damaris.data.Accumulation

class damaris.data.Accumulation(x: ndarray | list | None = None, y: list[ndarray] | None = None, y_2: list[ndarray] | ndarray | None = None, n: int | None = None, index: list[tuple[int, int]] | None = None, sampl_freq: float | None = None, error: bool = False)

Represents an accumulation of sampled data with various attributes and methods for data manipulation, analysis, and exporting.

This class handles statistical computations and provides mechanisms for thread-safe operations and data interaction.

Attributes: xlabel (str): Label for the x-axis. ylabel (str): Label for the y-axis. lock (threading.RLock): A reentrant lock to ensure thread-safe operations. common_descriptions (dict|None): A dictionary containing common descriptions of the data. time_period (list): A list to store the periods of time relevant to the accumulation. job_id (int|None): An identifier for the job associated with this accumulation. use_error (bool): Indicates whether statistical error calculations are enabled. sampling_rate (float): Sampling frequency of the data. n (int): Number of accumulations. cont_data (bool): Indicates whether the object contains valid data. index (list): List of index bounds for data segments. x (list): The x-axis data points. y (list): The y-axis data points.

__init__(x: ndarray | list | None = None, y: list[ndarray] | None = None, y_2: list[ndarray] | ndarray | None = None, n: int | None = None, index: list[tuple[int, int]] | None = None, sampl_freq: float | None = None, error: bool = False) → None

Methods

__init__([x, y, y_2, n, index, sampl_freq, ...])
autophase()	Automatically phases the data to maximize real part.
bartlett_window()	Symmetric centered window (bartlett)
baseline([last_part])	Correct the baseline of your data by subtracting the mean of the last_part fraction of your data.
blackman_window()	Symmetric centered window (blackmann)
clip([start, stop])	Method for clipping data, returns only the data between start and stop start and stop can be either time or frequency.
contains_data()	Return whether the measurement contains samples.
dexp_window([line_broadening, ...])
exp_window([line_broadening])	Exponential window function
fft([samples])	Calculate the Fourier transform of the data inplace.
gauss_window([line_broadening])	Gaussian window function
get_Result_by_index(index)
get_accu_by_index(index)
get_bars_above()	Gets bars-above property of errorplot
get_error_color()	Returns the error-bar color
get_index_bounds(index)	Returns a tuple with (start, end) of the wanted result
get_job_id()	Returns the job identifier associated with this accumulation.
get_legend()	Returns the legend of the plot (Dictionary)
get_number_of_channels()	Return the number of data channels.
get_phase([pos])	Return the phase in degrees from the first two channels.
get_result_by_index(index)
get_sampling_rate()	Returns the sampling frequency
get_style()	Returns a reference to plot-styles (dictionary)
get_text(index)	Returns labels to be plotted (List)
get_title()	Returns the title of the plot
get_xdata()	Returns a reference to the x-Plotdata (array)
get_xerr()	Returns a reference to x-Error (array)
get_xlabel()	Returns the label for the x-axis
get_xmax()	Returns maximum of x
get_xmin()	Returns minimun of x
get_xminpos()	Returns smallest positive value of x
get_ydata([channel])	return mean data
get_yerr(channel)	return error (std.dev/sqrt(n)) of mean
get_ylabel()	Gets the label for the y-axis
get_ymax()	Returns the global maximum value of all channels.
get_ymax_channels()	Return the maximum value of each channel.
get_ymean([start, stop])	Return the mean of every channel over a time interval.
get_ymin()	Return the global minimum across all channels.
get_ymin_channels()	Return the minimum value of each channel.
get_yminpos()	Returns smallest positive value of y
get_yrms()	Return the root-mean-square value of each channel.
get_ysquare(channel)
get_ystd()	Return the population standard deviation of each channel.
hamming_window()	Symmetric centered window (hamming)
hanning_window()	Symmetric centered window (hanning)
kaiser_window([beta, use_scipy])	Symmetric centered window (kaiser)
magnitude()	Return absolute signal, i.e.:
phase(degrees)	rotate signal by degrees for phase cycling, etc.
ppm(f_ref)	Return result scaled to PPM compared to f_ref
ready_for_drawing_error()	Returns true if more than one result have been accumulated
set_bars_above(bars_above)	Sets bars-above property of errorplot
set_error_color(color)	Sets the error-bar color
set_legend(channel, value)	Sets the legend of the plot
set_style(channel, value)	Sets a channel to a certain plot-style
set_text(index, text)	Sets labels to be plotted
set_title(title)	Sets the title of the plot
set_xdata(pos, value)	Sets a point in x
set_xerr(pos, value)	Sets a point in x-Error
set_xlabel(label)	Sets the label for the x-axis
set_xmax(xmax)	Sets maximum of x
set_xmin(xmin)	Sets minimum of x
set_ydata(channel, pos, value)	Sets a point in y
set_yerr(channel, pos, value)	Sets a point in y-Error
set_ylabel(label)	Sets the label for the y-axis
set_ymax(ymax)	Sets maximum of y
set_ymin(ymin)	Sets minimum of y
traf_window([line_broadening])
uses_statistics()
write_to_csv([destination, delimiter])	Write the accumulation data to a whitespace-delimited text file with comment headers.
write_to_hdf(hdffile, where, name, title[, ...])	Write the accumulation data to an HDF5 file using PyTables.
write_to_simpson([destination, delimiter, ...])	Write the accumulation data to a text file in Simpson format for NMRnotebook.
write_to_tecmag([destination, nrecords, ...])	Write the accumulation data to a binary TecMag file.