diff --git a/Bootstrap.ipynb b/Bootstrap.ipynb index f252656..5f5f944 100644 --- a/Bootstrap.ipynb +++ b/Bootstrap.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Demonstration of Boostrap \n", + "# Demonstration of Bootstrap \n", "# Contact: Michael Pyrcz, University of Texas at Austin, Geostatistics Course\n", "#\n", "# Steps:\n", @@ -14,7 +14,7 @@ "# 2. Draw from this initial sample set, with replacement, $ndata$ times to build a new realization of the sample. \n", "# Repeat this $nreal$ times to make realizations of the sample.\n", "# 3. Calculate the statistic of interest for each realization. This demonstration considers with mean and variance. \n", - "# We could have considered any sstatistic including median, 13th percentile, skew etc. \n", + "# We could have considered any statistic including median, 13th percentile, skew etc. \n", "# 4. - 6. Quantify and visualize uncertainty with histograms and summary statistics.\n", "#\n", "# Efron, 1982, The jackknife, the bootstrap, and other resampling plans, Society of Industrial and Applied Math, \n", @@ -298,7 +298,7 @@ } ], "source": [ - "# 2. Perform ndata random draws with replacement, nreal times. Here we aquire the nreal realizations of the distribution of \n", + "# 2. Perform ndata random draws with replacement, nreal times. Here we acquire the nreal realizations of the distribution of \n", "# ndata, samples.\n", "draw = np.zeros((ndata,nreal)) \n", "for ireal in range(0, nreal):\n", diff --git a/Declustering.ipynb b/Declustering.ipynb index 9967a1c..f3c7797 100644 --- a/Declustering.ipynb +++ b/Declustering.ipynb @@ -14,7 +14,7 @@ "\n", "This is a tutorial for / demonstration of **spatial declustering in Python with simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Almost every spatial dataset is based on biased sampling. This includes clustering (increased density of samples) over specific ranges of values. For example, more samples in an area of high feature values. Spatial declustering is a process of assigning data weights based on local data density. The cell-based declustering approach (Deutsch and Journel, 1997; Pyrcz and Deutsch, 2014; Pyrcz and Deutsch, 2003, paper is available here: http://gaa.org.au/pdf/DeclusterDebias-CCG.pdf) is based on the use of a mesh over the area of interest. Each datum's weight is inverse to the number of data in each cell. Cell offsets of applied to smooth out influence of mesh origin. Multiple cell sizes are applied and typically the cell size that minimizes the declustered distribution mean is applied for preferential sampling in the high-valued locations (the maximizing cell size is applied if the data is preferential sampled in the low-valued locations). If there is a nominal data spacing with local clusters, then this spacing is the best cell size.\n", "\n", - "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n", + "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplementation of GSLIB methods. The steps include:\n", "\n", "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n", "2. apply regular sampling to the 2D realization\n", @@ -24,7 +24,7 @@ "\n", "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n", "\n", - "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n", + "1. sgsim - sequential Gaussian simulation limited to 2D and unconditional\n", "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n", "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n", "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n", @@ -39,7 +39,7 @@ "\n", "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html. For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.\n", "\n", - "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB. The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/. \n", + "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familiar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB. The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/. \n", "\n", "I used this tutorial in my Introduction to Geostatistics undergraduate class (PGE337 at UT Austin) as part of a first introduction to geostatistics and Python for the engineering undergraduate students. It is assumed that students have no previous Python, geostatistics nor machine learning experience; therefore, all steps of the code and workflow are explored and described. This tutorial is augmented with course notes in my class. The Python code and markdown was developed and tested in Jupyter. \n", "\n", diff --git a/Experiential_Bootstrap_MCS.ipynb b/Experiential_Bootstrap_MCS.ipynb index 2cb7ddb..f8038f9 100644 --- a/Experiential_Bootstrap_MCS.ipynb +++ b/Experiential_Bootstrap_MCS.ipynb @@ -81,9 +81,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Declare Functions and Provide Code Snipets\n", + "#### Declare Functions and Provide Code Snippets\n", "\n", - "Declare convenience functions and code snipets to help with the workflow construction." + "Declare convenience functions and code snippets to help with the workflow construction." ] }, { diff --git a/Experiential_DecisionTree.ipynb b/Experiential_DecisionTree.ipynb index b32ad82..895b4db 100644 --- a/Experiential_DecisionTree.ipynb +++ b/Experiential_DecisionTree.ipynb @@ -33,9 +33,9 @@ "\n", "1. method for supervised learning\n", "2. categorical prediction with a classification tree and continuous prediction with a regression tree\n", - "3. fundamental idea is to divide feature space into exhastive, mutually exclusive regions (terminal or leaf nodes in the tree)\n", + "3. fundamental idea is to divide feature space into exhaustive, mutually exclusive regions (terminal or leaf nodes in the tree)\n", "4. estimate with the average of data in each region for continuous prediction or the majority category for the data in each region for categorical prediction\n", - "5. segment the feature space with hierarchical, binary splitting that may be respresented as a decision tree\n", + "5. segment the feature space with hierarchical, binary splitting that may be represented as a decision tree\n", "6. apply a greedy method to find the sequential splits for any feature that minimizes the residual sum of squares\n", "\n", "Let's build some decision trees together. You'll get a chance to see the trees and the divided feature space graphically. \n", @@ -50,11 +50,11 @@ "\n", "* the prediction is of the form $\\hat{Y} = \\hat{f}(X_1,\\ldots,X_m)$ \n", "\n", - "**Suppervised Learning**\n", + "**Supervised Learning**\n", "\n", "* the response feature label, $Y$, is available over the training and testing data\n", " \n", - "**Hiearchical, Binary Segmentation of the Feature Space**\n", + "**Hierarchical, Binary Segmentation of the Feature Space**\n", "\n", "The fundamental idea is to divide the predictor space, $𝑋_1,\\ldots,X_m$, into $J$ mutually exclusive, exhaustive regions\n", "\n", @@ -93,7 +93,7 @@ "\n", "* **predicts with the average of training response features** in each region $\\hat{Y}(R_j)$. \n", "\n", - "**Proceedure for Tree Construction**\n", + "**Procedure for Tree Construction**\n", "\n", "The tree is constructed from the top down. We begin with a sigle region that covers the entire feature space and then proceed with a sequence of splits.\n", "\n", diff --git a/GeostatsPy_Confidence_Hypothesis.ipynb b/GeostatsPy_Confidence_Hypothesis.ipynb index b36336a..512605f 100644 --- a/GeostatsPy_Confidence_Hypothesis.ipynb +++ b/GeostatsPy_Confidence_Hypothesis.ipynb @@ -84,7 +84,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_Monte_Carlo_simulation.ipynb b/GeostatsPy_Monte_Carlo_simulation.ipynb index 212d0e4..71c2a81 100644 --- a/GeostatsPy_Monte_Carlo_simulation.ipynb +++ b/GeostatsPy_Monte_Carlo_simulation.ipynb @@ -111,7 +111,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_bootstrap.ipynb b/GeostatsPy_bootstrap.ipynb index 569a0d1..ce777d3 100644 --- a/GeostatsPy_bootstrap.ipynb +++ b/GeostatsPy_bootstrap.ipynb @@ -37,7 +37,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -74,7 +74,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -108,7 +108,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, @@ -629,7 +629,7 @@ " samples = random.choices(df['Porosity'].values, k=len(df)) # n Monte Carlo simulations\n", " por_avg_real.append(np.average(samples)) # calculate the statistic of interest from the new bootstrap dataset\n", "plt.hist(por_avg_real,color = 'darkorange',alpha = 0.8,edgecolor = 'black') # plot the distribution, could also calculate any summary statistics\n", - "plt.xlabel('Boostrap Realizations of Average Porosity'); plt.ylabel('Frequency'); plt.title('Uncertainty Distribution for Average Porosity')\n", + "plt.xlabel('Bootstrap Realizations of Average Porosity'); plt.ylabel('Frequency'); plt.title('Uncertainty Distribution for Average Porosity')\n", "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.2); plt.show()" ] }, @@ -681,9 +681,9 @@ "source": [ "##### A Couple of Bootstrap Realizations\n", "\n", - "We will attempt boostrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n", + "We will attempt bootstrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n", "\n", - "This command returns a ndarray with k samples with replacment from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n", + "This command returns a ndarray with k samples with replacement from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n", "```p\n", "samples1 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n", "```\n", diff --git a/GeostatsPy_cosimulation.ipynb b/GeostatsPy_cosimulation.ipynb index 01ebecb..8502431 100644 --- a/GeostatsPy_cosimulation.ipynb +++ b/GeostatsPy_cosimulation.ipynb @@ -456,7 +456,7 @@ "\n", "Let's jump right to building two independent simulations and visualizing the results. \n", "\n", - "* independently simulate porosity and permability \n", + "* independently simulate porosity and permeability \n", "* check the porosity an permeability relationship, the scatter plot.