diff --git a/docs/contributing.md b/docs/contributing.md
deleted file mode 100644
index 435d357312c9a3443fbc5ed9cde0dc956f71594f..0000000000000000000000000000000000000000
--- a/docs/contributing.md
+++ /dev/null
@@ -1,2 +0,0 @@
-```{include} ../CONTRIBUTING.md
-```
\ No newline at end of file
diff --git a/docs/examples/emulator_robustgasp.ipynb b/docs/examples/emulator_robustgasp.ipynb
deleted file mode 100644
index 530bd449ef8651302545abae75bd397ed798437d..0000000000000000000000000000000000000000
--- a/docs/examples/emulator_robustgasp.ipynb
+++ /dev/null
@@ -1,479 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3bae4a70",
- "metadata": {},
- "source": [
- "# Gaussian process emulation\n",
- "\n",
- "This example demonstrates how to conduct Gaussian process emulation using our wrapper of the R package RobustGaSP. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "894be984",
- "metadata": {},
- "source": [
- "## Single-output case\n",
- "\n",
- "Let's look at an example with a single output model given by the equation below:\n",
- "\n",
- "$y = x + 3\\sin(x/2)$"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ea09e63c",
- "metadata": {},
- "source": [
- "### Import\n",
- "\n",
- "First, we need to import the class `ScalarGaSP` from the module `psimpy.emulator.robustgasp`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "17e42357",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.emulator.robustgasp import ScalarGaSP"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6e0c72a7",
- "metadata": {},
- "source": [
- "### Instantiation\n",
- "\n",
- "Then we need to create a new instance of `ScalarGaSP`. In order to initialize `ScalarGaSP`, training input points `design` and corresponding training outputs `response` need to be defined. One can also specify optional arguments such as `trend`, `nugget_est`, `method`, `kernel_type`, etc.\n",
- "\n",
- "Below we create a new instance of `ScalarGaSP` using six selected points from $y = x + 3\\sin(x/2)$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "093b140d",
- "metadata": {},
- "outputs": [],
- "source": [
- "import math\n",
- "import numpy as np\n",
- "\n",
- "def f(x):\n",
- " return x + 3*np.sin(x/2)\n",
- "\n",
- "x = np.arange(0,16,3)\n",
- "y = f(x)\n",
- "\n",
- "emulator = ScalarGaSP(design=x, response=y)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4832524f",
- "metadata": {},
- "source": [
- "### Training\n",
- "\n",
- "Next, we need to train the `ScalarGaSP` emulator."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "f70b21ab",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The upper bounds of the range parameters are 3839.746 \n",
- "The initial values of range parameters are 76.79491 \n",
- "Start of the optimization 1 : \n",
- "The number of iterations is 7 \n",
- " The value of the marginal posterior function is -11.13375 \n",
- " Optimized range parameters are 11.02656 \n",
- " Optimized nugget parameter is 0 \n",
- " Convergence: TRUE \n",
- "The initial values of range parameters are 0.8333333 \n",
- "Start of the optimization 2 : \n",
- "The number of iterations is 9 \n",
- " The value of the marginal posterior function is -11.13375 \n",
- " Optimized range parameters are 11.02656 \n",
- " Optimized nugget parameter is 0 \n",
- " Convergence: TRUE \n"
- ]
- }
- ],
- "source": [
- "emulator.train()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27c8db21",
- "metadata": {},
- "source": [
- "### Validation\n",
- "\n",
- "We can validate the performance of the trained emulator using additional validation data. In case that \n",
- "there is no extra computational budget to generate validation data, the leave-one-out cross validation\n",
- "can be used. It is implemented by the `loo_validate()` method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "f602b061",
- "metadata": {},
- "outputs": [],
- "source": [
- "predictions = emulator.loo_validate()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2df7e4d9",
- "metadata": {},
- "source": [
- "Let's ilustraste emulator predictions vs actual outputs:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "ee605292",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Emulator prediction')"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 288x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(4,4))\n",
