damaris.data.ADC_Result

class damaris.data.ADC_Result(x: ndarray | list | None = None, y: list[ndarray] | None = None, index: list[tuple[int, int]] | None = None, sampl_freq: float | None = None, desc: dict[str, str] | None = None, job_id: int | None = None, job_date: datetime | None = None)

__init__(x: ndarray | list | None = None, y: list[ndarray] | None = None, index: list[tuple[int, int]] | None = None, sampl_freq: float | None = None, desc: dict[str, str] | None = None, job_id: int | None = None, job_date: datetime | None = None)

Methods

__init__([x, y, index, sampl_freq, desc, ...])
add_sample_space(samples)	Adds space for n samples, where n can also be negative (deletes space).
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)
check_clipping([bit_depth])	Check if any data in y is clipped.
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()	Returns true if ADC_Result contains data.
create_data_space(channels, samples)	Initialises the internal data-structures
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_description(key)	Returns the description value for a given key
get_description_dictionary()	Returns a reference to the description (Dictionary)
get_index_bounds(index)	Returns a tuple with (start, end) of the wanted result
get_job_date()	Gets the date of this result
get_job_id()	Returns the job-id of this result
get_legend()	Returns the legend of the plot (Dictionary)
get_nChannels()	Gets the number of channels
get_number_of_channels()	Returns the number of channels in y
get_result_by_index(index)
get_sampling_rate()	Returns the samplingfrequency
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_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])	Returns the y-Plotdata of channel n (array)
get_ylabel()	Gets the label for the y-axis
get_ymax()	Returns maximimum of y
get_ymin()	Returns minimum of y
get_yminpos()	Returns smallest positive value of y
hamming_window()	Symmetric centered window (hamming)
hanning_window()	Symmetric centered window (hanning)
kaiser_window([beta, use_scipy])	Symmetric centered window (kaiser)
lowpass(cutoff[, numtaps])	Apply a zero-phase lowpass FIR filter to all channels.
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
set_description(key, value)	Adds a attribute to the description
set_description_dictionary(dictionary)	Sets the entire description
set_job_date(date)	Sets the date of this result
set_job_id(_id)	Sets the job-id of this result
set_legend(channel, value)	Sets the legend of the plot
set_nChannels(channels)	Sets the number of channels
set_sampling_rate(hz)	Sets the samplingfrequency in hz
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_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_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])	writes the data to a file or to sys.stdout destination can be a file or a filename suitable for further processing
write_to_hdf(hdffile, where, name, title[, ...])
write_to_simpson([destination, delimiter])	writes the data to a text file or sys.stdout in Simpson format, for further processing with the NMRnotebook software; destination can be a file or a filename

Attributes

default_bit_depth

Represents the result of an ADC, encapsulating data and metadata for processing, visualization, and export.