repository_name stringclasses 316
values | func_path_in_repository stringlengths 6 223 | func_name stringlengths 1 134 | language stringclasses 1
value | func_code_string stringlengths 57 65.5k | func_documentation_string stringlengths 1 46.3k | split_name stringclasses 1
value | func_code_url stringlengths 91 315 | called_functions listlengths 1 156 ⌀ | enclosing_scope stringlengths 2 1.48M |
|---|---|---|---|---|---|---|---|---|---|
scivision/gridaurora | gridaurora/__init__.py | chapman_profile | python | def chapman_profile(Z0: float, zKM: np.ndarray, H: float):
return np.exp(.5*(1-(zKM-Z0)/H - np.exp((Z0-zKM)/H))) | Z0: altitude [km] of intensity peak
zKM: altitude grid [km]
H: scale height [km]
example:
pz = chapman_profile(110,np.arange(90,200,1),20) | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/__init__.py#L64-L73 | null | from datetime import datetime, date
from dateutil.parser import parse
import numpy as np
import logging
from typing import Union
def toyearmon(time: datetime) -> int:
# %% date handle
if isinstance(time, (tuple, list, np.ndarray)):
logging.warning(f'taking only first time {time[0]}, would you like mul... |
scivision/gridaurora | gridaurora/calcemissions.py | calcemissions | python | def calcemissions(rates: xarray.DataArray, sim) -> Tuple[xarray.DataArray, np.ndarray, np.ndarray]:
if not sim.reacreq:
return 0., 0., 0.
ver = None
lamb = None
br = None
# %% METASTABLE
if 'metastable' in sim.reacreq:
ver, lamb, br = getMetastable(rates, ver, lamb, br, sim.reaction... | Franck-Condon factor
http://chemistry.illinoisstate.edu/standard/che460/handouts/460-Feb28lec-S13.pdf
http://assign3.chem.usyd.edu.au/spectroscopy/index.php | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L20-L59 | [
"def sortelimlambda(lamb, ver, br):\n assert lamb.ndim == 1\n assert lamb.size == ver.shape[-1]\n# %% eliminate unused wavelengths and Einstein coeff\n mask = np.isfinite(lamb)\n ver = ver[..., mask]\n lamb = lamb[mask]\n br = br[:, mask]\n# %% sort by lambda\n lambSortInd = lamb.argsort() # l... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | getMetastable | python | def getMetastable(rates, ver: np.ndarray, lamb, br, reactfn: Path):
with h5py.File(reactfn, 'r') as f:
A = f['/metastable/A'][:]
lambnew = f['/metastable/lambda'].value.ravel(order='F') # some are not 1-D!
vnew = np.concatenate((A[:2] * rates.loc[..., 'no1s'].values[:, None],
... | concatenate along the reaction dimension, axis=-1 | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L62-L76 | [
"def catvl(z, ver, vnew, lamb, lambnew, br):\n \"\"\"\n trapz integrates over altitude axis, axis = -2\n concatenate over reaction dimension, axis = -1\n\n br: column integrated brightness\n lamb: wavelength [nm]\n ver: volume emission rate [photons / cm^-3 s^-3 ...]\n \"\"\"\n if ver is no... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | getAtomic | python | def getAtomic(rates, ver, lamb, br, reactfn):
with h5py.File(reactfn, 'r') as f:
lambnew = f['/atomic/lambda'].value.ravel(order='F') # some are not 1-D!
vnew = np.concatenate((rates.loc[..., 'po3p3p'].values[..., None],
rates.loc[..., 'po3p5p'].values[..., None]), axis=-1)
... | prompt atomic emissions (nm)
844.6 777.4 | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L79-L89 | [
"def catvl(z, ver, vnew, lamb, lambnew, br):\n \"\"\"\n trapz integrates over altitude axis, axis = -2\n concatenate over reaction dimension, axis = -1\n\n br: column integrated brightness\n lamb: wavelength [nm]\n ver: volume emission rate [photons / cm^-3 s^-3 ...]\n \"\"\"\n if ver is no... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | getN21NG | python | def getN21NG(rates, ver, lamb, br, reactfn):
with h5py.File(str(reactfn), 'r', libver='latest') as f:
A = f['/N2+1NG/A'].value
lambdaA = f['/N2+1NG/lambda'].value.ravel(order='F')
franckcondon = f['/N2+1NG/fc'].value
return doBandTrapz(A, lambdaA, franckcondon, rates.loc[..., 'p1ng'], l... | excitation Franck-Condon factors (derived from Vallance Jones, 1974) | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L92-L101 | [
"def doBandTrapz(Aein, lambnew, fc, kin, lamb, ver, z, br):\n \"\"\"\n ver dimensions: wavelength, altitude, time\n\n A and lambda dimensions:\n axis 0 is upper state vib. level (nu')\n axis 1 is bottom state vib level (nu'')\n there is a Franck-Condon parameter (variable fc) for each upper state... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | getN21PG | python | def getN21PG(rates, ver, lamb, br, reactfn):
with h5py.File(str(reactfn), 'r', libver='latest') as fid:
A = fid['/N2_1PG/A'].value
lambnew = fid['/N2_1PG/lambda'].value.ravel(order='F')
franckcondon = fid['/N2_1PG/fc'].value
tau1PG = 1 / np.nansum(A, axis=1)
consfac = franckcondon... | solve for base concentration
confac=[1.66;1.56;1.31;1.07;.77;.5;.33;.17;.08;.04;.02;.004;.001]; %Cartwright, 1973b, stop at nuprime==12
Gattinger and Vallance Jones 1974
confac=array([1.66,1.86,1.57,1.07,.76,.45,.25,.14,.07,.03,.01,.004,.001]) | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L125-L148 | [
"def catvl(z, ver, vnew, lamb, lambnew, br):\n \"\"\"\n trapz integrates over altitude axis, axis = -2\n concatenate over reaction dimension, axis = -1\n\n br: column integrated brightness\n lamb: wavelength [nm]\n ver: volume emission rate [photons / cm^-3 s^-3 ...]\n \"\"\"\n if ver is no... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | doBandTrapz | python | def doBandTrapz(Aein, lambnew, fc, kin, lamb, ver, z, br):
tau = 1/np.nansum(Aein, axis=1)
scalevec = (Aein * tau[:, None] * fc[:, None]).ravel(order='F')
vnew = scalevec[None, None, :]*kin.values[..., None]
return catvl(z, ver, vnew, lamb, lambnew, br) | ver dimensions: wavelength, altitude, time
A and lambda dimensions:
axis 0 is upper state vib. level (nu')
axis 1 is bottom state vib level (nu'')
there is a Franck-Condon parameter (variable fc) for each upper state nu' | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L151-L166 | [
"def catvl(z, ver, vnew, lamb, lambnew, br):\n \"\"\"\n trapz integrates over altitude axis, axis = -2\n concatenate over reaction dimension, axis = -1\n\n br: column integrated brightness\n lamb: wavelength [nm]\n ver: volume emission rate [photons / cm^-3 s^-3 ...]\n \"\"\"\n if ver is no... | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/calcemissions.py | catvl | python | def catvl(z, ver, vnew, lamb, lambnew, br):
if ver is not None:
br = np.concatenate((br, np.trapz(vnew, z, axis=-2)), axis=-1) # must come first!
ver = np.concatenate((ver, vnew), axis=-1)
lamb = np.concatenate((lamb, lambnew))
else:
ver = vnew.copy(order='F')
lamb = lam... | trapz integrates over altitude axis, axis = -2
concatenate over reaction dimension, axis = -1
br: column integrated brightness
lamb: wavelength [nm]
ver: volume emission rate [photons / cm^-3 s^-3 ...] | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/calcemissions.py#L169-L187 | null | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
import xarray
"""
inputs:
spec: excitation rates, 3-D , dimensions time x altitude x reaction
output:
ver: a pandas DataFrame, wavelength x altitude
br: flux-tube integrated intensity, dimension lamb
See Eqn 9 of A... |
scivision/gridaurora | gridaurora/solarangle.py | solarzenithangle | python | def solarzenithangle(time: datetime, glat: float, glon: float, alt_m: float) -> tuple:
time = totime(time)
obs = EarthLocation(lat=glat*u.deg, lon=glon*u.deg, height=alt_m*u.m)
times = Time(time, scale='ut1')
sun = get_sun(times)
sunobs = sun.transform_to(AltAz(obstime=times, location=obs))
re... | Input:
t: scalar or array of datetime | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/solarangle.py#L8-L21 | [
"def totime(time: Union[str, datetime, np.datetime64]) -> np.ndarray:\n time = np.atleast_1d(time)\n\n if isinstance(time[0], (datetime, np.datetime64)):\n pass\n elif isinstance(time[0], str):\n time = np.atleast_1d(list(map(parse, time)))\n\n return time.squeeze()[()]\n"
] | from datetime import datetime
import astropy.units as u
from astropy.coordinates import get_sun, EarthLocation, AltAz
from astropy.time import Time
from . import totime
|
scivision/gridaurora | gridaurora/eFluxGen.py | maxwellian | python | def maxwellian(E: np.ndarray, E0: np.ndarray, Q0: np.ndarray) -> Tuple[np.ndarray, float]:
E0 = np.atleast_1d(E0)
Q0 = np.atleast_1d(Q0)
assert E0.ndim == Q0.ndim == 1
assert (Q0.size == 1 or Q0.size == E0.size)
Phi = Q0/(2*pi*E0**3) * E[:, None] * np.exp(-E[:, None]/E0)
Q = np.trapz(Phi, E, a... | input:
------
E: 1-D vector of energy bins [eV]
E0: characteristic energy (scalar or vector) [eV]
Q0: flux coefficient (scalar or vector) (to yield overall flux Q)
output:
-------
Phi: differential number flux
Q: total flux
Tanaka 2006 Eqn. 1
http://odin.gi.alaska.edu/lumm/Pape... | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/eFluxGen.py#L14-L39 | null | """
Michael Hirsch
based on Strickland 1993
"""
import logging
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
pi = np.pi
def fluxgen(E, E0, Q0, Wbc, bl, bm, bh, Bm, Bhf, verbose: int = 0) -> tuple:
Wb = Wbc*E0
isimE0 = abs(E - E0).argmin()
base = gaussflux(E, Wb, E... |
scivision/gridaurora | gridaurora/eFluxGen.py | hitail | python | def hitail(E: np.ndarray, diffnumflux: np.ndarray, isimE0: np.ndarray, E0: np.ndarray,
Bhf: np.ndarray, bh: float, verbose: int = 0):
Bh = np.empty_like(E0)
for iE0 in np.arange(E0.size):
Bh[iE0] = Bhf[iE0]*diffnumflux[isimE0[iE0], iE0] # 4100.
