PythonNumericalDemos

A collection of Python demos for spatial data analytics, geostatistics and machine learning methods.

I use these demonstrations for:

in-lecture walkthroughs
hands-on experiential learning with specific tasks suggested in the accompanying lecture notes
example workflows to support my students

Other resources that you might be interested in include:

While I have more than 20 years experience coding in FORTRAN, C++ and VBA, I just started with Python a couple of years ago. I love it. I code less and get more done! I recommend Python to any engineers or scientists. Here's some reasons to learn to code:

Transparency – no compiler accepts hand waiving! Coding forces your logic to be uncovered for any other scientist or engineer to review.
Reproducibility – run it, get an answer, hand it over, run it, get the same answer. This is a main principle of the scientific method.
Quantification – programs need numbers. Feed the program and discover new ways to look at the world.
Open-source – leverage a world of brilliance. Check out packages, snippets and be amazed with what great minds have freely shared.
Break Down Barriers – don’t throw it over the fence. Sit at the table with the developers and share more of your subject matter expertise for a better product.
Deployment – share it with others and multiply the impact. Performance metrics or altruism, your good work benefits many others.
Efficiency – minimize the boring parts of the job. Build a suite of scripts for automation of common tasks and spend more time doing science and engineering!
Always Time to Do it Again! – how many times did you only do it once? It probably takes 2-4 times as long to script and automate a workflow. Usually worth it.
Be Like Us – it will change you. Users feel limited, programmers truly harness the power of their applications and hardware.

Alright, that is enough of my ranting. Especially, since I haven't even introduced myself yet!

The Author:

Michael Pyrcz, Associate Professor, University of Texas at Austin

Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions

With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.

For more about Michael check out these links:

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Want to Work Together?

I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.

Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you!
Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!
I can be reached at [email protected].

I'm always happy to discuss,

Michael

Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin

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PythonNumericalDemos

The Author:

Michael Pyrcz, Associate Professor, University of Texas at Austin

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Want to Work Together?

More Resources Available at: Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