- "ax.plot([np.min(y)-1,np.max(y)+1], [np.min(y)-1,np.max(y)+1])\n",
- "ax.errorbar(y,predictions[:,0],predictions[:,1],fmt='.', linestyle='')\n",
- "ax.set_xlabel('Actual output')\n",
- "ax.set_ylabel('Emulator prediction')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ebcd4774",
- "metadata": {},
- "source": [
- "### Prediction\n",
- "With the trained emulator at our deposit, we can use the `predict()` method to make predictions at \n",
- "any arbitrary set of input points `testing_input`. The `testing_trend` should be set according to \n",
- "the `trend` used for emulator training. \n",
- "\n",
- "The `predict()` method returns a matrix consisting of four columns:\n",
- "\n",
- " - predictions[:,0] - mean\n",
- " - predictions[:,1] - lower bound of the 95% confidence interval\n",
- " - predictions[:,2] - upper bound of the 95% confidence interval\n",
- " - predictions[:,3] - standard deviation\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "a4fd6bbc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "testing_input = np.arange(0,15,0.1)\n",
- "predictions = emulator.predict(testing_input)\n",
- "\n",
- "plt.plot(testing_input, predictions[:, 0], 'r-', label= \"mean\")\n",
- "plt.fill_between(testing_input, predictions[:, 1], predictions[:, 2], alpha=0.3)\n",
- "plt.xlabel('x')\n",
- "plt.ylabel('emulator prediction')\n",
- "_ = plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "20c9346b",
- "metadata": {},
- "source": [
- "### Sampling\n",
- "We can also draw any number of samples at the desirable input points `testing_input` using the `sample()` method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "baf358cc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "num_sample = 1\n",
- "samples = emulator.sample(testing_input, num_sample=num_sample)\n",
- "\n",
- "for i in range(num_sample):\n",
- " plt.plot(testing_input, samples[:,i], label=f'sample{i+1}')\n",
- "plt.xlabel('x')\n",
- "plt.ylabel('y')\n",
- "_ = plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6e078033",
- "metadata": {},
- "source": [
- "## Multi-output case\n",
- "\n",
- "For the multi-output case, let's have a look at the data set from the `DIAMOND` simulator included in the `RobustGaSP` package."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "daa3cefc",
- "metadata": {},
- "outputs": [],
- "source": [
- "humanityX = np.genfromtxt('../../tests/data/humanityX.csv', delimiter=',')\n",
- "humanityXt = np.genfromtxt('../../tests/data/humanityXt.csv', delimiter=',')\n",
- "humanityY = np.genfromtxt('../../tests/data/humanityY.csv', delimiter=',')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d76e05c9",
- "metadata": {},
- "source": [
- "### Import\n",
- "\n",
- "First, we need to import the class `PPGaSP` from the module `psimpy.emulator.robustgasp`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "3267da6c",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.emulator.robustgasp import PPGaSP"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "26d1a75f",
- "metadata": {},
- "source": [
- "### Instantiation\n",
- "\n",
- "Then we need to create a new instance of `PPGaSP` similar as what we did in the single output case."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "ca25ee2c",
- "metadata": {},
- "outputs": [],
- "source": [
- "emulator = PPGaSP(design=humanityX, response=humanityY)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5b6211b5",
- "metadata": {},
- "source": [
- "### Training\n",
- "\n",
- "Next, we need to train the `PPGaSP` emulator."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "859f5799",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The upper bounds of the range parameters are 296.974 297.1814 294.7672 295.9345 296.563 295.935 297.3198 296.4444 296.9323 298.2373 298.0619 298.7249 298.7249 \n",
- "The initial values of range parameters are 5.93948 5.943628 5.895344 5.918691 5.93126 5.918701 5.946395 5.928888 5.938645 5.964745 5.961238 5.974498 5.974498 \n",
- "Start of the optimization 1 : \n",
- "The number of iterations is 44 \n",
- " The value of the marginal posterior function is -5279.534 \n",
- " Optimized range parameters are 25.46132 2.891428 5.30812 26.95519 291.1828 48.17274 84.56133 2.874385 39.12121 51.81304 0.5407651 1.728696 1.020605 \n",
- " Optimized nugget parameter is 0 \n",
- " Convergence: TRUE \n",
