index
int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
stringlengths 0
281k
⌀ | code
stringlengths 4
8.19k
| signature
stringlengths 2
42.8k
⌀ | embed_func_code
listlengths 768
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52,068 |
uciwebauth.uciwebauth
|
logout_url
|
Return URL for logging out of UCI WebAuth system.
|
def logout_url(self):
"""Return URL for logging out of UCI WebAuth system."""
return self.auth.logout_url(self.url)
|
(self)
|
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] |
52,069 |
uciwebauth.uciwebauth
|
WebAuthError
|
Base class for errors in the WebAuth class.
|
class WebAuthError(Exception):
"""Base class for errors in the WebAuth class."""
| null |
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] |
52,071 |
tussik.zpl.zplconfig
|
ZplConfig
| null |
class ZplConfig:
__slots__ = ['timeout', 'width', 'height', 'dpmm']
def __init__(self,
timeout: Optional[int] = None,
width: Union[None, float, int] = None,
height: Union[None, float, int] = None,
dpmm: Union[None, float, int] = None):
self.timeout: int = min(500, max(1, timeout if isinstance(timeout, int) else 10))
self.width: float = float(min(100, max(1, width if isinstance(width, (int, float)) else 4)))
self.height: float = float(min(100, max(1, height if isinstance(height, (int, float)) else 6)))
self.dpmm: float = float(min(100, max(1, dpmm if isinstance(dpmm, (int, float)) else 8)))
def copy(self) -> "ZplConfig":
return ZplConfig(self.timeout, self.width, self.height, self.dpmm)
|
(timeout: Optional[int] = None, width: Union[NoneType, float, int] = None, height: Union[NoneType, float, int] = None, dpmm: Union[NoneType, float, int] = None)
|
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] |
52,072 |
tussik.zpl.zplconfig
|
__init__
| null |
def __init__(self,
timeout: Optional[int] = None,
width: Union[None, float, int] = None,
height: Union[None, float, int] = None,
dpmm: Union[None, float, int] = None):
self.timeout: int = min(500, max(1, timeout if isinstance(timeout, int) else 10))
self.width: float = float(min(100, max(1, width if isinstance(width, (int, float)) else 4)))
self.height: float = float(min(100, max(1, height if isinstance(height, (int, float)) else 6)))
self.dpmm: float = float(min(100, max(1, dpmm if isinstance(dpmm, (int, float)) else 8)))
|
(self, timeout: Optional[int] = None, width: Union[NoneType, float, int] = None, height: Union[NoneType, float, int] = None, dpmm: Union[NoneType, float, int] = None)
|
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] |
52,073 |
tussik.zpl.zplconfig
|
copy
| null |
def copy(self) -> "ZplConfig":
return ZplConfig(self.timeout, self.width, self.height, self.dpmm)
|
(self) -> tussik.zpl.zplconfig.ZplConfig
|
[
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] |
52,074 |
tussik.zpl.zplwriter
|
ZplWriter
| null |
class ZplWriter:
__slots__ = ['_stream', '_attrib', '_config', '_font', '_reverse']
def __init__(self, config: Optional[ZplConfig] = None):
self._attrib: List[str] = []
self._stream: List[str] = []
self._font: str = ''
self._reverse: str = ''
self._config: ZplConfig = config.copy() if isinstance(config, ZplConfig) else ZplConfig()
def export(self) -> str:
config = "".join(self._attrib)
stream = "".join(self._stream)
return f"^XA^MT{config}{stream}^XZ"
def clear(self):
self._stream: List[str] = []
def add(self, command: str):
self._stream.append(command)
def coordinates(self, left: int, top: int) -> (int, int):
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
return left, top
def to_reverse(self):
self._reverse = "^FR"
def to_normal(self):
self._reverse = ""
def print(self, ipaddress: str, port: int = 9100) -> bool:
mysocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
mysocket.settimeout(self._config.timeout)
mysocket.connect((ipaddress, port)) # connecting to host
content = self.export()
payload = bytes(content, "utf8")
mysocket.send(payload)
mysocket.close()
except OSError as e:
logger.exception(f"Unable to connect to {ipaddress}:{port}")
return False
except Exception as e:
logger.exception("Error with the connection")
return False
return True
def saveas(self, destination: Union[str, io.BytesIO]) -> bool:
#
# uses a 3rd party service not affiliated with this library in any way
#
url = f"http://api.labelary.com/v1/printers/{int(self._config.dpmm)}dpmm/labels" \
f"/{int(self._config.width)}x{int(self._config.height)}/0/"
headers = {'Accept': 'application/pdf'}
response = requests.post(url, self.export(), headers=headers)
if response.status_code == 200:
if isinstance(destination, str):
with open(destination, 'wb') as f:
f.write(response.content)
else:
destination.write(response.content)
return True
logging.error(response.content)
return False
def textblock(self, left: int = 0, top: int = 0, data: str = "",
width: int = 0, justify: str = "J", maxlines: int = 1) -> bool:
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
justify = justify.upper()
if justify not in ['L', 'C', 'R', 'J']:
return False
width = max(0, int(width * self._config.dpmm))
self.add(f"^FO{left},{top}{self._font}{self._reverse}"
f"^FB{width},{maxlines},0,{justify},0^FD{data}^FS")
return True
def font(self, height: int = 10, width: Optional[int] = None, flip: int = 0,
fontcode: str = "0", isdefault: bool = False):
height = int(min(max(height * self._config.dpmm, 0), 32000))
if isinstance(width, int):
width = int(min(max(width * self._config.dpmm, 0), 32000))
if flip >= 270:
orient = "B"
elif flip >= 180:
orient = "I"
elif flip >= 90:
orient = "R"
else:
orient = "N"
if width is None:
self._font = f"^A{fontcode}{orient},{height}"
if isdefault:
self.add(f'^CF{fontcode},{height}')
else:
self._font = f"^A{fontcode}{orient},{height},{width}"
if isdefault:
self.add(f'^CF{fontcode},{height},{width}')
def text(self, data: str, left: int = 0, top: int = 0):
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._font}{self._reverse}^FD{data}^FS")
def ellipse(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: int = 2):
width = int(min(max(width * self._config.dpmm, 3), 4095))
height = int(min(max(height * self._config.dpmm, 3), 4095))
thick = int(min(max(thick, 2), 4095))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GE{width},{height},{thick},B^FS")
def circle(self, left: int = 0, top: int = 0, diameter: int = 100, thick: int = 2):
diameter = int(min(max(diameter * self._config.dpmm, 3), 4095))
thick = int(min(max(thick, 2), 4095))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GC{diameter},{thick},B^FS")
def box(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: float = 2, round: int = 0):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = int(min(max(thick, int(width * self._config.dpmm)), 32000))
height = int(min(max(thick, int(height * self._config.dpmm)), 32000))
round = int(min(max(0, round), 8))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,{round}^FS")
def hline(self, left: int = 0, top: int = 0, width: int = 100, thick: int = 2):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = int(min(max(thick, int(width * self._config.dpmm)), 32000))
height = max(thick, 0)
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,0^FS")
def vline(self, left: int = 0, top: int = 0, height: int = 100, thick: int = 2):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = max(thick, 0)
height = int(min(max(thick, int(height * self._config.dpmm)), 32000))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,0^FS")
def image(self, image: Union[str, Image], left: int = 0, top: int = 0,
width: Optional[int] = None, height: Optional[int] = None) -> bool:
if Image is None:
raise Exception("Developer! pillow library is required for ZplWriter.image")
try:
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
if isinstance(image, str):
rc = requests.get(image, stream=True)
if not rc.ok:
return False
image = Image.open(rc.raw)
if width is None:
width = int(image.size[0])
if height is None:
# maintain aspect ratio
height = int(float(image.size[1]) / image.size[0] * width)
width = width * self._config.dpmm
height = height * self._config.dpmm
totalbytes = math.ceil(width / 8.0) * height
bytesperrow = math.ceil(width / 8.0)
image = image.resize((int(width), int(height)))
image = ImageOps.invert(image.convert('L')).convert('1')
data = image.tobytes().hex().upper()
self.add(f"^FO{left},{top}^GFA,{len(data)},{totalbytes},{bytesperrow},{data}")
return True
except Exception as e:
logger.exception("failed to load image")
return False
def qrcode(self, left: int = 0, top: int = 0, data: Union[str, dict, list, int, float, None] = None,
|
(config: Optional[tussik.zpl.zplconfig.ZplConfig] = None)
|
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] |
52,075 |
tussik.zpl.zplwriter
|
__init__
| null |
def __init__(self, config: Optional[ZplConfig] = None):
self._attrib: List[str] = []
self._stream: List[str] = []
self._font: str = ''
self._reverse: str = ''
self._config: ZplConfig = config.copy() if isinstance(config, ZplConfig) else ZplConfig()
|
(self, config: Optional[tussik.zpl.zplconfig.ZplConfig] = None)
|
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] |
52,076 |
tussik.zpl.zplwriter
|
add
| null |
def add(self, command: str):
self._stream.append(command)
|
(self, command: str)
|
[
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] |
52,077 |
tussik.zpl.zplwriter
|
box
| null |
def box(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: float = 2, round: int = 0):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = int(min(max(thick, int(width * self._config.dpmm)), 32000))
height = int(min(max(thick, int(height * self._config.dpmm)), 32000))
round = int(min(max(0, round), 8))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,{round}^FS")
|
(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: float = 2, round: int = 0)
|
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] |
52,078 |
tussik.zpl.zplwriter
|
circle
| null |
def circle(self, left: int = 0, top: int = 0, diameter: int = 100, thick: int = 2):
diameter = int(min(max(diameter * self._config.dpmm, 3), 4095))
thick = int(min(max(thick, 2), 4095))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GC{diameter},{thick},B^FS")
|
(self, left: int = 0, top: int = 0, diameter: int = 100, thick: int = 2)
|
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] |
52,079 |
tussik.zpl.zplwriter
|
clear
| null |
def clear(self):
self._stream: List[str] = []
|
(self)
|
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] |
52,080 |
tussik.zpl.zplwriter
|
coordinates
| null |
def coordinates(self, left: int, top: int) -> (int, int):
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
return left, top
|
(self, left: int, top: int) -> (<class 'int'>, <class 'int'>)
|
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] |
52,081 |
tussik.zpl.zplwriter
|
cut
| null |
def cut(self, kiosk_cut_amount: bool = True):
a = "0" if kiosk_cut_amount else "1"
self.add(f"^CN{a}")
|
(self, kiosk_cut_amount: bool = True)
|
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] |
52,082 |
tussik.zpl.zplwriter
|
ellipse
| null |
def ellipse(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: int = 2):
width = int(min(max(width * self._config.dpmm, 3), 4095))
height = int(min(max(height * self._config.dpmm, 3), 4095))
thick = int(min(max(thick, 2), 4095))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GE{width},{height},{thick},B^FS")
|
(self, left: int = 0, top: int = 0, width: int = 100, height: int = 100, thick: int = 2)
|
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] |
52,083 |
tussik.zpl.zplwriter
|
export
| null |
def export(self) -> str:
config = "".join(self._attrib)
stream = "".join(self._stream)
return f"^XA^MT{config}{stream}^XZ"
|
(self) -> str
|
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] |
52,084 |
tussik.zpl.zplwriter
|
font
| null |
def font(self, height: int = 10, width: Optional[int] = None, flip: int = 0,
fontcode: str = "0", isdefault: bool = False):
height = int(min(max(height * self._config.dpmm, 0), 32000))
if isinstance(width, int):
width = int(min(max(width * self._config.dpmm, 0), 32000))
if flip >= 270:
orient = "B"
elif flip >= 180:
orient = "I"
elif flip >= 90:
orient = "R"
else:
orient = "N"
if width is None:
self._font = f"^A{fontcode}{orient},{height}"
if isdefault:
self.add(f'^CF{fontcode},{height}')
else:
self._font = f"^A{fontcode}{orient},{height},{width}"
if isdefault:
self.add(f'^CF{fontcode},{height},{width}')
|
(self, height: int = 10, width: Optional[int] = None, flip: int = 0, fontcode: str = '0', isdefault: bool = False)
|
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] |
52,085 |
tussik.zpl.zplwriter
|
hline
| null |
def hline(self, left: int = 0, top: int = 0, width: int = 100, thick: int = 2):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = int(min(max(thick, int(width * self._config.dpmm)), 32000))
height = max(thick, 0)
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,0^FS")
|
(self, left: int = 0, top: int = 0, width: int = 100, thick: int = 2)
|
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] |
52,086 |
tussik.zpl.zplwriter
|
image
| null |
def image(self, image: Union[str, Image], left: int = 0, top: int = 0,
width: Optional[int] = None, height: Optional[int] = None) -> bool:
if Image is None:
raise Exception("Developer! pillow library is required for ZplWriter.image")
try:
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
if isinstance(image, str):
rc = requests.get(image, stream=True)
if not rc.ok:
return False
image = Image.open(rc.raw)
if width is None:
width = int(image.size[0])
if height is None:
# maintain aspect ratio
height = int(float(image.size[1]) / image.size[0] * width)
width = width * self._config.dpmm
height = height * self._config.dpmm
totalbytes = math.ceil(width / 8.0) * height
bytesperrow = math.ceil(width / 8.0)
image = image.resize((int(width), int(height)))
image = ImageOps.invert(image.convert('L')).convert('1')
data = image.tobytes().hex().upper()
self.add(f"^FO{left},{top}^GFA,{len(data)},{totalbytes},{bytesperrow},{data}")
return True
except Exception as e:
logger.exception("failed to load image")
return False
|
(self, image: Optional[str], left: int = 0, top: int = 0, width: Optional[int] = None, height: Optional[int] = None) -> bool
|
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] |
52,087 |
tussik.zpl.zplwriter
|
label_length
| null |
def label_length(self, length: int):
len = int(min(32000, max(1, length)) * self._config.dpmm)
self.add(f"^LL{len}")
|
(self, length: int)
|
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] |
52,088 |
tussik.zpl.zplwriter
|
label_reverse
| null |
def label_reverse(self, reverse: bool = False):
