kwinkunks

import pandas as pd
import mplstereonet as mpl

# Load the data.
df = pd.read_csv("https://raw.githubusercontent.com/ICWallis/fractoolbox/master/testdata-fractures.csv")

# Create a stereonet with grid lines.
fig, ax = mpl.subplots(figsize=(9, 6))
ax.grid(color='k', alpha=0.2)

kwinkunks

import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt


def wiggle_2d(data,
              time,
              ax=None,
              skip=1,
              perc=99.0,

kwinkunks

#!/usr/bin/env python
#-*- coding: utf-8 -*-
"""
Author: Matt Hall, Agile Scientific
Licence: Apache 2.0, please re-use this code!

To use the CLI type this on the command line:
    python polarity_cartoon.py --help
   
There is a web app running at agile.geosci.ai/polarity

kwinkunks

import numpy as np
from scipy.ndimage import generic_filter


def rms_1d(trace_segment):
    """
    This function runs on every piece of `size` defined in the
    generic_filter, below. It returns a single value.
    """
    return np.sqrt(np.sum(trace_segment**2)/trace_segment.size)

kwinkunks

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

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kwinkunks

import pandas as pd
from datetime import timedelta
from urllib.parse import urlencode


def birthquakes(birthday:str) -> pd.DataFrame:
    """
    Make a DataFrame of earthquakes on a given day.
    
    Example: birthquake("1971-05-26")

kwinkunks

	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

kwinkunks

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."""

kwinkunks

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.