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{
"filename": "multiprocess_test_deconfuser.py",
"repo_name": "MIT-STARLab/deconfuser",
"repo_path": "deconfuser_extracted/deconfuser-main/multiprocess_test_deconfuser.py",
"type": "Python"
}
|
import numpy as np
import multiprocessing
import itertools
import argparse
import os
import deconfuser.sample_planets as sample_planets
import deconfuser.orbit_fitting as orbit_fitting
import deconfuser.orbit_grouping as orbit_grouping
import deconfuser.partition_ranking as partition_ranking
mu_sun = 4*np.pi**2 #Sun's gravitational parameter in AU^3/year^2
parser = argparse.ArgumentParser(description="Monte-Carlo testing of the deconfuser")
parser.add_argument("--n_planets", type=int, default=3, help="number of planet per system (default: 3)")
parser.add_argument("--n_epochs", type=int, default=4, help="number of observation epochs (default: 4)")
parser.add_argument("--cadence", type=float, default=0.5, help="observation candence in years (default: 0.5)")
parser.add_argument("--mu", type=float, default=mu_sun, help="gravitational parameter in AU^3/year^2 (default: 4pi^2)")
parser.add_argument("--min_a", type=float, default=0.25, help="minimum semi-major axis in AU (default: 0.25)")
parser.add_argument("--max_a", type=float, default=2.0, help="maximum semi-major axis in AU (default: 2.0)")
parser.add_argument("--sep_a", type=float, default=0.3, help="minimum semi-major difference in AU (default: 0.3)")
parser.add_argument("--min_i", type=float, default=0, help="minimum inclination in radians (default: 0)")
parser.add_argument("--max_i", type=float, default=np.pi/2, help="maximum inclination in radians (default: pi/2)")
parser.add_argument("--max_e", type=float, default=0.3, help="maximum eccentricity (default: 0.3)")
parser.add_argument("--spread_i_O", type=float, default=0.0, help="spread of inclination and LAN in radians (default: 0.0 - coplanar)")
parser.add_argument("--n_processes", type=int, default=4, help="number of concurrent processes (default: 4)")
parser.add_argument("--n_systems", type=int, default=10, help="number of systems per process (default: 10)")
parser.add_argument("-v", "--verbose", action="store_true", help="print planet data")
parser.add_argument("toleranes", type=float, nargs="+", help="orbit fit tollerances")
args = parser.parse_args()
#observation epochs (years)
ts = args.cadence*np.arange(args.n_epochs)
#the correct partition of detection by planets
correct_partition = [tuple(range(i*len(ts),(i+1)*len(ts))) for i in range(args.n_planets)]
#to speed up computation, begin with coarsest tolerance and progress to finest:
#1. full orbit grouping will be performed with the coarsest tolerance (i.e., recursively consider all groupings of observation)
#2. only "full" groups that fit observation within a coarser tolerance will be fitted with a finer tolerance
#Note: "missed" detections are not simulataed here so confusion will only "arise" with full groups (n_epochs observations per planet)
tolerances = sorted(args.toleranes, reverse=True)
orbit_grouper = orbit_grouping.OrbitGrouper(args.mu, ts, args.min_a-tolerances[0], args.max_a+tolerances[0], args.max_e, tolerances[0], lazy_init=False)
orbit_fitters = [orbit_fitting.OrbitFitter(args.mu, ts, args.min_a-tol, args.max_a+tol, args.max_e, tol) for tol in tolerances[1:]]
#multi-process printing
printing_lock = multiprocessing.Lock()
def _print(*v):
printing_lock.acquire()
print(os.getpid(), *v)
os.sys.stdout.flush()
printing_lock.release()
#main function to be ran from multiple processors (the large lookup tables are read-only and shared between processes)
def generate_and_test_systems():
np.random.seed(os.getpid())
for _ in range(args.n_systems):
#choose random orbit parameters for each planet
a,e,i,o,O,M0 = sample_planets.random_planet_elements(args.n_planets, args.min_a, args.max_a, args.max_e, args.sep_a, args.min_i, args.max_i, args.spread_i_O)
#get coordinates of planets when observed
xs,ys = sample_planets.get_observations(a, e, i, o, O, M0, ts, args.mu)
observations = np.stack([xs,ys], axis=2).reshape((-1,2))
#add radially bounded astrometry error
noise_r = tolerances[-1]*np.random.random(len(observations))
noise_a = 2*np.pi*np.random.random(len(observations))
observations[:,0] += noise_r*np.cos(noise_a)
observations[:,1] += noise_r*np.sin(noise_a)
if args.verbose:
_print("ts =", list(ts))
for ip in range(args.n_planets):
_print("a,e,i,o,O,M0 = ", (a[ip],e[ip],i[ip],o[ip],O[ip],M0[ip]))
_print("xys =", list(map(list, observations[ip*len(ts):(ip+1)*len(ts)])))
#all detection times for all obesrvations
all_ts = np.tile(ts, args.n_planets)
#get all possible (full or patrial) groupings of detection by orbits that fit them with the coarsest tolerance
groupings = orbit_grouper.group_orbits(observations, all_ts)
#select only groupings that include all epochs (these will be most highly ranked, so no need to check the rest)
groupings = [g for g in groupings if len(g) == args.n_epochs]
#check for spurious orbits and repeat for finer tolerances
for j in range(len(tolerances)):
found_correct = sum(cg in groupings for cg in correct_partition)
_print("Tolerance %f: found %d correct and %d spurious orbits out of %d"%(tolerances[j], found_correct, len(groupings) - found_correct, args.n_planets))
if args.verbose:
_print("Tolerance %f:"%(tolerances[j]), groupings)
#find all partitions of observations to exactly n_planets groups
#note that since all partial grouping were filtered out, all partitions will have exactly n_planets groups
top_partitions = list(partition_ranking.get_ranked_partitions(groupings))
if found_correct < args.n_planets:
for ip in range(args.n_planets):
if not correct_partition[ip] in groupings:
_print("Failed to fit a correct orbit for planet %d!"%(ip))
elif len(top_partitions) == 1:
_print("Tolerance %f: no confusion"%(tolerances[j]))
else:
assert(len(top_partitions) > 1)
_print("Tolerance %f: found %d spurious \"good\" paritions of detections by planets (confusion)"%(tolerances[j], len(top_partitions) - 1))
if args.verbose:
_print("Tolerance %f:"%(tolerances[j]), top_partitions)
#move to a finer tolerance
if j < len(tolerances) - 1:
#only keep groupings that cna be fitted with an orbit with the finer tolerance
groupings = [g for g in groupings if any(err < tolerances[j+1] for err in orbit_fitters[j].fit(observations[list(g)], only_error=True))]
if __name__ == '__main__':
#run testing from multiple processes
processes = []
for i in range(args.n_processes):
p = multiprocessing.Process(target=generate_and_test_systems)
p.start()
processes.append(p)
#wait for all processes to finish
for p in processes:
p.join()
|
MIT-STARLabREPO_NAMEdeconfuserPATH_START.@deconfuser_extracted@deconfuser-main@multiprocess_test_deconfuser.py@.PATH_END.py
|
{
"filename": "test_close_scene.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/integrationtests/mayavi/test_close_scene.py",
"type": "Python"
}
|
#!/usr/bin/env mayavi2
# Author: Prabhu Ramachandran <prabhu [at] aero . iitb . ac . in>
# Copyright (c) 2008, Prabhu Ramachandran
# License: BSD Style.
"""This tests that closing a hidden TVTK scene window does not crash or
raise PyDeadObjectErrors.
"""
from common import TestCase
class TestCloseScene(TestCase):
def test(self):
self.main()
def do(self):
mayavi = self.script
engine = mayavi.engine
s1 = mayavi.new_scene()
s2 = mayavi.new_scene()
# This should not crash or throw any errors (PyDeadObjects etc.)
engine.close_scene(s1)
# Neither should this.
engine.close_scene(s2)
if __name__ == "__main__":
t = TestCloseScene()
t.test()
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@integrationtests@mayavi@test_close_scene.py@.PATH_END.py
|
{
"filename": "funcs.py",
"repo_name": "jtdinsmore/leakagelib",
"repo_path": "leakagelib_extracted/leakagelib-main/src/funcs.py",
"type": "Python"
}
|
import numpy as np
from scipy.interpolate import RegularGridInterpolator
from .settings import *
KERNEL_ZS = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0,-4, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]) / 4**2
KERNEL_QS = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0,-1, 0, 0, 0,-1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]) / 4**2
KERNEL_US = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0,-2, 0, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 2, 0,-2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]) / 4**2
KERNEL_ZK = np.array([
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0,-8, 0, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0,-8, 0,20, 0,-8, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0,-8, 0, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
]) / 4**4 / 4
KERNEL_QK = np.array([
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0,-4, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[-1, 0, 4, 0, 0, 0, 4, 0,-1],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0,-4, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
]) / 4**4 / 3
KERNEL_UK = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0,-2, 0, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0,-2, 0,12,0,-12, 0, 2, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 0,-12,0,12, 0,-2, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 2, 0,-2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]) / 4**4 / 3
KERNEL_XK = np.array([
[0, 0, 0, 0,-1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 6, 0,-8, 0, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[-1, 0,-8, 0,12, 0,-8, 0,-1],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 6, 0,-8, 0, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0,-1, 0, 0, 0, 0],
]) / 4**4 / 3
KERNEL_YK = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 4, 0,-4, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0,-4, 0, 0, 0, 0, 0, 4, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 4, 0, 0, 0, 0, 0,-4, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0,-4, 0, 4, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]) / 4**4 / 3
def override_matplotlib_defaults():
'''Load Jack's default matplotlib parameters to make prettier images'''
import matplotlib as mpl
from cycler import cycler
mpl.rcParams["font.size"] = 10
mpl.rcParams["axes.titlesize"] = 18
mpl.rcParams["axes.labelsize"] = 18
mpl.rcParams["axes.formatter.limits"] = (-4, 5)
mpl.rcParams["axes.formatter.use_mathtext"] = True
mpl.rcParams["axes.formatter.useoffset"] = False
mpl.rcParams["axes.prop_cycle"] = cycler('color', ["cadetblue", "darkorange", "forestgreen", "firebrick", "dodgerblue", "goldenrod", "slateblue", "darkmagenta", "saddlebrown"])
mpl.rcParams["figure.titlesize"] = 24
mpl.rcParams["figure.subplot.hspace"] = 0.12
mpl.rcParams["figure.subplot.wspace"] = 0.1
mpl.rcParams["path.simplify"] = True
mpl.rcParams["image.cmap"] = "inferno"
mpl.rcParams["hist.bins"] = 20
mpl.rcParams["legend.fontsize"] = 17
mpl.rcParams["xtick.labelsize"] = 15
mpl.rcParams["ytick.labelsize"] = 15
mpl.rcParams["legend.frameon"] = False
mpl.rcParams["font.family"] = "Courier"
mpl.rcParams["mathtext.fontset"] = "custom"
mpl.rcParams["mathtext.rm"] = "Courier"
mpl.rcParams["mathtext.it"] = "Courier:oblique"
mpl.rcParams["mathtext.bf"] = "Courier:bold"
mpl.rcParams["mathtext.fallback"] = "stixsans"
mpl.rcParams["errorbar.capsize"] = 3
mpl.rcParams["scatter.edgecolors"] = 'k'
mpl.rcParams["lines.markersize"] = 10
mpl.rcParams["lines.markeredgecolor"] = 'k'
mpl.rcParams["lines.linewidth"] = 3
mpl.rcParams["figure.figsize"] = (9,5.56)
mpl.rcParams["savefig.bbox"] = "tight"
mpl.rcParams["xtick.top"] = True
mpl.rcParams["xtick.bottom"] = True
mpl.rcParams["xtick.major.size"] = 6
mpl.rcParams["xtick.minor.size"] = 3.5
mpl.rcParams["xtick.major.width"] = 1
mpl.rcParams["xtick.minor.width"] = 0.8
mpl.rcParams["xtick.direction"] = "in"
mpl.rcParams["xtick.minor.visible"] = True
mpl.rcParams["ytick.left"] = True
mpl.rcParams["ytick.right"] = True
mpl.rcParams["ytick.major.size"] = 6
mpl.rcParams["ytick.minor.size"] = 3.5
mpl.rcParams["ytick.major.width"] = 1
mpl.rcParams["ytick.minor.width"] = 0.8
mpl.rcParams["ytick.direction"] = "in"
mpl.rcParams["ytick.minor.visible"] = True
def super_zoom(image, frac, force_odd=False):
'''Interpolate zoom when zooming in, integrate zoom when zooming out. This is the recommended zoom function. Zooming is centered.
# Arguments:
- frac: Zoom fracion
- force_odd: Set to True to round the image to an odd number of pixels
'''
if np.abs(frac - 1) < 1e-5:
return image
if frac > 1:
return interpolate_zoom(image, frac, force_odd)
else:
return integrate_zoom(image, frac, force_odd)
def interpolate_zoom(image, frac, force_odd=False):
'''Zoom by linearly interpolating between the known pixels to get the new pixels. Most suitable for zooming in.
# Arguments:
- frac: Zoom fracion
- force_odd: Set to True to round the image to an odd number of pixels
'''
assert(len(image.shape) == 2 and image.shape[0] == image.shape[1])
original_size = image.shape[0]
# choose an odd new_size
new_size = image.shape[0] * frac
if force_odd:
new_size = 2 * int(np.round((new_size + 1) / 2)) - 1
else:
new_size = int(np.round(new_size))
if new_size == original_size:
return image
original_edges = np.linspace(-1, 1, original_size + 1)
original_line = (original_edges[1:] + original_edges[:-1]) / 2
interpolator = RegularGridInterpolator((original_line, original_line), image, method="linear", bounds_error=False, fill_value=None)
edge_lim = (float(new_size) / 2) / frac / (original_size / 2)
new_edges = np.linspace(-edge_lim, edge_lim, new_size + 1)
new_line = (new_edges[1:] + new_edges[:-1]) / 2
xs, ys = np.meshgrid(new_line, new_line, indexing="ij")
new_image = interpolator((xs, ys))
return new_image
def integrate_zoom_unvectorized(image, frac, force_odd=False):
'''Zoom by integrating over the original pixels. This function is not vectorized and very slow. Use integrate_zoom instead.
# Arguments:
- frac: Zoom fracion
- force_odd: Set to True to round the image to an odd number of pixels
'''
old_size = image.shape[0]
new_size = image.shape[0] * frac
if force_odd:
new_size = 2 * int(np.round((new_size + 1) / 2)) - 1
else:
new_size = int(np.round(new_size))
edge_lim = (float(new_size) / 2) / frac / (old_size / 2)
old_edges = np.linspace(-1, 1, old_size + 1)
new_edges = np.linspace(-edge_lim, edge_lim, new_size + 1)
all_edges = np.concatenate([old_edges, new_edges])
all_edges = np.unique(all_edges)
all_edges = np.sort(all_edges)
old_pixel_area = (old_edges[1] - old_edges[0])**2
new_image = np.zeros((new_size, new_size))
for intersection_idx in range(len(all_edges)-1):
for intersection_idy in range(len(all_edges)-1):
pixel_width = all_edges[intersection_idx + 1] - all_edges[intersection_idx]
pixel_height = all_edges[intersection_idy + 1] - all_edges[intersection_idy]
area = (pixel_width * pixel_height) / old_pixel_area
if area <= 0:
continue
pixel_center_x = (all_edges[intersection_idx + 1] + all_edges[intersection_idx]) / 2
pixel_center_y = (all_edges[intersection_idy + 1] + all_edges[intersection_idy]) / 2
old_idx = np.searchsorted(old_edges, pixel_center_x) - 1
old_idy = np.searchsorted(old_edges, pixel_center_y) - 1
new_idx = np.searchsorted(new_edges, pixel_center_x) - 1
new_idy = np.searchsorted(new_edges, pixel_center_y) - 1
if new_idx < 0 or new_idy < 0 or new_idx >= new_size or new_idy >= new_size:
continue
if old_idx < 0 or old_idy < 0 or old_idx >= old_size or old_idy >= old_size:
continue
new_image[new_idx, new_idy] += image[old_idx, old_idy] * area
return new_image
def integrate_zoom(image, frac, force_odd=False):
'''Zoom by integrating over the original pixels. Most suitable for zooming out.
# Arguments:
- frac: Zoom fracion
- force_odd: Set to True to round the image to an odd number of pixels
'''
old_size = image.shape[0]
new_size = image.shape[0] * frac
if force_odd:
new_size = 2 * int(np.round((new_size + 1) / 2)) - 1
else:
new_size = int(np.round(new_size))
edge_lim = (float(new_size) / 2) / frac / (old_size / 2)
old_edges = np.linspace(-1, 1, old_size + 1)
new_edges = np.linspace(-edge_lim, edge_lim, new_size + 1)
all_edges = np.concatenate([old_edges, new_edges])
all_edges = np.unique(all_edges)
all_edges = np.sort(all_edges)
old_pixel_area = (old_edges[1] - old_edges[0])**2
new_image = np.zeros((new_size, new_size))
intersection_xs, intersection_ys = np.meshgrid(all_edges, all_edges, indexing="ij")
center_xs = (intersection_xs[1:,1:] + intersection_xs[:-1,:-1]) / 2
center_ys = (intersection_ys[1:,1:] + intersection_ys[:-1,:-1]) / 2
areas = ((intersection_xs[1:,1:] - intersection_xs[:-1,:-1]) * (intersection_ys[1:,1:] - intersection_ys[:-1,:-1])) / old_pixel_area
old_idx = np.searchsorted(old_edges, center_xs) - 1
old_idy = np.searchsorted(old_edges, center_ys) - 1
new_idx = np.searchsorted(new_edges, center_xs) - 1
new_idy = np.searchsorted(new_edges, center_ys) - 1
out_of_bounds_mask = \
(new_idx < 0) | (new_idy < 0) | (new_idx >= new_size) | (new_idy >= new_size) | \
(old_idx < 0) | (old_idy < 0) | (old_idx >= old_size) | (old_idy >= old_size)
areas = areas[~out_of_bounds_mask]
new_idx = new_idx[~out_of_bounds_mask]
new_idy = new_idy[~out_of_bounds_mask]
old_idx = old_idx[~out_of_bounds_mask]
old_idy = old_idy[~out_of_bounds_mask]
np.add.at(new_image, (new_idx, new_idy), image[old_idx, old_idy] * areas)
return new_image
|
jtdinsmoreREPO_NAMEleakagelibPATH_START.@leakagelib_extracted@leakagelib-main@src@funcs.py@.PATH_END.py
|
{
"filename": "idl_function.py",
"repo_name": "SAIL-Labs/AMICAL",
"repo_path": "AMICAL_extracted/AMICAL-main/amical/mf_pipeline/idl_function.py",
"type": "Python"
}
|
"""
@author: Anthony Soulain (University of Sydney)
-------------------------------------------------------------------------
AMICAL: Aperture Masking Interferometry Calibration and Analysis Library
-------------------------------------------------------------------------
Matched filter sub-pipeline method.
All required IDL function translated into python.
--------------------------------------------------------------------
"""
import sys
import numpy as np
from rich import print as rprint
from amical.externals.munch import munchify as dict2class
def regress_noc(x, y, weights):
"""Python version of IDL regress_noc."""
sx = x.shape
sy = y.shape
nterm = sx[0] # # OF TERMS
npts = sy[0] # # OF OBSERVATIONS
if (len(weights) != sy[0]) or (len(sx) != 2) or (sy[0] != sx[1]):
rprint("[red]Incompatible arrays to compute slope error.", file=sys.stderr)
xwy = np.dot(x, (weights * y))
wx = np.zeros([npts, nterm])
for i in range(npts):
wx[i, :] = x[:, i] * weights[i]
xwx = np.dot(x, wx)
cov = np.linalg.inv(xwx)
coeff = np.dot(cov, xwy)
yfit = np.dot(x.T, coeff)
if npts != nterm:
MSE = np.sum(weights * (yfit - y) ** 2) / (npts - nterm)
var_yfit = np.zeros(npts)
for i in range(npts):
var_yfit[i] = np.dot(np.dot(x[:, i].T, cov), x[:, i]) # Neter et al pg 233
dic = {"coeff": coeff, "cov": cov, "yfit": yfit, "MSE": MSE, "var_yfit": var_yfit}
return dict2class(dic)
def dist(naxis):
"""Returns a rectangular array in which the value of each element is proportional to its frequency.
>>> dist(3)
array([[ 0. , 1. , 1. ],
[ 1. , 1.41421356, 1.41421356],
[ 1. , 1.41421356, 1.41421356]])
>>> dist(4)
array([[ 0. , 1. , 2. , 1. ],
[ 1. , 1.41421356, 2.23606798, 1.41421356],
[ 2. , 2.23606798, 2.82842712, 2.23606798],
[ 1. , 1.41421356, 2.23606798, 1.41421356]])
"""
xx, yy = np.arange(naxis), np.arange(naxis)
xx2 = xx - naxis // 2
yy2 = naxis // 2 - yy
distance = np.sqrt(xx2**2 + yy2[:, np.newaxis] ** 2)
output = np.roll(distance, -1 * (naxis // 2), axis=(0, 1))
return output
def array_coords(ind, dim):
"""Transform 1-D coordinates indices (ind) into 2-D coordinates"""
x, y = np.arange(dim), np.arange(dim)
X, Y = np.meshgrid(x, y)
output = [X.ravel()[ind], Y.ravel()[ind]]
return np.array(output)
def dblarr(dim1, dim2=None):
"""Python version of idl dblarr"""
if dim2 is None:
tab = np.zeros(dim1)
else:
tab = np.zeros([dim1, dim2])
return tab
|
SAIL-LabsREPO_NAMEAMICALPATH_START.@AMICAL_extracted@AMICAL-main@amical@mf_pipeline@idl_function.py@.PATH_END.py
|
{
"filename": "test_convolution.py",
"repo_name": "sibirrer/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/test/test_ImSim/test_Numerics/test_convolution.py",
"type": "Python"
}
|
__author__ = "sibirrer"
import numpy as np
import numpy.testing as npt
from lenstronomy.ImSim.Numerics.convolution import (
MultiGaussianConvolution,
PixelKernelConvolution,
SubgridKernelConvolution,
MGEConvolution,
)
from lenstronomy.LightModel.light_model import LightModel
import lenstronomy.Util.util as util
import pytest
class TestPixelKernelConvolution(object):
def setup_method(self):
lightModel = LightModel(light_model_list=["GAUSSIAN"])
self.delta_pix = 1
x, y = util.make_grid(10, deltapix=self.delta_pix)
kwargs = [{"amp": 1, "sigma": 1, "center_x": 0, "center_y": 0}]
flux = lightModel.surface_brightness(x, y, kwargs)
self.model = util.array2image(flux)
def test_convolve2d(self):
kernel = np.zeros((3, 3))
kernel[1, 1] = 1
pixel_conv = PixelKernelConvolution(kernel=kernel)
image_convolved = pixel_conv.convolution2d(self.model)
npt.assert_almost_equal(np.sum(image_convolved), np.sum(self.model), decimal=2)
def test_copy_transpose(self):
kernel = np.zeros((3, 3))
kernel[1, 1] = 1
pixel_conv = PixelKernelConvolution(kernel=kernel)
pixel_conv_t = pixel_conv.copy_transpose()
image_convolved = pixel_conv.convolution2d(self.model)
image_convolved_t = pixel_conv_t.convolution2d(self.model)
npt.assert_equal(image_convolved, image_convolved_t)
def test_pixel_kernel(self):
kernel = np.zeros((5, 5))
kernel[1, 1] = 1
pixel_conv = PixelKernelConvolution(kernel=kernel)
npt.assert_equal(pixel_conv.pixel_kernel(), kernel)
npt.assert_equal(pixel_conv.pixel_kernel(num_pix=3), kernel[1:-1, 1:-1])
def test_centroiding(self):
"""This test convolves a Gaussian source centered in a centered pixel with a
Gaussian and checks whether the pixelated FFT convolution returns a centered
image as well.
:return:
"""
# constructing a source
lightModel = LightModel(light_model_list=["GAUSSIAN"])
delta_pix = 1
x, y = util.make_grid(11, deltapix=delta_pix)
kwargs_model = [{"amp": 1, "sigma": 2, "center_x": 0, "center_y": 0}]
flux = lightModel.surface_brightness(x, y, kwargs_model)
model = util.array2image(flux)
model /= np.sum(flux)
# compute moments of the source to check that it is centered in the center pixel
npt.assert_almost_equal(np.sum(flux * x), 0, decimal=5)
npt.assert_almost_equal(np.sum(flux * y), 0, decimal=5)
# PSF
x_psf, y_psf = util.make_grid(21, deltapix=delta_pix)
kwargs_model = [{"amp": 1, "sigma": 2, "center_x": 0, "center_y": 0}]
psf_1d = lightModel.surface_brightness(x_psf, y_psf, kwargs_model)
psf = util.array2image(psf_1d)
psf /= np.sum(psf)
npt.assert_almost_equal(np.sum(psf_1d * x_psf), 0, decimal=5)
npt.assert_almost_equal(np.sum(psf_1d * y_psf), 0, decimal=5)
conv = PixelKernelConvolution(kernel=psf, convolution_type="fft_static")
model_conv = conv.convolution2d(model)
model_conv_1d = util.image2array(model_conv)
npt.assert_almost_equal(np.sum(model_conv_1d * x), 0, decimal=5)
npt.assert_almost_equal(np.sum(model_conv_1d * y), 0, decimal=5)
conv = PixelKernelConvolution(kernel=psf, convolution_type="fft")
model_conv = conv.convolution2d(model)
model_conv_1d = util.image2array(model_conv)
npt.assert_almost_equal(np.sum(model_conv_1d * x), 0, decimal=5)
npt.assert_almost_equal(np.sum(model_conv_1d * y), 0, decimal=5)
conv = PixelKernelConvolution(kernel=psf, convolution_type="grid")
model_conv = conv.convolution2d(model)
model_conv_1d = util.image2array(model_conv)
npt.assert_almost_equal(np.sum(model_conv_1d * x), 0, decimal=5)
npt.assert_almost_equal(np.sum(model_conv_1d * y), 0, decimal=5)
class TestSubgridKernelConvolution(object):
def setup_method(self):
self.supersampling_factor = 3
lightModel = LightModel(light_model_list=["GAUSSIAN"])
self.delta_pix = 1.0
x, y = util.make_grid(20, deltapix=self.delta_pix)
x_sub, y_sub = util.make_grid(
20 * self.supersampling_factor,
deltapix=self.delta_pix / self.supersampling_factor,
)
kwargs = [{"amp": 1, "sigma": 2, "center_x": 0, "center_y": 0}]
flux = lightModel.surface_brightness(x, y, kwargs)
self.model = util.array2image(flux)
flux_sub = lightModel.surface_brightness(x_sub, y_sub, kwargs)
self.model_sub = util.array2image(flux_sub)
x, y = util.make_grid(5, deltapix=self.delta_pix)
kwargs_kernel = [{"amp": 1, "sigma": 1, "center_x": 0, "center_y": 0}]
kernel = lightModel.surface_brightness(x, y, kwargs_kernel)
self.kernel = util.array2image(kernel) / np.sum(kernel)
x_sub, y_sub = util.make_grid(
5 * self.supersampling_factor,
deltapix=self.delta_pix / self.supersampling_factor,
)
kernel_sub = lightModel.surface_brightness(x_sub, y_sub, kwargs_kernel)
self.kernel_sub = util.array2image(kernel_sub) / np.sum(kernel_sub)
def test_fft_scipy_static(self):
supersampling_factor = 2
conv_sicpy = SubgridKernelConvolution(
self.kernel,
supersampling_factor,
supersampling_kernel_size=None,
convolution_type="fft",
)
conv_static = SubgridKernelConvolution(
self.kernel,
supersampling_factor,
supersampling_kernel_size=None,
convolution_type="fft_static",
)
model_conv_scipy = conv_sicpy.convolution2d(self.model)
model_conv_static = conv_static.convolution2d(self.model)
npt.assert_almost_equal(model_conv_static, model_conv_scipy, decimal=3)
def test_convolve2d(self):
# kernel_supersampled = kernel_util.subgrid_kernel(self.kernel, self.supersampling_factor, odd=True, num_iter=5)
subgrid_conv = SubgridKernelConvolution(
self.kernel_sub,
self.supersampling_factor,
supersampling_kernel_size=None,
convolution_type="fft",
)
model_subgrid_conv = subgrid_conv.convolution2d(self.model_sub)
supersampling_factor = 1
conv = SubgridKernelConvolution(
self.kernel,
supersampling_factor,
supersampling_kernel_size=None,
convolution_type="fft",
)
model_conv = conv.convolution2d(self.model)
npt.assert_almost_equal(
np.sum(model_subgrid_conv), np.sum(model_conv), decimal=1
)
npt.assert_almost_equal(model_subgrid_conv, model_conv, decimal=1)
# kernel_supersampled = kernel_util.subgrid_kernel(self.kernel, self.supersampling_factor, odd=True, num_iter=5)
subgrid_conv_split = SubgridKernelConvolution(
self.kernel_sub,
self.supersampling_factor,
supersampling_kernel_size=5,
convolution_type="fft",
)
model_subgrid_conv_split = subgrid_conv_split.convolution2d(self.model_sub)
npt.assert_almost_equal(
np.sum(model_subgrid_conv), np.sum(model_subgrid_conv_split), decimal=8
)
npt.assert_almost_equal(model_subgrid_conv, model_subgrid_conv_split, decimal=8)
subgrid_conv_split = SubgridKernelConvolution(
self.kernel_sub,
self.supersampling_factor,
supersampling_kernel_size=3,
convolution_type="fft",
)
model_subgrid_conv_split = subgrid_conv_split.convolution2d(self.model_sub)
npt.assert_almost_equal(
np.sum(model_subgrid_conv), np.sum(model_subgrid_conv_split), decimal=5
)
npt.assert_almost_equal(model_subgrid_conv, model_subgrid_conv_split, decimal=3)
class TestMultiGaussianConvolution(object):
def setup_method(self):
lightModel = LightModel(light_model_list=["GAUSSIAN"])
self.delta_pix = 1
x, y = util.make_grid(10, deltapix=self.delta_pix)
kwargs = [{"amp": 1, "sigma": 1, "center_x": 0, "center_y": 0}]
flux = lightModel.surface_brightness(x, y, kwargs)
self.model = util.array2image(flux)
def test_convolve2d(self):
sigma_list = [0.5, 1, 2]
fraction_list = [0.5, 0.2, 0.3]
mge_conv = MultiGaussianConvolution(
sigma_list=sigma_list,
fraction_list=fraction_list,
pixel_scale=self.delta_pix,
)
image_convolved = mge_conv.convolution2d(self.model)
npt.assert_almost_equal(np.sum(image_convolved), np.sum(self.model), decimal=2)
class TestMGEConvolution(object):
def setup_method(self):
lightModel = LightModel(light_model_list=["GAUSSIAN"])
self.delta_pix = 1
x, y = util.make_grid(10, deltapix=self.delta_pix)
kwargs = [{"amp": 1, "sigma": 2, "center_x": 0, "center_y": 0}]
flux = lightModel.surface_brightness(x, y, kwargs)
self.model = util.array2image(flux)
def test_convolve2d(self):
sigma_list = [2, 3, 4]
fraction_list = [0.5, 0.2, 0.3]
mg_conv = MultiGaussianConvolution(
sigma_list=sigma_list,
fraction_list=fraction_list,
pixel_scale=self.delta_pix,
)
pixel_kernel = mg_conv.pixel_kernel(num_pix=11)
mge_conv = MGEConvolution(pixel_kernel, pixel_scale=self.delta_pix, order=20)
image_conv_mg = mg_conv.convolution2d(self.model)
image_conv_mge = mge_conv.convolution2d(self.model)
npt.assert_almost_equal(
image_conv_mge / np.max(image_conv_mg),
image_conv_mg / np.max(image_conv_mg),
decimal=2,
)
diff_kernel = mge_conv.kernel_difference()
npt.assert_almost_equal(
diff_kernel,
pixel_kernel - mge_conv._mge_conv.pixel_kernel(len(pixel_kernel)),
)
if __name__ == "__main__":
pytest.main()
|
sibirrerREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@test@test_ImSim@test_Numerics@test_convolution.py@.PATH_END.py
|
{
"filename": "base.py",
"repo_name": "ML4GW/amplfi",
"repo_path": "amplfi_extracted/amplfi-main/amplfi/train/data/datasets/base.py",
"type": "Python"
}
|
import logging
import os
import sys
from typing import Dict, List, Optional, Sequence
import h5py
import lightning.pytorch as pl
import torch
from ml4gw.dataloading import Hdf5TimeSeriesDataset, InMemoryDataset
from ml4gw.transforms import ChannelWiseScaler, Whiten
from ...augmentations import PsdEstimator, WaveformProjector
from ..utils import fs as fs_utils
from ..utils.utils import ZippedDataset
from ..waveforms.sampler import WaveformSampler
Tensor = torch.Tensor
Distribution = torch.distributions.Distribution
class AmplfiDataset(pl.LightningDataModule):
"""
Base LightningDataModule for loading data to train AMPLFI models.
Subclasses must define the `inject` method
which encodes how background strain,
cross/plus polarizations and parameters
are processed before being passed to a model
Args:
data_dir:
Path to directory containing training and testing data
inference_params:
List of parameters to perform inference on. Can be a subset
of the parameters that fully describes the waveforms
highpass:
Highpass frequency in Hz
sample_rate:
Rate data is sampled in Hz
kernel_length:
Length of the kernel seen by model in seconds
fduration:
The length of the whitening filter's impulse
response, in seconds. `fduration / 2` seconds
worth of data will be cropped from the edges
of the whitened timeseries.
psd_length:
Length of data used to calculate psd in seconds
batches_per_epoch:
Number of batches for each training epoch.
batch_size:
Number of samples in each batch
ifos:
List of interferometers
waveform_sampler:
`WaveformSampler` object that produces waveforms and parameters
for training, validation and testing.
See `train.data.waveforms.sampler`
for methods this object should define.
fftlength:
Length of the fft used to calculate the psd.
Defaults to `kernel_length`
min_valid_duration:
Minimum number of seconds of validation background data
"""
def __init__(
self,
data_dir: str,
inference_params: list[str],
highpass: float,
sample_rate: float,
kernel_length: float,
fduration: float,
psd_length: float,
batches_per_epoch: int,
batch_size: int,
ifos: List[str],
waveform_sampler: WaveformSampler,
fftlength: Optional[int] = None,
min_valid_duration: float = 10000,
verbose: bool = False,
):
super().__init__()
self.save_hyperparameters(ignore=["waveform_sampler"])
self.init_logging(verbose)
self.waveform_sampler = waveform_sampler
# generate our local node data directory
# if our specified data source is remote
self.data_dir = fs_utils.get_data_dir(self.hparams.data_dir)
def init_logging(self, verbose: bool):
log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
logging.basicConfig(
format=log_format,
level=logging.DEBUG if verbose else logging.INFO,
stream=sys.stdout,
)
def prepare_data(self):
"""
Download s3 data if it doesn't exist.
"""
logger = logging.getLogger(self.__class__.__name__)
bucket, _ = fs_utils.split_data_dir(self.hparams.data_dir)
if bucket is None:
return
logger.info(
"Downloading data from S3 bucket {} to {}".format(
bucket, self.data_dir
)
)
fs_utils.download_training_data(bucket, self.data_dir)
# ================================================ #
# Distribution utilities
# ================================================ #
def get_world_size_and_rank(self) -> tuple[int, int]:
"""
Name says it all, but generalizes to the case
where we aren't running distributed training.
"""
if not torch.distributed.is_initialized():
return 1, 0
else:
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
return world_size, rank
def get_logger(self, world_size, rank):
logger_name = self.__class__.__name__
if world_size > 1:
logger_name += f":{rank}"
return logging.getLogger(logger_name)
@property
def device(self):
"""Return the device of the associated lightning module"""
return self.trainer.lightning_module.device
# ================================================ #
# Helper utilities for preprocessing
# ================================================ #
def transform(self, parameters: Dict[str, Tensor]):
"""
Make transforms to parameters before scaling
and performing training/inference.
For example, taking logarithm of hrss
"""
return self.waveform_sampler.parameter_transformer(parameters)
def scale(self, parameters, reverse: bool = False):
"""
Apply standard scaling to transformed parameters
"""
parameters = parameters.transpose(1, 0)
scaled = self.scaler(parameters, reverse=reverse)
scaled = scaled.transpose(1, 0)
return scaled
# ================================================ #
# Re-parameterizing some attributes
# ================================================ #
@property
def sample_length(self):
return (
self.hparams.kernel_length
+ self.hparams.fduration
+ self.hparams.psd_length
)
@property
def num_ifos(self):
return len(self.hparams.ifos)
@property
def num_params(self):
return len(self.hparams.inference_params)
@property
def num_workers(self):
local_world_size = len(self.trainer.device_ids)
return min(6, int(os.cpu_count() / local_world_size))
@property
def val_batch_size(self):
"""Use larger batch sizes when we don't need gradients."""
return int(1 * self.hparams.batch_size)
@property
def train_val_fnames(self):
"""List of background files used for both training and validation"""
background_dir = self.data_dir / "train" / "background"
fnames = list(background_dir.glob("*.hdf5"))
return fnames
@property
def test_fnames(self):
"""List of background files used for testing a trained model"""
test_dir = self.data_dir / "test" / "background"
fnames = list(test_dir.glob("*.hdf5"))
return fnames
def train_val_split(self) -> Sequence[str]:
"""
Split background files into training and validation sets
based on the requested duration of the validation set
"""
fnames = sorted(self.train_val_fnames)
durations = [int(fname.stem.split("-")[-1]) for fname in fnames]
valid_fnames = []
valid_duration = 0
while valid_duration < self.hparams.min_valid_duration:
fname, duration = fnames.pop(-1), durations.pop(-1)
valid_duration += duration
valid_fnames.append(str(fname))
train_fnames = fnames
return train_fnames, valid_fnames
# ================================================ #
# Utilities for initial data loading and preparation
# ================================================ #
def transforms_to_device(self):
"""
Move all `torch.nn.Modules` to the local device
"""
for item in self.__dict__.values():
if isinstance(item, torch.nn.Module):
item.to(self.device)
def build_transforms(self, stage):
"""
Build torch.nn.Modules that will be used for on-device
augmentation and preprocessing. Transfer these modules
to the appropiate device
"""
self._logger.info("Building torch Modules and transferring to device")
window_length = self.hparams.kernel_length + self.hparams.fduration
fftlength = self.hparams.fftlength or window_length
self.psd_estimator = PsdEstimator(
window_length,
self.hparams.sample_rate,
fftlength,
fast=self.hparams.highpass is not None,
average="median",
)
self.whitener = Whiten(
self.hparams.fduration,
self.hparams.sample_rate,
self.hparams.highpass,
)
# build standard scaler object and fit to parameters;
# waveform_sampler subclasses will decide how to generate
# parameters to fit the scaler
self._logger.info("Fitting standard scaler to parameters")
scaler = ChannelWiseScaler(self.num_params)
self.scaler = self.waveform_sampler.fit_scaler(scaler)
self.projector = WaveformProjector(
self.hparams.ifos, self.hparams.sample_rate
)
def setup(self, stage: str) -> None:
world_size, rank = self.get_world_size_and_rank()
self._logger = self.get_logger(world_size, rank)
self.train_fnames, self.val_fnames = self.train_val_split()
self._logger.info(f"Setting up data for stage {stage}")
# infer sample rate directly from background data
with h5py.File(self.train_fnames[0], "r") as f:
sample_rate = 1 / f[self.hparams.ifos[0]].attrs["dx"]
assert sample_rate == self.hparams.sample_rate
self._logger.info(f"Inferred sample rate of {sample_rate} Hz")
# load validation/testing waveforms, parameters, and background
# and build the fixed background dataset while data and augmentations
# modules are all still on CPU.
# get_val_waveforms should be implemented by waveform_sampler object
if stage in ["fit", "validate"]:
self.val_background = self.load_background(self.val_fnames)
self._logger.info(
f"Loaded background files {self.val_fnames} for validation"
)
cross, plus, parameters = self.waveform_sampler.get_val_waveforms(
rank, world_size
)
self._logger.info(f"Loaded {len(cross)} waveforms for validation")
params = []
for k in self.hparams.inference_params:
if k in parameters.keys():
params.append(torch.Tensor(parameters[k]))
self.val_waveforms = torch.stack([cross, plus], dim=0)
self.val_parameters = torch.column_stack(params)
elif stage == "test":
self.test_background = self.load_background(self.test_fnames)
self._logger.info(
f"Loaded background files {self.test_fnames} for testing"
)
(
cross,
plus,
parameters,
) = self.waveform_sampler.get_test_waveforms()
self._logger.info(f"Loaded {len(cross)} waveforms for testing")
params = []
for k in self.hparams.inference_params:
if k in parameters.keys():
params.append(torch.Tensor(parameters[k]))
self.test_waveforms = torch.stack([cross, plus], dim=0)
self.test_parameters = torch.column_stack(params)
# once we've generated validation/testing waveforms on cpu,
# build data augmentation modules
# and transfer them to appropiate device
self.build_transforms(stage)
self.transforms_to_device()
def load_background(self, fnames: Sequence[str]):
background = []
for fname in fnames:
data = []
with h5py.File(fname, "r") as f:
for ifo in self.hparams.ifos:
back = f[ifo][:]
data.append(torch.tensor(back, dtype=torch.float32))
data = torch.stack(data)
background.append(data)
return background
def on_after_batch_transfer(self, batch, _):
"""
This is a Lightning `hook` that gets called after
data returned by a dataloader gets put on the local device,
but before it gets passed to model for inference.
Use this to do on-device augmentation/preprocessing
"""
if self.trainer.training:
[batch] = batch
cross, plus, parameters = self.waveform_sampler.sample(batch)
strain, asds, parameters = self.inject(
batch, cross, plus, parameters
)
elif self.trainer.validating or self.trainer.sanity_checking:
[cross, plus, parameters], [background] = batch
background = background[: len(cross)]
keys = [
k
for k in self.hparams.inference_params
if k not in ["dec", "psi", "phi"]
]
parameters = {k: parameters[:, i] for i, k in enumerate(keys)}
strain, asds, parameters = self.inject(
background, cross, plus, parameters
)
elif self.trainer.testing:
[cross, plus, parameters], [background] = batch
keys = [
k
for k in self.hparams.inference_params
if k not in ["dec", "psi", "phi"]
]
parameters = {k: parameters[:, i] for i, k in enumerate(keys)}
strain, asds, parameters = self.inject(
background, cross, plus, parameters
)
return strain, asds, parameters
# ================================================ #
# Dataloaders used by lightning
# ================================================ #
def train_dataloader(self):
# if we only have one training file
# load it into memory and use InMemoryDataset
if len(self.train_fnames) == 1:
train_background = self.load_background(self.train_fnames)[0]
dataset = InMemoryDataset(
train_background,
kernel_size=int(self.hparams.sample_rate * self.sample_length),
batch_size=self.hparams.batch_size,
coincident=False,
batches_per_epoch=self.hparams.batches_per_epoch,
shuffle=True,
)
else:
dataset = Hdf5TimeSeriesDataset(
self.train_fnames,
channels=self.hparams.ifos,
kernel_size=int(self.hparams.sample_rate * self.sample_length),
batch_size=self.hparams.batch_size,
batches_per_epoch=self.hparams.batches_per_epoch,
coincident=False,
)
self._logger.info(
f"Using a {dataset.__class__.__name__} class for training"
)
pin_memory = isinstance(
self.trainer.accelerator, pl.accelerators.CUDAAccelerator
)
dataloader = torch.utils.data.DataLoader(
dataset, num_workers=self.num_workers, pin_memory=pin_memory
)
return dataloader
def val_dataloader(self):
# TODO: allow for multiple validation segment files
# offset the start of the validation background data
# by the device id to add more diversity in the validation set
_, rank = self.get_world_size_and_rank()
# build waveform dataloader
cross, plus = self.val_waveforms
waveform_dataset = torch.utils.data.TensorDataset(
cross, plus, self.val_parameters
)
waveform_dataloader = torch.utils.data.DataLoader(
waveform_dataset,
batch_size=self.val_batch_size,
shuffle=False,
pin_memory=False,
)
# build background dataloader
val_background = self.val_background[0][:, rank:]
background_dataset = InMemoryDataset(
val_background,
kernel_size=int(self.hparams.sample_rate * self.sample_length),
batch_size=self.val_batch_size,
batches_per_epoch=len(waveform_dataloader),
coincident=False,
shuffle=False,
)
background_dataloader = torch.utils.data.DataLoader(
background_dataset, pin_memory=False
)
return ZippedDataset(
waveform_dataloader,
background_dataloader,
)
def test_dataloader(self):
# TODO: allow for multiple test segment files
# build waveform dataloader
cross, plus = self.test_waveforms
waveform_dataset = torch.utils.data.TensorDataset(
cross, plus, self.test_parameters
)
waveform_dataloader = torch.utils.data.DataLoader(
waveform_dataset,
batch_size=1,
shuffle=False,
pin_memory=False,
num_workers=10,
)
background_dataset = InMemoryDataset(
self.test_background[0],
kernel_size=int(self.hparams.sample_rate * self.sample_length),
batch_size=1,
batches_per_epoch=len(waveform_dataloader),
coincident=False,
shuffle=False,
)
background_dataloader = torch.utils.data.DataLoader(
background_dataset, pin_memory=False, num_workers=10
)
return ZippedDataset(
waveform_dataloader,
background_dataloader,
)
def inject(self, *args, **kwargs):
"""
Subclasses should implement this method
for different training use cases,
like training a similarity embedding
or training a normalizing flow. This is called
after the data is transferred to the local device
"""
raise NotImplementedError
|
ML4GWREPO_NAMEamplfiPATH_START.@amplfi_extracted@amplfi-main@amplfi@train@data@datasets@base.py@.PATH_END.py
|
{
"filename": "simRTC.py",
"repo_name": "jacotay7/pyRTC",
"repo_path": "pyRTC_extracted/pyRTC-main/examples/scao/simRTC.py",
"type": "Python"
}
|
# %% Imports
import matplotlib.pyplot as plt
import time
from tqdm import tqdm
#%%
import sys
tmp = sys.stdout
from pyRTC import *
from pyRTC.hardware.OOPAOInterface import OOPAOInterface
from OOPAO.calibration.compute_KL_modal_basis import compute_KL_basis
RECALIBRATE = False
sys.stdout = tmp
import logging
import matplotlib
logging.getLogger('matplotlib').setLevel(logging.WARNING)
#%% Read Config
conf = utils.read_yaml_file("pywfs_OOPAO_config.yaml")
#%%
"""
Create the OOPAO simulation interface object
Running this cell will initialize the dm, wfs, psf, and slopes objects,
but will not start their real time computations. This inialization includes
the creation of the Shared Memory Objects, and the simulation inialization.
"""
sim = OOPAOInterface(conf=conf, param=None)
wfs, dm, psf = sim.get_hardware()
"""
Start the processes. Here the real-time computations selected in
the config will begin.
"""
dm.start()
dm.flatten()
wfs.start()
#Comment out if not made yet
psf.loadModelPSF("./calib/modelPSF.npy")
psf.start()
#Remove the atmosphere from the simulation
sim.removeAtmosphere()
psf.takeModelPSF() #Take a new model for the strehl calculation
psf.saveModelPSF("./calib/modelPSF.npy")
"""
It's important to set the full basis and number of possible modes before
initializing the loop object. Here I define a KL basis for the system
"""
NUM_MODES = conf["wfc"]["numModes"] #must be less than total KL modes
M2C_KL = compute_KL_basis(sim.tel, sim.atm, sim.dm)
dm.setM2C(M2C_KL[:,:NUM_MODES])
#%% Create Slope computation
slopes = SlopesProcess(conf=conf["slopes"])
slopes.start()
# %% Initialize our AO loop object
#Adjust the config for predictive control test
confLoop = conf["loop"]
confLoop["T"] = 3
confLoop["K"] = 10
confLoop["hidden_size"] = 64
confLoop["num_layers"] = 1
confLoop["learning_rate"] = 1e-3
confLoop["num_epochs"] = 100
confLoop["batch_size"] = 32
confLoop["validSubApsFile"] = "./calib/validSubAps.npy"
confLoop["functions"] = ["predictiveIntegrator"]
from pyRTC.hardware.basicPredictLoop import basicPredictLoop
loop = basicPredictLoop(conf=confLoop)
loop.IMFile = "./calib/simIM.npy"
loop.loadIM()
#%%
if RECALIBRATE:
#Remove the atmosphere from the simulation
sim.removeAtmosphere()
psf.takeModelPSF() #Take a new model for the strehl calculation
psf.saveModelPSF("./calib/modelPSF.npy")
loop.pokeAmp = 1e-7
#Compute the IM, blocking
loop.computeIM()
loop.saveIM("./calib/simIM.npy")
loop.loadIM()
#Add the atmosphere back to the simulation
sim.addAtmosphere()
#%%Adjust frame delay
dm.setDelay(confLoop["T"])
for i in range(5):
dm.flatten()
loop.setGain(0.3)
time.sleep(1)
loop.start()
# %%
loop.listen(int(2**14))
# loop.slopesBuffer = np.load("./calib/slopesBuffer.npy")
loop.stop()
# %%
# loop.num_epochs = 1
# loop.train()
# loop.loadModels()
loop.num_epochs = 100
loop.train()
# %%
loop.start()
loop.setGain(0.2)
loop.predict=False
time.sleep(5)
loop.gamma = 0.6
loop.predict=True
#%%
def recordStrehl(strehlShm, N=10):
val = 0
for j in range(N):
val += np.mean(strehlShm.read())
return val/N
def resetLoop():
loop.stop()
loop.flatten()
dm.flatten()
time.sleep(0.5)
loop.start()
time.sleep(0.5)
return
strehlShm = initExistingShm("strehl")[0]
#%% Find best gain
# loop.gamma = 0
gains = np.linspace(0.1,1,10)
strehls = np.zeros_like(gains)
N = 100
for i in tqdm(range(gains.size), desc="Optimal Gain"):
g = gains[i]
loop.setGain(g)
resetLoop()
strehls[i] = recordStrehl(strehlShm, N=N)
plt.plot(gains, strehls)
plt.show()
#%% Find best gamma
loop.gamma = 0
loop.predict=True
loop.setGain(0.2)
gammas = np.linspace(0,1,10)
strehls = np.zeros_like(gammas)
N = 100
for i in tqdm(range(gammas.size), desc="Optimal Gamma"):
g = gammas[i]
loop.gamma = g
resetLoop()
strehls[i] = recordStrehl(strehlShm, N=N)
plt.plot(gammas, strehls)
plt.show()
# %% Find best gamma/gain
loop.gamma = 0
loop.predict=True
loop.setGain(0.0)
loop.start()
N = 100
gammas = np.linspace(0,1,10)
gains = np.linspace(0.1,1,10)
strehls = np.zeros((gammas.size,gains.size))
for i in tqdm(range(gammas.size), desc="Optimal Gamma"):
gamma = gammas[i]
for j in range(gains.size):
gain = gains[j]
loop.gamma = 0
loop.setGain(gain)
resetLoop()
time.sleep(1)
loop.gamma = gamma
loop.setGain(gain)
time.sleep(1)
strehls[i,j] = recordStrehl(strehlShm, N=N)
np.save("./calib/performance.npy",strehls )
#%%
pred = loop.runInference(loop.history)
x = np.arange(loop.history.shape[0]+2)
for i in range(loop.history.shape[1]):
if i > 0:
break
plt.plot(x[:-2],loop.history[:,i])
plt.plot(x[-1], pred[i], 'x')
plt.show()
plt.plot(pred)
plt.plot(loop.history[-1])
plt.show()
#%%
loop.stop()
loop.flatten()
dm.flatten()
# %%
|
jacotay7REPO_NAMEpyRTCPATH_START.@pyRTC_extracted@pyRTC-main@examples@scao@simRTC.py@.PATH_END.py
|
{
"filename": "report_header.md",
"repo_name": "litebird/litebird_sim",
"repo_path": "litebird_sim_extracted/litebird_sim-master/templates/report_header.md",
"type": "Markdown"
}
|
# {{ name }}
{% if description -%}
{{description}}
{% endif %}
The simulation starts at t0={{ start_time }} and lasts {{ duration_s
}} seconds.
The seed used for the random number generator is {{ random_seed }}.
[TOC]
|
litebirdREPO_NAMElitebird_simPATH_START.@litebird_sim_extracted@litebird_sim-master@templates@report_header.md@.PATH_END.py
|
{
"filename": "_cornerradius.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/treemap/marker/_cornerradius.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CornerradiusValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="cornerradius", parent_name="treemap.marker", **kwargs
):
super(CornerradiusValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@treemap@marker@_cornerradius.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "Fermipy/fermipy",
"repo_path": "fermipy_extracted/fermipy-master/fermipy/__init__.py",
"type": "Python"
}
|
from __future__ import absolute_import, division, print_function
import os
import subprocess
__version__ = "unknown"
try:
from .version import get_git_version
__version__ = get_git_version()
except Exception as message:
print(message)
__author__ = "Matthew Wood"
try:
import pyLikelihood
except ImportError:
pass
def get_st_version():
"""Get the version string of the ST release."""
try:
import st_version.__version__ as science_tools_version
return science_tools_version
except ImportError:
pass
try:
import ST_Version
if hasattr(ST_Version, 'version'):
vv = ST_Version.version()
elif hasattr(ST_Version, 'get_git_version'):
vv = ST_Version.get_git_version()
if vv == "unknown":
vv = get_ft_conda_version()
return vv
except ImportError:
return ''
except AttributeError:
return ''
def get_git_version_fp():
"""Get the version string of the ST release."""
try:
import ST_Version
return ST_Version.get_git_version()
except ImportError:
return ''
except AttributeError:
return ''
def get_ft_conda_version():
"""Get the version string from conda"""
try:
lines = subprocess.check_output(['conda', 'list', '-f', 'fermitools']).decode().split('\n')
except:
lines = subprocess.check_output(['conda', 'list', '-f', 'fermitools']).split('\n')
for l in lines:
if not l:
continue
if l[0] == '#':
continue
tokens = l.split()
return tokens[1]
return "unknown"
PACKAGE_ROOT = os.path.abspath(os.path.dirname(__file__))
PACKAGE_DATA = os.path.join(PACKAGE_ROOT, 'data')
os.environ['FERMIPY_ROOT'] = PACKAGE_ROOT
os.environ['FERMIPY_DATA_DIR'] = PACKAGE_DATA
if 'FERMI_DIR' in os.environ and 'FERMI_DIFFUSE_DIR' not in os.environ:
os.environ['FERMI_DIFFUSE_DIR'] = os.path.expandvars('$FERMI_DIR/refdata/fermi/galdiffuse')
def _get_test_runner():
import os
from astropy.tests.helper import TestRunner
return TestRunner(os.path.dirname(__file__))
def test(package=None, test_path=None, args=None, plugins=None,
verbose=False, pastebin=None, remote_data=False, pep8=False,
pdb=False, coverage=False, open_files=False, **kwargs):
"""Run the tests using `py.test <http://pytest.org/latest>`_. A
proper set of arguments is constructed and passed to `pytest.main
<http://pytest.org/latest/builtin.html#pytest.main>`_.
Parameters
----------
package : str, optional
The name of a specific package to test, e.g. 'io.fits' or 'utils'.
If nothing is specified all default tests are run.
test_path : str, optional
Specify location to test by path. May be a single file or
directory. Must be specified absolutely or relative to the
calling directory.
args : str, optional
Additional arguments to be passed to pytest.main in the ``args``
keyword argument.
plugins : list, optional
Plugins to be passed to pytest.main in the ``plugins`` keyword
argument.
verbose : bool, optional
Convenience option to turn on verbose output from `py.test
<http://pytest.org/latest>`_. Passing True is the same as
specifying ``'-v'`` in ``args``.
pastebin : {'failed','all',None}, optional
Convenience option for turning on py.test pastebin output. Set to
``'failed'`` to upload info for failed tests, or ``'all'`` to upload
info for all tests.
remote_data : bool, optional
Controls whether to run tests marked with @remote_data. These
tests use online data and are not run by default. Set to True to
run these tests.
pep8 : bool, optional
Turn on PEP8 checking via the `pytest-pep8 plugin
<http://pypi.python.org/pypi/pytest-pep8>`_ and disable normal
tests. Same as specifying ``'--pep8 -k pep8'`` in ``args``.
pdb : bool, optional
Turn on PDB post-mortem analysis for failing tests. Same as
specifying ``'--pdb'`` in ``args``.
coverage : bool, optional
Generate a test coverage report. The result will be placed in
the directory htmlcov.
open_files : bool, optional
Fail when any tests leave files open. Off by default, because
this adds extra run time to the test suite. Works only on
platforms with a working ``lsof`` command.
parallel : int, optional
When provided, run the tests in parallel on the specified
number of CPUs. If parallel is negative, it will use the all
the cores on the machine. Requires the
`pytest-xdist <https://pypi.python.org/pypi/pytest-xdist>`_ plugin
installed. Only available when using Astropy 0.3 or later.
kwargs
Any additional keywords passed into this function will be passed
on to the astropy test runner. This allows use of test-related
functionality implemented in later versions of astropy without
explicitly updating the package template.
"""
test_runner = _get_test_runner()
return test_runner.run_tests(
package=package, test_path=test_path, args=args,
plugins=plugins, verbose=verbose, pastebin=pastebin,
remote_data=remote_data, pep8=pep8, pdb=pdb,
coverage=coverage, open_files=open_files, **kwargs)
|
FermipyREPO_NAMEfermipyPATH_START.@fermipy_extracted@fermipy-master@fermipy@__init__.py@.PATH_END.py
|
{
"filename": "conditional_abunmatch_bin_based.py",
"repo_name": "astropy/halotools",
"repo_path": "halotools_extracted/halotools-master/halotools/empirical_models/abunmatch/conditional_abunmatch_bin_based.py",
"type": "Python"
}
|
""" Module storing the Numpy kernel for conditional abundance matching
"""
import numpy as np
from astropy.utils import NumpyRNGContext
from ...utils import inverse_transformation_sampling as its
from ...utils import unsorting_indices
__author__ = ('Andrew Hearin', 'Duncan Campbell')
__all__ = ('conditional_abunmatch_bin_based', 'randomly_resort')
def conditional_abunmatch_bin_based(haloprop, galprop, sigma=0., npts_lookup_table=1000, seed=None):
""" Function used to model a correlation between two variables,
``haloprop`` and ``galprop``, using conditional abundance matching (CAM).
The input ``galprop`` defines a PDF of the desired galaxy property being modeled.
We will use the `~halotools.utils.monte_carlo_from_cdf_lookup` function to generate
Monte Carlo realizations of this input PDF. If there are ``num_halos`` in the input
``haloprop`` array, we will draw ``num_halos`` times from this input PDF,
and we will do so in such a way that larger values of ``galprop`` will be associated
with larger values of ``haloprop``. The returned array will thus be a Monte Carlo realization
of the input ``galprop`` distribution, but a correlation between the halo property and galaxy property
has been introduced. The strength of this correlation can be controlled with the input ``sigma``.
An example application of this technique is age matching, in which it is supposed that
earlier forming halos host earlier forming galaxies
(See `Hearin and Watson 2013 <https://arxiv.org/abs/1304.5557/>`_).
Alternative applications are numerous. For example, conditional abundance matching
could be used to model a correlation between galaxy disk size and halo spin,
or to model intrinsic alignments by introducing a correlation between halo and galaxy orientation.
Parameters
-----------
haloprop : ndarray
Numpy array of shape (num_halos, ) typically storing a halo property
galprop : ndarray
Numpy array of shape (num_gals, ) typically storing a galaxy property
sigma : float, optional
Level of Gaussian noise that will be introduced
to the haloprop--galprop correlation.
Default is 0, for a perfect monotonic relation between haloprop and galprop.
npts_lookup_table : int, optional
Size of the lookup table used to approximate the ``galprop`` distribution.
Default is 1000.
seed : int, optional
Random number seed used to introduce noise in the haloprop--galprop correlation.
Default is None for stochastic results.
Returns
-------
model_galprop : ndarray
Numpy array of shape (num_halos, ) storing the modeled galprop-values associated
with each value of the input ``haloprop``.
Examples
--------
Suppose we would like to do some CAM-style modeling of a correlation between some
halo property ``haloprop`` and some galaxy property ``galprop``.
The `conditional_abunmatch_bin_based` function
can be used to map values of the galaxy property onto the halos in such a way that the
PDF of ``galprop`` is preserved and a correlation (of variable strength)
between ``haloprop`` and ``galprop`` is introduced.
>>> num_halos_in_mpeak_bin = int(1e4)
>>> mean, size, std = -1.5, num_halos_in_mpeak_bin, 0.3
>>> spin_at_fixed_mpeak = 10**np.random.normal(loc=mean, size=size, scale=std)
>>> num_gals_in_mstar_bin = int(1e3)
>>> some_galprop_at_fixed_mstar = np.random.power(2.5, size=num_gals_in_mstar_bin)
>>> modeled_galprop = conditional_abunmatch_bin_based(spin_at_fixed_mpeak, some_galprop_at_fixed_mstar)
Notes
-----
To approximate the input ``galprop`` distribution, the implementation of `conditional_abunmatch_bin_based`
builds a lookup table for the CDF of the input ``galprop`` using a simple call to `numpy.interp`,
which can result in undesired edge case behavior if
a large fraction of model galaxies lie outside the range of the data.
To ensure your results are not impacted by this, make sure that
num_gals >> npts_lookup_table. It is recommended that you always visually check histograms of the
distribution of returned values against the desired distribution defined by ``galprop``.
This function is not really intended for traditional abundance matching applications
involving Schechter-like abundance functions such as the stellar-to-halo mass relation,
where extrapolation at the exponentially decaying high-mass end requires special care.
For code that provides careful treatment of this extrapolation in such cases,
see the `deconvolution abundance matching code <https://bitbucket.org/yymao/abundancematching/>`_
written by Yao-Yuan Mao.
With the release of Halotools v0.7, this function had its name changed.
The previous name given to this function was "conditional_abunmatch".
Halotools v0.7 has a new function `~halotools.empirical_models.conditional_abunmatch`
with this name that largely replaces the functionality here.
See :ref:`cam_tutorial` demonstrating how to use the new
function in galaxy-halo modeling with several worked examples.
"""
haloprop_table, galprop_table = its.build_cdf_lookup(galprop, npts_lookup_table)
haloprop_percentiles = its.rank_order_percentile(haloprop)
noisy_haloprop_percentiles = randomly_resort(haloprop_percentiles, sigma, seed=seed)
return its.monte_carlo_from_cdf_lookup(haloprop_table, galprop_table,
mc_input=noisy_haloprop_percentiles)
def randomly_resort(x, sigma, seed=None):
""" Function randomizes the entries of ``x``
with an input level of stochasticity ``sigma``.
Parameters
-----------
x : ndarray
Input array of shape (npts, ) that will be randomly reordered
sigma : float
Input level of stochasticity in the randomization
seed : int, optional
Seed used to randomly add noise
Returns
-------
noisy_x : ndarray
Array of shape (npts, )
"""
npts = len(x)
idx_sorted = np.argsort(x)
x_sorted = x[idx_sorted]
noisy_indices = noisy_indexing_array(npts, sigma, seed=seed)
noisy_x_sorted = x_sorted[noisy_indices]
idx_unsorted = unsorting_indices(idx_sorted)
return noisy_x_sorted[idx_unsorted]
def noisy_indexing_array(npts, sigma, seed=None):
"""
Function calculates an indexing array that can be used to randomly reorder
elements of a sorted array. The level of stochasticity in this random reordering
is set by the input ``sigma``.
Parameters
----------
npts : int
Number of points in the sample
sigma : float or ndarray
Level of Gaussian noise to add to the rank-ordering.
When passing a float, noise will be constant. Otherwise,
must pass an array of shape (npts, ).
seed : int, optional
Seed used to randomly draw from a Gaussian
Returns
--------
indices_with_noise : ndarray
Numpy integer array of shape (npts, ) storing the integers
0, 1, 2, ..., npts-1 in a noisy order
"""
sigma = np.atleast_1d(sigma)
if len(sigma) == 1:
sigma = np.zeros(npts) + sigma[0]
if np.any(sigma) < 0:
msg = "Input ``sigma`` must be non-negative"
raise ValueError(msg)
elif np.all(sigma) == 0:
return np.arange(npts)
else:
sigma = np.maximum(sigma, 1e-3)
with NumpyRNGContext(seed):
noise = np.random.normal(loc=0, scale=sigma, size=npts)
sorted_ranks = np.arange(1, npts+1)
sorted_percentiles = sorted_ranks/float(npts+1)
noisy_percentiles = sorted_percentiles + noise
# rescale to the unit interval
noisy_percentiles -= np.min(noisy_percentiles)
noisy_percentiles /= np.max(noisy_percentiles)
# Now transform noisy_percentiles into an array of noisy indices
noisy_indices = np.array(noisy_percentiles*npts).astype(int)
# At this point, noisy_indices has the appropriate stochasticity but may have repeated entries
# Our goal is to return a length-npts array with no repeated entries
# that may be treated as a fancy indexing array to introduce a noisy ordering
# of some other length-npts array storing our galaxy property.
# So what we do next is address the issue of repeated entries,
# replacing them with their rank-order in sequence of their appearance
placeholder_negatives = np.zeros_like(noisy_indices) - 1.
rescaled_noisy_percentile = np.insert(placeholder_negatives, noisy_indices, sorted_ranks-1)
mask_out_placeholder_negatives = rescaled_noisy_percentile != -1
indices_with_noise = rescaled_noisy_percentile[mask_out_placeholder_negatives]
return indices_with_noise.astype(int)
|
astropyREPO_NAMEhalotoolsPATH_START.@halotools_extracted@halotools-master@halotools@empirical_models@abunmatch@conditional_abunmatch_bin_based.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/agents/agent_toolkits/sql/__init__.py",
"type": "Python"
}
|
"""SQL agent."""
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@agents@agent_toolkits@sql@__init__.py@.PATH_END.py
|
{
"filename": "_dtickrange.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/polar/radialaxis/tickformatstop/_dtickrange.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class DtickrangeValidator(_plotly_utils.basevalidators.InfoArrayValidator):
def __init__(
self,
plotly_name="dtickrange",
parent_name="layout.polar.radialaxis.tickformatstop",
**kwargs,
):
super(DtickrangeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
items=kwargs.pop(
"items",
[
{"editType": "plot", "valType": "any"},
{"editType": "plot", "valType": "any"},
],
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@polar@radialaxis@tickformatstop@_dtickrange.py@.PATH_END.py
|
{
"filename": "fits_array_code.py",
"repo_name": "myrafproject/myrafproject",
"repo_path": "myrafproject_extracted/myrafproject-main/source/code/fits_array_code.py",
"type": "Python"
}
|
from myraflib import FitsArray
fa = FitsArray.from_pattern("PATTERN/OF/FILES/*.fits")
fa.hedit(
["Ke1y", "Key2"], ["Value1", "Value2"], ["Comment1", "Comment2"]
)
aligned_fa = fa.align(reference=0)
|
myrafprojectREPO_NAMEmyrafprojectPATH_START.@myrafproject_extracted@myrafproject-main@source@code@fits_array_code.py@.PATH_END.py
|
{
"filename": "creating_databases.py",
"repo_name": "DebduttaPaul/luminosity_function_of_sGRBs",
"repo_path": "luminosity_function_of_sGRBs_extracted/luminosity_function_of_sGRBs-master/creating_databases.py",
"type": "Python"
}
|
from __future__ import division
from astropy.io import ascii
from astropy.table import Table
from scipy.stats import pearsonr as R
from scipy.stats import spearmanr as S
from scipy.stats import kendalltau as T
from scipy.optimize import curve_fit
from scipy.integrate import quad
import debduttaS_functions as mf
import specific_functions as sf
import time
import numpy as np
import matplotlib.pyplot as plt
plt.rc('axes', linewidth = 2)
plt.rc('font', family = 'serif', serif = 'cm10')
plt.rc('text', usetex = True)
plt.rcParams['text.latex.preamble'] = [r'\boldmath']
####################################################################################################################################################
P = np.pi # Dear old pi!
C = 2.998*1e5 # The speed of light in vacuum, in km.s^{-1}.
H_0 = 72 # Hubble's constant, in km.s^{-1}.Mpc^{-1}.
CC = 0.73 # Cosmological constant.
L_norm = 1e52 # in ergs.s^{-1}.
T90_cut = 2 # in sec.
cm_per_Mpc = 3.0857 * 1e24
erg_per_keV = 1.6022 * 1e-9
padding = 8 # The padding of the axes labels.
size_font = 16 # The fontsize in the images.
marker_size = 7 # The size of markers in scatter plots.
al = 0.8 # The brightness of plots.
z_min = 1e-1 # for the purposes of plotting
z_max = 1e+1 # for the purposes of plotting
x_in_keV_min = 1e01 ; x_in_keV_max = 5e04 # Ep(1+z), min & max.
y_in_eps_min = 1e48 ; y_in_eps_max = 1e55 # L_iso , min & max.
####################################################################################################################################################
####################################################################################################################################################
######## Defining the functions.
def choose( bigger, smaller ):
indices = []
for i, s in enumerate( smaller ):
ind = np.where(bigger == s)[0][0] # the index is of the bigger array.
indices.append( ind )
return np.array(indices)
####################################################################################################################################################
####################################################################################################################################################
######## Reading the data.
Fermi_GRBs_table = ascii.read( './../tables/Fermi_GRBs--with_spectral_parameters.txt', format = 'fixed_width' )
Fermi_name = Fermi_GRBs_table['Fermi name'].data
Fermi_Ttime = Fermi_GRBs_table['GBM Trigger-time'].data
Fermi_T90 = Fermi_GRBs_table['GBM T90'].data
Fermi_T90_error = Fermi_GRBs_table['GBM T90_error'].data
Fermi_flux = Fermi_GRBs_table['GBM flux'].data
Fermi_flux_error = Fermi_GRBs_table['GBM flux_error'].data
Fermi_fluence = Fermi_GRBs_table['GBM fluence'].data
Fermi_fluence_error = Fermi_GRBs_table['GBM fluence_error'].data
Fermi_Epeak = Fermi_GRBs_table['Epeak'].data
Fermi_Epeak_error = Fermi_GRBs_table['Epeak_error'].data
Fermi_alpha = Fermi_GRBs_table['alpha'].data
Fermi_alpha_error = Fermi_GRBs_table['alpha_error'].data
Fermi_beta = Fermi_GRBs_table['beta'].data
Fermi_beta_error = Fermi_GRBs_table['beta_error'].data
Fermi_num = Fermi_name.size
Swift_all_GRBs_table = ascii.read( './../tables/Swift_GRBs--all.txt', format = 'fixed_width' )
Swift_all_name = Swift_all_GRBs_table['Swift name'].data
Swift_all_Ttimes = Swift_all_GRBs_table['BAT Trigger-time'].data
Swift_all_T90 = Swift_all_GRBs_table['BAT T90'].data
Swift_all_flux = Swift_all_GRBs_table['BAT Phoflux'].data
Swift_all_flux_error = Swift_all_GRBs_table['BAT Phoflux_error'].data
Swift_all_fluence = Swift_all_GRBs_table['BAT fluence'].data
Swift_all_fluence_error = Swift_all_GRBs_table['BAT fluence_error'].data
Swift_all_num = Swift_all_name.size
Swift_wkr_GRBs_table = ascii.read( './../tables/Swift_GRBs--wkr.txt', format = 'fixed_width' )
Swift_wkr_name = Swift_wkr_GRBs_table['Swift name'].data
Swift_wkr_Ttimes = Swift_wkr_GRBs_table['BAT Trigger-time'].data
Swift_wkr_redhsift = Swift_wkr_GRBs_table['redshift'].data
Swift_wkr_T90 = Swift_wkr_GRBs_table['BAT T90'].data
Swift_wkr_flux = Swift_wkr_GRBs_table['BAT Phoflux'].data
Swift_wkr_flux_error = Swift_wkr_GRBs_table['BAT Phoflux_error'].data
Swift_wkr_fluence = Swift_wkr_GRBs_table['BAT fluence'].data
Swift_wkr_fluence_error = Swift_wkr_GRBs_table['BAT fluence_error'].data
Swift_wkr_num = Swift_wkr_name.size
Swift_wkr_num = Swift_wkr_name.size
common_all_GRBs_table = ascii.read( './../tables/common_GRBs--all.txt', format = 'fixed_width' )
common_all_ID = common_all_GRBs_table['common ID'].data
common_all_Swift_name = common_all_GRBs_table['Swift name'].data
common_all_Fermi_name = common_all_GRBs_table['Fermi name'].data
common_all_Swift_T90 = common_all_GRBs_table['BAT T90'].data
common_all_Fermi_T90 = common_all_GRBs_table['GBM T90'].data
common_all_Fermi_T90_error = common_all_GRBs_table['GBM T90_error'].data
common_all_Fermi_flux = common_all_GRBs_table['GBM flux'].data
common_all_Fermi_flux_error = common_all_GRBs_table['GBM flux_error'].data
common_all_Fermi_fluence = common_all_GRBs_table['GBM fluence'].data
common_all_Fermi_fluence_error = common_all_GRBs_table['GBM fluence_error'].data
common_all_Epeak = common_all_GRBs_table['Epeak'].data # in keV.
common_all_Epeak_error = common_all_GRBs_table['Epeak_error'].data # in keV.
common_all_alpha = common_all_GRBs_table['alpha'].data
common_all_alpha_error = common_all_GRBs_table['alpha_error'].data
common_all_beta = common_all_GRBs_table['beta'].data
common_all_beta_error = common_all_GRBs_table['beta_error'].data
common_all_num = common_all_ID.size
common_wkr_GRBs_table = ascii.read( './../tables/common_GRBs--wkr.txt', format = 'fixed_width' )
common_wkr_ID = common_wkr_GRBs_table['common ID'].data
common_wkr_Swift_name = common_wkr_GRBs_table['Swift name'].data
common_wkr_Fermi_name = common_wkr_GRBs_table['Fermi name'].data
common_wkr_Fermi_Ttime = common_wkr_GRBs_table['GBM Trigger-time'].data # in hours.
common_wkr_Swift_T90 = common_wkr_GRBs_table['BAT T90'].data
common_wkr_Fermi_T90 = common_wkr_GRBs_table['GBM T90'].data
common_wkr_Fermi_T90_error = common_wkr_GRBs_table['GBM T90_error'].data
common_wkr_redshift = common_wkr_GRBs_table['redshift'].data
common_wkr_Fermi_flux = common_wkr_GRBs_table['GBM flux'].data
common_wkr_Fermi_flux_error = common_wkr_GRBs_table['GBM flux_error'].data
common_wkr_Fermi_fluence = common_wkr_GRBs_table['GBM fluence'].data
common_wkr_Fermi_fluence_error = common_wkr_GRBs_table['GBM fluence_error'].data
common_wkr_Epeak = common_wkr_GRBs_table['Epeak'].data # in keV.
common_wkr_Epeak_error = common_wkr_GRBs_table['Epeak_error'].data # in keV.
common_wkr_alpha = common_wkr_GRBs_table['alpha'].data
common_wkr_alpha_error = common_wkr_GRBs_table['alpha_error'].data
common_wkr_beta = common_wkr_GRBs_table['beta'].data
common_wkr_beta_error = common_wkr_GRBs_table['beta_error'].data
common_wkr_Luminosity = common_wkr_GRBs_table['Luminosity'].data
common_wkr_Luminosity_error = common_wkr_GRBs_table['Luminosity_error'].data
common_wkr_num = common_wkr_ID.size
BATSE_GRBs_table = ascii.read( './../tables/BATSE_GRBs--measured.txt', format = 'fixed_width' )
BATSE_name = BATSE_GRBs_table['name'].data
BATSE_Ttime = BATSE_GRBs_table['T-time'].data
BATSE_T90 = BATSE_GRBs_table['T90'].data
BATSE_T90_error = BATSE_GRBs_table['T90_error'].data
BATSE_flux = BATSE_GRBs_table['flux'].data
BATSE_flux_error = BATSE_GRBs_table['flux_error'].data
BATSE_fluence1 = BATSE_GRBs_table['fluence_1'].data
BATSE_fluence1_error = BATSE_GRBs_table['fluence_1_error'].data
BATSE_fluence2 = BATSE_GRBs_table['fluence_2'].data
BATSE_fluence2_error = BATSE_GRBs_table['fluence_2_error'].data
BATSE_fluence3 = BATSE_GRBs_table['fluence_3'].data
BATSE_fluence3_error = BATSE_GRBs_table['fluence_3_error'].data
BATSE_fluence4 = BATSE_GRBs_table['fluence_4'].data
BATSE_fluence4_error = BATSE_GRBs_table['fluence_4_error'].data
BATSE_num = BATSE_name.size
print 'Number of BATSE GRBs : ' , BATSE_num
# inds = np.where( BATSE_flux > BATSE_sensitivity )[0]
inds = np.where( BATSE_flux != 0 )[0]
BATSE_name = BATSE_name[inds]
BATSE_Ttime = BATSE_Ttime[inds]
BATSE_T90 = BATSE_T90[inds]
BATSE_T90_error = BATSE_T90_error[inds]
BATSE_flux = BATSE_flux[inds]
BATSE_flux_error = BATSE_flux_error[inds]
BATSE_fluence1 = BATSE_fluence1[inds]
BATSE_fluence1_error = BATSE_fluence1_error[inds]
BATSE_fluence2 = BATSE_fluence2[inds]
BATSE_fluence2_error = BATSE_fluence2_error[inds]
BATSE_fluence3 = BATSE_fluence3[inds]
BATSE_fluence3_error = BATSE_fluence3_error[inds]
BATSE_fluence4 = BATSE_fluence4[inds]
BATSE_fluence4_error = BATSE_fluence4_error[inds]
BATSE_num = BATSE_name.size
print 'Number of BATSE GRBs : ' , BATSE_num
Fermi_short_exclusive_GRBs_table = ascii.read( './../tables/Fermi_GRBs--without_spectral_parameters--short.txt', format = 'fixed_width' )
Fermi_short_exclusive_name = Fermi_short_exclusive_GRBs_table['name'].data
Fermi_short_exclusive_Ttime = Fermi_short_exclusive_GRBs_table['Ttime'].data
Fermi_short_exclusive_T90 = Fermi_short_exclusive_GRBs_table['T90'].data
Fermi_short_exclusive_T90_error = Fermi_short_exclusive_GRBs_table['T90_error'].data
Fermi_short_exclusive_flux = Fermi_short_exclusive_GRBs_table['flux [erg.cm^{-2}.s^{-1}]'].data
Fermi_short_exclusive_flux_error = Fermi_short_exclusive_GRBs_table['flux_error [erg.cm^{-2}.s^{-1}]'].data
Fermi_short_exclusive_fluence = Fermi_short_exclusive_GRBs_table['fluence [erg.cm^{-2}]'].data
Fermi_short_exclusive_fluence_error = Fermi_short_exclusive_GRBs_table['fluence_error [erg.cm^{-2}]'].data
Fermi_short_exclusive_num = Fermi_short_exclusive_name.size
print '\n\n'
print Fermi_short_exclusive_flux.min(), Fermi_short_exclusive_flux.max()
print Fermi_flux.min(), Fermi_flux.max()
print '\n\n'
####################################################################################################################################################
####################################################################################################################################################
######## For the Fermi GRBs, including those common with Swift (since they have spectra), except those with known redshifts (L already known).
#### First for the long ones.
## Finding all the long GRBs.
print 'Number of common GRBs : ', common_all_num
inds_long_in_universal_common_sample_by_applying_Swift_criterion = np.where( common_all_Swift_T90 >= T90_cut )[0] # these indices run over the sample of all common GRBs (i.e. with/without redshift).
print 'Number of long ones amongst them : ', inds_long_in_universal_common_sample_by_applying_Swift_criterion.size
Fermi_name_for_long_in_universal_common_sample_by_applying_Swift_criterion = common_all_Fermi_name[inds_long_in_universal_common_sample_by_applying_Swift_criterion]
inds_in_Fermi_for_long_in_universal_common_sample_by_applying_Swift_criterion = choose( Fermi_name, Fermi_name_for_long_in_universal_common_sample_by_applying_Swift_criterion ) # these indices run over the universal Fermi sample.
print 'Total number of GRBs in the Fermi sample : ', Fermi_num
inds_in_Fermi_common_all = choose( Fermi_name, common_all_Fermi_name ) # these indices run over the universal Fermi sample.
print 'Number of common GRBs : ', inds_in_Fermi_common_all.size
inds_in_Fermi_uncommon_all = np.delete( np.arange(Fermi_num), inds_in_Fermi_common_all ) # these indices run over the universal Fermi sample.
print 'Out of which those detected only by Fermi : ', inds_in_Fermi_uncommon_all.size
Fermi_T90_uncommon_all = Fermi_T90[ inds_in_Fermi_uncommon_all]
Fermi_name_uncommon_all = Fermi_name[inds_in_Fermi_uncommon_all]
inds_long_in_Fermi_only_sample = np.where( Fermi_T90_uncommon_all >= T90_cut )[0] # these indices run over the Fermi-only sample (with/without redshift).
