Reference guide #

file_ascii_write()#: Save vector summary into a file

get_assignment()#: Get cluster of a vector (index between 0 and nb_pattern)

get_column_identifier()#: todo

get_deletion_distance()#: returns deletion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_distance()#: returns distance between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_insertion_distance()#: returns insertion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_length()#: returns length between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_cluster()#: Return number of clusters

get_nb_deletion()#: returns nb of deletion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_insertion()#: returns nb of insertion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_match()#: returns nb of match between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_substitution()#: returns nb of substitution between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_transposition()#: returns nb of transposition between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_plotable()#: Return a plotable object

get_row_identifier()#: todo

get_substitution_distance()#: returns substitution between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_transposition_distance()#: returns transposition between element i,j where i in [0, nbrow] and j in [0,nbcolum]

hierarchical_clustering()#: Clustering using hierarchical methods

property nb_column#: get number of columns

property nb_row#: get number of rows

old_plot(*args, **kargs)#: Old AML style plot

partitioning_clusters()#: to be done

partitioning_prototype()#: to be done

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Graphical output of a (frequency) distribution and the associate hazard or survival rates (ViewPoint=”Survival”): It is assumed that the (frequency) distribution represents lifetime and the hazard or survival rates are deduced from this lifetime distribution. Graphical output of the state profile given by the smoothed probabilities as a function of the index parameter t computed from the parameters of a hidden Markovian model for the sequence (ViewPoint=”StateProfile”).

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Saving of sets of sequences or ‘tops’ (ViewPoint=”Data”): the format “Column” corresponds to the ASCII file syntax for objects of type _Sequences or _Tops. For a given value of the index parameter, the different variables are successively written. With the format “Line”, the univariate sequence for each variable are written on consecutive lines. In the case of univariate sequences, the two formats give the same file.

Saving of a (frequency) distribution and the associate hazard or survival rates (ViewPoint=”Survival”): It is assumed that the (frequency) distribution represents lifetime and the hazard or survival rates are deduced from this lifetime distribution.

Saving of the state profile given by the smoothed probabilities as a function of the index parameter t computed from the parameters of a hidden Markovian model for the sequence (ViewPoint=”StateProfile”).

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

select_individual()#: Select individuals given a list of identifiers

spreadsheet_write()#: Write object to filename (spreadsheet format)

symmetrize()#: symmetrize distance matrix

test_symmetry()#: returns True if symmetric

unnormalize()#: symmetrize distance matrix

class openalea.stat_tool._stat_tool._Compound#

Bases: _StatInterface, _Distribution, StatInterface

Compound

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract_compound()#: Return the compound distribution

extract_data()#: Return the data

extract_elementary()#: Return the elementary distribution

extract_sum()#: Return the sum distribution

file_ascii_write()#: Save Compound into a file

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

get_plotable()#: Return a plotable object

get_plotable_list()#: Return a plotable for a list of distribution

property get_variance#: variance

mass()#: return probability of a given value

property nb_value#: Number of values above zero

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

simulate()#: Simulate nb_element elements

simulation()#: simulate one realization

spreadsheet_write()#: Write object to filename (spreadsheet format)

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._CompoundData#

Bases: _StatInterface, _FrequencyDistribution, StatInterface

Compound data

ascii_write()#: Return a string containing the object description (exhaustive or not)

cluster_information()#: Cluster with information

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: Comparison of frequency distributions

compare_histo(*args, **kargs)#

Comparison of frequency distributions.

Parameters:

histo1, histo2, … (histogram, mixture_data, convolution_data, compound_data),
type (string): variable type (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)).

Keywords:

FileName (string) : name of the result file
Format (string) : format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The comparison result.

Examples:

>>> compare_histo(histo1, histo2, ..., type, FileName="result",
... Format="ASCII")

See also

compound_estimation1()#: Compound distribution estimation

compound_estimation2()#: Compound distribution estimation

convolution_estimation1()#: Convolution estimation

convolution_estimation2()#: Convolution estimation

default_parametric_estimation(iident_id)#

discrete_mixture_estimation1()#: Discrete mixture estimation

discrete_mixture_estimation2()#: Discrete mixture estimation

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

estimate_DiscreteMixture(*args, **kargs)#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

extract()#: Return the data

extract_elementary()#: Return the elementary distribution

extract_sum()#: Return the sum distribution

f_comparison()#: F comparison of frequency distributions

fit()#: Fit frequency distribution

get_plotable()#: Return a plotable object

get_plotable_list()#: Return a plotable for a list of frequency distributions

property mean#

merge()#: Merge frequency distributions

property nb_value#: Get highest possible value (sup_bound)

property offset#: Get offset (inf_bound)

old_plot(*args, **kargs)#: Old AML style plot

parametric_estimation()#: Parametric model estimation

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

shift()#: Shift FrequencyDistribution

spreadsheet_write()#: Write object to filename (spreadsheet format)

t_comparison()#: T comparison of frequency distributions

transcode()#: See Transcode

value_select()#: Selection of individuals according to the values taken by a variable

