text stringlengths 0 1.05M | meta dict |
|---|---|
__all__ = ["get_centroids"]
import numpy as np
from .label_clusters import label_clusters
from .label_stats import label_stats
def get_centroids(image, clustparam=0):
"""
Reduces a variate/statistical/network image to a set of centroids
describing the center of each stand-alone non-zero component in the image
ANTsR function: `getCentroids`
Arguments
---------
image : ANTsImage
image from which centroids will be calculated
clustparam : integer
look at regions greater than or equal to this size
Returns
-------
ndarray
Example
-------
>>> import ants
>>> image = ants.image_read( ants.get_ants_data( "r16" ) )
>>> image = ants.threshold_image( image, 90, 120 )
>>> image = ants.label_clusters( image, 10 )
>>> cents = ants.get_centroids( image )
"""
imagedim = image.dimension
if clustparam > 0:
mypoints = label_clusters(image, clustparam, max_thresh=1e15)
if clustparam == 0:
mypoints = image.clone()
mypoints = label_stats(mypoints, mypoints)
nonzero = mypoints[["LabelValue"]] > 0
mypoints = mypoints[nonzero["LabelValue"]]
mypoints = mypoints.iloc[:, :]
x = mypoints.x
y = mypoints.y
if imagedim == 3:
z = mypoints.z
else:
z = np.zeros(mypoints.shape[0])
if imagedim == 4:
t = mypoints.t
else:
t = np.zeros(mypoints.shape[0])
centroids = np.stack([x, y, z, t]).T
return centroids
| {
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"path": "ants/utils/get_centroids.py",
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"license": "apache-2.0",
"hash": -5582113283343429000,
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"alpha_frac": 0.614,
"autogenerated": false,
"ratio": 3.51288056206089,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.962688056206089,
"avg_score": 0,
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__all__ = ["get_config", "get_defaults", "dump_defaults", "get_imagecrawler", "parse_yaml_file",
"ImageCrawlerSetupError"]
from os.path import dirname, join as path_join, realpath
from typing import Any, Dict, Optional
from nichtparasoup.core.imagecrawler import BaseImageCrawler
_SCHEMA_FILE = realpath(path_join(dirname(__file__), "schema.yaml"))
_schema = None # type: Optional[Any]
_DEFAULTS_FILE = realpath(path_join(dirname(__file__), "defaults.yaml"))
_defaults = None # type: Optional[Dict[str, Any]]
class ImageCrawlerSetupError(Exception):
def __init__(self, ic_name: str, ic_class: type, ic_config: Dict[Any, Any]) -> None: # pragma: no cover
self._name = ic_name
self._class = ic_class
self._config = ic_config
def __str__(self) -> str: # pragma: no cover
return 'Failed setup crawler {!r} of type {!r} with config {!r}'.format(self._name, self._class, self._config)
def get_imagecrawler(config_crawler: Dict[str, Any]) -> BaseImageCrawler:
from nichtparasoup.imagecrawler import get_imagecrawlers
imagecrawler_name = config_crawler['name']
imagecrawler_class = get_imagecrawlers().get_class(imagecrawler_name)
if not imagecrawler_class:
raise ValueError('unknown crawler name {!r}'.format(imagecrawler_name))
imagecrawler_config = config_crawler['config']
try:
return imagecrawler_class(**imagecrawler_config)
except Exception as e:
raise ImageCrawlerSetupError(imagecrawler_name, imagecrawler_class, imagecrawler_config) from e
def parse_yaml_file(file_path: str) -> Dict[str, Any]:
import yamale # type: ignore
global _schema
if not _schema:
_schema = yamale.make_schema(_SCHEMA_FILE, parser='ruamel')
_data = yamale.make_data(file_path, parser='ruamel')
yamale.validate(_schema, _data, strict=True)
config = _data[0][0] # type: Dict[str, Any]
config.setdefault('logging', dict())
config['logging'].setdefault('level', 'INFO')
for config_crawler in config['crawlers']:
config_crawler.setdefault("weight", 1)
config_crawler.setdefault('config', dict())
return config
def dump_defaults(file_path: str) -> None:
from shutil import copyfile
copyfile(_DEFAULTS_FILE, file_path)
def get_defaults() -> Dict[str, Any]:
global _defaults
if not _defaults:
_defaults = parse_yaml_file(_DEFAULTS_FILE)
from copy import deepcopy
return deepcopy(_defaults)
def get_config(config_file: Optional[str] = None) -> Dict[str, Any]:
if not config_file:
return get_defaults()
try:
return parse_yaml_file(config_file)
except Exception as e:
raise ValueError('invalid config file {!r}'.format(config_file)) from e
| {
"repo_name": "k4cg/nichtparasoup",
"path": "nichtparasoup/config/__init__.py",
"copies": "1",
"size": "2764",
"license": "mit",
"hash": -1896548258880934700,
"line_mean": 35.8533333333,
"line_max": 118,
"alpha_frac": 0.6751085384,
"autogenerated": false,
"ratio": 3.530012771392082,
"config_test": true,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9702621154042945,
"avg_score": 0.0005000311498272453,
"num_lines": 75
} |
__all__ = ['get_config_vars', 'get_path']
try:
# Python 2.7 or >=3.2
from sysconfig import get_config_vars, get_path
except ImportError:
from distutils.sysconfig import get_config_vars, get_python_lib
def get_path(name):
if name not in ('platlib', 'purelib'):
raise ValueError("Name must be purelib or platlib")
return get_python_lib(name=='platlib')
try:
# Python >=3.2
from tempfile import TemporaryDirectory
except ImportError:
import shutil
import tempfile
class TemporaryDirectory(object):
""""
Very simple temporary directory context manager.
Will try to delete afterward, but will also ignore OS and similar
errors on deletion.
"""
def __init__(self):
self.name = None # Handle mkdtemp raising an exception
self.name = tempfile.mkdtemp()
def __enter__(self):
return self.name
def __exit__(self, exctype, excvalue, exctrace):
try:
shutil.rmtree(self.name, True)
except OSError: #removal errors are not the only possible
pass
self.name = None
| {
"repo_name": "SurfasJones/djcmsrc3",
"path": "venv/lib/python2.7/site-packages/setuptools/py31compat.py",
"copies": "10",
"size": "1184",
"license": "mit",
"hash": -6427629324895659000,
"line_mean": 31,
"line_max": 73,
"alpha_frac": 0.6021959459,
"autogenerated": false,
"ratio": 4.369003690036901,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0035879960429942887,
"num_lines": 37
} |
__all__ = ['get_current_request']
import logging
import sys
from typing import Optional, Union
from starlette.requests import Request
from starlette.types import Receive, Scope
log = logging.getLogger(__name__)
if sys.version_info[:2] == (3, 6):
# Backport PEP 567
try:
import aiocontextvars
except ImportError:
# Do not raise an exception as the module is exported to package API
# but is still optional
log.error(
'Python 3.6 requires `aiocontextvars` package to be installed'
' to support global access to request objects'
)
try:
from contextvars import ContextVar
except ImportError:
ContextVar = None
if ContextVar:
_current_request: ContextVar[Optional[Request]] = ContextVar(
'rollbar-request-object', default=None
)
def get_current_request() -> Optional[Request]:
"""
Return current request.
Do NOT modify the returned request object.
"""
if ContextVar is None:
log.error(
'Python 3.7+ (or aiocontextvars package)'
' is required to receive current request.'
)
return None
request = _current_request.get()
if request is None:
log.error('Request is not available in the present context.')
return None
return request
def store_current_request(
request_or_scope: Union[Request, Scope], receive: Optional[Receive] = None
) -> Optional[Request]:
if ContextVar is None:
return None
if receive is None:
request = request_or_scope
else:
request = Request(request_or_scope, receive)
_current_request.set(request)
return request
def hasuser(request: Request) -> bool:
try:
return hasattr(request, 'user')
except AssertionError:
return False
| {
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"path": "rollbar/contrib/starlette/requests.py",
"copies": "1",
"size": "1825",
"license": "mit",
"hash": 7898874865958705000,
"line_mean": 22.7012987013,
"line_max": 78,
"alpha_frac": 0.6471232877,
"autogenerated": false,
"ratio": 4.176201372997712,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5323324660697711,
"avg_score": null,
"num_lines": null
} |
__all__ = ["get_day_boxoffice"]
import tushare as ts
from flask import json
TRANS = {"AvgPrice": "平均票价",
"AvpPeoPle": "场均人次",
"BoxOffice": "单日票房（万）",
"BoxOffice_Up": "环比变化 （%）",
"IRank": "排名",
"MovieDay": "上映天数",
"MovieName": "影片名",
"SumBoxOffice": "累计票房(万)",
"WomIndex": "口碑指数"
}
def get_day_boxoffice(day=None):
if day == None:
total = ts.day_boxoffice().to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
else:
try:
total = ts.day_boxoffice(day).to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {"error": True, "message": "can not get the data, format date as YYYY-M-D"}
return result
| {
"repo_name": "FinaceInfo/Chinese-box-office-info",
"path": "app/boxoffice/day_boxoffice.py",
"copies": "1",
"size": "1109",
"license": "mit",
"hash": -2256031181057465900,
"line_mean": 31.21875,
"line_max": 96,
"alpha_frac": 0.5053346266,
"autogenerated": false,
"ratio": 2.801630434782609,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.3806965061382609,
"avg_score": null,
"num_lines": null
} |
__all__ = ["get_day_cinema"]
import tushare as ts
from flask import json
TRANS = {"Attendance": "上座率",
"AvgPeople": "场均人次",
"CinemaName": "影院名称",
"RowNum": "排名",
"TodayAudienceCount": "当日观众人数",
"TodayBox": "当日票房",
"TodayShowCount": "当日场次",
"price": "场均票价（元）"
}
def get_day_cinema(day=None):
print(day)
if day == None:
try:
total = ts.day_cinema().to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {"error": "true", "message": str(e)}
else:
try:
total = ts.day_cinema(day).to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {"error": "true",
"message": "can not get the data, format date as YYYY-M-D"}
print("result")
print(result)
return result
| {
"repo_name": "FinaceInfo/Chinese-box-office-info",
"path": "app/boxoffice/day_cinema.py",
"copies": "1",
"size": "1257",
"license": "mit",
"hash": -1355990365481550600,
"line_mean": 29.4871794872,
"line_max": 81,
"alpha_frac": 0.4911690496,
"autogenerated": false,
"ratio": 3.056555269922879,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.903975058593939,
"avg_score": 0.0015947467166979362,
"num_lines": 39
} |
__all__ = ['getDpi', 'getContext', 'getPreset', 'getScreenContext']
import matplotlib as mpl
from math import sqrt
def getDpi():
return 72.27
_screenDpi = 96
_screenFontSize = 12
_goldenRatio = (1 + sqrt(5)) / 2
_presets = {
'revtex12-single': (468, 10.95, None), # Single-column revtex; 12pt.
'mnras': (240, 8, _goldenRatio), # Double-column MNRAS; default font size.
'mnras-2': (504, 8, _goldenRatio * 1.2), # Single-column MNRAS; default font size.
'thesis': (300, 8, _goldenRatio),
'thesis-wide': (426, 8, _goldenRatio)
}
def getPreset(preset):
return _presets[preset]
def getContext(returnParams=False, *args, **kwargs):
width, height, dpi, fontSize, tickFontSize = _getParams(*args, **kwargs)
rc = {}
rc['text.latex.unicode'] = True
rc['text.usetex'] = True
rc['font.family'] = 'serif'
rc['font.serif'] = ['Computer Modern']
rc['font.size'] = fontSize
rc['axes.labelsize'] = fontSize
rc['legend.fontsize']= fontSize
rc['xtick.labelsize'] = tickFontSize
rc['ytick.labelsize'] = tickFontSize
rc['axes.labelcolor'] = 'black'
rc['xtick.color'] = 'black'
rc['ytick.color'] = 'black'
rc['figure.figsize'] = width, height
rc['figure.dpi'] = dpi
if returnParams:
return rc
else:
return mpl.rc_context(rc)
def getScreenContext(pixelWidth=None, fontSize=None, aspectRatio=None, returnParams=False):
if aspectRatio is None:
aspectRatio = _goldenRatio
if fontSize is None:
fontSize = _screenFontSize
params = {
'figure.dpi': _screenDpi,
'text.usetex': False,
'mathtext.fontset': 'stixsans',
'font.family': 'Bitstream Vera Sans',
'font.size': fontSize,
'axes.labelsize': fontSize,
'legend.fontsize': fontSize,
'xtick.labelsize': fontSize * 0.8,
'ytick.labelsize': fontSize * 0.8,
}
if pixelWidth is not None:
pixelHeight = pixelWidth / aspectRatio
params['figure.figsize'] = (pixelWidth / _screenDpi, pixelHeight / _screenDpi)
if returnParams:
return params
else:
return mpl.rc_context(params)
def _getParams(preset=None, width=None, fontSize=None, aspectRatio=None, height=None):
if preset is not None:
assert preset in _presets, \
'Preset not found.'
width0, fontSize0, aspectRatio0 = _presets[preset]
else:
width0, fontSize0, aspectRatio0 = None, None, None
if width is not None:
if width <= 1:
assert width0 is not None
width *= width0
else:
width = width0
width = width if width is not None else width0
fontSize = fontSize if fontSize is not None else fontSize0
aspectRatio = aspectRatio if aspectRatio is not None else aspectRatio0
assert width is not None and fontSize is not None, \
'Column width or font size missing.'
dpi = getDpi() # One inch in points (according to TeX).
if height is None:
if aspectRatio is None:
aspectRatio = _goldenRatio
height = width / aspectRatio
width /= dpi
height /= dpi
return width, height, dpi, fontSize, fontSize * 0.8
| {
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"path": "mpltex/mpltex.py",
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"license": "mit",
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"line_mean": 28.9719626168,
"line_max": 91,
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"autogenerated": false,
"ratio": 3.4483870967741934,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.45723347113741936,
"avg_score": null,
"num_lines": null
} |
__all__ = ["GET_FIRST_CLIENT_RECT", \
"GET_LOCATION_IN_VIEW", \
"GET_PAGE_ZOOM", \
"IS_ELEMENT_CLICKABLE", \
"TOUCH_SINGLE_TAP", \
"CLEAR", \
"CLEAR_LOCAL_STORAGE", \
"CLEAR_SESSION_STORAGE", \
"CLICK", \
"EXECUTE_ASYNC_SCRIPT", \
"EXECUTE_SCRIPT", \
"EXECUTE_SQL", \
"FIND_ELEMENT", \
"FIND_ELEMENTS", \
"GET_APPCACHE_STATUS", \
"GET_ATTRIBUTE", \
"GET_EFFECTIVE_STYLE", \
"GET_IN_VIEW_LOCATION", \
"GET_LOCAL_STORAGE_ITEM", \
"GET_LOCAL_STORAGE_KEY", \
"GET_LOCAL_STORAGE_KEYS", \
"GET_LOCAL_STORAGE_SIZE", \
"GET_SESSION_STORAGE_ITEM", \
"GET_SESSION_STORAGE_KEY", \
"GET_SESSION_STORAGE_KEYS", \
"GET_SESSION_STORAGE_SIZE", \
"GET_LOCATION", \
"GET_SIZE", \
"GET_TEXT", \
"IS_DISPLAYED", \
"IS_ENABLED", \
"IS_ONLINE", \
"IS_SELECTED", \
"REMOVE_LOCAL_STORAGE_ITEM", \
"REMOVE_SESSION_STORAGE_ITEM", \
"SET_LOCAL_STORAGE_ITEM", \
"SET_SESSION_STORAGE_ITEM", \
"SUBMIT"]
GET_FIRST_CLIENT_RECT = \
"function(){return function(){var g=this;\nfunction h(a){var b=typeof a;"\
"if(\"object\"==b)if(a){if(a instanceof Array)return\"array\";if(a insta"\
"nceof Object)return b;var e=Object.prototype.toString.call(a);if(\"[obj"\
"ect Window]\"==e)return\"object\";if(\"[object Array]\"==e||\"number\"="\
"=typeof a.length&&\"undefined\"!=typeof a.splice&&\"undefined\"!=typeof"\
" a.propertyIsEnumerable&&!a.propertyIsEnumerable(\"splice\"))return\"ar"\
"ray\";if(\"[object Function]\"==e||\"undefined\"!=typeof a.call&&\"unde"\
"fined\"!=typeof a.propertyIsEnumerable&&!a.propertyIsEnumerable(\"call"\
"\"))return\"function\"}else return\"null\";else if(\"function\"==\nb&&"\
"\"undefined\"==typeof a.call)return\"object\";return b};var k;function "\
"l(a,b){this.x=void 0!==a?a:0;this.y=void 0!==b?b:0}l.prototype.toString"\
"=function(){return\"(\"+this.x+\", \"+this.y+\")\"};function m(a){retur"\
"n 9==a.nodeType?a:a.ownerDocument||a.document}function n(a){this.b=a||g"\
".document||document}function p(a){var b=a.b;a=b.body;b=b.parentWindow||"\
"b.defaultView;return new l(b.pageXOffset||a.scrollLeft,b.pageYOffset||a"\
".scrollTop)};function q(a,b,e,d){this.left=a;this.top=b;this.width=e;th"\
"is.height=d}q.prototype.toString=function(){return\"(\"+this.left+\", "\
"\"+this.top+\" - \"+this.width+\"w x \"+this.height+\"h)\"};function s("\
"a){var b;a:{b=m(a);if(b.defaultView&&b.defaultView.getComputedStyle&&(b"\
"=b.defaultView.getComputedStyle(a,null))){b=b.position||b.getPropertyVa"\
"lue(\"position\")||\"\";break a}b=\"\"}return b||(a.currentStyle?a.curr"\
"entStyle.position:null)||a.style&&a.style.position}function t(a){var b;"\
"try{b=a.getBoundingClientRect()}catch(e){return{left:0,top:0,right:0,bo"\
"ttom:0}}return b}\nfunction u(a){var b=m(a),e=s(a),d=\"fixed\"==e||\"ab"\
"solute\"==e;for(a=a.parentNode;a&&a!=b;a=a.parentNode)if(e=s(a),d=d&&\""\
"static\"==e&&a!=b.documentElement&&a!=b.body,!d&&(a.scrollWidth>a.clien"\
"tWidth||a.scrollHeight>a.clientHeight||\"fixed\"==e||\"absolute\"==e||"\
"\"relative\"==e))return a;return null};function v(a){var b=a.getClientR"\
"ects();if(0==b.length)throw Error(\"Element does not have any client re"\
"cts\");b=b[0];if(1==a.nodeType)if(a.getBoundingClientRect)a=t(a),a=new "\
"l(a.left,a.top);else{var e=p(a?new n(m(a)):k||(k=new n));var d=m(a),z=s"\
"(a),c=new l(0,0),r=(d?m(d):document).documentElement;if(a!=r)if(a.getBo"\
"undingClientRect)a=t(a),d=p(d?new n(m(d)):k||(k=new n)),c.x=a.left+d.x,"\
"c.y=a.top+d.y;else if(d.getBoxObjectFor)a=d.getBoxObjectFor(a),d=d.getB"\
"oxObjectFor(r),c.x=a.screenX-d.screenX,c.y=a.screenY-\nd.screenY;else{v"\
"ar f=a;do{c.x+=f.offsetLeft;c.y+=f.offsetTop;f!=a&&(c.x+=f.clientLeft||"\
"0,c.y+=f.clientTop||0);if(\"fixed\"==s(f)){c.x+=d.body.scrollLeft;c.y+="\
"d.body.scrollTop;break}f=f.offsetParent}while(f&&f!=a);\"absolute\"==z&"\
"&(c.y-=d.body.offsetTop);for(f=a;(f=u(f))&&f!=d.body&&f!=r;)c.x-=f.scro"\
"llLeft,c.y-=f.scrollTop}a=new l(c.x-e.x,c.y-e.y)}else e=\"function\"==h"\
"(a.a),c=a,a.targetTouches?c=a.targetTouches[0]:e&&a.a().targetTouches&&"\
"(c=a.a().targetTouches[0]),a=new l(c.clientX,c.clientY);return new q(b."\
"left-\na.x,b.top-a.y,b.right-b.left,b.bottom-b.top)}var w=[\"_\"],x=g;w"\
"[0]in x||!x.execScript||x.execScript(\"var \"+w[0]);for(var y;w.length&"\
"&(y=w.shift());)w.length||void 0===v?x=x[y]?x[y]:x[y]={}:x[y]=v;; retur"\
"n this._.apply(null,arguments);}.apply({navigator:typeof window!=undefi"\
"ned?window.navigator:null,document:typeof window!=undefined?window.docu"\
"ment:null}, arguments);}"
GET_LOCATION_IN_VIEW = \
"function(){return function(){var k=this;\nfunction l(a){var b=typeof a;"\
"if(\"object\"==b)if(a){if(a instanceof Array)return\"array\";if(a insta"\
"nceof Object)return b;var c=Object.prototype.toString.call(a);if(\"[obj"\
"ect Window]\"==c)return\"object\";if(\"[object Array]\"==c||\"number\"="\
"=typeof a.length&&\"undefined\"!=typeof a.splice&&\"undefined\"!=typeof"\
" a.propertyIsEnumerable&&!a.propertyIsEnumerable(\"splice\"))return\"ar"\
"ray\";if(\"[object Function]\"==c||\"undefined\"!=typeof a.call&&\"unde"\
"fined\"!=typeof a.propertyIsEnumerable&&!a.propertyIsEnumerable(\"call"\
"\"))return\"function\"}else return\"null\";else if(\"function\"==\nb&&"\
"\"undefined\"==typeof a.call)return\"object\";return b};var m;function "\
"n(a,b){this.x=void 0!==a?a:0;this.y=void 0!==b?b:0}n.prototype.toString"\
"=function(){return\"(\"+this.x+\", \"+this.y+\")\"};function p(a,b){thi"\
"s.width=a;this.height=b}p.prototype.toString=function(){return\"(\"+thi"\
"s.width+\" x \"+this.height+\")\"};function q(a){return a?new r(s(a)):m"\
"||(m=new r)}function s(a){return 9==a.nodeType?a:a.ownerDocument||a.doc"\
"ument}function r(a){this.a=a||k.document||document}function t(a){a=(a.a"\
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"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function e(){}e.prototype=a.prototype;f.b="\
"a.prototype;f.prototype=new e})();\nvar h=\"unknown error\",g={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};g[13]=h;g[9]=\"unknown "\
"command\";f.prototype.toString=function(){return this.name+\": \"+this."\
"message};var k=this.navigator;var l=-1!=(k&&k.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=d||d;switch(\"local_storage\"){case "\
"\"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return l?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new f(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.clear=function(){this.a.clear()};n.prototype.size=function(){retu"\
"rn this.a.length};n.prototype.key=function(a){return this.a.key(a)};fun"\
"ction p(){var a;if(!m())throw new f(13,\"Local storage undefined\");a=n"\
"ew n(d.localStorage);for(var e=[],b=a.size(),c=0;c<b;c++)e[c]=a.a.key(c"\
");return e}var q=[\"_\"],r=this;q[0]in r||!r.execScript||r.execScript("\
"\"var \"+q[0]);for(var s;q.length&&(s=q.shift());)q.length||void 0===p?"\
"r=r[s]?r[s]:r[s]={}:r[s]=p;; return this._.apply(null,arguments);}.appl"\
"y({navigator:typeof window!=undefined?window.navigator:null,document:ty"\
"peof window!=undefined?window.document:null}, arguments);}"
GET_LOCAL_STORAGE_SIZE = \
"function(){return function(){var c=window;function d(a,g){this.code=a;t"\
"his.state=e[a]||f;this.message=g||\"\";var b=this.state.replace(/((?:^|"\
"\\s+)[a-z])/g,function(a){return a.toUpperCase().replace(/^[\\s\\xa0]+/"\
"g,\"\")}),l=b.length-5;if(0>l||b.indexOf(\"Error\",l)!=l)b+=\"Error\";t"\
"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function g(){}g.prototype=a.prototype;d.b="\
"a.prototype;d.prototype=new g})();\nvar f=\"unknown error\",e={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};e[13]=f;e[9]=\"unknown "\
"command\";d.prototype.toString=function(){return this.name+\": \"+this."\
"message};var h=this.navigator;var k=-1!=(h&&h.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=c||c;switch(\"local_storage\"){case "\
"\"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return k?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new d(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.clear=function(){this.a.clear()};n.prototype.size=function(){retu"\
"rn this.a.length};function p(){if(!m())throw new d(13,\"Local storage u"\
"ndefined\");return(new n(c.localStorage)).size()}var q=[\"_\"],r=this;q"\
"[0]in r||!r.execScript||r.execScript(\"var \"+q[0]);for(var s;q.length&"\
"&(s=q.shift());)q.length||void 0===p?r=r[s]?r[s]:r[s]={}:r[s]=p;; retur"\
"n this._.apply(null,arguments);}.apply({navigator:typeof window!=undefi"\
"ned?window.navigator:null,document:typeof window!=undefined?window.docu"\
"ment:null}, arguments);}"
GET_SESSION_STORAGE_ITEM = \
"function(){return function(){var c=window;function e(a,d){this.code=a;t"\
"his.state=f[a]||g;this.message=d||\"\";var b=this.state.replace(/((?:^|"\
"\\s+)[a-z])/g,function(a){return a.toUpperCase().replace(/^[\\s\\xa0]+/"\
"g,\"\")}),l=b.length-5;if(0>l||b.indexOf(\"Error\",l)!=l)b+=\"Error\";t"\
"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function d(){}d.prototype=a.prototype;e.b="\
"a.prototype;e.prototype=new d})();\nvar g=\"unknown error\",f={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};f[13]=g;f[9]=\"unknown "\
"command\";e.prototype.toString=function(){return this.name+\": \"+this."\
"message};var h=this.navigator;var k=-1!=(h&&h.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=c||c;switch(\"session_storage\"){case"\
" \"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return k?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new e(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.getItem=function(a){return this.a.getItem(a)};n.prototype.clear=f"\
"unction(){this.a.clear()};function p(a){var d;if(m())d=new n(c.sessionS"\
"torage);else throw new e(13,\"Session storage undefined\");return d.get"\
"Item(a)}var q=[\"_\"],r=this;q[0]in r||!r.execScript||r.execScript(\"va"\
"r \"+q[0]);for(var s;q.length&&(s=q.shift());)q.length||void 0===p?r=r["\
"s]?r[s]:r[s]={}:r[s]=p;; return this._.apply(null,arguments);}.apply({n"\
"avigator:typeof window!=undefined?window.navigator:null,document:typeof"\
" window!=undefined?window.document:null}, arguments);}"
GET_SESSION_STORAGE_KEY = \
"function(){return function(){var c=window;function e(a,d){this.code=a;t"\
"his.state=f[a]||g;this.message=d||\"\";var b=this.state.replace(/((?:^|"\
"\\s+)[a-z])/g,function(a){return a.toUpperCase().replace(/^[\\s\\xa0]+/"\
"g,\"\")}),l=b.length-5;if(0>l||b.indexOf(\"Error\",l)!=l)b+=\"Error\";t"\
"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function d(){}d.prototype=a.prototype;e.b="\
"a.prototype;e.prototype=new d})();\nvar g=\"unknown error\",f={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};f[13]=g;f[9]=\"unknown "\
"command\";e.prototype.toString=function(){return this.name+\": \"+this."\
"message};var h=this.navigator;var k=-1!=(h&&h.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=c||c;switch(\"session_storage\"){case"\
" \"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return k?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new e(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.clear=function(){this.a.clear()};n.prototype.key=function(a){retu"\
"rn this.a.key(a)};function p(a){var d;if(m())d=new n(c.sessionStorage);"\
"else throw new e(13,\"Session storage undefined\");return d.key(a)}var "\
"q=[\"_\"],r=this;q[0]in r||!r.execScript||r.execScript(\"var \"+q[0]);f"\
"or(var s;q.length&&(s=q.shift());)q.length||void 0===p?r=r[s]?r[s]:r[s]"\
"={}:r[s]=p;; return this._.apply(null,arguments);}.apply({navigator:typ"\
"eof window!=undefined?window.navigator:null,document:typeof window!=und"\
"efined?window.document:null}, arguments);}"
GET_SESSION_STORAGE_KEYS = \
"function(){return function(){var d=window;function f(a,e){this.code=a;t"\
"his.state=g[a]||h;this.message=e||\"\";var b=this.state.replace(/((?:^|"\
"\\s+)[a-z])/g,function(a){return a.toUpperCase().replace(/^[\\s\\xa0]+/"\
"g,\"\")}),c=b.length-5;if(0>c||b.indexOf(\"Error\",c)!=c)b+=\"Error\";t"\
"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function e(){}e.prototype=a.prototype;f.b="\
"a.prototype;f.prototype=new e})();\nvar h=\"unknown error\",g={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};g[13]=h;g[9]=\"unknown "\
"command\";f.prototype.toString=function(){return this.name+\": \"+this."\
"message};var k=this.navigator;var l=-1!=(k&&k.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=d||d;switch(\"session_storage\"){case"\
" \"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return l?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new f(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.clear=function(){this.a.clear()};n.prototype.size=function(){retu"\
"rn this.a.length};n.prototype.key=function(a){return this.a.key(a)};fun"\
"ction p(){var a;if(m())a=new n(d.sessionStorage);else throw new f(13,\""\
"Session storage undefined\");for(var e=[],b=a.size(),c=0;c<b;c++)e[c]=a"\
".a.key(c);return e}var q=[\"_\"],r=this;q[0]in r||!r.execScript||r.exec"\
"Script(\"var \"+q[0]);for(var s;q.length&&(s=q.shift());)q.length||void"\
" 0===p?r=r[s]?r[s]:r[s]={}:r[s]=p;; return this._.apply(null,arguments)"\
";}.apply({navigator:typeof window!=undefined?window.navigator:null,docu"\
"ment:typeof window!=undefined?window.document:null}, arguments);}"
GET_SESSION_STORAGE_SIZE = \
"function(){return function(){var c=window;function d(a,g){this.code=a;t"\
"his.state=e[a]||f;this.message=g||\"\";var b=this.state.replace(/((?:^|"\
"\\s+)[a-z])/g,function(a){return a.toUpperCase().replace(/^[\\s\\xa0]+/"\
"g,\"\")}),l=b.length-5;if(0>l||b.indexOf(\"Error\",l)!=l)b+=\"Error\";t"\
"his.name=b;b=Error(this.message);b.name=this.name;this.stack=b.stack||"\
"\"\"}(function(){var a=Error;function g(){}g.prototype=a.prototype;d.b="\
"a.prototype;d.prototype=new g})();\nvar f=\"unknown error\",e={15:\"ele"\
"ment not selectable\",11:\"element not visible\",31:\"ime engine activa"\
"tion failed\",30:\"ime not available\",24:\"invalid cookie domain\",29:"\
"\"invalid element coordinates\",12:\"invalid element state\",32:\"inval"\
"id selector\",51:\"invalid selector\",52:\"invalid selector\",17:\"java"\
"script error\",405:\"unsupported operation\",34:\"move target out of bo"\
"unds\",27:\"no such alert\",7:\"no such element\",8:\"no such frame\",2"\
"3:\"no such window\",28:\"script timeout\",33:\"session not created\",1"\
"0:\"stale element reference\",\n0:\"success\",21:\"timeout\",25:\"unabl"\
"e to set cookie\",26:\"unexpected alert open\"};e[13]=f;e[9]=\"unknown "\
"command\";d.prototype.toString=function(){return this.name+\": \"+this."\
"message};var h=this.navigator;var k=-1!=(h&&h.platform||\"\").indexOf("\
"\"Win\")&&!1;\nfunction m(){var a=c||c;switch(\"session_storage\"){case"\
" \"appcache\":return null!=a.applicationCache;case \"browser_connection"\
"\":return null!=a.navigator&&null!=a.navigator.onLine;case \"database\""\
":return null!=a.openDatabase;case \"location\":return k?!1:null!=a.navi"\
"gator&&null!=a.navigator.geolocation;case \"local_storage\":return null"\
"!=a.localStorage;case \"session_storage\":return null!=a.sessionStorage"\
"&&null!=a.sessionStorage.clear;default:throw new d(13,\"Unsupported API"\
" identifier provided as parameter\");}}\n;function n(a){this.a=a}n.prot"\
"otype.clear=function(){this.a.clear()};n.prototype.size=function(){retu"\
"rn this.a.length};function p(){var a;if(m())a=new n(c.sessionStorage);e"\
"lse throw new d(13,\"Session storage undefined\");return a.size()}var q"\
"=[\"_\"],r=this;q[0]in r||!r.execScript||r.execScript(\"var \"+q[0]);fo"\
"r(var s;q.length&&(s=q.shift());)q.length||void 0===p?r=r[s]?r[s]:r[s]="\
"{}:r[s]=p;; return this._.apply(null,arguments);}.apply({navigator:type"\
"of window!=undefined?window.navigator:null,document:typeof window!=unde"\
"fined?window.document:null}, arguments);}"
GET_LOCATION = \
"function(){return function(){var g=this;var h;function k(a,b){this.x=vo"\
"id 0!==a?a:0;this.y=void 0!==b?b:0}k.prototype.toString=function(){retu"\
"rn\"(\"+this.x+\", \"+this.y+\")\"};function l(a){return 9==a.nodeType?"\
"a:a.ownerDocument||a.document}function m(a){this.a=a||g.document||docum"\
"ent};function n(a){var b;a:{b=l(a);if(b.defaultView&&b.defaultView.getC"\
"omputedStyle&&(b=b.defaultView.getComputedStyle(a,null))){b=b.position|"\
"|b.getPropertyValue(\"position\")||\"\";break a}b=\"\"}return b||(a.cur"\
"rentStyle?a.currentStyle.position:null)||a.style&&a.style.position}\nfu"\
"nction p(a){var b=l(a),f=n(a),c=\"fixed\"==f||\"absolute\"==f;for(a=a.p"\
"arentNode;a&&a!=b;a=a.parentNode)if(f=n(a),c=c&&\"static\"==f&&a!=b.doc"\
"umentElement&&a!=b.body,!c&&(a.scrollWidth>a.clientWidth||a.scrollHeigh"\
"t>a.clientHeight||\"fixed\"==f||\"absolute\"==f||\"relative\"==f))retur"\
"n a;return null};function q(a){var b=l(a),f=n(a),c=new k(0,0),e=(b?l(b)"\
":document).documentElement;if(a==e)return c;if(a.getBoundingClientRect)"\
"{a:{var d;try{d=a.getBoundingClientRect()}catch(u){a={left:0,top:0,righ"\
"t:0,bottom:0};break a}a=d}e=(b?new m(l(b)):h||(h=new m)).a;b=e.body;e=e"\
".parentWindow||e.defaultView;b=new k(e.pageXOffset||b.scrollLeft,e.page"\
"YOffset||b.scrollTop);c.x=a.left+b.x;c.y=a.top+b.y}else if(b.getBoxObje"\
"ctFor)a=b.getBoxObjectFor(a),b=b.getBoxObjectFor(e),c.x=a.screenX-b.scr"\
"eenX,c.y=a.screenY-b.screenY;\nelse{d=a;do{c.x+=d.offsetLeft;c.y+=d.off"\
"setTop;d!=a&&(c.x+=d.clientLeft||0,c.y+=d.clientTop||0);if(\"fixed\"==n"\
"(d)){c.x+=b.body.scrollLeft;c.y+=b.body.scrollTop;break}d=d.offsetParen"\
"t}while(d&&d!=a);\"absolute\"==f&&(c.y-=b.body.offsetTop);for(d=a;(d=p("\
"d))&&d!=b.body&&d!=e;)c.x-=d.scrollLeft,c.y-=d.scrollTop}return c}var r"\
"=[\"_\"],s=g;r[0]in s||!s.execScript||s.execScript(\"var \"+r[0]);for(v"\
"ar t;r.length&&(t=r.shift());)r.length||void 0===q?s=s[t]?s[t]:s[t]={}:"\
"s[t]=q;; return this._.apply(null,arguments);}.apply({navigator:typeof "\
"window!=undefined?window.navigator:null,document:typeof window!=undefin"\
"ed?window.document:null}, arguments);}"
GET_SIZE = \
"function(){return function(){function c(a,b){this.width=a;this.height=b"\
"}c.prototype.toString=function(){return\"(\"+this.width+\" x \"+this.he"\
"ight+\")\"};function d(a){var b=a.offsetWidth,f=a.offsetHeight;if((void"\
" 0===b||!b&&!f)&&a.getBoundingClientRect){a:{var g;try{g=a.getBoundingC"\
"lientRect()}catch(l){a={left:0,top:0,right:0,bottom:0};break a}a=g}retu"\
"rn new c(a.right-a.left,a.bottom-a.top)}return new c(b,f)};function e(a"\
"){var b;a:{b=9==a.nodeType?a:a.ownerDocument||a.document;if(b.defaultVi"\
"ew&&b.defaultView.getComputedStyle&&(b=b.defaultView.getComputedStyle(a"\
",null))){b=b.display||b.getPropertyValue(\"display\")||\"\";break a}b="\
"\"\"}if(\"none\"!=(b||(a.currentStyle?a.currentStyle.display:null)||a.s"\
"tyle&&a.style.display))return d(a);b=a.style;var f=b.display,g=b.visibi"\
"lity,l=b.position;b.visibility=\"hidden\";b.position=\"absolute\";b.dis"\
"play=\"inline\";a=d(a);b.display=f;b.position=l;b.visibility=g;return a"\
"}\nvar h=[\"_\"],k=this;h[0]in k||!k.execScript||k.execScript(\"var \"+"\
"h[0]);for(var m;h.length&&(m=h.shift());)h.length||void 0===e?k=k[m]?k["\
"m]:k[m]={}:k[m]=e;; return this._.apply(null,arguments);}.apply({naviga"\
"tor:typeof window!=undefined?window.navigator:null,document:typeof wind"\
"ow!=undefined?window.document:null}, arguments);}"
GET_TEXT = \
"function(){return function(){function f(a){return function(){return a}}"\
"var k=this;\nfunction l(a){var b=typeof a;if(\"object\"==b)if(a){if(a i"\
"nstanceof Array)return\"array\";if(a instanceof Object)return b;var c=O"\
"bject.prototype.toString.call(a);if(\"[object Window]\"==c)return\"obje"\
"ct\";if(\"[object Array]\"==c||\"number\"==typeof a.length&&\"undefined"\
"\"!=typeof a.splice&&\"undefined\"!=typeof a.propertyIsEnumerable&&!a.p"\
"ropertyIsEnumerable(\"splice\"))return\"array\";if(\"[object Function]"\
"\"==c||\"undefined\"!=typeof a.call&&\"undefined\"!=typeof a.propertyIs"\
"Enumerable&&!a.propertyIsEnumerable(\"call\"))return\"function\"}else r"\
"eturn\"null\";\nelse if(\"function\"==b&&\"undefined\"==typeof a.call)r"\
"eturn\"object\";return b}function m(a){return\"string\"==typeof a};func"\
"tion aa(a){var b=a.length-1;return 0<=b&&a.indexOf(\" \",b)==b}function"\
" ba(a){return String(a).replace(/\\-([a-z])/g,function(a,c){return c.to"\
"UpperCase()})};var ca=Array.prototype;function p(a,b){for(var c=a.lengt"\
"h,d=m(a)?a.split(\"\"):a,e=0;e<c;e++)e in d&&b.call(void 0,d[e],e,a)}fu"\
"nction da(a,b){if(a.reduce)return a.reduce(b,\"\");var c=\"\";p(a,funct"\
"ion(d,e){c=b.call(void 0,c,d,e,a)});return c}function ea(a,b){for(var c"\
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__all__ = ['get', 'get_pinyin', 'get_initial']
import os
# init pinyin dict
pinyin_dict = {}
dat = os.path.join(os.path.dirname(__file__), "Mandarin.dat")
with open(dat) as f:
for line in f:
k, v = line.strip().split('\t')
pinyin_dict[k] = v.lower().split(" ")[0][:-1]
from ._compat import u
def _pinyin_generator(chars):
"""Generate pinyin for chars, if char is not chinese character,
itself will be returned.
Chars must be unicode list.
"""
for char in chars:
key = "%X" % ord(char)
yield pinyin_dict.get(key, char)
def get(s, delimiter=''):
"""Return pinyin of string, the string must be unicode
"""
return delimiter.join(_pinyin_generator(u(s)))
def get_pinyin(s):
"""This function is only for backward compatibility, use `get` instead.
"""
import warnings
warnings.warn('Deprecated, use `get` instead.')
return get(s)
def get_initial(s, delimiter=' '):
"""Return the 1st char of pinyin of string, the string must be unicode
"""
return delimiter.join([p[0] for p in _pinyin_generator(u(s))])
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__all__ = ['getMainBranch', 'a2z', 'z2a', 'readHlist', 'SimulationAnalysis', \
'TargetHalo', 'getDistance', 'iter_grouped_subhalos_indices']
import os
import re
import math
import gzip
from itertools import izip
from urllib import urlretrieve
from collections import deque
import numpy as np
_islistlike = lambda l: hasattr(l, '__iter__')
a2z = lambda a: 1./a - 1.
z2a = lambda z: 1./(1.+z)
def getMainBranch(iterable, get_num_prog):
item = iter(iterable)
q = deque([(item.next(), True)])
X = []
while len(q):
i, i_mb = q.popleft()
X.append(i_mb)
n = get_num_prog(i)
prog_mb = [i_mb] + [False]*(n-1) if n else []
q.extend([(item.next(), mb) for mb in prog_mb])
return np.array(X)
class BaseParseFields():
def __init__(self, header, fields=None):
if len(header)==0:
if all([isinstance(f, int) for f in fields]):
self._usecols = fields
self._formats = [float]*len(fields)
self._names = ['f%d'%f for f in fields]
else:
raise ValueError('header is empty, so fields must be a list '\
'of int.')
else:
header_s = map(self._name_strip, header)
if fields is None or len(fields)==0:
self._names = header
names_s = header_s
self._usecols = range(len(names_s))
else:
if not _islistlike(fields):
fields = [fields]
self._names = [header[f] if isinstance(f, int) else str(f) \
for f in fields]
names_s = map(self._name_strip, self._names)
wrong_fields = filter(bool, [str(f) if s not in header_s \
else '' for s, f in zip(names_s, fields)])
if len(wrong_fields):
raise ValueError('The following field(s) are not available'\
': %s.\nAvailable fields: %s.'%(\
', '.join(wrong_fields), ', '.join(header)))
self._usecols = map(header_s.index, names_s)
self._formats = map(self._get_format, names_s)
def parse_line(self, l):
items = l.split()
return tuple([c(items[i]) for i, c in \
zip(self._usecols, self._formats)])
def pack(self, X):
return np.array(X, np.dtype({'names':self._names, \
'formats':self._formats}))
def _name_strip(self, s):
return self._re_name_strip.sub('', s).lower()
def _get_format(self, s):
return float if self._re_formats.search(s) is None else int
_re_name_strip = re.compile('\W|_')
_re_formats = re.compile('^phantom$|^mmp$|id$|^num|num$')
class BaseDirectory:
def __init__(self, dir_path='.'):
self.dir_path = os.path.expanduser(dir_path)
#get file_index
files = os.listdir(self.dir_path)
matches = filter(lambda m: m is not None, \
map(self._re_filename.match, files))
if len(matches) == 0:
raise ValueError('cannot find matching files in this directory: %s.'%(self.dir_path))
indices = np.array(map(self._get_file_index, matches))
s = indices.argsort()
self.files = [matches[i].group() for i in s]
self.file_indices = indices[s]
#get header and header_info
header_info_list = []
with open('%s/%s'%(self.dir_path, self.files[0]), 'r') as f:
for l in f:
if l[0] == '#':
header_info_list.append(l)
else:
break
if len(header_info_list):
self.header_info = ''.join(header_info_list)
self.header = [self._re_header_remove.sub('', s) for s in \
header_info_list[0][1:].split()]
else:
self.header_info = ''
self.header = []
self._ParseFields = self._Class_ParseFields(self.header, \
self._default_fields)
def _load(self, index, exact_index=False, additional_fields=[]):
p = self._get_ParseFields(additional_fields)
fn = '%s/%s'%(self.dir_path, self.get_filename(index, exact_index))
with open(fn, 'r') as f:
l = '#'
while l[0] == '#':
try:
l = f.next()
except StopIteration:
return p.pack([])
X = [p.parse_line(l)]
for l in f:
X.append(p.parse_line(l))
return p.pack(X)
def _get_file_index(self, match):
return match.group()
def get_filename(self, index, exact_index=False):
if exact_index:
i = self.file_indices.searchsorted(index)
if self.file_indices[i] != index:
raise ValueError('Cannot find the exact index %s.'%(str(index)))
else:
i = np.argmin(np.fabs(self.file_indices - index))
return self.files[i]
def _get_ParseFields(self, additional_fields):
if not _islistlike(additional_fields) or len(additional_fields)==0:
return self._ParseFields
else:
return self._Class_ParseFields(self.header, \
self._default_fields + list(additional_fields))
_re_filename = re.compile('.+')
_re_header_remove = re.compile('')
_Class_ParseFields = BaseParseFields
_default_fields = []
load = _load
class HlistsDir(BaseDirectory):
_re_filename = re.compile('^hlist_(\d+\.\d+).list$')
_re_header_remove = re.compile('\(\d+\)$')
_default_fields = ['id', 'upid', 'mvir', 'rvir', 'rs', 'x', 'y', 'z', \
'vmax', 'vpeak']
def _get_file_index(self, match):
return math.log10(float(match.groups()[0]))
def load(self, z=0, exact_index=False, additional_fields=[]):
return self._load(math.log10(z2a(z)), exact_index, additional_fields)
class RockstarDir(BaseDirectory):
_re_filename = re.compile('^out_(\d+).list$')
_re_header_remove = re.compile('')
_default_fields = ['id', 'mvir', 'rvir', 'rs', 'x', 'y', 'z', 'vmax']
def _get_file_index(self, match):
fn = '%s/%s'%(self.dir_path, match.group())
with open(fn, 'r') as f:
for l in f:
if l.startswith('#a '):
break
else:
raise ValueError('Cannot find the scale factor in this file %s'\
%(fn))
return math.log10(float(l.split()[-1]))
def load(self, z=0, exact_index=False, additional_fields=[]):
return self._load(math.log10(z2a(z)), exact_index, additional_fields)
class TreesDir(BaseDirectory):
_re_filename = re.compile('^tree_\d+_\d+_\d+.dat$')
_re_header_remove = re.compile('\(\d+\)$')
_default_fields = ['scale', 'id', 'num_prog', 'upid', 'mvir', 'rvir', \
'rs', 'x', 'y', 'z', 'vmax']
def load(self, tree_root_id, additional_fields=[]):
p = self._get_ParseFields(additional_fields)
tree_root_id_str = str(tree_root_id)
location_file = self.dir_path + '/locations.dat'
if os.path.isfile(location_file):
with open(location_file, 'r') as f:
f.readline()
for l in f:
items = l.split()
if items[0] == tree_root_id_str:
break
else:
raise ValueError("Cannot find this tree_root_id: %d."%(\
tree_root_id))
tree_file = '%s/%s'%(self.dir_path, items[-1])
with open(tree_file, 'r') as f:
f.seek(int(items[2]))
X = []
for l in f:
if l[0] == '#': break
X.append(p.parse_line(l))
else:
for fn in self.files:
tree_file = '%s/%s'%(self.dir_path, fn)
with open(tree_file, 'r') as f:
l = '#'
while l[0] == '#':
try:
l = f.next()
except StopIteration:
raise ValueError("Cannot find this tree_root_id: %d."%(\
tree_root_id))
num_trees = int(l)
for l in f:
if l[0] == '#' and l.split()[-1] == tree_root_id_str:
break #found tree_root_id
else:
continue #not in this file, check the next one
X = []
for l in f:
if l[0] == '#': break
X.append(p.parse_line(l))
break #because tree_root_id has found
else:
raise ValueError("Cannot find this tree_root_id: %d."%(\
tree_root_id))
return p.pack(X)
def readHlist(hlist, fields=None, buffering=100000000):
"""
Read the given fields of a hlist file (also works for tree_*.dat and out_*.list) as a numpy record array.
Parameters
----------
hlist : str or file obj
The path to the file (can be an URL) or a file object.
fields : str, int, array_like, optional
The desired fields. It can be a list of string or int. If fields is None (default), return all the fields listed in the header.
Returns
-------
arr : ndarray
A numpy record array contains the data of the desired fields.
Example
-------
>>> h = readHlist('hlist_1.00000.list', ['id', 'mvir', 'upid'])
>>> h.dtype
dtype([('id', '<i8'), ('mvir', '<f8'), ('upid', '<i8')])
>>> mass_of_hosts = h['mvir'][(h['upid'] == -1)]
>>> largest_halo_id = h['id'][h['mvir'].argmax()]
>>> mass_of_subs_of_largest_halo = h['mvir'][(h['upid'] == largest_halo_id)]
"""
if hasattr(hlist, 'read'):
f = hlist
else:
if re.match(r'(s?ftp|https?)://', hlist, re.I):
hlist = urlretrieve(hlist)[0]
if hlist.endswith('.gz'):
f = gzip.open(hlist, 'r')
else:
f = open(hlist, 'r', int(buffering))
try:
l = f.next()
header = l[1:].split()
header = [re.sub('\(\d+\)$', '', s) for s in header]
p = BaseParseFields(header, fields)
while l[0] == '#':
try:
l = f.next()
except StopIteration:
return p.pack([])
X = [p.parse_line(l)]
for l in f:
X.append(p.parse_line(l))
finally:
if not hasattr(hlist, 'read'):
f.close()
return p.pack(X)
def readHlistToSqlite3(db, table_name, hlist, fields=None, unique_id=True):
"""
Read the given fields of a hlist file (also works for tree_*.dat and out_*.list) and save it to a sqlite3 database.
Parameters
----------
db : sqlite3.Cursor
A sqlite3.Cursor object.
hlist : str
The path to the file.
fields : str, int, array_like, optional
The desired fields. It can be a list of string or int. If fields is None (default), return all the fields listed in the header.
Returns
-------
db : sqlite3.Cursor
The same cursor object that was given as input.
"""
with open(hlist, 'r') as f:
l = f.next()
header = l[1:].split()
header = [re.sub('\(\d+\)$', '', s) for s in header]
p = BaseParseFields(header, fields)
db_cols = map(lambda s: re.sub(r'\W+', '_', s), \
map(lambda s: re.sub(r'^\W+|\W+$', '', s), p._names))
db_create_stmt = 'create table if not exists %s (%s)'%(table_name, \
','.join(['%s %s%s'%(name, 'int' if (fmt is int) else 'real', \
' unique' if (name == 'id' and unique_id) else '') \
for name, fmt in zip(db_cols, p._formats)]))
db_insert_stmt = 'insert or replace into %s values (%s)'%(table_name, \
','.join(['?']*len(p._names)))
empty_file = False
while l[0] == '#':
try:
l = f.next()
except StopIteration:
empty_file = True
db.execute(db_create_stmt)
if not empty_file:
db.execute(db_insert_stmt, p.parse_line(l))
for l in f:
db.execute(db_insert_stmt, p.parse_line(l))
db.commit()
return db
class SimulationAnalysis:
def __init__(self, hlists_dir=None, trees_dir=None, rockstar_dir=None):
self._directories = {}
if hlists_dir is not None:
self.set_hlists_dir(hlists_dir)
if trees_dir is not None:
self.set_trees_dir(trees_dir)
if rockstar_dir is not None:
self.set_rockstar_dir(rockstar_dir)
if len(self._directories) == 0:
raise ValueError('Please specify at least one directory.')
def set_trees_dir(self, trees_dir):
self._directories['trees'] = TreesDir(trees_dir)
self._trees = {}
self._main_branches = {}
def set_rockstar_dir(self, rockstar_dir):
self._directories['olists'] = RockstarDir(rockstar_dir)
self._olists = {}
def set_hlists_dir(self, hlists_dir):
self._directories['hlists'] = HlistsDir(hlists_dir)
self._hlists = {}
def load_tree(self, tree_root_id=-1, npy_file=None, additional_fields=[]):
if 'trees' not in self._directories:
raise ValueError('You must set trees_dir before using this function.')
if npy_file is not None and os.path.isfile(npy_file):
data = np.load(npy_file)
if tree_root_id < 0:
tree_root_id = data['id'][0]
elif tree_root_id != data['id'][0]:
raise ValueError('tree_root_id does not match.')
self._trees[tree_root_id] = data
elif tree_root_id not in self._trees:
self._trees[tree_root_id] = \
self._directories['trees'].load(tree_root_id, \
additional_fields=additional_fields)
if npy_file is not None and not os.path.isfile(npy_file):
np.save(npy_file, self._trees[tree_root_id])
return self._trees[tree_root_id]
def load_main_branch(self, tree_root_id=-1, npy_file=None, keep_tree=False, \
additional_fields=[]):
if 'trees' not in self._directories:
raise ValueError('You must set trees_dir before using this function.')
if npy_file is not None and os.path.isfile(npy_file):
data = np.load(npy_file)
if tree_root_id < 0:
tree_root_id = data['id'][0]
elif tree_root_id != data['id'][0]:
raise ValueError('tree_root_id does not match.')
self._main_branches[tree_root_id] = data
elif tree_root_id not in self._main_branches:
t = self._directories['trees'].load(tree_root_id, \
additional_fields=additional_fields)
mb = getMainBranch(t, lambda s: s['num_prog'])
if keep_tree:
self._trees[tree_root_id] = t
self._main_branches[tree_root_id] = t[mb]
if npy_file is not None and not os.path.isfile(npy_file):
np.save(npy_file, self._main_branches[tree_root_id])
return self._main_branches[tree_root_id]
def _choose_hlists_or_olists(self, use_rockstar=False):
if 'hlists' not in self._directories and \
'olists' not in self._directories:
raise ValueError('You must set hlists_dir and/or rockstar_dir'\
'before using this function.')
elif 'olists' not in self._directories:
if use_rockstar:
print "Warning: ignore use_rockstar"
return self._directories['hlists'], self._hlists
elif use_rockstar or 'hlists' not in self._directories:
return self._directories['olists'], self._olists
else:
return self._directories['hlists'], self._hlists
def load_halos(self, z=0, npy_file=None, use_rockstar=False, \
additional_fields=[]):
d, s = self._choose_hlists_or_olists(use_rockstar)
fn = d.get_filename(math.log10(z2a(z)))
if npy_file is not None and os.path.isfile(npy_file):
data = np.load(npy_file)
s[fn] = data
elif fn not in s:
s[fn] = d.load(z, additional_fields=additional_fields)
if npy_file is not None and not os.path.isfile(npy_file):
np.save(npy_file, s[fn])
return s[fn]
def del_tree(self, tree_root_id):
if tree_root_id in self._trees:
del self._trees[tree_root_id]
def del_main_branch(self, tree_root_id):
if tree_root_id in self._main_branches:
del self._main_branches[tree_root_id]
def del_halos(self, z, use_rockstar=False):
d, s = self._choose_hlists_or_olists(use_rockstar)
fn = d.get_filename(math.log10(z2a(z)))
if fn in s:
del s[fn]
def clear_trees(self):
self._trees = {}
def clear_main_branches(self):
self._main_branches = {}
def clear_halos(self):
self._olists = {}
self._hlists = {}
class TargetHalo:
def __init__(self, target, halos, box_size=-1):
self.target = target
try:
self.target_id = target['id']
except KeyError:
pass
self.halos = halos
self.dists = np.zeros(len(halos), float)
self.box_size = box_size
half_box_size = 0.5*box_size
for ax in 'xyz':
d = halos[ax] - target[ax]
if box_size > 0:
d[(d > half_box_size)] -= box_size
d[(d < -half_box_size)] += box_size
self.dists += d*d
self.dists = np.sqrt(self.dists)
def getDistance(target, halos, box_size=-1):
t = TargetHalo(target, halos, box_size)
return t.dists
def iter_grouped_subhalos_indices(host_ids, sub_pids):
s = sub_pids.argsort()
k = np.where(sub_pids[s[1:]] != sub_pids[s[:-1]])[0]
k += 1
k = np.vstack((np.insert(k, 0, 0), np.append(k, len(s)))).T
d = np.searchsorted(sub_pids[s[k[:,0]]], host_ids)
for j, host_id in izip(d, host_ids):
if j < len(s) and sub_pids[s[k[j,0]]] == host_id:
yield s[slice(*k[j])]
else:
yield np.array([], dtype=int)
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"path": "helpers/SimulationAnalysis.py",
"copies": "1",
"size": "18602",
"license": "mit",
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"line_mean": 36.2785571142,
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"autogenerated": false,
"ratio": 3.5724985596312657,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9547878128857612,
"avg_score": 0.008396831014730826,
"num_lines": 499
} |
__all__ = ["get_month_boxoffice"]
import tushare as ts
from flask import json
TRANS = {"Irank": "排名",
"MovieName": '电影名称',
"WomIndex": '口碑指数',
"avgboxoffice": '平均票价',
"avgshowcount": "场均人次",
"box_pro": '月度占比',
"boxoffice": "单月票房(万)",
"days": "月内天数",
"releaseTime": "上映日期"
}
def get_month_boxoffice(day=None):
if day == None:
total = ts.month_boxoffice().to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
else:
try:
total = ts.month_boxoffice(day).to_csv().split()
head = [TRANS.get(i) for i in total[0].split(",")]
body = [line.split(",") for line in total[1:]]
result = {"head": head, "body": body}
except Exception as e:
result = {"error": True, "message": "can not get the data, format date as YYYY-M-D"}
return result
| {
"repo_name": "FinaceInfo/Chinese-box-office-info",
"path": "app/boxoffice/month_boxoffice.py",
"copies": "1",
"size": "1101",
"license": "mit",
"hash": -8501913709367740000,
"line_mean": 30.2424242424,
"line_max": 96,
"alpha_frac": 0.5101842871,
"autogenerated": false,
"ratio": 2.9711815561959654,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.8972228073848918,
"avg_score": 0.0018275538894095594,
"num_lines": 33
} |
__all__ = ['getMUSICregion']
from SimulationAnalysis import readHlist
from readGadgetSnapshot import readGadgetSnapshot
from findLagrangianVolume import findLagrangianVolume
_fmt = lambda a: ', '.join(map(str, a))
def getMUSICregion(target_id, rvir_mult, hlist, snapshot_prefix, ic_prefix,\
edges_file=None):
halos = readHlist(hlist, ['id', 'rvir', 'x', 'y', 'z'])
target = halos[(halos['id'] == target_id)][0]
c = [target[ax] for ax in 'xyz']
r = rvir_mult * target['rvir'] * 1.e-3
header = readGadgetSnapshot(snapshot_prefix+'.0')
box_size = header.BoxSize
rec_cor, rec_len = findLagrangianVolume(c, r, \
snapshot_prefix, ic_prefix, edges_file, rec_only=True)
print 'region = box'
print 'ref_offset =', _fmt(rec_cor/box_size)
print 'ref_extent =', _fmt(rec_len/box_size)
def main():
from sys import argv
from json import load
with open(argv[1], 'r') as fp:
d = load(fp)
getMUSICregion(**d)
if __name__ == '__main__':
main()
| {
"repo_name": "manodeep/yymao-helpers",
"path": "helpers/getMUSICregion.py",
"copies": "1",
"size": "1047",
"license": "mit",
"hash": -431601617417047700,
"line_mean": 31.71875,
"line_max": 76,
"alpha_frac": 0.6160458453,
"autogenerated": false,
"ratio": 2.982905982905983,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.8998332753648008,
"avg_score": 0.020123814911594762,
"num_lines": 32
} |
__all__ = ["get_netloc_port", "requote_uri", "timeout_manager"]
from urllib.parse import quote
from functools import wraps
from anyio import fail_after
from .errors import RequestTimeout
async def timeout_manager(timeout, coro, *args):
try:
async with fail_after(timeout):
return await coro(*args)
except TimeoutError as e:
raise RequestTimeout from e
def get_netloc_port(parsed_url):
port = parsed_url.port
if not port:
if parsed_url.scheme == "https":
port = "443"
else:
port = "80"
return parsed_url.hostname, str(port)
# The unreserved URI characters (RFC 3986)
UNRESERVED_SET = (
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" + "0123456789-._~"
)
def unquote_unreserved(uri):
"""Un-escape any percent-escape sequences in a URI that are unreserved
characters. This leaves all reserved, illegal and non-ASCII bytes encoded.
:rtype: str
"""
parts = uri.split("%")
for i in range(1, len(parts)):
h = parts[i][0:2]
if len(h) == 2 and h.isalnum():
try:
c = chr(int(h, 16))
except ValueError:
raise ValueError("Invalid percent-escape sequence: '%s'" % h)
if c in UNRESERVED_SET:
parts[i] = c + parts[i][2:]
else:
parts[i] = "%" + parts[i]
else:
parts[i] = "%" + parts[i]
return "".join(parts)
def requote_uri(uri):
"""Re-quote the given URI.
This function passes the given URI through an unquote/quote cycle to
ensure that it is fully and consistently quoted.
:rtype: str
"""
safe_with_percent = "!#$%&'()*+,/:;=?@[]~"
safe_without_percent = "!#$&'()*+,/:;=?@[]~"
try:
# Unquote only the unreserved characters
# Then quote only illegal characters (do not quote reserved,
# unreserved, or '%')
return quote(unquote_unreserved(uri), safe=safe_with_percent)
except ValueError:
# We couldn't unquote the given URI, so let's try quoting it, but
# there may be unquoted '%'s in the URI. We need to make sure they're
# properly quoted so they do not cause issues elsewhere.
return quote(uri, safe=safe_without_percent)
def processor(gen):
@wraps(gen)
def wrapper(*args, **kwargs):
g = gen(*args, **kwargs)
next(g)
return g
return wrapper
| {
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"path": "asks/utils.py",
"copies": "1",
"size": "2460",
"license": "mit",
"hash": 7855551274579279000,
"line_mean": 27.6046511628,
"line_max": 78,
"alpha_frac": 0.5922764228,
"autogenerated": false,
"ratio": 3.813953488372093,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9906229911172093,
"avg_score": 0,
"num_lines": 86
} |
__all__ = ['getSDSSid']
import re
from urllib import urlopen
_re1 = re.compile('http://skyserver\.sdss3\.org/dr8/en/tools/explore/obj\.asp\?(ra=[+-]?\d+\.\d+)&(dec=[+-]?\d+\.\d+)')
_re2 = re.compile('<td align=\'center\' width=\'33%\' class=\'t\'>(\d+)</td>')
_url1 = 'http://www.nsatlas.org/getAtlas.html?search=nsaid&nsaID=%s&submit_form=Submit'
_url2 = 'http://skyserver.sdss3.org/dr10/en/tools/quicklook/quickobj.aspx?%s&%s'
def getSDSSid(nsa_id):
i = str(nsa_id)
error_msg = '#Cannot find SDSS id for ' + i
m = _re1.search(urlopen(_url1%i).read())
if m is None:
return error_msg
m = _re2.search(urlopen(_url2%m.groups()).read())
if m is None:
return error_msg
return m.groups()[0]
def main():
from argparse import ArgumentParser
parser = ArgumentParser(description='Query SDSS website for object ID, given NSA ID.')
parser.add_argument('id', type=int, nargs='*', help='List of NSA IDs')
parser.add_argument('-f', nargs=2, help='Catalog containing NSA IDs in column X')
args = parser.parse_args()
ids = map(str, args.id)
if args.f is not None:
try:
i = int(args.f[1]) - 1
with open(args.f[0]) as f:
for l in f:
ids.append(l.split()[i])
except OSError:
parser.error("It seems the file %s cannot be access."%args.f[0])
except ValueError:
parser.error("It seems the column %s is not correct."%args.f[1])
except IndexError:
parser.error("It seems the column %s is not correct."%args.f[1])
for i in ids:
print getSDSSid(i)
if __name__ == "__main__":
main()
| {
"repo_name": "manodeep/yymao-helpers",
"path": "helpers/getSDSSid.py",
"copies": "1",
"size": "1685",
"license": "mit",
"hash": -2529281338476514300,
"line_mean": 34.1041666667,
"line_max": 123,
"alpha_frac": 0.5863501484,
"autogenerated": false,
"ratio": 3.0748175182481754,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9111210331834366,
"avg_score": 0.009991466962761724,
"num_lines": 48
} |
__all__ = ['getSnapshotEdges', 'getParticlesWithinSphere']
import os
import numpy as np
from fast3tree import fast3tree
from readGadgetSnapshot import readGadgetSnapshot
def getSnapshotEdges(snapshot_prefix, output_file=None, single_type=-1, lgadget=False):
print "[Info] Calculating the edges of each snapshot file..."
num_files = readGadgetSnapshot('{0}.0'.format(snapshot_prefix)).num_files
edges = np.zeros((num_files, 6))
for i, x in enumerate(edges):
__, pos = readGadgetSnapshot('{0}.{1}'.format(snapshot_prefix, i), \
read_pos=True, single_type=single_type, lgadget=lgadget)
if len(pos):
x[:3] = pos.min(axis=0)
x[3:] = pos.max(axis=0)
else:
x[:3] = np.inf
x[3:] = -np.inf
if output_file is not None:
np.save(output_file, edges)
return edges
def _yield_periodic_points(center, radius, box_size):
cc = np.array(center)
flag = (cc-radius < 0).astype(int) - (cc+radius >= box_size).astype(int)
cp = cc + flag*box_size
a = range(len(cc))
for j in xrange(1 << len(cc)):
for i in a:
if j >> i & 1 == 0:
cc[i] = center[i]
elif flag[i]:
cc[i] = cp[i]
else:
break
else:
yield cc
def _check_within(c, r, edges):
return all((c+r >= edges[:3]) & (c-r <= edges[3:]))
def _find_intersected_regions(center, radius, box_size, edges):
num_subregions = len(edges)
region_list = []
for ci in _yield_periodic_points(center, radius, box_size):
region_list.extend(np.where([_check_within(ci, radius, edges[i]) \
for i in xrange(num_subregions)])[0])
region_list = list(set(region_list))
region_list.sort()
return region_list
_valid_names = ('r', 'v', 'vr', 'x', 'y', 'z', 'vx', 'vy', 'vz', 'id')
_pos_names = ('r', 'vr', 'x', 'y', 'z')
def _is_string_like(obj):
'Return True if *obj* looks like a string'
try:
obj + ''
except:
return False
return True
def getParticlesWithinSphere(center, radius, snapshot_prefix, \
snapshot_edges=None, output_dtype=np.dtype([('r', float)]), \
vel_center=(0,0,0), single_type=-1, lgadget=False):
#check output fields
output_names = output_dtype.names
if not all((x in _valid_names for x in output_names)):
raise ValueError('Unknown names in output_dtype.')
if not any((x in output_names for x in _valid_names)):
raise ValueError('You do not need any output??')
need_pos = any((x in _pos_names for x in output_names))
need_vel = any((x.startswith('v') for x in output_names))
need_id = ('id' in output_names)
#load one header to get box_size and num_files
header = readGadgetSnapshot('{0}.0'.format(snapshot_prefix))
box_size = header.BoxSize
half_box_size = header.BoxSize * 0.5
num_files = header.num_files
#load edges file
if _is_string_like(snapshot_edges):
if os.path.isfile(snapshot_edges):
edges = np.load(snapshot_edges)
else:
edges = getSnapshotEdges(snapshot_prefix, snapshot_edges, \
single_type=single_type, lgadget=lgadget)
elif snapshot_edges is None:
edges = getSnapshotEdges(snapshot_prefix, \
single_type=single_type, lgadget=lgadget)
else:
edges = np.asarray(snapshot_edges).reshape((num_files, 6))
#actually load particles
region_list = _find_intersected_regions(center, radius, box_size, edges)
npart = 0
for region in region_list:
snapshot_data = readGadgetSnapshot('{0}.{1}'.format(snapshot_prefix, region), \
read_pos=True, read_vel=need_vel, read_id=need_id, \
single_type=single_type, lgadget=lgadget)
s_pos = snapshot_data[1]
if need_vel: s_vel = snapshot_data[2]
if need_id: s_id = snapshot_data[-1]
with fast3tree(s_pos) as tree:
tree.set_boundaries(0, box_size)
p = tree.query_radius(center, radius, True)
if not len(p):
continue
if npart:
out.resize(npart+len(p))
else:
out = np.empty(len(p), output_dtype)
for x in ('r', 'v', 'vr'):
if x in output_names:
out[x][npart:] = 0
if need_pos or need_vel:
for i, ax in enumerate('xyz'):
vax = 'v'+ax
if need_pos:
dx = s_pos[p,i] - center[i]
dx[dx > half_box_size] -= box_size
dx[dx < -half_box_size] += box_size
if need_vel: dvx = s_vel[p,i] - vel_center[i]
if ax in output_names: out[ax][npart:] = dx
if vax in output_names: out[vax][npart:] = dvx
if 'r' in output_names: out['r'][npart:] += dx*dx
if 'v' in output_names: out['v'][npart:] += dvx*dvx
if 'vr' in output_names: out['vr'][npart:] += dx*dvx
if need_id:
out['id'][npart:] = s_id[p]
npart += len(p)
if not npart:
return np.empty(0, output_dtype)
for x in ('r', 'v'):
if x in output_names:
out[x] **= 0.5
return out
| {
"repo_name": "manodeep/yymao-helpers",
"path": "helpers/findSpheresInSnapshots.py",
"copies": "1",
"size": "5323",
"license": "mit",
"hash": -8954748401139274000,
"line_mean": 34.7248322148,
"line_max": 87,
"alpha_frac": 0.5540108961,
"autogenerated": false,
"ratio": 3.324797001873829,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9300167557060425,
"avg_score": 0.01572806818268076,
"num_lines": 149
} |
__all__ = ['get_statements', 'get_statements_for_paper',
'get_statements_by_hash', 'submit_curation']
from indra.util import clockit
from indra.statements import stmts_from_json, Complex, SelfModification, \
ActiveForm, Translocation, Conversion
from indra.sources.indra_db_rest.processor import IndraDBRestSearchProcessor, \
IndraDBRestHashProcessor, IndraDBRestPaperProcessor
from indra.sources.indra_db_rest.util import make_db_rest_request, get_url_base
@clockit
def get_statements(subject=None, object=None, agents=None, stmt_type=None,
use_exact_type=False, persist=True, simple_response=False,
*api_args, **api_kwargs):
"""Get a processor for the INDRA DB web API matching given agents and type.
There are two types of responses available. You can just get a list of
INDRA Statements, or you can get an IndraDBRestProcessor object, which allow
Statements to be loaded in a background thread, providing a sample of the
best* content available promptly in the sample_statements attribute, and
populates the statements attribute when the paged load is complete.
The latter should be used in all new code, and where convenient the prior
should be converted to use the processor, as this option may be removed in
the future.
* In the sense of having the most supporting evidence.
Parameters
----------
subject/object : str
Optionally specify the subject and/or object of the statements in
you wish to get from the database. By default, the namespace is assumed
to be HGNC gene names, however you may specify another namespace by
including `@<namespace>` at the end of the name string. For example, if
you want to specify an agent by chebi, you could use `CHEBI:6801@CHEBI`,
or if you wanted to use the HGNC id, you could use `6871@HGNC`.
agents : list[str]
A list of agents, specified in the same manner as subject and object,
but without specifying their grammatical position.
stmt_type : str
Specify the types of interactions you are interested in, as indicated
by the sub-classes of INDRA's Statements. This argument is *not* case
sensitive. If the statement class given has sub-classes
(e.g. RegulateAmount has IncreaseAmount and DecreaseAmount), then both
the class itself, and its subclasses, will be queried, by default. If
you do not want this behavior, set use_exact_type=True. Note that if
max_stmts is set, it is possible only the exact statement type will
be returned, as this is the first searched. The processor then cycles
through the types, getting a page of results for each type and adding it
to the quota, until the max number of statements is reached.
use_exact_type : bool
If stmt_type is given, and you only want to search for that specific
statement type, set this to True. Default is False.
persist : bool
Default is True. When False, if a query comes back limited (not all
results returned), just give up and pass along what was returned.
Otherwise, make further queries to get the rest of the data (which may
take some time).
simple_response : bool
If True, a simple list of statements is returned (thus block should also
be True). If block is False, only the original sample will be returned
(as though persist was False), until the statements are done loading, in
which case the rest should appear in the list. This behavior is not
encouraged. Default is False (which breaks backwards compatibility with
usage of INDRA versions from before 1/22/2019). WE ENCOURAGE ALL NEW
USE-CASES TO USE THE PROCESSOR, AS THIS FEATURE MAY BE REMOVED AT A
LATER DATE.
timeout : positive int or None
If an int, block until the work is done and statements are retrieved, or
until the timeout has expired, in which case the results so far will be
returned in the response object, and further results will be added in
a separate thread as they become available. If simple_response is True,
all statements available will be returned. Otherwise (if None), block
indefinitely until all statements are retrieved. Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 10.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
max_stmts : int or None
Select the maximum number of statements to return. When set less than
1000 the effect is much the same as setting persist to false, and will
guarantee a faster response. Default is None.
Returns
-------
processor/stmt_list : :py:class:`IndraDBRestSearchProcessor` or list
See `simple_response` for details regarding the choice. If a processor:
An instance of the IndraDBRestProcessor, which has an attribute
`statements` which will be populated when the query/queries are done.
This is the default behavior, and is encouraged in all future cases,
however a simple list of statements may be returned using the
`simple_response` option described above.
"""
return _wrap_processor(
IndraDBRestSearchProcessor(subject, object, agents, stmt_type,
use_exact_type, persist, *api_args,
**api_kwargs),
simple_response
)
@clockit
def get_statements_by_hash(hash_list, simple_response=False, *args, **kwargs):
"""Get fully formed statements from a list of hashes.
Parameters
----------
hash_list : list[int or str]
A list of statement hashes.
simple_response : bool
If True, a simple list of statements is returned (thus block should also
be True). If block is False, only the original sample will be returned
(as though persist was False), until the statements are done loading, in
which case the rest should appear in the list. This behavior is not
encouraged. Default is False (which breaks backwards compatibility with
usage of INDRA versions from before 9/19/2019). WE ENCOURAGE ALL NEW
USE-CASES TO USE THE PROCESSOR, AS THIS FEATURE MAY BE REMOVED AT A
LATER DATE.
timeout : positive int or None
If an int, return after `timeout` seconds, even if query is not done.
Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 100.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can
also help gracefully handle an unreliable connection, if you're
willing to wait. Default is 2.
Returns
-------
processor/stmt_list : :py:class:`IndraDBRestSearchProcessor` or list
See `simple_response` for details regarding the choice. If a processor:
An instance of the IndraDBRestProcessor, which has an attribute
`statements` which will be populated when the query/queries are done.
This is the default behavior, and is encouraged in all future cases,
however a simple list of statements may be returned using the
`simple_response` option described above.
"""
return _wrap_processor(
IndraDBRestHashProcessor(hash_list, *args, **kwargs),
simple_response
)
@clockit
def get_statements_for_paper(ids, simple_response=False, *args, **kwargs):
"""Get the set of raw Statements extracted from a paper given by the id.
Parameters
----------
ids : list[(<id type>, <id value>)]
A list of tuples with ids and their type. The type can be any one of
'pmid', 'pmcid', 'doi', 'pii', 'manuscript id', or 'trid', which is the
primary key id of the text references in the database.
simple_response : bool
If True, a simple list of statements is returned (thus block should also
be True). If block is False, only the original sample will be returned
(as though persist was False), until the statements are done loading, in
which case the rest should appear in the list. This behavior is not
encouraged. Default is False (which breaks backwards compatibility with
usage of INDRA versions from before 9/19/2019). WE ENCOURAGE ALL NEW
USE-CASES TO USE THE PROCESSOR, AS THIS FEATURE MAY BE REMOVED AT A
LATER DATE.
timeout : positive int or None
If an int, return after `timeout` seconds, even if query is not done.
Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 10.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
max_stmts : int or None
Select a maximum number of statements to be returned. Default is None.
Returns
-------
processor/stmt_list : :py:class:`IndraDBRestSearchProcessor` or list
See `simple_response` for details regarding the choice. If a processor:
An instance of the IndraDBRestProcessor, which has an attribute
`statements` which will be populated when the query/queries are done.
This is the default behavior, and is encouraged in all future cases,
however a simple list of statements may be returned using the
`simple_response` option described above.
"""
return _wrap_processor(IndraDBRestPaperProcessor(ids, *args, **kwargs),
simple_response)
def _wrap_processor(processor, simple_response):
if simple_response:
ret = processor.statements
else:
ret = processor
return ret
def submit_curation(hash_val, tag, curator, text=None,
source='indra_rest_client', ev_hash=None, is_test=False):
"""Submit a curation for the given statement at the relevant level.
Parameters
----------
hash_val : int
The hash corresponding to the statement.
tag : str
A very short phrase categorizing the error or type of curation,
e.g. "grounding" for a grounding error, or "correct" if you are
marking a statement as correct.
curator : str
The name or identifier for the curator.
text : str
A brief description of the problem.
source : str
The name of the access point through which the curation was performed.
The default is 'direct_client', meaning this function was used
directly. Any higher-level application should identify itself here.
ev_hash : int
A hash of the sentence and other evidence information. Elsewhere
referred to as `source_hash`.
is_test : bool
Used in testing. If True, no curation will actually be added to the
database.
"""
data = {'tag': tag, 'text': text, 'curator': curator, 'source': source,
'ev_hash': ev_hash}
url = 'curation/submit/%s' % hash_val
if is_test:
qstr = '?test'
else:
qstr = ''
return make_db_rest_request('post', url, qstr, data=data)
def get_statement_queries(stmts, fallback_ns='NAME', pick_ns_fun=None,
**params):
"""Get queries used to search based on a statement.
In addition to the stmts, you can enter any parameters standard to the
query. See https://github.com/indralab/indra_db/rest_api for a full list.
Parameters
----------
stmts : list[Statement]
A list of INDRA statements.
fallback_ns : Optional[str]
The name space to search by when an Agent in a Statement is not
grounded to one of the standardized name spaces. Typically,
searching by 'NAME' (i.e., the Agent's name) is a good option if
(1) An Agent's grounding is missing but its name is
known to be standard in one of the name spaces. In this case the
name-based lookup will yield the same result as looking up by
grounding. Example: MAP2K1(db_refs={})
(2) Any Agent that is encountered with the same name as this Agent
is never standardized, so looking up by name yields the same result
as looking up by TEXT. Example: xyz(db_refs={'TEXT': 'xyz'})
Searching by TEXT is better in other cases e.g., when the given
specific Agent is not grounded but we have other Agents with the
same TEXT that are grounded and then standardized to a different name.
Example: Erk(db_refs={'TEXT': 'Erk'}).
Default: 'NAME'
pick_ns_fun : Optional[function]
An optional user-supplied function which takes an Agent as input and
returns a string of the form value@ns where 'value' will be looked
up in namespace 'ns' to search for the given Agent.
**params : kwargs
A set of keyword arguments that are added as parameters to the
query URLs.
"""
def pick_ns(ag):
# If the Agent has grounding, in order of preference, in any of these
# name spaces then we look it up based on grounding.
for ns in ['FPLX', 'HGNC', 'UP', 'CHEBI', 'GO', 'MESH']:
if ns in ag.db_refs:
dbid = ag.db_refs[ns]
return '%s@%s' % (dbid, ns)
# Otherwise we fall back on searching by NAME or TEXT
# (or any other given name space as long as the Agent name can be
# usefully looked up in that name space).
return '%s@%s' % (ag.name, fallback_ns)
pick_ns_fun = pick_ns if not pick_ns_fun else pick_ns_fun
queries = []
url_base = get_url_base('statements/from_agents')
non_binary_statements = (Complex, SelfModification, ActiveForm,
Translocation, Conversion)
for stmt in stmts:
kwargs = {}
if not isinstance(stmt, non_binary_statements):
for pos, ag in zip(['subject', 'object'], stmt.agent_list()):
if ag is not None:
kwargs[pos] = pick_ns_fun(ag)
else:
for i, ag in enumerate(stmt.agent_list()):
if ag is not None:
kwargs['agent%d' % i] = pick_ns_fun(ag)
kwargs['type'] = stmt.__class__.__name__
kwargs.update(params)
query_str = '?' + '&'.join(['%s=%s' % (k, v) for k, v in kwargs.items()
if v is not None])
queries.append(url_base + query_str)
return queries
| {
"repo_name": "johnbachman/belpy",
"path": "indra/sources/indra_db_rest/api.py",
"copies": "1",
"size": "16246",
"license": "mit",
"hash": -8000564772349501000,
"line_mean": 48.2303030303,
"line_max": 80,
"alpha_frac": 0.6649021298,
"autogenerated": false,
"ratio": 4.275263157894737,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5440165287694737,
"avg_score": null,
"num_lines": null
} |
__all__ = ['get_valid_residue', 'activity_types',
'amino_acids', 'amino_acids_reverse',
'InvalidLocationError', 'InvalidResidueError']
import os
def get_valid_residue(residue):
"""Check if the given string represents a valid amino acid residue."""
if residue is not None and amino_acids.get(residue) is None:
res = amino_acids_reverse.get(residue.lower())
if res is None:
raise InvalidResidueError(residue)
else:
return res
return residue
# FIXME: this list could be read from the ontology or another resource file
activity_types = {'transcription', 'activity', 'catalytic', 'gtpbound',
'kinase', 'phosphatase', 'gef', 'gap'}
def _read_amino_acids():
"""Read the amino acid information from a resource file."""
this_dir = os.path.dirname(os.path.abspath(__file__))
aa_file = os.path.join(this_dir, os.pardir, 'resources', 'amino_acids.tsv')
amino_acids = {}
amino_acids_reverse = {}
with open(aa_file, 'rt') as fh:
lines = fh.readlines()
for lin in lines[1:]:
terms = lin.strip().split('\t')
key = terms[2]
val = {'full_name': terms[0],
'short_name': terms[1],
'indra_name': terms[3]}
amino_acids[key] = val
for v in val.values():
amino_acids_reverse[v] = key
return amino_acids, amino_acids_reverse
amino_acids, amino_acids_reverse = _read_amino_acids()
class InvalidResidueError(ValueError):
"""Invalid residue (amino acid) name."""
def __init__(self, name):
ValueError.__init__(self, "Invalid residue name: '%s'" % name)
class InvalidLocationError(ValueError):
"""Invalid cellular component name."""
def __init__(self, name):
ValueError.__init__(self, "Invalid location name: '%s'" % name)
| {
"repo_name": "bgyori/indra",
"path": "indra/statements/resources.py",
"copies": "4",
"size": "1860",
"license": "bsd-2-clause",
"hash": -2829514765963313700,
"line_mean": 32.2142857143,
"line_max": 79,
"alpha_frac": 0.6091397849,
"autogenerated": false,
"ratio": 3.3214285714285716,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0,
"num_lines": 56
} |
__all__ = ["GitHubHooks"]
from flask import request, Response
from .base import Hook
class GitHubHooks(Hook):
def ok(self):
return Response()
def deploy(self, app, env):
payload = request.get_json()
event = request.headers.get("X-GitHub-Event")
if event != "push":
# Gracefully ignore everything except push events
return self.ok()
default_ref = app.get_default_ref(env)
ref = payload["ref"]
if ref != f"refs/heads/{default_ref}":
return self.ok()
head_commit = payload["head_commit"]
if not head_commit:
# Deleting a branch is one case, not sure of others
return self.ok()
committer = head_commit["committer"]
# If the committer is GitHub and the action was triggered from
# the web UI, ignore it and use the author instead
if (
committer["email"] == "noreply@github.com"
and committer["username"] == "web-flow"
):
committer = head_commit["author"]
return self.client().post(
"/api/0/deploys/",
data={
"env": env,
"app": app.name,
"ref": head_commit["id"],
"user": committer["email"],
},
)
| {
"repo_name": "getsentry/freight",
"path": "freight/hooks/github.py",
"copies": "1",
"size": "1335",
"license": "apache-2.0",
"hash": 1515199527182206500,
"line_mean": 26.2448979592,
"line_max": 70,
"alpha_frac": 0.5243445693,
"autogenerated": false,
"ratio": 4.145962732919255,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5170307302219255,
"avg_score": null,
"num_lines": null
} |
__all__ = ["GithubNotifier"]
from flask import current_app
from freight import http
from freight.models import App, Task, TaskStatus
from .base import Notifier, NotifierEvent
class GithubNotifier(Notifier):
def get_options(self):
return {"repo": {"required": True}, "api_root": {"required": False}}
def send_deploy(self, deploy, task, config, event):
token = current_app.config["GITHUB_TOKEN"]
if not token:
raise ValueError("GITHUB_TOKEN is not set")
headers = {
"Accept": "application/vnd.github.v3+json",
"Authorization": f"token {token}",
}
app = App.query.get(deploy.app_id)
task = Task.query.get(deploy.task_id)
api_root = (
config.get("api_root") or current_app.config["GITHUB_API_ROOT"]
).rstrip("/")
url = f"{api_root}/repos/{config['repo']}/statuses/{task.sha}"
target_url = http.absolute_uri(
f"/deploys/{app.name}/{deploy.environment}/{deploy.number}"
)
if event == NotifierEvent.TASK_QUEUED:
state = "pending"
description = f"Freight deploy #{deploy.number} is currently queued."
elif event == NotifierEvent.TASK_STARTED:
state = "pending"
description = f"Freight deploy #{deploy.number} has started."
elif task.status == TaskStatus.failed:
state = "failure"
description = f"Freight deploy #{deploy.number} has failed."
elif task.status == TaskStatus.cancelled:
state = "error"
description = f"Freight deploy #{deploy.number} has been cancelled."
elif task.status == TaskStatus.finished:
state = "success"
description = f"Freight deploy #{deploy.number} has finished successfully."
else:
raise NotImplementedError(task.status)
payload = {
"state": state,
"target_url": target_url,
"description": description,
"context": "continuous-integration/freight/deploy",
}
http.post(url, headers=headers, json=payload)
| {
"repo_name": "getsentry/freight",
"path": "freight/notifiers/github.py",
"copies": "1",
"size": "2151",
"license": "apache-2.0",
"hash": -6391701396802450000,
"line_mean": 33.6935483871,
"line_max": 87,
"alpha_frac": 0.5908879591,
"autogenerated": false,
"ratio": 4.043233082706767,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5134121041806766,
"avg_score": null,
"num_lines": null
} |
__all__ = ["GitVcs"]
import os
from urllib.parse import urlparse
from .base import Vcs, CommandError, UnknownRevision
class GitVcs(Vcs):
binary_path = "git"
def get_default_env(self):
return {"GIT_SSH": self.ssh_connect_path}
def get_default_revision(self):
return "master"
@property
def remote_url(self):
if self.url.startswith(("ssh:", "http:", "https:")):
parsed = urlparse(self.url)
port = ":%s" % (parsed.port,) if parsed.port else ""
url = "%s://%s@%s/%s" % (
parsed.scheme,
parsed.username or self.username or "git",
parsed.hostname + port,
parsed.path.lstrip("/"),
)
else:
url = self.url
return url
def run(self, cmd, **kwargs):
cmd = [self.binary_path] + cmd
try:
return super(GitVcs, self).run(cmd, **kwargs)
except CommandError as e:
if e.stderr and "unknown revision or path" in e.stderr:
raise UnknownRevision(
cmd=e.cmd, retcode=e.retcode, stdout=e.stdout, stderr=e.stderr
)
raise
def clone(self):
self.run(["clone", "--mirror", self.remote_url, self.path])
def update(self):
# in case we have a non-mirror checkout, wipe it out
if os.path.exists(os.path.join(self.workspace.path, ".git")):
self.run(["rm", "-rf", self.workspace.path])
self.clone()
else:
self.run(["fetch", "--all", "-p"])
def checkout(self, ref, new_workspace):
self.run(
["clone", self.workspace.path, new_workspace.path], workspace=new_workspace
)
self.run(["reset", "--hard", ref], workspace=new_workspace)
def get_sha(self, ref):
return self.run(["rev-parse", ref], capture=True)
| {
"repo_name": "getsentry/freight",
"path": "freight/vcs/git.py",
"copies": "1",
"size": "1907",
"license": "apache-2.0",
"hash": 3918890006389340700,
"line_mean": 29.2698412698,
"line_max": 87,
"alpha_frac": 0.5359202937,
"autogenerated": false,
"ratio": 3.814,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.48499202937,
"avg_score": null,
"num_lines": null
} |
__all__ = ["GO", "riboseq", "rnaseq", "general", 'which', 'qtools',
'mapping', 'annotations', 'expr_db', 'conservation']
# Copyright (c) 2001-2004 Twisted Matrix Laboratories.
# See LICENSE for details.
"""
Utilities for dealing with processes.
"""
import os
def which(name, flags=os.X_OK):
"""
Copied from Twisted 8.2 implementation (http://twistedmatrix
.com/trac/browser/tags/releases/twisted-8.2.0/twisted/python/procutils.py).
Can't find it in the latest (13
.1) implementation. - Olga
Search PATH for executable files with the given name.
On newer versions of MS-Windows, the PATHEXT environment variable will be
set to the list of file extensions for files considered executable. This
will normally include things like ".EXE". This fuction will also find files
with the given name ending with any of these extensions.
On MS-Windows the only flag that has any meaning is os.F_OK. Any other
flags will be ignored.
@type name: C{str}
@param name: The name for which to search.
@type flags: C{int}
@param flags: Arguments to L{os.access}.
@rtype: C{list}
@param: A list of the full paths to files found, in the
order in which they were found.
"""
result = []
exts = filter(None, os.environ.get('PATHEXT', '').split(os.pathsep))
path = os.environ.get('PATH', None)
if path is None:
return []
for p in os.environ.get('PATH', '').split(os.pathsep):
p = os.path.join(p, name)
if os.access(p, flags):
result.append(p)
for e in exts:
pext = p + e
if os.access(pext, flags):
result.append(pext)
return result
| {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/__init__.py",
"copies": "1",
"size": "1715",
"license": "mit",
"hash": 4139222376335739400,
"line_mean": 30.7592592593,
"line_max": 79,
"alpha_frac": 0.6379008746,
"autogenerated": false,
"ratio": 3.6723768736616704,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.481027774826167,
"avg_score": null,
"num_lines": null
} |
__all__ = ['GotoLoiter', 'ArmDisarm']
import time
import geopy
import geopy.distance
from pymavlink import mavutil
from .sandbox import Sandbox
from .connection import CommandLong
from ..state import State
from ..environment import Environment
from ..valueRange import ContinuousValueRange, DiscreteValueRange
from ..configuration import Configuration
from ..command import Parameter, Command
from ..specification import Specification, Idle
ArmNormally = Specification(
'arm-normal',
"""
(and
(= $arm true)
(= _armable true)
(or (= _mode "GUIDED") (= _mode "LOITER")))
""",
'(= __armed true)')
DisarmNormally = Specification(
'disarm-normal',
'(and (= $arm false) (= _armed true))',
'(= __armed false)')
GotoLoiter = Specification(
'loiter',
'(and (= _armed true) (= _mode "LOITER"))',
"""
(and
(= __longitude $longitude)
(= __latitude $latitude)
(= __altitude $altitude)
(= __mode "LOITER"))
""")
class ArmDisarm(Command):
"""
Behaviours:
Normal: if the robot is armable and is in either its 'GUIDED' or
'LOITER' modes, the robot will become armed.
Idle: if the conditions above cannot be met, the robot will ignore the
command.
"""
name = 'arm'
parameters = [
Parameter('arm', DiscreteValueRange([True, False]))
]
specifications = [
ArmNormally,
DisarmNormally,
Idle
]
def dispatch(self,
sandbox: Sandbox,
state: State,
environment: Environment,
configuration: Configuration
) -> None:
vehicle = sandbox.connection
arm_flag = 1 if self.arm else 0
msg = vehicle.message_factory.command_long_encode(
0, 0,
mavutil.mavlink.MAV_CMD_COMPONENT_ARM_DISARM,
0, arm_flag, 0, 0, 0, 0, 0, 0)
vehicle.send_mavlink(msg)
def to_message(self) -> CommandLong:
msg = CommandLong(
0, 0,
mavutil.mavlink.MAV_CMD_COMPONENT_ARM_DISARM,
0, 1 if self.arm else 0, 0, 0, 0, 0, 0, 0)
return msg
| {
"repo_name": "squaresLab/Houston",
"path": "houston/ardu/common.py",
"copies": "1",
"size": "2209",
"license": "mit",
"hash": -4949612154999521000,
"line_mean": 24.9882352941,
"line_max": 78,
"alpha_frac": 0.5753734722,
"autogenerated": false,
"ratio": 3.8551483420593367,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.49305218142593366,
"avg_score": null,
"num_lines": null
} |
__all__ = ['GoTo']
import dronekit
import geopy.distance
from ..connection import CommandLong
from ...configuration import Configuration
from ...state import State
from ...environment import Environment
from ...command import Command, Parameter
from ...specification import Idle, Specification
from ...valueRange import ContinuousValueRange, DiscreteValueRange
from ..common import GotoLoiter
def timeout(a, s, e, c) -> float:
from_loc = (s.latitude, s.longitude)
to_loc = (a['latitude'], a['longitude'])
dist = geopy.distance.great_circle(from_loc, to_loc).meters
timeout = dist * c.time_per_metre_travelled
timeout += c.constant_timeout_offset
return timeout
GotoNormally = Specification(
'normal',
'(and (= _armed true) (not (= _mode "LOITER")))',
'(and (= __longitude $longitude) (= __latitude $latitude))',
timeout)
class GoTo(Command):
uid = 'ardu:rover:goto'
name = 'goto'
parameters = [
Parameter('latitude', ContinuousValueRange(-90.0, 90.0, True)),
Parameter('longitude', ContinuousValueRange(-180.0, 180.0, True))
]
specifications = [
GotoNormally,
GotoLoiter,
Idle
]
def dispatch(self,
sandbox: 'Sandbox',
state: State,
environment: Environment,
config: Configuration
) -> None:
loc = dronekit.LocationGlobalRelative(self.latitude,
self.longitude,
state.altitude)
sandbox.connection.simple_goto(loc)
def to_message(self) -> CommandLong:
return CommandLong(0,
0,
cmd_id=mavutil.MAV_CMD_NAV_WAYPOINT,
param1=2, # FIXME frame?
param5=self.latitude,
param6=self.longitude)
| {
"repo_name": "squaresLab/Houston",
"path": "houston/ardu/rover/goto.py",
"copies": "1",
"size": "1940",
"license": "mit",
"hash": 3088568571071058400,
"line_mean": 30.2903225806,
"line_max": 73,
"alpha_frac": 0.5706185567,
"autogenerated": false,
"ratio": 4.163090128755365,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5233708685455365,
"avg_score": null,
"num_lines": null
} |
__all__ = ['GoTo']
import random
import dronekit
import geopy
from pymavlink.mavutil import mavlink
from ..connection import CommandLong
from ..common import GotoLoiter
from ...connection import Message
from ...specification import Specification
from ...configuration import Configuration
from ...command import Command, Parameter
from ...state import State
from ...specification import Specification
from ...environment import Environment
from ...specification import Idle
from ...valueRange import ContinuousValueRange, DiscreteValueRange
def timeout_goto_normally(cmd: Command,
s: State,
e: Environment,
c: Configuration
) -> float:
from_loc = (s['latitude'], s['longitude'])
to_loc = (cmd['latitude'], cmd['longitude'])
dist = geopy.distance.great_circle(from_loc, to_loc).meters
timeout = dist * c.time_per_metre_travelled
timeout += c.constant_timeout_offset
return timeout
GotoNormally = Specification(
'normal',
"""
(and
(= _armed true)
(not (= _mode "LOITER"))
(> _altitude 0.3))
""",
"""
(and
(= __longitude $longitude)
(= __latitude $latitude)
(= __altitude $altitude))
""",
timeout_goto_normally)
class GoTo(Command):
uid = 'ardu:copter:goto'
name = 'goto'
parameters = [
Parameter('latitude', ContinuousValueRange(-90.0, 90.0, True)),
Parameter('longitude', ContinuousValueRange(-180.0, 180.0, True)),
Parameter('altitude', ContinuousValueRange(0.3, 100.0))
]
specifications = [
GotoNormally,
GotoLoiter,
Idle
]
@classmethod
def generate(cls, rng: random.Random) -> 'GoTo':
(lat, lon) = (-35.3632607, 149.1652351) # FIXME
heading = rng.uniform(0.0, 360.0)
dist = rng.uniform(0.0, 2.0) # FIXME
params = {}
origin = geopy.Point(latitude=lat, longitude=lon)
dist = geopy.distance.distance(meters=dist)
destination = dist.destination(origin, heading)
params['latitude'] = destination.latitude
params['longitude'] = destination.longitude
params['altitude'] = 5.0 # FIXME
command = cls(**params)
return command
def to_message(self) -> Message:
return CommandLong(target_system=0,
target_component=0,
cmd_id=mavlink.MAV_CMD_NAV_WAYPOINT,
param_5=self.latitude,
param_6=self.longitude,
param_7=self.altitude)
def dispatch(self,
sandbox: 'Sandbox',
state: State,
environment: Environment,
config: Configuration
) -> None:
loc = dronekit.LocationGlobalRelative(self.latitude,
self.longitude,
self.altitude)
sandbox.connection.simple_goto(loc)
| {
"repo_name": "squaresLab/Houston",
"path": "houston/ardu/copter/goto.py",
"copies": "1",
"size": "3090",
"license": "mit",
"hash": 7724019581158283000,
"line_mean": 30.2121212121,
"line_max": 74,
"alpha_frac": 0.5650485437,
"autogenerated": false,
"ratio": 4.238683127572016,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0,
"num_lines": 99
} |
__all__ = ["GP"]
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as optim
import logging
from scipy.linalg import cholesky, cho_solve
from copy import copy, deepcopy
from .ext import gp_c
logger = logging.getLogger("gp.gp")
DTYPE = np.float64
EPS = np.finfo(DTYPE).eps
MIN = np.log(np.exp2(DTYPE(np.finfo(DTYPE).minexp + 4)))
def memoprop(f):
"""
Memoized property.
When the property is accessed for the first time, the return value
is stored and that value is given on subsequent calls. The memoized
value can be cleared by calling 'del prop', where prop is the name
of the property.
"""
fname = f.__name__
def fget(self):
if fname not in self._memoized:
self._memoized[fname] = f(self)
return self._memoized[fname]
def fdel(self):
del self._memoized[fname]
prop = property(fget=fget, fdel=fdel, doc=f.__doc__)
return prop
class GP(object):
r"""
Gaussian Process object.
Parameters
----------
K : :class:`~gp.kernels.base.Kernel`
Kernel object
x : numpy.ndarray
:math:`n` array of input locations
y : numpy.ndarray
:math:`n` array of input observations
s : number (default=0)
Standard deviation of observation noise
"""
def __init__(self, K, x, y, s=0):
r"""
Initialize the GP.
"""
#: Kernel for the gaussian process, of type
#: :class:`~gp.kernels.base.Kernel`
self.K = K
self._x = None
self._y = None
self._s = None
self._memoized = {}
self.x = x
self.y = y
self.s = s
def __getstate__(self):
state = {}
state['K'] = self.K
state['_x'] = self._x
state['_y'] = self._y
state['_s'] = self._s
state['_memoized'] = self._memoized
return state
def __setstate__(self, state):
self.K = state['K']
self._x = state['_x']
self._y = state['_y']
self._s = state['_s']
self._memoized = state['_memoized']
def __copy__(self):
state = self.__getstate__()
cls = type(self)
gp = cls.__new__(cls)
gp.__setstate__(state)
return gp
def __deepcopy__(self, memo):
state = deepcopy(self.__getstate__(), memo)
cls = type(self)
gp = cls.__new__(cls)
gp.__setstate__(state)
return gp
def copy(self, deep=True):
"""
Create a copy of the gaussian process object.
Parameters
----------
deep : bool (default=True)
Whether to return a deep or shallow copy
Returns
-------
gp : :class:`~gp.gp.GP`
New gaussian process object
"""
if deep:
gp = deepcopy(self)
else:
gp = copy(self)
return gp
@property
def x(self):
r"""
Vector of input locations.
Returns
-------
x : numpy.ndarray
:math:`n` array, where :math:`n` is the number of
locations.
"""
return self._x
@x.setter
def x(self, val):
if np.any(val != self._x):
self._memoized = {}
self._x = np.array(val, copy=True, dtype=DTYPE)
self._x.flags.writeable = False
else: # pragma: no cover
pass
@property
def y(self):
r"""
Vector of input observations.
Returns
-------
y : numpy.ndarray
:math:`n` array, where :math:`n` is the number of
observations.
"""
return self._y
@y.setter
def y(self, val):
if np.any(val != self._y):
self._memoized = {}
self._y = np.array(val, copy=True, dtype=DTYPE)
self._y.flags.writeable = False
if self.y.shape != self.x.shape:
raise ValueError("invalid shape for y: %s" % str(self.y.shape))
else: # pragma: no cover
pass
@property
def s(self):
r"""
Standard deviation of the observation noise for the gaussian
process.
Returns
-------
s : numpy.float64
"""
return self._s
@s.setter
def s(self, val):
if val < 0:
raise ValueError("invalid value for s: %s" % val)
if val != self._s:
self._memoized = {}
self._s = DTYPE(val)
@property
def params(self):
r"""
Gaussian process parameters.
Returns
-------
params : numpy.ndarray
Consists of the kernel's parameters, `self.K.params`, and the
observation noise parameter, :math:`s`, in that order.
"""
_params = np.empty(self.K.params.size + 1)
_params[:-1] = self.K.params
_params[-1] = self._s
return _params
@params.setter
def params(self, val):
if np.any(self.params != val):
self._memoized = {}
self.K.params = val[:-1]
self.s = val[-1]
else: # pragma: no cover
pass
def get_param(self, name):
if name == 's':
return self.s
else:
return getattr(self.K, name)
def set_param(self, name, val):
if name == 's':
self.s = val
else:
p = getattr(self.K, name)
if p != val:
self._memoized = {}
self.K.set_param(name, val)
else: # pragma: no cover
pass
@memoprop
def Kxx(self):
r"""
Kernel covariance matrix :math:`\mathbf{K}_{xx}`.
Returns
-------
Kxx : numpy.ndarray
:math:`n\times n` covariance matrix
Notes
-----
The entry at index :math:`(i, j)` is defined as:
.. math:: K_{x_ix_j} = K(x_i, x_j) + s^2\delta(x_i-x_j),
where :math:`K(\cdot{})` is the kernel function, :math:`s` is the
standard deviation of the observation noise, and :math:`\delta`
is the Dirac delta function.
"""
x, s = self._x, self._s
K = self.K(x, x)
K += np.eye(x.size, dtype=DTYPE) * (s ** 2)
return K
@memoprop
def Kxx_J(self):
x = self._x
return self.K.jacobian(x, x)
@memoprop
def Kxx_H(self):
x = self._x
return self.K.hessian(x, x)
@memoprop
def Lxx(self):
r"""
Cholesky decomposition of the kernel covariance matrix.
Returns
-------
Lxx : numpy.ndarray
:math:`n\times n` lower triangular matrix
Notes
-----
The value is :math:`\mathbf{L}_{xx}`, such that
:math:`\mathbf{K}_{xx} = \mathbf{L}_{xx}\mathbf{L}_{xx}^\top`.
"""
return cholesky(self.Kxx, lower=True, overwrite_a=False, check_finite=True)
@memoprop
def inv_Kxx(self):
r"""
Inverse kernel covariance matrix, :math:`\mathbf{K}_{xx}^{-1}`.
Note that this inverse is provided mostly just for
reference. If you actually need to use it, use the Cholesky
decomposition (`self.Lxx`) instead.
Returns
-------
inv_Kxx : numpy.ndarray
:math:`n\times n` matrix
"""
inv_Lxx = np.linalg.inv(self.Lxx)
return np.dot(inv_Lxx.T, inv_Lxx)
@memoprop
def inv_Kxx_y(self):
r"""
Dot product of the inverse kernel covariance matrix and of
observation vector.
This uses scipy's cholesky solver to compute the solution.
Returns
-------
inv_Kxx_y : numpy.ndarray
:math:`n` array
Notes
-----
This is defined as :math:`\mathbf{K}_{xx}^{-1}\mathbf{y}`.
"""
inv_Kxx_y = cho_solve(
(self.Lxx, True), self._y,
overwrite_b=False, check_finite=True)
return inv_Kxx_y
@memoprop
def log_lh(self):
r"""
Marginal log likelihood.
Returns
-------
log_lh : numpy.float64
Marginal log likelihood
Notes
-----
This is the log likelihood of observations :math:`\mathbf{y}`
given locations :math:`\mathbf{x}` and kernel parameters
:math:`\theta`. It is defined by Eq. 5.8 of [RW06]_:
.. math::
\log{p(\mathbf{y} | \mathbf{x}, \mathbf{\theta})} = -\frac{1}{2}\mathbf{y}^\top \mathbf{K}_{xx}^{-1}\mathbf{y} - \frac{1}{2}\log{\left|\mathbf{K}_{xx}\right|}-\frac{d}{2}\log{2\pi},
where :math:`d` is the dimensionality of :math:`\mathbf{x}`.
"""
y = self._y
K = self.Kxx
try:
Kiy = self.inv_Kxx_y
except np.linalg.LinAlgError:
return -np.inf
return DTYPE(gp_c.log_lh(y, K, Kiy))
@memoprop
def lh(self):
r"""
Marginal likelihood.
Returns
-------
lh : numpy.float64
Marginal likelihood
Notes
-----
This is the likelihood of observations :math:`\mathbf{y}` given
locations :math:`\mathbf{x}` and kernel parameters
:math:`\theta`. It is defined as:
.. math::
p(\mathbf{y} | \mathbf{x}, \mathbf{\theta}) = \left(2\pi\right)^{-\frac{d}{2}}\left|\mathbf{K}_{xx}\right|^{-\frac{1}{2}}\exp\left(-\frac{1}{2}\mathbf{y}^\top\mathbf{K}_{xx}^{-1}\mathbf{y}\right)
where :math:`d` is the dimensionality of :math:`\mathbf{x}`.
"""
llh = self.log_lh
if llh < MIN:
return 0
else:
return np.exp(self.log_lh)
@memoprop
def dloglh_dtheta(self):
r"""
Derivative of the marginal log likelihood.
Returns
-------
dloglh_dtheta : numpy.ndarray
:math:`n_\theta`-length vector of derivatives, where
:math:`n_\theta` is the number of parameters (equivalent to
``len(self.params)``).
Notes
-----
This is a vector of first partial derivatives of the log
likelihood with respect to its parameters :math:`\theta`. It is
defined by Equation 5.9 of [RW06]_:
.. math::
\frac{\partial}{\partial\theta_i}\log{p(\mathbf{y}|\mathbf{x},\theta)}=\frac{1}{2}\mathbf{y}^\top\mathbf{K}_{xx}^{-1}\frac{\partial\mathbf{K}_{xx}}{\partial\theta_i}\mathbf{K}_{xx}^{-1}\mathbf{y}-\frac{1}{2}\mathbf{tr}\left(\mathbf{K}_{xx}^{-1}\frac{\partial\mathbf{K}_{xx}}{\partial\theta_i}\right)
"""
y = self._y
dloglh = np.empty(len(self.params))
try:
Ki = self.inv_Kxx
except np.linalg.LinAlgError:
dloglh.fill(np.nan)
return dloglh
Kj = self.Kxx_J
Kiy = self.inv_Kxx_y
gp_c.dloglh_dtheta(y, Ki, Kj, Kiy, self._s, dloglh)
return dloglh
@memoprop
def dlh_dtheta(self):
r"""
Derivative of the marginal likelihood.
Returns
-------
dlh_dtheta : numpy.ndarray
:math:`n_\theta`-length vector of derivatives, where
:math:`n_\theta` is the number of parameters (equivalent to
``len(self.params)``).
Notes
-----
This is a vector of first partial derivatives of the likelihood
with respect to its parameters :math:`\theta`.
"""
y = self._y
dlh = np.empty(len(self.params))
try:
Ki = self.inv_Kxx
except np.linalg.LinAlgError:
dlh.fill(np.nan)
return dlh
Kj = self.Kxx_J
Kiy = self.inv_Kxx_y
lh = self.lh
gp_c.dlh_dtheta(y, Ki, Kj, Kiy, self._s, lh, dlh)
return dlh
@memoprop
def d2lh_dtheta2(self):
r"""
Second derivative of the marginal likelihood.
Returns
-------
d2lh_dtheta2 : numpy.ndarray
:math:`n_\theta`-length vector of derivatives, where
:math:`n_\theta` is the number of parameters (equivalent to
``len(self.params)``).
Notes
-----
This is a matrix of second partial derivatives of the likelihood
with respect to its parameters :math:`\theta`.
"""
y = self._y
d2lh = np.empty((len(self.params), len(self.params)))
try:
Ki = self.inv_Kxx
except np.linalg.LinAlgError:
d2lh.fill(np.nan)
return d2lh
Kj = self.Kxx_J
Kh = self.Kxx_H
Kiy = self.inv_Kxx_y
lh = self.lh
dlh = self.dlh_dtheta
gp_c.d2lh_dtheta2(y, Ki, Kj, Kh, Kiy, self._s, lh, dlh, d2lh)
return d2lh
def Kxoxo(self, xo):
r"""
Kernel covariance matrix of new sample locations.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
Kxoxo : numpy.ndarray
:math:`m\times m` covariance matrix
Notes
-----
This is defined as :math:`K(\mathbf{x^*}, \mathbf{x^*})`, where
:math:`\mathbf{x^*}` are the new locations.
"""
return self.K(xo, xo)
def Kxxo(self, xo):
r"""
Kernel covariance matrix between given locations and new sample
locations.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
Kxxo : numpy.ndarray
:math:`n\times m` covariance matrix
Notes
-----
This is defined as :math:`K(\mathbf{x},\mathbf{x^*})`, where
:math:`\mathbf{x}` are the given locations and
:math:`\mathbf{x^*}` are the new sample locations.
"""
return self.K(self._x, xo)
def Kxox(self, xo):
r"""
Kernel covariance matrix between new sample locations and given
locations.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
Kxox : numpy.ndarray
:math:`m\times n` covariance matrix
Notes
-----
This is defined as :math:`K(\mathbf{x^*},\mathbf{x})`, where
:math:`\mathbf{x^*}` are the new sample locations and
:math:`\mathbf{x}` are the given locations
"""
return self.K(xo, self._x)
def mean(self, xo):
r"""
Predictive mean of the gaussian process.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
mean : numpy.ndarray
:math:`m` array of predictive means
Notes
-----
This is defined by Equation 2.23 of [RW06]_:
.. math::
\mathbf{m}(\mathbf{x^*})=K(\mathbf{x^*}, \mathbf{x})\mathbf{K}_{xx}^{-1}\mathbf{y}
"""
return np.dot(self.Kxox(xo), self.inv_Kxx_y)
def cov(self, xo):
r"""
Predictive covariance of the gaussian process.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
cov : numpy.ndarray
:math:`m\times m` array of predictive covariances
Notes
-----
This is defined by Eq. 2.24 of [RW06]_:
.. math::
\mathbf{C}=K(\mathbf{x^*}, \mathbf{x^*}) - K(\mathbf{x^*}, \mathbf{x})\mathbf{K}_{xx}^{-1}K(\mathbf{x}, \mathbf{x^*})
"""
Kxoxo = self.Kxoxo(xo)
Kxox = self.Kxox(xo)
Kxxo = self.Kxxo(xo)
return Kxoxo - np.dot(Kxox, np.dot(self.inv_Kxx, Kxxo))
def dm_dtheta(self, xo):
r"""
Derivative of the mean of the gaussian process with respect to
its parameters, and evaluated at `xo`.
Parameters
----------
xo : numpy.ndarray
:math:`m` array of new sample locations
Returns
-------
dm_dtheta : numpy.ndarray
:math:`n_p\times m` array, where :math:`n_p` is the
number of parameters (see `params`).
Notes
-----
The analytic form is:
.. math::
\frac{\partial}{\partial \theta_i}m(\mathbf{x^*})=\frac{\partial K(\mathbf{x^*}, \mathbf{x})}{\partial \theta_i}\mathbf{K}_{xx}^{-1}\mathbf{y} - K(\mathbf{x^*}, \mathbf{x})\mathbf{K}_{xx}^{-1}\frac{\partial \mathbf{K}_{xx}}{\partial \theta_i}\mathbf{K}_{xx}^{-1}\mathbf{y}
"""
y = self._y
Ki = self.inv_Kxx
Kj = self.Kxx_J
Kjxo = self.K.jacobian(xo, self._x)
Kxox = self.Kxox(xo)
dm = np.empty((len(self.params), xo.size))
gp_c.dm_dtheta(y, Ki, Kj, Kjxo, Kxox, self._s, dm)
return dm
def plot(self, ax=None, xlim=None, color='k', markercolor='r'):
"""
Plot the predictive mean and variance of the gaussian process.
Parameters
----------
ax : `matplotlib.pyplot.axes.Axes` (optional)
The axes on which to draw the graph. Defaults to
``plt.gca()`` if not given.
xlim : (lower x limit, upper x limit) (optional)
The limits of the x-axis. Defaults to the minimum and
maximum of `x` if not given.
color : str (optional)
The line color to use. The default is 'k' (black).
markercolor : str (optional)
The marker color to use. The default is 'r' (red).
"""
x, y = self._x, self._y
if ax is None:
ax = plt.gca()
if xlim is None:
xlim = (x.min(), x.max())
X = np.linspace(xlim[0], xlim[1], 1000)
mean = self.mean(X)
cov = self.cov(X)
std = np.sqrt(np.diag(cov))
upper = mean + std
lower = mean - std
ax.fill_between(X, lower, upper, color=color, alpha=0.3)
ax.plot(X, mean, lw=2, color=color)
ax.plot(x, y, 'o', ms=5, color=markercolor)
ax.set_xlim(*xlim)
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__all__ = ['GroundingMapper', 'load_grounding_map', 'default_grounding_map',
'default_agent_map', 'default_ignores', 'default_misgrounding_map',
'default_mapper', 'gm']
import os
import csv
import json
import logging
from copy import deepcopy
from indra.statements import Agent
from indra.databases import hgnc_client
from indra.util import read_unicode_csv
from indra.preassembler.grounding_mapper.gilda import get_gilda_models
from indra.ontology.standardize import standardize_db_refs, \
standardize_agent_name
from .disambiguate import adeft_disambiguators, DisambManager
logger = logging.getLogger(__name__)
class GroundingMapper(object):
"""Maps grounding of INDRA Agents based on a given grounding map.
Each parameter, if not provided will result in loading the corresponding
built-in grounding resource. To explicitly avoid loading the default,
pass in an empty data structure as the given parameter, e.g., ignores=[].
Parameters
----------
grounding_map : Optional[dict]
The grounding map, a dictionary mapping strings (entity names) to
a dictionary of database identifiers.
agent_map : Optional[dict]
A dictionary mapping strings to grounded INDRA Agents with given state.
ignores : Optional[list]
A list of entity strings that, if encountered will result in the
corresponding Statement being discarded.
misgrounding_map : Optional[dict]
A mapping dict similar to the grounding map which maps entity strings
to a given grounding which is known to be incorrect and should be
removed if encountered (making the remaining Agent ungrounded).
use_adeft : Optional[bool]
If True, Adeft will be attempted to be used for disambiguation of
acronyms. Default: True
gilda_mode : Optional[str]
If None, Gilda will not be used at all. If 'web', the GILDA_URL
setting from the config file or as an environmental variable
is assumed to be the web service endpoint through which Gilda is used.
If 'local', we assume that the gilda Python package is installed
and will be used.
"""
def __init__(self, grounding_map=None, agent_map=None, ignores=None,
misgrounding_map=None, use_adeft=True, gilda_mode=None):
self.grounding_map = grounding_map if grounding_map is not None \
else default_grounding_map
self.check_grounding_map(self.grounding_map)
self.agent_map = agent_map if agent_map is not None \
else default_agent_map
self.ignores = set(ignores) if ignores else default_ignores
self.misgrounding_map = misgrounding_map if misgrounding_map \
else default_misgrounding_map
self.use_adeft = use_adeft
self.disamb_manager = DisambManager()
self.gilda_mode = gilda_mode
self._gilda_models = None
@property
def gilda_models(self):
if self._gilda_models is None:
self._gilda_models = get_gilda_models(self.gilda_mode) \
if self.gilda_mode else []
return self._gilda_models
@gilda_models.setter
def gilda_models(self, models):
self._gilda_models = models
@staticmethod
def check_grounding_map(gm):
"""Run sanity checks on the grounding map, raise error if needed."""
for key, refs in gm.items():
if not refs:
continue
if 'HGNC' in refs and \
hgnc_client.get_hgnc_name(refs['HGNC']) is None:
raise ValueError('HGNC:%s for key %s in the grounding map is '
'not a valid ID' % (refs['HGNC'], key))
def map_stmts(self, stmts, do_rename=True):
"""Return a new list of statements whose agents have been mapped
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
The statements whose agents need mapping
do_rename: Optional[bool]
If True, the Agent name is updated based on the mapped grounding.
If do_rename is True the priority for setting the name is
FamPlex ID, HGNC symbol, then the gene name
from Uniprot. Default: True
Returns
-------
mapped_stmts : list of :py:class:`indra.statements.Statement`
A list of statements given by mapping the agents from each
statement in the input list
"""
# Make a copy of the stmts
mapped_stmts = []
num_skipped = 0
# Iterate over the statements
for stmt in stmts:
mapped_stmt = self.map_agents_for_stmt(stmt, do_rename)
# Check if we should skip the statement
if mapped_stmt is not None:
mapped_stmts.append(mapped_stmt)
else:
num_skipped += 1
logger.info('%s statements filtered out' % num_skipped)
return mapped_stmts
def map_agents_for_stmt(self, stmt, do_rename=True):
"""Return a new Statement whose agents have been grounding mapped.
Parameters
----------
stmt : :py:class:`indra.statements.Statement`
The Statement whose agents need mapping.
do_rename: Optional[bool]
If True, the Agent name is updated based on the mapped grounding.
If do_rename is True the priority for setting the name is
FamPlex ID, HGNC symbol, then the gene name
from Uniprot. Default: True
Returns
-------
mapped_stmt : :py:class:`indra.statements.Statement`
The mapped Statement.
"""
mapped_stmt = deepcopy(stmt)
# Iterate over the agents
# Update agents directly participating in the statement
agent_list = mapped_stmt.agent_list()
for idx, agent in enumerate(agent_list):
# If the agent is None, we do nothing
if agent is None:
continue
# If the agent's TEXT is in the ignores list, we return None to
# then filter out the Statement
agent_txts = {agent.db_refs[t] for t in {'TEXT', 'TEXT_NORM'}
if t in agent.db_refs}
if agent_txts and agent_txts & set(self.ignores):
return None
# Check if an adeft model exists for agent text
adeft_success = False
if self.use_adeft and agent_txts and agent_txts & \
set(adeft_disambiguators):
try:
# Us the longest match for disambiguation
txt_for_adeft = sorted(agent_txts &
set(adeft_disambiguators),
key=lambda x: len(x))[-1]
adeft_success = self.disamb_manager.\
run_adeft_disambiguation(mapped_stmt, agent, idx,
txt_for_adeft)
except Exception as e:
logger.error('There was an error during Adeft'
' disambiguation of %s.' % str(agent_txts))
logger.error(e)
gilda_success = False
# Gilda is not used if agent text is in the grounding map
if not adeft_success and self.gilda_mode and \
not agent_txts & set(self.grounding_map) and \
agent_txts & set(self.gilda_models):
try:
# Us the longest match for disambiguation
txt_for_gilda = sorted(agent_txts & set(self.gilda_models),
key=lambda x: len(x))[-1]
gilda_success = self.disamb_manager.\
run_gilda_disambiguation(mapped_stmt, agent, idx,
txt_for_gilda,
mode=self.gilda_mode)
except Exception as e:
logger.error('There was an error during Gilda'
' disambiguation of %s.' % str(agent_txts))
logger.error(e)
# If Adeft and Gilda were not used or didn't succeed, we do
# grounding mapping
new_agent = self.map_agent(agent, do_rename) \
if not (adeft_success or gilda_success) else agent
# If the old agent had bound conditions, but the new agent does
# not, copy the bound conditions over
if new_agent is not None and len(new_agent.bound_conditions) == 0:
new_agent.bound_conditions = agent.bound_conditions
agent_list[idx] = new_agent
mapped_stmt.set_agent_list(agent_list)
# Update agents in the bound conditions
for agent in agent_list:
if agent is not None:
for bc in agent.bound_conditions:
bc.agent = self.map_agent(bc.agent, do_rename)
if not bc.agent:
# Skip the entire statement if the agent maps to None
# in the grounding map
return None
return mapped_stmt
def map_agent(self, agent, do_rename):
"""Return the given Agent with its grounding mapped.
This function grounds a single agent. It returns the new Agent object
(which might be a different object if we load a new agent state
from json) or the same object otherwise.
Parameters
----------
agent : :py:class:`indra.statements.Agent`
The Agent to map.
do_rename: bool
If True, the Agent name is updated based on the mapped grounding.
If do_rename is True the priority for setting the name is
FamPlex ID, HGNC symbol, then the gene name
from Uniprot.
Returns
-------
grounded_agent : :py:class:`indra.statements.Agent`
The grounded Agent.
"""
# We always standardize DB refs as a functionality in the
# GroundingMapper. If a new module is implemented which is
# responsible for standardizing grounding, this can be removed.
agent.db_refs = self.standardize_db_refs(agent.db_refs)
# If there is no TEXT available, we can return immediately since we
# can't do mapping
agent_txts = sorted({agent.db_refs[t] for t in {'TEXT', 'TEXT_NORM'}
# Note that get here will correctly handle both
# a non-existent entry and a None entry which
# sometimes appears
if agent.db_refs.get(t)}, key=lambda x: len(x),
reverse=True)
if not agent_txts:
# We still do the name standardization here
if do_rename:
self.standardize_agent_name(agent, standardize_refs=False)
return agent
# 1. Check if there is a full agent mapping and apply if there is
for agent_text in agent_txts:
if agent_text in self.agent_map:
mapped_to_agent = \
Agent._from_json(self.agent_map[agent_text]['agent'])
return mapped_to_agent
# 2. Look agent text up in the grounding map
for agent_text in agent_txts:
if agent_text in self.grounding_map:
self.update_agent_db_refs(agent, self.grounding_map[agent_text],
do_rename)
return agent
# 3. Look agent text up in the misgrounding map
for agent_text in agent_txts:
if agent_text in self.misgrounding_map:
self.remove_agent_db_refs(agent,
self.misgrounding_map[agent_text])
# This happens when there is an Agent text but it is not in the
# grounding map. We still do the name standardization here.
if do_rename:
self.standardize_agent_name(agent, standardize_refs=False)
# Otherwise just return
return agent
def update_agent_db_refs(self, agent, db_refs, do_rename=True):
"""Update db_refs of agent using the grounding map
If the grounding map is missing one of the HGNC symbol or Uniprot ID,
attempts to reconstruct one from the other.
Parameters
----------
agent : :py:class:`indra.statements.Agent`
The agent whose db_refs will be updated
db_refs : dict
The db_refs so set for the agent.
do_rename: Optional[bool]
If True, the Agent name is updated based on the mapped grounding.
If do_rename is True the priority for setting the name is
FamPlex ID, HGNC symbol, then the gene name
from Uniprot. Default: True
"""
# Standardize the IDs in the db_refs dict and set it as the Agent's
# db_refs
txt = agent.db_refs.get('TEXT')
agent.db_refs = self.standardize_db_refs(deepcopy(db_refs))
if txt:
agent.db_refs['TEXT'] = txt
# Finally, if renaming is needed we standardize the Agent's name
if do_rename:
self.standardize_agent_name(agent, standardize_refs=False)
def remove_agent_db_refs(self, agent, db_refs):
# Standardize the IDs in the db_refs dict and set it as the Agent's
# db_refs
standard_refs = self.standardize_db_refs(deepcopy(db_refs))
# If there is any overlap between the Agent's db_refs and the db_refs
# that are to be eliminated, we consider the Agent's db_refs to be
# invalid and remove them. We then reset the Agent's name to
# its TEXT value if available.
preserve_refs = {k: agent.db_refs[k] for k in {'TEXT', 'TEXT_NORM'}
if k in agent.db_refs}
if set(standard_refs.items()) & set(agent.db_refs.items()):
agent.db_refs = preserve_refs
if 'TEXT_NORM' in agent.db_refs:
agent.name = agent.db_refs['TEXT_NORM']
elif 'TEXT' in agent.db_refs:
agent.name = agent.db_refs['TEXT']
@staticmethod
def standardize_db_refs(db_refs):
"""Return a standardized db refs dict for a given db refs dict.
Parameters
----------
db_refs : dict
A dict of db refs that may not be standardized, i.e., may be
missing an available UP ID corresponding to an existing HGNC ID.
Returns
-------
dict
The db_refs dict with standardized entries.
"""
return standardize_db_refs(db_refs)
@staticmethod
def standardize_agent_name(agent, standardize_refs=True):
"""Standardize the name of an Agent based on grounding information.
If an agent contains a FamPlex grounding, the FamPlex ID is used as a
name. Otherwise if it contains a Uniprot ID, an attempt is made to find
the associated HGNC gene name. If one can be found it is used as the
agent name and the associated HGNC ID is added as an entry to the
db_refs. Similarly, CHEBI, MESH and GO IDs are used in this order of
priority to assign a standardized name to the Agent. If no relevant
IDs are found, the name is not changed.
Parameters
----------
agent : indra.statements.Agent
An INDRA Agent whose name attribute should be standardized based
on grounding information.
standardize_refs : Optional[bool]
If True, this function assumes that the Agent's db_refs need to
be standardized, e.g., HGNC mapped to UP.
Default: True
"""
return standardize_agent_name(agent,
standardize_refs=standardize_refs)
@staticmethod
def rename_agents(stmts):
"""Return a list of mapped statements with updated agent names.
Creates a new list of statements without modifying the original list.
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
List of statements whose Agents need their names updated.
Returns
-------
mapped_stmts : list of :py:class:`indra.statements.Statement`
A new list of Statements with updated Agent names
"""
# Make a copy of the stmts
mapped_stmts = deepcopy(stmts)
# Iterate over the statements
for _, stmt in enumerate(mapped_stmts):
# Iterate over the agents
for agent in stmt.agent_list():
GroundingMapper.standardize_agent_name(agent, True)
return mapped_stmts
# TODO: handle the cases when there is more than one entry for the same
# key (e.g., ROS, ER)
def load_grounding_map(grounding_map_path, lineterminator='\r\n',
hgnc_symbols=True):
"""Return a grounding map dictionary loaded from a csv file.
In the file pointed to by grounding_map_path, the number of name_space ID
pairs can vary per row and commas are
used to pad out entries containing fewer than the maximum amount of
name spaces appearing in the file. Lines should be terminated with \r\n
both a carriage return and a new line by default.
Optionally, one can specify another csv file (pointed to by ignore_path)
containing agent texts that are degenerate and should be filtered out.
It is important to note that this function assumes that the mapping file
entries for the HGNC key are symbols not IDs. These symbols are converted
to IDs upon loading here.
Parameters
----------
grounding_map_path : str
Path to csv file containing grounding map information. Rows of the file
should be of the form <agent_text>,<name_space_1>,<ID_1>,...
<name_space_n>,<ID_n>
lineterminator : Optional[str]
Line terminator used in input csv file. Default: \r\n
hgnc_symbols : Optional[bool]
Set to True if the grounding map file contains HGNC symbols rather than
IDs. In this case, the entries are replaced by IDs. Default: True
Returns
-------
g_map : dict
The grounding map constructed from the given files.
"""
gmap = {}
map_rows = read_unicode_csv(grounding_map_path, delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
lineterminator=lineterminator)
for row in map_rows:
txt = row[0]
keys = [entry for entry in row[1::2] if entry]
values = [entry for entry in row[2::2] if entry]
if not keys or not values:
logger.warning('Missing grounding entries for %s, skipping.' % txt)
continue
if len(keys) != len(values):
logger.warning('Mismatched keys and values in row %s, skipping.' %
str(row))
continue
gmap[txt] = dict(zip(keys, values))
if hgnc_symbols:
gmap = replace_hgnc_symbols(gmap)
return gmap
def replace_hgnc_symbols(gmap):
"""Replace HGNC symbols with IDs in a grounding map."""
for txt, mapped_refs in deepcopy(gmap).items():
hgnc_sym = mapped_refs.get('HGNC')
if hgnc_sym:
hgnc_id = hgnc_client.get_hgnc_id(hgnc_sym)
# Override the HGNC symbol entry from the grounding
# map with an HGNC ID
if hgnc_id:
mapped_refs['HGNC'] = hgnc_id
else:
logger.error('No HGNC ID corresponding to gene '
'symbol %s in grounding map.' % hgnc_sym)
# Remove the HGNC symbol in this case
mapped_refs.pop('HGNC')
# In case the only grounding was eliminated, we remove the entry
# completely
if mapped_refs:
gmap[txt] = mapped_refs
return gmap
def _get_resource_path(*suffixes):
return os.path.join(os.path.dirname(__file__), os.pardir, os.pardir,
'resources', *suffixes)
def _load_default_grounding_map():
default_grounding_map_path = \
_get_resource_path('grounding', 'grounding_map.csv')
gmap = load_grounding_map(default_grounding_map_path, hgnc_symbols=True)
return gmap
def _load_default_misgrounding_map():
default_misgrounding_map_path = \
_get_resource_path('grounding', 'misgrounding_map.csv')
gmap = load_grounding_map(default_misgrounding_map_path, hgnc_symbols=False)
return gmap
def _load_default_agent_map():
default_agent_grounding_path = \
_get_resource_path('grounding', 'agents.json')
with open(default_agent_grounding_path, 'r') as fh:
agent_map = json.load(fh)
return agent_map
def _load_default_ignores():
default_ignore_path = _get_resource_path('grounding', 'ignore.csv')
with open(default_ignore_path, 'r') as fh:
ignores = [l.strip() for l in fh.readlines()]
return ignores
default_grounding_map = _load_default_grounding_map()
gm = default_grounding_map # For backwards compatibility, redundant
default_misgrounding_map = _load_default_misgrounding_map()
default_agent_map = _load_default_agent_map()
default_ignores = _load_default_ignores()
default_mapper = GroundingMapper(default_grounding_map,
agent_map=default_agent_map,
ignores=default_ignores,
misgrounding_map=default_misgrounding_map)
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.00008357809776249492,
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} |
__all__ = ['gt', 'lt', 'gte', 'lte', 'Condition']
class Condition(object):
"""This is a base class to represent a SQL filtering condition. These
can be used in a Query.filter clause to implement various equality
statements. For example, take the following query:
Select('person').filter(age=30)
This is great and all, but it will only filter on the exact age. A
mechanism was needed for implementing other checks. Django handles this
by doing special checks on the **kwargs that get passed into the
filter, and the user can append text to the variable name to change
the comparison. For example, instead of doing "age=30", if you wanted
to do a greater than comparison, you would do "age__gt=30". This is
fine, but it doesn't allow the end user to easily create their own
comparison operators. I found this to be a problem for non-standard
database servers.
Conrad implements these comparisons as classes. This allows you to
subclass the "Condition" class to create your own SQL comparison. A
Condition has a statement, a variable, and an operator. When the Query
generates its SQL statement, it will insert the "statement" property
from any Conditions included in the filters. It will then include the
value of the "variable" property in the arguments to the adapter. By
default, the "statement" property is {{name}} {operator} {{placeholder}}
where operator is just the class' operator property. The name and
placeholder variables are escaped, and inserted by the Query itself.
The name is the kwarg that was assigned, and the placeholder is the
query's placeholder value.
So, for example, let's use this in a Select:
Select('person').filter(age=gt(30))
This filter will return all 'person' rows where `age` > 30. In the
above description of the "statement", {{name}} is "age" and
{{placeholder}} is probably "?". In the gt() condition class below,
the operator is defined as ">". So that's how the Select query can
generate a SQL query of "SELECT * FROM person WHERE age > 30".
"""
# Override the operator in subclasses for simple condition modification
operator = '='
def __init__(self, variable):
self.variable = variable
@property
def statement(self):
# Override this property for more extreme conditions
return '{{name}} {operator} {{placeholder}}'.format(
operator=self.operator)
def __repr__(self):
return self.statement
class GreaterThan(Condition):
operator = '>'
class LessThan(Condition):
operator = '<'
class GreaterThanOrEqualTo(Condition):
operator = '>='
class LessThanOrEqualTo(Condition):
operator = '<='
gt = GreaterThan
lt = LessThan
gte = GreaterThanOrEqualTo
lte = LessThanOrEqualTo
| {
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"quality_score": 1,
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__all__ = ["GUID"]
from sqlalchemy.types import TypeDecorator, CHAR
from sqlalchemy.dialects.postgresql import UUID
import uuid
class GUID(TypeDecorator):
"""
Platform-independent GUID type.
Uses Postgresql's UUID type, otherwise uses
CHAR(32), storing as stringified hex values.
"""
impl = CHAR
def load_dialect_impl(self, dialect):
if dialect.name == "postgresql":
return dialect.type_descriptor(UUID())
else:
return dialect.type_descriptor(CHAR(32))
def process_bind_param(self, value, dialect):
if value is None:
return value
elif dialect.name == "postgresql":
return str(value)
else:
if not isinstance(value, uuid.UUID):
return "%.32x" % uuid.UUID(value)
else:
# hexstring
return "%.32x" % value
def process_result_value(self, value, dialect):
if value is None:
return value
else:
return uuid.UUID(value)
@classmethod
def default_value(cls):
return uuid.uuid1()
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"path": "zeus/db/types/guid.py",
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0,
"num_lines": 50
} |
__all__ = ['GuiManager']
import string
from kivy.uix.relativelayout import RelativeLayout
from kivy.core.window import Window
from pocketthrone.entities.event import *
from pocketthrone.managers.eventmanager import EventManager
from pocketthrone.gui import *
from pocketthrone.gui.sidebar import SideBar
from pocketthrone.managers.pipe import L
class GuiManager:
# layouts
_tag = "[GuiManager] "
gamestate = GAMESTATE_MENU
widgets_by_id = {}
def __init__(self):
EventManager.register(self)
self.set_gamestate(GAMESTATE_LOADING)
# set gamestate
def set_gamestate(self, value):
self.gamestate = value
# fire GameStateChangedEvent
ev_state_changed = GameStateChangedEvent(value)
EventManager.fire(ev_state_changed)
# returns gamestate
def get_gamestate(self):
return self.gamestate
# returns a new gui panel id
def next_panel_id(self):
self._last_panel_id += 1
return self._last_panel_id
# returns the screen size in px as tuple
def get_screen_size(self):
return (Window.width, Window.height)
# register widget_id in GuiManager
def register_widget(self, widget_id, widget):
# check if widget registration is valid
if string.strip(widget_id) == "untagged":
print(self._tag + "ERROR widget registration aborted for " + string.strip(widget_id))
return None
self.widgets_by_id[string.strip(widget_id)] = widget
print(self._tag + "registered widget_id=" + string.strip(widget_id))
return widget
# returns widget with string widget_id or None
def get_widget(self, widget_id):
widget = None
stripped_id = widget_id.strip()
success = False
# filter untagged
if stripped_id == "untagged":
success = False
try:
# try to get from widget list
widget = self.widgets_by_id[stripped_id]
success = True
finally:
print(self._tag + "get widget _id=" + string.strip(widget_id) + " 2nd_acc=" + str(success) + " widget=" + repr(widget))
return widget
# removes widget entity from GuiManager
def remove_widget(self, widget_id):
widget = self.get_widget(widget_id)
root.remove_widget(widget)
# fire ButtonClickedEvent when a button is pressed
def button_clicked(self, button):
ev_button_clicked = GuiButtonClickedEvent(button.tag, button)
EventManager.fire(ev_button_clicked)
def on_event(self, event):
if isinstance(event, GameStartedEvent):
self.gamestate = GAMESTATE_INGAME
# clear BottomBar Labels on TileUnselectedEvent
if isinstance(event, TileUnselectedEvent):
# change labels
self.get_widget("heading").set_text("")
self.get_widget("details").set_text("")
# get actionbutton
actionbutton = self.get_widget("actionbutton")
actionbutton.set_button_state("DEFAULT")
# show unit data in BottomBar on UnitSelectedEvent
if isinstance(event, UnitSelectedEvent):
heading = self.get_widget("heading")
details = self.get_widget("details")
heading.set_plaintext("Unit: " + event.unit.name)
details.set_plaintext("HP: " + str(event.unit.hp) + " | MP: " + str(event.unit.mp))
# show city data in BottomBar on CitySelectedEvent
if isinstance(event, CitySelectedEvent):
city = event.city
# get production info text
txt_prod_info = "nothing"
if city.is_recruiting():
txt_prod_info = str(city.get_unit_in_production().name) + " (" + str(city._recruition().get_duration()) + ")"
# make text for heading and detail label
txt_city_heading = city.get_size_name() + ": " + city.get_name()
txt_city_details = "HP: " + str(city.get_hp()) + " | In Production: " + txt_prod_info
# get labels & actionbutton
heading = self.get_widget("heading")
details = self.get_widget("details")
actionbutton = self.get_widget("actionbutton")
# change labels
heading.set_text(txt_city_heading)
details.set_text(txt_city_details)
# set actionbutton state BUILD
actionbutton.set_button_state("BUILD")
# handle Button clicks
if isinstance(event, GuiButtonClickedEvent):
print(self._tag + "GuiButtonClickedEvent widget_id=" + event.widget_id)
# ACTION button
if event.widget_id == "actionbutton":
# BUILD action inside a city
if event.button_state == "BUILD":
selected_city = L.CITY_MGR.get_selected_city()
# abort if no city is selected
if not selected_city:
return
# add sidebar
sidebar = SideBar()
root = self.get_widget("root")
root.add_widget(sidebar)
# show recruitable units on it
recruitable_units = L.CITY_MGR.get_recruitable_units(selected_city)
sidebar.show_recruitable_units(recruitable_units)
# gamestate changes
if isinstance(event, GameStateChangedEvent):
# menu initialized
if event.state == GAMESTATE_MENU:
pass
# loading...
elif event.state == GAMESTATE_LOADING:
pass
# ingame
else:
pass
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"few_assignments": false,
"quality_score": 0.9717758398236603,
"avg_score": 0.03094332280601266,
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__all__ = ['guinieranalysis']
import os
import ipy_table
import matplotlib.pyplot as plt
import numpy as np
from IPython.core.getipython import get_ipython
from IPython.display import display
from .atsas import autorg, gnom, datgnom, shanum, datporod
from .io import getsascurve
from .utils import writemarkdown
def guinieranalysis(samplenames, qranges=None, qmax_from_shanum=True, prfunctions_postfix='', dist=None,
plotguinier=True, graph_extension='.png', dmax=None, dmax_from_shanum=False):
"""Perform Guinier analysis on the samples.
Inputs:
samplenames: list of sample names
qranges: dictionary of q ranges for each sample. The keys are sample names. The special '__default__' key
corresponds to all samples which do not have a key in the dict.
qmax_from_shanum: use the qmax determined by the shanum program for the GNOM input.
prfunctions_postfix: The figure showing the P(r) functions will be saved as
prfunctions_<prfunctions_postfix><graph_extension>
dist: the sample-to-detector distance to use.
plotguinier: if Guinier plots are needed.
graph_extension: the extension of the saved graph image files.
dmax: Dict of Dmax parameters. If not found or None, determine automatically using DATGNOM. If found,
GNOM is used. The special key '__default__' works in a similar fashion as for `qranges`."""
figpr = plt.figure()
ip = get_ipython()
axpr = figpr.add_subplot(1, 1, 1)
if qranges is None:
qranges = {'__default__': (0, 1000000)}
if dmax is None:
dmax = {'__default__': None}
if '__default__' not in qranges:
qranges['__default__'] = (0, 1000000)
if '__default__' not in dmax:
dmax['__default__'] = None
table_autorg = [['Name', 'Rg (nm)', 'I$_0$ (cm$^{-1}$ sr$^{-1}$)',
'q$_{min}$ (nm$^{-1}$)', 'q$_{max}$ (nm$^{-1}$)',
'qmin*Rg', 'qmax*Rg', 'quality', 'aggregation',
'Dmax (nm)', 'q$_{shanum}$ (nm$^{-1}$)']]
table_gnom = [['Name', 'Rg (nm)', 'I$_0$ (cm$^{-1}$ sr$^{-1}$)',
'qmin (nm$^{-1}$)', 'qmax (nm$^{-1}$)',
'Dmin (nm)', 'Dmax (nm)', 'Total estimate', 'Porod volume (nm$^3$)']]
results = {}
for sn in samplenames:
if sn not in qranges:
print('Q-range not given for sample {}: using default one'.format(sn))
qrange = qranges['__default__']
else:
qrange = qranges[sn]
if sn not in dmax:
dmax_ = dmax['__default__']
else:
dmax_ = dmax[sn]
print('Using q-range for sample {}: {} <= q <= {}'.format(sn, qrange[0], qrange[1]))
curve = getsascurve(sn, dist)[0].trim(*qrange).sanitize()
curve.save(sn + '.dat')
try:
Rg, I0, qmin, qmax, quality, aggregation = autorg(sn + '.dat')
except ValueError:
print('Error running autorg on %s' % sn)
continue
dmax_shanum, nsh, nopt, qmaxopt = shanum(sn + '.dat')
if qmax_from_shanum:
curve_trim = curve.trim(qmin, qmaxopt)
else:
curve_trim = curve.trim(qmin, qrange[1])
if dmax_from_shanum:
dmax_ = dmax_from_shanum
curve_trim.save(sn + '_optrange.dat')
if dmax_ is None:
print('Calling DATGNOM for sample {} with Rg={}, q-range from {} to {}'.format(
sn, Rg.val, curve_trim.q.min(), curve_trim.q.max()))
gnompr, metadata = datgnom(sn + '_optrange.dat', Rg=Rg.val, noprint=True)
else:
print('Calling GNOM for sample {} with Rmax={}, q-range from {} to {}'.format(
sn, dmax_, curve_trim.q.min(), curve_trim.q.max()))
gnompr, metadata = gnom(curve_trim, dmax_)
rg, i0, vporod = datporod(sn + '_optrange.out')
axpr.errorbar(gnompr[:, 0], gnompr[:, 1], gnompr[:, 2], None, label=sn)
if plotguinier:
figsample = plt.figure()
axgnomfit = figsample.add_subplot(1, 2, 1)
curve.errorbar('b.', axes=axgnomfit, label='measured')
axgnomfit.errorbar(metadata['qj'], metadata['jexp'], metadata['jerror'], None, 'g.', label='gnom input')
axgnomfit.loglog(metadata['qj'], metadata['jreg'], 'r-', label='regularized by GNOM')
figsample.suptitle(sn)
axgnomfit.set_xlabel('q (nm$^{-1}$)')
axgnomfit.set_ylabel('$d\Sigma/d\Omega$ (cm$^{-1}$ sr$^{-1}$)')
axgnomfit.axvline(qmaxopt, 0, 1, linestyle='dashed', color='black', lw=2)
axgnomfit.grid(True, which='both')
axgnomfit.axis('tight')
axgnomfit.legend(loc='best')
axguinier = figsample.add_subplot(1, 2, 2)
axguinier.errorbar(curve.q, curve.Intensity, curve.Error, curve.qError, '.', label='Measured')
q = np.linspace(qmin, qmax, 100)
axguinier.plot(q, I0.val * np.exp(-q ** 2 * Rg.val ** 2 / 3), label='AutoRg')
axguinier.plot(q, metadata['I0_gnom'].val * np.exp(-q ** 2 * metadata['Rg_gnom'].val ** 2 / 3),
label='Gnom')
axguinier.set_xscale('power', exponent=2)
axguinier.set_yscale('log')
axguinier.set_xlabel('q (nm$^{-1}$)')
axguinier.set_ylabel('$d\Sigma/d\Omega$ (cm$^{-1}$ sr$^{-1}$)')
axguinier.legend(loc='best')
idxmin = np.arange(len(curve))[curve.q <= qmin].max()
idxmax = np.arange(len(curve))[curve.q >= qmax].min()
idxmin = max(0, idxmin - 5)
idxmax = min(len(curve) - 1, idxmax + 5)
if plotguinier:
curveguinier = curve.trim(curve.q[idxmin], curve.q[idxmax])
axguinier.axis(xmax=curve.q[idxmax], xmin=curve.q[idxmin], ymin=curveguinier.Intensity.min(),
ymax=curveguinier.Intensity.max())
axguinier.grid(True, which='both')
table_gnom.append(
[sn, metadata['Rg_gnom'].tostring(extra_digits=2), metadata['I0_gnom'].tostring(extra_digits=2),
metadata['qmin'], metadata['qmax'],
metadata['dmin'], metadata['dmax'], metadata['totalestimate_corrected'], vporod])
table_autorg.append([sn, Rg.tostring(extra_digits=2), I0, '%.3f' % qmin, '%.3f' % qmax, qmin * Rg, qmax * Rg,
'%.1f %%' % (quality * 100), aggregation, '%.3f' % dmax_shanum, '%.3f' % qmaxopt])
if plotguinier:
figsample.tight_layout()
figsample.savefig(os.path.join(ip.user_ns['auximages_dir'], 'guinier_%s%s' % (sn, graph_extension)),
dpi=600)
results[sn] = {
'Rg_autorg' : Rg, 'I0_autorg': I0,
'qmin_autorg': qmin, 'qmax_autorg': qmax,
'quality' : quality, 'aggregation': aggregation,
'dmax_autorg': dmax_shanum, 'qmax_shanum': qmaxopt,
'Rg_gnom' : metadata['Rg_gnom'],
'I0_gnom' : metadata['I0_gnom'],
'qmin_gnom' : metadata['qmin'],
'qmax_gnom' : metadata['qmax'],
'dmin_gnom' : metadata['dmin'],
'dmax_gnom' : metadata['dmax'],
'VPorod' : vporod,
}
axpr.set_xlabel('r (nm)')
axpr.set_ylabel('P(r)')
axpr.legend(loc='best')
axpr.grid(True, which='both')
writemarkdown('## Results from autorg and shanum')
tab = ipy_table.IpyTable(table_autorg)
tab.apply_theme('basic')
display(tab)
writemarkdown('## Results from gnom')
tab = ipy_table.IpyTable(table_gnom)
tab.apply_theme('basic')
if prfunctions_postfix and prfunctions_postfix[0] != '_':
prfunctions_postfix = '_' + prfunctions_postfix
figpr.tight_layout()
figpr.savefig(os.path.join(ip.user_ns['auximages_dir'], 'prfunctions%s%s' % (prfunctions_postfix, graph_extension)),
dpi=600)
display(tab)
return results
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__all__ = ['HaloAbundanceFunction', 'calc_number_densities', 'calc_number_densities_in_bins']
import numpy as np
def _to_float(x, default=np.nan, take_log=False):
try:
xf = float(x)
except (ValueError, TypeError):
return default
return np.log(xf) if take_log else xf
def calc_number_densities(x, box_size):
"""
Calculate the number densities for a list of values.
Number density = rank / volumn.
Parameters
----------
x : array_like
An 1-d array that contains the values of a halo property
(e.g. vpeak, mpeak).
box_size : float
The length of the cubic cosmological box.
Returns
-------
nd : array_like
Number density for each x.
"""
N = len(x)
inv_vol = 1.0/(box_size**3)
nd = np.empty(N, dtype=np.float64)
nd[np.argsort(x)] = np.linspace(N*inv_vol, inv_vol, N)
return nd
def calc_number_densities_in_bins(x, box_size, bins):
"""
Given a list of values, calculate the number densities of
at the positions of `bins`.
Number density = rank / volumn.
Parameters
----------
x : array_like
An 1-d array that contains the values of a halo property
(e.g. vpeak, mpeak).
box_size : float
The length of the cubic cosmological box.
bins : array_like
The position (in x) to calculate the number densities for.
Returns
-------
nd : array_like
Number density for each value in `bins`.
"""
return (len(x) - np.searchsorted(x, bins, sorter=np.argsort(x))).astype(float)/(box_size**3)
class HaloAbundanceFunction:
def __init__(self, x, box_size, fit_range=(None, None), fit_points=10,\
nbin=200, log_bins=True):
"""
Create (and extrapolate) a halo abundance function from
a halo property (e.g. vpeak, mpeak).
Parameters
----------
x : array_like or str
An 1-d array that contains the values of a halo property
(e.g. vpeak, mpeak).
box_size : float
The length of the cubic cosmological box.
fit_range : tuple
The range (a, b) to extrapolate the halo abundance function.
The extrapolation starts below b and fits down to a.
Should be in the same unit as x.
If (None, None), do not extrapolate.
fit_points : int
Number of bins to do the extrapolation mentioned above.
nbin : int, optional
Number of bins to interpolate the halo abundance function.
log_bins : bool, optional
Whether to take log of the halo property. Default: True.
"""
x = np.log(x) if log_bins else np.asarray(x)
x = x[np.isfinite(x)]
fit_to, fit_below = fit_range
fit_to = _to_float(fit_to, x.min(), log_bins)
fit_below = _to_float(fit_below, x.min(), log_bins)
bins = np.linspace(min(x.min(), fit_to), x.max(), int(nbin)+1)
nd = calc_number_densities_in_bins(x, box_size, bins)
if fit_to < fit_below:
dlog_nd = np.gradient(np.log(nd))
dx = (bins[-1]-bins[0])/int(nbin)
k = np.searchsorted(bins, fit_below)
s = slice(k, k+fit_points)
self._slope = dlog_nd[s].mean()/dx
nd[:k] = np.exp((bins[:k]-bins[k])*self._slope) * nd[k]
self._log_bins = log_bins
self._x = bins
self._nd_log = np.log(nd)
def __call__(self, x):
"""
Return the number density at x.
Parameters
----------
x : array_like
The halo abundance proxy, e.g. vpeak or mpeak.
Returns
-------
nd : array_like
Number densities at x.
"""
x = np.log(x) if self._log_bins else np.asarray(x)
return np.exp(np.interp(x, self._x, self._nd_log, np.nan, np.nan))
def get_number_density_table(self):
"""
Return the inter/extrapolated number density table.
Returns
-------
x : array_like
The halo abundance proxy.
nd : array_like
Number densities at x.