Fermi_name_for_long_in_Fermi_only_sample = Fermi_name_uncommon_all[inds_long_in_Fermi_only_sample]
print 'Long in Fermi-only sample, by Fermi criterion : ', inds_long_in_Fermi_only_sample.size
inds_long_in_Fermi_full_sample = choose( Fermi_name, Fermi_name_for_long_in_Fermi_only_sample ) # these indices run over the universal Fermi sample.
inds_long_in_Fermi = np.union1d( inds_long_in_Fermi_full_sample, inds_in_Fermi_for_long_in_universal_common_sample_by_applying_Swift_criterion ) # these indices run over the universal Fermi sample.
print 'Total number of long GRBs in the Fermi sample : ', inds_long_in_Fermi.size
## Finding only the long GRBs without redshift.
print '\n\n'
print 'Number of common GRBs with known redshift : ', common_wkr_num
inds_long_in_redshift_common_sample_by_applying_Swift_criterion = np.where( common_wkr_Swift_T90 >= T90_cut )[0] # these indices run over the sample of common GRBs with known redshift.
print 'Number of long ones amongst them : ', inds_long_in_redshift_common_sample_by_applying_Swift_criterion.size
Fermi_name_for_long_in_redshift_common_sample_by_applying_Swift_criterion = common_wkr_Fermi_name[inds_long_in_redshift_common_sample_by_applying_Swift_criterion]
inds_amongst_common_all_for_those_wkr = choose( Fermi_name_for_long_in_universal_common_sample_by_applying_Swift_criterion, Fermi_name_for_long_in_redshift_common_sample_by_applying_Swift_criterion )
inds_without_redshift_amongst_common_sample = np.delete( np.arange(inds_long_in_universal_common_sample_by_applying_Swift_criterion.size), inds_amongst_common_all_for_those_wkr ) # these indices run over all the common GRBs that are long (only, by Swift criterion).
Fermi_name_for_common_long_GRBs_without_redshift = Fermi_name_for_long_in_universal_common_sample_by_applying_Swift_criterion[inds_without_redshift_amongst_common_sample]
print 'Common GRBs with unknown redshift : ', Fermi_name_for_common_long_GRBs_without_redshift.size
inds_in_Fermi_for_common_long_GRBs_without_redshift = choose( Fermi_name, Fermi_name_for_common_long_GRBs_without_redshift ) # these indices run over the universal Fermi sample.
inds_long_in_Fermi_without_redshift = np.union1d( inds_long_in_Fermi_full_sample, inds_in_Fermi_for_common_long_GRBs_without_redshift ) # these indices run over the universal Fermi sample.
print 'Total number of Fermi l-GRBs without redshift : ', inds_long_in_Fermi_without_redshift.size
print '\n\n'
print '\n\n\n\n'
#### Similarly for short GRBs in Fermi.
print 'Number of common GRBs : ', common_all_num
inds_short_in_universal_common_sample_by_applying_Swift_criterion = np.where( common_all_Swift_T90 < T90_cut )[0] # these indices run over the sample of all common GRBs (i.e. with/without redshift).
print 'Number of short ones amongst them : ', inds_short_in_universal_common_sample_by_applying_Swift_criterion.size
Fermi_name_for_short_in_universal_common_sample_by_applying_Swift_criterion = common_all_Fermi_name[inds_short_in_universal_common_sample_by_applying_Swift_criterion]
inds_in_Fermi_for_short_in_universal_common_sample_by_applying_Swift_criterion = choose( Fermi_name, Fermi_name_for_short_in_universal_common_sample_by_applying_Swift_criterion ) # these indices run over the universal Fermi sample.
print 'Total number of GRBs in the Fermi sample : ', Fermi_num
print 'Out of which those detected only by Fermi : ', inds_in_Fermi_uncommon_all.size
inds_short_in_Fermi_only_sample = np.where( Fermi_T90_uncommon_all < T90_cut )[0] # these indices run over the Fermi-only sample (with/without redshift).
Fermi_name_for_short_in_Fermi_only_sample = Fermi_name_uncommon_all[inds_short_in_Fermi_only_sample]
print 'Short in Fermi-only sample, by Fermi criterion : ', inds_short_in_Fermi_only_sample.size
inds_short_in_Fermi_full_sample = choose( Fermi_name, Fermi_name_for_short_in_Fermi_only_sample ) # these indices run over the universal Fermi sample.
inds_short_in_Fermi = np.union1d( inds_short_in_Fermi_full_sample, inds_in_Fermi_for_short_in_universal_common_sample_by_applying_Swift_criterion ) # these indices run over the universal Fermi sample.
print 'Total number of short GRBs in the Fermi sample : ', inds_short_in_Fermi.size
## Finding only the short GRBs without redshift.
print '\n\n'
print 'Number of common GRBs with known redshift : ', common_wkr_num
inds_short_in_redshift_common_sample_by_applying_Swift_criterion = np.where( common_wkr_Swift_T90 < T90_cut )[0] # these indices run over the sample of common GRBs with known redshift.
print 'Number of short ones amongst them : ', inds_short_in_redshift_common_sample_by_applying_Swift_criterion.size
Fermi_name_for_short_in_redshift_common_sample_by_applying_Swift_criterion = common_wkr_Fermi_name[inds_short_in_redshift_common_sample_by_applying_Swift_criterion]
inds_amongst_common_all_for_those_wkr = choose( Fermi_name_for_short_in_universal_common_sample_by_applying_Swift_criterion, Fermi_name_for_short_in_redshift_common_sample_by_applying_Swift_criterion )
inds_without_redshift_amongst_common_sample = np.delete( np.arange(inds_short_in_universal_common_sample_by_applying_Swift_criterion.size), inds_amongst_common_all_for_those_wkr ) # these indices run over all the common GRBs that are long (only, by Swift criterion).
Fermi_name_for_common_short_GRBs_without_redshift = Fermi_name_for_short_in_universal_common_sample_by_applying_Swift_criterion[inds_without_redshift_amongst_common_sample]
print 'Common GRBs with unknown redshift : ', Fermi_name_for_common_short_GRBs_without_redshift.size
inds_in_Fermi_for_common_short_GRBs_without_redshift = choose( Fermi_name, Fermi_name_for_common_short_GRBs_without_redshift ) # these indices run over the universal Fermi sample..
inds_short_in_Fermi_without_redshift = np.union1d( inds_short_in_Fermi_full_sample, inds_in_Fermi_for_common_short_GRBs_without_redshift ) # these indices run over the universal Fermi sample.
print 'Total number of Fermi s-GRBs without redshift : ', inds_short_in_Fermi_without_redshift.size
print '\n\n'
print '\n\n\n\n'
Fermi_short_name = Fermi_name[inds_short_in_Fermi_without_redshift]
Fermi_short_Ttime = Fermi_Ttime[inds_short_in_Fermi_without_redshift]
Fermi_short_T90 = Fermi_T90[inds_short_in_Fermi_without_redshift]
Fermi_short_T90_error = Fermi_T90_error[inds_short_in_Fermi_without_redshift]
Fermi_short_flux = Fermi_flux[inds_short_in_Fermi_without_redshift]
Fermi_short_flux_error = Fermi_flux_error[inds_short_in_Fermi_without_redshift]
Fermi_short_fluence = Fermi_fluence[inds_short_in_Fermi_without_redshift]
Fermi_short_fluence_error = Fermi_fluence_error[inds_short_in_Fermi_without_redshift]
####################################################################################################################################################
####################################################################################################################################################
######## For the Swift GRBs, excluding those common with Fermi (since they have spectra), and those with known redshifts.
inds_common = choose( Swift_all_name, common_all_Swift_name )
inds_wkr = choose( Swift_all_name, Swift_wkr_name )
inds_to_delete = np.union1d( inds_common, inds_wkr )
inds_exclusively_Swift_GRBs_without_redshift = np.delete( np.arange(Swift_all_num), inds_to_delete )
Swift_num = inds_exclusively_Swift_GRBs_without_redshift.size
print '\n\n\n\n\n\n\n\n'
print ' #### Swift GRBs ####', '\n'
print '# of common GRBs : ', inds_common.size
print '# of GRBs with redshift : ', inds_wkr.size
print '# of common amongst these : ', np.intersect1d( inds_common, inds_wkr ).size
print '# to be finally deleted : ', inds_to_delete.size, '\n'
print '# of Swift GRBs, total : ', Swift_all_num
print '# to be selected : ', Swift_num
Swift_name = Swift_all_name[inds_exclusively_Swift_GRBs_without_redshift]
Swift_Ttime = Swift_all_Ttimes[inds_exclusively_Swift_GRBs_without_redshift]
Swift_T90 = Swift_all_T90[inds_exclusively_Swift_GRBs_without_redshift]
Swift_flux = Swift_all_flux[inds_exclusively_Swift_GRBs_without_redshift]
Swift_flux_error = Swift_all_flux_error[inds_exclusively_Swift_GRBs_without_redshift]
Swift_fluence = Swift_all_fluence[inds_exclusively_Swift_GRBs_without_redshift]
Swift_fluence_error = Swift_all_fluence_error[inds_exclusively_Swift_GRBs_without_redshift]
inds_long_in_Swift = np.where( Swift_T90 >= T90_cut )[0]
inds_short_in_Swift = np.delete( np.arange(Swift_num), inds_long_in_Swift )
Swift_long_num = inds_long_in_Swift.size
Swift_short_name = Swift_name[inds_short_in_Swift]
Swift_short_Ttime = Swift_Ttime[inds_short_in_Swift]
Swift_short_T90 = Swift_T90[inds_short_in_Swift]
Swift_short_flux = Swift_flux[inds_short_in_Swift]
Swift_short_flux_error = np.round( Swift_flux_error[inds_short_in_Swift] , 3)
Swift_short_fluence = Swift_fluence[inds_short_in_Swift]
Swift_short_fluence_error = np.round( Swift_fluence_error[inds_short_in_Swift], 3)
Swift_short_num = Swift_short_name.size
print 'Out of which, # of long GRBs : ', Swift_long_num
print ' short GRBs : ', Swift_short_num
print '\n\n\n\n'
print '#### Swift short GRBs ####', '\n'
print 'Number of GRBs put in : ', Swift_short_num, '\n'
print '\n\n\n\n'
####################################################################################################################################################
####################################################################################################################################################
######## For the Swift-only GRBs with known redshifts, called "other" GRBs.
inds_in_Swift_wkr_for_common_wkr = choose( Swift_wkr_name, common_wkr_Swift_name )
inds_exclusively_Swift_GRBs_with_redshift = np.delete( np.arange(Swift_wkr_num), inds_in_Swift_wkr_for_common_wkr )
other_num = inds_exclusively_Swift_GRBs_with_redshift.size
print '\n\n\n\n\n\n\n\n'
print ' #### other GRBs ####', '\n'
print '# of Swift GRBs wkr : ', Swift_wkr_num
print '# of common GRBs wkr : ', common_wkr_num
print '# of Swift-only wkr : ', other_num
other_Swift_name = Swift_wkr_name[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_Ttime = Swift_wkr_Ttimes[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_redshift = Swift_wkr_redhsift[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_T90 = Swift_wkr_T90[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_flux = Swift_wkr_flux[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_flux_error = Swift_wkr_flux_error[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_fluence = Swift_wkr_fluence[inds_exclusively_Swift_GRBs_with_redshift]
other_Swift_fluence_error = Swift_wkr_fluence_error[inds_exclusively_Swift_GRBs_with_redshift]
inds_other_long = np.where( other_Swift_T90 >= T90_cut )[0]
inds_other_short = np.delete( np.arange(other_num), inds_other_long )
other_long_num = inds_other_long.size
other_short_name = other_Swift_name[inds_other_short]
other_short_Ttime = other_Swift_Ttime[inds_other_short]
other_short_T90 = other_Swift_T90[inds_other_short]
other_short_redshift = other_Swift_redshift[inds_other_short]
other_short_flux = other_Swift_flux[inds_other_short]
other_short_flux_error = np.round( other_Swift_flux_error[inds_other_short] , 3 )
other_short_fluence = other_Swift_fluence[inds_other_short]
other_short_fluence_error = np.round( other_Swift_fluence_error[inds_other_short] , 3 )
other_short_num = inds_other_short.size
print 'Out of which, # of long : ', other_long_num
print ' short : ', other_short_num
print '\n\n\n\n'
####################################################################################################################################################
####################################################################################################################################################
######## For the common GRBs with known redshifts.
inds_common_long = np.where( common_wkr_Swift_T90 >= T90_cut )[0]
inds_common_short = np.delete( np.arange(common_wkr_num), inds_common_long )
print '\n\n\n\n\n\n\n\n'
print ' #### common GRBs ####'
print common_wkr_Fermi_name[inds_common_short]
print '\n\n\n\n'
known_long_Luminosity = common_wkr_Luminosity[inds_common_long ] * L_norm
known_short_Luminosity = common_wkr_Luminosity[inds_common_short] * L_norm
####################################################################################################################################################
####################################################################################################################################################
######## For the BATSE GRBs.
inds = np.where( BATSE_T90 < T90_cut )
BATSE_short_name = BATSE_name[inds]
BATSE_short_Ttime = BATSE_Ttime[inds]
BATSE_short_T90 = BATSE_T90[inds]
BATSE_short_T90_error = BATSE_T90_error[inds]
BATSE_short_flux = BATSE_flux[inds]
BATSE_short_flux_error = BATSE_flux_error[inds]
BATSE_short_fluence1 = BATSE_fluence1[inds]
BATSE_short_fluence1_error = BATSE_fluence1_error[inds]
BATSE_short_fluence2 = BATSE_fluence2[inds]
BATSE_short_fluence2_error = BATSE_fluence2_error[inds]
BATSE_short_fluence3 = BATSE_fluence3[inds]
BATSE_short_fluence3_error = BATSE_fluence3_error[inds]
BATSE_short_fluence4 = BATSE_fluence4[inds]
BATSE_short_fluence4_error = BATSE_fluence4_error[inds]
BATSE_short_num = BATSE_name.size
print '\n\n\n\n'
print '#### BATSE short GRBs ####', '\n'
print 'Number of GRBs put in : ', BATSE_short_flux.size, '\n'
print '\n\n\n\n'
####################################################################################################################################################
####################################################################################################################################################
######## For the exclsuive Fermi GRBs (i.e. not common to Swift) without spectral parameter measurement.
print '\n\n\n\n'
print '#### Fermi short exclusive GRBs ####', '\n'
print 'Number of exclusive Fermi short GRBs : ', Fermi_short_exclusive_num
Fermi_short_name = np.concatenate( [ Fermi_short_name, Fermi_short_exclusive_name] )
Fermi_short_Ttime = np.concatenate( [ Fermi_short_Ttime, Fermi_short_exclusive_Ttime] )
Fermi_short_T90 = np.concatenate( [ Fermi_short_T90, Fermi_short_exclusive_T90] )
Fermi_short_T90_error = np.concatenate( [ Fermi_short_T90_error, Fermi_short_exclusive_T90_error] )
Fermi_short_flux = np.concatenate( [ Fermi_short_flux, Fermi_short_exclusive_flux] )
Fermi_short_flux_error = np.concatenate( [ Fermi_short_flux_error, Fermi_short_exclusive_flux_error] )
Fermi_short_fluence = np.concatenate( [ Fermi_short_fluence_error, Fermi_short_exclusive_fluence] )
Fermi_short_fluence_error = np.concatenate( [ Fermi_short_fluence_error, Fermi_short_exclusive_fluence_error] )
print '\n\n\n\n'
####################################################################################################################################################
####################################################################################################################################################
######## Writing the data.
print '\n\n\n\n'
# print ( np.where( Fermi_short_exclusive_flux_error == 0 )[0] - np.where( Fermi_short_exclusive_pseudo_redshift_error == 0 )[0] == 0 ).all()
database_short__known = Table( [ common_wkr_Swift_name[inds_common_short], common_wkr_Fermi_name[inds_common_short], common_wkr_Fermi_Ttime[inds_common_short], common_wkr_Swift_T90[inds_common_short], common_wkr_Fermi_T90[inds_common_short], common_wkr_Fermi_T90_error[inds_common_short], common_wkr_Fermi_flux[inds_common_short], common_wkr_Fermi_flux_error[inds_common_short], common_wkr_Fermi_fluence[inds_common_short], common_wkr_Fermi_fluence_error[inds_common_short], common_wkr_redshift[inds_common_short] ], names = [ 'Swift name', 'Fermi name', 'T-time [hrs]', 'BAT T90 [s]', 'GBM T90 [s]', 'GBM T90_error [s]', 'GBM flux', 'GBM flux_error', 'GBM fluence', 'GBM fluence_error', 'measured z' ] )
database_short__Fermi = Table( [ Fermi_short_name, Fermi_short_Ttime, Fermi_short_T90, Fermi_short_T90_error, Fermi_short_flux, Fermi_short_flux_error, Fermi_short_fluence, Fermi_short_fluence_error ], names = [ 'name', 'T-time [hrs]', 'T90 [s]', 'T90_error [s]', 'flux', 'flux_error', 'fluence', 'fluence_error' ] )
database_short__Swift = Table( [ Swift_short_name, Swift_short_Ttime, Swift_short_T90, Swift_short_flux, Swift_short_flux_error, Swift_short_fluence, Swift_short_fluence_error ] , names = [ 'name', 'T-time [hrs]', 'T90 [s]', 'flux', 'flux_error', 'fluence', 'fluence_error' ] )
database_short__other = Table( [ other_short_name, other_short_Ttime, other_short_T90, other_short_flux, other_short_flux_error, other_short_fluence, other_short_fluence_error, other_short_redshift ] , names = [ 'name', 'T-time [hrs]', 'T90 [s]', 'flux', 'flux_error', 'fluence', 'fluence_error', 'measured z' ] )
database_short__BATSE = Table( [ BATSE_short_name, BATSE_short_Ttime, BATSE_short_T90, BATSE_short_T90_error, BATSE_short_flux, BATSE_short_flux_error, BATSE_short_fluence1, BATSE_short_fluence1_error, BATSE_short_fluence2, BATSE_short_fluence2_error, BATSE_short_fluence3, BATSE_short_fluence3_error, BATSE_short_fluence4, BATSE_short_fluence4_error ], names = [ 'name', 'T-time', 'T90 [s]', 'T90_error [s]', 'flux', 'flux_error', 'fluence_1', 'fluence_1_error', 'fluence_2', 'fluence_2_error', 'fluence_3', 'fluence_3_error', 'fluence_4', 'fluence_4_error' ] )
ascii.write( database_short__known, './../tables/database_short__known.txt', format = 'fixed_width', overwrite = True )
ascii.write( database_short__Fermi, './../tables/database_short__Fermi.txt', format = 'fixed_width', overwrite = True )
ascii.write( database_short__Swift, './../tables/database_short__Swift.txt', format = 'fixed_width', overwrite = True )
ascii.write( database_short__other, './../tables/database_short__other.txt', format = 'fixed_width', overwrite = True )
ascii.write( database_short__BATSE, './../tables/database_short__BATSE.txt', format = 'fixed_width', overwrite = True )
####################################################################################################################################################
|
DebduttaPaulREPO_NAMEluminosity_function_of_sGRBsPATH_START.@luminosity_function_of_sGRBs_extracted@luminosity_function_of_sGRBs-master@creating_databases.py@.PATH_END.py
|
{
"filename": "_sizemode.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolargl/marker/_sizemode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizemodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="sizemode", parent_name="scatterpolargl.marker", **kwargs
):
super(SizemodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
values=kwargs.pop("values", ["diameter", "area"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolargl@marker@_sizemode.py@.PATH_END.py
|
{
"filename": "sequential.py",
"repo_name": "astroufsc/chimera",
"repo_path": "chimera_extracted/chimera-master/src/chimera/controllers/scheduler/sequential.py",
"type": "Python"
}
|
from chimera.controllers.scheduler.ischeduler import IScheduler
from chimera.controllers.scheduler.model import Session, Program
from sqlalchemy import desc
import logging
log = logging.getLogger(__name__)
from queue import Queue
class SequentialScheduler(IScheduler):
def __init__(self):
self.rq = None
self.machine = None
def reschedule(self, machine):
self.machine = machine
self.rq = Queue(-1)
session = Session()
programs = (
session.query(Program)
.order_by(desc(Program.priority))
.filter(Program.finished == False)
.all()
)
if not programs:
return
log.debug("rescheduling, found %d runnable programs" % len(list(programs)))
for program in programs:
self.rq.put(program)
machine.wakeup()
def __next__(self):
if not self.rq.empty():
return self.rq.get()
return None
def done(self, task, error=None):
if error:
log.debug("Error processing program %s." % str(task))
log.exception(error)
else:
task.finished = True
self.rq.task_done()
self.machine.wakeup()
|
astroufscREPO_NAMEchimeraPATH_START.@chimera_extracted@chimera-master@src@chimera@controllers@scheduler@sequential.py@.PATH_END.py
|
{
"filename": "fitclass.py",
"repo_name": "rychallener/ThERESA",
"repo_path": "ThERESA_extracted/ThERESA-master/theresa/lib/fitclass.py",
"type": "Python"
}
|
import os
import sys
import numpy as np
import pickle
import configparser as cp
import configclass as cc
import scipy.constants as sc
import utils
class Fit:
"""
A class to hold attributes and methods related to fitting a model
or set of models to data.
"""
def read_config(self, cfile):
"""
Read a configuration file and set up attributes accordingly.
Note that self.cfg is a Configuration instance, and self.cfg.cfg
is a raw ConfigParser instance. The ConfigParser instance should
be parsed into attributes of the Configuration() instance for
simpler access within other routines that use the Fit class.
"""
config = cp.ConfigParser()
config.read(cfile)
self.cfg = cc.Configuration()
self.cfg.cfile = cfile
self.cfg.cfg = config
# General options
self.cfg.outdir = self.cfg.cfg.get('General', 'outdir')
# 2D options
self.cfg.twod.timefile = self.cfg.cfg.get('2D', 'timefile')
self.cfg.twod.fluxfile = self.cfg.cfg.get('2D', 'fluxfile')
self.cfg.twod.ferrfile = self.cfg.cfg.get('2D', 'ferrfile')
self.cfg.twod.filtfiles = self.cfg.cfg.get('2D', 'filtfiles').split()
nfilt = len(self.cfg.twod.filtfiles)
if len(self.cfg.cfg.get('2D', 'lmax').split()) == 1:
self.cfg.twod.lmax = np.ones(nfilt, dtype=int) * \
self.cfg.cfg.getint('2D', 'lmax')
else:
self.cfg.twod.lmax = np.array(
[int(a) for a in self.cfg.cfg.get('2D', 'lmax').split()])
if len(self.cfg.cfg.get('2D', 'ncurves').split()) == 1:
self.cfg.twod.ncurves = np.ones(nfilt, dtype=int) * \
self.cfg.cfg.getint('2D', 'ncurves')
else:
self.cfg.twod.ncurves = np.array(
[int(a) for a in self.cfg.cfg.get('2D', 'ncurves').split()])
self.cfg.twod.pca = self.cfg.cfg.get( '2D', 'pca')
self.cfg.twod.ncalc = self.cfg.cfg.getint('2D', 'ncalc')
self.cfg.twod.ncpu = self.cfg.cfg.getint('2D', 'ncpu')
self.cfg.twod.nsamples = self.cfg.cfg.getint('2D', 'nsamples')
self.cfg.twod.burnin = self.cfg.cfg.getint('2D', 'burnin')
self.cfg.twod.posflux = self.cfg.cfg.getboolean('2D', 'posflux')
self.cfg.twod.nlat = self.cfg.cfg.getint('2D', 'nlat')
self.cfg.twod.nlon = self.cfg.cfg.getint('2D', 'nlon')
self.cfg.twod.plots = self.cfg.cfg.getboolean('2D', 'plots')
self.cfg.twod.animations = self.cfg.cfg.getboolean('2D', 'animations')
self.cfg.twod.leastsq = self.cfg.cfg.get('2D', 'leastsq')
if (self.cfg.twod.leastsq == 'None' or
self.cfg.twod.leastsq == 'False'):
self.cfg.twod.leastsq = None
if self.cfg.cfg.has_option('2D', 'fgamma'):
self.cfg.twod.fgamma = self.cfg.cfg.getfloat('2D', 'fgamma')
else:
self.cfg.twod.fgamma = 1.0
if self.cfg.cfg.has_option('2D', 'baseline'):
self.cfg.twod.baseline = self.cfg.cfg.get('2D', 'baseline')
if (self.cfg.twod.baseline == 'None') or \
(self.cfg.twod.baseline == 'none'):
self.cfg.twod.baseline = None
else:
self.cfg.twod.baseline = None
# 3D options
self.cfg.threed.ncpu = self.cfg.cfg.getint('3D', 'ncpu')
self.cfg.threed.nsamples = self.cfg.cfg.getint('3D', 'nsamples')
self.cfg.threed.burnin = self.cfg.cfg.getint('3D', 'burnin')
self.cfg.threed.elemfile = self.cfg.cfg.get('3D', 'elemfile')
self.cfg.threed.ptop = self.cfg.cfg.getfloat('3D', 'ptop')
self.cfg.threed.pbot = self.cfg.cfg.getfloat('3D', 'pbot')
self.cfg.threed.atmtype = self.cfg.cfg.get( '3D', 'atmtype')
self.cfg.threed.nlayers = self.cfg.cfg.getint( '3D', 'nlayers')
self.cfg.threed.rtfunc = self.cfg.cfg.get('3D', 'rtfunc')
self.cfg.threed.mapfunc = self.cfg.cfg.get('3D', 'mapfunc')
self.cfg.threed.oob = self.cfg.cfg.get('3D', 'oob')
self.cfg.threed.interp = self.cfg.cfg.get('3D', 'interp')
self.cfg.threed.z = self.cfg.cfg.get('3D', 'z')
if self.cfg.threed.z != 'fit':
try:
self.cfg.threed.z = float(self.cfg.threed.z)
except ValueError:
print("Error: Metallicity must be either 'fit' or a number.")
sys.exit()
self.cfg.threed.mols = self.cfg.cfg.get('3D', 'mols').split()
self.cfg.threed.plots = self.cfg.cfg.getboolean('3D', 'plots')
self.cfg.threed.animations = self.cfg.cfg.getboolean('3D', 'animations')
self.cfg.threed.leastsq = self.cfg.cfg.get('3D', 'leastsq')
if (self.cfg.threed.leastsq == 'None' or
self.cfg.threed.leastsq == 'False'):
self.cfg.threed.leastsq = None
if self.cfg.cfg.has_option('3D', 'grbreak'):
self.cfg.threed.grbreak = self.cfg.cfg.getfloat('3D', 'grbreak')
else:
self.cfg.threed.grbreak = 0.0
self.cfg.threed.smooth = self.cfg.cfg.get('3D', 'smooth')
if self.cfg.threed.smooth == 'None':
self.cfg.threed.smooth = None
else:
self.cfg.threed.smooth = np.int(self.cfg.threed.smooth)
self.cfg.threed.fitcf = self.cfg.cfg.getboolean('3D', 'fitcf')
for item in ['params', 'pmin', 'pmax', 'pstep']:
if self.cfg.cfg.has_option('3D', item):
value = np.array(
self.cfg.cfg.get('3D', item).split()).astype(float)
setattr(self.cfg.threed, item, value)
if self.cfg.cfg.has_option('3D', 'pnames'):
self.cfg.threed.pnames = \
self.cfg.cfg.get('3D', 'pnames').split()
self.cfg.threed.resume = self.cfg.cfg.getboolean('3D', 'resume')
if self.cfg.cfg.has_option('3D', 'fgamma'):
self.cfg.threed.fgamma = self.cfg.cfg.getfloat('3D', 'fgamma')
else:
self.cfg.threed.fgamma = 1.0
if self.cfg.threed.atmtype == 'ggchem':
self.cfg.threed.tmin = self.cfg.cfg.getfloat('3D', 'tmin')
self.cfg.threed.tmax = self.cfg.cfg.getfloat('3D', 'tmax')
self.cfg.threed.numt = self.cfg.cfg.getint( '3D', 'numt')
self.cfg.threed.zmin = self.cfg.cfg.getfloat('3D', 'zmin')
self.cfg.threed.zmax = self.cfg.cfg.getfloat('3D', 'zmax')
self.cfg.threed.numz = self.cfg.cfg.getint( '3D', 'numz')
self.cfg.threed.condensates = \
self.cfg.cfg.getboolean('3D', 'condensates')
# Star options
self.cfg.star.m = self.cfg.cfg.getfloat('Star', 'm')
self.cfg.star.r = self.cfg.cfg.getfloat('Star', 'r')
self.cfg.star.prot = self.cfg.cfg.getfloat('Star', 'prot')
self.cfg.star.t = self.cfg.cfg.getfloat('Star', 't')
self.cfg.star.d = self.cfg.cfg.getfloat('Star', 'd')
self.cfg.star.z = self.cfg.cfg.getfloat('Star', 'z')
# Planet options
self.cfg.planet.m = self.cfg.cfg.getfloat('Planet', 'm')
self.cfg.planet.r = self.cfg.cfg.getfloat('Planet', 'r')
self.cfg.planet.p0 = self.cfg.cfg.getfloat('Planet', 'p0')
self.cfg.planet.porb = self.cfg.cfg.getfloat('Planet', 'porb')
self.cfg.planet.prot = self.cfg.cfg.getfloat('Planet', 'prot')
self.cfg.planet.Omega = self.cfg.cfg.getfloat('Planet', 'Omega')
self.cfg.planet.ecc = self.cfg.cfg.getfloat('Planet', 'ecc')
self.cfg.planet.inc = self.cfg.cfg.getfloat('Planet', 'inc')
self.cfg.planet.w = self.cfg.cfg.getfloat('Planet', 'w')
self.cfg.planet.t0 = self.cfg.cfg.getfloat('Planet', 't0')
self.cfg.planet.a = self.cfg.cfg.getfloat('Planet', 'a')
self.cfg.planet.b = self.cfg.cfg.getfloat('Planet', 'b')
def read_data(self):
self.t = np.loadtxt(self.cfg.twod.timefile, ndmin=1)
self.flux = np.loadtxt(self.cfg.twod.fluxfile, ndmin=2).T
self.ferr = np.loadtxt(self.cfg.twod.ferrfile, ndmin=2).T
if len(self.t) != self.flux.shape[1]:
print("WARNING: Number of times does not match the size " +
"of the flux array.")
sys.exit()
if len(self.t) != self.ferr.shape[1]:
print("WARNING: Number of times does not match the size " +
"of the ferr array.")
sys.exit()
def read_filters(self):
self.filtwl, self.filtwn, self.filttrans, self.wnmid, self.wlmid = \
utils.readfilters(self.cfg.twod.filtfiles)
def save(self, outdir, fname=None):
# Note: starry objects are not pickleable, so they
# cannot be added to the Fit object as attributes. Possible
# workaround by creating a custom Pickler?
if type(fname) == type(None):
fname = 'fit.pkl'
with open(os.path.join(outdir, fname), 'wb') as f:
pickle.dump(self, f)
class Map:
'''
A class to hold results from a fit to a single wavelength (a 2d map).
'''
pass
def load(outdir=None, filename=None):
"""
Load a Fit object from file.
Arguments
---------
outdir: string
Location of file to load. Default is an empty string (current
directory)
filename: string
Name of the file to load. Default is 'fit.pkl'.
Returns
-------
fit: Fit instance
Fit object loaded from filename
"""
if type(outdir) == type(None):
outdir = ''
if type(filename) == type(None):
filename = 'fit.pkl'
with open(os.path.join(outdir, filename), 'rb') as f:
return pickle.load(f)
|
rychallenerREPO_NAMEThERESAPATH_START.@ThERESA_extracted@ThERESA-master@theresa@lib@fitclass.py@.PATH_END.py
|
{
"filename": "drivers.py",
"repo_name": "simonsobs/socs",
"repo_path": "socs_extracted/socs-main/socs/agents/scpi_psu/drivers.py",
"type": "Python"
}
|
# Tucker Elleflot
import socket
import time
from socs.common.prologix_interface import PrologixInterface
# append new model strings as needed
ONE_CHANNEL_MODELS = ['2280S-60-3', '2280S-32-6', '9171',
'9172', '9181', '9182', '9183', '9184', '9185']
TWO_CHANNEL_MODELS = ['9173', '9174']
THREE_CHANNEL_MODELS = ['2230G-30-1']
# error codes from 2280S devices
# part of an attempt to query devices for the number
# of supported channels
UNDEFINED_HEADER = -113
HEADER_SUFFIX_OUT_OF_RANGE = -114
class ScpiPsuInterface:
def __init__(self, ip_address, port, **kwargs):
self.ip_address = ip_address
self.port = port
self.sock = None
self.model = None
self.num_channels = 0
self.conn_socket()
try:
self.configure()
except ValueError as err:
raise ValueError(err)
def conn_socket(self):
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.connect((self.ip_address, self.port))
self.sock.settimeout(5)
def read(self):
return self.sock.recv(128).decode().strip()
def write(self, msg):
message = msg + '\n'
self.sock.sendall(message.encode())
time.sleep(0.1) # to prevent flooding the connection
def identify(self):
self.write('*idn?')
return self.read()
def read_model(self):
idn_response = self.identify().split(',')[1]
if (idn_response.startswith('MODEL')):
return idn_response[6:]
else:
return idn_response
def configure(self):
self.model = self.read_model()
if (self.model in ONE_CHANNEL_MODELS):
self.num_channels = 1
if (self.model in TWO_CHANNEL_MODELS):
self.num_channels = 2
if (self.model in THREE_CHANNEL_MODELS):
self.num_channels = 3
if (self.num_channels == 0):
self.num_channels = 3
raise ValueError('Model number not found in known device models', self.model)
def enable(self, ch):
'''
Enables output for channel (1,2,3) but does not turn it on.
Depending on state of power supply, it might need to be called
before the output is set.
'''
if (self.num_channels != 1):
self.set_chan(ch)
self.write('OUTP:ENAB ON')
def disable(self, ch):
'''
disabled output from a channel (1,2,3). once called, enable must be
called to turn on the channel again
'''
self.write('OUTP:ENAB OFF')
def set_chan(self, ch):
if (self.num_channels != 1):
self.write('inst:nsel ' + str(ch))
def set_output(self, ch, out):
'''
set status of power supply channel
ch - channel (1,2,3) to set status
out - ON: True|1|'ON' OFF: False|0|'OFF'
Calls enable to ensure a channel can be turned on. We might want to
make them separate (and let us use disable as a safety feature) but
for now I am thinking we just want to thing to turn on when we tell
it to turn on.
'''
self.set_chan(ch)
self.enable(ch)
if (self.num_channels != 1):
if isinstance(out, str):
self.write('CHAN:OUTP ' + out)
elif out:
self.write('CHAN:OUTP ON')
else:
self.write('CHAN:OUTP OFF')
else:
if isinstance(out, str):
self.write('OUTP ' + out)
elif out:
self.write('OUTP ON')
else:
self.write('OUTP OFF')
def get_output(self, ch):
'''
check if the output of a channel (1,2,3) is on (True) or off (False)
'''
self.set_chan(ch)
if (self.num_channels != 1):
self.write('CHAN:OUTP:STAT?')
else:
self.write('OUTP:STAT?')
out = bool(float(self.read()))
return out
def set_volt(self, ch, volt):
self.set_chan(ch)
self.write('volt ' + str(volt))
def set_curr(self, ch, curr):
self.set_chan(ch)
self.write('curr ' + str(curr))
def get_volt(self, ch):
self.set_chan(ch)
if (self.num_channels != 1):
self.write('MEAS:VOLT? CH' + str(ch))
else:
self.write('MEAS:VOLT?')
voltage = float(self.read().split(',')[1].strip('V'))
return voltage
def get_curr(self, ch):
self.set_chan(ch)
if (self.num_channels != 1):
self.write('MEAS:CURR? CH' + str(ch))
else:
self.write('MEAS:CURR?')
current = float(self.read().split(',')[0].strip('A'))
return current
def clear(self):
return True
class PsuInterface(PrologixInterface):
def __init__(self, ip_address, gpibAddr, verbose=False, **kwargs):
self.verbose = verbose
self.num_channels = 0
super().__init__(ip_address, gpibAddr, **kwargs)
def enable(self, ch):
'''
Enables output for channel (1,2,3) but does not turn it on.
Depending on state of power supply, it might need to be called
before the output is set.
'''
self.set_chan(ch)
self.write('OUTP:ENAB ON')
def disable(self, ch):
'''
disabled output from a channel (1,2,3). once called, enable must be
called to turn on the channel again
'''
self.write('OUTP:ENAB OFF')
def set_chan(self, ch):
self.write('inst:nsel ' + str(ch))
def set_output(self, ch, out):
'''
set status of power supply channel
ch - channel (1,2,3) to set status
out - ON: True|1|'ON' OFF: False|0|'OFF'
Calls enable to ensure a channel can be turned on. We might want to
make them separate (and let us use disable as a safety feature) but
for now I am thinking we just want to thing to turn on when we tell
it to turn on.
'''
self.set_chan(ch)
self.enable(ch)
if isinstance(out, str):
self.write('CHAN:OUTP ' + out)
elif out:
self.write('CHAN:OUTP ON')
else:
self.write('CHAN:OUTP OFF')
def get_output(self, ch):
'''
check if the output of a channel (1,2,3) is on (True) or off (False)
'''
self.set_chan(ch)
self.write('CHAN:OUTP:STAT?')
out = bool(float(self.read()))
return out
def set_volt(self, ch, volt):
self.set_chan(ch)
self.write('volt ' + str(volt))
if self.verbose:
voltage = self.get_volt(ch)
print("CH " + str(ch) + " is set to " + str(voltage) + " V")
def set_curr(self, ch, curr):
self.set_chan(ch)
self.write('curr ' + str(curr))
if self.verbose:
current = self.get_curr(ch)
print("CH " + str(ch) + " is set to " + str(current) + " A")
def get_volt(self, ch):
self.set_chan(ch)
self.write('MEAS:VOLT? CH' + str(ch))
voltage = float(self.read())
return voltage
def get_curr(self, ch):
self.set_chan(ch)
self.write('MEAS:CURR? CH' + str(ch))
current = float(self.read())
return current
def clear(self):
# Clear all the event registers and error queue, using a query such as *ESR? or MEAS:X?
# instead of *CLS can confuse the PSU
self.write('*CLS')
return True
|
simonsobsREPO_NAMEsocsPATH_START.@socs_extracted@socs-main@socs@agents@scpi_psu@drivers.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "MichelleLochner/astronomaly",
"repo_path": "astronomaly_extracted/astronomaly-main/setup.py",
"type": "Python"
}
|
import setuptools
import re
import os
VERSIONFILE = os.path.join("astronomaly", "_version.py")
verstrline = open(VERSIONFILE, "rt").read()
VSRE = r"^__version__ = ['\"]([^'\"]*)['\"]"
mo = re.search(VSRE, verstrline, re.M)
if mo:
verstr = mo.group(1)
else:
raise RuntimeError("Unable to find version string in %s." % (VERSIONFILE,))
setuptools.setup(
name="astronomaly",
version=verstr,
author="Michelle Lochner",
author_email="dr.michelle.lochner@gmail.com",
description="A general anomaly detection framework for Astronomical data",
long_description_content_type="text/markdown",
url="https://github.com/MichelleLochner/astronomaly",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: BSD-3 License",
"Operating System :: OS Independent",
],
)
|
MichelleLochnerREPO_NAMEastronomalyPATH_START.@astronomaly_extracted@astronomaly-main@setup.py@.PATH_END.py
|
{
"filename": "kernelIntegrals.py",
"repo_name": "LLNL/spheral",
"repo_path": "spheral_extracted/spheral-main/tests/unit/Kernel/kernelIntegrals.py",
"type": "Python"
}
|
# Test out integrating kernels that are half-filled.
from math import *
from Spheral import *
class Wintegral(ScalarFunctor):
def __init__(self, W, ndim, useGradientAsKernel):
assert ndim in (1, 2, 3)
self.W = W
self.ndim = ndim
self.useGradientAsKernel = useGradientAsKernel
ScalarFunctor.__init__(self)
return
def __call__(self, x):
if self.useGradientAsKernel:
result = abs(W.gradValue(x, 1.0))
else:
result = W.kernelValue(x, 1.0)
if self.ndim == 1:
return result
elif self.ndim == 2:
return pi*x*result
else:
return 2.0*pi*x*x*result
nperh = 2.0
deta = 1.0/nperh
neta = 5
etas1d, etas2d, etas3d = [], [], []
for ix in range(neta):
etas1d.append(Vector1d((ix + 0.5)*deta))
for iy in range(-neta + 1, neta):
etas2d.append(Vector2d((ix + 0.5)*deta, (iy + 0.5)*deta))
for iz in range(-neta + 1, neta):
etas3d.append(Vector3d((ix + 0.5)*deta, (iy + 0.5)*deta, (iz + 0.5)*deta))
for (W, ndim, etas, zero) in ((TableKernel1d(BSplineKernel1d(), 1000), 1, etas1d, Vector1d.zero),
(TableKernel2d(BSplineKernel2d(), 1000), 2, etas2d, Vector2d.zero),
(TableKernel3d(BSplineKernel3d(), 1000), 3, etas3d, Vector3d.zero)):
result = simpsonsIntegrationDouble(Wintegral(W, ndim, True), 0.0, W.kernelExtent, 1000)
print("Expected half zeroth moment in %i dimensions: %g" % (ndim, result))
Wsum = 0.0
W1sum = zero
for eta in etas:
Wi = abs(W.gradValue(eta.magnitude(), 1.0))
Wsum += Wi
W1sum += Wi*eta
W1sum /= Wsum
print("Result of summing W: ", Wsum, Wsum**(1.0/ndim), W1sum.magnitude()) # , (Wsum/W.volumeNormalization)**(1.0/ndim), Wsum**(1.0/ndim)/W.volumeNormalization
|
LLNLREPO_NAMEspheralPATH_START.@spheral_extracted@spheral-main@tests@unit@Kernel@kernelIntegrals.py@.PATH_END.py
|
{
"filename": "folded.py",
"repo_name": "pyro-ppl/pyro",
"repo_path": "pyro_extracted/pyro-master/pyro/distributions/folded.py",
"type": "Python"
}
|
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
from torch.distributions import constraints
from torch.distributions.transforms import AbsTransform
from pyro.distributions.torch import TransformedDistribution
class FoldedDistribution(TransformedDistribution):
"""
Equivalent to ``TransformedDistribution(base_dist, AbsTransform())``,
but additionally supports :meth:`log_prob` .
:param ~torch.distributions.Distribution base_dist: The distribution to
reflect.
"""
support = constraints.positive
def __init__(self, base_dist, validate_args=None):
if base_dist.event_shape:
raise ValueError("Only univariate distributions can be folded.")
super().__init__(base_dist, AbsTransform(), validate_args)
def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(type(self), _instance)
return super().expand(batch_shape, _instance=new)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
dim = max(len(self.batch_shape), value.dim())
plus_minus = value.new_tensor([1.0, -1.0]).reshape((2,) + (1,) * dim)
return self.base_dist.log_prob(plus_minus * value).logsumexp(0)
|
pyro-pplREPO_NAMEpyroPATH_START.@pyro_extracted@pyro-master@pyro@distributions@folded.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/jedi/py2/jedi/evaluate/context/__init__.py",
"type": "Python"
}
|
from jedi.evaluate.context.module import ModuleContext
from jedi.evaluate.context.klass import ClassContext
from jedi.evaluate.context.function import FunctionContext, FunctionExecutionContext
from jedi.evaluate.context.instance import AnonymousInstance, BoundMethod, \
CompiledInstance, AbstractInstanceContext, TreeInstance
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@jedi@py2@jedi@evaluate@context@__init__.py@.PATH_END.py
|
{
"filename": "ring.py",
"repo_name": "mpi4py/mpi4py",
"repo_path": "mpi4py_extracted/mpi4py-master/demo/profiling/ring.py",
"type": "Python"
}
|
#!/usr/bin/env python
if False:
import mpi4py
name = "name" # lib{name}.so
path = []
mpi4py.profile(name, path=path)
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
src = rank-1
dest = rank+1
if rank == 0:
src = size-1
if rank == size-1:
dest = 0
try:
from numpy import zeros
a1 = zeros(1000000, 'd')
a2 = zeros(1000000, 'd')
except ImportError:
from array import array
a1 = array('d', [0]*1000); a1 *= 1000
a2 = array('d', [0]*1000); a2 *= 1000
comm.Sendrecv(
sendbuf=a1, recvbuf=a2,
source=src, dest=dest,
)
MPI.Request.Waitall([
comm.Isend(a1, dest=dest),
comm.Irecv(a2, source=src),
])
|
mpi4pyREPO_NAMEmpi4pyPATH_START.@mpi4py_extracted@mpi4py-master@demo@profiling@ring.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/__init__.py",
"type": "Python"
}
|
# Copyright 2015 The TensorFlow Authors. 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-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Bring in all of the public TensorFlow interface into this
# module.
# pylint: disable=g-bad-import-order
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
from tensorflow.python.platform import flags # pylint: disable=g-import-not-at-top
from tensorflow.python.platform import app # pylint: disable=g-import-not-at-top
app.flags = flags
# These symbols appear because we import the python package which
# in turn imports from tensorflow.core and tensorflow.python. They
# must come from this module. So python adds these symbols for the
# resolution to succeed.
# pylint: disable=undefined-variable
del python
del core
# pylint: enable=undefined-variable
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@__init__.py@.PATH_END.py
|
{
"filename": "Untitled-checkpoint.ipynb",
"repo_name": "stevepur/DR25-occurrence-public",
"repo_path": "DR25-occurrence-public_extracted/DR25-occurrence-public-main/GKbaseline_gaiaRadCut/.ipynb_checkpoints/Untitled-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import requests
import pandas as pd
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.patches as patches
```
```python
stellarCatalog =
stellarCatalog =
pcCatalog = "koiCatalogs/dr25_GK_PCs.csv"
period_rng = (50, 400)
rp_rng = (0.75, 2.5)
bergerGK = pd.read_csv("../stellarCatalogs/dr25_stellar_supp_gaia_clean_GaiaRadCut_GK.txt")
dr25GK = pd.read_csv("../stellarCatalogs/dr25_stellar_supp_gaia_clean_DR25RadCut_GK.txt")
base_kois = pd.read_csv(pcCatalog)
m = (period_rng[0] <= base_kois.koi_period) & (base_kois.koi_period <= period_rng[1])
thisRadii = getRadii(base_kois)
m &= np.isfinite(thisRadii) & (rp_rng[0] <= thisRadii) & (thisRadii <= rp_rng[1])
```
|
stevepurREPO_NAMEDR25-occurrence-publicPATH_START.@DR25-occurrence-public_extracted@DR25-occurrence-public-main@GKbaseline_gaiaRadCut@.ipynb_checkpoints@Untitled-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "results_arma.py",
"repo_name": "statsmodels/statsmodels",
"repo_path": "statsmodels_extracted/statsmodels-main/statsmodels/tsa/tests/results/results_arma.py",
"type": "Python"
}
|
"""
Results for ARMA models. Produced by gretl.
"""
import os
from numpy import genfromtxt
current_path = os.path.dirname(os.path.abspath(__file__))
with open(current_path+"/yhat_exact_nc.csv", "rb") as fd:
yhat_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/yhat_css_nc.csv", "rb") as fd:
yhat_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/yhat_exact_c.csv", "rb") as fd:
yhatc_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/yhat_css_c.csv", "rb") as fd:
yhatc_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/resids_exact_nc.csv", "rb") as fd:
resids_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/resids_css_nc.csv", "rb") as fd:
resids_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/resids_exact_c.csv", "rb") as fd:
residsc_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/resids_css_c.csv", "rb") as fd:
residsc_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float)
with open(current_path+"/results_arma_forecasts.csv", "rb") as fd:
forecast_results = genfromtxt(fd, names=True, delimiter=",", dtype=float)
class Y_arma11:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.788452102751, 0.381793815167]
self.aic = 714.489820273473
self.bic = 725.054203027060
self.arroots = 1.2683 + 0j
self.maroots = -2.6192 + 0j
self.bse = [0.042075906061, 0.060925105865]
self.hqic = 718.741675179309
self.llf = -354.244910136737
self.resid = resids_mle[:, 0]
self.fittedvalues = yhat_mle[:, 0]
self.pvalues = [2.39e-78, 3.69e-10]
self.tvalues = [18.74, 6.267]
self.sigma2 = 0.994743350844 ** 2
self.cov_params = [[0.0017704, -0.0010612],
[-0.0010612, 0.0037119]]
self.forecast = forecast_results['fc11']
self.forecasterr = forecast_results['fe11']
elif method == "css":
self.params = [0.791515576984, 0.383078056824]
self.aic = 710.994047176570
self.bic = 721.546405865964
self.arroots = [1.2634 + 0.0000j]
self.maroots = [-2.6104 + 0.0000j]
# NOTE: bse, cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: finish this sentence]
# self.bse = [0.042369318062, 0.065703859674]
self.bse = [0.0424015620491, 0.0608752234378]
# self.cov_params = [
# [0.0017952, -0.0010996],
# [-0.0010996, 0.0043170]]
self.cov_params = [
[0.00179789246421, -0.00106195321540],
[-0.00106195321540, 0.00370579282860]]
self.hqic = 715.241545108550
self.llf = -352.497023588285
self.resid = resids_css[1:, 0]
self.fittedvalues = yhat_css[1:, 0]
self.pvalues = [7.02e-78, 5.53e-09]
# self.tvalues = [18.68, 5.830]
self.tvalues = [18.6671317239, 6.2928857557]
self.sigma2 = 0.996717562780**2
class Y_arma14:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.763798613302, 0.306453049063, -0.835653786888,
0.151382611965, 0.421169903784]
self.aic = 736.001094752429
self.bic = 757.129860259603
self.arroots = 1.3092 + 0j
self.maroots = [1.0392 - 0.7070j, 1.0392 + 0.7070j,
-1.2189 - 0.1310j, -1.2189 + 0.1310j]
self.bse = [0.064888368113, 0.078031359430, 0.076246826219,
0.069267771804, 0.071567389557]
self.cov_params = [
[0.0042105, -0.0031074, -0.0027947, -0.00027766, -0.00037373],
[-0.0031074, 0.0060889, 0.0033958, -0.0026825, -0.00062289],
[-0.0027947, 0.0033958, 0.0058136, -0.00063747, -0.0028984],
[-0.00027766, -0.0026825, -0.00063747, 0.0047980, 0.0026998],
[-0.00037373, -0.00062289, -0.0028984, 0.0026998, 0.0051219]]
self.hqic = 744.504804564101
self.llf = -362.000547376215
self.resid = resids_mle[:, 1]
self.fittedvalues = yhat_mle[:, 1]
self.pvalues = [5.51e-32, 8.59e-05, 5.96e-28, 0.0289, 3.98e-09]
self.tvalues = [11.77, 3.927, -10.96, 2.185, 5.885]
self.sigma2 = 1.022607088673 ** 2
self.bse = [0.064888368113, 0.078031359430, 0.076246826219,
0.069267771804, 0.071567389557]
elif method == "css":
self.params = [0.772072791055, 0.283961556581, -0.834797380642,
0.157773469382, 0.431616426021]
self.aic = 734.294057687460
self.bic = 755.398775066249
self.arroots = [1.2952 + 0.0000j]
self.maroots = [1.0280 - 0.6987j, 1.0280 + 0.6987j,
-1.2108 - 0.1835j, -1.2108 + 0.1835j]
# NOTE: bse, cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: finish this sentence]
# self.bse = [0.083423762397, 0.086852297123, 0.093883465705,
# 0.068170451942, 0.065938183073]
self.bse = [0.06106330, 0.07381130, 0.07257705, 0.06857992,
0.07046048]
# self.cov_params = [
# [0.0069595, -0.0053083, -0.0054522, -0.0016324, -0.00099984],
# [-0.0053083, 0.0075433, 0.0052442, -0.00071680, 0.0010335],
# [-0.0054522, 0.0052442, 0.0088141, 0.0019754, -0.0018231],
# [-0.0016324, -0.00071680, 0.0019754, 0.0046472, 0.0011853],
# [-0.00099984, 0.0010335, -0.0018231, 0.0011853, 0.0043478]]
self.cov_params = [
[0.0037287270, -0.0025337305, -0.0023475489,
-0.0001894180, -0.0002716368],
[-0.0025337305, 0.0054481087, 0.0029356374,
-0.0027307668, -0.0008073432],
[-0.0023475489, 0.0029356374, 0.0052674275,
-0.0007578638, -0.0028534882],
[-0.0001894180, -0.0027307668, -0.0007578638,
0.0047032056, 0.0026710177],
[-0.0002716368, -0.0008073432, -0.0028534882,
0.0026710177, 0.0049646795]
]
self.hqic = 742.789053551421
self.llf = -361.147028843730
self.resid = resids_css[1:, 1]
self.fittedvalues = yhat_css[1:, 1]
self.pvalues = [2.15e-20, 0.0011, 6.01e-19, 0.0206, 5.92e-11]
# self.tvalues = [9.255, 3.269, -8.892, 2.314, 6.546]
self.tvalues = [12.643194, 3.847252, -11.501785, 2.301399,
6.126120]
self.sigma2 = 1.031950951582**2
class Y_arma41:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.859167822255, -0.445990454620, -0.094364739597,
0.633504596270, 0.039251240870]
self.aic = 680.801215465509
self.bic = 701.929980972682
self.arroots = [1.0209-0j, 0.2966-0.9835j, 0.2966+0.9835j,
-1.4652 + 0.0000j]
self.maroots = [-25.4769 + 0.0000]
self.bse = [0.097363938243, 0.136020728785, 0.128467873077,
0.081059611396, 0.138536155409]
self.cov_params = [
[0.0094797, -0.012908, 0.011870, -0.0073247, -0.011669],
[-0.012908, 0.018502, -0.017103, 0.010456, 0.015892],
[0.011870, -0.017103, 0.016504, -0.010091, -0.014626],
[-0.0073247, 0.010456, -0.010091, 0.0065707, 0.0089767],
[-0.011669, 0.015892, -0.014626, 0.0089767, 0.019192]]
self.hqic = 689.304925277181
self.llf = -334.400607732754
self.resid = resids_mle[:, 2]
self.fittedvalues = yhat_mle[:, 2]
self.pvalues = [1.10e-18, 0.0010, 0.4626, 5.48e-15, 0.7769]
self.tvalues = [8.824, -3.279, -.7345, 7.815, .2833]
self.sigma2 = 0.911409665692 ** 2
self.forecast = forecast_results['fc41']
self.forecasterr = forecast_results['fe41']
elif method == "css":
self.params = [0.868370308475, -0.459433478113, -0.086098063077,
0.635050245511, 0.033645204508]
self.aic = 666.171731561927
self.bic = 687.203720777521
self.arroots = [1.0184+0.0000j, 0.2960-0.9803j, 0.2960+0.9803j,
-1.4747+0.0000j]
self.maroots = [-29.7219 + 0.0000j]
# NOTE: bse, cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: finish this sentence]
# self.bse = [0.077822066628, 0.112199961491, 0.104986211369,
# 0.068394652456, 0.113996438269]
self.bse = [0.09554032, 0.13387533, 0.12691479, 0.08045129,
0.13456419]
# self.cov_params = [
# [0.0060563, -0.0083712, 0.0076270, -0.0047067, -0.0070610],
# [-0.0083712, 0.012589, -0.011391, 0.0069576, 0.0098601],
# [0.0076270, -0.011391, 0.011022, -0.0067771, -0.0089971],
# [-0.0047067, 0.0069576, -0.0067771, 0.0046778, 0.0054205],
# [-0.0070610, 0.0098601, -0.0089971, 0.0054205, 0.012995]
# ]
self.cov_params = [
[.009127952, -.01243259, .011488329, -.007070855, -.011031907],
[-.012432590, .01792260, -.016597806, .010136298, .015053122],
[.011488329, -.01659781, .016107364, -.009851695, -.013923062],
[-.007070855, .01013630, -.009851695, .006472410, .008562476],
[-.011031907, .01505312, -.013923062, .008562476, .018107521]
]
self.hqic = 674.640335476392
self.llf = -327.085865780964
self.resid = resids_css[4:, 2]
self.fittedvalues = yhat_css[4:, 2]
self.pvalues = [6.51e-29, 4.23e-05, 0.4122, 1.62e-20, 0.7679]
# self.tvalues = [11.16, -4.095, -0.8201, 9.285, 0.2951]
self.tvalues = [9.0887381, -3.4315100, -0.6786792, 7.8938778,
0.2503143]
self.sigma2 = 0.914551777765**2
class Y_arma22:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.810898877154, -0.535753742985, 0.101765385197,
-0.691891368356]
self.aic = 756.286535543453
self.bic = 773.893840132765
self.arroots = [0.7568 - 1.1375j, 0.7568 + 1.1375j]
self.maroots = [-1.1309, 1.2780]
self.bse = [0.065073834100, 0.060522519771, 0.065569474599,
0.071275323591]
self.cov_params = [
[0.0042346, -0.0012416, -0.0024319, -0.0012756],
[-0.0012416, 0.0036630, -0.00022460, -0.0019999],
[-0.0024319, -0.00022460, 0.0042994, 0.0017842],
[-0.0012756, -0.0019999, 0.0017842, 0.0050802]]
self.hqic = 763.372960386513
self.llf = -373.143267771727
self.resid = resids_mle[:, 3]
self.fittedvalues = yhat_mle[:, 3]
self.pvalues = [1.22e-35, 8.59e-19, 0.1207, 2.81e-22]
self.tvalues = [12.46, -8.852, 1.552, -9.707]
self.sigma2 = 1.069529754715**2
elif method == "css":
self.params = [0.811172493623, -0.538952207139, 0.108020549805,
-0.697398037845]
self.aic = 749.652327535412
self.bic = 767.219471266237
self.arroots = [0.7525 - 1.1354j, 0.7525 + 1.1354j]
self.maroots = [-1.1225 + 0.0000j, 1.2774 + 0.0000j]
# NOTE: bse, cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: finish this sentence]
# self.bse = [0.063356402845, 0.064719801680, 0.058293106832,
# 0.061453528114]
self.bse = [0.06549657, 0.06127495, 0.06514116, 0.07148213]
# self.cov_params = [
# [0.0040140, -0.0016670, -0.0019069, -0.0011369],
# [-0.0016670, 0.0041887, -0.00019356, -0.0014322],
# [-0.0019069, -0.00019356, 0.0033981, 0.0020063],
# [-0.0011369, -0.0014322, 0.0020063, 0.0037765]]
self.cov_params = [
[0.004289801, -0.0012980774, -0.0024461381, -0.001244467],
[-0.001298077, 0.0037546193, -0.0001725373, -0.002039177],
[-0.002446138, -0.0001725373, 0.0042433713, 0.001720042],
[-0.001244467, -0.0020391767, 0.0017200417, 0.005109695]
]
self.hqic = 756.724194601530
self.llf = -369.826163767706
self.resid = resids_css[2:, 3]
self.fittedvalues = yhat_css[2:, 3]
self.pvalues = [1.57e-37, 8.26e-17, 0.0639, 7.55e-30]
# self.tvalues = [12.80, -8.327, 1.853, -11.35]
self.tvalues = [12.385077, -8.795883, 1.657944, -9.755738]
self.sigma2 = 1.074973483083**2
class Y_arma50:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.726892679311, -0.312619864536, 0.323740181610,
0.226499145083, -0.089562902305]
self.aic = 691.422630427314
self.bic = 712.551395934487
self.arroots = [1.0772 + 0.0000j, 0.0087 - 1.2400j,
0.0087 + 1.2400j, -1.9764 + 0.0000j,
3.4107 + 0.0000j]
self.maroots = None # TODO: empty array?
self.bse = [0.062942787895, 0.076539691571, 0.076608230545,
0.077330717503, 0.063499540628]
self.cov_params = [
[0.0039618, -0.0028252, 0.0013351, -0.0013901, -0.00066624],
[-0.0028252, 0.0058583, -0.0040200, 0.0026059, -0.0014275],
[0.0013351, -0.0040200, 0.0058688, -0.0041018, 0.0013917],
[-0.0013901, 0.0026059, -0.0041018, 0.0059800, -0.0028959],
[-0.00066624, -0.0014275, 0.0013917, -0.0028959, 0.0040322]]
self.hqic = 699.926340238986
self.llf = -339.711315213657
self.resid = resids_mle[:, 4]
self.fittedvalues = yhat_mle[:, 4]
self.pvalues = [7.51e-31, 4.42e-05, 2.38e-05, 0.0034, 0.1584]
self.tvalues = [11.55, -4.084, 4.226, 2.929, -1.410]
self.sigma2 = 0.938374940397 ** 2
self.forecast = forecast_results['fc50']
self.forecasterr = forecast_results['fe50']
elif method == "css":
# NOTE: some results use x-12 arima because gretl uses
# LS estimates for AR CSS
self.params = [0.725706505843, -0.305501865989, 0.320719417706,
0.226552951649, -0.089852608091]
# self.aic = 674.817286564674
self.aic = 676.8173
# self.bic = 692.323577617397
self.bic = 697.8248
self.arroots = [1.0755 + 0.0000j, 0.0075-1.2434j, 0.0075 + 1.2434j,
-1.9686 + 0.0000j, 3.3994 + 0.0000j]
self.maroots = None
self.bse = [0.064344956583, 0.078060866211, 0.077980166982,
0.078390791831, 0.064384559496]
self.cov_params = [
[0.0041403, -0.0029335, 0.0013775, -0.0014298, -0.00068813],
[-0.0029335, 0.0060935, -0.0041786, 0.0026980, -0.0014765],
[0.0013775, -0.0041786, 0.0060809, -0.0042177, 0.0014572],
[-0.0014298, 0.0026980, -0.0042177, 0.0061451, -0.0029853],
[-0.00068813, -0.0014765, 0.0014572, -0.0029853, 0.0041454]]
# self.hqic = 681.867054880965
self.hqic = 685.2770
self.llf = -332.408643282337
self.resid = resids_css[5:, 4]
self.fittedvalues = yhat_css[5:, 4]
self.pvalues = [1.68e-29, 9.09e-05, 3.91e-05, 0.0039, 0.1628]
self.tvalues = [11.28, -3.914, 4.113, 2.890, -1.396]
# self.sigma2 = 0.949462810435**2
self.sigma2 = .939724 ** 2
class Y_arma02:
def __init__(self, method="mle"):
if method == "mle":
self.params = [0.169096401142, -0.683713393265]
self.aic = 775.017701544762
self.bic = 785.582084298349
self.arroots = None
self.maroots = [-1.0920 + 0j, 1.3393 + 0j]
self.bse = [0.049254112414, 0.050541821979]
self.cov_params = [[0.0024260, 0.00078704],
[0.00078704, 0.0025545]]
self.hqic = 779.269556450598
self.llf = -384.508850772381
self.resid = resids_mle[:, 5]
self.fittedvalues = yhat_mle[:, 5]
self.pvalues = [.0006, 1.07e-41]
self.tvalues = [3.433, -13.53]
self.sigma2 = 1.122887152869 ** 2
elif method == "css":
# NOTE: bse, cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: finish this sentence]
self.params = [0.175605240783, -0.688421349504]
self.aic = 773.725350463014
self.bic = 784.289733216601
self.arroots = None
self.maroots = [-1.0844 + 0.j, 1.3395 + 0.j]
# self.bse = [0.044465497496, 0.045000813836]
self.bse = [0.04850046, 0.05023068]
# self.cov_params = [
# [0.0019772, 0.00090016],
# [0.00090016, 0.0020251]]
self.cov_params = [
[0.0023522942, 0.0007545702],
[0.0007545702, 0.0025231209]
]
self.hqic = 777.977205368850
self.llf = -383.862675231507
self.resid = resids_css[:, 5]
self.fittedvalues = yhat_css[:, 5]
self.pvalues = [7.84e-05, 7.89e-53]
# self.tvalues = [3.949, -15.30]
self.tvalues = [3.620967, -13.705514]
self.sigma2 = 1.123571177436**2
class Y_arma11c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [4.856475759430, 0.664363281011, 0.407547531124]
self.aic = 737.922644877973
self.bic = 752.008488549422
self.arroots = [1.5052 + 0j]
self.maroots = [-2.4537 + 0j]
self.bse = [0.273164176960, 0.055495689209, 0.068249092654]
self.cov_params = [
[0.074619, -0.00012834, 1.5413e-05],
[-0.00012834, 0.0030798, -0.0020242],
[1.5413e-05, -0.0020242, 0.0046579]]
self.hqic = 743.591784752421
self.llf = -364.961322438987
self.resid = residsc_mle[:, 0]
self.fittedvalues = yhatc_mle[:, 0]
self.pvalues = [1.04e-70, 5.02e-33, 2.35e-9]
self.tvalues = [17.78, 11.97, 5.971]
self.sigma2 = 1.039168068701 ** 2
self.forecast = forecast_results['fc11c']
self.forecasterr = forecast_results['fe11c']
elif method == "css":
# NOTE: params, bse, cov_params, tvalues taken from R;
# commented-out versions below are from gretl
# NOTE: gretl gives the intercept not the mean, x-12-arima
# and R agree with us
# self.params = [1.625462134333, 0.666386002049, 0.409512270580]
self.params = [4.872477127267, 0.666395534262, 0.409517026658]
self.aic = 734.613526514951
self.bic = 748.683338100810
self.arroots = [1.5006 + 0.0000j]
self.maroots = [-2.4419 + 0.0000]
# self.bse = [0.294788633992, 0.057503298669, 0.063059352497]
self.bse = [0.2777238133284, 0.0557583459688, 0.0681432545482]
# self.cov_params = [
# [0.086900, -0.016074, 0.010536],
# [-0.016074, 0.0033066, -0.0021977],
# [0.010536, -0.0021977, 0.0039765]
# ]
self.cov_params = [
[7.71305164897e-02, 5.65375305967e-06, 1.29481824075e-06],
[5.65375305967e-06, 3.10899314518e-03, -2.02754322743e-03],
[1.29481824075e-06, -2.02754322743e-03, 4.64350314042e-03]
]
self.hqic = 740.276857090925
self.llf = -363.306763257476
self.resid = residsc_css[1:, 0]
self.fittedvalues = yhatc_css[1:, 0]
self.pvalues = [3.51e-08, 4.70e-31, 8.35e-11]
# self.tvalues = [5.514, 11.59, 6.494]
self.tvalues = [17.544326, 11.951494, 6.009649]
self.sigma2 = 1.040940645447**2
class Y_arma14c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [4.773779823083, 0.591149657917, 0.322267595204,
-0.702933089342, 0.116129490967, 0.323009574097]
self.aic = 720.814886758937
self.bic = 745.465113183973
self.arroots = [1.6916 + 0.0000j]
# TODO: had to change order in maroots?
self.maroots = [1.1071 - 0.7821j, 1.1071 + 0.7821j,
-1.2868 - 0.1705j, -1.2868 + 0.1705j]
self.bse = [0.160891073193, 0.151756542096, 0.152996852330,
0.140231020145, 0.064663675882, 0.065045468010]
self.cov_params = [
[0.025886, 0.00026606, -0.00020969,
-0.00021435, 4.2558e-05, 5.2904e-05],
[0.00026606, 0.023030, -0.021269,
-0.018787, 0.0015423, 0.0011363],
[-0.00020969, -0.021269, 0.023408,
0.018469, -0.0035048, -0.0010750],
[-0.00021435, -0.018787, 0.018469,
0.019665, -0.00085717, -0.0033840],
[4.2558e-05, 0.0015423, -0.0035048,
-0.00085717, 0.0041814, 0.0014543],
[5.2904e-05, 0.0011363, -0.0010750,
-0.0033840, 0.0014543, 0.0042309]]
self.hqic = 730.735881539221
self.llf = -353.407443379469
self.resid = residsc_mle[:, 1]
self.fittedvalues = yhatc_mle[:, 1]
self.pvalues = [1.82e-193, 9.80e-05, 0.0352,
5.37e-07, 0.0725, 6.84e-07]
self.tvalues = [29.67, 3.895, 2.106, -5.013, 1.796, 4.966]
self.sigma2 = 0.990262659233 ** 2
elif method == "css":
# NOTE: params, bse, cov_params, tvalues taken from R;
# commented-out versions below are from
# [TODO: Finish this sentence]
# self.params = [1.502401748545, 0.683090744792, 0.197636417391,
# -0.763847295045, 0.137000823589, 0.304781097398]
self.params = [4.740785760452, 0.683056278882, 0.197681128402,
-0.763804443884, 0.136991271488, 0.304776424257]
self.aic = 719.977407193363
self.bic = 744.599577468616
self.arroots = [1.4639 + 0.0000j]
self.maroots = [1.1306-0.7071j, 1.1306+0.7071j, -1.3554 - 0.0896j,
-1.3554 + 0.0896j]
# self.bse = [0.534723749868, 0.111273280223, 0.119840296133,
# 0.111263606843, 0.070759105676, 0.061783181500]
self.bse = [0.1750455599911, 0.0942341854820, 0.0999988749541,
0.0929630759694, 0.0628352649371, 0.0645444272345]
# self.cov_params = [
# [0.28593, -0.059175, 0.053968,
# 0.046974, 0.00085168, 0.0028000],
# [-0.059175, 0.012382, -0.011333,
# -0.0098375, -0.00012631, -0.00058518],
# [0.053968, -0.011333, 0.014362,
# 0.010298, -0.0028117, -0.00011132],
# [0.046974, -0.0098375, 0.010298,
# 0.012380, 0.00031018, -0.0021617],
# [0.00085168, -0.00012631, -0.0028117,
# 0.00031018, 0.0050069, 0.00079958],
# [.0028000, -0.00058518, -0.00011132,
# -0.0021617, 0.00079958, 0.0038172]]
self.cov_params = [
[0.030640948072601, -1.61599091345e-03, 0.001707084515950,
0.001163372764659, -1.78587340563e-04, 0.000116062673743],
[-0.001615990913449, 8.88008171345e-03, -0.007454252059003,
-0.006468410832237, 5.66645379098e-05, -0.000381880917361],
[0.001707084515950, -7.45425205900e-03, 0.009999774992092,
0.005860013051220, -2.27726197200e-03, 0.000757683049669],
[0.001163372764659, -6.46841083224e-03, 0.005860013051220,
0.008642133493695, 4.40550745987e-04, -0.002170706208320],
[-0.000178587340563, 5.66645379098e-05, -0.002277261972002,
0.000440550745987, 3.94827051971e-03, 0.000884171120090],
[0.000116062673743, -3.81880917361e-04, 0.000757683049669,
-0.002170706208320, 8.84171120090e-04, 0.004165983087027]
]
self.hqic = 729.888235701317
self.llf = -352.988703596681
self.resid = residsc_css[1:, 1]
self.fittedvalues = yhatc_css[1:, 1]
self.pvalues = [.0050, 8.31e-10, .0991, 6.64e-12, .0528, 8.09e-7]
# self.tvalues = [2.810, 6.139, 1.649, -6.865, 1.936, 4.933]
self.tvalues = [27.08315344127, 7.24849772286, 1.97683352430,
-8.21621311385, 2.18016541548, 4.72196341831]
self.sigma2 = 0.998687642867**2
class Y_arma41c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [1.062980233899, 0.768972932892, -0.264824839032,
-0.279936544064, 0.756963578430, 0.231557444097]
self.aic = 686.468309958027
self.bic = 711.118536383063
self.arroots = [1.0077 + 0j, .3044-.9793j, .3044+.9793j,
-1.2466 + 0j]
self.maroots = [-4.3186 + 0.j]
self.bse = [2.781653916478, 0.063404432598, 0.091047664068,
0.084679571389, 0.054747989396, 0.098952817806]
self.cov_params = [
[7.7376, 0.0080220, -0.0039840,
0.0064925, 0.0022936, -0.0098015],
[0.0080220, 0.0040201, -0.0054843,
0.0046548, -0.0029922, -0.0047964],
[-0.0039840, -0.0054843, 0.0082897,
-0.0072913, 0.0043566, 0.0067289],
[0.0064925, 0.0046548, -0.0072913,
0.0071706, -0.0043610, -0.0057962],
[0.0022936, -0.0029922, 0.0043566,
-0.0043610, 0.0029973, 0.0036193],
[-0.0098015, -0.0047964, 0.0067289,
-0.0057962, 0.0036193, 0.0097917]]
self.hqic = 696.389304738311
self.llf = -336.234154979014
self.resid = residsc_mle[:, 2]
self.fittedvalues = yhatc_mle[:, 2]
self.pvalues = [0.7024, 7.50e-34, 0.0036, 0.0009, 1.77e-43, 0.0193]
self.tvalues = [0.3821, 12.13, -2.909, -3.306, 13.83, 2.340]
self.sigma2 = 0.915487643192 ** 2
self.forecast = forecast_results['fc41c']
self.forecasterr = forecast_results['fe41c']
elif method == "css":
# NOTE: params, bse, cov_params, tvalues taken from R;
# commented-out versions below are from
# [TODO: Finish this sentence]
# self.params = [-0.077068926631, 0.763816531155, -0.270949972390,
# -0.284496499726, 0.757135838677, 0.225247299659]
self.params = [-2.234160612756, 0.763815335585, -0.270946894536,
-0.284497190744, 0.757136686518, 0.225260672575]
self.aic = 668.907200379791
self.bic = 693.444521131318
self.arroots = [1.0141+0.0000j, 0.3036-0.9765j, 0.3036+0.9765j,
-1.2455+0.0000j]
self.maroots = [-4.4396+0.0000j]
# self.bse = [0.076048453921, 0.067854052128, 0.098041415680,
# 0.090698349822, 0.057331126067, 0.099985455449]
self.bse = [2.1842857865614, 0.0644148863289, 0.0923502391706,
0.0860004491012, 0.0558014467639, 0.1003832271008]
# self.cov_params = [
# [0.0057834, 0.00052477, -0.00079965,
# 0.00061291, -0.00013618, -0.0018963],
# [0.00052477, 0.0046042, -0.0062505,
# 0.0053416, -0.0032941, -0.0047957],
# [-0.00079965, -0.0062505, 0.0096121,
# -0.0084500, 0.0047967, 0.0064755],
# [0.00061291, 0.0053416, -0.0084500,
# 0.0082262, -0.0048029, -0.0057908],
# [-0.00013618, -0.0032941, 0.0047967,
# -0.0048029, 0.0032869, 0.0035716],
# [-0.0018963, -0.0047957, 0.0064755,
# -0.0057908, 0.0035716, 0.0099971]
# ]
self.cov_params = [
[4.77110439737413, -0.00908682223670, 0.00330914414276,
-0.00684678121434, -0.00232348925409, 0.00950558295301],
[-0.00908682223670, -0.00562941039954, 0.00852856667488,
-0.00749429397372, -0.00304322809665, -0.00494984519949],
[0.00330914414276, -0.00562941039954, 0.00852856667488,
-0.00749429397372, 0.00443590637587, 0.00693146988144],
[-0.00684678121434, 0.00482359594764, -0.00749429397372,
0.00739607724561, -0.00448059420947, -0.00600908311031],
[-0.00232348925409, -0.00304322809665, 0.00443590637587,
-0.00448059420947, 0.00311380146095, 0.00373734623817],
[0.00950558295301, -0.00494984519949, 0.00693146988144,
-0.00600908311031, 0.00373734623817, 0.01007679228317]]
self.hqic = 678.787238280001
self.llf = -327.453600189896
self.resid = residsc_css[4:, 2]
self.fittedvalues = yhatc_css[4:, 2]
self.pvalues = [0.3109, 2.15e-29, 0.0057, 0.0017, 8.06e-40, 0.0243]
# self.tvalues = [-1.013, 11.26, -2.764, -3.137, 13.21, 2.253]
self.tvalues = [-1.02283347101, 11.85774561000, -2.93390571556,
-3.30808959392, 13.56840602577, 2.24400708246]
self.sigma2 = 0.915919923456**2
class Y_arma22c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [4.507728587708, 0.788365037622, -0.358656861792,
0.035886565643, -0.699600200796]
self.aic = 813.417242529788
self.bic = 834.546008036962
self.arroots = [1.0991 - 1.2571j, 1.0991 + 1.2571j]
self.maroots = [-1.1702 + 0.0000j, 1.2215 + 0.0000j]
self.bse = [0.045346684035, 0.078382496509, 0.07004802526,
0.069227816205, 0.070668181454]
self.cov_params = [
[0.0020563, -2.3845e-05, -6.3775e-06, 4.6698e-05, 5.8515e-05],
[-2.3845e-05, 0.0061438, -0.0014403, -0.0035405, -0.0019265],
[-6.3775e-06, -0.0014403, 0.0049067, -0.00059888, -0.0025716],
[4.6698e-05, -0.0035405, -0.00059888, 0.0047925, 0.0022931],
[5.8515e-05, -0.0019265, -0.0025716, 0.0022931, 0.0049940]]
self.hqic = 821.920952341460
self.llf = -400.708621264894
self.resid = residsc_mle[:, 3]
self.fittedvalues = yhatc_mle[:, 3]
self.pvalues = [0.0000, 8.48e-24, 3.05e-07, 0.6042, 4.17e-23]
self.tvalues = [99.41, 10.06, -5.120, 0.5184, -9.900]
self.sigma2 = 1.196309833136 ** 2
elif method == "css":
# NOTE: params, bse, cov_params, tvalues taken from R;
# commented-out versions below are from
# [TODO: Finish this sentence]
# self.params = [2.571274348147, 0.793030965872, -0.363511071688,
# 0.033543918525, -0.702593972949]
self.params = [4.507207454494, 0.793055048760, -0.363521072479,
0.033519062805, -0.702595834943]
self.aic = 806.807171655455
self.bic = 827.887744132445
# self.bse = [0.369201481343, 0.076041378729, 0.070029488852,
# 0.062547355221, 0.068166970089]
self.bse = [0.0446913896589, 0.0783060902603, 0.0697866176073,
0.0681463870772, 0.068958002297]
# self.cov_params = [
# [0.13631, -0.017255, -0.012852, 0.014091, 0.017241],
# [-0.017255, 0.0057823, -0.0020013, -0.0026493, -0.0014131],
# [-0.012852, -0.0020013, 0.0049041, -0.00042960, -0.0023845],
# [0.014091, -0.0026493, -0.00042960, 0.0039122, 0.0022028],
# [0.017241, -0.0014131, -0.0023845, 0.0022028, 0.0046467]
# ]
self.cov_params = [
[1.99732030964e-03, -2.22972353619e-05, -0.000009957435095,
4.64825632252e-05, 5.98134427402e-05],
[-2.22972353619e-05, 6.13184377186e-03, -0.001435210779968,
-3.47284237940e-03, -1.95077811843e-03],
[-9.95743509501e-06, -1.43521077997e-03, 0.004870171997068,
-6.54767224831e-04, -2.44459075151e-03],
[4.64825632252e-05, -3.47284237940e-03, -0.000654767224831,
4.64393007167e-03, 2.34032945541e-03],
[5.98134427402e-05, -1.95077811843e-03, -0.002444590751509,
2.34032945541e-03, 4.75520608091e-03]]
self.arroots = [1.0908 - 1.2494j, 1.0908 + 1.2494j]
self.maroots = [-1.1694 + 0.0000j, 1.2171 + 0.0000j]
self.hqic = 815.293412134796
self.llf = -397.403585827727
self.resid = residsc_css[2:, 3]
self.fittedvalues = yhatc_css[2:, 3]
self.pvalues = [3.30e-12, 1.83e-25, 2.09e-07, 0.5918, 6.55e-25]
# self.tvalues = [6.964, 10.43, -5.191, 0.5363, -10.31]
self.tvalues = [100.851808120009, 10.127629231947, -5.209036989363,
0.491868523669, -10.188749840927]
self.sigma2 = 1.201409294941**2
class Y_arma50c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [4.562207236168, 0.754284447885, -0.305849188005,
0.253824706641, 0.281161230244, -0.172263847479]
self.aic = 711.817562780112
self.bic = 736.467789205148
self.arroots = [-1.6535 + 0.j, .0129 - 1.2018j, .0129 + 1.2018j,
1.1546 + 0.j, 2.1052 + 0j]
self.maroots = None
self.bse = [0.318447388812, 0.062272737541, 0.076600312879,
0.077310728819, 0.076837326995, 0.062642955733]
self.cov_params = [
[0.10141, -6.6930e-05, -7.3157e-05,
-4.4815e-05, 7.7676e-05, -0.00013170],
[-6.6930e-05, 0.0038779, -0.0028465,
0.0013770, -0.0012194, -0.00058978],
[-7.3157e-05, -0.0028465, 0.0058676,
-0.0040145, 0.0024694, -0.0012307],
[-4.4815e-05, 0.0013770, -0.0040145,
0.0059769, -0.0040413, 0.0013481],
[7.7676e-05, -0.0012194, 0.0024694,
-0.0040413, 0.0059040, -0.0028575],
[-0.00013170, -0.00058978, -0.0012307,
0.0013481, -0.0028575, 0.0039241]]
self.hqic = 721.738557560396
self.llf = -348.908781390056
self.resid = residsc_mle[:, 4]
self.fittedvalues = yhatc_mle[:, 4]
self.pvalues = [1.50e-46, 9.06e-34, 6.53e-05, .0010, .0003, .0060]
self.tvalues = [14.33, 12.11, -3.993, 3.283, 3.659, -2.750]
self.sigma2 = 0.973930886014 ** 2
self.forecast = forecast_results['fc50c']
self.forecasterr = forecast_results['fe50c']
elif method == "css":
# NOTE: params, bse, cov_params, tvalues taken from R;
# commented-out versions below are from
# [TODO: Finish this sentence]
# likelihood based results from x-12 arima
# self.params = [0.843173779572, 0.755433266689, -0.296886816205,
# 0.253572751789, 0.276975022313, -0.172637420881]
self.params = [4.593494860193, 0.755427402630, -0.296867127441,
0.253556723526, 0.276987447724, -0.172647993470]
# self.aic = 694.843378847617
self.aic = 696.8434
# self.bic = 715.850928110886
self.bic = 721.3522
self.arroots = [-1.6539+0.0000j, 0.0091-1.2069j, 0.0091+1.2069j,
1.1508+0.0000j, 2.0892+0.0000j]
self.maroots = None
# self.bse = [0.236922950898, 0.063573574389, 0.078206936773,
# 0.078927252266, 0.078183651496, 0.063596048046]
self.bse = [0.3359627893565, 0.0621593755265, 0.0764672280408,
0.0771715117870, 0.0764444608104, 0.0621813373935]
# self.cov_params = [
# [0.056132, -0.0028895, -0.0012291,
# -0.0031424, -0.0012502, -0.0028739],
# [-0.0028895, 0.0040416, -0.0029508,
# 0.0014229, -0.0012546, -0.00062818],
# [-0.0012291, -0.0029508, 0.0061163,
# -0.0041939, 0.0025537, -0.0012585],
# [-0.0031424, 0.0014229, -0.0041939,
# 0.0062295, -0.0041928, 0.0014204],
# [-0.0012502, -0.0012546, 0.0025537,
# -0.0041928, 0.0061127, -0.0029479],
# [-0.0028739, -0.00062818, -0.0012585,
# 0.0014204, -0.0029479, 0.0040445]
# ]
self.cov_params = [
[1.12870995832e-01, 4.32810158586e-05, -1.89697385245e-05,
0.0000465331836881, -0.000024151327384, 0.000109807500875],
[4.32810158586e-05, 3.86378796585e-03, -2.82098637123e-03,
0.001360256141301, -0.001199382243647, -0.000600542191229],
[-1.89697385245e-05, -2.82098637123e-03, 5.84723696424e-03,
-0.004009391809667, 0.002441359768335, -0.001203154760767],
[4.65331836880e-05, 1.36025614130e-03, -4.00939180967e-03,
0.005955442231484, -0.004008307295820, 0.001357917028471],
[-2.41513273840e-05, -1.19938224365e-03, 2.44135976834e-03,
-0.004008307295820, 0.005843755588588, -0.002818181279545],
[1.09807500875e-04, -6.00542191229e-04, -1.20315476077e-03,
0.001357917028471, -0.002818181279545, 0.003866518720043]]
# self.hqic = 703.303100827167
self.hqic = 706.7131
self.llf = -341.421689423809
self.resid = residsc_css[5:, 4]
self.fittedvalues = yhatc_css[5:, 4]
self.pvalues = [0.0004, 1.45e-32, 0.0001, 0.0013, 0.0004, 0.0066]
# self.tvalues = [3.559, 11.88, -3.796, 3.213, 3.543, -2.715]
self.tvalues = [13.67262984389, 12.15307258528, -3.88227918086,
3.28562597329, 3.62338153462, -2.77652428699]
# self.sigma2 = 0.987100631424**2
self.sigma2 = 0.974939 ** 2
class Y_arma02c:
def __init__(self, method="mle"):
if method == "mle":
self.params = [4.519277801954, 0.200385403960, -0.643766305844]
self.aic = 758.051194540770
self.bic = 772.137038212219
self.arroots = None
self.maroots = [-1.1004 + 0.j, 1.4117 + 0.j]
self.bse = [0.038397713362, 0.049314652466, 0.048961366071]
self.cov_params = [
[0.0014744, 6.2363e-05, 6.4093e-05],
[6.2363e-05, 0.0024319, 0.0014083],
[6.4093e-05, 0.0014083, 0.0023972]]
self.hqic = 763.720334415218
self.llf = -375.025597270385
self.resid = residsc_mle[:, 5]
self.fittedvalues = yhatc_mle[:, 5]
self.pvalues = [0.0000, 4.84e-5, 1.74e-39]
self.tvalues = [117.7, 4.063, -13.15]
self.sigma2 = 1.081406299967 ** 2
elif method == "css":
# NOTE: cov_params, tvalues taken from R; commented-out
# versions below are from [TODO: Finish this sentence]
self.params = [4.519869870853, 0.202414429306, -0.647482560461]
self.aic = 756.679105324347
self.bic = 770.764948995796
self.arroots = None
self.maroots = [-1.0962 + 0.0000j, 1.4089 + 0.0000j]
self.bse = [0.038411589816, 0.047983057239, 0.043400749866]
# self.cov_params = [
# [0.0014755, 9.0191e-05, 7.3561e-06],
# [9.0191e-05, 0.0023024, 0.0012479],
# [7.3561e-06, 0.0012479, 0.0018836]]
self.cov_params = [
[1.46121526606e-03, 5.30770136338e-05, 5.34796521051e-05],
[5.30770136338e-05, 2.37105883909e-03, 1.41090983316e-03],
[5.34796521051e-05, 1.41090983316e-03, 2.35584355080e-03]]
self.hqic = 762.348245198795
self.llf = -374.339552662174
self.resid = residsc_css[:, 5]
self.fittedvalues = yhatc_css[:, 5]
self.pvalues = [0.0000, 2.46e-05, 2.49e-50]
# self.tvalues = [117.7, 4.218, -14.92]
self.tvalues = [118.24120637494, 4.15691796413, -13.33981086206]
self.sigma2 = 1.081576475937**2
|
statsmodelsREPO_NAMEstatsmodelsPATH_START.@statsmodels_extracted@statsmodels-main@statsmodels@tsa@tests@results@results_arma.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "D-arioSpace/astroquery",
"repo_path": "astroquery_extracted/astroquery-main/astroquery/esa/iso/tests/__init__.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
=====================
ISO Astroquery Module
=====================
European Space Astronomy Centre (ESAC)
European Space Agency (ESA)
"""
|
D-arioSpaceREPO_NAMEastroqueryPATH_START.@astroquery_extracted@astroquery-main@astroquery@esa@iso@tests@__init__.py@.PATH_END.py
|
{
"filename": "pysep.py",
"repo_name": "sdss/lvmagp",
"repo_path": "lvmagp_extracted/lvmagp-main/python/lvmagp/images/processors/detection/pysep.py",
"type": "Python"
}
|
import asyncio
from functools import partial
from typing import Tuple, TYPE_CHECKING, Any, Optional
from astropy.table import Table, Column
import logging
import numpy as np
import numpy.typing as npt
import pandas as pd
from .sourcedetection import SourceDetection
from lvmagp.images import Image
if TYPE_CHECKING:
from sep import Background
log = logging.getLogger(__name__)
class SepSourceDetection(SourceDetection):
"""Detect sources using SEP."""
