nrweir

# 2 functions to create big pickle files (>2 gb) and read them back into python

def write_big_pkl(obj, path):
  """Pickle obj, where obj is a >2 gb object.
  
  Args:
      obj: The object to be pickled.
      path: The absolute path to save the pickle to.
  """
  

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# c_foci = an array with numbers indicating where foci are (e.g. all voxels corresponding to focus #1 = 1, all vx corresp to foci #2 = 2, etc.)
# segmented_cells = an array with #s indicating where cells are, same format as c_foci
# x = focus ID that is being checked for parent cells


if verbose:
  print('current ID: ' + str(x))
parent_cell, cell_cts = np.unique(
  self.segmented_cells[i][c_foci == x],
  return_counts=True

nrweir

foci_cts_dict = {}
for i in np.unique(cells):  # gets the unique IDs of the cells
  if i == 0:  # if it's bgrd
    continue
  else
    overlapping_foci = np.unique(seg_foci[cells == i])  # gets the unique IDs for each focus in the cell, as well as the bgrd
    num_foci = overlapping_foci.shape[0]-1  # shape[0] is the length of the overlapping_foci array; subtract bgrd 
    foci_cts_dict[i] = num_foci  # add a key:value pair to foci_cts_dict where key is cell # and val is num of foci

nrweir

    os.chdir(img_dir)
    flist = os.listdir()
    imgs = [f for f in flist if '.tif' in f.lower()]
    pex_imgs = [im for im in imgs if '594' in im]
    mito_imgs = [im for im in imgs if '447' in im]
    pex_imgs.sort()
    mito_imgs.sort()
    if len(pex_imgs) != len(mito_imgs):
        raise ValueError('Length of pex and mito img sets do not match.')
    ims_per_job = int(len(pex_imgs)/array_l)

nrweir

# imports
import pandas as pd
import os
import ftplib as FTP
import urllib
from xml.etree import ElementTree as ET
import subprocess

# next line is the request url that I was using; everything after the first ? is the query terms.
# there's a place somewhere on pubmed (can't remember where) where you can get the string to use there.

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    data = {'x': ["0-7 days", "7-30 days", "31-90 days", ">90 days"],
            'All dogs': [41.4391, 34.8107, 15.6709, 4.4533],
            'Filtered': [0, 0, 0, 0]}
    source = ColumnDataSource(data=data)
    callback = CustomJS(args=dict(source=source), code="""
                $.ajax({
                    url: '/_ajax',
                    data: $("#filter_form").serialize(),
                    type: 'GET',
                    success: function(response) {

nrweir

import spacenetutilities.labeltools.coreLabelTools as cLT
import os
import argparse
import re

argparser = argparse.ArgumentParser()
argparser.add_argument('--geojson_src_dir', '-j', type=str, required=True,
                       help='Path to the directory containing geojsons. If ' +
                       'the referenced directory contains subdirectories ' +
                       'that must be searched, the `--recursive` argument ' +

nrweir

import rasterio
import geopandas as gpd

def transform_gdf(gdf, geotiff):
    """Transform the coordinates of a GeoDataFrame.

    Arguments
    ---------
    gdf : gpd.GeoDataFrame
        A geodataframe with an identity affine xform.

nrweir