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import sympy as sp | |
pi = sp.pi | |
def perley_lmn_from_icrs(alpha, dec, alpha0, dec0): | |
dra = alpha - alpha0 | |
l = sp.cos(dec) * sp.sin(dra) | |
m = sp.sin(dec) * sp.cos(dec0) - sp.cos(dec) * sp.sin(dec0) * sp.cos(dra) |
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from __future__ import (absolute_import, unicode_literals, division, print_function) | |
from astropy.coordinates import AltAz | |
from astropy.coordinates.attributes import (TimeAttribute, EarthLocationAttribute) | |
from astropy.coordinates.baseframe import (BaseCoordinateFrame, RepresentationMapping, frame_transform_graph) | |
from astropy.coordinates.representation import (CartesianRepresentation) | |
from astropy.coordinates.transformations import FunctionTransform | |
class ENU(BaseCoordinateFrame): |
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import asyncio | |
from collections import deque | |
class FairAsyncRLock: | |
""" | |
A fair reentrant lock for async programming. Fair means that it respects the order of acquisition. | |
""" | |
def __init__(self): | |
self._owner: asyncio.Task | None = None | |
self._count = 0 |
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def fill_in_empty_cells(voxels, length_scale_voxels=3, support=9, zero_threshold=1e-5): | |
""" | |
Fill in zero-values (or values less than zero_threshold) with smoothed values. | |
Leave the non-zero bins as they are. | |
Args: | |
voxels: [batch, voxels_per_dimension, voxels_per_dimension, voxels_per_dimension, num_properties] | |
support: float, length scale for exponential kernel how "near" in pixels to interpolate. | |
support: int, how big to make the kernel, should be big enough that there are no regions of this size without a value. |
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def speed_test_jax(): | |
import numpy as np | |
from jax import jit, value_and_grad, random, numpy as jnp | |
from jax.scipy.optimize import minimize as minimize_jax | |
from scipy.optimize import minimize as minimize_np | |
import pylab as plt | |
from timeit import default_timer | |
import jax | |
JAX_VERSION = jax.__version__ |
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def speed_test_jax(): | |
import numpy as np | |
from jax import jit,value_and_grad, random | |
from jax.scipy.optimize import minimize as minimize_jax | |
from scipy.optimize import minimize as minimize_np | |
import pylab as plt | |
from timeit import default_timer | |
S = 3 |
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--------------------------------------------------------------------------- | |
ValueError Traceback (most recent call last) | |
<ipython-input-65-a4128a1c8e7f> in <module>() | |
14 gp = pm.gp.Marginal(cov_func=cov_func) | |
15 f = gp.marginal_likelihood('f',X,y,0.1) | |
---> 16 f_star = gp.conditional('fstar',Xnew) | |
17 | |
~/anaconda3/envs/kerastf/lib/python3.6/site-packages/pymc3-3.3rc2-py3.6.egg/pymc3/gp/gp.py in conditional(self, name, Xnew, pred_noise, given, **kwargs) | |
501 |
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import numpy as np | |
import tensorflow as tf | |
from gpflow.likelihoods import Likelihood | |
from gpflow import densities | |
from gpflow.decors import params_as_tensors | |
from gpflow.params import Parameter | |
from gpflow.transforms import Transform, positive, Chain | |
from gpflow import settings |
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import gpflow as gp | |
import numpy as np | |
import pylab as plt | |
import tensorflow as tf | |
X = np.array([[ 1.16527441e+09], | |
[ 1.16527442e+09], | |
[ 1.16527443e+09], | |
[ 1.16527443e+09], | |
[ 1.16527444e+09], |
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package com.tactico.tm.bugs; | |
import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
import org.deeplearning4j.nn.conf.BackpropType; | |
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; | |
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
import org.deeplearning4j.nn.conf.Updater; | |
import org.deeplearning4j.nn.conf.inputs.InputType; | |
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer; | |
import org.deeplearning4j.nn.weights.WeightInit; |
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