# bh = 4 #2.9
het = B... | strickland 1993 said 0.2, but 0.145 gives better match to peak flux at 2500 = E0 | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/eFluxGen.py#L90-L103 | null | """
Michael Hirsch
based on Strickland 1993
"""
import logging
from pathlib import Path
import numpy as np
import h5py
from typing import Tuple
pi = np.pi
def maxwellian(E: np.ndarray, E0: np.ndarray, Q0: np.ndarray) -> Tuple[np.ndarray, float]:
"""
input:
------
E: 1-D vector of energy bins [eV]
... |
scivision/gridaurora | gridaurora/plots.py | plotOptMod | python | def plotOptMod(verNObg3gray, VERgray):
if VERgray is None and verNObg3gray is None:
return
fg = figure()
ax2 = fg.gca() # summed (as camera would see)
if VERgray is not None:
z = VERgray.alt_km
Ek = VERgray.energy_ev.values
# ax1.semilogx(VERgray, z, marker='',label='f... | called from either readTranscar.py or hist-feasibility/plotsnew.py | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/plots.py#L260-L331 | null | import logging
from datetime import datetime
from pathlib import Path
import h5py
import xarray
from numpy.ma import masked_invalid # for pcolormesh, which doesn't like NaN
from matplotlib.pyplot import figure, draw, close
from matplotlib.colors import LogNorm
from matplotlib.ticker import MultipleLocator
from matplot... |
scivision/gridaurora | gridaurora/opticalmod.py | opticalModel | python | def opticalModel(sim, ver: xarray.DataArray, obsAlt_km: float, zenithang: float):
assert isinstance(ver, xarray.DataArray)
# %% get system optical transmission T
optT = getSystemT(ver.wavelength_nm, sim.bg3fn, sim.windowfn, sim.qefn, obsAlt_km, zenithang)
# %% first multiply VER by T, THEN sum overall wavelengt... | ver: Nalt x Nwavelength | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/opticalmod.py#L7-L25 | [
"def getSystemT(newLambda, bg3fn: Path, windfn: Path, qefn: Path,\n obsalt_km, zenang_deg, verbose: bool = False) -> xarray.Dataset:\n\n bg3fn = Path(bg3fn).expanduser()\n windfn = Path(windfn).expanduser()\n qefn = Path(qefn).expanduser()\n\n newLambda = np.asarray(newLambda)\n# %% atmosp... | #!/usr/bin/env python
import logging
import xarray
from .filterload import getSystemT
|
scivision/gridaurora | MakeIonoEigenprofile.py | main | python | def main():
p = ArgumentParser(description='Makes unit flux eV^-1 as input to GLOW or Transcar to create ionospheric eigenprofiles')
p.add_argument('-i', '--inputgridfn', help='original Zettergren input flux grid to base off of', default='zettflux.csv')
p.add_argument('-o', '--outfn', help='hdf5 file to wri... | three output eigenprofiles
1) ver (optical emissions) 4-D array: time x energy x altitude x wavelength
2) prates (production) 4-D array: time x energy x altitude x reaction
3) lrates (loss) 4-D array: time x energy x altitude x reaction | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/MakeIonoEigenprofile.py#L35-L112 | [
"def ploteigver(EKpcolor, zKM, eigenprofile,\n vlim=(None,)*6, sim=None, tInd=None, makeplot=None, prefix=None, progms=None):\n try:\n fg = figure()\n ax = fg.gca()\n # pcolormesh canNOT handle nan at all\n pcm = ax.pcolormesh(EKpcolor, zKM, masked_invalid(eigenprofile),... | #!/usr/bin/env python
"""
Computes Eigenprofiles of Ionospheric response to flux tube input via the following steps:
1. Generate unit input differential number flux vs. energy
2. Compute ionospheric energy deposition and hence production/loss rates for the modeled kinetic chemistries (12 in total)
unverified for prope... |
scivision/gridaurora | gridaurora/ztanh.py | setupz | python | def setupz(Np: int, zmin: float, gridmin: float, gridmax: float) -> np.ndarray:
dz = _ztanh(Np, gridmin, gridmax)
return np.insert(np.cumsum(dz)+zmin, 0, zmin)[:-1] | np: number of grid points
zmin: minimum STEP SIZE at minimum grid altitude [km]
gridmin: minimum altitude of grid [km]
gridmax: maximum altitude of grid [km] | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/ztanh.py#L9-L19 | [
"def _ztanh(Np: int, gridmin: float, gridmax: float) -> np.ndarray:\n \"\"\"\n typically call via setupz instead\n \"\"\"\n x0 = np.linspace(0, 3.14, Np) # arbitrarily picking 3.14 as where tanh gets to 99% of asymptote\n return np.tanh(x0)*gridmax+gridmin\n"
] | #!/usr/bin/env python
"""
inspired by Matt Zettergren
Michael Hirsch
"""
import numpy as np
def _ztanh(Np: int, gridmin: float, gridmax: float) -> np.ndarray:
"""
typically call via setupz instead
"""
x0 = np.linspace(0, 3.14, Np) # arbitrarily picking 3.14 as where tanh gets to 99% of asymptote
... |
scivision/gridaurora | gridaurora/ztanh.py | _ztanh | python | def _ztanh(Np: int, gridmin: float, gridmax: float) -> np.ndarray:
x0 = np.linspace(0, 3.14, Np) # arbitrarily picking 3.14 as where tanh gets to 99% of asymptote
return np.tanh(x0)*gridmax+gridmin | typically call via setupz instead | train | https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/ztanh.py#L22-L27 | null | #!/usr/bin/env python
"""
inspired by Matt Zettergren
Michael Hirsch
"""
import numpy as np
def setupz(Np: int, zmin: float, gridmin: float, gridmax: float) -> np.ndarray:
"""
np: number of grid points
zmin: minimum STEP SIZE at minimum grid altitude [km]
gridmin: minimum altitude of grid [km]
gri... |
jacebrowning/comparable | comparable/tools.py | match_similar | python | def match_similar(base, items):
finds = list(find_similar(base, items))
if finds:
return max(finds, key=base.similarity) # TODO: make O(n)
return None | Get the most similar matching item from a list of items.
@param base: base item to locate best match
@param items: list of items for comparison
@return: most similar matching item or None | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/tools.py#L40-L52 | [
"def find_similar(base, items):\n \"\"\"Get an iterator of items similar to the base.\n\n @param base: base item to locate best match\n @param items: list of items for comparison\n @return: generator of similar items\n\n \"\"\"\n return (item for item in items if base.similarity(item))\n"
] | """Functions to utilize lists of Comparable objects."""
def find_equal(base, items):
"""Get an iterator of items equal to the base.
@param base: base item to find equality
@param items: list of items for comparison
@return: generator of equal items
"""
return (item for item in items if base.... |
jacebrowning/comparable | comparable/tools.py | duplicates | python | def duplicates(base, items):
for item in items:
if item.similarity(base) and not item.equality(base):
yield item | Get an iterator of items similar but not equal to the base.
@param base: base item to perform comparison against
@param items: list of items to compare to the base
@return: generator of items sorted by similarity to the base | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/tools.py#L55-L65 | null | """Functions to utilize lists of Comparable objects."""
def find_equal(base, items):
"""Get an iterator of items equal to the base.
@param base: base item to find equality
@param items: list of items for comparison
@return: generator of equal items
"""
return (item for item in items if base.... |
jacebrowning/comparable | comparable/tools.py | sort | python | def sort(base, items):
return sorted(items, key=base.similarity, reverse=True) | Get a sorted list of items ranked in descending similarity.
@param base: base item to perform comparison against
@param items: list of items to compare to the base
@return: list of items sorted by similarity to the base | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/tools.py#L68-L76 | null | """Functions to utilize lists of Comparable objects."""
def find_equal(base, items):
"""Get an iterator of items equal to the base.