"""
return np.exp(self._x), np.exp(self._nd_log)
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"""All handlers for CtC projects."""
from google.appengine.api import users
from google.appengine.ext import ndb
from ctc.helpers import csrf
from ctc.helpers import templates
from ctc.models import collaborator as collaborator_model
from ctc.models import project as project_model
from ctc.models import user as user_model
class BaseHandler(csrf.CsrfHandler):
"""Superclass for all CtC handlers."""
def require_login(self):
"""Redirect to the login page and abort if the user is not logged in."""
if not users.get_current_user():
self.redirect(users.create_login_url(self.request.uri), abort=True)
def dispatch(self):
"""Initializes default values and dispatches the request."""
self.values['login_url'] = users.create_login_url(self.request.uri)
self.values['logout_url'] = generate_logout_url()
super(BaseHandler, self).dispatch()
class MainPage(BaseHandler):
"""The handler for the root page."""
def get(self):
"""Renders the main landing page in response to a GET request."""
self.response.write(templates.render('main.html', self.values))
class DisplayDashboard(BaseHandler):
"""The handler for displaying a which projects the user is working on"""
def get(self):
"""Renders the dashboard corresponding to the logged in user"""
self.require_login()
user_key = user_model.get_current_user_key()
self.values['own'] = project_model.get_by_owner(user_key)
self.values['contributing'] = collaborator_model.get_projects(user_key)
self.response.write(templates.render('dashboard.html', self.values))
class DisplayUser(BaseHandler):
"""The handler for displaying a user page."""
def get(self, user_id):
"""Renders the user page in response to a GET request."""
self.require_login()
requesting_user_id = user_model.get_current_user_key().id()
self.values['profile'] = user_model.User.get_by_id(user_id)
is_profile_owner = (user_id == requesting_user_id)
if is_profile_owner:
self.values['edit_link'] = self.uri_for(EditUser, user_id=user_id)
self.response.write(templates.render('display_user.html', self.values))
class EditUser(BaseHandler):
"""The handler for editing a user profile."""
def require_owner(self, user_id):
"""Aborts the current request if the user isn't the profile's owner."""
current_user_id = user_model.get_current_user_key().id()
if current_user_id != user_id:
self.abort(403)
def get(self, user_id):
"""Renders the edit user page in response to a GET request."""
self.require_login()
self.require_owner(user_id)
self.values['profile'] = user_model.User.get_by_id(user_id)
self.values['action_link'] = self.uri_for(EditUser, user_id=user_id)
self.values['action'] = 'Update'
self.response.write(templates.render('edit_user.html', self.values))
def post(self, user_id):
"""Edits the provided project."""
self.require_login()
self.require_owner(user_id)
profile_object = user_model.User.get_by_id(user_id)
profile_object.populate(self.request).put()
self.redirect_to(DisplayUser, user_id=user_id)
class DisplayProject(BaseHandler):
"""The handler for displaying a project."""
def _set_action_and_link(self, project_id, is_collaborating, is_logged_in):
"""Sets action, action_link, and csrf_token in self.values."""
if is_collaborating:
self.values['action'] = 'Leave'
action_link = self.uri_for(LeaveProject, project_id=project_id)
if not is_collaborating and is_logged_in:
self.values['action'] = 'Join'
action_link = self.uri_for(JoinProject, project_id=project_id)
if not is_logged_in:
self.values['action'] = 'Login to Join'
action_link = users.create_login_url(self.request.uri)
self.values['action_link'] = action_link
self.values['csrf_token'] = csrf.make_token(action_link)
def get(self, project_id):
"""Renders the project page in response to a GET request."""
project = ndb.Key(project_model.Project, int(project_id)).get()
user_key = user_model.get_current_user_key()
collaborator_emails = []
# Initialize some truthy objects for the following display logic.
is_logged_in = user_key
self.values['user_is_logged_out'] = not is_logged_in
is_collaborating = collaborator_model.get_collaborator(
user_key, project.key)
is_project_owner = is_logged_in and project.owner_key == user_key
should_show_collaborator_emails = is_collaborating or is_project_owner
# Use the above as booleans to guide permissions.
if is_project_owner:
self.values['edit_link'] = self.uri_for(
EditProject, project_id=project_id)
if should_show_collaborator_emails:
collaborator_emails = collaborator_model.get_collaborator_emails(
ndb.Key(project_model.Project, int(project_id)))
self._set_action_and_link(project_id, is_collaborating, is_logged_in)
num_contributors = collaborator_model.get_collaborator_count(
ndb.Key(project_model.Project, int(project_id)))
self.values['num_contributors'] = num_contributors
self.values['collaborator_emails'] = collaborator_emails
self.values['project'] = project
self.response.write(
templates.render('display_project.html', self.values))
class EditProject(BaseHandler):
"""The handler for editing a project."""
def require_project_owner(self, project):
"""Aborts the current request if the user isn't the project's owner."""
current_user_key = user_model.get_current_user_key()
if current_user_key != project.owner_key:
self.abort(403)
def get(self, project_id):
"""Renders the edit project page in response to a GET request."""
self.require_login()
project = ndb.Key(project_model.Project, int(project_id)).get()
self.require_project_owner(project)
self.values['project'] = project
self.values['action_link'] = self.uri_for(
EditProject, project_id=project_id)
self.values['action'] = 'Edit Your'
self.response.write(templates.render('edit_project.html', self.values))
def post(self, project_id):
"""Edits the provided project."""
self.require_login()
project = ndb.Key(project_model.Project, int(project_id)).get()
self.require_project_owner(project)
project.populate(self.request).put()
self.redirect_to(DisplayProject, project_id=project_id)
class ListProjects(BaseHandler):
"""The handler for the projects list."""
def get(self):
"""Renders the projects page in response to a GET request."""
query = project_model.Project.query()
query = query.order(project_model.Project.updated_date)
projects = query.fetch()
links = []
for curr_project in projects:
project_id = curr_project.key.id()
links.append(self.uri_for(DisplayProject, project_id=project_id))
self.values['projects_and_links'] = zip(projects, links)
self.response.write(templates.render('list_projects.html', self.values))
class NewProject(BaseHandler):
"""The handler for a new project."""
def get(self):
"""Renders the new project page in response to a GET request."""
self.require_login()
self.values['action'] = 'Create a New'
self.values['action_link'] = self.uri_for(NewProject)
self.response.write(templates.render('edit_project.html', self.values))
def post(self):
"""Accepts a request to create a new project."""
self.require_login()
current_user_key = user_model.get_current_user_key()
new_project = project_model.Project().populate(self.request)
new_project.owner_key = current_user_key
new_project_key = new_project.put()
self.redirect_to(DisplayProject, project_id=new_project_key.id())
class JoinProject(BaseHandler):
"""Handler for a request to join a project."""
def post(self, project_id):
"""Accepts a request to join a project."""
self.require_login()
current_user_key = user_model.get_current_user_key()
collaborator_model.Collaborator.get_or_insert(
current_user_key.id(),
user_key=current_user_key,
parent=ndb.Key(project_model.Project, int(project_id))
)
self.redirect_to(DisplayProject, project_id=project_id)
class LeaveProject(BaseHandler):
"""Handler for a request to leave a project."""
def post(self, project_id):
"""Accepts a request to leave a project."""
self.require_login()
current_user_key = user_model.get_current_user_key()
collaborator = collaborator_model.get_collaborator(
current_user_key, ndb.Key(project_model.Project, int(project_id)))
if collaborator:
collaborator.key.delete()
self.redirect_to(DisplayProject, project_id=project_id)
def generate_logout_url():
"""Returns logout url if user is logged in; otherwise returns None."""
return users.create_logout_url('/') if users.get_current_user() else None
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"""All handy, general utility functionality used throughout the package."""
import os
import os.path as osp
import sys
import fnmatch
import shutil
def load_module_path(path, name=None, remove_byte_version=False):
"""Load a python module source file python version aware."""
name = name if name else osp.splitext(osp.basename(path))[0]
# remove byte versions if they are older than 3 seconds to avoid
# cuncurrency issues
if remove_byte_version:
for f in [path+'c', path+'o']:
try:
os.remove(f)
except OSError:
pass
# remove module references
try:
del sys.modules[name]
except KeyError:
pass
if sys.version_info < (3,):
import imp
m = imp.load_source(name, path)
elif sys.version_info >= (3, 5):
import importlib.util as iu
spec = iu.spec_from_file_location(name, path)
m = iu.module_from_spec(spec)
spec.loader.exec_module(m)
else:
raise ImportError('This python version is not supported: %s'
% sys.version_info)
return m
def get_paths_pattern(pattern, startdir):
"""
Get all paths (including in subdirectories) matching pattern.
Returns list of relative paths from startdir.
"""
matches = []
for root, dirnames, filenames in os.walk(startdir):
fpaths = [os.path.relpath(os.path.join(root, fn), startdir)
for fn in filenames]
matches += fnmatch.filter(fpaths, pattern)
return matches
def copy_resources(sourcedir, destinationdir, overwrite=False,
ignorepatterns=[], linkpatterns=[], verbose=False):
"""
Copy/sync resource file tree from sourcedir to destinationdir.
overwrite: Overwrite existing files.
"""
def printverbose(args):
if verbose:
print(args)
return
pj = osp.join
if not osp.exists(destinationdir):
printverbose('mkdir %s' % destinationdir)
os.mkdir(destinationdir)
walker = os.walk(sourcedir, topdown=True)
for path, dirs, files in walker:
rpath = osp.relpath(path, sourcedir).replace('.', '')
# dirs
subsetdirs = []
for d in dirs:
rdir = pj(rpath, d)
src = pj(path, d)
dest = pj(destinationdir, rpath, d)
if any(fnmatch.fnmatch(rdir, p) for p in ignorepatterns):
printverbose('Ignoring %s' % rdir)
# dir to symlink with relative path
elif any(fnmatch.fnmatch(rdir, p) for p in linkpatterns):
rsrc = osp.relpath(pj(path, d), osp.dirname(dest))
printverbose('Linking %s to %s' % (dest, rsrc))
os.symlink(rsrc, dest)
# copy/relink existing symlinks
elif osp.islink(src):
lnabs = osp.abspath(pj(path, os.readlink(src)))
rsrc = osp.relpath(lnabs, osp.dirname(dest))
printverbose('Linking %s to %s' % (dest, rsrc))
os.symlink(rsrc, dest)
# create new dir
else:
if not osp.exists(dest):
printverbose('mkdir %s' % dest)
os.mkdir(dest)
subsetdirs.append(d)
# update dirs (change in place will prevent walking into them)
dirs[:] = subsetdirs
# files
for f in files:
rfil = osp.join(rpath, f)
dest = pj(destinationdir, rpath, f)
src = pj(path, f)
# ignored files
if any(fnmatch.fnmatch(rfil, p) for p in ignorepatterns):
printverbose('Ignoring %s' % rfil)
continue
# file to symlink with relative path
elif any(fnmatch.fnmatch(rfil, p) for p in linkpatterns):
rsrc = osp.relpath(pj(path, f), osp.dirname(dest))
printverbose('Linking %s to %s' % (dest, rsrc))
os.symlink(rsrc, dest)
# copy/relink existing symlinks
elif osp.islink(src):
lnabs = osp.abspath(pj(path, os.readlink(src)))
rsrc = osp.relpath(lnabs, osp.dirname(dest))
printverbose('Linking %s to %s' % (dest, rsrc))
os.symlink(rsrc, dest)
# copy file
elif not osp.exists(dest) or overwrite:
printverbose('cp %s to %s' % (src, dest))
shutil.copy(src, dest)
return
class propertyplugin(property):
"""
Class decorator to create a plugin that is instantiated and returned when
the project attribute is used to conveniently combine class + constructor.
Like all plugins, the propertyplugin __init__ method must only accept one
positional argument, i.e. the project.
Usage:
------
```
@propertyplugin
class result:
def __init__(self, project):
pass
project = swim.Project()
project.result -> result instance
```
"""
def __init__(self, cls):
def plugin_instatiator(project):
# take project from plugin if decorator used inside plugins
if hasattr(project, 'project'):
project = project.project
return cls(project)
super(propertyplugin, self).__init__(plugin_instatiator)
self.__doc__ = cls.__doc__
self.__name__ = cls.__name__
# so sphinx recognises the property as not imported
self.__module__ = getattr(cls, '__module__', None)
self.plugin = cls
return
class GroupPlugin(object):
"""
An abstract class to group functionality.
"""
def __init__(self, project):
self.project = project
return
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# All harcoded (names, paths etc), refactor it if needed
sln_file = '''
Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2013
VisualStudioVersion = 12.0.31101.0
MinimumVisualStudioVersion = 10.0.40219.1
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MyProject", "MyProject\MyProject.vcxproj", "{143D99A7-C9F3-434F-BA39-514BB63835E8}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|ARM = Debug|ARM
Debug|x64 = Debug|x64
Debug|x86 = Debug|x86
Release|ARM = Release|ARM
Release|x64 = Release|x64
Release|x86 = Release|x86
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|ARM.ActiveCfg = Debug|ARM
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|ARM.Build.0 = Debug|ARM
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|x64.ActiveCfg = Debug|x64
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|x64.Build.0 = Debug|x64
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|x86.ActiveCfg = Debug|Win32
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Debug|x86.Build.0 = Debug|Win32
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|ARM.ActiveCfg = Release|ARM
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|ARM.Build.0 = Release|ARM
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|x64.ActiveCfg = Release|x64
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|x64.Build.0 = Release|x64
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|x86.ActiveCfg = Release|Win32
{143D99A7-C9F3-434F-BA39-514BB63835E8}.Release|x86.Build.0 = Release|Win32
EndGlobalSection
GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE
EndGlobalSection
EndGlobal
'''
vcxproj_file = '''<?xml version="1.0" encoding="utf-8"?>
<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
<ItemGroup Label="ProjectConfigurations">
<ProjectConfiguration Include="Debug|ARM">
<Configuration>Debug</Configuration>
<Platform>ARM</Platform>
</ProjectConfiguration>
<ProjectConfiguration Include="Debug|Win32">
<Configuration>Debug</Configuration>
<Platform>Win32</Platform>
</ProjectConfiguration>
<ProjectConfiguration Include="Debug|x64">
<Configuration>Debug</Configuration>
<Platform>x64</Platform>
</ProjectConfiguration>
<ProjectConfiguration Include="Release|ARM">
<Configuration>Release</Configuration>
<Platform>ARM</Platform>
</ProjectConfiguration>
<ProjectConfiguration Include="Release|Win32">
<Configuration>Release</Configuration>
<Platform>Win32</Platform>
</ProjectConfiguration>
<ProjectConfiguration Include="Release|x64">
<Configuration>Release</Configuration>
<Platform>x64</Platform>
</ProjectConfiguration>
</ItemGroup>
<PropertyGroup Label="Globals">
<ProjectGuid>{143D99A7-C9F3-434F-BA39-514BB63835E8}</ProjectGuid>
<RootNamespace>MyProject</RootNamespace>
</PropertyGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>true</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<CharacterSet>MultiByte</CharacterSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>true</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<CharacterSet>MultiByte</CharacterSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|ARM'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>true</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<CharacterSet>MultiByte</CharacterSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>false</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<WholeProgramOptimization>true</WholeProgramOptimization>
<CharacterSet>MultiByte</CharacterSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>false</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<WholeProgramOptimization>true</WholeProgramOptimization>
<CharacterSet>MultiByte</CharacterSet>
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<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|ARM'" Label="Configuration">
<ConfigurationType>Application</ConfigurationType>
<UseDebugLibraries>false</UseDebugLibraries>
<PlatformToolset>v140</PlatformToolset>
<WholeProgramOptimization>true</WholeProgramOptimization>
<CharacterSet>MultiByte</CharacterSet>
</PropertyGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
<ImportGroup Label="ExtensionSettings">
</ImportGroup>
<ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
<Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
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<PropertyGroup Label="UserMacros" />
<PropertyGroup />
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>Disabled</Optimization>
<SDLCheck>true</SDLCheck>
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<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
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<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>Disabled</Optimization>
<SDLCheck>true</SDLCheck>
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<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
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<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|ARM'">
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<WarningLevel>Level3</WarningLevel>
<Optimization>Disabled</Optimization>
<SDLCheck>true</SDLCheck>
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<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
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<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>MaxSpeed</Optimization>
<FunctionLevelLinking>true</FunctionLevelLinking>
<IntrinsicFunctions>true</IntrinsicFunctions>
<SDLCheck>true</SDLCheck>
</ClCompile>
<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
<EnableCOMDATFolding>true</EnableCOMDATFolding>
<OptimizeReferences>true</OptimizeReferences>
</Link>
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<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>MaxSpeed</Optimization>
<FunctionLevelLinking>true</FunctionLevelLinking>
<IntrinsicFunctions>true</IntrinsicFunctions>
<SDLCheck>true</SDLCheck>
</ClCompile>
<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
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</Link>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|ARM'">
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>MaxSpeed</Optimization>
<FunctionLevelLinking>true</FunctionLevelLinking>
<IntrinsicFunctions>true</IntrinsicFunctions>
<SDLCheck>true</SDLCheck>
</ClCompile>
<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
<EnableCOMDATFolding>true</EnableCOMDATFolding>
<OptimizeReferences>true</OptimizeReferences>
</Link>
</ItemDefinitionGroup>
<ItemGroup>
<ClCompile Include="main.cpp" />
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<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
<ImportGroup Label="ExtensionTargets">
</ImportGroup>
</Project>'''
filters_file = '''<?xml version="1.0" encoding="utf-8"?>
<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
<ItemGroup>
<Filter Include="Source Files">
<UniqueIdentifier>{4FC737F1-C7A5-4376-A066-2A32D752A2FF}</UniqueIdentifier>
<Extensions>cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx</Extensions>
</Filter>
<Filter Include="Header Files">
<UniqueIdentifier>{93995380-89BD-4b04-88EB-625FBE52EBFB}</UniqueIdentifier>
<Extensions>h;hh;hpp;hxx;hm;inl;inc;xsd</Extensions>
</Filter>
<Filter Include="Resource Files">
<UniqueIdentifier>{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}</UniqueIdentifier>
<Extensions>rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms</Extensions>
</Filter>
</ItemGroup>
<ItemGroup>
<ClCompile Include="main.cpp">
<Filter>Source Files</Filter>
</ClCompile>
</ItemGroup>
</Project>'''
main_file = '''#include <iostream>
int main()
{
std::cout << "Hello World!" << std::endl;
return 0;
}
'''
def get_vs_project_files():
return {"MyProject.sln": sln_file,
"MyProject/MyProject.vcxproj": vcxproj_file,
"MyProject/MyProject.vcxproj.filters": filters_file,
"MyProject/main.cpp": main_file}
| {
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"path": "conans/test/utils/visual_project_files.py",
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"quality_score": 0.6751409394365769,
"avg_score": null,
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__all__ = ['HardwareSupportError', 'GLSLError', 'MAX_COLOR_ATTACHMENTS',
'require_extension', 'hardware_info']
from pyglet import gl
import pyglet.gl.gl_info as gli
import ctypes
MAX_COLOR_ATTACHMENTS = gl.GLint()
gl.glGetIntegerv(gl.GL_MAX_COLOR_ATTACHMENTS,
ctypes.byref(MAX_COLOR_ATTACHMENTS))
MAX_COLOR_ATTACHMENTS = MAX_COLOR_ATTACHMENTS.value
class HardwareSupportError(Exception):
def __init__(self, message):
self.message = "Your graphics hardware does not support %s." % \
message
def __str__(self):
return self.message
class DriverError(Exception):
pass
class GLSLError(Exception):
pass
def require_extension(ext):
"""Ensure that the given graphics extension is supported.
"""
if not gl.gl_info.have_extension('GL_' + ext):
raise HardwareSupportError("the %s extension" % ext)
hardware_info = {'vendor': gli.get_vendor(),
'renderer': gli.get_renderer(),
'version': gli.get_version()}
# Check hardware support
_opengl_version = hardware_info['version'].split(' ')[0]
if _opengl_version < "2.0":
raise DriverError("This package requires OpenGL v2.0 or higher. "
"Your version is %s." % _opengl_version)
# This extension is required to return floats outside [0, 1]
# in gl_FragColor
require_extension('ARB_color_buffer_float')
require_extension('ARB_texture_float')
gl.glClampColorARB(gl.GL_CLAMP_VERTEX_COLOR_ARB, False)
gl.glClampColorARB(gl.GL_CLAMP_FRAGMENT_COLOR_ARB, False)
gl.glClampColorARB(gl.GL_CLAMP_READ_COLOR_ARB, False)
| {
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"path": "scikits/gpu/config.py",
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"autogenerated": false,
"ratio": 3.43312101910828,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.510411669010828,
"avg_score": null,
"num_lines": null
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__all__ = ['HashCol']
import sqlobject.col
class DbHash:
""" Presents a comparison object for hashes, allowing plain text to be
automagically compared with the base content. """
def __init__( self, hash, hashMethod ):
self.hash = hash
self.hashMethod = hashMethod
def __cmp__( self, other ):
if other is None:
if self.hash is None:
return 0
return True
if not isinstance( other, basestring ):
raise TypeError( "A hash may only be compared with a string, or None." )
return cmp( self.hashMethod( other ), self.hash )
def __repr__( self ):
return "<DbHash>"
class HashValidator( sqlobject.col.StringValidator ):
""" Provides formal SQLObject validation services for the HashCol. """
def to_python( self, value, state ):
""" Passes out a hash object. """
if value is None:
return None
return DbHash( hash = value, hashMethod = self.hashMethod )
def from_python( self, value, state ):
""" Store the given value as a MD5 hash, or None if specified. """
if value is None:
return None
return self.hashMethod( value )
class SOHashCol( sqlobject.col.SOStringCol ):
""" The internal HashCol definition. By default, enforces a md5 digest. """
def __init__( self, **kw ):
if 'hashMethod' not in kw:
from md5 import md5
self.hashMethod = lambda v: md5( v ).hexdigest()
if 'length' not in kw:
kw['length'] = 32
else:
self.hashMethod = kw['hashMethod']
del kw['hashMethod']
super( sqlobject.col.SOStringCol, self ).__init__( **kw )
def createValidators( self ):
return [HashValidator( name=self.name, hashMethod=self.hashMethod )] + \
super( SOHashCol, self ).createValidators()
class HashCol( sqlobject.col.StringCol ):
""" End-user HashCol class. May be instantiated with 'hashMethod', a function
which returns the string hash of any other string (i.e. basestring). """
baseClass = SOHashCol
| {
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"path": "serieswatcher/sqlobject/include/hashcol.py",
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"autogenerated": false,
"ratio": 4.083969465648855,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.6684904045048855,
"avg_score": null,
"num_lines": null
} |
__all__ = ['HDO6104']
from auspex.log import logger
from .instrument import SCPIInstrument, StringCommand, FloatCommand, IntCommand, Command
import numpy as np
import time
class HDO6104(SCPIInstrument):
channel_enabled = Command(scpi_string="C{channel}:TRA",
additional_args=["channel"],value_map={True:"ON",False:"OFF"})
sample_points = IntCommand(scpi_string="MEMORY_SIZE")
trig_mode = StringCommand(scpi_string="TRIG_MODE")
time_div = FloatCommand(scpi_string="TIME_DIV")
trig_delay = FloatCommand(scpi_string="TRIG_DELAY")
vol_div = Command(scpi_string="C{channel}:VOLT_DIV",additional_args=["channel"])
vol_offset = Command(scpi_string="C{channel}:OFFSET",additional_args=["channel"])
def connect(self, resource_name=None, interface_type=None):
super(HDO6104,self).connect(resource_name=resource_name,interface_type=interface_type)
self.interface.write("COMM_HEADER OFF")
self.interface._resource.read_termination = u"\n"
def get_info(self,channel=1):
raw_info = self.interface.query("C%d:INSPECT? WAVEDESC" %channel).split("\r\n")[1:-1]
info = [item.split(':') for item in raw_info]
return {k[0].strip(): k[1].strip() for k in info}
def fetch_waveform(self,channel):
# Send the MSB first
self.interface.write("COMM_ORDER HI")
self.interface.write("COMM_FORMAT DEF9,WORD,BIN")
mydict = self.get_info(channel=channel)
points = int(mydict["PNTS_PER_SCREEN"])
xincrement = float(mydict["HORIZ_INTERVAL"])
xorigin = float(mydict["HORIZ_OFFSET"])
yincrement = float(mydict["VERTICAL_GAIN"])
yorigin = float(mydict["VERTICAL_OFFSET"])
# Read waveform data
y_axis = np.array(self.interface.query_binary_values('C%d:WAVEFORM? DAT1' % channel, datatype='h', is_big_endian=True))
y_axis = y_axis*yincrement - yorigin
x_axis = xorigin + np.arange(0, xincrement*len(y_axis), xincrement)
return x_axis, y_axis
| {
"repo_name": "BBN-Q/Auspex",
"path": "src/auspex/instruments/lecroy.py",
"copies": "1",
"size": "2020",
"license": "apache-2.0",
"hash": -374128563342368200,
"line_mean": 45.976744186,
"line_max": 127,
"alpha_frac": 0.6653465347,
"autogenerated": false,
"ratio": 3.3223684210526314,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9454114198863116,
"avg_score": 0.006720151377903214,
"num_lines": 43
} |
__all__ = ["HeapSnapshotTaker"]
from devtools_event_listener import DevToolsEventListener
from status import *
from base.log import VLOG
# Take the heap snapshot.
class HeapSnapshotTaker(DevToolsEventListener):
def __init__(self, client):
self.client = client
self.client.AddListener(self)
self.snapshot_uid = -1
self.snapshot = ""
# return status and snapshot<value>
def TakeSnapshot(self):
snapshot = None
status1 = self._TakeSnapshotInternal()
params = {}
status2 = self.client.SendCommand("Debugger.disable", params)
status3 = Status(kOk)
if self.snapshot_uid != -1:
# Clear the snapshot cached in xwalk.
status3 = self.client.SendCommand("HeapProfiler.clearProfiles", params)
status4 = Status(kOk)
if status1.IsOk() and status2.IsOk() and status3.IsOk():
try:
snapshot = json.loads(self.snapshot)
except:
status4 = Status(kUnknownError, "heap snapshot not in JSON format")
self.snapshot_uid = -1
self.snapshot = ""
if status1.IsError():
return (status1, snapshot)
elif status2.IsError():
return (status2, snapshot)
elif status3.IsError():
return (status3, snapshot)
else:
return (status4, snapshot)
# Overridden from DevToolsEventListener:
def OnEvent(self, client, method, params):
if method == "HeapProfiler.addProfileHeader":
#self.snapshot_uid = params.get("header.uid", None)
self.snapshot_uid = params["header"].get("uid", None)
if self.snapshot_uid != -1:
VLOG(3, "multiple heap snapshot triggered")
#TODO: header.uid format
elif type(params["header"].get("uid")) != int:
return Status(kUnknownError, "HeapProfiler.addProfileHeader has invalid 'header.uid'")
elif method == "HeapProfiler.addHeapSnapshotChunk":
uid = -1
uid = params.get("uid")
if type(uid) != int:
return Status(kUnknownError, "HeapProfiler.addHeapSnapshotChunk has no 'uid'")
elif uid == self.snapshot_uid:
chunk = params.get("chunk")
if type(chunk) != str:
return Status(kUnknownError, "HeapProfiler.addHeapSnapshotChunk has no 'chunk'")
self.snapshot += chunk
else:
VLOG(3, "expect chunk event uid " + self.snapshot_uid + ", but got " + str(uid))
return Status(kOk)
def _TakeSnapshotInternal(self):
if self.snapshot_uid != -1:
return Status(kUnknownError, "unexpected heap snapshot was triggered")
params = {}
kMethods = ["Debugger.enable", "HeapProfiler.collectGarbage", "HeapProfiler.takeHeapSnapshot"]
for i in kMethods:
status = self.client.SendCommand(i, params)
if status.IsError():
return status
if self.snapshot_uid == -1:
return Status(kUnknownError, "failed to receive snapshot uid")
uid_params = {}
uid_params["uid"] = self.snapshot_uid
status = self.client.SendCommand("HeapProfiler.getHeapSnapshot", uid_params)
if status.IsError():
return status
return Status(kOk)
| {
"repo_name": "PeterWangIntel/crosswalk-webdriver-python",
"path": "browser/heap_snapshot_taker.py",
"copies": "1",
"size": "3030",
"license": "bsd-3-clause",
"hash": 3854860914793223000,
"line_mean": 35.0714285714,
"line_max": 98,
"alpha_frac": 0.6623762376,
"autogenerated": false,
"ratio": 3.7223587223587224,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9753297904579767,
"avg_score": 0.02628741107579106,
"num_lines": 84
} |
__all__ = ('HeapStorage',)
import struct
from pyoram.util.virtual_heap import SizedVirtualHeap
from pyoram.storage.block_storage import (BlockStorageInterface,
BlockStorageTypeFactory)
class HeapStorageInterface(object):
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
#
# Abstract Interface
#
def clone_device(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@classmethod
def compute_storage_size(cls, *args, **kwds):
raise NotImplementedError # pragma: no cover
@classmethod
def setup(cls, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def header_data(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def bucket_count(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def bucket_size(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def blocks_per_bucket(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def storage_name(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def virtual_heap(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def bucket_storage(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
def update_header_data(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
def close(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
def read_path(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
def write_path(self, *args, **kwds):
raise NotImplementedError # pragma: no cover
@property
def bytes_sent(self):
raise NotImplementedError # pragma: no cover
@property
def bytes_received(self):
raise NotImplementedError # pragma: no cover
class HeapStorage(HeapStorageInterface):
_header_struct_string = "!LLL"
_header_offset = struct.calcsize(_header_struct_string)
def _new_storage(self, storage, **kwds):
storage_type = kwds.pop('storage_type', 'file')
def __init__(self, storage, **kwds):
if isinstance(storage, BlockStorageInterface):
self._storage = storage
if len(kwds):
raise ValueError(
"Keywords not used when initializing "
"with a storage device: %s"
% (str(kwds)))
else:
storage_type = kwds.pop('storage_type', 'file')
self._storage = BlockStorageTypeFactory(storage_type)\
(storage, **kwds)
heap_base, heap_height, blocks_per_bucket = \
struct.unpack(
self._header_struct_string,
self._storage.header_data[:self._header_offset])
self._vheap = SizedVirtualHeap(
heap_base,
heap_height,
blocks_per_bucket=blocks_per_bucket)
#
# Define HeapStorageInterface Methods
#
def clone_device(self):
return HeapStorage(self._storage.clone_device())
@classmethod
def compute_storage_size(cls,
block_size,
heap_height,
blocks_per_bucket=1,
heap_base=2,
ignore_header=False,
storage_type='file',
**kwds):
assert (block_size > 0) and (block_size == int(block_size))
assert heap_height >= 0
assert blocks_per_bucket >= 1
assert heap_base >= 2
assert 'block_count' not in kwds
vheap = SizedVirtualHeap(
heap_base,
heap_height,
blocks_per_bucket=blocks_per_bucket)
if ignore_header:
return BlockStorageTypeFactory(storage_type).\
compute_storage_size(
vheap.blocks_per_bucket * block_size,
vheap.bucket_count(),
ignore_header=True,
**kwds)
else:
return cls._header_offset + \
BlockStorageTypeFactory(storage_type).\
compute_storage_size(
vheap.blocks_per_bucket * block_size,
vheap.bucket_count(),
ignore_header=False,
**kwds)
@classmethod
def setup(cls,
storage_name,
block_size,
heap_height,
blocks_per_bucket=1,
heap_base=2,
storage_type='file',
**kwds):
if 'block_count' in kwds:
raise ValueError("'block_count' keyword is not accepted")
if heap_height < 0:
raise ValueError(
"heap height must be 0 or greater. Invalid value: %s"
% (heap_height))
if blocks_per_bucket < 1:
raise ValueError(
"blocks_per_bucket must be 1 or greater. "
"Invalid value: %s" % (blocks_per_bucket))
if heap_base < 2:
raise ValueError(
"heap base must be 2 or greater. Invalid value: %s"
% (heap_base))
vheap = SizedVirtualHeap(
heap_base,
heap_height,
blocks_per_bucket=blocks_per_bucket)
user_header_data = kwds.pop('header_data', bytes())
if type(user_header_data) is not bytes:
raise TypeError(
"'header_data' must be of type bytes. "
"Invalid type: %s" % (type(user_header_data)))
kwds['header_data'] = \
struct.pack(cls._header_struct_string,
heap_base,
heap_height,
blocks_per_bucket) + \
user_header_data
return HeapStorage(
BlockStorageTypeFactory(storage_type).setup(
storage_name,
vheap.blocks_per_bucket * block_size,
vheap.bucket_count(),
**kwds))
@property
def header_data(self):
return self._storage.header_data[self._header_offset:]
@property
def bucket_count(self):
return self._storage.block_count
@property
def bucket_size(self):
return self._storage.block_size
@property
def blocks_per_bucket(self):
return self._vheap.blocks_per_bucket
@property
def storage_name(self):
return self._storage.storage_name
@property
def virtual_heap(self):
return self._vheap
@property
def bucket_storage(self):
return self._storage
def update_header_data(self, new_header_data):
self._storage.update_header_data(
self._storage.header_data[:self._header_offset] + \
new_header_data)
def close(self):
self._storage.close()
def read_path(self, b, level_start=0):
assert 0 <= b < self._vheap.bucket_count()
bucket_list = self._vheap.Node(b).bucket_path_from_root()
assert 0 <= level_start < len(bucket_list)
return self._storage.read_blocks(bucket_list[level_start:])
def write_path(self, b, buckets, level_start=0):
assert 0 <= b < self._vheap.bucket_count()
bucket_list = self._vheap.Node(b).bucket_path_from_root()
assert 0 <= level_start < len(bucket_list)
self._storage.write_blocks(bucket_list[level_start:],
buckets)
@property
def bytes_sent(self):
return self._storage.bytes_sent
@property
def bytes_received(self):
return self._storage.bytes_received
| {
"repo_name": "ghackebeil/PyORAM",
"path": "src/pyoram/storage/heap_storage.py",
"copies": "1",
"size": "8257",
"license": "mit",
"hash": 692533584230819000,
"line_mean": 32.9794238683,
"line_max": 73,
"alpha_frac": 0.53130677,
"autogenerated": false,
"ratio": 4.489940184883088,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0032802361512752148,
"num_lines": 243
} |
__all__ = ['HelloWorldBinaryLabelsClassifier', 'HelloWorldMultiLabelsClassifier']
import numpy as np
from sklearn.svm import SVC
from ycml.classifiers import BinaryLabelsClassifier, MultiLabelsClassifier, MulticlassLabelsClassifier
class HelloWorldBinaryLabelsClassifier(BinaryLabelsClassifier):
def fit_binarized(self, X_featurized, Y_binarized, **kwargs):
self.classifier_ = SVC(probability=True).fit(X_featurized, Y_binarized[:, 1])
return self
#end def
def _predict_proba(self, X_featurized, **kwargs):
return self.classifier_.predict_proba(X_featurized)
#end class
class HelloWorldMultiLabelsClassifier(MultiLabelsClassifier):
def fit_binarized(self, X_featurized, Y_binarized, **kwargs):
self.classifiers_ = {}
for j, c in enumerate(self.classes_):
self.classifiers_[c] = SVC(probability=True).fit(X_featurized, Y_binarized[:, j])
return self
#end def
def _predict_proba(self, X_featurized, **kwargs):
Y_proba = np.zeros((X_featurized.shape[0], len(self.classes_)), dtype=np.float)
for j, c in enumerate(self.classes_):
Y_proba[:, j] = self.classifiers_[c].predict_proba(X_featurized, **kwargs)[:, 1]
return Y_proba
#end def
#end class
class HelloWorldMulticlassClassifier(MulticlassLabelsClassifier):
def fit_binarized(self, X_featurized, Y_binarized, **kwargs):
self.classifier_ = SVC(probability=True).fit(X_featurized, Y_binarized)
return self
#end def
def _predict_proba(self, X_featurized, **kwargs):
return self.classifier_.predict_proba(X_featurized, **kwargs)
# Y_proba = np.zeros((X_featurized.shape[0], len(self.classes_)), dtype=np.float)
# for j, c in enumerate(self.classes_):
# Y_proba[:, j] = self.classifiers_[c].predict_proba(X_featurized, **kwargs)[:, 1]
# return Y_proba
#end def
#end class
| {
"repo_name": "skylander86/ycml",
"path": "helloworld/classifiers.py",
"copies": "1",
"size": "1936",
"license": "apache-2.0",
"hash": -2603075957997008400,
"line_mean": 32.3793103448,
"line_max": 102,
"alpha_frac": 0.6751033058,
"autogenerated": false,
"ratio": 3.4144620811287476,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4589565386928747,
"avg_score": null,
"num_lines": null
} |
"""All helpers for the URL."""
import urllib2
from mimetypes import MimeTypes
from ..base import FORMAT
from ..exceptions import URLError
def check_url(url):
"""
Check if URL is accessible and what data format it is.
Parameters
----------
url : str
URL to check.
Returns
-------
str
Data format of the URL.
"""
dataformat = None
response = None
errors = []
try:
response = urllib2.urlopen(url)
if response.headers.get('Access-Control-Allow-Origin') != '*':
errors.append(
'The server does not allow to use this URL externally. Make '
'sure that CORS is enabled on the server.'
)
else:
# Try to guess content type first
content_type = MimeTypes().guess_type(url)[0]
# If unsuccessful, get it from the headers
if content_type is None:
content_type = response.headers.get('Content-Type')
if 'application/json' in content_type:
dataformat = FORMAT.GeoJSON
elif 'application/vnd.google-earth.kml+xml' in content_type:
dataformat = FORMAT.KML
else:
errors.append('This data format is currently not supported.')
except urllib2.URLError as error:
if hasattr(error, 'code'):
errors.append('The server returned %s error.' % error.code)
if hasattr(error, 'reason'):
errors.append('Failed to reach the server: %s.' % error.reason)
if errors:
raise URLError('The URL cannot be used due to:', errors)
return dataformat
| {
"repo_name": "ExCiteS/geokey-webresources",
"path": "geokey_webresources/helpers/url_helpers.py",
"copies": "1",
"size": "1674",
"license": "mit",
"hash": 1982329427628254700,
"line_mean": 26.9,
"line_max": 77,
"alpha_frac": 0.5758661888,
"autogenerated": false,
"ratio": 4.405263157894737,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5481129346694738,
"avg_score": null,
"num_lines": null
} |
__all__ = ['Hero']
class Hero(object):
'''Represents a hero in the game.
Attributes:
id (int): the hero's id.
name (string): the bot's name.
user_id (string): the bot's id (None in training mode).
elo (int): the bot's ELO (None in training mode).
crashed (bool): True if the bot has been disconnected.
mine_count (int): the number of mines this hero owns.
gold (int): current amount of gold earned by this hero.
life (int): current hero's life.
last_dir (string): last bot movement (may be None).
x (int): the bot's position in the X axis.
y (int): the bot's position in the Y axis.
spawn_x (int): the bot's spawn position in X.
spawn_y (int): the bot's spawn position in Y.
'''
def __init__(self, hero):
'''Constructor.
Args:
hero (dict): the hero data from the server.
'''
# Constants
self.id = hero['id']
self.name = hero['name']
self.user_id = hero.get('userId')
self.elo = hero.get('elo')
# Variables
self.crashed = hero['crashed']
self.mine_count = hero['mineCount']
self.gold = hero['gold']
self.life = hero['life']
self.last_dir = hero.get('lastDir')
self.x = hero['pos']['y']
self.y = hero['pos']['x']
self.spawn_x = hero['spawnPos']['y']
self.spawn_y = hero['spawnPos']['x']
| {
"repo_name": "renatopp/vindinium-python",
"path": "vindinium/models/hero.py",
"copies": "1",
"size": "1552",
"license": "mit",
"hash": -2982725426382311000,
"line_mean": 35.0930232558,
"line_max": 63,
"alpha_frac": 0.5128865979,
"autogenerated": false,
"ratio": 3.5596330275229358,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4572519625422936,
"avg_score": null,
"num_lines": null
} |
__all__ = ["HostInfo"]
from panda3d.core import HashVal, Filename, PandaSystem, DocumentSpec, Ramfile
from panda3d.core import HTTPChannel, ConfigVariableInt
from panda3d import core
from direct.p3d.PackageInfo import PackageInfo
from direct.p3d.FileSpec import FileSpec
from direct.directnotify.DirectNotifyGlobal import directNotify
import time
class HostInfo:
""" This class represents a particular download host serving up
Panda3D packages. It is the Python equivalent of the P3DHost
class in the core API. """
notify = directNotify.newCategory("HostInfo")
def __init__(self, hostUrl, appRunner = None, hostDir = None,
rootDir = None, asMirror = False, perPlatform = None):
""" You must specify either an appRunner or a hostDir to the
HostInfo constructor.
If you pass asMirror = True, it means that this HostInfo
object is to be used to populate a "mirror" folder, a
duplicate (or subset) of the contents hosted by a server.
This means when you use this HostInfo to download packages, it
will only download the compressed archive file and leave it
there. At the moment, mirror folders do not download old
patch files from the server.
If you pass perPlatform = True, then files are unpacked into a
platform-specific directory, which is appropriate when you
might be downloading multiple platforms. The default is
perPlatform = False, which means all files are unpacked into
the host directory directly, without an intervening
platform-specific directory name. If asMirror is True, then
the default is perPlatform = True.
Note that perPlatform is also restricted by the individual
package's specification. """
self.__setHostUrl(hostUrl)
self.appRunner = appRunner
self.rootDir = rootDir
if rootDir is None and appRunner:
self.rootDir = appRunner.rootDir
if hostDir and not isinstance(hostDir, Filename):
hostDir = Filename.fromOsSpecific(hostDir)
self.hostDir = hostDir
self.asMirror = asMirror
self.perPlatform = perPlatform
if perPlatform is None:
self.perPlatform = asMirror
# Initially false, this is set true when the contents file is
# successfully read.
self.hasContentsFile = False
# This is the time value at which the current contents file is
# no longer valid.
self.contentsExpiration = 0
# Contains the md5 hash of the original contents.xml file.
self.contentsSpec = FileSpec()
# descriptiveName will be filled in later, when the
# contents file is read.
self.descriptiveName = None
# A list of known mirrors for this host, all URL's guaranteed
# to end with a slash.
self.mirrors = []
# A map of keyword -> altHost URL's. An altHost is different
# than a mirror; an altHost is an alternate URL to download a
# different (e.g. testing) version of this host's contents.
# It is rarely used.
self.altHosts = {}
# This is a dictionary of packages by (name, version). It
# will be filled in when the contents file is read.
self.packages = {}
if self.appRunner and self.appRunner.verifyContents != self.appRunner.P3DVCForce:
# Attempt to pre-read the existing contents.xml; maybe it
# will be current enough for our purposes.
self.readContentsFile()
def __setHostUrl(self, hostUrl):
""" Assigns self.hostUrl, and related values. """
self.hostUrl = hostUrl
if not self.hostUrl:
# A special case: the URL will be set later.
self.hostUrlPrefix = None
self.downloadUrlPrefix = None
else:
# hostUrlPrefix is the host URL, but it is guaranteed to end
# with a slash.
self.hostUrlPrefix = hostUrl
if self.hostUrlPrefix[-1] != '/':
self.hostUrlPrefix += '/'
# downloadUrlPrefix is the URL prefix that should be used for
# everything other than the contents.xml file. It might be
# the same as hostUrlPrefix, but in the case of an
# https-protected hostUrl, it will be the cleartext channel.
self.downloadUrlPrefix = self.hostUrlPrefix
def freshenFile(self, http, fileSpec, localPathname):
""" Ensures that the localPathname is the most current version
of the file defined by fileSpec, as offered by host. If not,
it downloads a new version on-the-spot. Returns true on
success, false on failure. """
if fileSpec.quickVerify(pathname = localPathname):
# It's good, keep it.
return True
# It's stale, get a new one.
doc = None
if self.appRunner and self.appRunner.superMirrorUrl:
# Use the "super mirror" first.
url = core.URLSpec(self.appRunner.superMirrorUrl + fileSpec.filename)
self.notify.info("Freshening %s" % (url))
doc = http.getDocument(url)
if not doc or not doc.isValid():
# Failing the super mirror, contact the actual host.
url = core.URLSpec(self.hostUrlPrefix + fileSpec.filename)
self.notify.info("Freshening %s" % (url))
doc = http.getDocument(url)
if not doc.isValid():
return False
file = Filename.temporary('', 'p3d_')
if not doc.downloadToFile(file):
# Failed to download.
file.unlink()
return False
# Successfully downloaded!
localPathname.makeDir()
if not file.renameTo(localPathname):
# Couldn't move it into place.
file.unlink()
return False
if not fileSpec.fullVerify(pathname = localPathname, notify = self.notify):
# No good after download.
self.notify.info("%s is still no good after downloading." % (url))
return False
return True
def downloadContentsFile(self, http, redownload = False,
hashVal = None):
""" Downloads the contents.xml file for this particular host,
synchronously, and then reads it. Returns true on success,
false on failure. If hashVal is not None, it should be a
HashVal object, which will be filled with the hash from the
new contents.xml file."""
if self.hasCurrentContentsFile():
# We've already got one.
return True
if self.appRunner and self.appRunner.verifyContents == self.appRunner.P3DVCNever:
# Not allowed to.
return False
rf = None
if http:
if not redownload and self.appRunner and self.appRunner.superMirrorUrl:
# We start with the "super mirror", if it's defined.
url = self.appRunner.superMirrorUrl + 'contents.xml'
request = DocumentSpec(url)
self.notify.info("Downloading contents file %s" % (request))
rf = Ramfile()
channel = http.makeChannel(False)
channel.getDocument(request)
if not channel.downloadToRam(rf):
self.notify.warning("Unable to download %s" % (url))
rf = None
if not rf:
# Then go to the main host, if our super mirror let us
# down.
url = self.hostUrlPrefix + 'contents.xml'
# Append a uniquifying query string to the URL to force the
# download to go all the way through any caches. We use the
# time in seconds; that's unique enough.
url += '?' + str(int(time.time()))
# We might as well explicitly request the cache to be disabled
# too, since we have an interface for that via HTTPChannel.
request = DocumentSpec(url)
request.setCacheControl(DocumentSpec.CCNoCache)
self.notify.info("Downloading contents file %s" % (request))
statusCode = None
statusString = ''
for attempt in range(int(ConfigVariableInt('contents-xml-dl-attempts', 3))):
if attempt > 0:
self.notify.info("Retrying (%s)..."%(attempt,))
rf = Ramfile()
channel = http.makeChannel(False)
channel.getDocument(request)
if channel.downloadToRam(rf):
self.notify.info("Successfully downloaded %s" % (url,))
break
else:
rf = None
statusCode = channel.getStatusCode()
statusString = channel.getStatusString()
self.notify.warning("Could not contact download server at %s" % (url,))
self.notify.warning("Status code = %s %s" % (statusCode, statusString))
if not rf:
self.notify.warning("Unable to download %s" % (url,))
try:
# Something screwed up.
if statusCode == HTTPChannel.SCDownloadOpenError or \
statusCode == HTTPChannel.SCDownloadWriteError:
launcher.setPandaErrorCode(2)
elif statusCode == 404:
# 404 not found
launcher.setPandaErrorCode(5)
elif statusCode < 100:
# statusCode < 100 implies the connection attempt itself
# failed. This is usually due to firewall software
# interfering. Apparently some firewall software might
# allow the first connection and disallow subsequent
# connections; how strange.
launcher.setPandaErrorCode(4)
else:
# There are other kinds of failures, but these will
# generally have been caught already by the first test; so
# if we get here there may be some bigger problem. Just
# give the generic "big problem" message.
launcher.setPandaErrorCode(6)
except NameError,e:
# no launcher
pass
except AttributeError, e:
self.notify.warning("%s" % (str(e),))
pass
return False
tempFilename = Filename.temporary('', 'p3d_', '.xml')
if rf:
f = open(tempFilename.toOsSpecific(), 'wb')
f.write(rf.getData())
f.close()
if hashVal:
hashVal.hashString(rf.getData())
if not self.readContentsFile(tempFilename, freshDownload = True):
self.notify.warning("Failure reading %s" % (url))
tempFilename.unlink()
return False
tempFilename.unlink()
return True
# Couldn't download the file. Maybe we should look for a
# previously-downloaded copy already on disk?
return False
def redownloadContentsFile(self, http):
""" Downloads a new contents.xml file in case it has changed.
Returns true if the file has indeed changed, false if it has
not. """
assert self.hasContentsFile
if self.appRunner and self.appRunner.verifyContents == self.appRunner.P3DVCNever:
# Not allowed to.
return False
url = self.hostUrlPrefix + 'contents.xml'
self.notify.info("Redownloading %s" % (url))
# Get the hash of the original file.
assert self.hostDir
hv1 = HashVal()
if self.contentsSpec.hash:
hv1.setFromHex(self.contentsSpec.hash)
else:
filename = Filename(self.hostDir, 'contents.xml')
hv1.hashFile(filename)
# Now download it again.
self.hasContentsFile = False
hv2 = HashVal()
if not self.downloadContentsFile(http, redownload = True,
hashVal = hv2):
return False
if hv2 == HashVal():
self.notify.info("%s didn't actually redownload." % (url))
return False
elif hv1 != hv2:
self.notify.info("%s has changed." % (url))
return True
else:
self.notify.info("%s has not changed." % (url))
return False
def hasCurrentContentsFile(self):
""" Returns true if a contents.xml file has been successfully
read for this host and is still current, false otherwise. """
if not self.appRunner \
or self.appRunner.verifyContents == self.appRunner.P3DVCNone \
or self.appRunner.verifyContents == self.appRunner.P3DVCNever:
# If we're not asking to verify contents, then
# contents.xml files never expires.
return self.hasContentsFile
now = int(time.time())
return now < self.contentsExpiration and self.hasContentsFile
def readContentsFile(self, tempFilename = None, freshDownload = False):
""" Reads the contents.xml file for this particular host, once
it has been downloaded into the indicated temporary file.
Returns true on success, false if the contents file is not
already on disk or is unreadable.
If tempFilename is specified, it is the filename read, and it
is copied the file into the standard location if it's not
there already. If tempFilename is not specified, the standard
filename is read if it is known. """
if not hasattr(core, 'TiXmlDocument'):
return False
if not tempFilename:
if self.hostDir:
# If the filename is not specified, we can infer it
# if we already know our hostDir
hostDir = self.hostDir
else:
# Otherwise, we have to guess the hostDir.
hostDir = self.__determineHostDir(None, self.hostUrl)
tempFilename = Filename(hostDir, 'contents.xml')
doc = core.TiXmlDocument(tempFilename.toOsSpecific())
if not doc.LoadFile():
return False
xcontents = doc.FirstChildElement('contents')
if not xcontents:
return False
maxAge = xcontents.Attribute('max_age')
if maxAge:
try:
maxAge = int(maxAge)
except:
maxAge = None
if maxAge is None:
# Default max_age if unspecified (see p3d_plugin.h).
from direct.p3d.AppRunner import AppRunner
maxAge = AppRunner.P3D_CONTENTS_DEFAULT_MAX_AGE
# Get the latest possible expiration time, based on the max_age
# indication. Any expiration time later than this is in error.
now = int(time.time())
self.contentsExpiration = now + maxAge
if freshDownload:
self.contentsSpec.readHash(tempFilename)
# Update the XML with the new download information.
xorig = xcontents.FirstChildElement('orig')
while xorig:
xcontents.RemoveChild(xorig)
xorig = xcontents.FirstChildElement('orig')
xorig = core.TiXmlElement('orig')
self.contentsSpec.storeXml(xorig)
xorig.SetAttribute('expiration', str(self.contentsExpiration))
xcontents.InsertEndChild(xorig)
else:
# Read the download hash and expiration time from the XML.
expiration = None
xorig = xcontents.FirstChildElement('orig')
if xorig:
self.contentsSpec.loadXml(xorig)
expiration = xorig.Attribute('expiration')
if expiration:
try:
expiration = int(expiration)
except:
expiration = None
if not self.contentsSpec.hash:
self.contentsSpec.readHash(tempFilename)
if expiration is not None:
self.contentsExpiration = min(self.contentsExpiration, expiration)
# Look for our own entry in the hosts table.
if self.hostUrl:
self.__findHostXml(xcontents)
else:
assert self.hostDir
self.__findHostXmlForHostDir(xcontents)
if self.rootDir and not self.hostDir:
self.hostDir = self.__determineHostDir(None, self.hostUrl)
# Get the list of packages available for download and/or import.
xpackage = xcontents.FirstChildElement('package')
while xpackage:
name = xpackage.Attribute('name')
platform = xpackage.Attribute('platform')
version = xpackage.Attribute('version')
try:
solo = int(xpackage.Attribute('solo') or '')
except ValueError:
solo = False
try:
perPlatform = int(xpackage.Attribute('per_platform') or '')
except ValueError:
perPlatform = False
package = self.__makePackage(name, platform, version, solo, perPlatform)
package.descFile = FileSpec()
package.descFile.loadXml(xpackage)
package.setupFilenames()
package.importDescFile = None
ximport = xpackage.FirstChildElement('import')
if ximport:
package.importDescFile = FileSpec()
package.importDescFile.loadXml(ximport)
xpackage = xpackage.NextSiblingElement('package')
self.hasContentsFile = True
# Now save the contents.xml file into the standard location.
if self.appRunner and self.appRunner.verifyContents != self.appRunner.P3DVCNever:
assert self.hostDir
filename = Filename(self.hostDir, 'contents.xml')
filename.makeDir()
if freshDownload:
doc.SaveFile(filename.toOsSpecific())
else:
if filename != tempFilename:
tempFilename.copyTo(filename)
return True
def __findHostXml(self, xcontents):
""" Looks for the <host> or <alt_host> entry in the
contents.xml that corresponds to the URL that we actually
downloaded from. """
xhost = xcontents.FirstChildElement('host')
while xhost:
url = xhost.Attribute('url')
if url == self.hostUrl:
self.readHostXml(xhost)
return
xalthost = xhost.FirstChildElement('alt_host')
while xalthost:
url = xalthost.Attribute('url')
if url == self.hostUrl:
self.readHostXml(xalthost)
return
xalthost = xalthost.NextSiblingElement('alt_host')
xhost = xhost.NextSiblingElement('host')
def __findHostXmlForHostDir(self, xcontents):
""" Looks for the <host> or <alt_host> entry in the
contents.xml that corresponds to the host dir that we read the
contents.xml from. This is used when reading a contents.xml
file found on disk, as opposed to downloading it from a
site. """
xhost = xcontents.FirstChildElement('host')
while xhost:
url = xhost.Attribute('url')
hostDirBasename = xhost.Attribute('host_dir')
hostDir = self.__determineHostDir(hostDirBasename, url)
if hostDir == self.hostDir:
self.__setHostUrl(url)
self.readHostXml(xhost)
return
xalthost = xhost.FirstChildElement('alt_host')
while xalthost:
url = xalthost.Attribute('url')
hostDirBasename = xalthost.Attribute('host_dir')
hostDir = self.__determineHostDir(hostDirBasename, url)
if hostDir == self.hostDir:
self.__setHostUrl(url)
self.readHostXml(xalthost)
return
xalthost = xalthost.NextSiblingElement('alt_host')
xhost = xhost.NextSiblingElement('host')
def readHostXml(self, xhost):
""" Reads a <host> or <alt_host> entry and applies the data to
this object. """
descriptiveName = xhost.Attribute('descriptive_name')
if descriptiveName and not self.descriptiveName:
self.descriptiveName = descriptiveName
hostDirBasename = xhost.Attribute('host_dir')
if self.rootDir and not self.hostDir:
self.hostDir = self.__determineHostDir(hostDirBasename, self.hostUrl)
# Get the "download" URL, which is the source from which we
# download everything other than the contents.xml file.
downloadUrl = xhost.Attribute('download_url')
if downloadUrl:
self.downloadUrlPrefix = downloadUrl
if self.downloadUrlPrefix[-1] != '/':
self.downloadUrlPrefix += '/'
else:
self.downloadUrlPrefix = self.hostUrlPrefix
xmirror = xhost.FirstChildElement('mirror')
while xmirror:
url = xmirror.Attribute('url')
if url:
if url[-1] != '/':
url += '/'
if url not in self.mirrors:
self.mirrors.append(url)
xmirror = xmirror.NextSiblingElement('mirror')
xalthost = xhost.FirstChildElement('alt_host')
while xalthost:
keyword = xalthost.Attribute('keyword')
url = xalthost.Attribute('url')
if url and keyword:
self.altHosts[keyword] = url
xalthost = xalthost.NextSiblingElement('alt_host')
def __makePackage(self, name, platform, version, solo, perPlatform):
""" Creates a new PackageInfo entry for the given name,
version, and platform. If there is already a matching
PackageInfo, returns it. """
if not platform:
platform = None
platforms = self.packages.setdefault((name, version or ""), {})
package = platforms.get("", None)
if not package:
package = PackageInfo(self, name, version, platform = platform,
solo = solo, asMirror = self.asMirror,
perPlatform = perPlatform)
platforms[platform or ""] = package
return package
def getPackage(self, name, version, platform = None):
""" Returns a PackageInfo that matches the indicated name and
version and the indicated platform or the current runtime
platform, if one is provided by this host, or None if not. """
assert self.hasContentsFile
platforms = self.packages.get((name, version or ""), {})
if platform:
# In this case, we are looking for a specific platform
# only.
return platforms.get(platform, None)
# We are looking for one matching the current runtime
# platform. First, look for a package matching the current
# platform exactly.
package = platforms.get(PandaSystem.getPlatform(), None)
# If not found, look for one matching no particular platform.
if not package:
package = platforms.get("", None)
return package
def getPackages(self, name = None, platform = None):
""" Returns a list of PackageInfo objects that match the
indicated name and/or platform, with no particular regards to
version. If name is None, all packages are returned. """
assert self.hasContentsFile
packages = []
for (pn, version), platforms in self.packages.items():
if name and pn != name:
continue
if not platform:
for p2 in platforms:
package = self.getPackage(pn, version, platform = p2)
if package:
packages.append(package)
else:
package = self.getPackage(pn, version, platform = platform)
if package:
packages.append(package)
return packages
def getAllPackages(self, includeAllPlatforms = False):
""" Returns a list of all available packages provided by this
host. """
result = []
items = sorted(self.packages.items())
for key, platforms in items:
if self.perPlatform or includeAllPlatforms:
# If we maintain a different answer per platform,
# return all of them.
pitems = sorted(platforms.items())
for pkey, package in pitems:
result.append(package)
else:
# If we maintain a host for the current platform
# only (e.g. a client copy), then return only the
# current platform, or no particular platform.
package = platforms.get(PandaSystem.getPlatform(), None)
if not package:
package = platforms.get("", None)
if package:
result.append(package)
return result
def deletePackages(self, packages):
""" Removes all of the indicated packages from the disk,
uninstalling them and deleting all of their files. The
packages parameter must be a list of one or more PackageInfo
objects, for instance as returned by getPackage(). Returns
the list of packages that were NOT found. """
packages = packages[:]
for key, platforms in self.packages.items():
for platform, package in platforms.items():
if package in packages:
self.__deletePackageFiles(package)
del platforms[platform]
packages.remove(package)
if not platforms:
# If we've removed all the platforms for a given
# package, remove the key from the toplevel map.
del self.packages[key]
return packages
def __deletePackageFiles(self, package):
""" Called by deletePackage(), this actually removes the files
for the indicated package. """
if self.appRunner:
self.notify.info("Deleting package %s: %s" % (package.packageName, package.getPackageDir()))
self.appRunner.rmtree(package.getPackageDir())
self.appRunner.sendRequest('forget_package', self.hostUrl, package.packageName, package.packageVersion or '')
def __determineHostDir(self, hostDirBasename, hostUrl):
""" Hashes the host URL into a (mostly) unique directory
string, which will be the root of the host's install tree.
Returns the resulting path, as a Filename.
This code is duplicated in C++, in
P3DHost::determine_host_dir(). """
if hostDirBasename:
# If the contents.xml specified a host_dir parameter, use
# it.
hostDir = str(self.rootDir) + '/hosts'
for component in hostDirBasename.split('/'):
if component:
if component[0] == '.':
# Forbid ".foo" or "..".
component = 'x' + component
hostDir += '/'
hostDir += component
return Filename(hostDir)
hostDir = 'hosts/'
# Look for a server name in the URL. Including this string in the
# directory name makes it friendlier for people browsing the
# directory.
# We could use URLSpec, but we do it by hand instead, to make
# it more likely that our hash code will exactly match the
# similar logic in P3DHost.
p = hostUrl.find('://')
hostname = ''
if p != -1:
start = p + 3
end = hostUrl.find('/', start)
# Now start .. end is something like "username@host:port".
at = hostUrl.find('@', start)
if at != -1 and at < end:
start = at + 1
colon = hostUrl.find(':', start)
if colon != -1 and colon < end:
end = colon
# Now start .. end is just the hostname.
hostname = hostUrl[start : end]
# Now build a hash string of the whole URL. We'll use MD5 to
# get a pretty good hash, with a minimum chance of collision.
# Even if there is a hash collision, though, it's not the end
# of the world; it just means that both hosts will dump their
# packages into the same directory, and they'll fight over the
# toplevel contents.xml file. Assuming they use different
# version numbers (which should be safe since they have the
# same hostname), there will be minimal redownloading.
hashSize = 16
keepHash = hashSize
if hostname:
hostDir += hostname + '_'
# If we successfully got a hostname, we don't really need the
# full hash. We'll keep half of it.
keepHash = keepHash // 2
md = HashVal()
md.hashString(hostUrl)
hostDir += md.asHex()[:keepHash * 2]
hostDir = Filename(self.rootDir, hostDir)
return hostDir
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"has_no_keywords": false,
"few_assignments": false,
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__all__ = ['hough']
from itertools import izip
import numpy as np
def _hough(img, theta=None):
if img.ndim != 2:
raise ValueError('The input image must be 2-D')
if theta is None:
theta = np.linspace(-np.pi / 2, np.pi / 2, 180)
# compute the vertical bins (the distances)
d = np.ceil(np.hypot(*img.shape))
nr_bins = 2 * d
bins = np.linspace(-d, d, nr_bins)
# allocate the output image
out = np.zeros((nr_bins, len(theta)), dtype=np.uint64)
# precompute the sin and cos of the angles
cos_theta = np.cos(theta)
sin_theta = np.sin(theta)
# find the indices of the non-zero values in
# the input image
y, x = np.nonzero(img)
# x and y can be large, so we can't just broadcast to 2D
# arrays as we may run out of memory. Instead we process
# one vertical slice at a time.
for i, (cT, sT) in enumerate(izip(cos_theta, sin_theta)):
# compute the base distances
distances = x * cT + y * sT
# round the distances to the nearest integer
# and shift them to a nonzero bin
shifted = np.round(distances) - bins[0]
# cast the shifted values to ints to use as indices
indices = shifted.astype(np.int)
# use bin count to accumulate the coefficients
bincount = np.bincount(indices)
# finally assign the proper values to the out array
out[:len(bincount), i] = bincount
return out, theta, bins
_py_hough = _hough
# try to import and use the faster Cython version if it exists
try:
from ._hough_transform import _hough
except ImportError:
pass
def hough(img, theta=None):
"""Perform a straight line Hough transform.
Parameters
----------
img : (M, N) ndarray
Input image with nonzero values representing edges.
theta : 1D ndarray of double
Angles at which to compute the transform, in radians.
Defaults to -pi/2 - pi/2
Returns
-------
H : 2-D ndarray of uint64
Hough transform accumulator.
distances : ndarray
Distance values.
theta : ndarray
Angles at which the transform was computed.
Examples
--------
Generate a test image:
>>> img = np.zeros((100, 150), dtype=bool)
>>> img[30, :] = 1
>>> img[:, 65] = 1
>>> img[35:45, 35:50] = 1
>>> for i in range(90):
>>> img[i, i] = 1
>>> img += np.random.random(img.shape) > 0.95
Apply the Hough transform:
>>> out, angles, d = hough(img)
Plot the results:
>>> import matplotlib.pyplot as plt
>>> plt.imshow(out, cmap=plt.cm.bone)
>>> plt.xlabel('Angle (degree)')
>>> plt.ylabel('Distance %d (pixel)' % d[0])
>>> plt.show()
.. plot:: hough_tf.py
"""
return _hough(img, theta)
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__all__ = ['hough', 'probabilistic_hough']
from itertools import izip as zip
import numpy as np
from ._hough_transform import _probabilistic_hough
def _hough(img, theta=None):
if img.ndim != 2:
raise ValueError('The input image must be 2-D')
if theta is None:
theta = np.linspace(-np.pi / 2, np.pi / 2, 180)
# compute the vertical bins (the distances)
d = np.ceil(np.hypot(*img.shape))
nr_bins = 2 * d
bins = np.linspace(-d, d, nr_bins)
# allocate the output image
out = np.zeros((nr_bins, len(theta)), dtype=np.uint64)
# precompute the sin and cos of the angles
cos_theta = np.cos(theta)
sin_theta = np.sin(theta)
# find the indices of the non-zero values in
# the input image
y, x = np.nonzero(img)
# x and y can be large, so we can't just broadcast to 2D
# arrays as we may run out of memory. Instead we process
# one vertical slice at a time.
for i, (cT, sT) in enumerate(zip(cos_theta, sin_theta)):
# compute the base distances
distances = x * cT + y * sT
# round the distances to the nearest integer
# and shift them to a nonzero bin
shifted = np.round(distances) - bins[0]
# cast the shifted values to ints to use as indices
indices = shifted.astype(np.int)
# use bin count to accumulate the coefficients
bincount = np.bincount(indices)
# finally assign the proper values to the out array
out[:len(bincount), i] = bincount
return out, theta, bins
_py_hough = _hough
# try to import and use the faster Cython version if it exists
try:
from ._hough_transform import _hough
except ImportError:
pass
def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10, theta=None):
"""Performs a progressive probabilistic line Hough transform and returns the detected lines.
Parameters
----------
img : (M, N) ndarray
Input image with nonzero values representing edges.
threshold : int
Threshold
line_length : int, optional (default 50)
Minimum accepted length of detected lines.
Increase the parameter to extract longer lines.
line_gap : int, optional, (default 10)
Maximum gap between pixels to still form a line.
Increase the parameter to merge broken lines more aggresively.
theta : 1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
Angles at which to compute the transform, in radians.
Returns
-------
lines : list
List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating
line start and end.
References
----------
.. [1] C. Galamhos, J. Matas and J. Kittler,"Progressive probabilistic Hough
transform for line detection", in IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 1999.
"""
return _probabilistic_hough(img, threshold, line_length, line_gap, theta)
def hough(img, theta=None):
"""Perform a straight line Hough transform.
Parameters
----------
img : (M, N) ndarray
Input image with nonzero values representing edges.
theta : 1D ndarray of double
Angles at which to compute the transform, in radians.
Defaults to -pi/2 .. pi/2
Returns
-------
H : 2-D ndarray of uint64
Hough transform accumulator.
distances : ndarray
Distance values.
theta : ndarray
Angles at which the transform was computed.
Examples
--------
Generate a test image:
>>> img = np.zeros((100, 150), dtype=bool)
>>> img[30, :] = 1
>>> img[:, 65] = 1
>>> img[35:45, 35:50] = 1
>>> for i in range(90):
>>> img[i, i] = 1
>>> img += np.random.random(img.shape) > 0.95
Apply the Hough transform:
>>> out, angles, d = hough(img)
Plot the results:
>>> import matplotlib.pyplot as plt
>>> plt.imshow(out, cmap=plt.cm.bone)
>>> plt.xlabel('Angle (degree)')
>>> plt.ylabel('Distance %d (pixel)' % d[0])
>>> plt.show()
.. plot:: hough_tf.py
"""
return _hough(img, theta)
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__all__ = ('HoverBehavior', )
from kivy.properties import BooleanProperty, NumericProperty, ObjectProperty
from kivy.weakproxy import WeakProxy
from kivy.core.window import Window
class HoverBehavior(object):
'''
The HoverBehavior `mixin <https://en.wikipedia.org/wiki/Mixin>`_ provides
Hover behavior. When combined with a widget, hovering mouse cursor
above it's position will call it's on_hovering event.
'''
_hover_grab_widget = None
'''WeakProxy of widget which has grabbed focus or None'''
_hover_widgets = []
'''List of hover widget WeakProxy references'''
min_hover_height = 0
'''Numeric class attribute of minimum height where "hovering" property
will be updated'''
hovering = BooleanProperty(False)
'''Hover state, is True when mouse enters it's position
:attr:`hovering` is a :class:`~kivy.properties.BooleanProperty` and
defaults to `False`.
'''
hover_height = NumericProperty(0)
'''Number that is compared to min_hover_height.
:attr:`hover_height` is a :class:`~kivy.properties.NumericProperty` and
defaults to `0`.
'''
def _on_hover_mouse_move(win, pos):
'''Internal method that is binded on Window.mouse_pos.
Compares mouse position with widget positions,
ignoring disabled widgets and widgets with hover_height below
HoverBehavior.min_hover_height,
then sets widget hovering property to False or True'''
collided = [] # widget weak proxies that collide with mouse pos
if HoverBehavior._hover_grab_widget:
HoverBehavior.hover_widget_refs = [HoverBehavior._hover_grab_widget]
else:
HoverBehavior.hover_widget_refs = HoverBehavior._hover_widgets
for ref in HoverBehavior.hover_widget_refs:
if not ref.disabled and ref._collide_point_window(*pos):
# Get all widgets that are at mouse pos
collided.append(ref)
# Remove hover from all widgets that are not at mouse pos
elif ref.hovering:
ref.hovering = False
if collided:
# Find the highest widget and set it's hover to True
# Set hover to False for other widgets
highest = collided[0]
if len(collided) > 1:
for ref in collided:
if ref.hover_height > highest.hover_height:
if highest.hovering:
highest.hovering = False
highest = ref
elif ref.hovering:
ref.hovering = False
if HoverBehavior._hover_grab_widget:
if not highest.hovering:
highest.hovering = True
elif highest.hover_height >= HoverBehavior.min_hover_height:
if not highest.hovering:
highest.hovering = True
@staticmethod
def force_update_hover():
'''Gets window mouse position and updates hover state for all widgets'''
HoverBehavior._on_hover_mouse_move(Window, Window.mouse_pos)
@staticmethod
def set_min_hover_height(number):
'''Sets min_hover_height for HoverBehavior class'''
HoverBehavior.min_hover_height = number
@staticmethod
def get_min_hover_height():
'''Gets min_hover_height from HoverBehavior class'''
return HoverBehavior.min_hover_height
def __init__(self, **kwargs):
super(HoverBehavior, self).__init__(**kwargs)
self.bind(parent=self._on_parent_update_hover)
def _on_parent_update_hover(self, _, parent):
'''Adds self to hover system when has a parent,
otherwise removes self from hover system'''
if parent:
self.hoverable_add()
else:
self.hoverable_remove()
def hoverable_add(self):
'''Add widget in hover system. By default, is called when widget
is added to a parent'''
HoverBehavior._hover_widgets.append(WeakProxy(self))
def hoverable_remove(self):
'''Remove widget from hover system. By default is called when widget
is removed from a parent'''
HoverBehavior._hover_widgets.remove(self)
def grab_hover(self):
'''Prevents other widgets from receiving hover'''
HoverBehavior._hover_grab_widget = WeakProxy(self)
@staticmethod
def get_hover_grab_widget():
'''Returns widget which has grabbed hover currently or None'''
return HoverBehavior._hover_grab_widget
@staticmethod
def remove_hover_grab():
'''Removes widget WeakProxy from hover system'''
HoverBehavior._hover_grab_widget = None
def _collide_point_window(self, x, y):
'''Widget collide point method that compares arguments to
"self.to_window(self.x, self.y)" instead of "self.x, self.y"'''
sx, sy = self.to_window(self.x, self.y)
return sx <= x <= sx + self.width and sy <= y <= sy + self.height
Window.bind(mouse_pos=HoverBehavior._on_hover_mouse_move)
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__all__ = ['HPX_grid_step', 'HPX_grid_size', 'FITS_to_HPPX']
import numpy as np
from scipy import sparse
# Kapteyn software contains tie-ins to WCS standard.
try:
from kapteyn import wcs
except ImportError:
print ("kapteyn package required: download at\n"
"http://www.astro.rug.nl/software/kapteyn/")
raise
from .grid_interpolation import GridInterpolation
from .util import regrid
def HPX_grid_size(Nside):
"""Return the size of the pixel grid (Nx, Ny) for a given Nside"""
Nx = 8 * Nside
Ny = 4 * Nside + 1
return Nx, Ny
def HPX_grid_step(Nside):
"""Return the size of the step between pixels in degrees"""
return 45. / Nside
def FITS_to_HPX(header, data, Nside, return_sparse=False):
"""Convert data from FITS format to sparse HPX grid
Parameters
----------
header : dict or PyFITS header
WCS header describing the coordinates of the input array
data : array_like
Input data array
Nside : int
HEALPix gridding parameter
Returns
-------
hpx_data : csr matrix
The HPX-projected data
"""
# Here's what we do for this function: we're working in "IMG coords"
# (i.e. the projection of the input data) and "HPX coords" (i.e. the
# projection of the output data). In between, we use "WCS coords".
#
# These are the steps involved:
# 1. Create an array of image edge-pixels in IMG coords, and project
# these to HPX coords.
# 2. From these bounds, create a regular grid of HPX coords that covers
# the image. Project this grid to IMG coords.
# 3. In IMG coords, interpolate the image data to the healpix grid.
# 4. Use this data to construct a sparse array in HPX coords.
if header['NAXIS'] != 2:
raise ValueError("input data & header must be two dimensional")
if data.shape != (header['NAXIS2'], header['NAXIS1']):
raise ValueError("data shape must match header metadata")
# Create wcs projection instance from the header
proj_img = wcs.Projection(header)
# Create wcs projection for healpix grid
# Note that the "pixel" coordinates here are measured in degrees...
# 0 to 360 in x/RA and -90 to 90 in y/DEC
proj_hpx = wcs.Projection({'NAXIS': 2,
'CTYPE1': 'RA---HPX',
'CTYPE2': 'DEC--HPX'})
# Define the dimension of the HEALPIX SciDB grid
Nx_hpx, Ny_hpx = HPX_grid_size(Nside)
dx_hpx = dy_hpx = HPX_grid_step(Nside)
#x_hpx = np.linspace(0, 360, Nx_hpx, endpoint=False)
#y_hpx = np.linspace(-90, 90, Ny_hpx)
# Find the coordinates of the pixels at the edge of the image
# Projecting these onto the healpix grid will give the bounds we need.
img_bounds_x = np.arange(header['NAXIS2'])
zeros_x = np.zeros_like(img_bounds_x)
img_bounds_y = np.arange(header['NAXIS1'])
zeros_y = np.zeros_like(img_bounds_y)
img_bounds_pix = np.concatenate(
[img_bounds_x, img_bounds_x, zeros_y, zeros_y + img_bounds_x[-1],
zeros_x, zeros_x + img_bounds_y[-1], img_bounds_y, img_bounds_y]
).reshape((2, -1)).T
x_bound_hpx, y_bound_hpx =\
proj_hpx.topixel(proj_img.toworld(img_bounds_pix)).T
# here we take the pixels at the edge of the boundaries of the image,
# transform them to HPX coordinates, and find the required extent
# of the HPX pixel grid.
# [TODO: check for crossing the pole]
# first we need to calculate pixel number
i_bound_hpx = x_bound_hpx / dx_hpx
j_bound_hpx = (y_bound_hpx + 90.) / dy_hpx
i_hpx = np.arange(int(np.floor(i_bound_hpx.min())),
int(np.ceil(i_bound_hpx.max()) + 1))
j_hpx = np.arange(int(np.floor(j_bound_hpx.min())),
int(np.ceil(j_bound_hpx.max()) + 1))
x_hpx = i_hpx * dx_hpx
y_hpx = j_hpx * dy_hpx - 90.
