dpohanlon

import os

import subprocess as sp

import argparser

def buildName(sampleID, s = '1', lane = "001", read = "1", number = "001"):

    name = f'{sampleID}_S{s}_L{lane}_R{read}_{number}.fastq'

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import re

import argparse

# pip install PyMuPDF
import fitz


def pageToFigNo(file, pages):

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# From https://gitlab.cern.ch/snippets/1303

import numpy as np
import cmath

from scipy.special import erf, erfc, wofz

from scipy.fftpack import fft, ifft, fft2, ifft2

from scipy.integrate import quad

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# I robbed most of this from:
# https://github.com/ContinuumIO/docker-images/blob/master/miniconda3/debian/Dockerfile
# https://github.com/root-project/root-docker/blob/6.22.00-conda/conda/Dockerfile

FROM ubuntu:20.04

ENV LANG=C.UTF-8 LC_ALL=C.UTF-8

ENV TZ=Europe/London
RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone

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# I robbed most of this from:
# https://github.com/ContinuumIO/docker-images/blob/master/miniconda3/debian/Dockerfile
# https://github.com/root-project/root-docker/blob/6.22.00-conda/conda/Dockerfile

# docker buildx build --push --platform linux/arm64/v8 -t dpohanlon/laura-arm .

FROM ubuntu:20.04

ENV LANG=C.UTF-8 LC_ALL=C.UTF-8

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import json

import numpy as np

def sigmoid(x):
    return 1./(1 + np.exp(-x))

d = 3 # number of features
h = 4 # number of hidden units
n = 1 # number of tracks

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JSON file available at /eos/lhcb/user/d/dohanlon/FT/mvTestModelParams.json

In mvTestModelParams.json,

input: (5 timestep, 3 feature) input matrix

output: (5 timestep, 4 hidden) output matrix (one hidden (length 4) output for each timestep)

GRU_* : Weights and biases correspond to those in https://en.wikipedia.org/wiki/Gated_recurrent_unit#Fully_gated_unit, 4 hidden units

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import json

import tensorflow.keras as keras
from tensorflow.keras.models import Model

import numpy as np

from pprint import pprint

def testModel(reset_after):

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#!/usr/bin/python3.6

import subprocess as sp
import os
import re

import argparse

def writeBashScript(location, name, command):

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import h5py

import numpy as np

import json

def getTracks(fileName = '/data/1/dan/veloSim/veloData.h5', nEvents = 100):

    file = h5py.File(fileName, 'r')