self.path
def __exit__(self, *args, **kwargs):
shutil.rmtree(self.path)
def decode_text(text, initial='utf-8'):
"""
Try to decode byte input as a string.
Tries initial guess (UTF-8) then if that fails it
uses chardet to try another guess before failing.
Parameters
------------
text : bytes
Data that might be a string
initial : str
Initial guess for text encoding.
Returns
------------
decoded : str
Data as a string
"""
# if not bytes just return input
if not hasattr(text, 'decode'):
return text
try:
# initially guess file is UTF-8 or specified encoding
text = text.decode(initial)
except UnicodeDecodeError:
# detect different file encodings
import chardet
# try to detect the encoding of the file
detect = chardet.detect(text)
# warn on files that aren't UTF-8
log.warning(
'Data not {}! Trying {} (confidence {})'.format(
initial,
detect['encoding'],
detect['confidence']))
# try to decode again, unwrapped in try
text = text.decode(detect['encoding'])
return text
def to_ascii(text):
"""
Force a string or other to ASCII text ignoring errors.
Parameters
-----------
text : any
Input to be converted to ASCII string
Returns
-----------
ascii : str
Input as an ASCII string
"""
if hasattr(text, 'encode'):
# case for existing strings
return text.encode(
'ascii', errors='ignore').decode('ascii')
elif hasattr(text, 'decode'):
# case for bytes
return text.decode('ascii', errors='ignore')
# otherwise just wrap as a string
return str(text)
def is_ccw(points):
"""
Check if connected 2D points are counterclockwise.
Parameters
-----------
points : (n, 2) float
Connected points on a plane
Returns
----------
ccw : bool
True if points are counter-clockwise
"""
points = np.asanyarray(points, dtype=np.float64)
if (len(points.shape) != 2 or points.shape[1] != 2):
raise ValueError('CCW is only defined for 2D')
xd = np.diff(points[:, 0])
# sum along axis=1 with a dot product
yd = np.dot(np.column_stack((
points[:, 1],
points[:, 1])).reshape(-1)[1:-1].reshape((-1, 2)), [1, 1])
area = np.sum(xd * yd) * .5
ccw = area < 0
return ccw
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