115 lines
11 KiB
Markdown
115 lines
11 KiB
Markdown
# Bibliography
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## Mathematics
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- Gene H. Golub and Charles F. Van Loan, *Matrix Computations*. John Hopkins University Press, 2013.
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- Mark H. Holmes, *Introduction to scientific computing and data analysis*. Vol. 13. Springer Nature, 2023.
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- David C. Lay, Steven R. Lay, and Judith J. McDonald, *Linear Algebra and Its Applications*, Pearson, 2021. ISBN 013588280X.
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- https://ubcmath.github.io/MATH307/index.html
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- https://eecs16b.org/notes/fa23/note16.pdf
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- https://en.wikipedia.org/wiki/Low-rank_approximation
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- https://www-labs.iro.umontreal.ca/~grabus/courses/ift6760_W20_files/lecture-5.pdf
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- https://www.statology.org/polynomial-regression-python/
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- https://en.wikipedia.org/wiki/Mean_squared_error
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- https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
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## Python
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### Numpy (https://numpy.org/doc/stable/index.html)
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- numpy basics: https://numpy.org/doc/stable/user/absolute_beginners.html
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- numpy.array: https://numpy.org/doc/stable/reference/generated/numpy.array.html
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- numpy.hstack: https://numpy.org/doc/stable/reference/generated/numpy.hstack.html (Stack arrays in sequence horizontally (column wise).)
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- numpy.column_stack: https://numpy.org/doc/stable/reference/generated/numpy.column_stack.html (Stack 1-D arrays as columns into a 2-D array.)
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- numpy.shape: https://numpy.org/doc/stable/reference/generated/numpy.shape.html (Return the shape of an array.)
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- numpy.polyfit: https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html (Least squares polynomial fit.)
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- numpy.mean: https://numpy.org/doc/stable/reference/generated/numpy.mean.html (Compute the arithmetic mean along the specified axis.)
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- numyp.poly1d: https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html (A one-dimensional polynomial class.)
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- numpy.set_printoptions: https://numpy.org/doc/stable/reference/generated/numpy.set_printoptions.html (These options determine the way floating point numbers, arrays and other NumPy objects are displayed.)
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- numpy.finfo: https://numpy.org/doc/stable/reference/generated/numpy.finfo.html (Machine limits for floating point types.)
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- numpy.logspace: https://numpy.org/doc/stable/reference/generated/numpy.logspace.html (Return numbers spaced evenly on a log scale.)
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- numpy.sum: https://numpy.org/doc/stable/reference/generated/numpy.sum.html (Sum of array elements over a given axis.)
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- numpy.abs: https://numpy.org/doc/stable/reference/generated/numpy.absolute.html (Calculate the absolute value element-wise.)
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- numpy.ndarray.T: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.T.html (View of the transposed array.)
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- numpy.ones: https://numpy.org/doc/stable/reference/generated/numpy.ones.html (Return a new array of given shape and type, filled with ones.)
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- numpy.zeros: https://numpy.org/doc/stable/reference/generated/numpy.zeros.html (Return a new array of given shape and type, filled with zeros.)
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- numpy.diag: https://numpy.org/doc/stable/reference/generated/numpy.diag.html (Extract a diagonal or construct a diagonal array.)
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- numpy.cumsum: https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html (Return the cumulative sum of the elements along a given axis.)
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- numpy.meshgrid: https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html (Return a tuple of coordinate matrices from coordinate vectors.)
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- numpy.linspace: https://numpy.org/doc/stable/reference/generated/numpy.linspace.html (Return evenly spaced numbers over a specified interval.)
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- numpy.ravel: https://numpy.org/doc/stable/reference/generated/numpy.ravel.html (Return a contiguous flattened array.)
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- numpy.vstack: https://numpy.org/doc/stable/reference/generated/numpy.vstack.html (Stack arrays in sequence vertically (row wise).)
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#### numpy.random (https://numpy.org/doc/stable/reference/random/index.html)
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- numpy.random.seed: https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html (Reseed the singleton RandomState instance.)
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- numpy.random.normal: https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html (Draw random samples from a normal (Gaussian) distribution.)
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- numpy.random.default_rng: https://numpy.org/doc/stable/reference/random/generator.html (Construct a new Generator with the default BitGenerator (PCG64).)
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- numpy.random.uniform: https://numpy.org/doc/stable/reference/random/generated/numpy.random.uniform.html (Draw samples from a uniform distribution.)
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#### numpy.linalg (https://numpy.org/doc/stable/reference/routines.linalg.html)
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- numpy.linalg.qr: https://numpy.org/doc/stable/reference/generated/numpy.linalg.qr.html (Compute the qr factorization of a matrix.
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- numpy.linalg.svd: https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html (Singular Value Decomposition.)
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- numpy.linalg.solve: https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html (Solve a linear matrix equation, or system of linear scalar equations.)