property variance#: Get variance

wmw_comparison()#: Wilcoxon-Mann-Whitney comparison of frequency distributions

class openalea.stat_tool._stat_tool._Convolution#

Bases: _Distribution, _StatInterface, StatInterface

Convolution Distribution

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract()#: Extract a particular element. First index is 1

extract_convolution()#: Return a _ParametricModel object

extract_data()#: Return the associated _ConvolutionData

extract_elementary()#: Extract a particular element. First index is 1

file_ascii_write()#: Save Convolution into a file

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

get_plotable()#: Return a plotable

get_plotable_list()#: Return a plotable for a list of distribution

property get_variance#: variance

mass()#: return probability of a given value

nb_distribution()#: Return the number of components

property nb_value#: Number of values above zero

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

simulate()#: Simulate elements

simulation()#: simulate one realization

spreadsheet_write()#: Write object to filename (spreadsheet format)

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._ConvolutionData#

Bases: _FrequencyDistribution, _StatInterface, StatInterface

Convolution Data

ascii_write()#: Return a string containing the object description (exhaustive or not)

cluster_information()#: Cluster with information

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: Comparison of frequency distributions

compare_histo(*args, **kargs)#

Comparison of frequency distributions.

Parameters:

histo1, histo2, … (histogram, mixture_data, convolution_data, compound_data),
type (string): variable type (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)).

Keywords:

FileName (string) : name of the result file
Format (string) : format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The comparison result.

Examples:

>>> compare_histo(histo1, histo2, ..., type, FileName="result",
... Format="ASCII")

See also

compound_estimation1()#: Compound distribution estimation

compound_estimation2()#: Compound distribution estimation

convolution_estimation1()#: Convolution estimation

convolution_estimation2()#: Convolution estimation

default_parametric_estimation(iident_id)#

discrete_mixture_estimation1()#: Discrete mixture estimation

discrete_mixture_estimation2()#: Discrete mixture estimation

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

estimate_DiscreteMixture(*args, **kargs)#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

extract()#: Extract a particular element. First index is 1

extract_convolution()#: Return a _DistributionData

extract_elementary()#: Extract a particular element. First index is 1

f_comparison()#: F comparison of frequency distributions

fit()#: Fit frequency distribution

get_frequency_distribution()#: todo

get_plotable()#: Return a plotable (no parameters)

get_plotable_list()#: Return a plotable for a list of frequency distributions

property mean#

merge()#: Merge frequency distributions

nb_distribution()#

property nb_value#: Get highest possible value (sup_bound)

property offset#: Get offset (inf_bound)

old_plot(*args, **kargs)#: Old AML style plot

parametric_estimation()#: Parametric model estimation

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

shift()#: Shift FrequencyDistribution

spreadsheet_write()#: Write object to filename (spreadsheet format)

t_comparison()#: T comparison of frequency distributions

transcode()#: See Transcode

value_select()#: Selection of individuals according to the values taken by a variable

property variance#: Get variance

wmw_comparison()#: Wilcoxon-Mann-Whitney comparison of frequency distributions

class openalea.stat_tool._stat_tool._Curves#

Bases: instance

Curves

property length#

property nb_curve#

property offset#

class openalea.stat_tool._stat_tool._Dendrogram#

Dendrogram

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

get_plotable()#: Return a plotable object

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

spreadsheet_write()#: Write object to filename (spreadsheet format)

class openalea.stat_tool._stat_tool._DiscreteDistributionData#

Bases: _FrequencyDistribution, _StatInterface, StatInterface

ascii_write()#: Return a string containing the object description (exhaustive or not)

cluster_information()#: Cluster with information

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: Comparison of frequency distributions

compare_histo(*args, **kargs)#

Comparison of frequency distributions.

Parameters:

histo1, histo2, … (histogram, mixture_data, convolution_data, compound_data),
type (string): variable type (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)).

Keywords:

FileName (string) : name of the result file
Format (string) : format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The comparison result.

Examples:

>>> compare_histo(histo1, histo2, ..., type, FileName="result",
... Format="ASCII")

See also

compound_estimation1()#: Compound distribution estimation

compound_estimation2()#: Compound distribution estimation

convolution_estimation1()#: Convolution estimation

convolution_estimation2()#: Convolution estimation

default_parametric_estimation(iident_id)#

discrete_mixture_estimation1()#: Discrete mixture estimation

discrete_mixture_estimation2()#: Discrete mixture estimation

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

estimate_DiscreteMixture(*args, **kargs)#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

extract_model()#: Return the ‘model’ part of the frequency distribution

f_comparison()#: F comparison of frequency distributions

file_ascii_write()#: Save frequency distribution into a file

fit()#: Fit frequency distribution

get_plotable()#: Return a plotable (no parameters)

get_plotable_list()#: Return a plotable for a list of frequency distributions

property mean#

merge()#: Merge frequency distributions

property nb_value#: Get highest possible value (sup_bound)

property offset#: Get offset (inf_bound)

old_plot(*args, **kargs)#: Old AML style plot

parametric_estimation()#: Parametric model estimation

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

shift()#: Shift FrequencyDistribution

spreadsheet_write()#: Write object to filename (spreadsheet format)

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a plotable object

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

t_comparison()#: T comparison of frequency distributions

transcode()#: See Transcode

value_select()#: Selection of individuals according to the values taken by a variable

property variance#: Get variance

wmw_comparison()#: Wilcoxon-Mann-Whitney comparison of frequency distributions

class openalea.stat_tool._stat_tool._DiscreteMixture#

Bases: _Distribution, _StatInterface, StatInterface

Construction of a mixture of distributions from elementary distributions and associated weights or from an ASCII file.