- "The initial values of range parameters are 12.37503 12.38367 12.28307 12.33172 12.3579 12.33174 12.38944 12.35296 12.37329 12.42767 12.42037 12.44799 12.44799 \n",
- "Start of the optimization 2 : \n",
- "The number of iterations is 45 \n",
- " The value of the marginal posterior function is -5279.534 \n",
- " Optimized range parameters are 25.46132 2.891428 5.308121 26.95519 291.183 48.17274 84.56133 2.874385 39.12121 51.81304 0.5407651 1.728696 1.020605 \n",
- " Optimized nugget parameter is 0 \n",
- " Convergence: TRUE \n"
- ]
- }
- ],
- "source": [
- "emulator.train()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5ffec175",
- "metadata": {},
- "source": [
- "### Prediction\n",
- "Now using the `predict()` method, we can make predictions for any arbitrary set of input points `testing_input`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "2cfd8d13",
- "metadata": {},
- "outputs": [],
- "source": [
- "predictions = emulator.predict(humanityXt)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1ea6b470",
- "metadata": {},
- "source": [
- "### Validation\n",
- "\n",
- "We can validate the performance of the trained emulator based on the validation data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "0fc7a66a",
- "metadata": {},
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 288x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "humanityYt = np.genfromtxt('../../tests/data/humanityYt.csv', delimiter=',')\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(4,4))\n",
- "ax.plot(humanityYt, predictions[:, :, 0], 'o')\n",
- "ax.set_xlabel('Actual output')\n",
- "_ = ax.set_ylabel('Emulator prediction')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b9c199fa",
- "metadata": {},
- "source": [
- "### Sampling\n",
- "Using the `sample()` method, we can draw any number of samples at the desirable input points `testing_input`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "c1c51f8a",
- "metadata": {},
- "outputs": [],
- "source": [
- "samples = emulator.sample(humanityXt, num_sample=2)"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "d9212ca1597032c1672af9541e93e14614267f9205bb9b68575454894387c12e"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/examples/run_simulator.ipynb b/docs/examples/run_simulator.ipynb
deleted file mode 100644
index 5f461b3b0d0ccee8c6b3325c05e613bba74ef3aa..0000000000000000000000000000000000000000
--- a/docs/examples/run_simulator.ipynb
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "74f2899d",
- "metadata": {},
- "source": [
- "# Run Simulator\n",
- "\n",
- "This example demonstrates how to execute a simulator (in serial or parallel) at a set of samples."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a8d41a8",
- "metadata": {},
- "source": [
- "### Import\n",
- "\n",
- "First, we need to import the class `RunSimulator` from the module `psimpy.simulator.run_simulator`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "727743d3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.simulator.run_simulator import RunSimulator"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5b18c1d6",
- "metadata": {},
- "source": [
- "### Instantiation\n",
- "\n",
- "Then we need to create a new class that inherets from the class `RunSimulator` and override the abstract method `simulator()`. The `simulator()` method cannot have any positional-only arguments, has to contain the `**kwargs` argument and it has to return the results in form of a numpy array.\n",
- "\n",
- "Here we're going to use the method `run` of the class `MPM` from the module `psimpy.simulator.mass_point_model` as our simulator. This method solves the mass point model using the ode solver `scipy.integrate.ode` given following inputs: topography, friction parameters, and initial condition of the mass point (location and velocity).\n",
- "\n",
- "To do this, we first define the child class `MpmSolver`, override the `__init__` method to create a new instance of `MPM`, then we override the `simulator()` method in order to return the outputs of the method `run()` from the `MPM` class. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "fb475422",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.simulator.mass_point_model import MPM\n",
- "\n",
- "class MpmSolver(RunSimulator):\n",
- " def __init__(self, *args, **kwargs):\n",
- " self.mpm = MPM()\n",
- " super(MpmSolver, self).__init__(*args, **kwargs)\n",
- " \n",
