GCBallesteros

git ls-files --exclude-standard | entr -p ./scp-git-file 'root@10.187.0.59' /_

GCBallesteros

import numpy as np

DIM = 4  # Dimension of matrix which represents the homography
IGNORE_DIM = 2  # This would be t on the blog post example

# We can't remove the last dim of the homography because it's
# special.
assert 0 <= IGNORE_DIM <= (DIM - 2)

assert IGNORE_DIM

GCBallesteros

import numpy as np
import torch.utils.data as data


class SimpleDataset(data.Dataset):
    def __init__(self):
        super().__init__()
        self.data = np.random.randn(1000, 5)

    def __getitem__(self, idxs):

GCBallesteros

GCBallesteros

from sklearn.base import BaseEstimator

import torchtuples as tt


class DeepSURVSklearnAdapter(BaseEstimator):
    def __init__(
        self,
        learning_rate=1e-4,
        batch_norm=True,

GCBallesteros

import numpy as np
import pandas as pd
import holoviews as hv
from holoviews import opts
from bokeh.models import HoverTool
from holoviews import streams

hv.extension("bokeh")

GCBallesteros

import fs from "fs"

export enum ByteEncoding {
  Base64,
  Hex,
}

export function hexToBuff(data: string): Buffer {
  return Buffer.from(data, "hex");
}

GCBallesteros

\documentclass{article}
\usepackage{amsmath}

\begin{document}

Transformation matrix from $|jm>$ to $|m_1 m_2>$ basis


This is not the actual matrix; elements are represented as in the
CG tables. Do element-wise sqrt but keep the sign for the matrix.

GCBallesteros

import sympy as sp
import sympy.physics.quantum as spq
from sympy.matrices.dense import matrix_multiply_elementwise as mme

import numpy as np

import itertools
from fractions import Fraction
from argparse import ArgumentParser

GCBallesteros

from numpy.random import uniform

class MISER:
    def __init__(self, miser_params):
        """
        Parameters
        ----------
        MNBS : int
            If less than MNBS evaluations are left we do
            vanilla MC.