a = "Y" if reverse else "N"
self.add(f"^LR{a}")
|
(self, reverse: bool = False)
|
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] |
52,089 |
tussik.zpl.zplwriter
|
orientation
| null |
def orientation(self, rotate: int = 0, justify: int = 2) -> None:
if rotate < 90:
rotate = 0
elif rotate < 180:
rotate = 90
elif rotate < 270:
rotate = 180
else:
rotate = 270
justify = min(2, max(0, justify))
self.add(f"^FW{rotate},{justify}")
|
(self, rotate: int = 0, justify: int = 2) -> NoneType
|
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] |
52,090 |
tussik.zpl.zplwriter
|
print
| null |
def print(self, ipaddress: str, port: int = 9100) -> bool:
mysocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
mysocket.settimeout(self._config.timeout)
mysocket.connect((ipaddress, port)) # connecting to host
content = self.export()
payload = bytes(content, "utf8")
mysocket.send(payload)
mysocket.close()
except OSError as e:
logger.exception(f"Unable to connect to {ipaddress}:{port}")
return False
except Exception as e:
logger.exception("Error with the connection")
return False
return True
|
(self, ipaddress: str, port: int = 9100) -> bool
|
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] |
52,091 |
tussik.zpl.zplwriter
|
qrcode
| null |
def qrcode(self, left: int = 0, top: int = 0, data: Union[str, dict, list, int, float, None] = None,
magnify: Optional[int] = None) -> None:
if data is None:
return
elif isinstance(data, str):
content = data
elif isinstance(data, (int, float)):
content = str(data)
else:
content = json.dumps(data)
if magnify is None:
magnify = ''
left, top = self.coordinates(left, top)
self.add(f"^FO{left},{top}^BQ,2,{magnify},H^FDQA,{content}^FS")
|
(self, left: int = 0, top: int = 0, data: Union[str, dict, list, int, float, NoneType] = None, magnify: Optional[int] = None) -> NoneType
|
[
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] |
52,092 |
tussik.zpl.zplwriter
|
saveas
| null |
def saveas(self, destination: Union[str, io.BytesIO]) -> bool:
#
# uses a 3rd party service not affiliated with this library in any way
#
url = f"http://api.labelary.com/v1/printers/{int(self._config.dpmm)}dpmm/labels" \
f"/{int(self._config.width)}x{int(self._config.height)}/0/"
headers = {'Accept': 'application/pdf'}
response = requests.post(url, self.export(), headers=headers)
if response.status_code == 200:
if isinstance(destination, str):
with open(destination, 'wb') as f:
f.write(response.content)
else:
destination.write(response.content)
return True
logging.error(response.content)
return False
|
(self, destination: Union[str, _io.BytesIO]) -> bool
|
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] |
52,093 |
tussik.zpl.zplwriter
|
text
| null |
def text(self, data: str, left: int = 0, top: int = 0):
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._font}{self._reverse}^FD{data}^FS")
|
(self, data: str, left: int = 0, top: int = 0)
|
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] |
52,094 |
tussik.zpl.zplwriter
|
textblock
| null |
def textblock(self, left: int = 0, top: int = 0, data: str = "",
width: int = 0, justify: str = "J", maxlines: int = 1) -> bool:
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
justify = justify.upper()
if justify not in ['L', 'C', 'R', 'J']:
return False
width = max(0, int(width * self._config.dpmm))
self.add(f"^FO{left},{top}{self._font}{self._reverse}"
f"^FB{width},{maxlines},0,{justify},0^FD{data}^FS")
return True
|
(self, left: int = 0, top: int = 0, data: str = '', width: int = 0, justify: str = 'J', maxlines: int = 1) -> bool
|
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] |
52,095 |
tussik.zpl.zplwriter
|
to_normal
| null |
def to_normal(self):
self._reverse = ""
|
(self)
|
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] |
52,096 |
tussik.zpl.zplwriter
|
to_reverse
| null |
def to_reverse(self):
self._reverse = "^FR"
|
(self)
|
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] |
52,097 |
tussik.zpl.zplwriter
|
vline
| null |
def vline(self, left: int = 0, top: int = 0, height: int = 100, thick: int = 2):
thick = int(min(max(1, int(thick * self._config.dpmm)), 32000))
width = max(thick, 0)
height = int(min(max(thick, int(height * self._config.dpmm)), 32000))
left = min(max(int(left * self._config.dpmm), 0), 32000)
top = min(max(int(top * self._config.dpmm), 0), 32000)
self.add(f"^FO{left},{top}{self._reverse}^GB{width},{height},{thick},B,0^FS")
|
(self, left: int = 0, top: int = 0, height: int = 100, thick: int = 2)
|
[
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] |
52,102 |
segindex.segregation_index
|
estimate_Hp
|
Arguments:
vv_s (List[List[values]]): list of lists of values
ww_s: weights over vv_s
K: counts of thresholds
m: degree of regression equation used for the estimation
|
def estimate_Hp(vv_s: List[List[Any]], ww_s=None, K=14, m=4):
'''
Arguments:
vv_s (List[List[values]]): list of lists of values
ww_s: weights over vv_s
K: counts of thresholds
m: degree of regression equation used for the estimation
'''
vv_s = from_vvs_to_rrs(vv_s, ww_s=ww_s) # rank-order transformation
kk = np.array([e/K for e in range(1,K)]) # 1/K ~ (K-1)/K
e_kk = np.array([entropy_p(k) for k in kk])
w_kk = e_kk**2
#
tt = [len(vv) for vv in vv_s]
Hp_kk = []
for k in kk:
pp = []
for vv in vv_s:
vv_k = [e for e in vv if e<=k]
pp.append(len(vv_k)/len(vv))
Hp_k = Theil_p(tt,pp)
Hp_kk.append(Hp_k)
#
d = pd.DataFrame({'Hp':Hp_kk, 'p':kk, 'weight': w_kk})
for n in range(2,m+1):
d[f"p_{n}"] = d['p']**n
exog = d[[c for c in d.columns if 'p' in c and 'Hp' not in c]]
exog = sm.add_constant(exog)
global f
f = sm.WLS(endog=d['Hp'], exog=exog, weights=d['weight']).fit()
#
betas = [f.params['const'], f.params['p']]
for n in range(2,m+1):
betas.append(f.params[f"p_{n}"])
#
deltas = [delta_m(e) for e in range(m+1)]
betas, deltas = map(np.array, [betas, deltas])
#
H_r = np.sum(betas*deltas)
return H_r
|
(vv_s: List[List[Any]], ww_s=None, K=14, m=4)
|
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] |
52,104 |
pebble.pool.base_pool
|
MapFuture
| null |
class MapFuture(PebbleFuture):
def __init__(self, futures: list):
super().__init__()
self._futures = futures
@property
def futures(self) -> list:
return self._futures
def cancel(self) -> bool:
"""Cancel the future.
Returns True if any of the elements of the iterables is cancelled.
False otherwise.
"""
super().cancel()
return any(tuple(f.cancel() for f in self._futures))
|
(futures: list)
|
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] |
52,105 |
concurrent.futures._base
|
__get_result
| null |
def __get_result(self):
if self._exception:
try:
raise self._exception
finally:
# Break a reference cycle with the exception in self._exception
self = None
else:
return self._result
|
(self)
|
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] |
52,106 |
pebble.pool.base_pool
|
__init__
| null |
def __init__(self, futures: list):
super().__init__()
self._futures = futures
|
(self, futures: list)
|
[
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] |
52,107 |
concurrent.futures._base
|
__repr__
| null |
def __repr__(self):
with self._condition:
if self._state == FINISHED:
if self._exception:
return '<%s at %#x state=%s raised %s>' % (
self.__class__.__name__,
id(self),
_STATE_TO_DESCRIPTION_MAP[self._state],
self._exception.__class__.__name__)
else:
return '<%s at %#x state=%s returned %s>' % (
self.__class__.__name__,
id(self),
_STATE_TO_DESCRIPTION_MAP[self._state],
self._result.__class__.__name__)
return '<%s at %#x state=%s>' % (
self.__class__.__name__,
id(self),
_STATE_TO_DESCRIPTION_MAP[self._state])
|
(self)
|
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] |
52,108 |
concurrent.futures._base
|
_invoke_callbacks
| null |
def _invoke_callbacks(self):
for callback in self._done_callbacks:
try:
callback(self)
except Exception:
LOGGER.exception('exception calling callback for %r', self)
|
(self)
|
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] |
52,109 |
concurrent.futures._base
|
add_done_callback
|
Attaches a callable that will be called when the future finishes.
Args:
fn: A callable that will be called with this future as its only
argument when the future completes or is cancelled. The callable
will always be called by a thread in the same process in which
it was added. If the future has already completed or been
cancelled then the callable will be called immediately. These
callables are called in the order that they were added.
|
def add_done_callback(self, fn):
"""Attaches a callable that will be called when the future finishes.
Args:
fn: A callable that will be called with this future as its only
argument when the future completes or is cancelled. The callable
will always be called by a thread in the same process in which
it was added. If the future has already completed or been
cancelled then the callable will be called immediately. These
callables are called in the order that they were added.
"""
with self._condition:
if self._state not in [CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED]:
self._done_callbacks.append(fn)
return
try:
fn(self)
except Exception:
LOGGER.exception('exception calling callback for %r', self)
|
(self, fn)
|
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] |
52,110 |
pebble.pool.base_pool
|
cancel
|
Cancel the future.
Returns True if any of the elements of the iterables is cancelled.
False otherwise.
|
def cancel(self) -> bool:
"""Cancel the future.
Returns True if any of the elements of the iterables is cancelled.
False otherwise.
"""
super().cancel()
return any(tuple(f.cancel() for f in self._futures))
|
(self) -> bool
|
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] |
52,111 |
concurrent.futures._base
|
cancelled
|
Return True if the future was cancelled.
|
def cancelled(self):
"""Return True if the future was cancelled."""
with self._condition:
return self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]
|
(self)
|
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] |
52,112 |
concurrent.futures._base
|
done
|
Return True if the future was cancelled or finished executing.
|
def done(self):
"""Return True if the future was cancelled or finished executing."""
with self._condition:
return self._state in [CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED]
|
(self)
|
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] |
52,113 |
concurrent.futures._base
|
exception
|
Return the exception raised by the call that the future represents.
Args:
timeout: The number of seconds to wait for the exception if the
future isn't done. If None, then there is no limit on the wait
time.
Returns:
The exception raised by the call that the future represents or None
if the call completed without raising.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
timeout.
|
def exception(self, timeout=None):
"""Return the exception raised by the call that the future represents.
Args:
timeout: The number of seconds to wait for the exception if the
future isn't done. If None, then there is no limit on the wait
time.
Returns:
The exception raised by the call that the future represents or None
if the call completed without raising.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
timeout.
"""
with self._condition:
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self._exception
self._condition.wait(timeout)
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self._exception
else:
raise TimeoutError()
|
(self, timeout=None)
|
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] |
52,114 |
concurrent.futures._base
|
result
|
Return the result of the call that the future represents.
Args:
timeout: The number of seconds to wait for the result if the future
isn't done. If None, then there is no limit on the wait time.
Returns:
The result of the call that the future represents.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
timeout.
Exception: If the call raised then that exception will be raised.
|
def result(self, timeout=None):
"""Return the result of the call that the future represents.
Args:
timeout: The number of seconds to wait for the result if the future
isn't done. If None, then there is no limit on the wait time.
Returns:
The result of the call that the future represents.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
timeout.
Exception: If the call raised then that exception will be raised.
"""
try:
with self._condition:
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self.__get_result()
self._condition.wait(timeout)
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self.__get_result()
else:
raise TimeoutError()
finally:
# Break a reference cycle with the exception in self._exception
self = None
|
(self, timeout=None)
|
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] |
52,115 |
concurrent.futures._base
|
running
|
Return True if the future is currently executing.
|
def running(self):
"""Return True if the future is currently executing."""
with self._condition:
return self._state == RUNNING
|
(self)
|
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] |
52,116 |
concurrent.futures._base
|
set_exception
|
Sets the result of the future as being the given exception.
Should only be used by Executor implementations and unit tests.
|
def set_exception(self, exception):
"""Sets the result of the future as being the given exception.
Should only be used by Executor implementations and unit tests.
"""
with self._condition:
if self._state in {CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED}:
raise InvalidStateError('{}: {!r}'.format(self._state, self))
self._exception = exception
self._state = FINISHED
for waiter in self._waiters:
waiter.add_exception(self)
self._condition.notify_all()
self._invoke_callbacks()
|
(self, exception)
|
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] |
52,117 |
concurrent.futures._base
|
set_result
|
Sets the return value of work associated with the future.
Should only be used by Executor implementations and unit tests.
|
def set_result(self, result):
"""Sets the return value of work associated with the future.
Should only be used by Executor implementations and unit tests.
"""
with self._condition:
if self._state in {CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED}:
raise InvalidStateError('{}: {!r}'.format(self._state, self))
self._result = result
self._state = FINISHED
for waiter in self._waiters:
waiter.add_result(self)
self._condition.notify_all()
self._invoke_callbacks()
|
(self, result)
|
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] |
52,118 |
pebble.common
|
set_running_or_notify_cancel
|
Mark the future as running or process any cancel notifications.