__module__ = "lvmagp.images.processors.detection"
def __init__(
self,
threshold: float = 1.5,
minarea: int = 5,
deblend_nthresh: int = 32,
deblend_cont: float = 0.005,
clean: bool = True,
clean_param: float = 1.0,
**kwargs: Any,
):
"""Initializes a wrapper for SEP. See its documentation for details.
Highly inspired by LCO's wrapper for SEP, see:
https://github.com/LCOGT/banzai/blob/master/banzai/photometry.py
Args:
threshold: Threshold pixel value for detection.
minarea: Minimum number of pixels required for detection.
deblend_nthresh: Number of thresholds used for object deblending.
deblend_cont: Minimum contrast ratio used for object deblending.
clean: Perform cleaning?
clean_param: Cleaning parameter (see SExtractor manual).
"""
SourceDetection.__init__(self, **kwargs)
# store
self.threshold = threshold
self.minarea = minarea
self.deblend_nthresh = deblend_nthresh
self.deblend_cont = deblend_cont
self.clean = clean
self.clean_param = clean_param
def __call__(self, image: Image) -> Image:
"""Find stars in given image and append catalog.
Args:
image: Image to find stars in.
Returns:
Image with attached catalog.
"""
import sep
loop = asyncio.get_running_loop()
# got data?
if image.data is None:
log.warning("No data found in image.")
return image
# no mask?
mask = image.mask if image.mask is not None else np.zeros(image.data.shape, dtype=bool)
# remove background
data, bkg = SepSourceDetection.remove_background(image.data, mask)
# extract sources
#sources = await loop.run_in_executor(
#None,
#partial(
#sep.extract,
#data,
#self.threshold,
#err=bkg.globalrms,
#minarea=self.minarea,
#deblend_nthresh=self.deblend_nthresh,
#deblend_cont=self.deblend_cont,
#clean=self.clean,
#clean_param=self.clean_param,
#mask=image.mask,
#),
#)
sources = sep.extract(
data,
self.threshold,
err=bkg.globalrms,
minarea=self.minarea,
deblend_nthresh=self.deblend_nthresh,
deblend_cont=self.deblend_cont,
clean=self.clean,
clean_param=self.clean_param,
mask=image.mask,
)
# convert to astropy table
sources = pd.DataFrame(sources)
# only keep sources with detection flag < 8
sources = sources[sources["flag"] < 8]
x, y = sources["x"], sources["y"]
# Calculate the ellipticity
sources["ellipticity"] = 1.0 - (sources["b"] / sources["a"])
# calculate the FWHMs of the stars
fwhm = 2.0 * (np.log(2) * (sources["a"] ** 2.0 + sources["b"] ** 2.0)) ** 0.5
sources["fwhm"] = fwhm
# clip theta to [-pi/2,pi/2]
sources["theta"] = sources["theta"].clip(lower=np.pi / 2, upper=np.pi / 2)
# Kron radius
kronrad, krflag = sep.kron_radius(data, x, y, sources["a"], sources["b"], sources["theta"], 6.0)
sources["flag"] |= krflag
sources["kronrad"] = kronrad
# equivalent of FLUX_AUTO
gain = image.header["GAIN"] if "GAIN" in image.header else None
#flux, fluxerr, flag = await loop.run_in_executor(
#None,
#partial(
#sep.sum_ellipse,
#data,
#x,
#y,
#sources["a"],
#sources["b"],
#sources["theta"],
#2.5 * kronrad,
#subpix=5,
#mask=image.mask,
#gain=gain,
#),
#)
flux, fluxerr, flag = sep.sum_ellipse(
data,
x,
y,
sources["a"],
sources["b"],
sources["theta"],
2.5 * kronrad,
subpix=5,
mask=image.mask,
gain=gain,
)
sources["flag"] |= flag
sources["flux"] = flux
# radii at 0.25, 0.5, and 0.75 flux
flux_radii, flag = sep.flux_radius(
data, x, y, 6.0 * sources["a"], [0.25, 0.5, 0.75], normflux=sources["flux"], subpix=5
)
sources["flag"] |= flag
sources["fluxrad25"] = flux_radii[:, 0]
sources["fluxrad50"] = flux_radii[:, 1]
sources["fluxrad75"] = flux_radii[:, 2]
# xwin/ywin
sig = 2.0 / 2.35 * sources["fluxrad50"]
xwin, ywin, flag = sep.winpos(data, x, y, sig)
sources["flag"] |= flag
sources["xwin"] = xwin
sources["ywin"] = ywin
# theta in degrees
sources["theta"] = np.degrees(sources["theta"])
# only keep sources with detection flag < 8
sources = sources[sources["flag"] < 8]
# match fits conventions
sources["x"] += 1
sources["y"] += 1
x, y = x.to_numpy(), y.to_numpy()
# Create a distance table of point (row) vs point (column)
sources_dist = np.sqrt((x - x[:,None])**2 + (y - y[:,None])**2)
# The diagonals are 0, as the distance of a point to itself is 0,
# but we want that to have a large value so it comes last in sorting
np.fill_diagonal(sources_dist, np.inf)
# Get the sorted index for each row
dist_idx = sources_dist.argsort(axis=1)
# add only the nearest
dist_next = [sources_dist[idx, dist_idx[idx]][0] for idx in range(len(sources))]
sources['dist'] = dist_next
# pick columns for catalog
cat = sources[
[
"x",
"y",
"peak",
"flux",
"fwhm",
"a",
"b",
"theta",
"ellipticity",
"tnpix",
"kronrad",
"fluxrad25",
"fluxrad50",
"fluxrad75",
"xwin",
"ywin",
"dist",
]
]
# copy image, set catalog and return it
img = image.copy()
img.catalog = Table.from_pandas(cat)
return img
@staticmethod
def remove_background(
data: npt.NDArray[float], mask: Optional[npt.NDArray[float]] = None
) -> Tuple[npt.NDArray[float], "Background"]:
"""Remove background from image in data.
Args:
data: Data to remove background from.
mask: Mask to use for estimating background.
Returns:
Image without background.
"""
import sep
# get data and make it continuous
d = data.astype(float)
# estimate background, probably we need to byte swap
try:
bkg = sep.Background(d, mask=mask, bw=32, bh=32, fw=3, fh=3)
except ValueError as e:
d = d.byteswap(True).newbyteorder()
bkg = sep.Background(d, mask=mask, bw=32, bh=32, fw=3, fh=3)
# subtract it
bkg.subfrom(d)
# return data without background and background
return d, bkg
__all__ = ["SepSourceDetection"]
|
sdssREPO_NAMElvmagpPATH_START.@lvmagp_extracted@lvmagp-main@python@lvmagp@images@processors@detection@pysep.py@.PATH_END.py
|
{
"filename": "_title.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattermap/marker/colorbar/_title.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TitleValidator(_plotly_utils.basevalidators.TitleValidator):
def __init__(
self, plotly_name="title", parent_name="scattermap.marker.colorbar", **kwargs
):
super(TitleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Title"),
data_docs=kwargs.pop(
"data_docs",
"""
font
Sets this color bar's title font.
side
Determines the location of color bar's title
with respect to the color bar. Defaults to
"top" when `orientation` if "v" and defaults
to "right" when `orientation` if "h".
text
Sets the title of the color bar.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattermap@marker@colorbar@_title.py@.PATH_END.py
|
{
"filename": "test_diagonal_cosmo_bnn_prior.py",
"repo_name": "jiwoncpark/baobab",
"repo_path": "baobab_extracted/baobab-master/baobab/tests/test_bnn_priors/test_diagonal_cosmo_bnn_prior.py",
"type": "Python"
}
|
import unittest
class TestDiagonalCosmoBNNPrior(unittest.TestCase):
"""A suite of tests alerting us for breakge, e.g. errors in
instantiation of classes or execution of scripts, for DiagonalBNNPrior
"""
def test_tdlmc_diagonal_cosmo_config(self):
"""Tests instantiation of TDLMC diagonal Config
"""
import baobab.configs as configs
cfg = configs.BaobabConfig.from_file(configs.tdlmc_diagonal_cosmo_config.__file__)
return cfg
def test_diagonal_cosmo_bnn_prior(self):
"""Tests instantiation and sampling of DiagonalBNNPrior
"""
from baobab.bnn_priors import DiagonalCosmoBNNPrior
cfg = self.test_tdlmc_diagonal_cosmo_config()
diagonal_cosmo_bnn_prior = DiagonalCosmoBNNPrior(cfg.bnn_omega, cfg.components)
return diagonal_cosmo_bnn_prior.sample()
if __name__ == '__main__':
unittest.main()
|
jiwoncparkREPO_NAMEbaobabPATH_START.@baobab_extracted@baobab-master@baobab@tests@test_bnn_priors@test_diagonal_cosmo_bnn_prior.py@.PATH_END.py
|
{
"filename": "_fields.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/hypothesis/py3/hypothesis/extra/django/_fields.py",
"type": "Python"
}
|
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
import re
import string
from datetime import datetime, timedelta
from decimal import Decimal
from functools import lru_cache
from typing import Any, Callable, Dict, Type, TypeVar, Union
import django
from django import forms as df
from django.contrib.auth.forms import UsernameField
from django.core.validators import (
validate_ipv4_address,
validate_ipv6_address,
validate_ipv46_address,
)
from django.db import models as dm
from hypothesis import strategies as st
from hypothesis.errors import InvalidArgument, ResolutionFailed
from hypothesis.internal.validation import check_type
from hypothesis.provisional import urls
from hypothesis.strategies import emails
AnyField = Union[dm.Field, df.Field]
F = TypeVar("F", bound=AnyField)
def numeric_bounds_from_validators(
field, min_value=float("-inf"), max_value=float("inf")
):
for v in field.validators:
if isinstance(v, django.core.validators.MinValueValidator):
min_value = max(min_value, v.limit_value)
elif isinstance(v, django.core.validators.MaxValueValidator):
max_value = min(max_value, v.limit_value)
return min_value, max_value
def integers_for_field(min_value, max_value):
def inner(field):
return st.integers(*numeric_bounds_from_validators(field, min_value, max_value))
return inner
@lru_cache
def timezones():
# From Django 4.0, the default is to use zoneinfo instead of pytz.
assert getattr(django.conf.settings, "USE_TZ", False)
if django.VERSION < (5, 0, 0) and getattr(
django.conf.settings, "USE_DEPRECATED_PYTZ", True
):
from hypothesis.extra.pytz import timezones
else:
from hypothesis.strategies import timezones
return timezones()
# Mapping of field types, to strategy objects or functions of (type) -> strategy
_FieldLookUpType = Dict[
Type[AnyField],
Union[st.SearchStrategy, Callable[[Any], st.SearchStrategy]],
]
_global_field_lookup: _FieldLookUpType = {
dm.SmallIntegerField: integers_for_field(-32768, 32767),
dm.IntegerField: integers_for_field(-2147483648, 2147483647),
dm.BigIntegerField: integers_for_field(-9223372036854775808, 9223372036854775807),
dm.PositiveIntegerField: integers_for_field(0, 2147483647),
dm.PositiveSmallIntegerField: integers_for_field(0, 32767),
dm.BooleanField: st.booleans(),
dm.DateField: st.dates(),
dm.EmailField: emails(),
dm.FloatField: st.floats(),
dm.NullBooleanField: st.one_of(st.none(), st.booleans()),
dm.URLField: urls(),
dm.UUIDField: st.uuids(),
df.DateField: st.dates(),
df.DurationField: st.timedeltas(),
df.EmailField: emails(),
df.FloatField: lambda field: st.floats(
*numeric_bounds_from_validators(field), allow_nan=False, allow_infinity=False
),
df.IntegerField: integers_for_field(-2147483648, 2147483647),
df.NullBooleanField: st.one_of(st.none(), st.booleans()),
df.URLField: urls(),
df.UUIDField: st.uuids(),
}
_ipv6_strings = st.one_of(
st.ip_addresses(v=6).map(str),
st.ip_addresses(v=6).map(lambda addr: addr.exploded),
)
def register_for(field_type):
def inner(func):
_global_field_lookup[field_type] = func
return func
return inner
@register_for(dm.DateTimeField)
@register_for(df.DateTimeField)
def _for_datetime(field):
if getattr(django.conf.settings, "USE_TZ", False):
# avoid https://code.djangoproject.com/ticket/35683
return st.datetimes(
min_value=datetime.min + timedelta(days=1),
max_value=datetime.max - timedelta(days=1),
timezones=timezones(),
)
return st.datetimes()
def using_sqlite():
try:
return (
getattr(django.conf.settings, "DATABASES", {})
.get("default", {})
.get("ENGINE", "")
.endswith(".sqlite3")
)
except django.core.exceptions.ImproperlyConfigured:
return None
@register_for(dm.TimeField)
def _for_model_time(field):
# SQLITE supports TZ-aware datetimes, but not TZ-aware times.
if getattr(django.conf.settings, "USE_TZ", False) and not using_sqlite():
return st.times(timezones=timezones())
return st.times()
@register_for(df.TimeField)
def _for_form_time(field):
if getattr(django.conf.settings, "USE_TZ", False):
return st.times(timezones=timezones())
return st.times()
@register_for(dm.DurationField)
def _for_duration(field):
# SQLite stores timedeltas as six bytes of microseconds
if using_sqlite():
delta = timedelta(microseconds=2**47 - 1)
return st.timedeltas(-delta, delta)
return st.timedeltas()
@register_for(dm.SlugField)
@register_for(df.SlugField)
def _for_slug(field):
min_size = 1
if getattr(field, "blank", False) or not getattr(field, "required", True):
min_size = 0
return st.text(
alphabet=string.ascii_letters + string.digits,
min_size=min_size,
max_size=field.max_length,
)
@register_for(dm.GenericIPAddressField)
def _for_model_ip(field):
return {
"ipv4": st.ip_addresses(v=4).map(str),
"ipv6": _ipv6_strings,
"both": st.ip_addresses(v=4).map(str) | _ipv6_strings,
}[field.protocol.lower()]
@register_for(df.GenericIPAddressField)
def _for_form_ip(field):
# the IP address form fields have no direct indication of which type
# of address they want, so direct comparison with the validator
# function has to be used instead. Sorry for the potato logic here
if validate_ipv46_address in field.default_validators:
return st.ip_addresses(v=4).map(str) | _ipv6_strings
if validate_ipv4_address in field.default_validators:
return st.ip_addresses(v=4).map(str)
if validate_ipv6_address in field.default_validators:
return _ipv6_strings
raise ResolutionFailed(f"No IP version validator on {field=}")
@register_for(dm.DecimalField)
@register_for(df.DecimalField)
def _for_decimal(field):
min_value, max_value = numeric_bounds_from_validators(field)
bound = Decimal(10**field.max_digits - 1) / (10**field.decimal_places)
return st.decimals(
min_value=max(min_value, -bound),
max_value=min(max_value, bound),
places=field.decimal_places,
)
def length_bounds_from_validators(field):
min_size = 1
max_size = field.max_length
for v in field.validators:
if isinstance(v, django.core.validators.MinLengthValidator):
min_size = max(min_size, v.limit_value)
elif isinstance(v, django.core.validators.MaxLengthValidator):
max_size = min(max_size or v.limit_value, v.limit_value)
return min_size, max_size
@register_for(dm.BinaryField)
def _for_binary(field):
min_size, max_size = length_bounds_from_validators(field)
if getattr(field, "blank", False) or not getattr(field, "required", True):
return st.just(b"") | st.binary(min_size=min_size, max_size=max_size)
return st.binary(min_size=min_size, max_size=max_size)
@register_for(dm.CharField)
@register_for(dm.TextField)
@register_for(df.CharField)
@register_for(df.RegexField)
@register_for(UsernameField)
def _for_text(field):
# We can infer a vastly more precise strategy by considering the
# validators as well as the field type. This is a minimal proof of
# concept, but we intend to leverage the idea much more heavily soon.
# See https://github.com/HypothesisWorks/hypothesis-python/issues/1116
regexes = [
re.compile(v.regex, v.flags) if isinstance(v.regex, str) else v.regex
for v in field.validators
if isinstance(v, django.core.validators.RegexValidator) and not v.inverse_match
]
if regexes:
# This strategy generates according to one of the regexes, and
# filters using the others. It can therefore learn to generate
# from the most restrictive and filter with permissive patterns.
# Not maximally efficient, but it makes pathological cases rarer.
# If you want a challenge: extend https://qntm.org/greenery to
# compute intersections of the full Python regex language.
return st.one_of(*(st.from_regex(r) for r in regexes))
# If there are no (usable) regexes, we use a standard text strategy.
min_size, max_size = length_bounds_from_validators(field)
strategy = st.text(
alphabet=st.characters(exclude_characters="\x00", exclude_categories=("Cs",)),
min_size=min_size,
max_size=max_size,
).filter(lambda s: min_size <= len(s.strip()))
if getattr(field, "blank", False) or not getattr(field, "required", True):
return st.just("") | strategy
return strategy
@register_for(df.BooleanField)
def _for_form_boolean(field):
if field.required:
return st.just(True)
return st.booleans()
def _model_choice_strategy(field):
def _strategy():
if field.choices is None:
# The field was instantiated with queryset=None, and not
# subsequently updated.
raise InvalidArgument(
"Cannot create strategy for ModelChoicesField with no choices"
)
elif hasattr(field, "_choices"):
# The choices property was set manually.
choices = field._choices
else:
# choices is not None, and was not set manually, so we
# must have a QuerySet.
choices = field.queryset
if not choices.ordered:
raise InvalidArgument(
f"Cannot create strategy for {field.__class__.__name__} with a choices "
"attribute derived from a QuerySet without an explicit ordering - this may "
"cause Hypothesis to produce unstable results between runs."
)
return st.sampled_from(
[
(
choice.value
if isinstance(choice, df.models.ModelChoiceIteratorValue)
else choice # Empty value, if included.
)
for choice, _ in field.choices
]
)
# Accessing field.choices causes database access, so defer the strategy.
return st.deferred(_strategy)
@register_for(df.ModelChoiceField)
def _for_model_choice(field):
return _model_choice_strategy(field)
@register_for(df.ModelMultipleChoiceField)
def _for_model_multiple_choice(field):
min_size = 1 if field.required else 0
return st.lists(_model_choice_strategy(field), min_size=min_size, unique=True)
def register_field_strategy(
field_type: Type[AnyField], strategy: st.SearchStrategy
) -> None:
"""Add an entry to the global field-to-strategy lookup used by
:func:`~hypothesis.extra.django.from_field`.
``field_type`` must be a subtype of :class:`django.db.models.Field` or
:class:`django.forms.Field`, which must not already be registered.
``strategy`` must be a :class:`~hypothesis.strategies.SearchStrategy`.
"""
if not issubclass(field_type, (dm.Field, df.Field)):
raise InvalidArgument(f"{field_type=} must be a subtype of Field")
check_type(st.SearchStrategy, strategy, "strategy")
if field_type in _global_field_lookup:
raise InvalidArgument(
f"{field_type=} already has a registered "
f"strategy ({_global_field_lookup[field_type]!r})"
)
if issubclass(field_type, dm.AutoField):
raise InvalidArgument("Cannot register a strategy for an AutoField")
_global_field_lookup[field_type] = strategy
def from_field(field: F) -> st.SearchStrategy[Union[F, None]]:
"""Return a strategy for values that fit the given field.
This function is used by :func:`~hypothesis.extra.django.from_form` and
:func:`~hypothesis.extra.django.from_model` for any fields that require
a value, or for which you passed ``...`` (:obj:`python:Ellipsis`) to infer
a strategy from an annotation.
It's pretty similar to the core :func:`~hypothesis.strategies.from_type`
function, with a subtle but important difference: ``from_field`` takes a
Field *instance*, rather than a Field *subtype*, so that it has access to
instance attributes such as string length and validators.
"""
check_type((dm.Field, df.Field), field, "field")
# The following isinstance check must occur *before* the getattr
# check. In the case of ModelChoicesField, evaluating
# field.choices causes database access, which we want to avoid if
# we don't have a connection (the generated strategies for
# ModelChoicesField defer evaluation of `choices').
if not isinstance(field, df.ModelChoiceField) and getattr(field, "choices", False):
choices: list = []
for value, name_or_optgroup in field.choices:
if isinstance(name_or_optgroup, (list, tuple)):
choices.extend(key for key, _ in name_or_optgroup)
else:
choices.append(value)
# form fields automatically include an empty choice, strip it out
if "" in choices:
choices.remove("")
min_size = 1
if isinstance(field, (dm.CharField, dm.TextField)) and field.blank:
choices.insert(0, "")
elif isinstance(field, (df.Field)) and not field.required:
choices.insert(0, "")
min_size = 0
strategy = st.sampled_from(choices)
if isinstance(field, (df.MultipleChoiceField, df.TypedMultipleChoiceField)):
strategy = st.lists(st.sampled_from(choices), min_size=min_size)
else:
if type(field) not in _global_field_lookup:
if getattr(field, "null", False):
return st.none()
raise ResolutionFailed(f"Could not infer a strategy for {field!r}")
strategy = _global_field_lookup[type(field)] # type: ignore
if not isinstance(strategy, st.SearchStrategy):
strategy = strategy(field)
assert isinstance(strategy, st.SearchStrategy)
if field.validators:
def validate(value):
try:
field.run_validators(value)
return True
except django.core.exceptions.ValidationError:
return False
strategy = strategy.filter(validate)
if getattr(field, "null", False):
return st.none() | strategy
return strategy
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@hypothesis@py3@hypothesis@extra@django@_fields.py@.PATH_END.py
|
{
"filename": "LICENSE.md",
"repo_name": "kboone/avocado",
"repo_path": "avocado_extracted/avocado-master/LICENSE.md",
"type": "Markdown"
}
|
MIT License
Copyright (c) 2019 Kyle Boone
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
kbooneREPO_NAMEavocadoPATH_START.@avocado_extracted@avocado-master@LICENSE.md@.PATH_END.py
|
{
"filename": "Comentarios_paper-checkpoint.ipynb",
"repo_name": "Monsalves-Gonzalez-N/Paper_OGLE",
"repo_path": "Paper_OGLE_extracted/Paper_OGLE-main/.ipynb_checkpoints/Comentarios_paper-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
```python
# Importaciones de bibliotecas estándar
# Importaciones de bibliotecas de sistema
import os
import gc
import time
import shutil
# Importaciones de bibliotecas de terceros
import wget
import scipy.signal
import h5py
import psutil
import ray
# Importaciones de TensorFlow
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import (
Conv2D,
Dense,
Dropout,
Flatten,
MaxPooling2D
)
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import CSVLogger, EarlyStopping
from keras import backend as K
# Importaciones de sklearn
from sklearn.model_selection import train_test_split
from sklearn import preprocessing, metrics
from sklearn.datasets import make_classification
from sklearn.utils import class_weight
from sklearn.metrics import f1_score
from sklearn.ensemble import RandomForestClassifier
# Importaciones de pandas
import pandas as pd
from pandarallel import pandarallel
pandarallel.initialize(progress_bar=True)
# Importaciones de matplotlib
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.ticker import FormatStrFormatter
from IPython.core.pylabtools import figsize, getfigs
%matplotlib inline
# Importaciones de seaborn
import seaborn as sns
gyr = ["#ffa600",
'#003f5c',
"#58508d",
"#ff6361",
"#ffd380",
"#bc5090",
"#129675"
]
palet = sns.palplot(sns.color_palette(gyr))
sns.set_context("paper")
# Importaciones de plotly
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Importaciones de numpy
import numpy as np
# Importaciones de astropy
from astropy.io import fits
from astropy.timeseries import LombScargle
from astropy.coordinates import SkyCoord
import astropy.units as u
# Importaciones para el equilibrio de los datos
from imblearn.keras import BalancedBatchGenerator
from imblearn.under_sampling import RandomUnderSampler
# Establecer la semilla para TensorFlow
tf.random.set_seed(42)
# Obtén el número de CPUs
num_cpus = psutil.cpu_count(logical=False)
class BalancedDataGenerator(tf.keras.utils.Sequence):
"""Generates data for Keras Sequence based data generator.
Suitable for building data generator for training and prediction.
"""
def __init__(self, x, y, batch_size=64):
self.x = x
self.y = y
self.batch_size = batch_size
self.classes = np.unique(y)
self.class_indices = [np.where(y == i)[0] for i in self.classes]
self.length = min([len(i) for i in self.class_indices]) // self.batch_size * len(self.classes)
def __len__(self):
return self.length
def __getitem__(self, idx):
batch_x = []
batch_y = []
for class_index in self.class_indices:
i = idx % (len(class_index) // self.batch_size)
batch_x.append(self.x[class_index[i * self.batch_size:(i + 1) * self.batch_size]])
batch_y.append(self.y[class_index[i * self.batch_size:(i + 1) * self.batch_size]])
return np.concatenate(batch_x), np.concatenate(batch_y)
def on_epoch_end(self):
for class_index in self.class_indices:
np.random.shuffle(class_index)
# Funciones
def descarga_wget(database,ID,path_3,path_4):
_,field,types,_ = ID.lower().split("-")
try :
if types=="ell":
types="ecl"
if database==4:
if ((field =="blg") |(field =="gd"))&((types =="ecl")|(types =="lpv")|(types =="dsct")):
url = "http://ftp.astrouw.edu.pl/ogle/ogle4/OCVS/"+field+"/"+types+"/phot_ogle4/I/"+ ID +".dat"
wget.download(url,path_4)
return 1
else:
url = "http://ftp.astrouw.edu.pl/ogle/ogle4/OCVS/"+field+"/"+types+"/phot/I/"+ ID +".dat"
wget.download(url,path_4)
return 1
if database==3:
url = "http://ftp.astrouw.edu.pl/ogle/ogle3/OIII-CVS/" +field+"/"+types+"/phot/I/"+ ID +".dat"
wget.download(url,path_3)
return 1
except:
return 0
@ray.remote
def review_open_data(nomb,path_datos,database):
path= path_datos[database]
try :
df = pd.read_csv(f"{path}/{nomb}.dat",delim_whitespace=True,names=["jd","mag","err"])
df_sigma = df.loc[(df["mag"] < np.mean(df["mag"]) + 3*np.std(df["mag"])) & ( df["mag"] > np.mean(df["mag"]) - 3*np.std(df["mag"]) )]
obs_eliminadas = len(df) - len(df_sigma)
amplitud = df_sigma["mag"].max() - df_sigma["mag"].min()
mag_mean = df_sigma["mag"].mean()
mag_std = df_sigma["mag"].std()
err_mean = df_sigma["err"].mean()
err_std = df_sigma["err"].std()
obs_final = len(df_sigma)
obs_inicial = len(df)
return 1,nomb,database,obs_eliminadas,amplitud,mag_mean,mag_std,err_mean,err_std,obs_final,obs_inicial
except:
return 0,nomb,database,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan
def ra_dec_to_degrees(ra_str, dec_str):
# Convertir las coordenadas RA y DEC en objetos SkyCoord
coord = SkyCoord(ra=ra_str, dec=dec_str, unit=(u.hourangle, u.deg))
# Obtener las coordenadas en grados
ra_deg = coord.ra.degree
dec_deg = coord.dec.degree
return ra_deg, dec_deg
def fase_datos(path_datos,database,nomb,per_vsx):
path= path_datos[database]
df = pd.read_csv(f"{path}/{nomb}.dat",delim_whitespace=True,names=["jd","mag","err"])
df_sigma = df.loc[(df["mag"] < np.mean(df["mag"]) + 3*np.std(df["mag"])) & ( df["mag"] > np.mean(df["mag"]) - 3*np.std(df["mag"]) )]
if len(df_sigma)>2000:
df_sigma = df_sigma.sample(2000,random_state=42).reset_index(drop=True)
fase_vsx = np.mod(df_sigma.jd, per_vsx) / per_vsx
mag_vsx,t_vsx,err_vsx =df_sigma.mag,df_sigma.jd,df_sigma.err
return fase_vsx,mag_vsx,t_vsx
def make_2d_histogram(n_bins_x,n_bins_y,data_mag,data_fase,norm_max):
bins_x = np.linspace(0,1, n_bins_x) # Curves in phase between 0 and 2.
bins_y = np.linspace( data_mag.min(), data_mag.max(), n_bins_y)
hist_data, _xbins, _ybins = np.histogram2d(data_fase, data_mag, bins=(bins_x, bins_y))
# Data in histogram is transposed, then transpose it just once:
if norm_max=="max":
norm_max = hist_data.max()
hist_data_norm = hist_data / norm_max
hist_data_transposed = hist_data_norm.transpose()
hdu = fits.PrimaryHDU(data=hist_data_transposed)
return hdu
else:
norm_max = float(norm_max)
hist_data[hist_data > norm_max ] = norm_max
hist_data_norm = hist_data / norm_max
hist_data_transposed = hist_data_norm.transpose()
hdu = fits.PrimaryHDU(data=hist_data_transposed)
return hdu
def split_random(df,numero_dividir,col_name):
for types in df["types"].unique():
df_var = df.loc[df["types"]==types].sample(numero_dividir,random_state=42)
df_train,df_test = train_test_split(df_var,random_state=42,test_size=0.13)
df_train,df_val = train_test_split(df_train,random_state=42,test_size=0.15)
df.loc[df_train.index,col_name] = "train"
df.loc[df_val.index,col_name] = "val"
df.loc[df_test.index,col_name] = "test"
return df
def split_data_balanced(df,numero_dividir):
df["combined"] = list(zip(df["obs_final"],
df["amplitud"],
df["mag_mean"],
df["mag_std"],
df["field"],
df["err_mean"],
df["per"],
df["err_std"]))
combined_weight = df['combined'].value_counts(normalize=True)
df['combined_weight'] = df['combined'].apply(lambda x: combined_weight[x])
subsample = df.sample(numero_dividir, weights=df['combined_weight'])
for types in df["types"].unique():
df_var = df.loc[df["types"]==types].sample(numero_dividir,
weights=df['combined_weight'],
random_state=42)
df_train = df_var.sample(frac=0.8,
weights=df['combined_weight'],
random_state=42)
df_var = df_var.drop(df_train.index)
df_val = df_var.sample(frac=0.5,
weights=df['combined_weight'],
random_state=42)
df_test = df_var.drop(df_val.index)
df.loc[df_train.index,"entrenamiento_8mil_balanced"] = "train"
df.loc[df_val.index,"entrenamiento_8mil_balanced"] = "val"
df.loc[df_test.index,"entrenamiento_8mil_balanced"] = "test"
return df
def plot_obs_dist(df,split_name):
sns.set_context("paper")
gyr = ["#890B96",'#FFCF3D',"#129675"]
sns.set_palette(gyr)
columns = ["obs_final", "amplitud", "mag_mean", "field", "err_mean", "per", "mag_std", "err_std"]
labels = [r'$n_{obs}$', r'$Amplitude$', 'Mean Magnitude', "Field", 'Mean Error', 'Period',
'Magnitude standard deviations', 'Magnitude Error standard deviations']
log_scales = [True, True, False, False, True, True, True, False]
x_ticks = [[10**2,10**3,10**4], [10**-1,1,10**1], None, None, [10**-2,10**-1,10**0], [10**-1,10**1,10**3],
None, [0,0.3,0.6]]
y_scale_log = [False, False, False, False, False, False, False, True]
fig, axes = plt.subplots(4, 2, figsize=(7,15))
for i, ax in enumerate(axes.flatten()):
sns.histplot(ax=ax, data=df, x=columns[i], hue=split_name, bins=30,
stat="density", log_scale=log_scales[i], fill=True, common_norm=True)
ax.set(xlabel=labels[i], ylabel="")
if x_ticks[i] is not None:
ax.set_xticks(x_ticks[i])
if y_scale_log[i]:
ax.set_yscale("log")
if i != 0: # remove legend for all but the first subplot
ax.get_legend().remove()
plt.rc('xtick', labelsize=13)
plt.rc('ytick', labelsize=13)
fig.tight_layout()
fig.text(-0.01,0.5,"Density", size=13, rotation=90)
fig.tight_layout()
def plot_histograms(df, estrellas, path_datos,norm):
estrellas_plot = df.loc[df["ID"].isin(estrellas)].reset_index(drop=True)
fig, ax = plt.subplots(len(estrellas), 2, figsize=(5,10), sharex="col")
for i in range(len(estrellas_plot)):
fase, mag, t_vsx = fase_datos(path_datos, estrellas_plot["database"][i], estrellas_plot["ID"][i], estrellas_plot["per"][i])
ax[i,0].set_ylim(mag.max(), mag.min())
ax[i,0].set_yticks(np.linspace(mag.min() + (mag.max() - mag.min()) /10, mag.max() - (mag.max() - mag.min()) /10, 4))
ax[i,0].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
sns.scatterplot(x=fase, y=mag, c=t_vsx, s=5, ax=ax[i,0])
hdu = make_2d_histogram(32+1, 32+1, mag, fase, norm_max=norm)
ax[i,1].imshow(hdu.data, interpolation='nearest', aspect='auto')
ax[i,1].set_yticklabels([])
ax[i,1].set_xticklabels([])
fig.text(0.5, 0, "Phase", size=13)
fig.text(-0.01, 0.5, "I Mag", size=13, rotation=90)
fig.tight_layout()
plt.subplots_adjust(wspace=0.01, hspace=0.01)
plt.savefig("hist_2d_fase.pdf", bbox_inches="tight", pad_inches=0)
return hdu
@ray.remote
def make_lc_hist(nomb,
per_vsx,
path_datos,
database,
aug,
rng,
g,
bins,
N):
path= path_datos[database]
df = pd.read_csv(f"{path}/{nomb}.dat",delim_whitespace=True,names=["d","mag","e"])
df_sigma = df.loc[(df["mag"] < np.mean(df["mag"]) + 3*np.std(df["mag"])) & ( df["mag"] > np.mean(df["mag"]) - 3*np.std(df["mag"]) )].reset_index(drop=True)
if int(aug) == 0:
df_sigma["fase"] = np.mod(df_sigma.d, per_vsx) / per_vsx
if len(df_sigma)>2000:
df_sigma = df_sigma.sample(2000,random_state=42)
hdu =make_2d_histogram(32+1,32+1,df_sigma.mag,df_sigma.fase, norm_max=7)
return hdu.data
if int(aug) == 1:
df_sigma["fase"] = np.mod(df_sigma.d - g, per_vsx) / per_vsx
df_sigma["mag"] = df_sigma["mag"] + rng.normal(0, df_sigma["e"],len(df_sigma))
df_sigma["fase_bin"] = pd.cut(df_sigma["fase"],bins=int(bins))
df_bins = pd.DataFrame(df_sigma.groupby("fase_bin")["fase"].mean())
df_bins["mag"] = df_sigma.groupby("fase_bin")["mag"].mean()
df_bins["e"] = df_sigma.groupby("fase_bin")["e"].mean()
df_bins["d"] = df_sigma.groupby("fase_bin")["d"].mean()
hdu =make_2d_histogram(32+1,32+1,df_bins.mag,df_bins.fase,norm_max=N)
return hdu.data
def create_hdf5(df,path_datos,rng):
results_ids = []
for i in range(len(df)):
hdu = make_lc_hist.remote(df["ID"][i],
df["per"][i],
path_datos,
df["database"][i],
df["aug"][i],
rng,
df["g"][i],
df["bins"][i],
df["N"][i])
results_ids.append((hdu))
x = np.empty((len(df), 32, 32))
for i,key in enumerate(results_ids):
ima = ray.get(key)
x[i] = ima
x = np.expand_dims(x, axis=3)
return x
def make_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, (3,3), input_shape=(32, 32, 1),activation="relu",padding="same"),
tf.keras.layers.Conv2D(16, (3,3),activation="relu",padding="same"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32, (3,3),activation="relu",padding="same"),
tf.keras.layers.Conv2D(32, (3, 3),activation="relu",padding="same"),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1024,activation="relu"),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(512,activation="relu"),
tf.keras.layers.Dropout(0.3),
# tf.keras.layers.Dense(1, activation='sigmoid')
tf.keras.layers.Dense(8, activation='softmax')
])
model.compile(optimizer=tf.keras.optimizers.Adam(
learning_rate=1e-4,
beta_1=0.9,
beta_2=0.999,
epsilon=0.1), loss="sparse_categorical_crossentropy", metrics=['acc'])
return model
def train_models(df_lista, keys_lista, data, prueba_8mil,epochs=200, use_balanced_generator=False):
tf.random.set_seed(42)
validation_datagen = ImageDataGenerator()
idx_val = prueba_8mil.loc[prueba_8mil['prueba_13080mil']=="val"].index.values
val_label = data['prueba_13080mil_label'][idx_val]
val_data = data["prueba_13080mil"][idx_val]
val_gen = validation_datagen.flow(val_data, val_label, batch_size=32, shuffle=True)
for df, test_name in zip(df_lista, keys_lista):
K.clear_session()
early_stopping = EarlyStopping(monitor='val_loss', patience=15, verbose=1)
model_history_log_file = f"history_softmax_{'batchBalanced_' if use_balanced_generator else ''}{test_name}.csv"
csv_logger = CSVLogger(model_history_log_file, append=False)
checkpoint_path = f"training_softmax_{'batchBalanced_' if use_balanced_generator else ''}{test_name}/cp.ckpt"
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, save_best_only=False, verbose=1)
callbacks = [csv_logger, cp_callback, early_stopping]
if use_balanced_generator:
idx_train = df.loc[(df[test_name]!="test")&(df[test_name]!="val")&(df["aug"]==0)].index.values
else :
idx_train = df.loc[(df[test_name]!="test")&(df[test_name]!="val")].index.values
bz = int((len(idx_train) * 64)/ len(prueba_8mil.loc[prueba_8mil['prueba_13080mil']=="train"]))
train_label = data[f'{test_name}_label'][idx_train]
train_data = data[test_name][idx_train]
if use_balanced_generator:
train_gen = BalancedDataGenerator(train_data, train_label, batch_size=64)
else:
train_datagen = ImageDataGenerator()
train_gen = train_datagen.flow(train_data, train_label, batch_size=bz, shuffle=True)
model = make_model()
print(f"Use balanced Generator [{use_balanced_generator}] \n Data: {len(train_data)} \n -----------------------------------------------------------------------------------")
history = model.fit(train_gen, epochs=epochs, validation_data=val_gen, callbacks=callbacks)
return
def augmented_to(ID,count,df):
a = (np.linspace(0,9,count)*100).astype(int)
np.sort(rng.uniform(low=0 + 1/32, high=1 - 1/32, size=1000))[a]
df.loc[df["ID"]==ID,"g"] = np.sort(rng.uniform(low=0 + 1/32, high=1 - 1/32, size=1000))[a]
return
def plot_accuracy_and_loss(path,file_names, title_names, amarillo_train, purpura_val, output_file="training_.pdf"):
plt.rcParams["figure.figsize"] = (18,8)
sns.set_context("paper", font_scale=1.5, rc={"lines.linewidth": 1.5})
sns.set_style("whitegrid")
fig, axs = plt.subplots(2, len(file_names), sharex="col", sharey="row")
metrics = ["acc", "val_acc", "loss", "val_loss"]
labels = ['Training data Augmentation', 'Validation data Augmentation', 'Training data Augmentation', 'Validation data Augmentation']
labels_batch = ['Training batch balanced', 'Validation batch balanced', 'Training batch balanced', 'Validation batch balanced']
colors = [amarillo_train, purpura_val, amarillo_train, purpura_val]
linestyles = ["-.", ".as", "dashdot", "dashdot"]
for i, file_name in enumerate(file_names):
batch_name = file_name.split("softmax")[0]+"softmax_batchBalanced"+file_name.split("softmax")[1]
df = pd.read_csv(f"{path}/{file_name}.csv")
df_batch = pd.read_csv(f"{path}/{batch_name}.csv")
for j, metric in enumerate(metrics):
sns.lineplot(ax=axs[j//2, i], data=df, x="epoch", y=metric,
color=colors[j], label=labels[j], linestyle="solid")
if i > 0:
sns.lineplot(ax=axs[j//2, i], data=df_batch, x="epoch", y=metric,
color=colors[j], label=labels_batch[j], linestyle="dashed")
axs[0,i].set_title(title_names[i])
axs[0,i].set_ylim([0.6,1])
axs[1,i].set_ylim([0.6,1])
axs[0,i].set_yticks(np.linspace(0.6,1,8))
axs[1,i].set_yticks(np.linspace(0,0.9,8))
axs[0,i].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
axs[1,i].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
axs[1,i].set_xlabel('Epoch')
axs[0,i].get_legend().remove()
axs[1,i].get_legend().remove()
axs[0,0].set_ylabel('Accuracy')
axs[1,0].set_ylabel('Loss')
handles, labels = axs[1,2].get_legend_handles_labels()
fig.legend(handles, labels, loc=(0.19,0.5), ncol=4, fancybox=True, shadow=True)
plt.tight_layout()
plt.subplots_adjust(hspace=0.05)
plt.savefig(output_file, bbox_inches="tight")
return
def run_analysis(tests,titles,entrenamiento,filename="../data_Paper_OGLE/Data_08Sep.hdf5",
csv_file="../data_Paper_OGLE/catalogos/prueba_8mil.csv"):
# Load data
data = h5py.File(filename, 'r+')
df_8mil = pd.read_csv(csv_file)
idx_test = df_8mil.loc[df_8mil["prueba_8mil"]=="test"].index.values
test = df_8mil.loc[df_8mil["prueba_8mil"]=="test"]
test = test.drop(columns={"prueba_8mil","aug","g","bins","GroupID","GroupSize"})
# Prepare data generator
test_datagen = ImageDataGenerator()
test_gen = test_datagen.flow(
data["prueba_13080mil"][idx_test],
data["prueba_13080mil_label"][idx_test],
batch_size=32
)
sns.set_context("paper",font_scale=3)
model = make_model()
num_tests = len(tests)
rows = 2 # Ahora queremos 2 filas
cols = 3 # Y 3 columnas
fig, ax = plt.subplots(rows, cols, figsize=(11*3, 10*2), sharey="row")
plt.subplots_adjust(wspace=0, hspace=0.2, right=0.7)
# Aplanar el array de ejes para iterar fácilmente
ax = ax.ravel()
for i, prueba in enumerate(tests):
model.load_weights(f"{entrenamiento}/{prueba}/cp.ckpt")
prediction(test, test_gen, model, prueba)
# Calculate F1 Score
f1 = f1_score(test_gen.y, test[f"label_predict_{prueba}"], average='weighted')
# Plots
array, annot = C_M(test_gen.y, test[f"label_predict_{prueba}"])
sns.heatmap(array, annot=annot, fmt='', vmin=0, vmax=np.sum(array, axis=1)[0], cmap="BuPu",
annot_kws={"fontsize":25}, linewidth=1, ax=ax[i], cbar=False)
ax[i].set_yticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5])
ax[i].set_xticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5])
ax[i].set_yticklabels(['ELL', 'Mira', 'CEP', 'DST', 'ECL', 'LPV', 'RRL', "Rndm"])
ax[i].set_xticklabels(['ELL', 'Mira', 'CEP', 'DST', 'ECL', 'LPV', 'RRL', "Rndm"], rotation=45)
prueba = prueba.split("prueba")[1]
# Add title and F1 Score
ax[i].set_title(f'{titles[i]}\nF1 Score: {f1:.3f}')
# Eliminar el último subplot si el número de tests no llena todos los subplots
if num_tests < rows * cols:
fig.delaxes(ax[-1])
# Etiqueta general para el eje y
fig.text(-0.02, 0.5, 'True Label', va='center', rotation='vertical', fontsize=30)
# Etiqueta general para el eje x
fig.text(0.5, -0.02, 'Predicted Label', ha='center', fontsize=30)
fig.tight_layout(pad=0)
plt.savefig("CM.pdf", bbox_inches="tight")
return test
def prediction(df,image_gen,model,prueba):
%matplotlib inline
grupos = ['ELL', 'Mira', 'cep', 'dsct', 'ecl', 'lpv', 'rrlyr',"random"]
label_predict = []
porcentaje_predict = []
nombres = []
for i in model.predict(image_gen.x):
idx = np.argmax(i)
label_predict.append(np.argmax(i))
porcentaje_predict.append(i[idx])
nombres.append(grupos[np.argmax(i)])
df[f"label_predict_{prueba}"] = label_predict
df[f"porcentaje_predict_{prueba}"] = porcentaje_predict
df[f"nombres_predict_{prueba}"] = nombres
return
def C_M(label,predict_label):
array = np.array(tf.math.confusion_matrix(label,predict_label) )
df = pd.DataFrame(array)
perc = df.copy()
cols=perc.columns.values
perc[cols]=perc[cols].div(perc[cols].sum(axis=1), axis=0).multiply(100)
annot=df.round(2).astype(str) + "\n" + perc.round(1).astype(str) + "%"
return array,annot
def metricas(labels,predict):
print("Accuracy:", "%0.2f" % metrics.accuracy_score(labels,predict))
print("macro precision: ","%0.2f" % metrics.precision_score(labels,predict, average='macro'))
print("macro recall: ","%0.2f" % metrics.recall_score(labels,predict, average='macro'))
print("macro F1: ","%0.2f" % metrics.f1_score(labels,predict, average='macro'))
print(metrics.classification_report(labels,predict, digits=2))
report = metrics.classification_report(labels,predict, output_dict=True, digits=2)
return report
def train_random_forest(X_train, y_train, X_test, y_test, n_estimators=500, random_state=42):
# Crea el clasificador Random Forest
clf = RandomForestClassifier(n_estimators=n_estimators, random_state=random_state)
# Entrena el clasificador
clf.fit(X_train, y_train)
# Predice las clases para el conjunto de test
y_pred = clf.predict(X_test)
# Crea una figura y ejes para la trama
fig, ax = plt.subplots(figsize=(8, 8))
array, annot = C_M(y_test, y_pred)
sns.heatmap(array, annot=annot, fmt='', vmin=0, vmax=np.sum(array, axis=1)[0], cmap="BuPu",
annot_kws={"fontsize":15}, linewidth=1, ax=ax, cbar=False)
ax.set_yticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5])
ax.set_yticklabels(['ELL', 'Mira', 'CEP', 'DST', 'ECL', 'LPV', 'RRL'], fontsize=20)
ax.set_ylabel('True Label', fontsize=20)
ax.set_xticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5])
ax.set_xticklabels(['ELL', 'Mira', 'CEP', 'DST', 'ECL', 'LPV', 'RRL'], fontsize=20)
ax.set_xlabel('Predicted Label', fontsize=20)
# Add title and F1 Score
# Calculate F1 Score
f1 = f1_score(y_test, y_pred, average='weighted')
ax.set_title(f'Random Forest\nF1 Score: {f1:.2f}', fontsize=20)
fig.tight_layout(pad=0)
plt.savefig("CNN_And_RF.pdf", bbox_inches="tight")
return clf, y_pred
from sklearn import tree
def train_tree(X_train, y_train, X_test, y_test, max_depth, random_state=42):
# Crea el clasificador Random Forest
clf = tree.DecisionTreeClassifier(max_depth=max_depth,random_state=42)
# Entrena el clasificador
clf.fit(X_train, y_train)
# Predice las clases para el conjunto de test
y_pred = clf.predict(X_test)
# Crea una figura y ejes para la trama
fig, ax = plt.subplots(figsize=(8, 8))
array, annot = C_M(y_test, y_pred)
sns.heatmap(array, annot=annot, fmt='', vmin=0, vmax=np.sum(array, axis=1)[0], cmap="BuPu",
annot_kws={"fontsize":15}, linewidth=1, ax=ax, cbar=False)
ax.set_yticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5])
ax.set_yticklabels(['ELL', 'Mira', 'Cep', 'Dsct', 'Ecl', 'Lpv', 'RRlyr'], fontsize=20)
ax.set_ylabel('True Label', fontsize=20)
ax.set_xticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5])
ax.set_xticklabels(['ELL', 'Mira', 'Cep', 'Dsct', 'Ecl', 'Lpv', 'RRlyr'], fontsize=20)
ax.set_xlabel('Predicted Label', fontsize=20)
# Add title and F1 Score
# Calculate F1 Score
f1 = f1_score(y_test, y_pred, average='weighted')
ax.set_title(f'Random Forest\nF1 Score: {f1:.2f}', fontsize=20)
fig.tight_layout(pad=0)
plt.savefig("CNN_And_RF.pdf", bbox_inches="tight")
return clf, y_pred
datos = "/dataworkspace/datos_ogle/datos"
path_datos_4 = datos + "/datos_ogle_4/I"
path_datos_3 = datos + "/datos_ogle_3/I"
path_datos = ["_","_","_",path_datos_3,path_datos_4]
rng = np.random.default_rng(42)
path = "/home/nicolas/nico/Data/data_Paper_OGLE/7_01_2024/"
```
INFO: Pandarallel will run on 24 workers.
INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.
```python
%ls $path
```
[0m[01;32m0_catalogo.csv[0m*
0_Catalog_TimeSerieInformation_AT.csv
0_Catalog_TimeSerieInformation.csv
0_Catalog_TimeSerieInformation_DT_Augmented.csv
0_Catalog_TimeSerieInformation_DT.csv
0_Catalog_TimeSerieInformation_DT_possible_blended.csv
0_Catalog_TimeSerieInformation_DT_Train_8.csv
0_Catalog_TimeSerieInformation_DT_unique.csv
[01;34mBZ_64[0m/
Data.hdf5
Distribution_splits.pdf
history_softmax_batchBalanced_Number_CEP.csv
history_softmax_batchBalanced_Number_DST.csv
history_softmax_batchBalanced_Number_ELL.csv
history_softmax_batchBalanced_Number_M.csv
history_softmax_Number_CEP.csv
history_softmax_Number_DST.csv
history_softmax_Number_ELL.csv
history_softmax_Number_M.csv
I_filter_data_missing.csv
OGLE.hdf5
prueba_8mil.csv
[01;34mtraining_softmax_batchBalanced_Number_CEP[0m/
[01;34mtraining_softmax_batchBalanced_Number_DST[0m/
[01;34mtraining_softmax_batchBalanced_Number_ELL[0m/
[01;34mtraining_softmax_batchBalanced_Number_M[0m/
[01;34mtraining_softmax_Number_CEP[0m/
[01;34mtraining_softmax_Number_DST[0m/
[01;34mtraining_softmax_Number_ELL[0m/
[01;34mtraining_softmax_Number_M[0m/
train_number_DST.csv
train_number_ELL.csv
train_number_M.csv
```python
catalog = pd.read_csv(f"{path}/0_Catalog_TimeSerieInformation_DT_unique.csv")
catalog = catalog.loc[catalog["per"]!=0]
```
```python
catalog.groupby(["types","field"])[["per","amplitud"]].quantile(0.9)
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>per</th>
<th>amplitud</th>
</tr>
<tr>
<th>types</th>
<th>field</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="3" valign="top">ELL</th>
<th>blg</th>
<td>149.520056</td>
<td>0.2800</td>
</tr>
<tr>
<th>lmc</th>
<td>98.679154</td>
<td>0.3594</td>
</tr>
<tr>
<th>smc</th>
<td>5.491819</td>
<td>0.2820</td>
</tr>
<tr>
<th rowspan="4" valign="top">Mira</th>
<th>blg</th>
<td>453.500000</td>
<td>4.0058</td>
</tr>
<tr>
<th>gd</th>
<td>485.500000</td>
<td>4.0290</td>
</tr>
<tr>
<th>lmc</th>
<td>539.650000</td>
<td>4.0611</td>
</tr>
<tr>
<th>smc</th>
<td>541.970000</td>
<td>4.4957</td>
</tr>
<tr>
<th rowspan="4" valign="top">cep</th>
<th>blg</th>
<td>14.268906</td>
<td>0.8328</td>
</tr>
<tr>
<th>gd</th>
<td>12.855141</td>
<td>0.7050</td>
</tr>
<tr>
<th>lmc</th>
<td>5.577311</td>
<td>0.5740</td>
</tr>
<tr>
<th>smc</th>
<td>4.206347</td>
<td>0.7512</td>
</tr>
<tr>
<th rowspan="4" valign="top">dsct</th>
<th>blg</th>
<td>0.120244</td>
<td>0.5830</td>
</tr>
<tr>
<th>gd</th>
<td>0.161330</td>
<td>0.4795</td>
</tr>
<tr>
<th>lmc</th>
<td>0.103021</td>
<td>1.3310</td>
</tr>
<tr>
<th>smc</th>
<td>0.105664</td>
<td>1.6064</td>
</tr>
<tr>
<th rowspan="4" valign="top">ecl</th>
<th>blg</th>
<td>4.205470</td>
<td>1.0290</td>
</tr>
<tr>
<th>gd</th>
<td>2.688949</td>
<td>1.2322</td>
</tr>
<tr>
<th>lmc</th>
<td>19.492928</td>
<td>0.9620</td>
</tr>
<tr>
<th>smc</th>
<td>16.297099</td>
<td>0.9520</td>
</tr>
<tr>
<th rowspan="4" valign="top">lpv</th>
<th>blg</th>
<td>89.764000</td>
<td>0.4520</td>
</tr>
<tr>
<th>gd</th>
<td>214.100000</td>
<td>0.8330</td>
</tr>
<tr>
<th>lmc</th>
<td>343.600000</td>
<td>0.3860</td>
</tr>
<tr>
<th>smc</th>
<td>344.440000</td>
<td>0.3340</td>
</tr>
<tr>
<th rowspan="4" valign="top">rrlyr</th>
<th>blg</th>
<td>0.642998</td>
<td>0.9050</td>
</tr>
<tr>
<th>gd</th>
<td>0.653132</td>
<td>0.9388</td>
</tr>
<tr>
<th>lmc</th>
<td>0.647808</td>
<td>0.9630</td>
</tr>
<tr>
<th>smc</th>
<td>0.654344</td>
<td>0.9720</td>
</tr>
</tbody>
</table>
</div>
```python
catalog.groupby(["types","field"])[["per","amplitud"]].quantile(0.1)
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>per</th>
<th>amplitud</th>
</tr>
<tr>
<th>types</th>
<th>field</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="3" valign="top">ELL</th>
<th>blg</th>
<td>0.458964</td>
<td>0.0700</td>
</tr>
<tr>
<th>lmc</th>
<td>0.850813</td>
<td>0.0910</td>
</tr>
<tr>
<th>smc</th>
<td>0.852220</td>
<td>0.1004</td>
</tr>
<tr>
<th rowspan="4" valign="top">Mira</th>
<th>blg</th>
<td>192.450000</td>
<td>1.5800</td>
</tr>
<tr>
<th>gd</th>
<td>225.200000</td>
<td>1.6360</td>
</tr>
<tr>
<th>lmc</th>
<td>183.858000</td>
<td>1.3833</td>
</tr>
<tr>
<th>smc</th>
<td>261.510000</td>
<td>1.6717</td>
</tr>
<tr>
<th rowspan="4" valign="top">cep</th>
<th>blg</th>
<td>0.371298</td>
<td>0.1979</td>
</tr>
<tr>
<th>gd</th>
<td>0.943978</td>
<td>0.2100</td>
</tr>
<tr>
<th>lmc</th>
<td>0.898014</td>
<td>0.2040</td>
</tr>
<tr>
<th>smc</th>
<td>0.779121</td>
<td>0.2410</td>
</tr>
<tr>
<th rowspan="4" valign="top">dsct</th>
<th>blg</th>
<td>0.052255</td>
<td>0.1599</td>
</tr>
<tr>
<th>gd</th>
<td>0.062806</td>
<td>0.1000</td>
</tr>
<tr>
<th>lmc</th>
<td>0.058713</td>
<td>0.4850</td>
</tr>
<tr>
<th>smc</th>
<td>0.055858</td>
<td>0.6170</td>
</tr>
<tr>
<th rowspan="4" valign="top">ecl</th>
<th>blg</th>
<td>0.335296</td>
<td>0.2370</td>
</tr>
<tr>
<th>gd</th>
<td>0.279837</td>
<td>0.1668</td>
</tr>
<tr>
<th>lmc</th>
<td>1.106557</td>
<td>0.1540</td>
</tr>
<tr>
<th>smc</th>
<td>0.790070</td>
<td>0.1450</td>
</tr>
<tr>
<th rowspan="4" valign="top">lpv</th>
<th>blg</th>
<td>11.192000</td>
<td>0.0460</td>
</tr>
<tr>
<th>gd</th>
<td>119.600000</td>
<td>0.3210</td>
</tr>
<tr>
<th>lmc</th>
<td>13.911300</td>
<td>0.0450</td>
</tr>
<tr>
<th>smc</th>
<td>13.605600</td>
<td>0.0470</td>
</tr>
<tr>
<th rowspan="4" valign="top">rrlyr</th>
<th>blg</th>
<td>0.292640</td>
<td>0.2940</td>
</tr>
<tr>
<th>gd</th>
<td>0.305633</td>
<td>0.3070</td>
</tr>
<tr>
<th>lmc</th>
<td>0.318043</td>
<td>0.4300</td>
</tr>
<tr>
<th>smc</th>
<td>0.363502</td>
<td>0.5180</td>
</tr>
</tbody>
</table>
</div>
```python
import pandas as pd
# Suponiendo que 'catalog' es tu DataFrame
# Calcular los deciles para 'per' y 'amplitud'
per_d10 = catalog.groupby("types")["per"].quantile(0.1)
per_d90 = catalog.groupby("types")["per"].quantile(0.9)
per_median = catalog.groupby("types")["per"].median()
amp_d10 = catalog.groupby("types")["amplitud"].quantile(0.1)
amp_d90 = catalog.groupby("types")["amplitud"].quantile(0.9)
amp_median = catalog.groupby("types")["amplitud"].median()
# Crear un nuevo DataFrame con estos valores
result = pd.DataFrame({
'Period D10': per_d10,
'Period D90': per_d90,
'Period Median': per_median,
'Amplitude D10': amp_d10,
'Amplitude D90': amp_d90,
'Amplitude Median': amp_median
})
# Mantener 4 cifras significativas
result = result.round(4)
# Imprimir en formato LaTeX
print(result.to_latex())
```
\begin{tabular}{lrrrrrr}
\toprule
& Period D10 & Period D90 & Period Median & Amplitude D10 & Amplitude D90 & Amplitude Median \\
types & & & & & & \\
\midrule
ELL & 0.465000 & 147.697100 & 5.851300 & 0.071000 & 0.283000 & 0.150000 \\
Mira & 201.300000 & 469.600000 & 331.100000 & 1.585000 & 4.020000 & 2.824000 \\
cep & 0.822400 & 5.963100 & 2.210100 & 0.212800 & 0.688000 & 0.402000 \\
dsct & 0.054300 & 0.141000 & 0.081100 & 0.135000 & 0.759000 & 0.330000 \\
ecl & 0.338500 & 5.316500 & 0.639300 & 0.223000 & 1.027000 & 0.538000 \\
lpv & 11.955000 & 156.230000 & 25.568000 & 0.046000 & 0.436000 & 0.088000 \\
rrlyr & 0.303200 & 0.646700 & 0.523800 & 0.315000 & 0.936000 & 0.629000 \\
\bottomrule
\end{tabular}
```python
%ls $path
```
[0m[01;32m0_catalogo.csv[0m*
0_Catalog_TimeSerieInformation_AT.csv
0_Catalog_TimeSerieInformation.csv
0_Catalog_TimeSerieInformation_DT_Augmented.csv
0_Catalog_TimeSerieInformation_DT.csv
0_Catalog_TimeSerieInformation_DT_possible_blended.csv
0_Catalog_TimeSerieInformation_DT_Train_8.csv
0_Catalog_TimeSerieInformation_DT_unique.csv
[01;34mBZ_64[0m/
Data.hdf5
Distribution_splits.pdf
history_softmax_batchBalanced_Number_CEP.csv
history_softmax_batchBalanced_Number_DST.csv
history_softmax_batchBalanced_Number_ELL.csv
history_softmax_batchBalanced_Number_M.csv
history_softmax_Number_CEP.csv
history_softmax_Number_DST.csv
history_softmax_Number_ELL.csv
history_softmax_Number_M.csv
I_filter_data_missing.csv
OGLE.hdf5
prueba_8mil.csv
[01;34mtraining_softmax_batchBalanced_Number_CEP[0m/
[01;34mtraining_softmax_batchBalanced_Number_DST[0m/
[01;34mtraining_softmax_batchBalanced_Number_ELL[0m/
[01;34mtraining_softmax_batchBalanced_Number_M[0m/
[01;34mtraining_softmax_Number_CEP[0m/
[01;34mtraining_softmax_Number_DST[0m/
[01;34mtraining_softmax_Number_ELL[0m/
[01;34mtraining_softmax_Number_M[0m/
train_number_DST.csv
train_number_ELL.csv
train_number_M.csv
```python
df = pd.read_csv(f"{path}/train_number_M.csv")
```
```python
data = h5py.File(f"{path}Data.hdf5", 'r+')
```
```python
ID = df.loc[(df["types"]=="ELL")&(df["count"]==5)].sample(1)["ID"].values[0]
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[21], line 1
----> 1 ID = df.loc[(df["types"]=="ELL")&(df["count"]==5)].sample(1)["ID"].values[0]
File ~/miniconda3/envs/deep/lib/python3.8/site-packages/pandas/core/generic.py:5858, in NDFrame.sample(self, n, frac, replace, weights, random_state, axis, ignore_index)
5855 if weights is not None:
5856 weights = sample.preprocess_weights(self, weights, axis)
-> 5858 sampled_indices = sample.sample(obj_len, size, replace, weights, rs)
5859 result = self.take(sampled_indices, axis=axis)
5861 if ignore_index:
File ~/miniconda3/envs/deep/lib/python3.8/site-packages/pandas/core/sample.py:151, in sample(obj_len, size, replace, weights, random_state)
148 else:
149 raise ValueError("Invalid weights: weights sum to zero")
--> 151 return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(
152 np.intp, copy=False
153 )
File mtrand.pyx:909, in numpy.random.mtrand.RandomState.choice()
ValueError: a must be greater than 0 unless no samples are taken
```python
ID_cep = 'OGLE-LMC-CEP-4447'
```
```python
ID_dsct = 'OGLE-BLG-DSCT-06772'
```
```python
ID_ELL = 'OGLE-BLG-ELL-021739'
```
```python
def fill_subplot(ax, df_plot, star, data):
"""
Rellena un subplot específico con los datos proporcionados.
:param ax: El eje en el que se dibujará el subplot.
:param df_plot: DataFrame con los datos para el subplot.
:param star: Identificador de la estrella para este subplot.
:param data: Diccionario con los datos de la imagen para el subplot.