About

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Name		Name	Last commit message	Last commit date
Latest commit History 318 Commits
About_Michael_Pyrcz.pdf		About_Michael_Pyrcz.pdf
Binomial_Demo_Environmental.xlsx		Binomial_Demo_Environmental.xlsx
Bootstrap.ipynb		Bootstrap.ipynb
CITATION.cff		CITATION.cff
Convolution_Simulation_Demo.ipynb		Convolution_Simulation_Demo.ipynb
DT_Demo.ipynb		DT_Demo.ipynb
Declustering.ipynb		Declustering.ipynb
Experiential_DecisionTree.ipynb		Experiential_DecisionTree.ipynb
GeostatsPy_Confidence_Hypothesis.ipynb		GeostatsPy_Confidence_Hypothesis.ipynb
GeostatsPy_Monte_Carlo_simulation.ipynb		GeostatsPy_Monte_Carlo_simulation.ipynb
GeostatsPy_bootstrap.ipynb		GeostatsPy_bootstrap.ipynb
GeostatsPy_datadistributions.ipynb		GeostatsPy_datadistributions.ipynb
GeostatsPy_declustering.ipynb		GeostatsPy_declustering.ipynb
GeostatsPy_ensemble_declustering.ipynb		GeostatsPy_ensemble_declustering.ipynb
GeostatsPy_indicator_kriging.ipynb		GeostatsPy_indicator_kriging.ipynb
GeostatsPy_inv_distance.ipynb		GeostatsPy_inv_distance.ipynb
GeostatsPy_kriging.ipynb		GeostatsPy_kriging.ipynb
GeostatsPy_multivariate.ipynb		GeostatsPy_multivariate.ipynb
GeostatsPy_overfit.ipynb		GeostatsPy_overfit.ipynb
GeostatsPy_plottingdata.ipynb		GeostatsPy_plottingdata.ipynb
GeostatsPy_simulation.ipynb		GeostatsPy_simulation.ipynb
GeostatsPy_simulation_postsim.ipynb		GeostatsPy_simulation_postsim.ipynb
GeostatsPy_sisim.ipynb		GeostatsPy_sisim.ipynb
GeostatsPy_spatial_continuity_directions.ipynb		GeostatsPy_spatial_continuity_directions.ipynb
GeostatsPy_spatial_updating.ipynb		GeostatsPy_spatial_updating.ipynb
GeostatsPy_synthetic_well_maker.ipynb		GeostatsPy_synthetic_well_maker.ipynb
GeostatsPy_transformations.ipynb		GeostatsPy_transformations.ipynb
GeostatsPy_trends.ipynb		GeostatsPy_trends.ipynb
GeostatsPy_univariate_simulation.ipynb		GeostatsPy_univariate_simulation.ipynb
GeostatsPy_variable_ranking.ipynb		GeostatsPy_variable_ranking.ipynb
GeostatsPy_variogram_calculation.ipynb		GeostatsPy_variogram_calculation.ipynb
GeostatsPy_variogram_modeling.ipynb		GeostatsPy_variogram_modeling.ipynb
GeostatsPy_volume_variance.ipynb		GeostatsPy_volume_variance.ipynb
Interactive_Bootstrap.ipynb		Interactive_Bootstrap.ipynb
Interactive_Bootstrap_Simple.ipynb		Interactive_Bootstrap_Simple.ipynb
Interactive_Central_Limit_Theorem.ipynb		Interactive_Central_Limit_Theorem.ipynb
Interactive_Confidence_Interval.ipynb		Interactive_Confidence_Interval.ipynb
Interactive_Correlation_Coefficient.ipynb		Interactive_Correlation_Coefficient.ipynb
Interactive_Correlation_Coefficient_Issues.ipynb		Interactive_Correlation_Coefficient_Issues.ipynb
Interactive_DecisionMaking.ipynb		Interactive_DecisionMaking.ipynb
Interactive_Declustering.ipynb		Interactive_Declustering.ipynb
Interactive_Distribution_Transformations.ipynb		Interactive_Distribution_Transformations.ipynb
Interactive_Hypothesis_Testing.ipynb		Interactive_Hypothesis_Testing.ipynb
Interactive_LASSO_Regression.ipynb		Interactive_LASSO_Regression.ipynb
Interactive_Monte_Carlo_simulation.ipynb		Interactive_Monte_Carlo_simulation.ipynb
Interactive_NeuralNetwork_SingleLayer.ipynb		Interactive_NeuralNetwork_SingleLayer.ipynb
Interactive_Norms.ipynb		Interactive_Norms.ipynb
Interactive_Optimization.ipynb		Interactive_Optimization.ipynb
Interactive_Overfit.ipynb		Interactive_Overfit.ipynb
Interactive_PCA.ipynb		Interactive_PCA.ipynb
Interactive_ParametricDistributions.ipynb		Interactive_ParametricDistributions.ipynb
Interactive_Ridge_Regresion.ipynb		Interactive_Ridge_Regresion.ipynb
Interactive_Shapley_Values.ipynb		Interactive_Shapley_Values.ipynb
Interactive_Simple_Kriging.ipynb		Interactive_Simple_Kriging.ipynb
Interactive_Simulation.ipynb		Interactive_Simulation.ipynb