# Create the grid of HPX pixels
pixel_ind_hpx = np.vstack(map(np.ravel, np.meshgrid(i_hpx, j_hpx))).T
pixel_locs_hpx = np.vstack(map(np.ravel, np.meshgrid(x_hpx, y_hpx))).T
pixel_locs_img = proj_img.topixel(proj_hpx.toworld(pixel_locs_hpx))
## DEBUG: Plot the borders & grid in the HPX projection
#import matplotlib.pyplot as plt
#plt.plot(i_bound_hpx, j_bound_hpx, '.k')
#plt.plot(pixel_ind_hpx[:, 0], pixel_ind_hpx[:, 1], '.r')
#plt.show()
#exit()
## DEBUG: Plot the HPX grid in the IMG projection
#import matplotlib.pyplot as plt
#plt.plot(img_bounds_pix[:, 0], img_bounds_pix[:, 1], '.k')
#plt.plot(pixel_locs_img[:, 0], pixel_locs_img[:, 1], '.r')
#plt.show()
#exit()
# Interpolate from data to pixel locations
I = GridInterpolation(data, [0, 0], [1, 1])
HPX_vals = I(pixel_locs_img)#.reshape(len(y_hpx), len(x_hpx))
# # DEBUG: Plot regridded input data next to the interpolated HPX data
# import matplotlib.pyplot as plt
# plt.figure(figsize=(8, 8))
# plt.subplot(211, aspect='equal')
# plt.contourf(x_hpx, y_hpx, HPX_vals)
# plt.subplot(212, aspect='equal')
# plt.contourf(regrid(data, 5))
# plt.show()
# exit()
good_vals = ~np.isnan(HPX_vals)
x, y = pixel_ind_hpx[good_vals].T
HPX_vals = HPX_vals[good_vals]
if return_sparse:
return sparse.coo_matrix((HPX_vals, (x, y)),
shape=(Nx_hpx, Ny_hpx))
else:
output = np.zeros(len(HPX_vals),
dtype=[('time', np.int64),
('x', np.int64),
('y', np.int64),
('val', np.float64)])
# use MJD in seconds
output['time'] = int(header['TAI'] * 24 * 60 * 60)
output['x'] = x
output['y'] = y
output['val'] = HPX_vals
return output
| {
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__all__ = ['HTTPView', 'TCPView']
from again.utils import unique_hex
from .utils.helpers import default_preflight_response
from .utils.ordered_class_member import OrderedClassMembers
class BaseView:
'''base class for views'''
_host = None
@property
def host(self):
return self._host
@host.setter
def host(self, host):
self._host = host
class BaseHTTPView(BaseView, metaclass=OrderedClassMembers):
'''base class for HTTP views'''
middlewares = []
def __init__(self):
super(BaseHTTPView, self).__init__()
class HTTPView(BaseHTTPView):
def __init__(self, allow_cross_domain=True,
preflight_response=default_preflight_response):
super(HTTPView, self).__init__()
self._allow_cross_domain = allow_cross_domain
self._preflight_response = preflight_response
@property
def cross_domain_allowed(self):
return self._allow_cross_domain
@property
def preflight_response(self):
return self._preflight_response
class BaseTCPView(BaseView):
'''base class for TCP views'''
def __init__(self):
super(BaseTCPView, self).__init__()
class TCPView(BaseTCPView):
def __init__(self):
super(TCPView, self).__init__()
@staticmethod
def _make_response_packet(request_id: str, from_id: str, entity: str, result: object, error: object,
failed: bool, old_api=None, replacement_api=None):
from .services import _Service
if failed:
payload = {'request_id': request_id, 'error': error, 'failed': failed}
else:
payload = {'request_id': request_id, 'result': result}
if old_api:
payload['old_api'] = old_api
if replacement_api:
payload['replacement_api'] = replacement_api
packet = {'pid': unique_hex(),
'to': from_id,
'entity': entity,
'type': _Service._RES_PKT_STR,
'payload': payload}
return packet
| {
"repo_name": "technomaniac/trellio",
"path": "trellio/views.py",
"copies": "2",
"size": "2073",
"license": "mit",
"hash": -8676553279200657000,
"line_mean": 27.0135135135,
"line_max": 104,
"alpha_frac": 0.5904486252,
"autogenerated": false,
"ratio": 4.017441860465116,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.00045834704469242625,
"num_lines": 74
} |
__all__ = ['HttpWhoHas']
from gevent import monkey; monkey.patch_all() # flake8: noqa
import gevent
from gevent.queue import Queue
from random import sample
import urllib2
import logging
class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):
def http_error_default(self, req, fp, code, msg, headers):
result = urllib2.HTTPError(req.get_full_url(), code, msg, headers, fp)
result.status = code
return result
class HttpWhoHas(object):
"""Finds the HTTP server that store a specific file from a list of HTTP servers.
This object allow to defines clusters of nodes, each nodes of the cluster should have
the same data.
The resole process will try to find a node in a cluster storing a specific file and
will return the full url to the filename.
"""
def __init__(self, per_cluster=3, user_agent='HttpWhoHas.py', proxy=None, timeout=5):
"""Initializes the resolver.
Args:
per_cluster (int): number of node to query from the same cluster.
user_agent (str): the user agent that will be used for queries.
proxy (str): a proxy server with IP/HOST:PORT format (eg: '127.0.0.1:8888').
timeout (int): timeout of the queries.
"""
self.clusters = {}
self.per_cluster = per_cluster
self.user_agent = user_agent
self.timeout = timeout
if proxy:
urllib2.install_opener(
urllib2.build_opener(urllib2.ProxyHandler({'http': proxy})))
urllib2.install_opener(urllib2.build_opener(DefaultErrorHandler()))
self.logger = logging.getLogger('katana.httpwhohas')
def set_cluster(self, name, ips, headers=None):
"""Adds a new cluster in the resolver.
Args:
name (str): the name of the cluster.
ips (list): a list of ips of nodes in this cluster.
headers (dict): a dict of headers that will be passed to every queries.
Returns:
None
"""
self.clusters[name] = {
'ips': ips,
'headers': headers if headers else {},
'per_cluster': min(self.per_cluster, len(ips)),
}
def _do_req(self, name, req, res):
full_url = req.get_full_url()
try:
resp = urllib2.urlopen(req, timeout=self.timeout)
status_code = resp.code
if status_code in (200, 304) and not hasattr(req, 'redirect_dict'):
host = req.get_header('Host')
modified = status_code == 200
self.logger.debug(
'found url=%s filer=%s host=%s modified=%s', full_url, name, host, modified)
res.put({
'filer': name,
'url': full_url,
'host': host,
'modified': modified,
'headers': dict(resp.headers),
})
else:
self.logger.debug(
'%s url=%s returned code %d', name, full_url, status_code)
except (urllib2.HTTPError, urllib2.URLError) as exc:
self.logger.debug('%s url=%s error: %s', name, full_url, exc)
except Exception as exc:
self.logger.exception('%s url=%s got an exception', name, full_url)
def _do_reqs(self, reqs, res):
jobs = [gevent.spawn(self._do_req, name, req, res)
for name, req in reqs]
gevent.joinall(jobs, timeout=self.timeout)
res.put(None)
gevent.killall(jobs)
def resolve(self, filename, etag=None, last_modified=None):
"""Resolves a filename on the clusters.
Args:
filename (str): the filename that we are looking for on the clusters.
etag (str): the etag to user for the query if any.
last_modified (str): the date in the same format as returned by Last-Modified.
Returns:
A dict if the filename was found otherwise None.
The dict has the following keys:
* filer (str): the name of the cluster.
* url (str): the full url of the filename.
* host (str): the value of the Host header.
* modified (bool): True if the file has been modified since the previous request.
* headers (dict): the HTTP response headers.
"""
self.logger.debug('resolving %s', filename)
res = Queue()
reqs = []
for name, info in self.clusters.items():
headers = {'User-Agent': self.user_agent}
headers.update(info['headers'])
if etag:
headers['If-None-Match'] = etag
elif last_modified:
headers['If-Modified-Since'] = last_modified
for ip in sample(info['ips'], info['per_cluster']):
self.logger.debug(
'looking for %s on %s with headers %s', filename, ip, headers)
req = urllib2.Request(
'http://%s%s' % (ip, filename), headers=headers)
req.get_method = lambda: 'HEAD'
reqs.append((name, req))
gevent.spawn(self._do_reqs, reqs, res)
result = res.get()
if result:
self.logger.debug('found %s on %s: url=%s host=%s', filename,
result['filer'], result['url'], result['host'])
else:
self.logger.debug('%s not found', filename)
return result
| {
"repo_name": "ktosiu/katana",
"path": "katana/httpwhohas.py",
"copies": "1",
"size": "5522",
"license": "mpl-2.0",
"hash": -1780310059016073200,
"line_mean": 36.8219178082,
"line_max": 96,
"alpha_frac": 0.5579500181,
"autogenerated": false,
"ratio": 4.186504927975739,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0010777129214134167,
"num_lines": 146
} |
__all__ = ['HttpWhoHas']
from gevent import monkey; monkey.patch_all() # flake8: noqa
import gevent
from gevent.queue import Queue
from random import sample
import urllib.request, urllib.error, urllib.parse
import logging
class DefaultErrorHandler(urllib.request.HTTPDefaultErrorHandler):
def http_error_default(self, req, fp, code, msg, headers):
result = urllib.error.HTTPError(req.get_full_url(), code, msg, headers, fp)
result.status = code
return result
class HttpWhoHas(object):
"""Finds the HTTP server that store a specific file from a list of HTTP servers.
This object allow to defines clusters of nodes, each nodes of the cluster should have
the same data.
The resole process will try to find a node in a cluster storing a specific file and
will return the full url to the filename.
"""
def __init__(self, per_cluster=3, user_agent='HttpWhoHas.py', proxy=None, timeout=5):
"""Initializes the resolver.
Args:
per_cluster (int): number of node to query from the same cluster.
user_agent (str): the user agent that will be used for queries.
proxy (str): a proxy server with IP/HOST:PORT format (eg: '127.0.0.1:8888').
timeout (int): timeout of the queries.
"""
self.clusters = {}
self.per_cluster = per_cluster
self.user_agent = user_agent
self.timeout = timeout
if proxy:
urllib.request.install_opener(
urllib.request.build_opener(urllib.request.ProxyHandler({'http': proxy})))
urllib.request.install_opener(urllib.request.build_opener(DefaultErrorHandler()))
self.logger = logging.getLogger('katana.httpwhohas')
def set_cluster(self, name, ips, headers=None):
"""Adds a new cluster in the resolver.
Args:
name (str): the name of the cluster.
ips (list): a list of ips of nodes in this cluster.
headers (dict): a dict of headers that will be passed to every queries.
Returns:
None
"""
self.clusters[name] = {
'ips': ips,
'headers': headers if headers else {},
'per_cluster': min(self.per_cluster, len(ips)),
}
def _do_req(self, name, req, res):
full_url = req.get_full_url()
try:
resp = urllib.request.urlopen(req, timeout=self.timeout)
status_code = resp.code
if status_code in (200, 304) and not hasattr(req, 'redirect_dict'):
host = req.get_header('Host')
modified = status_code == 200
self.logger.debug(
'found url=%s filer=%s host=%s modified=%s', full_url, name, host, modified)
res.put({
'filer': name,
'url': full_url,
'host': host,
'modified': modified,
'headers': dict(resp.headers),
})
else:
self.logger.debug(
'%s url=%s returned code %d', name, full_url, status_code)
except (urllib.error.HTTPError, urllib.error.URLError) as exc:
self.logger.debug('%s url=%s error: %s', name, full_url, exc)
except Exception as exc:
self.logger.exception('%s url=%s got an exception', name, full_url)
def _do_reqs(self, reqs, res):
jobs = [gevent.spawn(self._do_req, name, req, res)
for name, req in reqs]
gevent.joinall(jobs, timeout=self.timeout)
res.put(None)
gevent.killall(jobs)
def resolve(self, filename, etag=None, last_modified=None):
"""Resolves a filename on the clusters.
Args:
filename (str): the filename that we are looking for on the clusters.
etag (str): the etag to user for the query if any.
last_modified (str): the date in the same format as returned by Last-Modified.
Returns:
A dict if the filename was found otherwise None.
The dict has the following keys:
* filer (str): the name of the cluster.
* url (str): the full url of the filename.
* host (str): the value of the Host header.
* modified (bool): True if the file has been modified since the previous request.
* headers (dict): the HTTP response headers.
"""
self.logger.debug('resolving %s', filename)
res = Queue()
reqs = []
for name, info in list(self.clusters.items()):
headers = {'User-Agent': self.user_agent}
headers.update(info['headers'])
if etag:
headers['If-None-Match'] = etag
elif last_modified:
headers['If-Modified-Since'] = last_modified
for ip in sample(info['ips'], info['per_cluster']):
self.logger.debug(
'looking for %s on %s with headers %s', filename, ip, headers)
req = urllib.request.Request(
'http://%s%s' % (ip, filename), headers=headers)
req.get_method = lambda: 'HEAD'
reqs.append((name, req))
gevent.spawn(self._do_reqs, reqs, res)
result = res.get()
if result:
self.logger.debug('found %s on %s: url=%s host=%s', filename,
result['filer'], result['url'], result['host'])
else:
self.logger.debug('%s not found', filename)
return result
| {
"repo_name": "sebest/katana",
"path": "katana/httpwhohas.py",
"copies": "1",
"size": "5634",
"license": "mpl-2.0",
"hash": 4968513432039562000,
"line_mean": 37.5890410959,
"line_max": 96,
"alpha_frac": 0.5631877884,
"autogenerated": false,
"ratio": 4.201342281879195,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0014476093872002936,
"num_lines": 146
} |
__all__ = ['HyperparameterGridsearchMixin']
import logging
from joblib import Parallel, delayed
from sklearn.metrics import accuracy_score
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection import train_test_split
from ycml.utils import get_class_from_module_path
logger = logging.getLogger(__name__)
class HyperparameterGridsearchMixin(object):
def __init__(self, classifier='sklearn.svm.SVC', classifier_args={}, param_grid={}, metric=accuracy_score, validation_size=0.2, **kwargs):
self.classifier = classifier
self.classifier_args = classifier_args
self.param_grid = param_grid
self.validation_size = validation_size
self.metric = metric
if isinstance(metric, str):
if metric.startswith('lambda'): self.metric = eval(metric)
else: self.metric = get_class_from_module_path(metric)
#end if
#end def
def fit_binarized(self, X_featurized, Y_binarized, validation_data=None, **kwargs):
klass = get_class_from_module_path(self.classifier)
if validation_data is None: # use 0.2 for validation data
X_train, X_validation, Y_train, Y_validation = train_test_split(X_featurized, Y_binarized, test_size=self.validation_size)
logger.info('Using {} of training data ({} instances) for validation.'.format(self.validation_size, Y_validation.shape[0]))
else:
X_train, X_validation, Y_train, Y_validation = X_featurized, validation_data[0], Y_binarized, validation_data[1]
#end if
best_score, best_param = 0.0, None
if self.n_jobs > 1: logger.info('Performing hyperparameter gridsearch in parallel using {} jobs.'.format(self.n_jobs))
else: logger.debug('Performing hyperparameter gridsearch in parallel using {} jobs.'.format(self.n_jobs))
param_scores = Parallel(n_jobs=self.n_jobs)(delayed(_fit_classifier)(klass, self.classifier_args, param, self.metric, X_train, Y_train, X_validation, Y_validation) for param in ParameterGrid(self.param_grid))
best_param, best_score = max(param_scores, key=lambda x: x[1])
logger.info('Best scoring param is {} with score {}.'.format(best_param, best_score))
classifier_args = {}
classifier_args.update(self.classifier_args)
classifier_args.update(best_param)
self.classifier_ = klass(**classifier_args)
logger.info('Fitting final model <{}> on full data with param {}.'.format(self.classifier_, best_param))
self.classifier_.fit(X_featurized, Y_binarized)
return self
#end def
#end class
def _fit_classifier(klass, classifier_args, param, metric, X_train, Y_train, X_validation, Y_validation):
local_classifier_args = {}
local_classifier_args.update(classifier_args)
local_classifier_args.update(param)
classifier = klass(**local_classifier_args).fit(X_train, Y_train)
Y_predict = classifier.predict(X_validation)
score = metric(Y_validation, Y_predict)
logger.info('<{}> with param {} has micro F1 of {}.'.format(classifier, param, score))
return (param, score)
#end def
| {
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"path": "ycml/classifiers/gridsearch.py",
"copies": "1",
"size": "3151",
"license": "apache-2.0",
"hash": 683475633627162000,
"line_mean": 41.5810810811,
"line_max": 216,
"alpha_frac": 0.684544589,
"autogenerated": false,
"ratio": 3.668218859138533,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.48527634481385334,
"avg_score": null,
"num_lines": null
} |
''' Alliance vs. Horde Game Mode, based on an idea that a friend of mine suggested to me once. Draft cards randomly from the entire set with the only restriction being that they are (1) a class card or (2) not a class card BUT of a certain faction (Horde/Alliance). Definitions of card factions have been customly patched as defined in the factions.json file (see root directory of repository). '''
from __init__ import gameMode as mode, HS_NUM_CARDS_IN_SET
from random import uniform, choice
import copy
name = 'Alliance vs. Horde'
key = 'allianceVsHorde'
options = ['Alliance','Horde']
description = "Battle a friend as one of the <strong>factions</strong> of Azeroth! Pandarens, dragons, and other related creatures are <em>neutral</em>."
class gameMode(mode):
def __init__(self,coll,hero,info):
super(gameMode,self).__init__(coll,hero,info)
self.cards = list()
def tooManyCards(self,card): # Quick hack; make sure the draft only has enough cards in it that you can put into a normal hearthstone deck.
if card.getRarity() == 'Legendary': return (card in self.cards)
else: return (self.cards.count(card) == 2)
def getSet(self):
coll = self.collection
hero = self.hero
cards = [card for card in coll.iterCards() if self.isApplicableCard(card)]
set = list()
for x in xrange(HS_NUM_CARDS_IN_SET):
card = choice(cards)
while (card in set or self.tooManyCards(card)):
card = choice(cards)
self.cards.append(card)
set.append(copy.deepcopy(card))
return tuple(set)
def isApplicableCard(self,card):
return (card.getHero() and card.getHero() == self.hero.getHero()
) or (not card.getHero() and card.getFaction() == self.info['faction'])
def getDraft(self,numCards):
sets = []
for set in xrange(numCards): sets.append(self.getSet())
return sets
| {
"repo_name": "AlexSafatli/HearthstoneDrafter",
"path": "modes/allianceVsHorde.py",
"copies": "1",
"size": "2002",
"license": "mit",
"hash": -4646840250615443000,
"line_mean": 44.5,
"line_max": 398,
"alpha_frac": 0.6448551449,
"autogenerated": false,
"ratio": 3.633393829401089,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4778248974301089,
"avg_score": null,
"num_lines": null
} |
__all__ = ["Image", "ImageCollection"]
from typing import Any, Set
from uuid import uuid4
ImageUri = str
SourceUri = str
class Image(object):
"""Describe an image
`uri`
The absolute URI of the image. This basically identifies the Image and makes it unique.
This absolute URI must include: ``scheme``, ``host``.
``schema`` must be either 'http' or 'https' - use 'https' if possible!
Optional are: ``port``, ``path``, ``query``, ``fragment``.
`source`
The URI where did the image is originally found?
This URI can point to a ImageBoardThread, or a comment section in a Forum, or a news article...
In the idea of fair use, it is encouraged to point to the source as good as possible.
This absolute URI must include: ``scheme``, ``host``.
``schema`` must be either 'http' or 'https' - the last one is preferred.
Optional are: ``port``, ``path``, ``query``, ``fragment``.
Good examples are:
* https://www.reddit.com/r/Awww/comments/e1er0c/say_hi_to_loki_hes_just_contemplating/
* https://giphy.com/gifs/10kABVanhwykJW
`is_generic`
If a generic image crawler is used, its common that each image URI looks exactly the same.
To make this known, use this flag.
`more`
A dictionary of additional information an image crawler might want to deliver.
This dictionary's data types are intended to the basic ones: string, int, float, list, set, dict, bool, None
Good examples are:
* image-dimensions
* author, copyright information
* valid-until
"""
def __init__(self, *,
uri: ImageUri, source: SourceUri,
is_generic: bool = False,
**more: Any) -> None: # pragma: no cover
self.uri = uri
self.source = source
self.more = more
self.is_generic = is_generic
self.__hash = hash(uuid4()) if self.is_generic else hash(self.uri)
def __hash__(self) -> int:
return self.__hash
def __eq__(self, other: Any) -> bool:
if type(other) is type(self):
return hash(self) == hash(other)
return False
def __repr__(self) -> str: # pragma: no cover
return '<{0.__module__}.{0.__name__} object at {1:#x} {2.uri!r}>'.format(type(self), id(self), self)
class ImageCollection(Set[Image]):
pass
| {
"repo_name": "k4cg/nichtparasoup",
"path": "nichtparasoup/core/image.py",
"copies": "1",
"size": "2453",
"license": "mit",
"hash": -6929420835666320000,
"line_mean": 32.1486486486,
"line_max": 116,
"alpha_frac": 0.5923359152,
"autogenerated": false,
"ratio": 3.9185303514377,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0014040654237436009,
"num_lines": 74
} |
__all__ = ['Image', 'imread', 'imread_collection', 'imsave', 'imshow', 'show',
'push', 'pop']
try:
from urllib.request import urlopen
except ImportError:
from urllib2 import urlopen
import os
import re
import tempfile
from io import BytesIO
import numpy as np
import six
from skimage.io._plugins import call as call_plugin
from skimage.color import rgb2grey
# Shared image queue
_image_stack = []
URL_REGEX = re.compile(r'http://|https://|ftp://|file://|file:\\')
def is_url(filename):
"""Return True if string is an http or ftp path."""
return (isinstance(filename, six.string_types) and
URL_REGEX.match(filename) is not None)
class Image(np.ndarray):
"""Class representing Image data.
These objects have tags for image metadata and IPython display protocol
methods for image display.
Parameters
----------
arr : ndarray
Image data.
kwargs : Image tags as keywords
Specified in the form ``tag0=value``, ``tag1=value``.
Attributes
----------
tags : dict
Meta-data.
"""
def __new__(cls, arr, **kwargs):
"""Set the image data and tags according to given parameters.
"""
x = np.asarray(arr).view(cls)
x.tags = kwargs
return x
def __array_finalize__(self, obj):
self.tags = getattr(obj, 'tags', {})
def _repr_png_(self):
return self._repr_image_format('png')
def _repr_jpeg_(self):
return self._repr_image_format('jpeg')
def _repr_image_format(self, format_str):
str_buffer = BytesIO()
imsave(str_buffer, self, format_str=format_str)
return_str = str_buffer.getvalue()
str_buffer.close()
return return_str
def push(img):
"""Push an image onto the shared image stack.
Parameters
----------
img : ndarray
Image to push.
"""
if not isinstance(img, np.ndarray):
raise ValueError("Can only push ndarrays to the image stack.")
_image_stack.append(img)
def pop():
"""Pop an image from the shared image stack.
Returns
-------
img : ndarray
Image popped from the stack.
"""
return _image_stack.pop()
def imread(fname, as_grey=False, plugin=None, flatten=None,
**plugin_args):
"""Load an image from file.
Parameters
----------
fname : string
Image file name, e.g. ``test.jpg`` or URL.
as_grey : bool
If True, convert color images to grey-scale (32-bit floats).
Images that are already in grey-scale format are not converted.
plugin : str
Name of plugin to use (Python Imaging Library by default).
Other Parameters
----------------
flatten : bool
Backward compatible keyword, superseded by `as_grey`.
Returns
-------
img_array : ndarray
The different colour bands/channels are stored in the
third dimension, such that a grey-image is MxN, an
RGB-image MxNx3 and an RGBA-image MxNx4.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
# Backward compatibility
if flatten is not None:
as_grey = flatten
if is_url(fname):
_, ext = os.path.splitext(fname)
with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as f:
u = urlopen(fname)
f.write(u.read())
img = call_plugin('imread', f.name, plugin=plugin, **plugin_args)
os.remove(f.name)
else:
img = call_plugin('imread', fname, plugin=plugin, **plugin_args)
if as_grey and getattr(img, 'ndim', 0) >= 3:
img = rgb2grey(img)
return img
def imread_collection(load_pattern, conserve_memory=True,
plugin=None, **plugin_args):
"""
Load a collection of images.
Parameters
----------
load_pattern : str or list
List of objects to load. These are usually filenames, but may
vary depending on the currently active plugin. See the docstring
for ``ImageCollection`` for the default behaviour of this parameter.
conserve_memory : bool, optional
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
return call_plugin('imread_collection', load_pattern, conserve_memory,
plugin=plugin, **plugin_args)
def imsave(fname, arr, plugin=None, **plugin_args):
"""Save an image to file.
Parameters
----------
fname : str
Target filename.
arr : ndarray of shape (M,N) or (M,N,3) or (M,N,4)
Image data.
plugin : str
Name of plugin to use. By default, the different plugins are
tried (starting with the Python Imaging Library) until a suitable
candidate is found.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
return call_plugin('imsave', fname, arr, plugin=plugin, **plugin_args)
def imshow(arr, plugin=None, **plugin_args):
"""Display an image.
Parameters
----------
arr : ndarray or str
Image data or name of image file.
plugin : str
Name of plugin to use. By default, the different plugins are
tried (starting with the Python Imaging Library) until a suitable
candidate is found.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
if isinstance(arr, six.string_types):
arr = call_plugin('imread', arr, plugin=plugin)
return call_plugin('imshow', arr, plugin=plugin, **plugin_args)
def show():
'''Display pending images.
Launch the event loop of the current gui plugin, and display all
pending images, queued via `imshow`. This is required when using
`imshow` from non-interactive scripts.
A call to `show` will block execution of code until all windows
have been closed.
Examples
--------
>>> import skimage.io as io
>>> for i in range(4):
... io.imshow(np.random.random((50, 50)))
>>> io.show() # doctest: +SKIP
'''
return call_plugin('_app_show')
| {
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"path": "skimage/io/_io.py",
"copies": "1",
"size": "6424",
"license": "bsd-3-clause",
"hash": 695960431952372000,
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5180380221030159,
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# all imports needed by the client
from direct.showbase.DirectObject import DirectObject
from direct.distributed.ClientRepository import ClientRepository
from pandac.PandaModules import URLSpec
class DungeonClientRepository(ClientRepository):
"""the main client repository class"""
def __init__(self):
# list of all needed .dc files
dcFileNames = ['distributed/direct.dc', 'distributed/net.dc']
# initialise the client repository on this
# machine with the dc filenames
ClientRepository.__init__(self, dcFileNames = dcFileNames)
class Client(DirectObject):
"""The main client class, which contains all the logic
and stuff you can see in the application and handles the
connection to the server"""
def __init__(self, ip):
""" Default constructor for the Clinet class """
# get the port number from the configuration file
# if it doesn't exist, use 4400 as the default
tcpPort = base.config.GetInt('server-port', 4400)
# get the host name from the configuration file
# which we want to connect to. If it doesn't exit
# we use loopback to connect to
hostname = base.config.GetString('server-host', ip)
# now build the url from the data given above
self.url = URLSpec('http://%s:%s' % (hostname, tcpPort))
# create the Repository for the client
self.cr = DungeonClientRepository()
# and finaly try to connect to the server
self.cr.connect([self.url],
successCallback = self.connectSuccess,
failureCallback = self.connectFailure)
def connectFailure(self, statusCode, statusString):
""" some error occured while try to connect to the server """
# send a message, that should show the client the error message
base.messenger.send(
"showerror",
["Failed to connect to %s: %s." % (self.url, statusString)])
def connectSuccess(self):
""" Successfully connected. But we still can't really do
anything until we've got the doID range. """
print "Connection established, waiting for server."
self.cr.setInterestZones([1])
self.acceptOnce('gotTimeSync', self.syncReady)
def syncReady(self):
""" Now we've got the TimeManager manifested, and we're in
sync with the server time. Now we can enter the world. Check
to see if we've received our doIdBase yet. """
if self.cr.haveCreateAuthority():
self.createReady()
else:
# Not yet, keep waiting a bit longer.
self.acceptOnce('createReady', self.createReady)
def createReady(self):
""" Now we're ready to go! """
print "server connection done"
self.player = self.cr.createDistributedObject(
className = "DistributedPlayer", zoneId = 1)
self.player.startPosHprBroadcast()
# Unsure?? This maybe a good place to run a precheck to get
# everything ready or something like that :P
self.accept("clickPosition", self.walkTo)
def walkTo(self, pos):
self.player.lookAt(pos)
moveInterval = self.player.posInterval(1, pos)
moveInterval.start()
#self.player.setPos(pos)
| {
"repo_name": "grimfang/owp_dungeon_crawler",
"path": "src/client/client.py",
"copies": "1",
"size": "3309",
"license": "mit",
"hash": 1157125451503840000,
"line_mean": 40.3625,
"line_max": 72,
"alpha_frac": 0.6452100332,
"autogenerated": false,
"ratio": 4.325490196078431,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.002422765983253022,
"num_lines": 80
} |
# all imports
import sqlite3
from flask import Flask, request, session, g, redirect, url_for,\
abort, render_template, flash
from contextlib import closing
# configuration
DATABASE = '/temp/bloggify.db'
DEBUG = True
SECRET_KEY = 'development key'
USERNAME = 'admin'
PASSWORD = 'default'
# create our little application
app = Flask(__name__)
app.config.from_envvar('BLOGGIFY_SETTINGS', silent=True)
def connect_db():
""" Connects to a specific database """
return sqlite3.connect(app.config['DATABASE'])
def init_db():
""" innitializes a connexion """
with closing(connect_db()) as db:
with app.open_resource('schema.sql', mode='r') as f:
db.cursor().executescript(f.read())
db.commit()
@app.before_request
def before_request():
g.db = connect_db()
@app.teardown_request
def teardown_request(exception):
db = getattr(g, 'db', None )
if db is not None:
db.close()
@app.route('/')
def show_entries():
cur = g.db.execute('select title, text from entries order by id desc')
entries = [dict(title=row[0],text=row[1]) for row in cur.fetchall()]
return render_template('show_entries.html', entries=entries)
@app.route('/add', methods=['POST'])
def add_entry():
if not session.get('logged_in'):
abort(401)
g.db.execute('insert into entries (title, text) values (?,?)', [request.form['title'], request.form[text]])
g.db.commit()
flash('New entry was successfully posted')
return redirect(url_for('show_entries'))
# Login and logout
@app.route('/login', methods=['GET', 'POST'])
def login():
error = None
if request.method == 'POST':
if request.form['username'] != app.config['USERNAME']:
rerror = 'Invalid username'
elif request.form['password'] != app.config['PASSWORD']:
error = 'Invalid password'
else:
session['logged_in'] = True
flash('You were logged in')
return redirect(url_for('show_entries'))
return render_template('login.html', error=error)
if __name__ == '__main__':
app.run()
| {
"repo_name": "ledrui/blogify",
"path": "bloggify.py",
"copies": "1",
"size": "2105",
"license": "mit",
"hash": 5855447274542126000,
"line_mean": 26.3376623377,
"line_max": 111,
"alpha_frac": 0.6332541568,
"autogenerated": false,
"ratio": 3.5738539898132426,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.47071081466132425,
"avg_score": null,
"num_lines": null
} |
# all imports
import sqlite3
import os
from flask import Flask , request , session , g , redirect , url_for , \
abort , render_template , flash
from contextlib import closing
# Database configuration
DATABASE = os.environ['OPENSHIFT_DATA_DIR'] + 'flaskr.db'
DEBUG = True
SECRET_KEY = 'development key'
USERNAME = 'admin'
PASSWORD = 'admin'
# create our little application :)
app = Flask(__name__)
app.config.from_object(__name__)
app.config.from_envvar('FLASKR_SETTINGS', silent=True)
def connect_db():
return sqlite3.connect(app.config['DATABASE'])
def init_db():
with closing(connect_db()) as db:
with app.open_resource('schema.sql' , mode='r') as f:
db.cursor().executescript(f.read())
db.commit()
@app.before_request
def before_request():
g.db = connect_db()
@app.teardown_request
def teardown_request(exception):
db = getattr(g, 'db',None)
if db is not None:
db.close()
@app.route('/')
def show_entries():
cur = g.db.execute('select title , text from entries order by id desc')
entries = [dict(title=row[0] , text=row[1]) for row in cur.fetchall()]
return render_template('show_entries.html' , entries = entries)
@app.route('/add',methods=['POST'])
def add_entry():
if not session.get('logged_in'):
abort(401)
g.db.execute('insert into entries (title , text) values (? , ?)',
[request.form['title'], request.form['text']])
g.db.commit()
flash('New entry was successfully posted')
return redirect(url_for('show_entries'))
@app.route('/login' , methods=['GET' , 'POST'])
def login():
error = None
if request.method == 'POST':
if request.form['username'] != app.config['USERNAME']:
error = 'Invalid username'
elif request.form['password'] != app.config['PASSWORD']:
error = 'Invalid password'
else :
session['logged_in'] = True
flash('You are logged in')
return redirect(url_for('show_entries'))
return render_template('login.html' , error = error)
@app.route('/logout')
def logout():
session.pop('logged_in' , None)
flash('You were logged out')
return redirect(url_for('show_entries'))
if __name__ == '__main__':
# init_db()
app.run()
| {
"repo_name": "coursemdetw/flaskr_on_openshift",
"path": "wsgi/flaskr.py",
"copies": "1",
"size": "2111",
"license": "apache-2.0",
"hash": -4971371700237716000,
"line_mean": 25.0617283951,
"line_max": 72,
"alpha_frac": 0.6750355282,
"autogenerated": false,
"ratio": 3.0998531571218795,
"config_test": true,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9130882110950461,
"avg_score": 0.028801314874283602,
"num_lines": 81
} |
__all__ = ['imread', 'imread_collection', 'imsave', 'imshow', 'show',
'push', 'pop']
from scikits.image.io._plugins import call as call_plugin
from scikits.image.color import rgb2grey
import numpy as np
# Shared image queue
_image_stack = []
def push(img):
"""Push an image onto the shared image stack.
Parameters
----------
img : ndarray
Image to push.
"""
if not isinstance(img, np.ndarray):
raise ValueError("Can only push ndarrays to the image stack.")
_image_stack.append(img)
def pop():
"""Pop an image from the shared image stack.
Returns
-------
img : ndarray
Image popped from the stack.
"""
return _image_stack.pop()
def imread(fname, as_grey=False, plugin=None, flatten=None,
**plugin_args):
"""Load an image from file.
Parameters
----------
fname : string
Image file name, e.g. ``test.jpg``.
as_grey : bool
If True, convert color images to grey-scale (32-bit floats).
Images that are already in grey-scale format are not converted.
plugin : str
Name of plugin to use (Python Imaging Library by default).
Other Parameters
----------------
flatten : bool
Backward compatible keyword, superseded by `as_grey`.
Returns
-------
img_array : ndarray
The different colour bands/channels are stored in the
third dimension, such that a grey-image is MxN, an
RGB-image MxNx3 and an RGBA-image MxNx4.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
# Backward compatibility
if flatten is not None:
as_grey = flatten
img = call_plugin('imread', fname, plugin=plugin, **plugin_args)
if as_grey and getattr(img, 'ndim', 0) >= 3:
img = rgb2grey(img)
return img
def imread_collection(load_pattern, conserve_memory=True,
plugin=None, **plugin_args):
"""
Load a collection of images.
Parameters
----------
load_pattern : str or list
List of objects to load. These are usually filenames, but may
vary depending on the currently active plugin. See the docstring
for ``ImageCollection`` for the default behaviour of this parameter.
conserve_memory : bool, optional
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
return call_plugin('imread_collection', load_pattern, conserve_memory,
plugin=plugin, **plugin_args)
def imsave(fname, arr, plugin=None, **plugin_args):
"""Save an image to file.
Parameters
----------
fname : str
Target filename.
arr : ndarray of shape (M,N) or (M,N,3) or (M,N,4)
Image data.
plugin : str
Name of plugin to use. By default, the different plugins are
tried (starting with the Python Imaging Library) until a suitable
candidate is found.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
return call_plugin('imsave', fname, arr, plugin=plugin, **plugin_args)
def imshow(arr, plugin=None, **plugin_args):
"""Display an image.
Parameters
----------
arr : ndarray
Image data.
plugin : str
Name of plugin to use. By default, the different plugins are
tried (starting with the Python Imaging Library) until a suitable
candidate is found.
Other parameters
----------------
plugin_args : keywords
Passed to the given plugin.
"""
return call_plugin('imshow', arr, plugin=plugin, **plugin_args)
def show():
'''Display pending images.
Launch the event loop of the current gui plugin, and display all
pending images, queued via `imshow`. This is required when using
`imshow` from non-interactive scripts.
A call to `show` will block execution of code until all windows
have been closed.
Examples
--------
>>> import scikits.image.io as io
>>> for i in range(4):
... io.imshow(np.random.random((50, 50)))
>>> io.show()
'''
return call_plugin('_app_show')
| {
"repo_name": "GaelVaroquaux/scikits.image",
"path": "scikits/image/io/io.py",
"copies": "1",
"size": "4461",
"license": "bsd-3-clause",
"hash": 6169115537938402000,
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"autogenerated": false,
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0017472903476960272,
"num_lines": 170
} |
__all__ = ['imread', 'imread_collection']
import numpy as np
import scikits.image.io as io
try:
import pyfits
except ImportError:
raise ImportError("PyFITS could not be found. Please refer to\n"
"http://www.stsci.edu/resources/software_hardware/pyfits\n"
"for further instructions.")
def imread(fname, dtype=None):
"""Load an image from a FITS file.
Parameters
----------
fname : string
Image file name, e.g. ``test.fits``.
dtype : dtype, optional
For FITS, this argument is ignored because Stefan is planning on
removing the dtype argument from imread anyway.
Returns
-------
img_array : ndarray
Unlike plugins such as PIL, where different colour bands/channels are
stored in the third dimension, FITS images are greyscale-only and can
be N-dimensional, so an array of the native FITS dimensionality is
returned, without colour channels.
Currently if no image is found in the file, None will be returned
Notes
-----
Currently FITS ``imread()`` always returns the first image extension when
given a Multi-Extension FITS file; use ``imread_collection()`` (which does
lazy loading) to get all the extensions at once.
"""
hdulist = pyfits.open(fname)
# Iterate over FITS image extensions, ignoring any other extension types
# such as binary tables, and get the first image data array:
img_array = None
for hdu in hdulist:
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
if hdu.data is not None:
img_array = hdu.data
break
hdulist.close()
return img_array
def imread_collection(load_pattern, conserve_memory=True):
"""Load a collection of images from one or more FITS files
Parameters
----------
load_pattern : str or list
List of extensions to load. Filename globbing is currently
unsupported.
converve_memory : bool
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
"""
intype = type(load_pattern)
if intype is not list and intype is not str:
raise TypeError("Input must be a filename or list of filenames")
# Ensure we have a list, otherwise we'll end up iterating over the string:
if intype is not list:
load_pattern = [load_pattern]
# Generate a list of filename/extension pairs by opening the list of
# files and finding the image extensions in each one:
ext_list = []
for filename in load_pattern:
hdulist = pyfits.open(filename)
for n, hdu in zip(range(len(hdulist)), hdulist):
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
# Ignore (primary) header units with no data (use '.size'
# rather than '.data' to avoid actually loading the image):
if hdu.size() > 0:
ext_list.append((filename, n))
hdulist.close()
return io.ImageCollection(ext_list, load_func=FITSFactory,
conserve_memory=conserve_memory)
def FITSFactory(image_ext):
"""Load an image extension from a FITS file and return a NumPy array
Parameters
----------
image_ext : tuple
FITS extension to load, in the format ``(filename, ext_num)``.
The FITS ``(extname, extver)`` format is unsupported, since this
function is not called directly by the user and
``imread_collection()`` does the work of figuring out which
extensions need loading.
"""
# Expect a length-2 tuple with a filename as the first element:
if not isinstance(image_ext, tuple):
raise TypeError("Expected a tuple")
if len(image_ext) != 2:
raise ValueError("Expected a tuple of length 2")
filename = image_ext[0]
extnum = image_ext[1]
if type(filename) is not str or type(extnum) is not int:
raise ValueError("Expected a (filename, extension) tuple")
hdulist = pyfits.open(filename)
data = hdulist[extnum].data
hdulist.close()
if data is None:
raise RuntimeError("Extension %d of %s has no data" %
(extnum, filename))
return data
| {
"repo_name": "GaelVaroquaux/scikits.image",
"path": "scikits/image/io/_plugins/fits_plugin.py",
"copies": "1",
"size": "4494",
"license": "bsd-3-clause",
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"autogenerated": false,
"ratio": 4.219718309859155,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5347666685459155,
"avg_score": null,
"num_lines": null
} |
__all__ = ['imread', 'imread_collection']
import numpy as np
import skimage.io as io
try:
import pyfits
except ImportError:
raise ImportError("PyFITS could not be found. Please refer to\n"
"http://www.stsci.edu/resources/software_hardware/pyfits\n"
"for further instructions.")
def imread(fname, dtype=None):
"""Load an image from a FITS file.
Parameters
----------
fname : string
Image file name, e.g. ``test.fits``.
dtype : dtype, optional
For FITS, this argument is ignored because Stefan is planning on
removing the dtype argument from imread anyway.
Returns
-------
img_array : ndarray
Unlike plugins such as PIL, where different colour bands/channels are
stored in the third dimension, FITS images are greyscale-only and can
be N-dimensional, so an array of the native FITS dimensionality is
returned, without colour channels.
Currently if no image is found in the file, None will be returned
Notes
-----
Currently FITS ``imread()`` always returns the first image extension when
given a Multi-Extension FITS file; use ``imread_collection()`` (which does
lazy loading) to get all the extensions at once.
"""
hdulist = pyfits.open(fname)
# Iterate over FITS image extensions, ignoring any other extension types
# such as binary tables, and get the first image data array:
img_array = None
for hdu in hdulist:
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
if hdu.data is not None:
img_array = hdu.data
break
hdulist.close()
return img_array
def imread_collection(load_pattern, conserve_memory=True):
"""Load a collection of images from one or more FITS files
Parameters
----------
load_pattern : str or list
List of extensions to load. Filename globbing is currently
unsupported.
converve_memory : bool
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
"""
intype = type(load_pattern)
if intype is not list and intype is not str:
raise TypeError("Input must be a filename or list of filenames")
# Ensure we have a list, otherwise we'll end up iterating over the string:
if intype is not list:
load_pattern = [load_pattern]
# Generate a list of filename/extension pairs by opening the list of
# files and finding the image extensions in each one:
ext_list = []
for filename in load_pattern:
hdulist = pyfits.open(filename)
for n, hdu in zip(range(len(hdulist)), hdulist):
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
# Ignore (primary) header units with no data (use '.size'
# rather than '.data' to avoid actually loading the image):
try:
data_size = hdu.size()
except TypeError: # (size changed to int in PyFITS 3.1)
data_size = hdu.size
if data_size > 0:
ext_list.append((filename, n))
hdulist.close()
return io.ImageCollection(ext_list, load_func=FITSFactory,
conserve_memory=conserve_memory)
def FITSFactory(image_ext):
"""Load an image extension from a FITS file and return a NumPy array
Parameters
----------
image_ext : tuple
FITS extension to load, in the format ``(filename, ext_num)``.
The FITS ``(extname, extver)`` format is unsupported, since this
function is not called directly by the user and
``imread_collection()`` does the work of figuring out which
extensions need loading.
"""
# Expect a length-2 tuple with a filename as the first element:
if not isinstance(image_ext, tuple):
raise TypeError("Expected a tuple")
if len(image_ext) != 2:
raise ValueError("Expected a tuple of length 2")
filename = image_ext[0]
extnum = image_ext[1]
if type(filename) is not str or type(extnum) is not int:
raise ValueError("Expected a (filename, extension) tuple")
hdulist = pyfits.open(filename)
data = hdulist[extnum].data
hdulist.close()
if data is None:
raise RuntimeError("Extension %d of %s has no data" %
(extnum, filename))
return data
| {
"repo_name": "emmanuelle/scikits.image",
"path": "skimage/io/_plugins/fits_plugin.py",
"copies": "2",
"size": "4655",
"license": "bsd-3-clause",
"hash": 2907905309740414000,
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"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.0004597885057679217,
"num_lines": 148
} |
__all__ = ['imread', 'imread_collection']
import skimage.io as io
try:
from astropy.io import fits as pyfits
except ImportError:
try:
import pyfits
except ImportError:
raise ImportError("PyFITS could not be found. Please refer to\n"
"http://www.stsci.edu/resources/software_hardware/pyfits\n"
"for further instructions.")
def imread(fname, dtype=None):
"""Load an image from a FITS file.
Parameters
----------
fname : string
Image file name, e.g. ``test.fits``.
dtype : dtype, optional
For FITS, this argument is ignored because Stefan is planning on
removing the dtype argument from imread anyway.
Returns
-------
img_array : ndarray
Unlike plugins such as PIL, where different colour bands/channels are
stored in the third dimension, FITS images are greyscale-only and can
be N-dimensional, so an array of the native FITS dimensionality is
returned, without colour channels.
Currently if no image is found in the file, None will be returned
Notes
-----
Currently FITS ``imread()`` always returns the first image extension when
given a Multi-Extension FITS file; use ``imread_collection()`` (which does
lazy loading) to get all the extensions at once.
"""
hdulist = pyfits.open(fname)
# Iterate over FITS image extensions, ignoring any other extension types
# such as binary tables, and get the first image data array:
img_array = None
for hdu in hdulist:
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
if hdu.data is not None:
img_array = hdu.data
break
hdulist.close()
return img_array
def imread_collection(load_pattern, conserve_memory=True):
"""Load a collection of images from one or more FITS files
Parameters
----------
load_pattern : str or list
List of extensions to load. Filename globbing is currently
unsupported.
converve_memory : bool
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
"""
intype = type(load_pattern)
if intype is not list and intype is not str:
raise TypeError("Input must be a filename or list of filenames")
# Ensure we have a list, otherwise we'll end up iterating over the string:
if intype is not list:
load_pattern = [load_pattern]
# Generate a list of filename/extension pairs by opening the list of
# files and finding the image extensions in each one:
ext_list = []
for filename in load_pattern:
hdulist = pyfits.open(filename)
for n, hdu in zip(range(len(hdulist)), hdulist):
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
# Ignore (primary) header units with no data (use '.size'
# rather than '.data' to avoid actually loading the image):
try:
data_size = hdu.size()
except TypeError: # (size changed to int in PyFITS 3.1)
data_size = hdu.size
if data_size > 0:
ext_list.append((filename, n))
hdulist.close()
return io.ImageCollection(ext_list, load_func=FITSFactory,
conserve_memory=conserve_memory)
def FITSFactory(image_ext):
"""Load an image extension from a FITS file and return a NumPy array
Parameters
----------
image_ext : tuple
FITS extension to load, in the format ``(filename, ext_num)``.
The FITS ``(extname, extver)`` format is unsupported, since this
function is not called directly by the user and
``imread_collection()`` does the work of figuring out which
extensions need loading.
"""
# Expect a length-2 tuple with a filename as the first element:
if not isinstance(image_ext, tuple):
raise TypeError("Expected a tuple")
if len(image_ext) != 2:
raise ValueError("Expected a tuple of length 2")
filename = image_ext[0]
extnum = image_ext[1]
if type(filename) is not str or type(extnum) is not int:
raise ValueError("Expected a (filename, extension) tuple")
hdulist = pyfits.open(filename)
data = hdulist[extnum].data
hdulist.close()
if data is None:
raise RuntimeError("Extension %d of %s has no data" %
(extnum, filename))
return data
| {
"repo_name": "SamHames/scikit-image",
"path": "skimage/io/_plugins/fits_plugin.py",
"copies": "9",
"size": "4727",
"license": "bsd-3-clause",
"hash": -3250211166962519000,
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"quality_score": 0.9370319631775539,
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} |
__all__ = ['imread', 'imread_collection']
import skimage.io as io
try:
import pyfits
except ImportError:
raise ImportError("PyFITS could not be found. Please refer to\n"
"http://www.stsci.edu/resources/software_hardware/pyfits\n"
"for further instructions.")
def imread(fname, dtype=None):
"""Load an image from a FITS file.
Parameters
----------
fname : string
Image file name, e.g. ``test.fits``.
dtype : dtype, optional
For FITS, this argument is ignored because Stefan is planning on
removing the dtype argument from imread anyway.
Returns
-------
img_array : ndarray
Unlike plugins such as PIL, where different colour bands/channels are
stored in the third dimension, FITS images are greyscale-only and can
be N-dimensional, so an array of the native FITS dimensionality is
returned, without colour channels.
Currently if no image is found in the file, None will be returned
Notes
-----
Currently FITS ``imread()`` always returns the first image extension when
given a Multi-Extension FITS file; use ``imread_collection()`` (which does
lazy loading) to get all the extensions at once.
"""
hdulist = pyfits.open(fname)
# Iterate over FITS image extensions, ignoring any other extension types
# such as binary tables, and get the first image data array:
img_array = None
for hdu in hdulist:
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
if hdu.data is not None:
img_array = hdu.data
break
hdulist.close()
return img_array
def imread_collection(load_pattern, conserve_memory=True):
"""Load a collection of images from one or more FITS files
Parameters
----------
load_pattern : str or list
List of extensions to load. Filename globbing is currently
unsupported.
converve_memory : bool
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Returns
-------
ic : ImageCollection
Collection of images.
"""
intype = type(load_pattern)
if intype is not list and intype is not str:
raise TypeError("Input must be a filename or list of filenames")
# Ensure we have a list, otherwise we'll end up iterating over the string:
if intype is not list:
load_pattern = [load_pattern]
# Generate a list of filename/extension pairs by opening the list of
# files and finding the image extensions in each one:
ext_list = []
for filename in load_pattern:
hdulist = pyfits.open(filename)
for n, hdu in zip(range(len(hdulist)), hdulist):
if isinstance(hdu, pyfits.ImageHDU) or \
isinstance(hdu, pyfits.PrimaryHDU):
# Ignore (primary) header units with no data (use '.size'
# rather than '.data' to avoid actually loading the image):
try:
data_size = hdu.size()
except TypeError: # (size changed to int in PyFITS 3.1)
data_size = hdu.size
if data_size > 0:
ext_list.append((filename, n))
hdulist.close()
return io.ImageCollection(ext_list, load_func=FITSFactory,
conserve_memory=conserve_memory)
def FITSFactory(image_ext):
"""Load an image extension from a FITS file and return a NumPy array
Parameters
----------
image_ext : tuple
FITS extension to load, in the format ``(filename, ext_num)``.
The FITS ``(extname, extver)`` format is unsupported, since this
function is not called directly by the user and
``imread_collection()`` does the work of figuring out which
extensions need loading.
"""
# Expect a length-2 tuple with a filename as the first element:
if not isinstance(image_ext, tuple):
raise TypeError("Expected a tuple")
if len(image_ext) != 2:
raise ValueError("Expected a tuple of length 2")
filename = image_ext[0]
extnum = image_ext[1]
if type(filename) is not str or type(extnum) is not int:
raise ValueError("Expected a (filename, extension) tuple")
hdulist = pyfits.open(filename)
data = hdulist[extnum].data
hdulist.close()
if data is None:
raise RuntimeError("Extension %d of %s has no data" %
(extnum, filename))
return data
| {
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"path": "skimage/io/_plugins/fits_plugin.py",
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"size": "4636",
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"hash": -4676620609502751000,
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"autogenerated": false,
"ratio": 4.233789954337899,
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5854368038937899,
"avg_score": null,
"num_lines": null
} |
__all__ = ['imread', 'imsave']
import numpy as np
from six import string_types
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
from ...external.tifffile import imread as tif_imread, imsave as tif_imsave
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str
File name.
dtype : numpy dtype object or string specifier
Specifies data type of array elements.
img_num : int, optional
Specifies which image to read in a file with multiple images
(zero-indexed).
kwargs : keyword pairs, optional
Addition keyword arguments to pass through (only applicable to Tiff
files for now, see `tifffile`'s `imread` function).
Notes
-----
Tiff files are handled by Christophe Golhke's tifffile.py [1]_, and support many
advanced image types including multi-page and floating point.
All other files are read using the Python Imaging Libary.
See PIL docs [2]_ for a list of supported formats.
References
----------
.. [1] http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html
.. [2] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
if hasattr(fname, 'lower') and dtype is None:
kwargs.setdefault('key', img_num)
if fname.lower().endswith(('.tiff', '.tif')):
return tif_imread(fname, **kwargs)
im = Image.open(fname)
try:
# this will raise an IOError if the file is not readable
im.getdata()[0]
except IOError:
site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries"
raise ValueError('Could not load "%s"\nPlease see documentation at: %s' % (fname, site))
else:
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
def pil_to_ndarray(im, dtype=None, img_num=None):
"""Import a PIL Image object to an ndarray, in memory.
Parameters
----------
Refer to ``imread``.
"""
frames = []
grayscale = None
i = 0
while 1:
try:
im.seek(i)
except EOFError:
break
frame = im
if img_num is not None and img_num != i:
im.getdata()[0]
i += 1
continue
if im.mode == 'P':
if grayscale is None:
grayscale = _palette_is_grayscale(im)
if grayscale:
frame = im.convert('L')
else:
frame = im.convert('RGB')
elif im.mode == '1':
frame = im.convert('L')
elif 'A' in im.mode:
frame = im.convert('RGBA')
elif im.mode == 'CMYK':
frame = im.convert('RGB')
if im.mode.startswith('I;16'):
shape = im.size
dtype = '>u2' if im.mode.endswith('B') else '<u2'
if 'S' in im.mode:
dtype = dtype.replace('u', 'i')
frame = np.fromstring(frame.tobytes(), dtype)
frame.shape = shape[::-1]
else:
frame = np.array(frame, dtype=dtype)
frames.append(frame)
i += 1
if hasattr(im, 'fp') and im.fp:
im.fp.close()
if img_num is None and len(frames) > 1:
return np.array(frames)
elif frames:
return frames[0]
elif img_num:
raise IndexError('Could not find image #%s' % img_num)
def _palette_is_grayscale(pil_image):
"""Return True if PIL image in palette mode is grayscale.
Parameters
----------
pil_image : PIL image
PIL Image that is in Palette mode.
Returns
-------
is_grayscale : bool
True if all colors in image palette are gray.
"""
assert pil_image.mode == 'P'
# get palette as an array with R, G, B columns
palette = np.asarray(pil_image.getpalette()).reshape((256, 3))
# Not all palette colors are used; unused colors have junk values.
start, stop = pil_image.getextrema()
valid_palette = palette[start:stop]
# Image is grayscale if channel differences (R - G and G - B)
# are all zero.
return np.allclose(np.diff(valid_palette), 0)
def ndarray_to_pil(arr, format_str=None):
"""Export an ndarray to a PIL object.
Parameters
----------
Refer to ``imsave``.
"""
if arr.ndim == 3:
arr = img_as_ubyte(arr)
mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]]
elif format_str in ['png', 'PNG']:
mode = 'I;16'
mode_base = 'I'
if arr.dtype.kind == 'f':
arr = img_as_uint(arr)
elif arr.max() < 256 and arr.min() >= 0:
arr = arr.astype(np.uint8)
mode = mode_base = 'L'
else:
arr = img_as_uint(arr)
else:
arr = img_as_ubyte(arr)
mode = 'L'
mode_base = 'L'
try:
array_buffer = arr.tobytes()
except AttributeError:
array_buffer = arr.tostring() # Numpy < 1.9
if arr.ndim == 2:
im = Image.new(mode_base, arr.T.shape)
try:
im.frombytes(array_buffer, 'raw', mode)
except AttributeError:
im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7
else:
image_shape = (arr.shape[1], arr.shape[0])
try:
im = Image.frombytes(mode, image_shape, array_buffer)
except AttributeError:
im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7
return im
def imsave(fname, arr, format_str=None, **kwargs):
"""Save an image to disk.
Parameters
----------
fname : str or file-like object
Name of destination file.
arr : ndarray of uint8 or float
Array (image) to save. Arrays of data-type uint8 should have
values in [0, 255], whereas floating-point arrays must be
in [0, 1].
format_str: str
Format to save as, this is defaulted to PNG if using a file-like
object; this will be derived from the extension if fname is a string
kwargs: dict
Keyword arguments to the Pillow save function (or tifffile save
function, for Tiff files). These are format dependent. For example,
Pillow's JPEG save function supports an integer ``quality`` argument
with values in [1, 95], while TIFFFile supports a ``compress``
integer argument with values in [0, 9].
Notes
-----
Tiff files are handled by Christophe Golhke's tifffile.py [1]_,
and support many advanced image types including multi-page and
floating point.
All other image formats use the Python Imaging Libary.
See PIL docs [2]_ for a list of other supported formats.
All images besides single channel PNGs are converted using `img_as_uint8`.
Single Channel PNGs have the following behavior:
- Integer values in [0, 255] and Boolean types -> img_as_uint8
- Floating point and other integers -> img_as_uint16
References
----------
.. [1] http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html
.. [2] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
# default to PNG if file-like object
if not isinstance(fname, string_types) and format_str is None:
format_str = "PNG"
# Check for png in filename
if (isinstance(fname, string_types)
and fname.lower().endswith(".png")):
format_str = "PNG"
arr = np.asanyarray(arr).squeeze()
if arr.dtype.kind == 'b':
arr = arr.astype(np.uint8)
use_tif = False
if hasattr(fname, 'lower'):
if fname.lower().endswith(('.tiff', '.tif')):
use_tif = True
if not format_str is None:
if format_str.lower() in ['tiff', 'tif']:
use_tif = True
if use_tif:
tif_imsave(fname, arr, **kwargs)
return
if arr.ndim not in (2, 3):
raise ValueError("Invalid shape for image array: %s" % arr.shape)
if arr.ndim == 3:
if arr.shape[2] not in (3, 4):
raise ValueError("Invalid number of channels in image array.")
img = ndarray_to_pil(arr, format_str=format_str)
img.save(fname, format=format_str, **kwargs)
| {
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__all__ = ['imread', 'imsave']
import numpy as np
from six import string_types
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
from .tifffile_plugin import imread as tif_imread, imsave as tif_imsave
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy dtype object or string specifier
Specifies data type of array elements.
img_num : int, optional
Specifies which image to read in a file with multiple images
(zero-indexed).
kwargs : keyword pairs, optional
Addition keyword arguments to pass through (only applicable to Tiff
files for now, see `tifffile`'s `imread` function).
Notes
-----
Tiff files are handled by Christophe Golhke's tifffile.py [1]_, and support
many advanced image types including multi-page and floating point.
All other files are read using the Python Imaging Libary.
See PIL docs [2]_ for a list of supported formats.
References
----------
.. [1] http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html
.. [2] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
if hasattr(fname, 'lower') and dtype is None:
kwargs.setdefault('key', img_num)
if fname.lower().endswith(('.tiff', '.tif')):
return tif_imread(fname, **kwargs)
if isinstance(fname, string_types):
with open(fname, 'rb') as f:
im = Image.open(f)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
else:
im = Image.open(fname)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
def pil_to_ndarray(im, dtype=None, img_num=None):
"""Import a PIL Image object to an ndarray, in memory.
Parameters
----------
Refer to ``imread``.
"""
try:
# this will raise an IOError if the file is not readable
im.getdata()[0]
except IOError as e:
site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries"
pillow_error_message = str(e)
error_message = ('Could not load "%s" \n'
'Reason: "%s"\n'
'Please see documentation at: %s'
% (im.filename, pillow_error_message, site))
raise ValueError(error_message)
frames = []
grayscale = None
i = 0
while 1:
try:
im.seek(i)
except EOFError:
break
frame = im
if img_num is not None and img_num != i:
im.getdata()[0]
i += 1
continue
if im.format == 'PNG' and im.mode == 'I' and dtype is None:
dtype = 'uint16'
if im.mode == 'P':
if grayscale is None:
grayscale = _palette_is_grayscale(im)
if grayscale:
frame = im.convert('L')
else:
frame = im.convert('RGB')
elif im.mode == '1':
frame = im.convert('L')
elif 'A' in im.mode:
frame = im.convert('RGBA')
elif im.mode == 'CMYK':
frame = im.convert('RGB')
if im.mode.startswith('I;16'):
shape = im.size
dtype = '>u2' if im.mode.endswith('B') else '<u2'
if 'S' in im.mode:
dtype = dtype.replace('u', 'i')
frame = np.fromstring(frame.tobytes(), dtype)
frame.shape = shape[::-1]
else:
frame = np.array(frame, dtype=dtype)
frames.append(frame)
i += 1
if img_num is not None:
break
if hasattr(im, 'fp') and im.fp:
im.fp.close()
if img_num is None and len(frames) > 1:
return np.array(frames)
elif frames:
return frames[0]
elif img_num:
raise IndexError('Could not find image #%s' % img_num)
def _palette_is_grayscale(pil_image):
"""Return True if PIL image in palette mode is grayscale.
Parameters
----------
pil_image : PIL image
PIL Image that is in Palette mode.
Returns
-------
is_grayscale : bool
True if all colors in image palette are gray.
"""
assert pil_image.mode == 'P'
# get palette as an array with R, G, B columns
palette = np.asarray(pil_image.getpalette()).reshape((256, 3))
# Not all palette colors are used; unused colors have junk values.
start, stop = pil_image.getextrema()
valid_palette = palette[start:stop]
# Image is grayscale if channel differences (R - G and G - B)
# are all zero.
return np.allclose(np.diff(valid_palette), 0)
def ndarray_to_pil(arr, format_str=None):
"""Export an ndarray to a PIL object.
Parameters
----------
Refer to ``imsave``.
"""
if arr.ndim == 3:
arr = img_as_ubyte(arr)
mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]]
elif format_str in ['png', 'PNG']:
mode = 'I;16'
mode_base = 'I'
if arr.dtype.kind == 'f':
arr = img_as_uint(arr)
elif arr.max() < 256 and arr.min() >= 0:
arr = arr.astype(np.uint8)
mode = mode_base = 'L'
else:
arr = img_as_uint(arr)
else:
arr = img_as_ubyte(arr)
mode = 'L'
mode_base = 'L'
try:
array_buffer = arr.tobytes()
except AttributeError:
array_buffer = arr.tostring() # Numpy < 1.9
if arr.ndim == 2:
im = Image.new(mode_base, arr.T.shape)
try:
im.frombytes(array_buffer, 'raw', mode)
except AttributeError:
im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7
else:
image_shape = (arr.shape[1], arr.shape[0])
try:
im = Image.frombytes(mode, image_shape, array_buffer)
except AttributeError:
im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7
return im
def imsave(fname, arr, format_str=None, **kwargs):
"""Save an image to disk.
Parameters
----------
fname : str or file-like object
Name of destination file.
arr : ndarray of uint8 or float
Array (image) to save. Arrays of data-type uint8 should have
values in [0, 255], whereas floating-point arrays must be
in [0, 1].
format_str: str
Format to save as, this is defaulted to PNG if using a file-like
object; this will be derived from the extension if fname is a string
kwargs: dict
Keyword arguments to the Pillow save function (or tifffile save
function, for Tiff files). These are format dependent. For example,
Pillow's JPEG save function supports an integer ``quality`` argument
with values in [1, 95], while TIFFFile supports a ``compress``
integer argument with values in [0, 9].
Notes
-----
Tiff files are handled by Christophe Golhke's tifffile.py [1]_,
and support many advanced image types including multi-page and
floating point.
All other image formats use the Python Imaging Libary.
See PIL docs [2]_ for a list of other supported formats.
All images besides single channel PNGs are converted using `img_as_uint8`.
Single Channel PNGs have the following behavior:
- Integer values in [0, 255] and Boolean types -> img_as_uint8
- Floating point and other integers -> img_as_uint16
References
----------
.. [1] http://www.lfd.uci.edu/~gohlke/code/tifffile.py.html
.. [2] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
# default to PNG if file-like object
if not isinstance(fname, string_types) and format_str is None:
format_str = "PNG"
# Check for png in filename
if (isinstance(fname, string_types)
and fname.lower().endswith(".png")):
format_str = "PNG"
arr = np.asanyarray(arr)
if arr.dtype.kind == 'b':
arr = arr.astype(np.uint8)
use_tif = False
if hasattr(fname, 'lower'):
if fname.lower().endswith(('.tiff', '.tif')):
use_tif = True
if format_str is not None:
if format_str.lower() in ['tiff', 'tif']:
use_tif = True
if use_tif:
tif_imsave(fname, arr, **kwargs)
return
if arr.ndim not in (2, 3):
raise ValueError("Invalid shape for image array: %s" % arr.shape)
if arr.ndim == 3:
if arr.shape[2] not in (3, 4):
raise ValueError("Invalid number of channels in image array.")
img = ndarray_to_pil(arr, format_str=format_str)
img.save(fname, format=format_str, **kwargs)
| {
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"path": "skimage/io/_plugins/pil_plugin.py",
"copies": "5",
"size": "8721",
"license": "bsd-3-clause",
"hash": 6582274928026409000,
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"autogenerated": false,
"ratio": 3.6859678782755707,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.00012044692895756726,
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} |
__all__ = ['imread', 'imsave']
import numpy as np
from six import string_types
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy dtype object or string specifier
Specifies data type of array elements.
img_num : int, optional
Specifies which image to read in a file with multiple images
(zero-indexed).
kwargs : keyword pairs, optional
Addition keyword arguments to pass through.
Notes
-----
Files are read using the Python Imaging Libary.
See PIL docs [1]_ for a list of supported formats.
References
----------
.. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
if isinstance(fname, string_types):
with open(fname, 'rb') as f:
im = Image.open(f)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
else:
im = Image.open(fname)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
def pil_to_ndarray(im, dtype=None, img_num=None):
"""Import a PIL Image object to an ndarray, in memory.
Parameters
----------
Refer to ``imread``.
"""
try:
# this will raise an IOError if the file is not readable
im.getdata()[0]
except IOError as e:
site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries"
pillow_error_message = str(e)
error_message = ('Could not load "%s" \n'
'Reason: "%s"\n'
'Please see documentation at: %s'
% (im.filename, pillow_error_message, site))
raise ValueError(error_message)
frames = []
grayscale = None
i = 0
while 1:
try:
im.seek(i)
except EOFError:
break
frame = im
if img_num is not None and img_num != i:
im.getdata()[0]
i += 1
continue
if im.format == 'PNG' and im.mode == 'I' and dtype is None:
dtype = 'uint16'
if im.mode == 'P':
if grayscale is None:
grayscale = _palette_is_grayscale(im)
if grayscale:
frame = im.convert('L')
else:
if im.format == 'PNG' and 'transparency' in im.info:
frame = im.convert('RGBA')
else:
frame = im.convert('RGB')
elif im.mode == '1':
frame = im.convert('L')
elif 'A' in im.mode:
frame = im.convert('RGBA')
elif im.mode == 'CMYK':
frame = im.convert('RGB')
if im.mode.startswith('I;16'):
shape = im.size
dtype = '>u2' if im.mode.endswith('B') else '<u2'
if 'S' in im.mode:
dtype = dtype.replace('u', 'i')
frame = np.fromstring(frame.tobytes(), dtype)
frame.shape = shape[::-1]
else:
frame = np.array(frame, dtype=dtype)
frames.append(frame)
i += 1
if img_num is not None:
break
if hasattr(im, 'fp') and im.fp:
im.fp.close()
if img_num is None and len(frames) > 1:
return np.array(frames)
elif frames:
return frames[0]
elif img_num:
raise IndexError('Could not find image #%s' % img_num)
def _palette_is_grayscale(pil_image):
"""Return True if PIL image in palette mode is grayscale.
Parameters
----------
pil_image : PIL image
PIL Image that is in Palette mode.
Returns
-------
is_grayscale : bool
True if all colors in image palette are gray.
"""
assert pil_image.mode == 'P'
# get palette as an array with R, G, B columns
palette = np.asarray(pil_image.getpalette()).reshape((256, 3))
# Not all palette colors are used; unused colors have junk values.
start, stop = pil_image.getextrema()
valid_palette = palette[start:stop]
# Image is grayscale if channel differences (R - G and G - B)
# are all zero.
return np.allclose(np.diff(valid_palette), 0)
def ndarray_to_pil(arr, format_str=None):
"""Export an ndarray to a PIL object.
Parameters
----------
Refer to ``imsave``.
"""
if arr.ndim == 3:
arr = img_as_ubyte(arr)
mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]]
elif format_str in ['png', 'PNG']:
mode = 'I;16'
mode_base = 'I'
if arr.dtype.kind == 'f':
arr = img_as_uint(arr)
elif arr.max() < 256 and arr.min() >= 0:
arr = arr.astype(np.uint8)
mode = mode_base = 'L'
else:
arr = img_as_uint(arr)
else:
arr = img_as_ubyte(arr)
mode = 'L'
mode_base = 'L'
try:
array_buffer = arr.tobytes()
except AttributeError:
array_buffer = arr.tostring() # Numpy < 1.9
if arr.ndim == 2:
im = Image.new(mode_base, arr.T.shape)
try:
im.frombytes(array_buffer, 'raw', mode)
except AttributeError:
im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7
else:
image_shape = (arr.shape[1], arr.shape[0])
try:
im = Image.frombytes(mode, image_shape, array_buffer)
except AttributeError:
im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7
return im
def imsave(fname, arr, format_str=None, **kwargs):
"""Save an image to disk.
Parameters
----------
fname : str or file-like object
Name of destination file.
arr : ndarray of uint8 or float
Array (image) to save. Arrays of data-type uint8 should have
values in [0, 255], whereas floating-point arrays must be
in [0, 1].
format_str: str
Format to save as, this is defaulted to PNG if using a file-like
object; this will be derived from the extension if fname is a string
kwargs: dict
Keyword arguments to the Pillow save function (or tifffile save
function, for Tiff files). These are format dependent. For example,
Pillow's JPEG save function supports an integer ``quality`` argument
with values in [1, 95], while TIFFFile supports a ``compress``
integer argument with values in [0, 9].
Notes
-----
Use the Python Imaging Libary.
See PIL docs [1]_ for a list of other supported formats.
All images besides single channel PNGs are converted using `img_as_uint8`.
Single Channel PNGs have the following behavior:
- Integer values in [0, 255] and Boolean types -> img_as_uint8
- Floating point and other integers -> img_as_uint16
References
----------
.. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
# default to PNG if file-like object
if not isinstance(fname, string_types) and format_str is None:
format_str = "PNG"
# Check for png in filename
if (isinstance(fname, string_types)
and fname.lower().endswith(".png")):
format_str = "PNG"
arr = np.asanyarray(arr)
if arr.dtype.kind == 'b':
arr = arr.astype(np.uint8)
if arr.ndim not in (2, 3):
raise ValueError("Invalid shape for image array: %s" % arr.shape)
if arr.ndim == 3:
if arr.shape[2] not in (3, 4):
raise ValueError("Invalid number of channels in image array.")
img = ndarray_to_pil(arr, format_str=format_str)
img.save(fname, format=format_str, **kwargs)
| {
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"path": "skimage/io/_plugins/pil_plugin.py",
"copies": "15",
"size": "7726",
"license": "bsd-3-clause",
"hash": 7467518209125070000,
"line_mean": 28.7153846154,
"line_max": 93,
"alpha_frac": 0.56549314,
"autogenerated": false,
"ratio": 3.743217054263566,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": null,
"num_lines": null
} |
__all__ = ['imread']
import numpy as np
try:
from PIL import Image
except ImportError:
raise ImportError("The Python Image Library could not be found. "
"Please refer to http://pypi.python.org/pypi/PIL/ "
"for further instructions.")
def imread(fname, dtype=None):
"""Load an image from file.
"""
im = Image.open(fname)
if im.mode == 'P':
if _palette_is_grayscale(im):
im = im.convert('L')
else:
im = im.convert('RGB')
if 'A' in im.mode:
im = im.convert('RGBA')
return np.array(im, dtype=dtype)
def _palette_is_grayscale(pil_image):
"""Return True if PIL image in palette mode is grayscale.
Parameters
----------
pil_image : PIL image
PIL Image that is in Palette mode.
Returns
-------
is_grayscale : bool
True if all colors in image palette are gray.
"""
assert pil_image.mode == 'P'
# get palette as an array with R, G, B columns
palette = np.asarray(pil_image.getpalette()).reshape((256, 3))
# Not all palette colors are used; unused colors have junk values.
start, stop = pil_image.getextrema()
valid_palette = palette[start:stop]
# Image is grayscale if channel differences (R - G and G - B)
# are all zero.
return np.allclose(np.diff(valid_palette), 0)
def imsave(fname, arr):
"""Save an image to disk.
Parameters
----------
fname : str
Name of destination file.
arr : ndarray of uint8 or float
Array (image) to save. Arrays of data-type uint8 should have
values in [0, 255], whereas floating-point arrays must be
in [0, 1].
Notes
-----
Currently, only 8-bit precision is supported.
"""
arr = np.asarray(arr).squeeze()
if arr.ndim not in (2, 3):
raise ValueError("Invalid shape for image array: %s" % arr.shape)
if arr.ndim == 3:
if arr.shape[2] not in (3, 4):
raise ValueError("Invalid number of channels in image array.")
# Image is floating point, assume in [0, 1]
if np.issubdtype(arr.dtype, float):
arr = arr * 255
arr = arr.astype(np.uint8)
if arr.ndim == 2:
mode = 'L'
elif arr.shape[2] in (3, 4):
mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]]
# Force all integers to bytes
arr = arr.astype(np.uint8)
img = Image.fromstring(mode, (arr.shape[1], arr.shape[0]), arr.tostring())
img.save(fname)
def imshow(arr):
"""Display an image, using PIL's default display command.
Parameters
----------
arr : ndarray
Image to display. Images of dtype float are assumed to be in
[0, 1]. Images of dtype uint8 are in [0, 255].
"""
if np.issubdtype(arr.dtype, float):
arr = (arr * 255).astype(np.uint8)
Image.fromarray(arr).show()
| {
"repo_name": "GaelVaroquaux/scikits.image",
"path": "scikits/image/io/_plugins/pil_plugin.py",
"copies": "1",
"size": "2886",
"license": "bsd-3-clause",
"hash": -7233221457519247000,
"line_mean": 25.7222222222,
"line_max": 78,
"alpha_frac": 0.5838530839,
"autogenerated": false,
"ratio": 3.5940224159402243,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9670511423448176,
"avg_score": 0.0014728152784096722,
"num_lines": 108
} |
__all__ = ['Index']
import os
import sys
import mmap
import struct
class Index(object):
def __init__(self, sstable, t, columns, path=None):
self.sstable = sstable
self.t = t
self.columns = columns
self.mm = None
self.f = None
def get_path(self):
table = self.sstable.table
filename = 'index-%s-%s.data' % (self.t, '-'.join(self.columns))
path = os.path.join(table.get_path(), filename)
return path
def open(self):
'''
Open file for reading.
'''
self.f = open(self.get_path(), 'r+b')
self.mm = mmap.mmap(self.f.fileno(), 0)
def close(self):
'''
Open file for reading.
'''
self.mm.close()
self.f.close()
def w_open(self):
'''
Open file for writing.
'''
self.f = open(self.get_path(), 'wb')
def w_close(self):
'''
Close file for writing.
