Matt Hall kwinkunks

## Mountain_heights.ipynb

      
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                kwinkunks
                / Mountain_heights.ipynb
            
            
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              August 29, 2015 14:09
            
              
                Heights of mountains, compared to the centre of the earth. View IPython Notebook... http://nbviewer.ipython.org/gist/kwinkunks/a449bff4c8c35952e6ce
              
          
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## Seismic_acquisition_oo.ipynb

      
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                / Seismic_acquisition_oo.ipynb
            
            
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              August 29, 2015 14:11
            
              
                Simple seismic acquisition modeling
              
          
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## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## Xmas_2017.py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import requests
from io import StringIO
from welly import Well

# Fetch LAS file.
url = "https://dropbox.com/s/n52qezp5byap4mi/WellA.las?raw=1"
r = requests.get(url)

## blur_pure_NumPy.py
import numpy as np

arr = np.random.randint(0, 256, (200, 200), dtype=np.uint8)

def func(arr1d):
    kernel = np.ones(3) / 3
    return np.convolve(arr1d, kernel, mode='same')

first_pass = np.apply_along_axis(func, axis=0, arr=arr)
final_result = np.apply_along_axis(func, axis=1, arr=first_pass)

## volumes.py
import numpy as np
import scipy.signal as ss


def to_volume(points, max_mb=10):
    """
    Convert N x 3 array of points in a point cloud to a 3D image
    or 'volume'. The degree of upscaling is controlled by ``max_mb``
    which is the target size of the 3D image in memory.


## holiday19.py
import io
import requests
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap as LSC
from scipy.interpolate import Rbf

class HolidayCard():
    """A holiday card class."""

## SEG-Y_revisions_and_segyio.md

      
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                kwinkunks
                / SEG-Y_revisions_and_segyio.md
            
            
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              January 27, 2020 17:21
            
              
                How various features map to SEG-Y Revision numbers and `segyio`'s capability
              
          
Rev 0
Rev 1
Rev 2

segyio r
segyio w


Byte order


Big endian
1
1
1


Little endian
0
0
1


Pairwise byte-swapped
0
0
1


Number formats


8-bit int
0
1
1


## Colours.py
colours = {
 'Alkali-feldpar syenite': (244, 60, 108),
 'Alkali-feldspar granite': (255, 209, 220),
 'Alkali-feldspar rhyolite': (254, 220, 126),
 'Alkali-feldspar trachyte': (254, 183, 134),
 'Alkalic intrusive rock': (255, 111, 145),
 'Alkalic volcanic rock': (194, 65, 0),
 'Alkaline basalt': (169, 101, 55),
 'Alluvial fan': (255, 255, 183),
 'Alluvial terrace': (250, 238, 122),

## Nonspatial_clustering_of_map_features.py
import numpy as np

# Make some fake data
# Make array with 100 rows, 100 columns, and 6 'features' (different maps)
shape = (100, 100, 3)
data = np.random.random(shape)

# Pretend it has NaNs around edge.
data[:10] = np.nan
data[-10:] = np.nan
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	import numpy as np
	import matplotlib.pyplot as plt
	from matplotlib.patches import Rectangle
	import requests
	from io import StringIO
	from welly import Well

	# Fetch LAS file.
	url = "https://dropbox.com/s/n52qezp5byap4mi/WellA.las?raw=1"
	r = requests.get(url)
	import numpy as np

	arr = np.random.randint(0, 256, (200, 200), dtype=np.uint8)

	def func(arr1d):
	kernel = np.ones(3) / 3
	return np.convolve(arr1d, kernel, mode='same')

	first_pass = np.apply_along_axis(func, axis=0, arr=arr)
	final_result = np.apply_along_axis(func, axis=1, arr=first_pass)
	import numpy as np
	import scipy.signal as ss


	def to_volume(points, max_mb=10):
	"""
	Convert N x 3 array of points in a point cloud to a 3D image
	or 'volume'. The degree of upscaling is controlled by ``max_mb``
	which is the target size of the 3D image in memory.
	import io
	import requests
	import numpy as np
	from PIL import Image
	import matplotlib.pyplot as plt
	from matplotlib.colors import LinearSegmentedColormap as LSC
	from scipy.interpolate import Rbf

	class HolidayCard():
	"""A holiday card class."""
	Rev 0	Rev 1	Rev 2

Byte order
Big endian	1	1	1
Little endian	0	0	1
Pairwise byte-swapped	0	0	1

Number formats
8-bit int	0	1	1
	colours = {
	'Alkali-feldpar syenite': (244, 60, 108),
	'Alkali-feldspar granite': (255, 209, 220),
	'Alkali-feldspar rhyolite': (254, 220, 126),
	'Alkali-feldspar trachyte': (254, 183, 134),
	'Alkalic intrusive rock': (255, 111, 145),
	'Alkalic volcanic rock': (194, 65, 0),
	'Alkaline basalt': (169, 101, 55),
	'Alluvial fan': (255, 255, 183),
	'Alluvial terrace': (250, 238, 122),
	import numpy as np

	# Make some fake data
	# Make array with 100 rows, 100 columns, and 6 'features' (different maps)
	shape = (100, 100, 3)
	data = np.random.random(shape)

	# Pretend it has NaNs around edge.
	data[:10] = np.nan
	data[-10:] = np.nan