Should only be used by Executor implementations and unit tests.
If the future has been cancelled (cancel() was called and returned
True) then any threads waiting on the future completing (though calls
to as_completed() or wait()) are notified and False is returned.
If the future was not cancelled then it is put in the running state
(future calls to running() will return True) and True is returned.
This method should be called by Executor implementations before
executing the work associated with this future. If this method returns
False then the work should not be executed.
Returns:
False if the Future was cancelled, True otherwise.
Raises:
RuntimeError: if set_result() or set_exception() was called.
|
def set_running_or_notify_cancel(self):
"""Mark the future as running or process any cancel notifications.
Should only be used by Executor implementations and unit tests.
If the future has been cancelled (cancel() was called and returned
True) then any threads waiting on the future completing (though calls
to as_completed() or wait()) are notified and False is returned.
If the future was not cancelled then it is put in the running state
(future calls to running() will return True) and True is returned.
This method should be called by Executor implementations before
executing the work associated with this future. If this method returns
False then the work should not be executed.
Returns:
False if the Future was cancelled, True otherwise.
Raises:
RuntimeError: if set_result() or set_exception() was called.
"""
with self._condition:
if self._state == CANCELLED:
self._state = CANCELLED_AND_NOTIFIED
for waiter in self._waiters:
waiter.add_cancelled(self)
return False
elif self._state == PENDING:
self._state = RUNNING
return True
else:
raise RuntimeError('Future in unexpected state')
|
(self)
|
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] |
52,119 |
pebble.common
|
ProcessExpired
|
Raised when process dies unexpectedly.
|
class ProcessExpired(OSError):
"""Raised when process dies unexpectedly."""
def __init__(self, msg, code=0):
super(ProcessExpired, self).__init__(msg)
self.exitcode = code
|
(msg, code=0)
|
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] |
52,120 |
pebble.common
|
__init__
| null |
def __init__(self, msg, code=0):
super(ProcessExpired, self).__init__(msg)
self.exitcode = code
|
(self, msg, code=0)
|
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] |
52,121 |
pebble.common
|
ProcessFuture
| null |
class ProcessFuture(PebbleFuture):
def cancel(self):
"""Cancel the future.
Returns True if the future was cancelled, False otherwise. A future
cannot be cancelled if it has already completed.
"""
with self._condition:
if self._state == FINISHED:
return False
if self._state in (CANCELLED, CANCELLED_AND_NOTIFIED):
return True
self._state = CANCELLED
self._condition.notify_all()
self._invoke_callbacks()
return True
|
()
|
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] |
52,123 |
concurrent.futures._base
|
__init__
|
Initializes the future. Should not be called by clients.
|
def __init__(self):
"""Initializes the future. Should not be called by clients."""
self._condition = threading.Condition()
self._state = PENDING
self._result = None
self._exception = None
self._waiters = []
self._done_callbacks = []
|
(self)
|
[
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0.05126450955867767,
0.04482003673911095
] |
52,127 |
pebble.common
|
cancel
|
Cancel the future.
Returns True if the future was cancelled, False otherwise. A future
cannot be cancelled if it has already completed.
|
def cancel(self):
"""Cancel the future.
Returns True if the future was cancelled, False otherwise. A future
cannot be cancelled if it has already completed.
"""
with self._condition:
if self._state == FINISHED:
return False
if self._state in (CANCELLED, CANCELLED_AND_NOTIFIED):
return True
self._state = CANCELLED
self._condition.notify_all()
self._invoke_callbacks()
return True
|
(self)
|
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] |
52,136 |
pebble.pool.base_pool
|
ProcessMapFuture
| null |
class ProcessMapFuture(ProcessFuture):
def __init__(self, futures: list):
super().__init__()
self._futures = futures
@property
def futures(self) -> list:
return self._futures
def cancel(self) -> bool:
"""Cancel the future.
Returns True if any of the elements of the iterables is cancelled.
False otherwise.
"""
super().cancel()
return any(tuple(f.cancel() for f in self._futures))
|
(futures: list)
|
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] |
52,151 |
pebble.pool.process
|
ProcessPool
|
Allows to schedule jobs within a Pool of Processes.
max_workers is an integer representing the amount of desired process workers
managed by the pool.
If max_tasks is a number greater than zero,
each worker will be restarted after performing an equal amount of tasks.
initializer must be callable, if passed, it will be called
every time a worker is started, receiving initargs as arguments.
|
class ProcessPool(BasePool):
"""Allows to schedule jobs within a Pool of Processes.
max_workers is an integer representing the amount of desired process workers
managed by the pool.
If max_tasks is a number greater than zero,
each worker will be restarted after performing an equal amount of tasks.
initializer must be callable, if passed, it will be called
every time a worker is started, receiving initargs as arguments.
"""
def __init__(self, max_workers: int = multiprocessing.cpu_count(),
max_tasks: int = 0,
initializer: Callable = None,
initargs: list = (),
context: multiprocessing.context.BaseContext = None):
super().__init__(max_workers, max_tasks, initializer, initargs)
mp_context = multiprocessing if context is None else context
self._pool_manager = PoolManager(self._context, mp_context)
self._task_scheduler_loop = None
self._pool_manager_loop = None
self._message_manager_loop = None
def _start_pool(self):
with self._context.state_mutex:
if self._context.state == CREATED:
self._pool_manager.start()
self._task_scheduler_loop = launch_thread(
None, task_scheduler_loop, True, self._pool_manager)
self._pool_manager_loop = launch_thread(
None, pool_manager_loop, True, self._pool_manager)
self._message_manager_loop = launch_thread(
None, message_manager_loop, True, self._pool_manager)
self._context.state = RUNNING
def _stop_pool(self):
if self._pool_manager_loop is not None:
self._pool_manager_loop.join()
self._pool_manager.stop()
if self._task_scheduler_loop is not None:
self._task_scheduler_loop.join()
if self._message_manager_loop is not None:
self._message_manager_loop.join()
def schedule(self, function: Callable,
args: list = (),
kwargs: dict = {},
timeout: float = None) -> ProcessFuture:
"""Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
*timeout* is an integer, if expires the task will be terminated
and *Future.result()* will raise *TimeoutError*.
A *pebble.ProcessFuture* object is returned.
"""
self._check_pool_state()
future = ProcessFuture()
payload = TaskPayload(function, args, kwargs)
task = Task(next(self._task_counter), future, timeout, payload)
self._context.task_queue.put(task)
return future
def submit(self, function: Callable,
timeout: Optional[float],
*args, **kwargs) -> ProcessFuture:
"""This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
"""
return self.schedule(
function, args=args, kwargs=kwargs, timeout=timeout)
def map(self, function: Callable,
*iterables, **kwargs) -> ProcessMapFuture:
"""Computes the *function* using arguments from
each of the iterables. Stops when the shortest iterable is exhausted.
*timeout* is an integer, if expires the task will be terminated
and the call to next will raise *TimeoutError*.
The *timeout* is applied to each chunk of the iterable.
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function.
A *pebble.ProcessFuture* object is returned.
"""
self._check_pool_state()
timeout = kwargs.get('timeout')
chunksize = kwargs.get('chunksize', 1)
if chunksize < 1:
raise ValueError("chunksize must be >= 1")
futures = [self.schedule(
process_chunk, args=(function, chunk), timeout=timeout)
for chunk in iter_chunks(chunksize, *iterables)]
return map_results(ProcessMapFuture(futures), timeout)
|
(max_workers: int = 60, max_tasks: int = 0, initializer: Callable = None, initargs: list = (), context: multiprocessing.context.BaseContext = None)
|
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] |
52,153 |
pebble.pool.base_pool
|
__exit__
| null |
def __exit__(self, *args):
self.close()
self.join()
|
(self, *args)
|
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] |
52,154 |
pebble.pool.process
|
__init__
| null |
def __init__(self, max_workers: int = multiprocessing.cpu_count(),
max_tasks: int = 0,
initializer: Callable = None,
initargs: list = (),
context: multiprocessing.context.BaseContext = None):
super().__init__(max_workers, max_tasks, initializer, initargs)
mp_context = multiprocessing if context is None else context
self._pool_manager = PoolManager(self._context, mp_context)
self._task_scheduler_loop = None
self._pool_manager_loop = None
self._message_manager_loop = None
|
(self, max_workers: int = 60, max_tasks: int = 0, initializer: Optional[Callable] = None, initargs: list = (), context: Optional[multiprocessing.context.BaseContext] = None)
|
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] |
52,155 |
pebble.pool.base_pool
|
_check_pool_state
| null |
def _check_pool_state(self):
self._update_pool_state()
if self._context.state == ERROR:
raise RuntimeError('Unexpected error within the Pool')
elif self._context.state != RUNNING:
raise RuntimeError('The Pool is not active')
|
(self)
|
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] |
52,156 |
pebble.pool.process
|
_start_pool
| null |
def _start_pool(self):
with self._context.state_mutex:
if self._context.state == CREATED:
self._pool_manager.start()
self._task_scheduler_loop = launch_thread(
None, task_scheduler_loop, True, self._pool_manager)
self._pool_manager_loop = launch_thread(
None, pool_manager_loop, True, self._pool_manager)
self._message_manager_loop = launch_thread(
None, message_manager_loop, True, self._pool_manager)
self._context.state = RUNNING
|
(self)
|
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] |
52,157 |
pebble.pool.process
|
_stop_pool
| null |
def _stop_pool(self):
if self._pool_manager_loop is not None:
self._pool_manager_loop.join()
self._pool_manager.stop()
if self._task_scheduler_loop is not None:
self._task_scheduler_loop.join()
if self._message_manager_loop is not None:
self._message_manager_loop.join()
|
(self)
|
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0.002957309130579233,
0.016172295436263084
] |
52,158 |
pebble.pool.base_pool
|
_update_pool_state
| null |
def _update_pool_state(self):
if self._context.state == CREATED:
self._start_pool()
for loop in self._loops:
if not loop.is_alive():
self._context.state = ERROR
|
(self)
|
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] |
52,159 |
pebble.pool.base_pool
|
_wait_queue_depletion
| null |
def _wait_queue_depletion(self, timeout: Optional[float]):
tick = time.time()
while self.active:
if timeout is not None and time.time() - tick > timeout:
raise TimeoutError("Tasks are still being executed")
elif self._context.task_queue.unfinished_tasks:
time.sleep(SLEEP_UNIT)
else:
return
|
(self, timeout: Optional[float])
|
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] |
52,160 |
pebble.pool.base_pool
|
close
|
Closes the Pool preventing new tasks from being accepted.
Pending tasks will be completed.
|
def close(self):
"""Closes the Pool preventing new tasks from being accepted.
Pending tasks will be completed.
"""
self._context.state = CLOSED
|
(self)
|
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] |
52,161 |
pebble.pool.base_pool
|
join
|
Joins the pool waiting until all workers exited.
If *timeout* is set, it block until all workers are done
or raises TimeoutError.
|
def join(self, timeout: float = None):
"""Joins the pool waiting until all workers exited.
If *timeout* is set, it block until all workers are done
or raises TimeoutError.
"""
if self._context.state == RUNNING:
raise RuntimeError('The Pool is still running')
if self._context.state == CLOSED:
self._wait_queue_depletion(timeout)
self.stop()
self.join()
else:
self._context.task_queue.put(None)
self._stop_pool()
|
(self, timeout: Optional[float] = None)
|
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] |
52,162 |
pebble.pool.process
|
map
|
Computes the *function* using arguments from
each of the iterables. Stops when the shortest iterable is exhausted.
*timeout* is an integer, if expires the task will be terminated
and the call to next will raise *TimeoutError*.
The *timeout* is applied to each chunk of the iterable.
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function.
A *pebble.ProcessFuture* object is returned.
|
def map(self, function: Callable,
*iterables, **kwargs) -> ProcessMapFuture:
"""Computes the *function* using arguments from
each of the iterables. Stops when the shortest iterable is exhausted.
*timeout* is an integer, if expires the task will be terminated
and the call to next will raise *TimeoutError*.
The *timeout* is applied to each chunk of the iterable.
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function.
A *pebble.ProcessFuture* object is returned.
"""
self._check_pool_state()
timeout = kwargs.get('timeout')
chunksize = kwargs.get('chunksize', 1)
if chunksize < 1:
raise ValueError("chunksize must be >= 1")
futures = [self.schedule(
process_chunk, args=(function, chunk), timeout=timeout)
for chunk in iter_chunks(chunksize, *iterables)]
return map_results(ProcessMapFuture(futures), timeout)
|
(self, function: Callable, *iterables, **kwargs) -> pebble.pool.base_pool.ProcessMapFuture
|
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] |
52,163 |
pebble.pool.process
|
schedule
|
Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
*timeout* is an integer, if expires the task will be terminated
and *Future.result()* will raise *TimeoutError*.
A *pebble.ProcessFuture* object is returned.
|
def schedule(self, function: Callable,
args: list = (),
kwargs: dict = {},
timeout: float = None) -> ProcessFuture:
"""Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
*timeout* is an integer, if expires the task will be terminated
and *Future.result()* will raise *TimeoutError*.
A *pebble.ProcessFuture* object is returned.
