Brendan Collins brendancol

## datetime_range_micro_dsl.py
from datetime import datetime
from enum import Enum
from dateutil.relativedelta import relativedelta
from typing import Tuple, List


def get_today() -> datetime:
    tz = pytz.timezone('America/New_York')
    return datetime.now(tz)

## paint-colors.json
{"abbey-white": "#EBE5D0", "abyss": "#DFE6EA", "acapulco-cliffs": "#4E9AA8", "acceleration": "#808EA1", "accolade": "#F1EEE2", "acorn-squash": "#FFDF38", "admiralty": "#404E61", "adobe-white": "#F2ECD9", "adorable": "#E3BEB0", "adventure": "#34788C", "after-the-storm": "#919FA1", "afternoon-tea": "#594E40", "ageless": "#ECECDF", "airy": "#DAE6E9", "alaskan-blue": "#7E9EC2", "alaskan-mist": "#ECF0E5", "aldabra": "#AAA492", "all-about-olive": "#676C58", "allegro": "#B28959", "alley-cat": "#656874", "allspice": "#CC974D", "almond-brittle": "#B5865A", "almond-cream": "#E1CFB2", "almond-paste": "#E5DBC5", "almond-roca": "#B89068", "almost-aloe": "#BFE5B1", "almost-aqua": "#98DDC5", "aloe-essence": "#ECF1E2", "aloe-vera": "#DBE5B9", "aloha": "#1DB394", "aloof": "#D4E2E6", "alpaca-wool": "#E4D5BC", "alpine-blue": "#DBE4E5", "always-almond": "#EBE5D2", "always-apple": "#A0A667", "always-blue": "#A2BACB", "alyssa": "#F4E2D6", "amaretto": "#AB6F60", "amaretto-sours": "#C09856", "ambrosia": "#F4DED3", "american-anthem":

## rgb_bigtiff_cogs_notes.md

      
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                brendancol
                / rgb_bigtiff_cogs_notes.md
            
            
              Created
              March 5, 2020 07:40
                — forked from palmerj/rgb_bigtiff_cogs_notes.md
            
              
                Creating BigTiff COGS for raster RGB photos from a tile mosaic directory using GDAL
              
          
    Creating a Cloud Optimised Geotiffs (COGs) for raster photo imagery

This process outlines the process for creating Cloud Optimised Geotiffs
suitable for hosting in services such as AWS S3. COGs enables more
efficient workflows use cases such as fast access from Functions as a Services
(E.g AWS Lambda), or comsumption into client desktop GIS systems (e.g QGIS).
For more details on COGs please see https://www.cogeo.org/in-depth.html
1. Create a mosaic


## abort-example.js
import React, { Component } from 'react';
import './Fetch.css';
import 'abortcontroller-polyfill/dist/polyfill-patch-fetch';

const AbortController = window.AbortController;
const controller = new AbortController();
const signal = controller.signal;

class Fetch extends Component {

## rolling_window.py
def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.

    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

## environment.yml
name: point-clouds
channels:
- menpo
- conda-forge
- defaults
dependencies:
- libiconv=1.14=4
- libxslt=1.1.29=3
- pyside=1.2.4=py27_6
- configobj=5.0.6=py27_0

## dkdtree.py
from functools import partial
import numpy as np
from scipy.spatial import cKDTree
from dask.delayed import delayed
import dask.bag as db

class DaskKDTree(object):
    """

    Usage Example:

## names.py
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing

import pandas as pd
import numpy as np

from functools import partial


def encode_data(df, cols):

## py
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing

import pandas as pd
import numpy as np

from functools import partial


def encode_data(df, cols):

## hdf_to_parquet.py
# Install fastparquet and pytables
# conda install pytables
# conda install -c conda-forge fastparquet
# conda install python-snappy

import pandas as pd
import fastparquet as fp

# Write file
df = pd.read_hdf(data_path, base)
	from datetime import datetime
	from enum import Enum
	from dateutil.relativedelta import relativedelta
	from typing import Tuple, List



	def get_today() -> datetime:
	tz = pytz.timezone('America/New_York')
	return datetime.now(tz)
	import React, { Component } from 'react';
	import './Fetch.css';
	import 'abortcontroller-polyfill/dist/polyfill-patch-fetch';

	const AbortController = window.AbortController;
	const controller = new AbortController();
	const signal = controller.signal;

	class Fetch extends Component {
	def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
	"""Create a view of `array` which for every point gives the n-dimensional
	neighbourhood of size window. New dimensions are added at the end of
	`array` or after the corresponding original dimension.

	Parameters
	----------
	array : array_like
	Array to which the rolling window is applied.
	window : int or tuple
	name: point-clouds
	channels:
	- menpo
	- conda-forge
	- defaults
	dependencies:
	- libiconv=1.14=4
	- libxslt=1.1.29=3
	- pyside=1.2.4=py27_6
	- configobj=5.0.6=py27_0
	from functools import partial
	import numpy as np
	from scipy.spatial import cKDTree
	from dask.delayed import delayed
	import dask.bag as db

	class DaskKDTree(object):
	"""

	Usage Example:
	from sklearn.ensemble import RandomForestClassifier
	from sklearn import preprocessing

	import pandas as pd
	import numpy as np

	from functools import partial


	def encode_data(df, cols):
	# Install fastparquet and pytables
	# conda install pytables
	# conda install -c conda-forge fastparquet
	# conda install python-snappy

	import pandas as pd
	import fastparquet as fp

	# Write file
	df = pd.read_hdf(data_path, base)