- " def simulator(self, elevation, coulomb_friction, turbulent_friction, x0, y0,\n",
- " ux0=0, uy0=0, dt=1, tend=300, t0=0, g=9.8, atol=1e-4, rtol=1e-4,\n",
- " curvature=False, **kwargs):\n",
- " \n",
- " return self.mpm.run(elevation=elevation, coulomb_friction=coulomb_friction,\n",
- " turbulent_friction=turbulent_friction, x0=x0, y0=y0, ux0=ux0,\n",
- " uy0=uy0, dt=dt, tend=tend, t0=t0, g=g, atol=atol, rtol=rtol,\n",
- " curvature=curvature)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3546d6b4",
- "metadata": {},
- "source": [
- "Now we can create a new instance of `MpmSolver`. In order to initialize `MpmSolver`, which has the same `__init__()` method as `RunSimulator`, the user needs to define the parent folder to save simulation outputs `sim_dir`, a list consists of all variable input names `var_inp_name` and a dictionary consists of all fixed input name-value pairs `fix_inp`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "bd2936f9",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "sim_dir = os.path.join(os.path.abspath(''), \"../../tests/sim_log\")\n",
- "fix_inp = {\n",
- " \"elevation\" : os.path.join(os.path.abspath(''), \"../../tests/data/mpm_topo.asc\"), \n",
- " \"x0\" : 800,\n",
- " \"y0\" : 2000,\n",
- " \"ux0\" : 1e-10,\n",
- " \"uy0\" : 1e-10,\n",
- " \"dt\" : 2,\n",
- " \"tend\" : 600\n",
- "}\n",
- "var_inp_name=[\"coulomb_friction\", \"turbulent_friction\"]\n",
- "\n",
- "mpmsolver = MpmSolver(sim_dir=sim_dir, var_inp_name=var_inp_name, fix_inp=fix_inp)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b1535843",
- "metadata": {},
- "source": [
- "`coulomb_friction` and `turbulent_friction` are variable inputs and the other inputs are fixed. We may vary `coulomb_friction` between 0.1 to 0.3 and `turbulent_friction` between 500 to 2000."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "6e2270ee",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import itertools\n",
- "\n",
- "cf = np.arange(0.1, 0.3, 0.05)\n",
- "tf = np.arange(500, 2000, 150)\n",
- "\n",
- "var_samples = np.array([x for x in itertools.product(cf, tf)])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8f7da917",
- "metadata": {},
- "source": [
- "## Serial Execution\n",
- "In order to perform a serial execution of the simulator, we need to pass `var_samples` to the `serial_run()` method. The user can also specify optional arguments like `prefixes`, `append` or `save_out`. Here we like to save the results with the \"serial\" prefixes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "31f7328b",
- "metadata": {},
- "outputs": [],
- "source": [
- "serial_prefixes = [\"serial\"+str(i) for i in range(len(var_samples))]\n",
- "\n",
- "mpmsolver.serial_run(var_samples=var_samples, prefixes=serial_prefixes, save_out=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8149f7b0",
- "metadata": {},
- "source": [
- "To check the results one could either check `mpmsolver.outputs` or load the files available in `sim_dir`.\n",
- "\n",
- "## Parallel Execution\n",
- "Now let's run the same simulator in parallel. Once again we prepare samples of the variable inputs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "d5a94bbf",
- "metadata": {},
- "outputs": [],
- "source": [
- "cf = np.arange(0.1, 0.30, 0.04)\n",
- "tf = np.arange(500, 2000, 100)\n",
- "\n",
- "var_samples = np.array([x for x in itertools.product(cf, tf)])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c87aeaf8",
- "metadata": {},
- "source": [
- "This time we will pass the first 40 samples and then append the rest. We can also specify the maximum number of tasks running in parallel `max_workers` as well as `prefixes`, `append` and `save_out`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "9e772a13",
- "metadata": {},
- "outputs": [],
- "source": [
- "mpmsolver.parallel_run(var_samples=var_samples[:40], max_workers=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f05e6619",
- "metadata": {},
- "source": [
- "Now, let's append the rest of the samples."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "127faf15",
- "metadata": {},
- "outputs": [],
- "source": [
- "mpmsolver.parallel_run(var_samples=var_samples[40:], append=True, max_workers=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6e83cd2f",
- "metadata": {},
- "source": [