"""
self._check_pool_state()
future = ProcessFuture()
payload = TaskPayload(function, args, kwargs)
task = Task(next(self._task_counter), future, timeout, payload)
self._context.task_queue.put(task)
return future
|
(self, function: Callable, args: list = (), kwargs: dict = {}, timeout: Optional[float] = None) -> pebble.common.ProcessFuture
|
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] |
52,164 |
pebble.pool.base_pool
|
stop
|
Stops the pool without performing any pending task.
|
def stop(self):
"""Stops the pool without performing any pending task."""
self._context.state = STOPPED
|
(self)
|
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] |
52,165 |
pebble.pool.process
|
submit
|
This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
|
def submit(self, function: Callable,
timeout: Optional[float],
*args, **kwargs) -> ProcessFuture:
"""This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
"""
return self.schedule(
function, args=args, kwargs=kwargs, timeout=timeout)
|
(self, function: Callable, timeout: Optional[float], *args, **kwargs) -> pebble.common.ProcessFuture
|
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] |
52,166 |
pebble.pool.thread
|
ThreadPool
|
Allows to schedule jobs within a Pool of Threads.
max_workers is an integer representing the amount of desired process workers
managed by the pool.
If max_tasks is a number greater than zero,
each worker will be restarted after performing an equal amount of tasks.
initializer must be callable, if passed, it will be called
every time a worker is started, receiving initargs as arguments.
|
class ThreadPool(BasePool):
"""Allows to schedule jobs within a Pool of Threads.
max_workers is an integer representing the amount of desired process workers
managed by the pool.
If max_tasks is a number greater than zero,
each worker will be restarted after performing an equal amount of tasks.
initializer must be callable, if passed, it will be called
every time a worker is started, receiving initargs as arguments.
"""
def __init__(self, max_workers: int = multiprocessing.cpu_count(),
max_tasks: int = 0,
initializer: Callable = None,
initargs: list = ()):
super().__init__(max_workers, max_tasks, initializer, initargs)
self._pool_manager = PoolManager(self._context)
self._pool_manager_loop = None
def _start_pool(self):
with self._context.state_mutex:
if self._context.state == CREATED:
self._pool_manager.start()
self._pool_manager_loop = launch_thread(
None, pool_manager_loop, True, self._pool_manager)
self._context.state = RUNNING
def _stop_pool(self):
if self._pool_manager_loop is not None:
self._pool_manager_loop.join()
self._pool_manager.stop()
def schedule(self, function, args=(), kwargs={}) -> Future:
"""Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
A *concurrent.futures.Future* object is returned.
"""
self._check_pool_state()
future = Future()
payload = TaskPayload(function, args, kwargs)
task = Task(next(self._task_counter), future, None, payload)
self._context.task_queue.put(task)
return future
def submit(self, function: Callable, *args, **kwargs) -> Future:
"""This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
"""
return self.schedule(function, args=args, kwargs=kwargs)
def map(self, function: Callable, *iterables, **kwargs) -> MapFuture:
"""Returns an iterator equivalent to map(function, iterables).
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function. If None
the size will be controlled by the Pool.
"""
self._check_pool_state()
timeout = kwargs.get('timeout')
chunksize = kwargs.get('chunksize', 1)
if chunksize < 1:
raise ValueError("chunksize must be >= 1")
futures = [self.schedule(process_chunk, args=(function, chunk))
for chunk in iter_chunks(chunksize, *iterables)]
return map_results(MapFuture(futures), timeout)
|
(max_workers: int = 60, max_tasks: int = 0, initializer: Callable = None, initargs: list = ())
|
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] |
52,169 |
pebble.pool.thread
|
__init__
| null |
def __init__(self, max_workers: int = multiprocessing.cpu_count(),
max_tasks: int = 0,
initializer: Callable = None,
initargs: list = ()):
super().__init__(max_workers, max_tasks, initializer, initargs)
self._pool_manager = PoolManager(self._context)
self._pool_manager_loop = None
|
(self, max_workers: int = 60, max_tasks: int = 0, initializer: Optional[Callable] = None, initargs: list = ())
|
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] |
52,171 |
pebble.pool.thread
|
_start_pool
| null |
def _start_pool(self):
with self._context.state_mutex:
if self._context.state == CREATED:
self._pool_manager.start()
self._pool_manager_loop = launch_thread(
None, pool_manager_loop, True, self._pool_manager)
self._context.state = RUNNING
|
(self)
|
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] |
52,172 |
pebble.pool.thread
|
_stop_pool
| null |
def _stop_pool(self):
if self._pool_manager_loop is not None:
self._pool_manager_loop.join()
self._pool_manager.stop()
|
(self)
|
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] |
52,177 |
pebble.pool.thread
|
map
|
Returns an iterator equivalent to map(function, iterables).
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function. If None
the size will be controlled by the Pool.
|
def map(self, function: Callable, *iterables, **kwargs) -> MapFuture:
"""Returns an iterator equivalent to map(function, iterables).
*chunksize* controls the size of the chunks the iterable will
be broken into before being passed to the function. If None
the size will be controlled by the Pool.
"""
self._check_pool_state()
timeout = kwargs.get('timeout')
chunksize = kwargs.get('chunksize', 1)
if chunksize < 1:
raise ValueError("chunksize must be >= 1")
futures = [self.schedule(process_chunk, args=(function, chunk))
for chunk in iter_chunks(chunksize, *iterables)]
return map_results(MapFuture(futures), timeout)
|
(self, function: Callable, *iterables, **kwargs) -> pebble.pool.base_pool.MapFuture
|
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] |
52,178 |
pebble.pool.thread
|
schedule
|
Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
A *concurrent.futures.Future* object is returned.
|
def schedule(self, function, args=(), kwargs={}) -> Future:
"""Schedules *function* to be run the Pool.
*args* and *kwargs* will be forwareded to the scheduled function
respectively as arguments and keyword arguments.
A *concurrent.futures.Future* object is returned.
"""
self._check_pool_state()
future = Future()
payload = TaskPayload(function, args, kwargs)
task = Task(next(self._task_counter), future, None, payload)
self._context.task_queue.put(task)
return future
|
(self, function, args=(), kwargs={}) -> concurrent.futures._base.Future
|
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] |
52,180 |
pebble.pool.thread
|
submit
|
This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
|
def submit(self, function: Callable, *args, **kwargs) -> Future:
"""This function is provided for compatibility with
`asyncio.loop.run_in_executor`.
For scheduling jobs within the pool use `schedule` instead.
"""
return self.schedule(function, args=args, kwargs=kwargs)
|
(self, function: Callable, *args, **kwargs) -> concurrent.futures._base.Future
|
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] |
52,185 |
pebble.decorators
|
sighandler
|
Sets the decorated function as signal handler of given *signals*.
*signals* can be either a single signal or a list/tuple
of multiple ones.
|
def sighandler(signals: list) -> Callable:
"""Sets the decorated function as signal handler of given *signals*.
*signals* can be either a single signal or a list/tuple
of multiple ones.
"""
def wrap(function):
set_signal_handlers(signals, function)
@wraps(function)
def wrapper(*args, **kwargs):
return function(*args, **kwargs)
return wrapper
return wrap
|
(signals: list) -> Callable
|
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] |
52,186 |
pebble.decorators
|
synchronized
|
A synchronized function prevents two or more callers to interleave
its execution preventing race conditions.
The synchronized decorator accepts as optional parameter a Lock, RLock or
Semaphore object which will be employed to ensure the function's atomicity.
If no synchronization object is given, a single threading.Lock will be used.
This implies that between different decorated function only one at a time
will be executed.
|
def synchronized(*args) -> Callable:
"""A synchronized function prevents two or more callers to interleave
its execution preventing race conditions.
The synchronized decorator accepts as optional parameter a Lock, RLock or
Semaphore object which will be employed to ensure the function's atomicity.
If no synchronization object is given, a single threading.Lock will be used.
This implies that between different decorated function only one at a time
will be executed.
"""
if callable(args[0]):
return decorate_synchronized(args[0], _synchronized_lock)
else:
def wrap(function) -> type:
return decorate_synchronized(function, args[0])
return wrap
|
(*args) -> Callable
|
[
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0.0228592399507761,
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] |
52,187 |
pebble.functions
|
waitforqueues
|
Waits for one or more *Queue* to be ready or until *timeout* expires.
*queues* is a list containing one or more *Queue.Queue* objects.
If *timeout* is not None the function will block
for the specified amount of seconds.
The function returns a list containing the ready *Queues*.
|
def waitforqueues(queues: list, timeout: float = None) -> filter:
"""Waits for one or more *Queue* to be ready or until *timeout* expires.
*queues* is a list containing one or more *Queue.Queue* objects.
If *timeout* is not None the function will block
for the specified amount of seconds.
The function returns a list containing the ready *Queues*.
"""
lock = threading.Condition(threading.Lock())
prepare_queues(queues, lock)
try:
wait_queues(queues, lock, timeout)
finally:
reset_queues(queues)
return filter(lambda q: not q.empty(), queues)
|
(queues: list, timeout: Optional[float] = None) -> filter
|
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] |
52,188 |
pebble.functions
|
waitforthreads
|
Waits for one or more *Thread* to exit or until *timeout* expires.
.. note::
Expired *Threads* are not joined by *waitforthreads*.
*threads* is a list containing one or more *threading.Thread* objects.
If *timeout* is not None the function will block
for the specified amount of seconds.
The function returns a list containing the ready *Threads*.
|
def waitforthreads(threads: list, timeout: float = None) -> filter:
"""Waits for one or more *Thread* to exit or until *timeout* expires.
.. note::
Expired *Threads* are not joined by *waitforthreads*.
*threads* is a list containing one or more *threading.Thread* objects.
If *timeout* is not None the function will block
for the specified amount of seconds.
The function returns a list containing the ready *Threads*.
"""
old_function = None
lock = threading.Condition(threading.Lock())
def new_function(*args):
old_function(*args)
with lock:
lock.notify_all()
old_function = prepare_threads(new_function)
try:
wait_threads(threads, lock, timeout)
finally:
reset_threads(old_function)
return filter(lambda t: not t.is_alive(), threads)
|
(threads: list, timeout: Optional[float] = None) -> filter
|
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] |
52,195 |
osmnx.bearing
|
add_edge_bearings
|
Add compass `bearing` attributes to all graph edges.
Vectorized function to calculate (initial) bearing from origin node to
destination node for each edge in a directed, unprojected graph then add
these bearings as new edge attributes. Bearing represents angle in degrees
(clockwise) between north and the geodesic line from the origin node to
the destination node. Ignores self-loop edges as their bearings are
undefined.
Parameters
----------
G : networkx.MultiDiGraph
unprojected graph
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with edge bearing attributes
|
def add_edge_bearings(G, precision=None):
"""
Add compass `bearing` attributes to all graph edges.
Vectorized function to calculate (initial) bearing from origin node to
destination node for each edge in a directed, unprojected graph then add
these bearings as new edge attributes. Bearing represents angle in degrees
(clockwise) between north and the geodesic line from the origin node to
the destination node. Ignores self-loop edges as their bearings are
undefined.
Parameters
----------
G : networkx.MultiDiGraph
unprojected graph
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with edge bearing attributes
"""
if precision is None:
precision = 1
else:
warn(
"The `precision` parameter is deprecated and will be removed in the v2.0.0 release. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
if projection.is_projected(G.graph["crs"]): # pragma: no cover
msg = "graph must be unprojected to add edge bearings"
raise ValueError(msg)
# extract edge IDs and corresponding coordinates from their nodes
uvk = [(u, v, k) for u, v, k in G.edges if u != v]
x = G.nodes(data="x")
y = G.nodes(data="y")
coords = np.array([(y[u], x[u], y[v], x[v]) for u, v, k in uvk])
# calculate bearings then set as edge attributes
bearings = calculate_bearing(coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3])
values = zip(uvk, bearings.round(precision))
nx.set_edge_attributes(G, dict(values), name="bearing")
return G
|
(G, precision=None)
|
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0.0049476600252091885,
-0.023630909621715546,
-0.006845695432275534,
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] |
52,196 |
osmnx.elevation
|
add_edge_grades
|
Add `grade` attribute to each graph edge.
Vectorized function to calculate the directed grade (ie, rise over run)
for each edge in the graph and add it to the edge as an attribute. Nodes
must already have `elevation` attributes to use this function.
See also the `add_node_elevations_raster` and `add_node_elevations_google`
functions.
Parameters
----------
G : networkx.MultiDiGraph
input graph with `elevation` node attribute
add_absolute : bool
if True, also add absolute value of grade as `grade_abs` attribute
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with edge `grade` (and optionally `grade_abs`) attributes
|
def add_edge_grades(G, add_absolute=True, precision=None):
"""
Add `grade` attribute to each graph edge.
Vectorized function to calculate the directed grade (ie, rise over run)
for each edge in the graph and add it to the edge as an attribute. Nodes
must already have `elevation` attributes to use this function.
See also the `add_node_elevations_raster` and `add_node_elevations_google`
functions.
Parameters
----------
G : networkx.MultiDiGraph
input graph with `elevation` node attribute
add_absolute : bool
if True, also add absolute value of grade as `grade_abs` attribute
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with edge `grade` (and optionally `grade_abs`) attributes
"""
if precision is None:
precision = 3
else:
warn(
"The `precision` parameter is deprecated and will be removed in the v2.0.0 release. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
elev_lookup = G.nodes(data="elevation")
u, v, k, lengths = zip(*G.edges(keys=True, data="length"))
uvk = tuple(zip(u, v, k))
# calculate edges' elevation changes from u to v then divide by lengths
elevs = np.array([(elev_lookup[u], elev_lookup[v]) for u, v, k in uvk])
grades = ((elevs[:, 1] - elevs[:, 0]) / np.array(lengths)).round(precision)
nx.set_edge_attributes(G, dict(zip(uvk, grades)), name="grade")
# optionally add grade absolute value to the edge attributes
if add_absolute:
nx.set_edge_attributes(G, dict(zip(uvk, np.abs(grades))), name="grade_abs")
utils.log("Added grade attributes to all edges.")
return G
|
(G, add_absolute=True, precision=None)
|
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] |
52,197 |
osmnx.routing
|
add_edge_speeds
|
Add edge speeds (km per hour) to graph as new `speed_kph` edge attributes.