'''
self.f.close()
def _write_key(self, row, sstable_pos):
table = self.sstable.table
key_blob_items = []
for c in self.columns:
t = table.schema[c]
b = t._get_column_packed(row[c])
key_blob_items.append(b)
key_blob = b''.join(key_blob_items)
pos_blob = struct.pack('!Q', sstable_pos)
self.f.write(key_blob)
self.f.write(pos_blob)
def _read_key(self, pos):
table = self.sstable.table
key = []
p = pos
for c in self.columns:
t = table.schema[c]
v, p = t._get_column_unpacked(self.mm, p)
key.append(v)
key = tuple(key)
sstable_pos, = struct.unpack_from('!Q', self.mm, p)
return key, sstable_pos
def _get_key_size(self, key):
table = self.sstable.table
size = 0
for c in self.columns:
t = table.schema[c]
i = self.columns.index(c)
s = t._get_column_size(key[i])
size += s
return size
def get_sstable_pos(self, key):
sstable = self.sstable
table = self.sstable.table
step = 8 + self._get_key_size(key) # !Q + KEY
# binary search
low = 0
high = (self.mm.size() // step) - 1
key_pos = None
offset_pos = None
while low <= high:
mid = (low + high) // 2
key_pos = mid * step
offset_pos = mid
cur_key, sstable_pos = self._read_key(key_pos)
# print 'cur_key:', cur_key
if cur_key > key:
high = mid - 1
elif cur_key < key:
low = mid + 1
else:
break
else:
sstable_pos = None
return offset_pos, sstable_pos
def get_lt_sstable_pos(self, key):
sstable = self.sstable
table = self.sstable.table
step = 8 + self._get_key_size(key) # !Q + KEY
# binary search
low = 0
high = (self.mm.size() // step) - 1
key_pos = None
while low < high:
mid = (low + high) // 2
key_pos = mid * step
cur_key, sstable_pos = self._read_key(key_pos)
# print 'left cur_key:', cur_key
_key = tuple(x for x, y in zip(key, cur_key) if x != None)
_cur_key = tuple(y for x, y in zip(key, cur_key) if x != None)
if _cur_key < _key:
low = mid + 1
else:
high = mid
offset_pos = low - 1
_, sstable_pos = self._read_key(offset_pos * step)
return offset_pos, sstable_pos
def get_le_sstable_pos(self, key):
sstable = self.sstable
table = self.sstable.table
step = 8 + self._get_key_size(key) # !Q + KEY
# binary search
low = 0
high = (self.mm.size() // step) - 1
key_pos = None
while low < high:
mid = (low + high) // 2
key_pos = mid * step
cur_key, sstable_pos = self._read_key(key_pos)
# print 'left cur_key:', cur_key
_key = tuple(x for x, y in zip(key, cur_key) if x != None)
_cur_key = tuple(y for x, y in zip(key, cur_key) if x != None)
if _key < _cur_key:
high = mid
else:
low = mid + 1
offset_pos = low - 1
_, sstable_pos = self._read_key(offset_pos * step)
return offset_pos, sstable_pos
def get_gt_sstable_pos(self, key):
sstable = self.sstable
table = self.sstable.table
step = 8 + self._get_key_size(key) # !Q + KEY
# binary search
low = 0
high = (self.mm.size() // step) - 1
key_pos = None
while low < high:
mid = (low + high) // 2
key_pos = mid * step
cur_key, sstable_pos = self._read_key(key_pos)
# print 'left cur_key:', cur_key
_key = tuple(x for x, y in zip(key, cur_key) if x != None)
_cur_key = tuple(y for x, y in zip(key, cur_key) if x != None)
if _key < _cur_key:
high = mid
else:
low = mid + 1
offset_pos = low
_, sstable_pos = self._read_key(offset_pos * step)
return offset_pos, sstable_pos
def get_ge_sstable_pos(self, key):
sstable = self.sstable
table = self.sstable.table
step = 8 + self._get_key_size(key) # !Q + KEY
# binary search
low = 0
high = (self.mm.size() // step) - 1
key_pos = None
while low < high:
mid = (low + high) // 2
key_pos = mid * step
cur_key, sstable_pos = self._read_key(key_pos)
# print 'left cur_key:', cur_key
_key = tuple(x for x, y in zip(key, cur_key) if x != None)
_cur_key = tuple(y for x, y in zip(key, cur_key) if x != None)
if _cur_key < _key:
low = mid + 1
else:
high = mid
offset_pos = low
_, sstable_pos = self._read_key(offset_pos * step)
return offset_pos, sstable_pos
| {
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"path": "backup/store/index.py",
"copies": "1",
"size": "6298",
"license": "mit",
"hash": -986041783436472700,
"line_mean": 26.7444933921,
"line_max": 74,
"alpha_frac": 0.4680851064,
"autogenerated": false,
"ratio": 3.530269058295964,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4498354164695964,
"avg_score": null,
"num_lines": null
} |
__all__ = ['IndraDBRestSearchProcessor', 'IndraDBRestHashProcessor']
import logging
from copy import deepcopy
from threading import Thread
from datetime import datetime
from collections import OrderedDict, defaultdict
from indra.statements import stmts_from_json, get_statement_by_name, \
get_all_descendants
from indra.sources.indra_db_rest.util import submit_query_request, \
submit_statement_request
from indra.sources.indra_db_rest.exceptions import IndraDBRestResponseError
from indra.util.statement_presentation import get_available_source_counts, \
get_available_ev_counts, standardize_counts
logger = logging.getLogger(__name__)
class RemoveParam(object):
pass
class IndraDBRestProcessor(object):
"""The generalized packaging for query responses.
General Parameters
------------------
timeout : positive int or None
If an int, block until the work is done and statements are retrieved, or
until the timeout has expired, in which case the results so far will be
returned in the response object, and further results will be added in
a separate thread as they become available. If simple_response is True,
all statements available will be returned. Otherwise (if None), block
indefinitely until all statements are retrieved. Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 10.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
max_stmts : int or None
Select the maximum number of statements to return. When set less than
1000 the effect is much the same as setting persist to false, and will
guarantee a faster response. Default is None.
use_obtained_counts : Optional[bool]
If set to True, the statement evidence counts and source counts are
calculated based on the actual obtained statements and evidences
rather than the counts provided by the DB API. Default: False.
Attributes
----------
statements : list[:py:class:`indra.statements.Statement`]
A list of INDRA Statements that will be filled once all queries have
been completed.
"""
_override_default_api_params = {}
def __init__(self, *args, **kwargs):
self.statements = []
self.__statement_jsons = {}
self.__evidence_counts = {}
self.__source_counts = {}
self.use_obtained_counts = kwargs.pop('use_obtained_counts', False)
# Define the basic generic defaults.
default_api_params = dict(timeout=None, ev_limit=10, best_first=True,
tries=2, max_stmts=None)
# Update with any overrides.
default_api_params.update(self._override_default_api_params)
# Some overrides may be RemoveParam objects, indicating the key should
# be removed. Filter those out.
default_api_params = {k: v for k, v in default_api_params.items()
if not isinstance(v, RemoveParam)}
# Update the kwargs to include these default values, if not already
# specified by the user.
kwargs.update((k, kwargs.get(k, default_api_params[k]))
for k in default_api_params.keys())
self._run(*args, **kwargs)
return
def get_ev_count(self, stmt):
"""Get the total evidence count for a statement."""
return self.get_ev_count_by_hash(stmt.get_hash(shallow=True))
def get_ev_count_by_hash(self, stmt_hash):
"""Get the total evidence count for a statement hash."""
return self.__evidence_counts.get(stmt_hash, 0)
def get_source_counts(self):
"""Get the source counts as a dict per statement hash."""
return deepcopy(self.__source_counts)
def get_source_count(self, stmt):
"""Get the source counts for a given statement."""
return self.get_source_count_by_hash(stmt.get_hash(shallow=True))
def get_source_count_by_hash(self, stmt_hash):
"""Get the source counts for a given statement."""
return self.__source_counts.get(stmt_hash, {})
def get_ev_counts(self):
"""Get a dictionary of evidence counts."""
return self.__evidence_counts.copy()
def get_hash_statements_dict(self):
"""Return a dict of Statements keyed by hashes."""
res = {stmt_hash: stmts_from_json([stmt])[0]
for stmt_hash, stmt in self.__statement_jsons.items()}
return res
def merge_results(self, other_processor):
"""Merge the results of this processor with those of another."""
if not isinstance(other_processor, self.__class__):
raise ValueError("Can only extend with another %s instance."
% self.__class__.__name__)
self.statements.extend(other_processor.statements)
self._merge_json(other_processor.__statement_jsons,
other_processor.__evidence_counts,
other_processor.__source_counts)
return
def _merge_json(self, stmt_json, ev_counts, source_counts):
"""Merge these statement jsons with new jsons."""
# Where there is overlap, there _should_ be agreement.
self.__evidence_counts.update(standardize_counts(ev_counts))
# We turn source counts into an int-keyed dict and update it that way
self.__source_counts.update(standardize_counts(source_counts))
for k, sj in stmt_json.items():
if k not in self.__statement_jsons:
self.__statement_jsons[k] = sj # This should be most of them
else:
# This should only happen rarely.
for evj in sj['evidence']:
self.__statement_jsons[k]['evidence'].append(evj)
return
def _compile_statements(self):
"""Generate statements from the jsons."""
self.statements = stmts_from_json(self.__statement_jsons.values())
if self.use_obtained_counts:
self.__source_counts = get_available_source_counts(self.statements)
self.__evidence_counts = get_available_ev_counts(self.statements)
def _unload_and_merge_resp(self, resp):
resp_dict = resp.json(object_pairs_hook=OrderedDict)
stmts_json = resp_dict['statements']
ev_totals = resp_dict['evidence_totals']
source_counts = resp_dict['source_counts']
eos = resp_dict['end_of_statements']
limit = resp_dict['statement_limit']
num_returned = resp_dict['statements_returned']
# Update the result
self._merge_json(stmts_json, ev_totals, source_counts)
return eos, num_returned, limit
def _run(self, *args, **kwargs):
raise NotImplementedError("_run must be defined in subclass.")
class IndraDBRestHashProcessor(IndraDBRestProcessor):
"""The packaging and processor for hash lookup of statements.
Parameters
----------
hash_list : list[int or str]
A list of the matches-key hashes for the statements you want to get.
Keyword Parameters
------------------
timeout : positive int or None
If an int, block until the work is done and statements are retrieved, or
until the timeout has expired, in which case the results so far will be
returned in the response object, and further results will be added in
a separate thread as they become available. If simple_response is True,
all statements available will be returned. Otherwise (if None), block
indefinitely until all statements are retrieved. Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 100.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
Attributes
----------
statements : list[:py:class:`indra.statements.Statement`]
A list of INDRA Statements that will be filled once all queries have
been completed.
"""
_default_api_params = {'ev_limit': 100, 'max_stmts': RemoveParam()}
def _run(self, hash_list, **api_params):
# Make sure the input is a list (not just a single hash).
if not isinstance(hash_list, list):
raise ValueError("The `hash_list` input is a list, not %s."
% type(hash_list))
# If there is nothing in the list, don't waste time with a query.
if not hash_list:
return
# Regularize and check the types of elements in the hash list.
if isinstance(hash_list[0], str):
hash_list = [int(h) for h in hash_list]
if not all([isinstance(h, int) for h in hash_list]):
raise ValueError("Hashes must be ints or strings that can be "
"converted into ints.")
# Execute the query and load the results.
resp = submit_statement_request('post', 'from_hashes',
data={'hashes': hash_list},
**api_params)
self._unload_and_merge_resp(resp)
self._compile_statements()
return
class IndraDBRestPaperProcessor(IndraDBRestProcessor):
"""The packaging and processor for hash lookup of statements.
Parameters
----------
ids : list[(<id type>, <id value>)]
A list of tuples with ids and their type. The type can be any one of
'pmid', 'pmcid', 'doi', 'pii', 'manuscript id', or 'trid', which is the
primary key id of the text references in the database.
Keyword Parameters
------------------
timeout : positive int or None
If an int, block until the work is done and statements are retrieved, or
until the timeout has expired, in which case the results so far will be
returned in the response object, and further results will be added in
a separate thread as they become available. If simple_response is True,
all statements available will be returned. Otherwise (if None), block
indefinitely until all statements are retrieved. Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 100.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
max_stmts : int or None
Select the maximum number of statements to return. When set less than
1000 the effect is much the same as setting persist to false, and will
guarantee a faster response. Default is None.
Attributes
----------
statements : list[:py:class:`indra.statements.Statement`]
A list of INDRA Statements that will be filled once all queries have
been completed.
"""
def _run(self, ids, **api_params):
id_l = [{'id': id_val, 'type': id_type} for id_type, id_val in ids]
resp = submit_statement_request('post', 'from_papers',
data={'ids': id_l},
**api_params)
self._unload_and_merge_resp(resp)
self._compile_statements()
return
class IndraDBRestSearchProcessor(IndraDBRestProcessor):
"""The packaging for agent and statement type search query responses.
Parameters
----------
subject/object : str
Optionally specify the subject and/or object of the statements in
you wish to get from the database. By default, the namespace is assumed
to be HGNC gene names, however you may specify another namespace by
including `@<namespace>` at the end of the name string. For example, if
you want to specify an agent by chebi, you could use `CHEBI:6801@CHEBI`,
or if you wanted to use the HGNC id, you could use `6871@HGNC`.
agents : list[str]
A list of agents, specified in the same manner as subject and object,
but without specifying their grammatical position.
stmt_type : str
Specify the types of interactions you are interested in, as indicated
by the sub-classes of INDRA's Statements. This argument is *not* case
sensitive. If the statement class given has sub-classes
(e.g. RegulateAmount has IncreaseAmount and DecreaseAmount), then both
the class itself, and its subclasses, will be queried, by default. If
you do not want this behavior, set use_exact_type=True. Note that if
max_stmts is set, it is possible only the exact statement type will
be returned, as this is the first searched. The processor then cycles
through the types, getting a page of results for each type and adding it
to the quota, until the max number of statements is reached.
use_exact_type : bool
If stmt_type is given, and you only want to search for that specific
statement type, set this to True. Default is False.
persist : bool
Default is True. When False, if a query comes back limited (not all
results returned), just give up and pass along what was returned.
Otherwise, make further queries to get the rest of the data (which may
take some time).
Keyword Parameters
------------------
timeout : positive int or None
If an int, block until the work is done and statements are retrieved, or
until the timeout has expired, in which case the results so far will be
returned in the response object, and further results will be added in
a separate thread as they become available. If simple_response is True,
all statements available will be returned. Otherwise (if None), block
indefinitely until all statements are retrieved. Default is None.
ev_limit : int or None
Limit the amount of evidence returned per Statement. Default is 10.
best_first : bool
If True, the preassembled statements will be sorted by the amount of
evidence they have, and those with the most evidence will be
prioritized. When using `max_stmts`, this means you will get the "best"
statements. If False, statements will be queried in arbitrary order.
tries : int > 0
Set the number of times to try the query. The database often caches
results, so if a query times out the first time, trying again after a
timeout will often succeed fast enough to avoid a timeout. This can also
help gracefully handle an unreliable connection, if you're willing to
wait. Default is 2.
max_stmts : int or None
Select the maximum number of statements to return. When set less than
1000 the effect is much the same as setting persist to false, and will
guarantee a faster response. Default is None.
Attributes
----------
statements : list[:py:class:`indra.statements.Statement`]
A list of INDRA Statements that will be filled once all queries have
been completed.
statements_sample : list[:py:class:`indra.statements.Statement`]
A list of the INDRA Statements received from the first query. In
general these will be the "best" (currently this means they have the
most evidence) Statements available.
"""
def __init__(self, *args, **kwargs):
self.statements_sample = None
super(self.__class__, self).__init__(*args, **kwargs)
return
def is_working(self):
"""Check if the thread is running."""
if not self.__th:
return False
return self.__th.is_alive()
def wait_until_done(self, timeout=None):
"""Wait for the background load to complete."""
start = datetime.now()
if not self.__th:
raise IndraDBRestResponseError("There is no thread waiting to "
"complete.")
self.__th.join(timeout)
now = datetime.now()
dt = now - start
if self.__th.is_alive():
logger.warning("Timed out after %0.3f seconds waiting for "
"statement load to complete." % dt.total_seconds())
ret = False
else:
logger.info("Waited %0.3f seconds for statements to finish"
"loading." % dt.total_seconds())
ret = True
return ret
def _all_done(self):
every_type_done = (len(self.__done_dict) > 0
and all(self.__done_dict.values()))
quota_done = (self.__quota is not None and self.__quota <= 0)
return every_type_done or quota_done
def _query_and_extract(self, agent_strs, params, stmt_type=None):
assert not self._all_done(), "Tried to run query but I'm done!"
params['offset'] = self.__page_dict[stmt_type]
params['max_stmts'] = self.__quota
if stmt_type is not None:
params['type'] = stmt_type
resp = submit_query_request('from_agents', *agent_strs, **params)
eos, num_returned, page_step = self._unload_and_merge_resp(resp)
# NOTE: this is technically not a direct conclusion, and could be
# wrong, resulting in a single unnecessary extra query, but that
# should almost never happen, and if it does, it isn't the end of
# the world.
self.__done_dict[stmt_type] = eos
# Update the quota
if self.__quota is not None:
self.__quota -= num_returned
# Increment the page
self.__page_dict[stmt_type] += page_step
return
def _query_over_statement_types(self, agent_strs, stmt_types, params):
if not stmt_types:
self._query_and_extract(agent_strs, params.copy())
else:
for stmt_type in stmt_types:
if self.__done_dict[stmt_type]:
continue
self._query_and_extract(agent_strs, params.copy(), stmt_type)
# Check the quota
if self.__quota is not None and self.__quota <= 0:
break
return
def _run_queries(self, agent_strs, stmt_types, params, persist):
"""Use paging to get all statements requested."""
self._query_over_statement_types(agent_strs, stmt_types, params)
assert len(self.__done_dict) == len(stmt_types) \
or None in self.__done_dict.keys(), \
"Done dict was not initiated for all stmt_type's."
# Check if we want to keep going.
if not persist:
self._compile_statements()
return
# Get the rest of the content.
while not self._all_done():
self._query_over_statement_types(agent_strs, stmt_types, params)
# Create the actual statements.
self._compile_statements()
return
def merge_results(self, other_processor):
super(self.__class__, self).merge_results(other_processor)
if other_processor.statements_sample is not None:
if self.statements_sample is None:
self.statements_sample = other_processor.statements_sample
else:
self.statements_sample.extend(other_processor.statements_sample)
def _merge_json(self, stmt_json, ev_counts, source_counts):
super(self.__class__, self)._merge_json(stmt_json, ev_counts,
source_counts)
if not self.__started:
self.statements_sample = stmts_from_json(stmt_json.values())
self.__started = True
return
def _run(self, subject=None, object=None, agents=None, stmt_type=None,
use_exact_type=False, persist=True, strict_stop=False,
**api_params):
self.__started = False
self.__done_dict = defaultdict(lambda: False)
self.__page_dict = defaultdict(lambda: 0)
self.__th = None
self.__quota = api_params['max_stmts']
# Make sure we got at least SOME agents (the remote API will error if
# we proceed with no arguments).
if subject is None and object is None and not agents:
raise ValueError("At least one agent must be specified, or else "
"the scope will be too large.")
# Make timeouts apply differently in this case
if not strict_stop:
timeout = api_params.pop('timeout', None)
else:
timeout = api_params.get('timeout', None)
# Formulate inputs for the agents..
key_val_list = [('subject', subject), ('object', object)]
params = {param_key: param_val for param_key, param_val in key_val_list
if param_val is not None}
params.update(api_params)
agent_strs = [] if agents is None else ['agent%d=%s' % (i, ag)
for i, ag in enumerate(agents)]
# Handle the type(s).
stmt_types = [stmt_type] if stmt_type else []
if stmt_type is not None and not use_exact_type:
stmt_class = get_statement_by_name(stmt_type)
descendant_classes = get_all_descendants(stmt_class)
stmt_types += [cls.__name__ for cls in descendant_classes]
# Handle the content if we were limited.
args = [agent_strs, stmt_types, params, persist]
logger.debug("The remainder of the query will be performed in a "
"thread...")
self.__th = Thread(target=self._run_queries, args=args)
self.__th.start()
if timeout is None:
logger.debug("Waiting for thread to complete...")
self.__th.join()
elif timeout: # is not 0
logger.debug("Waiting at most %d seconds for thread to complete..."
% timeout)
self.__th.join(timeout)
return
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"quality_score": 0.5468419224866208,
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__all__ = ["infantry_bot", "heavy_bot", "rocket_bot"]
from .tools import create_unit
INFANTRY_BOT_BASIS = {
'c_size': 1,
'firing_range': 4,
'rate_of_fire': 1,
'speed': 5,
'type': 'infantryBot'
}
INFANTRY_BOTS = {
1: dict(damage_per_shot=50, hit_points=120, **INFANTRY_BOT_BASIS),
2: dict(damage_per_shot=60, hit_points=150, **INFANTRY_BOT_BASIS),
3: dict(damage_per_shot=75, hit_points=200, **INFANTRY_BOT_BASIS),
}
HEAVY_BOT_BASIS = {
'c_size': 4,
'firing_range': 2.5,
'rate_of_fire': 10,
'speed': 3,
'type': 'heavyBot'
}
HEAVY_BOTS = {
1: dict(damage_per_shot=10, hit_points=1000, **HEAVY_BOT_BASIS),
2: dict(damage_per_shot=13, hit_points=1200, **HEAVY_BOT_BASIS),
3: dict(damage_per_shot=16, hit_points=1400, **HEAVY_BOT_BASIS),
}
ROCKET_BOT_BASIS = {
'c_size': 2,
'firing_range': 8,
'rate_of_fire': 0.5,
'speed': 4,
'type': 'rocketBot'
}
ROCKET_BOTS = {
1: dict(damage_per_shot=150, hit_points=50, **ROCKET_BOT_BASIS),
2: dict(damage_per_shot=200, hit_points=60, **ROCKET_BOT_BASIS),
3: dict(damage_per_shot=300, hit_points=80, **ROCKET_BOT_BASIS),
}
def infantry_bot(level: int) -> dict:
return create_unit(INFANTRY_BOTS, level)
def heavy_bot(level: int) -> dict:
return create_unit(HEAVY_BOTS, level)
def rocket_bot(level: int) -> dict:
return create_unit(ROCKET_BOTS, level)
| {
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"path": "battle_mocks/units.py",
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.34997980545550084,
"avg_score": null,
"num_lines": null
} |
__all__ = ["InitLogging", \
"VLOG", \
"DEBUG", \
"INFO", \
"WARNING", \
"ERROR", \
"CRITICAL"]
import time
import sys
import logging
import copy
DEFAULT_LOGGER_NAME = "\033[1;42m" + "Log" + "\033[1;0m"
DEBUG = 0
INFO = 1
WARNING = 2
ERROR = 3
CRITICAL = 4
""" InitLogging should only be invoked on main thread, aka before the http handler,
and we only use one true logger whose name is set by "DEFAULT_LOGGER_NAME", wherever
need a log you just call VLOG(), the only logger is global scoped """
def InitLogging(opts):
logging.addLevelName(logging.DEBUG, "\033[1;44m%s\033[1;0m" % logging.getLevelName(logging.DEBUG))
logging.addLevelName(logging.INFO, "\033[1;45m%s\033[1;0m" % logging.getLevelName(logging.INFO))
logging.addLevelName(logging.WARNING, "\033[1;41m%s\033[1;0m" % logging.getLevelName(logging.WARNING))
logging.addLevelName(logging.ERROR, "\033[1;41m%s\033[1;0m" % logging.getLevelName(logging.ERROR))
logging.addLevelName(logging.CRITICAL, "\033[1;41m%s\033[1;0m" % logging.getLevelName(logging.CRITICAL))
g_level = logging.DEBUG
if '--verbose' in sys.argv:
g_level = logging.DEBUG
if '--silent' in sys.argv:
g_level = logging.NOTSET
logger = logging.getLogger(DEFAULT_LOGGER_NAME)
logger.setLevel(g_level)
g_start_time = time.asctime()
g_formatter = logging.Formatter('\033[1;33m%(asctime)s\033[1;0m %(name)s %(levelname)s %(message)s')
if opts.log_path:
try:
log_file = open(opts.log_path, "w")
except:
print "Failed to redirect stderr to log file"
return False
sys.stderr = log_file
# create a handler to write the log to a given file
fh = logging.FileHandler(log_path)
fh.setLevel(g_level)
fh.setFormatter(g_formatter)
logger.addHandler(fh)
# we direct the log information to stdout
ch = logging.StreamHandler(sys.__stdout__)
ch.setLevel(g_level)
ch.setFormatter(g_formatter)
logger.addHandler(ch)
return True
# dispatch difference level to logger created by InitLogging()
def VLOG(level, msg):
logger = logging.getLogger(DEFAULT_LOGGER_NAME)
if level == DEBUG:
logger.debug('\033[1;34m' + msg + '\033[1;0m')
elif level == INFO:
logger.info('\033[1;35m' + msg + '\033[1;0m')
elif level == WARNING:
logger.warning('\033[1;31m' + msg + '\033[1;0m')
elif level == ERROR:
logger.error('\033[1;31m' + msg + '\033[1;0m')
elif level == CRITICAL:
logger.critical('\033[1;31m' + msg + '\033[1;0m')
else:
logger.debug('\033[1;34m' + msg + '\033[1;0m')
return
| {
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.9066670373381962,
"avg_score": 0.03459126272618212,
"num_lines": 80
} |
__all__ = ['inject', 'signature']
import os
import sys
def _ensure_path(p):
p = os.path.realpath(p)
if isinstance(p, unicode):
p = p.encode(sys.getfilesystemencoding())
return p
def inject(pid, bundle, useMainThread=True):
"""Loads the given MH_BUNDLE in the target process identified by pid"""
try:
from _objc import _inject
from _dyld import dyld_find
except ImportError:
raise NotImplementedError("objc.inject is only supported on Mac OS X 10.3 and later")
bundlePath = bundle
systemPath = dyld_find('/usr/lib/libSystem.dylib')
carbonPath = dyld_find('/System/Library/Frameworks/Carbon.framework/Carbon')
paths = map(_ensure_path, (bundlePath, systemPath, carbonPath))
return _inject(
pid,
useMainThread,
*paths
)
def signature(signature, **kw):
"""
A Python method decorator that allows easy specification
of Objective-C selectors.
Usage::
@objc.signature('i@:if')
def methodWithX_andY_(self, x, y):
return 0
"""
from _objc import selector
kw['signature'] = signature
def makeSignature(func):
return selector(func, **kw)
return makeSignature
| {
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4986389619819626,
"avg_score": null,
"num_lines": null
} |
"""All in one NTM. Encapsulation of all components."""
import torch
from torch import nn
from .ntm import NTM
from .controller import LSTMController
from .head import NTMReadHead, NTMWriteHead
from .memory import NTMMemory
class EncapsulatedNTM(nn.Module):
def __init__(self, num_inputs, num_outputs,
controller_size, controller_layers, num_heads, N, M):
"""Initialize an EncapsulatedNTM.
:param num_inputs: External number of inputs.
:param num_outputs: External number of outputs.
:param controller_size: The size of the internal representation.
:param controller_layers: Controller number of layers.
:param num_heads: Number of heads.
:param N: Number of rows in the memory bank.
:param M: Number of cols/features in the memory bank.
"""
super(EncapsulatedNTM, self).__init__()
# Save args
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.controller_size = controller_size
self.controller_layers = controller_layers
self.num_heads = num_heads
self.N = N
self.M = M
# Create the NTM components
memory = NTMMemory(N, M)
controller = LSTMController(num_inputs + M*num_heads, controller_size, controller_layers)
heads = nn.ModuleList([])
for i in range(num_heads):
heads += [
NTMReadHead(memory, controller_size),
NTMWriteHead(memory, controller_size)
]
self.ntm = NTM(num_inputs, num_outputs, controller, memory, heads)
self.memory = memory
def init_sequence(self, batch_size):
"""Initializing the state."""
self.batch_size = batch_size
self.memory.reset(batch_size)
self.previous_state = self.ntm.create_new_state(batch_size)
def forward(self, x=None):
if x is None:
x = torch.zeros(self.batch_size, self.num_inputs)
o, self.previous_state = self.ntm(x, self.previous_state)
return o, self.previous_state
def calculate_num_params(self):
"""Returns the total number of parameters."""
num_params = 0
for p in self.parameters():
num_params += p.data.view(-1).size(0)
return num_params
| {
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"path": "ntm/aio.py",
"copies": "1",
"size": "2311",
"license": "bsd-3-clause",
"hash": -637902669801121200,
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"ratio": 3.923599320882852,
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5046273487882852,
"avg_score": null,
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} |
__all__ = ['input', 'middleware']
''' a middleware to build http params '''
'''#makingitpossible no file uploads handled yet '''
import io, cgi, collections
def middleware(app):
def process_fieldstorage(fieldstorage):
''' not processing file uploads '''
return isinstance(fieldstorage, list) and [process_fieldstorage(fs) for fs in fieldstorage] or fieldstorage.value
def input_app(environ):
if environ['REQUEST_METHOD'] in ('POST', 'PUT'):
length = int(environ.get('CONTENT_LENGTH', '0'))
environ['data'] = data = environ['wsgi.input'].read(length)
data_input = cgi.FieldStorage(fp=io.BytesIO(data), environ=environ, keep_blank_values=True, encoding='utf8')
environ['input'] = {key: process_fieldstorage(data_input[key]) for key in data_input.keys()}
else:
query_input = cgi.FieldStorage(environ=environ, keep_blank_values=True)
environ['input'] = {key: process_fieldstorage(query_input[key]) for key in query_input.keys()}
return app(environ)
return input_app
def input():
return middleware
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__all__ = ['INPUT', 'OUTPUT', 'INOUT']
__all__ += ['flip']
__all__ += ['Port']
INPUT = 'input'
OUTPUT = 'output'
INOUT = 'inout'
def flip(direction):
assert direction in [INPUT, OUTPUT, INOUT]
if direction == INPUT: return OUTPUT
elif direction == OUTPUT: return INPUT
elif direction == INOUT: return INOUT
def mergewires(new, old):
for i in old.inputs:
if i not in new.inputs:
new.inputs.append(i)
i.wires = new
for o in old.outputs:
if o not in new.outputs:
if len(new.outputs) > 0:
print("Error: connecting more than one output to an input", o)
new.outputs.append(o)
o.wires = new
#
# A Wire has a list of input and output Ports.
#
class Wire:
def __init__(self):
self.inputs = []
self.outputs = []
def connect( self, o, i ):
# anon Ports are added to the input or output list of this wire
#
# connecting to a non-anonymous port to an anonymous port
# add the non-anonymous port to the wire associated with the
# anonymous port
#print str(o), o.anon(), o.bit.isinput(), o.bit.isoutput()
#print str(i), i.anon(), i.bit.isinput(), i.bit.isoutput()
if not o.anon():
#assert o.bit.direction is not None
if o.bit.isinput():
print("Error: using an input as an output", str(o))
return
if o not in self.outputs:
if len(self.outputs) != 0:
print("Warning: adding an output to a wire with an output", str(o))
#print('adding output', o)
self.outputs.append(o)
if not i.anon():
#assert i.bit.direction is not None
if i.bit.isoutput():
print("Error: using an output as an input", str(i))
return
if i not in self.inputs:
#print('adding input', i)
self.inputs.append(i)
# print(o.wires,i.wires,self,self.outputs,self.inputs)
# always update wires
o.wires = self
i.wires = self
def check(self):
for o in self.inputs:
if o.isoutput():
print("Error: output in the wire inputs:",)
for o in self.outputs:
if o.isinput():
print("Error: input in the wire outputs:",)
return False
# check that this wire is only driven by a single output
if len(self.outputs) > 1:
print("Error: Multiple outputs on a wire:",)
return False
return True
#
# Port implements wiring
#
# Each port is represented by a Bit()
#
class Port:
def __init__(self, bit):
self.bit = bit
self.wires = Wire()
def __str__(self):
return str(self.bit)
def anon(self):
return self.bit.anon()
# wire a port to a port
def wire(i, o):
#if o.bit.direction is None:
# o.bit.direction = OUTPUT
#if i.bit.direction is None:
# i.bit.direction = INPUT
#print("Wiring", o.bit.direction, str(o), "->", i.bit.direction, str(i))
if i.wires and o.wires and i.wires is not o.wires:
# print('merging', i.wires.inputs, i.wires.outputs)
# print('merging', o.wires.inputs, o.wires.outputs)
w = Wire()
mergewires(w, i.wires)
mergewires(w, o.wires)
# print('after merge', w.inputs, w.outputs)
elif o.wires:
w = o.wires
elif i.wires:
w = i.wires
else:
w = Wire()
w.connect(o, i)
#print("after",o,"->",i, w)
# if the port is an input or inout, return the output
# if the port is an output, return the first input
def trace(self):
if not self.wires:
return None
if self in self.wires.inputs:
if len(self.wires.outputs) < 1:
# print('Warning:', str(self), 'is not connected to an output')
return None
assert len(self.wires.outputs) == 1
return self.wires.outputs[0]
if self in self.wires.outputs:
if len(self.wires.inputs) < 1:
# print('Warning:', str(self), 'is not connected to an input')
return None
assert len(self.wires.inputs) == 1
return self.wires.inputs[0]
return None
# if the port is in the inputs, return the output
def value(self):
if not self.wires:
return None
if self in self.wires.inputs:
if len(self.wires.outputs) < 1:
# print('Warning:', str(self), 'is not connected to an output')
return None
#assert len(self.wires.outputs) == 1
return self.wires.outputs[0]
return None
def driven(self):
return self.value() is not None
def wired(self):
return self.trace() is not None
| {
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__all__ = ["install"]
from direct.directnotify.DirectNotifyGlobal import directNotify
from direct.showbase.PythonUtil import fastRepr
import sys
import traceback
notify = directNotify.newCategory("ExceptionVarDump")
reentry = 0
def _varDump__init__(self, *args, **kArgs):
global reentry
if reentry > 0:
return
reentry += 1
# frame zero is this frame
f = 1
self._savedExcString = None
self._savedStackFrames = []
while True:
try:
frame = sys._getframe(f)
except ValueError as e:
break
else:
f += 1
self._savedStackFrames.append(frame)
self._moved__init__(*args, **kArgs)
reentry -= 1
sReentry = 0
def _varDump__print(exc):
global sReentry
global notify
if sReentry > 0:
return
sReentry += 1
if not exc._savedExcString:
s = ''
foundRun = False
for frame in reversed(exc._savedStackFrames):
filename = frame.f_code.co_filename
codename = frame.f_code.co_name
if not foundRun and codename != 'run':
# don't print stack frames before run(),
# they contain builtins and are huge
continue
foundRun = True
s += '\nlocals for %s:%s\n' % (filename, codename)
locals = frame.f_locals
for var in locals:
obj = locals[var]
rep = fastRepr(obj)
s += '::%s = %s\n' % (var, rep)
exc._savedExcString = s
exc._savedStackFrames = None
notify.info(exc._savedExcString)
sReentry -= 1
oldExcepthook = None
# store these values here so that Task.py can always reliably access them
# from its main exception handler
wantStackDumpLog = False
wantStackDumpUpload = False
variableDumpReasons = []
dumpOnExceptionInit = False
class _AttrNotFound:
pass
def _excepthookDumpVars(eType, eValue, tb):
origTb = tb
excStrs = traceback.format_exception(eType, eValue, origTb)
s = 'printing traceback in case variable repr crashes the process...\n'
for excStr in excStrs:
s += excStr
notify.info(s)
s = 'DUMPING STACK FRAME VARIABLES'
#import pdb;pdb.set_trace()
#foundRun = False
foundRun = True
while tb is not None:
frame = tb.tb_frame
code = frame.f_code
# this is a list of every string identifier used in this stack frame's code
codeNames = set(code.co_names)
# skip everything before the 'run' method, those frames have lots of
# not-useful information
if not foundRun:
if code.co_name == 'run':
foundRun = True
else:
tb = tb.tb_next
continue
s += '\n File "%s", line %s, in %s' % (
code.co_filename, frame.f_lineno, code.co_name)
stateStack = Stack()
# prime the stack with the variables we should visit from the frame's data structures
# grab all of the local, builtin and global variables that appear in the code's name list
name2obj = {}
for name, obj in frame.f_builtins.items():
if name in codeNames:
name2obj[name] = obj
for name, obj in frame.f_globals.items():
if name in codeNames:
name2obj[name] = obj
for name, obj in frame.f_locals.items():
if name in codeNames:
name2obj[name] = obj
# show them in alphabetical order
names = list(name2obj.keys())
names.sort()
# push them in reverse order so they'll be popped in the correct order
names.reverse()
traversedIds = set()
for name in names:
stateStack.push([name, name2obj[name], traversedIds])
while len(stateStack) > 0:
name, obj, traversedIds = stateStack.pop()
#notify.info('%s, %s, %s' % (name, fastRepr(obj), traversedIds))
r = fastRepr(obj, maxLen=10)
if type(r) is str:
r = r.replace('\n', '\\n')
s += '\n %s = %s' % (name, r)
# if we've already traversed through this object, don't traverse through it again
if id(obj) not in traversedIds:
attrName2obj = {}
for attrName in codeNames:
attr = getattr(obj, attrName, _AttrNotFound)
if (attr is not _AttrNotFound):
# prevent infinite recursion on method wrappers (__init__.__init__.__init__...)
try:
className = attr.__class__.__name__
except:
pass
else:
if className == 'method-wrapper':
continue
attrName2obj[attrName] = attr
if len(attrName2obj):
# show them in alphabetical order
attrNames = list(attrName2obj.keys())
attrNames.sort()
# push them in reverse order so they'll be popped in the correct order
attrNames.reverse()
ids = set(traversedIds)
ids.add(id(obj))
for attrName in attrNames:
obj = attrName2obj[attrName]
stateStack.push(['%s.%s' % (name, attrName), obj, ids])
tb = tb.tb_next
if foundRun:
s += '\n'
if wantStackDumpLog:
notify.info(s)
if wantStackDumpUpload:
excStrs = traceback.format_exception(eType, eValue, origTb)
for excStr in excStrs:
s += excStr
timeMgr = None
try:
timeMgr = base.cr.timeManager
except:
try:
timeMgr = simbase.air.timeManager
except:
pass
if timeMgr:
timeMgr.setStackDump(s)
oldExcepthook(eType, eValue, origTb)
def install(log, upload):
global oldExcepthook
global wantStackDumpLog
global wantStackDumpUpload
global dumpOnExceptionInit
wantStackDumpLog = log
wantStackDumpUpload = upload
dumpOnExceptionInit = ConfigVariableBool('variable-dump-on-exception-init', False)
if dumpOnExceptionInit:
# this mode doesn't completely work because exception objects
# thrown by the interpreter don't get created until the
# stack has been unwound and an except block has been reached
if not hasattr(Exception, '_moved__init__'):
Exception._moved__init__ = Exception.__init__
Exception.__init__ = _varDump__init__
else:
if sys.excepthook is not _excepthookDumpVars:
oldExcepthook = sys.excepthook
sys.excepthook = _excepthookDumpVars
| {
"repo_name": "brakhane/panda3d",
"path": "direct/src/showbase/ExceptionVarDump.py",
"copies": "1",
"size": "6972",
"license": "bsd-3-clause",
"hash": 1340529052280700000,
"line_mean": 33.86,
"line_max": 103,
"alpha_frac": 0.5499139415,
"autogenerated": false,
"ratio": 4.184873949579832,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5234787891079832,
"avg_score": null,
"num_lines": null
} |
__all__ = ["install"]
from panda3d.core import *
from direct.directnotify.DirectNotifyGlobal import directNotify
from direct.showbase.PythonUtil import fastRepr
import sys
import traceback
notify = directNotify.newCategory("ExceptionVarDump")
reentry = 0
def _varDump__init__(self, *args, **kArgs):
global reentry
if reentry > 0:
return
reentry += 1
# frame zero is this frame
f = 1
self._savedExcString = None
self._savedStackFrames = []
while True:
try:
frame = sys._getframe(f)
except ValueError as e:
break
else:
f += 1
self._savedStackFrames.append(frame)
self._moved__init__(*args, **kArgs)
reentry -= 1
sReentry = 0
def _varDump__print(exc):
global sReentry
global notify
if sReentry > 0:
return
sReentry += 1
if not exc._savedExcString:
s = ''
foundRun = False
for frame in reversed(exc._savedStackFrames):
filename = frame.f_code.co_filename
codename = frame.f_code.co_name
if not foundRun and codename != 'run':
# don't print stack frames before run(),
# they contain builtins and are huge
continue
foundRun = True
s += '\nlocals for %s:%s\n' % (filename, codename)
locals = frame.f_locals
for var in locals:
obj = locals[var]
rep = fastRepr(obj)
s += '::%s = %s\n' % (var, rep)
exc._savedExcString = s
exc._savedStackFrames = None
notify.info(exc._savedExcString)
sReentry -= 1
oldExcepthook = None
# store these values here so that Task.py can always reliably access them
# from its main exception handler
wantStackDumpLog = False
wantStackDumpUpload = False
variableDumpReasons = []
dumpOnExceptionInit = False
class _AttrNotFound:
pass
def _excepthookDumpVars(eType, eValue, tb):
origTb = tb
excStrs = traceback.format_exception(eType, eValue, origTb)
s = 'printing traceback in case variable repr crashes the process...\n'
for excStr in excStrs:
s += excStr
notify.info(s)
s = 'DUMPING STACK FRAME VARIABLES'
#import pdb;pdb.set_trace()
#foundRun = False
foundRun = True
while tb is not None:
frame = tb.tb_frame
code = frame.f_code
# this is a list of every string identifier used in this stack frame's code
codeNames = set(code.co_names)
# skip everything before the 'run' method, those frames have lots of
# not-useful information
if not foundRun:
if code.co_name == 'run':
foundRun = True
else:
tb = tb.tb_next
continue
s += '\n File "%s", line %s, in %s' % (
code.co_filename, frame.f_lineno, code.co_name)
stateStack = Stack()
# prime the stack with the variables we should visit from the frame's data structures
# grab all of the local, builtin and global variables that appear in the code's name list
name2obj = {}
for name, obj in frame.f_builtins.items():
if name in codeNames:
name2obj[name] = obj
for name, obj in frame.f_globals.items():
if name in codeNames:
name2obj[name] = obj
for name, obj in frame.f_locals.items():
if name in codeNames:
name2obj[name] = obj
# show them in alphabetical order
names = list(name2obj.keys())
names.sort()
# push them in reverse order so they'll be popped in the correct order
names.reverse()
traversedIds = set()
for name in names:
stateStack.push([name, name2obj[name], traversedIds])
while len(stateStack) > 0:
name, obj, traversedIds = stateStack.pop()
#notify.info('%s, %s, %s' % (name, fastRepr(obj), traversedIds))
r = fastRepr(obj, maxLen=10)
if type(r) is str:
r = r.replace('\n', '\\n')
s += '\n %s = %s' % (name, r)
# if we've already traversed through this object, don't traverse through it again
if id(obj) not in traversedIds:
attrName2obj = {}
for attrName in codeNames:
attr = getattr(obj, attrName, _AttrNotFound)
if (attr is not _AttrNotFound):
# prevent infinite recursion on method wrappers (__init__.__init__.__init__...)
try:
className = attr.__class__.__name__
except:
pass
else:
if className == 'method-wrapper':
continue
attrName2obj[attrName] = attr
if len(attrName2obj):
# show them in alphabetical order
attrNames = list(attrName2obj.keys())
attrNames.sort()
# push them in reverse order so they'll be popped in the correct order
attrNames.reverse()
ids = set(traversedIds)
ids.add(id(obj))
for attrName in attrNames:
obj = attrName2obj[attrName]
stateStack.push(['%s.%s' % (name, attrName), obj, ids])
tb = tb.tb_next
if foundRun:
s += '\n'
if wantStackDumpLog:
notify.info(s)
if wantStackDumpUpload:
excStrs = traceback.format_exception(eType, eValue, origTb)
for excStr in excStrs:
s += excStr
timeMgr = None
try:
timeMgr = base.cr.timeManager
except:
try:
timeMgr = simbase.air.timeManager
except:
pass
if timeMgr:
timeMgr.setStackDump(s)
oldExcepthook(eType, eValue, origTb)
def install(log, upload):
global oldExcepthook
global wantStackDumpLog
global wantStackDumpUpload
global dumpOnExceptionInit
wantStackDumpLog = log
wantStackDumpUpload = upload
dumpOnExceptionInit = ConfigVariableBool('variable-dump-on-exception-init', False)
if dumpOnExceptionInit:
# this mode doesn't completely work because exception objects
# thrown by the interpreter don't get created until the
# stack has been unwound and an except block has been reached
if not hasattr(Exception, '_moved__init__'):
Exception._moved__init__ = Exception.__init__
Exception.__init__ = _varDump__init__
else:
if sys.excepthook is not _excepthookDumpVars:
oldExcepthook = sys.excepthook
sys.excepthook = _excepthookDumpVars
| {
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__all__ = ['intAllen']
from scipy.interpolate import interp1d
import numpy as np
def intAllen(wavelength, mu):
"""
Return the intensity at a given wavelength and mu as tabulated by Allen
"""
CLight = 2.99792458e10
HPlanck = 6.62606876e-27
l = 1e4 * np.asarray([0.20,0.22,0.24,0.26,0.28,0.30,0.32,0.34,0.36,0.37,0.38,0.39,0.40,0.41,0.42,0.43,0.44,0.45,\
0.46,0.48,0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.90,1.00,1.10,1.20,1.40,1.60,1.80,2.00,2.50,\
3.00,4.00,5.00,6.00,8.00,10.0,12.0])
i0 = 1e14 * (l*1e-8)**2 / CLight * np.asarray([0.06,0.21,0.29,0.60,1.30,2.45,3.25,3.77,4.13,4.23,4.63,4.95,5.15,5.26,5.28,5.24,5.19,5.10,5.00,\
4.79,4.55,4.02,3.52,3.06,2.69,2.28,2.03,1.57,1.26,1.01,0.81,0.53,0.36,0.238,0.160,0.078,0.041,0.0142,0.0062,0.0032,0.00095,0.00035,0.00018])
cl0 = 1e4 * np.asarray([0.20, 0.22, 0.245,0.265,0.28,0.30,0.32,0.35,0.37,0.38,0.40,0.45,0.50,0.55,0.60,0.80,1.0,1.5,2.0,3.0,5.0,10.0])
cl1 = np.asarray([0.12,-1.3 ,-0.1 ,-0.1 , 0.38, 0.74, 0.88, 0.98, 1.03, 0.92, 0.91, 0.99, 0.97, 0.93, 0.88, 0.73, 0.64, 0.57, 0.48, 0.35, 0.22, 0.15])
cl2 = np.asarray([0.33, 1.6, 0.85, 0.90, 0.57, 0.20, 0.03,-0.1,-0.16,-0.05,-0.05,-0.17,-0.22,-0.23,-0.23,-0.22,-0.20,-0.21,-0.18,-0.12,-0.07,-0.07])
u = interp1d(cl0, cl1)(wavelength)
v = interp1d(cl0, cl2)(wavelength)
i0 = interp1d(l, i0)(wavelength)
return (1.0 - u - v + u * mu + v * mu**2)*i0 | {
"repo_name": "aasensio/pyiacsun",
"path": "pyiacsun/util/intAllen.py",
"copies": "2",
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"license": "mit",
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"quality_score": 0.6978898683879018,
"avg_score": 0.24695484051484276,
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} |
__all__ = ["integrate_velocity_field"]
from .process_args import _int_antsProcessArguments
from .. import utils
def integrate_velocity_field(
reference_image,
velocity_field_filename,
deformation_field_filename,
time_0=0,
time_1=1,
delta_time=0.01,
):
"""
Integrate a velocityfield
ANTsR function: `integrateVelocityField`
Arguments
---------
reference_image : ANTsImage
Reference image domain, same as velocity field space
velocity_field_filename : string
Filename to velocity field, output from ants.registration
deformation_field_filename : string
Filename to output deformation field
time_0 : scalar
Typically one or zero but can take intermediate values
time_1 : scalar
Typically one or zero but can take intermediate values
delta_time : scalar
Time step value in zero to one; typically 0.01
Returns
-------
None
Example
-------
>>> import ants
>>> fi = ants.image_read( ants.get_data( "r16" ) )
>>> mi = ants.image_read( ants.get_data( "r27" ) )
>>> mytx2 = ants.registration( fi, mi, "TV[2]" )
>>> ants.integrate_velocity_field( fi, mytx2['velocityfield'][0], "/tmp/def.nii.gz", 0, 1, 0.01 )
>>> mydef = ants.apply_transforms( fi, mi, ["/tmp/def.nii.gz", mytx2['fwdtransforms'][1]] )
>>> ants.image_mutual_information(fi,mi)
>>> ants.image_mutual_information(fi,mytx2['warpedmovout'])
>>> ants.image_mutual_information(fi,mydef)
>>> ants.integrate_velocity_field( fi, mytx2['velocityfield'][0], "/tmp/defi.nii.gz", 1, 0, 0.5 )
>>> mydefi = ants.apply_transforms( mi, fi, [ mytx2['fwdtransforms'][1], "/tmp/defi.nii.gz" ] )
>>> ants.image_mutual_information(mi,mydefi)
>>> ants.image_mutual_information(mi,mytx2['warpedfixout'])
"""
libfn = utils.get_lib_fn("integrateVelocityField")
if reference_image.dimension == 2:
libfn.integrateVelocityField2D(
reference_image.pointer,
velocity_field_filename,
deformation_field_filename,
time_0,
time_1,
delta_time,
)
if reference_image.dimension == 3:
libfn.integrateVelocityField3D(
reference_image.pointer,
velocity_field_filename,
deformation_field_filename,
time_0,
time_1,
delta_time,
)
| {
"repo_name": "ANTsX/ANTsPy",
"path": "ants/utils/ants_integrate_velocity_field.py",
"copies": "1",
"size": "2435",
"license": "apache-2.0",
"hash": 7797265809526752000,
"line_mean": 29.8227848101,
"line_max": 102,
"alpha_frac": 0.6135523614,
"autogenerated": false,
"ratio": 3.4985632183908044,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4612115579790804,
"avg_score": null,
"num_lines": null
} |
__all__ = ["interact"]
import jnupy
import sys
def input(prompt=None):
if prompt is not None:
print(prompt, end="")
result = jnupy.input()
if result is None:
raise EOFError
return result
def mp_repl_continue_with_input(line):
# check for blank input
if not line:
return False
# check for escape char in terminal
if "\x1b" in line:
return False
# check if input starts with a certain keyword
starts_with_compound_keyword = False
for keyword in "@", "if", "while", "for", "try", "with", "def", "class":
starts_with_compound_keyword = starts_with_compound_keyword or line.startswith(keyword)
# check for unmatched open bracket or triple quote
# TODO don't look at triple quotes inside single quotes
n_paren = n_brack = n_brace = 0
in_triple_quote = 0
passed = 0
for charno, char in enumerate(line):
if passed:
passed -= 1
continue
elif char == '(': n_paren += 1
elif char == ')': n_paren -= 1
elif char == '[': n_brack += 1
elif char == ']': n_brack -= 1
elif char == '{': n_brace += 1
elif char == '}': n_brace -= 1
elif char == "'":
if chr(in_triple_quote) != '"' and line[charno+1:charno+2] == line[charno+2:charno+3] == "'":
passed += 2;
in_triple_quote = ord("'") - in_triple_quote
elif char == '"':
if chr(in_triple_quote) != "'" and line[charno+1:charno+2] == line[charno+2:charno+3] == '"':
passed += 2;
in_triple_quote = ord('"') - in_triple_quote
# continue if unmatched brackets or quotes
if n_paren > 0 or n_brack > 0 or n_brace > 0 or in_triple_quote != 0:
return True
# continue if last character was backslash (for line continuation)
if line.endswith('\\'):
return True
# continue if compound keyword and last line was not empty
if starts_with_compound_keyword and not line.endswith('\n'):
return True
# otherwise, don't continue
return False
def interact(banner=None, readfunc=None, local=None):
if readfunc is None:
readfunc = input
if banner is None:
banner = "Micro Python {} on {}; {} version".format(jnupy.get_version("MICROPY_GIT_TAG"), jnupy.get_version("MICROPY_BUILD_DATE"), sys.platform)
if local is None:
local = dict()
print(banner)
while True:
try:
code = readfunc(">>> ")
except EOFError:
print()
continue
while mp_repl_continue_with_input(code):
try:
code += "\n" + readfunc("... ")
except EOFError:
print()
continue
try:
fun = compile(code, "<stdin>", "single")
except SyntaxError:
sys.print_exception(sys.exc_info()[1])
continue
try:
exec(fun, local, local)
except SystemExit:
raise
except:
sys.print_exception(sys.exc_info()[1])
| {
"repo_name": "EcmaXp/micropython",
"path": "opencom/lib/code.py",
"copies": "1",
"size": "3157",
"license": "mit",
"hash": -7980299781826150000,
"line_mean": 28.7830188679,
"line_max": 152,
"alpha_frac": 0.5388026608,
"autogenerated": false,
"ratio": 3.9760705289672544,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.5014873189767254,
"avg_score": null,
"num_lines": null
} |
"""All internal ansible-lint rules."""
import glob
import importlib.util
import logging
import os
import re
from collections import defaultdict
from importlib.abc import Loader
from time import sleep
from typing import List
import ansiblelint.utils
from ansiblelint.errors import MatchError
from ansiblelint.skip_utils import append_skipped_rules, get_rule_skips_from_line
_logger = logging.getLogger(__name__)
class AnsibleLintRule(object):
def __repr__(self) -> str:
"""Return a AnsibleLintRule instance representation."""
return self.id + ": " + self.shortdesc
def verbose(self) -> str:
return self.id + ": " + self.shortdesc + "\n " + self.description
id: str = ""
tags: List[str] = []
shortdesc: str = ""
description: str = ""
version_added: str = ""
severity: str = ""
match = None
matchtask = None
matchplay = None
@staticmethod
def unjinja(text):
text = re.sub(r"{{.+?}}", "JINJA_EXPRESSION", text)
text = re.sub(r"{%.+?%}", "JINJA_STATEMENT", text)
text = re.sub(r"{#.+?#}", "JINJA_COMMENT", text)
return text
def create_matcherror(
self,
message: str = None,
linenumber: int = 0,
details: str = "",
filename: str = None) -> MatchError:
return MatchError(
message=message,
linenumber=linenumber,
details=details,
filename=filename,
rule=self
)
def matchlines(self, file, text) -> List[MatchError]:
matches: List[MatchError] = []
if not self.match:
return matches
# arrays are 0-based, line numbers are 1-based
# so use prev_line_no as the counter
for (prev_line_no, line) in enumerate(text.split("\n")):
if line.lstrip().startswith('#'):
continue
rule_id_list = get_rule_skips_from_line(line)
if self.id in rule_id_list:
continue
result = self.match(file, line)
if not result:
continue
message = None
if isinstance(result, str):
message = result
m = self.create_matcherror(
message=message,
linenumber=prev_line_no + 1,
details=line,
filename=file['path'])
matches.append(m)
return matches
# TODO(ssbarnea): Reduce mccabe complexity
# https://github.com/ansible/ansible-lint/issues/744
def matchtasks(self, file: str, text: str) -> List[MatchError]: # noqa: C901
matches: List[MatchError] = []
if not self.matchtask:
return matches
if file['type'] == 'meta':
return matches
yaml = ansiblelint.utils.parse_yaml_linenumbers(text, file['path'])
if not yaml:
return matches
yaml = append_skipped_rules(yaml, text, file['type'])
try:
tasks = ansiblelint.utils.get_normalized_tasks(yaml, file)
except MatchError as e:
return [e]
for task in tasks:
if self.id in task.get('skipped_rules', ()):
continue
if 'action' not in task:
continue
result = self.matchtask(file, task)
if not result:
continue
message = None
if isinstance(result, str):
message = result
task_msg = "Task/Handler: " + ansiblelint.utils.task_to_str(task)
m = self.create_matcherror(
message=message,
linenumber=task[ansiblelint.utils.LINE_NUMBER_KEY],
details=task_msg,
filename=file['path'])
matches.append(m)
return matches
@staticmethod
def _matchplay_linenumber(play, optional_linenumber):
try:
linenumber, = optional_linenumber
except ValueError:
linenumber = play[ansiblelint.utils.LINE_NUMBER_KEY]
return linenumber
def matchyaml(self, file: str, text: str) -> List[MatchError]:
matches: List[MatchError] = []
if not self.matchplay:
return matches
yaml = ansiblelint.utils.parse_yaml_linenumbers(text, file['path'])
if not yaml:
return matches
if isinstance(yaml, dict):
yaml = [yaml]
yaml = ansiblelint.skip_utils.append_skipped_rules(yaml, text, file['type'])
for play in yaml:
if self.id in play.get('skipped_rules', ()):
continue
result = self.matchplay(file, play)
if not result:
continue
if isinstance(result, tuple):
result = [result]
if not isinstance(result, list):
raise TypeError("{} is not a list".format(result))
for section, message, *optional_linenumber in result:
linenumber = self._matchplay_linenumber(play, optional_linenumber)
matches.append(self.create_matcherror(
message=message,
linenumber=linenumber,
details=str(section),
filename=file['path']
))
return matches
def load_plugins(directory: str) -> List[AnsibleLintRule]:
"""Return a list of rule classes."""
result = []
for pluginfile in glob.glob(os.path.join(directory, '[A-Za-z]*.py')):
pluginname = os.path.basename(pluginfile.replace('.py', ''))
spec = importlib.util.spec_from_file_location(pluginname, pluginfile)
# https://github.com/python/typeshed/issues/2793
if spec and isinstance(spec.loader, Loader):
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
obj = getattr(module, pluginname)()
result.append(obj)
return result
class RulesCollection(object):
def __init__(self, rulesdirs=None) -> None:
"""Initialize a RulesCollection instance."""
if rulesdirs is None:
rulesdirs = []
self.rulesdirs = ansiblelint.utils.expand_paths_vars(rulesdirs)
self.rules: List[AnsibleLintRule] = []
for rulesdir in self.rulesdirs:
_logger.debug("Loading rules from %s", rulesdir)
self.extend(load_plugins(rulesdir))
self.rules = sorted(self.rules, key=lambda r: r.id)
def register(self, obj: AnsibleLintRule):
self.rules.append(obj)
def __iter__(self):
"""Return the iterator over the rules in the RulesCollection."""
return iter(self.rules)
def __len__(self):
"""Return the length of the RulesCollection data."""
return len(self.rules)
def extend(self, more: List[AnsibleLintRule]) -> None:
self.rules.extend(more)
def run(self, playbookfile, tags=set(), skip_list=frozenset()) -> List:
text = ""
matches: List = list()
for i in range(3):
try:
with open(playbookfile['path'], mode='r', encoding='utf-8') as f:
text = f.read()
break
except IOError as e:
_logger.warning(
"Couldn't open %s - %s [try:%s]",
playbookfile['path'],
e.strerror,
i)
sleep(1)
continue
if i and not text:
return matches
for rule in self.rules:
if not tags or not set(rule.tags).union([rule.id]).isdisjoint(tags):
rule_definition = set(rule.tags)
rule_definition.add(rule.id)
if set(rule_definition).isdisjoint(skip_list):
matches.extend(rule.matchlines(playbookfile, text))
matches.extend(rule.matchtasks(playbookfile, text))
matches.extend(rule.matchyaml(playbookfile, text))
return matches
def __repr__(self) -> str:
"""Return a RulesCollection instance representation."""
return "\n".join([rule.verbose()
for rule in sorted(self.rules, key=lambda x: x.id)])
def listtags(self) -> str:
tags = defaultdict(list)
for rule in self.rules:
for tag in rule.tags:
tags[tag].append("[{0}]".format(rule.id))
results = []
for tag in sorted(tags):
results.append("{0} {1}".format(tag, tags[tag]))
return "\n".join(results)
| {
"repo_name": "ansible/ansible-lint",
"path": "lib/ansiblelint/rules/__init__.py",
"copies": "1",
"size": "8648",
"license": "mit",
"hash": -2846238959904863000,
"line_mean": 31.6339622642,
"line_max": 84,
"alpha_frac": 0.5513413506,
"autogenerated": false,
"ratio": 4.23921568627451,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.00022848611912536598,
"num_lines": 265
} |
__all__ = ['interp1d', 'interp2d', 'lagrange', 'PPoly', 'BPoly', 'NdPPoly',
'RegularGridInterpolator', 'interpn']
import itertools
import warnings
import functools
import operator
import numpy as np
from numpy import (array, transpose, searchsorted, atleast_1d, atleast_2d,
ravel, poly1d, asarray, intp)
import scipy.special as spec
from scipy.special import comb
from scipy._lib._util import prod
from . import fitpack
from . import dfitpack
from . import _fitpack
from .polyint import _Interpolator1D
from . import _ppoly
from .fitpack2 import RectBivariateSpline
from .interpnd import _ndim_coords_from_arrays
from ._bsplines import make_interp_spline, BSpline
def lagrange(x, w):
r"""
Return a Lagrange interpolating polynomial.
Given two 1-D arrays `x` and `w,` returns the Lagrange interpolating
polynomial through the points ``(x, w)``.
Warning: This implementation is numerically unstable. Do not expect to
be able to use more than about 20 points even if they are chosen optimally.
Parameters
----------
x : array_like
`x` represents the x-coordinates of a set of datapoints.
w : array_like
`w` represents the y-coordinates of a set of datapoints, i.e., f(`x`).
Returns
-------
lagrange : `numpy.poly1d` instance
The Lagrange interpolating polynomial.
Examples
--------
Interpolate :math:`f(x) = x^3` by 3 points.
>>> from scipy.interpolate import lagrange
>>> x = np.array([0, 1, 2])
>>> y = x**3
>>> poly = lagrange(x, y)
Since there are only 3 points, Lagrange polynomial has degree 2. Explicitly,
it is given by
.. math::
\begin{aligned}
L(x) &= 1\times \frac{x (x - 2)}{-1} + 8\times \frac{x (x-1)}{2} \\
&= x (-2 + 3x)
\end{aligned}
>>> from numpy.polynomial.polynomial import Polynomial
>>> Polynomial(poly).coef
array([ 3., -2., 0.])
"""
M = len(x)
p = poly1d(0.0)
for j in range(M):
pt = poly1d(w[j])
for k in range(M):
if k == j:
continue
fac = x[j]-x[k]
pt *= poly1d([1.0, -x[k]])/fac
p += pt
return p
# !! Need to find argument for keeping initialize. If it isn't
# !! found, get rid of it!
class interp2d(object):
"""
interp2d(x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None)
Interpolate over a 2-D grid.
`x`, `y` and `z` are arrays of values used to approximate some function
f: ``z = f(x, y)``. This class returns a function whose call method uses
spline interpolation to find the value of new points.
If `x` and `y` represent a regular grid, consider using
RectBivariateSpline.
Note that calling `interp2d` with NaNs present in input values results in
undefined behaviour.
Methods
-------
__call__
Parameters
----------
x, y : array_like
Arrays defining the data point coordinates.
If the points lie on a regular grid, `x` can specify the column
coordinates and `y` the row coordinates, for example::
>>> x = [0,1,2]; y = [0,3]; z = [[1,2,3], [4,5,6]]
Otherwise, `x` and `y` must specify the full coordinates for each
point, for example::
>>> x = [0,1,2,0,1,2]; y = [0,0,0,3,3,3]; z = [1,2,3,4,5,6]
If `x` and `y` are multidimensional, they are flattened before use.
z : array_like
The values of the function to interpolate at the data points. If
`z` is a multidimensional array, it is flattened before use. The
length of a flattened `z` array is either
len(`x`)*len(`y`) if `x` and `y` specify the column and row coordinates
or ``len(z) == len(x) == len(y)`` if `x` and `y` specify coordinates
for each point.
kind : {'linear', 'cubic', 'quintic'}, optional
The kind of spline interpolation to use. Default is 'linear'.
copy : bool, optional
If True, the class makes internal copies of x, y and z.
If False, references may be used. The default is to copy.
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data (x,y), a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If omitted (None), values outside
the domain are extrapolated via nearest-neighbor extrapolation.
See Also
--------
RectBivariateSpline :
Much faster 2-D interpolation if your input data is on a grid
bisplrep, bisplev :
Spline interpolation based on FITPACK
BivariateSpline : a more recent wrapper of the FITPACK routines
interp1d : 1-D version of this function
Notes
-----
The minimum number of data points required along the interpolation
axis is ``(k+1)**2``, with k=1 for linear, k=3 for cubic and k=5 for
quintic interpolation.
The interpolator is constructed by `bisplrep`, with a smoothing factor
of 0. If more control over smoothing is needed, `bisplrep` should be
used directly.
Examples
--------
Construct a 2-D grid and interpolate on it:
>>> from scipy import interpolate
>>> x = np.arange(-5.01, 5.01, 0.25)
>>> y = np.arange(-5.01, 5.01, 0.25)
>>> xx, yy = np.meshgrid(x, y)
>>> z = np.sin(xx**2+yy**2)
>>> f = interpolate.interp2d(x, y, z, kind='cubic')
Now use the obtained interpolation function and plot the result:
>>> import matplotlib.pyplot as plt
>>> xnew = np.arange(-5.01, 5.01, 1e-2)
>>> ynew = np.arange(-5.01, 5.01, 1e-2)
>>> znew = f(xnew, ynew)
>>> plt.plot(x, z[0, :], 'ro-', xnew, znew[0, :], 'b-')
>>> plt.show()
"""
def __init__(self, x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None):
x = ravel(x)
y = ravel(y)
z = asarray(z)
rectangular_grid = (z.size == len(x) * len(y))
if rectangular_grid:
if z.ndim == 2:
if z.shape != (len(y), len(x)):
raise ValueError("When on a regular grid with x.size = m "
"and y.size = n, if z.ndim == 2, then z "
"must have shape (n, m)")
if not np.all(x[1:] >= x[:-1]):
j = np.argsort(x)
x = x[j]
z = z[:, j]
if not np.all(y[1:] >= y[:-1]):
j = np.argsort(y)
y = y[j]
z = z[j, :]
z = ravel(z.T)
else:
z = ravel(z)
if len(x) != len(y):
raise ValueError(
"x and y must have equal lengths for non rectangular grid")
if len(z) != len(x):
raise ValueError(
"Invalid length for input z for non rectangular grid")
try:
kx = ky = {'linear': 1,
'cubic': 3,
'quintic': 5}[kind]
except KeyError:
raise ValueError("Unsupported interpolation type.")
if not rectangular_grid:
# TODO: surfit is really not meant for interpolation!
self.tck = fitpack.bisplrep(x, y, z, kx=kx, ky=ky, s=0.0)
else:
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(
x, y, z, None, None, None, None,
kx=kx, ky=ky, s=0.0)
self.tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)],
kx, ky)
self.bounds_error = bounds_error
self.fill_value = fill_value
self.x, self.y, self.z = [array(a, copy=copy) for a in (x, y, z)]
self.x_min, self.x_max = np.amin(x), np.amax(x)
self.y_min, self.y_max = np.amin(y), np.amax(y)
def __call__(self, x, y, dx=0, dy=0, assume_sorted=False):
"""Interpolate the function.
Parameters
----------
x : 1-D array
x-coordinates of the mesh on which to interpolate.
y : 1-D array
y-coordinates of the mesh on which to interpolate.
dx : int >= 0, < kx
Order of partial derivatives in x.
dy : int >= 0, < ky
Order of partial derivatives in y.
assume_sorted : bool, optional
If False, values of `x` and `y` can be in any order and they are
sorted first.
If True, `x` and `y` have to be arrays of monotonically
increasing values.
Returns
-------
z : 2-D array with shape (len(y), len(x))
The interpolated values.
"""
x = atleast_1d(x)
y = atleast_1d(y)
if x.ndim != 1 or y.ndim != 1:
raise ValueError("x and y should both be 1-D arrays")
if not assume_sorted:
x = np.sort(x)
y = np.sort(y)
if self.bounds_error or self.fill_value is not None:
out_of_bounds_x = (x < self.x_min) | (x > self.x_max)
out_of_bounds_y = (y < self.y_min) | (y > self.y_max)
any_out_of_bounds_x = np.any(out_of_bounds_x)
any_out_of_bounds_y = np.any(out_of_bounds_y)
if self.bounds_error and (any_out_of_bounds_x or any_out_of_bounds_y):
raise ValueError("Values out of range; x must be in %r, y in %r"
% ((self.x_min, self.x_max),
(self.y_min, self.y_max)))
z = fitpack.bisplev(x, y, self.tck, dx, dy)
z = atleast_2d(z)
z = transpose(z)
if self.fill_value is not None:
if any_out_of_bounds_x:
z[:, out_of_bounds_x] = self.fill_value
if any_out_of_bounds_y:
z[out_of_bounds_y, :] = self.fill_value
if len(z) == 1:
z = z[0]
return array(z)
def _check_broadcast_up_to(arr_from, shape_to, name):
"""Helper to check that arr_from broadcasts up to shape_to"""
shape_from = arr_from.shape
if len(shape_to) >= len(shape_from):
for t, f in zip(shape_to[::-1], shape_from[::-1]):
if f != 1 and f != t:
break
else: # all checks pass, do the upcasting that we need later
if arr_from.size != 1 and arr_from.shape != shape_to:
arr_from = np.ones(shape_to, arr_from.dtype) * arr_from
return arr_from.ravel()
# at least one check failed
raise ValueError('%s argument must be able to broadcast up '
'to shape %s but had shape %s'
% (name, shape_to, shape_from))
def _do_extrapolate(fill_value):
"""Helper to check if fill_value == "extrapolate" without warnings"""
return (isinstance(fill_value, str) and
fill_value == 'extrapolate')
class interp1d(_Interpolator1D):
"""
Interpolate a 1-D function.
`x` and `y` are arrays of values used to approximate some function f:
``y = f(x)``. This class returns a function whose call method uses
interpolation to find the value of new points.
Note that calling `interp1d` with NaNs present in input values results in
undefined behaviour.
Parameters
----------
x : (N,) array_like
A 1-D array of real values.
y : (...,N,...) array_like
A N-D array of real values. The length of `y` along the interpolation
axis must be equal to the length of `x`.
kind : str or int, optional
Specifies the kind of interpolation as a string
('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'previous', 'next', where 'zero', 'slinear', 'quadratic' and 'cubic'
refer to a spline interpolation of zeroth, first, second or third
order; 'previous' and 'next' simply return the previous or next value
of the point) or as an integer specifying the order of the spline
interpolator to use.
Default is 'linear'.
axis : int, optional
Specifies the axis of `y` along which to interpolate.
Interpolation defaults to the last axis of `y`.
copy : bool, optional
If True, the class makes internal copies of x and y.
If False, references to `x` and `y` are used. The default is to copy.
bounds_error : bool, optional
If True, a ValueError is raised any time interpolation is attempted on
a value outside of the range of x (where extrapolation is
necessary). If False, out of bounds values are assigned `fill_value`.
By default, an error is raised unless ``fill_value="extrapolate"``.
fill_value : array-like or (array-like, array_like) or "extrapolate", optional
- if a ndarray (or float), this value will be used to fill in for
requested points outside of the data range. If not provided, then
the default is NaN. The array-like must broadcast properly to the
dimensions of the non-interpolation axes.
- If a two-element tuple, then the first element is used as a
fill value for ``x_new < x[0]`` and the second element is used for
``x_new > x[-1]``. Anything that is not a 2-element tuple (e.g.,
list or ndarray, regardless of shape) is taken to be a single
array-like argument meant to be used for both bounds as
``below, above = fill_value, fill_value``.
.. versionadded:: 0.17.0
- If "extrapolate", then points outside the data range will be
extrapolated.
.. versionadded:: 0.17.0
assume_sorted : bool, optional
If False, values of `x` can be in any order and they are sorted first.
If True, `x` has to be an array of monotonically increasing values.
Attributes
----------
fill_value
Methods
-------
__call__
See Also
--------
splrep, splev
Spline interpolation/smoothing based on FITPACK.
UnivariateSpline : An object-oriented wrapper of the FITPACK routines.
interp2d : 2-D interpolation
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy import interpolate
>>> x = np.arange(0, 10)
>>> y = np.exp(-x/3.0)
>>> f = interpolate.interp1d(x, y)
>>> xnew = np.arange(0, 9, 0.1)
>>> ynew = f(xnew) # use interpolation function returned by `interp1d`
>>> plt.plot(x, y, 'o', xnew, ynew, '-')
>>> plt.show()
"""
def __init__(self, x, y, kind='linear', axis=-1,
copy=True, bounds_error=None, fill_value=np.nan,
assume_sorted=False):
""" Initialize a 1-D linear interpolation class."""
_Interpolator1D.__init__(self, x, y, axis=axis)
self.bounds_error = bounds_error # used by fill_value setter
self.copy = copy
if kind in ['zero', 'slinear', 'quadratic', 'cubic']:
order = {'zero': 0, 'slinear': 1,
'quadratic': 2, 'cubic': 3}[kind]
kind = 'spline'
elif isinstance(kind, int):
order = kind
kind = 'spline'
elif kind not in ('linear', 'nearest', 'previous', 'next'):
raise NotImplementedError("%s is unsupported: Use fitpack "
"routines for other types." % kind)
x = array(x, copy=self.copy)
y = array(y, copy=self.copy)
if not assume_sorted:
ind = np.argsort(x)
x = x[ind]
y = np.take(y, ind, axis=axis)
if x.ndim != 1:
raise ValueError("the x array must have exactly one dimension.")
if y.ndim == 0:
raise ValueError("the y array must have at least one dimension.")
# Force-cast y to a floating-point type, if it's not yet one
if not issubclass(y.dtype.type, np.inexact):
y = y.astype(np.float_)
# Backward compatibility
self.axis = axis % y.ndim
# Interpolation goes internally along the first axis
self.y = y
self._y = self._reshape_yi(self.y)
self.x = x
del y, x # clean up namespace to prevent misuse; use attributes
self._kind = kind
self.fill_value = fill_value # calls the setter, can modify bounds_err
# Adjust to interpolation kind; store reference to *unbound*
# interpolation methods, in order to avoid circular references to self
# stored in the bound instance methods, and therefore delayed garbage
# collection. See: https://docs.python.org/reference/datamodel.html
if kind in ('linear', 'nearest', 'previous', 'next'):
# Make a "view" of the y array that is rotated to the interpolation
# axis.
minval = 2
if kind == 'nearest':
# Do division before addition to prevent possible integer
# overflow
self.x_bds = self.x / 2.0
self.x_bds = self.x_bds[1:] + self.x_bds[:-1]
self._call = self.__class__._call_nearest
elif kind == 'previous':
# Side for np.searchsorted and index for clipping
self._side = 'left'
self._ind = 0
# Move x by one floating point value to the left
self._x_shift = np.nextafter(self.x, -np.inf)
self._call = self.__class__._call_previousnext
elif kind == 'next':
self._side = 'right'
self._ind = 1
# Move x by one floating point value to the right
self._x_shift = np.nextafter(self.x, np.inf)
self._call = self.__class__._call_previousnext
else:
# Check if we can delegate to numpy.interp (2x-10x faster).
cond = self.x.dtype == np.float_ and self.y.dtype == np.float_
cond = cond and self.y.ndim == 1
cond = cond and not _do_extrapolate(fill_value)
if cond:
self._call = self.__class__._call_linear_np
else:
self._call = self.__class__._call_linear
else:
minval = order + 1
rewrite_nan = False
xx, yy = self.x, self._y
if order > 1:
# Quadratic or cubic spline. If input contains even a single
# nan, then the output is all nans. We cannot just feed data
# with nans to make_interp_spline because it calls LAPACK.
# So, we make up a bogus x and y with no nans and use it
# to get the correct shape of the output, which we then fill
# with nans.
# For slinear or zero order spline, we just pass nans through.
mask = np.isnan(self.x)
if mask.any():
sx = self.x[~mask]
if sx.size == 0:
raise ValueError("`x` array is all-nan")
xx = np.linspace(np.nanmin(self.x),
np.nanmax(self.x),
len(self.x))
rewrite_nan = True
if np.isnan(self._y).any():
yy = np.ones_like(self._y)
rewrite_nan = True
self._spline = make_interp_spline(xx, yy, k=order,
check_finite=False)
if rewrite_nan:
self._call = self.__class__._call_nan_spline
else:
self._call = self.__class__._call_spline
if len(self.x) < minval:
raise ValueError("x and y arrays must have at "
"least %d entries" % minval)
@property
def fill_value(self):
"""The fill value."""