A mixture is a parametric model of classification where each elementary distribution or component represents a class with its associated weight.

Parameters:

weight1, weight2, … (float) - weights of each component.
These weights should sum to one (they constitute a discrete distribution).
dist1, dist2, … (_DiscreteParametricModel, _DiscreteMixture, _Convolution, _Compound) elementary distributions (or components).
filename (string) -

Returns:

If the construction succeeds, an object of type mixture is returned, otherwise no object is returned.

Examples:

>>> Mixture(weight1, dist1, weight2, dist2,...)
>>> Mixture(filename)

See also

Save(), Estimate(), Simulate().

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract_component()#: Get a particular component. First index is 1

extract_data()#: Return the associated _DiscreteMixtureData object

extract_mixture()#: Return the DiscreteMixture distribution

extract_weight()#: Return the weight distribution

file_ascii_write()#: Save Mixture into a file

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

get_plotable()#: return plotable

get_plotable_list()#: Return a plotable for a list of distribution

property get_variance#: variance

mass()#: return probability of a given value

property nb_component#: Return the number of components

property nb_value#: Number of values above zero

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

simulate()#: See Simulate

simulation()#: simulate one realization

spreadsheet_write()#: save data in spreadsheet format

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._DiscreteMixtureData#

Bases: _FrequencyDistribution, _StatInterface, StatInterface

DiscreteMixture Data

ascii_write()#: Return a string containing the object description (exhaustive or not)

cluster_information()#: Cluster with information

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: Comparison of frequency distributions

compare_histo(*args, **kargs)#

Comparison of frequency distributions.

Parameters:

histo1, histo2, … (histogram, mixture_data, convolution_data, compound_data),
type (string): variable type (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)).

Keywords:

FileName (string) : name of the result file
Format (string) : format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The comparison result.

Examples:

>>> compare_histo(histo1, histo2, ..., type, FileName="result",
... Format="ASCII")

See also

compound_estimation1()#: Compound distribution estimation

compound_estimation2()#: Compound distribution estimation

convolution_estimation1()#: Convolution estimation

convolution_estimation2()#: Convolution estimation

default_parametric_estimation(iident_id)#

discrete_mixture_estimation1()#: Discrete mixture estimation

discrete_mixture_estimation2()#: Discrete mixture estimation

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

estimate_DiscreteMixture(*args, **kargs)#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

extract_component()#: Get a particular component. First index is 1

extract_mixture()#: Return a _DistributionData

extract_weight()#: Return a _DistributionData

f_comparison()#: F comparison of frequency distributions

file_ascii_write()#: Save Compound into a file

fit()#: Fit frequency distribution

get_component()#: Return the number of components.

get_plotable()#: Return a plotable (no parameters)

get_plotable_list()#: Return a plotable for a list of frequency distributions

property mean#

merge()#: Merge frequency distributions

property nb_component#: Return the number of components.

property nb_value#: Get highest possible value (sup_bound)

property offset#: Get offset (inf_bound)

old_plot(*args, **kargs)#: Old AML style plot

parametric_estimation()#: Parametric model estimation

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

shift()#: Shift FrequencyDistribution

spreadsheet_write()#: Write object to filename (spreadsheet format)

t_comparison()#: T comparison of frequency distributions

transcode()#: See Transcode

value_select()#: Selection of individuals according to the values taken by a variable

property variance#: Get variance

wmw_comparison()#: Wilcoxon-Mann-Whitney comparison of frequency distributions

class openalea.stat_tool._stat_tool._DiscreteParametric#

Bases: _Distribution

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

get_ident()#

property get_inf_bound#

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

property get_parameter#

get_plotable()#: Return a plotable

get_plotable_list()#: Return a plotable for a list of distribution

property get_probability#

property get_sup_bound#

property get_variance#: variance

mass()#: return probability of a given value

property nb_value#: Number of values above zero

simulate()#: Simulation one value

simulation()#: simulate one realization

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._DiscreteParametricModel#

Bases: _DiscreteParametric, _StatInterface, StatInterface

Parametric model

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract_data()#: Return the ‘data’ part of the model

file_ascii_write()#: Return a string containing the object description

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

get_ident()#

property get_inf_bound#

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

property get_parameter#

get_plotable()#

Return a plotable for a list of distribution

Return a plotable (no parameters)

get_plotable_list()#: Return a plotable for a list of distribution

property get_probability#

property get_sup_bound#

property get_variance#: variance

likelihood()#

Return loglikelihood value

mass()#: return probability of a given value

property nb_value#: Number of values above zero

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

simulate()#

Simulate values

Simulate one value

simulation()#: simulate one realization

spreadsheet_write()#: Write object to filename (spreadsheet format)