Interactive_Sivia_Coin_Toss.ipynb		Interactive_Sivia_Coin_Toss.ipynb
Interactive_Spatial_Aggregate_Uncertainty.ipynb		Interactive_Spatial_Aggregate_Uncertainty.ipynb
Interactive_Spectral_Clustering.ipynb		Interactive_Spectral_Clustering.ipynb
Interactive_Spurious_Correlations.ipynb		Interactive_Spurious_Correlations.ipynb
Interactive_String_Effect.ipynb		Interactive_String_Effect.ipynb
Interactive_Trend.ipynb		Interactive_Trend.ipynb
Interactive_Uncertainty_Checking.ipynb		Interactive_Uncertainty_Checking.ipynb
Interactive_Variogram_Calculation.ipynb		Interactive_Variogram_Calculation.ipynb
Interactive_Variogram_Calculation_Modeling.ipynb		Interactive_Variogram_Calculation_Modeling.ipynb
Interactive_Variogram_Modeling.ipynb		Interactive_Variogram_Modeling.ipynb
LICENSE		LICENSE
LU+Decomposition.ipynb		LU+Decomposition.ipynb
Lorenz_coefficient_demo.ipynb		Lorenz_coefficient_demo.ipynb
Lorenz_coefficient_function.ipynb		Lorenz_coefficient_function.ipynb
NSCORE.ipynb		NSCORE.ipynb
PCA_Demo.ipynb		PCA_Demo.ipynb
PyGSLIB_declus_python_demo.ipynb		PyGSLIB_declus_python_demo.ipynb
PyGSLIB_variogram_python_demo.ipynb		PyGSLIB_variogram_python_demo.ipynb
PythonDataBasic_Univariate_Statistics.ipynb		PythonDataBasic_Univariate_Statistics.ipynb
PythonDataBasics_Bivariate_Statistics.ipynb		PythonDataBasics_Bivariate_Statistics.ipynb
PythonDataBasics_BoostrapHypothesis.ipynb		PythonDataBasics_BoostrapHypothesis.ipynb
PythonDataBasics_Bootstrap.ipynb		PythonDataBasics_Bootstrap.ipynb
PythonDataBasics_BootstrapConfidence.ipynb		PythonDataBasics_BootstrapConfidence.ipynb
PythonDataBasics_DataFrame.ipynb		PythonDataBasics_DataFrame.ipynb
PythonDataBasics_DistributionsTransformations.ipynb		PythonDataBasics_DistributionsTransformations.ipynb
PythonDataBasics_Feature_Ranking.ipynb		PythonDataBasics_Feature_Ranking.ipynb
PythonDataBasics_Hypothesis.ipynb		PythonDataBasics_Hypothesis.ipynb
PythonDataBasics_HypothesisTesting.ipynb		PythonDataBasics_HypothesisTesting.ipynb
PythonDataBasics_LinearRegression.ipynb		PythonDataBasics_LinearRegression.ipynb
PythonDataBasics_MachineLearning.ipynb		PythonDataBasics_MachineLearning.ipynb
PythonDataBasics_ParametricDistributions.ipynb		PythonDataBasics_ParametricDistributions.ipynb
PythonDataBasics_ParametricDistributions_Fitting.ipynb		PythonDataBasics_ParametricDistributions_Fitting.ipynb
PythonDataBasics_PedictiveMachineLearning.ipynb		PythonDataBasics_PedictiveMachineLearning.ipynb
PythonDataBasics_Pipelines.ipynb		PythonDataBasics_Pipelines.ipynb
PythonDataBasics_Statistics.ipynb		PythonDataBasics_Statistics.ipynb
PythonDataBasics_Univariate_Statistics.ipynb		PythonDataBasics_Univariate_Statistics.ipynb
PythonDataBasics_Univariate_Visualization.ipynb		PythonDataBasics_Univariate_Visualization.ipynb
PythonDataBasics_ndarrays.ipynb		PythonDataBasics_ndarrays.ipynb
README.md		README.md
Ripley_K_demo.ipynb		Ripley_K_demo.ipynb
Spatial_Bootstrap.ipynb		Spatial_Bootstrap.ipynb
SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb		SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb
SubsurfaceDataAnalytics_BayesianRegression.ipynb		SubsurfaceDataAnalytics_BayesianRegression.ipynb
SubsurfaceDataAnalytics_Clustering.ipynb		SubsurfaceDataAnalytics_Clustering.ipynb
SubsurfaceDataAnalytics_ConditionalGenerativeAdversarialNetwork.ipynb		SubsurfaceDataAnalytics_ConditionalGenerativeAdversarialNetwork.ipynb

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siddharthfcb/PythonNumericalDemos

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PythonNumericalDemos

The Author:

Michael Pyrcz, Associate Professor, University of Texas at Austin

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Want to Work Together?

More Resources Available at: Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

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