# Extract curves into striplogs#

Sometimes you’d like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).

We’ll start by making some fake CSV text — we’ll make 5 formations called A, B, C, D and E:

data = """Comp Formation,Depth
A,100
B,200
C,250
D,400
E,600"""


If you have a CSV file, you can do:

s = Striplog.from_csv(filename=filename)


But we have text, so we do something slightly different, passing the text argument instead. We also pass a stop argument to tell Striplog to make the last unit (E) 50 m thick. (If you don’t do this, it will be 1 m thick).

from striplog import Striplog

s = Striplog.from_csv(text=data, stop=650)

/opt/hostedtoolcache/Python/3.10.4/x64/lib/python3.10/site-packages/striplog/striplog.py:512: UserWarning: No lexicon provided, using the default.
warnings.warn(w)


Each element of the striplog is an Interval object, which has a top, base and one or more Components, which represent whatever is in the interval (maybe a rock type, or in this case a formation). There is also a data field, which we will use later.

s[0]

top100.0
primary
 formation A
summary100.00 m of A
description
data
base200.0

We can plot the striplog. By default, it will use a random legend for the colours:

s.plot(aspect=3)


Or we can plot in the ‘tops’ style:

s.plot(style='tops', field='formation', aspect=1)


## Random curve data#

Make some fake data:

from welly import Curve
import numpy as np

depth = np.linspace(0, 699, 700)
data = np.sin(depth/10)
curve = Curve(data=data, index=depth)


Plot it:

import matplotlib.pyplot as plt

fig, axs = plt.subplots(ncols=2, sharey=True)

axs[0] = s.plot(ax=axs[0])
axs[1] = curve.plot(ax=axs[1])


## Extract data from the curve into the striplog#

s = s.extract(curve.values, basis=depth, name='GR')


Now we have some the GR data from each unit stored in that unit:

s[1]

top200.0
primary
 formation B
summary50.00 m of B
description
data
 GR [ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568 -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204 -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599 -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573 -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513 -0.23064571 -0.13235175]
base250.0

So we could plot a segment of curve, say:

plt.plot(s[1].data['GR'])

[<matplotlib.lines.Line2D at 0x7f2cfa844d60>]


## Extract and reduce data#

We don’t have to store all the data points. We can optionaly pass a function to produce anything we like, and store the result of that:

s = s.extract(curve, basis=depth, name='GRmean', function=np.nanmean)

s[1]

top200.0
primary
 formation B
summary50.00 m of B
description
data
 GR [ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568 -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204 -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599 -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573 -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513 -0.23064571 -0.13235175] GRmean -0.12697991702493913
base250.0

• np.nanmedian — median average (ignoring nans)

• np.product — product

• np.nansum — sum (ignoring nans)

• np.nanmin — minimum (ignoring nans)

• np.nanmax — maximum (ignoring nans)

• scipy.stats.mstats.mode — mode average

• scipy.stats.mstats.hmean — harmonic mean

• scipy.stats.mstats.gmean — geometric mean

Or you can write your own, for example:

def trim_mean(a):
"""Compute trimmed mean, trimming min and max"""
return (np.nansum(a) - np.nanmin(a) - np.nanmax(a)) / a.size


Then do:

s.extract(curve, basis=basis, name='GRtrim', function=trim_mean)


The function doesn’t have to return a single number like this, it could return anything you like, including a dictionary.

We can also add bits to the data dictionary manually:

s[1].data['foo'] = 'bar'
s[1]

top200.0
primary
 formation B
summary50.00 m of B
description
data
 GR [ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568 -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204 -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599 -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573 -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513 -0.23064571 -0.13235175] GRmean -0.12697991702493913 foo bar
base250.0