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- LineSpec(data=None, data_filter_expr=None, freq_style='K_INTEGRAL', include_coeff=False, zero_padding_ok=True, hertz_sample_rate=None, **generic_arguments)
- DESCRIPTION:
The LineSpec() function identifies periodicity that may be inherent in
an input series.
The following procedure is an example of how to use LineSpec:
1. Use ArimaEstimate() to identify spectral candidates.
2. Use ArimaValidate() to validate spectral candidates.
3. Use LineSpec() with "freq_style" parameter set to K_PERIODICITY
to perform spectral analysis.
4. Use DataFrame.plot() to plot the results.
5. Compute the test statistic.
6. Use SignifPeriodicities() on the periodicities of interest.
More than one periodicity can be entered using the "periodicities"
parameter.
PARAMETERS:
data:
Required Argument.
Specifies an input time series whose payload content has one of the
following values:
* REAL
* COMPLEX
* MULTIVAR_REAL
* MULTIVAR_COMPLEX
Types: TDSeries
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
freq_style:
Optional Argument.
Specifies the format or values associated with the x-axis of the
output.
Permitted Values:
* K_INTEGRAL: Integer representation.
* K_SAMPLE_RATE: Integer normalized to number entries, with
ranges from -0.5 to +0.5.
* K_RADIANS: Radian ranges from -π to +π.
* K_PERIODICITY: Periodicity.
Default Value: K_INTEGRAL
Types: str
include_coeff:
Optional Argument.
Specifies whether to include the calculated αk and βk values
in the output. The formula for the magnitude at time k in the
line spectrum is:
(n/2) * ((αk)**2 + (βk)**2)
Default Value: False
Types: bool
zero_padding_ok:
Optional Argument.
Specifies whether to add zeros to the end of time series
to make computation more efficient. When set to False, does
not add zeros, otherwise zeros are added.
Default Value: True
Types: bool
hertz_sample_rate:
Optional Argument.
Specifies the sample rate as a floating point constant, in hertz.
A value of 10000.0 indicates that the sample points were obtained
by sampling at a rate of 10,000 hertz. This hertz interpretation
applies to both the ROW_I and COLUMN_I indices.
Types: float
**generic_arguments:
Specifies the generic keyword arguments of UAF functions.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Note that, when UAF function is executed, an
analytic result table (ART) is created.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile ART or not. When set to
True, results are stored in a volatile ART,
otherwise not.
Default Value: False
Types: bool
output_table_name:
Optional Argument.
Specifies the name of the table to store results.
If not specified, a unique table name is internally
generated.
Types: str
output_db_name:
Optional Argument.
Specifies the name of the database to create output
table into. If not specified, table is created into
database specified by the user at the time of context
creation or configuration parameter. Argument is ignored,
if "output_table_name" is not specified.
Types: str
RETURNS:
Instance of LineSpec.
Output teradataml DataFrames can be accessed using attribute
references, such as LineSpec_obj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
# Load the example data.
load_example_data("uaf", ["TestRiver"])
# Create teradataml DataFrame object.
df = DataFrame.from_table("TestRiver")
# Create teradataml TDSeries object.
result = TDSeries(data=df, id="BuoyID", row_index="N_SeqNo",
row_index_style="SEQUENCE", payload_field="MAGNITUDE",
payload_content="REAL")
# Example 1 : Execute function to identify the periodicity of the input
# series.
uaf_out = LineSpec(data=result)
# Print the result DataFrame.
print(uaf_out.result)
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