\n", "\n", "Note we have already demonstrated univariate simulation checks for:\n", diff --git a/GeostatsPy_datadistributions.ipynb b/GeostatsPy_datadistributions.ipynb index 9f404f2..77a182f 100644 --- a/GeostatsPy_datadistributions.ipynb +++ b/GeostatsPy_datadistributions.ipynb @@ -56,7 +56,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_declustering.ipynb b/GeostatsPy_declustering.ipynb index f9ec562..9960cc9 100644 --- a/GeostatsPy_declustering.ipynb +++ b/GeostatsPy_declustering.ipynb @@ -96,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python \n", "import warnings \n", "warnings.filterwarnings('ignore') # suppress warnings" diff --git a/GeostatsPy_ensemble_declustering.ipynb b/GeostatsPy_ensemble_declustering.ipynb index 2748c42..79d5589 100644 --- a/GeostatsPy_ensemble_declustering.ipynb +++ b/GeostatsPy_ensemble_declustering.ipynb @@ -99,7 +99,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_inv_distance.ipynb b/GeostatsPy_inv_distance.ipynb index e872cc9..50e06c4 100644 --- a/GeostatsPy_inv_distance.ipynb +++ b/GeostatsPy_inv_distance.ipynb @@ -82,7 +82,7 @@ }, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_kriging.ipynb b/GeostatsPy_kriging.ipynb index 94153be..ddfd5bf 100644 --- a/GeostatsPy_kriging.ipynb +++ b/GeostatsPy_kriging.ipynb @@ -121,7 +121,7 @@ }, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_kriging_byfacies.ipynb b/GeostatsPy_kriging_byfacies.ipynb index 1c7c98c..0d1a7a0 100644 --- a/GeostatsPy_kriging_byfacies.ipynb +++ b/GeostatsPy_kriging_byfacies.ipynb @@ -121,7 +121,7 @@ }, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_multivariate.ipynb b/GeostatsPy_multivariate.ipynb index a8d84a2..cb91ef7 100644 --- a/GeostatsPy_multivariate.ipynb +++ b/GeostatsPy_multivariate.ipynb @@ -117,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_overfit.ipynb b/GeostatsPy_overfit.ipynb index 9363244..96c6b9f 100644 --- a/GeostatsPy_overfit.ipynb +++ b/GeostatsPy_overfit.ipynb @@ -148,7 +148,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_plottingdata.ipynb b/GeostatsPy_plottingdata.ipynb index f9feec0..640e066 100644 --- a/GeostatsPy_plottingdata.ipynb +++ b/GeostatsPy_plottingdata.ipynb @@ -52,7 +52,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_spatial_continuity_directions.ipynb b/GeostatsPy_spatial_continuity_directions.ipynb index 5b404da..c73555d 100644 --- a/GeostatsPy_spatial_continuity_directions.ipynb +++ b/GeostatsPy_spatial_continuity_directions.ipynb @@ -177,7 +177,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_spatial_updating.ipynb b/GeostatsPy_spatial_updating.ipynb index 96b14c7..20e277d 100644 --- a/GeostatsPy_spatial_updating.ipynb +++ b/GeostatsPy_spatial_updating.ipynb @@ -142,7 +142,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_synthetic_well_maker.ipynb b/GeostatsPy_synthetic_well_maker.ipynb index 7e687d7..9344ea3 100644 --- a/GeostatsPy_synthetic_well_maker.ipynb +++ b/GeostatsPy_synthetic_well_maker.ipynb @@ -56,7 +56,7 @@ }, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_transformations.ipynb b/GeostatsPy_transformations.ipynb index 42e41cd..7c226b9 100644 --- a/GeostatsPy_transformations.ipynb +++ b/GeostatsPy_transformations.ipynb @@ -84,7 +84,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_trends.ipynb b/GeostatsPy_trends.ipynb index ca32a45..6a2e501 100644 --- a/GeostatsPy_trends.ipynb +++ b/GeostatsPy_trends.ipynb @@ -113,7 +113,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_variable_ranking.ipynb b/GeostatsPy_variable_ranking.ipynb index 17ed200..402c4aa 100644 --- a/GeostatsPy_variable_ranking.ipynb +++ b/GeostatsPy_variable_ranking.ipynb @@ -77,7 +77,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_variogram_calculation.ipynb b/GeostatsPy_variogram_calculation.ipynb index 09b2345..9c719da 100644 --- a/GeostatsPy_variogram_calculation.ipynb +++ b/GeostatsPy_variogram_calculation.ipynb @@ -152,7 +152,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_variogram_from_image.ipynb b/GeostatsPy_variogram_from_image.ipynb index afed290..04a25ee 100644 --- a/GeostatsPy_variogram_from_image.ipynb +++ b/GeostatsPy_variogram_from_image.ipynb @@ -181,7 +181,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # variogram calculations " ] }, diff --git a/GeostatsPy_variogram_modeling.ipynb b/GeostatsPy_variogram_modeling.ipynb index dad493c..c8ba13f 100644 --- a/GeostatsPy_variogram_modeling.ipynb +++ b/GeostatsPy_variogram_modeling.ipynb @@ -168,7 +168,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/GeostatsPy_widearray_declustering.ipynb b/GeostatsPy_widearray_declustering.ipynb index 5ae8cea..f2f8287 100644 --- a/GeostatsPy_widearray_declustering.ipynb +++ b/GeostatsPy_widearray_declustering.ipynb @@ -93,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Bootstrap.ipynb b/Interactive_Bootstrap.ipynb index 97d9069..0ca8528 100644 --- a/Interactive_Bootstrap.ipynb +++ b/Interactive_Bootstrap.ipynb @@ -11,7 +11,7 @@ "\n", "## Interactive Bootstrap Demonstration\n", "\n", - "### Boostrap for Uncertainty in Sample Statistics Tutorial\n", + "### Bootstrap for Uncertainty in Sample Statistics Tutorial\n", "\n", "* interactive plot demonstration with ipywidget package\n", "\n", @@ -31,7 +31,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -68,7 +68,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -80,7 +80,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of statistical boostrap \n", + "2. provide an intuitive hands-on example of statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -143,7 +143,7 @@ "bins = np.linspace(0,1000,1000)\n", "\n", "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", - "l = widgets.Text(value=' Boostrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l = widgets.Text(value=' Bootstrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "dist = widgets.Dropdown(\n", " options=['Triangular', 'Uniform', 'Gaussian'],\n", " value='Gaussian',\n", @@ -229,7 +229,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Boostrap Demonstration, Michael Pyrcz, Associ…" + "VBox(children=(Text(value=' Bootstrap Demonstration, Michael Pyrcz, Associ…" ] }, "metadata": {}, @@ -355,7 +355,7 @@ "# parameters for the synthetic dataset\n", "bins = np.linspace(0,1000,1000)\n", "\n", - "l = widgets.Text(value=' Boostrap Demonstration with Modified Number of Data, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l = widgets.Text(value=' Bootstrap Demonstration with Modified Number of Data, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "\n", "n = widgets.IntSlider(min = 2, max = 1000, value = 100, description = 'New Number Samples',orientation='horizontal',layout=Layout(width='800px', height='20px'),continuous_update=False)\n", "n.style.handle_color = 'gray'\n", @@ -411,7 +411,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Boostrap Demonstration with Modified Number of Data, Mic…" + "VBox(children=(Text(value=' Bootstrap Demonstration with Modified Number of Data, Mic…" ] }, "metadata": {}, diff --git a/Interactive_Bootstrap_Simple.ipynb b/Interactive_Bootstrap_Simple.ipynb index e2a4679..9cbabdf 100644 --- a/Interactive_Bootstrap_Simple.ipynb +++ b/Interactive_Bootstrap_Simple.ipynb @@ -11,7 +11,7 @@ "\n", "## Interactive Simple Bootstrap Demonstration\n", "\n", - "### Boostrap for Uncertainty in Sample Statistics, The Red and Yellow Blocks with a Cowboy Hat Tutorial\n", + "### Bootstrap for Uncertainty in Sample Statistics, The Red and Yellow Blocks with a Cowboy Hat Tutorial\n", "\n", "* in class I bring in 3 red blocks, 2 yellow blocks and my cowboy hat, yes I have one, recall I was a farmhand in Alberta, Canada\n", "\n", @@ -37,7 +37,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -74,7 +74,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -86,7 +86,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of statistical boostrap \n", + "2. provide an intuitive hands-on example of statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -155,7 +155,7 @@ "bins = np.linspace(0,1000,1000)\n", "\n", "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", - "l = widgets.Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l = widgets.Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "\n", "a = widgets.IntSlider(min=0, max = 100, value = 2, step = 1, description = '$n_{red}$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n", "a.style.handle_color = 'red'\n", @@ -239,7 +239,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Assoc…" + "VBox(children=(Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Assoc…" ] }, "metadata": {}, @@ -283,7 +283,7 @@ "bins = np.linspace(0,1000,1000)\n", "\n", "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", - "l = widgets.Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l = widgets.Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "\n", "a = widgets.IntSlider(min=0, max = 100, value = 2, step = 1, description = '$n_{red}$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n", "a.style.handle_color = 'red'\n", @@ -368,7 +368,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Assoc…" + "VBox(children=(Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Assoc…" ] }, "metadata": {}, @@ -418,7 +418,7 @@ "bins = np.linspace(0,1000,1000)\n", "\n", "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", - "l2 = widgets.Text(value=' Simple Boostrap Demonstration, Uncertainty in Proportion of Balls by Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l2 = widgets.Text(value=' Simple Bootstrap Demonstration, Uncertainty in Proportion of Balls by Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "\n", "a2 = widgets.IntSlider(min=0, max = 100, value = 2, step = 1, description = '$n_{red}$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n", "a2.style.handle_color = 'red'\n", @@ -524,7 +524,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Simple Boostrap Demonstration, Uncertainty in Proportion of Balls by…" + "VBox(children=(Text(value=' Simple Bootstrap Demonstration, Uncertainty in Proportion of Balls by…" ] }, "metadata": {}, diff --git a/Interactive_Confidence_Interval.ipynb b/Interactive_Confidence_Interval.ipynb index 9906611..1638199 100644 --- a/Interactive_Confidence_Interval.ipynb +++ b/Interactive_Confidence_Interval.ipynb @@ -11,9 +11,9 @@ "\n", "## Interactive Confidence Interval Demonstration\n", "\n", - "### Boostrap and Analytical Confidence Intervals\n", + "### Bootstrap and Analytical Confidence Intervals\n", "\n", - "* we calculate the confidence interval in the mean with boostrap and compare to the analytical expression\n", + "* we calculate the confidence interval in the mean with bootstrap and compare to the analytical expression\n", "\n", "* with this workflow we all provide an interactive plot demonstration with matplotlib and ipywidget packages\n", "\n", @@ -59,7 +59,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -96,7 +96,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -108,7 +108,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of confidence intervals and compare to statistical boostrap \n", + "2. provide an intuitive hands-on example of confidence intervals and compare to statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -177,7 +177,7 @@ "bins = np.linspace(0,1000,1000)\n", "\n", "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", - "l = widgets.Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", + "l = widgets.Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "\n", "a = widgets.IntSlider(min=0, max = 100, value = 2, step = 1, description = '$n_{red}$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n", "a.style.handle_color = 'red'\n", @@ -262,7 +262,7 @@ "version_minor": 0 }, "text/plain": [ - "VBox(children=(Text(value=' Simple Boostrap Demonstration, Michael Pyrcz, Assoc…" + "VBox(children=(Text(value=' Simple Bootstrap Demonstration, Michael Pyrcz, Assoc…" ] }, "metadata": {}, diff --git a/Interactive_Correlation_Coefficient.ipynb b/Interactive_Correlation_Coefficient.ipynb index 66f9d76..2fd8986 100644 --- a/Interactive_Correlation_Coefficient.ipynb +++ b/Interactive_Correlation_Coefficient.ipynb @@ -134,7 +134,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Correlation_Coefficient_Issues.ipynb b/Interactive_Correlation_Coefficient_Issues.ipynb index f6238be..2d8bb97 100644 --- a/Interactive_Correlation_Coefficient_Issues.ipynb +++ b/Interactive_Correlation_Coefficient_Issues.ipynb @@ -123,7 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_DecisionMaking.ipynb b/Interactive_DecisionMaking.ipynb index 517d8ef..635bd94 100644 --- a/Interactive_DecisionMaking.ipynb +++ b/Interactive_DecisionMaking.ipynb @@ -27,9 +27,9 @@ "source": [ "Optimum Decision Making in Python \n", "\n", - "Here's a simple interactive demonstration of optimum decision making in the precense of uncertainty. This should help you get started with using your uncertainty models to make decisions. \n", + "Here's a simple interactive demonstration of optimum decision making in the presence of uncertainty. This should help you get started with using your uncertainty models to make decisions. \n", "\n", - "#### Optimum Decision Making in the Precense of Uncertainty \n", + "#### Optimum Decision Making in the presence of Uncertainty \n", "\n", "Definition: the decision that minimizes the expected loss or maximizes the expected profit\n", "\n", @@ -70,7 +70,7 @@ "\n", "#### Limitations\n", "\n", - "The optimum decision making in the precense of uncertainty assumes:\n", + "The optimum decision making in the presence of uncertainty assumes:\n", "1. **representativity** - the uncertainty distribution is representative, a good uncertainty model\n", "2. **independence** - between the variables / features impacting the uncertainty distribuiton and those impacting the lose function\n", "3. **stationarity** - the uncertainty model and the loss function are invariant under translation over included spatial samples and for location of implementation\n", @@ -93,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, @@ -326,7 +326,7 @@ "source": [ "#### Comments\n", "\n", - "This was a basic demonstration of optimum decision making in the precense of uncertainty. \n", + "This was a basic demonstration of optimum decision making in the presence of uncertainty. \n", "\n", "* we build uncertainty models all the time to support decision making. Here's how it gets done.\n", "\n", diff --git a/Interactive_Declustering.ipynb b/Interactive_Declustering.ipynb index 40a1a17..1f2aa12 100644 --- a/Interactive_Declustering.ipynb +++ b/Interactive_Declustering.ipynb @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Hypothesis_Testing.ipynb b/Interactive_Hypothesis_Testing.ipynb index e1cd866..3f3b68d 100644 --- a/Interactive_Hypothesis_Testing.ipynb +++ b/Interactive_Hypothesis_Testing.ipynb @@ -11,9 +11,9 @@ "\n", "## Interactive Hypothesis Testing Demonstration\n", "\n", - "### Boostrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", + "### Bootstrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", "\n", - "* we calculate the hypothesis test for different in means with boostrap and compare to the analytical expression\n", + "* we calculate the hypothesis test for different in means with bootstrap and compare to the analytical expression\n", "\n", "* **Welch's t-test**: we assume the features are Gaussian distributed and the variance are unequal\n", "\n", @@ -78,9 +78,9 @@ " \n", "* pool the results to assemble the $t_{statistic}$ sampling distribution\n", "\n", - "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the boostrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", + "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the bootstrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", "\n", - "Here's some prerequisite information on the boostrap.\n", + "Here's some prerequisite information on the bootstrap.\n", "\n", "#### Bootstrap\n", "\n", @@ -94,7 +94,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -131,7 +131,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -143,7 +143,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of confidence intervals and compare to statistical boostrap \n", + "2. provide an intuitive hands-on example of confidence intervals and compare to statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -314,7 +314,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Boostrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", + "### Bootstrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", "\n", "* including the analytical and bootstrap methods for testing the difference in means\n", "* interactive plot demonstration with ipywidget, matplotlib packages\n", diff --git a/Interactive_LASSO_Regression.ipynb b/Interactive_LASSO_Regression.ipynb index 10f7391..366b099 100644 --- a/Interactive_LASSO_Regression.ipynb +++ b/Interactive_LASSO_Regression.ipynb @@ -182,7 +182,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Monte_Carlo_simulation.ipynb b/Interactive_Monte_Carlo_simulation.ipynb index 3437645..10b4822 100644 --- a/Interactive_Monte_Carlo_simulation.ipynb +++ b/Interactive_Monte_Carlo_simulation.ipynb @@ -99,7 +99,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Overfit.ipynb b/Interactive_Overfit.ipynb index 6280990..dbb5539 100644 --- a/Interactive_Overfit.ipynb +++ b/Interactive_Overfit.ipynb @@ -107,7 +107,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_PreDrill_Prediction.ipynb b/Interactive_PreDrill_Prediction.ipynb index 80e4f99..2dd321f 100644 --- a/Interactive_PreDrill_Prediction.ipynb +++ b/Interactive_PreDrill_Prediction.ipynb @@ -170,7 +170,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_RadialBasisFunctions.ipynb b/Interactive_RadialBasisFunctions.ipynb index 03d8e3c..a45cfa2 100644 --- a/Interactive_RadialBasisFunctions.ipynb +++ b/Interactive_RadialBasisFunctions.ipynb @@ -148,7 +148,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Ridge_Regresion.ipynb b/Interactive_Ridge_Regresion.ipynb index 92b54f9..e470ffd 100644 --- a/Interactive_Ridge_Regresion.ipynb +++ b/Interactive_Ridge_Regresion.ipynb @@ -173,7 +173,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Simple_Kriging.ipynb b/Interactive_Simple_Kriging.ipynb index eaf818d..fe1cb76 100644 --- a/Interactive_Simple_Kriging.ipynb +++ b/Interactive_Simple_Kriging.ipynb @@ -162,7 +162,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Simple_Kriging_Behavoir.ipynb b/Interactive_Simple_Kriging_Behavoir.ipynb index b3aa5be..17c6733 100644 --- a/Interactive_Simple_Kriging_Behavoir.ipynb +++ b/Interactive_Simple_Kriging_Behavoir.ipynb @@ -166,7 +166,7 @@ }, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Simulation.ipynb b/Interactive_Simulation.ipynb index f043cde..812406e 100644 --- a/Interactive_Simulation.ipynb +++ b/Interactive_Simulation.ipynb @@ -172,7 +172,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Spatial_Aggregate_Uncertainty.ipynb b/Interactive_Spatial_Aggregate_Uncertainty.ipynb index 7aa2932..d016d31 100644 --- a/Interactive_Spatial_Aggregate_Uncertainty.ipynb +++ b/Interactive_Spatial_Aggregate_Uncertainty.ipynb @@ -218,7 +218,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_String_Effect.ipynb b/Interactive_String_Effect.ipynb index 3afc88d..43a0a72 100644 --- a/Interactive_String_Effect.ipynb +++ b/Interactive_String_Effect.ipynb @@ -182,7 +182,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Uncertainty_Checking.ipynb b/Interactive_Uncertainty_Checking.ipynb index dddd723..ac28ac5 100644 --- a/Interactive_Uncertainty_Checking.ipynb +++ b/Interactive_Uncertainty_Checking.ipynb @@ -175,7 +175,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Variogram_Calculation.ipynb b/Interactive_Variogram_Calculation.ipynb index 2fddca2..fd5869f 100644 --- a/Interactive_Variogram_Calculation.ipynb +++ b/Interactive_Variogram_Calculation.ipynb @@ -189,7 +189,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Variogram_Calculation_Modeling.ipynb b/Interactive_Variogram_Calculation_Modeling.ipynb index 0ab8dbc..51c32b6 100644 --- a/Interactive_Variogram_Calculation_Modeling.ipynb +++ b/Interactive_Variogram_Calculation_Modeling.ipynb @@ -246,7 +246,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Variogram_Calculation_Modeling_Krige.ipynb b/Interactive_Variogram_Calculation_Modeling_Krige.ipynb index 93def87..6c7588a 100644 --- a/Interactive_Variogram_Calculation_Modeling_Krige.ipynb +++ b/Interactive_Variogram_Calculation_Modeling_Krige.ipynb @@ -399,7 +399,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/Interactive_Variogram_Modeling.ipynb b/Interactive_Variogram_Modeling.ipynb index b4652ec..f5a2695 100644 --- a/Interactive_Variogram_Modeling.ipynb +++ b/Interactive_Variogram_Modeling.ipynb @@ -223,7 +223,7 @@ } ], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/PyGSLIB_declus_python_demo.ipynb b/PyGSLIB_declus_python_demo.ipynb index fe45cf4..87ece7b 100644 --- a/PyGSLIB_declus_python_demo.ipynb +++ b/PyGSLIB_declus_python_demo.ipynb @@ -12,13 +12,13 @@ "\n", "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n", "\n", - "This is a tutorial for / demonstration of **spatial declustering in Python** with the PyGSLIB package (by Adrian Martinez Vargas) that wraps some of the FORTRAN algorithms from GSLIB, Geostatistical Library (Deutsch and Journel, 1997). The GeostatPy (by Michael Pyrcz) is an informal collection of Python programs that write parameter files, data files, run GSLIB executables and then read results back. There has been some reimplimentation of visualization with MatPlotLib methods. \n", + "This is a tutorial for / demonstration of **spatial declustering in Python** with the PyGSLIB package (by Adrian Martinez Vargas) that wraps some of the FORTRAN algorithms from GSLIB, Geostatistical Library (Deutsch and Journel, 1997). The GeostatPy (by Michael Pyrcz) is an informal collection of Python programs that write parameter files, data files, run GSLIB executables and then read results back. There has been some reimplementation of visualization with MatPlotLib methods. \n", "\n", "Cell-based declustering is available in the declus algorithm from GSLIB. The methods is written about in the GSLIB book (Deutsch and Journel, 1998) and other textbooks such as Pyrcz and Deutsch (2014). The method provides declustering weights that acocunt for spatial clustering of the data samples. It is not sensitive to data boundaries, but relies on a cell size assumption. The common method is to attempt a range of cell sizes and retain that one that minimizes the declustered mean if locations with high values have been over sampled (and visa versa).\n", "\n", "We will demonstration calculation of declustering weights on regular and irregular spaced data. \n", "\n", - "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n", + "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplementation of GSLIB methods. The steps include:\n", "\n", "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n", "2. extract regular and irregular sample sets from the exhaustive realization\n", @@ -376,7 +376,7 @@ "11. vmodel\n", "12. sgsim\n", "\n", - "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the precense of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." + "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the presence of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." ] }, { diff --git a/PyGSLIB_variogram_python_demo.ipynb b/PyGSLIB_variogram_python_demo.ipynb index 6cdd080..ddddced 100644 --- a/PyGSLIB_variogram_python_demo.ipynb +++ b/PyGSLIB_variogram_python_demo.ipynb @@ -12,7 +12,7 @@ "\n", "#### [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube Lectures](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)\n", "\n", - "This is a tutorial for / demonstration of **variogram calculation in Python** with the PyGSLIB package (by Adrian Martinez Vargas) that wraps some of the FORTRAN algorithms from GSLIB, Geostatistical Library (Deutsch and Journel, 1997). I also use a couple functions form GeostatPy (by Michael Pyrcz, an informal collection of Python programs that write parameter files, data files, run GSLIB executables and then read results back and some reimplimentation of GSLIB visualization with MatPlotLib methods). The GeostatPy functions are embeded directly in the workflow.\n", + "This is a tutorial for / demonstration of **variogram calculation in Python** with the PyGSLIB package (by Adrian Martinez Vargas) that wraps some of the FORTRAN algorithms from GSLIB, Geostatistical Library (Deutsch and Journel, 1997). I also use a couple functions form GeostatPy (by Michael Pyrcz, an informal collection of Python programs that write parameter files, data files, run GSLIB executables and then read results back and some reimplementation of GSLIB visualization with MatPlotLib methods). The GeostatPy functions are embeded directly in the workflow.\n", "\n", "Variogram calculation for irregularly-spaced data is available in the $gam$ algorithm from GSLIB. The methods is written about in the GSLIB book by Deutsch and Journel (1998) and other textbooks such as Pyrcz and Deutsch (2014). The implementation is true to the GSLIB parameterization and includes the ability to calculate an array of outputs with multiple vairogram types and multiple directions all at once. \n", "\n", @@ -70,7 +70,7 @@ "2. locmap\n", "3. affine\n", "\n", - "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the precense of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." + "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the presence of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." ] }, { diff --git a/PythonDataBasics_BoostrapHypothesis.ipynb b/PythonDataBasics_BoostrapHypothesis.ipynb index 835d11c..e3d63fb 100644 --- a/PythonDataBasics_BoostrapHypothesis.ipynb +++ b/PythonDataBasics_BoostrapHypothesis.ipynb @@ -37,9 +37,9 @@ "\n", "* the tests we will demonstrated have analytical solutions and we will show that our bootstrap methods reproduce these analytically-known distributions while providing a more general workflow for any significance problem.\n", "\n", - "### Boostrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", + "### Bootstrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", "\n", - "* we calculate the hypothesis test for different in means with boostrap and compare to the analytical expression\n", + "* we calculate the hypothesis test for different in means with bootstrap and compare to the analytical expression\n", "\n", "* **Welch's t-test**: we assume the features are Gaussian distributed and the variance are unequal\n", "\n", @@ -104,9 +104,9 @@ " \n", "* pool the results to assemble the $t_{statistic}$ sampling distribution\n", "\n", - "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the boostrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", + "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the bootstrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", "\n", - "Here's some prerequisite information on the boostrap.\n", + "Here's some prerequisite information on the bootstrap.\n", "\n", "#### Bootstrap\n", "\n", @@ -120,7 +120,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -157,7 +157,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -169,7 +169,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of confidence intervals and compare to statistical boostrap \n", + "2. provide an intuitive hands-on example of confidence intervals and compare to statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -663,7 +663,7 @@ "source": [ "### Bootstrap Hypothesis Test\n", "\n", - "#### Boostrap Student's t-test, Welch's Test Difference in Means\n", + "#### Bootstrap Student's t-test, Welch's Test Difference in Means\n", "\n", "First let's calculate the t-statistic observed in our dataset, assuming unequal variances with the Welch's t-test.\n", "\n", diff --git a/PythonDataBasics_Bootstrap.ipynb b/PythonDataBasics_Bootstrap.ipynb index a5a5324..f2e40f5 100644 --- a/PythonDataBasics_Bootstrap.ipynb +++ b/PythonDataBasics_Bootstrap.ipynb @@ -124,7 +124,7 @@ "\n", "These are convenience functions to visualize the data and bootstrap results.\n", "* **custom_histogram** - data histogram with summary statistics \n", - "* **custom_histogram_with_uncert** - data histogram with boostrap uncertainty model \n", + "* **custom_histogram_with_uncert** - data histogram with bootstrap uncertainty model \n", "* **display_bootstrap** - bootstrap realizations' histogram with analytical or empirical PDF\n", "\n", "I include these for concise, readable workflows." @@ -165,12 +165,12 @@ " ht = np.max(freq)\n", " if analytical is None:\n", " sns.kdeplot(x=zreal,color = 'grey',alpha = 0.1,levels = 1,bw_adjust = 1,label='bootstrap PDF') \n", - " plt.xlabel('Boostrap Realizations and Kernel Density Estimate') \n", + " plt.xlabel('Bootstrap Realizations and Kernel Density Estimate') \n", " else: \n", " plt.plot(abin,analytical,color = 'black',label = 'analytical',alpha=0.4)\n", " plt.fill_between(abin, 0, analytical, where = abin <= np.percentile(zreal,10), facecolor='red', interpolate=True, alpha = 0.5)\n", " plt.fill_between(abin, 0, analytical, where = abin >= np.percentile(zreal,90), facecolor='red', interpolate=True, alpha = 0.5)\n", - " plt.xlabel('Boostrap Realizations and Analytical Sampling Distributions') \n", + " plt.xlabel('Bootstrap Realizations and Analytical Sampling Distributions') \n", " plt.axvline(x=stat(zdata),linestyle=\"--\",c='black')\n", " plt.text(stat(zdata)+offset,ht*0.95, r'Average = ' + str(round(np.average(zreal),1)), fontsize=12)\n", " plt.text(stat(zdata)+offset,ht*0.90, r'St.Dev. = ' + str(round(np.std(zreal),1)), fontsize=12)\n", @@ -833,7 +833,7 @@ " \n", "* calculate a bootstrap realization of the dataset with $n$ samples with replacement\n", "* calculate the mean and standard deviation from this bootstrapped realization of the dataset\n", - "* calculate a boostrap realization of the coefficient of variation as the standard deviation realizaiton divided by the mean realization\n", + "* calculate a bootstrap realization of the coefficient of variation as the standard deviation realizaiton divided by the mean realization\n", "\n", "Repeat this $L$ time on $L$ realizations of the data and then evaluate the resulting distribution.