@param base: base item to find equality
@param items: list of items for comparison
@return: generator of equal items
"""
return (item for item in items if base.... |
jacebrowning/comparable | comparable/simple.py | Number.similarity | python | def similarity(self, other):
numerator, denominator = sorted((self.value, other.value))
try:
ratio = float(numerator) / denominator
except ZeroDivisionError:
ratio = 0.0 if numerator else 1.0
similarity = self.Similarity(ratio)
return similarity | Get similarity as a ratio of the two numbers. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/simple.py#L44-L52 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n"
] | class Number(_Simple):
"""Comparable positive number."""
threshold = 0.999 # 99.9% similar
def __init__(self, value):
super().__init__(value)
if value < 0:
raise ValueError("Number objects can only be positive")
def equality(self, other):
"""Get equality using fl... |
jacebrowning/comparable | comparable/simple.py | Text.similarity | python | def similarity(self, other):
ratio = SequenceMatcher(a=self.value, b=other.value).ratio()
similarity = self.Similarity(ratio)
return similarity | Get similarity as a ratio of the two texts. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/simple.py#L65-L69 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n"
] | class Text(_Simple):
"""Comparable generic text."""
threshold = 0.83 # "Hello, world!" ~ "hello world"
def equality(self, other):
"""Get equality using string comparison."""
return str(self) == str(other)
|
jacebrowning/comparable | comparable/simple.py | TextEnum.similarity | python | def similarity(self, other):
ratio = 1.0 if (str(self).lower() == str(other).lower()) else 0.0
similarity = self.Similarity(ratio)
return similarity | Get similarity as a discrete ratio (1.0 or 0.0). | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/simple.py#L78-L82 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n"
] | class TextEnum(Text):
"""Comparable case-insensitive textual enumeration."""
threshold = 1.0 # enumerations must match
|
jacebrowning/comparable | comparable/simple.py | TextTitle._strip | python | def _strip(text):
text = text.strip()
text = text.replace(' ', ' ') # remove duplicate spaces
text = text.lower()
for joiner in TextTitle.JOINERS:
text = text.replace(joiner, 'and')
for article in TextTitle.ARTICLES:
if text.startswith(article + ' '):
... | Strip articles/whitespace and remove case. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/simple.py#L100-L111 | null | class TextTitle(Text):
"""Comparable case-insensitive textual titles."""
threshold = 0.93 # "The Cat and the Hat" ~ "cat an' the hat"
ARTICLES = 'a', 'an', 'the' # stripped from the front
JOINERS = '&', '+' # replaced with 'and'
def __init__(self, value):
super().__init__(value)
... |
jacebrowning/comparable | comparable/simple.py | TextTitle.similarity | python | def similarity(self, other):
logging.debug("comparing %r and %r...", self.stripped, other.stripped)
ratio = SequenceMatcher(a=self.stripped, b=other.stripped).ratio()
similarity = self.Similarity(ratio)
return similarity | Get similarity as a ratio of the stripped text. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/simple.py#L113-L118 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n"
] | class TextTitle(Text):
"""Comparable case-insensitive textual titles."""
threshold = 0.93 # "The Cat and the Hat" ~ "cat an' the hat"
ARTICLES = 'a', 'an', 'the' # stripped from the front
JOINERS = '&', '+' # replaced with 'and'
def __init__(self, value):
super().__init__(value)
... |
jacebrowning/comparable | comparable/base.py | equal | python | def equal(obj1, obj2):
Comparable.log(obj1, obj2, '==')
equality = obj1.equality(obj2)
Comparable.log(obj1, obj2, '==', result=equality)
return equality | Calculate equality between two (Comparable) objects. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L127-L132 | [
"def equality(self, other):\n \"\"\"Compare two objects for equality.\n\n @param self: first object to compare\n @param other: second object to compare\n\n @return: boolean result of comparison\n\n \"\"\"\n # Compare specified attributes for equality\n cname = self.__class__.__name__\n for a... | """Abstract base class and similarity functions."""
import logging
from collections import OrderedDict
from abc import ABCMeta, abstractmethod, abstractproperty # pylint: disable=W0611
class _Base(object): # pylint: disable=R0903
"""Shared base class."""
def _repr(self, *args, **kwargs):
"""Retur... |
jacebrowning/comparable | comparable/base.py | similar | python | def similar(obj1, obj2):
Comparable.log(obj1, obj2, '%')
similarity = obj1.similarity(obj2)
Comparable.log(obj1, obj2, '%', result=similarity)
return similarity | Calculate similarity between two (Comparable) objects. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L135-L140 | [
"def similarity(self, other):\n \"\"\"Compare two objects for similarity.\n\n @param self: first object to compare\n @param other: second object to compare\n\n @return: L{Similarity} result of comparison\n\n \"\"\"\n sim = self.Similarity()\n total = 0.0\n\n # Calculate similarity ratio for ... | """Abstract base class and similarity functions."""
import logging
from collections import OrderedDict
from abc import ABCMeta, abstractmethod, abstractproperty # pylint: disable=W0611
class _Base(object): # pylint: disable=R0903
"""Shared base class."""
def _repr(self, *args, **kwargs):
"""Retur... |
jacebrowning/comparable | comparable/base.py | _Base._repr | python | def _repr(self, *args, **kwargs):
# Remove unnecessary empty keywords arguments and sort the arguments
kwargs = {k: v for k, v in kwargs.items() if v is not None}
kwargs = OrderedDict(sorted(kwargs.items()))
# Build the __repr__ string pieces
args_repr = ', '.join(repr(arg) for ... | Return a __repr__ string from the arguments provided to __init__.
@param args: list of arguments to __init__
@param kwargs: dictionary of keyword arguments to __init__
@return: __repr__ string | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L12-L31 | null | class _Base(object): # pylint: disable=R0903
"""Shared base class."""
|
jacebrowning/comparable | comparable/base.py | Comparable.equality | python | def equality(self, other):
# Compare specified attributes for equality
cname = self.__class__.__name__
for aname in self.attributes:
try:
attr1 = getattr(self, aname)
attr2 = getattr(other, aname)
except AttributeError as error:
... | Compare two objects for equality.
@param self: first object to compare
@param other: second object to compare
@return: boolean result of comparison | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L179-L203 | [
"def log(obj1, obj2, sym, cname=None, aname=None, result=None): # pylint: disable=R0913\n \"\"\"Log the objects being compared and the result.\n\n When no result object is specified, subsequence calls will have an\n increased indentation level. The indentation level is decreased\n once a result object ... | class Comparable(_Base, metaclass=ABCMeta):
"""Abstract Base Class for objects that are comparable.
Subclasses directly comparable must override the 'equality' and
'similarity' methods to return a bool and 'Similarity' object,
respectively.
Subclasses comparable by attributes must override the
... |
jacebrowning/comparable | comparable/base.py | Comparable.similarity | python | def similarity(self, other):
sim = self.Similarity()
total = 0.0
# Calculate similarity ratio for each attribute
cname = self.__class__.__name__
for aname, weight in self.attributes.items():
attr1 = getattr(self, aname, None)
attr2 = getattr(other, aname... | Compare two objects for similarity.
@param self: first object to compare
@param other: second object to compare
@return: L{Similarity} result of comparison | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L206-L253 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n",
"def log(obj1, obj2, sym, cname=None, aname=None, result=None): # pylint: disable=R0913\n ... | class Comparable(_Base, metaclass=ABCMeta):
"""Abstract Base Class for objects that are comparable.
Subclasses directly comparable must override the 'equality' and
'similarity' methods to return a bool and 'Similarity' object,
respectively.
Subclasses comparable by attributes must override the
... |
jacebrowning/comparable | comparable/base.py | Comparable.Similarity | python | def Similarity(self, value=None): # pylint: disable=C0103
if value is None:
value = 0.0
return Similarity(value, threshold=self.threshold) | Constructor for new default Similarities. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L255-L259 | null | class Comparable(_Base, metaclass=ABCMeta):
"""Abstract Base Class for objects that are comparable.
Subclasses directly comparable must override the 'equality' and
'similarity' methods to return a bool and 'Similarity' object,
respectively.
Subclasses comparable by attributes must override the
... |
jacebrowning/comparable | comparable/base.py | Comparable.log | python | def log(obj1, obj2, sym, cname=None, aname=None, result=None): # pylint: disable=R0913
fmt = "{o1} {sym} {o2} : {r}"
if cname or aname:
assert cname and aname # both must be specified
fmt = "{c}.{a}: " + fmt
if result is None:
result = '...'
fmt... | Log the objects being compared and the result.
When no result object is specified, subsequence calls will have an
increased indentation level. The indentation level is decreased
once a result object is provided.
@param obj1: first object
@param obj2: second object
@para... | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/base.py#L262-L292 | [
"def more(cls):\n \"\"\"Increase the indent level.\"\"\"\n cls.level += 1\n",
"def less(cls):\n \"\"\"Decrease the indent level.\"\"\"\n cls.level = max(cls.level - 1, 0)\n",
"def indent(cls, fmt):\n \"\"\"Get a new format string with indentation.\"\"\"\n return '| ' * cls.level + fmt\n"
] | class Comparable(_Base, metaclass=ABCMeta):
"""Abstract Base Class for objects that are comparable.
Subclasses directly comparable must override the 'equality' and
'similarity' methods to return a bool and 'Similarity' object,
respectively.
Subclasses comparable by attributes must override the
... |
jacebrowning/comparable | comparable/compound.py | Group.equality | python | def equality(self, other):
if not len(self) == len(other):
return False
return super().equality(other) | Calculate equality based on equality of all group items. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/compound.py#L42-L46 | [
"def equality(self, other):\n \"\"\"A compound comparable's equality is based on attributes.\"\"\"\n return super().equality(other)\n"
] | class Group(CompoundComparable): # pylint: disable=W0223
"""Comparable list of Comparable items."""
attributes = None # created dynamically
def __init__(self, items):
self.items = items
names = ("item{0}".format(n + 1) for n in range(len(items)))
self.attributes = {name: 1 for n... |
jacebrowning/comparable | comparable/compound.py | Group.similarity | python | def similarity(self, other):
# Select the longer list as the basis for comparison
if len(self.items) > len(other.items):
first, second = self, other
else:
first, second = other, self
items = list(first.items) # backup items list
length = len(items)
... | Calculate similarity based on best matching permutation of items. | train | https://github.com/jacebrowning/comparable/blob/48455e613650e22412d31109681368fcc479298d/comparable/compound.py#L48-L75 | [
"def Similarity(self, value=None): # pylint: disable=C0103\n \"\"\"Constructor for new default Similarities.\"\"\"\n if value is None:\n value = 0.0\n return Similarity(value, threshold=self.threshold)\n",
"def log(obj1, obj2, sym, cname=None, aname=None, result=None): # pylint: disable=R0913\n ... | class Group(CompoundComparable): # pylint: disable=W0223
"""Comparable list of Comparable items."""
attributes = None # created dynamically
def __init__(self, items):
self.items = items
names = ("item{0}".format(n + 1) for n in range(len(items)))
self.attributes = {name: 1 for n... |
timothydmorton/simpledist | simpledist/distributions.py | double_lorgauss | python | def double_lorgauss(x,p):
mu,sig1,sig2,gam1,gam2,G1,G2 = p
gam1 = float(gam1)
gam2 = float(gam2)
G1 = abs(G1)
G2 = abs(G2)
sig1 = abs(sig1)
sig2 = abs(sig2)
gam1 = abs(gam1)
gab2 = abs(gam2)
L2 = (gam1/(gam1 + gam2)) * ((gam2*np.pi*G1)/(sig1*np.sqrt(2*np.pi)) -
... | Evaluates a normalized distribution that is a mixture of a double-sided Gaussian and Double-sided Lorentzian.