- "Same as serial execution, we can check the results either by using `mpmsolver.outputs` or loading the files available in `sim_dir`."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/examples/sampler_latin.ipynb b/docs/examples/sampler_latin.ipynb
deleted file mode 100644
index 9e60956a3b392117a2d7d9837bfd926eba64b871..0000000000000000000000000000000000000000
--- a/docs/examples/sampler_latin.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "1a9ca136",
- "metadata": {},
- "source": [
- "# Latin Hypercube Sampling\n",
- "\n",
- "This example demonstrates how to draw samples using the Latin hypercube sampler."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "deb749b4",
- "metadata": {},
- "source": [
- "## Import \n",
- "\n",
- "First, we need to import the class `LHS` from the module `psimpy.sampler.latin`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "4c78bba4-eb05-4ddb-a425-ba2ebe43048a",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.sampler.latin import LHS"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "23866440",
- "metadata": {},
- "source": [
- "## Instantiation\n",
- "\n",
- "Then we need to create a new instance of `LHS`. In order to initialize `LHS`,\n",
- "the user needs to define the dimension of parameters `d`. The user can also \n",
- "specify optional arguments including `bounds`, `seed`, and `criterion`.\n",
- "\n",
- "Below we create a new instance of `LHS` for a two-dimensional problem, with the\n",
- "other three arguments taking default values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "120945ce",
- "metadata": {},
- "outputs": [],
- "source": [
- "sampler = LHS(2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "63617382",
- "metadata": {},
- "source": [
- "## Draw samples\n",
- "Now that we created our sampler, we can draw any number of samples using \n",
- "the method `sample()`. Below 15 samples from the two-dimensional parameter space\n",
- "are drawn."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "f63fea15",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0.77079997, 0.26794708],\n",
- " [0.55721183, 0.52528333],\n",
- " [0.9769387 , 0.25009117],\n",
- " [0.34294648, 0.34372567],\n",
- " [0.153813 , 0.58300717],\n",
- " [0.61467445, 0.99535443],\n",
- " [0.41090769, 0.03218058],\n",
- " [0.09325676, 0.68595842],\n",
- " [0.05398797, 0.89904516],\n",
- " [0.90967252, 0.16312322],\n",
- " [0.69818303, 0.10100304],\n",
- " [0.47076742, 0.62137445],\n",
- " [0.27380325, 0.43684061],\n",
- " [0.25991701, 0.84513203],\n",
- " [0.85445131, 0.77589623]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "samples = sampler.sample(15)\n",
- "samples"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30e22288",
- "metadata": {},
- "source": [
- "## Plot\n",
- "Let's illustrate the samples using matplotlib library."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "faabd74b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.scatter(samples[:, 0], samples[:, 1])\n",
- "plt.title(\"Latin hypercube samples\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d3726e8",
- "metadata": {},
- "source": [
- "## Optional arguments\n",
- "Now let's look at a second example.<br>\n",
- "Here we like to pass specific `seed`, `bounds`, and `criterion` when we create\n",
- "the sampler. If we choose 'maximin' as the criterion, we can specify the number \n",
- "of itrations when we call the `sample()` method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "ad6fbc68",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from psimpy.sampler.latin import LHS\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "sampler = LHS(2, bounds=[[1, 20], [0, 2]], seed=123, criterion='maximin')\n",
- "samples = sampler.sample(40, iteration=1000)\n",
- "\n",
- "plt.scatter(samples[:, 0], samples[:, 1])\n",
- "plt.title('Latin hypercube samples')\n",
- "\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "d9212ca1597032c1672af9541e93e14614267f9205bb9b68575454894387c12e"
- },
- "kernelspec": {
- "display_name": "Python 3.9.12 ('psimpy-dev')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/examples/sampler_saltelli.ipynb b/docs/examples/sampler_saltelli.ipynb
deleted file mode 100644
index ae14ee650e4bbbcd2d4f84ec6f48b05f197085c8..0000000000000000000000000000000000000000
--- a/docs/examples/sampler_saltelli.ipynb
+++ /dev/null
@@ -1,226 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3bae4a70",
- "metadata": {},