\n", "\n", @@ -1006,7 +1006,7 @@ " plt.text(np.average(shale_prop_real)+0.07, 6.6, r'P90 = ' + str(round(np.percentile(shale_prop_real,90),2)), fontsize=12)\n", " plt.text(np.average(shale_prop_real)+0.07, 6.2, r'P10 = ' + str(round(np.percentile(shale_prop_real,10),2)), fontsize=12)\n", " \n", - " plt.xlabel('Boostrap Realizations and Analytical Sampling Distributions'); plt.ylabel('Frequency'); plt.title('Distribution of Bootstrap Proportions')\n", + " plt.xlabel('Bootstrap Realizations and Analytical Sampling Distributions'); plt.ylabel('Frequency'); plt.title('Distribution of Bootstrap Proportions')\n", " plt.legend(loc = 'upper left')\n", " \n", " return sand_prop_real, shale_prop_real" diff --git a/PythonDataBasics_BootstrapConfidence.ipynb b/PythonDataBasics_BootstrapConfidence.ipynb index 9bc2002..86072bd 100644 --- a/PythonDataBasics_BootstrapConfidence.ipynb +++ b/PythonDataBasics_BootstrapConfidence.ipynb @@ -49,7 +49,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -86,7 +86,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -788,7 +788,7 @@ "plt.text(np.percentile(iqr_real,90)+0.009, L*0.036, r'St.Dev. = ' + str(round(np.std(iqr_real),3)), fontsize=12)\n", "plt.text(np.percentile(iqr_real,90)+0.009, L*0.032, r'P90 = ' + str(round(np.percentile(iqr_real,90),3)), fontsize=12)\n", "plt.text(np.percentile(iqr_real,90)+0.009, L*0.028, r'P10 = ' + str(round(np.percentile(iqr_real,10),3)), fontsize=12)\n", - "plt.xlabel('Boostrap Realizations of Interquartile Range'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Interquartile Range')\n", + "plt.xlabel('Bootstrap Realizations of Interquartile Range'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Interquartile Range')\n", "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n", "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n", "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n", @@ -811,7 +811,7 @@ " \n", "* calculate a bootstrap realization of the dataset with $n$ samples with replacement\n", "* calculate the mean and standard deviation from this bootstrapped realization of the dataset\n", - "* calculate a boostrap realization of the coefficient of variation as the standard deviation divided by the mean\n", + "* calculate a bootstrap realization of the coefficient of variation as the standard deviation divided by the mean\n", "\n", "Repeat this $L$ times and then evaluate the resulting distribution." ] @@ -854,7 +854,7 @@ "plt.text(np.percentile(cv_real,90)+0.009, 15, r'St.Dev. = ' + str(round(np.std(cv_real),3)), fontsize=12)\n", "plt.text(np.percentile(cv_real,90)+0.009, 14, r'P90 = ' + str(round(np.percentile(cv_real,90),3)), fontsize=12)\n", "plt.text(np.percentile(cv_real,90)+0.009, 13, r'P10 = ' + str(round(np.percentile(cv_real,10),3)), fontsize=12)\n", - "plt.xlabel('Boostrap Realizations of Coefficient of Variation'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Coefficient of Variation')\n", + "plt.xlabel('Bootstrap Realizations of Coefficient of Variation'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Coefficient of Variation')\n", "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n", "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n", "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n", @@ -870,7 +870,7 @@ "#### Bootstrap of the Correlation Coefficient\n", "\n", "Here's a statistic that requires us to work with multiple, paired features at once. \n", - "* this reinforces that we boostrap for a new realization of the dataset, a set of samples with all their features.\n", + "* this reinforces that we bootstrap for a new realization of the dataset, a set of samples with all their features.\n", "\n", "For the correlation coefficient we will:\n", " \n", @@ -918,7 +918,7 @@ "# plt.text(np.percentile(corr_real,90)+0.009, 15, r'St.Dev. = ' + str(round(np.std(corr_real),3)), fontsize=12)\n", "# plt.text(np.percentile(corr_real,90)+0.009, 14, r'P90 = ' + str(round(np.percentile(corr_real,90),3)), fontsize=12)\n", "# plt.text(np.percentile(corr_real,90)+0.009, 13, r'P10 = ' + str(round(np.percentile(corr_real,10),3)), fontsize=12)\n", - "plt.xlabel('Boostrap Realizations of Correlation Coefficient'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Correlation Coefficient')\n", + "plt.xlabel('Bootstrap Realizations of Correlation Coefficient'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Correlation Coefficient')\n", "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n", "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n", "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n", diff --git a/PythonDataBasics_Distribution_Transformations.ipynb b/PythonDataBasics_Distribution_Transformations.ipynb index ec1964f..a735527 100644 --- a/PythonDataBasics_Distribution_Transformations.ipynb +++ b/PythonDataBasics_Distribution_Transformations.ipynb @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/PythonDataBasics_Feature_Ranking.ipynb b/PythonDataBasics_Feature_Ranking.ipynb index f3667c7..307e809 100644 --- a/PythonDataBasics_Feature_Ranking.ipynb +++ b/PythonDataBasics_Feature_Ranking.ipynb @@ -66,7 +66,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/PythonDataBasics_HypothesisTesting.ipynb b/PythonDataBasics_HypothesisTesting.ipynb index 4ab0caf..53affb8 100644 --- a/PythonDataBasics_HypothesisTesting.ipynb +++ b/PythonDataBasics_HypothesisTesting.ipynb @@ -11,9 +11,9 @@ "\n", "## Bootstrap-based Hypothesis Testing Demonstration\n", "\n", - "### Boostrap and Methods for Hypothesis Testing, Difference in Means\n", + "### Bootstrap and Methods for Hypothesis Testing, Difference in Means\n", "\n", - "* we calculate the hypothesis test for different in means with boostrap and compare to the analytical expression\n", + "* we calculate the hypothesis test for different in means with bootstrap and compare to the analytical expression\n", "\n", "* **Welch's t-test**: we assume the features are Gaussian distributed and the variance are unequal\n", "\n", @@ -78,16 +78,16 @@ " \n", "* pool the results to assemble the $t_{statistic}$ sampling distribution\n", "\n", - "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the boostrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", + "* calculate the cumulative probability of the observed t_{statistic}m, $\\hat{t}$, from the bootstrap distribution based on $\\hat{t}^{\\ell}$, $\\ell = 1,\\ldots,L$.\n", "\n", - "Here's some prerequisite information on the boostrap.\n", + "Here's some prerequisite information on the bootstrap.\n", "\n", "#### Bootstrap\n", "\n", "Bootstrap is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -124,7 +124,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -136,7 +136,7 @@ "Provide an example and demonstration for:\n", "\n", "1. interactive plotting in Jupyter Notebooks with Python packages matplotlib and ipywidgets\n", - "2. provide an intuitive hands-on example of confidence intervals and compare to statistical boostrap \n", + "2. provide an intuitive hands-on example of confidence intervals and compare to statistical bootstrap \n", "\n", "#### Getting Started\n", "\n", @@ -307,7 +307,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Boostrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", + "### Bootstrap and Analytical Methods for Hypothesis Testing, Difference in Means\n", "\n", "* including the analytical and bootstrap methods for testing the difference in means\n", "* interactive plot demonstration with ipywidget, matplotlib packages\n", diff --git a/PythonDataBasics_SpatialBootstrap.ipynb b/PythonDataBasics_SpatialBootstrap.ipynb index 5ad7b08..18b8122 100644 --- a/PythonDataBasics_SpatialBootstrap.ipynb +++ b/PythonDataBasics_SpatialBootstrap.ipynb @@ -41,7 +41,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -78,7 +78,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", diff --git a/PythonDataBasics_ndarrays.ipynb b/PythonDataBasics_ndarrays.ipynb index 572f5c5..1bd17dc 100644 --- a/PythonDataBasics_ndarrays.ipynb +++ b/PythonDataBasics_ndarrays.ipynb @@ -1384,7 +1384,7 @@ "source": [ "#### Constant Value Imputation for an ndarray \n", "\n", - "Now that we have identified the precense of missing values we can update them.\n", + "Now that we have identified the presence of missing values we can update them.\n", "\n", "* NumPy.nan_to_num() function can be to replace the NaN values with a constant" ] diff --git a/Spatial_Bootstrap.ipynb b/Spatial_Bootstrap.ipynb index e0d83c0..057b721 100644 --- a/Spatial_Bootstrap.ipynb +++ b/Spatial_Bootstrap.ipynb @@ -18,7 +18,7 @@ "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n", "\n", "\n", - "### A Demonstration of Spatial Boostrap \n", + "### A Demonstration of Spatial Bootstrap \n", "\n", "Here's a simple workflow to demonstrate spatial bootstrap and to compare with regular (non-spatial) bootstrap.