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a plotable object

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._DistanceMatrix#

Distance Matrix

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

file_ascii_write()#: Save vector summary into a file

get_column_identifier()#: todo

get_deletion_distance()#: returns deletion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_distance()#: returns distance between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_insertion_distance()#: returns insertion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_length()#: returns length between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_deletion()#: returns nb of deletion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_insertion()#: returns nb of insertion between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_match()#: returns nb of match between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_substitution()#: returns nb of substitution between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_nb_transposition()#: returns nb of transposition between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_plotable()#: Return a plotable object

get_row_identifier()#: todo

get_substitution_distance()#: returns substitution between element i,j where i in [0, nbrow] and j in [0,nbcolum]

get_transposition_distance()#: returns transposition between element i,j where i in [0, nbrow] and j in [0,nbcolum]

hierarchical_clustering()#: Clustering using hierarchical methods

property nb_column#: get number of columns

property nb_row#: get number of rows

old_plot(*args, **kargs)#: Old AML style plot

partitioning_clusters()#: to be done

partitioning_prototype()#: to be done

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

select_individual()#: Select individuals given a list of identifiers

spreadsheet_write()#: Write object to filename (spreadsheet format)

symmetrize()#: symmetrize distance matrix

test_symmetry()#: returns True if symmetric

unnormalize()#: symmetrize distance matrix

class openalea.stat_tool._stat_tool._Distribution#

Bases: instance

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

get_plotable()#: Return a plotable

get_plotable_list()#: Return a plotable for a list of distribution

property get_variance#: variance

mass()#: return probability of a given value

property nb_value#: Number of values above zero

simulation()#: simulate one realization

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._Forward#

Bases: _DiscreteParametric

property get_alloc_nb_value#: Number of values with zero probability

property get_complement#: complementary probability

get_ident()#

property get_inf_bound#

property get_max#: probability maximum

property get_mean#: mean

property get_nb_parameter#: number of unknown parameters

property get_parameter#

get_plotable()#: Return a plotable

get_plotable_list()#: Return a plotable for a list of distribution

property get_probability#

property get_sup_bound#

property get_variance#: variance

mass()#: return probability of a given value

property nb_value#: Number of values above zero

simulate()#: Simulation one value

simulation()#: simulate one realization

survival_ascii_write()#: Return a string containing the object description (survival viewpoint)

survival_get_plotable()#: Return a survival plotable

survival_plot_write()#: Write GNUPLOT files (survival viewpoint)

survival_spreadsheet_write()#: Write object to filename (spreadsheet format)

truncate()#: See Truncate

class openalea.stat_tool._stat_tool._FrequencyDistribution#

Bases: instance, EstimateFunctions

cluster_information()#: Cluster with information

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: Comparison of frequency distributions

compare_histo(*args, **kargs)#

Comparison of frequency distributions.

Parameters:

histo1, histo2, … (histogram, mixture_data, convolution_data, compound_data),
type (string): variable type (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)).

Keywords:

FileName (string) : name of the result file
Format (string) : format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The comparison result.

Examples:

>>> compare_histo(histo1, histo2, ..., type, FileName="result",
... Format="ASCII")

See also

compound_estimation1()#: Compound distribution estimation

compound_estimation2()#: Compound distribution estimation

convolution_estimation1()#: Convolution estimation

convolution_estimation2()#: Convolution estimation

default_parametric_estimation(iident_id)#

discrete_mixture_estimation1()#: Discrete mixture estimation

discrete_mixture_estimation2()#: Discrete mixture estimation

estimate_DiscreteMixture(*args, **kargs)#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

f_comparison()#: F comparison of frequency distributions

fit()#: Fit frequency distribution

get_plotable()#: Return a plotable (no parameters)

get_plotable_list()#: Return a plotable for a list of frequency distributions

property mean#

merge()#: Merge frequency distributions

property nb_value#: Get highest possible value (sup_bound)

property offset#: Get offset (inf_bound)

parametric_estimation()#: Parametric model estimation

shift()#: Shift FrequencyDistribution

t_comparison()#: T comparison of frequency distributions

transcode()#: See Transcode

value_select()#: Selection of individuals according to the values taken by a variable

property variance#: Get variance

wmw_comparison()#: Wilcoxon-Mann-Whitney comparison of frequency distributions

class openalea.stat_tool._stat_tool._MultivariateMixture#

Multivariate Mixture Distribution

_criteria()#: Extract the value of each selection criterion

_is_parametric(self, variable) → bool.#: Return True if the variable is parametric

ascii_write()#: Return a string containing the object description (exhaustive or not)

cluster_data(self, Vectors, entropy) → _MultivariateMixtureData.#: Cluster data using the _MultivariateMixture model

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract_data()#: Return the associated _MultivariateMixtureData object

extract_distribution()#: Extraction of marginal for a given variable

extract_mixture(self, variable) → _Distribution.#: Return the _MultivariateMixture distribution

extract_weight()#: Return the weight distribution

file_ascii_write(self, path, exhaustive_flag) → None.#: Save _MultivariateMixture into a file

get_plotable()#: return plotable

property nb_component#: Return the number of variables

property nb_variable#: Return the number of variables

old_plot(variable, Title='')#: Plot a given variable

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(variable, Title='')#: Print a given variable into .ps file

plot_write(self, prefix, title) → None.#: Write GNUPLOT files

save(file_name, format='ASCII', overwrite=False)#

Save MultivariateMixture object into a file.

