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 = ""
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]
   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(
/home/vsts/work/1/s/examples/04-dcip/ UserWarning:

code under construction - API might change in the future


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

# clean up
plot read DC data with IO class

Moving Forward#

If you have suggestions for improving this example, please create a pull request on the example in SimPEG

You might try:
  • changing the contour levels

  • try with you own dataset

  • create a mask for negative apparent resistivities

Total running time of the script: (0 minutes 2.379 seconds)

Estimated memory usage: 9 MB

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