Parameters
----------
x : float or array-like
Value(s) at which to evaluate distribution
p : array-like
Input parameters: mu (mode of distribution),
s... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L615-L664 | null | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | fit_double_lorgauss | python | def fit_double_lorgauss(bins,h,Ntry=5):
try:
from lmfit import minimize, Parameters, Parameter, report_fit
except ImportError:
raise ImportError('you need lmfit to use this function.')
#make sure histogram is normalized
h /= np.trapz(h,bins)
#zero-pad the ends of the distri... | Uses lmfit to fit a "Double LorGauss" distribution to a provided histogram.
Uses a grid of starting guesses to try to avoid local minima.
Parameters
----------
bins, h : array-like
Bins and heights of a histogram, as returned by, e.g., `np.histogram`.
Ntry : int, optional
Spacing ... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L666-L754 | [
"def double_lorgauss(x,p):\n \"\"\"Evaluates a normalized distribution that is a mixture of a double-sided Gaussian and Double-sided Lorentzian.\n\n Parameters\n ----------\n x : float or array-like\n Value(s) at which to evaluate distribution\n\n p : array-like\n Input parameters: mu (... | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | doublegauss | python | def doublegauss(x,p):
mu,sig1,sig2 = p
x = np.atleast_1d(x)
A = 1./(np.sqrt(2*np.pi)*(sig1+sig2)/2.)
ylo = A*np.exp(-(x-mu)**2/(2*sig1**2))
yhi = A*np.exp(-(x-mu)**2/(2*sig2**2))
y = x*0
wlo = np.where(x < mu)
whi = np.where(x >= mu)
y[wlo] = ylo[wlo]
y[whi] = yhi[whi]
if np.... | Evaluates normalized two-sided Gaussian distribution
Parameters
----------
x : float or array-like
Value(s) at which to evaluate distribution
p : array-like
Parameters of distribution: (mu: mode of distribution,
sig1: LH width,
... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L792-L824 | null | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | doublegauss_cdf | python | def doublegauss_cdf(x,p):
x = np.atleast_1d(x)
mu,sig1,sig2 = p
sig1 = np.absolute(sig1)
sig2 = np.absolute(sig2)
ylo = float(sig1)/(sig1 + sig2)*(1 + erf((x-mu)/np.sqrt(2*sig1**2)))
yhi = float(sig1)/(sig1 + sig2) + float(sig2)/(sig1+sig2)*(erf((x-mu)/np.sqrt(2*sig2**2)))
lo = x < mu
hi... | Cumulative distribution function for two-sided Gaussian
Parameters
----------
x : float
Input values at which to calculate CDF.
p : array-like
Parameters of distribution: (mu: mode of distribution,
sig1: LH width,
... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L826-L847 | null | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | fit_doublegauss_samples | python | def fit_doublegauss_samples(samples,**kwargs):
sorted_samples = np.sort(samples)
N = len(samples)
med = sorted_samples[N/2]
siglo = med - sorted_samples[int(0.16*N)]
sighi = sorted_samples[int(0.84*N)] - med
return fit_doublegauss(med,siglo,sighi,median=True,**kwargs) | Fits a two-sided Gaussian to a set of samples.
Calculates 0.16, 0.5, and 0.84 quantiles and passes these to
`fit_doublegauss` for fitting.
Parameters
----------
samples : array-like
Samples to which to fit the Gaussian.
kwargs
Keyword arguments passed to `fit_doublegauss`. | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L849-L868 | [
"def fit_doublegauss(med,siglo,sighi,interval=0.683,p0=None,median=False,return_distribution=True):\n \"\"\"Fits a two-sided Gaussian distribution to match a given confidence interval.\n\n The center of the distribution may be either the median or the mode.\n\n Parameters\n ----------\n med : float\n... | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | fit_doublegauss | python | def fit_doublegauss(med,siglo,sighi,interval=0.683,p0=None,median=False,return_distribution=True):
if median:
q1 = 0.5 - (interval/2)
q2 = 0.5 + (interval/2)
targetvals = np.array([med-siglo,med,med+sighi])
qvals = np.array([q1,0.5,q2])
def objfn(pars):
logging.de... | Fits a two-sided Gaussian distribution to match a given confidence interval.
The center of the distribution may be either the median or the mode.
Parameters
----------
med : float
The center of the distribution to which to fit. Default this
will be the mode unless the `median` keyword... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L871-L937 | null | from __future__ import absolute_import, division, print_function
__author__ = 'Timothy D. Morton <tim.morton@gmail.com>'
"""
Defines objects useful for describing probability distributions.
"""
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.interpolate import UnivariateSpline as interpol... |
timothydmorton/simpledist | simpledist/distributions.py | Distribution.pctile | python | def pctile(self,pct,res=1000):
grid = np.linspace(self.minval,self.maxval,res)
return grid[np.argmin(np.absolute(pct-self.cdf(grid)))] | Returns the desired percentile of the distribution.
Will only work if properly normalized. Designed to mimic
the `ppf` method of the `scipy.stats` random variate objects.
Works by gridding the CDF at a given resolution and matching the nearest
point. NB, this is of course not as preci... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L136-L158 | [
"def cdf(x):\n x = np.atleast_1d(x)\n y = np.atleast_1d(cdf_fn(x))\n y[np.where(x < self.minval)] = 0\n y[np.where(x > self.maxval)] = 1\n return y\n"
] | class Distribution(object):
"""Base class to describe probability distribution.
Has some minimal functional overlap with scipy.stats random variates
(e.g. `ppf`, `rvs`)
Parameters
----------
pdf : callable
The probability density function to be used. Does not have to be
normal... |
timothydmorton/simpledist | simpledist/distributions.py | Distribution.save_hdf | python | def save_hdf(self,filename,path='',res=1000,logspace=False):
if logspace:
vals = np.logspace(np.log10(self.minval),
np.log10(self.maxval),
res)
else:
vals = np.linspace(self.minval,self.maxval,res)
d = {'vals':... | Saves distribution to an HDF5 file.
Saves a pandas `dataframe` object containing tabulated pdf and cdf
values at a specfied resolution. After saving to a particular path, a
distribution may be regenerated using the `Distribution_FromH5` subclass.
Parameters
----------
... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L162-L206 | [
"def cdf(x):\n x = np.atleast_1d(x)\n y = np.atleast_1d(cdf_fn(x))\n y[np.where(x < self.minval)] = 0\n y[np.where(x > self.maxval)] = 1\n return y\n"
] | class Distribution(object):
"""Base class to describe probability distribution.
Has some minimal functional overlap with scipy.stats random variates
(e.g. `ppf`, `rvs`)
Parameters
----------
pdf : callable
The probability density function to be used. Does not have to be
normal... |
timothydmorton/simpledist | simpledist/distributions.py | Distribution.plot | python | def plot(self,minval=None,maxval=None,fig=None,log=False,
npts=500,**kwargs):
if minval is None:
minval = self.minval
if maxval is None:
maxval = self.maxval
if maxval==np.inf or minval==-np.inf:
raise ValueError('must have finite upper and lower ... | Plots distribution.
Parameters
----------
minval : float,optional
minimum value to plot. Required if minval of Distribution is
`-np.inf`.
maxval : float, optional
maximum value to plot. Required if maxval of Distribution is
`np.inf`.
... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L244-L294 | null | class Distribution(object):
"""Base class to describe probability distribution.
Has some minimal functional overlap with scipy.stats random variates
(e.g. `ppf`, `rvs`)
Parameters
----------
pdf : callable
The probability density function to be used. Does not have to be
normal... |
timothydmorton/simpledist | simpledist/distributions.py | Distribution.resample | python | def resample(self,N,minval=None,maxval=None,log=False,res=1e4):
N = int(N)
if minval is None:
if hasattr(self,'minval_cdf'):
minval = self.minval_cdf
else:
minval = self.minval
if maxval is None:
if hasattr(self,'maxval_cdf'):
... | Returns random samples generated according to the distribution
Mirrors basic functionality of `rvs` method for `scipy.stats`
random variates. Implemented by mapping uniform numbers onto the
inverse CDF using a closest-matching grid approach.
Parameters
----------
N : i... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L296-L357 | [
"def cdf(x):\n x = np.atleast_1d(x)\n y = np.atleast_1d(cdf_fn(x))\n y[np.where(x < self.minval)] = 0\n y[np.where(x > self.maxval)] = 1\n return y\n"
] | class Distribution(object):
"""Base class to describe probability distribution.
Has some minimal functional overlap with scipy.stats random variates
(e.g. `ppf`, `rvs`)
Parameters
----------
pdf : callable
The probability density function to be used. Does not have to be
normal... |
timothydmorton/simpledist | simpledist/distributions.py | Hist_Distribution.plothist | python | def plothist(self,fig=None,**kwargs):
setfig(fig)
plt.hist(self.samples,bins=self.bins,**kwargs) | Plots a histogram of samples using provided bins.