# backwards compat: mimic a public attribute
return self._fill_value_orig
@fill_value.setter
def fill_value(self, fill_value):
# extrapolation only works for nearest neighbor and linear methods
if _do_extrapolate(fill_value):
if self.bounds_error:
raise ValueError("Cannot extrapolate and raise "
"at the same time.")
self.bounds_error = False
self._extrapolate = True
else:
broadcast_shape = (self.y.shape[:self.axis] +
self.y.shape[self.axis + 1:])
if len(broadcast_shape) == 0:
broadcast_shape = (1,)
# it's either a pair (_below_range, _above_range) or a single value
# for both above and below range
if isinstance(fill_value, tuple) and len(fill_value) == 2:
below_above = [np.asarray(fill_value[0]),
np.asarray(fill_value[1])]
names = ('fill_value (below)', 'fill_value (above)')
for ii in range(2):
below_above[ii] = _check_broadcast_up_to(
below_above[ii], broadcast_shape, names[ii])
else:
fill_value = np.asarray(fill_value)
below_above = [_check_broadcast_up_to(
fill_value, broadcast_shape, 'fill_value')] * 2
self._fill_value_below, self._fill_value_above = below_above
self._extrapolate = False
if self.bounds_error is None:
self.bounds_error = True
# backwards compat: fill_value was a public attr; make it writeable
self._fill_value_orig = fill_value
def _call_linear_np(self, x_new):
# Note that out-of-bounds values are taken care of in self._evaluate
return np.interp(x_new, self.x, self.y)
def _call_linear(self, x_new):
# 2. Find where in the original data, the values to interpolate
# would be inserted.
# Note: If x_new[n] == x[m], then m is returned by searchsorted.
x_new_indices = searchsorted(self.x, x_new)
# 3. Clip x_new_indices so that they are within the range of
# self.x indices and at least 1. Removes mis-interpolation
# of x_new[n] = x[0]
x_new_indices = x_new_indices.clip(1, len(self.x)-1).astype(int)
# 4. Calculate the slope of regions that each x_new value falls in.
lo = x_new_indices - 1
hi = x_new_indices
x_lo = self.x[lo]
x_hi = self.x[hi]
y_lo = self._y[lo]
y_hi = self._y[hi]
# Note that the following two expressions rely on the specifics of the
# broadcasting semantics.
slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]
# 5. Calculate the actual value for each entry in x_new.
y_new = slope*(x_new - x_lo)[:, None] + y_lo
return y_new
def _call_nearest(self, x_new):
""" Find nearest neighbor interpolated y_new = f(x_new)."""
# 2. Find where in the averaged data the values to interpolate
# would be inserted.
# Note: use side='left' (right) to searchsorted() to define the
# halfway point to be nearest to the left (right) neighbor
x_new_indices = searchsorted(self.x_bds, x_new, side='left')
# 3. Clip x_new_indices so that they are within the range of x indices.
x_new_indices = x_new_indices.clip(0, len(self.x)-1).astype(intp)
# 4. Calculate the actual value for each entry in x_new.
y_new = self._y[x_new_indices]
return y_new
def _call_previousnext(self, x_new):
"""Use previous/next neighbor of x_new, y_new = f(x_new)."""
# 1. Get index of left/right value
x_new_indices = searchsorted(self._x_shift, x_new, side=self._side)
# 2. Clip x_new_indices so that they are within the range of x indices.
x_new_indices = x_new_indices.clip(1-self._ind,
len(self.x)-self._ind).astype(intp)
# 3. Calculate the actual value for each entry in x_new.
y_new = self._y[x_new_indices+self._ind-1]
return y_new
def _call_spline(self, x_new):
return self._spline(x_new)
def _call_nan_spline(self, x_new):
out = self._spline(x_new)
out[...] = np.nan
return out
def _evaluate(self, x_new):
# 1. Handle values in x_new that are outside of x. Throw error,
# or return a list of mask array indicating the outofbounds values.
# The behavior is set by the bounds_error variable.
x_new = asarray(x_new)
y_new = self._call(self, x_new)
if not self._extrapolate:
below_bounds, above_bounds = self._check_bounds(x_new)
if len(y_new) > 0:
# Note fill_value must be broadcast up to the proper size
# and flattened to work here
y_new[below_bounds] = self._fill_value_below
y_new[above_bounds] = self._fill_value_above
return y_new
def _check_bounds(self, x_new):
"""Check the inputs for being in the bounds of the interpolated data.
Parameters
----------
x_new : array
Returns
-------
out_of_bounds : bool array
The mask on x_new of values that are out of the bounds.
"""
# If self.bounds_error is True, we raise an error if any x_new values
# fall outside the range of x. Otherwise, we return an array indicating
# which values are outside the boundary region.
below_bounds = x_new < self.x[0]
above_bounds = x_new > self.x[-1]
# !! Could provide more information about which values are out of bounds
if self.bounds_error and below_bounds.any():
raise ValueError("A value in x_new is below the interpolation "
"range.")
if self.bounds_error and above_bounds.any():
raise ValueError("A value in x_new is above the interpolation "
"range.")
# !! Should we emit a warning if some values are out of bounds?
# !! matlab does not.
return below_bounds, above_bounds
class _PPolyBase(object):
"""Base class for piecewise polynomials."""
__slots__ = ('c', 'x', 'extrapolate', 'axis')
def __init__(self, c, x, extrapolate=None, axis=0):
self.c = np.asarray(c)
self.x = np.ascontiguousarray(x, dtype=np.float64)
if extrapolate is None:
extrapolate = True
elif extrapolate != 'periodic':
extrapolate = bool(extrapolate)
self.extrapolate = extrapolate
if self.c.ndim < 2:
raise ValueError("Coefficients array must be at least "
"2-dimensional.")
if not (0 <= axis < self.c.ndim - 1):
raise ValueError("axis=%s must be between 0 and %s" %
(axis, self.c.ndim-1))
self.axis = axis
if axis != 0:
# roll the interpolation axis to be the first one in self.c
# More specifically, the target shape for self.c is (k, m, ...),
# and axis !=0 means that we have c.shape (..., k, m, ...)
# ^
# axis
# So we roll two of them.
self.c = np.rollaxis(self.c, axis+1)
self.c = np.rollaxis(self.c, axis+1)
if self.x.ndim != 1:
raise ValueError("x must be 1-dimensional")
if self.x.size < 2:
raise ValueError("at least 2 breakpoints are needed")
if self.c.ndim < 2:
raise ValueError("c must have at least 2 dimensions")
if self.c.shape[0] == 0:
raise ValueError("polynomial must be at least of order 0")
if self.c.shape[1] != self.x.size-1:
raise ValueError("number of coefficients != len(x)-1")
dx = np.diff(self.x)
if not (np.all(dx >= 0) or np.all(dx <= 0)):
raise ValueError("`x` must be strictly increasing or decreasing.")
dtype = self._get_dtype(self.c.dtype)
self.c = np.ascontiguousarray(self.c, dtype=dtype)
def _get_dtype(self, dtype):
if np.issubdtype(dtype, np.complexfloating) \
or np.issubdtype(self.c.dtype, np.complexfloating):
return np.complex_
else:
return np.float_
@classmethod
def construct_fast(cls, c, x, extrapolate=None, axis=0):
"""
Construct the piecewise polynomial without making checks.
Takes the same parameters as the constructor. Input arguments
``c`` and ``x`` must be arrays of the correct shape and type. The
``c`` array can only be of dtypes float and complex, and ``x``
array must have dtype float.
"""
self = object.__new__(cls)
self.c = c
self.x = x
self.axis = axis
if extrapolate is None:
extrapolate = True
self.extrapolate = extrapolate
return self
def _ensure_c_contiguous(self):
"""
c and x may be modified by the user. The Cython code expects
that they are C contiguous.
"""
if not self.x.flags.c_contiguous:
self.x = self.x.copy()
if not self.c.flags.c_contiguous:
self.c = self.c.copy()
def extend(self, c, x, right=None):
"""
Add additional breakpoints and coefficients to the polynomial.
Parameters
----------
c : ndarray, size (k, m, ...)
Additional coefficients for polynomials in intervals. Note that
the first additional interval will be formed using one of the
``self.x`` end points.
x : ndarray, size (m,)
Additional breakpoints. Must be sorted in the same order as
``self.x`` and either to the right or to the left of the current
breakpoints.
right
Deprecated argument. Has no effect.
.. deprecated:: 0.19
"""
if right is not None:
warnings.warn("`right` is deprecated and will be removed.")
c = np.asarray(c)
x = np.asarray(x)
if c.ndim < 2:
raise ValueError("invalid dimensions for c")
if x.ndim != 1:
raise ValueError("invalid dimensions for x")
if x.shape[0] != c.shape[1]:
raise ValueError("Shapes of x {} and c {} are incompatible"
.format(x.shape, c.shape))
if c.shape[2:] != self.c.shape[2:] or c.ndim != self.c.ndim:
raise ValueError("Shapes of c {} and self.c {} are incompatible"
.format(c.shape, self.c.shape))
if c.size == 0:
return
dx = np.diff(x)
if not (np.all(dx >= 0) or np.all(dx <= 0)):
raise ValueError("`x` is not sorted.")
if self.x[-1] >= self.x[0]:
if not x[-1] >= x[0]:
raise ValueError("`x` is in the different order "
"than `self.x`.")
if x[0] >= self.x[-1]:
action = 'append'
elif x[-1] <= self.x[0]:
action = 'prepend'
else:
raise ValueError("`x` is neither on the left or on the right "
"from `self.x`.")
else:
if not x[-1] <= x[0]:
raise ValueError("`x` is in the different order "
"than `self.x`.")
if x[0] <= self.x[-1]:
action = 'append'
elif x[-1] >= self.x[0]:
action = 'prepend'
else:
raise ValueError("`x` is neither on the left or on the right "
"from `self.x`.")
dtype = self._get_dtype(c.dtype)
k2 = max(c.shape[0], self.c.shape[0])
c2 = np.zeros((k2, self.c.shape[1] + c.shape[1]) + self.c.shape[2:],
dtype=dtype)
if action == 'append':
c2[k2-self.c.shape[0]:, :self.c.shape[1]] = self.c
c2[k2-c.shape[0]:, self.c.shape[1]:] = c
self.x = np.r_[self.x, x]
elif action == 'prepend':
c2[k2-self.c.shape[0]:, :c.shape[1]] = c
c2[k2-c.shape[0]:, c.shape[1]:] = self.c
self.x = np.r_[x, self.x]
self.c = c2
def __call__(self, x, nu=0, extrapolate=None):
"""
Evaluate the piecewise polynomial or its derivative.
Parameters
----------
x : array_like
Points to evaluate the interpolant at.
nu : int, optional
Order of derivative to evaluate. Must be non-negative.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used.
If None (default), use `self.extrapolate`.
Returns
-------
y : array_like
Interpolated values. Shape is determined by replacing
the interpolation axis in the original array with the shape of x.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if extrapolate is None:
extrapolate = self.extrapolate
x = np.asarray(x)
x_shape, x_ndim = x.shape, x.ndim
x = np.ascontiguousarray(x.ravel(), dtype=np.float_)
# With periodic extrapolation we map x to the segment
# [self.x[0], self.x[-1]].
if extrapolate == 'periodic':
x = self.x[0] + (x - self.x[0]) % (self.x[-1] - self.x[0])
extrapolate = False
out = np.empty((len(x), prod(self.c.shape[2:])), dtype=self.c.dtype)
self._ensure_c_contiguous()
self._evaluate(x, nu, extrapolate, out)
out = out.reshape(x_shape + self.c.shape[2:])
if self.axis != 0:
# transpose to move the calculated values to the interpolation axis
l = list(range(out.ndim))
l = l[x_ndim:x_ndim+self.axis] + l[:x_ndim] + l[x_ndim+self.axis:]
out = out.transpose(l)
return out
class PPoly(_PPolyBase):
"""
Piecewise polynomial in terms of coefficients and breakpoints
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
local power basis::
S = sum(c[m, i] * (xp - x[i])**(k-m) for m in range(k+1))
where ``k`` is the degree of the polynomial.
Parameters
----------
c : ndarray, shape (k, m, ...)
Polynomial coefficients, order `k` and `m` intervals.
x : ndarray, shape (m+1,)
Polynomial breakpoints. Must be sorted in either increasing or
decreasing order.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs. If 'periodic',
periodic extrapolation is used. Default is True.
axis : int, optional
Interpolation axis. Default is zero.
Attributes
----------
x : ndarray
Breakpoints.
c : ndarray
Coefficients of the polynomials. They are reshaped
to a 3-D array with the last dimension representing
the trailing dimensions of the original coefficient array.
axis : int
Interpolation axis.
Methods
-------
__call__
derivative
antiderivative
integrate
solve
roots
extend
from_spline
from_bernstein_basis
construct_fast
See also
--------
BPoly : piecewise polynomials in the Bernstein basis
Notes
-----
High-order polynomials in the power basis can be numerically
unstable. Precision problems can start to appear for orders
larger than 20-30.
"""
def _evaluate(self, x, nu, extrapolate, out):
_ppoly.evaluate(self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, x, nu, bool(extrapolate), out)
def derivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : int, optional
Order of derivative to evaluate. Default is 1, i.e., compute the
first derivative. If negative, the antiderivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k - n representing the derivative
of this polynomial.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if nu < 0:
return self.antiderivative(-nu)
# reduce order
if nu == 0:
c2 = self.c.copy()
else:
c2 = self.c[:-nu, :].copy()
if c2.shape[0] == 0:
# derivative of order 0 is zero
c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype)
# multiply by the correct rising factorials
factor = spec.poch(np.arange(c2.shape[0], 0, -1), nu)
c2 *= factor[(slice(None),) + (None,)*(c2.ndim-1)]
# construct a compatible polynomial
return self.construct_fast(c2, self.x, self.extrapolate, self.axis)
def antiderivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the antiderivative.
Antiderivative is also the indefinite integral of the function,
and derivative is its inverse operation.
Parameters
----------
nu : int, optional
Order of antiderivative to evaluate. Default is 1, i.e., compute
the first integral. If negative, the derivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k + n representing
the antiderivative of this polynomial.
Notes
-----
The antiderivative returned by this function is continuous and
continuously differentiable to order n-1, up to floating point
rounding error.
If antiderivative is computed and ``self.extrapolate='periodic'``,
it will be set to False for the returned instance. This is done because
the antiderivative is no longer periodic and its correct evaluation
outside of the initially given x interval is difficult.
"""
if nu <= 0:
return self.derivative(-nu)
c = np.zeros((self.c.shape[0] + nu, self.c.shape[1]) + self.c.shape[2:],
dtype=self.c.dtype)
c[:-nu] = self.c
# divide by the correct rising factorials
factor = spec.poch(np.arange(self.c.shape[0], 0, -1), nu)
c[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)]
# fix continuity of added degrees of freedom
self._ensure_c_contiguous()
_ppoly.fix_continuity(c.reshape(c.shape[0], c.shape[1], -1),
self.x, nu - 1)
if self.extrapolate == 'periodic':
extrapolate = False
else:
extrapolate = self.extrapolate
# construct a compatible polynomial
return self.construct_fast(c, self.x, extrapolate, self.axis)
def integrate(self, a, b, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
a : float
Lower integration bound
b : float
Upper integration bound
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used.
If None (default), use `self.extrapolate`.
Returns
-------
ig : array_like
Definite integral of the piecewise polynomial over [a, b]
"""
if extrapolate is None:
extrapolate = self.extrapolate
# Swap integration bounds if needed
sign = 1
if b < a:
a, b = b, a
sign = -1
range_int = np.empty((prod(self.c.shape[2:]),), dtype=self.c.dtype)
self._ensure_c_contiguous()
# Compute the integral.
if extrapolate == 'periodic':
# Split the integral into the part over period (can be several
# of them) and the remaining part.
xs, xe = self.x[0], self.x[-1]
period = xe - xs
interval = b - a
n_periods, left = divmod(interval, period)
if n_periods > 0:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, xs, xe, False, out=range_int)
range_int *= n_periods
else:
range_int.fill(0)
# Map a to [xs, xe], b is always a + left.
a = xs + (a - xs) % period
b = a + left
# If b <= xe then we need to integrate over [a, b], otherwise
# over [a, xe] and from xs to what is remained.
remainder_int = np.empty_like(range_int)
if b <= xe:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, b, False, out=remainder_int)
range_int += remainder_int
else:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, xe, False, out=remainder_int)
range_int += remainder_int
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, xs, xs + left + a - xe, False, out=remainder_int)
range_int += remainder_int
else:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, b, bool(extrapolate), out=range_int)
# Return
range_int *= sign
return range_int.reshape(self.c.shape[2:])
def solve(self, y=0., discontinuity=True, extrapolate=None):
"""
Find real solutions of the the equation ``pp(x) == y``.
Parameters
----------
y : float, optional
Right-hand side. Default is zero.
discontinuity : bool, optional
Whether to report sign changes across discontinuities at
breakpoints as roots.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to return roots from the polynomial
extrapolated based on first and last intervals, 'periodic' works
the same as False. If None (default), use `self.extrapolate`.
Returns
-------
roots : ndarray
Roots of the polynomial(s).
If the PPoly object describes multiple polynomials, the
return value is an object array whose each element is an
ndarray containing the roots.
Notes
-----
This routine works only on real-valued polynomials.
If the piecewise polynomial contains sections that are
identically zero, the root list will contain the start point
of the corresponding interval, followed by a ``nan`` value.
If the polynomial is discontinuous across a breakpoint, and
there is a sign change across the breakpoint, this is reported
if the `discont` parameter is True.
Examples
--------
Finding roots of ``[x**2 - 1, (x - 1)**2]`` defined on intervals
``[-2, 1], [1, 2]``:
>>> from scipy.interpolate import PPoly
>>> pp = PPoly(np.array([[1, -4, 3], [1, 0, 0]]).T, [-2, 1, 2])
>>> pp.solve()
array([-1., 1.])
"""
if extrapolate is None:
extrapolate = self.extrapolate
self._ensure_c_contiguous()
if np.issubdtype(self.c.dtype, np.complexfloating):
raise ValueError("Root finding is only for "
"real-valued polynomials")
y = float(y)
r = _ppoly.real_roots(self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, y, bool(discontinuity),
bool(extrapolate))
if self.c.ndim == 2:
return r[0]
else:
r2 = np.empty(prod(self.c.shape[2:]), dtype=object)
# this for-loop is equivalent to ``r2[...] = r``, but that's broken
# in NumPy 1.6.0
for ii, root in enumerate(r):
r2[ii] = root
return r2.reshape(self.c.shape[2:])
def roots(self, discontinuity=True, extrapolate=None):
"""
Find real roots of the the piecewise polynomial.
Parameters
----------
discontinuity : bool, optional
Whether to report sign changes across discontinuities at
breakpoints as roots.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to return roots from the polynomial
extrapolated based on first and last intervals, 'periodic' works
the same as False. If None (default), use `self.extrapolate`.
Returns
-------
roots : ndarray
Roots of the polynomial(s).
If the PPoly object describes multiple polynomials, the
return value is an object array whose each element is an
ndarray containing the roots.
See Also
--------
PPoly.solve
"""
return self.solve(0, discontinuity, extrapolate)
@classmethod
def from_spline(cls, tck, extrapolate=None):
"""
Construct a piecewise polynomial from a spline
Parameters
----------
tck
A spline, as returned by `splrep` or a BSpline object.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if isinstance(tck, BSpline):
t, c, k = tck.tck
if extrapolate is None:
extrapolate = tck.extrapolate
else:
t, c, k = tck
cvals = np.empty((k + 1, len(t)-1), dtype=c.dtype)
for m in range(k, -1, -1):
y = fitpack.splev(t[:-1], tck, der=m)
cvals[k - m, :] = y/spec.gamma(m+1)
return cls.construct_fast(cvals, t, extrapolate)
@classmethod
def from_bernstein_basis(cls, bp, extrapolate=None):
"""
Construct a piecewise polynomial in the power basis
from a polynomial in Bernstein basis.
Parameters
----------
bp : BPoly
A Bernstein basis polynomial, as created by BPoly
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if not isinstance(bp, BPoly):
raise TypeError(".from_bernstein_basis only accepts BPoly instances. "
"Got %s instead." % type(bp))
dx = np.diff(bp.x)
k = bp.c.shape[0] - 1 # polynomial order
rest = (None,)*(bp.c.ndim-2)
c = np.zeros_like(bp.c)
for a in range(k+1):
factor = (-1)**a * comb(k, a) * bp.c[a]
for s in range(a, k+1):
val = comb(k-a, s-a) * (-1)**s
c[k-s] += factor * val / dx[(slice(None),)+rest]**s
if extrapolate is None:
extrapolate = bp.extrapolate
return cls.construct_fast(c, bp.x, extrapolate, bp.axis)
class BPoly(_PPolyBase):
"""Piecewise polynomial in terms of coefficients and breakpoints.
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
Bernstein polynomial basis::
S = sum(c[a, i] * b(a, k; x) for a in range(k+1)),
where ``k`` is the degree of the polynomial, and::
b(a, k; x) = binom(k, a) * t**a * (1 - t)**(k - a),
with ``t = (x - x[i]) / (x[i+1] - x[i])`` and ``binom`` is the binomial
coefficient.
Parameters
----------
c : ndarray, shape (k, m, ...)
Polynomial coefficients, order `k` and `m` intervals
x : ndarray, shape (m+1,)
Polynomial breakpoints. Must be sorted in either increasing or
decreasing order.
extrapolate : bool, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs. If 'periodic',
periodic extrapolation is used. Default is True.
axis : int, optional
Interpolation axis. Default is zero.
Attributes
----------
x : ndarray
Breakpoints.
c : ndarray
Coefficients of the polynomials. They are reshaped
to a 3-D array with the last dimension representing
the trailing dimensions of the original coefficient array.
axis : int
Interpolation axis.
Methods
-------
__call__
extend
derivative
antiderivative
integrate
construct_fast
from_power_basis
from_derivatives
See also
--------
PPoly : piecewise polynomials in the power basis
Notes
-----
Properties of Bernstein polynomials are well documented in the literature,
see for example [1]_ [2]_ [3]_.
References
----------
.. [1] https://en.wikipedia.org/wiki/Bernstein_polynomial
.. [2] Kenneth I. Joy, Bernstein polynomials,
http://www.idav.ucdavis.edu/education/CAGDNotes/Bernstein-Polynomials.pdf
.. [3] E. H. Doha, A. H. Bhrawy, and M. A. Saker, Boundary Value Problems,
vol 2011, article ID 829546, :doi:`10.1155/2011/829543`.
Examples
--------
>>> from scipy.interpolate import BPoly
>>> x = [0, 1]
>>> c = [[1], [2], [3]]
>>> bp = BPoly(c, x)
This creates a 2nd order polynomial
.. math::
B(x) = 1 \\times b_{0, 2}(x) + 2 \\times b_{1, 2}(x) + 3 \\times b_{2, 2}(x) \\\\
= 1 \\times (1-x)^2 + 2 \\times 2 x (1 - x) + 3 \\times x^2
"""
def _evaluate(self, x, nu, extrapolate, out):
_ppoly.evaluate_bernstein(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, x, nu, bool(extrapolate), out)
def derivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : int, optional
Order of derivative to evaluate. Default is 1, i.e., compute the
first derivative. If negative, the antiderivative is returned.
Returns
-------
bp : BPoly
Piecewise polynomial of order k - nu representing the derivative of
this polynomial.
"""
if nu < 0:
return self.antiderivative(-nu)
if nu > 1:
bp = self
for k in range(nu):
bp = bp.derivative()
return bp
# reduce order
if nu == 0:
c2 = self.c.copy()
else:
# For a polynomial
# B(x) = \sum_{a=0}^{k} c_a b_{a, k}(x),
# we use the fact that
# b'_{a, k} = k ( b_{a-1, k-1} - b_{a, k-1} ),
# which leads to
# B'(x) = \sum_{a=0}^{k-1} (c_{a+1} - c_a) b_{a, k-1}
#
# finally, for an interval [y, y + dy] with dy != 1,
# we need to correct for an extra power of dy
rest = (None,)*(self.c.ndim-2)
k = self.c.shape[0] - 1
dx = np.diff(self.x)[(None, slice(None))+rest]
c2 = k * np.diff(self.c, axis=0) / dx
if c2.shape[0] == 0:
# derivative of order 0 is zero
c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype)
# construct a compatible polynomial
return self.construct_fast(c2, self.x, self.extrapolate, self.axis)
def antiderivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the antiderivative.
Parameters
----------
nu : int, optional
Order of antiderivative to evaluate. Default is 1, i.e., compute
the first integral. If negative, the derivative is returned.
Returns
-------
bp : BPoly
Piecewise polynomial of order k + nu representing the
antiderivative of this polynomial.
Notes
-----
If antiderivative is computed and ``self.extrapolate='periodic'``,
it will be set to False for the returned instance. This is done because
the antiderivative is no longer periodic and its correct evaluation
outside of the initially given x interval is difficult.
"""
if nu <= 0:
return self.derivative(-nu)
if nu > 1:
bp = self
for k in range(nu):
bp = bp.antiderivative()
return bp
# Construct the indefinite integrals on individual intervals
c, x = self.c, self.x
k = c.shape[0]
c2 = np.zeros((k+1,) + c.shape[1:], dtype=c.dtype)
c2[1:, ...] = np.cumsum(c, axis=0) / k
delta = x[1:] - x[:-1]
c2 *= delta[(None, slice(None)) + (None,)*(c.ndim-2)]
# Now fix continuity: on the very first interval, take the integration
# constant to be zero; on an interval [x_j, x_{j+1}) with j>0,
# the integration constant is then equal to the jump of the `bp` at x_j.
# The latter is given by the coefficient of B_{n+1, n+1}
# *on the previous interval* (other B. polynomials are zero at the
# breakpoint). Finally, use the fact that BPs form a partition of unity.
c2[:,1:] += np.cumsum(c2[k, :], axis=0)[:-1]
if self.extrapolate == 'periodic':
extrapolate = False
else:
extrapolate = self.extrapolate
return self.construct_fast(c2, x, extrapolate, axis=self.axis)
def integrate(self, a, b, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
a : float
Lower integration bound
b : float
Upper integration bound
extrapolate : {bool, 'periodic', None}, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs. If 'periodic', periodic
extrapolation is used. If None (default), use `self.extrapolate`.
Returns
-------
array_like
Definite integral of the piecewise polynomial over [a, b]
"""
# XXX: can probably use instead the fact that
# \int_0^{1} B_{j, n}(x) \dx = 1/(n+1)
ib = self.antiderivative()
if extrapolate is None:
extrapolate = self.extrapolate
# ib.extrapolate shouldn't be 'periodic', it is converted to
# False for 'periodic. in antiderivative() call.
if extrapolate != 'periodic':
ib.extrapolate = extrapolate
if extrapolate == 'periodic':
# Split the integral into the part over period (can be several
# of them) and the remaining part.
# For simplicity and clarity convert to a <= b case.
if a <= b:
sign = 1
else:
a, b = b, a
sign = -1
xs, xe = self.x[0], self.x[-1]
period = xe - xs
interval = b - a
n_periods, left = divmod(interval, period)
res = n_periods * (ib(xe) - ib(xs))
# Map a and b to [xs, xe].
a = xs + (a - xs) % period
b = a + left
# If b <= xe then we need to integrate over [a, b], otherwise
# over [a, xe] and from xs to what is remained.
if b <= xe:
res += ib(b) - ib(a)
else:
res += ib(xe) - ib(a) + ib(xs + left + a - xe) - ib(xs)
return sign * res
else:
return ib(b) - ib(a)
def extend(self, c, x, right=None):
k = max(self.c.shape[0], c.shape[0])
self.c = self._raise_degree(self.c, k - self.c.shape[0])
c = self._raise_degree(c, k - c.shape[0])
return _PPolyBase.extend(self, c, x, right)
extend.__doc__ = _PPolyBase.extend.__doc__
@classmethod
def from_power_basis(cls, pp, extrapolate=None):
"""
Construct a piecewise polynomial in Bernstein basis
from a power basis polynomial.
Parameters
----------
pp : PPoly
A piecewise polynomial in the power basis
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if not isinstance(pp, PPoly):
raise TypeError(".from_power_basis only accepts PPoly instances. "
"Got %s instead." % type(pp))
dx = np.diff(pp.x)
k = pp.c.shape[0] - 1 # polynomial order
rest = (None,)*(pp.c.ndim-2)
c = np.zeros_like(pp.c)
for a in range(k+1):
factor = pp.c[a] / comb(k, k-a) * dx[(slice(None),)+rest]**(k-a)
for j in range(k-a, k+1):
c[j] += factor * comb(j, k-a)
if extrapolate is None:
extrapolate = pp.extrapolate
return cls.construct_fast(c, pp.x, extrapolate, pp.axis)
@classmethod
def from_derivatives(cls, xi, yi, orders=None, extrapolate=None):
"""Construct a piecewise polynomial in the Bernstein basis,
compatible with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1-D array of x-coordinates
yi : array_like or list of array_likes
``yi[i][j]`` is the ``j``th derivative known at ``xi[i]``
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
Notes
-----
If ``k`` derivatives are specified at a breakpoint ``x``, the
constructed polynomial is exactly ``k`` times continuously
differentiable at ``x``, unless the ``order`` is provided explicitly.
In the latter case, the smoothness of the polynomial at
the breakpoint is controlled by the ``order``.
Deduces the number of derivatives to match at each end
from ``order`` and the number of derivatives available. If
possible it uses the same number of derivatives from
each end; if the number is odd it tries to take the
extra one from y2. In any case if not enough derivatives
are available at one end or another it draws enough to
make up the total from the other end.
If the order is too high and not enough derivatives are available,
an exception is raised.
Examples
--------
>>> from scipy.interpolate import BPoly
>>> BPoly.from_derivatives([0, 1], [[1, 2], [3, 4]])
Creates a polynomial `f(x)` of degree 3, defined on `[0, 1]`
such that `f(0) = 1, df/dx(0) = 2, f(1) = 3, df/dx(1) = 4`
>>> BPoly.from_derivatives([0, 1, 2], [[0, 1], [0], [2]])
Creates a piecewise polynomial `f(x)`, such that
`f(0) = f(1) = 0`, `f(2) = 2`, and `df/dx(0) = 1`.
Based on the number of derivatives provided, the order of the
local polynomials is 2 on `[0, 1]` and 1 on `[1, 2]`.
Notice that no restriction is imposed on the derivatives at
``x = 1`` and ``x = 2``.
Indeed, the explicit form of the polynomial is::
f(x) = | x * (1 - x), 0 <= x < 1
| 2 * (x - 1), 1 <= x <= 2
So that f'(1-0) = -1 and f'(1+0) = 2
"""
xi = np.asarray(xi)
if len(xi) != len(yi):
raise ValueError("xi and yi need to have the same length")
if np.any(xi[1:] - xi[:1] <= 0):
raise ValueError("x coordinates are not in increasing order")
# number of intervals
m = len(xi) - 1
# global poly order is k-1, local orders are <=k and can vary
try:
k = max(len(yi[i]) + len(yi[i+1]) for i in range(m))
except TypeError:
raise ValueError("Using a 1-D array for y? Please .reshape(-1, 1).")
if orders is None:
orders = [None] * m
else:
if isinstance(orders, (int, np.integer)):
orders = [orders] * m
k = max(k, max(orders))
if any(o <= 0 for o in orders):
raise ValueError("Orders must be positive.")
c = []
for i in range(m):
y1, y2 = yi[i], yi[i+1]
if orders[i] is None:
n1, n2 = len(y1), len(y2)
else:
n = orders[i]+1
n1 = min(n//2, len(y1))
n2 = min(n - n1, len(y2))
n1 = min(n - n2, len(y2))
if n1+n2 != n:
mesg = ("Point %g has %d derivatives, point %g"
" has %d derivatives, but order %d requested" % (
xi[i], len(y1), xi[i+1], len(y2), orders[i]))
raise ValueError(mesg)
if not (n1 <= len(y1) and n2 <= len(y2)):
raise ValueError("`order` input incompatible with"
" length y1 or y2.")
b = BPoly._construct_from_derivatives(xi[i], xi[i+1],
y1[:n1], y2[:n2])
if len(b) < k:
b = BPoly._raise_degree(b, k - len(b))
c.append(b)
c = np.asarray(c)
return cls(c.swapaxes(0, 1), xi, extrapolate)
@staticmethod
def _construct_from_derivatives(xa, xb, ya, yb):
r"""Compute the coefficients of a polynomial in the Bernstein basis
given the values and derivatives at the edges.
Return the coefficients of a polynomial in the Bernstein basis
defined on ``[xa, xb]`` and having the values and derivatives at the
endpoints `xa` and `xb` as specified by `ya`` and `yb`.
The polynomial constructed is of the minimal possible degree, i.e.,
if the lengths of `ya` and `yb` are `na` and `nb`, the degree
of the polynomial is ``na + nb - 1``.
Parameters
----------
xa : float
Left-hand end point of the interval
xb : float
Right-hand end point of the interval
ya : array_like
Derivatives at `xa`. `ya[0]` is the value of the function, and
`ya[i]` for ``i > 0`` is the value of the ``i``th derivative.
yb : array_like
Derivatives at `xb`.
Returns
-------
array
coefficient array of a polynomial having specified derivatives
Notes
-----
This uses several facts from life of Bernstein basis functions.
First of all,
.. math:: b'_{a, n} = n (b_{a-1, n-1} - b_{a, n-1})
If B(x) is a linear combination of the form
.. math:: B(x) = \sum_{a=0}^{n} c_a b_{a, n},
then :math: B'(x) = n \sum_{a=0}^{n-1} (c_{a+1} - c_{a}) b_{a, n-1}.
Iterating the latter one, one finds for the q-th derivative
.. math:: B^{q}(x) = n!/(n-q)! \sum_{a=0}^{n-q} Q_a b_{a, n-q},
with
.. math:: Q_a = \sum_{j=0}^{q} (-)^{j+q} comb(q, j) c_{j+a}
This way, only `a=0` contributes to :math: `B^{q}(x = xa)`, and
`c_q` are found one by one by iterating `q = 0, ..., na`.
At ``x = xb`` it's the same with ``a = n - q``.
"""
ya, yb = np.asarray(ya), np.asarray(yb)
if ya.shape[1:] != yb.shape[1:]:
raise ValueError('Shapes of ya {} and yb {} are incompatible'
.format(ya.shape, yb.shape))
dta, dtb = ya.dtype, yb.dtype
if (np.issubdtype(dta, np.complexfloating) or
np.issubdtype(dtb, np.complexfloating)):
dt = np.complex_
else:
dt = np.float_
na, nb = len(ya), len(yb)
n = na + nb
c = np.empty((na+nb,) + ya.shape[1:], dtype=dt)
# compute coefficients of a polynomial degree na+nb-1
# walk left-to-right
for q in range(0, na):
c[q] = ya[q] / spec.poch(n - q, q) * (xb - xa)**q
for j in range(0, q):
c[q] -= (-1)**(j+q) * comb(q, j) * c[j]
# now walk right-to-left
for q in range(0, nb):
c[-q-1] = yb[q] / spec.poch(n - q, q) * (-1)**q * (xb - xa)**q
for j in range(0, q):
c[-q-1] -= (-1)**(j+1) * comb(q, j+1) * c[-q+j]
return c
@staticmethod
def _raise_degree(c, d):
r"""Raise a degree of a polynomial in the Bernstein basis.
Given the coefficients of a polynomial degree `k`, return (the
coefficients of) the equivalent polynomial of degree `k+d`.
Parameters
----------
c : array_like
coefficient array, 1-D
d : integer
Returns
-------
array
coefficient array, 1-D array of length `c.shape[0] + d`
Notes
-----
This uses the fact that a Bernstein polynomial `b_{a, k}` can be
identically represented as a linear combination of polynomials of
a higher degree `k+d`:
.. math:: b_{a, k} = comb(k, a) \sum_{j=0}^{d} b_{a+j, k+d} \
comb(d, j) / comb(k+d, a+j)
"""
if d == 0:
return c
k = c.shape[0] - 1
out = np.zeros((c.shape[0] + d,) + c.shape[1:], dtype=c.dtype)
for a in range(c.shape[0]):
f = c[a] * comb(k, a)
for j in range(d+1):
out[a+j] += f * comb(d, j) / comb(k+d, a+j)
return out
class NdPPoly(object):
"""
Piecewise tensor product polynomial
The value at point ``xp = (x', y', z', ...)`` is evaluated by first
computing the interval indices `i` such that::
x[0][i[0]] <= x' < x[0][i[0]+1]
x[1][i[1]] <= y' < x[1][i[1]+1]
...
and then computing::
S = sum(c[k0-m0-1,...,kn-mn-1,i[0],...,i[n]]
* (xp[0] - x[0][i[0]])**m0
* ...
* (xp[n] - x[n][i[n]])**mn
for m0 in range(k[0]+1)
...
for mn in range(k[n]+1))
where ``k[j]`` is the degree of the polynomial in dimension j. This
representation is the piecewise multivariate power basis.
Parameters
----------
c : ndarray, shape (k0, ..., kn, m0, ..., mn, ...)
Polynomial coefficients, with polynomial order `kj` and
`mj+1` intervals for each dimension `j`.
x : ndim-tuple of ndarrays, shapes (mj+1,)
Polynomial breakpoints for each dimension. These must be
sorted in increasing order.
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs. Default: True.
Attributes
----------
x : tuple of ndarrays
Breakpoints.
c : ndarray
Coefficients of the polynomials.
Methods
-------
__call__
construct_fast
See also
--------
PPoly : piecewise polynomials in 1D
Notes
-----
High-order polynomials in the power basis can be numerically
unstable.
"""
def __init__(self, c, x, extrapolate=None):
self.x = tuple(np.ascontiguousarray(v, dtype=np.float64) for v in x)
self.c = np.asarray(c)
if extrapolate is None:
extrapolate = True
self.extrapolate = bool(extrapolate)
ndim = len(self.x)
if any(v.ndim != 1 for v in self.x):
raise ValueError("x arrays must all be 1-dimensional")
if any(v.size < 2 for v in self.x):
raise ValueError("x arrays must all contain at least 2 points")
if c.ndim < 2*ndim:
raise ValueError("c must have at least 2*len(x) dimensions")
if any(np.any(v[1:] - v[:-1] < 0) for v in self.x):
raise ValueError("x-coordinates are not in increasing order")
if any(a != b.size - 1 for a, b in zip(c.shape[ndim:2*ndim], self.x)):
raise ValueError("x and c do not agree on the number of intervals")
dtype = self._get_dtype(self.c.dtype)
self.c = np.ascontiguousarray(self.c, dtype=dtype)
@classmethod
def construct_fast(cls, c, x, extrapolate=None):
"""
Construct the piecewise polynomial without making checks.
Takes the same parameters as the constructor. Input arguments
``c`` and ``x`` must be arrays of the correct shape and type. The
``c`` array can only be of dtypes float and complex, and ``x``
array must have dtype float.
"""
self = object.__new__(cls)
self.c = c
self.x = x
if extrapolate is None:
extrapolate = True
self.extrapolate = extrapolate
return self
def _get_dtype(self, dtype):
if np.issubdtype(dtype, np.complexfloating) \
or np.issubdtype(self.c.dtype, np.complexfloating):
return np.complex_
else:
return np.float_
def _ensure_c_contiguous(self):
if not self.c.flags.c_contiguous:
self.c = self.c.copy()
if not isinstance(self.x, tuple):
self.x = tuple(self.x)
def __call__(self, x, nu=None, extrapolate=None):
"""
Evaluate the piecewise polynomial or its derivative
Parameters
----------
x : array-like
Points to evaluate the interpolant at.
nu : tuple, optional
Orders of derivatives to evaluate. Each must be non-negative.
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
y : array-like
Interpolated values. Shape is determined by replacing
the interpolation axis in the original array with the shape of x.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
ndim = len(self.x)
x = _ndim_coords_from_arrays(x)
x_shape = x.shape
x = np.ascontiguousarray(x.reshape(-1, x.shape[-1]), dtype=np.float_)
if nu is None:
nu = np.zeros((ndim,), dtype=np.intc)
else:
nu = np.asarray(nu, dtype=np.intc)
if nu.ndim != 1 or nu.shape[0] != ndim:
raise ValueError("invalid number of derivative orders nu")
dim1 = prod(self.c.shape[:ndim])
dim2 = prod(self.c.shape[ndim:2*ndim])
dim3 = prod(self.c.shape[2*ndim:])
ks = np.array(self.c.shape[:ndim], dtype=np.intc)
out = np.empty((x.shape[0], dim3), dtype=self.c.dtype)
self._ensure_c_contiguous()
_ppoly.evaluate_nd(self.c.reshape(dim1, dim2, dim3),
self.x,
ks,
x,
nu,
bool(extrapolate),
out)
return out.reshape(x_shape[:-1] + self.c.shape[2*ndim:])
def _derivative_inplace(self, nu, axis):
"""
Compute 1-D derivative along a selected dimension in-place
May result to non-contiguous c array.
"""
if nu < 0:
return self._antiderivative_inplace(-nu, axis)
ndim = len(self.x)
axis = axis % ndim
# reduce order
if nu == 0:
# noop
return
else:
sl = [slice(None)]*ndim
sl[axis] = slice(None, -nu, None)
c2 = self.c[tuple(sl)]
if c2.shape[axis] == 0:
# derivative of order 0 is zero
shp = list(c2.shape)
shp[axis] = 1
c2 = np.zeros(shp, dtype=c2.dtype)
# multiply by the correct rising factorials
factor = spec.poch(np.arange(c2.shape[axis], 0, -1), nu)
sl = [None]*c2.ndim
sl[axis] = slice(None)
c2 *= factor[tuple(sl)]
self.c = c2
def _antiderivative_inplace(self, nu, axis):
"""
Compute 1-D antiderivative along a selected dimension
May result to non-contiguous c array.
"""
if nu <= 0:
return self._derivative_inplace(-nu, axis)
ndim = len(self.x)
axis = axis % ndim
perm = list(range(ndim))
perm[0], perm[axis] = perm[axis], perm[0]
perm = perm + list(range(ndim, self.c.ndim))
c = self.c.transpose(perm)
c2 = np.zeros((c.shape[0] + nu,) + c.shape[1:],
dtype=c.dtype)
c2[:-nu] = c
# divide by the correct rising factorials
factor = spec.poch(np.arange(c.shape[0], 0, -1), nu)
c2[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)]
# fix continuity of added degrees of freedom
perm2 = list(range(c2.ndim))
perm2[1], perm2[ndim+axis] = perm2[ndim+axis], perm2[1]
c2 = c2.transpose(perm2)
c2 = c2.copy()
_ppoly.fix_continuity(c2.reshape(c2.shape[0], c2.shape[1], -1),
self.x[axis], nu-1)
c2 = c2.transpose(perm2)
c2 = c2.transpose(perm)
# Done
self.c = c2
def derivative(self, nu):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : ndim-tuple of int
Order of derivatives to evaluate for each dimension.
If negative, the antiderivative is returned.
Returns
-------
pp : NdPPoly
Piecewise polynomial of orders (k[0] - nu[0], ..., k[n] - nu[n])
representing the derivative of this polynomial.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals in each dimension are
considered half-open, ``[a, b)``, except for the last interval
which is closed ``[a, b]``.
"""
p = self.construct_fast(self.c.copy(), self.x, self.extrapolate)
for axis, n in enumerate(nu):
p._derivative_inplace(n, axis)
p._ensure_c_contiguous()
return p
def antiderivative(self, nu):
"""
Construct a new piecewise polynomial representing the antiderivative.
Antiderivative is also the indefinite integral of the function,
and derivative is its inverse operation.
Parameters
----------
nu : ndim-tuple of int
Order of derivatives to evaluate for each dimension.
If negative, the derivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k + n representing
the antiderivative of this polynomial.
Notes
-----
The antiderivative returned by this function is continuous and
continuously differentiable to order n-1, up to floating point
rounding error.
"""
p = self.construct_fast(self.c.copy(), self.x, self.extrapolate)
for axis, n in enumerate(nu):
p._antiderivative_inplace(n, axis)
p._ensure_c_contiguous()
return p
def integrate_1d(self, a, b, axis, extrapolate=None):
r"""
Compute NdPPoly representation for one dimensional definite integral
The result is a piecewise polynomial representing the integral:
.. math::
p(y, z, ...) = \int_a^b dx\, p(x, y, z, ...)
where the dimension integrated over is specified with the
`axis` parameter.
Parameters
----------
a, b : float
Lower and upper bound for integration.
axis : int
Dimension over which to compute the 1-D integrals
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
ig : NdPPoly or array-like
Definite integral of the piecewise polynomial over [a, b].
If the polynomial was 1D, an array is returned,
otherwise, an NdPPoly object.
"""
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
ndim = len(self.x)
axis = int(axis) % ndim
# reuse 1-D integration routines
c = self.c
swap = list(range(c.ndim))
swap.insert(0, swap[axis])
del swap[axis + 1]
swap.insert(1, swap[ndim + axis])
del swap[ndim + axis + 1]
c = c.transpose(swap)
p = PPoly.construct_fast(c.reshape(c.shape[0], c.shape[1], -1),
self.x[axis],
extrapolate=extrapolate)
out = p.integrate(a, b, extrapolate=extrapolate)
# Construct result
if ndim == 1:
return out.reshape(c.shape[2:])
else:
c = out.reshape(c.shape[2:])
x = self.x[:axis] + self.x[axis+1:]
return self.construct_fast(c, x, extrapolate=extrapolate)
def integrate(self, ranges, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
ranges : ndim-tuple of 2-tuples float
Sequence of lower and upper bounds for each dimension,
``[(a[0], b[0]), ..., (a[ndim-1], b[ndim-1])]``
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
ig : array_like
Definite integral of the piecewise polynomial over
[a[0], b[0]] x ... x [a[ndim-1], b[ndim-1]]
"""
ndim = len(self.x)
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
if not hasattr(ranges, '__len__') or len(ranges) != ndim:
raise ValueError("Range not a sequence of correct length")
self._ensure_c_contiguous()
# Reuse 1D integration routine
c = self.c
for n, (a, b) in enumerate(ranges):
swap = list(range(c.ndim))
swap.insert(1, swap[ndim - n])
del swap[ndim - n + 1]
c = c.transpose(swap)
p = PPoly.construct_fast(c, self.x[n], extrapolate=extrapolate)
out = p.integrate(a, b, extrapolate=extrapolate)
c = out.reshape(c.shape[2:])
return c
class RegularGridInterpolator(object):
"""
Interpolation on a regular grid in arbitrary dimensions
The data must be defined on a regular grid; the grid spacing however may be
uneven. Linear and nearest-neighbor interpolation are supported. After
setting up the interpolator object, the interpolation method (*linear* or
*nearest*) may be chosen at each evaluation.
Parameters
----------
points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
The points defining the regular grid in n dimensions.
values : array_like, shape (m1, ..., mn, ...)
The data on the regular grid in n dimensions.
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest". This parameter will become the default for the object's
``__call__`` method. Default is "linear".
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated.
Methods
-------
__call__
Notes
-----
Contrary to LinearNDInterpolator and NearestNDInterpolator, this class
avoids expensive triangulation of the input data by taking advantage of the
regular grid structure.
If any of `points` have a dimension of size 1, linear interpolation will
return an array of `nan` values. Nearest-neighbor interpolation will work
as usual in this case.
.. versionadded:: 0.14
Examples
--------
Evaluate a simple example function on the points of a 3-D grid:
>>> from scipy.interpolate import RegularGridInterpolator
>>> def f(x, y, z):
... return 2 * x**3 + 3 * y**2 - z
>>> x = np.linspace(1, 4, 11)
>>> y = np.linspace(4, 7, 22)
>>> z = np.linspace(7, 9, 33)
>>> data = f(*np.meshgrid(x, y, z, indexing='ij', sparse=True))
``data`` is now a 3-D array with ``data[i,j,k] = f(x[i], y[j], z[k])``.
Next, define an interpolating function from this data:
>>> my_interpolating_function = RegularGridInterpolator((x, y, z), data)
Evaluate the interpolating function at the two points
``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``:
>>> pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]])
>>> my_interpolating_function(pts)
array([ 125.80469388, 146.30069388])
which is indeed a close approximation to
``[f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)]``.
See also
--------
NearestNDInterpolator : Nearest neighbor interpolation on unstructured
data in N dimensions
LinearNDInterpolator : Piecewise linear interpolant on unstructured data
in N dimensions
References
----------
.. [1] Python package *regulargrid* by Johannes Buchner, see
https://pypi.python.org/pypi/regulargrid/
.. [2] Wikipedia, "Trilinear interpolation",
https://en.wikipedia.org/wiki/Trilinear_interpolation
.. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear
and multilinear table interpolation in many dimensions." MATH.
COMPUT. 50.181 (1988): 189-196.
https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf
"""
# this class is based on code originally programmed by Johannes Buchner,
# see https://github.com/JohannesBuchner/regulargrid
def __init__(self, points, values, method="linear", bounds_error=True,
fill_value=np.nan):
if method not in ["linear", "nearest"]:
raise ValueError("Method '%s' is not defined" % method)
self.method = method
self.bounds_error = bounds_error
if not hasattr(values, 'ndim'):
# allow reasonable duck-typed values
values = np.asarray(values)
if len(points) > values.ndim:
raise ValueError("There are %d point arrays, but values has %d "
"dimensions" % (len(points), values.ndim))
if hasattr(values, 'dtype') and hasattr(values, 'astype'):
if not np.issubdtype(values.dtype, np.inexact):
values = values.astype(float)
self.fill_value = fill_value
if fill_value is not None:
fill_value_dtype = np.asarray(fill_value).dtype
if (hasattr(values, 'dtype') and not
np.can_cast(fill_value_dtype, values.dtype,
casting='same_kind')):
raise ValueError("fill_value must be either 'None' or "
"of a type compatible with values")
for i, p in enumerate(points):
if not np.all(np.diff(p) > 0.):
raise ValueError("The points in dimension %d must be strictly "
"ascending" % i)
if not np.asarray(p).ndim == 1:
raise ValueError("The points in dimension %d must be "
"1-dimensional" % i)
if not values.shape[i] == len(p):
raise ValueError("There are %d points and %d values in "
"dimension %d" % (len(p), values.shape[i], i))
self.grid = tuple([np.asarray(p) for p in points])
self.values = values
def __call__(self, xi, method=None):
"""
Interpolation at coordinates
Parameters
----------
xi : ndarray of shape (..., ndim)
The coordinates to sample the gridded data at
method : str
The method of interpolation to perform. Supported are "linear" and
"nearest".
"""
method = self.method if method is None else method
if method not in ["linear", "nearest"]:
raise ValueError("Method '%s' is not defined" % method)
ndim = len(self.grid)
xi = _ndim_coords_from_arrays(xi, ndim=ndim)
if xi.shape[-1] != len(self.grid):
raise ValueError("The requested sample points xi have dimension "
"%d, but this RegularGridInterpolator has "
"dimension %d" % (xi.shape[1], ndim))
xi_shape = xi.shape
xi = xi.reshape(-1, xi_shape[-1])
if self.bounds_error:
for i, p in enumerate(xi.T):
if not np.logical_and(np.all(self.grid[i][0] <= p),
np.all(p <= self.grid[i][-1])):
raise ValueError("One of the requested xi is out of bounds "
"in dimension %d" % i)
indices, norm_distances, out_of_bounds = self._find_indices(xi.T)
if method == "linear":
result = self._evaluate_linear(indices,
norm_distances,
out_of_bounds)
elif method == "nearest":
result = self._evaluate_nearest(indices,
norm_distances,
out_of_bounds)
if not self.bounds_error and self.fill_value is not None:
result[out_of_bounds] = self.fill_value
return result.reshape(xi_shape[:-1] + self.values.shape[ndim:])
def _evaluate_linear(self, indices, norm_distances, out_of_bounds):
# slice for broadcasting over trailing dimensions in self.values
vslice = (slice(None),) + (None,)*(self.values.ndim - len(indices))
# find relevant values
# each i and i+1 represents a edge
edges = itertools.product(*[[i, i + 1] for i in indices])
values = 0.
for edge_indices in edges:
weight = 1.
for ei, i, yi in zip(edge_indices, indices, norm_distances):
weight *= np.where(ei == i, 1 - yi, yi)
values += np.asarray(self.values[edge_indices]) * weight[vslice]
return values
def _evaluate_nearest(self, indices, norm_distances, out_of_bounds):
idx_res = [np.where(yi <= .5, i, i + 1)
for i, yi in zip(indices, norm_distances)]
return self.values[tuple(idx_res)]
def _find_indices(self, xi):
# find relevant edges between which xi are situated
indices = []
# compute distance to lower edge in unity units
norm_distances = []
# check for out of bounds xi
out_of_bounds = np.zeros((xi.shape[1]), dtype=bool)
# iterate through dimensions
for x, grid in zip(xi, self.grid):
i = np.searchsorted(grid, x) - 1
i[i < 0] = 0
i[i > grid.size - 2] = grid.size - 2
indices.append(i)
norm_distances.append((x - grid[i]) /
(grid[i + 1] - grid[i]))
if not self.bounds_error:
out_of_bounds += x < grid[0]
out_of_bounds += x > grid[-1]
return indices, norm_distances, out_of_bounds
def interpn(points, values, xi, method="linear", bounds_error=True,
fill_value=np.nan):
"""
Multidimensional interpolation on regular grids.
Parameters
----------
points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
The points defining the regular grid in n dimensions.
values : array_like, shape (m1, ..., mn, ...)
The data on the regular grid in n dimensions.
xi : ndarray of shape (..., ndim)
The coordinates to sample the gridded data at
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest", and "splinef2d". "splinef2d" is only supported for
2-dimensional data.
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated. Extrapolation is not supported by method
"splinef2d".
Returns
-------
values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:]
Interpolated values at input coordinates.
Notes
-----
.. versionadded:: 0.14
Examples
--------
Evaluate a simple example function on the points of a regular 3-D grid:
>>> from scipy.interpolate import interpn
>>> def value_func_3d(x, y, z):
... return 2 * x + 3 * y - z
>>> x = np.linspace(0, 5)
>>> y = np.linspace(0, 5)
>>> z = np.linspace(0, 5)
>>> points = (x, y, z)
>>> values = value_func_3d(*np.meshgrid(*points))
Evaluate the interpolating function at a point
>>> point = np.array([2.21, 3.12, 1.15])
>>> print(interpn(points, values, point))
[11.72]
See also
--------
NearestNDInterpolator : Nearest neighbor interpolation on unstructured
data in N dimensions
LinearNDInterpolator : Piecewise linear interpolant on unstructured data
in N dimensions
RegularGridInterpolator : Linear and nearest-neighbor Interpolation on a
regular grid in arbitrary dimensions
RectBivariateSpline : Bivariate spline approximation over a rectangular mesh
"""
# sanity check 'method' kwarg
if method not in ["linear", "nearest", "splinef2d"]:
raise ValueError("interpn only understands the methods 'linear', "
"'nearest', and 'splinef2d'. You provided %s." %
method)
if not hasattr(values, 'ndim'):
values = np.asarray(values)
ndim = values.ndim
if ndim > 2 and method == "splinef2d":
raise ValueError("The method splinef2d can only be used for "
"2-dimensional input data")
if not bounds_error and fill_value is None and method == "splinef2d":
raise ValueError("The method splinef2d does not support extrapolation.")
# sanity check consistency of input dimensions
if len(points) > ndim:
raise ValueError("There are %d point arrays, but values has %d "
"dimensions" % (len(points), ndim))
if len(points) != ndim and method == 'splinef2d':
raise ValueError("The method splinef2d can only be used for "
"scalar data with one point per coordinate")
# sanity check input grid
for i, p in enumerate(points):
if not np.all(np.diff(p) > 0.):
raise ValueError("The points in dimension %d must be strictly "
"ascending" % i)
if not np.asarray(p).ndim == 1:
raise ValueError("The points in dimension %d must be "
"1-dimensional" % i)
if not values.shape[i] == len(p):
raise ValueError("There are %d points and %d values in "
"dimension %d" % (len(p), values.shape[i], i))
grid = tuple([np.asarray(p) for p in points])
# sanity check requested xi
xi = _ndim_coords_from_arrays(xi, ndim=len(grid))
if xi.shape[-1] != len(grid):
raise ValueError("The requested sample points xi have dimension "
"%d, but this RegularGridInterpolator has "
"dimension %d" % (xi.shape[1], len(grid)))
for i, p in enumerate(xi.T):
if bounds_error and not np.logical_and(np.all(grid[i][0] <= p),
np.all(p <= grid[i][-1])):
raise ValueError("One of the requested xi is out of bounds "
"in dimension %d" % i)
# perform interpolation
if method == "linear":
interp = RegularGridInterpolator(points, values, method="linear",
bounds_error=bounds_error,
fill_value=fill_value)
return interp(xi)
elif method == "nearest":
interp = RegularGridInterpolator(points, values, method="nearest",
bounds_error=bounds_error,
fill_value=fill_value)
return interp(xi)
elif method == "splinef2d":
xi_shape = xi.shape
xi = xi.reshape(-1, xi.shape[-1])
# RectBivariateSpline doesn't support fill_value; we need to wrap here
idx_valid = np.all((grid[0][0] <= xi[:, 0], xi[:, 0] <= grid[0][-1],
grid[1][0] <= xi[:, 1], xi[:, 1] <= grid[1][-1]),
axis=0)
result = np.empty_like(xi[:, 0])
# make a copy of values for RectBivariateSpline
interp = RectBivariateSpline(points[0], points[1], values[:])
result[idx_valid] = interp.ev(xi[idx_valid, 0], xi[idx_valid, 1])
result[np.logical_not(idx_valid)] = fill_value
return result.reshape(xi_shape[:-1])
# backward compatibility wrapper
class _ppform(PPoly):
"""
Deprecated piecewise polynomial class.
New code should use the `PPoly` class instead.
"""
def __init__(self, coeffs, breaks, fill=0.0, sort=False):
warnings.warn("_ppform is deprecated -- use PPoly instead",
category=DeprecationWarning)
if sort:
breaks = np.sort(breaks)
else:
breaks = np.asarray(breaks)
PPoly.__init__(self, coeffs, breaks)
self.coeffs = self.c
self.breaks = self.x
self.K = self.coeffs.shape[0]
self.fill = fill
self.a = self.breaks[0]
self.b = self.breaks[-1]
def __call__(self, x):
return PPoly.__call__(self, x, 0, False)
def _evaluate(self, x, nu, extrapolate, out):
PPoly._evaluate(self, x, nu, extrapolate, out)
out[~((x >= self.a) & (x <= self.b))] = self.fill
return out
@classmethod
def fromspline(cls, xk, cvals, order, fill=0.0):
# Note: this spline representation is incompatible with FITPACK
N = len(xk)-1
sivals = np.empty((order+1, N), dtype=float)
for m in range(order, -1, -1):
fact = spec.gamma(m+1)
res = _fitpack._bspleval(xk[:-1], xk, cvals, order, m)
res /= fact
sivals[order-m, :] = res
return cls(sivals, xk, fill=fill)
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__all__ = ['interp1d', 'interp2d', 'lagrange', 'PPoly', 'BPoly', 'NdPPoly',
'RegularGridInterpolator', 'interpn']
import itertools
import warnings
import numpy as np
from numpy import (array, transpose, searchsorted, atleast_1d, atleast_2d,
ravel, poly1d, asarray, intp)
import scipy.special as spec
from scipy.special import comb
from scipy._lib._util import prod
from . import fitpack
from . import dfitpack
from . import _fitpack
from .polyint import _Interpolator1D
from . import _ppoly
from .fitpack2 import RectBivariateSpline
from .interpnd import _ndim_coords_from_arrays
from ._bsplines import make_interp_spline, BSpline
def lagrange(x, w):
r"""
Return a Lagrange interpolating polynomial.
Given two 1-D arrays `x` and `w,` returns the Lagrange interpolating
polynomial through the points ``(x, w)``.
Warning: This implementation is numerically unstable. Do not expect to
be able to use more than about 20 points even if they are chosen optimally.
Parameters
----------
x : array_like
`x` represents the x-coordinates of a set of datapoints.
w : array_like
`w` represents the y-coordinates of a set of datapoints, i.e., f(`x`).
Returns
-------
lagrange : `numpy.poly1d` instance
The Lagrange interpolating polynomial.
Examples
--------
Interpolate :math:`f(x) = x^3` by 3 points.
>>> from scipy.interpolate import lagrange
>>> x = np.array([0, 1, 2])
>>> y = x**3
>>> poly = lagrange(x, y)
Since there are only 3 points, Lagrange polynomial has degree 2. Explicitly,
it is given by
.. math::
\begin{aligned}
L(x) &= 1\times \frac{x (x - 2)}{-1} + 8\times \frac{x (x-1)}{2} \\
&= x (-2 + 3x)
\end{aligned}
>>> from numpy.polynomial.polynomial import Polynomial
>>> Polynomial(poly).coef
array([ 3., -2., 0.])
"""
M = len(x)
p = poly1d(0.0)
for j in range(M):
pt = poly1d(w[j])
for k in range(M):
if k == j:
continue
fac = x[j]-x[k]
pt *= poly1d([1.0, -x[k]])/fac
p += pt
return p
# !! Need to find argument for keeping initialize. If it isn't
# !! found, get rid of it!
class interp2d(object):
"""
interp2d(x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None)
Interpolate over a 2-D grid.
`x`, `y` and `z` are arrays of values used to approximate some function
f: ``z = f(x, y)``. This class returns a function whose call method uses
spline interpolation to find the value of new points.
If `x` and `y` represent a regular grid, consider using
RectBivariateSpline.
Note that calling `interp2d` with NaNs present in input values results in
undefined behaviour.
Methods
-------
__call__
Parameters
----------
x, y : array_like
Arrays defining the data point coordinates.
If the points lie on a regular grid, `x` can specify the column
coordinates and `y` the row coordinates, for example::
>>> x = [0,1,2]; y = [0,3]; z = [[1,2,3], [4,5,6]]
Otherwise, `x` and `y` must specify the full coordinates for each
point, for example::
>>> x = [0,1,2,0,1,2]; y = [0,0,0,3,3,3]; z = [1,2,3,4,5,6]
If `x` and `y` are multidimensional, they are flattened before use.
z : array_like
The values of the function to interpolate at the data points. If
`z` is a multidimensional array, it is flattened before use. The
length of a flattened `z` array is either
len(`x`)*len(`y`) if `x` and `y` specify the column and row coordinates
or ``len(z) == len(x) == len(y)`` if `x` and `y` specify coordinates
for each point.
kind : {'linear', 'cubic', 'quintic'}, optional
The kind of spline interpolation to use. Default is 'linear'.
copy : bool, optional
If True, the class makes internal copies of x, y and z.
If False, references may be used. The default is to copy.
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data (x,y), a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If omitted (None), values outside
the domain are extrapolated via nearest-neighbor extrapolation.
See Also
--------
RectBivariateSpline :
Much faster 2-D interpolation if your input data is on a grid
bisplrep, bisplev :
Spline interpolation based on FITPACK
BivariateSpline : a more recent wrapper of the FITPACK routines
interp1d : 1-D version of this function
Notes
-----
The minimum number of data points required along the interpolation
axis is ``(k+1)**2``, with k=1 for linear, k=3 for cubic and k=5 for
quintic interpolation.
The interpolator is constructed by `bisplrep`, with a smoothing factor
of 0. If more control over smoothing is needed, `bisplrep` should be
used directly.
Examples
--------
Construct a 2-D grid and interpolate on it:
>>> from scipy import interpolate
>>> x = np.arange(-5.01, 5.01, 0.25)
>>> y = np.arange(-5.01, 5.01, 0.25)
>>> xx, yy = np.meshgrid(x, y)
>>> z = np.sin(xx**2+yy**2)
>>> f = interpolate.interp2d(x, y, z, kind='cubic')
Now use the obtained interpolation function and plot the result:
>>> import matplotlib.pyplot as plt
>>> xnew = np.arange(-5.01, 5.01, 1e-2)
>>> ynew = np.arange(-5.01, 5.01, 1e-2)
>>> znew = f(xnew, ynew)
>>> plt.plot(x, z[0, :], 'ro-', xnew, znew[0, :], 'b-')
>>> plt.show()
"""
def __init__(self, x, y, z, kind='linear', copy=True, bounds_error=False,
fill_value=None):
x = ravel(x)
y = ravel(y)
z = asarray(z)
rectangular_grid = (z.size == len(x) * len(y))
if rectangular_grid:
if z.ndim == 2:
if z.shape != (len(y), len(x)):
raise ValueError("When on a regular grid with x.size = m "
"and y.size = n, if z.ndim == 2, then z "
"must have shape (n, m)")
if not np.all(x[1:] >= x[:-1]):
j = np.argsort(x)
x = x[j]
z = z[:, j]
if not np.all(y[1:] >= y[:-1]):
j = np.argsort(y)
y = y[j]
z = z[j, :]
z = ravel(z.T)
else:
z = ravel(z)
if len(x) != len(y):
raise ValueError(
"x and y must have equal lengths for non rectangular grid")
if len(z) != len(x):
raise ValueError(
"Invalid length for input z for non rectangular grid")
interpolation_types = {'linear': 1, 'cubic': 3, 'quintic': 5}
try:
kx = ky = interpolation_types[kind]
except KeyError as e:
raise ValueError(
f"Unsupported interpolation type {repr(kind)}, must be "
f"either of {', '.join(map(repr, interpolation_types))}."
) from e
if not rectangular_grid:
# TODO: surfit is really not meant for interpolation!
self.tck = fitpack.bisplrep(x, y, z, kx=kx, ky=ky, s=0.0)
else:
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(
x, y, z, None, None, None, None,
kx=kx, ky=ky, s=0.0)
self.tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)],
kx, ky)
self.bounds_error = bounds_error
self.fill_value = fill_value
self.x, self.y, self.z = [array(a, copy=copy) for a in (x, y, z)]
self.x_min, self.x_max = np.amin(x), np.amax(x)
self.y_min, self.y_max = np.amin(y), np.amax(y)
def __call__(self, x, y, dx=0, dy=0, assume_sorted=False):
"""Interpolate the function.
Parameters
----------
x : 1-D array
x-coordinates of the mesh on which to interpolate.
y : 1-D array
y-coordinates of the mesh on which to interpolate.
dx : int >= 0, < kx
Order of partial derivatives in x.
dy : int >= 0, < ky
Order of partial derivatives in y.
assume_sorted : bool, optional
If False, values of `x` and `y` can be in any order and they are
sorted first.
If True, `x` and `y` have to be arrays of monotonically
increasing values.
Returns
-------
z : 2-D array with shape (len(y), len(x))
The interpolated values.
"""
x = atleast_1d(x)
y = atleast_1d(y)
if x.ndim != 1 or y.ndim != 1:
raise ValueError("x and y should both be 1-D arrays")
if not assume_sorted:
x = np.sort(x, kind="mergesort")
y = np.sort(y, kind="mergesort")
if self.bounds_error or self.fill_value is not None:
out_of_bounds_x = (x < self.x_min) | (x > self.x_max)
out_of_bounds_y = (y < self.y_min) | (y > self.y_max)
any_out_of_bounds_x = np.any(out_of_bounds_x)
any_out_of_bounds_y = np.any(out_of_bounds_y)
if self.bounds_error and (any_out_of_bounds_x or any_out_of_bounds_y):
raise ValueError("Values out of range; x must be in %r, y in %r"
% ((self.x_min, self.x_max),
(self.y_min, self.y_max)))
z = fitpack.bisplev(x, y, self.tck, dx, dy)
z = atleast_2d(z)
z = transpose(z)
if self.fill_value is not None:
if any_out_of_bounds_x:
z[:, out_of_bounds_x] = self.fill_value
if any_out_of_bounds_y:
z[out_of_bounds_y, :] = self.fill_value
if len(z) == 1:
z = z[0]
return array(z)
def _check_broadcast_up_to(arr_from, shape_to, name):
"""Helper to check that arr_from broadcasts up to shape_to"""
shape_from = arr_from.shape
if len(shape_to) >= len(shape_from):
for t, f in zip(shape_to[::-1], shape_from[::-1]):
if f != 1 and f != t:
break
else: # all checks pass, do the upcasting that we need later
if arr_from.size != 1 and arr_from.shape != shape_to:
arr_from = np.ones(shape_to, arr_from.dtype) * arr_from
return arr_from.ravel()
# at least one check failed
raise ValueError('%s argument must be able to broadcast up '
'to shape %s but had shape %s'
% (name, shape_to, shape_from))
def _do_extrapolate(fill_value):
"""Helper to check if fill_value == "extrapolate" without warnings"""
return (isinstance(fill_value, str) and
fill_value == 'extrapolate')
class interp1d(_Interpolator1D):
"""
Interpolate a 1-D function.
`x` and `y` are arrays of values used to approximate some function f:
``y = f(x)``. This class returns a function whose call method uses
interpolation to find the value of new points.
Parameters
----------
x : (N,) array_like
A 1-D array of real values.
y : (...,N,...) array_like
A N-D array of real values. The length of `y` along the interpolation
axis must be equal to the length of `x`.
kind : str or int, optional
Specifies the kind of interpolation as a string
('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'previous', 'next', where 'zero', 'slinear', 'quadratic' and 'cubic'
refer to a spline interpolation of zeroth, first, second or third
order; 'previous' and 'next' simply return the previous or next value
of the point) or as an integer specifying the order of the spline
interpolator to use.
Default is 'linear'.
axis : int, optional
Specifies the axis of `y` along which to interpolate.
Interpolation defaults to the last axis of `y`.
copy : bool, optional
If True, the class makes internal copies of x and y.
If False, references to `x` and `y` are used. The default is to copy.
bounds_error : bool, optional
If True, a ValueError is raised any time interpolation is attempted on
a value outside of the range of x (where extrapolation is
necessary). If False, out of bounds values are assigned `fill_value`.
By default, an error is raised unless ``fill_value="extrapolate"``.
fill_value : array-like or (array-like, array_like) or "extrapolate", optional
- if a ndarray (or float), this value will be used to fill in for
requested points outside of the data range. If not provided, then
the default is NaN. The array-like must broadcast properly to the
dimensions of the non-interpolation axes.
- If a two-element tuple, then the first element is used as a
fill value for ``x_new < x[0]`` and the second element is used for
``x_new > x[-1]``. Anything that is not a 2-element tuple (e.g.,
list or ndarray, regardless of shape) is taken to be a single
array-like argument meant to be used for both bounds as
``below, above = fill_value, fill_value``.
.. versionadded:: 0.17.0
- If "extrapolate", then points outside the data range will be
extrapolated.
.. versionadded:: 0.17.0
assume_sorted : bool, optional
If False, values of `x` can be in any order and they are sorted first.
If True, `x` has to be an array of monotonically increasing values.
Attributes
----------
fill_value
Methods
-------
__call__
See Also
--------
splrep, splev
Spline interpolation/smoothing based on FITPACK.
UnivariateSpline : An object-oriented wrapper of the FITPACK routines.
interp2d : 2-D interpolation
Notes
-----
Calling `interp1d` with NaNs present in input values results in
undefined behaviour.
Input values `x` and `y` must be convertible to `float` values like
`int` or `float`.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy import interpolate
>>> x = np.arange(0, 10)
>>> y = np.exp(-x/3.0)
>>> f = interpolate.interp1d(x, y)
>>> xnew = np.arange(0, 9, 0.1)
>>> ynew = f(xnew) # use interpolation function returned by `interp1d`
>>> plt.plot(x, y, 'o', xnew, ynew, '-')
>>> plt.show()
"""
def __init__(self, x, y, kind='linear', axis=-1,
copy=True, bounds_error=None, fill_value=np.nan,
assume_sorted=False):
""" Initialize a 1-D linear interpolation class."""
_Interpolator1D.__init__(self, x, y, axis=axis)
self.bounds_error = bounds_error # used by fill_value setter
self.copy = copy
if kind in ['zero', 'slinear', 'quadratic', 'cubic']:
order = {'zero': 0, 'slinear': 1,
'quadratic': 2, 'cubic': 3}[kind]
kind = 'spline'
elif isinstance(kind, int):
order = kind
kind = 'spline'
elif kind not in ('linear', 'nearest', 'previous', 'next'):
raise NotImplementedError("%s is unsupported: Use fitpack "
"routines for other types." % kind)
x = array(x, copy=self.copy)
y = array(y, copy=self.copy)
if not assume_sorted:
ind = np.argsort(x, kind="mergesort")
x = x[ind]
y = np.take(y, ind, axis=axis)
if x.ndim != 1:
raise ValueError("the x array must have exactly one dimension.")
if y.ndim == 0:
raise ValueError("the y array must have at least one dimension.")
# Force-cast y to a floating-point type, if it's not yet one
if not issubclass(y.dtype.type, np.inexact):
y = y.astype(np.float_)
# Backward compatibility
self.axis = axis % y.ndim
# Interpolation goes internally along the first axis
self.y = y
self._y = self._reshape_yi(self.y)
self.x = x
del y, x # clean up namespace to prevent misuse; use attributes
self._kind = kind
self.fill_value = fill_value # calls the setter, can modify bounds_err
# Adjust to interpolation kind; store reference to *unbound*
# interpolation methods, in order to avoid circular references to self
# stored in the bound instance methods, and therefore delayed garbage
# collection. See: https://docs.python.org/reference/datamodel.html
if kind in ('linear', 'nearest', 'previous', 'next'):
# Make a "view" of the y array that is rotated to the interpolation
# axis.
minval = 2
if kind == 'nearest':
# Do division before addition to prevent possible integer
# overflow
self.x_bds = self.x / 2.0
self.x_bds = self.x_bds[1:] + self.x_bds[:-1]
self._call = self.__class__._call_nearest
elif kind == 'previous':
# Side for np.searchsorted and index for clipping
self._side = 'left'
self._ind = 0
# Move x by one floating point value to the left
self._x_shift = np.nextafter(self.x, -np.inf)
self._call = self.__class__._call_previousnext
elif kind == 'next':
self._side = 'right'
self._ind = 1
# Move x by one floating point value to the right
self._x_shift = np.nextafter(self.x, np.inf)
self._call = self.__class__._call_previousnext
else:
# Check if we can delegate to numpy.interp (2x-10x faster).
cond = self.x.dtype == np.float_ and self.y.dtype == np.float_
cond = cond and self.y.ndim == 1
cond = cond and not _do_extrapolate(fill_value)
if cond:
self._call = self.__class__._call_linear_np
else:
self._call = self.__class__._call_linear
else:
minval = order + 1
rewrite_nan = False
xx, yy = self.x, self._y
if order > 1:
# Quadratic or cubic spline. If input contains even a single
# nan, then the output is all nans. We cannot just feed data
# with nans to make_interp_spline because it calls LAPACK.
# So, we make up a bogus x and y with no nans and use it
# to get the correct shape of the output, which we then fill
# with nans.
# For slinear or zero order spline, we just pass nans through.
mask = np.isnan(self.x)
if mask.any():
sx = self.x[~mask]
if sx.size == 0:
raise ValueError("`x` array is all-nan")
xx = np.linspace(np.nanmin(self.x),
np.nanmax(self.x),
len(self.x))
rewrite_nan = True
if np.isnan(self._y).any():
yy = np.ones_like(self._y)
rewrite_nan = True
self._spline = make_interp_spline(xx, yy, k=order,
check_finite=False)
if rewrite_nan:
self._call = self.__class__._call_nan_spline
else:
self._call = self.__class__._call_spline
if len(self.x) < minval:
raise ValueError("x and y arrays must have at "
"least %d entries" % minval)
@property
def fill_value(self):
"""The fill value."""
