Steps methods

Basic methods

Steps.copy()	Return a shallow copy of this steps object
Steps.clear()	Clear all the step data defined within the steps object, the same as defining a new Steps object with no data, except will retain the assigned datetime flag and base.
Steps.first()	The first key or start value of the steps, if the steps extend to negative infinity, the first value will be the first finite key value.
Steps.last()	The last key or start value of the steps, if the steps extend to positive infinity, the last value will be the last finite key value.
Steps.series([xdata, ydata])	A convenience function to either return the internal steps data in a Pandas Series object with the steps keys as the index or convert a provided 2-D set of data into a Pandas Series.
Steps.iloc(idx[, raw_keys])	The individual step changes at each array index, these are the delta values that add and subtract across the series to realise the entire step function.
Steps.using_datetime()	Check if this steps object is using datetime step keys or not.
Steps.step_keys([convert_keys])	The step key values within this object, can be returned either in raw float format or converted if using datetime.
Steps.step_values()	The cummulative step values at each key value.
Steps.step_data([delta_values, convert_keys])	A clean multi-dimensional numpy array of the step keys and either the cummulative values or the step change values all in floats and ready to use in further analysis.
Steps.steps()	Return all the raw steps data within this steps object.

Plotting methods

Steps.plot([method, smooth_factor, …])	Plot the steps function using different parameters and methods.
Steps.smooth_plot([smooth_factor, …])	Plot a smoothed steps function using different parameters and methods.
Steps.plot_rolling_step([rolling_function, …])	Plot the result of applying a reduction function to a rolling window across the step values.
Steps.histogram_plot([bins, axis, …])	Plot a histogram of the cummulative step values.
Steps.ecdf_plot([ax])	Plot an empirical cummulative distribution function of the cummulative step values.
Steps.pacf_plot([lags, minlags, ax])	Plot an partial auto-correlation function of the cummulative step values.
Steps.acf_plot([lags, minlags, ax])	Plot an auto-correlation function of the cummulative step values.
Steps.summary([plot_params])	Plot and display summary details and statistics for the Steps object.

Class Loader methods

Steps.read_dataframe(data[, start, end, …])	Read a Pandas dataframe with values that represent either the cummulative value of the data steps or the direct step values seperately.
Steps.read_array([start, end, weight, …])	Read arrays of values for start, end and weight values that represent either the cummulative value of the data steps or the direct step values seperately, indexed by the start and possibly end arrays.
Steps.read_dict(data[, use_datetime, …])	Read a dictionary with values that represent either the cummulative value of the data steps or the direct step values seperately, indexed by the dictionary key values.

Instance Loader methods

Steps.add(steps)	Add an array of step or steps objects to this collection of steps.
Steps.add_steps(step_data_np)	Add an array of internal step data as per the DataModel used internally by HotStepper.
Steps.add_direct([data_start, data_end, …])	Add an array of individual step objects to this collection of steps.

Step methods

Steps.step(xdata[, process_input])	This is a mathematical function definition of the Steps object, this is a dynamically created formula representation that can be passed an array of values to evaluate the steps function at.
Steps.smooth_step(xdata[, smooth_factor, …])	This is a mathematical function definition of the Steps object, this is a dynamically created formula representation that can be passed an array of values to evaluate the steps function at.
Steps.fast_step(xdata[, process_input, side])	This will evaluate the cummulative steps function at the provided input values.
Steps.rebase([new_basis])	Change the basis function to apply to the steps data when evaluating as a mathematical function, the default basis is the Heaviside step function.
Steps.basis()	Return a reference to the assigned Basis.

Arithmetic operators

Steps.__neg__()	Equivalent to multiplying the Steps by -1.
Steps.__add__(other)	The + operation to add int, float, step and steps objects like they are numbers.
Steps.__sub__(other)	The - operation to subtract int, float, step and steps objects like they are numbers.
Steps.__mul__(other)	The * operation to multiply int, float, step and steps objects like they are numbers.
Steps.__truediv__(other)	The / operation to divide int, float, step and steps objects like they are numbers.

Logical operators

Steps.normalise([normalise_value])	Create new steps with a constant weight of 1 everywhere the steps are not equal to zero.
Steps.invert([normalise_value])	Create new steps with a constant weight of 1 everywhere the steps are equal to zero.
Steps.reflect([reflect_point])
Steps.__eq__(other)	Equal to boolean comparison.
Steps.__ne__(other)	Not equal to boolean comparison.
Steps.__gt__(other)	Greater than boolean comparison.
Steps.__lt__(other)	Less than boolean comparison.
Steps.__ge__(other)	Greater than or equal to boolean comparison.
Steps.__le__(other)	Less than or equal to boolean comparison.

Statistical operators

Steps.covariance(other)	Returns the covariance between this steps object and another.
Steps.correlation(other)	Returns the correlation between this steps object and another.
Steps.span_and_weights()	Returns a number of data items for the step keys within the dataset, such as;

Summary statistics

Steps.integrate()	Returns the integral (area under the curve) for the cummulative steps values across all step keys.
Steps.describe([precision, return_dataframe])	Generate a table containing a number of standard statistical metrics for the steps data.
Steps.min([include_zero])	Returns the minimum value for the cummulative steps values across all step keys.
Steps.max()	Returns the maximum value for the cummulative steps values across all step keys.
Steps.var()	Returns the weighted variance of the cummulative steps values across all step keys.
Steps.std()	Returns the weighted standard deviation of the cummulative steps values across all step keys.
Steps.mean()	Returns the weighted mean of the cummulative steps values across all step keys.
Steps.median()	Returns the median value for the cummulative steps values across all step keys.
Steps.percentile(percent)	Return the value of the percentile of the cummulative steps values.
Steps.ecdf()	Calculates the Empirical Cummulative Distribution Function for the steps data.
Steps.histogram([bins, axis])	Calculates a histogram for the corresponding step function values

Miscellaneous methods

Steps.clip([lbound, ubound])	Clip the steps between lower and upper limits, this function is equivalent to taking a slice of the steps and returning a new steps object only containing data between the clip boundries.
Steps.clamp([lbound, ubound])	Clamp the steps between lower and upper limits, this function is equivalent to zeroing out the steps beyond the lower and upper limits.
Steps.rshift(other)	Right shift operator >>, used to shift the step foward.
Steps.lshift(other)	Left shift operator <<, used to shift the step backwards.
Steps.compare(other)	Compare the steps function with another to determine if the two are equivalent based on their cummulative values and step keys.