"""
n_obs, phi, N = df_plot.loc[star, ["bins", "phi", "N"]]
if n_obs > 2000:
n_obs = 2000.0
# Insertar gráfico (reemplazar con el código de gráfico real)
ax.imshow(data["number_M"][star], aspect="auto")
# Construir el texto con los valores y luego aplicar LaTeX
text_str = r'$n_{\mathrm{obs}} = ' + str(int(n_obs)) + \
r'$, $\phi\prime = ' + f'{phi:.2f}' + \
r'$, $N = ' + str(N) + r'$'
ax.text(0.5, 1.05, text_str, transform=ax.transAxes, fontsize=25,
verticalalignment='bottom', horizontalalignment='center',
bbox=dict(facecolor='white', alpha=0.5))
# Ejemplo de uso:
# create_plots(df_plot, data, rows=3, cols=n)
```
```python
# Creación de la figura y los ejes
fig, axes = plt.subplots(3, 5, figsize=(5 * 6, 3 * 6),sharex=True,sharey=True)
plt.tight_layout(pad=0.3)
# Ciclo para cada grupo de estrellas
for fila,stars_group in enumerate([ID_cep, ID_dsct, ID_ELL]):
df_plot = df.loc[(df["ID"]==stars_group)&(df["types"].str.split("_").str[1]!="random")]
df_plot.loc[df_plot["bins"].isna(), "bins"] = df_plot["obs_final"]
df_plot.loc[df_plot["aug"] == 0, "g"] = 0
df_plot.loc[df_plot["aug"] == 0, "N"] = 7
df_plot["phi"] = df_plot["g"] / df_plot["per"]
df_plot = df_plot.sort_values(by="phi")
df_plot.loc[df_plot["types"]=="cep","types"] = "Cepheids"
df_plot.loc[df_plot["types"]=="dsct","types"] = "Delta Scuti"
df_plot.loc[df_plot["types"]=="ELL","types"] = "Ellipsoidal"
for col,star in enumerate(df_plot.index):
fill_subplot(axes[fila,col], df_plot, star, data)
axes[fila,col].set_yticks([])
if col == 0:
axes[fila,col].set_ylabel(df_plot.loc[star]["types"],fontsize=25)
if (col ==0 )&(fila==2):
axes[fila,col].set_xlabel("Original",fontsize=25)
if (col !=0 )&(fila==2):
axes[fila,col].set_xlabel(f"Augmented {col}",fontsize=25)
axes[fila,col].set_xticks([])
plt.subplots_adjust(hspace=0.15)
plt.savefig("Augmented_stars.pdf", bbox_inches="tight")
# Ajustar el layout y mostrar la figura
plt.show()
```
/tmp/ipykernel_800151/2366859100.py:9: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_plot.loc[df_plot["aug"] == 0, "N"] = 7
/tmp/ipykernel_800151/2366859100.py:10: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_plot["phi"] = df_plot["g"] / df_plot["per"]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[28], line 16
14 df_plot.loc[df_plot["types"]=="ELL","types"] = "Ellipsoidal"
15 for col,star in enumerate(df_plot.index):
---> 16 fill_subplot(axes[fila,col], df_plot, star, data)
17 axes[fila,col].set_yticks([])
18 if col == 0:
Cell In[27], line 15, in fill_subplot(ax, df_plot, star, data)
12 n_obs = 2000.0
14 # Insertar gráfico (reemplazar con el código de gráfico real)
---> 15 ax.imshow(data["train_number_M"][star], aspect="auto")
17 # Construir el texto con los valores y luego aplicar LaTeX
18 text_str = r'$n_{\mathrm{obs}} = ' + str(int(n_obs)) + \
19 r'$, $\phi\prime = ' + f'{phi:.2f}' + \
20 r'$, $N = ' + str(N) + r'$'
File h5py/_objects.pyx:54, in h5py._objects.with_phil.wrapper()
File h5py/_objects.pyx:55, in h5py._objects.with_phil.wrapper()
File ~/miniconda3/envs/deep/lib/python3.8/site-packages/h5py/_hl/group.py:264, in Group.__getitem__(self, name)
262 raise ValueError("Invalid HDF5 object reference")
263 else:
--> 264 oid = h5o.open(self.id, self._e(name), lapl=self._lapl)
266 otype = h5i.get_type(oid)
267 if otype == h5i.GROUP:
File h5py/_objects.pyx:54, in h5py._objects.with_phil.wrapper()
File h5py/_objects.pyx:55, in h5py._objects.with_phil.wrapper()
File h5py/h5o.pyx:190, in h5py.h5o.open()
KeyError: "Unable to open object (object 'train_number_M' doesn't exist)"
```python
rng = np.random.default_rng(42)
datos = f"{path}Data/datos_ogle/datos"
path_datos_4 = datos + "/datos_ogle_4/I"
path_datos_3 = datos + "/datos_ogle_3/I"
path_datos = ["_","_","_",path_datos_3,path_datos_4]
```
```python
prueba_8mil = pd.read_csv(f"{path}catalogos/prueba_8mil.csv")
```
```python
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
def plot_obs_dist(df, split_name):
sns.set_context("paper")
gyr = ['#FFCF3D', "#129675", "#890B96"]
sns.set_palette(gyr)
columns = ["obs_final", "amplitud", "mag_mean", "field", "err_mean", "per", "mag_std", "err_std"]
labels = [r'$n_{obs}$', r'$Amplitude$', 'Mean Magnitude', "Field", 'Mean Error', 'Period',
'Magnitude standard deviations', 'Error standard deviations']
log_scales = [True, True, False, False, True, True, True, False]
x_ticks = [[10**2, 10**3, 10**4], [10**-1, 1, 10**1], None, None, [10**-2, 10**-1, 10**0], [10**-1, 10**1, 10**3],
None, [0, 0.3, 0.6]]
y_scale_log = [True, True, True, True, True, True, True, True]
fig, axes = plt.subplots(2, 4, figsize=(15, 7))
# Crear las leyendas una vez
legend_labels = df[split_name].unique()
legend_colors = gyr[:len(legend_labels)]
for i, ax in enumerate(axes.flatten()):
sns.histplot(ax=ax, data=df, x=columns[i], hue=split_name, bins=30, log_scale=log_scales[i], fill=True, common_norm=True, multiple="stack")
ax.set(xlabel=labels[i], ylabel="")
if x_ticks[i] is not None:
ax.set_xticks(x_ticks[i])
if y_scale_log[i]:
ax.set_yscale("log")
# Calcular y establecer 4 y-ticks para cada subplot
ymin, ymax = ax.get_ylim()
yticks = np.logspace(np.log10(ymin+1e-3), np.log10(ymax), 4) # Añadir un pequeño offset para evitar log(0) print(yticks)# Añadir un pequeño offset para evitar log(0)
ax.set_yticks(yticks)
ax.set_yticklabels(yticks)
ax.get_yaxis().set_major_formatter(ticker.FuncFormatter(lambda y, _: '${{10^{{{:d}}}}}$'.format(int(np.log10(y)))))
ax.get_legend().remove() # Remove the individual legend from each subplot
plt.rc('xtick', labelsize=13)
# Crear una leyenda para toda la figura
legend_elements = [plt.Line2D([0], [0], color=color, lw=4, label=label)
for label, color in zip(["Train", "Test", "Validation"], legend_colors)]
fig.legend(handles=legend_elements, loc='upper center', ncol=len(legend_labels), bbox_to_anchor=(0.5, 1.05))
fig.tight_layout()
plt.savefig("Distribution_splits.pdf", bbox_inches="tight")
plt.show()
```
```python
plot_obs_dist(prueba_8mil,"entrenamiento_8mil")
```
## Resultados
```python
amarillo_train = "#FFCF3D"
purpura_val = "#890B96"
file_names = ["history_softmax_prueba_13080mil",
"history_softmax_prueba_27620mil",
"history_softmax_prueba_69721mil"]
title_names = [
'Train-9',
'Train-24',
'Train-60']
#plot_accuracy_and_loss("entrenamientos/entrenamiento_8_sep/",file_names, title_names, amarillo_train, purpura_val)
```
```python
ls {path}/catalogos/DataSets
```
prueba_23mil.csv prueba_60mil.csv prueba_8mil.csv
```python
tests = ['training_softmax_prueba_13080mil','training_softmax_prueba_27620mil',
'training_softmax_batchBalanced_prueba_27620mil',
'training_softmax_prueba_69721mil',
'training_softmax_batchBalanced_prueba_69721mil']
titles = ['Train-9 Undersampling','Train-24 Data Augmentation',
'Train-24 Batch Balanced',
'Train-60 Data Augmentation',
'Train-60 Batch Balanced']
test = run_analysis(tests,titles,
f"{path}/../../git/Paper_OGLE/entrenamientos/entrenamiento_8_sep/",
f"{path}Data_08Sep.hdf5",
f"{path}/catalogos/DataSets/prueba_8mil.csv"
)
```
```python
df = metricas(test["categorical_label"],test["label_predict_training_softmax_batchBalanced_prueba_27620mil"])
```
Accuracy: 0.91
macro precision: 0.91
macro recall: 0.91
macro F1: 0.91
precision recall f1-score support
0 0.95 0.93 0.94 1488
1 0.93 0.95 0.94 1488
2 0.91 0.85 0.88 1488
3 0.84 0.92 0.88 1488
4 0.92 0.94 0.93 1488
5 0.88 0.92 0.90 1488
6 0.91 0.88 0.89 1488
7 0.96 0.88 0.92 1488
accuracy 0.91 11904
macro avg 0.91 0.91 0.91 11904
weighted avg 0.91 0.91 0.91 11904
```python
print(pd.DataFrame(df).T.to_latex())
```
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.945504 & 0.932796 & 0.939107 & 1488.000000 \\
1 & 0.928665 & 0.953629 & 0.940981 & 1488.000000 \\
2 & 0.914986 & 0.853495 & 0.883171 & 1488.000000 \\
3 & 0.838631 & 0.922043 & 0.878361 & 1488.000000 \\
4 & 0.920760 & 0.944892 & 0.932670 & 1488.000000 \\
5 & 0.879510 & 0.917339 & 0.898026 & 1488.000000 \\
6 & 0.908460 & 0.880376 & 0.894198 & 1488.000000 \\
7 & 0.957447 & 0.877016 & 0.915468 & 1488.000000 \\
accuracy & 0.910198 & 0.910198 & 0.910198 & 0.910198 \\
macro avg & 0.911745 & 0.910198 & 0.910248 & 11904.000000 \\
weighted avg & 0.911745 & 0.910198 & 0.910248 & 11904.000000 \\
\bottomrule
\end{tabular}
```python
data = h5py.File(f"{path}Data_08Sep.hdf5", 'r+')
```
```python
plt.imshow(data["prueba_69721mil"][77670])
```
<matplotlib.image.AxesImage at 0x7f9b620a06a0>
```python
test.keys()
```
Index(['ID', 'RA', 'DEC', 'types', 'database', 'field', 'Subtype', 'per',
'error', 'obs_eliminadas', 'amplitud', 'mag_mean', 'mag_std',
'err_mean', 'err_std', 'obs_final', 'obs_inicial', 'ra_deg', 'dec_deg',
'categorical_label', 'N',
'label_predict_training_softmax_prueba_13080mil',
'porcentaje_predict_training_softmax_prueba_13080mil',
'nombres_predict_training_softmax_prueba_13080mil',
'label_predict_training_softmax_prueba_27620mil',
'porcentaje_predict_training_softmax_prueba_27620mil',
'nombres_predict_training_softmax_prueba_27620mil',
'label_predict_training_softmax_batchBalanced_prueba_27620mil',
'porcentaje_predict_training_softmax_batchBalanced_prueba_27620mil',
'nombres_predict_training_softmax_batchBalanced_prueba_27620mil',
'label_predict_training_softmax_prueba_69721mil',
'porcentaje_predict_training_softmax_prueba_69721mil',
'nombres_predict_training_softmax_prueba_69721mil',
'label_predict_training_softmax_batchBalanced_prueba_69721mil',
'porcentaje_predict_training_softmax_batchBalanced_prueba_69721mil',
'nombres_predict_training_softmax_batchBalanced_prueba_69721mil'],
dtype='object')
```python
miss_class = test.loc[(test["categorical_label"]!=7)&
(test["label_predict_training_softmax_batchBalanced_prueba_27620mil"]==7)].sample(25)
```
```python
test.keys()
```
Index(['ID', 'RA', 'DEC', 'types', 'database', 'field', 'Subtype', 'per',
'error', 'obs_eliminadas', 'amplitud', 'mag_mean', 'mag_std',
'err_mean', 'err_std', 'obs_final', 'obs_inicial', 'ra_deg', 'dec_deg',
'categorical_label', 'N',
'label_predict_training_softmax_prueba_13080mil',
'porcentaje_predict_training_softmax_prueba_13080mil',
'nombres_predict_training_softmax_prueba_13080mil',
'label_predict_training_softmax_prueba_27620mil',
'porcentaje_predict_training_softmax_prueba_27620mil',
'nombres_predict_training_softmax_prueba_27620mil',
'label_predict_training_softmax_batchBalanced_prueba_27620mil',
'porcentaje_predict_training_softmax_batchBalanced_prueba_27620mil',
'nombres_predict_training_softmax_batchBalanced_prueba_27620mil',
'label_predict_training_softmax_prueba_69721mil',
'porcentaje_predict_training_softmax_prueba_69721mil',
'nombres_predict_training_softmax_prueba_69721mil',
'label_predict_training_softmax_batchBalanced_prueba_69721mil',
'porcentaje_predict_training_softmax_batchBalanced_prueba_69721mil',
'nombres_predict_training_softmax_batchBalanced_prueba_69721mil'],
dtype='object')
```python
import matplotlib.pyplot as plt
# Suponiendo que 'data' y 'miss_class' están definidos correctamente
# Creación de la figura y los ejes
fig, axes = plt.subplots(5, 5, figsize=(14, 14), sharex=True, sharey=True)
# Aplanar el array de ejes para facilitar la iteración
axes_flat = axes.flatten()
# Ciclo para cada grupo de estrellas
for idx, star in enumerate(miss_class.index):
# Asegurarse de que no se exceda el número de ejes disponibles
if idx < len(axes_flat):
axes_flat[idx].imshow(data["prueba_13080mil"][star], aspect="auto")
axes_flat[idx].set_xticks([])
axes_flat[idx].set_yticks([])
predict = round(miss_class["porcentaje_predict_training_softmax_batchBalanced_prueba_69721mil"][star],2)
tex = miss_class["ID"][star]+ " "+"[P " + str(predict)+"]"
text_str = tex
axes_flat[idx].text(0.5, 1.05, text_str, transform=axes_flat[idx].transAxes, fontsize=10,
verticalalignment='bottom', horizontalalignment='center',
bbox=dict(facecolor='white', alpha=0.5))
# Ajustes adicionales de la figura
plt.tight_layout(pad=0.15)
plt.subplots_adjust(hspace=0.15)
plt.savefig("Predicted_as_random.pdf", bbox_inches="tight")
plt.show()
```
```python
```
```python
f"{path}/../../git/Paper_OGLE/entrenamientos/entrenamiento_8_sep/",
f"{path}Data_08Sep.hdf5",
f"{path}/catalogos/DataSets/prueba_8mil.csv"
```
```python
#data = h5py.File("../data_Paper_OGLE/Data_08Sep.hdf5", 'r+')
#ogle = pd.read_csv("catalogos/ogle_no_usado.csv")
df_8mil = pd.read_csv(f"{path}/catalogos/DataSets/prueba_8mil.csv")
idx_test = df_8mil.loc[df_8mil["prueba_8mil"]=="test"].index.values
model_softmax = make_model()
model_softmax.load_weights(f"{path}/../../git/Paper_OGLE/entrenamientos/entrenamiento_8_sep/training_softmax_batchBalanced_prueba_27620mil/cp.ckpt")
df = pd.DataFrame(model_softmax.predict(data["prueba_13080mil"][:]),columns=['ELL', 'Mira', 'cep', 'dsct', 'ecl', 'lpv', 'rrlyr',"Random"])
df_8mil = pd.concat([df_8mil,df],axis=1)
df_8mil = df_8mil.loc[df_8mil["categorical_label"]!=7]
X_train = df_8mil.loc[df_8mil["prueba_8mil"]!="test"][['ELL', 'Mira', 'cep', 'dsct', 'ecl', 'lpv', 'rrlyr',"Random",
"per","amplitud","types","categorical_label"]]
X_test = df_8mil.loc[df_8mil["prueba_8mil"]=="test"][['ELL', 'Mira', 'cep', 'dsct', 'ecl', 'lpv', 'rrlyr',"Random",
"per","amplitud","types","categorical_label"]]
```
```python
X_test
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ELL</th>
<th>Mira</th>
<th>cep</th>
<th>dsct</th>
<th>ecl</th>
<th>lpv</th>
<th>rrlyr</th>
<th>Random</th>
<th>per</th>
<th>amplitud</th>
<th>types</th>
<th>categorical_label</th>
</tr>
</thead>
<tbody>
<tr>
<th>6</th>
<td>2.723986e-04</td>
<td>1.049594e-14</td>
<td>7.219313e-14</td>
<td>1.519476e-12</td>
<td>9.997275e-01</td>
<td>2.169540e-12</td>
<td>6.886375e-15</td>
<td>5.944822e-11</td>
<td>0.426101</td>
<td>0.863</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>11</th>
<td>5.086940e-05</td>
<td>2.551360e-12</td>
<td>1.713711e-14</td>
<td>6.517447e-17</td>
<td>9.999491e-01</td>
<td>2.309529e-09</td>
<td>1.061885e-16</td>
<td>2.331338e-10</td>
<td>0.574601</td>
<td>0.894</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>16</th>
<td>1.220247e-03</td>
<td>2.402624e-08</td>
<td>1.542982e-09</td>
<td>3.034349e-05</td>
<td>1.249477e-01</td>
<td>2.080750e-02</td>
<td>6.775367e-08</td>
<td>8.529941e-01</td>
<td>217.713940</td>
<td>0.137</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>25</th>
<td>6.793228e-03</td>
<td>5.545616e-10</td>
<td>6.420836e-12</td>
<td>6.178395e-11</td>
<td>9.932064e-01</td>
<td>1.746676e-08</td>
<td>1.465342e-10</td>
<td>3.399267e-07</td>
<td>0.407592</td>
<td>0.657</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>26</th>
<td>2.116934e-03</td>
<td>1.072764e-14</td>
<td>1.285526e-14</td>
<td>4.844122e-12</td>
<td>9.978830e-01</td>
<td>8.824531e-12</td>
<td>3.752517e-13</td>
<td>6.743773e-10</td>
<td>1.161228</td>
<td>0.178</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>80078</th>
<td>1.173109e-04</td>
<td>2.766868e-04</td>
<td>2.855800e-03</td>
<td>1.439554e-01</td>
<td>1.605881e-04</td>
<td>8.443494e-01</td>
<td>8.067787e-03</td>
<td>2.169938e-04</td>
<td>115.160000</td>
<td>0.274</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80082</th>
<td>1.128164e-06</td>
<td>1.028469e-06</td>
<td>1.653618e-04</td>
<td>3.257132e-03</td>
<td>4.555328e-06</td>
<td>9.943915e-01</td>
<td>4.611811e-05</td>
<td>2.133176e-03</td>
<td>15.976000</td>
<td>0.058</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80091</th>
<td>1.004801e-06</td>
<td>1.116740e-07</td>
<td>1.026062e-05</td>
<td>9.715824e-04</td>
<td>1.789106e-05</td>
<td>9.981526e-01</td>
<td>2.815519e-06</td>
<td>8.436771e-04</td>
<td>23.485000</td>
<td>0.085</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80099</th>
<td>2.333280e-06</td>
<td>2.685732e-08</td>
<td>2.058074e-06</td>
<td>2.572711e-05</td>
<td>2.731292e-05</td>
<td>9.952452e-01</td>
<td>1.497755e-06</td>
<td>4.695841e-03</td>
<td>25.305000</td>
<td>0.073</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80102</th>
<td>9.449904e-08</td>
<td>1.083972e-07</td>
<td>1.690062e-05</td>
<td>2.648227e-03</td>
<td>6.685579e-07</td>
<td>9.972674e-01</td>
<td>1.553458e-05</td>
<td>5.108954e-05</td>
<td>19.453000</td>
<td>0.053</td>
<td>lpv</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>10416 rows × 12 columns</p>
</div>
```python
def train_tree(X_train, y_train, X_test, y_test, max_depth, random_state=42):
```
```python
a,b = train_random_forest(X_train.drop(columns={"types","categorical_label"}),
X_train["categorical_label"],
X_test.drop(columns={"types","categorical_label"}),
X_test["categorical_label"],7)
```
```python
catalo
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ID</th>
<th>RA</th>
<th>DEC</th>
<th>types</th>
<th>database</th>
<th>field</th>
<th>Subtype</th>
<th>per</th>
<th>l</th>
<th>b</th>
<th>error</th>
<th>obs_eliminadas</th>
<th>amplitud</th>
<th>mag_mean</th>
<th>mag_std</th>
<th>err_mean</th>
<th>err_std</th>
<th>obs_final</th>
<th>obs_inicial</th>
<th>categorical_label</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>OGLE-LMC-ECL-17495</td>
<td>05:30:01.51</td>
<td>-69:30:57.2</td>
<td>ecl</td>
<td>4</td>
<td>lmc</td>
<td>NC</td>
<td>1.029283</td>
<td>-1.395018</td>
<td>-0.565071</td>
<td>1</td>
<td>3.0</td>
<td>0.353</td>
<td>18.177608</td>
<td>0.066634</td>
<td>0.030425</td>
<td>0.006698</td>
<td>609.0</td>
<td>612.0</td>
<td>4</td>
</tr>
<tr>
<th>1</th>
<td>OGLE-BLG-ECL-270986</td>
<td>18:03:00.33</td>
<td>-28:07:41.1</td>
<td>ecl</td>
<td>4</td>
<td>blg</td>
<td>NC</td>
<td>1.944729</td>
<td>0.045888</td>
<td>-0.050359</td>
<td>1</td>
<td>526.0</td>
<td>0.202</td>
<td>15.089699</td>
<td>0.019399</td>
<td>0.004058</td>
<td>0.000238</td>
<td>10010.0</td>
<td>10536.0</td>
<td>4</td>
</tr>
<tr>
<th>2</th>
<td>OGLE-BLG-ECL-270978</td>
<td>18:03:00.30</td>
<td>-28:05:36.3</td>
<td>ecl</td>
<td>4</td>
<td>blg</td>
<td>NC</td>
<td>0.379665</td>
<td>0.046415</td>
<td>-0.050060</td>
<td>1</td>
<td>66.0</td>
<td>0.337</td>
<td>18.287165</td>
<td>0.053646</td>
<td>0.027392</td>
<td>0.006585</td>
<td>10471.0</td>
<td>10537.0</td>
<td>4</td>
</tr>
<tr>
<th>3</th>
<td>OGLE-BLG-ECL-270979</td>
<td>18:03:00.31</td>
<td>-32:58:13.6</td>
<td>ecl</td>
<td>4</td>
<td>blg</td>
<td>NC</td>
<td>2.464727</td>
<td>-0.028040</td>
<td>-0.091660</td>
<td>1</td>
<td>12.0</td>
<td>0.560</td>
<td>18.191190</td>
<td>0.091937</td>
<td>0.026330</td>
<td>0.007748</td>
<td>373.0</td>
<td>385.0</td>
<td>4</td>
</tr>
<tr>
<th>4</th>
<td>OGLE-BLG-ECL-270980</td>
<td>18:03:00.32</td>
<td>-34:40:16.2</td>
<td>ecl</td>
<td>4</td>
<td>blg</td>
<td>NC</td>
<td>1.078398</td>
<td>-0.054124</td>
<td>-0.106060</td>
<td>1</td>
<td>0.0</td>
<td>0.308</td>
<td>17.712623</td>
<td>0.062966</td>
<td>0.021575</td>
<td>0.005255</td>
<td>252.0</td>
<td>252.0</td>
<td>4</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>1055934</th>
<td>OGLE-LMC-ECL-00041</td>
<td>04:33:50.14</td>
<td>-67:53:54.0</td>
<td>ecl</td>
<td>3</td>
<td>lmc</td>
<td>EC</td>
<td>0.365729</td>
<td>-1.399284</td>
<td>-0.658162</td>
<td>1</td>
<td>0.0</td>
<td>0.486</td>
<td>15.386158</td>
<td>0.133404</td>
<td>0.006551</td>
<td>0.000569</td>
<td>450.0</td>
<td>450.0</td>
<td>4</td>
</tr>
<tr>
<th>1055935</th>
<td>OGLE-LMC-ECL-00029</td>
<td>04:33:06.86</td>
<td>-69:41:17.1</td>
<td>ecl</td>
<td>3</td>
<td>lmc</td>
<td>ED</td>
<td>2.860338</td>
<td>-1.362424</td>
<td>-0.647225</td>
<td>1</td>
<td>4.0</td>
<td>1.155</td>
<td>19.122611</td>
<td>0.186475</td>
<td>0.097419</td>
<td>0.024627</td>
<td>167.0</td>
<td>171.0</td>
<td>4</td>
</tr>
<tr>
<th>1055936</th>
<td>OGLE-LMC-ECL-00012</td>
<td>04:31:28.89</td>
<td>-69:01:33.5</td>
<td>ecl</td>
<td>3</td>
<td>lmc</td>
<td>ESD</td>
<td>4.744795</td>
<td>-1.374519</td>
<td>-0.654099</td>
<td>1</td>
<td>8.0</td>
<td>1.251</td>
<td>19.902106</td>
<td>0.192003</td>
<td>0.133802</td>
<td>0.043903</td>
<td>424.0</td>
<td>432.0</td>
<td>4</td>
</tr>
<tr>
<th>1055937</th>
<td>OGLE-LMC-ECL-00008</td>
<td>04:31:13.02</td>
<td>-70:03:32.6</td>
<td>ecl</td>
<td>3</td>
<td>lmc</td>
<td>ELL_EC</td>
<td>2.806298</td>
<td>-1.353557</td>
<td>-0.647236</td>
<td>1</td>
<td>1.0</td>
<td>0.068</td>
<td>16.288839</td>
<td>0.012584</td>
<td>0.009227</td>
<td>0.001140</td>
<td>428.0</td>
<td>429.0</td>
<td>4</td>
</tr>
<tr>
<th>1055938</th>
<td>OGLE-LMC-ECL-00005</td>
<td>04:30:43.21</td>
<td>-68:59:20.8</td>
<td>ecl</td>
<td>3</td>
<td>lmc</td>
<td>ED</td>
<td>100.423523</td>
<td>-1.374669</td>
<td>-0.655446</td>
<td>1</td>
<td>12.0</td>
<td>0.255</td>
<td>18.114472</td>
<td>0.036303</td>
<td>0.026843</td>
<td>0.005167</td>
<td>432.0</td>
<td>444.0</td>
<td>4</td>
</tr>
</tbody>
</table>
<p>1055939 rows × 20 columns</p>
</div>
```python
prueba_8mil.loc[(prueba_8mil["types"]=="cep")]#&(prueba_8mil["prueba_8mil"]=="train")]
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ID</th>
<th>RA</th>
<th>DEC</th>
<th>types</th>
<th>database</th>
<th>field</th>
<th>Subtype</th>
<th>per</th>
<th>error</th>
<th>obs_eliminadas</th>
<th>...</th>
<th>ra_deg</th>
<th>dec_deg</th>
<th>GroupID</th>
<th>GroupSize</th>
<th>categorical_label</th>
<th>prueba_8mil</th>
<th>aug</th>
<th>g</th>
<th>bins</th>
<th>N</th>
</tr>
</thead>
<tbody>
<tr>
<th>19965</th>
<td>OGLE-SMC-CEP-4851</td>
<td>01:18:57.39</td>
<td>-70:19:21.9</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.895894</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.739125</td>
<td>-70.322750</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19966</th>
<td>OGLE-SMC-CEP-4852</td>
<td>01:19:17.53</td>
<td>-71:15:23.6</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O/2O</td>
<td>0.458678</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.823042</td>
<td>-71.256556</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19967</th>
<td>OGLE-SMC-CEP-4853</td>
<td>01:19:18.93</td>
<td>-70:51:37.6</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>F/1O</td>
<td>1.612018</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.828875</td>
<td>-70.860444</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19968</th>
<td>OGLE-SMC-CEP-4854</td>
<td>01:20:07.87</td>
<td>-74:16:07.0</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.686529</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>20.032792</td>
<td>-74.268611</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19969</th>
<td>OGLE-SMC-CEP-4855</td>
<td>01:20:16.90</td>
<td>-74:18:22.3</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.653218</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>20.070417</td>
<td>-74.306194</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>69279</th>
<td>OGLE-LMC-CEP-0059</td>
<td>04:44:54.90</td>
<td>-68:38:21.7</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>F</td>
<td>4.390393</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>71.228750</td>
<td>-68.639361</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69280</th>
<td>OGLE-LMC-CEP-0073</td>
<td>04:46:38.50</td>
<td>-67:08:43.9</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>1O/2O</td>
<td>0.837148</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>71.660417</td>
<td>-67.145528</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69281</th>
<td>OGLE-LMC-CEP-0036</td>
<td>04:42:11.79</td>
<td>-70:14:02.0</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>F/1O</td>
<td>1.573822</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>70.549125</td>
<td>-70.233889</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69282</th>
<td>OGLE-GD-CEP-0017</td>
<td>13:27:30.10</td>
<td>-64:40:37.7</td>
<td>cep</td>
<td>3</td>
<td>gd</td>
<td>1O</td>
<td>1.915025</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>201.875417</td>
<td>-64.677139</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69283</th>
<td>OGLE-GD-CEP-0013</td>
<td>11:33:02.68</td>
<td>-60:52:04.5</td>
<td>cep</td>
<td>3</td>
<td>gd</td>
<td>1O</td>
<td>5.243600</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>173.261167</td>
<td>-60.867917</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>11445 rows × 27 columns</p>
</div>
```python
X_train
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ELL</th>
<th>Mira</th>
<th>cep</th>
<th>dsct</th>
<th>ecl</th>
<th>lpv</th>
<th>rrlyr</th>
<th>Random</th>
<th>per</th>
<th>amplitud</th>
<th>types</th>
<th>categorical_label</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1.642817e-01</td>
<td>1.572727e-09</td>
<td>2.296300e-10</td>
<td>4.981265e-06</td>
<td>8.357080e-01</td>
<td>6.587572e-08</td>
<td>1.439532e-08</td>
<td>5.205571e-06</td>
<td>0.428245</td>
<td>0.576</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>1</th>
<td>7.013346e-05</td>
<td>3.915452e-15</td>
<td>5.605886e-16</td>
<td>3.305101e-05</td>
<td>9.504156e-01</td>
<td>2.538087e-09</td>
<td>4.124067e-14</td>
<td>4.948131e-02</td>
<td>3.463970</td>
<td>0.662</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>2</th>
<td>4.658592e-06</td>
<td>9.737916e-22</td>
<td>5.952192e-22</td>
<td>8.390437e-17</td>
<td>9.999954e-01</td>
<td>5.952139e-17</td>
<td>2.499581e-20</td>
<td>2.940820e-12</td>
<td>1.560932</td>
<td>0.723</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>3</th>
<td>6.215497e-01</td>
<td>2.834021e-09</td>
<td>3.658616e-09</td>
<td>1.358648e-09</td>
<td>3.784485e-01</td>
<td>2.946799e-08</td>
<td>6.666316e-10</td>
<td>1.654385e-06</td>
<td>0.260708</td>
<td>0.184</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>4</th>
<td>1.466834e-08</td>
<td>2.368128e-21</td>
<td>3.016977e-23</td>
<td>4.755060e-20</td>
<td>1.000000e+00</td>
<td>1.037125e-16</td>
<td>3.419298e-22</td>
<td>7.601812e-12</td>
<td>1.181536</td>
<td>1.226</td>
<td>ecl</td>
<td>4</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>80110</th>
<td>9.438141e-06</td>
<td>1.411484e-05</td>
<td>6.001598e-03</td>
<td>5.174115e-01</td>
<td>2.308806e-06</td>
<td>4.642476e-01</td>
<td>1.231173e-02</td>
<td>1.650714e-06</td>
<td>40.150000</td>
<td>0.106</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80111</th>
<td>9.143809e-07</td>
<td>1.686532e-07</td>
<td>2.920850e-04</td>
<td>5.437627e-02</td>
<td>3.395374e-06</td>
<td>9.450446e-01</td>
<td>9.196552e-05</td>
<td>1.907397e-04</td>
<td>27.258000</td>
<td>0.076</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80112</th>
<td>1.042230e-05</td>
<td>3.806272e-04</td>
<td>2.167285e-03</td>
<td>1.316011e-01</td>
<td>3.749256e-05</td>
<td>8.620260e-01</td>
<td>3.646558e-03</td>
<td>1.305378e-04</td>
<td>185.740000</td>
<td>0.435</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80113</th>
<td>2.154076e-05</td>
<td>2.378728e-05</td>
<td>1.227048e-03</td>
<td>5.295302e-03</td>
<td>4.007520e-05</td>
<td>9.912550e-01</td>
<td>8.851961e-04</td>
<td>1.251945e-03</td>
<td>80.420000</td>
<td>0.297</td>
<td>lpv</td>
<td>5</td>
</tr>
<tr>
<th>80114</th>
<td>1.380259e-06</td>
<td>1.368487e-05</td>
<td>1.699156e-02</td>
<td>1.479474e-01</td>
<td>5.673784e-07</td>
<td>7.923765e-01</td>
<td>4.266181e-02</td>
<td>7.096150e-06</td>
<td>118.190000</td>
<td>0.069</td>
<td>lpv</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>69699 rows × 12 columns</p>
</div>
```python
feature_names = X_train.drop(columns={"types","categorical_label"}).keys().values.tolist()
class_names = X_train["types"].values.tolist()
```
```python
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
# Ajustar el tamaño de la figura
plt.figure(figsize=(30, 10)) # Puedes ajustar estos valores según tus necesidades
# Graficar el árbol
plot_tree(a,
filled=True, # Rellena los nodos con el color de la clase mayoritaria
rounded=True, # Redondea las esquinas de los nodos
feature_names=feature_names, # Lista con los nombres de las características
class_names=class_names, # Lista con los nombres de las clases
fontsize=12,
max_depth=5) # Tamaño de la fuente de los textos
# Mostrar el gráfico
plt.show()
```
```python
ls {path}/catalogos/DataSets/
```
prueba_23mil.csv prueba_60mil.csv prueba_8mil.csv
```python
df_8mil = pd.read_csv(f"{path}/catalogos/DataSets/prueba_8mil.csv")
```
```python
df_8mil.loc[(df_8mil["types"]=="cep")&(df_8mil["obs_final"]>60)]
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ID</th>
<th>RA</th>
<th>DEC</th>
<th>types</th>
<th>database</th>
<th>field</th>
<th>Subtype</th>
<th>per</th>
<th>error</th>
<th>obs_eliminadas</th>
<th>...</th>
<th>ra_deg</th>
<th>dec_deg</th>
<th>GroupID</th>
<th>GroupSize</th>
<th>categorical_label</th>
<th>prueba_8mil</th>
<th>aug</th>
<th>g</th>
<th>bins</th>
<th>N</th>
</tr>
</thead>
<tbody>
<tr>
<th>19965</th>
<td>OGLE-SMC-CEP-4851</td>
<td>01:18:57.39</td>
<td>-70:19:21.9</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.895894</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.739125</td>
<td>-70.322750</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19966</th>
<td>OGLE-SMC-CEP-4852</td>
<td>01:19:17.53</td>
<td>-71:15:23.6</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O/2O</td>
<td>0.458678</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.823042</td>
<td>-71.256556</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19967</th>
<td>OGLE-SMC-CEP-4853</td>
<td>01:19:18.93</td>
<td>-70:51:37.6</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>F/1O</td>
<td>1.612018</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>19.828875</td>
<td>-70.860444</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19968</th>
<td>OGLE-SMC-CEP-4854</td>
<td>01:20:07.87</td>
<td>-74:16:07.0</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.686529</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>20.032792</td>
<td>-74.268611</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>19969</th>
<td>OGLE-SMC-CEP-4855</td>
<td>01:20:16.90</td>
<td>-74:18:22.3</td>
<td>cep</td>
<td>4</td>
<td>smc</td>
<td>1O</td>
<td>0.653218</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>20.070417</td>
<td>-74.306194</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>69279</th>
<td>OGLE-LMC-CEP-0059</td>
<td>04:44:54.90</td>
<td>-68:38:21.7</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>F</td>
<td>4.390393</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>71.228750</td>
<td>-68.639361</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69280</th>
<td>OGLE-LMC-CEP-0073</td>
<td>04:46:38.50</td>
<td>-67:08:43.9</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>1O/2O</td>
<td>0.837148</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>71.660417</td>
<td>-67.145528</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69281</th>
<td>OGLE-LMC-CEP-0036</td>
<td>04:42:11.79</td>
<td>-70:14:02.0</td>
<td>cep</td>
<td>3</td>
<td>lmc</td>
<td>F/1O</td>
<td>1.573822</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>70.549125</td>
<td>-70.233889</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69282</th>
<td>OGLE-GD-CEP-0017</td>
<td>13:27:30.10</td>
<td>-64:40:37.7</td>
<td>cep</td>
<td>3</td>
<td>gd</td>
<td>1O</td>
<td>1.915025</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>201.875417</td>
<td>-64.677139</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>69283</th>
<td>OGLE-GD-CEP-0013</td>
<td>11:33:02.68</td>
<td>-60:52:04.5</td>
<td>cep</td>
<td>3</td>
<td>gd</td>
<td>1O</td>
<td>5.243600</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>173.261167</td>
<td>-60.867917</td>
<td>NaN</td>
<td>NaN</td>
<td>2</td>
<td>train</td>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>11445 rows × 27 columns</p>
</div>
```python
df_8mil.groupby("prueba_8mil").count() /8
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ID</th>
<th>RA</th>
<th>DEC</th>
<th>types</th>
<th>database</th>
<th>field</th>
<th>Subtype</th>
<th>per</th>
<th>error</th>
<th>obs_eliminadas</th>
<th>...</th>
<th>obs_inicial</th>
<th>ra_deg</th>
<th>dec_deg</th>
<th>GroupID</th>
<th>GroupSize</th>
<th>categorical_label</th>
<th>aug</th>
<th>g</th>
<th>bins</th>
<th>N</th>
</tr>
<tr>
<th>prueba_8mil</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>test</th>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1264.625</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>...</td>
<td>1488.0</td>
<td>1488.0</td>
<td>1488.0</td>
<td>5.375</td>
<td>5.375</td>
<td>1488.0</td>
<td>1488.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
</tr>
<tr>
<th>train</th>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>7187.875</td>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>...</td>
<td>8463.0</td>
<td>8463.0</td>
<td>8463.0</td>
<td>36.250</td>
<td>36.250</td>
<td>8463.0</td>
<td>8463.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
</tr>
<tr>
<th>val</th>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1270.125</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>...</td>
<td>1494.0</td>
<td>1494.0</td>
<td>1494.0</td>
<td>8.250</td>
<td>8.250</td>
<td>1494.0</td>
<td>1494.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
</tr>
</tbody>
</table>
<p>3 rows × 26 columns</p>
</div>
```python
df_8mil["types"].unique()
```
array(['ecl', 'rrlyr', 'Mira', 'dsct', 'cep', 'ELL', 'lpv', 'ELL_random',
'Mira_random', 'cep_random', 'dsct_random', 'ecl_random',
'lpv_random', 'rrlyr_random'], dtype=object)
```python
```
|
Monsalves-Gonzalez-NREPO_NAMEPaper_OGLEPATH_START.@Paper_OGLE_extracted@Paper_OGLE-main@.ipynb_checkpoints@Comentarios_paper-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "_btype.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/contourcarpet/_btype.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BtypeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="btype", parent_name="contourcarpet", **kwargs):
super(BtypeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc+clearAxisTypes"),
values=kwargs.pop("values", ["array", "scaled"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@contourcarpet@_btype.py@.PATH_END.py
|
{
"filename": "extract_problempar.py",
"repo_name": "piernik-dev/piernik",
"repo_path": "piernik_extracted/piernik-master/python/extract_problempar.py",
"type": "Python"
}
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import sys
import h5py
from colored_io import die, prtinfo, prtwarn
# This script reads problem.par field from provided
# h5/res file, extracts it and saves it in CWD,
# so it can be used in new run.
if (len(sys.argv) > 1):
hdf5_filename = sys.argv[1]
prtinfo("File to process is %s" % hdf5_filename)
else:
die("No file provided, exiting.")
try:
fh5 = h5py.File(hdf5_filename, 'r')
parfile_out = open("problem.par.%s" % hdf5_filename.strip(".res").strip(".h5"), 'w')
except:
die("Problem opening %s file. " % hdf5_filename)
parameter_object = fh5["problem.par"]
for line in parameter_object:
# print(line.decode("utf-8"))
parfile_out.write(line.decode("utf-8") + "\n")
prtinfo("Successfully read parameters from %s file, list of parameters saved to %s" % (hdf5_filename, parfile_out.name))
parfile_out.close()
fh5.close()
|
piernik-devREPO_NAMEpiernikPATH_START.@piernik_extracted@piernik-master@python@extract_problempar.py@.PATH_END.py
|
{
"filename": "test_pickling.py",
"repo_name": "dmlc/xgboost",
"repo_path": "xgboost_extracted/xgboost-master/tests/python/test_pickling.py",
"type": "Python"
}
|
import json
import os
import pickle
import numpy as np
import xgboost as xgb
kRows = 100
kCols = 10
def generate_data():
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
return X, y
class TestPickling:
def run_model_pickling(self, xgb_params) -> str:
X, y = generate_data()
dtrain = xgb.DMatrix(X, y)
bst = xgb.train(xgb_params, dtrain)
dump_0 = bst.get_dump(dump_format='json')
assert dump_0
config_0 = bst.save_config()
filename = 'model.pkl'
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
assert bst.get_dump(dump_format='json') == dump_0
if os.path.exists(filename):
os.remove(filename)
config_1 = bst.save_config()
assert config_0 == config_1
return json.loads(config_0)
def test_model_pickling_json(self):
def check(config):
tree_param = config["learner"]["gradient_booster"]["tree_train_param"]
subsample = tree_param["subsample"]
assert float(subsample) == 0.5
params = {"nthread": 8, "tree_method": "hist", "subsample": 0.5}
config = self.run_model_pickling(params)
check(config)
params = {"nthread": 8, "tree_method": "exact", "subsample": 0.5}
config = self.run_model_pickling(params)
check(config)
|
dmlcREPO_NAMExgboostPATH_START.@xgboost_extracted@xgboost-master@tests@python@test_pickling.py@.PATH_END.py
|
{
"filename": "nvidia_riva.ipynb",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/docs/docs/integrations/tools/nvidia_riva.ipynb",
"type": "Jupyter Notebook"
}
|
# NVIDIA Riva: ASR and TTS
## NVIDIA Riva
[NVIDIA Riva](https://www.nvidia.com/en-us/ai-data-science/products/riva/) is a GPU-accelerated multilingual speech and translation AI software development kit for building fully customizable, real-time conversational AI pipelines—including automatic speech recognition (ASR), text-to-speech (TTS), and neural machine translation (NMT) applications—that can be deployed in clouds, in data centers, at the edge, or on embedded devices.
The Riva Speech API server exposes a simple API for performing speech recognition, speech synthesis, and a variety of natural language processing inferences and is integrated into LangChain for ASR and TTS. See instructions on how to [setup a Riva Speech API](#3-setup) server below.
## Integrating NVIDIA Riva to LangChain Chains
The `NVIDIARivaASR`, `NVIDIARivaTTS` utility runnables are LangChain runnables that integrate [NVIDIA Riva](https://www.nvidia.com/en-us/ai-data-science/products/riva/) into LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech (TTS).
This example goes over how to use these LangChain runnables to:
1. Accept streamed audio,
2. convert the audio to text,
3. send the text to an LLM,
4. stream a textual LLM response, and
5. convert the response to streamed human-sounding audio.
## 1. NVIDIA Riva Runnables
There are 2 Riva Runnables:
a. **RivaASR**: Converts audio bytes into text for an LLM using NVIDIA Riva.
b. **RivaTTS**: Converts text into audio bytes using NVIDIA Riva.
### a. RivaASR
The [**RivaASR**](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/nvidia_riva.py#L404) runnable converts audio bytes into a string for an LLM using NVIDIA Riva.
It's useful for sending an audio stream (a message containing streaming audio) into a chain and preprocessing that audio by converting it to a string to create an LLM prompt.
```
ASRInputType = AudioStream # the AudioStream type is a custom type for a message queue containing streaming audio
ASROutputType = str
class RivaASR(
RivaAuthMixin,
RivaCommonConfigMixin,
RunnableSerializable[ASRInputType, ASROutputType],
):
"""A runnable that performs Automatic Speech Recognition (ASR) using NVIDIA Riva."""
name: str = "nvidia_riva_asr"
description: str = (
"A Runnable for converting audio bytes to a string."
"This is useful for feeding an audio stream into a chain and"
"preprocessing that audio to create an LLM prompt."
)
# riva options
audio_channel_count: int = Field(
1, description="The number of audio channels in the input audio stream."
)
profanity_filter: bool = Field(
True,
description=(
"Controls whether or not Riva should attempt to filter "
"profanity out of the transcribed text."
),
)
enable_automatic_punctuation: bool = Field(
True,
description=(
"Controls whether Riva should attempt to correct "
"senetence puncuation in the transcribed text."
),
)
```
When this runnable is called on an input, it takes an input audio stream that acts as a queue and concatenates transcription as chunks are returned.After a response is fully generated, a string is returned.
* Note that since the LLM requires a full query the ASR is concatenated and not streamed in token-by-token.
### b. RivaTTS
The [**RivaTTS**](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/nvidia_riva.py#L511) runnable converts text output to audio bytes.
It's useful for processing the streamed textual response from an LLM by converting the text to audio bytes. These audio bytes sound like a natural human voice to be played back to the user.
```
TTSInputType = Union[str, AnyMessage, PromptValue]
TTSOutputType = byte
class RivaTTS(
RivaAuthMixin,
RivaCommonConfigMixin,
RunnableSerializable[TTSInputType, TTSOutputType],
):
"""A runnable that performs Text-to-Speech (TTS) with NVIDIA Riva."""
name: str = "nvidia_riva_tts"
description: str = (
"A tool for converting text to speech."
"This is useful for converting LLM output into audio bytes."
)
# riva options
voice_name: str = Field(
"English-US.Female-1",
description=(
"The voice model in Riva to use for speech. "
"Pre-trained models are documented in "
"[the Riva documentation]"
"(https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-overview.html)."
),
)
output_directory: Optional[str] = Field(
None,
description=(
"The directory where all audio files should be saved. "
"A null value indicates that wave files should not be saved. "
"This is useful for debugging purposes."
),
```
When this runnable is called on an input, it takes iterable text chunks and streams them into output audio bytes that are either written to a `.wav` file or played out loud.
## 2. Installation
The NVIDIA Riva client library must be installed.
```python
%pip install --upgrade --quiet nvidia-riva-client
```
Note: you may need to restart the kernel to use updated packages.
## 3. Setup
**To get started with NVIDIA Riva:**
1. Follow the Riva Quick Start setup instructions for [Local Deployment Using Quick Start Scripts](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/quick-start-guide.html#local-deployment-using-quick-start-scripts).
## 4. Import and Inspect Runnables
Import the RivaASR and RivaTTS runnables and inspect their schemas to understand their fields.
```python
import json
from langchain_community.utilities.nvidia_riva import (
RivaASR,
RivaTTS,
)
```
Let's view the schemas.
```python
print(json.dumps(RivaASR.schema(), indent=2))
print(json.dumps(RivaTTS.schema(), indent=2))
```
{
"title": "RivaASR",
"description": "A runnable that performs Automatic Speech Recognition (ASR) using NVIDIA Riva.",
"type": "object",
"properties": {
"name": {
"title": "Name",
"default": "nvidia_riva_asr",
"type": "string"
},
"encoding": {
"description": "The encoding on the audio stream.",
"default": "LINEAR_PCM",
"allOf": [
{
"$ref": "#/definitions/RivaAudioEncoding"
}
]
},
"sample_rate_hertz": {
"title": "Sample Rate Hertz",
"description": "The sample rate frequency of audio stream.",
"default": 8000,
"type": "integer"
},
"language_code": {
"title": "Language Code",
"description": "The [BCP-47 language code](https://www.rfc-editor.org/rfc/bcp/bcp47.txt) for the target language.",
"default": "en-US",
"type": "string"
},
"url": {
"title": "Url",
"description": "The full URL where the Riva service can be found.",
"default": "http://localhost:50051",
"examples": [
"http://localhost:50051",
"https://user@pass:riva.example.com"
],
"anyOf": [
{
"type": "string",
"minLength": 1,
"maxLength": 65536,
"format": "uri"
},
{
"type": "string"
}
]
},
"ssl_cert": {
"title": "Ssl Cert",
"description": "A full path to the file where Riva's public ssl key can be read.",
"type": "string"
},
"description": {
"title": "Description",
"default": "A Runnable for converting audio bytes to a string.This is useful for feeding an audio stream into a chain andpreprocessing that audio to create an LLM prompt.",
"type": "string"
},
"audio_channel_count": {
"title": "Audio Channel Count",
"description": "The number of audio channels in the input audio stream.",
"default": 1,
"type": "integer"
},
"profanity_filter": {
"title": "Profanity Filter",
"description": "Controls whether or not Riva should attempt to filter profanity out of the transcribed text.",
"default": true,
"type": "boolean"
},
"enable_automatic_punctuation": {
"title": "Enable Automatic Punctuation",
"description": "Controls whether Riva should attempt to correct senetence puncuation in the transcribed text.",
"default": true,
"type": "boolean"
}
},
"definitions": {
"RivaAudioEncoding": {
"title": "RivaAudioEncoding",
"description": "An enum of the possible choices for Riva audio encoding.\n\nThe list of types exposed by the Riva GRPC Protobuf files can be found\nwith the following commands:\n```python\nimport riva.client\nprint(riva.client.AudioEncoding.keys()) # noqa: T201\n```",
"enum": [
"ALAW",
"ENCODING_UNSPECIFIED",
"FLAC",
"LINEAR_PCM",
"MULAW",
"OGGOPUS"
],
"type": "string"
}
}
}
{
"title": "RivaTTS",
"description": "A runnable that performs Text-to-Speech (TTS) with NVIDIA Riva.",
"type": "object",
"properties": {
"name": {
"title": "Name",
"default": "nvidia_riva_tts",
"type": "string"
},
"encoding": {
"description": "The encoding on the audio stream.",
"default": "LINEAR_PCM",
"allOf": [
{
"$ref": "#/definitions/RivaAudioEncoding"
}
]
},
"sample_rate_hertz": {
"title": "Sample Rate Hertz",
"description": "The sample rate frequency of audio stream.",
"default": 8000,
"type": "integer"
},
"language_code": {
"title": "Language Code",
"description": "The [BCP-47 language code](https://www.rfc-editor.org/rfc/bcp/bcp47.txt) for the target language.",
"default": "en-US",
"type": "string"
},
"url": {
"title": "Url",
"description": "The full URL where the Riva service can be found.",
"default": "http://localhost:50051",
"examples": [
"http://localhost:50051",
"https://user@pass:riva.example.com"
],
"anyOf": [
{
"type": "string",
"minLength": 1,
"maxLength": 65536,
"format": "uri"
},
{
"type": "string"
}
]
},
"ssl_cert": {
"title": "Ssl Cert",
"description": "A full path to the file where Riva's public ssl key can be read.",
"type": "string"
},
"description": {
"title": "Description",
"default": "A tool for converting text to speech.This is useful for converting LLM output into audio bytes.",
"type": "string"
},
"voice_name": {
"title": "Voice Name",
"description": "The voice model in Riva to use for speech. Pre-trained models are documented in [the Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-overview.html).",
"default": "English-US.Female-1",
"type": "string"
},
"output_directory": {
"title": "Output Directory",
"description": "The directory where all audio files should be saved. A null value indicates that wave files should not be saved. This is useful for debugging purposes.",
"type": "string"
}
},
"definitions": {
"RivaAudioEncoding": {
"title": "RivaAudioEncoding",
"description": "An enum of the possible choices for Riva audio encoding.\n\nThe list of types exposed by the Riva GRPC Protobuf files can be found\nwith the following commands:\n```python\nimport riva.client\nprint(riva.client.AudioEncoding.keys()) # noqa: T201\n```",
"enum": [
"ALAW",
"ENCODING_UNSPECIFIED",
"FLAC",
"LINEAR_PCM",
"MULAW",
"OGGOPUS"
],
"type": "string"
}
}
}
## 5. Declare Riva ASR and Riva TTS Runnables
For this example, a single-channel audio file (mulaw format, so `.wav`) is used.
You will need a Riva speech server setup, so if you don't have a Riva speech server, go to [Setup](#3-setup).
### a. Set Audio Parameters
Some parameters of audio can be inferred by the mulaw file, but others are set explicitly.
Replace `audio_file` with the path of your audio file.
```python
import pywav # pywav is used instead of built-in wave because of mulaw support
from langchain_community.utilities.nvidia_riva import RivaAudioEncoding
audio_file = "./audio_files/en-US_sample2.wav"
wav_file = pywav.WavRead(audio_file)
audio_data = wav_file.getdata()
audio_encoding = RivaAudioEncoding.from_wave_format_code(wav_file.getaudioformat())
sample_rate = wav_file.getsamplerate()
delay_time = 1 / 4
chunk_size = int(sample_rate * delay_time)
delay_time = 1 / 8
num_channels = wav_file.getnumofchannels()
```
```python
import IPython
IPython.display.Audio(audio_file)
```
<audio controls="controls" >
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type="audio/x-wav" />
Your browser does not support the audio element.
</audio>
### b. Set the Speech Server and Declare Riva LangChain Runnables
Be sure to set `RIVA_SPEECH_URL` to be the URI of your Riva speech server.
The runnables act as clients to the speech server. Many of the fields set in this example are configured based on the sample audio data.
```python
RIVA_SPEECH_URL = "http://localhost:50051/"
riva_asr = RivaASR(
url=RIVA_SPEECH_URL, # the location of the Riva ASR server
encoding=audio_encoding,
audio_channel_count=num_channels,
sample_rate_hertz=sample_rate,
profanity_filter=True,
enable_automatic_punctuation=True,
language_code="en-US",
)
riva_tts = RivaTTS(
url=RIVA_SPEECH_URL, # the location of the Riva TTS server
output_directory="./scratch", # location of the output .wav files
language_code="en-US",
voice_name="English-US.Female-1",
)
```
## 6. Create Additional Chain Components
As usual, declare the other parts of the chain. In this case, it's just a prompt template and an LLM.
You can use any [LangChain compatible LLM](https://python.langchain.com/v0.1/docs/integrations/llms/) in the chain. In this example, we use a [Mixtral8x7b NIM from NVIDIA](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/). NVIDIA NIMs are supported in LangChain via the `langchain-nvidia-ai-endpoints` package, so you can easily build applications with best in class throughput and latency.
LangChain compatible NVIDIA LLMs from [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) can also be used by following these [instructions](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints).
```python
%pip install --upgrade --quiet langchain-nvidia-ai-endpoints
```
Follow the [instructions for LangChain](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/) to use NVIDIA NIM in your speech-enabled LangChain application.
Set your key for NVIDIA API catalog, where NIMs are hosted for you to try.
```python
import getpass
import os
nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key"
os.environ["NVIDIA_API_KEY"] = nvapi_key
```
Instantiate LLM.
```python
from langchain_core.prompts import PromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
prompt = PromptTemplate.from_template("{user_input}")
llm = ChatNVIDIA(model="mistralai/mixtral-8x7b-instruct-v0.1")
```
Now, tie together all the parts of the chain including RivaASR and RivaTTS.
```python
chain = {"user_input": riva_asr} | prompt | llm | riva_tts
```
## 7. Run the Chain with Streamed Inputs and Outputs
### a. Mimic Audio Streaming
To mimic streaming, first convert the processed audio data to iterable chunks of audio bytes.
Two functions, `producer` and `consumer`, respectively handle asynchronously passing audio data into the chain and consuming audio data out of the chain.
```python
import asyncio
from langchain_community.utilities.nvidia_riva import AudioStream
audio_chunks = [
audio_data[0 + i : chunk_size + i] for i in range(0, len(audio_data), chunk_size)
]
async def producer(input_stream) -> None:
"""Produces audio chunk bytes into an AudioStream as streaming audio input."""
for chunk in audio_chunks:
await input_stream.aput(chunk)
input_stream.close()
async def consumer(input_stream, output_stream) -> None:
"""
Consumes audio chunks from input stream and passes them along the chain
constructed comprised of ASR -> text based prompt for an LLM -> TTS chunks
with synthesized voice of LLM response put in an output stream.
"""
while not input_stream.complete:
async for chunk in chain.astream(input_stream):
await output_stream.put(
chunk
) # for production code don't forget to add a timeout
input_stream = AudioStream(maxsize=1000)
output_stream = asyncio.Queue()
# send data into the chain
producer_task = asyncio.create_task(producer(input_stream))
# get data out of the chain
consumer_task = asyncio.create_task(consumer(input_stream, output_stream))
while not consumer_task.done():
try:
generated_audio = await asyncio.wait_for(
output_stream.get(), timeout=2
) # for production code don't forget to add a timeout
except asyncio.TimeoutError:
continue
await producer_task
await consumer_task
```
## 8. Listen to Voice Response
The audio response is written to `./scratch` and should contain an audio clip that is a response to the input audio.
```python
import glob
import os
output_path = os.path.join(os.getcwd(), "scratch")
file_type = "*.wav"
files_path = os.path.join(output_path, file_type)
files = glob.glob(files_path)
IPython.display.Audio(files[0])
```
<audio controls="controls" >
<source 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langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@docs@docs@integrations@tools@nvidia_riva.ipynb@.PATH_END.py
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{
"filename": "grid_potential.py",
"repo_name": "amusecode/amuse",
"repo_path": "amuse_extracted/amuse-main/examples/simple/grid_potential.py",
"type": "Python"
}
|
# -*- coding: ascii -*-
from __future__ import print_function
from amuse.units import units, nbody_system
from amuse.datamodel import Particle
from amuse.community.athena.interface import Athena
from amuse.community.hermite.interface import Hermite
from matplotlib import pyplot
def hydro_grid_in_potential_well(mass=1 | units.MSun, length=100 | units.AU):
converter = nbody_system.nbody_to_si(mass, length)
# calculate density in field based on solar wind
# gives a very low number
molar_mass_hydrogen_proton = 1 | units.g / units.mol
density_hydrogen_in_stellar_wind = 10 | 1 / units.cm**3
particles_per_mol = 6.022e23 | 1 / units.mol
density_hydrogen_in_stellar_wind_in_moles = (
density_hydrogen_in_stellar_wind
/ particles_per_mol
)
density_gas = 100 * (
density_hydrogen_in_stellar_wind_in_moles
* molar_mass_hydrogen_proton
).as_quantity_in(units.MSun / units.AU**3)
# override with higher number for plotting
density_gas = 1e-3 | units.MSun / units.AU**3
instance = Athena(converter)
instance.initialize_code()
instance.parameters.nx = 50
instance.parameters.ny = 50
instance.parameters.nz = 1
instance.parameters.length_x = length
instance.parameters.length_y = length
instance.parameters.length_z = length
instance.parameters.x_boundary_conditions = ("periodic", "periodic")
instance.parameters.y_boundary_conditions = ("periodic", "periodic")
instance.parameters.z_boundary_conditions = ("outflow", "outflow")
# instance.stopping_conditions.number_of_steps_detection.enable()
instance.set_has_external_gravitational_potential(1)
instance.commit_parameters()
grid_in_memory = instance.grid.copy()
grid_in_memory.rho = density_gas
pressure = 1 | units.Pa
grid_in_memory.energy = pressure / (instance.parameters.gamma - 1)
channel = grid_in_memory.new_channel_to(instance.grid)
channel.copy()
instance.initialize_grid()
particle = Particle(
mass=mass,
position=length * [0.5, 0.5, 0.5],
velocity=[0.0, 0.0, 0.0] | units.kms
)
gravity = Hermite(converter)
dx = (grid_in_memory.x[1][0][0] - grid_in_memory.x[0]
[0][0]).as_quantity_in(units.AU)
gravity.parameters.epsilon_squared = dx**2
gravity.particles.add_particle(particle)
potential = gravity.get_potential_at_point(
0 * instance.potential_grid.x.flatten(),
instance.potential_grid.x.flatten(),
instance.potential_grid.y.flatten(),
instance.potential_grid.z.flatten()
)
potential = potential.reshape(instance.potential_grid.x.shape)
instance.potential_grid.potential = potential
instance.evolve_model(100 | units.yr)
print(instance.get_timestep().value_in(units.yr))
value_to_plot = instance.grid.rho[:, :, 0].value_in(
units.MSun / units.AU**3)
# value_to_plot = potential[...,...,0].value_in(potential.unit)
plot_grid(value_to_plot)
def plot_grid(x):
figure = pyplot.figure(figsize=(6, 6))
plot = figure.add_subplot(1, 1, 1)
mappable = plot.imshow(x, origin='lower')
pyplot.colorbar(mappable)
# figure.savefig('orszag_tang.png')
pyplot.show()
if __name__ == '__main__':
hydro_grid_in_potential_well()
|
amusecodeREPO_NAMEamusePATH_START.@amuse_extracted@amuse-main@examples@simple@grid_potential.py@.PATH_END.py
|
{
"filename": "apero.py",
"repo_name": "njcuk9999/lbl",
"repo_path": "lbl_extracted/lbl-main/lbl/science/apero.py",
"type": "Python"
}
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
# CODE NAME HERE
# CODE DESCRIPTION HERE
Created on 2021-11-01
@author: cook
"""
import warnings
from typing import Tuple
import numpy as np
from lbl.core import base
from lbl.core import base_classes
from lbl.core import io
from lbl.core import math as mp
from lbl.instruments import select
# =============================================================================
# Define variables
# =============================================================================
__NAME__ = 'lbl_template.py'
__STRNAME__ = 'LBL Template'
__version__ = base.__version__
__date__ = base.__date__
__authors__ = base.__authors__
# get classes
InstrumentsList = select.InstrumentsList
InstrumentsType = select.InstrumentsType
ParamDict = base_classes.ParamDict
LblException = base_classes.LblException
log = io.log
# =============================================================================
# Define functions
# =============================================================================
def e2ds_to_s1d(params: ParamDict, wavemap: np.ndarray, e2ds: np.ndarray,
blaze: np.ndarray, wavegrid: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""
E2DS to S1D function (taken from apero - with adjustments)
:param params: ParamDict, parameter dictionary of constants
:param wavemap: np.ndarray (2D), the wave map for the E2DS
:param e2ds: np.ndarray (2D), the E2DS 2D numpy array (norders x npixels)
must not be blaze corrected
:param blaze: np.ndarray (2D), the blaze function of the E2DS - used for
weighting orders
:param wavegrid: np.ndarray (1D), the output s1d wave grid
:return: tuple, 1. np.array (1D) the s1d flux, 2. np.array (1D) the weight
assigned to each order
"""
# get quantities from parameter dictionary of constants
smooth_size = params['BLAZE_SMOOTH_SIZE']
blazethres = params['BLAZE_THRESHOLD']
# -------------------------------------------------------------------------
# get size from e2ds
nord, npix = e2ds.shape
# -------------------------------------------------------------------------
# define a smooth transition mask at the edges of the image
# this ensures that the s1d has no discontinuity when going from one order
# to the next. We define a scale for this mask
# smoothing scale
# -------------------------------------------------------------------------
# define a kernel that goes from -3 to +3 smooth_sizes of the mask
xker = np.arange(-smooth_size * 3, smooth_size * 3, 1)
ker = np.exp(-0.5 * (xker / smooth_size) ** 2)
# set up the edge vector
edges = np.ones(npix, dtype=bool)
# set edges of the image to 0 so that we get a sloping weight
edges[:int(3 * smooth_size)] = False
edges[-int(3 * smooth_size):] = False
# define the weighting for the edges (slopevector)
slopevector = np.zeros_like(blaze)
# for each order find the sloping weight vector
for order_num in range(nord):
# get the blaze for this order
oblaze = np.array(blaze[order_num])
# find the valid pixels
cond1 = np.isfinite(oblaze) & np.isfinite(e2ds[order_num])
with warnings.catch_warnings(record=True) as _:
cond2 = oblaze > (blazethres * mp.nanmax(oblaze))
valid = cond1 & cond2 & edges
# convolve with the edge kernel
oweight = np.convolve(valid, ker, mode='same')
# normalise to the maximum
with warnings.catch_warnings(record=True) as _:
oweight = oweight - mp.nanmin(oweight)
oweight = oweight / mp.nanmax(oweight)
# append to sloping vector storage
slopevector[order_num] = oweight
# multiple the spectrum and blaze by the sloping vector
sblaze = np.array(blaze) * slopevector
se2ds = np.array(e2ds) * slopevector
# -------------------------------------------------------------------------
# Perform a weighted mean of overlapping orders
# by performing a spline of both the blaze and the spectrum
# -------------------------------------------------------------------------
out_spec = np.zeros_like(wavegrid)
# out_spec_err = np.zeros_like(wavegrid)
weight = np.zeros_like(wavegrid)
# loop around all orders
for order_num in range(nord):
# get wavelength mask - if there are NaNs in wavemap have to deal with
# them (happens at least for polar)
wavemask = np.isfinite(wavemap[order_num])
# identify the valid pixels
valid = np.isfinite(se2ds[order_num]) & np.isfinite(sblaze[order_num])
valid &= wavemask
# if we have no valid points we need to skip
if np.sum(valid) == 0:
continue
# get this orders vectors
owave = wavemap[order_num]
oe2ds = se2ds[order_num, valid]
oblaze = sblaze[order_num, valid]
# check that all points for this order are zero
if np.sum(owave == 0) != 0:
# log message about skipping this order
msg = ('\tOrder {0}: Some points in wavelength '
'grid are zero. Skipping order.')
log.info(msg.format(order_num))
# skip this order
continue
# check that the grid increases or decreases in a monotonic way
gradwave = np.gradient(owave)
# check the signs of wave map gradient
if np.sign(np.min(gradwave)) != np.sign(np.max(gradwave)):
msg = ('\tOrder {0}: Wavelength grid curves around. '
'Skipping order')
log.info(msg.format(order_num))
continue
# create the splines for this order
spline_sp = mp.iuv_spline(owave[valid], oe2ds, k=5, ext=1)
spline_bl = mp.iuv_spline(owave[valid], oblaze, k=1, ext=1)
# valid must be cast as float for splining
valid_float = valid.astype(float)
# we mask pixels that are neighbours to a NaN.
valid_float = np.convolve(valid_float, np.ones(3) / 3.0, mode='same')
spline_valid = mp.iuv_spline(owave[wavemask], valid_float[wavemask],
k=1, ext=1)
# can only spline in domain of the wave
useful_range = (wavegrid > mp.nanmin(owave[valid]))
useful_range &= (wavegrid < mp.nanmax(owave[valid]))
# finding pixels where we have immediate neighbours that are
# considered valid in the spline (to avoid interpolating over large
# gaps in validity)
maskvalid = np.zeros_like(wavegrid, dtype=bool)
maskvalid[useful_range] = spline_valid(wavegrid[useful_range]) > 0.9
useful_range &= maskvalid
# get splines and add to outputs
weight[useful_range] += spline_bl(wavegrid[useful_range])
out_spec[useful_range] += spline_sp(wavegrid[useful_range])
# where out_spec is exactly zero set to NaN
out_spec[out_spec == 0] = np.nan
# return properties
return out_spec, weight
# =============================================================================
# Start of code
# =============================================================================
if __name__ == "__main__":
# print hello world
print('Hello World')
# =============================================================================
# End of code
# =============================================================================
|
njcuk9999REPO_NAMElblPATH_START.@lbl_extracted@lbl-main@lbl@science@apero.py@.PATH_END.py
|
{
"filename": "degradation.py",
"repo_name": "RobertJaro/InstrumentToInstrument",
"repo_path": "InstrumentToInstrument_extracted/InstrumentToInstrument-master/itipy/data/stereo/degradation.py",
"type": "Python"
}
|
import glob
import os
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from dateutil.parser import parse
from tqdm import tqdm
import matplotlib.dates as mdates
from itipy.data.editor import LoadMapEditor, NormalizeRadiusEditor, MapToDataEditor, EITCheckEditor, RemoveOffLimbEditor, \
AIAPrepEditor, SECCHIPrepEditor
stereo_path = '/gpfs/gpfs0/robert.jarolim/data/iti/stereo_iti2021_prep'
evaluation_path = '/gpfs/gpfs0/robert.jarolim/iti/euv_calibration'
soho_channels = ['195', '284']
secchi_files = [sorted(glob.glob(os.path.join(stereo_path, c, '*.fits'))) for c in soho_channels]
def getQSdata(f):
s_map, _ = LoadMapEditor().call(f)
s_map = SECCHIPrepEditor().call(s_map)
s_map = NormalizeRadiusEditor(1024).call(s_map)
s_map = RemoveOffLimbEditor(fill_value=np.nan).call(s_map)
data, _ = MapToDataEditor().call(s_map)
threshold = np.nanmedian(data) + np.nanstd(data)
data[data > threshold] = np.nan
return data
secchi_means = {}
for c, c_files in zip(soho_channels, secchi_files):
c_files = c_files[::len(c_files) // 100]
dates = [parse(os.path.basename(f).replace('.fits', '')) for f in c_files]
with Pool(4) as p:
means = [np.nanmean(m) for m in tqdm(p.imap(getQSdata, c_files), total=len(c_files))]
secchi_means[c] = (dates, means)
for c, (secchi_dates, y) in secchi_means.items():
x = mdates.date2num(secchi_dates)
fit_params = np.polyfit(x, y, 1)
fit = np.poly1d(fit_params)
d0 = fit(x[0])
print(c, fit_params / d0)
plt.plot(x, np.array(y) / fit(x), label='Quiet-Sun Mean')
# plt.plot(x, fit(x), label='Fit', linestyle='--')
plt.legend()
plt.savefig(os.path.join(evaluation_path, '%s_mean.jpg' % c))
plt.close()
|
RobertJaroREPO_NAMEInstrumentToInstrumentPATH_START.@InstrumentToInstrument_extracted@InstrumentToInstrument-master@itipy@data@stereo@degradation.py@.PATH_END.py
|
{
"filename": "_stream.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/contour/_stream.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Stream(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "contour"
_path_str = "contour.stream"
_valid_props = {"maxpoints", "token"}
# maxpoints
# ---------
@property
def maxpoints(self):
"""
Sets the maximum number of points to keep on the plots from an
incoming stream. If `maxpoints` is set to 50, only the newest
50 points will be displayed on the plot.
The 'maxpoints' property is a number and may be specified as:
- An int or float in the interval [0, 10000]
Returns
-------
int|float
"""
return self["maxpoints"]
@maxpoints.setter
def maxpoints(self, val):
self["maxpoints"] = val
# token
# -----
@property
def token(self):
"""
The stream id number links a data trace on a plot with a
stream. See https://chart-studio.plotly.com/settings for more
details.
The 'token' property is a string and must be specified as:
- A non-empty string
Returns
-------
str
"""
return self["token"]
@token.setter
def token(self, val):
self["token"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
maxpoints
Sets the maximum number of points to keep on the plots
from an incoming stream. If `maxpoints` is set to 50,
only the newest 50 points will be displayed on the
plot.
token
The stream id number links a data trace on a plot with
a stream. See https://chart-studio.plotly.com/settings
for more details.
"""
def __init__(self, arg=None, maxpoints=None, token=None, **kwargs):
"""
Construct a new Stream object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.contour.Stream`
maxpoints
Sets the maximum number of points to keep on the plots
from an incoming stream. If `maxpoints` is set to 50,
only the newest 50 points will be displayed on the
plot.
token
The stream id number links a data trace on a plot with
a stream. See https://chart-studio.plotly.com/settings
for more details.
Returns
-------
Stream
"""
super(Stream, self).__init__("stream")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.contour.Stream
constructor must be a dict or
an instance of :class:`plotly.graph_objs.contour.Stream`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("maxpoints", None)
_v = maxpoints if maxpoints is not None else _v
if _v is not None:
self["maxpoints"] = _v
_v = arg.pop("token", None)
_v = token if token is not None else _v
if _v is not None:
self["token"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@contour@_stream.py@.PATH_END.py
|
{
"filename": "argilla.ipynb",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/docs/docs/integrations/callbacks/argilla.ipynb",
"type": "Jupyter Notebook"
}
|
# Argilla
>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.
> Using Argilla, everyone can build robust language models through faster data curation
> using both human and machine feedback. We provide support for each step in the MLOps cycle,
> from data labeling to model monitoring.
<a target="_blank" href="https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/integrations/callbacks/argilla.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
In this guide we will demonstrate how to track the inputs and responses of your LLM to generate a dataset in Argilla, using the `ArgillaCallbackHandler`.
It's useful to keep track of the inputs and outputs of your LLMs to generate datasets for future fine-tuning. This is especially useful when you're using a LLM to generate data for a specific task, such as question answering, summarization, or translation.
## Installation and Setup
```python
%pip install --upgrade --quiet langchain langchain-openai argilla
```
### Getting API Credentials
To get the Argilla API credentials, follow the next steps:
1. Go to your Argilla UI.
2. Click on your profile picture and go to "My settings".
3. Then copy the API Key.
In Argilla the API URL will be the same as the URL of your Argilla UI.
To get the OpenAI API credentials, please visit https://platform.openai.com/account/api-keys
```python
import os
os.environ["ARGILLA_API_URL"] = "..."
os.environ["ARGILLA_API_KEY"] = "..."
os.environ["OPENAI_API_KEY"] = "..."
```
### Setup Argilla
To use the `ArgillaCallbackHandler` we will need to create a new `FeedbackDataset` in Argilla to keep track of your LLM experiments. To do so, please use the following code:
```python
import argilla as rg
```
```python
from packaging.version import parse as parse_version
if parse_version(rg.__version__) < parse_version("1.8.0"):
raise RuntimeError(
"`FeedbackDataset` is only available in Argilla v1.8.0 or higher, please "
"upgrade `argilla` as `pip install argilla --upgrade`."
)
```
```python
dataset = rg.FeedbackDataset(
fields=[
rg.TextField(name="prompt"),
rg.TextField(name="response"),
],
questions=[
rg.RatingQuestion(
name="response-rating",
description="How would you rate the quality of the response?",
values=[1, 2, 3, 4, 5],
required=True,
),
rg.TextQuestion(
name="response-feedback",
description="What feedback do you have for the response?",
required=False,
),
],
guidelines="You're asked to rate the quality of the response and provide feedback.",
)
rg.init(
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
dataset.push_to_argilla("langchain-dataset")
```
> 📌 NOTE: at the moment, just the prompt-response pairs are supported as `FeedbackDataset.fields`, so the `ArgillaCallbackHandler` will just track the prompt i.e. the LLM input, and the response i.e. the LLM output.
## Tracking
To use the `ArgillaCallbackHandler` you can either use the following code, or just reproduce one of the examples presented in the following sections.
```python
from langchain_community.callbacks.argilla_callback import ArgillaCallbackHandler
argilla_callback = ArgillaCallbackHandler(
dataset_name="langchain-dataset",
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
```
### Scenario 1: Tracking an LLM
First, let's just run a single LLM a few times and capture the resulting prompt-response pairs in Argilla.
```python
from langchain_core.callbacks.stdout import StdOutCallbackHandler
from langchain_openai import OpenAI
argilla_callback = ArgillaCallbackHandler(
dataset_name="langchain-dataset",
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
```
LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the air with love.\n\nEver changing and renewing,\nA never-ending light of grace.\nThe moon remains a constant view,\nA reminder of life’s gentle pace.\n\nThrough time and space it guides us on,\nA never-fading beacon of hope.\nThe moon shines down on us all,\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ. What did one magnet say to the other magnet?\nA. "I find you very attractive!"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nThe world is charged with the grandeur of God.\nIt will flame out, like shining from shook foil;\nIt gathers to a greatness, like the ooze of oil\nCrushed. Why do men then now not reck his rod?\n\nGenerations have trod, have trod, have trod;\nAnd all is seared with trade; bleared, smeared with toil;\nAnd wears man's smudge and shares man's smell: the soil\nIs bare now, nor can foot feel, being shod.\n\nAnd for all this, nature is never spent;\nThere lives the dearest freshness deep down things;\nAnd though the last lights off the black West went\nOh, morning, at the brown brink eastward, springs —\n\nBecause the Holy Ghost over the bent\nWorld broods with warm breast and with ah! bright wings.\n\n~Gerard Manley Hopkins", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ: What did one ocean say to the other ocean?\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed with depth\n\nIn the morning, when dawn arrives\n\nA fresh start, no reason to hide\n\nSomewhere down the road, there's a heart that beats\n\nBelieve in yourself, you'll always succeed.", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})
### Scenario 2: Tracking an LLM in a chain
Then we can create a chain using a prompt template, and then track the initial prompt and the final response in Argilla.
```python
from langchain.chains import LLMChain
from langchain_core.callbacks.stdout import StdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
argilla_callback = ArgillaCallbackHandler(
dataset_name="langchain-dataset",
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]
synopsis_chain.apply(test_prompts)
```
[1m> Entering new LLMChain chain...[0m
Prompt after formatting:
[32;1m[1;3mYou are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: Documentary about Bigfoot in Paris
Playwright: This is a synopsis for the above play:[0m
[1m> Finished chain.[0m
[{'text': "\n\nDocumentary about Bigfoot in Paris focuses on the story of a documentary filmmaker and their search for evidence of the legendary Bigfoot creature in the city of Paris. The play follows the filmmaker as they explore the city, meeting people from all walks of life who have had encounters with the mysterious creature. Through their conversations, the filmmaker unravels the story of Bigfoot and finds out the truth about the creature's presence in Paris. As the story progresses, the filmmaker learns more and more about the mysterious creature, as well as the different perspectives of the people living in the city, and what they think of the creature. In the end, the filmmaker's findings lead them to some surprising and heartwarming conclusions about the creature's existence and the importance it holds in the lives of the people in Paris."}]
### Scenario 3: Using an Agent with Tools
Finally, as a more advanced workflow, you can create an agent that uses some tools. So that `ArgillaCallbackHandler` will keep track of the input and the output, but not about the intermediate steps/thoughts, so that given a prompt we log the original prompt and the final response to that given prompt.
> Note that for this scenario we'll be using Google Search API (Serp API) so you will need to both install `google-search-results` as `pip install google-search-results`, and to set the Serp API Key as `os.environ["SERPAPI_API_KEY"] = "..."` (you can find it at https://serpapi.com/dashboard), otherwise the example below won't work.
```python
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_core.callbacks.stdout import StdOutCallbackHandler
from langchain_openai import OpenAI
argilla_callback = ArgillaCallbackHandler(
dataset_name="langchain-dataset",
api_url=os.environ["ARGILLA_API_URL"],
api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
tools = load_tools(["serpapi"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callbacks=callbacks,
)
agent.run("Who was the first president of the United States of America?")
```
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m I need to answer a historical question
Action: Search
Action Input: "who was the first president of the United States of America" [0m
Observation: [36;1m[1;3mGeorge Washington[0m
Thought:[32;1m[1;3m George Washington was the first president
Final Answer: George Washington was the first president of the United States of America.[0m
[1m> Finished chain.[0m
'George Washington was the first president of the United States of America.'
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@docs@docs@integrations@callbacks@argilla.ipynb@.PATH_END.py
|
{
"filename": "test_max_tree.py",
"repo_name": "scikit-image/scikit-image",
"repo_path": "scikit-image_extracted/scikit-image-main/skimage/morphology/tests/test_max_tree.py",
"type": "Python"
}
|
import numpy as np
from skimage.morphology import max_tree, area_closing, area_opening
from skimage.morphology import max_tree_local_maxima, diameter_opening
from skimage.morphology import diameter_closing
from skimage.util import invert
from skimage._shared.testing import assert_array_equal, TestCase
eps = 1e-12
def _full_type_test(img, param, expected, func, param_scale=False, **keywords):
# images as they are
out = func(img, param, **keywords)
assert_array_equal(out, expected)
# unsigned int
for dt in [np.uint32, np.uint64]:
img_cast = img.astype(dt)
out = func(img_cast, param, **keywords)
exp_cast = expected.astype(dt)
assert_array_equal(out, exp_cast)
# float
data_float = img.astype(np.float64)
data_float = data_float / 255.0
expected_float = expected.astype(np.float64)
expected_float = expected_float / 255.0
if param_scale:
param_cast = param / 255.0
else:
param_cast = param
for dt in [np.float32, np.float64]:
data_cast = data_float.astype(dt)
out = func(data_cast, param_cast, **keywords)
exp_cast = expected_float.astype(dt)
error_img = 255.0 * exp_cast - 255.0 * out
error = (error_img >= 1.0).sum()
assert error < eps
# signed images
img_signed = img.astype(np.int16)
img_signed = img_signed - 128
exp_signed = expected.astype(np.int16)
exp_signed = exp_signed - 128
for dt in [np.int8, np.int16, np.int32, np.int64]:
img_s = img_signed.astype(dt)
out = func(img_s, param, **keywords)
exp_s = exp_signed.astype(dt)
assert_array_equal(out, exp_s)
class TestMaxtree(TestCase):
def test_max_tree(self):
"Test for max tree"
img_type = np.uint8
img = np.array(
[[10, 8, 8, 9], [7, 7, 9, 9], [8, 7, 10, 10], [9, 9, 10, 10]],
dtype=img_type,
)
P_exp = np.array(
[[1, 4, 1, 1], [4, 4, 3, 3], [1, 4, 3, 10], [3, 3, 10, 10]], dtype=np.int64
)
S_exp = np.array(
[4, 5, 9, 1, 2, 8, 3, 6, 7, 12, 13, 0, 10, 11, 14, 15], dtype=np.int64
)
for img_type in [np.uint8, np.uint16, np.uint32, np.uint64]:
img = img.astype(img_type)
P, S = max_tree(img, connectivity=2)
assert_array_equal(P, P_exp)
assert_array_equal(S, S_exp)
for img_type in [np.int8, np.int16, np.int32, np.int64]:
img = img.astype(img_type)
img_shifted = img - 9
P, S = max_tree(img_shifted, connectivity=2)
assert_array_equal(P, P_exp)
assert_array_equal(S, S_exp)
img_float = img.astype(float)
img_float = (img_float - 8) / 2.0
for img_type in [np.float32, np.float64]:
img_float = img_float.astype(img_type)
P, S = max_tree(img_float, connectivity=2)
assert_array_equal(P, P_exp)
assert_array_equal(S, S_exp)
return
def test_area_closing(self):
"Test for Area Closing (2 thresholds, all types)"
# original image
img = np.array(
[
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 200, 200, 240, 200, 240, 200, 200, 240, 240, 200, 240],
[240, 200, 40, 240, 240, 240, 240, 240, 240, 240, 40, 240],
[240, 240, 240, 240, 100, 240, 100, 100, 240, 240, 200, 240],
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[200, 200, 200, 200, 200, 200, 200, 240, 200, 200, 255, 255],
[200, 255, 200, 200, 200, 255, 200, 240, 255, 255, 255, 40],
[200, 200, 200, 100, 200, 200, 200, 240, 255, 255, 255, 255],
[200, 200, 200, 100, 200, 200, 200, 240, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 40, 200, 240, 240, 100, 255, 255],
[200, 40, 255, 255, 255, 40, 200, 255, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 255, 255, 255, 255, 255],
],
dtype=np.uint8,
)
# expected area closing with area 2
expected_2 = np.array(
[
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 200, 200, 240, 240, 240, 200, 200, 240, 240, 200, 240],
[240, 200, 200, 240, 240, 240, 240, 240, 240, 240, 200, 240],
[240, 240, 240, 240, 240, 240, 100, 100, 240, 240, 200, 240],
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[200, 200, 200, 200, 200, 200, 200, 240, 200, 200, 255, 255],
[200, 255, 200, 200, 200, 255, 200, 240, 255, 255, 255, 255],
[200, 200, 200, 100, 200, 200, 200, 240, 255, 255, 255, 255],
[200, 200, 200, 100, 200, 200, 200, 240, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 40, 200, 240, 240, 200, 255, 255],
[200, 200, 255, 255, 255, 40, 200, 255, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 255, 255, 255, 255, 255],
],
dtype=np.uint8,
)
# expected diameter closing with diameter 4
expected_4 = np.array(
[
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 200, 200, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 200, 200, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240],
[200, 200, 200, 200, 200, 200, 200, 240, 240, 240, 255, 255],
[200, 255, 200, 200, 200, 255, 200, 240, 255, 255, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 240, 255, 255, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 240, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 240, 240, 200, 255, 255],
[200, 200, 255, 255, 255, 200, 200, 255, 200, 200, 255, 255],
[200, 200, 200, 200, 200, 200, 200, 255, 255, 255, 255, 255],
],
dtype=np.uint8,
)
# _full_type_test makes a test with many image types.
_full_type_test(img, 2, expected_2, area_closing, connectivity=2)
_full_type_test(img, 4, expected_4, area_closing, connectivity=2)
P, S = max_tree(invert(img), connectivity=2)
_full_type_test(img, 4, expected_4, area_closing, parent=P, tree_traverser=S)
def test_area_opening(self):
"Test for Area Opening (2 thresholds, all types)"
# original image
img = np.array(
[
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 55, 55, 15, 55, 15, 55, 55, 15, 15, 55, 15],
[15, 55, 215, 15, 15, 15, 15, 15, 15, 15, 215, 15],
[15, 15, 15, 15, 155, 15, 155, 155, 15, 15, 55, 15],
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[55, 55, 55, 55, 55, 55, 55, 15, 55, 55, 0, 0],
[55, 0, 55, 55, 55, 0, 55, 15, 0, 0, 0, 215],
[55, 55, 55, 155, 55, 55, 55, 15, 0, 0, 0, 0],
[55, 55, 55, 155, 55, 55, 55, 15, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 215, 55, 15, 15, 155, 0, 0],
[55, 215, 0, 0, 0, 215, 55, 0, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
# expected area closing with area 2
expected_2 = np.array(
[
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 55, 55, 15, 15, 15, 55, 55, 15, 15, 55, 15],
[15, 55, 55, 15, 15, 15, 15, 15, 15, 15, 55, 15],
[15, 15, 15, 15, 15, 15, 155, 155, 15, 15, 55, 15],
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[55, 55, 55, 55, 55, 55, 55, 15, 55, 55, 0, 0],
[55, 0, 55, 55, 55, 0, 55, 15, 0, 0, 0, 0],
[55, 55, 55, 155, 55, 55, 55, 15, 0, 0, 0, 0],
[55, 55, 55, 155, 55, 55, 55, 15, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 215, 55, 15, 15, 55, 0, 0],
[55, 55, 0, 0, 0, 215, 55, 0, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
# expected diameter closing with diameter 4
expected_4 = np.array(
[
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 55, 55, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 55, 55, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15],
[55, 55, 55, 55, 55, 55, 55, 15, 15, 15, 0, 0],
[55, 0, 55, 55, 55, 0, 55, 15, 0, 0, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 15, 0, 0, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 15, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 15, 15, 55, 0, 0],
[55, 55, 0, 0, 0, 55, 55, 0, 55, 55, 0, 0],
[55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
# _full_type_test makes a test with many image types.
_full_type_test(img, 2, expected_2, area_opening, connectivity=2)
_full_type_test(img, 4, expected_4, area_opening, connectivity=2)
P, S = max_tree(img, connectivity=2)
_full_type_test(img, 4, expected_4, area_opening, parent=P, tree_traverser=S)
def test_diameter_closing(self):
"Test for Diameter Opening (2 thresholds, all types)"
img = np.array(
[
[97, 95, 93, 92, 91, 90, 90, 90, 91, 92, 93, 95],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
[93, 63, 63, 63, 63, 86, 86, 86, 87, 43, 43, 91],
[92, 89, 88, 86, 85, 85, 84, 85, 85, 43, 43, 89],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[90, 88, 86, 84, 83, 83, 82, 83, 83, 84, 86, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[92, 89, 23, 23, 85, 85, 84, 85, 85, 3, 3, 89],
[93, 91, 23, 23, 87, 86, 86, 86, 87, 88, 3, 91],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
],
dtype=np.uint8,
)
ex2 = np.array(
[
[97, 95, 93, 92, 91, 90, 90, 90, 91, 92, 93, 95],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
[93, 63, 63, 63, 63, 86, 86, 86, 87, 43, 43, 91],
[92, 89, 88, 86, 85, 85, 84, 85, 85, 43, 43, 89],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[90, 88, 86, 84, 83, 83, 83, 83, 83, 84, 86, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[92, 89, 23, 23, 85, 85, 84, 85, 85, 3, 3, 89],
[93, 91, 23, 23, 87, 86, 86, 86, 87, 88, 3, 91],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
],
dtype=np.uint8,
)
ex4 = np.array(
[
[97, 95, 93, 92, 91, 90, 90, 90, 91, 92, 93, 95],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
[93, 63, 63, 63, 63, 86, 86, 86, 87, 84, 84, 91],
[92, 89, 88, 86, 85, 85, 84, 85, 85, 84, 84, 89],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[90, 88, 86, 84, 83, 83, 83, 83, 83, 84, 86, 88],
[90, 88, 86, 85, 84, 83, 83, 83, 84, 85, 86, 88],
[91, 88, 87, 85, 84, 84, 83, 84, 84, 85, 87, 88],
[92, 89, 84, 84, 85, 85, 84, 85, 85, 84, 84, 89],
[93, 91, 84, 84, 87, 86, 86, 86, 87, 88, 84, 91],
[95, 93, 91, 89, 88, 88, 88, 88, 88, 89, 91, 93],
],
dtype=np.uint8,
)
# _full_type_test makes a test with many image types.
_full_type_test(img, 2, ex2, diameter_closing, connectivity=2)
_full_type_test(img, 4, ex4, diameter_closing, connectivity=2)
P, S = max_tree(invert(img), connectivity=2)
_full_type_test(img, 4, ex4, diameter_opening, parent=P, tree_traverser=S)
def test_diameter_opening(self):
"Test for Diameter Opening (2 thresholds, all types)"
img = np.array(
[
[5, 7, 9, 11, 12, 12, 12, 12, 12, 11, 9, 7],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
[9, 40, 40, 40, 40, 16, 16, 16, 16, 60, 60, 11],
[11, 13, 15, 16, 17, 18, 18, 18, 17, 60, 60, 13],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 15, 16, 18, 19, 19, 20, 19, 19, 18, 16, 15],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[11, 13, 80, 80, 17, 18, 18, 18, 17, 100, 100, 13],
[9, 11, 80, 80, 16, 16, 16, 16, 16, 15, 100, 11],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
]
)
ex2 = np.array(
[
[5, 7, 9, 11, 12, 12, 12, 12, 12, 11, 9, 7],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
[9, 40, 40, 40, 40, 16, 16, 16, 16, 60, 60, 11],
[11, 13, 15, 16, 17, 18, 18, 18, 17, 60, 60, 13],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 15, 16, 18, 19, 19, 19, 19, 19, 18, 16, 15],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[11, 13, 80, 80, 17, 18, 18, 18, 17, 100, 100, 13],
[9, 11, 80, 80, 16, 16, 16, 16, 16, 15, 100, 11],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
]
)
ex4 = np.array(
[
[5, 7, 9, 11, 12, 12, 12, 12, 12, 11, 9, 7],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
[9, 40, 40, 40, 40, 16, 16, 16, 16, 18, 18, 11],
[11, 13, 15, 16, 17, 18, 18, 18, 17, 18, 18, 13],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 15, 16, 18, 19, 19, 19, 19, 19, 18, 16, 15],
[12, 14, 16, 18, 19, 19, 19, 19, 19, 18, 16, 14],
[12, 14, 16, 17, 18, 19, 19, 19, 18, 17, 16, 14],
[11, 13, 18, 18, 17, 18, 18, 18, 17, 18, 18, 13],
[9, 11, 18, 18, 16, 16, 16, 16, 16, 15, 18, 11],
[7, 10, 11, 13, 14, 14, 15, 14, 14, 13, 11, 10],
]
)
# _full_type_test makes a test with many image types.
_full_type_test(img, 2, ex2, diameter_opening, connectivity=2)
_full_type_test(img, 4, ex4, diameter_opening, connectivity=2)
P, S = max_tree(img, connectivity=2)
_full_type_test(img, 4, ex4, diameter_opening, parent=P, tree_traverser=S)
def test_local_maxima(self):
"local maxima for various data types"
data = np.array(
[
[10, 11, 13, 14, 14, 15, 14, 14, 13, 11],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13],
[13, 15, 40, 40, 18, 18, 18, 60, 60, 15],
[14, 16, 40, 40, 19, 19, 19, 60, 60, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[15, 16, 18, 19, 19, 20, 19, 19, 18, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[14, 16, 80, 80, 19, 19, 19, 100, 100, 16],
[13, 15, 80, 80, 18, 18, 18, 100, 100, 15],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13],
],
dtype=np.uint8,
)
expected_result = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
dtype=np.uint64,
)
for dtype in [np.uint8, np.uint64, np.int8, np.int64]:
test_data = data.astype(dtype)
out = max_tree_local_maxima(test_data, connectivity=1)
out_bin = out > 0
assert_array_equal(expected_result, out_bin)
assert out.dtype == expected_result.dtype
assert np.max(out) == 5
P, S = max_tree(test_data)
out = max_tree_local_maxima(test_data, parent=P, tree_traverser=S)
assert_array_equal(expected_result, out_bin)
assert out.dtype == expected_result.dtype
assert np.max(out) == 5
def test_extrema_float(self):
"specific tests for float type"
data = np.array(
[
[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14, 0.14, 0.13, 0.11],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13],
[0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18, 0.60, 0.60, 0.15],
[0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19, 0.60, 0.60, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19, 4.0, 1.0, 0.16],
[0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18, 1.0, 1.0, 0.15],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13],
],
dtype=np.float32,
)
expected_result = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
# test for local maxima
out = max_tree_local_maxima(data, connectivity=1)
out_bin = out > 0
assert_array_equal(expected_result, out_bin)
assert np.max(out) == 6
def test_3d(self):
"""tests the detection of maxima in 3D."""
img = np.zeros((8, 8, 8), dtype=np.uint8)
local_maxima = np.zeros((8, 8, 8), dtype=np.uint64)
# first maximum: only one pixel
img[1, 1:3, 1:3] = 100
img[2, 2, 2] = 200
img[3, 1:3, 1:3] = 100
local_maxima[2, 2, 2] = 1
# second maximum: three pixels in z-direction
img[5:8, 1, 1] = 200
local_maxima[5:8, 1, 1] = 1
# third: two maxima in 0 and 3.
img[0, 5:8, 5:8] = 200
img[1, 6, 6] = 100
img[2, 5:7, 5:7] = 200
img[0:3, 5:8, 5:8] += 50
local_maxima[0, 5:8, 5:8] = 1
local_maxima[2, 5:7, 5:7] = 1
# four : one maximum in the corner of the square
img[6:8, 6:8, 6:8] = 200
img[7, 7, 7] = 255
local_maxima[7, 7, 7] = 1
out = max_tree_local_maxima(img)
out_bin = out > 0
assert_array_equal(local_maxima, out_bin)
assert np.max(out) == 5
|
scikit-imageREPO_NAMEscikit-imagePATH_START.@scikit-image_extracted@scikit-image-main@skimage@morphology@tests@test_max_tree.py@.PATH_END.py
|
{
"filename": "_hoverlabel.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/surface/_hoverlabel.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Hoverlabel(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "surface"
_path_str = "surface.hoverlabel"
_valid_props = {
"align",
"alignsrc",
"bgcolor",
"bgcolorsrc",
"bordercolor",
"bordercolorsrc",
"font",
"namelength",
"namelengthsrc",
}
# align
# -----
@property
def align(self):
"""
Sets the horizontal alignment of the text content within hover
label box. Has an effect only if the hover label text spans
more two or more lines
The 'align' property is an enumeration that may be specified as:
- One of the following enumeration values:
['left', 'right', 'auto']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["align"]
@align.setter
def align(self, val):
self["align"] = val
# alignsrc
# --------
@property
def alignsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `align`.