Parameters
----------
fig : None or int
Parameter passed to `setfig`.
kwargs
Keyword arguments passed to `plt.hist`. | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L548-L560 | null | class Hist_Distribution(Distribution):
"""Generates a distribution from a histogram of provided samples.
Uses `np.histogram` to create a histogram using the bins keyword,
then interpolates this histogram to create the pdf to pass to the
`Distribution` constructor.
Parameters
----------
sam... |
timothydmorton/simpledist | simpledist/distributions.py | Hist_Distribution.resample | python | def resample(self,N):
inds = rand.randint(len(self.samples),size=N)
return self.samples[inds] | Returns a bootstrap resampling of provided samples.
Parameters
----------
N : int
Number of samples. | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L562-L571 | null | class Hist_Distribution(Distribution):
"""Generates a distribution from a histogram of provided samples.
Uses `np.histogram` to create a histogram using the bins keyword,
then interpolates this histogram to create the pdf to pass to the
`Distribution` constructor.
Parameters
----------
sam... |
timothydmorton/simpledist | simpledist/distributions.py | Box_Distribution.resample | python | def resample(self,N):
return rand.random(size=N)*(self.maxval - self.minval) + self.minval | Returns a random sampling. | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L606-L609 | null | class Box_Distribution(Distribution):
"""Simple distribution uniform between provided lower and upper limits.
Parameters
----------
lo,hi : float
Lower/upper limits of the distribution.
kwargs
Keyword arguments passed to `Distribution` constructor.
"""
def __init__(self,lo... |
timothydmorton/simpledist | simpledist/distributions.py | DoubleGauss_Distribution.resample | python | def resample(self,N,**kwargs):
lovals = self.mu - np.absolute(rand.normal(size=N)*self.siglo)
hivals = self.mu + np.absolute(rand.normal(size=N)*self.sighi)
u = rand.random(size=N)
hi = (u < float(self.sighi)/(self.sighi + self.siglo))
lo = (u >= float(self.sighi)/(self.sighi + ... | Random resampling of the doublegauss distribution | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/distributions.py#L982-L995 | null | class DoubleGauss_Distribution(Distribution):
"""A Distribution oject representing a two-sided Gaussian distribution
This can be used to represent a slightly asymmetric distribution,
and consists of two half-Normal distributions patched together at the
mode, and normalized appropriately. The pdf and c... |
timothydmorton/simpledist | simpledist/kde.py | deriv | python | def deriv(f,c,dx=0.0001):
return (f(c+dx)-f(c-dx))/(2*dx) | deriv(f,c,dx) --> float
Returns f'(x), computed as a symmetric difference quotient. | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/kde.py#L224-L230 | null | from __future__ import absolute_import, division, print_function
import numpy as np
from scipy.stats import gaussian_kde
import numpy.random as rand
from scipy.integrate import quad
class KDE(object):
"""An implementation of a kernel density estimator allowing for adaptive kernels.
If the `adaptive` keyword i... |
timothydmorton/simpledist | simpledist/kde.py | newton | python | def newton(f,c,tol=0.0001,restrict=None):
#print(c)
if restrict:
lo,hi = restrict
if c < lo or c > hi:
print(c)
c = random*(hi-lo)+lo
if fuzzyequals(f(c),0,tol):
return c
else:
try:
return newton(f,c-f(c)/deriv(f,c,tol),tol,restrict)
... | newton(f,c) --> float
Returns the x closest to c such that f(x) = 0 | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/kde.py#L235-L254 | [
"def newton(f,c,tol=0.0001,restrict=None):\n \"\"\"\n newton(f,c) --> float\n\n Returns the x closest to c such that f(x) = 0\n \"\"\"\n #print(c)\n if restrict:\n lo,hi = restrict\n if c < lo or c > hi:\n print(c)\n c = random*(hi-lo)+lo\n\n if fuzzyequals(f... | from __future__ import absolute_import, division, print_function
import numpy as np
from scipy.stats import gaussian_kde
import numpy.random as rand
from scipy.integrate import quad
class KDE(object):
"""An implementation of a kernel density estimator allowing for adaptive kernels.
If the `adaptive` keyword i... |
timothydmorton/simpledist | simpledist/kde.py | KDE.integrate_box | python | def integrate_box(self,low,high,forcequad=False,**kwargs):
if not self.adaptive and not forcequad:
return self.gauss_kde.integrate_box_1d(low,high)*self.norm
return quad(self.evaluate,low,high,**kwargs)[0] | Integrates over a box. Optionally force quad integration, even for non-adaptive.
If adaptive mode is not being used, this will just call the
`scipy.stats.gaussian_kde` method `integrate_box_1d`. Else,
by default, it will call `scipy.integrate.quad`. If the
`forcequad` flag is turned o... | train | https://github.com/timothydmorton/simpledist/blob/d9807c90a935bd125213445ffed6255af558f1ca/simpledist/kde.py#L131-L156 | null | class KDE(object):
"""An implementation of a kernel density estimator allowing for adaptive kernels.
If the `adaptive` keyword is set to `False`, then this will essentially be just
a wrapper for the `scipy.stats.gaussian_kde` class. If adaptive, though, it
allows for different kernels and different ke... |
ttm/socialLegacy | social/twiter.py | Twitter.searchTag | python | def searchTag(self,HTAG="#arenaNETmundial"):
search = t.search(q=HTAG,count=100,result_type="recent")
ss=search[:]
search = t.search(q=HTAG,count=150,max_id=ss[-1]['id']-1,result_type="recent")
#search = t.search(q=HTAG,count=150,since_id=ss[-1]['id'],result_type="recent")
while ... | Set Twitter search or stream criteria for the selection of tweets | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/twiter.py#L24-L32 | null | class Twitter:
"""Simplified Twitter interface for Stability observance
# function to set authentication: __init__()
# function to set hashtag and other tweets selection criteria: searchTag()
# function to search tweets: searchTag()
# function to stream tweets: void
"""
def __init__(self,ap... |
ttm/socialLegacy | social/utils.py | makeRetweetNetwork | python | def makeRetweetNetwork(tweets):
G=x.DiGraph()
G_=x.DiGraph()
for tweet in tweets:
text=tweet["text"]
us=tweet["user"]["screen_name"]
if text.startswith("RT @"):
prev_us=text.split(":")[0].split("@")[1]
#print(us,prev_us,text)
if G.has_edge(prev_us,... | Receives tweets, returns directed retweet networks.
Without and with isolated nodes. | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/utils.py#L2-L23 | null | import networkx as x
def makeRetweetNetwork(tweets):
"""Receives tweets, returns directed retweet networks.
Without and with isolated nodes.
"""
G=x.DiGraph()
G_=x.DiGraph()
for tweet in tweets:
text=tweet["text"]
us=tweet["user"]["screen_name"]
if text.startswith("RT @"... |
ttm/socialLegacy | social/utils.py | GDFgraph.makeNetwork | python | def makeNetwork(self):
if "weight" in self.data_friendships.keys():
self.G=G=x.DiGraph()
else:
self.G=G=x.Graph()
F=self.data_friends
for friendn in range(self.n_friends):
if "posts" in F.keys():
G.add_node(F["name"][friendn],
... | Makes graph object from .gdf loaded data | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/utils.py#L57-L85 | null | class GDFgraph:
"""Read GDF graph into networkX"""
def __init__(self,filename="../data/RenatoFabbri06022014.gdf"):
with open(filename,"r") as f:
self.data=f.read()
self.lines=self.data.split("\n")
columns=self.lines[0].split(">")[1].split(",")
column_names=[i.split(" ... |
ttm/socialLegacy | social/fb/gml2rdf.py | triplifyGML | python | def triplifyGML(dpath="../data/fb/",fname="foo.gdf",fnamei="foo_interaction.gdf",
fpath="./fb/",scriptpath=None,uid=None,sid=None,fb_link=None,ego=True,umbrella_dir=None):
c("iniciado tripgml")
if sum(c.isdigit() for c in fname)==4:
year=re.findall(r".*(\d\d\d\d).gml",fname)[0][0]
B.date... | Produce a linked data publication tree from a standard GML file.
INPUTS:
======
=> the data directory path
=> the file name (fname) of the friendship network
=> the file name (fnamei) of the interaction network
=> the final path (fpath) for the tree of files to be created
=> a path to the s... | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fb/gml2rdf.py#L4-L67 | [
"def rdfFriendshipNetwork(fnet):\n tg=P.rdf.makeBasicGraph([[\"po\",\"fb\"],[P.rdf.ns.per,P.rdf.ns.fb]],\"Facebook friendship network from {} . Ego: {}\".format(B.name,B.ego))\n #if sum([(\"user\" in i) for i in fnet[\"individuals\"][\"label\"]])==len(fnet[\"individuals\"][\"label\"]):\n # # nomes falso... | import percolation as P, social as S, rdflib as r, builtins as B, re, datetime, os, shutil
c=P.utils.check
def trans(tkey):
if tkey=="name":
return "numericID"
if tkey=="label":
return "name"
return tkey
def rdfInteractionNetwork(fnet):
tg=P.rdf.makeBasicGraph([["po","fb"],[P.rdf.ns... |
ttm/socialLegacy | social/fb/fb.py | triplifyGML | python | def triplifyGML(fname="foo.gml",fpath="./fb/",scriptpath=None,uid=None,sid=None,extra_info=None):
# aname=fname.split("/")[-1].split(".")[0]
aname=fname.split("/")[-1].split(".")[0]
if "RonaldCosta" in fname:
aname=fname.split("/")[-1].split(".")[0]
name,day,month,year=re.findall(".*/([a-zA-... | Produce a linked data publication tree from a standard GML file.
INPUTS:
=> the file name (fname, with path) where the gdf file
of the friendship network is.
=> the final path (fpath) for the tree of files to be created.
=> a path to the script that is calling this function (scriptpath).
... | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fb/fb.py#L11-L167 | null | import time, os, pickle, shutil, datetime, re
import networkx as x, rdflib as r
from splinter import Browser
from bs4 import BeautifulSoup
import percolation as P
c=P.utils.check
this_dir = os.path.split(__file__)[0]
NS=P.rdf.ns
a=NS.rdf.type
def triplifyGML(fname="foo.gml",fpath="./fb/",scriptpath=None,uid=None,sid=N... |
ttm/socialLegacy | social/fb/fb.py | triplifyGDFInteraction | python | def triplifyGDFInteraction(fname="foo.gdf",fpath="./fb/",scriptpath=None,uid=None,sid=None,dlink=None):
#aname=fname.split("/")[-1].split(".")[0]+"_fb"
aname=fname.split("/")[-1].split(".")[0]
if re.findall("[a-zA-Z]*_[0-9]",fname):
name,year,month,day,hour,minute=re.findall(".*/([a-zA-Z]*).*(\d\d\d... | Produce a linked data publication tree from GDF files of a Facebook interaction network.
INPUTS:
=> the file name (fname, with path) where the gdf file
of the friendship network is.
=> the final path (fpath) for the tree of files to be created.
=> a path to the script that is calling this fun... | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fb/fb.py#L170-L326 | [
"def readGDF(filename=\"../data/RenatoFabbri06022014.gdf\"):\n \"\"\"Made to work with my own network. Check file to ease adaptation\"\"\"\n with open(filename,\"r\") as f:\n data=f.read()\n lines=data.split(\"\\n\")\n columns=lines[0].split(\">\")[1].split(\",\")\n column_names=[i.split(\" \"... | import time, os, pickle, shutil, datetime, re
import networkx as x, rdflib as r
from splinter import Browser
from bs4 import BeautifulSoup
import percolation as P
c=P.utils.check
this_dir = os.path.split(__file__)[0]
NS=P.rdf.ns
a=NS.rdf.type
def triplifyGML(fname="foo.gml",fpath="./fb/",scriptpath=None,uid=None,sid=N... |
ttm/socialLegacy | social/fb/fb.py | ScrapyBrowser.getFriends | python | def getFriends(self,user_id="astronauta.mecanico",write=True):
while user_id not in self.browser.url:
self.browser.visit("http://www.facebook.com/{}/friends".format(user_id), wait_time=3)
#self.go("http://www.facebook.com/{}/friends".format(user_id))
T=time.time()
while 1:
... | Returns user_ids (that you have access) of the friends of your friend with user_ids | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fb/fb.py#L611-L655 | null | class ScrapyBrowser:
"""Opens a browser for user to login to facebook.
Such browser pulls data as requested by user."""
def __init__(self,user_email=None, user_password=None,basedir="~/.social/"):
self._BASE_DIR=basedir.replace("~",os.path.expanduser("~"))
if not os.path.isdir(self._BASE_DI... |
ttm/socialLegacy | social/fb/read.py | readGDF | python | def readGDF(filename="../data/RenatoFabbri06022014.gdf"):
with open(filename,"r") as f:
data=f.read()
lines=data.split("\n")
columns=lines[0].split(">")[1].split(",")
column_names=[i.split(" ")[0] for i in columns]
data_friends={cn:[] for cn in column_names}
for line in lines[1:]:
... | Made to work with gdf files from my own network and friends and groups | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fb/read.py#L164-L192 | null | import networkx as x, percolation as P, re
c=P.utils.check
def readGML2(filename="../data/RenatoFabbri06022014.gml"):
with open(filename,"r") as f:
data=f.read()
lines=data.split("\n")
nodes=[] # list of dicts, each a node
edges=[] # list of tuples
state="receive"
for line in lines:
... |
ttm/socialLegacy | social/tw.py | Twitter.searchTag | python | def searchTag(self,HTAG="#python"):
self.t = Twython(app_key =self.app_key ,
app_secret =self.app_secret ,
oauth_token =self.oauth_token ,
oauth_token_secret =self.oauth_token_secret)
... | Set Twitter search or stream criteria for the selection of tweets | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/tw.py#L293-L307 | null | class Twitter:
"""Simplified Twitter interface for Stability observance
# function to set authentication: __init__()
# function to set hashtag and other tweets selection criteria: searchTag()
# function to search tweets: searchTag()
# function to stream tweets: void
"""
TWITTER_API_KEY ... |
ttm/socialLegacy | social/fsong.py | FSong.makePartitions | python | def makePartitions(self):
class NetworkMeasures:
pass
self.nm=nm=NetworkMeasures()
nm.degrees=self.network.degree()
nm.nodes_= sorted(self.network.nodes(), key=lambda x : nm.degrees[x])
nm.degrees_=[nm.degrees[i] for i in nm.nodes_]
nm.edges= self.network.... | Make partitions with gmane help. | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L29-L41 | null | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.makeImages | python | def makeImages(self):
# make layout
self.makeLayout()
self.setAgraph()
# make function that accepts a mode, a sector
# and nodes and edges True and False
self.plotGraph()
self.plotGraph("reversed",filename="tgraphR.png")
agents=n.concatenate(self.np.sector... | Make spiral images in sectors and steps.
Plain, reversed,
sectorialized, negative sectorialized
outline, outline reversed, lonely
only nodes, only edges, both | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L42-L63 | [
"def plotGraph(self,mode=\"plain\",nodes=None,filename=\"tgraph.png\"):\n \"\"\"Plot graph with nodes (iterable) into filename\n \"\"\"\n if nodes==None:\n nodes=self.nodes\n else:\n nodes=[i for i in self.nodes if i in nodes]\n for node in self.nodes:\n n_=self.A.get_node(node)\... | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.plotGraph | python | def plotGraph(self,mode="plain",nodes=None,filename="tgraph.png"):
if nodes==None:
nodes=self.nodes
else:
nodes=[i for i in self.nodes if i in nodes]
for node in self.nodes:
n_=self.A.get_node(node)
if mode=="plain":
nmode=1
... | Plot graph with nodes (iterable) into filename | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L72-L109 | null | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.makeSong | python | def makeSong(self):
self.makeVisualSong()
self.makeAudibleSong()
if self.make_video:
self.makeAnimation() | Render abstract animation | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L127-L133 | [
"def makeVisualSong(self):\n \"\"\"Return a sequence of images and durations.\n \"\"\"\n self.files=os.listdir(self.basedir)\n self.stairs=[i for i in self.files if (\"stair\" in i) and (\"R\" in i)]\n self.sectors=[i for i in self.files if \"sector\" in i]\n self.stairs.sort()\n self.sectors.s... | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.makeVisualSong | python | def makeVisualSong(self):
self.files=os.listdir(self.basedir)
self.stairs=[i for i in self.files if ("stair" in i) and ("R" in i)]
self.sectors=[i for i in self.files if "sector" in i]
self.stairs.sort()
self.sectors.sort()
filenames=[self.basedir+i for i in self.sectors[... | Return a sequence of images and durations. | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L134-L188 | null | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.makeAudibleSong | python | def makeAudibleSong(self):
sound0=n.hstack((sy.render(220,d=1.5),
sy.render(220*(2**(7/12)),d=2.5),
sy.render(220*(2**(-5/12)),d=.5),
sy.render(220*(2**(0/12)),d=1.5),
))
sound1=n.hstack((sy.render(220*(2**(0... | Use mass to render wav soundtrack. | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L194-L239 | null | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
ttm/socialLegacy | social/fsong.py | FSong.makeAnimation | python | def makeAnimation(self):
aclip=mpy.AudioFileClip("sound.wav")
self.iS=self.iS.set_audio(aclip)
self.iS.write_videofile("mixedVideo.webm",15,audio=True)
print("wrote "+"mixedVideo.webm") | Use pymovie to render (visual+audio)+text overlays. | train | https://github.com/ttm/socialLegacy/blob/c0930cfe6e84392729449bf7c92569e1556fd109/social/fsong.py#L240-L246 | null | class FSong:
"""Create song from undirected (friendship) network
"""
def __init__(self, network,basedir="fsong/",clean=False,render_images=False,render_images2=False,make_video=False):
os.system("mkdir {}".format(basedir))
if clean:
os.system("rm {}*".format(basedir))
sel... |
harlowja/failure | failure/finders.py | match_modules | python | def match_modules(allowed_modules):
cleaned_allowed_modules = [
utils.mod_to_mod_name(tmp_mod)
for tmp_mod in allowed_modules
]
cleaned_split_allowed_modules = [
tmp_mod.split(".")
for tmp_mod in cleaned_allowed_modules
]
cleaned_allowed_modules = []
del cleaned_a... | Creates a matcher that matches a list/set/tuple of allowed modules. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/finders.py#L44-L73 | null | # -*- coding: utf-8 -*-
# Copyright (C) 2016 GoDaddy Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... |
harlowja/failure | failure/finders.py | match_classes | python | def match_classes(allowed_classes):
cleaned_allowed_classes = [
utils.cls_to_cls_name(tmp_cls)
for tmp_cls in allowed_classes
]
def matcher(cause):
cause_cls = None
cause_type_name = cause.exception_type_names[0]
try:
cause_cls_idx = cleaned_allowed_class... | Creates a matcher that matches a list/tuple of allowed classes. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/finders.py#L76-L97 | null | # -*- coding: utf-8 -*-
# Copyright (C) 2016 GoDaddy Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... |
harlowja/failure | failure/finders.py | combine_or | python | def combine_or(matcher, *more_matchers):
def matcher(cause):
for sub_matcher in itertools.chain([matcher], more_matchers):
cause_cls = sub_matcher(cause)
if cause_cls is not None:
return cause_cls
return None
return matcher | Combines more than one matcher together (first that matches wins). | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/finders.py#L100-L110 | null | # -*- coding: utf-8 -*-
# Copyright (C) 2016 GoDaddy Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... |
harlowja/failure | failure/failure.py | WrappedFailure.check | python | def check(self, *exc_classes):
if not exc_classes:
return None
for cause in self:
result = cause.check(*exc_classes)
if result is not None:
return result
return None | Check if any of exception classes caused the failure/s.