- "source": [
- "# Saltelli's extension of the Sobol sequence\n",
- "\n",
- "This example demonstrates how to draw samples using Saltelli's extension of \n",
- "the Sobol sequence."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "894be984",
- "metadata": {},
- "source": [
- "## Import\n",
- "\n",
- "First, we need to import the class `Saltelli` from the module `psimpy.sampler.saaltelli`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "42cb1b83",
- "metadata": {},
- "outputs": [],
- "source": [
- "from psimpy.sampler.saltelli import Saltelli"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6e0c72a7",
- "metadata": {},
- "source": [
- "## Instantiation\n",
- "\n",
- "Then we need to create a new instance of `Saltelli`. In order to initialize `Saltelli`,\n",
- "the user needs to define the dimension of parameters `d`. The user can also \n",
- "specify the optional argument `bounds`.\n",
- "\n",
- "Below we create a new instance of `Saltelli` for a two-dimensional problem, with \n",
- "`bounds` set to default."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "7ad79500",
- "metadata": {},
- "outputs": [],
- "source": [
- "sampler = Saltelli(2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4832524f",
- "metadata": {},
- "source": [
- "## Draw samples\n",
- "\n",
- "Now that we created our sampler, we can draw samples by calling the method `sample()`.\n",
- "`sample()` has one mandatory argument `nsamples` and two optional arguments `calc_second_order`\n",
- "and `skip_values`.\n",
- "\n",
- "We draw samples by setting `nsamples=4` and keep the two optional arguments as default values\n",
- "as follows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "f70b21ab",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0.09375, 0.46875],\n",
- " [0.46875, 0.46875],\n",
- " [0.09375, 0.65625],\n",
- " [0.09375, 0.65625],\n",
- " [0.46875, 0.46875],\n",
- " [0.46875, 0.65625],\n",
- " [0.59375, 0.96875],\n",
- " [0.96875, 0.96875],\n",
- " [0.59375, 0.15625],\n",
- " [0.59375, 0.15625],\n",
- " [0.96875, 0.96875],\n",
- " [0.96875, 0.15625],\n",
- " [0.84375, 0.21875],\n",
- " [0.21875, 0.21875],\n",
- " [0.84375, 0.90625],\n",
- " [0.84375, 0.90625],\n",
- " [0.21875, 0.21875],\n",
- " [0.21875, 0.90625],\n",
- " [0.34375, 0.71875],\n",
- " [0.71875, 0.71875],\n",
- " [0.34375, 0.40625],\n",
- " [0.34375, 0.40625],\n",
- " [0.71875, 0.71875],\n",
- " [0.71875, 0.40625]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "samples = sampler.sample(4)\n",
- "samples"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27c8db21",
- "metadata": {},
- "source": [
- "## Plot\n",
- "Let's illustrate the samples using matplotlib library."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "f602b061",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.scatter(samples[:, 0], samples[:, 1])\n",
- "plt.title(\"Saltelli samples\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b418875d",
- "metadata": {},
- "source": [
- "## Optional arguments\n",
- "\n",
- "Now let's look at a second example.<br>\n",
- "Here we have specific bounds for the two parameters and want to specify the `bounds`\n",
- "when create the sampler. And we don't want to calculate the second order sensitivity \n",
- "indices, so we set `calc_second_order` to False. We also specify the number of points \n",
- "to skip `skit_values`in the Sobol' sequence. This number should be ideally a value of base 2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "a4fd6bbc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from psimpy.sampler.saltelli import Saltelli\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "sampler = Saltelli(2, bounds=[[0, 8], [0, 5]])\n",
- "samples = sampler.sample(64, calc_second_order=False, skip_values=128)\n",
- "\n",
- "plt.scatter(samples[:, 0], samples[:, 1])\n",
- "plt.title('Saltelli samples')\n",
- "\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "d9212ca1597032c1672af9541e93e14614267f9205bb9b68575454894387c12e"
- },
- "kernelspec": {
- "display_name": "Python 3.9.12 ('psimpy-dev')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/index.md b/docs/index.md
index 5179ea83c7d5598af8b01f26820e7e94ee9fb227..e3acefaaf22aa3a1dc6e06d07d7cedcbd0418b19 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,7 +6,6 @@
:hidden:
changelog.md
-contributing.md
conduct.md
autoapi/index
```