The 'alignsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["alignsrc"]
@alignsrc.setter
def alignsrc(self, val):
self["alignsrc"] = val
# bgcolor
# -------
@property
def bgcolor(self):
"""
Sets the background color of the hover labels for this trace
The 'bgcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["bgcolor"]
@bgcolor.setter
def bgcolor(self, val):
self["bgcolor"] = val
# bgcolorsrc
# ----------
@property
def bgcolorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `bgcolor`.
The 'bgcolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["bgcolorsrc"]
@bgcolorsrc.setter
def bgcolorsrc(self, val):
self["bgcolorsrc"] = val
# bordercolor
# -----------
@property
def bordercolor(self):
"""
Sets the border color of the hover labels for this trace.
The 'bordercolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["bordercolor"]
@bordercolor.setter
def bordercolor(self, val):
self["bordercolor"] = val
# bordercolorsrc
# --------------
@property
def bordercolorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
The 'bordercolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["bordercolorsrc"]
@bordercolorsrc.setter
def bordercolorsrc(self, val):
self["bordercolorsrc"] = val
# font
# ----
@property
def font(self):
"""
Sets the font used in hover labels.
The 'font' property is an instance of Font
that may be specified as:
- An instance of :class:`plotly.graph_objs.surface.hoverlabel.Font`
- A dict of string/value properties that will be passed
to the Font constructor
Supported dict properties:
color
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for `family`.
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud
for `lineposition`.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
shadowsrc
Sets the source reference on Chart Studio Cloud
for `shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
style
Sets whether a font should be styled with a
normal or italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud
for `style`.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud
for `textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud
for `variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud
for `weight`.
Returns
-------
plotly.graph_objs.surface.hoverlabel.Font
"""
return self["font"]
@font.setter
def font(self, val):
self["font"] = val
# namelength
# ----------
@property
def namelength(self):
"""
Sets the default length (in number of characters) of the trace
name in the hover labels for all traces. -1 shows the whole
name regardless of length. 0-3 shows the first 0-3 characters,
and an integer >3 will show the whole name if it is less than
that many characters, but if it is longer, will truncate to
`namelength - 3` characters and add an ellipsis.
The 'namelength' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [-1, 9223372036854775807]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|numpy.ndarray
"""
return self["namelength"]
@namelength.setter
def namelength(self, val):
self["namelength"] = val
# namelengthsrc
# -------------
@property
def namelengthsrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`namelength`.
The 'namelengthsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["namelengthsrc"]
@namelengthsrc.setter
def namelengthsrc(self, val):
self["namelengthsrc"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on Chart Studio Cloud for
`align`.
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud for
`bgcolor`.
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud for
`namelength`.
"""
def __init__(
self,
arg=None,
align=None,
alignsrc=None,
bgcolor=None,
bgcolorsrc=None,
bordercolor=None,
bordercolorsrc=None,
font=None,
namelength=None,
namelengthsrc=None,
**kwargs,
):
"""
Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.surface.Hoverlabel`
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on Chart Studio Cloud for
`align`.
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud for
`bgcolor`.
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud for
`bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud for
`namelength`.
Returns
-------
Hoverlabel
"""
super(Hoverlabel, self).__init__("hoverlabel")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.surface.Hoverlabel
constructor must be a dict or
an instance of :class:`plotly.graph_objs.surface.Hoverlabel`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("align", None)
_v = align if align is not None else _v
if _v is not None:
self["align"] = _v
_v = arg.pop("alignsrc", None)
_v = alignsrc if alignsrc is not None else _v
if _v is not None:
self["alignsrc"] = _v
_v = arg.pop("bgcolor", None)
_v = bgcolor if bgcolor is not None else _v
if _v is not None:
self["bgcolor"] = _v
_v = arg.pop("bgcolorsrc", None)
_v = bgcolorsrc if bgcolorsrc is not None else _v
if _v is not None:
self["bgcolorsrc"] = _v
_v = arg.pop("bordercolor", None)
_v = bordercolor if bordercolor is not None else _v
if _v is not None:
self["bordercolor"] = _v
_v = arg.pop("bordercolorsrc", None)
_v = bordercolorsrc if bordercolorsrc is not None else _v
if _v is not None:
self["bordercolorsrc"] = _v
_v = arg.pop("font", None)
_v = font if font is not None else _v
if _v is not None:
self["font"] = _v
_v = arg.pop("namelength", None)
_v = namelength if namelength is not None else _v
if _v is not None:
self["namelength"] = _v
_v = arg.pop("namelengthsrc", None)
_v = namelengthsrc if namelengthsrc is not None else _v
if _v is not None:
self["namelengthsrc"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@surface@_hoverlabel.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/densitymap/colorbar/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._yref import YrefValidator
from ._ypad import YpadValidator
from ._yanchor import YanchorValidator
from ._y import YValidator
from ._xref import XrefValidator
from ._xpad import XpadValidator
from ._xanchor import XanchorValidator
from ._x import XValidator
from ._title import TitleValidator
from ._tickwidth import TickwidthValidator
from ._tickvalssrc import TickvalssrcValidator
from ._tickvals import TickvalsValidator
from ._ticktextsrc import TicktextsrcValidator
from ._ticktext import TicktextValidator
from ._ticksuffix import TicksuffixValidator
from ._ticks import TicksValidator
from ._tickprefix import TickprefixValidator
from ._tickmode import TickmodeValidator
from ._ticklen import TicklenValidator
from ._ticklabelstep import TicklabelstepValidator
from ._ticklabelposition import TicklabelpositionValidator
from ._ticklabeloverflow import TicklabeloverflowValidator
from ._tickformatstopdefaults import TickformatstopdefaultsValidator
from ._tickformatstops import TickformatstopsValidator
from ._tickformat import TickformatValidator
from ._tickfont import TickfontValidator
from ._tickcolor import TickcolorValidator
from ._tickangle import TickangleValidator
from ._tick0 import Tick0Validator
from ._thicknessmode import ThicknessmodeValidator
from ._thickness import ThicknessValidator
from ._showticksuffix import ShowticksuffixValidator
from ._showtickprefix import ShowtickprefixValidator
from ._showticklabels import ShowticklabelsValidator
from ._showexponent import ShowexponentValidator
from ._separatethousands import SeparatethousandsValidator
from ._outlinewidth import OutlinewidthValidator
from ._outlinecolor import OutlinecolorValidator
from ._orientation import OrientationValidator
from ._nticks import NticksValidator
from ._minexponent import MinexponentValidator
from ._lenmode import LenmodeValidator
from ._len import LenValidator
from ._labelalias import LabelaliasValidator
from ._exponentformat import ExponentformatValidator
from ._dtick import DtickValidator
from ._borderwidth import BorderwidthValidator
from ._bordercolor import BordercolorValidator
from ._bgcolor import BgcolorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._yref.YrefValidator",
"._ypad.YpadValidator",
"._yanchor.YanchorValidator",
"._y.YValidator",
"._xref.XrefValidator",
"._xpad.XpadValidator",
"._xanchor.XanchorValidator",
"._x.XValidator",
"._title.TitleValidator",
"._tickwidth.TickwidthValidator",
"._tickvalssrc.TickvalssrcValidator",
"._tickvals.TickvalsValidator",
"._ticktextsrc.TicktextsrcValidator",
"._ticktext.TicktextValidator",
"._ticksuffix.TicksuffixValidator",
"._ticks.TicksValidator",
"._tickprefix.TickprefixValidator",
"._tickmode.TickmodeValidator",
"._ticklen.TicklenValidator",
"._ticklabelstep.TicklabelstepValidator",
"._ticklabelposition.TicklabelpositionValidator",
"._ticklabeloverflow.TicklabeloverflowValidator",
"._tickformatstopdefaults.TickformatstopdefaultsValidator",
"._tickformatstops.TickformatstopsValidator",
"._tickformat.TickformatValidator",
"._tickfont.TickfontValidator",
"._tickcolor.TickcolorValidator",
"._tickangle.TickangleValidator",
"._tick0.Tick0Validator",
"._thicknessmode.ThicknessmodeValidator",
"._thickness.ThicknessValidator",
"._showticksuffix.ShowticksuffixValidator",
"._showtickprefix.ShowtickprefixValidator",
"._showticklabels.ShowticklabelsValidator",
"._showexponent.ShowexponentValidator",
"._separatethousands.SeparatethousandsValidator",
"._outlinewidth.OutlinewidthValidator",
"._outlinecolor.OutlinecolorValidator",
"._orientation.OrientationValidator",
"._nticks.NticksValidator",
"._minexponent.MinexponentValidator",
"._lenmode.LenmodeValidator",
"._len.LenValidator",
"._labelalias.LabelaliasValidator",
"._exponentformat.ExponentformatValidator",
"._dtick.DtickValidator",
"._borderwidth.BorderwidthValidator",
"._bordercolor.BordercolorValidator",
"._bgcolor.BgcolorValidator",
],
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@densitymap@colorbar@__init__.py@.PATH_END.py
|
{
"filename": "export.md",
"repo_name": "ultralytics/ultralytics",
"repo_path": "ultralytics_extracted/ultralytics-main/docs/en/modes/export.md",
"type": "Markdown"
}
|
---
comments: true
description: Learn how to export your YOLO11 model to various formats like ONNX, TensorRT, and CoreML. Achieve maximum compatibility and performance.
keywords: YOLO11, Model Export, ONNX, TensorRT, CoreML, Ultralytics, AI, Machine Learning, Inference, Deployment
---
# Model Export with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
The ultimate goal of training a model is to deploy it for real-world applications. Export mode in Ultralytics YOLO11 offers a versatile range of options for exporting your trained model to different formats, making it deployable across various platforms and devices. This comprehensive guide aims to walk you through the nuances of model exporting, showcasing how to achieve maximum compatibility and performance.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/WbomGeoOT_k?si=aGmuyooWftA0ue9X"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How To Export Custom Trained Ultralytics YOLO Model and Run Live Inference on Webcam.
</p>
## Why Choose YOLO11's Export Mode?
- **Versatility:** Export to multiple formats including ONNX, TensorRT, CoreML, and more.
- **Performance:** Gain up to 5x GPU speedup with TensorRT and 3x CPU speedup with ONNX or OpenVINO.
- **Compatibility:** Make your model universally deployable across numerous hardware and software environments.
- **Ease of Use:** Simple CLI and Python API for quick and straightforward model exporting.
### Key Features of Export Mode
Here are some of the standout functionalities:
- **One-Click Export:** Simple commands for exporting to different formats.
- **Batch Export:** Export batched-inference capable models.
- **Optimized Inference:** Exported models are optimized for quicker inference times.
- **Tutorial Videos:** In-depth guides and tutorials for a smooth exporting experience.
!!! tip
* Export to [ONNX](../integrations/onnx.md) or [OpenVINO](../integrations/openvino.md) for up to 3x CPU speedup.
* Export to [TensorRT](../integrations/tensorrt.md) for up to 5x GPU speedup.
## Usage Examples
Export a YOLO11n model to a different format like ONNX or TensorRT. See the Arguments section below for a full list of export arguments.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
## Arguments
This table details the configurations and options available for exporting YOLO models to different formats. These settings are critical for optimizing the exported model's performance, size, and compatibility across various platforms and environments. Proper configuration ensures that the model is ready for deployment in the intended application with optimal efficiency.
{% include "macros/export-args.md" %}
Adjusting these parameters allows for customization of the export process to fit specific requirements, such as deployment environment, hardware constraints, and performance targets. Selecting the appropriate format and settings is essential for achieving the best balance between model size, speed, and [accuracy](https://www.ultralytics.com/glossary/accuracy).
## Export Formats
Available YOLO11 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n.onnx`. Usage examples are shown for your model after export completes.
{% include "macros/export-table.md" %}
## FAQ
### How do I export a YOLO11 model to ONNX format?
Exporting a YOLO11 model to ONNX format is straightforward with Ultralytics. It provides both Python and CLI methods for exporting models.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
For more details on the process, including advanced options like handling different input sizes, refer to the [ONNX section](../integrations/onnx.md).
### What are the benefits of using TensorRT for model export?
Using TensorRT for model export offers significant performance improvements. YOLO11 models exported to TensorRT can achieve up to a 5x GPU speedup, making it ideal for real-time inference applications.
- **Versatility:** Optimize models for a specific hardware setup.
- **Speed:** Achieve faster inference through advanced optimizations.
- **Compatibility:** Integrate smoothly with NVIDIA hardware.
To learn more about integrating TensorRT, see the [TensorRT integration guide](../integrations/tensorrt.md).
### How do I enable INT8 quantization when exporting my YOLO11 model?
INT8 quantization is an excellent way to compress the model and speed up inference, especially on edge devices. Here's how you can enable INT8 quantization:
!!! example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt") # Load a model
model.export(format="engine", int8=True)
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=engine int8=True # export TensorRT model with INT8 quantization
```
INT8 quantization can be applied to various formats, such as TensorRT and CoreML. More details can be found in the [Export section](../modes/export.md).
### Why is dynamic input size important when exporting models?
Dynamic input size allows the exported model to handle varying image dimensions, providing flexibility and optimizing processing efficiency for different use cases. When exporting to formats like ONNX or TensorRT, enabling dynamic input size ensures that the model can adapt to different input shapes seamlessly.
To enable this feature, use the `dynamic=True` flag during export:
!!! example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.export(format="onnx", dynamic=True)
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx dynamic=True
```
For additional context, refer to the [dynamic input size configuration](#arguments).
### What are the key export arguments to consider for optimizing model performance?
Understanding and configuring export arguments is crucial for optimizing model performance:
- **`format:`** The target format for the exported model (e.g., `onnx`, `torchscript`, `tensorflow`).
- **`imgsz:`** Desired image size for the model input (e.g., `640` or `(height, width)`).
- **`half:`** Enables FP16 quantization, reducing model size and potentially speeding up inference.
- **`optimize:`** Applies specific optimizations for mobile or constrained environments.
- **`int8:`** Enables INT8 quantization, highly beneficial for edge deployments.
For a detailed list and explanations of all the export arguments, visit the [Export Arguments section](#arguments).
|
ultralyticsREPO_NAMEultralyticsPATH_START.@ultralytics_extracted@ultralytics-main@docs@en@modes@export.md@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "lynx-x-ray-observatory/soxs",
"repo_path": "soxs_extracted/soxs-main/setup.py",
"type": "Python"
}
|
#!/usr/bin/env python
import glob
import os
import numpy as np
from setuptools import find_packages, setup
from setuptools.extension import Extension
if os.name == "nt":
std_libs = []
else:
std_libs = ["m"]
scripts = glob.glob("scripts/*")
cython_extensions = [
Extension(
"soxs.lib.broaden_lines",
["soxs/lib/broaden_lines.pyx"],
language="c",
libraries=std_libs,
include_dirs=[np.get_include()],
),
Extension(
"soxs.lib.psf_cdf",
["soxs/lib/psf_cdf.pyx"],
language="c",
libraries=std_libs,
include_dirs=[np.get_include()],
),
]
setup(
packages=find_packages(),
url="https://github.com/lynx-x-ray-observatory/soxs/",
include_package_data=True,
scripts=scripts,
ext_modules=cython_extensions,
)
|
lynx-x-ray-observatoryREPO_NAMEsoxsPATH_START.@soxs_extracted@soxs-main@setup.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "jakevdp/nfft",
"repo_path": "nfft_extracted/nfft-master/README.md",
"type": "Markdown"
}
|
# nfft package
[](https://travis-ci.org/jakevdp/nfft/)[](https://pypi.python.org/pypi/nfft)
[](https://github.com/jakevdp/nfft/blob/master/LICENSE)
The ``nfft`` package is a lightweight implementation of the non-equispaced
fast Fourier transform (NFFT), implemented via numpy and scipy and released
under the MIT license.
For information about the NFFT algorithm, see the paper
[*Using NFFT 3 – a software library for various nonequispaced fast Fourier transforms*](http://dl.acm.org/citation.cfm?id=1555388).
The ``nfft`` package achieves comparable performance to the C package
described in that paper, without any customized compiled code. Rather, it
makes use of the computational building blocks available in NumPy and SciPy.
For a discussion of the algorithm and this implementation, see the
[Implementation Walkthrough](notebooks/ImplementationWalkthrough.ipynb)
notebook.
## About
The ``nfft`` package implements one-dimensional versions of the forward and
adjoint non-equispaced fast Fourier transforms;
The forward transform:
And the adjoint transform:
In both cases, the wavenumbers *k* are on a regular grid from -N/2 to N/2,
while the data values *x_j* are irregularly spaced between -1/2 and 1/2.
The direct and fast version of these algorithms are implemented in the following
functions:
- ``nfft.ndft``: direct forward non-equispaced Fourier transform
- ``nfft.nfft``: fast forward non-equispaced Fourier transform
- ``nfft.ndft_adjoint``: direct adjoint non-equispaced Fourier transform
- ``nfft.nfft_adjoint``: fast adjoint non-equispaced Fourier transform
### Computational complexity
The direct version of each transform has a computational complexity of
approximately *O[NM]*, while the NFFT has a computational complexity of
approximately *O[N log(N) + M log(1/ϵ)]*, where *ϵ* is the desired precision
of the result. In the current implementation, memory requirements scale as
approximately *O[N + M log(1/ϵ)]*.
### Comparison to pynfft
Another option for computing the NFFT in Python is to use the
[pynfft](https://github.com/ghisvail/pyNFFT/) package, which provides a
Python wrapper to the C library referenced in the above paper.
The advantage of ``pynfft`` is that, compared to ``nfft``, it provides a more
complete set of routines, including multi-dimensional NFFTs, several related
extensions, and a range of computing strategies.
The disadvantage is that ``pynfft`` is GPL-licensed (and thus can't be used
in much of the more permissively licensed Python scientific world), and has
a much more complicated set of dependencies.
Performance-wise, ``nfft`` and ``pynfft`` are comparable, with the
implementation within ``nfft`` package being up to a factor of 2 faster
in most cases of interest (see [Benchmarks.ipynb](notebooks/Benchmarks.ipynb)
for some simple benchmarks).
If you're curious about the implementation and how ``nfft`` attains such
performance without a custom compiled extension, see the [Implementation
Walkthrough](notebooks/ImplementationWalkthrough.ipynb) notebook.
### Basic Usage
```python
import numpy as np
from nfft import nfft
# define evaluation points
x = -0.5 + np.random.rand(1000)
# define Fourier coefficients
N = 10000
k = - N // 2 + np.arange(N)
f_k = np.random.randn(N)
# non-equispaced fast Fourier transform
f = nfft(x, f_k)
```
For some more examples, see the notebooks in the [notebooks](notebooks)
directory.
## Installation
The ``nfft`` package can be installed directly from the Python Package Index:
```
$ pip install nfft
```
Dependencies are [numpy](http://www.numpy.org), [scipy](http://www.scipy.org),
and [pytest](http://www.pytest.org), and the package is tested in Python
versions 2.7. 3.5, and 3.6.
## Testing
Unit tests can be run using [pytest](http://pytest.org):
```
$ pytest --pyargs nfft
```
## License
This code is released under the [MIT License](LICENSE). For more information,
see the [Open Source Initiative](https://opensource.org/licenses/MIT)
## Support
Development of this package is supported by the
[UW eScience Institute](http://escience.washington.edu/),
with funding from
the [Gordon & Betty Moore Foundation](https://www.moore.org/),
the [Alfred P. Sloan Foundation](https://sloan.org/),
and the [Washington Research Foundation](http://www.wrfseattle.org/)
|
jakevdpREPO_NAMEnfftPATH_START.@nfft_extracted@nfft-master@README.md@.PATH_END.py
|
{
"filename": "time_gaussian.py",
"repo_name": "GalSim-developers/GalSim",
"repo_path": "GalSim_extracted/GalSim-main/devel/external/time_photon_shooting/time_gaussian.py",
"type": "Python"
}
|
# Copyright (c) 2012-2023 by the GalSim developers team on GitHub
# https://github.com/GalSim-developers
#
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# https://github.com/GalSim-developers/GalSim
#
# GalSim is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
#
"""An example script to evaluate timing for shooting photons through a Gaussian distribution with
the GalSim library.
"""
import sys
import logging
import time
# This machinery lets us run Python examples even though they aren't positioned
# properly to find galsim as a package in the current directory.
try:
import galsim
except ImportError:
path, filename = os.path.split(__file__)
sys.path.append(os.path.abspath(os.path.join(path, "..")))
import galsim
NIMAGES = 100
NPHOTONS = 500000 # Number of photons per draw
PIXEL_SCALE = 1.0 # arcsec (size units in input catalog are pixels)
IMAGE_XMAX = 64 # pixels
IMAGE_YMAX = 64 # pixels
GAUSSIAN_SIGMA = 5.
RANDOM_SEED = 3231139901
def time_gaussian_shoot():
"""Shoot photons through a Gaussian profile recording times for comparison between USE_COS_SIN
method in SBProfile.cpp and the unit circle rejection method.
"""
logger = logging.getLogger("time_gaussian")
# Initialize the random number generator we will be using.
rng = galsim.UniformDeviate(RANDOM_SEED)
# Build the image for drawing the galaxy into
image = galsim.ImageF(IMAGE_XMAX, IMAGE_YMAX, PIXEL_SCALE)
# Start the timer
t1 = time.time()
for i in range(NIMAGES):
# Build the galaxy
gal = galsim.Gaussian(sigma=GAUSSIAN_SIGMA)
# Build the image for drawing the galaxy into
image = galsim.ImageF(IMAGE_XMAX, IMAGE_YMAX)
# Shoot the galaxy
gal.drawShoot(image, NPHOTONS)
# Get the time
t2 = time.time()
logger.info(
'time_gaussian_shoot: NIMAGES = %d, NPHOTONS = %d, total time = %f sec', NIMAGES,
NPHOTONS, t2-t1
)
if __name__ == "__main__":
logging.basicConfig(
format="%(message)s",
level=logging.DEBUG,
stream=sys.stdout
)
time_gaussian_shoot()
|
GalSim-developersREPO_NAMEGalSimPATH_START.@GalSim_extracted@GalSim-main@devel@external@time_photon_shooting@time_gaussian.py@.PATH_END.py
|
{
"filename": "hashutils.py",
"repo_name": "timothydmorton/VESPA",
"repo_path": "VESPA_extracted/VESPA-master/vespa/hashutils.py",
"type": "Python"
}
|
try:
import numpy as np
import hashlib
from hashlib import sha1
from numpy import all, array, uint8
except ImportError:
np, hashlib, sha1 = (None, None, None)
all, array, uint8 = (None, None, None)
class hashable(object):
r'''Hashable wrapper for ndarray objects.
Instances of ndarray are not hashable, meaning they cannot be added to
sets, nor used as keys in dictionaries. This is by design - ndarray
objects are mutable, and therefore cannot reliably implement the
__hash__() method.
The hashable class allows a way around this limitation. It implements
the required methods for hashable objects in terms of an encapsulated
ndarray object. This can be either a copied instance (which is safer)
or the original object (which requires the user to be careful enough
not to modify it).
This class taken from `here <http://stackoverflow.com/questions/1939228/constructing-a-python-set-from-a-numpy-matrix/5173201#5173201>`_; edited only slightly.
'''
def __init__(self, wrapped, tight=False):
r'''Creates a new hashable object encapsulating an ndarray.
wrapped
The wrapped ndarray.
tight
Optional. If True, a copy of the input ndaray is created.
Defaults to False.
'''
self.__tight = tight
self.__wrapped = array(wrapped) if tight else wrapped
#self.__hash = int(sha1(wrapped.view(uint8)).hexdigest(), 16)
self.__hash = int(sha1(np.ascontiguousarray(wrapped)).hexdigest(), 16)
def __eq__(self, other):
return all(self.__wrapped == other.__wrapped)
def __hash__(self):
return self.__hash
def unwrap(self):
r'''Returns the encapsulated ndarray.
If the wrapper is "tight", a copy of the encapsulated ndarray is
returned. Otherwise, the encapsulated ndarray itself is returned.
'''
if self.__tight:
return array(self.__wrapped)
return self.__wrapped
def hasharray(arr):
"""
Hashes array-like object (except DataFrame)
"""
#return hash(hashlib.sha1(np.ascontiguousarray(arr)).hexdigest())
return hash(hashable(np.array(arr)))
def hashdf(df):
"""hashes a pandas dataframe, forcing values to float
"""
return hasharray(df.values.astype(float))
def hashcombine(*xs):
"""
Combines multiple hashes using xor
"""
k = 0
for x in xs:
k ^= hash(x)
k ^= hash(xs)
return k
def hashdict(d):
"""Hash a dictionary
"""
k = 0
for key,val in d.items():
k ^= hash(key) ^ hash(val)
return k
|
timothydmortonREPO_NAMEVESPAPATH_START.@VESPA_extracted@VESPA-master@vespa@hashutils.py@.PATH_END.py
|
{
"filename": "2-particle-NBody-2d.py",
"repo_name": "LLNL/spheral",
"repo_path": "spheral_extracted/spheral-main/tests/functional/Gravity/2-particle-NBody-2d.py",
"type": "Python"
}
|
#-------------------------------------------------------------------------------
# Set up a pair of equal mass N-body points in a simple circular orbit of each
# other.
#-------------------------------------------------------------------------------
from Spheral2d import *
from SpheralTestUtilities import *
from SpheralGnuPlotUtilities import *
from NodeHistory import *
from SpheralVisitDump import dumpPhysicsState
from math import *
print("3-D N-Body Gravity test -- two particle problem")
#-------------------------------------------------------------------------------
# Generic problem parameters
#-------------------------------------------------------------------------------
commandLine(
# Initial particle stuff
r0 = 1.0, # (m) Start stuff out at 1 m from center of mass
m0 = 1.0e11, # (kg) particle mass
plummerLength = 1.0e-10, # (m) Plummer softening scale
opening = 0.5, # (dimensionless, OctTreeGravity) opening parameter for tree walk
fdt = 0.1, # (dimensionless, OctTreeGravity) timestep multiplier
# Problem control
steps = None,
numOrbits = 2, # How many orbits do we want to follow?
# Output
dataDir = "two-particle-2d",
baseName = "2_particle_nbody",
restoreCycle = None,
restartStep = 100,
numViz = 100,
)
# Compute the velocity necessary for a circular orbit.
G = MKS().G
v0 = 0.25*G*m0
orbitTime = 2.0*pi*r0/v0
print("Calcualted (velocity, orbit time) = (%g, %g)" % (v0, orbitTime))
# Miscellaneous problem control parameters.
dt = orbitTime / 180
goalTime = orbitTime * numOrbits
dtMin, dtMax = dt, dt # 0.1*dt, 100.0*dt
dtGrowth = 2.0
maxSteps = None
statsStep = 10
smoothIters = 0
vizTime = goalTime / numViz
restartDir = os.path.join(dataDir, "restarts")
visitDir = os.path.join(dataDir, "visit")
restartBaseName = os.path.join(restartDir, baseName + "_restart")
#-------------------------------------------------------------------------------
# Check if the necessary output directories exist. If not, create them.
#-------------------------------------------------------------------------------
import os, sys
if mpi.rank == 0:
if not os.path.exists(restartDir):
os.makedirs(restartDir)
if not os.path.exists(visitDir):
os.makedirs(visitDir)
mpi.barrier()
#-------------------------------------------------------------------------------
# For now we have set up a fluid node list, even though this is collisionless
# problem. Fix at some point!
# In the meantime, set up the hydro objects this script isn't really going to
# need.
#-------------------------------------------------------------------------------
WT = TableKernel(BSplineKernel(), 1000)
eos = GammaLawGasMKS(gamma = 5.0/3.0, mu = 1.0)
#-------------------------------------------------------------------------------
# Make the NodeList, and set our initial properties.
#-------------------------------------------------------------------------------
nodes = makeFluidNodeList("nodes", eos,
numInternal = 2,
xmin = Vector(-100*r0, -100*r0),
xmax = Vector( 100*r0, 100*r0),
hmin = 1e-10,
hmax = 1.0)
mass = nodes.mass()
pos = nodes.positions()
vel = nodes.velocity()
mass[0] = m0
mass[1] = m0
pos[0] = Vector(-r0, 0.0)
pos[1] = Vector( r0, 0.0)
vel[0] = Vector(0.0, -v0)
vel[1] = Vector(0.0, v0)
# These are fluid variables we shouldn't need. Just set them to valid values.
H = nodes.Hfield()
rho = nodes.massDensity()
H[0] = 1.0/(1.0e-3*r0) * SymTensor.one
H[1] = 1.0/(1.0e-3*r0) * SymTensor.one
rho[0] = 1.0
rho[1] = 1.0
#-------------------------------------------------------------------------------
# DataBase
#-------------------------------------------------------------------------------
db = DataBase()
db.appendNodeList(nodes)
#-------------------------------------------------------------------------------
# Gimme gravity.
#-------------------------------------------------------------------------------
gravity = QuadTreeGravity(G = G,
softeningLength = plummerLength,
opening = opening,
ftimestep = fdt)
#-------------------------------------------------------------------------------
# Construct a time integrator.
#-------------------------------------------------------------------------------
integrator = SynchronousRK2Integrator(db)
integrator.appendPhysicsPackage(gravity)
integrator.lastDt = 1e-10 # seconds
if dtMin:
integrator.dtMin = dtMin
if dtMax:
integrator.dtMax = dtMax
integrator.dtGrowth = dtGrowth
#-------------------------------------------------------------------------------
# Build the problem controller to follow the problem evolution.
#-------------------------------------------------------------------------------
control = SpheralController(integrator, WT,
statsStep = statsStep,
restartStep = restartStep,
restartBaseName = restartBaseName,
vizBaseName = os.path.join(visitDir, baseName),
vizTime = vizTime,
vizMethod = dumpPhysicsState)
#-------------------------------------------------------------------------------
# Build a diagnostic to maintain the history of our points.
#-------------------------------------------------------------------------------
def sampleMethod(nodes, indices):
m = nodes.mass()
pos = nodes.positions()
vel = nodes.velocity()
assert nodes.numInternalNodes == 2
return (m[0], pos[0].x, pos[0].y, vel[0].x, vel[0].y,
m[1], pos[1].x, pos[1].y, vel[1].x, vel[1].y)
sampleNodes = [0, 1] # We're going to sample both of our nodes!
history = NodeHistory(nodes, sampleNodes, sampleMethod,
os.path.join(dataDir, "node_history.txt"),
header = "# Orbit history of a 2 earth (no sun) system.",
labels = ("m1", "x1", "y1", "vx1", "vy1",
"m2", "x2", "y2", "vx2", "vy2"))
control.appendPeriodicTimeWork(history.sample, vizTime)
#-------------------------------------------------------------------------------
# If we're restarting, read in the restart file.
#-------------------------------------------------------------------------------
if restoreCycle:
control.loadRestartFile(restoreCycle)
#-------------------------------------------------------------------------------
# Advance to the end time.
#-------------------------------------------------------------------------------
if not steps is None:
control.step(steps)
else:
control.advance(goalTime)
# Plot the final state.
x1 = [stuff[1] for stuff in history.sampleHistory]
y1 = [stuff[2] for stuff in history.sampleHistory]
x2 = [stuff[6] for stuff in history.sampleHistory]
y2 = [stuff[7] for stuff in history.sampleHistory]
import Gnuplot
gdata1 = Gnuplot.Data(x1, y1,
with_ = 'linespoints ps 3',
title = 'Particle 1')
gdata2 = Gnuplot.Data(x2, y2,
with_ = 'linespoints ps 3',
title = 'Particle 2')
plot = Gnuplot.Gnuplot()
plot.plot(gdata1)
plot.replot(gdata2)
plot('set size square; set xrange [-1.1:1.1]; set yrange [-1.1:1.1]')
plot.xlabel = 'x'
plot.ylabel = 'y'
plot.refresh()
|
LLNLREPO_NAMEspheralPATH_START.@spheral_extracted@spheral-main@tests@functional@Gravity@2-particle-NBody-2d.py@.PATH_END.py
|
{
"filename": "_size.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/densitymap/hoverlabel/font/_size.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="size", parent_name="densitymap.hoverlabel.font", **kwargs
):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@densitymap@hoverlabel@font@_size.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/matplotlib/py3/matplotlib/style/__init__.py",
"type": "Python"
}
|
from .core import available, context, library, reload_library, use
__all__ = ["available", "context", "library", "reload_library", "use"]
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@matplotlib@py3@matplotlib@style@__init__.py@.PATH_END.py
|
{
"filename": "_cmid.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/bar/marker/_cmid.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CmidValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmid", parent_name="bar.marker", **kwargs):
super(CmidValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {}),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@bar@marker@_cmid.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatter3d/hoverlabel/font/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="textcase", parent_name="scatter3d.hoverlabel.font", **kwargs
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatter3d@hoverlabel@font@_textcase.py@.PATH_END.py
|
{
"filename": "deprecation_test.py",
"repo_name": "jax-ml/jax",
"repo_path": "jax_extracted/jax-main/tests/deprecation_test.py",
"type": "Python"
}
|
# Copyright 2022 The JAX Authors.
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from absl.testing import absltest
from jax._src import deprecations
from jax._src import test_util as jtu
from jax._src.internal_test_util import deprecation_module as m
class DeprecationTest(absltest.TestCase):
def testModuleDeprecation(self):
with warnings.catch_warnings():
warnings.simplefilter("error")
self.assertEqual(m.x, 42)
with self.assertWarnsRegex(DeprecationWarning, "Please use x"):
self.assertEqual(m.y, 101)
with self.assertRaisesRegex(AttributeError, "Please do not use z"):
_ = m.z
with self.assertRaisesRegex(AttributeError,
"module .* has no attribute 'w'"):
_ = m.w
def testNamedDeprecation(self):
some_unique_id = "some-unique-id"
try:
deprecations.register(some_unique_id)
self.assertFalse(deprecations.is_accelerated(some_unique_id))
deprecations.accelerate(some_unique_id)
self.assertTrue(deprecations.is_accelerated(some_unique_id))
finally:
deprecations.unregister(some_unique_id)
msg = f"deprecation_id={some_unique_id!r} not registered"
with self.assertRaisesRegex(ValueError, msg):
deprecations.accelerate(some_unique_id)
with self.assertRaisesRegex(ValueError, msg):
deprecations.is_accelerated(some_unique_id)
with self.assertRaisesRegex(ValueError, msg):
deprecations.unregister(some_unique_id)
if __name__ == "__main__":
absltest.main(testLoader=jtu.JaxTestLoader())
|
jax-mlREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@tests@deprecation_test.py@.PATH_END.py
|
{
"filename": "hello-world.py",
"repo_name": "OxfordSKA/OSKAR",
"repo_path": "OSKAR_extracted/OSKAR-master/docs/python/hello-world.py",
"type": "Python"
}
|
#!/usr/bin/env python3
"""Script to run a simple test example of an OSKAR simulation."""
import matplotlib
matplotlib.use("Agg")
# pylint: disable=wrong-import-position
import matplotlib.pyplot as plt
import numpy
import oskar
# Basic settings. (Note that the sky model is set up later.)
params = {
"simulator": {
"use_gpus": False
},
"observation" : {
"num_channels": 3,
"start_frequency_hz": 100e6,
"frequency_inc_hz": 20e6,
"phase_centre_ra_deg": 20,
"phase_centre_dec_deg": -30,
"num_time_steps": 24,
"start_time_utc": "01-01-2000 12:00:00.000",
"length": "12:00:00.000"
},
"telescope": {
"input_directory": "telescope.tm"
},
"interferometer": {
"oskar_vis_filename": "example.vis",
"ms_filename": "",
"channel_bandwidth_hz": 1e6,
"time_average_sec": 10
}
}
settings = oskar.SettingsTree("oskar_sim_interferometer")
settings.from_dict(params)
# Set the numerical precision to use.
precision = "single"
if precision == "single":
settings["simulator/double_precision"] = False
# Create a sky model containing three sources from a numpy array.
sky_data = numpy.array([
[20.0, -30.0, 1, 0, 0, 0, 100.0e6, -0.7, 0.0, 0, 0, 0],
[20.0, -30.5, 3, 2, 2, 0, 100.0e6, -0.7, 0.0, 600, 50, 45],
[20.5, -30.5, 3, 0, 0, 2, 100.0e6, -0.7, 0.0, 700, 10, -10]])
sky = oskar.Sky.from_array(sky_data, precision) # Pass precision here.
# Set the sky model and run the simulation.
sim = oskar.Interferometer(settings=settings)
sim.set_sky_model(sky)
sim.run()
# Make an image 4 degrees across and return it to Python.
# (It will also be saved with the filename "example_I.fits".)
imager = oskar.Imager(precision)
imager.set(fov_deg=4, image_size=512)
imager.set(input_file="example.vis", output_root="example")
output = imager.run(return_images=1)
image = output["images"][0]
# Render the image using matplotlib and save it as a PNG file.
im = plt.imshow(image, cmap="jet")
plt.gca().invert_yaxis()
plt.colorbar(im)
plt.savefig("%s.png" % imager.output_root)
plt.close("all")
|
OxfordSKAREPO_NAMEOSKARPATH_START.@OSKAR_extracted@OSKAR-master@docs@python@hello-world.py@.PATH_END.py
|
{
"filename": "models_architecture.py",
"repo_name": "zivmaaya/NeuralCMS",
"repo_path": "NeuralCMS_extracted/NeuralCMS-main/models_architecture.py",
"type": "Python"
}
|
import torch
import torch.nn as nn
class FCNN(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_1 = 2 * 512
self.hidden_2 = 2 * 256
self.hidden_3 = 2 * 128
self.hidden_4 = 2 * 64
self.seq = nn.Sequential(nn.Linear(self.input_size, self.hidden_1, bias=True),
nn.ReLU(),
nn.Linear(self.hidden_1, self.hidden_2, bias=True),
nn.ReLU(),
nn.Linear(self.hidden_2, self.hidden_3, bias=True),
nn.ReLU(),
nn.Linear(self.hidden_3, self.hidden_4, bias=True),
nn.ReLU(),
nn.Linear(self.hidden_4, self.output_size, bias=True),
)
def forward(self, x):
out = self.seq(x)
return out
class SharedNN(nn.Module):
def __init__(self, input_size):
super().__init__()
self.input_size = input_size
self.hidden_size = 1024
self.shared = nn.Sequential(nn.Linear(self.input_size, self.hidden_size, bias=True), nn.ReLU())
self.private1 = nn.Sequential(nn.Linear(self.hidden_size, self.hidden_size, bias=True), nn.ReLU())
self.private2 = nn.Sequential(nn.Linear(self.hidden_size, self.hidden_size, bias=True), nn.ReLU())
self.private3 = nn.Sequential(nn.Linear(self.hidden_size, self.hidden_size, bias=True), nn.ReLU())
self.private4 = nn.Sequential(nn.Linear(self.hidden_size, self.hidden_size, bias=True), nn.ReLU())
self.private5 = nn.Sequential(nn.Linear(self.hidden_size, self.hidden_size, bias=True), nn.ReLU())
self.private1_1 = nn.Linear(self.hidden_size, 1, bias=True)
self.private2_1 = nn.Linear(self.hidden_size, 1, bias=True)
self.private3_1 = nn.Linear(self.hidden_size, 1, bias=True)
self.private4_1 = nn.Linear(self.hidden_size, 1, bias=True)
self.private5_1 = nn.Linear(self.hidden_size, 1, bias=True)
def forward(self, x):
out = self.shared(x)
out1 = self.private1(out)
out2 = self.private2(out)
out3 = self.private3(out)
out4 = self.private4(out)
out5 = self.private5(out)
out1 = self.private1_1(out1)
out2 = self.private2_1(out2)
out3 = self.private3_1(out3)
out4 = self.private4_1(out4)
out5 = self.private5_1(out5)
out = torch.stack([out1, out2, out3, out4, out5], 0)
out = torch.transpose(out, 0, 1)
dim1 = out.size(dim=0)
dim2 = out.size(dim=1)
out = torch.reshape(out, (dim1, dim2))
return out
|
zivmaayaREPO_NAMENeuralCMSPATH_START.@NeuralCMS_extracted@NeuralCMS-main@models_architecture.py@.PATH_END.py
|
{
"filename": "glyph.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/docs/source/mayavi/auto/glyph.py",
"type": "Python"
}
|
#!/usr/bin/env python
"""
This script demonstrates using the Mayavi core API to add a VectorCutPlane,
split the pipeline using a MaskPoints filter and then view the filtered data
with the Glyph module.
"""
# Author: Prabhu Ramachandran <prabhu_r@users.sf.net>
# Copyright (c) 2005-2020, Enthought, Inc.
# License: BSD Style.
# Standard library imports
from os.path import join, abspath, dirname
# Enthought library imports
from mayavi.scripts import mayavi2
from mayavi.sources.vtk_xml_file_reader import VTKXMLFileReader
from mayavi.modules.outline import Outline
from mayavi.modules.glyph import Glyph
from mayavi.modules.vector_cut_plane import VectorCutPlane
from mayavi.filters.mask_points import MaskPoints
@mayavi2.standalone
def glyph():
"""The script itself. We needn't have defined a function but
having a function makes this more reusable.
"""
# 'mayavi' is always defined on the interpreter.
# Create a new VTK scene.
mayavi.new_scene()
# Read a VTK (old style) data file.
r = VTKXMLFileReader()
r.initialize(join(mayavi2.get_data_dir(dirname(abspath(__file__))),
'fire_ug.vtu'))
mayavi.add_source(r)
# Create an outline and a vector cut plane.
mayavi.add_module(Outline())
v = VectorCutPlane()
mayavi.add_module(v)
v.glyph.color_mode = 'color_by_scalar'
# Now mask the points and show glyphs (we could also use
# Vectors but glyphs are a bit more generic)
m = MaskPoints()
m.filter.trait_set(on_ratio=10, random_mode=True)
mayavi.add_filter(m)
g = Glyph()
mayavi.add_module(g)
# Note that this adds the module to the filtered output.
g.glyph.scale_mode = 'scale_by_vector'
# Use arrows to view the scalars.
gs = g.glyph.glyph_source
gs.glyph_source = gs.glyph_dict['arrow_source']
if __name__ == '__main__':
glyph()
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@docs@source@mayavi@auto@glyph.py@.PATH_END.py
|
{
"filename": "materialize_test.py",
"repo_name": "vaexio/vaex",
"repo_path": "vaex_extracted/vaex-master/tests/materialize_test.py",
"type": "Python"
}
|
from common import *
def test_materialize_virtual(ds_local):
ds = ds_local
print(ds)
ds['new_r'] = np.sqrt(ds.x**2 + ds.y**2)
assert 'new_r' in ds.virtual_columns
assert hasattr(ds, 'new_r')
ds = ds.materialize(ds.new_r)
assert 'new_r' not in ds.virtual_columns
assert 'new_r' in ds.columns
assert hasattr(ds, 'new_r')
assert ds.new_r.evaluate().tolist() == np.sqrt(ds.x.to_numpy()**2 + ds.y.to_numpy()**2).tolist()
def test_materialize_dataset():
df = vaex.from_scalars(x=1)
df = df.materialize('x')
assert df.dataset.names == ['x']
df = vaex.from_scalars(x=1, __y=2)
df = df.materialize()
assert df.dataset.names == ['x', '__y']
|
vaexioREPO_NAMEvaexPATH_START.@vaex_extracted@vaex-master@tests@materialize_test.py@.PATH_END.py
|
{
"filename": "paramdesc.py",
"repo_name": "j0r1/GRALE2",
"repo_path": "GRALE2_extracted/GRALE2-master/pygrale/grale/paramdesc.py",
"type": "Python"
}
|
"""This module contains tools for parametric inversion: something to analyze
a description of a lens model that can be optimized parametrically, and
a routine to start such a description based on an existing lens model."""
from .constants import *
from . import lenses
import pprint
import copy
import math
import uuid
class ParametricDescriptionException(Exception):
"""An exception that will be thrown in case something goes wrong when
analyzing or creating a lens description for parametric inversion."""
pass
def _getInitialParameterValue(params, paramKey):
value = params[paramKey]
if type(value) == dict:
return (value["initmin"] + value["initmax"])*0.5, False
if type(value) == list or type(value) == tuple:
if len(value) == 1 or len(value) == 2 or len(value) == 3:
return value[0], False
raise ParametricDescriptionException("Too many entries for parameter")
return value, True
def _getInitialMinOrMaxParameterValue(params, paramKey, fraction, isMin):
value = params[paramKey]
key = "initmin" if isMin else "initmax"
fracSign = -1 if isMin else 1
if type(value) == dict:
return value[key], False
if type(value) == list or type(value) == tuple:
if len(value) < 1 or len(value) > 3:
raise ParametricDescriptionException("Incorrect number of entries for parameter")
if value[0] < 0:
fracSign = -fracSign
if len(value) == 1:
fullFrac = 1 + fracSign*abs(fraction)
else: # 2 params
fullFrac = 1 + fracSign*abs(value[1])
return value[0]*fullFrac, False
return value, True
def _checkParameterValues(lensParams, paramMapping, getParamValue, positionNames):
remainingLensParams = lensParams.copy() # shallow copy is enough
newParams = { }
for x,y in paramMapping:
newParams[x], isFixed = getParamValue(lensParams, x)
del remainingLensParams[x]
if not isFixed:
positionNames.append(y)
return remainingLensParams, newParams
def _processPlummerLens(lensParams, Dd, getParamValue):
positionNames = [ ]
lensParams, newParams = _checkParameterValues(lensParams, [ ("mass", "mass_scaled"),
("width", "width_scaled")],
getParamValue, positionNames)
if lensParams:
raise ParametricDescriptionException("Excess parameters for PlummerLens")
return newParams, positionNames
def _processCompositeLens(lensParams, Dd, getParamValue):
compParams = []
positionNames = []
for idx, subParams in enumerate(lensParams):
subParams, newParams = _checkParameterValues(subParams,
[ ("x", f"x_{idx}_scaled"),
("y", f"y_{idx}_scaled"),
("factor", f"factor_{idx}"),
("angle", f"angle_{idx}") ],
getParamValue, positionNames)
l, posNames = _createTemplateLens_helper(subParams["lens"], Dd, getParamValue)
newParams["lens"] = l
del subParams["lens"]
for p in posNames:
positionNames.append(f"lens_{idx}," + p)
if subParams:
raise ParametricDescriptionException("Excess parameters for CompositeLens")
compParams.append(newParams)
return compParams, positionNames
def _processNSIELens(lensParams, Dd, getParamValue):
positionNames = [ ]
lensParams, newParams = _checkParameterValues(lensParams, [ ("ellipticity", "ellipticity"),
("coreRadius", "core_scaled"),
("velocityDispersion", "sigma_scaled") ],
getParamValue, positionNames)
if lensParams:
raise ParametricDescriptionException("Excess parameters for NSIELens")
return newParams, positionNames
def _processSISLens(lensParams, Dd, getParamValue):
positionNames = [ ]
lensParams, newParams = _checkParameterValues(lensParams, [ ("velocityDispersion", "sigma_scaled") ],
getParamValue, positionNames)
if lensParams:
raise ParametricDescriptionException("Excess parameters for SISLens")
return newParams, positionNames
def _processMassSheetLens(lensParams, Dd, getParamValue):
positionNames = []
if "Ds" in lensParams or "Dds" in lensParams:
raise ParametricDescriptionException("For a mass sheet lens, 'Ds' and 'Dds' cannot be used, use 'density' instead")
lensParams, newParams = _checkParameterValues(lensParams, [ ("density", "density_scaled") ],
getParamValue, positionNames)
if lensParams:
raise ParametricDescriptionException("Excess parameters for MassSheetLens")
return newParams, positionNames
def _processMultiplePlummerLens(lensParams, Dd, getParamValue):
positionNames = []
newMultiParams = []
for i,subParams in enumerate(lensParams):
subParams, newParams = _checkParameterValues(subParams, [ ("x", f"x_{i}_scaled"),
("y", f"y_{i}_scaled"),
("mass", f"mass_{i}_scaled"),
("width", f"width_{i}_scaled"),
],
getParamValue, positionNames)
if subParams:
raise ParametricDescriptionException("Excess parameters for MultiplePlummerLens")
newMultiParams.append(newParams)
return newMultiParams, positionNames
def _processDeflectionGridLens(lensParams, Dd, getParamValue):
# No parameters can be changed
lpCopy = lensParams.copy()
for k in [ "bottomleft", "topright" ]:
if not k in lpCopy:
raise ParametricDescriptionException(f"Expecting key '{key}' in DeflectionGridLens parameters")
try:
x, y = lpCopy[k][0], lpCopy[k][1]
x, y = float(x), float(y)
except Exception as e:
raise ParametricDescriptionException(f"Value for '{key}' should be a fixed 2D coordinate") from e
del lpCopy[k]
if not "angles" in lpCopy:
raise ParametricDescriptionException("Expecting key 'angles' in DeflectionGridLens parameters")
angles = lpCopy["angles"]
try:
shp = angles.shape
except Exception as e:
raise ParametricDescriptionException("Expeting the 'angles' in DeflectionGridLens parameters to be a fixed numpy 2D array")
del lpCopy["angles"]
if lpCopy:
raise ParametricDescriptionException("Excess parameters for DeflectionGridLens")
return lensParams.copy(), [] # No variable parameters
def _processPIEMDLens(lensParams, Dd, getParamValue):
positionNames = [ ]
lensParams, newParams = _checkParameterValues(lensParams, [ ("epsilon", "epsilon"),
("coreradius", "coreradius_scaled"),
("scaleradius", "scaleradius_difference_scaled"),
("centraldensity", "centraldensity_scaled")],
getParamValue, positionNames)
if lensParams:
raise ParametricDescriptionException("Excess parameters for PIEMDLens")
return newParams, positionNames
def _createTemplateLens_helper(parametricLensDescription, Dd, getParamValue):
lensType = parametricLensDescription["type"]
lensParams = parametricLensDescription["params"]
if not lensType in _supportedLensTypes:
raise ParametricDescriptionException("Unknown lens type for parametric inversion")
t = _supportedLensTypes[lensType]
handler, lensClass = t["handler"], t["lens"]
newParams, positionNames = handler(lensParams, Dd, getParamValue) # Need Dd as a parameter because of possible recursion
lens = lensClass(Dd, newParams)
return lens, positionNames
def _createParamOffsetInfo(l, paramNames, deflectionscale, potentialscale):
adjustableParams = l.getCLAdjustableFloatingPointParameterInfo(deflectionscale, potentialscale)
adjustableParamsDict = { }
for p in adjustableParams:
name = p["name"]
if name in adjustableParamsDict:
raise ParametricDescriptionException(f"Internal error: name {name} already exists in adjustable params dict")
adjustableParamsDict[name] = p
paramOffsetInfo = []
for n in paramNames:
if not n in adjustableParamsDict:
raise ParametricDescriptionException(f"Internal error: specified adjustable param {n} does not seem to be valid")
paramOffsetInfo.append(adjustableParamsDict[n])
return sorted(paramOffsetInfo, key=lambda x: x["offset"])
def _createTemplateLens(parametricLensDescription, Dd):
l, paramNames = _createTemplateLens_helper(parametricLensDescription, Dd, _getInitialParameterValue)
scales = l.getSuggestedScales()
intParam, floatParams = l.getCLParameters(**scales)
ret = { "templatelens": l,
"paramoffsets": _createParamOffsetInfo(l, paramNames, **scales),
"paramnames": paramNames, # in original order
"scales": scales,
"floatparams": floatParams,
"description": copy.deepcopy(parametricLensDescription)
}
return ret
def _getShouldBeSameArray(templateLensDescription):
numParams = templateLensDescription["floatparams"].shape[0]
shouldBeSame = [ True for _ in range(numParams) ]
for paramInfo in templateLensDescription["paramoffsets"]:
offset = paramInfo["offset"]
assert shouldBeSame[offset], "Internal error: changing same floating point value twice"
shouldBeSame[offset] = False
return shouldBeSame
def _checkShouldBeSame(templateLensDescription, minParams, maxParams):
shouldBeSame = _getShouldBeSameArray(templateLensDescription)
for idx, value in enumerate(shouldBeSame):
# print(f"Comparing index {idx}: {initMinParams[idx]} vs {initMaxParams[idx]}")
if value: # everything should be same
assert minParams[idx] == maxParams[idx], "Internal error: min/max should be same at this position"
assert minParams[idx] == templateLensDescription["floatparams"][idx], "Internal error: min and template values should match at this position"
else:
if minParams[idx] == maxParams[idx]:
raise ParametricDescriptionException(f"Values at floating point offset {idx} should be allowed to change, but initial min/max values are the same (so no variation will be introduced)")
if minParams[idx] > maxParams[idx]:
raise ParametricDescriptionException(f"Min/max value at floating point offset {idx} should be other way around")
def _createInitialMinMaxParameters(templateLensDesciption, defaultFraction):
scales = templateLensDesciption["scales"]
Dd = templateLensDesciption["templatelens"].getLensDistance()
parametricLensDescription = templateLensDesciption["description"]
l, paramNames = _createTemplateLens_helper(parametricLensDescription, Dd,
lambda params, key: _getInitialMinOrMaxParameterValue(params, key, defaultFraction, True))
_, initMinParams = l.getCLParameters(**scales)
l, paramNames = _createTemplateLens_helper(parametricLensDescription, Dd,
lambda params, key: _getInitialMinOrMaxParameterValue(params, key, defaultFraction, False))
_, initMaxParams = l.getCLParameters(**scales)
assert templateLensDesciption["floatparams"].shape == initMinParams.shape, "Internal error: mismatch in floating point parameter shapes"
assert initMaxParams.shape == initMinParams.shape, "Internal error: mismatch in floating point parameter shapes (2)"
_checkShouldBeSame(templateLensDesciption, initMinParams, initMaxParams)
return initMinParams, initMaxParams
def _getHardMinOrMaxParameterValue(params, paramKey, isMin, knownParamNames, hardInfinite):
# We're expecting either a fixed value, or a dict { "start", "hardmin", "hardmax"}
value = params[paramKey]
if type(value) != dict:
return value, True
paramName = knownParamNames.pop(0)
key, infValue = ("hardmin", float("-inf")) if isMin else ("hardmax", float("inf"))
extValue = value[key]
if math.isinf(extValue):
paramValue = value["start"] # Don't use inf as a lens parameter
if extValue != infValue:
raise ParametricDescriptionException(f"Got {extValue} but expecting {infValue} for {paramName}")
hardInfinite.append(paramName) # Remember this parameter name
else:
paramValue = extValue
return paramValue, False
def _mergeHardMinOrMaxParameterValue(params, key, knownParamNames, paramOffsets):
# print("knownParamNames")
# pprint.pprint(knownParamNames)
# print("paramOffsets")
# pprint.pprint(paramOffsets)
startValue, isFixed = _getInitialParameterValue(params, key)
if isFixed:
return startValue, True
paramName = knownParamNames.pop(0)
paramInfo = paramOffsets[paramName]
value = params[key]
newDict = { "start": startValue }
if type(value) == dict:
for pyKey, cKey in [ ("hardmin", "hard_min"), ("hardmax", "hard_max")]:
if pyKey in value:
newDict[pyKey] = value[pyKey]
else:
newDict[pyKey] = paramInfo[cKey]
elif (type(value) == list or type(value) == tuple) and len(value) == 3:
# Third is a percentage describing the hard bounds
minVal, maxVal = startValue*(1-value[2]), startValue*(1+value[2])
if startValue < 0:
minVal, maxVal = maxVal, minVal
newDict["hardmin"] = minVal
newDict["hardmax"] = maxVal
else:
for pyKey, cKey in [ ("hardmin", "hard_min"), ("hardmax", "hard_max")]:
newDict[pyKey] = paramInfo[cKey]
# Change it!
params[key] = newDict
return startValue, False
def _createHardMinMaxParameters(templateLensDesciption):
# We're going to merge this with hard min/max values
parametricLensDescription = copy.deepcopy(templateLensDesciption["description"])
Dd = templateLensDesciption["templatelens"].getLensDistance()
knownParamNames = copy.deepcopy(templateLensDesciption["paramnames"])
paramOffsets = templateLensDesciption["paramoffsets"]
paramOffsets = { x["name"]:x for x in paramOffsets }
scales = templateLensDesciption["scales"]
l, paramNames = _createTemplateLens_helper(parametricLensDescription, Dd,
lambda params, key: _mergeHardMinOrMaxParameterValue(params, key, knownParamNames, paramOffsets))
# Now parametricLensDescription is modified, to contain
# "start", "hardmin" and "hardmax" values for all variable
# entries
knownParamNames = copy.deepcopy(templateLensDesciption["paramnames"])
hardInfMinNames = []
l, _ = _createTemplateLens_helper(parametricLensDescription, Dd,
lambda params, key: _getHardMinOrMaxParameterValue(params, key, True, knownParamNames, hardInfMinNames))
_, hardMinParams = l.getCLParameters(**scales) # These should all be finite
knownParamNames = copy.deepcopy(templateLensDesciption["paramnames"])
hardInfMaxNames = []
l, _ = _createTemplateLens_helper(parametricLensDescription, Dd,
lambda params, key: _getHardMinOrMaxParameterValue(params, key, False, knownParamNames, hardInfMaxNames))
_, hardMaxParams = l.getCLParameters(**scales) # These should all be finite
def isNumber(num):
return not (math.isinf(num) or math.isnan(num))
# print(templateLensDesciption["paramnames"])
# pprint.pprint(templateLensDesciption["floatparams"])
# pprint.pprint(hardMinParams)
# pprint.pprint(hardMaxParams)
for i in range(hardMinParams.shape[0]):
assert isNumber(hardMinParams[i]), f"Internal error: expecting all hard min parameters to start as a real number ({hardMinParams[i]})"
for i in range(hardMaxParams.shape[0]):
assert isNumber(hardMaxParams[i]), f"Internal error: expecting all hard max parameters to start as a real number ({hardMaxParams[i]})"
for paramName in hardInfMinNames:
offset = paramOffsets[paramName]["offset"]
hardMinParams[offset] = float("-inf")
for paramName in hardInfMaxNames:
offset = paramOffsets[paramName]["offset"]
hardMaxParams[offset] = float("inf")
assert templateLensDesciption["floatparams"].shape == hardMinParams.shape, "Internal error: mismatch in floating point parameter shapes (3)"
assert hardMaxParams.shape == hardMinParams.shape, "Internal error: mismatch in floating point parameter shapes (4)"
_checkShouldBeSame(templateLensDesciption, hardMinParams, hardMaxParams)
return hardMinParams, hardMaxParams
def analyzeParametricLensDescription(parametricLens, Dd, defaultFraction, clampToHardLimits = False):
"""Analyze the parametric lens description in `parametricLens`, which
should be a dictionary, for a lens at angular diameter distance `Dd`.
In case a parameter is set to change with some fraction about a value,
`defaultFraction` is used if no specific fraction is specified. By
default, an error will be generated if some initial value bounds exceed the
hard bounds, but if `clampToHardLimits` is set to ``True``, the hard
limit will be used as bound for the initial value.
Returns a dictionary with the following entries:
- ``templatelens``: a lens model constructed from the description
- ``floatparams``: the floating point parameters for the model, some of
which can be changed.
- ``scales``: itself a dictionary with entries for the angular and potential
scales that are used.
- ``variablefloatparams``: describes which of the floating point parameters
can be changed, and within which boundaries.
An example `parametricLens` object:
.. code:: python
parametricLens = {
"type": "MultiplePlummerLens",
"params": [
{
# This represents a fixed value
"x": 2*ANGLE_ARCSEC,
# This specifies an initial value that can change within 20% of the specified one.
# During optimization these bounds can be exceeded
"y": [ 1*ANGLE_ARCSEC, 0.20 ]
# Also a variable that can change, the amount determined by `defaultFraction`
"mass": [ 1e13*MASS_SUN ],
# When three entries are specified, the second is again to determine the
# range of initial values, the last one is a fraction to fix hard limits.
"width": [ 3*ANGLE_ARCSEC, 0.1, 0.5 ]
},
{
# Here the initial values will be chosen in the specified interval
"x": { "initmin": -2*ANGLE_ARCSEC, "initmax": 2*ANGLE_ARCSEC },
# Similar, but hard bounds for the parameter are specified as well
# In case none are specified, default values are used for these,
# depending on the parameter type. Specifying them overrides the
# defaults.
"y": { "initmin": -2*ANGLE_ARCSEC, "initmax": 2*ANGLE_ARCSEC,
"hardmin": -3*ANGLE_ARCSEC, "hardmax": 3*ANGLE_ARCSEC },
# And some fixed values
"mass": 1e13*MASS_SUN,
"width": 2*ANGLE_ARCSEC
}
]
}
"""
inf = _createTemplateLens(parametricLens, Dd)
def getNameForOffset(off):
for x in inf["paramoffsets"]:
if x["offset"] == off:
return x["name"]
return "unknown"
initMin, initMax = _createInitialMinMaxParameters(inf, defaultFraction)
hardMin, hardMax = _createHardMinMaxParameters(inf)
numParams = initMin.shape[0]
for i in range(numParams):
if hardMin[i] > initMin[i]:
if clampToHardLimits:
initMin[i] = hardMin[i]
else:
n = getNameForOffset(i)
raise ParametricDescriptionException(f"Parameter '{n}' (at offset {i}) has initial value that can be smaller than hard lower limit")
if hardMax[i] < initMax[i]:
if clampToHardLimits:
initMax[i] = hardMax[i]
else:
n = getNameForOffset(i)
raise ParametricDescriptionException(f"Parameter '{n}' (at offset {i}) has initial value that can be larger than hard upper limit")
if initMax[i] < initMin[i]:
n = getNameForOffset(i)
raise ParametricDescriptionException(f"Parameter '{n}' (at offset {i}) has invalid initial range (possibly after clamping): initMin = {initMin[i]}, initMax = {initMax[i]}")
paramRanges = [ ]
for offInf in inf["paramoffsets"]:
offset = offInf["offset"]
hardMinValue = hardMin[offset]
hardMaxValue = hardMax[offset]
initMinValue = initMin[offset]
initMaxValue = initMax[offset]
assert not (offset in paramRanges), f"Internal error: offset {offset} already set"
paramRanges.append({ "initialrange": [ initMinValue, initMaxValue ],
"hardlimits": [ hardMinValue, hardMaxValue ],
"name": offInf["name"],
"scalefactor": offInf["scalefactor"],
"offset": offset })
paramRanges = sorted(paramRanges, key = lambda x: x["offset"])
# Do a final consistency check
for p in paramRanges:
n = p["name"]
hardMin, hardMax = p["hardlimits"]
initMin, initMax = p["initialrange"]
if initMin == initMax:
raise ParametricDescriptionException(f"Consistency error: Paraameter '{n}' has emty initial range ({initMin})")
if not (hardMin <= initMin < initMax <= hardMax):
raise ParametricDescriptionException(f"Consistency error: Parameter '{n}' fails range check: hardMin = {hardMin}, initMin = {initMin}, initMax = {initMax}, hardMax = {hardMax}")
#print("variablefloatparams")
#pprint.pprint(paramRanges)
ret = {
"templatelens": inf["templatelens"],
"floatparams": inf["floatparams"],
"scales": inf["scales"],
"variablefloatparams": paramRanges
}
return ret
def _getUnitlessValue(x):
return f"{x:.10g}"
def _getUnitValue(x, unitStr):
unit = eval(unitStr)
y = x/unit
v = _getUnitlessValue(y)
return f"{v}*{unitStr}"
def _convertedValueToString(value, stringConverter):
if type(value) == list or type(value) == tuple:
if len(value) == 1:
return "[" + stringConverter(value[0]) + "]"
if len(value) == 2:
return "[" + stringConverter(value[0]) + ", " + _getUnitlessValue(value[1]) + "]"
if len(value) == 3:
return "[" + stringConverter(value[0]) + ", " + _getUnitlessValue(value[1]) + ", " + _getUnitlessValue(value[2]) + "]"
raise ParametricDescriptionException(f"List or tuple should have length 1, 2 or 3, but is {len(value)}")
if type(value) == dict:
d = "{ "
for k in value:
if not k in [ "initmin", "initmax", "hardmin", "hardmax" ]:
raise ParametricDescriptionException(f'Unexpected key {k}, expecting "initmin", "initmax", "hardmin", "hardmax"')
d += f'"{k}": ' + stringConverter(value[k]) + ", "
d += "}"
return d
return stringConverter(value)
def _analyzePlummerLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
params = lens.getLensParameters()
massStr = _convertedValueToString(convertValueFunction(params["mass"], ["PlummerLens"], "mass", "mass_scaled", params), lambda x: _getUnitValue(x, massUnitString))
widthStr = _convertedValueToString(convertValueFunction(params["width"], ["PlummerLens"], "width", "width_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
return [
'{',
f' "mass": {massStr},',
f' "width": {widthStr},',
'}'
]
def _analyzeNSIELens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
params = lens.getLensParameters()
sigmaStr = _convertedValueToString(convertValueFunction(params["velocityDispersion"], ["NSIELens"], "velocityDispersion", "sigma_scaled", params), _getUnitlessValue)
ellStr = _convertedValueToString(convertValueFunction(params["ellipticity"], ["NSIELens"], "ellipticity", "ellipticity", params), _getUnitlessValue)
coreStr = _convertedValueToString(convertValueFunction(params["coreRadius"], ["NSIELens"], "coreRadius", "core_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
return [
'{',
f' "velocityDispersion": {sigmaStr},',
f' "ellipticity": {ellStr},',
f' "coreRadius": {coreStr},',
'}'
]
def _analyzeMultiplePlummerLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
paramLines = ['[']
for i,params in enumerate(lens.getLensParameters()):
massStr = _convertedValueToString(convertValueFunction(params["mass"], ["MultiplePlummerLens"], f"mass_{i}", f"mass_{i}_scaled", params), lambda x: _getUnitValue(x, massUnitString))
widthStr = _convertedValueToString(convertValueFunction(params["width"], ["MultiplePlummerLens"], f"width_{i}", f"width_{i}_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
xStr = _convertedValueToString(convertValueFunction(params["x"], ["MultiplePlummerLens"], f"x_{i}", f"x_{i}_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
yStr = _convertedValueToString(convertValueFunction(params["y"], ["MultiplePlummerLens"], f"y_{i}", f"y_{i}_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
subParams = [
'{',
f' "mass": {massStr},',
f' "width": {widthStr},',
f' "x": {xStr},',
f' "y": {yStr},',
'},' ]
for p in subParams:
paramLines.append(' ' + p)
paramLines.append(']')
return paramLines
def _analyzeCompositeLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
paramLines = ['[']
for i,params in enumerate(lens.getLensParameters()):
xStr = _convertedValueToString(convertValueFunction(params["x"], ["CompositeLens"], f"x_{i}", f"x_{i}_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
yStr = _convertedValueToString(convertValueFunction(params["y"], ["CompositeLens"], f"y_{i}", f"y_{i}_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
angleStr = _convertedValueToString(convertValueFunction(params["angle"], ["CompositeLens"], f"angle_{i}", f"angle_{i}", params), _getUnitlessValue)
factorStr = _convertedValueToString(convertValueFunction(params["factor"], ["CompositeLens"], f"factor_{i}", f"factor_{i}", params), _getUnitlessValue)
subParams = [
'{',
f' "factor": {factorStr},',
f' "x": {xStr},',
f' "y": {yStr},',
f' "angle": {angleStr},' ]
def cvfWrapper(x, lensName, paramName, uniqueParamName, fullParams):
return convertValueFunction(x, [ "CompositeLens", f"lens_{i}" ] + lensName, paramName, f"lens_{i}," + uniqueParamName, fullParams)
subLensLines = createParametricDescription(params["lens"], massUnitString, angularUnitString, False, cvfWrapper, objectStore, objectStoreName)
subParams.append(' "lens": ' + subLensLines[0])
for sl in subLensLines[1:]:
subParams.append(' ' + sl)
subParams.append('},')
for p in subParams:
paramLines.append(' ' + p)
paramLines.append(']')
return paramLines
def _analyzeSISLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
params = lens.getLensParameters()
sigmaStr = _convertedValueToString(convertValueFunction(params["velocityDispersion"], ["SISLens"], "velocityDispersion", "sigma_scaled", params), _getUnitlessValue)
return [
'{',
f' "velocityDispersion": {sigmaStr},',
'}'
]
def _analyzeMassSheetLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
params = lens.getLensParameters()
densStr = _convertedValueToString(convertValueFunction(params["density"], ["MassSheetLens"], "density", "density_scaled", params), _getUnitlessValue)
return [
'{',
f' "density": {densStr},',
'}'
]
def _analyzeDeflectionGridLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
# Nothing can be changed about this lens
if objectStore is None or objectStoreName is None:
raise ParametricDescriptionException("To be able to store the deflection angles for a DeflectionGridLens, the objectStore and objectStoreName must be set")
params = lens.getLensParameters()
bl = params["bottomleft"]
tr = params["topright"]
bl_x = _getUnitValue(bl[0], angularUnitString)
bl_y = _getUnitValue(bl[1], angularUnitString)
tr_x = _getUnitValue(tr[0], angularUnitString)
tr_y = _getUnitValue(tr[1], angularUnitString)
objectUuid = str(uuid.uuid4())
objectStore[objectUuid] = params["angles"].copy()
return [
'{',
f' "bottomleft": [ {bl_x}, {bl_y} ],',
f' "topright": [ {tr_x}, {tr_y} ],',
f' "angles": {objectStoreName}["{objectUuid}"],',
'}'
]
def _analyzePIEMDLens(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName):
params = lens.getLensParameters()
sigma0Str = _convertedValueToString(convertValueFunction(params["centraldensity"], ["PIEMDLens"], "centraldensity", "centraldensity_scaled", params), _getUnitlessValue)
epsStr = _convertedValueToString(convertValueFunction(params["epsilon"], ["PIEMDLens"], "epsilon", "epsilon", params), _getUnitlessValue)
coreRadStr = _convertedValueToString(convertValueFunction(params["coreradius"], ["PIEMDLens"], "coreradius", "coreradius_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
scaleRadStr = _convertedValueToString(convertValueFunction(params["scaleradius"], ["PIEMDLens"], "scaleradius", "scaleradius_difference_scaled", params), lambda x: _getUnitValue(x, angularUnitString))
return [
'{',
f' "centraldensity": {sigma0Str},',
f' "epsilon": {epsStr},',
f' "coreradius": {coreRadStr},',
f' "scaleradius": {scaleRadStr},',
'}'
]
def createParametricDescription(lens, massUnitString = "MASS_SUN", angularUnitString = "ANGLE_ARCSEC",
asString = True, convertValueFunction = None,
objectStore = None, objectStoreName = None):
"""Create a basic representation of a parametric lens model, based on the
:class:`lens model<grale.lenses.GravitationalLens>` in `lens`. The result is
a string which represents python code and can be saved to a file for further
editing. This result has no parameters that can change, so it will need to be
adjusted.
To make the description more readable, values for masses will be represented
as a value times the mass unit, which needs to be represented as string in
`massUnitString`. Similarly, angular values will be represented using
`angularUnitString`.
By default a single large string is returned, if a list of separate lines is
more covenient, the `asString` parameter can be set to ``False``.
The `convertValueFunction` is a callback that can be used to convert a fixed
value to a variable one. The parameters are:
- `value`: the value itself
- `lensName`: this is a list, of which the last entry specifies the name
of the current (sub) model. This list could simply be ``[ "NSIELens" ]`` for
a simple lens, or e.g. ``[ "CompositeLens", "lens_0", "NSIELens" ]`` for a
more complex model.
- `paramName`: the name of the parameter, for example ``"mass"``
- `uniqueParamName`: internal name of the parameter, which is unique. This is
similar as the parameter names in ``variablefloatparams`` from the
:func:`analyzeParametricLensDescription` function.
- `fullParams`: the full lens model parameters of the current (sub) model. This
could be helpful if you need more information about which submodel is being
considered.
The return value can be:
- a value: in that case this is a fixed parameter
- a list ``[ value, fraction ]`` to initialize the parameter to vary between
bounds specified by the fraction, or just ``[ value ]`` if the default
fraction is to be used when calling :func:`analyzeParametricLensDescription`.
If a third fraction is specified, e.g. ``[ value, fraction, hardfraction ]``,
then that will be used for the hard bounds.
- a dictionary containing entries for ``"initmin"`` and ``"initmax"``, and
optionally ``"hardmin"`` and ``"hardmax"``.
For some lenses (for now only a :class:`DeflectionGridLens <grale.lenses.DeflectionGridLens>`)
it may be nessary to store a large amount of data somewhere (e.g. the deflection
angles on a grid). In this case you can specify a dictionary for `objectStore` where
this data will be stored with a unique key. In the final output of this function (which
is a string), the name `objectStoreName` will be used as the name of this dictionary.
"""
if convertValueFunction is None:
convertValueFunction = lambda value, lensName, paramName, uniqueParamName, allParams : value
if not type(lens) in _supportedLensTypesByClass:
raise ParametricDescriptionException(f"Can't create parametric description for lens type {type(lens)}")
info = _supportedLensTypesByClass[type(lens)]
name = info["name"]
if not "analysis" in info:
raise ParametricDescriptionException(f"No lens analysis function for {name}")
analyzer = info["analysis"]
paramLines = analyzer(lens, massUnitString, angularUnitString, convertValueFunction, objectStore, objectStoreName)
descLines = [
'{',
f' "type": "{name}",',
f' "params": ' + paramLines[0] ]
for p in paramLines[1:]:
descLines.append(' ' + p)
descLines.append('}')
if not asString:
return descLines
return "\n".join(descLines)
_supportedLensTypes = {
"PlummerLens": { "handler": _processPlummerLens, "lens": lenses.PlummerLens, "analysis": _analyzePlummerLens },
"MultiplePlummerLens": { "handler": _processMultiplePlummerLens, "lens": lenses.MultiplePlummerLens, "analysis": _analyzeMultiplePlummerLens },
"NSIELens": { "handler": _processNSIELens, "lens": lenses.NSIELens, "analysis": _analyzeNSIELens },
"CompositeLens": { "handler": _processCompositeLens, "lens": lenses.CompositeLens, "analysis": _analyzeCompositeLens },
"SISLens": { "handler": _processSISLens, "lens": lenses.SISLens, "analysis": _analyzeSISLens },
"MassSheetLens": { "handler": _processMassSheetLens, "lens": lenses.MassSheetLens, "analysis": _analyzeMassSheetLens },
"DeflectionGridLens": { "handler": _processDeflectionGridLens, "lens": lenses.DeflectionGridLens, "analysis": _analyzeDeflectionGridLens },
"PIEMDLens" : { "handler": _processPIEMDLens, "lens": lenses.PIEMDLens, "analysis": _analyzePIEMDLens },
}
_supportedLensTypesByClass = { _supportedLensTypes[name]["lens"]: { "name": name, **_supportedLensTypes[name] } for name in _supportedLensTypes }
def getSupportedLensTypes():
"""List which gravitational lens types are recognized in the parametric description."""
return [ (x, _supportedLensTypes[x]["lens"]) for x in _supportedLensTypes ]
def main2():
pprint.pprint(getSupportedLensTypes())
def main():
Dd = 1000*DIST_MPC
#l = lenses.PlummerLens(Dd, { "mass": 1e13*MASS_SUN, "width": 1*ANGLE_ARCSEC})
#l = lenses.NSIELens(Dd, { "velocityDispersion": 100000, "ellipticity": 0.8, "coreRadius": 0.5*ANGLE_ARCSEC})
# l = lenses.MultiplePlummerLens(Dd, [
# { "mass": 0.7e15*MASS_SUN, "width":3*ANGLE_ARCSEC, "x": -2*ANGLE_ARCSEC, "y": 1*ANGLE_ARCSEC },
# { "mass": 0.4e15*MASS_SUN, "width":4*ANGLE_ARCSEC, "x": 3*ANGLE_ARCSEC, "y": -1*ANGLE_ARCSEC },
# ])
l = lenses.CompositeLens(Dd, [
{"lens": lenses.PlummerLens(Dd, { "mass": 0.7e15*MASS_SUN, "width":3*ANGLE_ARCSEC}),
"x": -2*ANGLE_ARCSEC, "y": 1*ANGLE_ARCSEC, "angle": 10, "factor": 1.1},
{"lens": lenses.PlummerLens(Dd, { "mass": 0.4e15*MASS_SUN, "width":4*ANGLE_ARCSEC}),
"x": 3*ANGLE_ARCSEC, "y": -1*ANGLE_ARCSEC, "angle":30, "factor": 0.9}
])
#l = lenses.SISLens(Dd, { "velocityDispersion": 400000 })
#Ds = 1
#Dds = 0.8
#l = lenses.MassSheetLens(Dd, { "Ds": Ds, "Dds": Dds })
# l = lenses.GravitationalLens.load("/home/jori/projects/grale2-git/inversion_examples/example2/inv1.lensdata")
objStr = createParametricDescription(l)
print(objStr)
parametricLens = eval(objStr)
pprint.pprint(parametricLens)
inf = analyzeParametricLensDescription(parametricLens, Dd, 0.1)
pprint.pprint(inf)
pprint.pprint(inf["templatelens"].getLensParameters())
def main0():
#parametricLens = {
# "type": "PlummerLens",
# "params": {
# # voor vaste waarde
# "mass": [ 1e14*MASS_SUN ],
# "width": { "initmin": 2*ANGLE_ARCSEC, "initmax": 5*ANGLE_ARCSEC }
# }
#}
# parametricLens = {
# "type": "MultiplePlummerLens",
# "params": [
# { "x": [ 0.1*ANGLE_ARCSEC ], "y": 0, "mass": [1e13*MASS_SUN], "width": 3*ANGLE_ARCSEC },
# { "x": 0, "y": 0, "mass": 1e13*MASS_SUN, "width": [ 2*ANGLE_ARCSEC ] }
# ]
# }
parametricLens = {
"type": "CompositeLens",
"params": [
{ "x": 0, "y": [ 0.1*ANGLE_ARCSEC ], "factor": 1, "angle": 0,
"lens": {
"type": "PlummerLens",
"params": {
"mass": [ 1e14*MASS_SUN ],
"width": { "initmin": 2*ANGLE_ARCSEC, "initmax": 5*ANGLE_ARCSEC }
}
}
},
{ "x": [0.11*ANGLE_ARCSEC], "y": 0, "factor": [ 1 ], "angle": 0,
"lens": {
"type": "PlummerLens",
"params": {
"mass": 1e14*MASS_SUN,
"width": { "initmin": 2*ANGLE_ARCSEC, "initmax": 5*ANGLE_ARCSEC }
}
}
},
{ "x": [0.111*ANGLE_ARCSEC], "y": 0, "factor": 1,
"angle": { "initmin": -60, "initmax": 30 },
"lens": {
"type": "NSIELens",
"params": {
"velocityDispersion": [ 400000 ],
"ellipticity": [ 0.8 ],
"coreRadius": [ 2*ANGLE_ARCSEC ]
}
}
},
{ "x": 0, "y": 0, "factor": 1,
"angle": 0,
"lens": {
"type": "SISLens",
"params": {
"velocityDispersion": [ 400000 ],
}
}
},
{
"x": 0, "y": 0, "factor": 1, "angle": 0,
"lens": {
"type": "MassSheetLens",
"params": { "density": [ 4.44 ] }
}
}
]
}
inf = analyzeParametricLensDescription(parametricLens, 1000*DIST_MPC, 0.1)
pprint.pprint(inf)
if __name__ == "__main__":
main()
|
j0r1REPO_NAMEGRALE2PATH_START.@GRALE2_extracted@GRALE2-master@pygrale@grale@paramdesc.py@.PATH_END.py
|
{
"filename": "2_polynomial_tutorial.ipynb",
"repo_name": "frescigno/magpy_rv",
"repo_path": "magpy_rv_extracted/magpy_rv-main/source/tutorials/2_polynomial_tutorial.ipynb",
"type": "Jupyter Notebook"
}
|
# Tutorial 2 - Simple Model
This second tutorial explains the basics of creating a simple model along with the GP and running it through an MCMC to refine the model and GP parameters. It also introduces users to the mixing plots, the corner plots, and the saving function.
