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()

Mon Nov 04 09:00:37 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	0.23.1.dev1+g60e0c3a5d	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