GeostatsGuy · pitmonticone · Dec 21, 2023 · Dec 21, 2023 · Dec 21, 2023 · Dec 21, 2023
diff --git a/Bootstrap.ipynb b/Bootstrap.ipynb
@@ -6,15 +6,15 @@
    "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",
     "# 1. Build an initial sample set with $ndata$ samples.\n",
     "# 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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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        "
    ]
   },