```python
import numpy as np
from magpy_rv.mcmc_aux import get_model as get_model
import magpy_rv.parameters as par
import magpy_rv.models as mod
import magpy_rv.kernels as ker
import magpy_rv.gp_likelihood as gp
from magpy_rv.mcmc import run_MCMC as run
import magpy_rv.plotting as plot
from magpy_rv.saving import save as save
import magpy_rv.auxiliary as aux
```
## Creating fake data to work with
Much like before, a cosine with a small jitter term is created as a fake set of data, this will act as the activity to model our kernel from
```python
# time array with 20 values
time = np.arange(0,20,1)
# set up the amplitude and period of the cosine
A = 10.
P = 5.
err = []
# set up a random jitter to add to the data
for i in time:
err.append(np.random.uniform(-3,3))
# generate the rvs and errors
rv = A*np.cos(time*((2*np.pi)/P))+err
rv_err = np.ones_like(rv)*3
```
We will additionally now add to this data by creating a polynomial and adding it on to our rv values to simulate activity plus some polynomial signal
```python
# the polynomial we use will be y = 0.2x^2 + x -10, the polynomial model can take up to x^3
a0 = 5.
a1 = 1.
a2 = 0.2
a3 = 0
# create the polynomial and add it to the rv data
y = a3*(time**3) + a2*(time**2) + a1*time + a0
rv = rv + y
```
data_plot function will take the time, rv data, and rv errors and plot a scatter graph of the data, similar to before but now the polynomial part is visible. Axis labels, legend, and saving can all be controlled from the function inputs.
```python
plot.data_plot(time = time, rv = rv, y_err = rv_err)
```
## Creating the kernel
A cosine Kernel is created the same as before using the par_create funciton which will take only the name of the kernel and return an empty dictionary of hyperparameters to be filled out. This dictionary can be printed to view the hyperparamer names.
Currently available kernels along with their hyperparameter names can be viewed by running PrintKernelList:
```python
ker.PrintKernelList()
```
```python
# create the kernel
hparam = par.par_create("Cosine")
# print the hyperparameter dictionary
print(hparam)
```
Hyperparameters are then assigned in the same way as before but this time we give the errors and whether we want the value to vary in the mcmc. By default vary is set to True and the errors are 20% of the value.
```python
# assign values to the dictionary
hparam["gp_amp"] = par.parameter(value = 10., error = 0.5, vary = True)
hparam["gp_per"] = par.parameter(value = 5., error = 0.5, vary = True)
# printing now prints the filled dictionary
print(hparam)
```
Priors should then be created by pri_create function and appending it to the list of priors in the same way as before. The pri_create function takes the parameter name, the prior name, and the prior parameters as inputs which must be inputted in the correct form, this form can be viewed by running the PRINTPRIORDER function.
```python
# view the correct form of prior parameter inputs
par.PRINTPRIORDER()
```
```python
# create empty prior list
prior_list = []
# uniform parameters used here so prior parameters inputted as [minval, maxval], as the above function states
pri_amp = par.pri_create("gp_amp", "Uniform", [5.,15.])
# then append the prior to the list
prior_list.append(pri_amp)
pri_per = par.pri_create("gp_per", "Uniform", [0.,10.])
prior_list.append(pri_per)
# print the list of all the priors
print(prior_list)
```
## Model Parameters
Now the data contains a polynomial model, we must set up initial parameters for this model to supply to the mcmc and allow us to plot the model.
We will start by defining a model list that contains the name of all models present in the data, in this case this will just be a polynomial. Running PrintModelList will allow us to see all the available models and their parameter names. We then create the model parameter dictionary by running the mod_create function with the model list as the only input. We can then print this to view the required parameters.
```python
# see available models
mod.PrintModelList()
```
```python
# define the model list, in this case just polynomial
model_list = ["Polynomial"]
# create the model parameter dictionary
model_par = mod.mod_create(model_list)
print(model_par)
```
We must then define model parameters and priors in the same way as for the kernel using the parameter function and the pri_create function
```python
# initial parameter values are set up for the model
model_par["a0"]=par.parameter(value = 5., error=1., vary=True)
model_par["a1"]=par.parameter(value = 1., error=0.5, vary=True)
model_par["a2"]=par.parameter(value = 0.2, error=0.1, vary=True)
# we know a3 is 0 so there is no need to vary it. You can do it in two ways
# ATTENTION: if you chose not to vary a value, such as here, be aware of the following
# if you want all chains to start from the given value (here 0), you NEED to set the error to 0 (or leave it undefined)
# Giving an error will make so that the chains will all have different starting points within the error
# This will allow you to plot a corner plot even with a "non-varying parameter", but it is not the same as keeping a value set!
model_par["a3"]=par.parameter(value = 0., error = 0.1, vary=False)
# priors created in the same way as before
pri_val = par.pri_create("a0", "Uniform", [0.,10.])
prior_list.append(pri_val)
pri_val = par.pri_create("a1", "Uniform", [0.,3.])
prior_list.append(pri_val)
pri_val = par.pri_create("a2", "Uniform", [0.,1.])
prior_list.append(pri_val)
# printing the final prior list and model parameters
print("Prior List:")
print(prior_list)
print("Model Parameters:")
print(model_par)
```
## Obtaining LogL and GP values
As we are using a model, in order to run the GPLikelihood class we require the y values for the model. The get_model function allows the y values to be obtained for all models in the data given their parameters, names, and a time array. For plotting purposes this time array is better to be far smoother than the actual time array to produce a good plot. The GPLikelihood class should this time be defined and run with the time data, the rv data, the rv errors, the hyperparameters, the kernel name, the model y values, and the model parameters. This allows the GPLikelihood.LogL function to be run with the prior_list which returns the initial log likelihood of the GP model.
In order to return the y values and errors of the GP model, a predicted x array must first be defined which should be smoother and longer than the initial time array, in this case it begins at -1 and ends at 21 with intervals of 0.1 which is around 10 times more data points than the initial time array. This must be then inputted into the GPLikelihood.predict function to return the y values and the errors of the GP.
```python
# model_y in this case comes only from the polynomial in the data
model_y = get_model(model_list, time, model_par, to_ecc=False)
# GPLikelihood class called as loglik, run with the current inputs
loglik = gp.GPLikelihood(time, rv, rv_err, hparam, "Cosine", model_y, model_par)
# LogL obtained by running loglik.LogL with the prior_list as the only input
logL = loglik.LogL(prior_list)
# xpred is smoother and longer than time
xpred = np.arange(min(time)-1, max(time)+1, 0.1)
# GP_y and GP_err are arrays of the GP y values and errors of the same length as the xpred array
GP_y, GP_err = loglik.predict(xpred)
print('Initial Log Likelihood =', logL)
```
## Plotting the GP
The GP y values and model y values could be manually plotted against xpred once obtained in the previous step however the GP_plot function allows for an alternative faster way of plotting. This time, we must give the time array, the rv data, the hyperparameters, the kernel name, the rv errors, the model list, and the model parameters. This will now return a plot of the data with the GP plotted over it in orange and the combined model and GP plotted in blue along with its uncertainties in grey. Xpred, axis labels, residuals, legend, and saving can all be controlled by the function inputs.
```python
# GP_plot will plot the GP, the model and the data along with residuals if enabled and the uncertainty in grey
plot.GP_plot(time, rv, hparam, "Cosine", rv_err = rv_err, model_list = model_list, model_param = model_par, residuals = True)
```
## Running the MCMC
The MCMC can be run by defining the run_MCMC function as 4 outputs: the first is the LogL chain, this will be a 3d array of the log likelihood across all iterations and chain; the second is the final hyperparameters, this will be a 3d array of all hyperparameters where ncolumns = parameters, nrows = chains, and ndimensions = iterations; the third is the final model parameters, this will be a 3d array of all model parameters where ncolumns = parameters, nrows = chains, and ndimensions = iterations; the fourth is the completed iterations, this will be the number of iterations that the code ran for, this may not be the number that was set as it may reach convergence before that number is reached.
This function requires the inputs of iterations, the time array, the rv data, the rv error, the hyperparameters, and the kernel name. For this run, as there is a model we will also include the model parameters, the model list, the prior list, and the number of chains. If the number of chains is not entered it defaults to 100.
This function will print the initial parameters and hyperparameters, the initial log likelihood, the number of chains, the progress, the number of completed iterations, the acceptance rate, and the time taken.
```python
# set up iterations and chains
iterations = 100
numb_chains = 100
# run the mcmc function to return the 3d parameter arrays
logL_chain, fin_hparams, fin_model_param, completed_iterations = run(iterations, time, rv, rv_err, hparam, "Cosine", model_par, model_list, prior_list, numb_chains = numb_chains)
```
## Mixing Plots
The mixing_plot funciton takes in the hyperparameter array, the kernel name, the parameter array, the model list, and the logL array. It returns the MCMC chains for each parameter where if the code had run for a sufficient number of iterations it should be possible to see some convergence in the chains. This plot can be saved through the function inputs. This plot and the next will still plot the parameters that are not set to vary however they will easily be visible in the mixing plots by the straight lines as they are not varying. These plots do not reach convergence as very few iterations were used and the priors were likely too large.
```python
# show the mixing plots, in this case a_3 does not vary as we set it to not do so
plot.mixing_plot(fin_hparams, "Cosine", fin_model_param, model_list, logL_chain)
```
## Corner Plots
The corner_plot function takes the same inputs as the mixing plot function minus the logL array and will return 3 outputs. The first is a list of the final posterior values for each parameter and hyperparameter, the second and third are the upper and lower errors on thos values. These values are also all visible on top of each corner plot. This plot can also be saved through the function inputs.
The code will produce seperate plots for the hyperparameters, model parameters, and combined. These will also save individually.
```python
# corner plots also look poor in this case as only 100 iterations were run
final_param_values, final_param_erru, final_param_errd = plot.corner_plot(fin_hparams, "Cosine", fin_model_param, model_list, errors=True)
```
## Saving
The save function will save all outputs, initial conditions, final conditions, and posteriors in seperate files in a chosen folder. If this folder does not exist a new one will be created. These are all generated from the previous functions and shouold be inputted as done below. The input burnin is optional and will save the posteriors with the desired burn in, the input fin_to_skck defaults to False and is for Keplerians, this determines whether to return the final parameters as Sk and Ck (True) or ecc and omega (False). As well as a readable list of final parameter values, the function will output the parameter values in the form of a latex table in the file 'final_param_table'.
```python
# enter in desired file path to saving function
save('savedata', rv, time, rv_err, model_list = model_list, init_param = model_par, kernel = 'Cosine', init_hparam = hparam, prior_list = prior_list, fin_hparam_post = fin_hparams, fin_param_post = fin_model_param, logl_chain = logL_chain, fin_param_values = final_param_values, fin_param_erru = final_param_erru, fin_param_errd = final_param_errd, burnin = 20, fin_to_skck = False)
```
```python
```
|
frescignoREPO_NAMEmagpy_rvPATH_START.@magpy_rv_extracted@magpy_rv-main@source@tutorials@2_polynomial_tutorial.ipynb@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "andreicuceu/vega",
"repo_path": "vega_extracted/vega-master/vega/models/fvoigt_models/README.md",
"type": "Markdown"
}
|
# Fvoigt for HCD modelling
## Building fvoigt_models :
Use the code in the directory build_Fvoigt and read the README.md
## Adding files in fvoigt_models :
**always** : Fvoigt_whatever.txt :smile:
## How to use fvoigt_models :
Modification in the file **.ini :
* In [model] use model-pk = *pk_hcd*
* Add a new section in the data.ini files : [hcd_model]
* Add in this section : name_hcd_model = *whatever*
* In [parameters] : add HCD parameters (see below)
## HCD parameters :
* *bias_hcd* = real bias hcd (measured in the DLA-autocorrelation) * $Fvoigt^{non-norm}(0)$
* *beta_hcd* = real beta hcd
* *L0_hcd* == 1.0 (scale factor) --> hard to fit due to degenerancy (has to be one)
## Description of each Fvoigt function in fvoigt_models directory
* exp : implementation in eBOSS DR14
* london_6.0 : Implementation for mocks london_6.0
* saclay_4.4: Implementation for mocks saclay_4.4
I show in the 06/05/19 DESI Lyman-alpha meeting that my implementation is the correct way to model the DLAs in Lyman-alpha autocorrelation function.
* DR12_noterdame : built with Noterdame/Pasquier DLAs catalogue and DR12 QSO catalogue
* DR12_prochaska : built with Prochaska DLAs catalogue and DR12 QSO catalogue
|
andreicuceuREPO_NAMEvegaPATH_START.@vega_extracted@vega-master@vega@models@fvoigt_models@README.md@.PATH_END.py
|
{
"filename": "transpose.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/lite/testing/op_tests/transpose.py",
"type": "Python"
}
|
# Copyright 2019 The TensorFlow Authors. 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-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test configs for transpose."""
import numpy as np
import tensorflow as tf
from tensorflow.lite.testing.zip_test_utils import create_tensor_data
from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
from tensorflow.lite.testing.zip_test_utils import register_make_test_function
@register_make_test_function()
def make_transpose_tests(options):
"""Make a set of tests to do transpose."""
# TODO(nupurgarg): Add test for uint8.
test_parameters = [{
"dtype": [tf.int32, tf.int64, tf.float32],
"input_shape": [[2, 2, 3]],
"perm": [[0, 1, 2], [0, 2, 1]],
"constant_perm": [True, False],
"fully_quantize": [False],
}, {
"dtype": [tf.float32],
"input_shape": [[1, 2, 3, 4]],
"perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
"constant_perm": [True, False],
"fully_quantize": [False],
}, {
"dtype": [tf.float32],
"input_shape": [[1, 2, 3, 4, 5]],
"perm": [[4, 3, 2, 1, 0]],
"constant_perm": [True, False],
"fully_quantize": [False],
}, {
"dtype": [tf.float32],
"input_shape": [[2, 2, 3]],
"perm": [[0, 1, 2], [0, 2, 1]],
"constant_perm": [True],
"fully_quantize": [True],
}, {
"dtype": [tf.float32],
"input_shape": [[1, 2, 3, 4]],
"perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
"constant_perm": [True],
"fully_quantize": [True],
}, {
"dtype": [tf.float32],
"input_shape": [[1, 2, 3, 4, 5]],
"perm": [[0, 1, 2, 3, 4], [3, 4, 0, 1, 2]],
"constant_perm": [True],
"fully_quantize": [True, False],
}, {
"dtype": [tf.float32],
"input_shape": [[2, 2]],
"perm": [[-2, -1]],
"constant_perm": [True, False],
"fully_quantize": [False],
}]
def build_graph(parameters):
"""Build a transpose graph given `parameters`."""
input_tensor = tf.compat.v1.placeholder(
dtype=parameters["dtype"],
name="input",
shape=parameters["input_shape"])
if parameters["constant_perm"]:
perm = parameters["perm"]
input_tensors = [input_tensor]
else:
shape = len(parameters["perm"])
perm = tf.compat.v1.placeholder(dtype=tf.int32, name="perm", shape=shape)
input_tensors = [input_tensor, perm]
out = tf.transpose(a=input_tensor, perm=perm)
return input_tensors, [out]
def build_inputs(parameters, sess, inputs, outputs):
values = [
create_tensor_data(parameters["dtype"], parameters["input_shape"],
min_value=-1, max_value=1)
]
if not parameters["constant_perm"]:
values.append(np.array(parameters["perm"]))
return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
make_zip_of_tests(
options,
test_parameters,
build_graph,
build_inputs)
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@lite@testing@op_tests@transpose.py@.PATH_END.py
|
{
"filename": "flim_models.py",
"repo_name": "HETDEX/hetdex_api",
"repo_path": "hetdex_api_extracted/hetdex_api-master/hetdex_api/flux_limits/flim_models.py",
"type": "Python"
}
|
"""
This module stores different models
to convert between the values in the
sensitivity cubes and the flux at
50% detection completeness. It also
stores the tools to interpolate over
simulation results.
.. moduleauthor:: Daniel Farrow <dfarrow@mpe.mpg.de>
"""
from glob import glob
from os.path import join
from scipy.interpolate import (interp1d, interp2d, splrep, griddata, RectBivariateSpline,
NearestNDInterpolator)
from numpy import (polyval, mean, median, loadtxt, meshgrid, savetxt,
array, linspace, tile, ones, array, argmin, zeros,
sqrt)
from hetdex_api.config import HDRconfig
from hetdex_api.flux_limits import flim_models_old, flim_model_cache
class NoLineWidthModel(Exception):
pass
class FlimOptionNotSupported(Exception):
pass
class ModelInfo(object):
"""
Store information about the flux
limit model
Parameters
----------
snfile_version : str
which version of the sn?.?.use
files to use
snpoly : array
a polynomial to go from S/N
cut to noise multiplier
wavepoly : array or NoneType
a polynomial to adjust the
noise as a function of
wavelength (None to not use)
"""
def __init__(self, snfile_version, snpoly, wavepoly,
interp_sigmas, dont_interp_to_zero,
snlow=4.8, snhigh=7.0, lw_scaling=None,
wl_collapse=False, single_snfile=False):
self.snfile_version = snfile_version
self.snpoly = snpoly
self.wavepoly = wavepoly
self.interp_sigmas = interp_sigmas
self.snlow = snlow
self.snhigh = snhigh
self.dont_interp_to_zero = dont_interp_to_zero
self.lw_scaling = lw_scaling
self.wl_collapse = wl_collapse
self.single_snfile = single_snfile
class NoSNFilesException(Exception):
pass
def linewidth_f50_scaling_v1(linewidth, sncut):
"""
Model for how the 50% completeness flux
changes with linewidth. Based on Karl's
simulation calibrated model from
July 11th 2021, quoting from Karl
Gebhardt:
x12=min(x12,12.)
rfit=sqrt(x12/2.2)*(1.64-(sncut-4.8)/(9.5-4.8))/(1.+0.07*(12.-x12))
rfit=max(1.,rfit)
Parameters
----------
linewidth : array or float
linewidth in A
sncut : float
S/N cut
Returns
-------
rfit : array or float
scaling of 50% flux
with linewidth
"""
try:
linewidth = array(linewidth)
linewidth[linewidth > 12.0] = 12.0
except TypeError:
linewidth = max(linewidth, 12.0)
rfit=sqrt(linewidth/2.2)*(1.64-(sncut-4.8)/(9.5-4.8))/(1.+0.07*(12.-linewidth))
try:
rfit[rfit < 1.0] = 1.0
except TypeError:
rfit = max(1.0, rfit)
return rfit
def write_karl_file(fn, f50s, fcens, wlcens,
completeness_2d):
with open(fn, 'w') as fp:
fp.write("0 ")
for wl in wlcens:
fp.write("{:5.1f} ".format(wl))
fp.write("\n")
fp.write("0.500 ")
for f50 in f50s:
fp.write("{:5.3f} ".format(f50))
fp.write("\n")
out_arr = zeros((completeness_2d.shape[0] + 1, completeness_2d.shape[1]))
out_arr[0, :] = fcens
out_arr[1:, :] = completeness_2d
savetxt(fp, out_arr.T, fmt="%.4f ")
def write_sn_file(fn, sns, wlcens, completeness_2d):
out_arr = zeros((completeness_2d.shape[0] + 1, completeness_2d.shape[1] + 1))
out_arr[0, 0] = 0
out_arr[1:, 0] = wlcens
out_arr[0, 1:] = sns
out_arr[1:, 1:] = completeness_2d
savetxt(fn, out_arr.T, fmt="%.4f ")
def read_sn_file(fn):
compl_file = loadtxt(fn)
waves = compl_file[0,1:]
compl_curves = compl_file[1:, 1:].T
sns = compl_file[1:, 0]
return waves, sns, compl_curves
def read_karl_file(fn):
"""
Read in the results from
Karl Gebhardt's simulation
Parameters
----------
fn : str
filename of Karl's
fcor.use file
Returns
-------
waves : array
wavelengths at
which simulation has
results
f50 : array
50% completeness at
each wavelength (in
10^-17 erg/s/cm2 units)
compl_curves : 2D array
2D array of completeness
curves at every wavelength
fluxes : array
flux bins for completeness
curves
"""
karl_det_file = loadtxt(fn)
waves = karl_det_file[0,1:]
f50 = karl_det_file[1,1:]
compl_curves = karl_det_file[2:, 1:].T
fluxes = karl_det_file[2:, 0]
return waves, f50, compl_curves, fluxes
class SimulationInterpolator(object):
"""
Interpolate over the results of source
simulations. Use nearest neighbour
interpolation to select the shape of
the completeness given a S/N cut (i.e.
which of Karl's files to use)
Parameters
----------
fdir : str
A directory of sn?.?.use files,
containing Karl's simulation
results
single_snfile : bool
Use the same file of completeness
curves for all S/N cuts
**kwargs :
passed to SingleSNSimulationInterpolator
"""
def __init__(self, fdir, dont_interp_to_zero,
single_snfile=False,
snmode=False, verbose=False, **kwargs):
if single_snfile:
if snmode:
raise FlimOptionNotSupported("Using single_snfile and ",
"snmode at same time ",
"not supported")
if dont_interp_to_zero:
raise FlimOptionNotSupported("Using dont_interp_to_zero ",
"and single_snfile at same time ",
"not supported")
if single_snfile:
if verbose:
print("Using a single S/N file for everything!")
snfiles = glob(fdir + "/sn_all.use")
elif snmode:
snfiles = glob(fdir + "/sn_based_?.?.dat")
else:
snfiles = glob(fdir + "/sn?.?.use")
self.dont_interp_to_zero = dont_interp_to_zero
sns = []
self.sninterpolators = []
for snfile in snfiles:
if verbose:
print(snfile)
self.sninterpolators.append(SingleSNSimulationInterpolator(snfile, dont_interp_to_zero,
snmode=snmode, **kwargs))
if single_snfile:
# value doesn't matter
sns.append(5.5)
else:
sns.append(float(snfile[-7:-4]))
if len(sns) == 0:
raise NoSNFilesException("Could not find any simulation files! ")
if verbose:
print("Read S/N files for the following cuts: {:s}".format(str(sns)))
self.sns = array(sns)
def __call__(self, flux, f50, wave, sncut, verbose=False):
"""
Find the nearest S/N versus completeness curve
from the simulations and use it to predict
completeness
Parameters
----------
flux : array
fluxes to return completeness
at
f50 : array
50% completeness fluxes
wave : array
wavelengths (in A)
sncut : float
the S/N cut applied
"""
diffs = abs(self.sns - sncut)
iclosest = argmin(diffs)
if verbose:
print("Using simulation from S/N cut {:f}".format(self.sns[iclosest]))
return self.sninterpolators[iclosest](flux, f50, wave)
class SingleSNSimulationInterpolator(object):
"""
Interpolate over the results of source
completeness simulations, of the form
run by Karl Gebhardt, for one S/N cut only
Parameters
----------
filename : str
Karl's fcor.use file
wl_collapse : bool
If True collapse
wavelength bins in
the simulation file to
use one curve for all
wavelengths (default: False)
cmax : float
Maximum completeness to normalize
the curves to (default: 0.98)
Attributes
----------
waves : array
wavelengths at which
completeness curves are
measured
f50 : array
50% completeness values for
each wavelength
compl_curves : array
2D array of completeness
in flux and wavelength bins
fluxes : array
the fluxes (*1e17 erg/s/cm) at
which completeness is tabulated
completeness_model : callable
model of completeness given
flux, wavelength and noise
"""
def __init__(self, filename, dont_interp_to_zero, wl_collapse = False,
cmax = None, snmode = False):
if not snmode:
self.waves, self.f50, self.compl_curves, self.fluxes =\
read_karl_file(filename)
else:
self.waves, self.fluxes, self.compl_curves = \
read_sn_file(filename)
# set 50% to one as already in S/N units
self.f50 = ones(len(self.waves))
# Check bin spacing uniform
offs = self.fluxes[1:] - self.fluxes[:-1]
if max(abs(offs - offs[0])) > 1e-20:
raise Exception("Bin spacing must be uniform!")
self._snmode = snmode
self._wl_collapse = wl_collapse
self.cmax = cmax
self.dont_interp_to_zero = dont_interp_to_zero
# Optionally normalize all curves to cmax
if cmax:
print("Normalizing to {:f}".format(cmax))
for i in range(len(self.waves)):
self.compl_curves[i, :] /= max(self.compl_curves[i, :])
self.compl_curves[i, :] *= cmax
self.completeness_model = self.interpolated_model()
def interpolated_model(self, plot=False):
"""
Generate an interpolated model from
the completeness curves. Waves have to
be uniformly spaced
"""
if plot:
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use("tkagg")
# bins to interpolate all completeness curves to,
# in these coordinates 50% completeness always
# at 1.0, also should in combination will fill_value
# ensure 0 returns zero completeness in the
# RectBivariateSpline
fluxes_f50_units = linspace(0, 50, 5000)
c_all = []
fgrid_for_mask = []
wgrid_for_mask = []
# Offset by half a bin brighter, so the mask kicks in
# at the center location, not 1/2 a bin away. The
# 0.999 factor is to ensure the actual value itself
# is not in the mask
fbsizediv2 = 0.999*(self.fluxes[1] - self.fluxes[0])/2.0
for twave, tf50, c in zip(self.waves, self.f50, self.compl_curves):
if plot:
plt.plot(self.fluxes/tf50, c, linestyle="--")
# Shift grid to center of bin, so don't
# interpolate past value
fgrid_for_mask.append((self.fluxes + fbsizediv2)/tf50)
wgrid_for_mask.append(ones(len(self.fluxes))*twave)
# divide so 50% completeness to
# convert to flux units of per f50
interpolator = interp1d(self.fluxes/tf50, c, bounds_error = False,
fill_value=(0.0, c[-1]))
# interpolate to the coordinates where 50% is at 1.0
c_all.append(interpolator(fluxes_f50_units))
c_all = array(c_all)
# Make a combined model
if self._wl_collapse or (len(self.waves) == 1):
if len(self.waves) == 1:
cmean = c_all[0]
else:
# Don't use first two wavebins as
# big scatter
cmean = mean(c_all[2:], axis=0)
completeness_model = interp1d(fluxes_f50_units, cmean,
fill_value=(0.0, cmean[-1]),
bounds_error=False)
if plot:
vals_to_plot = completeness_model(fluxes_f50_units)
else:
# waves have to be uniformly spaced for this to work (? don't think so?)
interp = RectBivariateSpline(self.waves, fluxes_f50_units, c_all,
kx=3, ky=3)
if self.dont_interp_to_zero:
# Use this as a mask to not extrapolate toward 0.0
# if nearest point is zero
compl_mask = zeros(self.compl_curves.shape)
compl_mask[self.compl_curves > 0.0] = 1.0
interp_mask = NearestNDInterpolator(list(zip(array(wgrid_for_mask).ravel(),
array(fgrid_for_mask).ravel())),
compl_mask.ravel())
completeness_model = lambda x, y : interp_mask(x, y)*interp(x, y, grid=False)
else:
completeness_model = lambda x, y : interp(x, y, grid=False)
if plot:
vals_to_plot = completeness_model(self.waves[2]*ones(len(fluxes_f50_units)),
fluxes_f50_units)
if plot:
plt.plot(fluxes_f50_units, vals_to_plot, "k.", lw=2.0)
plt.xlim(0, 12.0)
plt.xlabel("Flux/(50% flux) [erg/s/cm^2]")
plt.ylabel("Normalized Completeness")
plt.show()
return completeness_model
def __call__(self, flux, f50, wave):
"""
Return the completeness
Parameters
----------
flux : array
fluxes to return completeness
at
f50 : array
50% completeness fluxes
wave : array
wavelengths (in A)
"""
flux = array(flux)
f50 = array(f50)
wave = array(wave)
if self._snmode:
return self.completeness_model(f50, flux/f50)
elif self._wl_collapse or (len(self.waves) == 1):
return self.completeness_model(flux/f50)
else:
return self.completeness_model(wave.flatten(), (flux/f50).flatten())
# Blue end noise adjustment
#params_wl = [6.18971170e-07, -5.15522003e-03, 1.17598910e+01]
#
#try:
# noise[lambda_ < 4000.0] = noise[lambda_ < 4000]*polyval(params_wl, lambda_[lambda_ < 4000.0])
#except TypeError:
# if lambda_ < 4000.0:
# noise = noise*polyval(params_wl, lambda_)
def return_flux_limit_model_old(flim_model):
"""
Return the noise -> 50% completeness
scaling and a function for the completeness
curves
"""
f50_from_noise = getattr(flim_models_old, "{:s}_f50_from_noise".format(flim_model))
return f50_from_noise, None, False
def return_flux_limit_model(flim_model, cache_sim_interp = True,
verbose = False):
"""
Return the noise -> 50% completeness
scaling and a function for the
completeness curves
"""
# old models for legacy support
if flim_model in ["hdr1", "hdr2pt1"]:
if verbose:
print("Using flim model: {:s}".format(flim_model))
return return_flux_limit_model_old(flim_model)
models = {
"one_sigma_nearest_pixel" : ModelInfo("curves_v1",
[1.0, 0.0],
None,
False, False,
snlow=0.999999, snhigh=1.000001),
"one_sigma_interpolate" : ModelInfo("curves_v1",
[1.0, 0.0],
None,
True, False,
snlow=0.999999, snhigh=1.000001),
"hdr2pt1pt1" : ModelInfo("curves_v1",
[2.76096687e-03, 2.09732448e-02, 7.21681512e-02, 3.36040017e+00],
None, False, False),
"hdr2pt1pt3" : ModelInfo("curves_v1",
[6.90111625e-04, 5.99169372e-02, 2.92352510e-01, 1.74348070e+00],
None, False, False),
"v1" : ModelInfo("curves_v1",
[-8.80650683e-02, 2.03488098e+00, -1.73733048e+01,
6.56038443e+01, -8.84158092e+01],
None, False, True),
"v1.1" : ModelInfo("curves_v1",
[-8.80650683e-02, 2.03488098e+00, -1.73733048e+01,
6.56038443e+01, -8.84158092e+01],
None, True, True),
"v2" : ModelInfo("curves_v1",
[1.0, 0.0],
[-1.59395767e-14, 3.10388106e-10,
-2.26855051e-06, 7.38507004e-03,
-8.06953973e+00], False, True,
lw_scaling=linewidth_f50_scaling_v1),
"v3" : ModelInfo("curves_v1",
[-0.04162407, 0.80167981, -4.11209695, 9.95753597],
[1.46963918e-15, -6.68766843e-11, 7.56849155e-07, -3.28661164e-03,
5.95152597e+00],
False, True,
lw_scaling=linewidth_f50_scaling_v1,
wl_collapse=True),
"v4" : ModelInfo("curves_v2",
[1.0, 0.0],
[-4.10848216e-11, 6.68571465e-07,
-3.55515621e-03, 6.99466106e+00],
False, False,
lw_scaling=linewidth_f50_scaling_v1,
single_snfile=True)
}
default = "v4"
if not flim_model:
flim_model = default
model = models[flim_model]
if verbose:
print("Using flim model: {:s}".format(flim_model))
if flim_model_cache.cached_model == flim_model and cache_sim_interp:
sinterp = flim_model_cache.cached_sim_interp
else:
conf = HDRconfig()
fdir = conf.flim_sim_completeness
fdir = join(fdir, model.snfile_version)
sinterp = SimulationInterpolator(fdir, model.dont_interp_to_zero,
snmode=False, verbose=verbose,
wl_collapse=model.wl_collapse,
single_snfile=model.single_snfile)
# save model in cache
if cache_sim_interp:
flim_model_cache.cached_model = flim_model
flim_model_cache.cached_sim_interp = sinterp
def f50_from_noise(noise, lambda_, sncut, linewidth = None):
"""
Return the 50% completeness
flux given noise and S/N cut.
Parameters
----------
noise : float
the noise from the
sensitivity cubes
sncut : float
the signal to noise
cut to assume
linewidth : float
the linewidth in A,
only used with supported
models
Returns
-------
f50s : array
the fluxes at 50%
completeness
"""
try:
if sncut < model.snlow or sncut > model.snhigh:
print("WARNING: model {:s} not calibrated for this S/N range".format(flim_model))
except ValueError:
if any(sncut < 4.5) or any(ncut > 7.5):
print("WARNING: model {:s} not calibrated for this S/N range".format(flim_model))
bad = noise > 998
if model.wavepoly:
noise = noise*polyval(model.wavepoly, lambda_)
if type(linewidth) != type(None):
if type(model.lw_scaling) != type(None):
lw_scale = model.lw_scaling(linewidth, sncut)
else:
raise NoLineWidthModel("Linewidth dependence not available for this flim model")
noise = noise*lw_scale
snmult = polyval(model.snpoly, sncut)
f50 = snmult*noise
# keep bad values unscaled
try:
f50[bad] = 999
except TypeError:
if bad:
f50 = 999
return f50
return f50_from_noise, sinterp, model.interp_sigmas
|
HETDEXREPO_NAMEhetdex_apiPATH_START.@hetdex_api_extracted@hetdex_api-master@hetdex_api@flux_limits@flim_models.py@.PATH_END.py
|
{
"filename": "popen_fork.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/tools/python3/Lib/multiprocessing/popen_fork.py",
"type": "Python"
}
|
import os
import signal
from . import util
__all__ = ['Popen']
#
# Start child process using fork
#
class Popen(object):
method = 'fork'
def __init__(self, process_obj):
util._flush_std_streams()
self.returncode = None
self.finalizer = None
self._launch(process_obj)
def duplicate_for_child(self, fd):
return fd
def poll(self, flag=os.WNOHANG):
if self.returncode is None:
try:
pid, sts = os.waitpid(self.pid, flag)
except OSError:
# Child process not yet created. See #1731717
# e.errno == errno.ECHILD == 10
return None
if pid == self.pid:
self.returncode = os.waitstatus_to_exitcode(sts)
return self.returncode
def wait(self, timeout=None):
if self.returncode is None:
if timeout is not None:
from multiprocessing.connection import wait
if not wait([self.sentinel], timeout):
return None
# This shouldn't block if wait() returned successfully.
return self.poll(os.WNOHANG if timeout == 0.0 else 0)
return self.returncode
def _send_signal(self, sig):
if self.returncode is None:
try:
os.kill(self.pid, sig)
except ProcessLookupError:
pass
except OSError:
if self.wait(timeout=0.1) is None:
raise
def terminate(self):
self._send_signal(signal.SIGTERM)
def kill(self):
self._send_signal(signal.SIGKILL)
def _launch(self, process_obj):
code = 1
parent_r, child_w = os.pipe()
child_r, parent_w = os.pipe()
self.pid = os.fork()
if self.pid == 0:
try:
os.close(parent_r)
os.close(parent_w)
code = process_obj._bootstrap(parent_sentinel=child_r)
finally:
os._exit(code)
else:
os.close(child_w)
os.close(child_r)
self.finalizer = util.Finalize(self, util.close_fds,
(parent_r, parent_w,))
self.sentinel = parent_r
def close(self):
if self.finalizer is not None:
self.finalizer()
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@tools@python3@Lib@multiprocessing@popen_fork.py@.PATH_END.py
|
{
"filename": "_circle.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/layout/mapbox/layer/_circle.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CircleValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="circle", parent_name="layout.mapbox.layer", **kwargs
):
super(CircleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Circle"),
data_docs=kwargs.pop(
"data_docs",
"""
radius
Sets the circle radius
(mapbox.layer.paint.circle-radius). Has an
effect only when `type` is set to "circle".
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@layout@mapbox@layer@_circle.py@.PATH_END.py
|
{
"filename": "test_ncdim.py",
"repo_name": "joshspeagle/dynesty",
"repo_path": "dynesty_extracted/dynesty-master/tests/test_ncdim.py",
"type": "Python"
}
|
import numpy as np
from numpy import linalg
import numpy.testing as npt
import dynesty
import pytest
import itertools
from dynesty import utils as dyfunc
from utils import get_rstate, get_printing
"""
A rudimentary test that ncdim parameter works
"""
nlive = 500
printing = get_printing
def bootstrap_tol(results, rstate):
""" Compute the uncertainty of means/covs by doing bootstrapping """
n = len(results['logz'])
niter = 50
pos = results.samples
wts = results.importance_weights()
means = []
covs = []
for i in range(niter):
xid = rstate.integers(n, size=n)
mean, cov = dyfunc.mean_and_cov(pos[xid], wts[xid])
means.append(mean)
covs.append(cov)
return np.std(means, axis=0), np.std(covs, axis=0)
def check_results(results,
mean_truth,
cov_truth,
logz_truth,
mean_tol,
cov_tol,
logz_tol,
sig=5):
""" Check if means and covariances match match expectations
within the tolerances
"""
pos = results.samples
wts = results.importance_weights()
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results['logz'][-1]
npt.assert_array_less(np.abs(mean - mean_truth), sig * mean_tol)
npt.assert_array_less(np.abs(cov - cov_truth), sig * cov_tol)
npt.assert_array_less(np.abs((logz_truth - logz)), sig * logz_tol)
# GAUSSIAN TEST
ndim_gau = 3
ntotdim = 5
mean_gau = np.linspace(-1, 1, ndim_gau)
cov_gau = np.identity(ndim_gau) # set covariance to identity matrix
cov_gau[cov_gau == 0] = 0.95 # set off-diagonal terms (strongly correlated)
cov_inv_gau = linalg.inv(cov_gau) # precision matrix
lnorm_gau = -0.5 * (np.log(2 * np.pi) * ndim_gau + np.log(linalg.det(cov_gau)))
prior_win = 10 # +/- 10 on both sides
logz_truth_gau = ndim_gau * (-np.log(2 * prior_win))
mean_vec = np.concatenate((mean_gau, np.zeros(ntotdim - ndim_gau)))
cov_true = np.zeros((ntotdim, ntotdim))
cov_true[:ndim_gau, :ndim_gau] = cov_gau
cov_true[-(ntotdim - ndim_gau):,
-(ntotdim - ndim_gau):] = np.eye(ntotdim -
ndim_gau) * prior_win**2 / 3
def check_results_gau(results, rstate, logz_tol, sig=5):
mean_tol, cov_tol = bootstrap_tol(results, rstate)
check_results(results,
mean_vec,
cov_true,
logz_truth_gau,
mean_tol,
cov_tol,
logz_tol,
sig=sig)
# 3-D correlated multivariate normal log-likelihood
def loglikelihood_gau(x0):
"""Multivariate normal log-likelihood."""
x = x0[:ndim_gau]
return -0.5 * np.dot((x - mean_gau), np.dot(cov_inv_gau,
(x - mean_gau))) + lnorm_gau
# prior transform
def prior_transform_gau(u):
"""Flat prior between -10. and 10."""
return prior_win * (2. * u - 1.)
def test_gaussian():
logz_tol = 1
rstate = get_rstate()
sampler = dynesty.NestedSampler(loglikelihood_gau,
prior_transform_gau,
ntotdim,
nlive=nlive,
ncdim=ndim_gau,
rstate=rstate)
sampler.run_nested(print_progress=printing)
# check that jitter/resample work
# for not dynamic sampler
dyfunc.jitter_run(sampler.results, rstate=rstate)
dyfunc.resample_run(sampler.results, rstate=rstate)
# add samples
# check continuation behavior
sampler.run_nested(dlogz=0.1, print_progress=printing)
# get errors
nerr = 2
result_list = []
for i in range(nerr):
sampler.reset()
sampler.run_nested(print_progress=False)
results = sampler.results
result_list.append(results)
pos = results.samples
wts = results.importance_weights()
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results['logz'][-1]
assert (np.abs(logz - logz_truth_gau) < logz_tol)
res_comb = dyfunc.merge_runs(result_list)
assert (np.abs(res_comb['logz'][-1] - logz_truth_gau) < logz_tol)
# check summary
res = sampler.results
res.summary()
def test_dynamic():
# check dynamic nested sampling behavior
logz_tol = 1
rstate = get_rstate()
dsampler = dynesty.DynamicNestedSampler(loglikelihood_gau,
prior_transform_gau,
ntotdim,
ncdim=ndim_gau,
rstate=rstate)
dsampler.run_nested(print_progress=printing)
check_results_gau(dsampler.results, rstate, logz_tol)
@pytest.mark.parametrize('bound,periodic',
itertools.product(['single', 'multi'], [False, True]))
def test_single_periodic(bound, periodic):
# check single/multi ellipse bound with and without periodic vars
logz_tol = 1
rstate = get_rstate()
if periodic:
periodic = [0]
else:
periodic = None
dsampler = dynesty.NestedSampler(loglikelihood_gau,
prior_transform_gau,
ntotdim,
ncdim=ndim_gau,
periodic=periodic,
bound=bound,
rstate=rstate)
dsampler.run_nested(print_progress=printing)
check_results_gau(dsampler.results, rstate, logz_tol)
|
joshspeagleREPO_NAMEdynestyPATH_START.@dynesty_extracted@dynesty-master@tests@test_ncdim.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "axgoujon/convex_ridge_regularizers",
"repo_path": "convex_ridge_regularizers_extracted/convex_ridge_regularizers-main/hyperparameter_tuning/README.md",
"type": "Markdown"
}
|
Given a score function, the script [validate_coarse_to_fine.py](https://github.com/axgoujon/convex_ridge_regularizers/blob/main/validate_coarse_to_fine.py) allows one to tune two hyperparameters with the simple coarse-to-fine approach given in the [paper](https://ieeexplore.ieee.org/document/10223264) (or [open access version](https://arxiv.org/pdf/2211.12461.pdf)).
Requirements
--------------
* python >= 3.8
* pandas
* numpy
How to
--------------
The goal is to tune two parameters (p1,p2), for e.g. $(\lambda,\mu)$.
**Requirements**
The routine requires a score function `score(p1, p2)`. This function takes the hyperparameters as input and should return a performance metric. Typically, this function loops over the validation set, solve the inverse problem for each image, then return the average performance in the form `(psnr, ssim, niter)`.
**Nb 1:** as implemented, the optimization is based only on the PSNR, the SSIM and the number of iterations to converge are used only for logging. Hence their value does not modify the outcome of the algorithm and you could put any other metric.
**Nb 2:** if optimizing a single parameter (```freeze_p2=True```), `score(p1)` is expected to only take a single input.
**Usage**
```python
# initialize the validation process
validator = ValidateCoarseToFine(score, dir_name="./", exp_name="CsMRI_Mask1", p1_init=0.1, p2_init=10, freeze_p2=False)
# run the validation
validator.run()
```
**Output**
Each time the score function is called, the results are stored in a local database, namely a simple *.csv* file. The process is stopped when the grid size is sufficiently small. Then, one can identify the more promising parameters from the *.csv*.
The *.csv* also allows to skip the calls to the score function for parameters that have already been used.
**Tuning one parameter**
The routine can be used to tune a single parameter by setting `freeze_p2=True`.
**Validation set**
For the experiments presented in the [paper](https://ieeexplore.ieee.org/document/10223264) (or [open access version](https://arxiv.org/pdf/2211.12461.pdf)), it was noticed that a small validation set (<=10 well chosen samples) suffices to generalize well. Hence the tuning phase is rather fast.
|
axgoujonREPO_NAMEconvex_ridge_regularizersPATH_START.@convex_ridge_regularizers_extracted@convex_ridge_regularizers-main@hyperparameter_tuning@README.md@.PATH_END.py
|
{
"filename": "primitives_gmos_spect.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/geminidr/gmos/primitives_gmos_spect.py",
"type": "Python"
}
|
#
# gemini_python
#
# primtives_gmos_spect.py
# ------------------------------------------------------------------------------
import gc
import os
import numpy as np
from importlib import import_module
from datetime import datetime
from copy import copy, deepcopy
from functools import partial
from astropy.modeling import models, Model
from astropy import units as u
from scipy.interpolate import UnivariateSpline
import geminidr
from geminidr.core import Spect
from gempy.library.fitting import fit_1D
from .primitives_gmos import GMOS
from . import parameters_gmos_spect
from geminidr.gemini.lookups import DQ_definitions as DQ
from geminidr.gmos.lookups import geometry_conf as geotable
from gempy.gemini import gemini_tools as gt
from gempy.library import astromodels as am
from gempy.library import transform, wavecal
from recipe_system.utils.decorators import parameter_override, capture_provenance
from ..interactive.fit.wavecal import WavelengthSolutionVisualizer
from ..interactive.interactive import UIParameters
# Put this here for now!
def qeModel(ext, use_iraf=False):
"""
This function returns a callable object that returns the QE of a CCD
(relative to CCD2) as a function of wavelength(s) in nm. The QE data is
provided as a dict, keyed by the array_name() descriptor of the CCD.
The value is either a list (interpreted as polynomial coefficients) or a
dict describing a spline.
In addition, if the model changes, the value can be a dict keyed by the
earliest UT date at which each model should be applied.
Parameters
----------
ext : single-slice AstroData object
the extension to calculate the QE coefficients for
use_iraf : bool
use IRAF fits rather than DRAGONS ones?
Returns
-------
callable: a function to convert wavelengths in nm to relative QE
"""
# All coefficients are for nm (not AA as in G-IRAF)
qeData = {
# GMOS-N EEV CCD1 and 3
"EEV 9273-16-03": [9.883090E-1, -1.390254E-5, 5.282149E-7, -6.847360E-10],
"EEV 9273-20-03": [9.699E-1, 1.330E-4, -2.082E-7, 1.206E-10],
# GMOS-N Hamamatsu CCD1 and 3
"BI13-20-4k-1": {"order": 3,
"knots": [366.5, 413.5, 435.5, 465., 478.5, 507.5, 693., 1062.],
"coeffs": [1.20848283, 1.59132929, 1.58317142, 1.25123198, 1.14410563,
0.98095206, 0.83416436, 1.03247587, 1.15355675, 1.10176507]},
"BI13-18-4k-2": {"order": 3,
"knots": [341.75, 389.5, 414., 447.5, 493., 592., 694.5, 1057.],
"coeffs": [0.90570141, 0.99834392, 1.6311227 , 1.47271364, 1.13843214,
0.91170917, 0.88454097, 1.06456595, 1.16684561, 1.10476059]},
# IRAF coefficients
("BI13-20-4k-1", "IRAF"): [-2.45481760e+03, 3.24130657e+01, -1.87380500e-01,
6.23494400e-04, -1.31713482e-06, 1.83308885e-09,
-1.68145852e-12, 9.80603592e-16, -3.30016761e-19,
4.88466076e-23],
("BI13-18-4k-2", "IRAF"): [3.48333720e+03, -5.27904605e+01, 3.48210500e-01,
-1.31286828e-03, 3.12154994e-06, -4.85949692e-09,
4.95886638e-12, -3.20198283e-15, 1.18833302e-18,
-1.93303639e-22],
# GMOS-S EEV CCD1 and 3
"EEV 2037-06-03": {"1900-01-01": [2.8197, -8.101e-3, 1.147e-5, -5.270e-9],
"2006-08-31": [2.225037, -4.441856E-3, 5.216792E-6, -1.977506E-9]},
"EEV 8261-07-04": {"1900-01-01": [1.3771, -1.863e-3, 2.559e-6, -1.0289e-9],
"2006-08-31": [8.694583E-1, 1.021462E-3, -2.396927E-6, 1.670948E-9]},
# GMOS-S Hamamatsu CCD1 (original) and 3
"BI5-36-4k-2": {"order": 3,
"knots": [374., 409., 451., 523.5, 584.5, 733.5, 922., 1070.75],
"coeffs": [1.04722893, 0.87968707, 0.70533794, 0.67657144, 0.71217743,
0.82421959, 0.94903734, 1.00847771, 0.98158784, 0.90798127]},
# CCD3 before and after replacement (it's the same CCD but this
# is a ratio to CCD2, which has changed)
"BI12-34-4k-1": {"1900-01-01": {"order": 3,
"knots": [340.25, 377.5, 406., 439.,
511.5, 601., 746., 916.5, 1070.],
"coeffs": [0.7433304, 1.07041859, 1.51006315,
1.4399747, 1.03126307, 0.84984109,
0.8944949, 1.02806209, 1.11960524,
1.1222421, 0.95279761]},
"2023-12-14": {"order": 3,
"knots": [342.75, 361.0, 371.0, 405.0, 432.0,
458.5, 582.0, 715.5, 1043.5],
"coeffs": [1.1720608, 1.6827764, 2.1932968,
0.6824087, 1.0712193, 1.0245441,
0.9778866, 0.9672609, 0.9682988,
0.97707780, 0.9745534]}},
# GMOS-S Hamamatsu new CCD1
"BI11-41-4k-2": {"order": 3,
"knots": [408.0, 487.0, 524.0, 568.5, 775.5, 1096.0],
"coeffs": [0.8045865, 0.9031578, 1.1704650, 1.1776077, 1.0853394,
0.8585280, 0.8748014, 0.8858184]},
# IRAF coefficients
("BI5-36-4k-2", "IRAF"): [-6.00810046e+02, 6.74834788e+00, -3.26251680e-02,
8.87677395e-05, -1.48699188e-07, 1.57120033e-10,
-1.02326999e-13, 3.75794380e-17, -5.96238257e-21],
("BI12-34-4k-1", "IRAF"): [7.44793105e+02, -1.22941630e+01, 8.83657074e-02,
-3.62949805e-04, 9.40246850e-07, -1.59549327e-09,
1.77557909e-12, -1.25086490e-15, 5.06582071e-19,
-8.99166534e-23]
}
array_name = ext.array_name().split(',')[0]
key = (array_name, "IRAF") if use_iraf else array_name
try:
data = qeData[key]
except KeyError:
try: # fallback for older CCDs where the IRAF solution isn't labelled
data = qeData[array_name]
except KeyError:
return None
# Deal with date-dependent changes
if isinstance(data, dict) and 'knots' not in data:
obs_date = ext.ut_date()
for k in sorted(data):
if obs_date >= datetime.strptime(k, "%Y-%m-%d").date():
use_data = data[k]
data = use_data
# data is either a dict defining a spline that defines QE
# or a list of polynomial coefficients that define QE
if 'knots' in data:
# Duplicate the knots at either end for the correct format
order = data["order"]
knots = data["knots"]
knots[0:0] = [knots[0]] * order
knots.extend(knots[-1:] * order)
coeffs = data["coeffs"] + [0] * (order+1)
spline = UnivariateSpline._from_tck((knots, coeffs, order))
return spline
else:
model_params = {'c{}'.format(i): c for i, c in enumerate(data)}
model = models.Polynomial1D(degree=len(data)-1, **model_params)
return model
# ------------------------------------------------------------------------------
@parameter_override
@capture_provenance
class GMOSSpect(Spect, GMOS):
"""
This is the class containing all of the preprocessing primitives
for the GMOSSpect level of the type hierarchy tree. It inherits all
the primitives from the level above
"""
tagset = {"GEMINI", "GMOS", "SPECT"}
def _initialize(self, adinputs, **kwargs):
super()._initialize(adinputs, **kwargs)
self._param_update(parameters_gmos_spect)
def QECorrect(self, adinputs=None, **params):
"""
This primitive applies a wavelength-dependent QE correction to
a 2D spectral image, based on the wavelength solution in the WCS
(from `attachWavelengthSolution` or, in non-SQ-modes, the initial
linear approximation).
It is only designed to work on FLATs, and therefore unmosaicked data.
Parameters
----------
suffix: str
suffix to be added to output files
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
sfx = params["suffix"]
use_iraf = params["use_iraf"]
do_cal = params["do_cal"]
if do_cal == 'skip':
log.warning("QE correction has been turned off.")
return adinputs
taper_locut, taper_losig = 350, 25 # nm
taper_hicut, taper_hisig = 1200, 200
xgrid = np.array([])
for ad in adinputs:
if ad.phu.get(timestamp_key):
log.warning(f"{ad.filename}: already processed by QECorrect. "
"Continuing.")
continue
if 'e2v' in ad.detector_name(pretty=True):
log.stdinfo(f"{ad.filename} has the e2v CCDs, so no QE "
"correction is necessary")
continue
if self.timestamp_keys['mosaicDetectors'] in ad.phu:
msg = (f"{ad.filename} has been processed by mosaicDetectors "
"so cannot correct QE for each CCD")
if 'sq' in self.mode or do_cal == 'force':
raise ValueError(msg)
log.warning(msg)
continue
# Determines whether to multiply or divide by QE correction
is_flat = 'FLAT' in ad.tags
array_info = gt.array_information(ad)
if array_info.detector_shape == (1, 3):
ccd2_indices = array_info.extensions[1]
else:
raise ValueError(f"{ad.filename} does not have 3 separate detectors")
for index, ext in enumerate(ad):
if index in ccd2_indices:
continue
# astropy issue #17094: need to copy the WCS to avoid
# enormous memory usag
trans = deepcopy(ext.wcs.forward_transform)
# There should always be a wavelength model (even if it's an
# approximation) as long as the data have been prepared, but
# check and produce a clear error if not:
try:
am.get_named_submodel(trans, 'WAVE')
except (AttributeError, IndexError):
raise ValueError('No wavelength solution for '
f'{ad.filename}, extension {ext.id}')
# For SQ, require that the distortion correction is included,
# either in the WCS or possibly by prior rectification (though
# this is a corner case since mosaicking is disallowed). This
# check might need revisiting if distortion correction gets
# included in any other resampling steps in future, but by that
# point we may be propagating an "already applied" WCS (from
# resampled to raw co-ordinates) that would make it easier.
if ('distortion_corrected' not in ext.wcs.available_frames and
self.timestamp_keys['distortionCorrect'] not in ad.phu):
msg = ('No distortion correction in WCS for '
f'{ad.filename}, extension {ext.id}')
if 'sq' in self.mode:
raise ValueError(msg)
log.warning(msg)
if xgrid.shape != ext.shape:
ygrid, xgrid = np.mgrid[:ext.shape[0], :ext.shape[1]]
taper = np.ones_like(ext.data)
waves = trans(xgrid, ygrid)[0] # Wavelength always axis 0
qe_correction = np.empty_like(ext.data)
else:
taper[:] = 1.
waves[:] = trans(xgrid, ygrid)[0] # Wavelength always axis 0
# Tapering required to prevent QE correction from blowing up
# at the extremes (remember, this is a ratio, not the actual QE)
# We use half-Gaussians to taper
taper[waves < taper_locut] = np.exp(-((waves
- taper_locut) / taper_losig) ** 2)[waves < taper_locut]
taper[waves > taper_hicut] = np.exp(-((waves
- taper_hicut) / taper_hisig) ** 2)[waves > taper_hicut]
try:
qe_correction[:] = (qeModel(ext, use_iraf=use_iraf)(
waves).astype(np.float32) - 1) * taper + 1
except TypeError: # qeModel() returns None
msg = f"No QE correction found for {ad.filename} extension {ext.id}"
if 'sq' in self.mode:
raise ValueError(msg)
else:
log.warning(msg)
continue
log.stdinfo(f"Mean relative QE of extension {ext.id} is "
f"{qe_correction.mean():.5f}")
if not is_flat:
qe_correction[:] = 1. / qe_correction
ext.multiply(qe_correction)
# The other half of preventing enormous memory usage
del trans
gc.collect()
# Timestamp and update the filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=sfx, strip=True)
return adinputs
def findAcquisitionSlits(self, adinputs=None, **params):
"""
This primitive determines which rows of a 2D spectroscopic frame
contain the stars used for target acquisition, primarily so they can
be used later to estimate the image FWHM. This is done by cross-
correlating a vertical cut of the image with a cartoon model of the
slit locations determined from the MDF.
Parameters
----------
suffix: str
suffix to be added to output files
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
for ad in adinputs:
# First, check we want to process this: not if it's already been
# processed; or has no MDF; or has no acquisition stars in the MDF
if ad.phu.get(timestamp_key):
log.warning("No changes will be made to {}, since it has "
"already been processed by findAcqusitionSlits".
format(ad.filename))
continue
try:
mdf = ad.MDF
except AttributeError:
log.warning("No MDF associated with {}".format(ad.filename))
continue
if 'priority' not in mdf.columns:
log.warning("No acquisition slits in {}".format(ad.filename))
continue
# Tile and collapse along wavelength direction
ad_tiled = self.tileArrays([ad], tile_all=True)[0]
# Ignore bad pixels (non-linear/saturated are OK)
if ad_tiled[0].mask is None:
mask = None
else:
mask = ad_tiled[0].mask & ~(DQ.non_linear | DQ.saturated)
spatial_profile = np.ma.array(ad_tiled[0].data,
mask=mask).sum(axis=1)
# Construct a theoretical illumination map from the MDF data
slits_profile = np.zeros_like(spatial_profile)
image_pix_scale = ad.pixel_scale()
shuffle = ad.shuffle_pixels() // ad.detector_y_bin()
# It is possible to use simply the MDF information in mm to get
# the necessary slit position data, but this relies on knowing
# the distortion correction. It seems better to use the MDF
# pixel information, if it exists.
try:
mdf_pix_scale = mdf.meta['header']['PIXSCALE']
except KeyError:
mdf_pix_scale = ad.pixel_scale() / ad.detector_y_bin()
# There was a problem with the mdf_pix_scale for GMOS-S pre-2009B
# Work around this because the two pixel scales should be in a
# simple ratio (3:2, 2:1, etc.)
ratios = np.array([1.*a/b for a in range(1,6) for b in range(1,6)])
# Here we have to account for the EEV->Hamamatsu change
# (I've future-proofed this for the same event on GMOS-N)
ratios = np.append(ratios,[ratios*0.73/0.8,ratios*0.727/0.807])
nearest_ratio = ratios[np.argmin(abs(mdf_pix_scale /
image_pix_scale - ratios))]
# -1 because python is zero-indexed (see +1 later)
slits_y = mdf['y_ccd'] * nearest_ratio - 1
try:
slits_width = mdf['slitsize_y']
except KeyError:
slits_width = mdf['slitsize_my'] * 1.611444
for (slit, width) in zip(slits_y, slits_width):
slit_ymin = slit - 0.5*width/image_pix_scale
slit_ymax = slit + 0.5*width/image_pix_scale
# Only add slit if it wasn't shuffled off top of CCD
if slit < ad_tiled[0].data.shape[0]-shuffle:
slits_profile[max(int(slit_ymin),0):
min(int(slit_ymax+1),len(slits_profile))] = 1
if slit_ymin > shuffle:
slits_profile[int(slit_ymin-shuffle):
int(slit_ymax-shuffle+1)] = 1
# Cross-correlate collapsed image with theoretical profile
c = np.correlate(spatial_profile, slits_profile, mode='full')
slit_offset = np.argmax(c)-len(spatial_profile) + 1
# Work out where the alignment slits actually are!
# NODAYOFF should possibly be incorporated here, to better estimate
# the locations of the positive traces, but I see inconsistencies
# in the sign (direction of +ve values) for different datasets.
acq_slits = np.logical_and(mdf['priority']=='0',
slits_y<ad_tiled[0].data.shape[0]-shuffle)
# Slits centers and widths
acq_slits_y = (slits_y[acq_slits] + slit_offset + 0.5).astype(int)
acq_slits_width = (slits_width[acq_slits] / image_pix_scale +
0.5).astype(int)
star_list = ' '.join('{}:{}'.format(y,w) for y,w in
zip(acq_slits_y,acq_slits_width))
ad.phu.set('ACQSLITS', star_list,
comment=self.keyword_comments['ACQSLITS'])
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=params["suffix"], strip=True)
return adinputs
def flagCosmicRays(self, adinputs=None, **params):
"""
Detect and clean cosmic rays in a 2D wavelength-dispersed image,
using the well-known LA Cosmic algorithm of van Dokkum (2001)*, as
implemented in McCully's optimized version for Python, "astroscrappy"+.
* LA Cosmic: http://www.astro.yale.edu/dokkum/lacosmic
+ astroscrappy: https://github.com/astropy/astroscrappy
Parameters
----------
suffix : str
Suffix to be added to output files.
spectral_order, spatial_order : int or None, optional
Order for fitting and subtracting object continuum and sky line
models, prior to running the main cosmic ray detection algorithm.
When None, defaults optimized for GMOS are used. To control which
fits are performed, use the bkgmodel parameter.
bkgmodel : {'both', 'object', 'skyline', 'none'}, optional
Set which background model(s) to use, between 'object', 'skyline',
'both', or 'none'. Different data may get better results with
different background models.
'both': Use both object and sky line models.
'object': Use object model only.
'skyline': Use sky line model only.
'none': Don't use a background model.
Default: 'skyline'.
bitmask : int, optional
Bits in the input data quality `flags` that are to be used to
exclude bad pixels from cosmic ray detection and cleaning. Default
65535 (all non-zero bits, up to 16 planes).
sigclip : float, optional
Laplacian-to-noise limit for cosmic ray detection. Lower values
will flag more pixels as cosmic rays. Default: 4.5.
sigfrac : float, optional
Fractional detection limit for neighboring pixels. For cosmic ray
neighbor pixels, a lapacian-to-noise detection limit of
sigfrac * sigclip will be used. Default: 0.3.
objlim : float, optional
Minimum contrast between Laplacian image and the fine structure
image. Increase this value if cores of bright stars are flagged
as cosmic rays. Default: 5.0.
niter : int, optional
Number of iterations of the LA Cosmic algorithm to perform.
Default: 4.
sepmed : boolean, optional
Use the separable median filter instead of the full median filter.
The separable median is not identical to the full median filter,
but they are approximately the same and the separable median filter
is significantly faster and still detects cosmic rays well.
Default: True
cleantype : {'median', 'medmask', 'meanmask', 'idw'}, optional
Set which clean algorithm is used:
'median': An umasked 5x5 median filter
'medmask': A masked 5x5 median filter
'meanmask': A masked 5x5 mean filter
'idw': A masked 5x5 inverse distance weighted interpolation
Default: "meanmask".
fsmode : {'median', 'convolve'}, optional
Method to build the fine structure image:
'median': Use the median filter in the standard LA Cosmic algorithm
'convolve': Convolve the image with the psf kernel to calculate the
fine structure image.
Default: 'median'.
psfmodel : {'gauss', 'gaussx', 'gaussy', 'moffat'}, optional
Model to use to generate the psf kernel if fsmode == 'convolve' and
psfk is None. The current choices are Gaussian and Moffat profiles.
'gauss' and 'moffat' produce circular PSF kernels. The 'gaussx' and
'gaussy' produce Gaussian kernels in the x and y directions
respectively. Default: "gauss".
psffwhm : float, optional
Full Width Half Maximum of the PSF to use to generate the kernel.
Default: 2.5.
psfsize : int, optional
Size of the kernel to calculate. Returned kernel will have size
psfsize x psfsize. psfsize should be odd. Default: 7.
psfbeta : float, optional
Moffat beta parameter. Only used if fsmode=='convolve' and
psfmodel=='moffat'. Default: 4.765.
verbose : boolean, optional
Print to the screen or not. Default: False.
debug : bool
Enable plots for debugging and store object and sky fits in the
ad objects.
"""
spectral_order_param = params.pop('spectral_order')
spatial_order_param = params.pop('spatial_order')
for ad in adinputs:
spectral_order = 9 if spectral_order_param is None else spectral_order_param
# Values selected to work on skyline-heavy data.
# Eg. R400, 750nm, >1000 sec.
# In some cases, the curvature of the lines lead to a really bad
# sky line model unless the order is rather large.
# The users should pay attention and adjust spatial_order when
# the defaults below do not work. We need a better solution.
# The values are set purely from empirical evidence, we don't
# fully understand.
if ad.detector_roi_setting() == 'Full Frame':
spatial_order = ((45 if ad.detector_x_bin() >= 2 else 5)
if spatial_order_param is None else spatial_order_param)
elif ad.detector_roi_setting() == 'Central Spectrum':
spatial_order = ((15 if ad.detector_x_bin() == 2 else 5)
if spatial_order_param is None else spatial_order_param)
else: # custom ROI? Use the generic flagCR default.
spatial_order = None
# flagCosmicRays() modifies in-place so just call it
if spatial_order is None:
super().flagCosmicRays([ad],
spectral_order=spectral_order,
**params)
else:
super().flagCosmicRays([ad],
spectral_order=spectral_order,
spatial_order=spatial_order,
**params)
return adinputs
def standardizeWCS(self, adinputs=None, **params):
"""
This primitive updates the WCS attribute of each NDAstroData extension
in the input AstroData objects. For spectroscopic data, it means
replacing an imaging WCS with an approximate spectroscopic WCS.
This is a GMOS-specific primitive due to the systematic offsets for
GMOS-S at central wavelengths > 950nm.
Parameters
----------
suffix: str/None
suffix to be added to output files
"""
log = self.log
timestamp_key = self.timestamp_keys[self.myself()]
log.debug(gt.log_message("primitive", self.myself(), "starting"))
super().standardizeWCS(adinputs, **params)
for ad in adinputs:
log.stdinfo(f"Adding spectroscopic WCS to {ad.filename}")
cenwave = ad.central_wavelength(asNanometers=True)
if ad.instrument() == "GMOS-S" and cenwave > 950:
cenwave += (6.89483617 - 0.00332086 * cenwave) * cenwave - 3555.048
else:
cenwave = cenwave
transform.add_longslit_wcs(ad, central_wavelength=cenwave)
# Timestamp. Suffix was updated in the super() call
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
return adinputs
def _get_arc_linelist(self, waves=None, ext=None):
# There aren't many lines in the very red, so one way to improve the
# wavecal might have been to take out any blocking filter to get all the
# lines from ~500 nm at twice the wavelength. The GMOS team doesn't do
# that, however, so it would just results in a bunch of extra lines that
# don't actually exist, so keep use_second_order = False here; the code
# is left as a template for how such stuff might operate.
use_second_order = waves.max() > 1000 and abs(np.diff(waves).mean()) < 0.2
use_second_order = False
lookup_dir = os.path.dirname(import_module('.__init__',
self.inst_lookups).__file__)
filename = os.path.join(lookup_dir,
'CuAr_GMOS{}.dat'.format('_mixord' if use_second_order else ''))
return wavecal.LineList(filename)
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@geminidr@gmos@primitives_gmos_spect.py@.PATH_END.py
|
{
"filename": "_dx.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/waterfall/_dx.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class DxValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="dx", parent_name="waterfall", **kwargs):
super(DxValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@waterfall@_dx.py@.PATH_END.py
|
{
"filename": "misc.py",
"repo_name": "ytree-project/ytree",
"repo_path": "ytree_extracted/ytree-main/ytree/frontends/rockstar/misc.py",
"type": "Python"
}
|
"""
RockstarArbor miscellany
"""
#-----------------------------------------------------------------------------
# Copyright (c) ytree development team. All rights reserved.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
class Group:
def __init__(self, name=None):
self.things = None
if name is not None:
self.add_thing(
lambda a: name.lower() in a.lower())
self.name = name
def add_thing(self, thing):
if self.things is None:
self.things = []
self.things.append(thing)
def in_group(self, item):
if self.things is None:
return False
for thing in self.things:
try:
if thing(item):
return True
except Exception:
continue
return False
|
ytree-projectREPO_NAMEytreePATH_START.@ytree_extracted@ytree-main@ytree@frontends@rockstar@misc.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/analysis_modules/halo_mass_function/__init__.py",
"type": "Python"
}
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@analysis_modules@halo_mass_function@__init__.py@.PATH_END.py
|
|
{
"filename": "time.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/tests/fixtures/time.py",
"type": "Python"
}
|
from datetime import timedelta
from typing import Callable, Optional, Union
import pendulum
import pytest
from pendulum import DateTime
from pendulum.tz.timezone import Timezone
@pytest.fixture
def frozen_time(monkeypatch: pytest.MonkeyPatch) -> pendulum.DateTime:
frozen = pendulum.now("UTC")
def frozen_time(tz: Optional[Union[str, Timezone]] = None):
if tz is None:
return frozen
return frozen.in_timezone(tz)
monkeypatch.setattr(pendulum, "now", frozen_time)
return frozen
@pytest.fixture
def advance_time(monkeypatch: pytest.MonkeyPatch) -> Callable[[timedelta], DateTime]:
clock = pendulum.now("UTC")
def advance(amount: timedelta):
nonlocal clock
clock += amount
return clock
def nowish(tz: Optional[Union[str, Timezone]] = None):
# each time this is called, advance by 1 microsecond so that time is moving
# forward bit-by-bit to avoid everything appearing to happen all at once
advance(timedelta(microseconds=1))
if tz is None:
return clock
return clock.in_timezone(tz)
monkeypatch.setattr(pendulum, "now", nowish)
return advance
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@tests@fixtures@time.py@.PATH_END.py
|
{
"filename": "base.py",
"repo_name": "florpi/sunbird",
"repo_path": "sunbird_extracted/sunbird-main/sunbird/emulators/models/base.py",
"type": "Python"
}
|
import torch
import numpy as np
from typing import Dict
from pathlib import Path
import yaml
import lightning as pl
from torch.optim.lr_scheduler import ReduceLROnPlateau
from flax.traverse_util import unflatten_dict
import sunbird.emulators.models as models
def convert_state_dict_from_pt(
model,
state,
):
"""
Converts a PyTorch parameter state dict to an equivalent Flax parameter state dict
"""
state = {k: v.numpy() for k, v in state.items()}
state = model.convert_from_pytorch(
state,
)
state = unflatten_dict({tuple(k.split(".")): v for k, v in state.items()})
return state
class BaseModel(pl.LightningModule):
def __init__(self, *args, **kwargs):
"""Base pytorch lightning model"""
super().__init__()
@classmethod
def from_folder(
cls,
path_to_model: str,
load_loss: bool = False,
) -> "BaseModel":
"""load a model from folder
Args:
path_to_model (str): path to model folder
Returns:
model: model loaded from checkpoint
"""
path_to_model = Path(path_to_model)
with open(path_to_model / "hparams.yaml") as f:
hparams = yaml.safe_load(f)
del hparams["load_loss"]
model = cls(**hparams, load_loss=False)
# find file with lowest validation loss
files = list((path_to_model / "checkpoints").glob("*.ckpt"))
file_idx = np.argmin(
[float(str(file).split(".ckpt")[0].split("=")[-1]) for file in files]
)
weights_dict = torch.load(
files[file_idx],
map_location=torch.device("cpu"),
)
state_dict = weights_dict["state_dict"]
model.load_state_dict(state_dict, strict=False)
return model
def training_step(self, batch, batch_idx) -> float:
"""Compute training loss
Args:
batch: batch
batch_idx: idx of batch
Returns:
float: loss
"""
loss = self._compute_loss(batch=batch, batch_idx=batch_idx)
self.log(
"train_loss",
loss,
logger=False,
)
#print(f'Epoch = {self.current_epoch}, step = {self.trainer.global_step}, wandb step = {self.logger.experiment.step}')
# self.logger.experiment.log({'train_loss': loss.item(), 'global_step': self.trainer.global_step+1}, step=self.trainer.global_step+1)
# self.log(
# "train_loss",
# loss.item(),
# prog_bar=True,
# logger=False,
# )
return loss
def validation_step(self, batch, batch_idx) -> float:
"""Compute validation loss
Args:
batch: batch
batch_idx: idx of batch
Returns:
float: loss
"""
loss = self._compute_loss(batch=batch, batch_idx=batch_idx)
self.log(
"val_loss",
loss,
logger=False,
)
# self.logger.experiment.log({'val_loss': loss.item(), 'global_step': self.trainer.global_step}, step=self.trainer.global_step)
# self.log(
# "val_loss",
# loss,
# prog_bar=True,
# logger=False,
# )
return loss
def test_step(self, batch, batch_idx):
"""Compute test loss
Args:
batch: batch
batch_idx: idx of batch
Returns:
float: loss
"""
loss = self._compute_loss(batch=batch, batch_idx=batch_idx)
self.log(
"test_loss",
loss,
)
return loss
def predict_step(
self,
batch,
batch_idx,
):
x, y = batch
return self(x)
def configure_optimizers(self) -> Dict:
"""configure optimizer and learning rate scheduler
Returns:
Dict: dictionary with configuration
"""
optimizer = torch.optim.AdamW(
self.parameters(),
lr=self.learning_rate,
weight_decay=self.weight_decay,
)
scheduler = ReduceLROnPlateau(
optimizer,
mode="min",
patience=self.scheduler_patience, #50,
factor=self.scheduler_factor, #0.5,
threshold=self.scheduler_threshold,
threshold_mode='abs',
min_lr=1.0e-6,
verbose=True,
)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
"interval": "epoch",
"frequency": 1,
},
}
@property
def flax_attributes(self,):
return
def to_jax(self,):
nn_model = getattr(models, f'Flax{self.__class__.__name__}')(
**self.flax_attributes,
)
flax_params = {'params': convert_state_dict_from_pt(
model=nn_model,
state=self.state_dict(),
)}
return nn_model, flax_params
|
florpiREPO_NAMEsunbirdPATH_START.@sunbird_extracted@sunbird-main@sunbird@emulators@models@base.py@.PATH_END.py
|
{
"filename": "test_extract.py",
"repo_name": "LCOGT/banzai-nres",
"repo_path": "banzai-nres_extracted/banzai-nres-main/banzai_nres/tests/test_extract.py",
"type": "Python"
}
|
import numpy as np
from banzai.data import CCDData
from banzai_nres.frames import NRESObservationFrame
from banzai_nres.extract import WeightedExtract, GetOptimalExtractionWeights
from banzai import context
class TestExtract:
def test_rejects_on_no_weights(self):
con = context.Context({})
assert WeightedExtract(con).do_stage(two_order_image()) is None
def test_rejects_on_no_wavelengths(self):
con = context.Context({})
image = type('image', (), {'weights': 'notnone', 'wavelengths': None})
assert WeightedExtract(con).do_stage(image) is None
def test_unit_weights_extraction(self):
image = two_order_image()
image.weights = np.ones_like(image.data)
expected_extracted_flux = np.max(image.data) * 3
expected_extracted_wavelength = np.max(image.wavelengths)
expected_extracted_uncertainty = np.sqrt(3) * np.max(image.data)
input_context = context.Context({})
stage = WeightedExtract(input_context)
output_image = stage.do_stage(image)
spectrum = output_image.spectrum
assert np.allclose(spectrum[0, 0]['flux'], expected_extracted_flux)
assert np.allclose(spectrum[0, 0]['wavelength'], expected_extracted_wavelength)
assert np.allclose(spectrum[0, 0]['uncertainty'], expected_extracted_uncertainty)
assert np.allclose(spectrum[0, 0]['id'], 1)
assert np.allclose(spectrum[1, 1]['flux'][1:-1], expected_extracted_flux)
assert np.allclose(spectrum[1, 1]['wavelength'][1:-1], expected_extracted_wavelength)
assert len(spectrum[1, 1]['flux']) == image.traces.shape[1] - 2
assert np.allclose(spectrum[1, 1]['uncertainty'][1:-1], expected_extracted_uncertainty)
assert len(spectrum[1, 1]['uncertainty']) == image.traces.shape[1] - 2
assert np.allclose(spectrum[1, 1]['id'], 2)
def test_extract_in_poisson_regime(self):
trace_width, number_traces = 20, 10
seed = 1408235915
image = five_hundred_square_image(50000, number_traces, trace_width, seed=seed)
expected_extracted_wavelength = np.max(image.wavelengths)
image2 = five_hundred_square_image(50000, number_traces, trace_width, seed=seed)
image2.profile = np.ones_like(image.data)
input_context = context.Context({})
stage = GetOptimalExtractionWeights(input_context)
image = stage.do_stage(image)
image2.weights = np.ones_like(image2.data)
stage2 = WeightedExtract(input_context)
optimal_image = stage2.do_stage(image)
box_image = stage2.do_stage(image2)
for i in range(1, number_traces + 1):
assert np.allclose(optimal_image.spectrum[i, i]['flux'], box_image.spectrum[i, i]['flux'], rtol=0.05)
assert np.allclose(optimal_image.spectrum[i, i]['wavelength'], expected_extracted_wavelength)
assert np.allclose(optimal_image.spectrum[i, i]['uncertainty'], box_image.spectrum[i, i]['uncertainty'],
rtol=0.05)
def test_extract_in_readnoise_regime(self):
trace_width, number_traces = 20, 10
seed = 192074123
image = five_hundred_square_image(100, number_traces, trace_width, read_noise=100, seed=seed)
image2 = five_hundred_square_image(100, number_traces, trace_width, read_noise=100, seed=seed)
image2.profile = np.ones_like(image.data)
input_context = context.Context({})
stage = GetOptimalExtractionWeights(input_context)
image = stage.do_stage(image)
image2.weights = np.ones_like(image2.data)
stage2 = WeightedExtract(input_context)
optimal_image = stage2.do_stage(image)
box_image = stage2.do_stage(image2)
for i in range(1, number_traces + 1):
optimal_median_sn = np.median(
optimal_image.spectrum[i, i]['flux'] / optimal_image.spectrum[i, i]['uncertainty'])
box_median_sn = np.median(box_image.spectrum[i, i]['flux'] / box_image.spectrum[i, i]['uncertainty'])
assert optimal_median_sn > 1.45 * box_median_sn
def test_if_robust_to_wavelength_region_mismatch_between_trace_region():
"""
This creates a simulated image where the trace region for the wavelength image
is different than image.traces by 1 pixel (a 1 pixel shift). This kind of shift
used to cause the wavelengths to be incorrect by
(trace width - 1)/trace width . This tests in particular this bug, because trace regions shift by 1 pixel nearly
every night (even though the traces themselves do not shift by much).