Argument file_name is a string designing the file name and path. String argument format must be “ASCII” or “SpreadSheet”. Boolean argument overwrite is false is the file should not be overwritten.

save_backup(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

simulate(self, nb_element) → _MultivariateMixtureData.#: Simulate nb_element elements

spreadsheet_write()#: save data in spreadsheet format

state_permutation(perm)#

Permutation of the states of self. perm[i]==j means that current state i will become new state j.

Usage: state_permutation(list)

state_permutation_backup()#: permutation of the model states

class openalea.stat_tool._stat_tool._MultivariateMixtureData#

Bases: _Vectors, StatInterface

Multivariate Mixture Data

ascii_data_write()#: Return a string with the object representation

ascii_write()#: Return a string containing the object description (exhaustive or not)

check()#: todo check vectors

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: TODOCompare Vectors given a VectorDistance

contingency_table()#: See ContingencyTable

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract()#: Extract histogram

extract_component()#: Get a particular component for a particular variable. First index is 1

extract_marginal()#: Return a _MultivariateMixtureData for a particular variable.

extract_mixture()#: Return a _MultivariateMixtureData for mixture model

extract_weight()#: Return a _MultivariateMixtureData for mixture weights.

file_ascii_data_write()#: Save vector data into a file

file_ascii_write()#: Save _MultivariateMixtureData into a file

file_spreadsheet_write()#: Save _MultivariateMixtureData into a file

get_identifiers()#: function that returns the list of identifiers

get_marginal_histogram()#: get_marginal_histogram(nb_variable) construct marginal histogram of the vector given for the variable provided. The variable must be >=0 and less than nb_variable.

get_max_value()#: Return the max value of a variable. Example: v.get_max_value(4)

get_mean()#: Return the mean value of a vector. examepl v.get_mean(4)

get_min_value()#: Return the min value of a variable. Example: v.get_min_value(4)

get_nb_component()#: Return the number of components.

get_plotable()#: return plotable

get_type()#: Return the type of a variable. example v.get_type(4)

linear_regression()#: TODO Linear regression

merge()#: See Merge

merge_variable()#: See MergeVariable

mixture_estimation(model, nb_iteration=-1, force_param=None)#: Estimate a mixture from _Vectors given initial model or number of components, the maximal number of iterations and a flag for using parametric observation distributions or not, within a given family

mixture_estimation_model()#: TODO Mixture estimation (EM algorithm with initial model)

mixture_estimation_nb_component()#: TODO Mixture estimation (EM algorithm with fixed number of components)

moving_average_regression_distribution()#: Linear regression (See MovingAverage)

moving_average_regression_values()#: Linear regression (MovingAverage)

property nb_variable#: Return the number of variables

property nb_vector#: Return the number of vectors

nearest_neighbours_regression()#: TODO Linear regression (nearest neighbours)

old_plot(variable, Title='')#: Plot a given variable

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files

rank_correlation_computation()#: Rank correlation computation

round()#: See Round

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

scaling()#: TODOScales vectors

select_bin_width()#: select_bin_width(bin_width) redefine the bin_width of the histogram. bin_width must be >0

select_individual()#: See SelectIndividual

select_variable()#: See SelectVariable

shift()#: See Shift

spreadsheet_write()#: save data in spreadsheet format

transcode()#: See Transcode

value_select()#: See ValueSelect

variance_analysis()#

See VarianceAnalysis

Return a string with the variance analysis

class openalea.stat_tool._stat_tool._Regression#

Regression class

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

file_ascii_write()#: Save regression summary into a file

file_spreadsheet_write()#: Save regression summary into a CSV file

get_plotable()#: Return a plotable object

get_residual()#: Return nb_vector

get_vectors()#: return vectors

property nb_vector#: Return nb_vector

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

spreadsheet_write()#: Write object to filename (spreadsheet format)

class openalea.stat_tool._stat_tool._RegressionKernel#

Bases: instance

Regression kernel class

class openalea.stat_tool._stat_tool._SetSeed#

Bases: instance

SetSeed

set_seed()#: Set seed

class openalea.stat_tool._stat_tool._StatError#

Bases: instance

correction_update()#

get_max_nb_error()#

get_nb_error()#

init()#

update()#

class openalea.stat_tool._stat_tool._StatInterface#

Bases: instance

ascii_write()#: Return a string containing the object description (exhaustive or not)

get_plotable()#: Return a plotable object

plot_write()#: Write GNUPLOT files (with prefix)

spreadsheet_write()#: Write object to filename (spreadsheet format)

class openalea.stat_tool._stat_tool._VectorDistance#

Distance class

ascii_write()#: Return a string containing the object description (exhaustive or not)