\n", "\n", diff --git a/SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb b/SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb index d82a294..48381c1 100644 --- a/SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb +++ b/SubsurfaceDataAnalytics_Basic_Data_Loading_Display.ipynb @@ -900,7 +900,7 @@ "\n", "* There is a lot to learn, a bit of a hurdle\n", "\n", - "* We will use the *pixelplt* reimplimentation from the GeostatsPy package. \n", + "* We will use the *pixelplt* reimplementation from the GeostatsPy package. \n", "\n", "This function uses MatPlotLib with the function parameters to build a nice figure, so we can procastinate learning MatPlotLib for now! \n", "\n", diff --git a/SubsurfaceDataAnalytics_Clustering.ipynb b/SubsurfaceDataAnalytics_Clustering.ipynb index 6b20d07..fcb2755 100644 --- a/SubsurfaceDataAnalytics_Clustering.ipynb +++ b/SubsurfaceDataAnalytics_Clustering.ipynb @@ -83,7 +83,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, @@ -104,7 +104,9 @@ "import pandas as pd # DataFrames for tabular data\n", "import os # set working directory, run executables\n", "import matplotlib.pyplot as plt # for plotting\n", - "import copy # for deep copies" + "import copy # for deep copies\n", + "import warnings # supress all warnings\n", + "warnings.filterwarnings('ignore')" ] }, { @@ -168,7 +170,7 @@ "metadata": {}, "outputs": [], "source": [ - "os.chdir(\"C:/PGE383\") # set the working directory with the input data file" + "#os.chdir(\"C:/PGE383\") # set the working directory with the input data file" ] }, { @@ -225,7 +227,8 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('12_sample_data.csv') # load our data table\n", + "#df = pd.read_csv('12_sample_data.csv') # load our data table\n", + "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv')\n", "df = df.sample(frac=.30, random_state = 73073); df = df.reset_index() # extract 30% random to reduce the number of data" ] }, @@ -289,6 +292,17 @@ " 478.000000\n", " \n", " \n", + " Unnamed: 0\n", + " 144.0\n", + " 440.291667\n", + " 245.046303\n", + " 2.000000\n", + " 217.250000\n", + " 459.000000\n", + " 659.250000\n", + " 825.000000\n", + " \n", + " \n", " X\n", " 144.0\n", " 449.375000\n", @@ -359,23 +373,25 @@ "" ], "text/plain": [ - " count mean std min 25% \\\n", - "index 144.0 261.048611 136.830267 1.000000 144.750000 \n", - "X 144.0 449.375000 263.691435 0.000000 242.500000 \n", - "Y 144.0 542.979167 289.228936 19.000000 300.000000 \n", - "Facies 144.0 0.659722 0.475456 0.000000 0.000000 \n", - "Porosity 144.0 0.190700 0.031972 0.131230 0.166621 \n", - "Perm 144.0 510.036736 1136.459068 0.039555 6.950509 \n", - "AI 144.0 3746.825725 793.196589 1961.600397 3167.631744 \n", + " count mean std min 25% \\\n", + "index 144.0 261.048611 136.830267 1.000000 144.750000 \n", + "Unnamed: 0 144.0 440.291667 245.046303 2.000000 217.250000 \n", + "X 144.0 449.375000 263.691435 0.000000 242.500000 \n", + "Y 144.0 542.979167 289.228936 19.000000 300.000000 \n", + "Facies 144.0 0.659722 0.475456 0.000000 0.000000 \n", + "Porosity 144.0 0.190700 0.031972 0.131230 0.166621 \n", + "Perm 144.0 510.036736 1136.459068 0.039555 6.950509 \n", + "AI 144.0 3746.825725 793.196589 1961.600397 3167.631744 \n", "\n", - " 50% 75% max \n", - "index 270.000000 381.250000 478.000000 \n", - "X 400.000000 650.000000 980.000000 \n", - "Y 579.000000 800.000000 979.000000 \n", - "Facies 1.000000 1.000000 1.000000 \n", - "Porosity 0.188733 0.217234 0.256172 \n", - "Perm 56.886770 356.658709 7452.343369 \n", - "AI 3668.526774 4244.264532 6194.573653 " + " 50% 75% max \n", + "index 270.000000 381.250000 478.000000 \n", + "Unnamed: 0 459.000000 659.250000 825.000000 \n", + "X 400.000000 650.000000 980.000000 \n", + "Y 579.000000 800.000000 979.000000 \n", + "Facies 1.000000 1.000000 1.000000 \n", + "Porosity 0.188733 0.217234 0.256172 \n", + "Perm 56.886770 356.658709 7452.343369 \n", + "AI 3668.526774 4244.264532 6194.573653 " ] }, "execution_count": 6, @@ -408,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -428,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -475,6 +491,17 @@ " 478.000000\n", " \n", " \n", + " Unnamed: 0\n", + " 144.0\n", + " 440.291667\n", + " 245.046303\n", + " 2.000000\n", + " 217.250000\n", + " 459.000000\n", + " 659.250000\n", + " 825.000000\n", + " \n", + " \n", " X\n", " 144.0\n", " 449.375000\n", @@ -569,6 +596,7 @@ "text/plain": [ " count mean std min 25% \\\n", "index 144.0 261.048611 136.830267 1.000000 144.750000 \n", + "Unnamed: 0 144.0 440.291667 245.046303 2.000000 217.250000 \n", "X 144.0 449.375000 263.691435 0.000000 242.500000 \n", "Y 144.0 542.979167 289.228936 19.000000 300.000000 \n", "Facies 144.0 0.659722 0.475456 0.000000 0.000000 \n", @@ -580,6 +608,7 @@ "\n", " 50% 75% max \n", "index 270.000000 381.250000 478.000000 \n", + "Unnamed: 0 459.000000 659.250000 825.000000 \n", "X 400.000000 650.000000 980.000000 \n", "Y 579.000000 800.000000 979.000000 \n", "Facies 1.000000 1.000000 1.000000 \n", @@ -590,7 +619,7 @@ "Norm_AI 0.403245 0.539258 1.000000 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -608,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -687,13 +716,13 @@ "4 0.216253 3959.934912 0.680501 0.472088" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_subset = df.iloc[:,[4,6,7,8]] # extract Porosity and AI for a simple 2D example\n", + "df_subset = df.iloc[:,[5,7,8,9]] # extract Porosity and AI for a simple 2D example\n", "df_subset.head() # preview the new DataFrame" ] }, @@ -718,14 +747,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "por_min = 0.12; por_max = 0.28\n", "AI_min = 1500; AI_max = 6500\n", - "np.random.seed(210)\n", - "K = 7 # number of prototypes\n", + "np.random.seed(712)\n", + "K = 3 # number of prototypes\n", "colmap = {1: 'r', 2: 'g', 3: 'b', 4: 'm', 5: 'c', 6: 'k', 7: 'w'}" ] }, @@ -746,30 +775,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# scatter plot our training data \n", "plt.subplot(121)\n", - "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", "\n", "plt.subplot(122)\n", - "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0.0,1.0)\n", "plt.ylim(0.0,1.0)\n", @@ -793,17 +824,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -816,18 +849,18 @@ " centroids[i+1] = [por,AI,norm_por,norm_AI]\n", " \n", "plt.subplot(121) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Porosity'], df['AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.2, hspace=0.2)\n", "\n", "plt.subplot(122) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=\"black\", alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0, 1)\n", "plt.ylim(0, 1)\n", @@ -853,17 +886,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -873,15 +908,15 @@ "plt.subplot(121) # plot the assigned training data and K prototypes\n", "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", "\n", "plt.subplot(122) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity with Initial Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0, 1)\n", "plt.ylim(0, 1)\n", @@ -899,19 +934,21 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -923,7 +960,7 @@ "ax = plt.gca()\n", "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity with Updated Prototypes and Vectors'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", @@ -939,7 +976,7 @@ "ax = plt.gca()\n", "plt.scatter(df['Norm_Porosity'], df['Norm_AI'], color=df['color'], alpha=0.5, edgecolor='k')\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity with Updated Prototypes and Vectors'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0, 1)\n", "plt.ylim(0, 1)\n", @@ -967,17 +1004,19 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -987,15 +1026,15 @@ "plt.subplot(121) # plot the assigned training data and K prototypes\n", "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity with Updated Training Data'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", "\n", "plt.subplot(122) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity with Updated Training Data'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0, 1)\n", "plt.ylim(0, 1)\n", @@ -1019,42 +1058,56 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration:\n", + "2 3 4 5 6 " + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# Continue until all assigned categories don't change any more (code modified from Ben Keen, http://benalexkeen.com/k-means-clustering-in-python/)\n", "\n", + "iteration = 2\n", + "print('Iteration:')\n", "while True:\n", + " print(iteration, end =\" \")\n", " closest_centroids = df['closest'].copy(deep=True)\n", " centroids = update(centroids,pormin,pormax,AImin,AImax)\n", " df = assignment(df, centroids)\n", " if closest_centroids.equals(df['closest']):\n", " break\n", - "\n", + " iteration = iteration + 1\n", + " \n", "plt.subplot(121) # plot the assigned training data and K prototypes\n", "plt.scatter(df['Porosity'], df['AI'], color=df['color'], alpha=0.5, edgecolor='k')\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[0], centroids.get(i)[1], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (fraction)'); plt.ylabel('Acoustic impedance (kg/m^3 x m/s x 10^3)')\n", "plt.xlim(por_min, por_max)\n", "plt.ylim(AI_min, AI_max)\n", "\n", "plt.subplot(122) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['color'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", "for i in centroids.keys():\n", - " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, verts=None, edgecolors=\"black\")\n", + " plt.scatter(centroids.get(i)[2], centroids.get(i)[3], color=colmap[i],linewidths=1.0, edgecolors=\"black\")\n", "plt.title('Normalized Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", "plt.xlim(0, 1)\n", "plt.ylim(0, 1)\n", @@ -1065,141 +1118,85 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have demonstrated k-means clustering by-hand, let's try out the scikit-learn implimentation.\n", + "#### Clustering with scikit-learn function\n", "\n", - "* we have the typical instantiate, fit and predict steps. In this case we will stop with fit and use the claster labels assigned at the sample data locations." + "Let's repeat with the scikit-learn function." ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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\n", "text/plain": [ - " Porosity AI Norm_Porosity Norm_AI clusters\n", - "0 0.139637 4747.274043 0.067289 0.658089 2\n", - "1 0.170732 4535.625583 0.316164 0.608089 2\n", - "2 0.244345 2696.102930 0.905345 0.173519 0\n", - "3 0.167125 5500.997419 0.287294 0.836149 2\n", - "4 0.216253 3959.934912 0.680501 0.472088 0" + "