"""
image = two_order_image()
image.weights = np.ones_like(image.data)
expected_extracted_flux = np.max(image.data) * 3
expected_extracted_wavelength = np.max(image.wavelengths)
expected_extracted_uncertainty = np.sqrt(3) * np.max(image.data)
input_context = context.Context({})
# now we shift the wavelength image up by 1 pixel. This creates a mismatch between image.wavelengths
# and image.traces
image.wavelengths = np.vstack([image.wavelengths[1:], np.zeros((1, len(image.wavelengths[0])), dtype=float)])
output_image = WeightedExtract(input_context).do_stage(image)
spectrum = output_image.spectrum
assert np.allclose(spectrum[0, 0]['flux'], expected_extracted_flux)
assert np.allclose(spectrum[0, 0]['wavelength'], expected_extracted_wavelength)
assert np.allclose(spectrum[0, 0]['uncertainty'], expected_extracted_uncertainty)
assert np.allclose(spectrum[0, 0]['id'], 1)
assert np.allclose(spectrum[1, 1]['flux'][1:-1], expected_extracted_flux)
assert np.allclose(spectrum[1, 1]['wavelength'][1:-1], expected_extracted_wavelength)
assert len(spectrum[1, 1]['flux']) == image.traces.shape[1] - 2
assert np.allclose(spectrum[1, 1]['uncertainty'][1:-1], expected_extracted_uncertainty)
assert len(spectrum[1, 1]['uncertainty']) == image.traces.shape[1] - 2
assert np.allclose(spectrum[1, 1]['id'], 2)
class TestGetWeights:
def test_rejects_on_no_profile(self):
con = context.Context({})
assert GetOptimalExtractionWeights(con).do_stage(two_order_image()) is None
def test_optimal_weights_zero_on_zero_profile(self):
image = two_order_image()
image.profile = np.zeros_like(image.traces, dtype=float)
input_context = context.Context({})
stage = GetOptimalExtractionWeights(input_context)
output_image = stage.do_stage(image)
assert np.allclose(output_image.weights, 0)
def test_optimal_weights_on_one_profile(self):
image = two_order_image()
image.profile = np.ones_like(image.traces, dtype=float)
input_context = context.Context({})
stage = GetOptimalExtractionWeights(input_context)
output_image = stage.do_stage(image)
assert np.allclose(output_image.weights[~np.isclose(image.traces, 0)], 1 / 3)
def test_optimal_weights_on_box_profile(self):
profile = np.zeros((5, 5))
variance = np.ones_like(profile, dtype=float)
mask = np.zeros_like(profile, dtype=float)
profile[1:4, :], variance[1:4, :] = 1., 1.
input_context = context.Context({})
stage = GetOptimalExtractionWeights(input_context)
weights = stage.weights(profile, variance, mask)
# check that the weights in the order are 1/width of the order:
assert np.allclose(weights[np.isclose(profile, 1)], 1. / 3.)
# check that the weights of the area with zero profile are zero:
assert np.allclose(weights[np.isclose(profile, 0)], 0)
def two_order_image():
# generate 2 flat traces.
traces = np.zeros((60, 20))
traces[[10, 11, 12], :] = 1
# the second trace that does not span the image entirely
traces[[50, 51, 52], :] = 2
traces[[50, 51, 52], 0] = 0
traces[[50, 51, 52], -1] = 0
# generate test data with zero noise
data = np.ones_like(traces, dtype=float)
data[~np.isclose(traces, 0)] = 100.
uncertainty = 1. * data
wavelengths = (traces > 0).astype(float) * 5
image = NRESObservationFrame([CCDData(data=data, uncertainty=uncertainty, meta={'OBJECTS': 'tung&tung&none'})],
'foo.fits')
image.wavelengths = wavelengths
image.traces = traces
image.fibers = {'fiber': np.arange(2), 'order': np.arange(2)}
image.blaze = {'id': np.arange(2), 'blaze': [np.arange(20), np.arange(20)],
'blaze_error': [np.arange(20), np.arange(20)]}
return image
def five_hundred_square_image(maxflux, number_traces, trace_width, read_noise=10, seed=None):
traces = np.zeros((500, 500))
data = np.ones_like(traces, dtype=float)
profile = np.zeros_like(traces, dtype=float)
ix = np.arange(trace_width)
for i in range(1, number_traces + 1):
traces[40 * i:40 * i + trace_width, :] = i
for j in range(0, trace_width):
data[40 * i + j, :] += maxflux * np.exp((-1.) * (ix[j] - trace_width / 2.) ** 2 / (trace_width / 6.) ** 2)
for j in range(0, trace_width):
profile[40 * i + j, :] = data[40 * i + j, :] / np.sum(data[40 * i: 40 * i + trace_width, 0])
np.random.seed(seed=seed)
data += np.random.poisson(data)
data += np.random.normal(0.0, read_noise, size=data.shape)
uncertainty = np.sqrt(data + read_noise ** 2)
wavelengths = np.ones_like(traces) * 5 # dummy wavelengths image that has values distinct from flux and traces.
image = NRESObservationFrame([CCDData(data=data, uncertainty=uncertainty, meta={'OBJECTS': 'tung&tung&none'})],
'foo.fits')
image.traces = traces
image.profile = profile
image.wavelengths = wavelengths
image.blaze = {'id': np.arange(number_traces) + 1,
'blaze': [np.ones(traces.shape[1]) for i in range(number_traces)],
'blaze_error': [np.ones(traces.shape[1]) for i in range(number_traces)]}
image.fibers = {'fiber': np.arange(number_traces) + 1, 'order': np.arange(number_traces) + 1}
return image
|
LCOGTREPO_NAMEbanzai-nresPATH_START.@banzai-nres_extracted@banzai-nres-main@banzai_nres@tests@test_extract.py@.PATH_END.py
|
{
"filename": "test_task_run_state_change_events.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/tests/events/client/instrumentation/test_task_run_state_change_events.py",
"type": "Python"
}
|
import pendulum
from prefect import flow, task
from prefect.client.orchestration import PrefectClient
from prefect.client.schemas.objects import State
from prefect.events.clients import AssertingEventsClient
from prefect.events.schemas.events import Resource
from prefect.events.worker import EventsWorker
from prefect.filesystems import LocalFileSystem
from prefect.task_worker import TaskWorker
async def test_task_state_change_happy_path(
asserting_events_worker: EventsWorker,
reset_worker_events: None,
prefect_client: PrefectClient,
events_pipeline,
):
@task
def happy_little_tree():
return "🌳"
@flow
def happy_path():
return happy_little_tree(return_state=True)
flow_state: State[State[str]] = happy_path(return_state=True)
await events_pipeline.process_events(dequeue_events=False)
task_state: State[str] = await flow_state.result()
task_run_id = task_state.state_details.task_run_id
task_run = await prefect_client.read_task_run(task_run_id)
task_run_states = await prefect_client.read_task_run_states(task_run_id)
await asserting_events_worker.drain()
assert isinstance(asserting_events_worker._client, AssertingEventsClient)
events = [
event
for event in asserting_events_worker._client.events
if event.event.startswith("prefect.task-run.")
]
assert len(task_run_states) == len(events) == 3
pending, running, completed = events
assert pending.event == "prefect.task-run.Pending"
assert pending.id == task_run_states[0].id
assert pending.occurred == task_run_states[0].timestamp
assert pending.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "PENDING",
"prefect.state-name": "Pending",
"prefect.state-timestamp": task_run_states[0].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(pending.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert pending.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert pending.payload == {
"initial_state": None,
"intended": {"from": None, "to": "PENDING"},
"validated_state": {
"type": "PENDING",
"name": "Pending",
"message": "",
"state_details": {},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 0,
"name": task_run.name,
"run_count": 0,
"tags": [],
"labels": {},
"task_inputs": {},
"total_run_time": 0.0,
},
}
assert running.event == "prefect.task-run.Running"
assert running.id == task_run_states[1].id
assert running.occurred == task_run_states[1].timestamp
assert running.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "RUNNING",
"prefect.state-name": "Running",
"prefect.state-timestamp": task_run_states[1].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(running.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert running.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert (
pendulum.parse(running.payload["task_run"].pop("start_time"))
== task_run.start_time
)
assert running.payload == {
"intended": {"from": "PENDING", "to": "RUNNING"},
"initial_state": {
"type": "PENDING",
"name": "Pending",
"message": "",
"state_details": {},
},
"validated_state": {
"type": "RUNNING",
"name": "Running",
"message": "",
"state_details": {},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 1,
"name": task_run.name,
"run_count": 1,
"tags": [],
"labels": {},
"task_inputs": {},
"total_run_time": 0.0,
},
}
assert completed.event == "prefect.task-run.Completed"
assert completed.id == task_run_states[2].id
assert completed.occurred == task_run_states[2].timestamp
assert completed.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "COMPLETED",
"prefect.state-name": "Completed",
"prefect.state-timestamp": task_run_states[2].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(completed.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert completed.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert (
pendulum.parse(completed.payload["task_run"].pop("start_time"))
== task_run.start_time
)
assert (
pendulum.parse(completed.payload["task_run"].pop("end_time"))
== task_run.end_time
)
assert completed.payload["task_run"].pop("total_run_time") > 0.0
assert completed.payload == {
"intended": {"from": "RUNNING", "to": "COMPLETED"},
"initial_state": {
"type": "RUNNING",
"name": "Running",
"message": "",
"state_details": {},
},
"validated_state": {
"type": "COMPLETED",
"name": "Completed",
"message": "",
"state_details": {},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 1,
"name": task_run.name,
"run_count": 1,
"tags": [],
"labels": {},
"task_inputs": {},
},
}
async def test_task_state_change_task_failure(
asserting_events_worker: EventsWorker,
reset_worker_events,
prefect_client,
events_pipeline,
):
@task
def happy_little_tree():
raise ValueError("Here's a happy little accident.")
@flow
def happy_path():
return happy_little_tree(return_state=True)
flow_state = happy_path(return_state=True)
await events_pipeline.process_events(dequeue_events=False)
task_state = await flow_state.result(raise_on_failure=False)
task_run_id = task_state.state_details.task_run_id
task_run = await prefect_client.read_task_run(task_run_id)
task_run_states = await prefect_client.read_task_run_states(task_run_id)
await asserting_events_worker.drain()
assert isinstance(asserting_events_worker._client, AssertingEventsClient)
events = [
event
for event in asserting_events_worker._client.events
if event.event.startswith("prefect.task-run.")
]
assert len(task_run_states) == len(events) == 3
pending, running, failed = events
assert pending.event == "prefect.task-run.Pending"
assert pending.id == task_run_states[0].id
assert pending.occurred == task_run_states[0].timestamp
assert pending.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "PENDING",
"prefect.state-name": "Pending",
"prefect.state-timestamp": task_run_states[0].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(pending.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert pending.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert pending.payload == {
"initial_state": None,
"intended": {"from": None, "to": "PENDING"},
"validated_state": {
"type": "PENDING",
"name": "Pending",
"message": "",
"state_details": {},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 0,
"name": task_run.name,
"run_count": 0,
"tags": [],
"labels": {},
"task_inputs": {},
"total_run_time": 0.0,
},
}
assert running.event == "prefect.task-run.Running"
assert running.id == task_run_states[1].id
assert running.occurred == task_run_states[1].timestamp
assert running.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "RUNNING",
"prefect.state-name": "Running",
"prefect.state-timestamp": task_run_states[1].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(running.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert (
pendulum.parse(running.payload["task_run"].pop("start_time"))
== task_run.start_time
)
assert running.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert running.payload == {
"intended": {"from": "PENDING", "to": "RUNNING"},
"initial_state": {
"type": "PENDING",
"name": "Pending",
"message": "",
"state_details": {},
},
"validated_state": {
"type": "RUNNING",
"name": "Running",
"message": "",
"state_details": {},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 1,
"name": task_run.name,
"run_count": 1,
"tags": [],
"labels": {},
"task_inputs": {},
"total_run_time": 0.0,
},
}
assert failed.event == "prefect.task-run.Failed"
assert failed.id == task_run_states[2].id
assert failed.occurred == task_run_states[2].timestamp
assert failed.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run.id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": (
"Task run encountered an exception ValueError: "
"Here's a happy little accident."
),
"prefect.state-type": "FAILED",
"prefect.state-name": "Failed",
"prefect.state-timestamp": task_run_states[2].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(failed.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert failed.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert (
pendulum.parse(failed.payload["task_run"].pop("start_time"))
== task_run.start_time
)
assert (
pendulum.parse(failed.payload["task_run"].pop("end_time")) == task_run.end_time
)
assert failed.payload["task_run"].pop("total_run_time") > 0
assert failed.payload == {
"intended": {"from": "RUNNING", "to": "FAILED"},
"initial_state": {
"type": "RUNNING",
"name": "Running",
"message": "",
"state_details": {},
},
"validated_state": {
"type": "FAILED",
"name": "Failed",
"message": (
"Task run encountered an exception ValueError: "
"Here's a happy little accident."
),
"state_details": {"retriable": False},
"data": None,
},
"task_run": {
"dynamic_key": task_run.dynamic_key,
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 1,
"name": task_run.name,
"run_count": 1,
"tags": [],
"labels": {},
"task_inputs": {},
},
}
async def test_background_task_state_changes(
asserting_events_worker: EventsWorker,
reset_worker_events,
prefect_client,
tmp_path,
events_pipeline,
):
storage = LocalFileSystem(basepath=tmp_path)
await storage.save("test")
@task(result_storage=storage)
def foo():
pass
task_run_future = foo.apply_async()
task_run = await prefect_client.read_task_run(task_run_future.task_run_id)
await TaskWorker(foo).execute_task_run(task_run)
await events_pipeline.process_events(dequeue_events=False)
task_run_states = await prefect_client.read_task_run_states(
task_run_future.task_run_id
)
await asserting_events_worker.drain()
events = sorted(asserting_events_worker._client.events, key=lambda e: e.occurred)
events = [e for e in events if e.event.startswith("prefect.task-run.")]
assert len(task_run_states) == len(events) == 4
assert [e.event for e in events] == [
"prefect.task-run.Scheduled",
"prefect.task-run.Pending",
"prefect.task-run.Running",
"prefect.task-run.Completed",
]
observed = [
(e.payload["intended"]["from"], e.payload["intended"]["to"])
for e in events
if e.event.startswith("prefect.task-run.")
]
expected = [
(None, "SCHEDULED"),
("SCHEDULED", "PENDING"),
("PENDING", "RUNNING"),
("RUNNING", "COMPLETED"),
]
assert observed == expected
async def test_apply_async_emits_scheduled_event(
asserting_events_worker,
prefect_client,
):
@task
def happy_little_tree():
return "🌳"
future = happy_little_tree.apply_async()
task_run_id = future.task_run_id
await asserting_events_worker.drain()
events = asserting_events_worker._client.events
assert len(events) == 1
scheduled = events[0]
task_run = await prefect_client.read_task_run(task_run_id)
assert task_run
assert task_run.id == task_run_id
task_run_states = await prefect_client.read_task_run_states(task_run_id)
assert len(task_run_states) == 1
assert scheduled.event == "prefect.task-run.Scheduled"
assert scheduled.id == task_run_states[0].id
assert scheduled.occurred == task_run_states[0].timestamp
assert scheduled.resource == Resource(
{
"prefect.resource.id": f"prefect.task-run.{task_run_id}",
"prefect.resource.name": task_run.name,
"prefect.state-message": "",
"prefect.state-type": "SCHEDULED",
"prefect.state-name": "Scheduled",
"prefect.state-timestamp": task_run_states[0].timestamp.isoformat(),
"prefect.orchestration": "client",
}
)
assert (
pendulum.parse(
scheduled.payload["validated_state"]["state_details"].pop("scheduled_time")
)
== task_run.expected_start_time
)
assert (
pendulum.parse(scheduled.payload["task_run"].pop("next_scheduled_start_time"))
== task_run.next_scheduled_start_time
)
assert (
pendulum.parse(scheduled.payload["task_run"].pop("expected_start_time"))
== task_run.expected_start_time
)
assert scheduled.payload["task_run"].pop("name").startswith("happy_little_tree")
assert (
scheduled.payload["task_run"].pop("dynamic_key").startswith("happy_little_tree")
)
assert scheduled.payload["task_run"].pop("task_key").startswith("happy_little_tree")
assert scheduled.payload == {
"initial_state": None,
"intended": {"from": None, "to": "SCHEDULED"},
"validated_state": {
"type": "SCHEDULED",
"name": "Scheduled",
"message": "",
"state_details": {
"pause_reschedule": False,
"untrackable_result": False,
"deferred": True,
},
"data": None,
},
"task_run": {
"empirical_policy": {
"max_retries": 0,
"retries": 0,
"retry_delay": 0,
"retry_delay_seconds": 0.0,
},
"flow_run_run_count": 0,
"run_count": 0,
"tags": [],
"labels": {},
"task_inputs": {},
"total_run_time": 0.0,
},
}
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@tests@events@client@instrumentation@test_task_run_state_change_events.py@.PATH_END.py
|
{
"filename": "passband.py",
"repo_name": "mikecokina/elisa",
"repo_path": "elisa_extracted/elisa-master/src/elisa/observer/passband.py",
"type": "Python"
}
|
import sys
import numpy as np
import pandas as pd
from scipy import interpolate
from .. import settings
def init_bolometric_passband():
"""
initializing bolometric passband and its wavelength boundaries
:return: Tuple;
"""
df = pd.DataFrame(
{
settings.PASSBAND_DATAFRAME_THROUGHPUT: [1.0, 1.0],
settings.PASSBAND_DATAFRAME_WAVE: [50.0, 2000000.0]
}
)
right_bandwidth = sys.float_info.max
left_bandwidth = 0.0
bol_passband = PassbandContainer(table=df, passband='bolometric')
return bol_passband, right_bandwidth, left_bandwidth
def init_rv_passband():
"""
Initializing passband used to calculate radial velocities
:return: Tuple
"""
df = pd.DataFrame(
{settings.PASSBAND_DATAFRAME_THROUGHPUT: [1.0, 1.0],
settings.PASSBAND_DATAFRAME_WAVE: settings.RV_LAMBDA_INTERVAL})
right_bandwidth = settings.RV_LAMBDA_INTERVAL[1]
left_bandwidth = settings.RV_LAMBDA_INTERVAL[0]
psmbnd = PassbandContainer(table=df, passband='rv_band')
return psmbnd, right_bandwidth, left_bandwidth
def bolometric(x):
"""
Bolometric passband interpolation function in way of lambda x: 1.0
:param x:
:return: float or numpy.array; 1.0s in shape of x
"""
if isinstance(x, (float, int)):
return 1.0
if isinstance(x, list):
return [1.0] * len(x)
if isinstance(x, np.ndarray):
return np.array([1.0] * len(x))
class PassbandContainer(object):
def __init__(self, table, passband):
"""
Data container used for storing passband response curves. Fully initialized PassbandContainers contain following
attributes:
- left_bandwidth, right_bandwidth: left and right wavelength boundary of the passband
- table: pandas.DataFrame; dataframe containing a `wavelength` column with corresponding `throughput` values
defining a given passband
- passband: name of the passband
The response curve is stored in a pandas.DataFrame
Setup PassbandContainier object. It carres dependedncies of throughputs on wavelengths for given passband.
:param table: pandas.DataFrame;
:param passband: str;
"""
self.left_bandwidth = np.nan
self.right_bandwidth = np.nan
self.akima = None
self._table = pd.DataFrame({})
self.wave_unit = "angstrom"
self.passband = passband
# in case this np.pi will stay here, there will be rendundant multiplication in intensity integration
self.wave_to_si_mult = 1e-10
setattr(self, 'table', table)
@property
def table(self):
"""
Return pandas dataframe which represent pasband table as dependecy of throughput on wavelength.
:return: pandas.DataFrame;
"""
return self._table
@table.setter
def table(self, df):
"""
Setter for passband table.
It precompute left and right bandwidth for given table and also interpolation function placeholder.
Akima1DInterpolator is used. If `bolometric` passband is used then interpolation function is like::
lambda x: 1.0
:param df: pandas.DataFrame;
"""
self._table = df
self.akima = bolometric if (self.passband.lower() in ['bolometric', 'rv_band']) else \
interpolate.Akima1DInterpolator(df[settings.PASSBAND_DATAFRAME_WAVE],
df[settings.PASSBAND_DATAFRAME_THROUGHPUT])
self.left_bandwidth = min(df[settings.PASSBAND_DATAFRAME_WAVE])
self.right_bandwidth = max(df[settings.PASSBAND_DATAFRAME_WAVE])
|
mikecokinaREPO_NAMEelisaPATH_START.@elisa_extracted@elisa-master@src@elisa@observer@passband.py@.PATH_END.py
|
{
"filename": "constraint.py",
"repo_name": "pmelchior/scarlet",
"repo_path": "scarlet_extracted/scarlet-master/scarlet/constraint.py",
"type": "Python"
}
|
from functools import partial
import numpy as np
import proxmin
from . import operator
from .cache import Cache
class Constraint:
"""Constraint base class
Constraints encode expected properties of the solution.
Mathematically, they are the consequence of adding potentially
non-differentiable penalty functions to the model fitting loss function.
As we use proximal gradient methods, all constraints act as proxmimal
operators, i.e. they need to have the following signature:
f(X, step) -> X'
where X' is the closest point to X that satisfies the feasibility criterion
of the penalty function.
For reference, every operator of the `proxmin` package yields a valid
`Constraint`.
"""
def __init__(self, f=None):
"""Constraint base class
Parameters
----------
f: proximal mapping
Signature: f(X, step) -> X'
"""
self.f = f
def __call__(self, X, step):
"""Proximal mapping
Parameters
----------
X: array
Optimimzation parameter
step: float or array of same shape as X
Step size for the proximal mapping
Returns
-------
X': closest feasible match to X
"""
if self.f is not None:
return self.f(X, step)
return X
class ConstraintChain:
"""An ordered list of `Constraint`s.
Uses the concept of alternating projections onto convex sets to find
solutions that are feasible according to a list of constraints.
Parameters
----------
constraints: list of `Constraint`
repeat: int
How often the constrain chain is repeated to ensure feasibility
"""
def __init__(self, *constraints, repeat=1):
assert isinstance(repeat, int) and repeat >= 1
self.constraints = constraints
self.repeat = repeat
def __call__(self, X, step):
for r in range(self.repeat):
for c in self.constraints:
X = c(X, step)
return X
class PositivityConstraint(Constraint):
"""Allow only values not smaller than `zero`.
"""
def __init__(self, zero=0):
self.zero = zero
def __call__(self, X, step):
X = np.maximum(X, self.zero)
return X
class NormalizationConstraint(Constraint):
def __init__(self, type="sum"):
"""Normalize X to unity.
Parameters
----------
type: in ['sum', 'max']
Whether the sum or the maximum is set to unity.
"""
type = type.lower()
assert type in ["sum", "max"]
self.type = type
def __call__(self, X, step):
if self.type == "sum":
X /= X.sum()
else:
X /= X.max()
return X
class L0Constraint(Constraint):
def __init__(self, thresh, type="absolute"):
"""L0 norm (sparsity) penalty
Parameters
----------
thresh: float
regularization strength
type: ['relative', 'absolute']
if the penalty is expressed in units of the function value (relative)
or in units of the variable X (absolute).
"""
super().__init__(
partial(proxmin.operators.prox_hard, thresh=thresh, type=type,)
)
class L1Constraint(Constraint):
def __init__(self, thresh, type="absolute"):
"""L1 norm (sparsity) penalty
Parameters
----------
thresh: regularization strength
type: ['relative', 'absolute']
if the penalty is expressed in units of the function value (relative)
or in units of the variable X (absolute).
"""
super().__init__(partial(proxmin.operators.prox_soft, thresh=thresh, type=type))
class ThresholdConstraint(Constraint):
"""Set a cutoff threshold for pixels below the noise
Use the log histogram of pixel values to determine when the
source is fitting noise. This function works well to prevent
faint sources from growing large footprints but for large
diffuse galaxies with a wide range of pixel values this
does not work as well.
The region that contains flux above the threshold is contained
in `component.bboxes["thresh"]`.
"""
def __call__(self, X, step):
thresh, _bins = self.threshold(X)
return proxmin.operators.prox_hard_plus(X, step, thresh=thresh, type="absolute")
def threshold(self, morph):
"""Find the threshold value for a given morphology
"""
_morph = morph[morph > 0]
_bins = 50
# Decrease the bin size for sources with a small number of pixels
if _morph.size < 500:
_bins = max(int(_morph.size / 10), 1)
if _bins == 1:
return 0, _bins
hist, bins = np.histogram(np.log10(_morph).reshape(-1), _bins)
cutoff = np.where(hist == 0)[0]
# If all of the pixels are used there is no need to threshold
if len(cutoff) == 0:
return 0, _bins
return 10 ** bins[cutoff[-1]], _bins
class MonotonicityConstraint(Constraint):
"""Make morphology monotonically decrease from the center
See `~scarlet.operator.prox_monotonic`
for a description of the other parameters.
"""
def __init__(
self,
neighbor_weight="flat",
min_gradient=0.1,
use_mask=False,
fit_center_radius=0,
):
self.neighbor_weight = neighbor_weight
self.min_gradient = min_gradient
self.use_mask = use_mask
self.fit_center = fit_center_radius > 0
self.fit_center_radius = fit_center_radius
def __call__(self, morph, step):
shape = morph.shape
center = (shape[0] // 2, shape[1] // 2)
if self.fit_center:
center = operator.get_center(morph, center, radius=self.fit_center_radius)
# get prox from the cache
prox_name = "operator.prox_weighted_monotonic"
key = (shape, center, self.neighbor_weight, self.min_gradient)
# The creation of this operator is expensive,
# so load it from memory if possible.
try:
prox = Cache.check(prox_name, key)
except KeyError:
prox = operator.prox_weighted_monotonic(
shape,
neighbor_weight=self.neighbor_weight,
min_gradient=self.min_gradient,
center=center,
)
Cache.set(prox_name, key, prox)
# apply the prox
_morph = morph.copy()
result = prox(morph, step)
if self.use_mask:
valid, _morph, _bounds = operator.prox_monotonic_mask(
_morph, step, center=center, center_radius=0, variance=0, max_iter=0,
)
result[valid] = _morph[valid]
return result
class MonotonicMaskConstraint(Constraint):
"""Make morphology monotonic by branching from the center
"""
def __init__(self, center, center_radius=1, variance=0.0, max_iter=3):
self.center = center
self.center_radius = center_radius
self.variance = variance
self.max_iter = max_iter
self.prox = partial(
operator.prox_monotonic_mask,
center=center,
center_radius=center_radius,
variance=variance,
max_iter=max_iter,
)
def __call__(self, morph, step):
if len(morph.shape) == 2:
valid, morph, bounds = self.prox(morph, step)
else:
morph = np.array([self.prox(morph_, step)[1] for morph_ in morph])
return morph
class SymmetryConstraint(Constraint):
"""Make the source symmetric about its center
See `~scarlet.operator.prox_uncentered_symmetry`
for a description of the parameters.
"""
def __init__(self, strength=1):
self.strength = strength
def __call__(self, morph, step):
return operator.prox_soft_symmetry(morph, step, strength=self.strength)
class CenterOnConstraint(Constraint):
"""Sets the center pixel to a tiny non-zero value
"""
def __init__(self, tiny=1e-6):
self.tiny = tiny
def __call__(self, morph, step):
shape = morph.shape
center = (shape[0] // 2, shape[1] // 2)
morph[center] = max(morph[center], self.tiny)
return morph
class LeakyConstraint(Constraint):
"""Make a constraint leak the original value with a configurable amount:
Updates `x = (1-leak) * prox(x, step) + leak * x`
"""
def __init__(self, constraint, leak=0.05):
self.constraint = constraint
self.leak = leak
def __call__(self, x, step):
return (1 - self.leak) * self.constraint(x, step) + self.leak * x
|
pmelchiorREPO_NAMEscarletPATH_START.@scarlet_extracted@scarlet-master@scarlet@constraint.py@.PATH_END.py
|
{
"filename": "_outsidetextfont.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/pie/_outsidetextfont.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class OutsidetextfontValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="outsidetextfont", parent_name="pie", **kwargs):
super(OutsidetextfontValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Outsidetextfont"),
data_docs=kwargs.pop(
"data_docs",
"""
color
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for `family`.
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud
for `lineposition`.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
shadowsrc
Sets the source reference on Chart Studio Cloud
for `shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud
for `size`.
style
Sets whether a font should be styled with a
normal or italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud
for `style`.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud
for `textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud
for `variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud
for `weight`.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@pie@_outsidetextfont.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/ternary/aaxis/title/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._text import TextValidator
from ._font import FontValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._text.TextValidator", "._font.FontValidator"]
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@ternary@aaxis@title@__init__.py@.PATH_END.py
|
{
"filename": "_zhoverformat.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/contour/_zhoverformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ZhoverformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="zhoverformat", parent_name="contour", **kwargs):
super(ZhoverformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@contour@_zhoverformat.py@.PATH_END.py
|
{
"filename": "SortAndDivideRedistributeNodes2d.py",
"repo_name": "LLNL/spheral",
"repo_path": "spheral_extracted/spheral-main/src/PYB11/Distributed/SortAndDivideRedistributeNodes2d.py",
"type": "Python"
}
|
#-------------------------------------------------------------------------------
# SortAndDivideRedistributeNodes2d
#-------------------------------------------------------------------------------
from PYB11Generator import *
from SortAndDivideRedistributeNodes import *
@PYB11template()
@PYB11template_dict({"Dimension": "Dim<2>"})
class SortAndDivideRedistributeNodes2d(SortAndDivideRedistributeNodes):
"""SortAndDivideRedistributeNodes2d -- 2-D implementation of the sort and
divide algorithm for domain decomposition."""
PYB11typedefs = """
typedef typename KeyTraits::Key Key;
typedef typename %(Dimension)s::Scalar Scalar;
typedef typename %(Dimension)s::Vector Vector;
typedef typename %(Dimension)s::Tensor Tensor;
typedef typename %(Dimension)s::SymTensor SymTensor;
"""
#...........................................................................
# Constructors
def pyinit(self,
Hextent = "const double"):
"Constructor"
#...........................................................................
# Virtual methods
@PYB11virtual
def redistributeNodes(self,
dataBase = "DataBase<%(Dimension)s>&",
boundaries = ("std::vector<Boundary<%(Dimension)s>*>", "std::vector<Boundary<%(Dimension)s>*>()")):
"""Given a Spheral++ data base of NodeLists, repartition it among the processors.
This is the method required of all descendent classes."""
return "void"
|
LLNLREPO_NAMEspheralPATH_START.@spheral_extracted@spheral-main@src@PYB11@Distributed@SortAndDivideRedistributeNodes2d.py@.PATH_END.py
|
{
"filename": "_yhoverformat.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/surface/_yhoverformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class YhoverformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="yhoverformat", parent_name="surface", **kwargs):
super(YhoverformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@surface@_yhoverformat.py@.PATH_END.py
|
{
"filename": "_dtick.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/icicle/marker/colorbar/_dtick.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class DtickValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(
self, plotly_name="dtick", parent_name="icicle.marker.colorbar", **kwargs
):
super(DtickValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
implied_edits=kwargs.pop("implied_edits", {"tickmode": "linear"}),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@icicle@marker@colorbar@_dtick.py@.PATH_END.py
|
{
"filename": "validation.py",
"repo_name": "ML4GW/aframe",
"repo_path": "aframe_extracted/aframe-main/projects/data/data/waveforms/validation.py",
"type": "Python"
}
|
from jsonargparse import ArgumentParser
from data.waveforms.rejection import rejection_sample
from ledger.injections import WaveformSet, waveform_class_factory
parser = ArgumentParser()
parser.add_function_arguments(rejection_sample)
parser.add_argument("--output_file", "-o", type=str)
def main(args):
args = args.validation_waveforms.as_dict()
output_file = args.pop("output_file")
cls = waveform_class_factory(
args["ifos"],
WaveformSet,
"IfoWaveformSet",
)
parameters, _ = rejection_sample(**args)
waveform_set = cls(**parameters)
waveform_set.write(output_file)
|
ML4GWREPO_NAMEaframePATH_START.@aframe_extracted@aframe-main@projects@data@data@waveforms@validation.py@.PATH_END.py
|
{
"filename": "test_api.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/tslibs/test_api.py",
"type": "Python"
}
|
"""Tests that the tslibs API is locked down"""
from pandas._libs import tslibs
def test_namespace():
submodules = [
"base",
"ccalendar",
"conversion",
"dtypes",
"fields",
"nattype",
"np_datetime",
"offsets",
"parsing",
"period",
"strptime",
"vectorized",
"timedeltas",
"timestamps",
"timezones",
"tzconversion",
]
api = [
"BaseOffset",
"NaT",
"NaTType",
"iNaT",
"nat_strings",
"OutOfBoundsDatetime",
"OutOfBoundsTimedelta",
"Period",
"IncompatibleFrequency",
"Resolution",
"Tick",
"Timedelta",
"dt64arr_to_periodarr",
"Timestamp",
"is_date_array_normalized",
"ints_to_pydatetime",
"normalize_i8_timestamps",
"get_resolution",
"delta_to_nanoseconds",
"ints_to_pytimedelta",
"localize_pydatetime",
"tz_convert_from_utc",
"tz_convert_from_utc_single",
"to_offset",
"tz_compare",
"is_unitless",
"astype_overflowsafe",
"get_unit_from_dtype",
"periods_per_day",
"periods_per_second",
"guess_datetime_format",
"add_overflowsafe",
"get_supported_dtype",
"is_supported_dtype",
]
expected = set(submodules + api)
names = [x for x in dir(tslibs) if not x.startswith("__")]
assert set(names) == expected
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@tslibs@test_api.py@.PATH_END.py
|
{
"filename": "fit_transformer_final.ipynb",
"repo_name": "astrockragh/Mangrove",
"repo_path": "Mangrove_extracted/Mangrove-main/transform/fit_transformer_final.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import torch, os, pickle, time
import torch_geometric as tg
from torch_geometric.data import Data
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
import os.path as osp
import networkx as nx
path='~/../../tigress/cj1223/merger_trees/isotrees/'
transform_path='~/../../tigress/cj1223/gmdata/transformer'
all_cols=np.array([0,2,4,10,11,12,13,14,15,16,23,24,25,35]+list(range(37,60)))
```
```python
os.listdir(osp.expanduser('~/../../../scratch/gpfs/cj1223/GraphStorage/'))
```
['vlarge_all_4t_z1.0_standard_quant',
'vlarge_all_4t_z0.3_quantile_raw',
'vlarge_4t_quantile_raw_redshift_75_all',
'vlarge_all_4t_z1.0_quantile_raw',
'vlarge_all_4t_z0.3_None',
'vlarge_all_4t_z3.0_quantile_raw',
'test_all_8t_z0.0_None',
'vlarge_all_4t_z2.0_standard_quant',
'vlarge_all_4t_z0.8_quantile_raw',
'tvt_idx',
'vlarge_all_4t_z2.0_None',
'redshift_scan_0',
'testid_all_4t_z2.0_None',
'vlarge_all_4t_z0.0_quantile_stand',
'vlarge_all_multi_try1',
'vlarge_4t_quantile_raw_redshift_99_all',
'vlarge_all_4t_z2.0_quantile_raw',
'vlarge_all_4t_z0.0_standard_quant',
'vlarge_all_4t_z0.5_quantile_quant',
'vlarge_4t_quantile_raw_redshift_50_all',
'vlarge_all_4t_z2.0_quantile_stand',
'vlarge_all_t_quantile_raw_rm_final',
'vlarge_all_4t_z1.0_quantile_quant',
'vlarge_all_allt_z0.0_quantile_raw_rm',
'transformers',
'vlarge_all_4t_z0.0_standard_raw',
'vlarge_all_4t_quantile_raw_final',
'vlarge_all_4t_z0.5_standard_stand',
'vlarge_all_4t_z1.8_quantile_raw',
'vlarge_all_allt_z0.0_quantile_raw_floor',
'vlarge_all_4t_z0.5_standard_quant',
'vlarge_all_4t_zall_quantile_raw_trainandtest',
'vlarge_all_4t_z0.0_quantile_raw',
'standard_raw_final_6t',
'old',
'vlarge_standard_raw_rm_final',
'vlarge_all_4t_z1.0_None',
'vlarge_all_4t_z1.5_quantile_raw',
'vlarge_all_4t_z1.0_standard_stand',
'vlarge_all_4t_z0.8_None',
'vlarge_all_4t_z1.8_None',
'vlarge_all_4t_z2.0_standard_raw',
'vlarge_4t_quantile_raw_redshift_95_all',
'testid_all_4t_z0.0_None',
'vlarge_all_all_t_z0.0_None',
'vlarge_all_4t_z3.0_None',
'vlarge_all_4t_z0.5_standard_raw',
'vlarge_all_4t_z1.5_None',
'vlarge_all_4t_z0.0_None',
'vlarge_4t_quantile_raw_redshift_85_all',
'vlarge_all_4t_z0.5_quantile_raw',
'vlarge_all_4t_z1.0_standard_raw',
'vlarge_all_4t_quantile_raw',
'vlarge__all_8t_z0.0_None',
'testt_all_4t_z0.0_None',
'vlarge_all_smass',
'vlarge_all_4t_z0.0_quantile_quant',
'vlarge_all_4t_z0.5_quantile_stand',
'vlarge_all_4t_zall_quantile_raw',
'vlarge_all_4t_z0.0_standard_stand',
'vlarge_all_4t_z1.0_quantile_stand',
'vlarge_all_4t_z2.0_quantile_quant',
'vlarge_all_4t_z2.0_standard_stand',
'vlarge_all_4t_z0.5_None',
'haloids.pkl']
```python
case='vlarge_all_all_t_z0.0_None'
# case = 'vlarge_all_allt_z0.0_quantile_raw_rm'
data=pickle.load(open(osp.expanduser(f'~/../../../scratch/gpfs/cj1223/GraphStorage/{case}/data.pkl'), 'rb'))
```
```python
xs=[]
ys=[]
ls=[]
# for d in data[:int(len(data)*0.8)]:
for d in data:
xs.append(d.x.numpy())
# xs.append(d.x.numpy()[0])
ys.append(d.y.numpy())
ls.append(len(d.x.numpy()))
xs=np.vstack(xs)
# xs[:,40]=np.log10(xs[:,40])
ys=np.vstack(ys)
ls=np.array(ls)
splits=np.cumsum(ls)
```
```python
halos=pd.read_table(path+f'isotree_0_0_0.dat', skiprows=0, nrows=1, delimiter='\s+')
halos.columns[all_cols]
```
Index(['#scale(0)', 'desc_scale(2)', 'num_prog(4)', 'Mvir(10)', 'Rvir(11)',
'rs(12)', 'vrms(13)', 'mmp?(14)', 'scale_of_last_MM(15)', 'vmax(16)',
'Jx(23)', 'Jy(24)', 'Jz(25)', 'Tidal_Force(35)', 'Rs_Klypin',
'Mvir_all', 'M200b', 'M200c', 'M500c', 'M2500c', 'Xoff', 'Voff',
'Spin_Bullock', 'b_to_a', 'c_to_a', 'A[x]', 'A[y]', 'A[z]',
'b_to_a(500c)', 'c_to_a(500c)', 'A[x](500c)', 'A[y](500c)',
'A[z](500c)', 'T/|U|', 'M_pe_Behroozi', 'M_pe_Diemer',
'Halfmass_Radius'],
dtype='object')
```python
cols_h = []
for i, col in enumerate(halos.columns[all_cols]):
if col[-1] == ')':
cols_h.append(col[:-3]+f'({i})')
else:
cols_h.append(col+f'({i})')
```
```python
fig,ax=plt.subplots(nrows=8,ncols=5, figsize=(30,23))
ax=ax.flatten()
for i in tqdm(range(len(cols_h))):
ax[i].hist(xs[:,i], bins=100, density=1, histtype='step');
ax[i].set(title=cols_h[i])
fig.tight_layout()
```
100%|███████████████████████████████████████████████████████████████| 37/37 [00:32<00:00, 1.12it/s]
```python
xs[:,16]
```
array([ 0.01129135, 0.01129135, 0.01129135, ..., -0.01129135,
0.01129135, 0.01129135], dtype=float32)
```python
z0_feats = np.array([ 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36])
targets = np.array([8, 15, 19, 21, 23, 27])
```
```python
xss = []
yss = []
for x, y in zip(xs, ys):
if np.all( y[targets] > 0):
xss.append(x[z0_feats])
yss.append(np.log10(y[targets]))
else:
continue
xss=np.vstack(xss)
yss=np.vstack(yss)
len(xss)
```
108630
```python
plt.hist(xss[:,0], bins=100);
```
```python
mus, scales = np.mean(xss,axis=0), np.std(xss,axis=0)
mus, scales
```
(array([ 1.3043818e+00, 1.0482839e+01, 6.8827255e+01, 5.5631461e+00,
6.1627102e+01, 3.5166168e-01, 6.4789871e+01, -6.0743326e-04,
-7.2524161e-04, -1.0647699e-03, 4.9036452e-01, 5.4335928e+00,
1.0488175e+01, 1.0514258e+01, 1.0427412e+01, 1.0331637e+01,
1.0102284e+01, 2.4800856e+00, 4.9633760e+00, 3.8996551e-02,
8.3892757e-01, 6.9374627e-01, 1.3949696e+00, 1.4162083e+00,
1.4180782e+00, 8.3473778e-01, 6.7949873e-01, 1.1404462e+00,
1.1499077e+00, 1.1641036e+00, 5.5684698e-01, 1.0600431e+01,
1.0140233e+01, 2.2547022e+01], dtype=float32),
array([1.5931481e+00, 4.6887007e-01, 3.7005737e+01, 7.3534260e+00,
3.1689447e+01, 2.3791949e-01, 3.1388607e+01, 7.0497632e-02,
9.0221323e-02, 9.6819788e-02, 3.3502483e-01, 5.4994922e+00,
4.6876633e-01, 4.7106558e-01, 4.6538469e-01, 4.6102148e-01,
4.5496431e-01, 3.6341157e+00, 7.2529106e+00, 2.8715109e-02,
1.3576227e-01, 1.3383374e-01, 2.8440909e+00, 2.8351688e+00,
2.8433828e+00, 1.2989864e-01, 1.2603475e-01, 2.2782235e+00,
2.2766337e+00, 2.2788014e+00, 4.6202425e-02, 4.5650178e-01,
5.0597996e-01, 1.5053839e+01], dtype=float32))
```python
np.array(cols_h)[z0_feats]
```
array(['num_prog(2)', 'Mvir((3)', 'Rvir((4)', 'rs((5)', 'vrms((6)',
'scale_of_last_MM((8)', 'vmax((9)', 'Jx((10)', 'Jy((11)',
'Jz((12)', 'Tidal_Force((13)', 'Rs_Klypin(14)', 'Mvir_all(15)',
'M200b(16)', 'M200c(17)', 'M500c(18)', 'M2500c(19)', 'Xoff(20)',
'Voff(21)', 'Spin_Bullock(22)', 'b_to_a(23)', 'c_to_a(24)',
'A[x](25)', 'A[y](26)', 'A[z](27)', 'b_to_a(50(28)',
'c_to_a(50(29)', 'A[x](50(30)', 'A[y](50(31)', 'A[z](50(32)',
'T/|U|(33)', 'M_pe_Behroozi(34)', 'M_pe_Diemer(35)',
'Halfmass_Radius(36)'], dtype='<U20')
```python
scale_feats = np.array([ 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33])
xss[:, scale_feats] = (xss[:,scale_feats]-mus[scale_feats])/scales[scale_feats]
```
```python
fig,ax=plt.subplots(nrows=7,ncols=5, figsize=(30,23))
ax=ax.flatten()
cols = []
for i, col in tqdm(enumerate(np.array(cols_h)[z0_feats])):
ax[i].hist(xss[:,i], bins=100, density=1, histtype='step');
ax[i].set(title=col)
cols.append(col)
fig.tight_layout()
```
34it [00:00, 252.81it/s]
```python
fig,ax=plt.subplots(nrows=3,ncols=2, figsize=(30,23))
ax=ax.flatten()
cols = []
for i in range(len(yss[0])):
ax[i].hist(yss[:,i], bins=100, density=1, histtype='step');
# ax[i].set(title=col)
# cols.append(col)
fig.tight_layout()
```
```python
plt.plot(xss[:,1], yss[:,0], 'ro', alpha=0.1, markersize=1)
```
[<matplotlib.lines.Line2D at 0x2ba3f967a520>]
```python
from torch.utils.data import DataLoader, Dataset
class HaloData(Dataset):
def __init__(self, x, y, xcols = np.arange(34), ycols = np.arange(6)):
self.x = x[:, xcols]
self.y = y[:, ycols]
def __len__(self):
return len(self.y)
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
train_data = HaloData(xss, yss)
test_data = HaloData(xss, yss)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)
for b in train_loader:
print(b)
break
```
[tensor([[-0.1911, -0.2815, -0.3105, ..., -0.4777, 0.1963, 0.1043],
[-0.1911, 0.3845, 0.1092, ..., 0.2727, 0.4602, 0.1736],
[-0.1911, 0.2082, -0.0119, ..., 0.2057, 0.1007, -0.0868],
...,
[-0.1911, 0.1124, -0.0744, ..., 0.0326, 0.1187, 0.0009],
[-0.1911, -0.5168, -0.4362, ..., -0.5527, -0.6191, -0.3826],
[-0.1911, 0.3156, 0.0610, ..., 0.4022, 0.0935, -0.1538]]), tensor([[-1.6120, -0.2127, -3.4076, -2.3220, -2.2132, -3.8851],
[-0.9214, 0.0718, -2.2610, -1.8508, -1.9125, -3.6769],
[-1.0591, -0.3564, -2.5394, -2.3152, -2.3495, -3.7439],
[-0.6603, -0.0833, -1.8325, -2.1393, -2.1564, -3.5840],
[-1.9348, 0.0561, -3.8487, -2.7380, -2.7743, -3.9574],
[-1.2194, -0.5419, -2.6493, -2.6426, -2.6419, -4.1541],
[-1.9007, -0.2074, -3.7384, -2.8091, -2.8402, -3.8848],
[-0.6118, -0.1035, -1.8109, -1.8658, -1.8915, -3.7437],
[-2.3853, 0.5621, -4.7431, -2.9621, -3.0172, -3.9576],
[-2.1313, -0.5848, -4.0876, -3.6850, -3.7519, -4.0960],
[-1.2484, -0.8407, -2.6731, -2.9431, -2.9762, -3.9932],
[-1.7927, -0.3505, -3.5493, -2.5917, -2.6260, -3.8226],
[-1.1165, -0.2701, -2.5431, -2.6895, -2.7287, -3.7687],
[-1.8568, -0.6846, -3.6399, -3.6573, -3.6808, -4.2206],
[-2.2537, -0.8676, -4.2927, -3.7063, -3.7990, -4.0959],
[-3.2644, -0.0863, -6.0722, -4.0136, -4.0693, -4.0456],
[-1.1590, -0.0495, -2.6640, -1.9424, -1.7225, -3.7688],
[-0.8611, 0.2658, -2.2049, -1.6957, -1.7289, -3.7436],
[-2.0205, -0.7416, -3.9301, -3.2305, -3.2599, -4.0448],
[-1.9088, -0.5312, -3.7136, -3.3304, -3.4021, -4.2205],
[-1.9845, -0.7916, -3.7472, -3.2352, -3.2518, -3.9576],
[-1.5249, -0.8176, -3.2030, -3.0986, -3.1743, -3.9199],
[-2.5592, 0.1705, -4.7121, -3.6056, -3.6386, -3.9580],
[-0.0184, -0.1376, -0.9500, -2.5173, -1.3962, -3.2751],
[-1.6696, -0.1313, -3.3900, -2.3502, -2.3775, -3.9987],
[-1.1875, 0.1468, -2.7411, -1.7981, -1.8567, -3.9577],
[-1.8056, -0.4783, -3.5636, -3.4688, -3.4927, -4.3966],
[-1.5689, -0.5431, -3.2726, -3.4255, -3.4511, -4.1536],
[-1.4610, -0.3855, -3.0271, -3.0264, -3.0452, -4.1539],
[-1.8216, 0.0586, -3.7750, -2.6608, -2.6902, -3.9195],
[-2.0850, -0.1842, -4.0727, -3.1476, -3.1802, -3.9198],
[-1.7672, -0.5088, -3.5175, -2.8860, -2.9012, -3.9196],
[-1.9793, -0.3290, -3.8747, -2.9453, -2.9977, -4.0958],
[-1.9470, -0.2132, -3.8451, -2.2476, -2.2800, -4.0451],
[-1.7473, -0.5718, -3.4977, -3.4850, -3.5206, -4.1537],
[-2.2330, 0.3826, -4.4072, -3.7675, -3.8024, -3.7689],
[ 0.6022, 0.1400, 0.1526, -0.5861, -0.6261, -2.9707],
[-1.1424, 0.6453, -2.7797, -1.7691, -1.8072, -3.4939],
[-1.4938, -0.6110, -3.0950, -3.1243, -3.1384, -4.0959],
[-0.5072, -0.5060, -1.6237, -2.3713, -2.3925, -3.6767],
[-2.1186, -0.5371, -4.1909, -3.2162, -3.2350, -4.0447],
[-1.7929, -0.3348, -3.6049, -2.8731, -2.3996, -3.9986],
[-0.7328, -0.2242, -1.9777, -2.1590, -2.1893, -3.9574],
[-1.5002, -0.9542, -3.1485, -3.3411, -3.3562, -3.8230],
[-1.4866, -0.3443, -3.1042, -2.8666, -2.9615, -3.9200],
[-1.9149, -0.8784, -3.7367, -3.3957, -3.4060, -3.9195],
[-1.8759, -0.2984, -3.7298, -2.3372, -2.3705, -3.8851],
[-0.7967, -0.1448, -2.0447, -1.8993, -1.9138, -4.0958],
[-1.0260, 0.0597, -2.5265, -2.4206, -2.4567, -3.7686],
[-2.2688, -0.7024, -4.2838, -3.6595, -3.7373, -3.9993],
[-2.4082, -0.1191, -4.6733, -3.2461, -3.2859, -4.0961],
[-1.5859, -0.0605, -3.2840, -2.7507, -2.7877, -3.9198],
[-1.7343, 0.6475, -3.6623, -2.0881, -2.1424, -3.8851],
[ 1.3892, 1.1630, 1.2793, 0.2945, 0.2452, -2.3911],
[-1.1720, 0.6752, -2.8111, -1.7952, -1.8314, -3.6981],
[-1.7662, -0.4545, -3.4421, -3.5305, -3.5653, -3.8528],
[-0.2092, 0.3286, -1.2627, -1.8161, -1.8446, -3.6006],
[-1.2714, 0.2646, -2.8799, -2.0364, -2.0653, -3.6772],
[-1.7379, -0.7587, -3.5111, -3.1569, -3.1661, -4.0444],
[-1.1434, 0.5650, -2.7451, -1.7915, -1.8235, -3.6979],
[-1.6551, -0.3511, -3.4084, -3.0154, -3.0379, -3.8527],
[-0.9614, 0.0414, -2.3821, -2.5702, -2.3190, -3.9575],
[-1.7503, -1.0355, -3.5318, -3.5273, -3.5370, -3.7200],
[-0.8478, -0.1299, -2.1467, -2.2368, -2.2497, -3.7437]])]
```python
# os.mkdir(osp.expanduser(f"~/../../../scratch/gpfs/cj1223/GraphStorage/standard_raw_final_6t"))
data_path=osp.expanduser(f"~/../../../scratch/gpfs/cj1223/GraphStorage/standard_raw_final_6t/xs.pkl")
with open(data_path, 'wb') as handle:
pickle.dump(xss, handle)
data_path=osp.expanduser(f"~/../../../scratch/gpfs/cj1223/GraphStorage/standard_raw_final_6t/ys.pkl")
with open(data_path, 'wb') as handle:
pickle.dump(yss, handle)
```
```python
```
|
astrockraghREPO_NAMEMangrovePATH_START.@Mangrove_extracted@Mangrove-main@transform@fit_transformer_final.ipynb@.PATH_END.py
|
{
"filename": "test_chandra2ixpe.py",
"repo_name": "lucabaldini/ixpeobssim",
"repo_path": "ixpeobssim_extracted/ixpeobssim-main/tests/test_chandra2ixpe.py",
"type": "Python"
}
|
#!/usr/bin/env python
#
# Copyright (C) 2018, the ixpeobssim team.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
from __future__ import print_function, division
import unittest
import os
import sys
import numpy
from ixpeobssim import IXPEOBSSIM_TEST, IXPEOBSSIM_CONFIG_REG
from ixpeobssim.srcmodel.roi import xChandraObservation, xChandraROIModel
from ixpeobssim.irf import load_irf_set, load_arf, load_vign
from ixpeobssim.srcmodel.polarization import constant
from ixpeobssim.utils.matplotlib_ import plt
from ixpeobssim.evt.gti import xUberGTIList
from ixpeobssim.utils.time_ import string_to_met_utc
from ixpeobssim.utils.astro import angular_separation, read_ds9
from ixpeobssim.utils.units_ import degrees_to_arcmin
import ixpeobssim.utils.chandra as chandra
import ixpeobssim.core.pipeline as pipeline
if sys.flags.interactive:
plt.ion()
class TestChandraToIxpe(unittest.TestCase):
"""Unit test for simulation and analysis pipeline(s).
"""
def test_chandra_irfs(self):
"""Try to load chandra ACIS-I and ACIS-S arf and vignetting, and
computing the effective area ratio.
"""
plt.figure('Chandra effective area')
acis_i = chandra.load_arf('ACIS-I')
acis_i.plot(label='ACIS-I')
acis_s = chandra.load_arf('ACIS-S')
acis_s.plot(label='ACIS-S')
plt.legend()
plt.figure('Chandra vignetting')
vign_c = chandra.load_vign()
vign_c.plot()
plt.figure('Effective area ratio')
aeff = load_arf()
vign = load_vign()
ratio = chandra.arf_ratio(aeff, vign, acis_i, vign_c)
ratio.plot()
def test_aeff_ratio(self):
"""Compare the effective area between Chandra and IXPE ratio defined as bivariate
spline with the one calculated using the effective exposure event by event.
"""
acis_i = chandra.load_arf('ACIS-I')
vign_c = chandra.load_vign()
aeff = load_arf()
vign = load_vign()
aeff_ratio = chandra.arf_ratio(aeff, vign, acis_i, vign_c)
evt_path = os.path.join(IXPEOBSSIM_TEST, 'data', 'cena.fits')
roi = xChandraROIModel(evt_path, acis='I')
mask = roi.theta_c < 8.5
energy = roi.energy_c[mask]
theta = roi.theta_c[mask]
ixpe_exp = aeff(energy) * vign(energy, theta) * roi.obs_time
_ratio1 = ixpe_exp / roi.effexp_c[mask]
_ratio2 = aeff_ratio(roi.energy_c[mask], roi.theta_c[mask])
_delta = _ratio1 / _ratio2 - 1
_delta_max = numpy.absolute(_delta).max()
self.assertTrue(_delta_max < 1e-2, 'diff. %.9f' % _delta_max)
def test_legacy_converter(self, duration=20000.):
"""Compare the new simulation flow using the effective exposure event by event with
the old one using the time scaling and the effective area ratio.
"""
numpy.random.seed(0)
acis_i = chandra.load_arf('ACIS-I')
vign_c = chandra.load_vign()
aeff = load_arf()
vign = load_vign()
aeff_ratio = chandra.arf_ratio(aeff, vign, acis_i, vign_c)
evt_path = os.path.join(IXPEOBSSIM_TEST, 'data', 'cena.fits')
roi = xChandraROIModel(evt_path, acis='I')
roi._reset_mask()
mc_energy, mc_ra, mc_dec, mc_effexp, mc_theta =\
roi.energy_c, roi.ra_c, roi.dec_c, roi.effexp_c, roi.theta_c
# Old way
# Scale the chandra events according to duration
scale = numpy.modf(duration / roi.obs_time)
_energy, _ra, _dec = xChandraObservation._time_scaling(scale, mc_energy, mc_ra, mc_dec)
# Throw an array of random numbers and accept the events based on the
# effective area ratio
rnd_ratio = numpy.random.random(len(_energy))
separation = angular_separation(_ra, _dec, roi.ra, roi.dec)
separation = degrees_to_arcmin(separation)
mask = rnd_ratio < aeff_ratio(_energy, separation)
old_num_events = mask.sum()
# New way
expect_repeat = aeff(mc_energy) * vign(mc_energy, mc_theta) * duration / mc_effexp
expect_repeat[numpy.logical_not(expect_repeat > 0.)] = 0.
new_num_events = expect_repeat.sum()
_delta = old_num_events / new_num_events - 1
_delta_max = numpy.absolute(_delta).max()
self.assertTrue(_delta_max < 1.4e-2, 'diff. %.9f' % _delta_max)
def test_chandra_converter(self):
"""Run a quick test converting data from Chandra.
Warning
-------
We should be checking *something* in the output, here.
"""
pipeline.reset('test_cena', overwrite=True)
# Run the converter
pipeline.xpobssim(duration=10000., saa=False, occult=False)
def test_nevents(self, N=2):
"""Test if, doubling the duration time, the number of converted events doubles.
"""
numpy.random.seed(0)
irf_set = load_irf_set()
kwargs = dict(startdate='2022-04-21', duration=200000., deadtime=0.,
roll=0., dithering=False)
kwargs['start_met'] = string_to_met_utc(kwargs.get('startdate'), lazy=True)
kwargs['stop_met'] = kwargs.get('start_met') + kwargs.get('duration')
kwargs['gti_list'] = xUberGTIList()
evt_path = os.path.join(IXPEOBSSIM_TEST, 'data', 'cena_mod.fits')
pol_degree = constant(0.)
pol_angle = constant(0.)
obs = xChandraObservation('Cen A', pol_degree, pol_angle)
roi_model = xChandraROIModel(evt_path, acis='I')
roi_model.add_source(obs)
event_list_1 = roi_model.rvs_event_list(irf_set, **kwargs)
kwargs['duration'] *= N
event_list_2 = roi_model.rvs_event_list(irf_set, **kwargs)
_ratio = event_list_2.num_events() / float(event_list_1.num_events())
_delta = _ratio / N - 1
self.assertTrue(abs(_delta) < 3e-2, 'diff. %.9f' % abs(_delta))
def test_overlap(self):
"""Test if the converter raises SystemExit exception in presence of
overlapping regions in the configuration file.
"""
irf_set = load_irf_set()
REG_SOURCE_FILE_PATH = os.path.join(IXPEOBSSIM_CONFIG_REG, 'cena_jet+core.reg')
regions = read_ds9(REG_SOURCE_FILE_PATH)
kwargs = dict(startdate='2022-04-21', duration=100000., deadtime=0., roll=0.)
kwargs['start_met'] = string_to_met_utc(kwargs.get('startdate'), lazy=True)
kwargs['stop_met'] = kwargs.get('start_met') + kwargs.get('duration')
kwargs['gti_list'] = xUberGTIList()
evt_path = os.path.join(IXPEOBSSIM_TEST, 'data', 'cena.fits')
roi_model = xChandraROIModel(evt_path, acis='I')
pol_degree = constant(0.)
pol_angle = constant(0.)
obs1 = xChandraObservation('Cen A', pol_degree, pol_angle)
roi_model.add_source(obs1)
obs2 = xChandraObservation('Core', pol_degree, pol_angle, regions[1])
roi_model.add_source(obs2)
self.assertRaises(SystemExit, roi_model.rvs_event_list, irf_set,
**kwargs)
if __name__ == '__main__':
unittest.main(exit=not sys.flags.interactive)
|
lucabaldiniREPO_NAMEixpeobssimPATH_START.@ixpeobssim_extracted@ixpeobssim-main@tests@test_chandra2ixpe.py@.PATH_END.py
|
{
"filename": "DaySequence.py",
"repo_name": "james-trayford/strauss",
"repo_path": "strauss_extracted/strauss-main/examples/DaySequence.py",
"type": "Python"
}
|
#!/usr/bin/env python
# coding: utf-8
# ### <u> Generate the sunrise to sunset sonification used in the "_Audible Universe_" planetarium show </u>
import matplotlib.pyplot as plt
import ffmpeg as ff
import wavio as wav
from strauss.sonification import Sonification
from strauss.sources import Objects
from strauss import channels
from strauss.score import Score
import numpy as np
from strauss.generator import Sampler
import IPython.display as ipd
import glob
import os
import copy
from pathlib import Path
print("\nSonifying the Sun's motion across the sky...")
# First we download the samples to the local data directory, if they haven't been already:
outdir = Path("..", "data", "samples", "day_sequence")
if list(Path(f"{outdir}").glob("*.wav")):
print(f"Directory {outdir} already exists.")
else:
print("Downloading files...")
import urllib.request
Path('..', 'data', 'samples', 'day_sequence').mkdir(parents=True, exist_ok=True)
files = ("sun_A4.wav", "scatter_B4.wav")
urls = ("https://drive.google.com/uc?export=download&id=15D7xHEKtKppTvzzwECIq_0UGhifdhrEy",
"https://drive.google.com/uc?export=download&id=1bnhZ_kagtWMUkj1VtEE6vzQGfnYexQfL")
for f, u in zip(files, urls):
with urllib.request.urlopen(u) as response, Path(f"{outdir}", f"{f}").open(mode='wb') as out_file:
print(f"\t getting {f}")
data = response.read() # a `bytes` object
out_file.write(data)
print("Done.")
# **Specify the audio system to use** _(use `'stereo'` by default but for the planetarium `'5.1'` is used)_
# specify audio system (e.g. mono, stereo, 5.1, ...)
system = "stereo"
# **Now, set-up the sampler:**
# set up sampler
sampler = Sampler(str(outdir))
sampler.modify_preset({'filter':'on'}) # want filtering on for sun altitude effect
# **Set mapping limits of mapped quantities** (truncated relative to planetarium show example)
maplims = {'azimuth': (0, 360),
'polar': (0, 180),
'pitch' : (0, 1),
'cutoff' : (0, 1),
'volume' : (0,1),
'time_evo' : (0,75)}
# **Initialise the score:**
# setup score
score = Score([['A4','B4']], 75)
# **Render sonification for specified planet...**
data = {'azimuth': np.array([90,90, 0, 330, 240,240]),
'polar': np.array([45,45,0, 40, 0, 0]), # constant polar of 90 deg
'pitch': 1, # constant pitch
'volume': np.ones(6),
'cutoff': np.array([0.5, 0.5, 1, 0.444, 0.1, 0]),
'time_evo': np.array([0, 33.5,45, 57.5, 72.5, 147])}
# set up source
events = Objects(maplims.keys())
events.fromdict(data)
events.apply_mapping_functions(map_lims=maplims)
print("Generating sonification of Sun alone...")
soni = Sonification(score, events, sampler, system)
soni.render()
# listen...
soni.hear()
# **Listen to and plot the waveforms from the sonification:**
print("Generating sonification with scattered light sound...")
data2 = {'azimuth': np.ones(8)*0,
'polar': np.zeros(8), # constant polar of 90 deg
'pitch': 1, # constant pitch
'volume': np.array([0.2,0.2,0.4,0.2,0.1,0.03, 0.01, 0.]),
'cutoff': np.ones(8),
'time_evo': np.array([0, 33.5,45, 57.5, 72.5, 90, 100, 147])}
# set up source
events2 = Objects(maplims.keys())
events2.fromdict(data2)
events2.apply_mapping_functions(map_lims=maplims)
sampler2 = copy.deepcopy(sampler)
sampler2.samples['A4'] = sampler2.samples['B4']
soni2 = Sonification(score, events2, sampler2, system)
soni2.out_channels = soni.out_channels
soni2.render()
# listen...
soni2.hear()
# **Combine and save sonification to a multi-channel wav**
#
# NOTE: Change `"../../FILENAME.wav"` to your filepath of choice. By default, the sound file is normalised to that of the highest amplitude sample, but can be set to a lower normalisation by setting the `master_volume` parameter to a value between `0.` and `1.`.
# soni2.save_combined(Path("..", "..", "day_sequence.wav"), True, master_volume=1.0)
|
james-trayfordREPO_NAMEstraussPATH_START.@strauss_extracted@strauss-main@examples@DaySequence.py@.PATH_END.py
|
{
"filename": "test_testing.py",
"repo_name": "scikit-image/scikit-image",
"repo_path": "scikit-image_extracted/scikit-image-main/skimage/_shared/tests/test_testing.py",
"type": "Python"
}
|
"""Testing decorators module"""
import inspect
import re
import warnings
import pytest
from numpy.testing import assert_equal
from skimage._shared.testing import (
doctest_skip_parser,
run_in_parallel,
assert_stacklevel,
)
from skimage._shared import testing
from skimage._shared._dependency_checks import is_wasm
from skimage._shared._warnings import expected_warnings
from warnings import warn
def test_skipper():
def f():
pass
class c:
def __init__(self):
self.me = "I think, therefore..."
docstring = """ Header
>>> something # skip if not HAVE_AMODULE
>>> something + else
>>> a = 1 # skip if not HAVE_BMODULE
>>> something2 # skip if HAVE_AMODULE
"""
f.__doc__ = docstring
c.__doc__ = docstring
global HAVE_AMODULE, HAVE_BMODULE
HAVE_AMODULE = False
HAVE_BMODULE = True
f2 = doctest_skip_parser(f)
c2 = doctest_skip_parser(c)
assert f is f2
assert c is c2
expected = """ Header
>>> something # doctest: +SKIP
>>> something + else
>>> a = 1
>>> something2
"""
assert_equal(f2.__doc__, expected)
assert_equal(c2.__doc__, expected)
HAVE_AMODULE = True
HAVE_BMODULE = False
f.__doc__ = docstring
c.__doc__ = docstring
f2 = doctest_skip_parser(f)
c2 = doctest_skip_parser(c)
assert f is f2
expected = """ Header
>>> something
>>> something + else
>>> a = 1 # doctest: +SKIP
>>> something2 # doctest: +SKIP
"""
assert_equal(f2.__doc__, expected)
assert_equal(c2.__doc__, expected)
del HAVE_AMODULE
f.__doc__ = docstring
c.__doc__ = docstring
with testing.raises(NameError):
doctest_skip_parser(f)
with testing.raises(NameError):
doctest_skip_parser(c)
@pytest.mark.skipif(is_wasm, reason="Cannot start threads in WASM")
def test_run_in_parallel():
state = []
@run_in_parallel()
def change_state1():
state.append(None)
change_state1()
assert len(state) == 2
@run_in_parallel(num_threads=1)
def change_state2():
state.append(None)
change_state2()
assert len(state) == 3
@run_in_parallel(num_threads=3)
def change_state3():
state.append(None)
change_state3()
assert len(state) == 6
@pytest.mark.skipif(is_wasm, reason="Cannot run parallel code in WASM")
def test_parallel_warning():
@run_in_parallel()
def change_state_warns_fails():
warn("Test warning for test parallel", stacklevel=2)
with expected_warnings(['Test warning for test parallel']):
change_state_warns_fails()
@run_in_parallel(warnings_matching=['Test warning for test parallel'])
def change_state_warns_passes():
warn("Test warning for test parallel", stacklevel=2)
change_state_warns_passes()
def test_expected_warnings_noop():
# This will ensure the line beolow it behaves like a no-op
with expected_warnings(['Expected warnings test']):
# This should behave as a no-op
with expected_warnings(None):
warn('Expected warnings test')
class Test_assert_stacklevel:
def raise_warning(self, *args, **kwargs):
warnings.warn(*args, **kwargs)
def test_correct_stacklevel(self):
# Should pass if stacklevel is set correctly
with pytest.warns(UserWarning, match="passes") as record:
self.raise_warning("passes", UserWarning, stacklevel=2)
assert_stacklevel(record)
@pytest.mark.parametrize("level", [1, 3])
def test_wrong_stacklevel(self, level):
# AssertionError should be raised for wrong stacklevel
with pytest.warns(UserWarning, match="wrong") as record:
self.raise_warning("wrong", UserWarning, stacklevel=level)
# Check that message contains expected line on right side
line_number = inspect.currentframe().f_lineno - 2
regex = ".*" + re.escape(f"Expected: {__file__}:{line_number}")
with pytest.raises(AssertionError, match=regex):
assert_stacklevel(record, offset=-5)
|
scikit-imageREPO_NAMEscikit-imagePATH_START.@scikit-image_extracted@scikit-image-main@skimage@_shared@tests@test_testing.py@.PATH_END.py
|
{
"filename": "joint_categorical.py",
"repo_name": "jmschrei/pomegranate",
"repo_path": "pomegranate_extracted/pomegranate-master/pomegranate/distributions/joint_categorical.py",
"type": "Python"
}
|
# joint_categorical.py
# Contact: Jacob Schreiber <jmschreiber91@gmail.com>
import numpy
import torch
from .._utils import _cast_as_tensor
from .._utils import _cast_as_parameter
from .._utils import _update_parameter
from .._utils import _check_parameter
from .._utils import _reshape_weights
from ._distribution import Distribution
from .categorical import Categorical
class JointCategorical(Distribution):
"""A joint categorical distribution.
A joint categorical distribution models the probability of a vector of
categorical values occurring without assuming that the dimensions are
independent from each other. Essentially, it is a Categorical distribution
without the assumption that the dimensions are independent of each other.
There are two ways to initialize this object. The first is to pass in
the tensor of probability parameters, at which point they can immediately be
used. The second is to not pass in the rate parameters and then call
either `fit` or `summary` + `from_summaries`, at which point the
probability parameters will be learned from data.
Parameters
----------
probs: list, numpy.ndarray, torch.tensor, or None, shape=*n_categories
A tensor where each dimension corresponds to one column in the data
set being modeled and the size of each dimension is the number of
categories in that column, e.g., if the data being modeled is binary
and has shape (5, 4), this will be a tensor with shape (2, 2, 2, 2).
Default is None.
n_categories: list, numpy.ndarray, torch.tensor, or None, shape=(d,)
A vector with the maximum number of categories that each column
can have. If not given, this will be inferred from the data. Default
is None.
inertia: float, [0, 1], optional
Indicates the proportion of the update to apply to the parameters
during training. When the inertia is 0.0, the update is applied in
its entirety and the previous parameters are ignored. When the
inertia is 1.0, the update is entirely ignored and the previous
parameters are kept, equivalently to if the parameters were frozen.
pseudocount: float, optional
A number of observations to add to each entry in the probability
distribution during training. A higher value will smooth the
distributions more. Default is 0.
inertia: float, [0, 1], optional
Indicates the proportion of the update to apply to the parameters
during training. When the inertia is 0.0, the update is applied in
its entirety and the previous parameters are ignored. When the
inertia is 1.0, the update is entirely ignored and the previous
parameters are kept, equivalently to if the parameters were frozen.
frozen: bool, optional
Whether all the parameters associated with this distribution are frozen.
If you want to freeze individual pameters, or individual values in those
parameters, you must modify the `frozen` attribute of the tensor or
parameter directly. Default is False.
check_data: bool, optional
Whether to check properties of the data and potentially recast it to
torch.tensors. This does not prevent checking of parameters but can
slightly speed up computation when you know that your inputs are valid.
Setting this to False is also necessary for compiling.
"""
def __init__(self, probs=None, n_categories=None, pseudocount=0,
inertia=0.0, frozen=False, check_data=True):
super().__init__(inertia=inertia, frozen=frozen, check_data=check_data)
self.name = "JointCategorical"
self.probs = _check_parameter(_cast_as_parameter(probs), "probs",
min_value=0, max_value=1, value_sum=1)
self.n_categories = _check_parameter(n_categories, "n_categories",
min_value=2)
self.pseudocount = _check_parameter(pseudocount, "pseudocount")
self._initialized = probs is not None
self.d = len(self.probs.shape) if self._initialized else None
if self._initialized:
if n_categories is None:
self.n_categories = tuple(self.probs.shape)
elif isinstance(n_categories, int):
self.n_categories = (n_categories for i in range(n_categories))
else:
self.n_categories = tuple(n_categories)
else:
self.n_categories = None
self._reset_cache()
def _initialize(self, d, n_categories):
"""Initialize the probability distribution.
This method is meant to only be called internally. It initializes the
parameters of the distribution and stores its dimensionality. For more
complex methods, this function will do more.
Parameters
----------
d: int
The dimensionality the distribution is being initialized to.
n_categories: list, numpy.ndarray, torch.tensor, or None, shape=(d,)
A vector with the maximum number of categories that each column
can have. If not given, this will be inferred from the data.
Default is None.
"""
self.probs = _cast_as_parameter(torch.zeros(*n_categories,
dtype=self.dtype, device=self.device))
self.n_categories = n_categories
self._initialized = True
super()._initialize(d)
def _reset_cache(self):
"""Reset the internally stored statistics.
This method is meant to only be called internally. It resets the
stored statistics used to update the model parameters as well as
recalculates the cached values meant to speed up log probability
calculations.
"""
if self._initialized == False:
return
self._w_sum = torch.zeros(self.d, dtype=self.probs.dtype)
self._xw_sum = torch.zeros(*self.n_categories, dtype=self.probs.dtype)
self._log_probs = torch.log(self.probs)
def sample(self, n):
"""Sample from the probability distribution.
This method will return `n` samples generated from the underlying
probability distribution. For a mixture model, this involves first
sampling the component using the prior probabilities, and then sampling
from the chosen distribution.
Parameters
----------
n: int
The number of samples to generate.
Returns
-------
X: torch.tensor, shape=(n, self.d)
Randomly generated samples.
"""
idxs = torch.multinomial(self.probs.flatten(), num_samples=n,
replacement=True)
X = numpy.unravel_index(idxs.numpy(), self.n_categories)
X = numpy.stack(X).T
return torch.from_numpy(X)
def log_probability(self, X):
"""Calculate the log probability of each example.
This method calculates the log probability of each example given the
parameters of the distribution. The examples must be given in a 2D
format. For a joint categorical distribution, each value must be an
integer category that is smaller than the maximum number of categories
for each feature.
Note: This differs from some other log probability calculation
functions, like those in torch.distributions, because it is not
returning the log probability of each feature independently, but rather
the total log probability of the entire example.
Parameters
----------
X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
A set of examples to evaluate.
Returns
-------
logp: torch.Tensor, shape=(-1,)
The log probability of each example.
"""
X = _check_parameter(_cast_as_tensor(X), "X",
value_set=tuple(range(max(self.n_categories)+1)), ndim=2,
shape=(-1, self.d), check_parameter=self.check_data)
logps = torch.zeros(len(X), dtype=self.probs.dtype)
for i in range(len(X)):
logps[i] = self._log_probs[tuple(X[i])]
return logps
def summarize(self, X, sample_weight=None):
"""Extract the sufficient statistics from a batch of data.
This method calculates the sufficient statistics from optionally
weighted data and adds them to the stored cache. The examples must be
given in a 2D format. Sample weights can either be provided as one
value per example or as a 2D matrix of weights for each feature in
each example.
Parameters
----------
X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
A set of examples to summarize.
sample_weight: list, tuple, numpy.ndarray, torch.Tensor, optional
A set of weights for the examples. This can be either of shape
(-1, self.d) or a vector of shape (-1,). Default is ones.
"""
if self.frozen == True:
return
X = _check_parameter(_cast_as_tensor(X), "X", ndim=2,
dtypes=(torch.int32, torch.int64), check_parameter=self.check_data)
if not self._initialized:
self._initialize(len(X[0]), torch.max(X, dim=0)[0]+1)
X = _check_parameter(X, "X", shape=(-1, self.d),
value_set=tuple(range(max(self.n_categories)+1)),
check_parameter=self.check_data)
sample_weight = _reshape_weights(X, _cast_as_tensor(sample_weight,
dtype=torch.float32))[:,0]
self._w_sum += torch.sum(sample_weight, dim=0)
for i in range(len(X)):
self._xw_sum[tuple(X[i])] += sample_weight[i]
def from_summaries(self):
"""Update the model parameters given the extracted statistics.
This method uses calculated statistics from calls to the `summarize`
method to update the distribution parameters. Hyperparameters for the
update are passed in at initialization time.
Note: Internally, a call to `fit` is just a successive call to the
`summarize` method followed by the `from_summaries` method.
"""
if self.frozen == True:
return
probs = self._xw_sum / self._w_sum[0]
_update_parameter(self.probs, probs, self.inertia)
self._reset_cache()
|
jmschreiREPO_NAMEpomegranatePATH_START.@pomegranate_extracted@pomegranate-master@pomegranate@distributions@joint_categorical.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "fchollet/keras",
"repo_path": "keras_extracted/keras-master/keras/api/utils/bounding_boxes/__init__.py",
"type": "Python"
}
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
affine_transform,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
clip_to_image_size,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
convert_format,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
crop,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
pad,
)
|
fcholletREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@api@utils@bounding_boxes@__init__.py@.PATH_END.py
|
{
"filename": "finetune.py",
"repo_name": "mwalmsley/zoobot",
"repo_path": "zoobot_extracted/zoobot-main/zoobot/tensorflow/training/finetune.py",
"type": "Python"
}
|
import logging
import os
import tensorflow as tf
from tensorflow.keras import layers
from zoobot.tensorflow.training import training_config
def run_finetuning(config, encoder, train_dataset, val_dataset, test_dataset, save_dir):
new_head = linear_classifier(config['finetune']['encoder_dim'], config['finetune']['label_dim'])
img_size = config['finetune']['img_size']
"""
Retrain the model. Only the new head will train as the rest is frozen.
"""
encoder.trainable = False
# stick the new head on the pretrained base model
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(img_size, img_size, 1)),
encoder,
new_head
])
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
model.compile(
loss=loss,
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # normal learning rate is okay
metrics=['accuracy']
)
model.summary()
trainer = training_config.Trainer(
# parameters for how to train e.g. epochs, patience
log_dir=os.path.join(save_dir, 'head_only'),
epochs=config['finetune']['n_epochs'],
patience=config['finetune']['patience']
)
model_with_trained_head = trainer.fit(
model,
train_dataset,
val_dataset,
eager=False
)
logging.info('Head finetuning complete')
if config['finetune']['n_layers'] == 0:
logging.info('n_layers = 0: not finetuning lower layers')
return model_with_trained_head
logging.info('Unfreezing layers')
# you can unfreeze layers like so:
unfreeze_model(model, unfreeze_names=['top'])
# or more...
# utils.unfreeze_model(model, unfreeze_names=['top', 'block7'])
# utils.unfreeze_model(model, unfreeze_names=['top', 'block7', 'block6'])
# utils.unfreeze_model(model, unfreeze_names=['top', 'block7', 'block6', 'block5'])
# utils.unfreeze_model(model, unfreeze_names=['top', 'block7', 'block6', 'block5', 'block4'])
# utils.unfreeze_model(model, unfreeze_names=[], unfreeze_all=True)
logging.info('Recompiling with lower learning rate and trainable upper layers')
model_with_trained_head.compile(
loss=tf.keras.losses.binary_crossentropy,
optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5), # 10x lower initial learning rate (adam will adapt anyway)
metrics=['accuracy']
)
model_with_trained_head.summary(print_fn=logging.info)
trainer = training_config.Trainer(
# parameters for how to train e.g. epochs, patience
log_dir=os.path.join(save_dir, 'full'),
epochs=config['finetune']['n_epochs'],
patience=config['finetune']['patience']
)
model_with_trained_lower_layers = trainer.fit(
model_with_trained_head,
train_dataset,
val_dataset,
eager=False
)
logging.info('Finetuning complete')
return model_with_trained_lower_layers
"""
Well done!
You can now use your finetuned models to make predictions on new data..
See make_predictions.py for a self-contained example.
