GenevieveBuckley

GenevieveBuckley

import math

import numpy as np
import pytest


def combine_slices(slices):
    starts = [s.start for s in slices if s.start is not None]
    stops = [s.stop for s in slices if s.stop is not None]
    steps = [s.step for s in slices if s.step is not None]

GenevieveBuckley

import matplotlib.pyplot as plt
%matplotlib inline

plt.errorbar(
    [1,2,3],
    [1,2,3],
    yerr=[.1, .2, .3],
    color='blue', marker='o', linestyle='dashed',
    )
plt.title("Example")

GenevieveBuckley

from html import escape


class HighLevelGraphHTML():
    def __init__(self, highlevelgraph):
        self.highlevelgraph = highlevelgraph
        pass

    def _repr_html_(self):
        highlevelgraph = self.highlevelgraph

GenevieveBuckley

GenevieveBuckley

TITLE:

Scaling Science: leveraging Dask for life sciences

SHORT ABSTRACT:

Managing the challenges associated with big data in life sciences can be difficult. Scalable scientific computing is required to cope with the increasing demands of modern biology and neuroscience. Dask is a python library for distributed computation. In this talk, we'll look at several case studies where Dask is used to scale up data processing for life sciences. It will include examples from statistical genetics, single cell analysis, and imaging visualization & analysis. This will give you a better understanding of how you can extend code with Dask to scale your analysis.

DESCRIPTION:

GenevieveBuckley

GenevieveBuckley

conda install -c conda-forge/label/cupy_rc cudatoolkit=11.2 cupy=9.0.0b3

GenevieveBuckley

PyConlineAU 2020 https://pretalx.com/pycon-au-2020/talk/review/RP9LMHZUMYZZWUB73ESTKG9SGT9QCBMJ

dask-image: distributed image processing for large data

Abstract

This talk introduces dask-image, a python library for distributed image processing. Targeted towards applications involving large array data too big to fit in memory, dask-image is built on top of numpy, scipy, and dask allowing easy scalability and portability from your laptop to the supercomputing cluster. It is of broad interest for a diverse range of data analysis applications such as video/streaming data, computer vision, and scientific fields including astronomy, microscopy and geosciences. We will provide a general overview of the dask-image library, then discuss mixing and matching with your own custom functions, and present a practical case study of a python image processing pipeline.

Detailed abstract

Image datasets are large, and becoming larger. The widely used benchmark dataset COCO (Common Objects in Context) contains 330,000

GenevieveBuckley

name: cellprofiler-dev
channels:
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=1_gnu
  - alsa-lib=1.2.3=h516909a_0
  - atk-1.0=2.36.0=h3371d22_4
  - brotlipy=0.7.0=py38h497a2fe_1001