" ] }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "import warnings # muted warnings due to the updating of a sliced DataFrame\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "from sklearn.cluster import KMeans # import the KMeans method from scikit-learn\n", - "n_init = 10 # number of random initial centroids (best solution is picked)\n", - "max_iter = 1000 # maximum number of interations to converge\n", - "seed = 73075 # random number seed\n", - "tol = 1e-6 # tolerance to determine solution has convereged\n", - "kmeans_clustering = KMeans(n_clusters=3, random_state = seed, n_init = n_init, max_iter = max_iter, tol = tol)\n", - "df_subset['clusters'] = kmeans_clustering.fit(df_subset[['Norm_Porosity','Norm_AI']]).labels_\n", - "df_subset.head()" + "from sklearn.cluster import KMeans # k-means clustering\n", + "\n", + "K = 5\n", + "\n", + "kmeans = KMeans(n_clusters=K, random_state=14, n_init = 100).fit(df.loc[:,['Norm_Porosity','Norm_AI']].values)\n", + "df['kMeans'] = kmeans.labels_ + 1\n", + "\n", + "plt.subplot(111) # plot the training data and K prototypes\n", + "plt.scatter(df_subset['Norm_Porosity'], df['Norm_AI'], c=df['kMeans'], alpha = 0.4, linewidths=1.0, edgecolors=\"black\")\n", + "plt.title('Normalized Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", + "plt.xlim(0, 1); plt.ylim(0, 1)\n", + "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Selecting the Optimum Number of Clusters\n", + "\n", + "One method to assist with selection of the optimum number of clusters is the elbow method.\n", + "\n", + "* calculate the loss function for a range of k = 1,...,K cases and look for an inflection point, rapid reduction in slope." ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.subplot(111) # plot the training data and K prototypes\n", - "plt.scatter(df_subset['Norm_Porosity'], df_subset['Norm_AI'], c=df_subset['clusters'], alpha = 0.4, linewidths=1.0, verts=None, edgecolors=\"black\")\n", - "plt.scatter(kmeans_clustering.cluster_centers_[:,0],kmeans_clustering.cluster_centers_[:,1], marker='x', c=['black'])\n", - "plt.title('Normalized Acoustic Impedence vs. Porosity with Final Prototypes'); plt.xlabel('Porosity (normalized)'); plt.ylabel('Acoustic impedance (normalized)')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, 1)\n", - "plt.subplots_adjust(left=0.0, bottom=0.0, right=0.95, top=1.0, wspace=0.2, hspace=0.2)" + "max_K = 50 # maximum number of clusters, k\n", + "inertia = []\n", + "for k in range(2,max_K+1):\n", + " kmeans_iter = KMeans(n_clusters=k, random_state=14, n_init = 10).fit(df.loc[:,['Norm_Porosity','Norm_AI']].values)\n", + " inertia.append(kmeans_iter.inertia_)\n", + "\n", + "plt.scatter(range(2,max_K+1),inertia,c='red',edgecolor='black',zorder=10)\n", + "plt.plot(range(2,max_K+1),inertia,c='red',ls='--',zorder=1)\n", + "plt.xlim(2,max_K); plt.xlabel('Number of Clusters'); plt.ylabel('Inertia'); plt.ylim(bottom=0)\n", + "plt.vlines(13,0,np.max(inertia),color='black'); plt.grid(True); plt.title('k-Means Clustering Intertia vs. Number of Clusters')\n", + "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.2)" ] }, { @@ -1274,7 +1271,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1288,7 +1285,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/SubsurfaceDataAnalytics_Confidence_Hypothesis.ipynb b/SubsurfaceDataAnalytics_Confidence_Hypothesis.ipynb index e456518..ebd5a30 100644 --- a/SubsurfaceDataAnalytics_Confidence_Hypothesis.ipynb +++ b/SubsurfaceDataAnalytics_Confidence_Hypothesis.ipynb @@ -102,7 +102,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_ConvolutionalNeuralNetworks_Percolation_Threshold.ipynb b/SubsurfaceDataAnalytics_ConvolutionalNeuralNetworks_Percolation_Threshold.ipynb index 757678e..bd666b3 100644 --- a/SubsurfaceDataAnalytics_ConvolutionalNeuralNetworks_Percolation_Threshold.ipynb +++ b/SubsurfaceDataAnalytics_ConvolutionalNeuralNetworks_Percolation_Threshold.ipynb @@ -126,7 +126,7 @@ "import scipy\n", "from scipy.ndimage import gaussian_filter # Gaussian filter for smoothing our images\n", "from sklearn.preprocessing import OneHotEncoder # one hot encoder for our response feature\n", - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python \n", "import random # pseudo-random values\n", "\n", diff --git a/SubsurfaceDataAnalytics_Feature_Imputation.ipynb b/SubsurfaceDataAnalytics_Feature_Imputation.ipynb index 8112780..87ac649 100644 --- a/SubsurfaceDataAnalytics_Feature_Imputation.ipynb +++ b/SubsurfaceDataAnalytics_Feature_Imputation.ipynb @@ -73,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_Feature_Ranking.ipynb b/SubsurfaceDataAnalytics_Feature_Ranking.ipynb index 01168ff..1c78cd3 100644 --- a/SubsurfaceDataAnalytics_Feature_Ranking.ipynb +++ b/SubsurfaceDataAnalytics_Feature_Ranking.ipynb @@ -85,7 +85,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_Feature_Transformations.ipynb b/SubsurfaceDataAnalytics_Feature_Transformations.ipynb index b131a6e..d657c02 100644 --- a/SubsurfaceDataAnalytics_Feature_Transformations.ipynb +++ b/SubsurfaceDataAnalytics_Feature_Transformations.ipynb @@ -82,7 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_Gridded_Data.ipynb b/SubsurfaceDataAnalytics_Gridded_Data.ipynb index f4ee74e..88d9443 100644 --- a/SubsurfaceDataAnalytics_Gridded_Data.ipynb +++ b/SubsurfaceDataAnalytics_Gridded_Data.ipynb @@ -292,7 +292,7 @@ "\n", "Let's look at the dataset that we loaded. \n", "\n", - "* Instead of working with the MatPlotLib package directly (common data visualization package for Python) we use *pixelplt* reimplimentation from our set of functions from my effort to bring GSLIB to Python\n", + "* Instead of working with the MatPlotLib package directly (common data visualization package for Python) we use *pixelplt* reimplementation from our set of functions from my effort to bring GSLIB to Python\n", "\n", "This function uses MatPlotLib with the function parameters to build a nice figure, so we can procastinate learning MatPlotLib for now! \n", "\n", diff --git a/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb b/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb index 3bc1a93..fa21882 100644 --- a/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb +++ b/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb @@ -159,7 +159,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_Multivariate.ipynb b/SubsurfaceDataAnalytics_Multivariate.ipynb index a6400a9..6750704 100644 --- a/SubsurfaceDataAnalytics_Multivariate.ipynb +++ b/SubsurfaceDataAnalytics_Multivariate.ipynb @@ -124,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_NaiveBayes.ipynb b/SubsurfaceDataAnalytics_NaiveBayes.ipynb index b01cf24..d030be2 100644 --- a/SubsurfaceDataAnalytics_NaiveBayes.ipynb +++ b/SubsurfaceDataAnalytics_NaiveBayes.ipynb @@ -141,7 +141,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_NeuralNet_Map.ipynb b/SubsurfaceDataAnalytics_NeuralNet_Map.ipynb index 3abe5d6..ffec854 100644 --- a/SubsurfaceDataAnalytics_NeuralNet_Map.ipynb +++ b/SubsurfaceDataAnalytics_NeuralNet_Map.ipynb @@ -139,7 +139,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_PolynomialRegression.ipynb b/SubsurfaceDataAnalytics_PolynomialRegression.ipynb index a563504..21305a8 100644 --- a/SubsurfaceDataAnalytics_PolynomialRegression.ipynb +++ b/SubsurfaceDataAnalytics_PolynomialRegression.ipynb @@ -1564,7 +1564,7 @@ "\n", "# functions taken (without modification) from http://davmre.github.io/blog/python/2013/12/15/orthogonal_poly\n", "# appreciation to Dave Moore for the great blog post on titled 'Orthogonal polynomial regression in Python'\n", - "# functions are Dave's reimplimentation of poly() from R\n", + "# functions are Dave's reimplementation of poly() from R\n", "\n", "def ortho_poly_fit(x, degree = 1):\n", " n = degree + 1\n", diff --git a/SubsurfaceDataAnalytics_Spatial_Bootstrap.ipynb b/SubsurfaceDataAnalytics_Spatial_Bootstrap.ipynb index 2115ce6..8f58baa 100644 --- a/SubsurfaceDataAnalytics_Spatial_Bootstrap.ipynb +++ b/SubsurfaceDataAnalytics_Spatial_Bootstrap.ipynb @@ -41,7 +41,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -78,7 +78,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", diff --git a/SubsurfaceDataAnalytics_TimeSeries.ipynb b/SubsurfaceDataAnalytics_TimeSeries.ipynb index 07453ba..c3ee9c2 100644 --- a/SubsurfaceDataAnalytics_TimeSeries.ipynb +++ b/SubsurfaceDataAnalytics_TimeSeries.ipynb @@ -90,7 +90,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_advanced_clustering.ipynb b/SubsurfaceDataAnalytics_advanced_clustering.ipynb index 574127f..61ed993 100644 --- a/SubsurfaceDataAnalytics_advanced_clustering.ipynb +++ b/SubsurfaceDataAnalytics_advanced_clustering.ipynb @@ -57,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SubsurfaceDataAnalytics_bootstrap.ipynb b/SubsurfaceDataAnalytics_bootstrap.ipynb index 48756a2..3877530 100644 --- a/SubsurfaceDataAnalytics_bootstrap.ipynb +++ b/SubsurfaceDataAnalytics_bootstrap.ipynb @@ -41,7 +41,7 @@ "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n", "\n", "Assumptions\n", - "* sufficient, representative sampling, identical, idependent samples\n", + "* sufficient, representative sampling, identical, independent samples\n", "\n", "Limitations\n", "1. assumes the samples are representative \n", @@ -78,7 +78,7 @@ "\n", " * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n", "\n", - "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", + "6. Calculate a realization of the summary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n", "\n", "7. Compile and summarize the $L$ realizations of the statistic of interest.