Joshuaalbert

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)

Joshuaalbert

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):

Joshuaalbert

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

Joshuaalbert

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.

Joshuaalbert

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__

Joshuaalbert

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

Joshuaalbert

---------------------------------------------------------------------------
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 

Joshuaalbert

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

Joshuaalbert

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],

Joshuaalbert

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;