By default, this imputes free-flow travel speeds for all edges via the
mean `maxspeed` value of the edges of each highway type. For highway types
in the graph that have no `maxspeed` value on any edge, it assigns the
mean of all `maxspeed` values in graph.
This default mean-imputation can obviously be imprecise, and the user can
override it by passing in `hwy_speeds` and/or `fallback` arguments that
correspond to local speed limit standards. The user can also specify a
different aggregation function (such as the median) to impute missing
values from the observed values.
If edge `maxspeed` attribute has "mph" in it, value will automatically be
converted from miles per hour to km per hour. Any other speed units should
be manually converted to km per hour prior to running this function,
otherwise there could be unexpected results. If "mph" does not appear in
the edge's maxspeed attribute string, then function assumes kph, per OSM
guidelines: https://wiki.openstreetmap.org/wiki/Map_Features/Units
Parameters
----------
G : networkx.MultiDiGraph
input graph
hwy_speeds : dict
dict keys = OSM highway types and values = typical speeds (km per
hour) to assign to edges of that highway type for any edges missing
speed data. Any edges with highway type not in `hwy_speeds` will be
assigned the mean preexisting speed value of all edges of that highway
type.
fallback : numeric
default speed value (km per hour) to assign to edges whose highway
type did not appear in `hwy_speeds` and had no preexisting speed
values on any edge
precision : int
deprecated, do not use
agg : function
aggregation function to impute missing values from observed values.
the default is numpy.mean, but you might also consider for example
numpy.median, numpy.nanmedian, or your own custom function
Returns
-------
G : networkx.MultiDiGraph
graph with speed_kph attributes on all edges
|
def add_edge_speeds(G, hwy_speeds=None, fallback=None, precision=None, agg=np.mean):
"""
Add edge speeds (km per hour) to graph as new `speed_kph` edge attributes.
By default, this imputes free-flow travel speeds for all edges via the
mean `maxspeed` value of the edges of each highway type. For highway types
in the graph that have no `maxspeed` value on any edge, it assigns the
mean of all `maxspeed` values in graph.
This default mean-imputation can obviously be imprecise, and the user can
override it by passing in `hwy_speeds` and/or `fallback` arguments that
correspond to local speed limit standards. The user can also specify a
different aggregation function (such as the median) to impute missing
values from the observed values.
If edge `maxspeed` attribute has "mph" in it, value will automatically be
converted from miles per hour to km per hour. Any other speed units should
be manually converted to km per hour prior to running this function,
otherwise there could be unexpected results. If "mph" does not appear in
the edge's maxspeed attribute string, then function assumes kph, per OSM
guidelines: https://wiki.openstreetmap.org/wiki/Map_Features/Units
Parameters
----------
G : networkx.MultiDiGraph
input graph
hwy_speeds : dict
dict keys = OSM highway types and values = typical speeds (km per
hour) to assign to edges of that highway type for any edges missing
speed data. Any edges with highway type not in `hwy_speeds` will be
assigned the mean preexisting speed value of all edges of that highway
type.
fallback : numeric
default speed value (km per hour) to assign to edges whose highway
type did not appear in `hwy_speeds` and had no preexisting speed
values on any edge
precision : int
deprecated, do not use
agg : function
aggregation function to impute missing values from observed values.
the default is numpy.mean, but you might also consider for example
numpy.median, numpy.nanmedian, or your own custom function
Returns
-------
G : networkx.MultiDiGraph
graph with speed_kph attributes on all edges
"""
if precision is None:
precision = 1
else:
warn(
"The `precision` parameter is deprecated and will be removed in the v2.0.0 release. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
if fallback is None:
fallback = np.nan
edges = convert.graph_to_gdfs(G, nodes=False, fill_edge_geometry=False)
# collapse any highway lists (can happen during graph simplification)
# into string values simply by keeping just the first element of the list
edges["highway"] = edges["highway"].map(lambda x: x[0] if isinstance(x, list) else x)
if "maxspeed" in edges.columns:
# collapse any maxspeed lists (can happen during graph simplification)
# into a single value
edges["maxspeed"] = edges["maxspeed"].apply(_collapse_multiple_maxspeed_values, agg=agg)
# create speed_kph by cleaning maxspeed strings and converting mph to
# kph if necessary
edges["speed_kph"] = edges["maxspeed"].astype(str).map(_clean_maxspeed).astype(float)
else:
# if no edges in graph had a maxspeed attribute
edges["speed_kph"] = None
# if user provided hwy_speeds, use them as default values, otherwise
# initialize an empty series to populate with values
hwy_speed_avg = pd.Series(dtype=float) if hwy_speeds is None else pd.Series(hwy_speeds).dropna()
# for each highway type that caller did not provide in hwy_speeds, impute
# speed of type by taking the mean of the preexisting speed values of that
# highway type
for hwy, group in edges.groupby("highway"):
if hwy not in hwy_speed_avg:
hwy_speed_avg.loc[hwy] = agg(group["speed_kph"])
# if any highway types had no preexisting speed values, impute their speed
# with fallback value provided by caller. if fallback=np.nan, impute speed
# as the mean speed of all highway types that did have preexisting values
hwy_speed_avg = hwy_speed_avg.fillna(fallback).fillna(agg(hwy_speed_avg))
# for each edge missing speed data, assign it the imputed value for its
# highway type
speed_kph = (
edges[["highway", "speed_kph"]].set_index("highway").iloc[:, 0].fillna(hwy_speed_avg)
)
# all speeds will be null if edges had no preexisting maxspeed data and
# caller did not pass in hwy_speeds or fallback arguments
if pd.isna(speed_kph).all():
msg = (
"this graph's edges have no preexisting `maxspeed` attribute "
"values so you must pass `hwy_speeds` or `fallback` arguments."
)
raise ValueError(msg)
# add speed kph attribute to graph edges
edges["speed_kph"] = speed_kph.round(precision).to_numpy()
nx.set_edge_attributes(G, values=edges["speed_kph"], name="speed_kph")
return G
|
(G, hwy_speeds=None, fallback=None, precision=None, agg=<function mean at 0x7efd24b4dd30>)
|
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] |
52,198 |
osmnx.routing
|
add_edge_travel_times
|
Add edge travel time (seconds) to graph as new `travel_time` edge attributes.
Calculates free-flow travel time along each edge, based on `length` and
`speed_kph` attributes. Note: run `add_edge_speeds` first to generate the
`speed_kph` attribute. All edges must have `length` and `speed_kph`
attributes and all their values must be non-null.
Parameters
----------
G : networkx.MultiDiGraph
input graph
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with travel_time attributes on all edges
|
def add_edge_travel_times(G, precision=None):
"""
Add edge travel time (seconds) to graph as new `travel_time` edge attributes.
Calculates free-flow travel time along each edge, based on `length` and
`speed_kph` attributes. Note: run `add_edge_speeds` first to generate the
`speed_kph` attribute. All edges must have `length` and `speed_kph`
attributes and all their values must be non-null.
Parameters
----------
G : networkx.MultiDiGraph
input graph
precision : int
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with travel_time attributes on all edges
"""
if precision is None:
precision = 1
else:
warn(
"The `precision` parameter is deprecated and will be removed in the v2.0.0 release. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
edges = convert.graph_to_gdfs(G, nodes=False)
# verify edge length and speed_kph attributes exist
if not ("length" in edges.columns and "speed_kph" in edges.columns): # pragma: no cover
msg = "all edges must have `length` and `speed_kph` attributes."
raise KeyError(msg)
# verify edge length and speed_kph attributes contain no nulls
if pd.isna(edges["length"]).any() or pd.isna(edges["speed_kph"]).any(): # pragma: no cover
msg = "edge `length` and `speed_kph` values must be non-null."
raise ValueError(msg)
# convert distance meters to km, and speed km per hour to km per second
distance_km = edges["length"] / 1000
speed_km_sec = edges["speed_kph"] / (60 * 60)
# calculate edge travel time in seconds
travel_time = distance_km / speed_km_sec
# add travel time attribute to graph edges
edges["travel_time"] = travel_time.round(precision).to_numpy()
nx.set_edge_attributes(G, values=edges["travel_time"], name="travel_time")
return G
|
(G, precision=None)
|
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] |
52,199 |
osmnx.elevation
|
add_node_elevations_google
|
Add an `elevation` (meters) attribute to each node using a web service.
By default, this uses the Google Maps Elevation API but you can optionally
use an equivalent API with the same interface and response format, such as
Open Topo Data, via the `settings` module's `elevation_url_template`. The
Google Maps Elevation API requires an API key but other providers may not.
For a free local alternative see the `add_node_elevations_raster`
function. See also the `add_edge_grades` function.
Parameters
----------
G : networkx.MultiDiGraph
input graph
api_key : string
a valid API key, can be None if the API does not require a key
batch_size : int
max number of coordinate pairs to submit in each API call (if this is
too high, the server will reject the request because its character
limit exceeds the max allowed)
pause : float
time to pause between API calls, which can be increased if you get
rate limited
max_locations_per_batch : int
deprecated, do not use
precision : int
deprecated, do not use
url_template : string
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with node elevation attributes
|
def add_node_elevations_google(
G,
api_key=None,
batch_size=350,
pause=0,
max_locations_per_batch=None,
precision=None,
url_template=None,
):
"""
Add an `elevation` (meters) attribute to each node using a web service.
By default, this uses the Google Maps Elevation API but you can optionally
use an equivalent API with the same interface and response format, such as
Open Topo Data, via the `settings` module's `elevation_url_template`. The
Google Maps Elevation API requires an API key but other providers may not.
For a free local alternative see the `add_node_elevations_raster`
function. See also the `add_edge_grades` function.
Parameters
----------
G : networkx.MultiDiGraph
input graph
api_key : string
a valid API key, can be None if the API does not require a key
batch_size : int
max number of coordinate pairs to submit in each API call (if this is
too high, the server will reject the request because its character
limit exceeds the max allowed)
pause : float
time to pause between API calls, which can be increased if you get
rate limited
max_locations_per_batch : int
deprecated, do not use
precision : int
deprecated, do not use
url_template : string
deprecated, do not use
Returns
-------
G : networkx.MultiDiGraph
graph with node elevation attributes
"""
if max_locations_per_batch is None:
max_locations_per_batch = batch_size
else:
warn(
"The `max_locations_per_batch` parameter is deprecated and will be "
"removed the v2.0.0 release, use the `batch_size` parameter instead. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
if precision is None:
precision = 3
else:
warn(
"The `precision` parameter is deprecated and will be removed in the v2.0.0 release. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
if url_template is None:
url_template = settings.elevation_url_template
else:
warn(
"The `url_template` parameter is deprecated and will be removed "
"in the v2.0.0 release. Configure the `settings` module's "
"`elevation_url_template` instead. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
# make a pandas series of all the nodes' coordinates as 'lat,lon'
# round coordinates to 5 decimal places (approx 1 meter) to be able to fit
# in more locations per API call
node_points = pd.Series(
{node: f'{data["y"]:.5f},{data["x"]:.5f}' for node, data in G.nodes(data=True)}
)
n_calls = int(np.ceil(len(node_points) / max_locations_per_batch))
domain = _downloader._hostname_from_url(url_template)
utils.log(f"Requesting node elevations from {domain!r} in {n_calls} request(s)")
# break the series of coordinates into chunks of max_locations_per_batch
# API format is locations=lat,lon|lat,lon|lat,lon|lat,lon...
results = []
for i in range(0, len(node_points), max_locations_per_batch):
chunk = node_points.iloc[i : i + max_locations_per_batch]
locations = "|".join(chunk)
url = url_template.format(locations=locations, key=api_key)
# download and append these elevation results to list of all results
response_json = _elevation_request(url, pause)
if "results" in response_json and len(response_json["results"]) > 0:
results.extend(response_json["results"])
else:
raise InsufficientResponseError(str(response_json))
# sanity check that all our vectors have the same number of elements
msg = f"Graph has {len(G):,} nodes and we received {len(results):,} results from {domain!r}"
utils.log(msg)
if not (len(results) == len(G) == len(node_points)): # pragma: no cover
err_msg = f"{msg}\n{response_json}"
raise InsufficientResponseError(err_msg)
# add elevation as an attribute to the nodes
df_elev = pd.DataFrame(node_points, columns=["node_points"])
df_elev["elevation"] = [result["elevation"] for result in results]
df_elev["elevation"] = df_elev["elevation"].round(precision)
nx.set_node_attributes(G, name="elevation", values=df_elev["elevation"].to_dict())
utils.log(f"Added elevation data from {domain!r} to all nodes.")
return G
|
(G, api_key=None, batch_size=350, pause=0, max_locations_per_batch=None, precision=None, url_template=None)
|
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52,200 |
osmnx.elevation
|
add_node_elevations_raster
|
Add `elevation` attribute to each node from local raster file(s).
If `filepath` is a list of paths, this will generate a virtual raster
composed of the files at those paths as an intermediate step.
See also the `add_edge_grades` function.
Parameters
----------
G : networkx.MultiDiGraph
input graph, in same CRS as raster
filepath : string or pathlib.Path or list of strings/Paths
path (or list of paths) to the raster file(s) to query
band : int
which raster band to query
cpus : int
how many CPU cores to use; if None, use all available
Returns
-------
G : networkx.MultiDiGraph
graph with node elevation attributes
|
def add_node_elevations_raster(G, filepath, band=1, cpus=None):
"""
Add `elevation` attribute to each node from local raster file(s).
