{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SciPy (the Scientific Python Library)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summary of what we have learned so far:\n", "\n", "1) `numpy` supplies many common statistics and math functions for operating on arrays.\n", "2) `netCDF4` and `pandas` allow you to read in multidimensional and labeled data.\n", "3) `matplotlib` generates many standard plot types (line plots, scatter, images, contours, histograms)\n", "\n", " What about more complex operations such as filtering? How do people generate such _fancy_ statistics plots? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[SciPy](https://scipy.org/) is an all-purpose scientific Python library that operates on NumPy arrays where you will find the following (and much more!):\n", "\n", "1) [scipy.signal](https://docs.scipy.org/doc/scipy/reference/signal.html): convolution, filtering, peak finding, spectral analysis\n", "\n", "2) [scipy.fft](https://docs.scipy.org/doc/scipy/reference/fft.html): fft, ifft, fftshift, sin and cos transforms\n", "\n", "3) [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/optimize.html): curve fitting, root finding, nonlinear least squares, minimization\n", "\n", "4) [scipy.linalg](https://docs.scipy.org/doc/scipy/reference/linalg.html): matrix functions, decompositions, inv, eig\n", "\n", "5) [scipy.interpolate](https://docs.scipy.org/doc/scipy/reference/interpolate.html): univariate and multivariate interpolation, cubic splines, smoothing and approximation\n", "\n", "6) [scipy.integrate](https://docs.scipy.org/doc/scipy/reference/integrate.html): single/double/triple definite integrals, integrating over fixed samples, numerical methods (Runge-Kutta, Initial Value problems)\n", "\n", "7) [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html): continuous distributions, discrete distributions, multivariate distributions, summary stats, frequency stats, correlation and association, resampling and Monte Carlo methods\n", "\n", "You can consider SciPy tools to be roughly equivalent to tools you would find in Matlab or R. SciPy is also heavily tested, has millions of users, and regular maintainers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtering" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from scipy.signal import butter, sosfiltfilt, detrend\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Curve Fitting" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted parameters: a=1.00, b=1.00\n" ] } ], "source": [ "from scipy.optimize import curve_fit\n", "import numpy as np\n", "\n", "# curve fitting\n", "def model_function(x, a, b):\n", " return a * np.exp(b * x)\n", "x_data = np.array([0, 1, 2, 3])\n", "y_data = np.array([1, 2.7, 7.4, 20.1])\n", "params, covariance = curve_fit(model_function, x_data, y_data)\n", "print(f\"Fitted parameters: a={params[0]:.2f}, b={params[1]:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Algebra" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Determinant: -2.0\n" ] } ], "source": [ "from scipy import linalg\n", "\n", "# determinant of a matrix \n", "matrix = np.array([[1, 2], [3, 4]])\n", "determinant = linalg.det(matrix)\n", "print(f\"Determinant: {determinant}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stats" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# examples adapted from https://xiaoganghe.github.io/python-climate-visuals/chapters/data-analytics/scipy-basic.html\n", "\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "\n", "bins = np.arange(5 - 3 * 2, 5 + 3 * 2, 0.01)\n", "\n", "PDF = stats.norm.pdf(bins, loc=5, scale=2) # generate PDF in bins\n", "CDF = stats.norm.cdf(bins, loc=5, scale=2) # generate CDF in bins\n", "SF = stats.norm.sf(bins, loc=5, scale=2) # generate survival function (1-CDF)\n", "PPF = stats.norm.ppf(0.5, loc=5, scale=2) # obtain percent point (inverse of CDF)\n", "RVS = stats.norm.rvs(loc=5, scale=2, size=1000) # generate 1000 random variates\n", "MMS = stats.norm.stats(loc=5, scale=2, moments='mvsk') # obtain the four moments\n", "\n", "plt.plot(bins, PDF)\n", "plt.ylabel(\"PDF\")\n", "plt.show()\n", "\n", "plt.plot(bins, CDF)\n", "plt.ylabel(\"CDF\")\n", "plt.show()\n", "\n", "# fit a dataset into a distribution\n", "samples = stats.norm.rvs(loc=5, scale=2, size=1000) # pesudo dataset\n", "mu, sigma = stats.norm.fit(samples, method=\"MLE\") # do a maximum-likelihood fit\n", "print(mu, sigma)\n", "\n", "# Plot figure\n", "bins = np.arange(mu - 3 * sigma, mu + 3 * sigma, 0.01)\n", "plt.hist(samples, density=True, histtype='stepfilled',\n", " alpha=0.2, label='Samples')\n", "plt.plot(bins, stats.norm.pdf(bins, loc=mu, scale=sigma),\n", " 'k-', lw=2, label='Fit')\n", "plt.legend(loc='best', frameon=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2.8614204830777963 0.005065297984663446\n" ] } ], "source": [ "# T-test and p value for statistical significance \n", "a = np.random.normal(0, 1, size=100) # Sample A\n", "b = np.random.normal(1, 1, size=10) # Sample B\n", "T, p = stats.ttest_ind(a, b) # T-test\n", "print(T, p)" ] } ], "metadata": { "kernelspec": { "display_name": "scicompute", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }