graphinglib.FitFromLog#

class graphinglib.FitFromLog(curve_to_be_fit: Curve | Scatter, label: str | None = None, log_base: float = 2.718281828459045, guesses: TypeAliasForwardRef('ArrayLike') | None = None, color: str | Inherit = Inherit, line_width: int | Inherit = Inherit, line_style: str | Inherit = Inherit, alpha: float | Inherit = Inherit, max_iterations: int = 10000)[source]#

Create a curve fit (continuous Curve) from an existing Curve object using a logarithmic fit.

Fits a logarithmic function of the form \(f(x) = a \log_{base}(x + b) + c\) to the given curve. All standard Curve attributes and methods are available.

Parameters:

curve_to_be_fitCurve or Scatter: The object to be fit.
labelstr, optional: Label to be displayed in the legend.
log_basefloat: Base of the logarithm. Default is e.
guessesArrayLike, optional: Initial guesses for the parameters of the fit. Order is a, b, c as written above.
colorstr: Color of the curve. Default depends on the figure_style configuration.
line_widthint: Line width of the curve. Typical range is 0.5 to 4. Default depends on the figure_style configuration.
line_stylestr: Line style of the curve. Values include "-", "--", "-.", ":", "solid", "dashed", "dashdot", and "dotted". Default depends on the figure_style configuration.
alphafloat: Opacity of the curve. Range is 0 (transparent) to 1 (opaque). Default depends on the figure_style configuration.
max_iterationsint: Maximum number of iterations for the fit. Default is 10000.

Attributes:

parametersnp.ndarray: Parameters of the fit (same order as guesses).
cov_matrixnp.ndarray: Covariance matrix of the parameters of the fit.
standard_deviationnp.ndarray: Standard deviation of the parameters of the fit.
functionCallable: Logarithmic function with the parameters of the fit.

Notes

Color parameters accept Matplotlib color formats: named colors ("blue"), short color strings ("b"), hex strings ("#0000ff"), grayscale strings ("0.5"), and RGB/RGBA tuples with values between 0 and 1 ((0, 0, 1) or (0, 0, 1, 0.5)).

Methods

`__init__`(curve_to_be_fit[, label, log_base, ...])	Create a curve fit (continuous `Curve`) from an existing `Curve` object using a logarithmic fit.
`add_error_curves`([y_error, ...])	Adds error curves to the `Curve`.
`add_errorbars`([x_error, y_error, cap_width, ...])	Adds errorbars to the `Curve`.
`copy`()	Returns a deep copy of the `Curve`.
`copy_with`(**kwargs)	Returns a deep copy of the Plottable with specified attributes overridden.
`create_derivative_curve`([label, color, ...])	Creates a new curve which is the derivative of the original curve.
`create_integral_curve`([initial_value, ...])	Creates a new curve which is the integral of the original curve.
`create_intersection_points`(other[, labels, ...])	Creates the intersection Points between two curves.
`create_normal_curve`(x[, label, color, ...])	Creates a new curve which is the normal to the original curve at a given x value.
`create_point_at_x`(x[, label, face_color, ...])	Gets the point on the curve at a given x value.
`create_points_at_y`(y[, interpolation_kind, ...])	Creates the Points on the curve at a given y value.
`create_slice_x`(x1, x2[, label, color, ...])	Creates a slice of the curve between two x values.
`create_slice_y`(y1, y2[, label, color, ...])	Creates a slice of the curve between two y values.
`create_tangent_curve`(x[, label, color, ...])	Creates a new curve which is the tangent to the original curve at a given x value.
`from_function`(func, x_min, x_max[, label, ...])	Creates a `Curve` from a function and a range of x values.
`get_Rsquared`()	Calculates the \(R^2\) value of the fit curve.
`get_arc_length_between`(x1, x2)	Calculates the arc length of the curve between two x values.
`get_area_between`(x1, x2[, fill_between, ...])	Calculates the area between the curve and the x axis between two x values.
`get_coordinates_at_x`(x)	Gets the coordinates of the curve at a given x value.
`get_coordinates_at_y`(y[, interpolation_method])	Gets the coordinates of the curve at a given y value.
`get_intersection_coordinates`(other)	Calculates the coordinates of the intersection points between two curves.
`get_residuals`()	Calculates the residuals of the fit curve.
`get_slope_at`(x)	Calculates the slope of the curve at a given x value.
`show_residual_curves`([sigma_multiplier, ...])	Displays two curves `"sigma_multiplier"` standard deviations above and below the fit curve.
`to_desmos`([decimal_precision, to_clipboard])	Gives the data points in a Desmos-readable format.