If `filepath` is a list of paths, this will generate a virtual raster
composed of the files at those paths as an intermediate step.
See also the `add_edge_grades` function.
Parameters
----------
G : networkx.MultiDiGraph
input graph, in same CRS as raster
filepath : string or pathlib.Path or list of strings/Paths
path (or list of paths) to the raster file(s) to query
band : int
which raster band to query
cpus : int
how many CPU cores to use; if None, use all available
Returns
-------
G : networkx.MultiDiGraph
graph with node elevation attributes
"""
if rasterio is None or gdal is None: # pragma: no cover
msg = "gdal and rasterio must be installed to query raster files"
raise ImportError(msg)
if cpus is None:
cpus = mp.cpu_count()
cpus = min(cpus, mp.cpu_count())
utils.log(f"Attaching elevations with {cpus} CPUs...")
# if a list of filepaths is passed, compose them all as a virtual raster
# use the sha1 hash of the filepaths list as the vrt filename
if not isinstance(filepath, (str, Path)):
filepaths = [str(p) for p in filepath]
sha = sha1(str(filepaths).encode("utf-8")).hexdigest()
filepath = f"./.osmnx_{sha}.vrt"
gdal.UseExceptions()
gdal.BuildVRT(filepath, filepaths).FlushCache()
nodes = convert.graph_to_gdfs(G, edges=False, node_geometry=False)[["x", "y"]]
if cpus == 1:
elevs = dict(_query_raster(nodes, filepath, band))
else:
# divide nodes into equal-sized chunks for multiprocessing
size = int(np.ceil(len(nodes) / cpus))
args = ((nodes.iloc[i : i + size], filepath, band) for i in range(0, len(nodes), size))
with mp.get_context("spawn").Pool(cpus) as pool:
results = pool.starmap_async(_query_raster, args).get()
elevs = {k: v for kv in results for k, v in kv}
assert len(G) == len(elevs)
nx.set_node_attributes(G, elevs, name="elevation")
utils.log("Added elevation data from raster to all nodes.")
return G
|
(G, filepath, band=1, cpus=None)
|
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] |
52,201 |
osmnx.stats
|
basic_stats
|
Calculate basic descriptive geometric and topological measures of a graph.
Density measures are only calculated if `area` is provided and clean
intersection measures are only calculated if `clean_int_tol` is provided.
Parameters
----------
G : networkx.MultiDiGraph
input graph
area : float
if not None, calculate density measures and use this value (in square
meters) as the denominator
clean_int_tol : float
if not None, calculate consolidated intersections count (and density,
if `area` is also provided) and use this tolerance value; refer to the
`simplification.consolidate_intersections` function documentation for
details
Returns
-------
stats : dict
dictionary containing the following keys
- `circuity_avg` - see `circuity_avg` function documentation
- `clean_intersection_count` - see `clean_intersection_count` function documentation
- `clean_intersection_density_km` - `clean_intersection_count` per sq km
- `edge_density_km` - `edge_length_total` per sq km
- `edge_length_avg` - `edge_length_total / m`
- `edge_length_total` - see `edge_length_total` function documentation
- `intersection_count` - see `intersection_count` function documentation
- `intersection_density_km` - `intersection_count` per sq km
- `k_avg` - graph's average node degree (in-degree and out-degree)
- `m` - count of edges in graph
- `n` - count of nodes in graph
- `node_density_km` - `n` per sq km
- `self_loop_proportion` - see `self_loop_proportion` function documentation
- `street_density_km` - `street_length_total` per sq km
- `street_length_avg` - `street_length_total / street_segment_count`
- `street_length_total` - see `street_length_total` function documentation
- `street_segment_count` - see `street_segment_count` function documentation
- `streets_per_node_avg` - see `streets_per_node_avg` function documentation
- `streets_per_node_counts` - see `streets_per_node_counts` function documentation
- `streets_per_node_proportions` - see `streets_per_node_proportions` function documentation
|
def basic_stats(G, area=None, clean_int_tol=None):
"""
Calculate basic descriptive geometric and topological measures of a graph.
Density measures are only calculated if `area` is provided and clean
intersection measures are only calculated if `clean_int_tol` is provided.
Parameters
----------
G : networkx.MultiDiGraph
input graph
area : float
if not None, calculate density measures and use this value (in square
meters) as the denominator
clean_int_tol : float
if not None, calculate consolidated intersections count (and density,
if `area` is also provided) and use this tolerance value; refer to the
`simplification.consolidate_intersections` function documentation for
details
Returns
-------
stats : dict
dictionary containing the following keys
- `circuity_avg` - see `circuity_avg` function documentation
- `clean_intersection_count` - see `clean_intersection_count` function documentation
- `clean_intersection_density_km` - `clean_intersection_count` per sq km
- `edge_density_km` - `edge_length_total` per sq km
- `edge_length_avg` - `edge_length_total / m`
- `edge_length_total` - see `edge_length_total` function documentation
- `intersection_count` - see `intersection_count` function documentation
- `intersection_density_km` - `intersection_count` per sq km
- `k_avg` - graph's average node degree (in-degree and out-degree)
- `m` - count of edges in graph
- `n` - count of nodes in graph
- `node_density_km` - `n` per sq km
- `self_loop_proportion` - see `self_loop_proportion` function documentation
- `street_density_km` - `street_length_total` per sq km
- `street_length_avg` - `street_length_total / street_segment_count`
- `street_length_total` - see `street_length_total` function documentation
- `street_segment_count` - see `street_segment_count` function documentation
- `streets_per_node_avg` - see `streets_per_node_avg` function documentation
- `streets_per_node_counts` - see `streets_per_node_counts` function documentation
- `streets_per_node_proportions` - see `streets_per_node_proportions` function documentation
"""
Gu = convert.to_undirected(G)
stats = {}
stats["n"] = len(G.nodes)
stats["m"] = len(G.edges)
stats["k_avg"] = 2 * stats["m"] / stats["n"]
stats["edge_length_total"] = edge_length_total(G)
stats["edge_length_avg"] = stats["edge_length_total"] / stats["m"]
stats["streets_per_node_avg"] = streets_per_node_avg(G)
stats["streets_per_node_counts"] = streets_per_node_counts(G)
stats["streets_per_node_proportions"] = streets_per_node_proportions(G)
stats["intersection_count"] = intersection_count(G)
stats["street_length_total"] = street_length_total(Gu)
stats["street_segment_count"] = street_segment_count(Gu)
stats["street_length_avg"] = stats["street_length_total"] / stats["street_segment_count"]
stats["circuity_avg"] = circuity_avg(Gu)
stats["self_loop_proportion"] = self_loop_proportion(Gu)
# calculate clean intersection counts if requested
if clean_int_tol:
stats["clean_intersection_count"] = len(
simplification.consolidate_intersections(
G, tolerance=clean_int_tol, rebuild_graph=False, dead_ends=False
)
)
# can only calculate density measures if area was provided
if area is not None:
area_km = area / 1_000_000 # convert m^2 to km^2
stats["node_density_km"] = stats["n"] / area_km
stats["intersection_density_km"] = stats["intersection_count"] / area_km
stats["edge_density_km"] = stats["edge_length_total"] / area_km
stats["street_density_km"] = stats["street_length_total"] / area_km
if clean_int_tol:
stats["clean_intersection_density_km"] = stats["clean_intersection_count"] / area_km
return stats
|
(G, area=None, clean_int_tol=None)
|
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] |
52,203 |
osmnx.utils
|
citation
|
Print the OSMnx package's citation information.
Boeing, G. (2024). Modeling and Analyzing Urban Networks and Amenities with
OSMnx. Working paper. https://geoffboeing.com/publications/osmnx-paper/
Parameters
----------
style : string {"apa", "bibtex", "ieee"}
citation format, either APA or BibTeX or IEEE
Returns
-------
None
|
def citation(style="bibtex"):
"""
Print the OSMnx package's citation information.
Boeing, G. (2024). Modeling and Analyzing Urban Networks and Amenities with
OSMnx. Working paper. https://geoffboeing.com/publications/osmnx-paper/
Parameters
----------
style : string {"apa", "bibtex", "ieee"}
citation format, either APA or BibTeX or IEEE
Returns
-------
None
"""
if style == "apa":
msg = (
"Boeing, G. (2024). Modeling and Analyzing Urban Networks and Amenities "
"with OSMnx. Working paper. https://geoffboeing.com/publications/osmnx-paper/"
)
elif style == "bibtex":
msg = (
"@techreport{boeing_osmnx_2024,\n"
" author = {Boeing, Geoff},\n"
" title = {{Modeling and Analyzing Urban Networks and Amenities with OSMnx}},\n"
" type = {Working paper},\n"
" url = {https://geoffboeing.com/publications/osmnx-paper/},\n"
" year = {2024}\n"
"}"
)
elif style == "ieee":
msg = (
'G. Boeing, "Modeling and Analyzing Urban Networks and Amenities with OSMnx," '
"Working paper, https://geoffboeing.com/publications/osmnx-paper/"
)
else: # pragma: no cover
err_msg = f"unrecognized citation style {style!r}"
raise ValueError(err_msg)
print(msg) # noqa: T201
|
(style='bibtex')
|
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] |
52,204 |
osmnx.utils
|
config
|
Do not use: deprecated. Use the settings module directly.
Parameters
----------
all_oneway : bool
deprecated
bidirectional_network_types : list
deprecated
cache_folder : string or pathlib.Path
deprecated
data_folder : string or pathlib.Path
deprecated
cache_only_mode : bool
deprecated
default_accept_language : string
deprecated
default_access : string
deprecated
default_crs : string
deprecated
default_referer : string
deprecated
default_user_agent : string
deprecated
imgs_folder : string or pathlib.Path
deprecated
log_file : bool
deprecated
log_filename : string
deprecated
log_console : bool
deprecated
log_level : int
deprecated
log_name : string
deprecated
logs_folder : string or pathlib.Path
deprecated
max_query_area_size : int
deprecated
memory : int
deprecated
nominatim_endpoint : string
deprecated
nominatim_key : string
deprecated
osm_xml_node_attrs : list
deprecated
osm_xml_node_tags : list
deprecated
osm_xml_way_attrs : list
deprecated
osm_xml_way_tags : list
deprecated
overpass_endpoint : string
deprecated
overpass_rate_limit : bool
deprecated
overpass_settings : string
deprecated
requests_kwargs : dict
deprecated
timeout : int
deprecated
use_cache : bool
deprecated
useful_tags_node : list
deprecated
useful_tags_way : list
deprecated
Returns
-------
None
|
def config(
all_oneway=settings.all_oneway,
bidirectional_network_types=settings.bidirectional_network_types,
cache_folder=settings.cache_folder,
cache_only_mode=settings.cache_only_mode,
data_folder=settings.data_folder,
default_accept_language=settings.default_accept_language,
default_access=settings.default_access,
default_crs=settings.default_crs,
default_referer=settings.default_referer,
default_user_agent=settings.default_user_agent,
imgs_folder=settings.imgs_folder,
log_console=settings.log_console,
log_file=settings.log_file,
log_filename=settings.log_filename,
log_level=settings.log_level,
log_name=settings.log_name,
logs_folder=settings.logs_folder,
max_query_area_size=settings.max_query_area_size,
memory=settings.memory,
nominatim_endpoint=settings.nominatim_endpoint,
nominatim_key=settings.nominatim_key,
osm_xml_node_attrs=settings.osm_xml_node_attrs,
osm_xml_node_tags=settings.osm_xml_node_tags,
osm_xml_way_attrs=settings.osm_xml_way_attrs,
osm_xml_way_tags=settings.osm_xml_way_tags,
overpass_endpoint=settings.overpass_endpoint,
overpass_rate_limit=settings.overpass_rate_limit,
overpass_settings=settings.overpass_settings,
requests_kwargs=settings.requests_kwargs,
timeout=settings.timeout,
use_cache=settings.use_cache,
useful_tags_node=settings.useful_tags_node,
useful_tags_way=settings.useful_tags_way,
):
"""
Do not use: deprecated. Use the settings module directly.
Parameters
----------
all_oneway : bool
deprecated
bidirectional_network_types : list
deprecated
cache_folder : string or pathlib.Path
deprecated
data_folder : string or pathlib.Path
deprecated
cache_only_mode : bool
deprecated
default_accept_language : string
deprecated
default_access : string
deprecated
default_crs : string
deprecated
default_referer : string
deprecated
default_user_agent : string
deprecated
imgs_folder : string or pathlib.Path
deprecated
log_file : bool
deprecated
log_filename : string
deprecated
log_console : bool
deprecated
log_level : int
deprecated
log_name : string
deprecated
logs_folder : string or pathlib.Path
deprecated
max_query_area_size : int
deprecated
memory : int
deprecated
nominatim_endpoint : string
deprecated
nominatim_key : string
deprecated
osm_xml_node_attrs : list
deprecated
osm_xml_node_tags : list
deprecated
osm_xml_way_attrs : list
deprecated
osm_xml_way_tags : list
deprecated
overpass_endpoint : string
deprecated
overpass_rate_limit : bool
deprecated
overpass_settings : string
deprecated
requests_kwargs : dict
deprecated
timeout : int
deprecated
use_cache : bool
deprecated
useful_tags_node : list
deprecated
useful_tags_way : list
deprecated
Returns
-------
None
"""
warn(
"The `utils.config` function is deprecated and will be removed in "
"the v2.0.0 release. Instead, use the `settings` module directly to "
"configure a global setting's value. For example, "
"`ox.settings.log_console=True`. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
# set each global setting to the argument value
settings.all_oneway = all_oneway
settings.bidirectional_network_types = bidirectional_network_types
settings.cache_folder = cache_folder
settings.cache_only_mode = cache_only_mode
settings.data_folder = data_folder
settings.default_accept_language = default_accept_language
settings.default_access = default_access
settings.default_crs = default_crs
settings.default_referer = default_referer
settings.default_user_agent = default_user_agent
settings.imgs_folder = imgs_folder
settings.log_console = log_console
settings.log_file = log_file
settings.log_filename = log_filename
settings.log_level = log_level
settings.log_name = log_name
settings.logs_folder = logs_folder
settings.max_query_area_size = max_query_area_size
settings.memory = memory
settings.nominatim_endpoint = nominatim_endpoint
settings.nominatim_key = nominatim_key
settings.osm_xml_node_attrs = osm_xml_node_attrs
settings.osm_xml_node_tags = osm_xml_node_tags
settings.osm_xml_way_attrs = osm_xml_way_attrs
settings.osm_xml_way_tags = osm_xml_way_tags
settings.overpass_endpoint = overpass_endpoint
settings.overpass_rate_limit = overpass_rate_limit
settings.overpass_settings = overpass_settings
settings.timeout = timeout
settings.use_cache = use_cache
settings.useful_tags_node = useful_tags_node
settings.useful_tags_way = useful_tags_way
settings.requests_kwargs = requests_kwargs
|
(all_oneway=False, bidirectional_network_types=['walk'], cache_folder='./cache', cache_only_mode=False, data_folder='./data', default_accept_language=None, default_access='["access"!~"private"]', default_crs='epsg:4326', default_referer=None, default_user_agent=None, imgs_folder='./images', log_console=False, log_file=False, log_filename='osmnx', log_level=20, log_name='OSMnx', logs_folder='./logs', max_query_area_size=2500000000, memory=None, nominatim_endpoint=None, nominatim_key=None, osm_xml_node_attrs=None, osm_xml_node_tags=None, osm_xml_way_attrs=None, osm_xml_way_tags=None, overpass_endpoint=None, overpass_rate_limit=True, overpass_settings='[out:json][timeout:{timeout}]{maxsize}', requests_kwargs={}, timeout=None, use_cache=True, useful_tags_node=['ref', 'highway'], useful_tags_way=['bridge', 'tunnel', 'oneway', 'lanes', 'ref', 'name', 'highway', 'maxspeed', 'service', 'access', 'area', 'landuse', 'width', 'est_width', 'junction'])
|
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] |
52,205 |
osmnx.simplification
|
consolidate_intersections
|
Consolidate intersections comprising clusters of nearby nodes.
Merges nearby nodes and returns either their centroids or a rebuilt graph
with consolidated intersections and reconnected edge geometries. The
tolerance argument should be adjusted to approximately match street design
standards in the specific street network, and you should always use a
projected graph to work in meaningful and consistent units like meters.
Note the tolerance represents a per-node buffering radius: for example, to
consolidate nodes within 10 meters of each other, use tolerance=5.
When rebuild_graph=False, it uses a purely geometrical (and relatively
fast) algorithm to identify "geometrically close" nodes, merge them, and
return just the merged intersections' centroids. When rebuild_graph=True,
it uses a topological (and slower but more accurate) algorithm to identify
"topologically close" nodes, merge them, then rebuild/return the graph.
Returned graph's node IDs represent clusters rather than osmids. Refer to
nodes' osmid_original attributes for original osmids. If multiple nodes
were merged together, the osmid_original attribute is a list of merged
nodes' osmids.
Divided roads are often represented by separate centerline edges. The
intersection of two divided roads thus creates 4 nodes, representing where
each edge intersects a perpendicular edge. These 4 nodes represent a
single intersection in the real world. A similar situation occurs with
roundabouts and traffic circles. This function consolidates nearby nodes
by buffering them to an arbitrary distance, merging overlapping buffers,
and taking their centroid.
Parameters
----------
G : networkx.MultiDiGraph
a projected graph
tolerance : float
nodes are buffered to this distance (in graph's geometry's units) and
subsequent overlaps are dissolved into a single node
rebuild_graph : bool
if True, consolidate the nodes topologically, rebuild the graph, and
return as networkx.MultiDiGraph. if False, consolidate the nodes
geometrically and return the consolidated node points as
geopandas.GeoSeries
dead_ends : bool
if False, discard dead-end nodes to return only street-intersection
points
reconnect_edges : bool
ignored if rebuild_graph is not True. if True, reconnect edges and
their geometries in rebuilt graph to the consolidated nodes and update
edge length attributes; if False, returned graph has no edges (which
is faster if you just need topologically consolidated intersection
counts).
Returns
-------
networkx.MultiDiGraph or geopandas.GeoSeries
if rebuild_graph=True, returns MultiDiGraph with consolidated
intersections and reconnected edge geometries. if rebuild_graph=False,
returns GeoSeries of shapely Points representing the centroids of
street intersections
|
def consolidate_intersections(
G, tolerance=10, rebuild_graph=True, dead_ends=False, reconnect_edges=True
):
"""
Consolidate intersections comprising clusters of nearby nodes.
Merges nearby nodes and returns either their centroids or a rebuilt graph
with consolidated intersections and reconnected edge geometries. The
tolerance argument should be adjusted to approximately match street design
standards in the specific street network, and you should always use a
projected graph to work in meaningful and consistent units like meters.
Note the tolerance represents a per-node buffering radius: for example, to
consolidate nodes within 10 meters of each other, use tolerance=5.
When rebuild_graph=False, it uses a purely geometrical (and relatively
fast) algorithm to identify "geometrically close" nodes, merge them, and
return just the merged intersections' centroids. When rebuild_graph=True,
it uses a topological (and slower but more accurate) algorithm to identify
"topologically close" nodes, merge them, then rebuild/return the graph.
Returned graph's node IDs represent clusters rather than osmids. Refer to
nodes' osmid_original attributes for original osmids. If multiple nodes
were merged together, the osmid_original attribute is a list of merged
nodes' osmids.
Divided roads are often represented by separate centerline edges. The
intersection of two divided roads thus creates 4 nodes, representing where
each edge intersects a perpendicular edge. These 4 nodes represent a
single intersection in the real world. A similar situation occurs with
roundabouts and traffic circles. This function consolidates nearby nodes
by buffering them to an arbitrary distance, merging overlapping buffers,
and taking their centroid.
Parameters
----------
G : networkx.MultiDiGraph
a projected graph
tolerance : float
nodes are buffered to this distance (in graph's geometry's units) and
subsequent overlaps are dissolved into a single node
rebuild_graph : bool
if True, consolidate the nodes topologically, rebuild the graph, and
return as networkx.MultiDiGraph. if False, consolidate the nodes
geometrically and return the consolidated node points as
geopandas.GeoSeries
dead_ends : bool
if False, discard dead-end nodes to return only street-intersection
points
reconnect_edges : bool
ignored if rebuild_graph is not True. if True, reconnect edges and
their geometries in rebuilt graph to the consolidated nodes and update
edge length attributes; if False, returned graph has no edges (which
is faster if you just need topologically consolidated intersection
counts).
Returns
-------
networkx.MultiDiGraph or geopandas.GeoSeries
if rebuild_graph=True, returns MultiDiGraph with consolidated
intersections and reconnected edge geometries. if rebuild_graph=False,
returns GeoSeries of shapely Points representing the centroids of
street intersections
"""
# if dead_ends is False, discard dead-ends to retain only intersections
if not dead_ends:
spn = stats.streets_per_node(G)
dead_end_nodes = [node for node, count in spn.items() if count <= 1]
# make a copy to not mutate original graph object caller passed in
G = G.copy()
G.remove_nodes_from(dead_end_nodes)
if rebuild_graph:
if not G or not G.edges:
# cannot rebuild a graph with no nodes or no edges, just return it
return G
# otherwise
return _consolidate_intersections_rebuild_graph(G, tolerance, reconnect_edges)
# otherwise, if we're not rebuilding the graph
if not G:
# if graph has no nodes, just return empty GeoSeries
return gpd.GeoSeries(crs=G.graph["crs"])
# otherwise, return the centroids of the merged intersection polygons
return _merge_nodes_geometric(G, tolerance).centroid
|
(G, tolerance=10, rebuild_graph=True, dead_ends=False, reconnect_edges=True)
|
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] |
52,210 |
osmnx.features
|
features_from_address
|
Create GeoDataFrame of OSM features within some distance N, S, E, W of address.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
address : string
the address to geocode and use as the central point around which to
get the features
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
dist : numeric
distance in meters
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_address(address, tags, dist=1000):
"""
Create GeoDataFrame of OSM features within some distance N, S, E, W of address.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
address : string
the address to geocode and use as the central point around which to
get the features
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
dist : numeric
distance in meters
Returns
-------
gdf : geopandas.GeoDataFrame
"""
# geocode the address string to a (lat, lon) point
center_point = geocoder.geocode(query=address)
# create GeoDataFrame of features around this point
return features_from_point(center_point, tags, dist=dist)
|
(address, tags, dist=1000)
|
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] |
52,211 |
osmnx.features
|
features_from_bbox
|
Create a GeoDataFrame of OSM features within a N, S, E, W bounding box.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
north : float
deprecated, do not use
south : float
deprecated, do not use
east : float
deprecated, do not use
west : float
deprecated, do not use
bbox : tuple of floats
bounding box as (north, south, east, west)
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_bbox(north=None, south=None, east=None, west=None, bbox=None, tags=None):
"""
Create a GeoDataFrame of OSM features within a N, S, E, W bounding box.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
north : float
deprecated, do not use
south : float
deprecated, do not use
east : float
deprecated, do not use
west : float
deprecated, do not use
bbox : tuple of floats
bounding box as (north, south, east, west)
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
if not (north is None and south is None and east is None and west is None):
msg = (
"The `north`, `south`, `east`, and `west` parameters are deprecated and "
"will be removed in the v2.0.0 release. Use the `bbox` parameter instead. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123"
)
warn(msg, FutureWarning, stacklevel=2)
bbox = (north, south, east, west)
# convert bounding box to a polygon
polygon = utils_geo.bbox_to_poly(bbox=bbox)
# create GeoDataFrame of features within this polygon
return features_from_polygon(polygon, tags)
|
(north=None, south=None, east=None, west=None, bbox=None, tags=None)
|
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] |
52,212 |
osmnx.features
|
features_from_place
|
Create GeoDataFrame of OSM features within boundaries of some place(s).
The query must be geocodable and OSM must have polygon boundaries for the
geocode result. If OSM does not have a polygon for this place, you can
instead get features within it using the `features_from_address`
function, which geocodes the place name to a point and gets the features
within some distance of that point.
If OSM does have polygon boundaries for this place but you're not finding
it, try to vary the query string, pass in a structured query dict, or vary
the `which_result` argument to use a different geocode result. If you know
the OSM ID of the place, you can retrieve its boundary polygon using the
`geocode_to_gdf` function, then pass it to the `features_from_polygon`
function.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
query : string or dict or list
the query or queries to geocode to get place boundary polygon(s)
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
which_result : int
which geocoding result to use. if None, auto-select the first
(Multi)Polygon or raise an error if OSM doesn't return one.
buffer_dist : float
deprecated, do not use
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_place(query, tags, which_result=None, buffer_dist=None):
"""
Create GeoDataFrame of OSM features within boundaries of some place(s).
The query must be geocodable and OSM must have polygon boundaries for the
geocode result. If OSM does not have a polygon for this place, you can
instead get features within it using the `features_from_address`
function, which geocodes the place name to a point and gets the features
within some distance of that point.
If OSM does have polygon boundaries for this place but you're not finding
it, try to vary the query string, pass in a structured query dict, or vary
the `which_result` argument to use a different geocode result. If you know
the OSM ID of the place, you can retrieve its boundary polygon using the
`geocode_to_gdf` function, then pass it to the `features_from_polygon`
function.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
query : string or dict or list
the query or queries to geocode to get place boundary polygon(s)
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
which_result : int
which geocoding result to use. if None, auto-select the first
(Multi)Polygon or raise an error if OSM doesn't return one.
buffer_dist : float
deprecated, do not use
Returns
-------
gdf : geopandas.GeoDataFrame
"""
if buffer_dist is not None:
warn(
"The buffer_dist argument has been deprecated and will be removed "
"in the v2.0.0 release. Buffer your query area directly, if desired. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
# create a GeoDataFrame with the spatial boundaries of the place(s)
if isinstance(query, (str, dict)):
# if it is a string (place name) or dict (structured place query),
# then it is a single place
gdf_place = geocoder.geocode_to_gdf(
query, which_result=which_result, buffer_dist=buffer_dist
)
elif isinstance(query, list):
# if it is a list, it contains multiple places to get
gdf_place = geocoder.geocode_to_gdf(query, buffer_dist=buffer_dist)
else: # pragma: no cover
msg = "query must be dict, string, or list of strings"
raise TypeError(msg)
# extract the geometry from the GeoDataFrame to use in API query
polygon = gdf_place["geometry"].unary_union
utils.log("Constructed place geometry polygon(s) to query API")
# create GeoDataFrame using this polygon(s) geometry
return features_from_polygon(polygon, tags)
|
(query, tags, which_result=None, buffer_dist=None)
|
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] |
52,213 |
osmnx.features
|
features_from_point
|
Create GeoDataFrame of OSM features within some distance N, S, E, W of a point.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
center_point : tuple
the (lat, lon) center point around which to get the features
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
dist : numeric
distance in meters
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_point(center_point, tags, dist=1000):
"""
Create GeoDataFrame of OSM features within some distance N, S, E, W of a point.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
center_point : tuple
the (lat, lon) center point around which to get the features
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
dist : numeric
distance in meters
Returns
-------
gdf : geopandas.GeoDataFrame
"""
# create bounding box from center point and distance in each direction
bbox = utils_geo.bbox_from_point(center_point, dist)
# convert the bounding box to a polygon
polygon = utils_geo.bbox_to_poly(bbox=bbox)
# create GeoDataFrame of features within this polygon
return features_from_polygon(polygon, tags)
|
(center_point, tags, dist=1000)
|
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] |
52,214 |
osmnx.features
|
features_from_polygon
|
Create GeoDataFrame of OSM features within boundaries of a (multi)polygon.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
polygon : shapely.geometry.Polygon or shapely.geometry.MultiPolygon
geographic boundaries to fetch features within
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_polygon(polygon, tags):
"""
Create GeoDataFrame of OSM features within boundaries of a (multi)polygon.