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- numpy.linalg.lstsq: https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html (Return the least-squares solution to a linear matrix equation.)
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- numpy.linalg.norm: https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html (Matrix or vector norm.)
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- numpy.linalg.pinv: https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html (Compute the (Moore-Penrose) pseudo-inverse of a matrix.)
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- numpy.linalg.cond: https://numpy.org/doc/stable/reference/generated/numpy.linalg.cond.html (Compute the condition number of a matrix.)
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### Matplotlib (https://matplotlib.org/stable/users/getting_started/)
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- matplotlib.pyplot: https://matplotlib.org/stable/api/pyplot_summary.html
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- matplotlib.figure: https://matplotlib.org/stable/api/figure_api.html (Implements the following classes: `Figure` and `SubFigure`)
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- mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface: https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface.html#mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface (Create a surface plot.)
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#### matplotlib.pyplot
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- matplotlib.pyplot.plot: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html (Plot y versus x as lines and/or markers.)
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- matplotlib.pyplot.quiver: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.quiver.html (Plot a 2D field of arrows.)
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- matplotlib.pyplot.tight_layout: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html (Adjust the padding between and around subplots.)
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- matplotlib.pyplot.legend: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html (Place a legend on the Axes.)
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- matplotlib.pyplot.show: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html (Display all open figures.)
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- matplotlib.pyplot.xlabel: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html (Set the label for the x-axis.)
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- matplotlib.pyplot.ylabel: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html (Set the label for the y-axis.)
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- matplotlib.pyplot.title: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html (Set a title for the Axes.)
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- matplotlib.pyplot.scatter: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html (A scatter plot of y vs. x with varying marker size and/or color.)
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- matplotlib.pyplot.imshow: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html (Display data as an image, i.e., on a 2D regular raster.)
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- matplotlib.pyplot.axis: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axis.html (Convenience method to get or set some axis properties.)
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- matplotlib.pyplot.semilogy: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.semilogy.html (Make a plot with log scaling on the y-axis.)
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- matplotlib.pyplot.subplots: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html (Create a figure and a set of subplots.)
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- matplotlib.pyplot.contour: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html (Plot contour lines.)
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- matplotlib.pyplot.contourf: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html (Plot filled contours.)
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- matplotlib.pyplot.axhline: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html (Add a horizontal line spanning the whole or fraction of the Axes.)
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- matplotlib.pyplot.axvline: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html (Add a vertical line spanning the whole or fraction of the Axes.)
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- matplotlib.pyplot.gca: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.gca.html (Get the current Axes.)
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#### matplotlib.figure
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- matplotlib.figure.Figure.add_subplot : https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.add_subplot.html (Add an `Axes` to the figure as part of a subplot arrangement.)]
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#### matplotlib.axes
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- matplotlib.axes.Axes.set_title: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html (Set a title for the Axes.)
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- matplotlib.axes.Axes.imshow: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html (Display data as an image, i.e., on a 2D regular raster.)
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- matplotlib.axes.Axes.axis: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.axis.html (Convenience method to get or set some axis properties.)
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- matplotlib.axes.Axes.text: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html (Add text to the Axes.)
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- matplotlib.axes.Axes.set_xlabel: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html (Set the label for the x-axis.)
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- matplotlib.axes.Axes.set_ylabel: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html (Set the label for the y-axis.)
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- matplotlib.axes.Axes.set_xlim: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html (Set the x-axis view limits.)
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- matplotlib.axes.Axes.set_aspect: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_aspect.html (Set the aspect ratio of the Axes scaling, i.e. y/x-scale.)
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#### Scatter plots with line of best fit
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- https://stackoverflow.com/questions/37234163/how-to-add-a-line-of-best-fit-to-scatter-plot
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- https://www.statology.org/line-of-best-fit-python/
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- https://stackoverflow.com/questions/6148207/linear-regression-with-matplotlib-numpy
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### Pandas
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- pandas basics: https://pandas.pydata.org/docs/user_guide/index.html
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- pandas.DataFrame: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html (Two-dimensional, size-mutable, potentially heterogeneous tabular data.)
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#### pandas.DataFrame
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- pandas.DataFrame.describe: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html (Generate descriptive statistics.)
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- pandas.DataFrame.corr: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html (Compute pairwise correlation of columns, excluding NA/null values.)
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- pandas.DataFrame.to_numpy: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html (Convert the DataFrame to a NumPy array.)
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- pandas.DataFrame.plot: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html (Make plots of Series or DataFrame.)
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### Pillow
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- PIL basics: https://pillow.readthedocs.io/en/stable/
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- PIL.Image: https://pillow.readthedocs.io/en/stable/reference/Image.html
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### Math
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- Math basics: https://docs.python.org/3/library/math.html
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- math.ceil: https://docs.python.org/3/library/math.html#math.ceil (Return the ceiling of x, the smallest integer greater than or equal to x)
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