\n", "\n", @@ -112,7 +112,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, @@ -681,9 +681,9 @@ "source": [ "##### A Couple of Bootstrap Realizations\n", "\n", - "We will attempt boostrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n", + "We will attempt bootstrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n", "\n", - "This command returns a ndarray with k samples with replacment from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n", + "This command returns a ndarray with k samples with replacement from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n", "```p\n", "samples1 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n", "```\n", diff --git a/SubsurfaceDataAnalytics_clustering.ipynb b/SubsurfaceDataAnalytics_clustering.ipynb index 2e48c1d..fcb2755 100644 --- a/SubsurfaceDataAnalytics_clustering.ipynb +++ b/SubsurfaceDataAnalytics_clustering.ipynb @@ -83,7 +83,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", + "import geostatspy.GSLIB as GSLIB # GSLIB utilities, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, diff --git a/SuportVectorMachines.ipynb b/SuportVectorMachines.ipynb index 9d4cda1..9767755 100644 --- a/SuportVectorMachines.ipynb +++ b/SuportVectorMachines.ipynb @@ -12,11 +12,11 @@ "\n", "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n", "\n", - "This is a tutorial for / demonstration of **support vector machine modeling in Python**. We have included in our workflow some simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Support vector machines are a powerful method for machine learning classification. The support vector machine is a generalization of the maximal margin classifier that deals with cateogries that cannot be separated linearly. \n", + "This is a tutorial for / demonstration of **support vector machine modeling in Python**. We have included in our workflow some simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Support vector machines are a powerful method for machine learning classification. The support vector machine is a generalization of the maximal margin classifier that deals with categories that cannot be separated linearly. \n", "\n", - "This exercise demonstrates the support vector machine approach in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n", + "This exercise demonstrates the support vector machine approach in Python with wrappers and reimplementation of GSLIB methods. The steps include:\n", "\n", - "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n", + "1. generate a 2D sequential Gaussian simulation using a wrapper of GSLIB's sgsim method\n", "2. add a trend (to simplify the segmentation problem) and truncate to build a categorical, exhaustive truth model\n", "3. extract random samples from the truth model\n", "4. separate into training and testing (20%) datasets\n", @@ -67,7 +67,7 @@ "import pandas as pd # DataFrames\n", "import matplotlib.pyplot as plt # plotting\n", "from sklearn.model_selection import train_test_split # training and testing datasets\n", - "from sklearn.metrics import confusion_matrix # for sumarizing model performance\n", + "from sklearn.metrics import confusion_matrix # for summarizing model performance\n", "import itertools # assist with iteration used in plot_confusion_matrix" ] }, @@ -97,7 +97,7 @@ "14. plot_svc_decision_function - visualize the model with margins included\n", "15. plot_confusion_matrix - plot confusion matrix\n", "\n", - "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the precense of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." + "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the presence of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." ] }, { diff --git a/Variogram.ipynb b/Variogram.ipynb index 03510f4..ef756e6 100644 --- a/Variogram.ipynb +++ b/Variogram.ipynb @@ -12,23 +12,23 @@ "\n", "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n", "\n", - "This is a tutorial for / demonstration of **spatial variogram calculation in Python with simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Variogram calculation is a valuable method for quantifying spaital continuity. We can interpret the resulting experimental variograms and then infer valid variogram models for use with spatial estimation and simulation. \n", + "This is a tutorial for / demonstration of **spatial variogram calculation in Python with simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Variogram calculation is a valuable method for quantifying spatial continuity. We can interpret the resulting experimental variograms and then infer valid variogram models for use with spatial estimation and simulation. \n", "\n", "We will demonstration calculation of variograms on regular and irregular spaced data. \n", "\n", - "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplimentation of GSLIB methods. The steps include:\n", + "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplementation of GSLIB methods. The steps include:\n", "\n", "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n", "2. calculatate the variogram map and anisotropic experimental variograms in the x and y directions with gam\n", "3. visualize the experimental variograms\n", "4. fit a positive definite variogram model with nested know licit variogram structures\n", - "5. resample to form a nonuniformaly sampled dataset\n", + "5. resample to form a nonuniformly sampled dataset\n", "6. calculate the variogram map, isotropic and directional experimental variograms with gamv\n", "7. fit a positive definite variogram model with nested know licit variogram structures\n", "\n", "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n", "\n", - "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n", + "1. sgsim - sequential Gaussian simulation limited to 2D and unconditional\n", "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n", "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n", "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n", @@ -50,7 +50,7 @@ "\n", "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html. For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.\n", "\n", - "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB. The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/. \n", + "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familiar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB. The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/. \n", "\n", "I used this tutorial in my Introduction to Geostatistics undergraduate class (PGE337 at UT Austin) as part of a first introduction to geostatistics and Python for the engineering undergraduate students. It is assumed that students have no previous Python, geostatistics nor machine learning experience; therefore, all steps of the code and workflow are explored and described. This tutorial is augmented with course notes in my class. The Python code and markdown was developed and tested in Jupyter. \n", "\n", @@ -94,7 +94,7 @@ "11. vmodel\n", "12. sgsim\n", "\n", - "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the precense of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." + "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package. Warning, there has been no attempt to make these functions robust in the presence of bad inputs. If you get a crazy error check the inputs. Are the arrays empty and are they the same size when they should be? Are the arrays the correct dimension? Is the parameter order mixed up? Make sure the inputs are consistent with the descriptions in this document." ] }, { @@ -198,7 +198,7 @@ " plt.show()\n", " return\n", " \n", - "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n", + "# pixel plot, reimplementation in Python of GSLIB pixelplt with MatPlotLib methods\n", "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n", " xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n", " plt.figure(figsize=(8,6))\n", diff --git a/make_nonlinear_MV_spatial_data.ipynb b/make_nonlinear_MV_spatial_data.ipynb index 6bf4b5d..99d80c5 100644 --- a/make_nonlinear_MV_spatial_data.ipynb +++ b/make_nonlinear_MV_spatial_data.ipynb @@ -32,7 +32,7 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import geostatspy.geostats as geostats # reimplimentation of GSLIB algorithms in Python\n", + "import geostatspy.geostats as geostats # reimplementation of GSLIB algorithms in Python\n", "import geostatspy.GSLIB as GSLIB # GSLIB visualization, subroutines and algorithm wrappers\n", "cmap = plt.cm.inferno # color map\n", "import seaborn as sns\n", diff --git a/make_nonlinear_MV_spatial_data_v3.ipynb b/make_nonlinear_MV_spatial_data_v3.ipynb index 29e6f9f..5d9bd42 100644 --- a/make_nonlinear_MV_spatial_data_v3.ipynb +++ b/make_nonlinear_MV_spatial_data_v3.ipynb @@ -32,7 +32,7 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import geostatspy.geostats as geostats # reimplimentation of GSLIB algorithms in Python\n", + "import geostatspy.geostats as geostats # reimplementation of GSLIB algorithms in Python\n", "import geostatspy.GSLIB as GSLIB # GSLIB visualization, subroutines and algorithm wrappers\n", "cmap = plt.cm.inferno # color map\n", "import seaborn as sns\n", diff --git a/make_nonlinear_MV_spatial_data_v7.ipynb b/make_nonlinear_MV_spatial_data_v7.ipynb index 10cc89e..f20a121 100644 --- a/make_nonlinear_MV_spatial_data_v7.ipynb +++ b/make_nonlinear_MV_spatial_data_v7.ipynb @@ -32,7 +32,7 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import geostatspy.geostats as geostats # reimplimentation of GSLIB algorithms in Python\n", + "import geostatspy.geostats as geostats # reimplementation of GSLIB algorithms in Python\n", "import geostatspy.GSLIB as GSLIB # GSLIB visualization, subroutines and algorithm wrappers\n", "cmap = plt.cm.inferno # color map\n", "import seaborn as sns\n", diff --git a/make_planview2D_spatial_data.ipynb b/make_planview2D_spatial_data.ipynb index 4c85ef3..8c4fd8c 100644 --- a/make_planview2D_spatial_data.ipynb +++ b/make_planview2D_spatial_data.ipynb @@ -63,7 +63,7 @@ "metadata": {}, "outputs": [], "source": [ - "import geostatspy.geostats as geostats # reimplimentation of GSLIB algorithms in Python\n", + "import geostatspy.geostats as geostats # reimplementation of GSLIB algorithms in Python\n", "import geostatspy.GSLIB as GSLIB # GSLIB visualization, subroutines and algorithm wrappers\n", "import numpy as np # ndarrys for gridded data\n", "import pandas as pd # DataFrames for tabular data\n", diff --git a/make_univariate_data_for_variograms.ipynb b/make_univariate_data_for_variograms.ipynb index 2b0b6d4..4dac97b 100644 --- a/make_univariate_data_for_variograms.ipynb +++ b/make_univariate_data_for_variograms.ipynb @@ -33,7 +33,7 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import pyvista as pv\n", - "import geostatspy.geostats as geostats # reimplimentation of GSLIB algorithms in Python\n", + "import geostatspy.geostats as geostats # reimplementation of GSLIB algorithms in Python\n", "import geostatspy.GSLIB as GSLIB # GSLIB visualization, subroutines and algorithm wrappers\n", "cmap = plt.cm.inferno # color map\n", "import seaborn as sns\n",