You can use the `settings` module to retrieve a snapshot of historical OSM
data as of a certain date, or to configure the Overpass server timeout,
memory allocation, and other custom settings.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
polygon : shapely.geometry.Polygon or shapely.geometry.MultiPolygon
geographic boundaries to fetch features within
tags : dict
Dict of tags used for finding elements in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
# verify that the geometry is valid and a Polygon/MultiPolygon
if not polygon.is_valid:
msg = "The geometry of `polygon` is invalid"
raise ValueError(msg)
if not isinstance(polygon, (Polygon, MultiPolygon)):
msg = (
"Boundaries must be a shapely Polygon or MultiPolygon. If you "
"requested features from place name, make sure your query resolves "
"to a Polygon or MultiPolygon, and not some other geometry, like a "
"Point. See OSMnx documentation for details."
)
raise TypeError(msg)
# download the data from OSM
response_jsons = _overpass._download_overpass_features(polygon, tags)
# create GeoDataFrame from the downloaded data
return _create_gdf(response_jsons, polygon, tags)
|
(polygon, tags)
|
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] |
52,215 |
osmnx.features
|
features_from_xml
|
Create a GeoDataFrame of OSM features in an OSM-formatted XML file.
Because this function creates a GeoDataFrame of features from an
OSM-formatted XML file that has already been downloaded (i.e. no query is
made to the Overpass API) the polygon and tags arguments are not required.
If they are not supplied to the function, features_from_xml() will
return features for all of the tagged elements in the file. If they are
supplied they will be used to filter the final GeoDataFrame.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
filepath : string or pathlib.Path
path to file containing OSM XML data
polygon : shapely.geometry.Polygon
optional geographic boundary to filter elements
tags : dict
optional dict of tags for filtering elements from the XML. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
encoding : string
the XML file's character encoding
Returns
-------
gdf : geopandas.GeoDataFrame
|
def features_from_xml(filepath, polygon=None, tags=None, encoding="utf-8"):
"""
Create a GeoDataFrame of OSM features in an OSM-formatted XML file.
Because this function creates a GeoDataFrame of features from an
OSM-formatted XML file that has already been downloaded (i.e. no query is
made to the Overpass API) the polygon and tags arguments are not required.
If they are not supplied to the function, features_from_xml() will
return features for all of the tagged elements in the file. If they are
supplied they will be used to filter the final GeoDataFrame.
For more details, see: https://wiki.openstreetmap.org/wiki/Map_features
Parameters
----------
filepath : string or pathlib.Path
path to file containing OSM XML data
polygon : shapely.geometry.Polygon
optional geographic boundary to filter elements
tags : dict
optional dict of tags for filtering elements from the XML. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
encoding : string
the XML file's character encoding
Returns
-------
gdf : geopandas.GeoDataFrame
"""
# transmogrify file of OSM XML data into JSON
response_jsons = [osm_xml._overpass_json_from_file(filepath, encoding)]
# create GeoDataFrame using this response JSON
return _create_gdf(response_jsons, polygon=polygon, tags=tags)
|
(filepath, polygon=None, tags=None, encoding='utf-8')
|
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] |
52,217 |
osmnx.geocoder
|
geocode
|
Geocode place names or addresses to (lat, lon) with the Nominatim API.
This geocodes the query via the Nominatim "search" endpoint.
Parameters
----------
query : string
the query string to geocode
Returns
-------
point : tuple
the (lat, lon) coordinates returned by the geocoder
|
def geocode(query):
"""
Geocode place names or addresses to (lat, lon) with the Nominatim API.
This geocodes the query via the Nominatim "search" endpoint.
Parameters
----------
query : string
the query string to geocode
Returns
-------
point : tuple
the (lat, lon) coordinates returned by the geocoder
"""
# define the parameters
params = OrderedDict()
params["format"] = "json"
params["limit"] = 1
params["dedupe"] = 0 # prevent deduping to get precise number of results
params["q"] = query
response_json = _nominatim._nominatim_request(params=params)
# if results were returned, parse lat and lon out of the result
if response_json and "lat" in response_json[0] and "lon" in response_json[0]:
lat = float(response_json[0]["lat"])
lon = float(response_json[0]["lon"])
point = (lat, lon)
utils.log(f"Geocoded {query!r} to {point}")
return point
# otherwise we got no results back
msg = f"Nominatim could not geocode query {query!r}"
raise InsufficientResponseError(msg)
|
(query)
|
[
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0.02339251898229122,
0.03208374232053757,
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0.0011707505909726024,
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] |
52,218 |
osmnx.geocoder
|
geocode_to_gdf
|
Retrieve OSM elements by place name or OSM ID with the Nominatim API.
If searching by place name, the `query` argument can be a string or
structured dict, or a list of such strings/dicts to send to the geocoder.
This uses the Nominatim "search" endpoint to geocode the place name to the
best-matching OSM element, then returns that element and its attribute
data.
You can instead query by OSM ID by passing `by_osmid=True`. This uses the
Nominatim "lookup" endpoint to retrieve the OSM element with that ID. In
this case, the function treats the `query` argument as an OSM ID (or list
of OSM IDs), which must be prepended with their types: node (N), way (W),
or relation (R) in accordance with the Nominatim API format. For example,
`query=["R2192363", "N240109189", "W427818536"]`.
If `query` is a list, then `which_result` must be either a single value or
a list with the same length as `query`. The queries you provide must be
resolvable to elements in the Nominatim database. The resulting
GeoDataFrame's geometry column contains place boundaries if they exist.
Parameters
----------
query : string or dict or list of strings/dicts
query string(s) or structured dict(s) to geocode
which_result : int
which search result to return. if None, auto-select the first
(Multi)Polygon or raise an error if OSM doesn't return one. to get
the top match regardless of geometry type, set which_result=1.
ignored if by_osmid=True.
by_osmid : bool
if True, treat query as an OSM ID lookup rather than text search
buffer_dist : float
deprecated, do not use
Returns
-------
gdf : geopandas.GeoDataFrame
a GeoDataFrame with one row for each query
|
def geocode_to_gdf(query, which_result=None, by_osmid=False, buffer_dist=None):
"""
Retrieve OSM elements by place name or OSM ID with the Nominatim API.
If searching by place name, the `query` argument can be a string or
structured dict, or a list of such strings/dicts to send to the geocoder.
This uses the Nominatim "search" endpoint to geocode the place name to the
best-matching OSM element, then returns that element and its attribute
data.
You can instead query by OSM ID by passing `by_osmid=True`. This uses the
Nominatim "lookup" endpoint to retrieve the OSM element with that ID. In
this case, the function treats the `query` argument as an OSM ID (or list
of OSM IDs), which must be prepended with their types: node (N), way (W),
or relation (R) in accordance with the Nominatim API format. For example,
`query=["R2192363", "N240109189", "W427818536"]`.
If `query` is a list, then `which_result` must be either a single value or
a list with the same length as `query`. The queries you provide must be
resolvable to elements in the Nominatim database. The resulting
GeoDataFrame's geometry column contains place boundaries if they exist.
Parameters
----------
query : string or dict or list of strings/dicts
query string(s) or structured dict(s) to geocode
which_result : int
which search result to return. if None, auto-select the first
(Multi)Polygon or raise an error if OSM doesn't return one. to get
the top match regardless of geometry type, set which_result=1.
ignored if by_osmid=True.
by_osmid : bool
if True, treat query as an OSM ID lookup rather than text search
buffer_dist : float
deprecated, do not use
Returns
-------
gdf : geopandas.GeoDataFrame
a GeoDataFrame with one row for each query
"""
if buffer_dist is not None:
warn(
"The buffer_dist argument has been deprecated and will be removed "
"in the v2.0.0 release. Buffer your results directly, if desired. "
"See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123",
FutureWarning,
stacklevel=2,
)
if not isinstance(query, (str, dict, list)): # pragma: no cover
msg = "query must be a string or dict or list"
raise TypeError(msg)
# if caller passed a list of queries but a scalar which_result value, then
# turn which_result into a list with same length as query list
if isinstance(query, list) and (isinstance(which_result, int) or which_result is None):
which_result = [which_result] * len(query)
# turn query and which_result into lists if they're not already
if not isinstance(query, list):
query = [query]
if not isinstance(which_result, list):
which_result = [which_result]
# ensure same length
if len(query) != len(which_result): # pragma: no cover
msg = "which_result length must equal query length"
raise ValueError(msg)
# ensure query type of each item
for q in query:
if not isinstance(q, (str, dict)): # pragma: no cover
msg = "each query must be a dict or a string"
raise TypeError(msg)
# geocode each query and add to GeoDataFrame as a new row
gdf = gpd.GeoDataFrame()
for q, wr in zip(query, which_result):
gdf = pd.concat([gdf, _geocode_query_to_gdf(q, wr, by_osmid)])
# reset GeoDataFrame index and set its CRS
gdf = gdf.reset_index(drop=True)
gdf = gdf.set_crs(settings.default_crs)
# if buffer_dist was passed in, project the geometry to UTM, buffer it in
# meters, then project it back to lat-lon
if buffer_dist is not None and len(gdf) > 0:
gdf_utm = projection.project_gdf(gdf)
gdf_utm["geometry"] = gdf_utm["geometry"].buffer(buffer_dist)
gdf = projection.project_gdf(gdf_utm, to_latlong=True)
utils.log(f"Buffered GeoDataFrame to {buffer_dist} meters")
utils.log(f"Created GeoDataFrame with {len(gdf)} rows from {len(query)} queries")
return gdf
|
(query, which_result=None, by_osmid=False, buffer_dist=None)
|
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] |
52,221 |
osmnx.geometries
|
geometries_from_address
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
address : string
Do not use: deprecated.
tags : dict
Do not use: deprecated.
dist : numeric
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_address(address, tags, dist=1000):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
address : string
Do not use: deprecated.
tags : dict
Do not use: deprecated.
dist : numeric
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_address(address, tags, dist)
|
(address, tags, dist=1000)
|
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] |
52,222 |
osmnx.geometries
|
geometries_from_bbox
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
north : float
Do not use: deprecated.
south : float
Do not use: deprecated.
east : float
Do not use: deprecated.
west : float
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_bbox(north, south, east, west, tags):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
north : float
Do not use: deprecated.
south : float
Do not use: deprecated.
east : float
Do not use: deprecated.
west : float
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_bbox(north, south, east, west, tags=tags)
|
(north, south, east, west, tags)
|
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] |
52,223 |
osmnx.geometries
|
geometries_from_place
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
query : string or dict or list
Do not use: deprecated.
tags : dict
Do not use: deprecated.
which_result : int
Do not use: deprecated.
buffer_dist : float
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_place(query, tags, which_result=None, buffer_dist=None):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
query : string or dict or list
Do not use: deprecated.
tags : dict
Do not use: deprecated.
which_result : int
Do not use: deprecated.
buffer_dist : float
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_place(query, tags, which_result, buffer_dist)
|
(query, tags, which_result=None, buffer_dist=None)
|
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] |
52,224 |
osmnx.geometries
|
geometries_from_point
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
center_point : tuple
Do not use: deprecated.
tags : dict
Do not use: deprecated.
dist : numeric
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_point(center_point, tags, dist=1000):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
center_point : tuple
Do not use: deprecated.
tags : dict
Do not use: deprecated.
dist : numeric
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_point(center_point, tags, dist)
|
(center_point, tags, dist=1000)
|
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] |
52,225 |
osmnx.geometries
|
geometries_from_polygon
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
polygon : shapely.geometry.Polygon or shapely.geometry.MultiPolygon
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_polygon(polygon, tags):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
polygon : shapely.geometry.Polygon or shapely.geometry.MultiPolygon
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_polygon(polygon, tags)
|
(polygon, tags)
|
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] |
52,226 |
osmnx.geometries
|
geometries_from_xml
|
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
filepath : string or pathlib.Path
Do not use: deprecated.
polygon : shapely.geometry.Polygon
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
|
def geometries_from_xml(filepath, polygon=None, tags=None):
"""
Do not use: deprecated.
The `geometries` module and `geometries_from_X` functions have been
renamed the `features` module and `features_from_X` functions. Use these
instead. The `geometries` module and functions are deprecated and will be
removed in the v2.0.0 release.
Parameters
----------
filepath : string or pathlib.Path
Do not use: deprecated.
polygon : shapely.geometry.Polygon
Do not use: deprecated.
tags : dict
Do not use: deprecated.
Returns
-------
gdf : geopandas.GeoDataFrame
"""
warn(DEP_MSG, FutureWarning, stacklevel=2)
return features.features_from_xml(filepath, polygon, tags)
|
(filepath, polygon=None, tags=None)
|
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