# backwards compat: mimic a public attribute
return self._fill_value_orig
@fill_value.setter
def fill_value(self, fill_value):
# extrapolation only works for nearest neighbor and linear methods
if _do_extrapolate(fill_value):
if self.bounds_error:
raise ValueError("Cannot extrapolate and raise "
"at the same time.")
self.bounds_error = False
self._extrapolate = True
else:
broadcast_shape = (self.y.shape[:self.axis] +
self.y.shape[self.axis + 1:])
if len(broadcast_shape) == 0:
broadcast_shape = (1,)
# it's either a pair (_below_range, _above_range) or a single value
# for both above and below range
if isinstance(fill_value, tuple) and len(fill_value) == 2:
below_above = [np.asarray(fill_value[0]),
np.asarray(fill_value[1])]
names = ('fill_value (below)', 'fill_value (above)')
for ii in range(2):
below_above[ii] = _check_broadcast_up_to(
below_above[ii], broadcast_shape, names[ii])
else:
fill_value = np.asarray(fill_value)
below_above = [_check_broadcast_up_to(
fill_value, broadcast_shape, 'fill_value')] * 2
self._fill_value_below, self._fill_value_above = below_above
self._extrapolate = False
if self.bounds_error is None:
self.bounds_error = True
# backwards compat: fill_value was a public attr; make it writeable
self._fill_value_orig = fill_value
def _call_linear_np(self, x_new):
# Note that out-of-bounds values are taken care of in self._evaluate
return np.interp(x_new, self.x, self.y)
def _call_linear(self, x_new):
# 2. Find where in the original data, the values to interpolate
# would be inserted.
# Note: If x_new[n] == x[m], then m is returned by searchsorted.
x_new_indices = searchsorted(self.x, x_new)
# 3. Clip x_new_indices so that they are within the range of
# self.x indices and at least 1. Removes mis-interpolation
# of x_new[n] = x[0]
x_new_indices = x_new_indices.clip(1, len(self.x)-1).astype(int)
# 4. Calculate the slope of regions that each x_new value falls in.
lo = x_new_indices - 1
hi = x_new_indices
x_lo = self.x[lo]
x_hi = self.x[hi]
y_lo = self._y[lo]
y_hi = self._y[hi]
# Note that the following two expressions rely on the specifics of the
# broadcasting semantics.
slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]
# 5. Calculate the actual value for each entry in x_new.
y_new = slope*(x_new - x_lo)[:, None] + y_lo
return y_new
def _call_nearest(self, x_new):
""" Find nearest neighbor interpolated y_new = f(x_new)."""
# 2. Find where in the averaged data the values to interpolate
# would be inserted.
# Note: use side='left' (right) to searchsorted() to define the
# halfway point to be nearest to the left (right) neighbor
x_new_indices = searchsorted(self.x_bds, x_new, side='left')
# 3. Clip x_new_indices so that they are within the range of x indices.
x_new_indices = x_new_indices.clip(0, len(self.x)-1).astype(intp)
# 4. Calculate the actual value for each entry in x_new.
y_new = self._y[x_new_indices]
return y_new
def _call_previousnext(self, x_new):
"""Use previous/next neighbor of x_new, y_new = f(x_new)."""
# 1. Get index of left/right value
x_new_indices = searchsorted(self._x_shift, x_new, side=self._side)
# 2. Clip x_new_indices so that they are within the range of x indices.
x_new_indices = x_new_indices.clip(1-self._ind,
len(self.x)-self._ind).astype(intp)
# 3. Calculate the actual value for each entry in x_new.
y_new = self._y[x_new_indices+self._ind-1]
return y_new
def _call_spline(self, x_new):
return self._spline(x_new)
def _call_nan_spline(self, x_new):
out = self._spline(x_new)
out[...] = np.nan
return out
def _evaluate(self, x_new):
# 1. Handle values in x_new that are outside of x. Throw error,
# or return a list of mask array indicating the outofbounds values.
# The behavior is set by the bounds_error variable.
x_new = asarray(x_new)
y_new = self._call(self, x_new)
if not self._extrapolate:
below_bounds, above_bounds = self._check_bounds(x_new)
if len(y_new) > 0:
# Note fill_value must be broadcast up to the proper size
# and flattened to work here
y_new[below_bounds] = self._fill_value_below
y_new[above_bounds] = self._fill_value_above
return y_new
def _check_bounds(self, x_new):
"""Check the inputs for being in the bounds of the interpolated data.
Parameters
----------
x_new : array
Returns
-------
out_of_bounds : bool array
The mask on x_new of values that are out of the bounds.
"""
# If self.bounds_error is True, we raise an error if any x_new values
# fall outside the range of x. Otherwise, we return an array indicating
# which values are outside the boundary region.
below_bounds = x_new < self.x[0]
above_bounds = x_new > self.x[-1]
# !! Could provide more information about which values are out of bounds
if self.bounds_error and below_bounds.any():
raise ValueError("A value in x_new is below the interpolation "
"range.")
if self.bounds_error and above_bounds.any():
raise ValueError("A value in x_new is above the interpolation "
"range.")
# !! Should we emit a warning if some values are out of bounds?
# !! matlab does not.
return below_bounds, above_bounds
class _PPolyBase(object):
"""Base class for piecewise polynomials."""
__slots__ = ('c', 'x', 'extrapolate', 'axis')
def __init__(self, c, x, extrapolate=None, axis=0):
self.c = np.asarray(c)
self.x = np.ascontiguousarray(x, dtype=np.float64)
if extrapolate is None:
extrapolate = True
elif extrapolate != 'periodic':
extrapolate = bool(extrapolate)
self.extrapolate = extrapolate
if self.c.ndim < 2:
raise ValueError("Coefficients array must be at least "
"2-dimensional.")
if not (0 <= axis < self.c.ndim - 1):
raise ValueError("axis=%s must be between 0 and %s" %
(axis, self.c.ndim-1))
self.axis = axis
if axis != 0:
# roll the interpolation axis to be the first one in self.c
# More specifically, the target shape for self.c is (k, m, ...),
# and axis !=0 means that we have c.shape (..., k, m, ...)
# ^
# axis
# So we roll two of them.
self.c = np.rollaxis(self.c, axis+1)
self.c = np.rollaxis(self.c, axis+1)
if self.x.ndim != 1:
raise ValueError("x must be 1-dimensional")
if self.x.size < 2:
raise ValueError("at least 2 breakpoints are needed")
if self.c.ndim < 2:
raise ValueError("c must have at least 2 dimensions")
if self.c.shape[0] == 0:
raise ValueError("polynomial must be at least of order 0")
if self.c.shape[1] != self.x.size-1:
raise ValueError("number of coefficients != len(x)-1")
dx = np.diff(self.x)
if not (np.all(dx >= 0) or np.all(dx <= 0)):
raise ValueError("`x` must be strictly increasing or decreasing.")
dtype = self._get_dtype(self.c.dtype)
self.c = np.ascontiguousarray(self.c, dtype=dtype)
def _get_dtype(self, dtype):
if np.issubdtype(dtype, np.complexfloating) \
or np.issubdtype(self.c.dtype, np.complexfloating):
return np.complex_
else:
return np.float_
@classmethod
def construct_fast(cls, c, x, extrapolate=None, axis=0):
"""
Construct the piecewise polynomial without making checks.
Takes the same parameters as the constructor. Input arguments
``c`` and ``x`` must be arrays of the correct shape and type. The
``c`` array can only be of dtypes float and complex, and ``x``
array must have dtype float.
"""
self = object.__new__(cls)
self.c = c
self.x = x
self.axis = axis
if extrapolate is None:
extrapolate = True
self.extrapolate = extrapolate
return self
def _ensure_c_contiguous(self):
"""
c and x may be modified by the user. The Cython code expects
that they are C contiguous.
"""
if not self.x.flags.c_contiguous:
self.x = self.x.copy()
if not self.c.flags.c_contiguous:
self.c = self.c.copy()
def extend(self, c, x, right=None):
"""
Add additional breakpoints and coefficients to the polynomial.
Parameters
----------
c : ndarray, size (k, m, ...)
Additional coefficients for polynomials in intervals. Note that
the first additional interval will be formed using one of the
``self.x`` end points.
x : ndarray, size (m,)
Additional breakpoints. Must be sorted in the same order as
``self.x`` and either to the right or to the left of the current
breakpoints.
right
Deprecated argument. Has no effect.
.. deprecated:: 0.19
"""
if right is not None:
warnings.warn("`right` is deprecated and will be removed.")
c = np.asarray(c)
x = np.asarray(x)
if c.ndim < 2:
raise ValueError("invalid dimensions for c")
if x.ndim != 1:
raise ValueError("invalid dimensions for x")
if x.shape[0] != c.shape[1]:
raise ValueError("Shapes of x {} and c {} are incompatible"
.format(x.shape, c.shape))
if c.shape[2:] != self.c.shape[2:] or c.ndim != self.c.ndim:
raise ValueError("Shapes of c {} and self.c {} are incompatible"
.format(c.shape, self.c.shape))
if c.size == 0:
return
dx = np.diff(x)
if not (np.all(dx >= 0) or np.all(dx <= 0)):
raise ValueError("`x` is not sorted.")
if self.x[-1] >= self.x[0]:
if not x[-1] >= x[0]:
raise ValueError("`x` is in the different order "
"than `self.x`.")
if x[0] >= self.x[-1]:
action = 'append'
elif x[-1] <= self.x[0]:
action = 'prepend'
else:
raise ValueError("`x` is neither on the left or on the right "
"from `self.x`.")
else:
if not x[-1] <= x[0]:
raise ValueError("`x` is in the different order "
"than `self.x`.")
if x[0] <= self.x[-1]:
action = 'append'
elif x[-1] >= self.x[0]:
action = 'prepend'
else:
raise ValueError("`x` is neither on the left or on the right "
"from `self.x`.")
dtype = self._get_dtype(c.dtype)
k2 = max(c.shape[0], self.c.shape[0])
c2 = np.zeros((k2, self.c.shape[1] + c.shape[1]) + self.c.shape[2:],
dtype=dtype)
if action == 'append':
c2[k2-self.c.shape[0]:, :self.c.shape[1]] = self.c
c2[k2-c.shape[0]:, self.c.shape[1]:] = c
self.x = np.r_[self.x, x]
elif action == 'prepend':
c2[k2-self.c.shape[0]:, :c.shape[1]] = c
c2[k2-c.shape[0]:, c.shape[1]:] = self.c
self.x = np.r_[x, self.x]
self.c = c2
def __call__(self, x, nu=0, extrapolate=None):
"""
Evaluate the piecewise polynomial or its derivative.
Parameters
----------
x : array_like
Points to evaluate the interpolant at.
nu : int, optional
Order of derivative to evaluate. Must be non-negative.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used.
If None (default), use `self.extrapolate`.
Returns
-------
y : array_like
Interpolated values. Shape is determined by replacing
the interpolation axis in the original array with the shape of x.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if extrapolate is None:
extrapolate = self.extrapolate
x = np.asarray(x)
x_shape, x_ndim = x.shape, x.ndim
x = np.ascontiguousarray(x.ravel(), dtype=np.float_)
# With periodic extrapolation we map x to the segment
# [self.x[0], self.x[-1]].
if extrapolate == 'periodic':
x = self.x[0] + (x - self.x[0]) % (self.x[-1] - self.x[0])
extrapolate = False
out = np.empty((len(x), prod(self.c.shape[2:])), dtype=self.c.dtype)
self._ensure_c_contiguous()
self._evaluate(x, nu, extrapolate, out)
out = out.reshape(x_shape + self.c.shape[2:])
if self.axis != 0:
# transpose to move the calculated values to the interpolation axis
l = list(range(out.ndim))
l = l[x_ndim:x_ndim+self.axis] + l[:x_ndim] + l[x_ndim+self.axis:]
out = out.transpose(l)
return out
class PPoly(_PPolyBase):
"""
Piecewise polynomial in terms of coefficients and breakpoints
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
local power basis::
S = sum(c[m, i] * (xp - x[i])**(k-m) for m in range(k+1))
where ``k`` is the degree of the polynomial.
Parameters
----------
c : ndarray, shape (k, m, ...)
Polynomial coefficients, order `k` and `m` intervals.
x : ndarray, shape (m+1,)
Polynomial breakpoints. Must be sorted in either increasing or
decreasing order.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs. If 'periodic',
periodic extrapolation is used. Default is True.
axis : int, optional
Interpolation axis. Default is zero.
Attributes
----------
x : ndarray
Breakpoints.
c : ndarray
Coefficients of the polynomials. They are reshaped
to a 3-D array with the last dimension representing
the trailing dimensions of the original coefficient array.
axis : int
Interpolation axis.
Methods
-------
__call__
derivative
antiderivative
integrate
solve
roots
extend
from_spline
from_bernstein_basis
construct_fast
See also
--------
BPoly : piecewise polynomials in the Bernstein basis
Notes
-----
High-order polynomials in the power basis can be numerically
unstable. Precision problems can start to appear for orders
larger than 20-30.
"""
def _evaluate(self, x, nu, extrapolate, out):
_ppoly.evaluate(self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, x, nu, bool(extrapolate), out)
def derivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : int, optional
Order of derivative to evaluate. Default is 1, i.e., compute the
first derivative. If negative, the antiderivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k - n representing the derivative
of this polynomial.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if nu < 0:
return self.antiderivative(-nu)
# reduce order
if nu == 0:
c2 = self.c.copy()
else:
c2 = self.c[:-nu, :].copy()
if c2.shape[0] == 0:
# derivative of order 0 is zero
c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype)
# multiply by the correct rising factorials
factor = spec.poch(np.arange(c2.shape[0], 0, -1), nu)
c2 *= factor[(slice(None),) + (None,)*(c2.ndim-1)]
# construct a compatible polynomial
return self.construct_fast(c2, self.x, self.extrapolate, self.axis)
def antiderivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the antiderivative.
Antiderivative is also the indefinite integral of the function,
and derivative is its inverse operation.
Parameters
----------
nu : int, optional
Order of antiderivative to evaluate. Default is 1, i.e., compute
the first integral. If negative, the derivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k + n representing
the antiderivative of this polynomial.
Notes
-----
The antiderivative returned by this function is continuous and
continuously differentiable to order n-1, up to floating point
rounding error.
If antiderivative is computed and ``self.extrapolate='periodic'``,
it will be set to False for the returned instance. This is done because
the antiderivative is no longer periodic and its correct evaluation
outside of the initially given x interval is difficult.
"""
if nu <= 0:
return self.derivative(-nu)
c = np.zeros((self.c.shape[0] + nu, self.c.shape[1]) + self.c.shape[2:],
dtype=self.c.dtype)
c[:-nu] = self.c
# divide by the correct rising factorials
factor = spec.poch(np.arange(self.c.shape[0], 0, -1), nu)
c[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)]
# fix continuity of added degrees of freedom
self._ensure_c_contiguous()
_ppoly.fix_continuity(c.reshape(c.shape[0], c.shape[1], -1),
self.x, nu - 1)
if self.extrapolate == 'periodic':
extrapolate = False
else:
extrapolate = self.extrapolate
# construct a compatible polynomial
return self.construct_fast(c, self.x, extrapolate, self.axis)
def integrate(self, a, b, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
a : float
Lower integration bound
b : float
Upper integration bound
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used.
If None (default), use `self.extrapolate`.
Returns
-------
ig : array_like
Definite integral of the piecewise polynomial over [a, b]
"""
if extrapolate is None:
extrapolate = self.extrapolate
# Swap integration bounds if needed
sign = 1
if b < a:
a, b = b, a
sign = -1
range_int = np.empty((prod(self.c.shape[2:]),), dtype=self.c.dtype)
self._ensure_c_contiguous()
# Compute the integral.
if extrapolate == 'periodic':
# Split the integral into the part over period (can be several
# of them) and the remaining part.
xs, xe = self.x[0], self.x[-1]
period = xe - xs
interval = b - a
n_periods, left = divmod(interval, period)
if n_periods > 0:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, xs, xe, False, out=range_int)
range_int *= n_periods
else:
range_int.fill(0)
# Map a to [xs, xe], b is always a + left.
a = xs + (a - xs) % period
b = a + left
# If b <= xe then we need to integrate over [a, b], otherwise
# over [a, xe] and from xs to what is remained.
remainder_int = np.empty_like(range_int)
if b <= xe:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, b, False, out=remainder_int)
range_int += remainder_int
else:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, xe, False, out=remainder_int)
range_int += remainder_int
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, xs, xs + left + a - xe, False, out=remainder_int)
range_int += remainder_int
else:
_ppoly.integrate(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, a, b, bool(extrapolate), out=range_int)
# Return
range_int *= sign
return range_int.reshape(self.c.shape[2:])
def solve(self, y=0., discontinuity=True, extrapolate=None):
"""
Find real solutions of the the equation ``pp(x) == y``.
Parameters
----------
y : float, optional
Right-hand side. Default is zero.
discontinuity : bool, optional
Whether to report sign changes across discontinuities at
breakpoints as roots.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to return roots from the polynomial
extrapolated based on first and last intervals, 'periodic' works
the same as False. If None (default), use `self.extrapolate`.
Returns
-------
roots : ndarray
Roots of the polynomial(s).
If the PPoly object describes multiple polynomials, the
return value is an object array whose each element is an
ndarray containing the roots.
Notes
-----
This routine works only on real-valued polynomials.
If the piecewise polynomial contains sections that are
identically zero, the root list will contain the start point
of the corresponding interval, followed by a ``nan`` value.
If the polynomial is discontinuous across a breakpoint, and
there is a sign change across the breakpoint, this is reported
if the `discont` parameter is True.
Examples
--------
Finding roots of ``[x**2 - 1, (x - 1)**2]`` defined on intervals
``[-2, 1], [1, 2]``:
>>> from scipy.interpolate import PPoly
>>> pp = PPoly(np.array([[1, -4, 3], [1, 0, 0]]).T, [-2, 1, 2])
>>> pp.solve()
array([-1., 1.])
"""
if extrapolate is None:
extrapolate = self.extrapolate
self._ensure_c_contiguous()
if np.issubdtype(self.c.dtype, np.complexfloating):
raise ValueError("Root finding is only for "
"real-valued polynomials")
y = float(y)
r = _ppoly.real_roots(self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, y, bool(discontinuity),
bool(extrapolate))
if self.c.ndim == 2:
return r[0]
else:
r2 = np.empty(prod(self.c.shape[2:]), dtype=object)
# this for-loop is equivalent to ``r2[...] = r``, but that's broken
# in NumPy 1.6.0
for ii, root in enumerate(r):
r2[ii] = root
return r2.reshape(self.c.shape[2:])
def roots(self, discontinuity=True, extrapolate=None):
"""
Find real roots of the the piecewise polynomial.
Parameters
----------
discontinuity : bool, optional
Whether to report sign changes across discontinuities at
breakpoints as roots.
extrapolate : {bool, 'periodic', None}, optional
If bool, determines whether to return roots from the polynomial
extrapolated based on first and last intervals, 'periodic' works
the same as False. If None (default), use `self.extrapolate`.
Returns
-------
roots : ndarray
Roots of the polynomial(s).
If the PPoly object describes multiple polynomials, the
return value is an object array whose each element is an
ndarray containing the roots.
See Also
--------
PPoly.solve
"""
return self.solve(0, discontinuity, extrapolate)
@classmethod
def from_spline(cls, tck, extrapolate=None):
"""
Construct a piecewise polynomial from a spline
Parameters
----------
tck
A spline, as returned by `splrep` or a BSpline object.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if isinstance(tck, BSpline):
t, c, k = tck.tck
if extrapolate is None:
extrapolate = tck.extrapolate
else:
t, c, k = tck
cvals = np.empty((k + 1, len(t)-1), dtype=c.dtype)
for m in range(k, -1, -1):
y = fitpack.splev(t[:-1], tck, der=m)
cvals[k - m, :] = y/spec.gamma(m+1)
return cls.construct_fast(cvals, t, extrapolate)
@classmethod
def from_bernstein_basis(cls, bp, extrapolate=None):
"""
Construct a piecewise polynomial in the power basis
from a polynomial in Bernstein basis.
Parameters
----------
bp : BPoly
A Bernstein basis polynomial, as created by BPoly
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if not isinstance(bp, BPoly):
raise TypeError(".from_bernstein_basis only accepts BPoly instances. "
"Got %s instead." % type(bp))
dx = np.diff(bp.x)
k = bp.c.shape[0] - 1 # polynomial order
rest = (None,)*(bp.c.ndim-2)
c = np.zeros_like(bp.c)
for a in range(k+1):
factor = (-1)**a * comb(k, a) * bp.c[a]
for s in range(a, k+1):
val = comb(k-a, s-a) * (-1)**s
c[k-s] += factor * val / dx[(slice(None),)+rest]**s
if extrapolate is None:
extrapolate = bp.extrapolate
return cls.construct_fast(c, bp.x, extrapolate, bp.axis)
class BPoly(_PPolyBase):
"""Piecewise polynomial in terms of coefficients and breakpoints.
The polynomial between ``x[i]`` and ``x[i + 1]`` is written in the
Bernstein polynomial basis::
S = sum(c[a, i] * b(a, k; x) for a in range(k+1)),
where ``k`` is the degree of the polynomial, and::
b(a, k; x) = binom(k, a) * t**a * (1 - t)**(k - a),
with ``t = (x - x[i]) / (x[i+1] - x[i])`` and ``binom`` is the binomial
coefficient.
Parameters
----------
c : ndarray, shape (k, m, ...)
Polynomial coefficients, order `k` and `m` intervals
x : ndarray, shape (m+1,)
Polynomial breakpoints. Must be sorted in either increasing or
decreasing order.
extrapolate : bool, optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs. If 'periodic',
periodic extrapolation is used. Default is True.
axis : int, optional
Interpolation axis. Default is zero.
Attributes
----------
x : ndarray
Breakpoints.
c : ndarray
Coefficients of the polynomials. They are reshaped
to a 3-D array with the last dimension representing
the trailing dimensions of the original coefficient array.
axis : int
Interpolation axis.
Methods
-------
__call__
extend
derivative
antiderivative
integrate
construct_fast
from_power_basis
from_derivatives
See also
--------
PPoly : piecewise polynomials in the power basis
Notes
-----
Properties of Bernstein polynomials are well documented in the literature,
see for example [1]_ [2]_ [3]_.
References
----------
.. [1] https://en.wikipedia.org/wiki/Bernstein_polynomial
.. [2] Kenneth I. Joy, Bernstein polynomials,
http://www.idav.ucdavis.edu/education/CAGDNotes/Bernstein-Polynomials.pdf
.. [3] E. H. Doha, A. H. Bhrawy, and M. A. Saker, Boundary Value Problems,
vol 2011, article ID 829546, :doi:`10.1155/2011/829543`.
Examples
--------
>>> from scipy.interpolate import BPoly
>>> x = [0, 1]
>>> c = [[1], [2], [3]]
>>> bp = BPoly(c, x)
This creates a 2nd order polynomial
.. math::
B(x) = 1 \\times b_{0, 2}(x) + 2 \\times b_{1, 2}(x) + 3 \\times b_{2, 2}(x) \\\\
= 1 \\times (1-x)^2 + 2 \\times 2 x (1 - x) + 3 \\times x^2
"""
def _evaluate(self, x, nu, extrapolate, out):
_ppoly.evaluate_bernstein(
self.c.reshape(self.c.shape[0], self.c.shape[1], -1),
self.x, x, nu, bool(extrapolate), out)
def derivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : int, optional
Order of derivative to evaluate. Default is 1, i.e., compute the
first derivative. If negative, the antiderivative is returned.
Returns
-------
bp : BPoly
Piecewise polynomial of order k - nu representing the derivative of
this polynomial.
"""
if nu < 0:
return self.antiderivative(-nu)
if nu > 1:
bp = self
for k in range(nu):
bp = bp.derivative()
return bp
# reduce order
if nu == 0:
c2 = self.c.copy()
else:
# For a polynomial
# B(x) = \sum_{a=0}^{k} c_a b_{a, k}(x),
# we use the fact that
# b'_{a, k} = k ( b_{a-1, k-1} - b_{a, k-1} ),
# which leads to
# B'(x) = \sum_{a=0}^{k-1} (c_{a+1} - c_a) b_{a, k-1}
#
# finally, for an interval [y, y + dy] with dy != 1,
# we need to correct for an extra power of dy
rest = (None,)*(self.c.ndim-2)
k = self.c.shape[0] - 1
dx = np.diff(self.x)[(None, slice(None))+rest]
c2 = k * np.diff(self.c, axis=0) / dx
if c2.shape[0] == 0:
# derivative of order 0 is zero
c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype)
# construct a compatible polynomial
return self.construct_fast(c2, self.x, self.extrapolate, self.axis)
def antiderivative(self, nu=1):
"""
Construct a new piecewise polynomial representing the antiderivative.
Parameters
----------
nu : int, optional
Order of antiderivative to evaluate. Default is 1, i.e., compute
the first integral. If negative, the derivative is returned.
Returns
-------
bp : BPoly
Piecewise polynomial of order k + nu representing the
antiderivative of this polynomial.
Notes
-----
If antiderivative is computed and ``self.extrapolate='periodic'``,
it will be set to False for the returned instance. This is done because
the antiderivative is no longer periodic and its correct evaluation
outside of the initially given x interval is difficult.
"""
if nu <= 0:
return self.derivative(-nu)
if nu > 1:
bp = self
for k in range(nu):
bp = bp.antiderivative()
return bp
# Construct the indefinite integrals on individual intervals
c, x = self.c, self.x
k = c.shape[0]
c2 = np.zeros((k+1,) + c.shape[1:], dtype=c.dtype)
c2[1:, ...] = np.cumsum(c, axis=0) / k
delta = x[1:] - x[:-1]
c2 *= delta[(None, slice(None)) + (None,)*(c.ndim-2)]
# Now fix continuity: on the very first interval, take the integration
# constant to be zero; on an interval [x_j, x_{j+1}) with j>0,
# the integration constant is then equal to the jump of the `bp` at x_j.
# The latter is given by the coefficient of B_{n+1, n+1}
# *on the previous interval* (other B. polynomials are zero at the
# breakpoint). Finally, use the fact that BPs form a partition of unity.
c2[:,1:] += np.cumsum(c2[k, :], axis=0)[:-1]
if self.extrapolate == 'periodic':
extrapolate = False
else:
extrapolate = self.extrapolate
return self.construct_fast(c2, x, extrapolate, axis=self.axis)
def integrate(self, a, b, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
a : float
Lower integration bound
b : float
Upper integration bound
extrapolate : {bool, 'periodic', None}, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs. If 'periodic', periodic
extrapolation is used. If None (default), use `self.extrapolate`.
Returns
-------
array_like
Definite integral of the piecewise polynomial over [a, b]
"""
# XXX: can probably use instead the fact that
# \int_0^{1} B_{j, n}(x) \dx = 1/(n+1)
ib = self.antiderivative()
if extrapolate is None:
extrapolate = self.extrapolate
# ib.extrapolate shouldn't be 'periodic', it is converted to
# False for 'periodic. in antiderivative() call.
if extrapolate != 'periodic':
ib.extrapolate = extrapolate
if extrapolate == 'periodic':
# Split the integral into the part over period (can be several
# of them) and the remaining part.
# For simplicity and clarity convert to a <= b case.
if a <= b:
sign = 1
else:
a, b = b, a
sign = -1
xs, xe = self.x[0], self.x[-1]
period = xe - xs
interval = b - a
n_periods, left = divmod(interval, period)
res = n_periods * (ib(xe) - ib(xs))
# Map a and b to [xs, xe].
a = xs + (a - xs) % period
b = a + left
# If b <= xe then we need to integrate over [a, b], otherwise
# over [a, xe] and from xs to what is remained.
if b <= xe:
res += ib(b) - ib(a)
else:
res += ib(xe) - ib(a) + ib(xs + left + a - xe) - ib(xs)
return sign * res
else:
return ib(b) - ib(a)
def extend(self, c, x, right=None):
k = max(self.c.shape[0], c.shape[0])
self.c = self._raise_degree(self.c, k - self.c.shape[0])
c = self._raise_degree(c, k - c.shape[0])
return _PPolyBase.extend(self, c, x, right)
extend.__doc__ = _PPolyBase.extend.__doc__
@classmethod
def from_power_basis(cls, pp, extrapolate=None):
"""
Construct a piecewise polynomial in Bernstein basis
from a power basis polynomial.
Parameters
----------
pp : PPoly
A piecewise polynomial in the power basis
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
"""
if not isinstance(pp, PPoly):
raise TypeError(".from_power_basis only accepts PPoly instances. "
"Got %s instead." % type(pp))
dx = np.diff(pp.x)
k = pp.c.shape[0] - 1 # polynomial order
rest = (None,)*(pp.c.ndim-2)
c = np.zeros_like(pp.c)
for a in range(k+1):
factor = pp.c[a] / comb(k, k-a) * dx[(slice(None),)+rest]**(k-a)
for j in range(k-a, k+1):
c[j] += factor * comb(j, k-a)
if extrapolate is None:
extrapolate = pp.extrapolate
return cls.construct_fast(c, pp.x, extrapolate, pp.axis)
@classmethod
def from_derivatives(cls, xi, yi, orders=None, extrapolate=None):
"""Construct a piecewise polynomial in the Bernstein basis,
compatible with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1-D array of x-coordinates
yi : array_like or list of array_likes
``yi[i][j]`` is the ``j``th derivative known at ``xi[i]``
orders : None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
extrapolate : bool or 'periodic', optional
If bool, determines whether to extrapolate to out-of-bounds points
based on first and last intervals, or to return NaNs.
If 'periodic', periodic extrapolation is used. Default is True.
Notes
-----
If ``k`` derivatives are specified at a breakpoint ``x``, the
constructed polynomial is exactly ``k`` times continuously
differentiable at ``x``, unless the ``order`` is provided explicitly.
In the latter case, the smoothness of the polynomial at
the breakpoint is controlled by the ``order``.
Deduces the number of derivatives to match at each end
from ``order`` and the number of derivatives available. If
possible it uses the same number of derivatives from
each end; if the number is odd it tries to take the
extra one from y2. In any case if not enough derivatives
are available at one end or another it draws enough to
make up the total from the other end.
If the order is too high and not enough derivatives are available,
an exception is raised.
Examples
--------
>>> from scipy.interpolate import BPoly
>>> BPoly.from_derivatives([0, 1], [[1, 2], [3, 4]])
Creates a polynomial `f(x)` of degree 3, defined on `[0, 1]`
such that `f(0) = 1, df/dx(0) = 2, f(1) = 3, df/dx(1) = 4`
>>> BPoly.from_derivatives([0, 1, 2], [[0, 1], [0], [2]])
Creates a piecewise polynomial `f(x)`, such that
`f(0) = f(1) = 0`, `f(2) = 2`, and `df/dx(0) = 1`.
Based on the number of derivatives provided, the order of the
local polynomials is 2 on `[0, 1]` and 1 on `[1, 2]`.
Notice that no restriction is imposed on the derivatives at
``x = 1`` and ``x = 2``.
Indeed, the explicit form of the polynomial is::
f(x) = | x * (1 - x), 0 <= x < 1
| 2 * (x - 1), 1 <= x <= 2
So that f'(1-0) = -1 and f'(1+0) = 2
"""
xi = np.asarray(xi)
if len(xi) != len(yi):
raise ValueError("xi and yi need to have the same length")
if np.any(xi[1:] - xi[:1] <= 0):
raise ValueError("x coordinates are not in increasing order")
# number of intervals
m = len(xi) - 1
# global poly order is k-1, local orders are <=k and can vary
try:
k = max(len(yi[i]) + len(yi[i+1]) for i in range(m))
except TypeError as e:
raise ValueError(
"Using a 1-D array for y? Please .reshape(-1, 1)."
) from e
if orders is None:
orders = [None] * m
else:
if isinstance(orders, (int, np.integer)):
orders = [orders] * m
k = max(k, max(orders))
if any(o <= 0 for o in orders):
raise ValueError("Orders must be positive.")
c = []
for i in range(m):
y1, y2 = yi[i], yi[i+1]
if orders[i] is None:
n1, n2 = len(y1), len(y2)
else:
n = orders[i]+1
n1 = min(n//2, len(y1))
n2 = min(n - n1, len(y2))
n1 = min(n - n2, len(y2))
if n1+n2 != n:
mesg = ("Point %g has %d derivatives, point %g"
" has %d derivatives, but order %d requested" % (
xi[i], len(y1), xi[i+1], len(y2), orders[i]))
raise ValueError(mesg)
if not (n1 <= len(y1) and n2 <= len(y2)):
raise ValueError("`order` input incompatible with"
" length y1 or y2.")
b = BPoly._construct_from_derivatives(xi[i], xi[i+1],
y1[:n1], y2[:n2])
if len(b) < k:
b = BPoly._raise_degree(b, k - len(b))
c.append(b)
c = np.asarray(c)
return cls(c.swapaxes(0, 1), xi, extrapolate)
@staticmethod
def _construct_from_derivatives(xa, xb, ya, yb):
r"""Compute the coefficients of a polynomial in the Bernstein basis
given the values and derivatives at the edges.
Return the coefficients of a polynomial in the Bernstein basis
defined on ``[xa, xb]`` and having the values and derivatives at the
endpoints `xa` and `xb` as specified by `ya`` and `yb`.
The polynomial constructed is of the minimal possible degree, i.e.,
if the lengths of `ya` and `yb` are `na` and `nb`, the degree
of the polynomial is ``na + nb - 1``.
Parameters
----------
xa : float
Left-hand end point of the interval
xb : float
Right-hand end point of the interval
ya : array_like
Derivatives at `xa`. `ya[0]` is the value of the function, and
`ya[i]` for ``i > 0`` is the value of the ``i``th derivative.
yb : array_like
Derivatives at `xb`.
Returns
-------
array
coefficient array of a polynomial having specified derivatives
Notes
-----
This uses several facts from life of Bernstein basis functions.
First of all,
.. math:: b'_{a, n} = n (b_{a-1, n-1} - b_{a, n-1})
If B(x) is a linear combination of the form
.. math:: B(x) = \sum_{a=0}^{n} c_a b_{a, n},
then :math: B'(x) = n \sum_{a=0}^{n-1} (c_{a+1} - c_{a}) b_{a, n-1}.
Iterating the latter one, one finds for the q-th derivative
.. math:: B^{q}(x) = n!/(n-q)! \sum_{a=0}^{n-q} Q_a b_{a, n-q},
with
.. math:: Q_a = \sum_{j=0}^{q} (-)^{j+q} comb(q, j) c_{j+a}
This way, only `a=0` contributes to :math: `B^{q}(x = xa)`, and
`c_q` are found one by one by iterating `q = 0, ..., na`.
At ``x = xb`` it's the same with ``a = n - q``.
"""
ya, yb = np.asarray(ya), np.asarray(yb)
if ya.shape[1:] != yb.shape[1:]:
raise ValueError('Shapes of ya {} and yb {} are incompatible'
.format(ya.shape, yb.shape))
dta, dtb = ya.dtype, yb.dtype
if (np.issubdtype(dta, np.complexfloating) or
np.issubdtype(dtb, np.complexfloating)):
dt = np.complex_
else:
dt = np.float_
na, nb = len(ya), len(yb)
n = na + nb
c = np.empty((na+nb,) + ya.shape[1:], dtype=dt)
# compute coefficients of a polynomial degree na+nb-1
# walk left-to-right
for q in range(0, na):
c[q] = ya[q] / spec.poch(n - q, q) * (xb - xa)**q
for j in range(0, q):
c[q] -= (-1)**(j+q) * comb(q, j) * c[j]
# now walk right-to-left
for q in range(0, nb):
c[-q-1] = yb[q] / spec.poch(n - q, q) * (-1)**q * (xb - xa)**q
for j in range(0, q):
c[-q-1] -= (-1)**(j+1) * comb(q, j+1) * c[-q+j]
return c
@staticmethod
def _raise_degree(c, d):
r"""Raise a degree of a polynomial in the Bernstein basis.
Given the coefficients of a polynomial degree `k`, return (the
coefficients of) the equivalent polynomial of degree `k+d`.
Parameters
----------
c : array_like
coefficient array, 1-D
d : integer
Returns
-------
array
coefficient array, 1-D array of length `c.shape[0] + d`
Notes
-----
This uses the fact that a Bernstein polynomial `b_{a, k}` can be
identically represented as a linear combination of polynomials of
a higher degree `k+d`:
.. math:: b_{a, k} = comb(k, a) \sum_{j=0}^{d} b_{a+j, k+d} \
comb(d, j) / comb(k+d, a+j)
"""
if d == 0:
return c
k = c.shape[0] - 1
out = np.zeros((c.shape[0] + d,) + c.shape[1:], dtype=c.dtype)
for a in range(c.shape[0]):
f = c[a] * comb(k, a)
for j in range(d+1):
out[a+j] += f * comb(d, j) / comb(k+d, a+j)
return out
class NdPPoly(object):
"""
Piecewise tensor product polynomial
The value at point ``xp = (x', y', z', ...)`` is evaluated by first
computing the interval indices `i` such that::
x[0][i[0]] <= x' < x[0][i[0]+1]
x[1][i[1]] <= y' < x[1][i[1]+1]
...
and then computing::
S = sum(c[k0-m0-1,...,kn-mn-1,i[0],...,i[n]]
* (xp[0] - x[0][i[0]])**m0
* ...
* (xp[n] - x[n][i[n]])**mn
for m0 in range(k[0]+1)
...
for mn in range(k[n]+1))
where ``k[j]`` is the degree of the polynomial in dimension j. This
representation is the piecewise multivariate power basis.
Parameters
----------
c : ndarray, shape (k0, ..., kn, m0, ..., mn, ...)
Polynomial coefficients, with polynomial order `kj` and
`mj+1` intervals for each dimension `j`.
x : ndim-tuple of ndarrays, shapes (mj+1,)
Polynomial breakpoints for each dimension. These must be
sorted in increasing order.
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs. Default: True.
Attributes
----------
x : tuple of ndarrays
Breakpoints.
c : ndarray
Coefficients of the polynomials.
Methods
-------
__call__
derivative
antiderivative
integrate
integrate_1d
construct_fast
See also
--------
PPoly : piecewise polynomials in 1D
Notes
-----
High-order polynomials in the power basis can be numerically
unstable.
"""
def __init__(self, c, x, extrapolate=None):
self.x = tuple(np.ascontiguousarray(v, dtype=np.float64) for v in x)
self.c = np.asarray(c)
if extrapolate is None:
extrapolate = True
self.extrapolate = bool(extrapolate)
ndim = len(self.x)
if any(v.ndim != 1 for v in self.x):
raise ValueError("x arrays must all be 1-dimensional")
if any(v.size < 2 for v in self.x):
raise ValueError("x arrays must all contain at least 2 points")
if c.ndim < 2*ndim:
raise ValueError("c must have at least 2*len(x) dimensions")
if any(np.any(v[1:] - v[:-1] < 0) for v in self.x):
raise ValueError("x-coordinates are not in increasing order")
if any(a != b.size - 1 for a, b in zip(c.shape[ndim:2*ndim], self.x)):
raise ValueError("x and c do not agree on the number of intervals")
dtype = self._get_dtype(self.c.dtype)
self.c = np.ascontiguousarray(self.c, dtype=dtype)
@classmethod
def construct_fast(cls, c, x, extrapolate=None):
"""
Construct the piecewise polynomial without making checks.
Takes the same parameters as the constructor. Input arguments
``c`` and ``x`` must be arrays of the correct shape and type. The
``c`` array can only be of dtypes float and complex, and ``x``
array must have dtype float.
"""
self = object.__new__(cls)
self.c = c
self.x = x
if extrapolate is None:
extrapolate = True
self.extrapolate = extrapolate
return self
def _get_dtype(self, dtype):
if np.issubdtype(dtype, np.complexfloating) \
or np.issubdtype(self.c.dtype, np.complexfloating):
return np.complex_
else:
return np.float_
def _ensure_c_contiguous(self):
if not self.c.flags.c_contiguous:
self.c = self.c.copy()
if not isinstance(self.x, tuple):
self.x = tuple(self.x)
def __call__(self, x, nu=None, extrapolate=None):
"""
Evaluate the piecewise polynomial or its derivative
Parameters
----------
x : array-like
Points to evaluate the interpolant at.
nu : tuple, optional
Orders of derivatives to evaluate. Each must be non-negative.
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
y : array-like
Interpolated values. Shape is determined by replacing
the interpolation axis in the original array with the shape of x.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals are considered half-open,
``[a, b)``, except for the last interval which is closed
``[a, b]``.
"""
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
ndim = len(self.x)
x = _ndim_coords_from_arrays(x)
x_shape = x.shape
x = np.ascontiguousarray(x.reshape(-1, x.shape[-1]), dtype=np.float_)
if nu is None:
nu = np.zeros((ndim,), dtype=np.intc)
else:
nu = np.asarray(nu, dtype=np.intc)
if nu.ndim != 1 or nu.shape[0] != ndim:
raise ValueError("invalid number of derivative orders nu")
dim1 = prod(self.c.shape[:ndim])
dim2 = prod(self.c.shape[ndim:2*ndim])
dim3 = prod(self.c.shape[2*ndim:])
ks = np.array(self.c.shape[:ndim], dtype=np.intc)
out = np.empty((x.shape[0], dim3), dtype=self.c.dtype)
self._ensure_c_contiguous()
_ppoly.evaluate_nd(self.c.reshape(dim1, dim2, dim3),
self.x,
ks,
x,
nu,
bool(extrapolate),
out)
return out.reshape(x_shape[:-1] + self.c.shape[2*ndim:])
def _derivative_inplace(self, nu, axis):
"""
Compute 1-D derivative along a selected dimension in-place
May result to non-contiguous c array.
"""
if nu < 0:
return self._antiderivative_inplace(-nu, axis)
ndim = len(self.x)
axis = axis % ndim
# reduce order
if nu == 0:
# noop
return
else:
sl = [slice(None)]*ndim
sl[axis] = slice(None, -nu, None)
c2 = self.c[tuple(sl)]
if c2.shape[axis] == 0:
# derivative of order 0 is zero
shp = list(c2.shape)
shp[axis] = 1
c2 = np.zeros(shp, dtype=c2.dtype)
# multiply by the correct rising factorials
factor = spec.poch(np.arange(c2.shape[axis], 0, -1), nu)
sl = [None]*c2.ndim
sl[axis] = slice(None)
c2 *= factor[tuple(sl)]
self.c = c2
def _antiderivative_inplace(self, nu, axis):
"""
Compute 1-D antiderivative along a selected dimension
May result to non-contiguous c array.
"""
if nu <= 0:
return self._derivative_inplace(-nu, axis)
ndim = len(self.x)
axis = axis % ndim
perm = list(range(ndim))
perm[0], perm[axis] = perm[axis], perm[0]
perm = perm + list(range(ndim, self.c.ndim))
c = self.c.transpose(perm)
c2 = np.zeros((c.shape[0] + nu,) + c.shape[1:],
dtype=c.dtype)
c2[:-nu] = c
# divide by the correct rising factorials
factor = spec.poch(np.arange(c.shape[0], 0, -1), nu)
c2[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)]
# fix continuity of added degrees of freedom
perm2 = list(range(c2.ndim))
perm2[1], perm2[ndim+axis] = perm2[ndim+axis], perm2[1]
c2 = c2.transpose(perm2)
c2 = c2.copy()
_ppoly.fix_continuity(c2.reshape(c2.shape[0], c2.shape[1], -1),
self.x[axis], nu-1)
c2 = c2.transpose(perm2)
c2 = c2.transpose(perm)
# Done
self.c = c2
def derivative(self, nu):
"""
Construct a new piecewise polynomial representing the derivative.
Parameters
----------
nu : ndim-tuple of int
Order of derivatives to evaluate for each dimension.
If negative, the antiderivative is returned.
Returns
-------
pp : NdPPoly
Piecewise polynomial of orders (k[0] - nu[0], ..., k[n] - nu[n])
representing the derivative of this polynomial.
Notes
-----
Derivatives are evaluated piecewise for each polynomial
segment, even if the polynomial is not differentiable at the
breakpoints. The polynomial intervals in each dimension are
considered half-open, ``[a, b)``, except for the last interval
which is closed ``[a, b]``.
"""
p = self.construct_fast(self.c.copy(), self.x, self.extrapolate)
for axis, n in enumerate(nu):
p._derivative_inplace(n, axis)
p._ensure_c_contiguous()
return p
def antiderivative(self, nu):
"""
Construct a new piecewise polynomial representing the antiderivative.
Antiderivative is also the indefinite integral of the function,
and derivative is its inverse operation.
Parameters
----------
nu : ndim-tuple of int
Order of derivatives to evaluate for each dimension.
If negative, the derivative is returned.
Returns
-------
pp : PPoly
Piecewise polynomial of order k2 = k + n representing
the antiderivative of this polynomial.
Notes
-----
The antiderivative returned by this function is continuous and
continuously differentiable to order n-1, up to floating point
rounding error.
"""
p = self.construct_fast(self.c.copy(), self.x, self.extrapolate)
for axis, n in enumerate(nu):
p._antiderivative_inplace(n, axis)
p._ensure_c_contiguous()
return p
def integrate_1d(self, a, b, axis, extrapolate=None):
r"""
Compute NdPPoly representation for one dimensional definite integral
The result is a piecewise polynomial representing the integral:
.. math::
p(y, z, ...) = \int_a^b dx\, p(x, y, z, ...)
where the dimension integrated over is specified with the
`axis` parameter.
Parameters
----------
a, b : float
Lower and upper bound for integration.
axis : int
Dimension over which to compute the 1-D integrals
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
ig : NdPPoly or array-like
Definite integral of the piecewise polynomial over [a, b].
If the polynomial was 1D, an array is returned,
otherwise, an NdPPoly object.
"""
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
ndim = len(self.x)
axis = int(axis) % ndim
# reuse 1-D integration routines
c = self.c
swap = list(range(c.ndim))
swap.insert(0, swap[axis])
del swap[axis + 1]
swap.insert(1, swap[ndim + axis])
del swap[ndim + axis + 1]
c = c.transpose(swap)
p = PPoly.construct_fast(c.reshape(c.shape[0], c.shape[1], -1),
self.x[axis],
extrapolate=extrapolate)
out = p.integrate(a, b, extrapolate=extrapolate)
# Construct result
if ndim == 1:
return out.reshape(c.shape[2:])
else:
c = out.reshape(c.shape[2:])
x = self.x[:axis] + self.x[axis+1:]
return self.construct_fast(c, x, extrapolate=extrapolate)
def integrate(self, ranges, extrapolate=None):
"""
Compute a definite integral over a piecewise polynomial.
Parameters
----------
ranges : ndim-tuple of 2-tuples float
Sequence of lower and upper bounds for each dimension,
``[(a[0], b[0]), ..., (a[ndim-1], b[ndim-1])]``
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
ig : array_like
Definite integral of the piecewise polynomial over
[a[0], b[0]] x ... x [a[ndim-1], b[ndim-1]]
"""
ndim = len(self.x)
if extrapolate is None:
extrapolate = self.extrapolate
else:
extrapolate = bool(extrapolate)
if not hasattr(ranges, '__len__') or len(ranges) != ndim:
raise ValueError("Range not a sequence of correct length")
self._ensure_c_contiguous()
# Reuse 1D integration routine
c = self.c
for n, (a, b) in enumerate(ranges):
swap = list(range(c.ndim))
swap.insert(1, swap[ndim - n])
del swap[ndim - n + 1]
c = c.transpose(swap)
p = PPoly.construct_fast(c, self.x[n], extrapolate=extrapolate)
out = p.integrate(a, b, extrapolate=extrapolate)
c = out.reshape(c.shape[2:])
return c
class RegularGridInterpolator(object):
"""
Interpolation on a regular grid in arbitrary dimensions
The data must be defined on a regular grid; the grid spacing however may be
uneven. Linear and nearest-neighbor interpolation are supported. After
setting up the interpolator object, the interpolation method (*linear* or
*nearest*) may be chosen at each evaluation.
Parameters
----------
points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
The points defining the regular grid in n dimensions.
values : array_like, shape (m1, ..., mn, ...)
The data on the regular grid in n dimensions.
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest". This parameter will become the default for the object's
``__call__`` method. Default is "linear".
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated.
Methods
-------
__call__
Notes
-----
Contrary to LinearNDInterpolator and NearestNDInterpolator, this class
avoids expensive triangulation of the input data by taking advantage of the
regular grid structure.
If any of `points` have a dimension of size 1, linear interpolation will
return an array of `nan` values. Nearest-neighbor interpolation will work
as usual in this case.
.. versionadded:: 0.14
Examples
--------
Evaluate a simple example function on the points of a 3-D grid:
>>> from scipy.interpolate import RegularGridInterpolator
>>> def f(x, y, z):
... return 2 * x**3 + 3 * y**2 - z
>>> x = np.linspace(1, 4, 11)
>>> y = np.linspace(4, 7, 22)
>>> z = np.linspace(7, 9, 33)
>>> data = f(*np.meshgrid(x, y, z, indexing='ij', sparse=True))
``data`` is now a 3-D array with ``data[i,j,k] = f(x[i], y[j], z[k])``.
Next, define an interpolating function from this data:
>>> my_interpolating_function = RegularGridInterpolator((x, y, z), data)
Evaluate the interpolating function at the two points
``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``:
>>> pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]])
>>> my_interpolating_function(pts)
array([ 125.80469388, 146.30069388])
which is indeed a close approximation to
``[f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)]``.
See also
--------
NearestNDInterpolator : Nearest neighbor interpolation on unstructured
data in N dimensions
LinearNDInterpolator : Piecewise linear interpolant on unstructured data
in N dimensions
References
----------
.. [1] Python package *regulargrid* by Johannes Buchner, see
https://pypi.python.org/pypi/regulargrid/
.. [2] Wikipedia, "Trilinear interpolation",
https://en.wikipedia.org/wiki/Trilinear_interpolation
.. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear
and multilinear table interpolation in many dimensions." MATH.
COMPUT. 50.181 (1988): 189-196.
https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf
"""
# this class is based on code originally programmed by Johannes Buchner,
# see https://github.com/JohannesBuchner/regulargrid
def __init__(self, points, values, method="linear", bounds_error=True,
fill_value=np.nan):
if method not in ["linear", "nearest"]:
raise ValueError("Method '%s' is not defined" % method)
self.method = method
self.bounds_error = bounds_error
if not hasattr(values, 'ndim'):
# allow reasonable duck-typed values
values = np.asarray(values)
if len(points) > values.ndim:
raise ValueError("There are %d point arrays, but values has %d "
"dimensions" % (len(points), values.ndim))
if hasattr(values, 'dtype') and hasattr(values, 'astype'):
if not np.issubdtype(values.dtype, np.inexact):
values = values.astype(float)
self.fill_value = fill_value
if fill_value is not None:
fill_value_dtype = np.asarray(fill_value).dtype
if (hasattr(values, 'dtype') and not
np.can_cast(fill_value_dtype, values.dtype,
casting='same_kind')):
raise ValueError("fill_value must be either 'None' or "
"of a type compatible with values")
for i, p in enumerate(points):
if not np.all(np.diff(p) > 0.):
raise ValueError("The points in dimension %d must be strictly "
"ascending" % i)
if not np.asarray(p).ndim == 1:
raise ValueError("The points in dimension %d must be "
"1-dimensional" % i)
if not values.shape[i] == len(p):
raise ValueError("There are %d points and %d values in "
"dimension %d" % (len(p), values.shape[i], i))
self.grid = tuple([np.asarray(p) for p in points])
self.values = values
def __call__(self, xi, method=None):
"""
Interpolation at coordinates
Parameters
----------
xi : ndarray of shape (..., ndim)
The coordinates to sample the gridded data at
method : str
The method of interpolation to perform. Supported are "linear" and
"nearest".
"""
method = self.method if method is None else method
if method not in ["linear", "nearest"]:
raise ValueError("Method '%s' is not defined" % method)
ndim = len(self.grid)
xi = _ndim_coords_from_arrays(xi, ndim=ndim)
if xi.shape[-1] != len(self.grid):
raise ValueError("The requested sample points xi have dimension "
"%d, but this RegularGridInterpolator has "
"dimension %d" % (xi.shape[1], ndim))
xi_shape = xi.shape
xi = xi.reshape(-1, xi_shape[-1])
if self.bounds_error:
for i, p in enumerate(xi.T):
if not np.logical_and(np.all(self.grid[i][0] <= p),
np.all(p <= self.grid[i][-1])):
raise ValueError("One of the requested xi is out of bounds "
"in dimension %d" % i)
indices, norm_distances, out_of_bounds = self._find_indices(xi.T)
if method == "linear":
result = self._evaluate_linear(indices,
norm_distances,
out_of_bounds)
elif method == "nearest":
result = self._evaluate_nearest(indices,
norm_distances,
out_of_bounds)
if not self.bounds_error and self.fill_value is not None:
result[out_of_bounds] = self.fill_value
return result.reshape(xi_shape[:-1] + self.values.shape[ndim:])
def _evaluate_linear(self, indices, norm_distances, out_of_bounds):
# slice for broadcasting over trailing dimensions in self.values
vslice = (slice(None),) + (None,)*(self.values.ndim - len(indices))
# find relevant values
# each i and i+1 represents a edge
edges = itertools.product(*[[i, i + 1] for i in indices])
values = 0.
for edge_indices in edges:
weight = 1.
for ei, i, yi in zip(edge_indices, indices, norm_distances):
weight *= np.where(ei == i, 1 - yi, yi)
values += np.asarray(self.values[edge_indices]) * weight[vslice]
return values
def _evaluate_nearest(self, indices, norm_distances, out_of_bounds):
idx_res = [np.where(yi <= .5, i, i + 1)
for i, yi in zip(indices, norm_distances)]
return self.values[tuple(idx_res)]
def _find_indices(self, xi):
# find relevant edges between which xi are situated
indices = []
# compute distance to lower edge in unity units
norm_distances = []
# check for out of bounds xi
out_of_bounds = np.zeros((xi.shape[1]), dtype=bool)
# iterate through dimensions
for x, grid in zip(xi, self.grid):
i = np.searchsorted(grid, x) - 1
i[i < 0] = 0
i[i > grid.size - 2] = grid.size - 2
indices.append(i)
norm_distances.append((x - grid[i]) /
(grid[i + 1] - grid[i]))
if not self.bounds_error:
out_of_bounds += x < grid[0]
out_of_bounds += x > grid[-1]
return indices, norm_distances, out_of_bounds
def interpn(points, values, xi, method="linear", bounds_error=True,
fill_value=np.nan):
"""
Multidimensional interpolation on regular grids.
Parameters
----------
points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
The points defining the regular grid in n dimensions.
values : array_like, shape (m1, ..., mn, ...)
The data on the regular grid in n dimensions.
xi : ndarray of shape (..., ndim)
The coordinates to sample the gridded data at
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest", and "splinef2d". "splinef2d" is only supported for
2-dimensional data.
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then `fill_value` is used.
fill_value : number, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated. Extrapolation is not supported by method
"splinef2d".
Returns
-------
values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:]
Interpolated values at input coordinates.
Notes
-----
.. versionadded:: 0.14
Examples
--------
Evaluate a simple example function on the points of a regular 3-D grid:
>>> from scipy.interpolate import interpn
>>> def value_func_3d(x, y, z):
... return 2 * x + 3 * y - z
>>> x = np.linspace(0, 5)
>>> y = np.linspace(0, 5)
>>> z = np.linspace(0, 5)
>>> points = (x, y, z)
>>> values = value_func_3d(*np.meshgrid(*points))
Evaluate the interpolating function at a point
>>> point = np.array([2.21, 3.12, 1.15])
>>> print(interpn(points, values, point))
[11.72]
See also
--------
NearestNDInterpolator : Nearest neighbor interpolation on unstructured
data in N dimensions
LinearNDInterpolator : Piecewise linear interpolant on unstructured data
in N dimensions
RegularGridInterpolator : Linear and nearest-neighbor Interpolation on a
regular grid in arbitrary dimensions
RectBivariateSpline : Bivariate spline approximation over a rectangular mesh
"""
# sanity check 'method' kwarg
if method not in ["linear", "nearest", "splinef2d"]:
raise ValueError("interpn only understands the methods 'linear', "
"'nearest', and 'splinef2d'. You provided %s." %
method)
if not hasattr(values, 'ndim'):
values = np.asarray(values)
ndim = values.ndim
if ndim > 2 and method == "splinef2d":
raise ValueError("The method splinef2d can only be used for "
"2-dimensional input data")
if not bounds_error and fill_value is None and method == "splinef2d":
raise ValueError("The method splinef2d does not support extrapolation.")
# sanity check consistency of input dimensions
if len(points) > ndim:
raise ValueError("There are %d point arrays, but values has %d "
"dimensions" % (len(points), ndim))
if len(points) != ndim and method == 'splinef2d':
raise ValueError("The method splinef2d can only be used for "
"scalar data with one point per coordinate")
# sanity check input grid
for i, p in enumerate(points):
if not np.all(np.diff(p) > 0.):
raise ValueError("The points in dimension %d must be strictly "
"ascending" % i)
if not np.asarray(p).ndim == 1:
raise ValueError("The points in dimension %d must be "
"1-dimensional" % i)
if not values.shape[i] == len(p):
raise ValueError("There are %d points and %d values in "
"dimension %d" % (len(p), values.shape[i], i))
grid = tuple([np.asarray(p) for p in points])
# sanity check requested xi
xi = _ndim_coords_from_arrays(xi, ndim=len(grid))
if xi.shape[-1] != len(grid):
raise ValueError("The requested sample points xi have dimension "
"%d, but this RegularGridInterpolator has "
"dimension %d" % (xi.shape[1], len(grid)))
if bounds_error:
for i, p in enumerate(xi.T):
if not np.logical_and(np.all(grid[i][0] <= p),
np.all(p <= grid[i][-1])):
raise ValueError("One of the requested xi is out of bounds "
"in dimension %d" % i)
# perform interpolation
if method == "linear":
interp = RegularGridInterpolator(points, values, method="linear",
bounds_error=bounds_error,
fill_value=fill_value)
return interp(xi)
elif method == "nearest":
interp = RegularGridInterpolator(points, values, method="nearest",
bounds_error=bounds_error,
fill_value=fill_value)
return interp(xi)
elif method == "splinef2d":
xi_shape = xi.shape
xi = xi.reshape(-1, xi.shape[-1])
# RectBivariateSpline doesn't support fill_value; we need to wrap here
idx_valid = np.all((grid[0][0] <= xi[:, 0], xi[:, 0] <= grid[0][-1],
grid[1][0] <= xi[:, 1], xi[:, 1] <= grid[1][-1]),
axis=0)
result = np.empty_like(xi[:, 0])
# make a copy of values for RectBivariateSpline
interp = RectBivariateSpline(points[0], points[1], values[:])
result[idx_valid] = interp.ev(xi[idx_valid, 0], xi[idx_valid, 1])
result[np.logical_not(idx_valid)] = fill_value
return result.reshape(xi_shape[:-1])
# backward compatibility wrapper
class _ppform(PPoly):
"""
Deprecated piecewise polynomial class.
New code should use the `PPoly` class instead.
"""
def __init__(self, coeffs, breaks, fill=0.0, sort=False):
warnings.warn("_ppform is deprecated -- use PPoly instead",
category=DeprecationWarning)
if sort:
breaks = np.sort(breaks)
else:
breaks = np.asarray(breaks)
PPoly.__init__(self, coeffs, breaks)
self.coeffs = self.c
self.breaks = self.x
self.K = self.coeffs.shape[0]
self.fill = fill
self.a = self.breaks[0]
self.b = self.breaks[-1]
def __call__(self, x):
return PPoly.__call__(self, x, 0, False)
def _evaluate(self, x, nu, extrapolate, out):
PPoly._evaluate(self, x, nu, extrapolate, out)
out[~((x >= self.a) & (x <= self.b))] = self.fill
return out
@classmethod
def fromspline(cls, xk, cvals, order, fill=0.0):
# Note: this spline representation is incompatible with FITPACK
N = len(xk)-1
sivals = np.empty((order+1, N), dtype=float)
for m in range(order, -1, -1):
fact = spec.gamma(m+1)
res = _fitpack._bspleval(xk[:-1], xk, cvals, order, m)
res /= fact
sivals[order-m, :] = res
return cls(sivals, xk, fill=fill)
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__all__ = ["interrogator",
"editor",
"plotter",
"conc",
"save_result",
"quickview",
"load_result",
"load_all_results",
"as_regex",
"new_project",
"download_large_file",
"extract_cnlp",
"get_corpus_filepaths",
"check_jdk",
"parse_corpus",
"move_parsed_files",
"corenlp_exists"]
__version__ = "1.58"
__author__ = "Daniel McDonald"
__license__ = "MIT"
import sys
import os
import inspect
# probably not needed, but adds corpkit to path for tregex.sh
corpath = inspect.getfile(inspect.currentframe())
baspat = os.path.dirname(corpath)
dicpath = os.path.join(baspat, 'dictionaries')
for p in [corpath, baspat, dicpath]:
if p not in sys.path:
sys.path.append(p)
if p not in os.environ["PATH"].split(':'):
os.environ["PATH"] += os.pathsep + p
from interrogator import interrogator
from editor import editor
from plotter import plotter
from conc import conc
from other import save_result
from other import load_result
from other import load_all_results
from other import quickview
from other import as_regex
from other import new_project
from build import download_large_file
from build import extract_cnlp
from build import get_corpus_filepaths
from build import check_jdk
from build import parse_corpus
from build import move_parsed_files
from build import corenlp_exists
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"autogenerated": false,
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"has_no_keywords": false,
"few_assignments": false,
"quality_score": 0.4708885847686747,
"avg_score": null,
"num_lines": null
} |
__all__ = [ 'Interval' ]
class Interval(object):
'''Interval - Arbitrary half-closed interval of the form [start, end)'''
def __init__(self, start, end):
self.start = start
self.end = end
def __call__(self, value):
return (1. - value) * self.start + value * self.end
def __str__(self):
return 'Interval({self.start}, {self.end})'.format(self=self)
def __repr__(self):
return 'Interval({self.start}, {self.end})'.format(self=self)
def __eq__(self, right):
return self.start == right.start and self.end == right.end
def contains(self, value):
return self.start <= value < self.end
def __intersection(self, right):
return (
max(self.start, right.start),
min(self.end, right.end)
)
def overlaps(self, other):
start, end = self.__intersection(other)
return start < end
def intersection(self, other):
start, end = self.__intersection(other)
if start < end:
return Interval(start, end)
else:
return None
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"few_assignments": false,
"quality_score": 0.5460072710604226,
"avg_score": null,
"num_lines": null
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__all__ = ['InvalidData', 'Step', 'ProcessManager']
class InvalidData(Exception):
"""
Raised for by step validation process
"""
pass
class Step(object):
"""
A single step in a multistep process
"""
def __str__(self):
raise NotImplementedError() # pragma: no cover
def validate(self):
raise NotImplementedError() # pragma: no cover
class ProcessManager(object):
"""
A multistep process handler
"""
def __iter__(self):
raise NotImplementedError() # pragma: no cover
def __getitem__(self, step_id):
for step in self:
if str(step) == step_id:
return step
raise KeyError('%r is not a valid step' % (step_id,))
def validate_step(self, step):
try:
step.validate()
except InvalidData:
return False
return True
def get_next_step(self):
for step in self:
if not self.validate_step(step):
return step
def get_errors(self):
errors = {}
for step in self:
try:
step.validate()
except InvalidData as error:
errors[str(step)] = error
return errors
def is_complete(self):
return self.get_next_step() is None
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"quality_score": 1,
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__all__ = [ "isbinary", "isiterable" ]
def _inttobyte(i):
"""Takes an integer in the 8-bit range and returns a single-character byte
object.
"""
return bytes((i,))
_text_characters = (b''.join(_inttobyte(i) for i in range(32, 127)) +
b'\n\r\t\f\b')
def isbinary(fileobj, blocksize=512):
"""Uses heuristics to guess whether the given file is a text or a binary
file, by reading a single block of bytes from the file.
If more than 30% of the bytes in the block are non-text, or there are NUL
('\x00') bytes in the block, assume this is a binary file.
"""
block = fileobj.read(blocksize)
if b'\x00' in block:
# Files with null bytes are binary.
return True
elif not block:
# An empty file is considered a valid text file.
return True
# Uses translate's 'deletechars' to argument to efficiently remove all
# occurrences of _text_characters from the block.
nontext = block.translate(None, _text_characters)
return float(len(nontext)) / len(block) > 30.0
def isiterable(obj):
"""Returns whether an object allows iteration.
True if obj provides __iter__, False otherwise.
Note that this function returns False when obj is of type str in Python 2.
"""
return getattr(obj, '__iter__', False)
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# all is object
def hi(name = 'yasoob'):
return "hi " + name
print(hi())
greet = hi
print(greet())
del hi
# print(hi())
print(greet())
def hi2(name = 'yasoob'):
print("now you are inside the hi2() function")
def greet2():
return "now you are in the greet2() function"
def welcome2():
return "now you are in the welcome2() function"
print(greet2())
print(welcome2())
print("now you are back in the hi2() function")
hi2()
# greet2()
def hi3(name = 'yasoob'):
def greet3():
return "now you are in the greet3() function"
def welcome3():
return "now you are in the welcome3() function"
if name == 'yasoob':
return greet3
else:
return welcome3
a = hi3()
print(a)
print(a())
b = hi3('abc')
print(b)
print(b())
print(hi3()())
def hi4():
return "hi yasoob!"
def doSomethingBeforeHi(func):
print("I am doing some boring work before executing hi()")
print(func())
doSomethingBeforeHi(hi4)
def a_new_decorator(a_func):
def wrapTheFunction():
print("I am doing some boring work before executing a_func")
a_func()
print("I am doing some boring work after executing a_func")
return wrapTheFunction
def a_function_requiring_decoration():
print("I am the function which needs some decoration to remove my foul smell")
a_function_requiring_decoration()
a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)
a_function_requiring_decoration()
@a_new_decorator
def a_function_requiring_decoration2():
'''hey you! decorate me'''
print("I am the function2")
a_function_requiring_decoration2()
print(a_function_requiring_decoration2.__name__)
from functools import wraps
def a_new_decorator2(a_func):
@wraps(a_func)
def wrapTheFunction():
print("I am doing some boring work before executing a_func")
a_func()
print("I am doing some boring work after executing a_func")
return wrapTheFunction
@a_new_decorator2
def a_function_requiring_decoration3():
"""he"""
print("I am the function3")
print(type(a_function_requiring_decoration3))
a_function_requiring_decoration3()
print(a_function_requiring_decoration3.__name__)
def decorator_name(f):
@wraps(f)
def decorated(*args, **kwargs):
if not can_run:
return "Function will not run"
return f(*args, **kwargs)
return decorated
@decorator_name
def func():
return ("Function is running")
can_run = True
print(func())
can_run = False
print(func())
def logit(func):
@wraps(func)
def with_logging(*args, **kwargs):
print(func.__name__ + " was called")
return func(*args, **kwargs)
return with_logging
@logit
def addition_func(x):
"""Do some math."""
return x + x
result = addition_func(5)
def logit2(logfile='out.log'):
def logging_decorator(func):
@wraps(func)
def wrapped_function(*args, **kwargs):
log_string = func.__name__ + " was called."