"""
def linear_classifier(input_dim, output_dim):
return tf.keras.Sequential([
# TODO move pooling
tf.keras.layers.InputLayer(input_shape=(input_dim)), # base model dim after GlobalAveragePooling (ignoring batch)
tf.keras.layers.Dense(output_dim, name='logits') # output should be N neurons w/ softmax for N-class classification
# layers.Dense(3, activation="softmax", name="softmax_output") # ...or
])
def freeze_model(model):
# Freeze the pretrained weights
# inplace
model.trainable = False
def unfreeze_model(model, unfreeze_names=['block7', 'top'], unfreeze_all=False):
if unfreeze_all and (len(unfreeze_names) > 0):
logging.warning('unfreeze_all is True; ignoring unfreeze_names and unfreezing all layers')
# https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/
# required for any layer to be trainable.
# however, setting to True sets *every* layer trainable (why, tf, why...)
# so need to then set each layer individually trainable or not trainable below
model.trainable = True # everything trainable, recursively.
for layer in model.layers:
# recursive
# if isinstance(layer, tf.keras.Sequential) or isinstance(layer, tf.python.keras.engine.functional.Functional): # layer is itself a model (effnet is functional due to residual connections)
if isinstance(layer, tf.keras.Model): # includes subclasses Sequential and Functional
unfreeze_model(layer, unfreeze_names=unfreeze_names, unfreeze_all=unfreeze_all) # recursive
elif any([layer.name.startswith(name) for name in unfreeze_names]) or unfreeze_all:
if isinstance(layer, layers.BatchNormalization):
logging.debug('freezing batch norm layer {}'.format(layer.name))
layer.trainable = False
else:
logging.debug('unfreezing {}'.format(layer.name))
layer.trainable = True
# print('Freezing batch norm layer')
# https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization?version=stable#note_that_2
# this will also switch layer to inference mode from tf2, no need to separately pass training=False
else:
logging.warning('Layer {} ({}) not in unfreeze list - freezing by default'.format(layer.name, layer))
layer.trainable = False # not a recursive call, and not with a name to unfreeze
# model will be trainable next time it is compiled
def check_batchnorm_frozen(model):
for layer in model.layers:
print(layer)
if isinstance(layer, tf.keras.Model):
check_batchnorm_frozen(layer)
elif isinstance(layer, layers.BatchNormalization):
assert not layer.trainable
print('checks out')
# import numpy as np
# import pandas as pd
# paths_pred = paths_val # TODO for simplicitly I'll just make more predictions on the validation images, but you'll want to change this
# raw_pred_dataset = image_datasets.get_image_dataset(paths_pred, file_format=file_format, requested_img_size=requested_img_size, batch_size=batch_size)
# ordered_paths = [x.numpy().decode('utf8') for batch in raw_pred_dataset for x in batch['id_str']]
# # must exactly match the preprocessing you used for training
# pred_config = preprocess.PreprocessingConfig(
# label_cols=[], # image_datasets.get_image_dataset will put the labels arg under 'label' key for each batch
# input_size=requested_img_size,
# make_greyscale=True,
# # normalise_from_uint8=True,
# permute_channels=False
# )
# pred_dataset = preprocess.preprocess_dataset(raw_pred_dataset, pred_config)
# predictions = model.predict(pred_dataset)
# data = [{'prediction': float(prediction), 'image_loc': local_png_loc} for prediction, local_png_loc in zip(predictions, ordered_paths)]
# pred_df = pd.DataFrame(data=data)
# example_predictions_loc = 'results/finetune_minimal/example_predictions.csv'
# pred_df.to_csv(example_predictions_loc, index=False)
# logging.info(f'Example predictions saved to {example_predictions_loc}')
|
mwalmsleyREPO_NAMEzoobotPATH_START.@zoobot_extracted@zoobot-main@zoobot@tensorflow@training@finetune.py@.PATH_END.py
|
{
"filename": "CAMB.py",
"repo_name": "Valcin/BE_HaPPy",
"repo_path": "BE_HaPPy_extracted/BE_HaPPy-master/coefficients/other neutrinos masses/0.13eV/CAMB.py",
"type": "Python"
}
|
import numpy as np
import camb
import sys,os
################################## INPUT ######################################
# neutrino parameters
hierarchy = 'degenerate' #'degenerate', 'normal', 'inverted'
Mnu = 0.13 #eV
Nnu = 3 #number of massive neutrinos
Neff = 3.046
#~ Neff = 0.00641
# cosmological parameters
h = 0.6711
Omega_c = 0.2685 - Mnu/(93.14*h**2)
Omega_b = 0.049
Omega_k = 0.0
tau = None
# initial P(k) parameters
ns = 0.9624
As = 2.13e-9
pivot_scalar = 0.05
pivot_tensor = 0.05
# redshifts and k-range
redshifts = [0.0, 0.5, 1, 2, 3, 99]
kmax = 10.0
k_per_logint = 10
# dz, relative difference dz/z to compute growths
dz = 0.01
###############################################################################
# create a new redshift list to compute growth rates
zs = []
for z in redshifts:
dz_abs = (1.0+z)*dz
if z==0.0:
zs.append(z); zs.append(z+dz_abs)
else:
zs.append(z-dz_abs); zs.append(z); zs.append(z+dz_abs)
z_list = redshifts; redshifts = zs
Omega_cb = Omega_c + Omega_b
pars = camb.CAMBparams()
# set accuracy of the calculation
pars.set_accuracy(AccuracyBoost=5.0, lSampleBoost=5.0,
lAccuracyBoost=5.0, HighAccuracyDefault=True,
DoLateRadTruncation=True)
# set value of the cosmological parameters
pars.set_cosmology(H0=h*100.0, ombh2=Omega_b*h**2, omch2=Omega_c*h**2,
mnu=Mnu, omk=Omega_k,
neutrino_hierarchy=hierarchy,
num_massive_neutrinos=Nnu,
nnu=Neff,
tau=tau)
# set the value of the primordial power spectrum parameters
pars.InitPower.set_params(As=As, ns=ns,
pivot_scalar=pivot_scalar,
pivot_tensor=pivot_tensor)
# set redshifts, k-range and k-sampling
pars.set_matter_power(redshifts=redshifts, kmax=kmax,
k_per_logint=k_per_logint)
# compute results
results = camb.get_results(pars)
# get raw matter power spectrum and transfer functions with strange k-binning
#k, zs, Pk = results.get_linear_matter_power_spectrum()
#Tk = (results.get_matter_transfer_data()).transfer_data
# interpolate to get Pmm, Pcc...etc
k, zs, Pkmm = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=400, var1=7, var2=7,
have_power_spectra=True,
params=None)
k, zs, Pkcc = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=400, var1=2, var2=2,
have_power_spectra=True,
params=None)
k, zs, Pkbb = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=400, var1=3, var2=3,
have_power_spectra=True,
params=None)
k, zs, Pkcb = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=400, var1=2, var2=3,
have_power_spectra=True,
params=None)
Pkcb = (Omega_c**2*Pkcc + Omega_b**2*Pkbb +\
2.0*Omega_b*Omega_c*Pkcb)/Omega_cb**2
k, zs, Pknn = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=400, var1=6, var2=6,
have_power_spectra=True,
params=None)
print pars
# get sigma_8 and Hz in km/s/(kpc/h)
s8 = np.array(results.get_sigma8())
Hz = results.hubble_parameter(99.0)
print 'H(z=99) = %.4f km/s/(kpc/h)'%(Hz/1e3/h)
print 'sigma_8(z=0) = %.4f'%s8[-1]
# do a loop over all redshifts
for i,z in enumerate(zs):
fout1 = 'Pk_mm_z=%.3f.txt'%z
fout2 = 'Pk_cc_z=%.3f.txt'%z
fout3 = 'Pk_bb_z=%.3f.txt'%z
fout4 = 'Pk_cb_z=%.3f.txt'%z
fout5 = 'Pk_nn_z=%.3f.txt'%z
np.savetxt(fout1,np.transpose([k,Pkmm[i,:]]))
np.savetxt(fout2,np.transpose([k,Pkcc[i,:]]))
np.savetxt(fout3,np.transpose([k,Pkbb[i,:]]))
np.savetxt(fout4,np.transpose([k,Pkcb[i,:]]))
np.savetxt(fout5,np.transpose([k,Pknn[i,:]]))
#fout = 'Pk_trans_z=%.3f.txt'%z
# notice that transfer functions have an inverted order:i=0 ==>z_max
#np.savetxt(fout,np.transpose([Tk[0,:,i],Tk[1,:,i],Tk[2,:,i],Tk[3,:,i],
# Tk[4,:,i],Tk[5,:,i],Tk[6,:,i]]))
# compute growth rates
for z in z_list:
dz_abs = (1.0+z)*dz
for suffix in ['mm','cb','nn']:
fout = 'f%s_z=%.3f.txt'%(suffix,z)
f2 = 'Pk_%s_z=%.3f.txt'%(suffix,z+dz_abs)
if z==0.0:
f1 = 'Pk_%s_z=%.3f.txt'%(suffix,z); fac = 1.0
else:
f1 = 'Pk_%s_z=%.3f.txt'%(suffix,z-dz_abs); fac = 2.0
k1,Pk1 = np.loadtxt(f1,unpack=True)
k2,Pk2 = np.loadtxt(f2,unpack=True)
if np.any(k1!=k2):
print 'Error!'; sys.exit()
f = -0.5*(1.0+z)*np.log(Pk2/Pk1)/(fac*dz_abs)
np.savetxt(fout,np.transpose([k1,f]))
os.system('rm '+f2)
if z!=0.0: os.system('rm '+f1)
|
ValcinREPO_NAMEBE_HaPPyPATH_START.@BE_HaPPy_extracted@BE_HaPPy-master@coefficients@other neutrinos masses@0.13eV@CAMB.py@.PATH_END.py
|
{
"filename": "_utils.py",
"repo_name": "21cmfast/21cmFAST",
"repo_path": "21cmFAST_extracted/21cmFAST-master/src/py21cmfast/_utils.py",
"type": "Python"
}
|
"""Utilities that help with wrapping various C structures."""
import glob
import h5py
import logging
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from bidict import bidict
from cffi import FFI
from enum import IntEnum
from hashlib import md5
from os import makedirs, path
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
from . import __version__
from ._cfg import config
from .c_21cmfast import lib
_ffi = FFI()
logger = logging.getLogger(__name__)
class ArrayStateError(ValueError):
"""Errors arising from incorrectly modifying array state."""
pass
class ArrayState:
"""Define the memory state of a struct array."""
def __init__(
self, initialized=False, c_memory=False, computed_in_mem=False, on_disk=False
):
self._initialized = initialized
self._c_memory = c_memory
self._computed_in_mem = computed_in_mem
self._on_disk = on_disk
@property
def initialized(self):
"""Whether the array is initialized (i.e. allocated memory)."""
return self._initialized
@initialized.setter
def initialized(self, val):
if not val:
# if its not initialized, can't be computed in memory
self.computed_in_mem = False
self._initialized = bool(val)
@property
def c_memory(self):
"""Whether the array's memory (if any) is controlled by C."""
return self._c_memory
@c_memory.setter
def c_memory(self, val):
self._c_memory = bool(val)
@property
def computed_in_mem(self):
"""Whether the array is computed and stored in memory."""
return self._computed_in_mem
@computed_in_mem.setter
def computed_in_mem(self, val):
if val:
# any time we pull something into memory, it must be initialized.
self.initialized = True
self._computed_in_mem = bool(val)
@property
def on_disk(self):
"""Whether the array is computed and store on disk."""
return self._on_disk
@on_disk.setter
def on_disk(self, val):
self._on_disk = bool(val)
@property
def computed(self):
"""Whether the array is computed anywhere."""
return self.computed_in_mem or self.on_disk
@property
def c_has_active_memory(self):
"""Whether C currently has initialized memory for this array."""
return self.c_memory and self.initialized
def __str__(self):
"""Returns a string representation of the ArrayState."""
if self.computed_in_mem:
return "computed (in mem)"
elif self.on_disk:
return "computed (on disk)"
elif self.initialized:
return "memory initialized (not computed)"
else:
return "uncomputed and uninitialized"
class ParameterError(RuntimeError):
"""An exception representing a bad choice of parameters."""
default_message = "21cmFAST does not support this combination of parameters."
def __init__(self, msg=None):
super().__init__(msg or self.default_message)
class FatalCError(Exception):
"""An exception representing something going wrong in C."""
default_message = "21cmFAST is exiting."
def __init__(self, msg=None):
super().__init__(msg or self.default_message)
class FileIOError(FatalCError):
"""An exception when an error occurs with file I/O."""
default_message = "Expected file could not be found! (check the LOG for more info)"
class GSLError(ParameterError):
"""An exception when a GSL routine encounters an error."""
default_message = "A GSL routine has errored! (check the LOG for more info)"
class ArgumentValueError(FatalCError):
"""An exception when a function takes an unexpected input."""
default_message = "An incorrect argument has been defined or passed! (check the LOG for more info)"
class PhotonConsError(ParameterError):
"""An exception when the photon non-conservation correction routine errors."""
default_message = "An error has occured with the Photon non-conservation correction! (check the LOG for more info)"
class TableGenerationError(ParameterError):
"""An exception when an issue arises populating one of the interpolation tables."""
default_message = """An error has occured when generating an interpolation table!
This has likely occured due to the choice of input AstroParams (check the LOG for more info)"""
class TableEvaluationError(ParameterError):
"""An exception when an issue arises populating one of the interpolation tables."""
default_message = """An error has occured when evaluating an interpolation table!
This can sometimes occur due to small boxes (either small DIM/HII_DIM or BOX_LEN) (check the LOG for more info)"""
class InfinityorNaNError(ParameterError):
"""An exception when an infinity or NaN is encountered in a calculated quantity."""
default_message = """Something has returned an infinity or a NaN! This could be due to an issue with an
input parameter choice (check the LOG for more info)"""
class MassDepZetaError(ParameterError):
"""An exception when determining the bisection for stellar mass/escape fraction."""
default_message = """There is an issue with the choice of parameters under MASS_DEPENDENT_ZETA. Could be an issue with
any of the chosen F_STAR10, ALPHA_STAR, F_ESC10 or ALPHA_ESC."""
class MemoryAllocError(FatalCError):
"""An exception when unable to allocated memory."""
default_message = """An error has occured while attempting to allocate memory! (check the LOG for more info)"""
SUCCESS = 0
IOERROR = 1
GSLERROR = 2
VALUEERROR = 3
PHOTONCONSERROR = 4
TABLEGENERATIONERROR = 5
TABLEEVALUATIONERROR = 6
INFINITYORNANERROR = 7
MASSDEPZETAERROR = 8
MEMORYALLOCERROR = 9
def _process_exitcode(exitcode, fnc, args):
"""Determine what happens for different values of the (integer) exit code from a C function."""
if exitcode != SUCCESS:
logger.error(f"In function: {fnc.__name__}. Arguments: {args}")
if exitcode:
try:
raise {
IOERROR: FileIOError,
GSLERROR: GSLError,
VALUEERROR: ArgumentValueError,
PHOTONCONSERROR: PhotonConsError,
TABLEGENERATIONERROR: TableGenerationError,
TABLEEVALUATIONERROR: TableEvaluationError,
INFINITYORNANERROR: InfinityorNaNError,
MASSDEPZETAERROR: MassDepZetaError,
MEMORYALLOCERROR: MemoryAllocError,
}[exitcode]
except KeyError: # pragma: no cover
raise FatalCError(
"Unknown error in C. Please report this error!"
) # Unknown C code
ctype2dtype = {}
# Integer types
for prefix in ("int", "uint"):
for log_bytes in range(4):
ctype = "%s%d_t" % (prefix, 8 * (2**log_bytes))
dtype = "%s%d" % (prefix[0], 2**log_bytes)
ctype2dtype[ctype] = np.dtype(dtype)
# Floating point types
ctype2dtype["float"] = np.dtype("f4")
ctype2dtype["double"] = np.dtype("f8")
ctype2dtype["int"] = np.dtype("i4")
def asarray(ptr, shape):
"""Get the canonical C type of the elements of ptr as a string."""
ctype = _ffi.getctype(_ffi.typeof(ptr).item).split("*")[0].strip()
if ctype not in ctype2dtype:
raise RuntimeError(
f"Cannot create an array for element type: {ctype}. Can do {list(ctype2dtype.values())}."
)
array = np.frombuffer(
_ffi.buffer(ptr, _ffi.sizeof(ctype) * np.prod(shape)), ctype2dtype[ctype]
)
array.shape = shape
return array
class StructWrapper:
"""
A base-class python wrapper for C structures (not instances of them).
Provides simple methods for creating new instances and accessing field names and values.
To implement wrappers of specific structures, make a subclass with the same name as the
appropriate C struct (which must be defined in the C code that has been compiled to the ``ffi``
object), *or* use an arbitrary name, but set the ``_name`` attribute to the C struct name.
"""
_name = None
_ffi = None
def __init__(self):
# Set the name of this struct in the C code
self._name = self._get_name()
@classmethod
def _get_name(cls):
return cls._name or cls.__name__
@property
def _cstruct(self):
"""
The actual structure which needs to be passed around to C functions.
.. note:: This is best accessed by calling the instance (see __call__).
The reason it is defined as this (manual) cached property is so that it can be created
dynamically, but not lost. It must not be lost, or else C functions which use it will lose
access to its memory. But it also must be created dynamically so that it can be recreated
after pickling (pickle can't handle CData).
"""
try:
return self.__cstruct
except AttributeError:
self.__cstruct = self._new()
return self.__cstruct
def _new(self):
"""Return a new empty C structure corresponding to this class."""
return self._ffi.new("struct " + self._name + "*")
@classmethod
def get_fields(cls, cstruct=None) -> Dict[str, Any]:
"""Obtain the C-side fields of this struct."""
if cstruct is None:
cstruct = cls._ffi.new("struct " + cls._get_name() + "*")
return dict(cls._ffi.typeof(cstruct[0]).fields)
@classmethod
def get_fieldnames(cls, cstruct=None) -> List[str]:
"""Obtain the C-side field names of this struct."""
fields = cls.get_fields(cstruct)
return [f for f, t in fields]
@classmethod
def get_pointer_fields(cls, cstruct=None) -> List[str]:
"""Obtain all pointer fields of the struct (typically simulation boxes)."""
return [f for f, t in cls.get_fields(cstruct) if t.type.kind == "pointer"]
@property
def fields(self) -> Dict[str, Any]:
"""List of fields of the underlying C struct (a list of tuples of "name, type")."""
return self.get_fields(self._cstruct)
@property
def fieldnames(self) -> List[str]:
"""List names of fields of the underlying C struct."""
return [f for f, t in self.fields.items()]
@property
def pointer_fields(self) -> List[str]:
"""List of names of fields which have pointer type in the C struct."""
return [f for f, t in self.fields.items() if t.type.kind == "pointer"]
@property
def primitive_fields(self) -> List[str]:
"""List of names of fields which have primitive type in the C struct."""
return [f for f, t in self.fields.items() if t.type.kind == "primitive"]
def __getstate__(self):
"""Return the current state of the class without pointers."""
return {
k: v
for k, v in self.__dict__.items()
if k not in ["_strings", "_StructWrapper__cstruct"]
}
def refresh_cstruct(self):
"""Delete the underlying C object, forcing it to be rebuilt."""
try:
del self.__cstruct
except AttributeError:
pass
def __call__(self):
"""Return an instance of the C struct."""
pass
class StructWithDefaults(StructWrapper):
"""
A convenient interface to create a C structure with defaults specified.
It is provided for the purpose of *creating* C structures in Python to be passed to C functions,
where sensible defaults are available. Structures which are created within C and passed back do
not need to be wrapped.
This provides a *fully initialised* structure, and will fail if not all fields are specified
with defaults.
.. note:: The actual C structure is gotten by calling an instance. This is auto-generated when
called, based on the parameters in the class.
.. warning:: This class will *not* deal well with parameters of the struct which are pointers.
All parameters should be primitive types, except for strings, which are dealt with
specially.
Parameters
----------
ffi : cffi object
The ffi object from any cffi-wrapped library.
"""
_defaults_ = {}
def __init__(self, *args, **kwargs):
super().__init__()
if args:
if len(args) > 1:
raise TypeError(
"%s takes up to one position argument, %s were given"
% (self.__class__.__name__, len(args))
)
elif args[0] is None:
pass
elif isinstance(args[0], self.__class__):
kwargs.update(args[0].self)
elif isinstance(args[0], dict):
kwargs.update(args[0])
else:
raise TypeError(
f"optional positional argument for {self.__class__.__name__} must be"
f" None, dict, or an instance of itself. Got {type(args[0])}"
)
for k, v in self._defaults_.items():
# Prefer arguments given to the constructor.
_v = kwargs.pop(k, None)
if _v is not None:
v = _v
try:
setattr(self, k, v)
except AttributeError:
# The attribute has been defined as a property, save it as a hidden variable
setattr(self, "_" + k, v)
if kwargs:
warnings.warn(
"The following parameters to {thisclass} are not supported: {lst}".format(
thisclass=self.__class__.__name__, lst=list(kwargs.keys())
)
)
def convert(self, key, val):
"""Make any conversions of values before saving to the instance."""
return val
def update(self, **kwargs):
"""
Update the parameters of an existing class structure.
This should always be used instead of attempting to *assign* values to instance attributes.
It consistently re-generates the underlying C memory space and sets some book-keeping
variables.
Parameters
----------
kwargs:
Any argument that may be passed to the class constructor.
"""
# Start a fresh cstruct.
if kwargs:
self.refresh_cstruct()
for k in self._defaults_:
# Prefer arguments given to the constructor.
if k in kwargs:
v = kwargs.pop(k)
try:
setattr(self, k, v)
except AttributeError:
# The attribute has been defined as a property, save it as a hidden variable
setattr(self, "_" + k, v)
# Also ensure that parameters that are part of the class, but not the defaults, are set
# this will fail if these parameters cannot be set for some reason, hence doing it
# last.
for k in list(kwargs.keys()):
if hasattr(self, k):
setattr(self, k, kwargs.pop(k))
if kwargs:
warnings.warn(
"The following arguments to be updated are not compatible with this class: %s"
% kwargs
)
def clone(self, **kwargs):
"""Make a fresh copy of the instance with arbitrary parameters updated."""
new = self.__class__(self.self)
new.update(**kwargs)
return new
def __call__(self):
"""Return a filled C Structure corresponding to this instance."""
for key, val in self.pystruct.items():
# Find the value of this key in the current class
if isinstance(val, str):
# If it is a string, need to convert it to C string ourselves.
val = self.ffi.new("char[]", getattr(self, key).encode())
try:
setattr(self._cstruct, key, val)
except TypeError:
logger.info(f"For key {key}, value {val}:")
raise
return self._cstruct
@property
def pystruct(self):
"""A pure-python dictionary representation of the corresponding C structure."""
return {fld: self.convert(fld, getattr(self, fld)) for fld in self.fieldnames}
@property
def defining_dict(self):
"""
Pure python dictionary representation of this class, as it would appear in C.
.. note:: This is not the same as :attr:`pystruct`, as it omits all variables that don't
need to be passed to the constructor, but appear in the C struct (some can be
calculated dynamically based on the inputs). It is also not the same as
:attr:`self`, as it includes the 'converted' values for each variable, which are
those actually passed to the C code.
"""
return {k: self.convert(k, getattr(self, k)) for k in self._defaults_}
@property
def self(self):
"""
Dictionary which if passed to its own constructor will yield an identical copy.
.. note:: This differs from :attr:`pystruct` and :attr:`defining_dict` in that it uses the
hidden variable value, if it exists, instead of the exposed one. This prevents
from, for example, passing a value which is 10**10**val (and recurring!).
"""
# Try to first use the hidden variable before using the non-hidden variety.
dct = {}
for k in self._defaults_:
if hasattr(self, "_" + k):
dct[k] = getattr(self, "_" + k)
else:
dct[k] = getattr(self, k)
return dct
def __repr__(self):
"""Full unique representation of the instance."""
return (
self.__class__.__name__
+ "("
+ ", ".join(
sorted(
k
+ ":"
+ (
float_to_string_precision(v, config["cache_redshift_sigfigs"])
if isinstance(v, (float, np.float32))
else str(v)
)
for k, v in self.defining_dict.items()
)
)
+ ")"
)
def __eq__(self, other):
"""Check whether this instance is equal to another object (by checking the __repr__)."""
return self.__repr__() == repr(other)
def __hash__(self):
"""Generate a unique hsh for the instance."""
return hash(self.__repr__())
def __str__(self):
"""Human-readable string representation of the object."""
biggest_k = max(len(k) for k in self.defining_dict)
params = "\n ".join(
sorted(f"{k:<{biggest_k}}: {v}" for k, v in self.defining_dict.items())
)
return f"""{self.__class__.__name__}:
{params}
"""
def snake_to_camel(word: str, publicize: bool = True):
"""Convert snake case to camel case."""
if publicize:
word = word.lstrip("_")
return "".join(x.capitalize() or "_" for x in word.split("_"))
def camel_to_snake(word: str, depublicize: bool = False):
"""Convert came case to snake case."""
word = "".join("_" + i.lower() if i.isupper() else i for i in word)
if not depublicize:
word = word.lstrip("_")
return word
def float_to_string_precision(x, n):
"""Prints out a standard float number at a given number of significant digits.
Code here: https://stackoverflow.com/a/48812729
"""
return f'{float(f"{x:.{int(n)}g}"):g}'
def get_all_subclasses(cls):
"""Get a list of all subclasses of a given class, recursively."""
all_subclasses = []
for subclass in cls.__subclasses__():
all_subclasses.append(subclass)
all_subclasses.extend(get_all_subclasses(subclass))
return all_subclasses
class OutputStruct(StructWrapper, metaclass=ABCMeta):
"""Base class for any class that wraps a C struct meant to be output from a C function."""
_meta = True
_fields_ = []
_global_params = None
_inputs = ("user_params", "cosmo_params", "_random_seed")
_filter_params = ["external_table_path", "wisdoms_path"]
_c_based_pointers = ()
_c_compute_function = None
_TYPEMAP = bidict({"float32": "float *", "float64": "double *", "int32": "int *"})
def __init__(self, *, random_seed=None, dummy=False, initial=False, **kwargs):
"""
Base type for output structures from C functions.
Parameters
----------
random_seed
Seed associated with the output.
dummy
Specify this as a dummy struct, in which no arrays are to be
initialized or computed.
initial
Specify this as an initial struct, where arrays are to be
initialized, but do not need to be computed to pass into another
struct's compute().
"""
super().__init__()
self.version = ".".join(__version__.split(".")[:2])
self.patch_version = ".".join(__version__.split(".")[2:])
self._paths = []
self._random_seed = random_seed
for k in self._inputs:
if k not in self.__dict__:
try:
setattr(self, k, kwargs.pop(k))
except KeyError:
raise KeyError(
f"{self.__class__.__name__} requires the keyword argument {k}"
)
if kwargs:
warnings.warn(
f"{self.__class__.__name__} received the following unexpected "
f"arguments: {list(kwargs.keys())}"
)
self.dummy = dummy
self.initial = initial
self._array_structure = self._get_box_structures()
self._array_state = {k: ArrayState() for k in self._array_structure}
self._array_state.update({k: ArrayState() for k in self._c_based_pointers})
for k in self._array_structure:
if k not in self.pointer_fields:
raise TypeError(f"Key {k} in {self} not a defined pointer field in C.")
@property
def path(self) -> Tuple[None, Path]:
"""The path to an on-disk version of this object."""
if not self._paths:
return None
for pth in self._paths:
if pth.exists():
return pth
logger.info(f"All paths that defined {self} have been deleted on disk.")
return None
@abstractmethod
def _get_box_structures(self) -> Dict[str, Union[Dict, Tuple[int]]]:
"""Return a dictionary of names mapping to shapes for each array in the struct.
The reason this is a function, not a simple attribute, is that we may need to
decide on what arrays need to be initialized based on the inputs (eg. if USE_2LPT
is True or False).
Each actual OutputStruct subclass needs to implement this. Note that the arrays
are not actually initialized here -- that's done automatically by :func:`_init_arrays`
using this information. This function means that the names of the actually required
arrays can be accessed without doing any actual initialization.
Note also that this only contains arrays allocated *by Python* not C. Arrays
allocated by C are specified in :func:`_c_shape`.
"""
pass
def _c_shape(self, cstruct) -> Dict[str, Tuple[int]]:
"""Return a dictionary of field: shape for arrays allocated within C."""
return {}
@classmethod
def _implementations(cls):
all_classes = get_all_subclasses(cls)
return [c for c in all_classes if not c._meta]
def _init_arrays(self):
for k, state in self._array_state.items():
# Don't initialize C-based pointers or already-inited stuff, or stuff
# that's computed on disk (if it's on disk, accessing the array should
# just give the computed version, which is what we would want, not a
# zero-inited array).
if k in self._c_based_pointers or state.initialized or state.on_disk:
continue
params = self._array_structure[k]
tp = self._TYPEMAP.inverse[self.fields[k].type.cname]
if isinstance(params, tuple):
shape = params
fnc = np.zeros
elif isinstance(params, dict):
fnc = params.get("init", np.zeros)
shape = params.get("shape")
else:
raise ValueError("params is not a tuple or dict")
setattr(self, k, fnc(shape, dtype=tp))
# Add it to initialized arrays.
state.initialized = True
@property
def random_seed(self):
"""The random seed for this particular instance."""
if self._random_seed is None:
self._random_seed = int(np.random.randint(1, int(1e12)))
return self._random_seed
def _init_cstruct(self):
# Initialize all uninitialized arrays.
self._init_arrays()
for k, state in self._array_state.items():
# We do *not* set COMPUTED_ON_DISK items to the C-struct here, because we have no
# way of knowing (in this function) what is required to load in, and we don't want
# to unnecessarily load things in. We leave it to the user to ensure that all
# required arrays are loaded into memory before calling this function.
if state.initialized:
setattr(self._cstruct, k, self._ary2buf(getattr(self, k)))
for k in self.primitive_fields:
try:
setattr(self._cstruct, k, getattr(self, k))
except AttributeError:
pass
def _ary2buf(self, ary):
if not isinstance(ary, np.ndarray):
raise ValueError("ary must be a numpy array")
return self._ffi.cast(
OutputStruct._TYPEMAP[ary.dtype.name], self._ffi.from_buffer(ary)
)
def __call__(self):
"""Initialize/allocate a fresh C struct in memory and return it."""
if not self.dummy:
self._init_cstruct()
return self._cstruct
def __expose(self):
"""Expose the non-array primitives of the ctype to the top-level object."""
for k in self.primitive_fields:
setattr(self, k, getattr(self._cstruct, k))
@property
def _fname_skeleton(self):
"""The filename without specifying the random seed."""
return self._name + "_" + self._md5 + "_r{seed}.h5"
def prepare(
self,
flush: Optional[Sequence[str]] = None,
keep: Optional[Sequence[str]] = None,
force: bool = False,
):
"""Prepare the instance for being passed to another function.
This will flush all arrays in "flush" from memory, and ensure all arrays
in "keep" are in memory. At least one of these must be provided. By default,
the complement of the given parameter is all flushed/kept.
Parameters
----------
flush
Arrays to flush out of memory. Note that if no file is associated with this
instance, these arrays will be lost forever.
keep
Arrays to keep or load into memory. Note that if these do not already
exist, they will be loaded from file (if the file exists). Only one of
``flush`` and ``keep`` should be specified.
force
Whether to force flushing arrays even if no disk storage exists.
"""
if flush is None and keep is None:
raise ValueError("Must provide either flush or keep")
if flush is not None and keep is None:
keep = [k for k in self._array_state if k not in flush]
elif flush is None:
flush = [
k
for k in self._array_state
if k not in keep and self._array_state[k].initialized
]
flush = flush or []
keep = keep or []
for k in flush:
self._remove_array(k, force)
# Accessing the array loads it into memory.
for k in keep:
getattr(self, k)
def _remove_array(self, k, force=False):
state = self._array_state[k]
if not state.initialized and k in self._array_structure:
warnings.warn(f"Trying to remove array that isn't yet created: {k}")
return
if state.computed_in_mem and not state.on_disk and not force:
raise OSError(
f"Trying to purge array '{k}' from memory that hasn't been stored! Use force=True if you meant to do this."
)
if state.c_has_active_memory:
lib.free(getattr(self._cstruct, k))
delattr(self, k)
state.initialized = False
def __getattr__(self, item):
"""Gets arrays that aren't already in memory."""
# Have to use __dict__ here to test membership, otherwise we get recursion error.
if "_array_state" not in self.__dict__ or item not in self._array_state:
raise self.__getattribute__(item)
if not self._array_state[item].on_disk:
raise OSError(
f"Cannot get {item} as it is not in memory, and this object is not cached to disk."
)
self.read(fname=self.path, keys=[item])
return getattr(self, item)
def purge(self, force=False):
"""Flush all the boxes out of memory.
Parameters
----------
force
Whether to force the purge even if no disk storage exists.
"""
self.prepare(keep=[], force=force)
def load_all(self):
"""Load all possible arrays into memory."""
self.prepare(flush=[])
@property
def filename(self):
"""The base filename of this object."""
if self._random_seed is None:
raise AttributeError("filename not defined until random_seed has been set")
return self._fname_skeleton.format(seed=self.random_seed)
def _get_fname(self, direc=None):
direc = path.abspath(path.expanduser(direc or config["direc"]))
return path.join(direc, self.filename)
def _find_file_without_seed(self, direc):
allfiles = glob.glob(path.join(direc, self._fname_skeleton.format(seed="*")))
if allfiles:
return allfiles[0]
else:
return None
def find_existing(self, direc=None):
"""
Try to find existing boxes which match the parameters of this instance.
Parameters
----------
direc : str, optional
The directory in which to search for the boxes. By default, this is the
centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
Returns
-------
str
The filename of an existing set of boxes, or None.
"""
# First, if appropriate, find a file without specifying seed.
# Need to do this first, otherwise the seed will be chosen randomly upon
# choosing a filename!
direc = path.expanduser(direc or config["direc"])
if not self._random_seed:
f = self._find_file_without_seed(direc)
if f and self._check_parameters(f):
return f
else:
f = self._get_fname(direc)
if path.exists(f) and self._check_parameters(f):
return f
return None
def _check_parameters(self, fname):
with h5py.File(fname, "r") as f:
for k in self._inputs + ("_global_params",):
q = getattr(self, k)
# The key name as it should appear in file.
kfile = k.lstrip("_")
# If this particular variable is set to None, this is interpreted
# as meaning that we don't care about matching it to file.
if q is None:
continue
if (
not isinstance(q, StructWithDefaults)
and not isinstance(q, StructInstanceWrapper)
and f.attrs[kfile] != q
):
return False
elif isinstance(q, (StructWithDefaults, StructInstanceWrapper)):
grp = f[kfile]
dct = q.self if isinstance(q, StructWithDefaults) else q
for kk, v in dct.items():
if kk not in self._filter_params:
file_v = grp.attrs[kk]
if file_v == "none":
file_v = None
if file_v != v:
logger.debug("For file %s:" % fname)
logger.debug(
f"\tThough md5 and seed matched, the parameter {kk} did not match,"
f" with values {file_v} and {v} in file and user respectively"
)
return False
return True
def exists(self, direc=None):
"""
Return a bool indicating whether a box matching the parameters of this instance is in cache.
Parameters
----------
direc : str, optional
The directory in which to search for the boxes. By default, this is the
centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
"""
return self.find_existing(direc) is not None
def write(
self,
direc=None,
fname: Union[str, Path, None, h5py.File, h5py.Group] = None,
write_inputs=True,
mode="w",
):
"""
Write the struct in standard HDF5 format.
Parameters
----------
direc : str, optional
The directory in which to write the boxes. By default, this is the
centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
fname : str, optional
The filename to write to. By default creates a unique filename from the hash.
write_inputs : bool, optional
Whether to write the inputs to the file. Can be useful to set to False if
the input file already exists and has parts already written.
"""
if not all(v.computed for v in self._array_state.values()):
raise OSError(
"Not all boxes have been computed (or maybe some have been purged). Cannot write."
f"Non-computed boxes: {[k for k, v in self._array_state.items() if not v.computed]}"
)
if not self._random_seed:
raise ValueError(
"Attempting to write when no random seed has been set. "
"Struct has been 'computed' inconsistently."
)
if not write_inputs:
mode = "a"
try:
if not isinstance(fname, (h5py.File, h5py.Group)):
direc = path.expanduser(direc or config["direc"])
if not path.exists(direc):
makedirs(direc)
fname = fname or self._get_fname(direc)
if not path.isabs(fname):
fname = path.abspath(path.join(direc, fname))
fl = h5py.File(fname, mode)
else:
fl = fname
try:
# Save input parameters to the file
if write_inputs:
for k in self._inputs + ("_global_params",):
q = getattr(self, k)
kfile = k.lstrip("_")
if isinstance(q, (StructWithDefaults, StructInstanceWrapper)):
grp = fl.create_group(kfile)
dct = q.self if isinstance(q, StructWithDefaults) else q
for kk, v in dct.items():
if kk not in self._filter_params:
try:
grp.attrs[kk] = "none" if v is None else v
except TypeError:
raise TypeError(
f"key {kk} with value {v} is not able to be written to HDF5 attrs!"
)
else:
fl.attrs[kfile] = q
# Write 21cmFAST version to the file
fl.attrs["version"] = __version__
# Save the boxes to the file
boxes = fl.create_group(self._name)
self.write_data_to_hdf5_group(boxes)
finally:
if not isinstance(fname, (h5py.File, h5py.Group)):
fl.close()
self._paths.insert(0, Path(fname))
except OSError as e:
logger.warning(
f"When attempting to write {self.__class__.__name__} to file, write failed with the following error. Continuing without caching."
)
logger.warning(e)
def write_data_to_hdf5_group(self, group: h5py.Group):
"""
Write out this object to a particular HDF5 subgroup.
Parameters
----------
group
The HDF5 group into which to write the object.
"""
# Go through all fields in this struct, and save
for k, state in self._array_state.items():
group.create_dataset(k, data=getattr(self, k))
state.on_disk = True
for k in self.primitive_fields:
group.attrs[k] = getattr(self, k)
def save(self, fname=None, direc=".", h5_group=None):
"""Save the box to disk.
In detail, this just calls write, but changes the default directory to the
local directory. This is more user-friendly, while :meth:`write` is for
automatic use under-the-hood.
Parameters
----------
fname : str, optional
The filename to write. Can be an absolute or relative path. If relative,
by default it is relative to the current directory (otherwise relative
to ``direc``). By default, the filename is auto-generated as unique to
the set of parameters that go into producing the data.
direc : str, optional
The directory into which to write the data. By default the current directory.
Ignored if ``fname`` is an absolute path.
"""
# If fname is absolute path, then get direc from it, otherwise assume current dir.
if path.isabs(fname):
direc = path.dirname(fname)
fname = path.basename(fname)
if h5_group is not None:
if not path.isabs(fname):
fname = path.abspath(path.join(direc, fname))
fl = h5py.File(fname, "a")
try:
grp = fl.create_group(h5_group)
self.write(direc, grp)
finally:
fl.close()
else:
self.write(direc, fname)
def _get_path(
self, direc: Union[str, Path, None] = None, fname: Union[str, Path, None] = None
) -> Path:
if direc is None and fname is None and self.path:
return self.path
if fname is None:
pth = self.find_existing(direc)
if pth is None:
raise OSError("No boxes exist for these parameters.")
else:
direc = Path(direc or config["direc"]).expanduser()
fname = Path(fname)
pth = fname if fname.exists() else direc / fname
return pth
def read(
self,
direc: Union[str, Path, None] = None,
fname: Union[str, Path, None, h5py.File, h5py.Group] = None,
keys: Optional[Sequence[str]] = None,
):
"""
Try find and read existing boxes from cache, which match the parameters of this instance.
Parameters
----------
direc
The directory in which to search for the boxes. By default, this is the
centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
fname
The filename to read. By default, use the filename associated with this
object. Can be an open h5py File or Group, which will be directly written to.
keys
The names of boxes to read in (can be a subset). By default, read everything.
"""
if not isinstance(fname, (h5py.File, h5py.Group)):
pth = self._get_path(direc, fname)
fl = h5py.File(pth, "r")
else:
fl = fname
keys = keys or []
try:
try:
boxes = fl[self._name]
except KeyError:
raise OSError(
f"While trying to read in {self._name}, the file exists, but does not have the "
"correct structure."
)
# Set our arrays.
for k in boxes.keys():
self._array_state[k].on_disk = True
if k in keys:
setattr(self, k, boxes[k][...])
self._array_state[k].computed_in_mem = True
setattr(self._cstruct, k, self._ary2buf(getattr(self, k)))
for k in boxes.attrs.keys():
if k == "version":
version = ".".join(boxes.attrs[k].split(".")[:2])
patch = ".".join(boxes.attrs[k].split(".")[2:])
if version != ".".join(__version__.split(".")[:2]):
# Ensure that the major and minor versions are the same.
warnings.warn(
f"The file {pth} is out of date (version = {version}.{patch}). "
f"Consider using another box and removing it!"
)
self.version = version
self.patch_version = patch
setattr(self, k, boxes.attrs[k])
try:
setattr(self._cstruct, k, getattr(self, k))
except AttributeError:
pass
# Need to make sure that the seed is set to the one that's read in.
seed = fl.attrs["random_seed"]
self._random_seed = int(seed)
finally:
self.__expose()
if isinstance(fl, h5py.File):
self._paths.insert(0, Path(fl.filename))
else:
self._paths.insert(0, Path(fl.file.filename))
if not isinstance(fname, (h5py.File, h5py.Group)):
fl.close()
@classmethod
def from_file(
cls,
fname,
direc=None,
load_data=True,
h5_group: Union[str, None] = None,
arrays_to_load=None,
):
"""Create an instance from a file on disk.
Parameters
----------
fname : str, optional
Path to the file on disk. May be relative or absolute.
direc : str, optional
The directory from which fname is relative to (if it is relative). By
default, will be the cache directory in config.
load_data : bool, optional
Whether to read in the data when creating the instance. If False, a bare
instance is created with input parameters -- the instance can read data
with the :func:`read` method.
h5_group
The path to the group within the file in which the object is stored.
"""
direc = path.expanduser(direc or config["direc"])
if not path.exists(fname):
fname = path.join(direc, fname)
with h5py.File(fname, "r") as fl:
if h5_group is not None:
self = cls(**cls._read_inputs(fl[h5_group]))
else:
self = cls(**cls._read_inputs(fl))
if not load_data:
arrays_to_load = []
if h5_group is not None:
with h5py.File(fname, "r") as fl:
self.read(fname=fl[h5_group], keys=arrays_to_load)
else:
self.read(fname=fname, keys=arrays_to_load)
return self
@classmethod
def _read_inputs(cls, grp: Union[h5py.File, h5py.Group]):
input_classes = [c.__name__ for c in StructWithDefaults.__subclasses__()]
# Read the input parameter dictionaries from file.
kwargs = {}
for k in cls._inputs:
kfile = k.lstrip("_")
input_class_name = snake_to_camel(kfile)
if input_class_name in input_classes:
input_class = StructWithDefaults.__subclasses__()[
input_classes.index(input_class_name)
]
subgrp = grp[kfile]
kwargs[k] = input_class(
{k: v for k, v in dict(subgrp.attrs).items() if v != "none"}
)
else:
kwargs[kfile] = grp.attrs[kfile]
return kwargs
def __repr__(self):
"""Return a fully unique representation of the instance."""
# This is the class name and all parameters which belong to C-based input structs,
# eg. InitialConditions(HII_DIM:100,SIGMA_8:0.8,...)
# eg. InitialConditions(HII_DIM:100,SIGMA_8:0.8,...)
return f"{self._seedless_repr()}_random_seed={self._random_seed}"
def _seedless_repr(self):
# The same as __repr__ except without the seed.
return (
(
self._name
+ "("
+ "; ".join(
(
repr(v)
if isinstance(v, StructWithDefaults)
else (
v.filtered_repr(self._filter_params)
if isinstance(v, StructInstanceWrapper)
else k.lstrip("_")
+ ":"
+ (
float_to_string_precision(
v, config["cache_param_sigfigs"]
)
if isinstance(v, (float, np.float32))
else repr(v)
)
)
)
for k, v in [
(k, getattr(self, k))
for k in self._inputs + ("_global_params",)
if k != "_random_seed"
]
)
)
+ f"; v{self.version}"
+ ")"
)
def __str__(self):
"""Return a human-readable representation of the instance."""
# this is *not* a unique representation, and doesn't include global params.
return (
self._name
+ "("
+ ";\n\t".join(
(
repr(v)
if isinstance(v, StructWithDefaults)
else k.lstrip("_") + ":" + repr(v)
)
for k, v in [(k, getattr(self, k)) for k in self._inputs]
)
) + ")"
def __hash__(self):
"""Return a unique hsh for this instance, even global params and random seed."""
return hash(repr(self))
@property
def _md5(self):
"""Return a unique hsh of the object, *not* taking into account the random seed."""
return md5(self._seedless_repr().encode()).hexdigest()
def __eq__(self, other):
"""Check equality with another object via its __repr__."""
return repr(self) == repr(other)
@property
def is_computed(self) -> bool:
"""Whether this instance has been computed at all.
This is true either if the current instance has called :meth:`compute`,
or if it has a current existing :attr:`path` pointing to stored data,
or if such a path exists.
Just because the instance has been computed does *not* mean that all
relevant quantities are available -- some may have been purged from
memory without writing. Use :meth:`has` to check whether certain arrays
are available.
"""
return any(v.computed for v in self._array_state.values())
def ensure_arrays_computed(self, *arrays, load=False) -> bool:
"""Check if the given arrays are computed (not just initialized)."""
if not self.is_computed:
return False
computed = all(self._array_state[k].computed for k in arrays)
if computed and load:
self.prepare(keep=arrays, flush=[])
return computed
def ensure_arrays_inited(self, *arrays, init=False) -> bool:
"""Check if the given arrays are initialized (or computed)."""
inited = all(self._array_state[k].initialized for k in arrays)
if init and not inited:
self._init_arrays()
return True
@abstractmethod
def get_required_input_arrays(self, input_box) -> List[str]:
"""Return all input arrays required to compute this object."""
pass
def ensure_input_computed(self, input_box, load=False) -> bool:
"""Ensure all the inputs have been computed."""
if input_box.dummy:
return True
arrays = self.get_required_input_arrays(input_box)
if input_box.initial:
return input_box.ensure_arrays_inited(*arrays, init=load)
return input_box.ensure_arrays_computed(*arrays, load=load)
def summarize(self, indent=0) -> str:
"""Generate a string summary of the struct."""
indent = indent * " "
out = f"\n{indent}{self.__class__.__name__}\n"
out += "".join(
f"{indent} {fieldname:>15}: {getattr(self, fieldname, 'non-existent')}\n"
for fieldname in self.primitive_fields
)
for fieldname, state in self._array_state.items():
if not state.initialized:
out += f"{indent} {fieldname:>15}: uninitialized\n"
elif not state.computed:
out += f"{indent} {fieldname:>15}: initialized\n"
elif not state.computed_in_mem:
out += f"{indent} {fieldname:>15}: computed on disk\n"
else:
x = getattr(self, fieldname).flatten()
if len(x) > 0:
out += f"{indent} {fieldname:>15}: {x[0]:1.4e}, {x[-1]:1.4e}, {x.min():1.4e}, {x.max():1.4e}, {np.mean(x):1.4e}\n"
else:
out += f"{indent} {fieldname:>15}: size zero\n"
return out
@classmethod
def _log_call_arguments(cls, *args):
logger.debug(f"Calling {cls._c_compute_function.__name__} with following args:")
for arg in args:
if isinstance(arg, OutputStruct):
for line in arg.summarize(indent=1).split("\n"):
logger.debug(line)
elif isinstance(arg, StructWithDefaults):
for line in str(arg).split("\n"):
logger.debug(f" {line}")
else:
logger.debug(f" {arg}")
def _ensure_arguments_exist(self, *args):
for arg in args:
if (
isinstance(arg, OutputStruct)
and not arg.dummy
and not self.ensure_input_computed(arg, load=True)
):
raise ValueError(
f"Trying to use {arg.__class__.__name__} to compute "
f"{self.__class__.__name__}, but some required arrays "
f"are not computed!\nArrays required: "
f"{self.get_required_input_arrays(arg)}\n"
f"Current State: {[(k, str(v)) for k, v in self._array_state.items()]}"
)
def _compute(
self, *args, hooks: Optional[Dict[Union[str, Callable], Dict[str, Any]]] = None
):
"""Compute the actual function that fills this struct."""
# Check that all required inputs are really computed, and load them into memory
# if they're not already.
self._ensure_arguments_exist(*args)
# Write a detailed message about call arguments if debug turned on.
if logger.getEffectiveLevel() <= logging.DEBUG:
self._log_call_arguments(*args)
# Construct the args. All StructWrapper objects need to actually pass their
# underlying cstruct, rather than themselves. OutputStructs also pass the
# class in that's calling this.
inputs = [arg() if isinstance(arg, StructWrapper) else arg for arg in args]
# Ensure we haven't already tried to compute this instance.
if self.is_computed:
raise ValueError(
f"You are trying to compute {self.__class__.__name__}, but it has already been computed."
)
# Perform the C computation
try:
exitcode = self._c_compute_function(*inputs, self())
except TypeError as e:
logger.error(
f"Arguments to {self._c_compute_function.__name__}: "
f"{[arg() if isinstance(arg, StructWrapper) else arg for arg in args]}"
)
raise e
_process_exitcode(exitcode, self._c_compute_function, args)
# Ensure memory created in C gets mapped to numpy arrays in this struct.
for k, state in self._array_state.items():
if state.initialized:
state.computed_in_mem = True
self.__memory_map()
self.__expose()
# Optionally do stuff with the result (like writing it)
self._call_hooks(hooks)
return self
def _call_hooks(self, hooks):
if hooks is None:
hooks = {"write": {"direc": config["direc"]}}
for hook, params in hooks.items():
if callable(hook):
hook(self, **params)
else:
getattr(self, hook)(**params)
def __memory_map(self):
shapes = self._c_shape(self._cstruct)
for item in self._c_based_pointers:
setattr(self, item, asarray(getattr(self._cstruct, item), shapes[item]))
self._array_state[item].c_memory = True
self._array_state[item].computed_in_mem = True
def __del__(self):
"""Safely delete the object and its C-allocated memory."""
for k in self._c_based_pointers:
if self._array_state[k].c_has_active_memory:
lib.free(getattr(self._cstruct, k))
class StructInstanceWrapper:
"""A wrapper for *instances* of C structs.
This is as opposed to :class:`StructWrapper`, which is for the un-instantiated structs.
Parameters
----------
wrapped :
The reference to the C object to wrap (contained in the ``cffi.lib`` object).
ffi :
The ``cffi.ffi`` object.
"""
def __init__(self, wrapped, ffi):
self._cobj = wrapped
self._ffi = ffi
for nm, tp in self._ffi.typeof(self._cobj).fields:
setattr(self, nm, getattr(self._cobj, nm))
# Get the name of the structure
self._ctype = self._ffi.typeof(self._cobj).cname.split()[-1]
def __setattr__(self, name, value):
"""Set an attribute of the instance, attempting to change it in the C struct as well."""
try:
setattr(self._cobj, name, value)
except AttributeError:
pass
object.__setattr__(self, name, value)
def items(self):
"""Yield (name, value) pairs for each element of the struct."""
for nm, tp in self._ffi.typeof(self._cobj).fields:
yield nm, getattr(self, nm)
def keys(self):
"""Return a list of names of elements in the struct."""
return [nm for nm, tp in self.items()]
def __repr__(self):
"""Return a unique representation of the instance."""
return (
self._ctype
+ "("
+ ";".join(k + "=" + str(v) for k, v in sorted(self.items()))
) + ")"
def filtered_repr(self, filter_params):
"""Get a fully unique representation of the instance that filters out some parameters.
Parameters
----------
filter_params : list of str
The parameter names which should not appear in the representation.
"""
return (
self._ctype
+ "("
+ ";".join(
k + "=" + str(v)
for k, v in sorted(self.items())
if k not in filter_params
)
) + ")"
def _check_compatible_inputs(*datasets, ignore=["redshift"]):
"""Ensure that all defined input parameters for the provided datasets are equal.
Parameters
----------
datasets : list of :class:`~_utils.OutputStruct`
A number of output datasets to cross-check.
ignore : list of str
Attributes to ignore when ensuring that parameter inputs are the same.
Raises
------
ValueError :
If datasets are not compatible.
"""
done = [] # keeps track of inputs we've checked so we don't double check.
for i, d in enumerate(datasets):
# If a dataset is None, just ignore and move on.
if d is None:
continue
# noinspection PyProtectedMember
for inp in d._inputs:
# Skip inputs that we want to ignore
if inp in ignore:
continue
if inp not in done:
for j, d2 in enumerate(datasets[(i + 1) :]):
if d2 is None:
continue
# noinspection PyProtectedMember
if inp in d2._inputs and getattr(d, inp) != getattr(d2, inp):
raise ValueError(
f"""
{d.__class__.__name__} and {d2.__class__.__name__} are incompatible.
{inp}: {getattr(d, inp)}
vs.
{inp}: {getattr(d2, inp)}
"""
)
done += [inp]
|
21cmfastREPO_NAME21cmFASTPATH_START.@21cmFAST_extracted@21cmFAST-master@src@py21cmfast@_utils.py@.PATH_END.py
|
{
"filename": "_sort.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/icicle/_sort.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SortValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(self, plotly_name="sort", parent_name="icicle", **kwargs):
super(SortValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@icicle@_sort.py@.PATH_END.py
|
{
"filename": "interface_generator.py",
"repo_name": "yacobozdalkiran/CLASS_mod",
"repo_path": "CLASS_mod_extracted/CLASS_mod-main/class_public-master/python/interface_generator.py",
"type": "Python"
}
|
"""
Automatically reads header files to generate an interface
"""
from __future__ import division, print_function
import sys
import logging
try:
from collections import OrderedDict as od
except ImportError:
try:
from ordereddict import OrderedDict as od
except ImportError:
raise ImportError(
"If you are running with Python v2.5 or 2.6"
" you need to manually install the ordereddict"
" package.")
try:
import colorlog
except ImportError:
raise ImportError(
"You have to install the colorlog module"
" with pip, or easy-install.")
SPACING = ' '
NAMING_CONVENTION = {
'precision': {'python': 'precision',
'function': 'precision'},
'background': {'python': 'background',
'function': 'background'},
'thermo': {'python': 'thermodynamics',
'function': 'thermodynamics'},
'perturbs': {'python': 'perturbations',
'function': 'perturb'},
'transfers': {'python': 'transfer',
'function': 'transfer'},
'primordial': {'python': 'primordial',
'function': 'primordial'},
'spectra': {'python': 'spectra',
'function': 'spectra'},
'lensing': {'python': 'lensing',
'function': 'lensing'},
'nonlinear': {'python': 'nonlinear',
'function': 'nonlinear'},
'output': {'python': 'output',
'function': 'output'},
}
def main():
# create logger
logger = create_logger()
# Recover all sub-header files
main_header = '../include/class.h'
headers = []
with open(main_header, 'r') as header_file:
in_modules = False
for line in header_file:
if in_modules:
if line.strip() == '':
in_modules = False
continue
if line.find('common') == -1 and line.find('input') == -1:
headers.append(
'../include/%s' % line.split()[-1].strip('"'))
if line.find('class modules') != -1:
in_modules = True
logger.info('Extracted the following headers: %s', ', '.join(headers))
output = 'classy.pyx'
logger.info('Creating %s', output)
structs = od()
output_file = open(output, 'w')
write_imports(output_file)
output_file.write('cdef extern from "class.h":\n')
# First write the first non automatic bits
output_file.write(
SPACING+'ctypedef char FileArg[40]\n' +
SPACING+'ctypedef char* ErrorMsg\n' +
SPACING+'cdef struct precision:\n' +
2*SPACING+'ErrorMsg error_message\n\n' +
SPACING+'cdef int _FAILURE_\n' +
SPACING+'cdef int _FALSE_\n' +
SPACING+'cdef int _TRUE_\n')
for header in headers:
extract_headers(header, structs, output_file, logger)
logger.info("Finished extracting headers")
for struct_name, struct in structs.items():
create_wrapper_class(struct_name, struct, output_file, logger)
return
def extract_headers(header, structs, output_file, logger):
"""toto"""
# Initialise the two flags controlling the exploration of the main
# structure
in_struct, main_struct_finished = False, False
# Flags for exploring enums (only the ones before the struct)
in_enum = False
# flag dealing with extracting docstrings
comment_partially_recovered = False
# Flag keeping track of multiple variables
multiple_var = False
# Flag recovering the functions
in_function_definitions, in_function, in_init = False, False, False
with open(header, 'r') as header_file:
logger.info("reading %s" % header)
for line in header_file:
# First case, recover the enums
if not main_struct_finished and not in_struct:
if line.find("enum ") != -1 and line.find("{") != -1:
enum_members = []
if line.find(";") == -1:
in_enum = True
enum_name = line.strip("enum").strip().strip('{')
else:
in_enum = False
line = line.strip("enum").strip().strip(';')
enum_name, enum_sign = line.split(' ', 1)
enum_sign = enum_sign.strip('}').strip('{')
for elem in enum_sign.split(','):
enum_members.append(elem.strip())
output_file.write(
SPACING + 'cdef enum %s:\n' % enum_name)
for elem in enum_members:
output_file.write(2*SPACING + elem + '\n')
output_file.write('\n')
elif in_enum:
if line.find('};') != -1:
in_enum = False
output_file.write(
SPACING + 'cdef enum %s:\n' % enum_name)
for elem in enum_members:
output_file.write(2*SPACING + elem + '\n')
output_file.write('\n')
else:
if line.strip() != '':
enum_members.append(line.split()[0].strip().strip(','))
if line.find("struct ") != -1 and not main_struct_finished:
in_struct = True
# Recover the name
logger.debug("in struct: %s" % line)
struct_name = line.strip().split()[1]
logger.debug("struct name: %s" % struct_name)
structs[struct_name] = {}
structs[struct_name].update(
NAMING_CONVENTION[struct_name])
output_file.write("%scdef struct %s:\n" % (
SPACING, struct_name))
continue
elif in_struct:
if line.find("};\n") != -1:
output_file.write('\n')
in_struct, main_struct_finished = False, True
else:
# if the line is not empty or does not contain only a
# comment:
if line.strip() == '' or line.strip()[:2] == '/*':
continue
logger.debug(
"potentially non empty line: %s" % line.strip())
#elif line.find('/**') != -1 or line.find('*/') != -1:
#continue
if line.find(';') == -1 and not comment_partially_recovered:
logger.debug("--> Discarded")
continue
elif line.find(';') != -1 and not comment_partially_recovered:
var_doc = ''
var_part, begin_comment = line.strip().split(';', 1)
var_doc += begin_comment.strip()[4:].strip()
# 2 things can happen: there can be arrays, and there
# can be several variables defined in one line...
# If array, slightly more complex
if var_part.find('*') != -1:
# if no comma is found, it means it is a single
# variable: good !
if var_part.find(',') == -1:
# remove if commented (starts with /*)
if var_part[:2] in ['/*', '//']:
continue
multiple_var = False
var_type, var_stars, var_name = var_part.strip().split()
structs[struct_name][var_name] = [
var_type, var_stars]
else:
# Count how many variables are defined
multiple_var = True
all_vars = [elem.strip() for elem in
var_part.split('*')[-1].split(',')]
var_type, var_stars = (var_part.strip().
split()[:2])
for var in all_vars:
structs[struct_name][var] = [
var_type, var_stars]
else:
# Again check for more than one variable
var_stars = ''
if var_part.find(',') == -1:
multiple_var = False
var_type, var_name = var_part.strip().split(' ', 1)
# Check if enum
if var_type == 'enum':
enum_name, var_name = var_name.split()
var_type += ' '+enum_name
structs[struct_name][var_name] = [
var_type, var_stars]
else:
multiple_var = True
all_vars = [elem.strip() for elem in
var_part.split()[2:].split(',')]
var_type = (var_part.strip().split()[0])
for var in all_vars:
structs[struct_name][var] = [
var_type, var_stars]
# If the comment is finished, pass
if var_doc[-2:] != '*/':
comment_partially_recovered = True
else:
var_doc = var_doc[:-2].replace('\\f$', '$').strip()
structs[struct_name][var_name].append(var_doc)
logger.debug(
"extracted the variable %s, " % var_name +
"of type %s, with docstring: %s" % (
''.join([var_stars, var_type]), var_doc))
if not multiple_var:
output_file.write(2*SPACING+' '.join(
[elem for elem in [var_type, var_stars, var_name]
if elem])+'\n')
else:
for var in all_vars:
output_file.write(2*SPACING+' '.join(
[elem for elem in [var_type, var_stars, var]
if elem])+'\n')
if comment_partially_recovered:
logger.debug("--> Accepted")
var_doc += ' '+line.strip()
if var_doc[-2:] == '*/':
comment_partially_recovered = False
var_doc = var_doc[:-2].replace('\\f$', '$').strip()
structs[struct_name][var_name].append(var_doc)
logger.debug(
"extracted the variable %s, " % var_name +
"of type %s, with docstring: %s" % (
''.join([var_stars, var_type]), var_doc))
elif main_struct_finished:
if line.find('extern "C"') != -1:
in_function_definitions = True
if not in_function_definitions:
continue
else:
if line.find('(') != -1:
in_function = True
logger.debug("Found a function")
func_type, func_name = line.split('(')[0].strip().split()
logger.debug('%s %s' % (func_name, func_type))
func_param = []
if func_name == structs[struct_name]['function']+'_init':
logger.info("found the init function")
in_init = True
structs[struct_name]['init'] = [func_name]
output_file.write(SPACING+'%s %s(' % (
func_type, func_name))
elif in_function:
# recover the signature of the function
line = line.strip().strip(',')
if line.find('struct') != -1:
if in_init:
name = line.split('*')[0].strip()[7:]
structs[struct_name]['init'].append(name)
func_param.append('void *')
elif line.find('*') != -1:
# Taking into account with or without spaces
temp = ''.join(line.strip(',').split())
last_star = len(temp)-temp[::-1].find('*')
func_param.append(temp[:last_star])
elif line.find(')') == -1:
if line != '':
func_param.append(line.split()[0])
else:
logger.debug('signature extracted')
in_function = False
if in_init:
in_init = False
output_file.write(', '.join(func_param) + ')\n')
elif line.find('}') != -1:
output_file.write('\n')
in_function_definitions = False
#print line.strip()
def create_wrapper_class(struct_name, struct, of, logger):
"""TODO"""
of.write('# Defining wrapper around struct %s\n' % struct_name)
of.write('cdef class %s:\n' % (
NAMING_CONVENTION[struct_name]['python'].capitalize()))
## recover the number of additional arguments:
init_name, argument_names = struct['init'][0], struct['init'][1:]
for companion in argument_names:
of.write(SPACING+'cdef %s _%s\n' % (companion, companion))
#logger.info("structure: %s, python name: %s" % (
#companion, NAMING_CONVENTION[companion]['python']))
of.write('\n')
# Define the array variables for all needed
array_variables = []
variables = []
for key, value in struct.items():
if key != 'init':
if value[1]:
array_variables.append(key)
variables.append(key)
of.write(SPACING+'cdef np.ndarray %s_arr\n' % key)
else:
variables.append(key)
of.write('\n')
# write the init
of.write(SPACING+'def __init__(self')
for companion in argument_names:
of.write(", %s py_%s" % (
NAMING_CONVENTION[companion]['python'].capitalize(), companion))
of.write('):\n\n')
# pointing the pointers where they belong
for companion in argument_names:
of.write(2*SPACING+"self._%s = py_%s._%s\n" % (
companion, companion, companion))
# Writing the call to structname_init()
of.write(2*SPACING+'%s_init(\n' % struct_name)
for companion in argument_names:
of.write(3*SPACING+'&(self._%s),\n' % companion)
of.write(3*SPACING+'&(self._%s))\n\n' % struct_name)
#of.write(2*SPACING+'%s_init(&(self._%s))\n\n' % (
#struct_name, struct_name))
for array in array_variables:
of.write(2*SPACING+'# Wrapping %s\n' % array)
of.write(2*SPACING+'%s_wrapper = ArrayWrapper()\n' % array)
of.write(
2*SPACING+"%s_wrapper.set_data(%d, '%s', "
"<void*> self._%s.%s)\n" % (
array, 2, struct[array].strip('*'), struct_name, array))
of.write(
2*SPACING+'self.%s_arr = np.array(%s_wrapper, '
'copy=False)\n' % (
array, array))
of.write(2*SPACING+'self.%s_arr.base = '
'<PyObject*> %s_wrapper\n' % (
array, array))
of.write(2*SPACING+'Py_INCREF(%s_wrapper)\n\n' % array)
#raise NotImplementedError('multiple init are not supported')
# Write the properties
for key in variables:
of.write(SPACING+'property %s:\n' % key)
if key not in array_variables:
of.write(2*SPACING+'def __get__(self):\n')
of.write(3*SPACING+'return self._%s.%s\n' % (struct_name, key))
of.write(2*SPACING+'def __set__(self, rhs):\n')
of.write(3*SPACING+'self._%s.%s = rhs\n' % (struct_name, key))
else:
of.write(2*SPACING+'def __get__(self):\n')
of.write(3*SPACING+'return self.%s_arr\n' % key)
of.write(2*SPACING+'def __set__(self, rhs):\n')
of.write(3*SPACING+'self.%s_arr[:] = rhs\n' % key)
of.write('\n')
# Add blank lines
of.write('\n\n')
def write_imports(output_file):
"""TODO"""
a = '''# Author: Gael Varoquaux
# License: BSD
from libc.stdlib cimport free
from cpython cimport PyObject, Py_INCREF
# Import the Python-level symbols of numpy
import numpy as np
# Import the C-level symbols of numpy
cimport numpy as np
# Numpy must be initialized. When using numpy from C or Cython you must
# _always_ do that, or you will have segfaults
np.import_array()
cdef class ArrayWrapper:
cdef void* data_ptr
cdef int size
cdef int type
cdef set_data(self, int size, char* type, void* data_ptr):
""" Set the data of the array
This cannot be done in the constructor as it must recieve C-level
arguments.
Parameters:
-----------
size: int
Length of the array.
data_ptr: void*
Pointer to the data
"""
self.data_ptr = data_ptr
self.size = size
if type.find('int') != -1:
self.type = np.NPY_INT
elif type.find('float') != -1:
self.type = np.NPY_FLOAT
elif type.find('double') != -1:
self.type = np.NPY_DOUBLE
elif type.find('long') != -1:
self.type = np.NPY_LONG
def __array__(self):
""" Here we use the __array__ method, that is called when numpy
tries to get an array from the object."""
cdef np.npy_intp shape[1]
shape[0] = <np.npy_intp> self.size
# Create a 1D array, of length 'size'
ndarray = np.PyArray_SimpleNewFromData(1, shape,
self.type, self.data_ptr)
return ndarray
def __dealloc__(self):
""" Frees the array. This is called by Python when all the
references to the object are gone. """
free(<void*>self.data_ptr)\n\n'''
output_file.write(a)
def create_logger():
"""Nothing"""
logger = logging.getLogger('simple_example')
#logger.setLevel(logging.DEBUG)
logger.setLevel(logging.INFO)
# create console handler and set level to debug
console_handler = logging.StreamHandler()
#console_handler.setLevel(logging.DEBUG)
console_handler.setLevel(logging.INFO)
# create formatter
#formatter = logging.Formatter(
#"%(asctime)s %(module)s: L%(lineno) 4s %(funcName) 15s"
#" | %(levelname) -10s --> %(message)s")
formatter = colorlog.ColoredFormatter(
"%(asctime)s %(module)s: L%(lineno) 4s %(blue)s%(funcName) 15s%(reset)s"
" | %(log_color)s%(levelname) -10s --> %(message)s%(reset)s",
datefmt=None,
reset=True,
log_colors={
'DEBUG': 'cyan',
'INFO': 'green',
'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red',
})
# add formatter to console_handler
console_handler.setFormatter(formatter)
# add console_handler to logger
logger.addHandler(console_handler)
return logger
if __name__ == "__main__":
sys.exit(main())
|
yacobozdalkiranREPO_NAMECLASS_modPATH_START.@CLASS_mod_extracted@CLASS_mod-main@class_public-master@python@interface_generator.py@.PATH_END.py
|
{
"filename": "_line.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/scatter3d/_line.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Line(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "scatter3d"
_path_str = "scatter3d.line"
_valid_props = {
"autocolorscale",
"cauto",
"cmax",
"cmid",
"cmin",
"color",
"coloraxis",
"colorbar",
"colorscale",
"colorsrc",
"dash",
"reversescale",
"showscale",
"width",
}
# autocolorscale
# --------------
@property
def autocolorscale(self):
"""
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`line.colorscale`. Has an effect only if in `line.color` is set
to a numerical array. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette will be chosen
according to whether numbers in the `color` array are all
positive, all negative or mixed.
The 'autocolorscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["autocolorscale"]
@autocolorscale.setter
def autocolorscale(self, val):
self["autocolorscale"] = val
# cauto
# -----
@property
def cauto(self):
"""
Determines whether or not the color domain is computed with
respect to the input data (here in `line.color`) or the bounds
set in `line.cmin` and `line.cmax` Has an effect only if in
`line.color` is set to a numerical array. Defaults to `false`
when `line.cmin` and `line.cmax` are set by the user.
The 'cauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["cauto"]
@cauto.setter
def cauto(self, val):
self["cauto"] = val
# cmax
# ----
@property
def cmax(self):
"""
Sets the upper bound of the color domain. Has an effect only if
in `line.color` is set to a numerical array. Value should have
the same units as in `line.color` and if set, `line.cmin` must
be set as well.
The 'cmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmax"]
@cmax.setter
def cmax(self, val):
self["cmax"] = val
# cmid
# ----
@property
def cmid(self):
"""
Sets the mid-point of the color domain by scaling `line.cmin`
and/or `line.cmax` to be equidistant to this point. Has an
effect only if in `line.color` is set to a numerical array.
Value should have the same units as in `line.color`. Has no
effect when `line.cauto` is `false`.
The 'cmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmid"]
@cmid.setter
def cmid(self, val):
self["cmid"] = val
# cmin
# ----
@property
def cmin(self):
"""
Sets the lower bound of the color domain. Has an effect only if
in `line.color` is set to a numerical array. Value should have
the same units as in `line.color` and if set, `line.cmax` must
be set as well.
The 'cmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmin"]
@cmin.setter
def cmin(self, val):
self["cmin"] = val
# color
# -----
@property
def color(self):
"""
Sets the line color. It accepts either a specific color or an
array of numbers that are mapped to the colorscale relative to
the max and min values of the array or relative to `line.cmin`
and `line.cmax` if set.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A number that will be interpreted as a color
according to scatter3d.line.colorscale
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# coloraxis
# ---------
@property
def coloraxis(self):
"""
Sets a reference to a shared color axis. References to these
shared color axes are "coloraxis", "coloraxis2", "coloraxis3",
etc. Settings for these shared color axes are set in the
layout, under `layout.coloraxis`, `layout.coloraxis2`, etc.
Note that multiple color scales can be linked to the same color
axis.
The 'coloraxis' property is an identifier of a particular
subplot, of type 'coloraxis', that may be specified as the string 'coloraxis'
optionally followed by an integer >= 1
(e.g. 'coloraxis', 'coloraxis1', 'coloraxis2', 'coloraxis3', etc.)
Returns
-------
str
"""
return self["coloraxis"]
@coloraxis.setter
def coloraxis(self, val):
self["coloraxis"] = val
# colorbar
# --------
@property
def colorbar(self):
"""
The 'colorbar' property is an instance of ColorBar
that may be specified as:
- An instance of :class:`plotly.graph_objs.scatter3d.line.ColorBar`
- A dict of string/value properties that will be passed
to the ColorBar constructor
Supported dict properties:
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
labelalias
Replacement text for specific tick or hover
labels. For example using {US: 'USA', CA:
'Canada'} changes US to USA and CA to Canada.
The labels we would have shown must match the
keys exactly, after adding any tickprefix or
ticksuffix. For negative numbers the minus sign
symbol used (U+2212) is wider than the regular
ascii dash. That means you need to use −1
instead of -1. labelalias can be used with any
axis type, and both keys (if needed) and values
(if desired) can include html-like tags or
MathJax.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this
number. This only has an effect when
`tickformat` is "SI" or "B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
orientation
Sets the orientation of the colorbar.
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see: h
ttps://github.com/d3/d3-format/tree/v1.4.5#d3-
format. And for dates see:
https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two
items to d3's date formatter: "%h" for half of
the year as a decimal number as well as "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
"09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.scatter
3d.line.colorbar.Tickformatstop` instances or
dicts with compatible properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.scatter3d.line.colorbar.tickformatstopdefault
s), sets the default property values to use for
elements of
scatter3d.line.colorbar.tickformatstops
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of
the axis. The default value for inside tick
labels is *hide past domain*. In other cases
the default is *hide past div*.
ticklabelposition
Determines where tick labels are drawn relative
to the ticks. Left and right options are used
when `orientation` is "h", top and bottom when
`orientation` is "v".
ticklabelstep
Sets the spacing between tick labels as
compared to the spacing between ticks. A value
of 1 (default) means each tick gets a label. A
value of 2 means shows every 2nd label. A
larger value n means only every nth tick is
labeled. `tick0` determines which labels are
shown. Not implemented for axes with `type`
"log" or "multicategory", or when `tickmode` is
"array".
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud
for `ticktext`.
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud
for `tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.scatter3d.line.col
orbar.Title` instance or dict with compatible
properties
x
Sets the x position with respect to `xref` of
the color bar (in plot fraction). When `xref`
is "paper", defaults to 1.02 when `orientation`
is "v" and 0.5 when `orientation` is "h". When
`xref` is "container", defaults to 1 when
`orientation` is "v" and 0.5 when `orientation`
is "h". Must be between 0 and 1 if `xref` is
"container" and between "-2" and 3 if `xref` is
"paper".
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar. Defaults to "left" when `orientation` is
"v" and "center" when `orientation` is "h".
xpad
Sets the amount of padding (in px) along the x
direction.
xref
Sets the container `x` refers to. "container"
spans the entire `width` of the plot. "paper"
refers to the width of the plotting area only.
y
Sets the y position with respect to `yref` of
the color bar (in plot fraction). When `yref`
is "paper", defaults to 0.5 when `orientation`
is "v" and 1.02 when `orientation` is "h". When
`yref` is "container", defaults to 0.5 when
`orientation` is "v" and 1 when `orientation`
is "h". Must be between 0 and 1 if `yref` is
"container" and between "-2" and 3 if `yref` is
"paper".
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
Defaults to "middle" when `orientation` is "v"
and "bottom" when `orientation` is "h".
ypad
Sets the amount of padding (in px) along the y
direction.
yref
Sets the container `y` refers to. "container"
spans the entire `height` of the plot. "paper"
refers to the height of the plotting area only.
Returns
-------
plotly.graph_objs.scatter3d.line.ColorBar
"""
return self["colorbar"]
@colorbar.setter
def colorbar(self, val):
self["colorbar"] = val
# colorscale
# ----------
@property
def colorscale(self):
"""
Sets the colorscale. Has an effect only if in `line.color` is
set to a numerical array. The colorscale must be an array
containing arrays mapping a normalized value to an rgb, rgba,
hex, hsl, hsv, or named color string. At minimum, a mapping for
the lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To
control the bounds of the colorscale in color space, use
`line.cmin` and `line.cmax`. Alternatively, `colorscale` may be
a palette name string of the following list: Blackbody,Bluered,
Blues,Cividis,Earth,Electric,Greens,Greys,Hot,Jet,Picnic,Portla
nd,Rainbow,RdBu,Reds,Viridis,YlGnBu,YlOrRd.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of colors that will be spaced evenly to create the colorscale.
Many predefined colorscale lists are included in the sequential, diverging,
and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
'ylorrd'].
Appending '_r' to a named colorscale reverses it.
Returns
-------
str
"""
return self["colorscale"]
@colorscale.setter
def colorscale(self, val):
self["colorscale"] = val
# colorsrc
# --------
@property
def colorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `color`.
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["colorsrc"]
@colorsrc.setter
def colorsrc(self, val):
self["colorsrc"] = val
# dash
# ----
@property
def dash(self):
"""
Sets the dash style of the lines.
The 'dash' property is an enumeration that may be specified as:
- One of the following enumeration values:
['dash', 'dashdot', 'dot', 'longdash', 'longdashdot',
'solid']
Returns
-------
Any
"""
return self["dash"]
@dash.setter
def dash(self, val):
self["dash"] = val
# reversescale
# ------------
@property
def reversescale(self):
"""
Reverses the color mapping if true. Has an effect only if in
`line.color` is set to a numerical array. If true, `line.cmin`
will correspond to the last color in the array and `line.cmax`
will correspond to the first color.
The 'reversescale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["reversescale"]
@reversescale.setter
def reversescale(self, val):
self["reversescale"] = val
# showscale
# ---------
@property
def showscale(self):
"""
Determines whether or not a colorbar is displayed for this
trace. Has an effect only if in `line.color` is set to a
numerical array.
The 'showscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showscale"]
@showscale.setter
def showscale(self, val):
self["showscale"] = val
# width
# -----
@property
def width(self):
"""
Sets the line width (in px).
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`line.colorscale`. Has an effect only if in
`line.color` is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in `line.color`)
or the bounds set in `line.cmin` and `line.cmax` Has an
effect only if in `line.color` is set to a numerical
array. Defaults to `false` when `line.cmin` and
`line.cmax` are set by the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `line.color` is set to a numerical array.
Value should have the same units as in `line.color` and
if set, `line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by scaling
`line.cmin` and/or `line.cmax` to be equidistant to
this point. Has an effect only if in `line.color` is
set to a numerical array. Value should have the same
units as in `line.color`. Has no effect when
`line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `line.color` is set to a numerical array.
Value should have the same units as in `line.color` and
if set, `line.cmax` must be set as well.
color
Sets the line color. It accepts either a specific color
or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `line.cmin` and `line.cmax` if
set.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorbar
:class:`plotly.graph_objects.scatter3d.line.ColorBar`
instance or dict with compatible properties
colorscale
Sets the colorscale. Has an effect only if in
`line.color` is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use `line.cmin` and `line.cmax`. Alternatively,
`colorscale` may be a palette name string of the
following list: Blackbody,Bluered,Blues,Cividis,Earth,E
lectric,Greens,Greys,Hot,Jet,Picnic,Portland,Rainbow,Rd
Bu,Reds,Viridis,YlGnBu,YlOrRd.
colorsrc
Sets the source reference on Chart Studio Cloud for
`color`.
dash
Sets the dash style of the lines.
reversescale
Reverses the color mapping if true. Has an effect only
if in `line.color` is set to a numerical array. If
true, `line.cmin` will correspond to the last color in
the array and `line.cmax` will correspond to the first
color.
showscale
Determines whether or not a colorbar is displayed for
this trace. Has an effect only if in `line.color` is
set to a numerical array.
width
Sets the line width (in px).
"""
def __init__(
self,
arg=None,
autocolorscale=None,
cauto=None,
cmax=None,
cmid=None,
cmin=None,
color=None,
coloraxis=None,
colorbar=None,
colorscale=None,
colorsrc=None,
dash=None,
reversescale=None,
showscale=None,
width=None,
**kwargs,
):
"""
Construct a new Line object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.scatter3d.Line`
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`line.colorscale`. Has an effect only if in
`line.color` is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in `line.color`)
or the bounds set in `line.cmin` and `line.cmax` Has an
effect only if in `line.color` is set to a numerical
array. Defaults to `false` when `line.cmin` and
`line.cmax` are set by the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `line.color` is set to a numerical array.
Value should have the same units as in `line.color` and
if set, `line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by scaling
`line.cmin` and/or `line.cmax` to be equidistant to
this point. Has an effect only if in `line.color` is
set to a numerical array. Value should have the same
units as in `line.color`. Has no effect when
`line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `line.color` is set to a numerical array.
Value should have the same units as in `line.color` and
if set, `line.cmax` must be set as well.
color
Sets the line color. It accepts either a specific color
or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `line.cmin` and `line.cmax` if
set.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorbar
:class:`plotly.graph_objects.scatter3d.line.ColorBar`
instance or dict with compatible properties
colorscale
Sets the colorscale. Has an effect only if in
`line.color` is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use `line.cmin` and `line.cmax`. Alternatively,
`colorscale` may be a palette name string of the
following list: Blackbody,Bluered,Blues,Cividis,Earth,E
lectric,Greens,Greys,Hot,Jet,Picnic,Portland,Rainbow,Rd
Bu,Reds,Viridis,YlGnBu,YlOrRd.
colorsrc
Sets the source reference on Chart Studio Cloud for
`color`.
dash
Sets the dash style of the lines.
reversescale
Reverses the color mapping if true. Has an effect only
if in `line.color` is set to a numerical array. If
true, `line.cmin` will correspond to the last color in
the array and `line.cmax` will correspond to the first
color.
showscale
Determines whether or not a colorbar is displayed for
this trace. Has an effect only if in `line.color` is
set to a numerical array.
width
Sets the line width (in px).
Returns
-------
Line
"""
super(Line, self).__init__("line")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.scatter3d.Line
constructor must be a dict or
an instance of :class:`plotly.graph_objs.scatter3d.Line`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("autocolorscale", None)
_v = autocolorscale if autocolorscale is not None else _v
if _v is not None:
self["autocolorscale"] = _v
_v = arg.pop("cauto", None)
_v = cauto if cauto is not None else _v
if _v is not None:
self["cauto"] = _v
_v = arg.pop("cmax", None)
_v = cmax if cmax is not None else _v
if _v is not None:
self["cmax"] = _v
_v = arg.pop("cmid", None)
_v = cmid if cmid is not None else _v
if _v is not None:
self["cmid"] = _v
_v = arg.pop("cmin", None)
_v = cmin if cmin is not None else _v
if _v is not None:
self["cmin"] = _v
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("coloraxis", None)
_v = coloraxis if coloraxis is not None else _v
if _v is not None:
self["coloraxis"] = _v
_v = arg.pop("colorbar", None)
_v = colorbar if colorbar is not None else _v
if _v is not None:
self["colorbar"] = _v
_v = arg.pop("colorscale", None)
_v = colorscale if colorscale is not None else _v
if _v is not None:
self["colorscale"] = _v
_v = arg.pop("colorsrc", None)
_v = colorsrc if colorsrc is not None else _v
if _v is not None:
self["colorsrc"] = _v
_v = arg.pop("dash", None)
_v = dash if dash is not None else _v
if _v is not None:
self["dash"] = _v
_v = arg.pop("reversescale", None)
_v = reversescale if reversescale is not None else _v
if _v is not None:
self["reversescale"] = _v
_v = arg.pop("showscale", None)
_v = showscale if showscale is not None else _v
if _v is not None:
self["showscale"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@scatter3d@_line.py@.PATH_END.py
|
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