:param exc_classes: exception types/exception type names to
search for.
If any of the contained failures were caused by an exception of a
given type, the corresponding argument that matched is returned. If
... | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L77-L93 | null | class WrappedFailure(utils.StrMixin, Exception):
"""Wraps one or several failure objects.
When exception/s cannot be re-raised (for example, because the value and
traceback are lost in serialization) or there are several exceptions active
at the same time (due to more than one thread raising exceptions... |
harlowja/failure | failure/failure.py | Failure.from_exc_info | python | def from_exc_info(cls, exc_info=None,
retain_exc_info=True,
cause=None, find_cause=True):
if exc_info is None:
exc_info = sys.exc_info()
if not any(exc_info):
raise NoActiveException("No exception currently"
... | Creates a failure object from a ``sys.exc_info()`` tuple. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L241-L291 | [
"def extract_roots(exc_type):\n return to_tuple(\n reflection.get_all_class_names(exc_type, up_to=BaseException,\n truncate_builtins=False))\n",
"def _extract_cause(cls, exc_val):\n \"\"\"Helper routine to extract nested cause (if any).\"\"\"\n # See: https://... | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.from_exception | python | def from_exception(cls, exception, retain_exc_info=True,
cause=None, find_cause=True):
exc_info = (
type(exception),
exception,
getattr(exception, '__traceback__', None)
)
return cls.from_exc_info(exc_info=exc_info,
... | Creates a failure object from a exception instance. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L294-L304 | [
"def from_exc_info(cls, exc_info=None,\n retain_exc_info=True,\n cause=None, find_cause=True):\n \"\"\"Creates a failure object from a ``sys.exc_info()`` tuple.\"\"\"\n if exc_info is None:\n exc_info = sys.exc_info()\n if not any(exc_info):\n raise N... | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.validate | python | def validate(cls, data):
try:
jsonschema.validate(
data, cls.SCHEMA,
# See: https://github.com/Julian/jsonschema/issues/148
types={'array': (list, tuple)})
except jsonschema.ValidationError as e:
raise InvalidFormat("Failure data no... | Validate input data matches expected failure ``dict`` format. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L307-L338 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.matches | python | def matches(self, other):
if not isinstance(other, Failure):
return False
if self.exc_info is None or other.exc_info is None:
return self._matches(other)
else:
return self == other | Checks if another object is equivalent to this object.
:returns: checks if another object is equivalent to this object
:rtype: boolean | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L351-L362 | [
"def _matches(self, other):\n if self is other:\n return True\n return (self.exception_type_names == other.exception_type_names and\n self.exception_args == other.exception_args and\n self.exception_kwargs == other.exception_kwargs and\n self.exception_str == other.exce... | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.reraise_if_any | python | def reraise_if_any(failures, cause_cls_finder=None):
if not isinstance(failures, (list, tuple)):
# Convert generators/other into a list...
failures = list(failures)
if len(failures) == 1:
failures[0].reraise(cause_cls_finder=cause_cls_finder)
elif len(failures... | Re-raise exceptions if argument is not empty.
If argument is empty list/tuple/iterator, this method returns
None. If argument is converted into a list with a
single ``Failure`` object in it, that failure is reraised. Else, a
:class:`~.WrappedFailure` exception is raised with the failure... | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L436-L451 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.reraise | python | def reraise(self, cause_cls_finder=None):
if self._exc_info:
six.reraise(*self._exc_info)
else:
# Attempt to regenerate the full chain (and then raise
# from the root); without a traceback, oh well...
root = None
parent = None
for c... | Re-raise captured exception (possibly trying to recreate). | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L453-L482 | [
"def iter_causes(self):\n \"\"\"Iterate over all causes.\"\"\"\n curr = self._cause\n while curr is not None:\n yield curr\n curr = curr._cause\n"
] | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.check | python | def check(self, *exc_classes):
for cls in exc_classes:
cls_name = utils.cls_to_cls_name(cls)
if cls_name in self._exc_type_names:
return cls
return None | Check if any of ``exc_classes`` caused the failure.
Arguments of this method can be exception types or type
names (strings **fully qualified**). If captured exception is
an instance of exception of given type, the corresponding argument
is returned, otherwise ``None`` is returned. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L484-L496 | [
"def cls_to_cls_name(cls):\n if isinstance(cls, type):\n cls_name = reflection.get_class_name(cls, truncate_builtins=False)\n else:\n cls_name = str(cls)\n return cls_name\n"
] | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.pformat | python | def pformat(self, traceback=False):
buf = six.StringIO()
if not self._exc_type_names:
buf.write('Failure: %s' % (self._exception_str))
else:
buf.write('Failure: %s: %s' % (self._exc_type_names[0],
self._exception_str))
if... | Pretty formats the failure object into a string. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L513-L532 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.iter_causes | python | def iter_causes(self):
curr = self._cause
while curr is not None:
yield curr
curr = curr._cause | Iterate over all causes. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L534-L539 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure._extract_cause | python | def _extract_cause(cls, exc_val):
# See: https://www.python.org/dev/peps/pep-3134/ for why/what
# these are...
#
# '__cause__' attribute for explicitly chained exceptions
# '__context__' attribute for implicitly chained exceptions
# '__traceback__' attribute for the trace... | Helper routine to extract nested cause (if any). | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L584-L620 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.from_dict | python | def from_dict(cls, data):
data = dict(data)
cause = data.get('cause')
if cause is not None:
data['cause'] = cls.from_dict(cause)
return cls(**data) | Converts this from a dictionary to a object. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L623-L629 | [
"def from_dict(cls, data):\n \"\"\"Converts this from a dictionary to a object.\"\"\"\n data = dict(data)\n cause = data.get('cause')\n if cause is not None:\n data['cause'] = cls.from_dict(cause)\n return cls(**data)\n"
] | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.to_dict | python | def to_dict(self, include_args=True, include_kwargs=True):
data = {
'exception_str': self.exception_str,
'traceback_str': self.traceback_str,
'exc_type_names': self.exception_type_names,
'exc_args': self.exception_args if include_args else tuple(),
'ex... | Converts this object to a dictionary.
:param include_args: boolean indicating whether to include the
exception args in the output.
:param include_kwargs: boolean indicating whether to include the
exception kwargs in the output. | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L631-L650 | null | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
harlowja/failure | failure/failure.py | Failure.copy | python | def copy(self, deep=False):
cause = self._cause
if cause is not None:
cause = cause.copy(deep=deep)
exc_info = utils.copy_exc_info(self.exc_info, deep=deep)
exc_args = self.exception_args
exc_kwargs = self.exception_kwargs
if deep:
exc_args = copy.... | Copies this object (shallow or deep).
:param deep: boolean indicating whether to do a deep copy (or a
shallow copy). | train | https://github.com/harlowja/failure/blob/9ea9a46ebb26c6d7da2553c80e36892f3997bd6f/failure/failure.py#L652-L684 | [
"def copy_exc_info(exc_info, deep=False):\n if exc_info is None:\n return None\n exc_type, exc_value, exc_tb = exc_info\n # NOTE(imelnikov): there is no need to copy the exception type, and\n # a shallow copy of the value is fine and we can't copy the traceback since\n # it contains reference ... | class Failure(utils.StrMixin):
"""An immutable object that represents failure.
Failure objects encapsulate exception information so that they can be
re-used later to re-raise, inspect, examine, log, print, serialize,
deserialize...
For those who are curious, here are a few reasons why the original... |
ambitioninc/django-entity-event | entity_event/context_loader.py | get_context_hints_per_source | python | def get_context_hints_per_source(context_renderers):
# Merge the context render hints for each source as there can be multiple context hints for
# sources depending on the render target. Merging them together involves combining select
# and prefetch related hints for each context renderer
context_hints_... | Given a list of context renderers, return a dictionary of context hints per source. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L21-L42 | null | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | get_querysets_for_context_hints | python | def get_querysets_for_context_hints(context_hints_per_source):
model_select_relateds = defaultdict(set)
model_prefetch_relateds = defaultdict(set)
model_querysets = {}
for context_hints in context_hints_per_source.values():
for hints in context_hints.values():
model = get_model(hints... | Given a list of context hint dictionaries, return a dictionary
of querysets for efficient context loading. The return value
is structured as follows:
{
model: queryset,
...
} | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L45-L74 | null | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | dict_find | python | def dict_find(d, which_key):
# If the starting point is a list, iterate recursively over all values
if isinstance(d, (list, tuple)):
for i in d:
for result in dict_find(i, which_key):
yield result
# Else, iterate over all key values of the dictionary
elif isinstance(... | Finds key values in a nested dictionary. Returns a tuple of the dictionary in which
the key was found along with the value | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L77-L94 | [
"def dict_find(d, which_key):\n \"\"\"\n Finds key values in a nested dictionary. Returns a tuple of the dictionary in which\n the key was found along with the value\n \"\"\"\n # If the starting point is a list, iterate recursively over all values\n if isinstance(d, (list, tuple)):\n for i ... | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | get_model_ids_to_fetch | python | def get_model_ids_to_fetch(events, context_hints_per_source):
number_types = (complex, float) + six.integer_types
model_ids_to_fetch = defaultdict(set)
for event in events:
context_hints = context_hints_per_source.get(event.source, {})
for context_key, hints in context_hints.items():
... | Obtains the ids of all models that need to be fetched. Returns a dictionary of models that
point to sets of ids that need to be fetched. Return output is as follows:
{
model: [id1, id2, ...],
...
} | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L97-L119 | [
"def dict_find(d, which_key):\n \"\"\"\n Finds key values in a nested dictionary. Returns a tuple of the dictionary in which\n the key was found along with the value\n \"\"\"\n # If the starting point is a list, iterate recursively over all values\n if isinstance(d, (list, tuple)):\n for i ... | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | fetch_model_data | python | def fetch_model_data(model_querysets, model_ids_to_fetch):
return {
model: id_dict(model_querysets[model].filter(id__in=ids_to_fetch))
for model, ids_to_fetch in model_ids_to_fetch.items()
} | Given a dictionary of models to querysets and model IDs to models, fetch the IDs
for every model and return the objects in the following structure.
{
model: {
id: obj,
...
},
...
} | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L122-L138 | null | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | load_fetched_objects_into_contexts | python | def load_fetched_objects_into_contexts(events, model_data, context_hints_per_source):
for event in events:
context_hints = context_hints_per_source.get(event.source, {})
for context_key, hints in context_hints.items():
model = get_model(hints['app_name'], hints['model_name'])
... | Given the fetched model data and the context hints for each source, go through each
event and populate the contexts with the loaded information. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L141-L155 | [
"def dict_find(d, which_key):\n \"\"\"\n Finds key values in a nested dictionary. Returns a tuple of the dictionary in which\n the key was found along with the value\n \"\"\"\n # If the starting point is a list, iterate recursively over all values\n if isinstance(d, (list, tuple)):\n for i ... | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | load_renderers_into_events | python | def load_renderers_into_events(events, mediums, context_renderers, default_rendering_style):
# Make a mapping of source groups and rendering styles to context renderers. Do
# the same for sources and rendering styles to context renderers
source_group_style_to_renderer = {
(cr.source_group_id, cr.ren... | Given the events and the context renderers, load the renderers into the event objects
so that they may be able to call the 'render' method later on. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L158-L191 | null | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/context_loader.py | load_contexts_and_renderers | python | def load_contexts_and_renderers(events, mediums):
sources = {event.source for event in events}
rendering_styles = {medium.rendering_style for medium in mediums if medium.rendering_style}
# Fetch the default rendering style and add it to the set of rendering styles
default_rendering_style = get_default_... | Given a list of events and mediums, load the context model data into the contexts of the events. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_loader.py#L202-L226 | [
"def get_context_hints_per_source(context_renderers):\n \"\"\"\n Given a list of context renderers, return a dictionary of context hints per source.\n \"\"\"\n # Merge the context render hints for each source as there can be multiple context hints for\n # sources depending on the render target. Mergi... | """
A module for loading contexts using context hints.
"""
from collections import defaultdict
import six
from django.conf import settings
from django.db.models import Q
try:
# Django 1.9
from django.apps import apps
get_model = apps.get_model
except ImportError: # pragma: no cover
# Django < 1.9
... |
ambitioninc/django-entity-event | entity_event/models.py | _unseen_event_ids | python | def _unseen_event_ids(medium):
query = '''
SELECT event.id
FROM entity_event_event AS event
LEFT OUTER JOIN (SELECT *
FROM entity_event_eventseen AS seen
WHERE seen.medium_id=%s) AS eventseen
ON event.id = eventseen.event_id
WHERE eve... | Return all events that have not been seen on this medium. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/models.py#L1191-L1206 | null | from collections import defaultdict
from datetime import datetime
from operator import or_
from six.moves import reduce
from cached_property import cached_property
from django.contrib.postgres.fields import JSONField
from django.core.serializers.json import DjangoJSONEncoder
from django.db import models, transaction
f... |
ambitioninc/django-entity-event | entity_event/models.py | EventQuerySet.mark_seen | python | def mark_seen(self, medium):
EventSeen.objects.bulk_create([
EventSeen(event=event, medium=medium) for event in self
]) | Creates EventSeen objects for the provided medium for every event
in the queryset.
Creating these EventSeen objects ensures they will not be
returned when passing ``seen=False`` to any of the medium
event retrieval functions, ``events``, ``entity_events``, or
``events_targets``. | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/models.py#L866-L878 | null | class EventQuerySet(QuerySet):
"""
A custom QuerySet for Events.
"""
def cache_related(self):
"""
Cache any related objects that we may use
:return:
"""
return self.select_related(
'source'
).prefetch_related(
'source__group'
... |
ambitioninc/django-entity-event | entity_event/models.py | EventManager.create_event | python | def create_event(self, actors=None, ignore_duplicates=False, **kwargs):
kwargs['actors'] = actors
kwargs['ignore_duplicates'] = ignore_duplicates
events = self.create_events([kwargs])
if events:
return events[0]
return None | Create events with actors.
This method can be used in place of ``Event.objects.create``
to create events, and the appropriate actors. It takes all the
same keywords as ``Event.objects.create`` for the event
creation, but additionally takes a list of actors, and can be
told to no... | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/models.py#L926-L988 | [
"def create_events(self, kwargs_list):\n \"\"\"\n Create events in bulk to save on queries. Each element in the kwargs list should be a dict with the same set\n of arguments you would normally pass to create_event\n :param kwargs_list: list of kwargs dicts\n :return: list of Event\n \"\"\"\n # ... | class EventManager(models.Manager):
"""
A custom Manager for Events.
"""
def get_queryset(self):
"""
Return the EventQuerySet.
"""
return EventQuerySet(self.model)
def cache_related(self):
"""
Return a queryset with prefetched values
:return:
... |
ambitioninc/django-entity-event | entity_event/models.py | EventManager.create_events | python | def create_events(self, kwargs_list):
# Build map of uuid to event info
uuid_map = {
kwargs.get('uuid', ''): {
'actors': kwargs.pop('actors', []),
'ignore_duplicates': kwargs.pop('ignore_duplicates', False),
'event_kwargs': kwargs
... | Create events in bulk to save on queries. Each element in the kwargs list should be a dict with the same set
of arguments you would normally pass to create_event
:param kwargs_list: list of kwargs dicts
:return: list of Event | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/models.py#L990-L1036 | null | class EventManager(models.Manager):
"""
A custom Manager for Events.
"""
def get_queryset(self):
"""
Return the EventQuerySet.
"""
return EventQuerySet(self.model)
def cache_related(self):
"""
Return a queryset with prefetched values
:return:
... |
ambitioninc/django-entity-event | entity_event/context_serializer.py | DefaultContextSerializer.serialize_value | python | def serialize_value(self, value):
# Create a list of serialize methods to run the value through
serialize_methods = [
self.serialize_model,
self.serialize_json_string,
self.serialize_list,
self.serialize_dict
]
# Run all of our serialize m... | Given a value, ensure that it is serialized properly
:param value:
:return: | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_serializer.py#L29-L48 | null | class DefaultContextSerializer(object):
"""
Default class for serializing context data
"""
def __init__(self, context):
super(DefaultContextSerializer, self).__init__()
self.context = context
@property
def data(self):
"""
Data property that will return the seria... |
ambitioninc/django-entity-event | entity_event/context_serializer.py | DefaultContextSerializer.serialize_model | python | def serialize_model(self, value):
# Check if the context value is a model
if not isinstance(value, models.Model):
return value
# Serialize the model
serialized_model = model_to_dict(value)
# Check the model for cached foreign keys
for model_field, model_val... | Serializes a model and all of its prefetched foreign keys
:param value:
:return: | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_serializer.py#L50-L79 | [
"def serialize_value(self, value):\n \"\"\"\n Given a value, ensure that it is serialized properly\n :param value:\n :return:\n \"\"\"\n # Create a list of serialize methods to run the value through\n serialize_methods = [\n self.serialize_model,\n self.serialize_json_string,\n ... | class DefaultContextSerializer(object):
"""
Default class for serializing context data
"""
def __init__(self, context):
super(DefaultContextSerializer, self).__init__()
self.context = context
@property
def data(self):
"""
Data property that will return the seria... |
ambitioninc/django-entity-event | entity_event/context_serializer.py | DefaultContextSerializer.serialize_json_string | python | def serialize_json_string(self, value):
# Check if the value might be a json string
if not isinstance(value, six.string_types):
return value
# Make sure it starts with a brace
if not value.startswith('{') or value.startswith('['):
return value
# Try to ... | Tries to load an encoded json string back into an object
:param json_string:
:return: | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_serializer.py#L81-L100 | null | class DefaultContextSerializer(object):
"""
Default class for serializing context data
"""
def __init__(self, context):
super(DefaultContextSerializer, self).__init__()
self.context = context
@property
def data(self):
"""
Data property that will return the seria... |
ambitioninc/django-entity-event | entity_event/context_serializer.py | DefaultContextSerializer.serialize_list | python | def serialize_list(self, value):
# Check if this is a list or a tuple
if not isinstance(value, (list, tuple)):
return value
# Loop over all the values and serialize the values
return [
self.serialize_value(list_value)
for list_value in value
... | Ensure that all values of a list or tuple are serialized
:return: | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_serializer.py#L102-L116 | null | class DefaultContextSerializer(object):
"""
Default class for serializing context data
"""
def __init__(self, context):
super(DefaultContextSerializer, self).__init__()
self.context = context
@property
def data(self):
"""
Data property that will return the seria... |
ambitioninc/django-entity-event | entity_event/context_serializer.py | DefaultContextSerializer.serialize_dict | python | def serialize_dict(self, value):
# Check if this is a dict
if not isinstance(value, dict):
return value
# Loop over all the values and serialize them
return {
dict_key: self.serialize_value(dict_value)
for dict_key, dict_value in value.items()
... | Ensure that all values of a dictionary are properly serialized
:param value:
:return: | train | https://github.com/ambitioninc/django-entity-event/blob/70f50df133e42a7bf38d0f07fccc6d2890e5fd12/entity_event/context_serializer.py#L118-L133 | null | class DefaultContextSerializer(object):
"""
Default class for serializing context data
"""
def __init__(self, context):
super(DefaultContextSerializer, self).__init__()
self.context = context
@property
def data(self):
"""
Data property that will return the seria... |
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