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

file_ascii_write()#: Save vector distance summary into a file

get_distance_type()#: return distance type

get_plotable()#: Return a plotable object

property nb_variable#: return number of variable

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files (with prefix)

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

spreadsheet_write()#: Save data into CSV file

class openalea.stat_tool._stat_tool._Vectors#

Vectors (2 dimensions list)

ascii_data_write()#: Return a string with the object representation

ascii_write()#: Return a string containing the object description (exhaustive or not)

check()#: todo check vectors

cluster_limit()#: See Cluster

cluster_step()#: See Cluster

compare()#: TODOCompare Vectors given a VectorDistance

contingency_table()#: See ContingencyTable

display(*args, **kargs)#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

extract()#: Extract histogram

file_ascii_data_write()#: Save vector data into a file

file_ascii_write()#: Save vector summary into a file

get_identifiers()#: function that returns the list of identifiers

get_marginal_histogram()#: get_marginal_histogram(nb_variable) construct marginal histogram of the vector given for the variable provided. The variable must be >=0 and less than nb_variable.

get_max_value()#: Return the max value of a variable. Example: v.get_max_value(4)

get_mean()#: Return the mean value of a vector. examepl v.get_mean(4)

get_min_value()#: Return the min value of a variable. Example: v.get_min_value(4)

get_plotable()#: Return a plotable

get_type()#: Return the type of a variable. example v.get_type(4)

linear_regression()#: TODO Linear regression

merge()#: See Merge

merge_variable()#: See MergeVariable

mixture_estimation(model, nb_iteration=-1, force_param=None)#: Estimate a mixture from _Vectors given initial model or number of components, the maximal number of iterations and a flag for using parametric observation distributions or not, within a given family

mixture_estimation_model()#: TODO Mixture estimation (EM algorithm with initial model)

mixture_estimation_nb_component()#: TODO Mixture estimation (EM algorithm with fixed number of components)

moving_average_regression_distribution()#: Linear regression (See MovingAverage)

moving_average_regression_values()#: Linear regression (MovingAverage)

property nb_variable#: Return the number of variables

property nb_vector#: Return the number of vectors

nearest_neighbours_regression()#: TODO Linear regression (nearest neighbours)

old_plot(*args, **kargs)#: Old AML style plot

plot(*args, **kargs)#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)#: Old AML style print into .ps file

plot_write()#: Write GNUPLOT files

rank_correlation_computation()#: Rank correlation computation

round()#: See Round

save(filename, Detail=2, ViewPoint='', Format='ASCII')#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

scaling()#: TODOScales vectors

select_bin_width()#: select_bin_width(bin_width) redefine the bin_width of the histogram. bin_width must be >0

select_individual()#: See SelectIndividual

select_variable()#: See SelectVariable

shift()#: See Shift

spreadsheet_write()#: Save data into CSV file

transcode()#: See Transcode

value_select()#: See ValueSelect

variance_analysis()#

See VarianceAnalysis

Return a string with the variance analysis

class openalea.stat_tool._stat_tool.moving_average_method#

Bases: enum

AVERAGING = openalea.stat_tool._stat_tool.moving_average_method.AVERAGING#

LEAST_SQUARES = openalea.stat_tool._stat_tool.moving_average_method.LEAST_SQUARES#

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.

name#

names = {'AVERAGING': openalea.stat_tool._stat_tool.moving_average_method.AVERAGING, 'LEAST_SQUARES': openalea.stat_tool._stat_tool.moving_average_method.LEAST_SQUARES}#

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.

values = {0: openalea.stat_tool._stat_tool.moving_average_method.AVERAGING, 1: openalea.stat_tool._stat_tool.moving_average_method.LEAST_SQUARES}#

class openalea.stat_tool._stat_tool.std_vector_int#

Bases: instance

append()#

extend()#

Data structures #

`openalea.stat_tool.compound` module#

Compound module

openalea.stat_tool.compound.Compound(*args, **kargs)[source]#

Construction of a compound of distributions from a sum distribution and an elementary distribution or from an ASCII file.

A compound (or stopped-sum) distribution is defined as the distribution of the sum of n independent and identically distributed random variables $X_i$ where n is the value taken by the random variable N. The distribution of $N$ is referred to as the sum distribution while the distribution of the $X_i$ is referred to as the elementary distribution.

Parameters:

sum_dist (distribution, mixture, convolution, compound) – sum distribution
dist (distribution, mixture, convolution, compound) – elementary distribution
filename (string)

Returns:

If the construction succeeds, an object of type COMPOUND is returned, otherwise no object is returned.

Examples:

>>> Compound(sum_dist, dist)
>>> Compound(sum_dist, dist, Threshold=0.999)
>>> Compound(filename)

from openalea.stat_tool import *
sum_dist = Binomial(0,10,0.5)
dist = Binomial(0,15,0.2)
c = Compound(sum_dist, dist)
c.plot()

See also

Save(), Estimate(), Simulate()

`openalea.stat_tool.convolution` module#

Convolution module

openalea.stat_tool.convolution.Convolution(*args)[source]#

Construction of an object of type convolution from elementary distributions or from an ASCII file.

