Diagnostic Output

Diagnostic Output#

Main Properties#

The data in a diagnostics file can be categorized as

non-update step
update-step: before update
update-step: after update (analysis)

The Diagnostic object therefore has the following result properties:

forecast (0 and 1)
forecast_at_update (1)
analysis (2)
result (0 and 2)

and the additional data properties:

innovation (y-Hx measurement-minus-model at 0, 1 and 2)
increment (2-1; the update itself)

All these properties are also diagnostic objects with similar functionality as the diagnostic object: min(), max(), std(), plot(), hist(), …

Main Properties for files without updates#

If the diagnostic files stem from a non-DA simulation it will only contain non-update steps (0). The object will therefore only contain:

forecast
result
innovation (y-Hx measurement-minus-model)

And forecast = result.

Diagnostic Collection#

It’s often convenient to load all diagnostic files from a simulation using FMDAp’s DiagnosticCollection. It can done with the

API#

The diagnostic_output module contains methods and classes for working with MIKE FM DA diagnostic outputs.

The entrance point is always the read_diagnostic() method which will return a specialized object depending on the type of diagnostic output.

The returned object will be of type:

Examples

>>> import fmdap
>>> d = fmdap.read_diagnostic("Diagnostics_Drogden_OI.dfs0", name="Drogden")
>>> d.increment.hist()
>>> d.analysis.innovation.hist()
>>> d.result.plot()

class fmdap.diagnostic_output.DiagnosticType(*values)#

as_integer_ratio()#

Return a pair of integers, whose ratio is equal to the original int.

The ratio is in lowest terms and has a positive denominator.

>>> (10).as_integer_ratio()
(10, 1)
>>> (-10).as_integer_ratio()
(-10, 1)
>>> (0).as_integer_ratio()
(0, 1)

bit_count()#

Number of ones in the binary representation of the absolute value of self.

Also known as the population count.

>>> bin(13)
'0b1101'
>>> (13).bit_count()
3

bit_length()#

Number of bits necessary to represent self in binary.

>>> bin(37)
'0b100101'
>>> (37).bit_length()
6

conjugate()#: Returns self, the complex conjugate of any int.

denominator#: the denominator of a rational number in lowest terms

classmethod from_bytes(bytes, byteorder='big', *, signed=False)#

Return the integer represented by the given array of bytes.

bytes: Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol.
byteorder: The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. Default is to use ‘big’.
signed: Indicates whether two’s complement is used to represent the integer.

imag#: the imaginary part of a complex number

is_integer()#: Returns True. Exists for duck type compatibility with float.is_integer.

numerator#: the numerator of a rational number in lowest terms

real#: the real part of a complex number

to_bytes(length=1, byteorder='big', *, signed=False)#

Return an array of bytes representing an integer.

length: Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. Default is length 1.
byteorder: The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. Default is to use ‘big’.
signed: Determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.

class fmdap.diagnostic_output.MeasurementDistributedDiagnostic(df, name, eumItem=None, filename=None)#

property has_updates#: has file any assimilation updates (duplicate index)

hist(**kwargs)#

plot histogram of values using plt.hist()

Parameters:: bins (int, optional) – histgram bins, by default 100

property idx_analysis#: index after assimilation updates (analysis)

property idx_forecast#: index before assimilation updates (forecast)

property idx_no_update#: index when there is no assimilation updates

property innovation#: innovation (y-Hx) object

property n_updates#: number of assimilation updates

property time#: the time vector (index as datetime)

property values#: all values as a nd array

class fmdap.diagnostic_output.MeasurementPointDiagnostic(df, name, eumItem=None, filename=None)#

property has_updates#: has file any assimilation updates (duplicate index)

hist(**kwargs)#

plot histogram of values using plt.hist()

Parameters:: bins (int, optional) – histgram bins, by default 100

property idx_analysis#: index after assimilation updates (analysis)

property idx_forecast#: index before assimilation updates (forecast)

property idx_no_update#: index when there is no assimilation updates

property innovation#: innovation (y-Hx) object

property n_updates#: number of assimilation updates

property time#: the time vector (index as datetime)

property values#: all values as a nd array

class fmdap.diagnostic_output.NonMeasurementPointDiagnostic(df, name, eumItem=None, filename=None)#

property has_updates#: has file any assimilation updates (duplicate index)

hist(bins=100, show_Gaussian=False, **kwargs)#

plot histogram of values using plt.hist()

Parameters:: bins (int, optional) – histgram bins, by default 100

property idx_analysis#: index after assimilation updates (analysis)

property idx_forecast#: index before assimilation updates (forecast)

property idx_no_update#: index when there is no assimilation updates

property n_updates#: number of assimilation updates

property time#: the time vector (index as datetime)

property values#: all values as a nd array

fmdap.diagnostic_output.read_diagnostic(filename, name=None)#: Read diagnostic output dfs0 file

class fmdap.diagnostic_collection.DiagnosticCollection(diagnostics=None, names=None, attrs=None)#

get(k[, d]) → D[k] if k in D, else d. d defaults to None.#

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

values() → an object providing a view on D's values#

Diagnostic Output

Contents

Diagnostic Output#

Main Properties#

Main Properties for files without updates#

Diagnostic Collection#

API#