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from pymongo import MongoClient
client = MongoClient()

aggregation = [
    {
        "$set": {
            "collection_date.has_date_value": {
                "$dateFromString": {
                    "dateString": "$collection_date.has_raw_value",
                },

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  graph TD;
      A(A);
      A-->B;
      A-->C;
      B-->D;
      C-->D;
      D-->A;
      D-->C;
 D-->B;

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source,target
1,2
2,3
3,4
4,5
4,6
4,7
7,2
7,3
7,4

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import sys

def superpixels_to_annotation(chunk_size, input_filename):
    output_file_prefix = '.'.join(input_filename.split('.')[:-1])
    with open(input_filename) as input_file:
        elements = []
        chunk = 0
        for line in input_file:
            output_line = ','.join(
                [f'[{val},0]' for val in line.strip().split(' ')[3:]])

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{
  "markdown": {
    "visible": true,
    "value": "# Hemibrain connectome\n\nThis is a 2D representation of the traced neurons in the <a href=\"https://www.janelia.org/project-team/flyem/hemibrain\">fly hemibrain connectome</a> (<a href=\"https://creativecommons.org/licenses/by/4.0/\">CC-BY</a>). Each dot represents a neuron, and each neuron is connected to other neurons through synapses. The links between neurons were used to position the neurons using a network layout algorithm. The size of each dot corresponds to the number of connections to other neurons. Hover over a neuron to see its well-connected neighbors (with 20 or more joint connections), which will be highlighted in yellow.\n\nThe color represents a value for each neuron. The default value is the brain region where the highest number of the neuron's connections reside. You may change the coloring with one of these options:\n\n* None: No coloring.\n* connections: The number of connections in the full unfiltered network.\n* degree: The number of 

jeffbaumes

export default {
  name: 'Farming',
  items: [
    {
      name: 'Wheat',
      color: 'goldenrod',
      icon: 'barley',
      cost: [],
    },
    {

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@import '~vuetify/src/stylus/settings/_variables'

$body-font-family = 'Barlow Condensed', sans-serif
$heading-font-family = 'Barlow Condensed', sans-serif
$headings = {
  h1: { size: 112px, weight: 300, line-height: 1, letter-spacing: -.04em, font-family: $heading-font-family },
  h2: { size: 56px, weight: 400, line-height: 1.35, letter-spacing: -.02em, font-family: $heading-font-family },
  h3: { size: 45px, weight: 400, line-height: 48px, letter-spacing: normal, font-family: $heading-font-family },
  h4: { size: 34px, weight: 400, line-height: 40px, letter-spacing: normal, font-family: $heading-font-family },
  h5: { size: 24px, weight: 400, line-height: 32px, letter-spacing: normal, font-family: $heading-font-family },

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def fit_regression(x, y, n_boot=1000, units=None, ci=95, order=1, logistic=False, lowess=False, robust=False, logx=False):
    """Fit the regression model."""
    # Create the grid for the regression
    x_min, x_max = [x.min(), x.max()]
    grid = np.linspace(x_min, x_max, 100)

    # Fit the regression
    if order > 1:
        yhat, yhat_boots = fit_poly(x, y, ci, grid, order, n_boot, units)
    elif logistic:

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