AlexandreAbraham

""" Unsupervised evaluation metrics. """

# License: BSD Style.

from itertools import combinations
import numpy as np

from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import distance_metrics
from sklearn.metrics.pairwise import pairwise_distances

ctrueden

# read in and display ImagePlus object(s)
from loci.plugins import BF
file = "/Users/curtis/data/tubhiswt4D.ome.tif"
imps = BF.openImagePlus(file)
for imp in imps:
    imp.show()

# read in and display ImagePlus(es) with arguments
from loci.common import Region
from loci.plugins.in import ImporterOptions

rxaviers

People

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😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

thirdwing

#include <iostream>
#include <fstream>
#include <unistd.h>

void process_mem_usage(double& vm_usage, double& resident_set)
{
    vm_usage     = 0.0;
    resident_set = 0.0;

    // the two fields we want

kevin-keraudren

#!/usr/bin/python

import SimpleITK as sitk
import vtk
import numpy as np
import sys

from vtk.util.vtkConstants import *

filename = sys.argv[1]

somada141

#!/usr/bin/python

import SimpleITK as sitk
import vtk
import numpy as np

from vtk.util.vtkConstants import *

def numpy2VTK(img,spacing=[1.0,1.0,1.0]):
    # evolved from code from Stou S.,

karenyyng

Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

lukas-h

Markdown License badges

Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.

Notes

• The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

• 🇫🇷 Cette liste en français

auxiliary

#!/usr/bin/python

import SimpleITK as sitk
import vtk
import numpy as np
import sys

from vtk.util.vtkConstants import *

filename = sys.argv[1]

dusenberrymw

Keras Tips & Tricks

fit_generator

• Can using either threading or multiprocessing for concurrent and parallel processing, respectively, of the data generator.
• In the threading approach (model.fit_generator(..., pickle_safe=False)), the generator can be run concurrently (but not parallel) in multiple threads, with each thread pulling the next available batch based on the shared state of the generator and placing it in a shared queue. However, the generator must be threadsafe (i.e. use locks at synchronization points).
• Due to the Python global interpreter lock (GIL), the threading option generally does not benefit from >1 worker (i.e. model.fit_generator(..., nb_worker=1) is best). One possible use case in which >1 threads could be beneficial is the presence of exceptionally long IO times, during which the GIL will be released to enable concurrency. Note also that TensorFlow's `session.run(