print(log_string)
with open(logfile, 'a') as opened_file:
opened_file.write(log_string + '\n')
return func(*args, **kwargs)
return wrapped_function
return logging_decorator
@logit2()
def myfunc1():
pass
myfunc1()
@logit2(logfile='func2.log')
def myfunc2():
pass
myfunc2()
class logit3(object):
def __init__(self, logfile='out.log'):
self.logfile = logfile
def __call__(self, func):
@wraps(func)
def wrapped_function(*args, **kwargs):
log_string = func.__name__ + " was called"
print(log_string)
with open(self.logfile, 'a') as opened_file:
opened_file.write(log_string + '\n')
self.notify()
return func(*args, **kwargs)
return wrapped_function
def notify(self):
pass
@logit3()
def myfunc3():
pass
# myfunc3 = logit3(myfunc3)
myfunc3()
class email_logit(logit3):
''' a subclass'''
def __init__(self, email='admin@myproject.com', *args, **kwargs):
self.email = email
super(logit3,self).__init__(*args, **kwargs)
def notify(self):
pass
@email_logit()
def myfunc4():
pass
myfunc4()
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# Allison Schubauer and Daisy Hernandez
# Created: 5/21/2013
# Last Updated: 6/13/2013
# For JCAP
from PyQt4 import QtGui, QtCore
from datareader import DATA_HEADINGS
import graph
import profilecreator
import date_helpers
import time
""" window that displays a single graph area and various
customization options """
class GraphWindow(QtGui.QWidget):
def __init__(self):
super(GraphWindow, self).__init__()
self.updating = False
self.initUI()
""" draws the user interface of the window """
def initUI(self):
# set window size and position on screen
self.setGeometry(300, 200, 1000, 600)
# get variables from spreadsheet
global DATA_HEADINGS
self.vars = []
for index in range(3, len(DATA_HEADINGS)):
self.vars += [DATA_HEADINGS.get(index)]
# initialize default graph
self.graph = graph.Graph(self, xvarname="Time",
yvarname=self.vars[0])
self.toolbar = graph.NavigationToolbar(self.graph, self)
self.updating = True
self.setWindowTitle(self.vars[0])
self.plotOptionMenu = QtGui.QComboBox()
self.plotOptionMenu.addItem('Switch graph')
self.plotOptionMenu.addItem('Add to left axis')
# make drop-down menu for selecting graphs
self.selectVar = QtGui.QComboBox()
for var in self.vars:
self.selectVar.addItem(var)
self.selectVar.activated[str].connect(self.selectGraph)
# set up layout and sub-layouts
self.layout = QtGui.QVBoxLayout(self)
self.optionslayout = QtGui.QGridLayout()
self.gridlayout = QtGui.QGridLayout()
self.axeslayout = QtGui.QGridLayout()
self.timelayout = QtGui.QGridLayout()
# this exists so auto axis buttons can move if necessary
self.autowidget = QtGui.QWidget(self)
self.autolayout = QtGui.QGridLayout()
# first column holds graph, second column holds graph options
# set the column stretches - 0 is the default
# set minimum column widths
self.gridlayout.setColumnStretch(0, 4)
self.gridlayout.setColumnStretch(1, 0)
self.gridlayout.setColumnMinimumWidth(0,300)
self.gridlayout.setRowMinimumHeight(0,375)
# add drop-down menus and MPL toolbar to top of window
self.layout.addLayout(self.optionslayout)
self.optionslayout.addWidget(self.plotOptionMenu, 0, 0)
self.optionslayout.addWidget(self.selectVar, 0, 1, 1, 3)
self.optionslayout.addWidget(self.toolbar, 1, 0, 1, 4)
# initialize checkbox that acts as pause button
self.hold_cb = QtGui.QCheckBox('Hold', self)
self.hold_cb.stateChanged.connect(self.hold)
# initialize input boxes for axis limits
self.minutes = QtGui.QLineEdit(self)
self.minutes.setFixedWidth(40)
self.hours = QtGui.QLineEdit(self)
self.hours.setFixedWidth(40)
self.days = QtGui.QLineEdit(self)
self.days.setFixedWidth(40)
self.Ymin = QtGui.QLineEdit(self)
self.Ymax = QtGui.QLineEdit(self)
self.YminR = QtGui.QLineEdit(self)
self.YmaxR = QtGui.QLineEdit(self)
# create labels for the input boxes
self.label_time = QtGui.QLabel('Show data from the last:')
self.label_minutes = QtGui.QLabel('minutes')
self.label_hours = QtGui.QLabel('hours')
self.label_days = QtGui.QLabel('days')
self.label_Ymin = QtGui.QLabel('Y Min (left):')
self.label_Ymax = QtGui.QLabel('Y Max (left):')
self.label_YminR = QtGui.QLabel('Y Min (right):')
self.label_YmaxR = QtGui.QLabel('Y Max (right):')
# initialize buttons and their connections
self.set_axes = QtGui.QPushButton('Enter')
self.auto_xaxes = QtGui.QPushButton('Auto X')
self.auto_yaxes = QtGui.QPushButton('Auto Y (left)')
self.auto_yraxes = QtGui.QPushButton('Auto Y (right)')
self.set_axes.clicked.connect(self.setAxes)
self.auto_xaxes.clicked.connect(self.autoXAxes)
self.auto_yaxes.clicked.connect(self.autoYAxes)
self.auto_yraxes.clicked.connect(self.autoYRAxes)
# set the possible streches of input boxes
self.Ymin.setSizePolicy(QtGui.QSizePolicy.Maximum, QtGui.QSizePolicy.Preferred)
self.Ymax.setSizePolicy(QtGui.QSizePolicy.Maximum, QtGui.QSizePolicy.Preferred)
self.YminR.setSizePolicy(QtGui.QSizePolicy.Maximum, QtGui.QSizePolicy.Preferred)
self.YmaxR.setSizePolicy(QtGui.QSizePolicy.Maximum, QtGui.QSizePolicy.Preferred)
# initialize menu to choose variable for right-hand axis
self.label_raxis = QtGui.QLabel('Choose a variable to plot on the right-hand axis:')
self.choose_var = QtGui.QComboBox()
for var in self.vars:
self.choose_var.addItem(var)
self.set_raxis = QtGui.QPushButton('Plot')
self.set_raxis.clicked.connect(self.addRAxis)
# place the main grid layout inside layout for window
self.layout.addLayout(self.gridlayout)
# add graph and options to main grid layout
self.gridlayout.addWidget(self.graph, 0, 0)
self.gridlayout.addLayout(self.axeslayout, 0, 1)
# set alignment for the widgets
self.axeslayout.setAlignment(QtCore.Qt.AlignTop)
# create spacers to separate fields in graph options layout
self.spacer1 = QtGui.QSpacerItem(1, 20)
self.spacer2 = QtGui.QSpacerItem(1, 20)
self.spacer3 = QtGui.QSpacerItem(1, 20)
self.spacer4 = QtGui.QSpacerItem(1, 20)
# add items to the graph options layout
self.axeslayout.addItem(self.spacer1, 0, 0)
self.axeslayout.addWidget(self.hold_cb, 1, 0)
self.axeslayout.addItem(self.spacer2, 2, 0)
self.axeslayout.addItem(self.spacer3, 3, 0)
self.axeslayout.addWidget(self.label_raxis, 4, 0)
self.axeslayout.addWidget(self.choose_var, 5, 0)
self.axeslayout.addWidget(self.set_raxis, 6, 0)
self.axeslayout.addItem(self.spacer4, 7, 0)
self.axeslayout.addWidget(self.label_time, 8, 0)
self.axeslayout.addLayout(self.timelayout, 9, 0)
# add options for time axis to a sub-grid
self.timelayout.addWidget(self.minutes, 0, 0)
self.timelayout.addWidget(self.label_minutes, 0, 1)
self.timelayout.addWidget(self.hours, 1, 0)
self.timelayout.addWidget(self.label_hours, 1, 1)
self.timelayout.addWidget(self.days, 2, 0)
self.timelayout.addWidget(self.label_days, 2, 1)
# add more items to graph options layout
self.axeslayout.addWidget(self.label_Ymin, 13, 0)
self.axeslayout.addWidget(self.Ymin, 14, 0)
self.axeslayout.addWidget(self.label_Ymax, 15, 0)
self.axeslayout.addWidget(self.Ymax, 16, 0)
self.axeslayout.addWidget(self.label_YminR, 17, 0)
self.axeslayout.addWidget(self.YminR, 18, 0)
self.axeslayout.addWidget(self.label_YmaxR, 19, 0)
self.axeslayout.addWidget(self.YmaxR, 20, 0)
self.axeslayout.addWidget(self.set_axes, 21, 0)
# hide options for second axis initially
self.label_YminR.hide()
self.YminR.hide()
self.label_YmaxR.hide()
self.YmaxR.hide()
# add widget that holds auto axis buttons
self.axeslayout.addWidget(self.autowidget, 22, 0, 1, 2)
self.autowidget.setLayout(self.autolayout)
# add auto axis buttons
self.autolayout.addWidget(self.auto_xaxes, 0 , 0)
self.autolayout.addWidget(self.auto_yaxes, 0 , 1)
self.autolayout.addWidget(self.auto_yraxes, 0 , 2)
# hide option for auto right axis initially
self.auto_yraxes.hide()
self.show()
""" called when variable to plot is selected """
def selectGraph(self, varName):
# convert QString to string
varString = str(varName)
if self.plotOptionMenu.currentText() == 'Switch graph':
# clear previous plot and set parent to None so it can be deleted
self.graph.clearPlot()
self.graph.setParent(None)
self.gridlayout.removeWidget(self.graph)
self.graph =None
self.graph = graph.Graph(self, xvarname = "Time",
yvarname = varString)
self.gridlayout.addWidget(self.graph, 0, 0)
self.setWindowTitle(varString)
# remove all options for right-hand axis because plot is initialized
# without it
self.label_YminR.hide()
self.YminR.hide()
self.label_YmaxR.hide()
self.YmaxR.hide()
self.auto_yraxes.hide()
# remove the "add to right axis" option from plotOptionMenu if
# it is currently displayed
self.plotOptionMenu.removeItem(2)
# clear axis label fields
self.minutes.clear()
self.hours.clear()
self.days.clear()
self.Ymin.clear()
self.Ymax.clear()
self.YminR.clear()
self.YmaxR.clear()
elif self.plotOptionMenu.currentText() == 'Add to left axis':
self.graph.addVarToAxis(varString)
return
else:
self.graph.addVarToAxis(varString, "right")
return
""" called when request to add plot to right-hand axis is made """
def addRAxis(self):
# get name of variable from selection menu
varName = self.choose_var.currentText()
# convert QString to string
varString = str(varName)
self.graph.addRightAxis(varString)
# reset right y-axis limit fields
self.YminR.clear()
self.YmaxR.clear()
# remove the "add to right axis" option from plotOptionMenu if
# it is currently displayed
self.plotOptionMenu.removeItem(2)
# show all options for right-hand axis
self.plotOptionMenu.addItem('Add to right axis')
self.label_YminR.show()
self.YminR.show()
self.label_YmaxR.show()
self.YmaxR.show()
self.auto_yraxes.show()
""" called whenever new data is ready to be plotted """
def updateWindow(self, newRow):
self.graph.updatePlot(newRow)
""" called by MainMenu every second """
def redrawWindow(self):
if self.updating:
self.graph.timeFrame()
self.graph.draw()
""" toggles auto-updating property of graphs in window """
def hold(self):
if self.updating == True:
self.updating = False
else:
self.updating = True
""" called when user gives input for axis limits """
def setAxes(self):
# [Are axes changing?, new min, new max]
setXAxes = [False, None, None]
setYAxes = [False, None, None]
# dealing with the current time and the time that we have to
# go back for x-axis limits
currTime = time.time()
# measured in seconds
timeBack = 0
# x-axis maximum is current time
setXAxes[2] = date_helpers.dateObj(currTime)
# get and save input from all fields
min_input = self.minutes.text()
hour_input = self.hours.text()
day_input = self.days.text()
Ymin_input = self.Ymin.text()
Ymax_input = self.Ymax.text()
axes_input = [('min', min_input), ('hour', hour_input),
('day', day_input), ('Ymin', Ymin_input),
('Ymax', Ymax_input)]
for axis_tuple in axes_input:
try:
value = float(axis_tuple[1])
if axis_tuple[0] == 'Ymin':
setYAxes[0] = True
setYAxes[1] = value
elif axis_tuple[0] == 'Ymax':
setYAxes[0] = True
setYAxes[2] = value
elif axis_tuple[0] == 'min':
setXAxes[0] = True
timeBack += value*60
elif axis_tuple[0] == 'hour':
setXAxes[0] = True
timeBack += value*60*60
elif axis_tuple[0] == 'day':
setXAxes[0] = True
timeBack += value*60*60*24
# if no input was given to field, ignore it
except ValueError:
pass
# set x-axis minimum to current time minus specified time window
setXAxes[1] = date_helpers.dateObj(currTime - timeBack)
# if y-axis limits have been changed
if setYAxes[0]:
self.graph.setYlim(amin=setYAxes[1], amax=setYAxes[2])
# if x-axis limits have been changed
if setXAxes[0]:
self.graph.auto = False
self.graph.timeWindow = timeBack
self.graph.setXlim(amin=setXAxes[1], amax=setXAxes[2])
if self.graph.hasRightAxis:
self.setRAxis()
""" called when user gives input for right-hand axis limits """
def setRAxis(self):
# [Are axes changing?, new min, new max]
setAxes = [False, None, None]
YminR_input = self.YminR.text()
YmaxR_input = self.YmaxR.text()
try:
setAxes[0] = True
setAxes[1] = float(YminR_input)
except ValueError:
pass
try:
setAxes[0] = True
setAxes[2] = float(YmaxR_input)
except ValueError:
pass
if setAxes:
self.graph.setRYlim(amin=setAxes[1], amax=setAxes[2])
""" called when 'Auto X' button is clicked
sets x axis limits automatically to fit all data """
def autoXAxes(self):
self.graph.auto = True
self.graph.axes.autoscale(axis ='x')
self.minutes.clear()
self.hours.clear()
self.days.clear()
""" called when 'Auto Y (left)' button is clicked
sets y axis limits automatically to fit all data """
def autoYAxes(self):
self.graph.axes.autoscale(axis ='y')
self.Ymin.clear()
self.Ymax.clear()
""" called when 'Auto Y (right)' button is clicked
sets right-hand y axis limits automatically to fit all data """
def autoYRAxes(self):
self.graph.rightAxes.autoscale(axis ='y')
self.YminR.clear()
self.YmaxR.clear()
| {
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"license": "bsd-3-clause",
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# Allison Schubauer and Daisy Hernandez
# Created: 5/23/2013
# Last Updated: 6/12/2013
# For JCAP
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from navigation_toolbar import NavigationToolbar
from matplotlib.figure import Figure
from pylab import matplotlib
import sys
import date_helpers
import yvariable
import copy
import itertools
import time
from dictionary_helpers import *
""" widget to represent an auto-updating graph """
class Graph(FigureCanvas):
""" sets up Figure object and plot """
def __init__(self, parent="None", width=3, height=2, dpi=80,
xvarname="None", yvarname="None"):
self.auto = True
self.timeWindow = 0
self.hasRightAxis = False
self.legendL = None
self.xvar = xvarname
# keeps track of label for coordinates on graph
self.xyLabel = None
# holds matplotlib keywords for color of plots
self.colors = itertools.cycle(["b","r","g","c","m","y","k"])
# put first y-var into list of y-vars on left axis
self.yvarsL = [yvariable.YVariable(varName = yvarname,
columnNumber = getCol(yvarname), color = self.colors.next())]
self.yvarsR = []
# used to access date/time data for formatting
# and displaying on the x-axis
self.colNums = [getCol("Date"),getCol(self.xvar)]
self.figure = Figure(figsize=(width, height), dpi=dpi, tight_layout=True)
FigureCanvas.__init__(self, self.figure)
# Graph will have a parent widget if contained in a layout
self.setParent(parent)
FigureCanvas.setSizePolicy(self,
QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
self.initPlot()
# ---- this is deprecated as of adding the toolbar ----
# clicking on graph gives x and y coordinates
# (not enabled when right y axis is present)
#self.figure.canvas.mpl_connect('button_press_event', self.onclick)
""" draws and labels axes """
def initPlot(self):
self.axes = self.figure.add_subplot(111)
self.axes.set_xlabel(self.xvar)
self.axes.set_ylabel(self.yvarsL[0].varName)
self.axes.xaxis_date()
self.figure.autofmt_xdate()
self.time_format = matplotlib.dates.DateFormatter('%m/%d/%y %H:%M:%S')
self.axes.xaxis.set_major_formatter(self.time_format)
self.axes.set_ylabel(self.yvarsL[0].varName)
# Assign first y-variable to left-hand axis
self.yvarsL[0].axis = self.axes
self.firstPlot(self.yvarsL[0])
""" plots initial data for a given y-var on the graph """
def firstPlot(self, yvarIns):
list_of_times = []
theAxes = yvarIns.axis
theYvar = yvarIns.varName
ydata = copy.deepcopy(DATA_DICT.get(theYvar))
time_array = copy.deepcopy(DATA_DICT.get(self.xvar))
date_array = copy.deepcopy(DATA_DICT.get("Date"))
for index in range(len(time_array)):
full_time = date_array[index] + " " + time_array[index]
formatted_time = date_helpers.dateObjFloat(full_time)
list_of_times += [formatted_time]
timeToPlot = matplotlib.dates.date2num(list_of_times)
# If reader is still reading, DATA_DICT is changing,
# and it is possible for timeToPlot to be a different
# length than ydata. In that case, we can wait for
# DATA_DICT to update and try again.
try:
theAxes.plot_date(timeToPlot, ydata, markerfacecolor=yvarIns.color, label = theYvar,
markeredgecolor=yvarIns.color)
self.timeFrame()
except ValueError:
time.sleep(0.5)
firstPlot(self, yvarIns)
""" adds new data to graph whenever reader sends new row """
def updatePlot(self, row):
# turn date/time strings into time objects
time_value = date_helpers.dateObjFloat(row[self.colNums[0]] + " " + row[self.colNums[1]])
# plot new point for all y-vars no matter which of the two axis
for axis in (self.yvarsL, self.yvarsR):
for graphPlots in axis:
graphPlots.axis.plot_date(time_value,row[graphPlots.columnNumber],
markerfacecolor=graphPlots.color,
markeredgecolor=graphPlots.color)
""" resets the x-axis limits when specific time window is selected """
def timeFrame(self):
if not self.auto:
currTime = time.time()
rightLim = date_helpers.dateObj(currTime)
leftLim = date_helpers.dateObj(currTime - self.timeWindow)
self.setXlim(amin=leftLim, amax=rightLim)
""" initializes the right-hand y-axis and adds the first variable """
def addRightAxis(self, rightvar):
# replace old right-hand axis
if self.hasRightAxis:
self.figure.delaxes(self.rightAxes)
self.hasRightAxis = True
# share x-axis with left-hand y-axis
self.rightAxes = self.axes.twinx()
self.rightAxes.yaxis.tick_right()
self.rightAxes.yaxis.set_label_position("right")
self.rightAxes.set_ylabel(rightvar)
self.rightAxes.xaxis.set_major_formatter(self.time_format)
self.rightAxes.get_xaxis().set_visible(False)
# save the right-hand axis information
self.yvarsR = [yvariable.YVariable(varName = rightvar, axis = self.rightAxes,
columnNumber = getCol(rightvar), color = self.colors.next())]
self.firstPlot(self.yvarsR[0])
# add legend if 2 or more variables on the same axis
if len(self.yvarsL) > 1:
self.addLegends()
""" adds another variable to the specified y-axis """
def addVarToAxis(self, varString, axis="left"):
newVar = yvariable.YVariable(varName = varString, axis = self.axes,
columnNumber = getCol(varString), color = self.colors.next())
if axis == "left":
self.axes.get_yaxis().get_label().set_visible(False)
self.yvarsL += [newVar]
elif axis == "right":
newVar.axis = self.rightAxes
self.rightAxes.get_yaxis().get_label().set_visible(False)
self.yvarsR += [newVar]
self.firstPlot(newVar)
self.addLegends()
""" adds left-hand and right-hand axis legends """
def addLegends(self):
# remove old legend if there is already one in place
if self.legendL:
self.legendL.set_visible(False)
linesL, labelsL = self.axes.get_legend_handles_labels()
if self.hasRightAxis:
linesR, labelsR = self.rightAxes.get_legend_handles_labels()
# place left-hand legend in upper left corner
self.legendL = self.rightAxes.legend(linesL, labelsL, loc=2,
title="Left", prop={"size":"small"})
# place right-hand legend in upper right corner
self.legendR = self.rightAxes.legend(linesR, labelsR, loc=1,
title = "Right", prop={"size":"small"})
# add left-hand legend to upper Axes object so
# it can be manipulated
self.rightAxes.add_artist(self.legendL)
self.legendR.draggable(state = True)
else:
self.legendL = self.axes.legend(linesL, labelsL, loc=2,
title="Left", prop={"size":"small"})
self.legendL.draggable(state = True)
""" called when user specifies a time window to display """
def setXlim(self, amin=None, amax=None):
self.axes.set_xlim(left=amin, right=amax)
""" called when user specifies left-hand y-axis limits """
def setYlim(self, amin=None, amax=None):
self.axes.set_ylim(bottom=amin, top=amax)
""" called when user specifies right-hand y-axis limits """
def setRYlim(self, amin=None, amax=None):
self.rightAxes.set_ylim(bottom=amin, top=amax)
""" called when new graph is selected for window """
def clearPlot(self):
self.figure.clf()
## ---- the function below is deprecated as of adding the toolbar ----
## """ called when user clicks on graph to display (x, y) data """
## def onclick(self,event):
## if not self.hasRightAxis:
## try:
## datetime_date = matplotlib.dates.num2date(event.xdata)
## formatted_xdate = datetime_date.strftime("%m/%d/%Y %H:%M:%S")
## if self.xyLabel:
## self.xyLabel.set_visible(False)
## # textcoords is the coordinate system used to place the
## # text on the axes; 0.55 is horizontal placement,
## # 1.05 is vertical placement (where 1,1 is upper right corner)
## self.xyLabel = self.axes.annotate('x = %s, y = %f'
## %(formatted_xdate, event.ydata),
## xy=(event.xdata, event.ydata),
## textcoords='axes fraction',
## xytext=(0.55,1.05))
## self.draw()
## except TypeError:
## if self.xyLabel:
## self.xyLabel.set_visible(False)
| {
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# Allison Schubauer and Daisy Hernandez
# Created: 5/23/2013
# Last Updated: 6/13/2013
# For JCAP
import csv
from PyQt4 import QtCore
DATA_DICT = {}
DATA_HEADINGS = {}
""" thread that reads data from file """
class DataReader(QtCore.QThread):
# initialize signal to data processor when a full line has been read
lineRead = QtCore.pyqtSignal(list)
def __init__(self, parent=None, filename='default.csv'):
super(DataReader, self).__init__()
self.initData(filename)
self.running = True
def initData(self, filename):
global DATA_DICT
global DATA_HEADINGS
self.datafile = open(filename, 'rb')
# read column headings and create lists to hold data
headings = self.datafile.readline().split(',')
# clear dictionary in case it has already been used for a different file
DATA_DICT.clear()
DATA_HEADINGS.clear()
# manually add time and date headings because they aren't in file
DATA_HEADINGS[0] = 'Time'
DATA_DICT['Time'] = []
DATA_HEADINGS[1] = 'Date'
DATA_DICT['Date'] = []
# initialize each heading with an array to store the column data
for col in range(3, len(headings)):
# strip extra " characters that may have been added to
# spreadsheet cells by Excel
colName = headings[col].strip('"')
# ignore empty columns at end of spreadsheet
if colName == '':
break
DATA_HEADINGS[col] = colName
DATA_DICT[colName] = []
self.numColumns = len(DATA_HEADINGS)
self.lastEOFpos = self.datafile.tell()
def run(self):
global DATA_DICT
global DATA_HEADINGS
# get column numbers that hold data in spreadsheet
dataColNums = DATA_HEADINGS.keys()
while self.running:
self.datafile.seek(self.lastEOFpos)
data = self.datafile.readline()
row = data.split(',')
strippedRow = []
# ignore empty third column in spreadsheet
for col in (row[:2] + row[3:]):
col = col.strip('"')
if col != '':
strippedRow += [col]
# ignore empty columns at end of spreadsheet
else:
break
# check if we have all data from row and have read
# up to the end of line character
if len(strippedRow) == self.numColumns and row[len(row)-1].endswith('\r\n'):
# re-insert empty third column to keep indices
# in strippedRow consistent with DATA_HEADINGS
strippedRow.insert(2, '')
# add the new info to the respective column
for col in dataColNums:
heading = DATA_HEADINGS.get(col)
DATA_DICT[heading].append(strippedRow[col])
# send signal
self.lineRead.emit(strippedRow)
# move the reader cursor only if we read in a full line
self.lastEOFpos = self.datafile.tell()
# close file after end() has been called
self.datafile.close()
""" called when application exits, experiment, ends or new file is loaded
to terminate thread """
def end(self):
self.running = False
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# Allison Schubauer and Daisy Hernandez
# Created: 5/23/2013
# Last Updated: 6/14/2013
# For JCAP
from PyQt4 import QtGui, QtCore
import datareader
import cPickle as pickle
""" widget to make profiles """
class ProfileCreator(QtGui.QWidget):
""" sets up the Widget """
def __init__(self):
super(ProfileCreator, self).__init__()
self.initUI()
""" creates all graphics in the Widget """
def initUI(self):
self.setGeometry(400, 200, 400, 300)
self.setWindowTitle('Create a New Profile')
# top-level vertical layout
vBox = QtGui.QVBoxLayout(self)
instructions = QtGui.QLabel()
instructions.setText("Choose up to 8 variables to graph.")
instructions.setMaximumHeight(20)
vBox.addWidget(instructions)
# make widget and layout to hold checkboxes
checkField = QtGui.QWidget()
hBox = QtGui.QHBoxLayout(checkField)
vBox.addWidget(checkField)
# checkboxes displayed in 2 columns
self.col1 = QtGui.QVBoxLayout()
self.col2 = QtGui.QVBoxLayout()
hBox.addLayout(self.col1)
hBox.addLayout(self.col2)
self.col1.setAlignment(QtCore.Qt.AlignTop)
self.col2.setAlignment(QtCore.Qt.AlignTop)
# add all checkboxes to col1 and col2
self.getVars()
# create OK and Cancel buttons
buttons = QtGui.QDialogButtonBox()
buttons.setStandardButtons(QtGui.QDialogButtonBox.Ok
| QtGui.QDialogButtonBox.Cancel)
buttons.accepted.connect(self.saveProfile)
buttons.rejected.connect(self.close)
vBox.addWidget(buttons)
""" makes checkboxes for each variable in data set """
def getVars(self):
global DATA_HEADINGS
self.checkboxes = []
# this ignores first date and time columns in spreadsheet
varNames = datareader.DATA_HEADINGS.values()
varNames.remove('Time')
varNames.remove('Date')
for index in range(len(varNames)):
self.checkboxes += [QtGui.QCheckBox(varNames[index], self)]
# first half of variables added to first column
if index <= len(varNames)/2:
self.col1.addWidget(self.checkboxes[index])
# second half of variables added to second column
else:
self.col2.addWidget(self.checkboxes[index])
""" uses cPickle to save profile for future use """
def saveProfile(self):
varsList = []
# get varsList from checked boxes
for box in self.checkboxes:
if box.isChecked():
varsList += [str(box.text())]
# error if more than 8 variables checked
if len(varsList) > 8:
self.tooManyVars()
return
# error if no variables selected
elif len(varsList) < 1:
self.tooFewVars()
return
# asks for name of profile
name, ok = QtGui.QInputDialog.getText(self, 'Create a New Profile',
'Please enter a name for this profile.')
# saves profile if name is entered and OK is clicked
if name and ok:
try:
# open file for reading
savefile = open('saved_profiles.txt', 'rb')
savedProfiles = pickle.load(savefile)
savefile.close()
except IOError:
savedProfiles = []
# save tuple of profile name and list of variables in profile
savedProfiles.append((str(name), varsList))
savefile = open('saved_profiles.txt', 'wb')
pickle.dump(savedProfiles, savefile)
savefile.close()
self.close()
""" sends error message if more than 8 variables selected """
def tooManyVars(self):
error = QtGui.QMessageBox.question(None, 'Error',
'You can only put 8 graphs in a profile.',
QtGui.QMessageBox.Ok)
""" sends error message if no variables selected """
def tooFewVars(self):
error = QtGui.QMessageBox.question(None, 'Error',
'Please select at least one graph for this profile.',
QtGui.QMessageBox.Ok | QtGui.QMessageBox.Cancel)
# exits profile creator if Cancel is clicked
if (error == QtGui.QMessageBox.Cancel):
self.close()
"""function called on all windows by MainMenu"""
def redrawWindow(self):
# no figures to draw
pass
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# Allison Schubauer and Daisy Hernandez
# Created: 5/28/2013
# Last Updated: 6/12/2013
# For JCAP
from PyQt4 import QtCore, QtGui
import sys
import graph
""" window that displays profile after loading """
class ProfileWindow(QtGui.QMainWindow):
""" takes in name that profile was saved under and list of
variables to be graphed """
def __init__(self, name = "None", varsList = []):
super(ProfileWindow, self).__init__()
self.name = name
self.varsList = varsList
self.graphs = []
self.initUI()
""" creates graphics in window """
def initUI(self):
# window width is based on number of graphs in profile
self.setGeometry(0, 50, 200*(len(self.varsList)+1)+50, 800)
self.setWindowTitle(self.name)
# displays graphs in a grid format
self.main_widget = QtGui.QWidget(self)
self.setCentralWidget(self.main_widget)
grid = QtGui.QGridLayout(self.main_widget)
# creates and adds graphs to appropriate row
num_graphs = len(self.varsList)
midpoint = (num_graphs+1)/2
for index in range(num_graphs):
graphLayout = QtGui.QVBoxLayout()
newGraph = graph.Graph(parent=None, xvarname="Time",
yvarname=self.varsList[index])
toolbar = graph.NavigationToolbar(newGraph, self)
self.graphs += [newGraph]
column = 0 if index < midpoint else 1
grid.addLayout(graphLayout, column, index%midpoint)
graphLayout.addWidget(toolbar)
graphLayout.addWidget(newGraph)
""" called whenever new data is ready to be plotted """
def updateWindow(self, newRow):
for graph in self.graphs:
graph.updatePlot(newRow)
""" called by MainMenu every second """
def redrawWindow(self):
for graph in self.graphs:
graph.draw()
""" menu that shows avaiable profiles and loads user's selection """
class LoadMenu(QtGui.QDialog):
# these signals will be sent to MainMenu
profileChosen = QtCore.pyqtSignal(str)
profileToDelete = QtCore.pyqtSignal(str)
""" takes in a list of profiles saved for the current data file """
def __init__(self, menuList = []):
super(LoadMenu, self).__init__()
self.setWindowTitle('Load Profile')
# create the main layout and add the list of profiles
self.layout = QtGui.QVBoxLayout(self)
self.list = QtGui.QListWidget(self)
self.list.addItems(menuList)
self.layout.addWidget(self.list)
# create and activate OK and Cancel buttons
buttons = QtGui.QDialogButtonBox()
buttons.setStandardButtons(QtGui.QDialogButtonBox.Ok
| QtGui.QDialogButtonBox.Cancel)
deleteButton = QtGui.QPushButton('Delete')
buttons.addButton(deleteButton, QtGui.QDialogButtonBox.DestructiveRole)
buttons.accepted.connect(self.sendName)
buttons.rejected.connect(self.close)
deleteButton.clicked.connect(self.deleteName)
self.layout.addWidget(buttons)
""" sends name of selected profile to MainMenu,
which will then load the profile in a new window """
def sendName(self):
name = str(self.list.currentItem().text())
self.profileChosen.emit(name)
self.close()
def deleteName(self):
name = str(self.list.currentItem().text())
confirm = QtGui.QMessageBox.question(None, 'Delete Profile',
'Are you sure you want to delete this profile?',
QtGui.QMessageBox.Yes |
QtGui.QMessageBox.No)
if (confirm == QtGui.QMessageBox.Yes):
self.profileToDelete.emit(name)
self.close()
""" called by MainMenu every second; nothing for this widget to do """
def redrawWindow(self):
pass
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# Allison Schubauer and Daisy Hernandez
# Created: 5/28/2013
# Last Updated: 6/13/2013
# For JCAP
from PyQt4 import QtGui
from dictionary_helpers import getCol
import datareader
import depositionwindow_data
import graphwindow_data
import profilewindow
import profilecreator
import os
import filename_handler
import process_deposition_data as pdd
import sys
import cPickle as pickle
DATA_FILE_DIR = ''
""" window that pops up when application launches """
class MainMenu(QtGui.QWidget):
def __init__(self):
super(MainMenu, self).__init__()
# holds all active windows
self.graphWindows = []
self.depWindows = []
self.miscWindows = []
# holds all profiles associated with current file
self.profiles = {}
# data processor is not initialized until FILE_INFO is complete
self.processor = None
# save name of file from which application will read
self.file = self.initReader()
# if there are no .csv files in working folder, have user
# choose file to load
if not self.file:
self.loadDataFile(1)
# check if filename is in valid format
filenameError = filename_handler.parseFilename(self.file)
# if not, ask user for experiment parameters
if filenameError:
self.requestFileInfo(1)
# otherwise, finish setting up program
else:
self.initData()
self.initUI()
""" automatically loads last modified data file
when application launches """
def initReader(self):
lastModifiedFile = ''
lastModifiedTime = 0
allFiles = os.listdir(DATA_FILE_DIR)
data = filter(lambda filename: filename.endswith('.csv'), allFiles)
for filename in data:
statbuf = os.stat(os.path.join(DATA_FILE_DIR, filename))
if statbuf.st_mtime > lastModifiedTime:
lastModifiedTime = statbuf.st_mtime
lastModifiedFile = filename
return lastModifiedFile
""" draws graphical user interface """
def initUI(self):
self.setGeometry(50, 150, 300, 400)
self.setWindowTitle('Deposition Monitor')
self.layout = QtGui.QVBoxLayout(self)
# load data file
loadFileButton = QtGui.QPushButton('Choose Data File')
self.layout.addWidget(loadFileButton)
loadFileButton.clicked.connect(self.loadDataFile)
# show single graph
makeGraphButton = QtGui.QPushButton('Show Graph')
self.layout.addWidget(makeGraphButton)
makeGraphButton.clicked.connect(self.makeGraph)
# create a profile
makeProfileButton = QtGui.QPushButton('Create a New Profile')
self.layout.addWidget(makeProfileButton)
makeProfileButton.clicked.connect(self.makeProfile)
# show a saved profile
loadProfileButton = QtGui.QPushButton('Load a Saved Profile')
self.layout.addWidget(loadProfileButton)
loadProfileButton.clicked.connect(self.selectProfile)
# show a deposition graph
makeDepositionButton = QtGui.QPushButton('Create Deposition Graph')
self.layout.addWidget(makeDepositionButton)
makeDepositionButton.clicked.connect(self.makeDeposition)
# end data collection session
endButton = QtGui.QPushButton('End Experiment')
self.layout.addWidget(endButton)
endButton.clicked.connect(self.endExperiment)
# initialize the timer that updates all graphs in application
timer = pdd.QtCore.QTimer(self)
timer.timeout.connect(self.redrawAll)
# update graph every 1000 milliseconds
timer.start(1000)
self.show()
""" initializes all elements of program that require experiment
information (FILE_INFO must be complete) """
def initData(self):
filepath = os.path.join(DATA_FILE_DIR, self.file)
# if application has just been opened
if not self.processor:
# initialize data processor (includes reader)
self.processor = pdd.ProcessorThread(parent=self, filename=filepath)
self.processor.lineRead.connect(self.newLineRead)
self.processor.newData.connect(self.depUpdate)
self.processor.srcError.connect(self.sourceError)
self.processor.start()
# if loading a new file
else:
self.processor.newFile(filepath)
self.initSupplyVars()
"""Initializes any variables that are useful for error checking"""
def initSupplyVars(self):
self.errors =[]
# number of rows read by checkValidity
self.rowsReadCV = 0
self.supply = int(filename_handler.FILE_INFO.get("Supply"))
try:
if self.supply % 2 == 0:
self.rfl = getCol("Power Supply" + str(self.supply) + " Rfl Power")
self.fwd = getCol("Power Supply" + str(self.supply) + " Fwd Power")
self.dcbias = getCol("Power Supply" + str(self.supply) + " DC Bias")
if self.supply % 2 == 1:
self.output_power = getCol("Power Supply" + str(self.supply) + \
" Output Power")
self.output_voltage = getCol("Power Supply" + str(self.supply) + \
" Output Voltage")
except IndexError:
self.processor.end()
message = "This is not a valid data file. Please load a new file."
invalidFileError = QtGui.QMessageBox.warning(None, "Invalid File",
message)
self.loadDataFile(1)
""" if filename is not in correct format, ask user to enter
experiment parameters manually
(mode is 1 if called when the application is first opened,
0 if called when the user loads a new file) """
def requestFileInfo(self, mode):
fileErrorDialog = FileInfoDialog(mode)
self.miscWindows.append(fileErrorDialog)
fileErrorDialog.fileInfoComplete.connect(self.initData)
fileErrorDialog.fileAborted.connect(self.loadDataFile)
""" allows user to choose another data file
(mode is 1 if called to replace default file,
0 if the user loads a new file through main menu) """
def loadDataFile(self, mode=0):
global DATA_FILE_DIR
global FILE_INFO
dirname = QtGui.QFileDialog.getOpenFileName(self, 'Open data file',
DATA_FILE_DIR,
'CSV files (*.csv)')
# if cancel is clicked, dirname will be empty string
if dirname != '':
filename_handler.FILE_INFO = {'Element':'', 'Source':'', 'Supply':'', 'TiltDeg':[],
'Z_mm':[]}
# converts Qstring to string
dirString = str(dirname)
# gets filename from current directory (will be changed eventually)
dirList = dirString.split('/')
DATA_FILE_DIR = '/'.join(dirList[:len(dirList)-1])
self.file = dirList[len(dirList)-1]
# hides all windows so they can be removed later
for window in (self.graphWindows + self.depWindows + self.miscWindows):
window.hide()
# check filename for correct format
filenameError = filename_handler.parseFilename(self.file)
if filenameError:
self.requestFileInfo(mode)
# set everything up for the new file being read
else:
self.initData()
# if user declines to use default file and declines to open a
# new file, quit the application
else:
if mode == 1:
self.close()
""" creates window for single graph """
def makeGraph(self):
graph = graphwindow_data.GraphWindow()
self.graphWindows.append(graph)
graph.show()
""" shows profile creator window """
def makeProfile(self):
profileCreator = profilecreator.ProfileCreator()
self.miscWindows.append(profileCreator)
profileCreator.show()
""" shows load profile window """
def selectProfile(self):
try:
# open file for reading
savefile = open('saved_profiles.txt', 'rb')
savedProfiles = pickle.load(savefile)
savefile.close()
# this will occur if profiles have never been saved before
# and saved_profiles.txt does not exist
except IOError:
savedProfiles = []
menuList = []
if not savedProfiles:
error = QtGui.QMessageBox.information(None, "Load Profile Error",
"There are no saved profiles.")
return
# only show profiles with headings that correspond to this file
for name, varsList in savedProfiles:
if all(var in datareader.DATA_HEADINGS.values() for var in varsList):
self.profiles[name] = varsList
menuList += [name]
loadMenu = profilewindow.LoadMenu(menuList)
self.miscWindows.append(loadMenu)
loadMenu.show()
loadMenu.profileChosen.connect(self.loadProfile)
loadMenu.profileToDelete.connect(self.deleteProfile)
""" shows deposition graph window """
def makeDeposition(self):
depWindow = depositionwindow_data.DepositionWindow()
self.depWindows.append(depWindow)
""" once profile is chosen, loads profile in new window """
def loadProfile(self, name):
varsList = self.profiles.get(str(name))
profileWindow = profilewindow.ProfileWindow(name, varsList)
self.graphWindows.append(profileWindow)
profileWindow.show()
""" deletes a chosen profile from the LoadMenu """
def deleteProfile(self, name):
# savefile holds list of all profiles
savefile = open('saved_profiles.txt', 'rb')
savedProfiles = pickle.load(savefile)
savefile.close()
for profile in savedProfiles:
if profile[0] == name:
savedProfiles.remove(profile)
# save updated list of profiles
savefile = open('saved_profiles.txt', 'wb')
pickle.dump(savedProfiles, savefile)
savefile.close()
""" sends new data received by reader to active graph windows """
def updateGraphs(self, newRow):
for window in self.graphWindows:
window.updateWindow(newRow)
""" sends new processed data to active deposition graph windows """
def depUpdate(self, newDepRates):
for window in self.depWindows:
window.updateWindow(newDepRates)
""" updates all active graph windows every second """
def redrawAll(self):
for windowType in (self.graphWindows,self.depWindows,self.miscWindows):
for window in windowType:
# release memory resources for all closed windows
if window.isHidden():
windowType.remove(window)
# graph windows will redraw; all other windows
# ignore this command
else:
window.redrawWindow()
""" processes final data set and terminates reader at end of experiment """
def endExperiment(self):
self.processor.onEndExperiment()
""" terminates reader (if still active) when main window is closed """
def closeEvent(self, event):
if self.processor:
self.processor.end()
event.accept()
""" handles signal from reader when new line has been read """
def newLineRead(self, newRow):
self.updateGraphs(newRow)
self.checkValidity(newRow)
# This can be changed to check for errors. Currently it checks one
# row at a time but it could be changed to use averages
""" shows an error message if data indicates experiment failure """
def checkValidity(self, row):
errors_list = []
# change the number so that it ignores the errors for the first
# calibratingNumber of lines we read in case we're callibrating
calibratingNumber = 30
if self.supply % 2 == 0:
fwdValue = float(row[self.fwd])
dcBiasValue = float(row[self.dcbias])
rflValue = float(row[self.rfl])
if fwdValue < 5: errors_list.append("FWD power is below 5.")
if dcBiasValue < 50: errors_list.append("DC bias is below 50.")
if .10*fwdValue < rflValue:
errors_list.append("RFL power is greater than 10% of FWD.")
if self.supply % 2 == 1:
opValue = float(row[self.output_power])
if opValue < 5: errors_list.append("Output power is below 5.")
newErrors = [error for error in errors_list if error not in self.errors]
# rows read by the checkValidity increased
self.rowsReadCV +=1
# do not consider these errors if it's for the first bufferNumber lines
# as it could be callibrating
if self.rowsReadCV < calibratingNumber:
newErrors = []
self.errors += newErrors
# show error warnings if necessary
if newErrors:
message = "You have the following errors: " + " ".join(newErrors)
self.validityError = QtGui.QErrorMessage()
self.validityError.setWindowTitle("Power Supply Warning")
self.validityError.showMessage(message)
""" kills data processor and prompts user to load new file if
invalid source number was provided """
def sourceError(self, srcNum):
self.processor.end()
message = "Source %d is not a valid source. Please load a new file." % srcNum
srcError = QtGui.QMessageBox.warning(None, "Source Error", message)
self.loadDataFile(1)
""" custom dialog box to request necessary file info from user """
class FileInfoDialog(QtGui.QWidget):
#sends signal and mode to MainMenu once all information
# has been entered
fileInfoComplete = pdd.QtCore.pyqtSignal(int)
# sends signal and mode to MainMenu if user closes dialog
fileAborted = pdd.QtCore.pyqtSignal(int)
""" mode is 1 if called when the application is first opened,
0 if called when the user loads a new file """
def __init__(self, mode):
super(FileInfoDialog, self).__init__()
self.mode = mode
self.setWindowModality(pdd.QtCore.Qt.ApplicationModal)
self.initUI()
""" draws user interface of window """
def initUI(self):
self.setWindowTitle('Experiment Information')
self.layout = QtGui.QVBoxLayout(self)
self.gridlayout = QtGui.QGridLayout(self)
self.setLayout(self.layout)
self.labels = []
self.lineEdits = []
# get necessary parameters from FILE_INFO and sort in alphabetical order
self.tagsList = [tag for tag in filename_handler.FILE_INFO.iteritems()]
self.tagsList.sort(key = lambda x: x[0])
self.layout.addWidget(QtGui.QLabel('Please enter values for the following parameters:'))
# make sub-layout to hold labels and text fields
self.layout.addLayout(self.gridlayout)
for i, (tag, val) in enumerate(self.tagsList):
# allow comma-separated lists for z and t values
if type(val) == list:
val = ','.join([str(x) for x in val])
# add label for this parameter
self.labels.append(QtGui.QLabel(tag+':'))
# if a value was obtained from the filename, fill it in the
# text input field
self.lineEdits.append(QtGui.QLineEdit(str(val)))
self.gridlayout.addWidget(self.labels[i], i, 0)
self.gridlayout.addWidget(self.lineEdits[i], i, 1)
self.enter = QtGui.QPushButton('Enter')
self.enter.setDefault(True)
self.enter.clicked.connect(self.sendInfo)
self.layout.addWidget(self.enter, alignment=pdd.QtCore.Qt.AlignRight)
self.show()
""" populates FILE_INFO dictionary and sends signal to MainMenu """
def sendInfo(self):
global FILE_INFO
for i, (tag, val) in enumerate(self.tagsList):
# convert Qstring to string
newValStr = str(self.lineEdits[i].text())
# if text input field is blank, bring up an error message
if not newValStr:
self.completionError()
return
# convert comma-separated entries into list
if type(filename_handler.FILE_INFO.get(tag)) == list:
newValStrList = newValStr.split(',')
try:
newValList = [float(x) for x in newValStrList]
filename_handler.FILE_INFO[tag] = newValList
# raise error if z and t aren't decimal values
except ValueError:
self.formatError()
return
# add user input values to FILE_INFO
else:
filename_handler.FILE_INFO[tag] = newValStr
# notify MainMenu that FILE_INFO is complete and close window
self.fileInfoComplete.emit(self.mode)
self.hide()
""" NOTE: We don't check the validity of any of the actual entries
here because we trust the user to know the parameters of his/her
own experiment. Our program doesn't use the element name anywhere,
but you could easily check if the element name given is an actual
element by checking if it's a key in the ELEMENTS dictionary. An
invalid power supply will raise an error in initSupplyVars in this
file; an invalid source number will raise an error in process_
deposition_data, which will be handled by MainMenu (see SourceError).
If the input t and z values are incorrect, the deposition graph
will simply be blank. """
""" brings up an error message if not all fields are filled in """
def completionError(self):
message = "Please enter values for all parameters."
error = QtGui.QMessageBox.warning(self, "Error", message,
QtGui.QMessageBox.Ok)
""" brings up an error message if z and t entries are invalid """
def formatError(self):
message = "Please enter comma-separated decimal values for Z and tilt."
error = QtGui.QMessageBox.warning(self, "Error", message,
QtGui.QMessageBox.Ok)
# clear text input fields for z and t
if error == QtGui.QMessageBox.Ok:
for i, (tag, val) in enumerate(self.tagsList):
if type(filename_handler.FILE_INFO.get(tag)) == list:
self.lineEdits[i].clear()
""" nothing to redraw every second """
def redrawWindow(self):
pass
""" confirms that user doesn't want to load data file if
user tries to close dialog """
def closeEvent(self, event):
message = "If you close this window, you will be prompted to open another file."
response = QtGui.QMessageBox.information(self, "Note", message,
QtGui.QMessageBox.Ok |
QtGui.QMessageBox.Cancel)
if response == QtGui.QMessageBox.Ok:
self.fileAborted.emit(self.mode)
event.accept()
else:
event.ignore()
""" main event loop """
def main():
global DATA_FILE_DIR
dirfile = open('DefaultDirectory.txt', 'rb')
DATA_FILE_DIR = dirfile.readline()
dirfile.close()
app = QtGui.QApplication(sys.argv)
menu = MainMenu()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
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# Allison Schubauer and Daisy Hernandez
# Created: 6/05/2013
# Last Updated: 6/14/2013
# For JCAP
from elements import ELEMENTS
# holds important information associated with data file
FILE_INFO = {'Element':'', 'Source':'', 'Supply':'', 'TiltDeg':[],
'Z_mm':[]}
""" gets information for FILE_INFO from name of data file """
def parseFilename(filename):
global FILE_INFO
# ignore '.csv' at end of filename
if filename.endswith('.csv'):
filename = filename[:-4]
# experiment parameters should be separated with underscores
rawFileInfo = filename.split('_')
# keys: keywords in filename; values: corresponding keys in FILE_INFO
tagsDict = {'Source': 'Source', 'Src': 'Source', 'SRC': 'Source',
'src': 'Source', 't': 'TiltDeg', 'tilt': 'TiltDeg', 'z': 'Z_mm',
'Z': 'Z_mm', 'Supply': 'Supply', 'Sup': 'Supply', 'sup': 'Supply'}
for tag in rawFileInfo:
# strippedTag is the keyword
strippedTag = filter(str.isalpha, tag)
# tagVal is the numerical value attached to the keyword
tagVal = filter(lambda x: not x.isalpha(), tag)
if strippedTag in tagsDict:
stdName = tagsDict.get(strippedTag)
# multiple z-values can be listed in filename
if (stdName == 'Z_mm' and stdName in FILE_INFO):
FILE_INFO[stdName] += [float(tagVal)]
# z and t values should be stored in a list (used in
# data processing)
elif (stdName == 'Z_mm' or stdName == 'TiltDeg'):
FILE_INFO[stdName] = [float(tagVal)]
# all other values can be saved as string
else:
FILE_INFO[stdName] = tagVal
elif tag in ELEMENTS:
FILE_INFO['Element'] = tag
# keep track of any parameters that were not found in filename
invalidTags = []
for tag in FILE_INFO:
if not FILE_INFO.get(tag):
invalidTags.append(tag)
# existence of invalidTags will be checked by MainMenu
return invalidTags
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# Allison Schubauer and Daisy Hernandez
# Created: 6/05/2013
# Last Updated: 6/14/2013
# For JCAP
import numpy as np
from PyQt4 import QtCore
from dictionary_helpers import *
import date_helpers
import filename_handler
import datareader
# global dictionary holds all processed (z, x, y, rate) data for the experiment
DEP_DATA = []
zndec = 1
tndec = 0
radius1 = 28.
radius2 = 45.
""" does all of the data processing necessary for deposition plots """
class ProcessorThread(QtCore.QThread):
# transfers new line from reader to MainMenu
lineRead = QtCore.pyqtSignal(list)
# transfers new processed data to deposition graph
newData = QtCore.pyqtSignal(tuple)
srcError = QtCore.pyqtSignal(int)
def __init__(self, parent=None, filename='default.csv'):
super(ProcessorThread, self).__init__()
self.file = filename
self.rowBuffer = []
self.changeZ = False
self.running = True
self.reader = datareader.DataReader(parent=self, filename=self.file)
self.reader.lineRead.connect(self.newLineRead)
def run(self):
self.reader.start()
# initialize DATA_DICT column numbers used for data processing
try:
self.tcolnum = getCol('Src%d Motor Tilt Position' %int(filename_handler.FILE_INFO['Source']))
except IndexError:
self.srcError.emit(int(filename_handler.FILE_INFO['Source']))
self.zcolnum = getCol('Platen Zshift Motor 1 Position')
self.anglecolnum = getCol('Platen Motor Position')
while self.running:
pass
""" called whenever the reader sends a full line """
def newLineRead(self, newRow):
self.lineRead.emit(newRow)
self.processRow(newRow)
""" adds a new row to its own row buffer and processes the
data in the row buffer if the azimuth or z-value of the
instrument has changed """
def processRow(self, row):
if self.rowBuffer == []:
self.rowBuffer += [row]
else:
angle = round(float(row[self.anglecolnum]))
zval = round(float(row[self.zcolnum]), 2)
prevangle = round(float(self.rowBuffer[-1][self.anglecolnum]), 0)
prevz = round(float(self.rowBuffer[-1][self.zcolnum]), 2)
if (angle == prevangle and zval == prevz):
self.rowBuffer += [row]
elif (angle == prevangle):
self.processData(prevz, prevangle, radius1)
self.processData(prevz, prevangle, radius2)
# indicates that center point will need to be
# computed in next round of processing
self.changeZ = True
# reset row buffer
self.rowBuffer = [row]
else:
self.processData(zval, prevangle, radius1)
self.processData(zval, prevangle, radius2)
self.rowBuffer = [row]
""" processes all rates at the same angle and z-value
to produce a single (z, x, y, rate) data point """
def processData(self, z, angle, radius):
global DEP_DATA
rowRange = self.getRowRange()
# only one or two data points indicates a transitional angle
# that can be ignored - Savitzky Golay can be used in the future
if rowRange[1] - rowRange[0] <= 2:
pass
else:
# get only valid rows from buffer
dataArray = self.rowBuffer[rowRange[0]:(rowRange[1]+1)]
# transpose matrix so that each column in the
# spreadsheet becomes a row
dataArrayT = np.array(dataArray).T
timespan = self.getTimeSpan(dataArrayT)
depRates = self.getDepRates(timespan, dataArrayT)
# normalize based on drifting center point
rate0 = self.getXtalRate(3, dataArrayT).mean()
rate = rate0
if radius == radius1:
if angle == 0 or self.changeZ:
# plot center point along with first set
# of data for this z-value
DEP_DATA.append((z, 0.0, 0.0, rate))
self.newData.emit((z, 0.0, 0.0, rate))
self.changeZ = False
x = radius * np.cos(angle * np.pi/180.)
y = radius * np.sin(angle * np.pi/180.)
# rate1 corresponds to Xtal4 Rate
rate = rate0 * depRates[2]/depRates[1]
else:
x = radius * np.cos(angle * np.pi/180. + np.pi)
y = radius * np.sin(angle * np.pi/180. + np.pi)
# rate2 corresponds to Xtal2 Rate
rate = rate0 * depRates[0]/depRates[1]
# store data points for initializing new graph
DEP_DATA.append((z, x, y, rate))
# indicate to exisiting graphs that there is
# new data to display
self.newData.emit((z, x, y, rate))
""" helper function to correct for instrument noise in measuring z-value """
def roundZ(self, zcol):
zrnd=np.round(zcol, decimals=zndec)
for i, zval in enumerate(zrnd):
if zval not in filename_handler.FILE_INFO['Z_mm']:
zrnd[i] = -1
return zrnd
""" helper function to correct for instrument noise in measuring tilt """
def roundT(self, tcol):
trnd=np.round(tcol, decimals=tndec)
for i, tval in enumerate(trnd):
if tval not in filename_handler.FILE_INFO['TiltDeg']:
trnd[i] = -1
return trnd
""" gets range of valid rows in row buffer based on
whether z and t values match experimental parameters """
def getRowRange(self):
data = np.array(self.rowBuffer)
datacols = data.T
zcol = map(float, datacols[self.zcolnum])
tcol = map(float, datacols[self.tcolnum])
inds_useful=np.where((self.roundZ(zcol)>=0)&(self.roundT(tcol)>=0))[0]
# if rowRange is nonzero, send it
if inds_useful.size:
return (inds_useful[0], inds_useful[-1])
# otherwise, send dummy rowRange to processData
return (0, 0)
""" gets time span of valid data set for given angle and z-value """
def getTimeSpan(self, dataArrayT):
datecol = getCol('Date')
timecol = getCol('Time')
datetimeTup = zip(dataArrayT[datecol], dataArrayT[timecol])
startStr = datetimeTup[0][0] + ' ' + datetimeTup[0][1]
endStr = datetimeTup[-1][0] + ' ' + datetimeTup[-1][1]
durationObj = date_helpers.dateObjFloat(endStr) - date_helpers.dateObjFloat(startStr)
return durationObj.total_seconds()
""" helper function to return column of Xtal rates from valid data set """
def getXtalRate(self, ratenum, dataArrayT):
rcolnum = getCol('Xtal%d Rate' % ratenum)
return np.array(map(float, dataArrayT[rcolnum]))
""" helper function to compute all deposition rates
as time-averaged Xtal rates """
def getDepRates(self, timespan, dataArrayT):
depRates = []
for x in range(2,5):
rateData = self.getXtalRate(x, dataArrayT)
rateDiff = rateData[-1] - rateData[0]
depRates += [rateDiff/timespan]
return depRates
""" re-initializes data sets and reader when a new
spreadsheet file is loaded """
def newFile(self, newfile):
global DEP_DATA
DEP_DATA = []
self.rowBuffer = []
if self.reader:
self.reader.end()
self.reader = datareader.DataReader(parent=self, filename=newfile)
self.reader.lineRead.connect(self.newLineRead)
self.reader.start()
# re-initialize DATA_DICT column numbers used for data processing
try:
self.tcolnum = getCol('Src%d Motor Tilt Position' %int(filename_handler.FILE_INFO['Source']))
except IndexError:
self.srcError.emit(int(filename_handler.FILE_INFO['Source']))
self.zcolnum = getCol('Platen Zshift Motor 1 Position')
self.anglecolnum = getCol('Platen Motor Position')
""" empties row buffer and kills reader when experiment has ended """
def onEndExperiment(self):
if self.rowBuffer:
angle = round(float(self.rowBuffer[0][self.anglecolnum]))
zval = round(float(self.rowBuffer[0][self.zcolnum]), 1)
self.processData(zval, angle, radius1)
self.processData(zval, angle, radius2)
self.rowBuffer = []
if self.reader:
self.reader.end()
self.reader = None
""" kills both the reader and data processor threads;
called when application exits """
def end(self):
if self.reader:
self.reader.end()
self.running = False
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# Allison Schubauer and Daisy Hernandez
# Created: 6/06/2013
# Last Updated: 6/14/2013
# For JCAP
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.colors as colors
import matplotlib.cm as cm
import process_deposition_data as pdd
import numpy as np
""" the deposition graph, its axes, and its data """
class DepositionGraph(FigureCanvas):
def __init__(self, parent="None", width=2, height=2, dpi=120):
# initialize matplotlib figure
self.figure = Figure(figsize=(width, height), dpi=dpi)
FigureCanvas.__init__(self, self.figure)
# let graph expand when window expands
FigureCanvas.setSizePolicy(self,
QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
# set up the axes and their properties
self.initPlotArea()
# conversion factor is scalar used for changing units
# maxRate is used for resetting the color scale
self.convFactor, self.maxRate = 1, 0
# currentZ is the z-position for which the graph
# is displaying deposition rate data
# zvars holds all z-values for this experiment
self.currentZ, self.zvars = None, []
# formatted string that represents the units of the data
# (defaults to 10^-8 g/(s cm^2))
self.units = r'$10^{-8}$'+'g/s cm'+r'$^2$'
""" initializes the graph's data and axes """
def initPlotArea(self):
self.xdata, self.ydata, self.ratedata = [], [], []
self.plot = self.figure.add_subplot(1, 1, 1, adjustable='box', aspect=1)
self.plot.set_xlim(-50, 50)
self.plot.set_ylim(-50, 50)
# keep x and y limits fixed because radius values are fixed
self.plot.autoscale(enable=False, tight=False)
""" plots all available data when window is opened """
def firstPlot(self, zval = None):
self.currentZ = zval
# default to first z-value in data file
if not zval:
self.currentZ = pdd.DEP_DATA[0][0]
# keep track of all valid z-values for experiment
if self.currentZ not in self.zvars:
self.zvars.append(self.currentZ)
for z, x, y, rate in pdd.DEP_DATA:
# save other z-values for user to access later
if z != self.currentZ:
if z not in self.zvars:
self.zvars.append(z)
continue
# otherwise, plot rate on this graph
self.xdata.append(x)
self.ydata.append(y)
modified_rate = rate*self.convFactor
self.ratedata.append(modified_rate)
# keep maxRate updated if we need to reset the scale
if modified_rate > self.maxRate:
self.maxRate = modified_rate
# holds information about the scale of the colorbar
self.scalarMap = cm.ScalarMappable(norm=colors.Normalize(vmin=0, vmax=self.maxRate))
self.scalarMap.set_array(np.array(self.ratedata))
# plot all available data and save scatter object for later deletion
self.datavis = self.plot.scatter(self.xdata, self.ydata,
c = self.ratedata, cmap=self.scalarMap.get_cmap(),
marker='o', edgecolor='none', s=60)
# initialize colorbar and set its properties
self.colorbar = self.figure.colorbar(self.scalarMap, ax = self.plot)
self.colorbar.set_array(np.array(self.ratedata))
self.colorbar.autoscale()
self.colorbar.set_label(self.units)
self.scalarMap.set_clim(0, self.maxRate)
self.scalarMap.changed()
self.draw()
""" plots newly-processed data on preexisting graph """
def updatePlot(self, newData):
# only plot data corresponding to current z-position
for z, x, y, rate in [newData]:
if z!= self.currentZ:
break
self.xdata.append(x)
self.ydata.append(y)
modified_rate = rate*self.convFactor
self.ratedata.append(modified_rate)
# reset colorbar scale if necessary
if modified_rate > self.maxRate:
self.maxRate = modified_rate
# redraw plot with new point
self.rescale()
self.draw()
""" NOTE: We redraw the entire plot every time a new point comes in
rather than simply adding the point to the preexisting plot because
matplotlib was often unable to associate the new point with the
existing colormap, resulting in a lot of dark blue dots (representing
0 on the scale, as far as we could tell) on the graph, which the user
would have to correct manually by clicking 'Reset Colors.' We decided
that it was worth a negligible amount of extra memory to provide the
user with a less frustrating experience. """
""" redraws the graph with new data and new color scale
(if maxRate has changed) """
def rescale(self):
# clear old color values from plot
self.datavis.remove()
# reset limits of color scale
self.scalarMap.set_clim(0, self.maxRate)
# plot entire set of data according to new scale
self.datavis = self.plot.scatter(self.xdata, self.ydata,
c = self.ratedata,
cmap=self.scalarMap.get_cmap(),
marker='o', edgecolor='none', s=60)
# rescale the colorbar
self.colorbar.draw_all()
""" reset figure prior to switching z-values """
def clearPlot(self):
self.figure.clear()
self.initPlotArea()
self.convFactor, self.maxRate = 1, 0
self.currentZ, self.zvars = None, []
self.units = r'$10^{-8}$'+'g/s cm'+r'$^2$'
""" convert rate data and change label on colorbar when converting units """
def convertPlot(self):
# used to make sure we got all the data values for the given z
lenOfRateData = len(self.ratedata)
currLocation = 0
for z, x, y, rate in pdd.DEP_DATA:
# convert only those rate with the desired z
if currLocation < lenOfRateData and z == self.currentZ:
self.ratedata[currLocation] = rate*self.convFactor
currLocation +=1
elif currLocation >= lenOfRateData:
break
# get new maxRate with max function to prevent errors due to precision
self.maxRate = max(self.ratedata)
self.scalarMap.set_clim(0, self.maxRate)
self.scalarMap.changed()
self.colorbar.draw_all()
self.draw()
""" called when user clicks on graph to display (x, y) data """
def onclick(self,event):
try:
xcorr = event.xdata
ycorr = event.ydata
except TypeError:
pass
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# Allison Schubauer and Daisy Hernandez
# Created: 6/24/2013
# Last Updated: 6/25/2013
# For JCAP
import logging, logging.handlers
class ErrorHandler():
def __init__(self, name, loggingfile, loglevel = logging.DEBUG):
# setting up the logger and the messages allowed to be logged
self.logger = logging.getLogger(name)
self.logger.setLevel(loglevel)
# assures that the messages from the logger are propogated upward
self.logger.propagate
self.loggingfile = loggingfile
""" Initializes the file handler. If we are using a child logger then
we don't have to do this. That is, if we will be relying on the root
logger and it's file handler. """
def initHandler(self,formatforhandler=None, logginglevel = logging.DEBUG):
self.errorFileHandler = self.fileHandlerCreator(self.loggingfile,logginglevel)
self.initFormat(self.errorFileHandler,formatforhandler)
self.addHandler(self.errorFileHandler)
""" Creates a fileHandler and sets its level. This does not have to be done
more than once if one is planning to rely on a root logger. """
def fileHandlerCreator(self, filename, logginglevel):
handler = logging.FileHandler(filename)
handler.setLevel(logginglevel)
return handler
""" Sets the format of the handler it is passed."""
def initFormat(self,hdlr,formattouse ='%(asctime)s - %(name)s - %(levelname)s - %(message)s'):
formatter = logging.Formatter(formattouse)
hdlr.setFormatter(formatter)
""" Adds the handler to the logger created. """
def addHandler(self,hdlr):
self.logger.addHandler(hdlr)
""" Logs the message using different levels. It defaults to DEBUG if
there is no level given or its not one of the other choices. """
def logMessage(self,message,level=logging.DEBUG,traceback=False):
if level == "INFO": level = logging.INFO
elif level == "WARNING": level = logging.WARNING
elif level == "ERROR": level = logging.ERROR
elif level == "CRITICAL": level = logging.CRITICAL
else: level = logging.DEBUG
self.logger.log(level,message,exc_info=traceback)
""" Closes the file handler. If there is more handler those need to be
closed as well. """
def close(self):
self.errorFileHandler.close()
# example of a way to use it
def example(path = 'C:\Users\Public\Documents\errors.log'):
testingLog = ErrorHandler('TestingLog', path)
testingLog.initHandler('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
a = [1,2,3,4,5]
while True:
try:
a.pop()
except:
testingLog.logMessage("Failed doing a.pop()")
break
testingLog.close()
# comment out to see a demstration of the sample
# example()
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# Allison Schubauer and Daisy Hernandez
# Created: 6/24/2013
# Last Updated: 6/27/2013
# For JCAP
# functions for converting our formatted data dictionaries to
# and from JSON files
import os
import csv
import json
import numpy
import datetime
#SAVEPATH = os.path.expanduser("~/Desktop/Working Folder/AutoAnalysisJSON")
##def toJSON(filename, fomdict, intermeddict, inputdict):
## savepath = os.path.join(SAVEPATH, filename+'.json')
## with open(savepath, 'w') as fileObj:
## dataTup = (fomdict, intermeddict, inputdict)
## listOfObjs = []
## for dataDict in dataTup:
## listOfObjs.append(convertNpTypes(dataDict))
## json.dump(listOfObjs, fileObj)
## return savepath
def toJSON(savepath, version, dataTup):
with open(savepath, 'w') as fileObj:
ObjDict['version']=version
for k, dataDict in zip(["measurement_info", "fom", "raw_arrays", "intermediate_arrays", "function_parameters"], dataTup):
ObjDict[k]=convertNpTypes(dataDict)
json.dump(ObjDict, fileObj)
return ObjDict
##def fromJSON(filepath):
## with open(filepath, 'r') as fileObj:
## dataTup = json.load(fileObj, object_hook=unicodeToString)
## print dataTup
## return dataTup
def getDataFromJSON(filepath):
with open(filepath, 'r') as fileObj:
jsonDict = json.load(fileObj, object_hook=unicodeToString)
return jsonDict
def convertNpTypes(container):
if isinstance(container, dict):
pydict = {}
for key, val in container.iteritems():
if isinstance(val, numpy.generic):
val = numpy.asscalar(val)
elif isinstance(val, numpy.ndarray):
val = convertNpTypes(list(val))
elif isinstance(val, datetime.datetime):
val=str(val)
pydict[key] = val
return pydict
elif isinstance(container, list):
for i, val in enumerate(container):
if isinstance(val, numpy.generic):
container[i] = numpy.asscalar(val)
elif isinstance(val, numpy.ndarray):
container[i] = convertNpTypes(list(val))
return container
elif isinstance(container, numpy.ndarray):
return convertNpTypes(list(container))
def unicodeToString(container):
if isinstance(container, dict):
strdict = {}
for key, val in container.iteritems():
if isinstance(key, unicode):
key = str(key)
if isinstance(val, unicode):
val = str(val)
elif isinstance(val, list):
val = unicodeToString(val)
elif isinstance(val, dict):
val = unicodeToString(val)
strdict[key] = val
return strdict
elif isinstance(container, list):
for i, val in enumerate(container):
if isinstance(val, unicode):
container[i] = str(val)
elif isinstance(val, list):
container[i] = unicodeToString(val)
elif isinstance(val, dict):
container[i] = unicodeToString(val)
return container
#def getFOMs(filename):
# dataTup = fromJSON(filename)
# fomdict = dataTup[0]
# idval = filename.split('_')[0]
# with open(filename+'.txt', 'wb') as fileToDB:
# fomwriter = csv.writer(fileToDB)
# rowToWrite = [idval]
# for fom in fomdict:
# rowToWrite.append(str(fom)+': '+str(fomdict.get(fom)))
# fomwriter.writerow(rowToWrite)
# print rowToWrite
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# Allison Schubauer and Daisy Hernandez
# Created: 6/25/2013
# Last Updated: 6/26/2013
# For JCAP
import sys, os
from PyQt4 import QtCore, QtGui
from time import strftime, localtime
import re
class MainMenu(QtGui.QMainWindow):
def __init__(self):
super(MainMenu, self).__init__()
self.versionName = None
self.verifiedName = None
self.initUI()
""" initializes the user interface for this commit menu """
def initUI(self):
self.setGeometry(500, 200, 600, 100)
self.setWindowTitle('Data Analysis File Committer')
self.mainWidget = QtGui.QWidget(self)
self.setCentralWidget(self.mainWidget)
self.secondaryWidget = QtGui.QWidget(self)
self.mainLayout= QtGui.QGridLayout()
self.secondaryLayout= QtGui.QGridLayout()
self.mainWidget.setLayout(self.mainLayout)
self.secondaryWidget.setLayout(self.secondaryLayout)
self.directions = QtGui.QLabel('Please select the folder you wish to.', self)
self.mainLayout.addWidget(self.directions, 0,0)
self.mainLayout.addWidget(self.secondaryWidget)
selectFolder = QtGui.QPushButton('Select Folder', self)
selectFolder.clicked.connect(self.selectProgram)
self.secondaryLayout.addWidget(selectFolder, 0, 0)
self.fileSelected = QtGui.QLineEdit(self)
self.fileSelected.setReadOnly(True)
self.secondaryLayout.addWidget(self.fileSelected, 0, 1)
self.status = QtGui.QLabel('', self)
self.mainLayout.addWidget(self.status)
self.show()
""" textFileTuple is signal received from file dialog; 0th item is string of
file/folder names to display in line edit, 1st item is list of filepaths
(basenames) to load """
def loadData(self, textFileTuple):
self.fileSelected.setText(textFileTuple[0])
self.files = textFileTuple[1]
print len(self.files)
""" deals with getting relevent information for the file ones wishes to commit """
def selectProgram(self):
self.programDialog = QtGui.QFileDialog(self,
caption = "Select a version folder containing data analysis scripts")
self.programDialog.setFileMode(QtGui.QFileDialog.Directory)
# if user clicks 'Choose'
if self.programDialog.exec_():
self.status.setText('')
# list of QStrings (only one folder is allowed to be selected)
dirList = self.programDialog.selectedFiles()
targetDir = os.path.normpath(str(dirList[0]))
pyFiles = filter(lambda f: f.endswith('.py'), os.listdir(targetDir))
# set the line edit and get save the location of the pyFiles
self.loadData(tuple((targetDir,pyFiles)))
print pyFiles
# is the name valid with our version naming standards
nameValidity = self.versionNameVerifier(targetDir)
# if a file's name was invalid to commit
if nameValidity[0] == False:
# deals with renaming the program
newTargetDir = self.renameProgram(targetDir,nameValidity[1])
pyFiles = filter(lambda f: f.endswith('.py'), os.listdir(newTargetDir))
self.loadData(tuple((newTargetDir,pyFiles)))
if nameValidity[0] is not None:
self.status.setText('Your file has been committed.')
""" verifies that the name of the new version folder matches the standard naming """
def versionNameVerifier(self,directory):
plainDirectory = os.path.dirname(directory)
self.versionName = os.path.basename(directory)
dateExpected = strftime("%Y%m%d", localtime())
pattern = '^v(' + dateExpected + ')([0-9])$'
result = re.match(pattern, self.versionName)
# go through all the valid names to check if either we have a match or we must
# renaming the name of the folder
for x in range(0,10):
pathToTest = os.path.join(plainDirectory, 'v' + dateExpected + str(x))
try:
if os.path.exists(pathToTest):
if directory == pathToTest and result:
return (True,None)
else:
pass
else:
return (False,pathToTest)
except:
print "TODO Something must have really gone wrong - put a logger maybe?"
print "It appears you might have done more than 10 commits in one day. \
We thus cannot commit your file. Please refrain from doing this in the future."
return (None,None)
""" deals with renaming the program with a valid name """
def renameProgram(self, oldpath, newpath):
newPath = os.path.normpath(newpath)
oldPath = os.path.normpath(oldpath)
os.rename(oldPath,newPath)
return newPath
""" TODO - A possible function to create """
def buildCompiled(self):
pass
# TODO: when it is already ready to go, perhaps call compiler.compileFile
# so that there is a .pyc file to make it faster. This only handles
# the startup being faster -- also make sure it doesn't get done everytime
# once a file gets compiled
def main():
app = QtGui.QApplication(sys.argv)
menu = MainMenu()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
| {
"repo_name": "johnmgregoire/2013JCAPDataProcess",
"path": "commitmenu.py",
"copies": "1",
"size": "5474",
"license": "bsd-3-clause",
"hash": 6886603468258203000,
"line_mean": 40.1578947368,
"line_max": 116,
"alpha_frac": 0.6251370113,
"autogenerated": false,
"ratio": 4.233565351894819,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.008489787004688212,
"num_lines": 133
} |
# Allison Schubauer and Daisy Hernandez
# Created: 6/26/2013
# Last Updated: 7/25/2013
# For JCAP
"""
runs functions to produce figures of merit automatically, and
replaces dictionaries of data produced by old versions with
updated data
"""
import sys, os
import argparse
import cPickle as pickle
from multiprocessing import Process, Pool, Manager
from inspect import *
from rawdataparser import RAW_DATA_PATH
from qhtest import * # this also imports queue
import jsontranslator
import xmltranslator
import importlib
import distutils.util
import path_helpers
import fomautomator_helpers
import filerunner
import time
import datetime
from infodbcomm import infoDictfromDB
# the directory where the versions of the fomfunctions are
FUNC_DIR = os.path.normpath(os.path.expanduser("~/Desktop/Working Folder/AutoAnalysisFunctions"))
MOD_NAME = 'fomfunctions'
UPDATE_MOD_NAME = 'fomfunctions_update'
""" The FOMAutomator class provides the framework for processing data files
automatically. Its main method, defined in fom_commandline, can be accessed
through the command line. Alternatively, the FOMAutomator can be started
with the user interface in fomautomator_menu. The automator can either
process files in sequence on a single process or use Python's multiprocessing
framework to process files on an optimal number of processes for your
system (determined by Python). Both options are available through the command
line and user interface, but the command line defaults to running sequentially.
In both implementations, status messages and errors are logged to a file in the
output directory, and the FileRunner class (defined in filerunner.py) is used
to process each individual file.
"""
class FOMAutomator(object):
""" initializes the automator with all necessary information """
def __init__(self, rawDataFiles, versionName, prevVersion,funcModule,
updateModule, technique_names, srcDir, dstDir, rawDataDir,errorNum,jobname):
# initializing all the basic info
self.version = versionName
self.lastVersion = prevVersion
# the os.path.insert in the gui or in main is what makes
# we select the correct function module
self.funcMod = __import__(funcModule)
self.modname = funcModule
self.updatemod = updateModule
self.technique_names = technique_names
self.srcDir = srcDir
self.dstDir = dstDir
self.rawDataDir = rawDataDir
# the max number of errors allowed by the user
self.errorNum = errorNum
self.jobname = jobname
self.files = rawDataFiles
self.infoDicts=infoDictfromDB(self.files) # required to have keys 'reference_Eo' and 'technique_name'
self.processFuncs()
""" returns a dictionary with all of the parameters and batch variables
for the fom functions that will be run """
def processFuncs(self):
self.params = {}
self.funcDicts = {}
self.allFuncs = []
# if we have the type of experiment, we can just get the specific functions
if self.technique_names:
for tech in self.technique_names:
techDict = self.funcMod.EXPERIMENT_FUNCTIONS.get(tech)
if techDict:
[self.allFuncs.append(func) for func in techDict
if func not in self.allFuncs]
# if not we just get them all
else:
self.allFuncs = [f[0] for f in getmembers(self.funcMod, isfunction)]
# now that we have all the functions, we get all the parameters
for fname in self.allFuncs:
funcObj = [f[1] for f in getmembers(self.funcMod, isfunction) if
f[0] == fname][0]
funcdict = {'batchvars': [], 'params': []}
try:
dictargs = funcObj.func_code.co_argcount - len(funcObj.func_defaults)
funcdict['numdictargs'] = dictargs
arglist = zip(funcObj.func_code.co_varnames[dictargs:],
funcObj.func_defaults)
except TypeError: # if there are no keyword arguments
dictargs = funcObj.func_code.co_argcount
funcdict['numdictargs'] = dictargs
arglist = []
# note: we're assuming any string argument to the functions that the user wrote is data
# for example t = 't(s)' in the function would mean t is equal to the raw data column t(s)
for arg, val in arglist:
if isinstance(val, list):
funcdict['batchvars'].append(arg)
funcdict['~'+arg] = val
elif isinstance(val, str):
funcdict[arg] = val
## ---- VSHIFT -------------------------------------------------------
## elif arg == 'vshift':
## pass
## -------------------------------------------------------------------
else:
self.params[fname+'_'+arg] = val
funcdict['params'].append(arg)
funcdict['#'+arg] = val
self.funcDicts[fname] = funcdict
""" Returns a list of functions and their parameters, which can be
changed by the user if running fomautomator_menu. This function is
only called by fomautomator_menu. If 'default' is true, the default
parameters defined in the fom functions file are used; otherwise, the
parameters are requested from the user. """
def requestParams(self,default=True):
funcNames = self.funcDicts.keys()
funcNames.sort()
params_full = [[ fname, [(pname,type(pval),pval) for pname in self.funcDicts[fname]['params']
for pval in [self.funcDicts[fname]['#'+pname]]]]
for fname in funcNames if self.funcDicts[fname]['params'] != []]
if not default:
return params_full
else:
funcs_names = [func[0] for func in params_full for num in range(len(func[1]))]
params_and_answers = [[pname,pval] for func in params_full for (pname,ptype,pval) in func[1]]
return funcs_names, params_and_answers
""" If the parameter values were changed by fomautomator_menu, save
the changed values in the automator's parameter dictionary and
function dictionary. """
def setParams(self, funcNames, paramsList):
for fname, params in zip(funcNames, paramsList):
fdict = self.funcDicts[fname]
param,val = params
fdict['#'+param] = val
self.params[fname+'_'+param] = val
""" processes the files in parallel, logs status messages and errors """
def runParallel(self):
# the path to which to log - will change depending on the way
# processing ends and if a statusFile with the same
# name already exists
statusFileName = path_helpers.createPathWExtention(self.dstDir,self.jobname,".run")
# set up the manager and objects required for logging due to multiprocessing
pmanager = Manager()
# this queue takes messages from individual processes and passes them
# to the QueueListener
loggingQueue = pmanager.Queue()
processPool = Pool()
# handler for the logging file
fileHandler = logging.FileHandler(statusFileName)
logFormat = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fileHandler.setFormatter(logFormat)
# the QueueListener takes messages from the logging queue and passes
# them through another queue to the fileHandler (logs safely because
# only this main process writes to the fileHandler)
fileLogger = QueueListener(loggingQueue, fileHandler)
fileLogger.start()
# keep track of when processing started
bTime = time.time()
# the jobs to process each of the files
# jobs = [(loggingQueue, filename, self.version, self.lastVersion,
# self.modname, self.updatemod,self.params, self.funcDicts,
# self.srcDir, self.dstDir, self.rawDataDir)
# for filename in self.files]
jobs = [(loggingQueue, filename, self.version, self.lastVersion,
self.modname, self.updatemod, self.params, self.funcDicts,
self.srcDir, self.dstDir, self.rawDataDir, infodict['reference_Eo'], infodict['technique_name']) \
for (filename, infodict)
in zip(self.files, self.infoDicts)]
processPool.map(makeFileRunner, jobs)
# keep track of when processing ended
eTime = time.time()
timeStamp = time.strftime('%Y%m%d%H%M%S',time.gmtime())
# clean up the pool
processPool.close()
processPool.join()
root = logging.getLogger()
if fileLogger.errorCount > self.errorNum:
root.info("The job encountered %d errors and the max number of them allowed is %d" %(fileLogger.errorCount,self.errorNum))
root.info("Processed for %s H:M:S" %(str(datetime.timedelta(seconds=eTime-bTime)),))
fileLogger.stop()
fileHandler.close()
if fileLogger.errorCount > self.errorNum:
try:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname,".error"))
except:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname+timeStamp,".error"))
else:
try:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname,".done"))
except:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname+timeStamp,".done"))
""" runs the files in order on a single process and logs errors """
def runSequentially(self):
# set up everything needed for logging the errors
root = logging.getLogger()
root.setLevel(logging.INFO)
statusFileName = path_helpers.createPathWExtention(self.dstDir,self.jobname,".run")
fileHandler = logging.FileHandler(statusFileName)
logFormat = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fileHandler.setFormatter(logFormat)
root.addHandler(fileHandler)
numberOfFiles = len(self.files)
numberOfErrors = 0
bTime= time.time()
# The file processing occurs here
logQueue = None
for i, (filename, infodict) in enumerate(zip(self.files, self.infoDicts)):
if numberOfErrors > self.errorNum:
root.info("The job encountered %d errors and the max number of them allowed is %d" %(numberOfErrors,self.errorNum))
break
try:
# returns 1 if file was processed and 0 if file was skipped
exitcode = filerunner.FileRunner(logQueue,filename, self.version,
self.lastVersion, self.modname, self.updatemod,
self.params, self.funcDicts,self.srcDir,
self.dstDir, self.rawDataDir, infodict['reference_Eo'], infodict['technique_name'])
if exitcode.exitSuccess:
root.info('File %s completed %d/%d' %(os.path.basename(filename),i+1,numberOfFiles))
except Exception as someException:
# root.exception will log an ERROR with printed traceback;
# root.error will log an ERROR without traceback
# root.exception(someException)
root.error('Exception raised in file %s:\n' %filename +repr(someException))
numberOfErrors +=1
exitcode = -1
eTime= time.time()
root.info("Processed for %s H:M:S" %(str(datetime.timedelta(seconds=eTime-bTime)),))
timeStamp = time.strftime('%Y%m%d%H%M%S',time.gmtime())
# closing the fileHandler is important or else we cannot rename the file
root.removeHandler(fileHandler)
fileHandler.close()
# the renaming of the run file based on the way the file processing ended
if numberOfErrors > self.errorNum:
try:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname,".error"))
except:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname+timeStamp,".error"))
else:
try:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname,".done"))
except:
os.rename(statusFileName, path_helpers.createPathWExtention(self.dstDir,self.jobname+timeStamp,".done"))
""" This function is started in a separate process by ProcessPool.map.
Here, a FileRunner is created and a processHandler is added temporarily
to log status or error messages from the FileRunner. The argument to
makeFileRunner is the list of arguments to the FileRunner, but this function
is only allowed a single argument because of ProcessPool.map. """
def makeFileRunner(args):
# the multiprocessing queue
queue = args[0]
filename = os.path.basename(args[1])
root = logging.getLogger()
root.setLevel(logging.INFO)
# a logging handler which sends messages to the multiprocessing queue
processHandler = QueueHandler(queue)
root.addHandler(processHandler)
try:
# exitSuccess is 1 if file was processed or 0 if file was too short
exitcode = filerunner.FileRunner(*args)
# if file was processed, write logging message
if exitcode.exitSuccess:
root.info('File %s completed' %filename)
except Exception as someException:
# root.exception will log an ERROR with printed traceback;
# root.error will log an ERROR without traceback
root.error('Exception raised in file %s:\n' %filename +repr(someException))
#root.exception(someException)
exitcode = -1
finally:
# remove handler for this file (because a new handler is created
# for every file)
root.removeHandler(processHandler)
return exitcode
| {
"repo_name": "johnmgregoire/2013JCAPDataProcess",
"path": "fomautomator.py",
"copies": "1",
"size": "14469",
"license": "bsd-3-clause",
"hash": -6962008753702540000,
"line_mean": 46.2843137255,
"line_max": 134,
"alpha_frac": 0.628516138,
"autogenerated": false,
"ratio": 4.3011296076099885,
"config_test": false,
"has_no_keywords": false,
"few_assignments": false,
"quality_score": 1,
"avg_score": 0.008522673552634322,
"num_lines": 306
} |
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