Reading and Plotting data with DC.IO class#

The DC.IO class is a convenient way to handle DC data and carry inversions within a same class. It also has several plotting utils such as pseudosections. We show here an example of plotting DC data based on a demonstration dataset.

import numpy as np
import pandas as pd
import shutil
import os
import matplotlib.pyplot as plt
from simpeg.electromagnetics.static import resistivity as DC
from simpeg import Report
from simpeg.utils.io_utils import download

Download an example DC data csv file#

# file origina and name
url = "https://storage.googleapis.com/simpeg/examples/dc_data.csv"
fname = download(url, folder="./test_url", overwrite=True)

# read csv using pandas
df = pd.read_csv(fname)
# header for ABMN locations
header_loc = ["Spa." + str(i + 1) for i in range(4)]
# Apparent resistivity
header_apprho = df.keys()[6]

Downloading https://storage.googleapis.com/simpeg/examples/dc_data.csv
   saved to: /home/vsts/work/1/s/examples/04-dcip/test_url/dc_data.csv
Download completed!

Convert file to DC.IO object#

# Number of the data
ndata = df[header_loc[0]].values.size
# ABMN locations
a = np.c_[df[header_loc[0]].values, np.zeros(ndata)]
b = np.c_[df[header_loc[1]].values, np.zeros(ndata)]
m = np.c_[df[header_loc[2]].values, np.zeros(ndata)]
n = np.c_[df[header_loc[3]].values, np.zeros(ndata)]
# Apparent resistivity
apprho = df[header_apprho].values

# Create DC.IO survey Object object
IO = DC.IO()
# Generate DC survey using IO object
dc_survey = IO.from_abmn_locations_to_survey(
    a,
    b,
    m,
    n,
    survey_type="dipole-dipole",
    data_dc=apprho,
    data_dc_type="apparent_resistivity",
)

/home/vsts/work/1/s/examples/04-dcip/plot_read_DC_data_with_IO_class.py:53: UserWarning:

code under construction - API might change in the future

Plot#

fig, ax = plt.subplots(1, 1, figsize=(10, 3))
IO.plotPseudoSection(
    data_type="apparent_resistivity", scale="linear", clim=(0, 1000), ncontour=3, ax=ax
)
plt.show()

# clean up
shutil.rmtree(os.path.expanduser("./test_url"))

Print the version of SimPEG and dependencies#

Report()

Sun Nov 03 19:19:00 2024 UTC
OS	Linux (Ubuntu 22.04)	CPU(s)	2	Machine	x86_64
Architecture	64bit	RAM	6.8 GiB	Environment	Python
File system	ext4
Python 3.10.15 \| packaged by conda-forge \| (main, Oct 16 2024, 01:24:24) [GCC 13.3.0]
simpeg	0.23.0	discretize	0.11.0	pymatsolver	0.3.1
numpy	2.0.2	scipy	1.14.1	matplotlib	3.9.2
geoana	0.7.2	libdlf	0.3.0	pydiso	0.1.2
numba	0.60.0	dask	2024.10.0	sklearn	1.5.2
pandas	2.2.3	sympy	1.13.3	plotly	5.24.1
memory_profiler	0.61.0	choclo	0.3.0