The convolution of independent random variables is the distribution of their sum.

Parameters:

dist1, dist2, …(distribution, mixture, convolution, compound) - elementary distributions,
file_name (string).

Returns:

If the construction succeeds, the returned object is of type convolution, otherwise no object is returned.

Examples:

>>> Convolution(dist1, dist2, ...)
>>> Convolution(file_name)

from openalea.stat_tool import *
dist1 = Binomial(0,10,0.5)
dist2 = Binomial(0,15,0.2)
c = Convolution(dist1, dist2)
c.plot()

See also

Save(), Estimate(), Simulate().

`openalea.stat_tool.distribution` module#

Distributions

openalea.stat_tool.distribution.Binomial(inf_bound, sup_bound=-1, proba=-1.0)[source]#

Construction of a binomial distribution

Parameters:

inf_bound (float) – lower bound to the range of possible values (shift parameter)
sup_bound (float) – upper bound to the range of possible values
proba (float) – probability of success

from openalea.stat_tool.distribution import Binomial
b = Binomial(0,10,0.5)
b.plot(legend_size=8)

openalea.stat_tool.distribution.Distribution(utype, *args)[source]#

Construction of a parametric discrete distribution (either binomial, Poisson, negative binomial or uniform) from the name and the parameters of the distribution or from an ASCII file.

A supplementary shift parameter (argument inf_bound) is required with respect to the usual definitions of these discrete distributions. Constraints over parameters are given in the file syntax corresponding to the type distribution(cf. File Syntax).

Parameters:

inf_bound (int) : lower bound to the range of possible values (shift parameter),
sup_bound (int) : upper bound to the range of possible values (only relevant for binomial or uniform distributions),
param (int, real) : parameter of either the Poisson distribution or the negative binomial distribution.
proba (int, float) : probability of success (only relevant for binomial or negative binomial distributions),
file_name (string).

Note

the names of the parametric discrete distributions can be summarized by their first letters:

“B” (“BINOMIAL”),
“P” (“POISSON”),
“NB” (“NEGATIVE_BINOMIAL”),
“U” (“UNIFORM”),
“M” (“MULTINOMIAL”),

Returns:

If the construction succeeds, an object of type distribution is returned, otherwise no object is returned.

Examples:

>>> Distribution("BINOMIAL", inf_bound, sup_bound, proba)
>>> Distribution("POISSON", inf_bound, param)
>>> Distribution("NEGATIVE_BINOMIAL", inf_bound, param, proba)
>>> Distribution("UNIFORM", inf_bound, sup_bound)
>>> Distribution(file_name)

See also

Save(), Estimate() Simulate().

openalea.stat_tool.distribution.Multinomial(probabilities)[source]#

Construction of a categorical distribution. A categorical distribution is implemented as a particular case of the multinomial distribution ${\mathcal{M}}(1; p_1, \ldots, p_K)$

Parameters:

probabilities (list): list of probabilities $p_1,\ldots,p_K$

from openalea.stat_tool.distribution import Multinomial
m = Multinomial([0.1, 0.6, .3])
from openalea.stat_tool import Histogram
Histogram([m.simulation() for i in range(100)]).plot()

openalea.stat_tool.distribution.NegativeBinomial(inf_bound, param=-1.0, proba=-1.0)[source]#

Construction of a negative binomial distribution The negative binomial distribution has the following parameterization:

$P(X-inf\_bound=i) = \frac{\Gamma(param+i)}{i! \Gamma(param)} p^{param} (1-p)^i$

Parameters:

inf_bound (int) : lower bound to the range of possible values (shift parameter)
param (int, float) : parameter of the Negative Binomial distribution
proba (int, float) : probability of ‘success’

from openalea.stat_tool.distribution import NegativeBinomial
b = NegativeBinomial(1,2,.5)
b.plot(legend_size=12)

openalea.stat_tool.distribution.Poisson(inf_bound, param=-1.0)[source]#

Construction of a Poisson distribution

Parameters:

inf_bound (int) : lower bound to the range of possible values (shift parameter)
param (int, float) : parameter of the Poisson distribution

from openalea.stat_tool.distribution import Poisson
b = Poisson(1,1.5)
b.plot(legend_size=8)

openalea.stat_tool.distribution.ToDistribution(histo)[source]#

Cast an object of type _DiscreteDistributionData into an object of type _Distribution.

Parameters:

histo (DiscreteDistributionData)

Returns:

If the object histo contains a ‘model’ part, an object of type _Distribution is returned, otherwise no object is returned.

Examples:

>>> ToDistribution(histo)

See also

ToHistogram()

openalea.stat_tool.distribution.ToHistogram(dist)[source]#

Cast an object of type _Distribution into an object of type _DiscreteDistributionData.

Parameters:

dist (distribution).

Returns:

If the object dist contains a ‘data’ part, an object of type _DiscreteDistributionData is returned, otherwise no object is returned.

