GenevieveBuckley

"""Choose a threshold value using a magicgui widget in napari.

Based on the magicgui excample script napari_parameter_sweep.py
https://github.com/napari/magicgui/blob/main/examples/napari_param_sweep.py
"""
import napari
import skimage.data
import skimage.filters
from napari.layers import Image
from napari.types import LabelsData

GenevieveBuckley

class CupyContextManager:
    def __init__(self):
        self.previous_np = None

    def __enter__(self):
        self.previous_np = globals().get("np")
        import cupy as np
        globals()["np"] = np
        return np

GenevieveBuckley

There's some kind of problem linking python to the existing CUDA 10 installation (CUDA 10.0 lives at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\)

(base) C:\Users\CryoEM>conda activate CNNectome

(CNNectome) C:\Users\CryoEM>ipython
Python 3.6.12 |Anaconda, Inc.| (default, Sep  9 2020, 00:29:25) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information

GenevieveBuckley

(CNNectome) C:\Users\CryoEM\Documents\GitHub\OpenOrganelle\CNNectome>pip install malis[malis_loss]@git+https://github.com/neptunes5thmoon/malis.git@fix_setup
Collecting malis[malis_loss]@ git+https://github.com/neptunes5thmoon/malis.git@fix_setup
  Cloning https://github.com/neptunes5thmoon/malis.git (to revision fix_setup) to c:\users\cryoem\appdata\local\temp\pip-install-835bihd5\malis_d558e69fd2aa477b8a3ada5875704cf7
  Running command git clone -q https://github.com/neptunes5thmoon/malis.git 'C:\Users\CryoEM\AppData\Local\Temp\pip-install-835bihd5\malis_d558e69fd2aa477b8a3ada5875704cf7'
  Running command git checkout -b fix_setup --track origin/fix_setup
  branch 'fix_setup' set up to track 'origin/fix_setup'.
  Switched to a new branch 'fix_setup'
  Resolved https://github.com/neptunes5thmoon/malis.git to commit 2a54eed480edc8b0e9948f24b1193a58c5714d2d
WARNING: malis 1.0 does not provide the extra 'malis_loss'

GenevieveBuckley

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: osx-arm64
@EXPLICIT
https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.18.1-h3422bc3_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2021.10.8-h4653dfc_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-12.0.1-h168391b_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h642e427_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.2.11-hee7b306_1013.tar.bz2

GenevieveBuckley

So, you want to make a new high level graph layer class?

Step 1:

It's common to want to convert something Dask already does, and convert it to use high level graph under the hood.

First you need to find the place in the code where the dask task dictionary is created. Typically this looks like a variable called dsk or dsk_out that is a dictionary mapping the key names to individual tasks.

Found it? Great, this is the spot we're going to insert an instance of your (new, not yet created) high level graph class, eg: dsk_out = MyNewLayer(input_args, ...)

GenevieveBuckley

GenevieveBuckley

# This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.


import os
import six
import collections

GenevieveBuckley

#!/bin/bash

# Fernando's nuke and reinstall conda script
# https://twitter.com/fperez_org/status/1447996737063317512

# Download and install miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O ~/Downloads/miniconda.sh
bash ~/Downloads/miniconda.sh -b -p $HOME/local/conda

conda config --add channels conda-forge

GenevieveBuckley

def match_key(key, keylist):
    # key to match
    key = list(key)    
    key[0] = key[0].rsplit('-', 1)[0]  # remove token
    # search for matches
    for k in keylist:
        temp = list(k)    
        temp[0] = temp[0].rsplit('-', 1)[0]  # remove token
        if temp == key:
            return tuple(k)