schrodinger.math.mathutils module¶
Contains math-related utility functions
Copyright Schrodinger, LLC. All rights reserved.
- schrodinger.math.mathutils.round_value(val, precision=3, significant_figures=None)¶
Return val as a string with the required precision or significant figures.
Either precision or significant_figures should be provided. Uses scientific notation for very large or small values, if the precision allows.
- Parameters
val (float) – The value to round
precision (int) – The precision needed after rounding. A precision of 2 means two decimal places should be kept after rounding. A negative precision indicates how many digits can be replaced with zeros before the decimal point. -5 means that 123456 can be shown as 1e5, -3 means it can be shown as 1.23e5.
significant_figures (int) – The number of significant figures that should remain after rounding. If provided, determines the rounding precision.
- Return type
str or None
- Returns
A string with the required precision, or None if the input is a string
- schrodinger.math.mathutils.deduplicate_xy_data(x_vals, y_vals)¶
Remove duplicate x values by averaging the y values for them.
- Parameters
x_vals (list) – The x values
y_vals (list) – The y values
- Return type
list, list
- Returns
The deduplicated xy data
- class schrodinger.math.mathutils.Interpolate1D(source_vals, target_vals, log_interp=False)¶
Bases:
object
Creates a map between values in a source and a target axis, allowing to get the equivalent target point for each source point.
Wrapper around
scipy.interpolate.interp1d
to allow logarithmic interpolation or extrapolation.- __init__(source_vals, target_vals, log_interp=False)¶
Create an instance.
- Parameters
source_vals (tuple) – The values of points in the source range
target_vals (tuple) – The values of points in the target range
log_interp (bool) – Whether the interpolation is logarithmic. If False, linear interpolation will be used.
- class schrodinger.math.mathutils.Interpolate2D(x_source_vals, x_target_vals, y_source_vals, y_target_vals, x_log_interp=False, y_log_interp=False)¶
Bases:
object
Creates two instances of Interpolate1D to map values between two source axes and two target axes. Example use case is mapping QGraphicsScene/QChart XY coordinates to a XY coordinate system being displayed in the scene/chart.
The two axes need to be independent.
- __init__(x_source_vals, x_target_vals, y_source_vals, y_target_vals, x_log_interp=False, y_log_interp=False)¶
Create an instance.
- Parameters
x_source_vals (tuple) – The values of points in the X source range
x_target_vals (tuple) – The values of points in the X target range
y_source_vals (tuple) – The values of points in the Y source range
y_target_vals (tuple) – The values of points in the Y target range
x_log_interp (bool) – Whether the X axis interpolation is logarithmic
y_log_interp (bool) – Whether the Y axis interpolation is logarithmic
- schrodinger.math.mathutils.roundup(inp)¶
Round away from zero (to +/-infinity).
- Parameters
inp (float or numpy.array) – value to be rounded
- Return type
float or numpy.array
- Returns
Rounded value
- schrodinger.math.mathutils.sig_fig_round(value, significant_figure=5)¶
- Parameters
value (float) – change the significant figures of this value
significant_figure (int) – significant figure of the displayed value
- Return type
str
- Returns
str representation of a number with proper significant figures
- schrodinger.math.mathutils.get_significant_figures(number)¶
Get significant digits of a number
- Parameters
number (float or int or str) – number to find significant digits
- Return type
int
- Returns
number of significant digits
- schrodinger.math.mathutils.set_significant_figures(number, sig_fig)¶
Set significant digits of a number
- Parameters
number (float or int) – number to set significant digits
sig_fig (int) – number of significant digits
- Return type
float
- Returns
number with desired significant digits
- schrodinger.math.mathutils.polyfit(xdata, ydata, degree)¶
Fit a polynomial of rang degree to data.
- Parameters
xdata (numpy.array) – X data
ydata (numpy.array) – Y data
degree (int) – Degree of the fitting polynomial
- Return type
numpy.array, float
- Returns
Fitting coefficients, coefficient of determination (R^2)
- schrodinger.math.mathutils.dbscan_cluster(objects, eps, key=None)¶
Cluster the given objects by numbers obtained via the given key function using a specified precision. Uses simple interface to sklearn.cluster.DBSCAN (density based spatial clustering).
- Parameters
objects (list) – the objects to cluster
eps (float) – the precision that controls the size of the clusters, for example if objects is [102, 104, 307, 309, 99, 919, 918] an eps of 6 will produce 3 clusters, see sklearn.cluster.DBSCAN documentation for more details
key (function or None) – the function to get a number from the object, if None is the identity function
- Raises
ValueError – if there is an issue
- Return type
dict
- Returns
clustered objects, keys are arbitrary cluster indices, values are lists of objects for the given cluster