Examples:

>>> ToHistogram(dist)

See also

ToDistribution()

openalea.stat_tool.distribution.Uniform(inf_bound, sup_bound=-1)[source]#

Construction of a uniform distribution

Parameters:

inf_bound (int) : lower bound to the range of possible values (shift parameter)
sup_bound (int) : upper bound to the range of possible values

from openalea.stat_tool.distribution import Uniform
b = Uniform(1,10)
b.plot(legend_size=8)

openalea.stat_tool.distribution.set_seed()#: Set seed

`openalea.stat_tool.histogram` module#

Histogram module

openalea.stat_tool.histogram.Histogram(*args)[source]#

Construction of a frequency distribution from an object of type list(int) or from an ASCII file.

In the file syntax, the frequencies fi for each possible value i are given in two columns. In the case of an argument of type (list(int)), it is simply assumed that each array element represents one data item.

Parameters:

list (integer) – a list of integer values
filename (string) – a valid filename in the proper format (see syntax part)

Returns:

If the construction succeeds, an object of type _DiscreteDistributionData is returned.

Usage:

>>> Histogram(list)
>>> Histogram(filename)

Examples:

from openalea.stat_tool import *
from numpy.random import randint
h = Histogram(randint(10, size=10000).tolist())
h.plot()

Note

works for integer values only.

`openalea.stat_tool.mixture` module#

Mixture object

openalea.stat_tool.mixture.Mixture(*args)[source]#

Construction of a mixture of distributions from elementary distributions and associated weights or from an ASCII file.

A mixture is a parametric model of classification where each elementary distribution or component represents a class with its associated weight.

Parameters:

weight1, weight2, … (float) - weights of each component.
These weights should sum to one (they constitute a discrete distribution).
dist1, dist2, … (_DiscreteParametricModel, _DiscreteMixture, _Convolution, _Compound) elementary distributions (or components).
filename (string) -

Returns:

If the construction succeeds, an object of type mixture is returned, otherwise no object is returned.

Examples:

>>> Mixture(weight1, dist1, weight2, dist2,...)
>>> Mixture(filename)

See also

Save(), Estimate(), Simulate().

`openalea.stat_tool.vectors` module#

Vectors function and class

openalea.stat_tool.vectors.ComputeRankCorrelation()[source]#

Computation of the rank correlation matrix.

Usage:

>>> vec = Vectors([1,2,3,4,5,4,3,2,1])
>>> ComputeRankCorrelation(vec, Type="Spearman", FileName='')

Arguments:

vec (vectors).

Optional Arguments:

Type (string): type of rank correlation coefficient: “Spearman” (the default) or “Kendall”.

Returned Object:

No object returned.

openalea.stat_tool.vectors.ContingencyTable(*args, **kargs)[source]#

Computation of a contingency table.

Parameters:

vec (_Vectors),
variable1, variable2 (int): variable indices,

Keywords:

FileName (string): name of the result file,
Format (string): format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The contingency table result as a string

Examples:

>>> ContingencyTable(vec, variable1, variable2, FileName="result", Format="SpreadSheet")

class openalea.stat_tool.vectors.OutputFormat#

Bases: enum

ASCII = openalea.stat_tool._stat_tool.OutputFormat.ASCII#

GNUPLOT = openalea.stat_tool._stat_tool.OutputFormat.GNUPLOT#

PLOT = openalea.stat_tool._stat_tool.OutputFormat.PLOT#

SPREADSHEET = openalea.stat_tool._stat_tool.OutputFormat.SPREADSHEET#

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.

name#

names = {'ASCII': openalea.stat_tool._stat_tool.OutputFormat.ASCII, 'GNUPLOT': openalea.stat_tool._stat_tool.OutputFormat.GNUPLOT, 'PLOT': openalea.stat_tool._stat_tool.OutputFormat.PLOT, 'SPREADSHEET': openalea.stat_tool._stat_tool.OutputFormat.SPREADSHEET}#

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.

values = {0: openalea.stat_tool._stat_tool.OutputFormat.ASCII, 1: openalea.stat_tool._stat_tool.OutputFormat.SPREADSHEET, 2: openalea.stat_tool._stat_tool.OutputFormat.GNUPLOT, 3: openalea.stat_tool._stat_tool.OutputFormat.PLOT}#

openalea.stat_tool.vectors.VarianceAnalysis(*args, **kargs)[source]#

One-way variance analysis.

Examples:

>>> VarianceAnalysis(vec, class_variable, response_variable,
... type, FileName="result", Format="SpreadSheet")

Parameters:

vec (_Vectors),
class_variable (int): index of the class or group variable,
response_variable (int): index of the response variable,
type (string): type of the response variable (“NUMERIC” (“N”) or “ORDINAL” (“O”)).

Keywords:

FileName (string): name of the result file,
Format (string): format of the result file: “ASCII” (default format) or “SpreadSheet”. This optional argument can only be used in conjunction with the optional argument FileName.

Returns:

The variance analysis result as a string

openalea.stat_tool.vectors.VectorDistance(*args, **kargs)[source]#

Construction of an object of type vector_distance from types (and eventually weights) of variables or from an ASCII file.

The type _VectorDistance implements standardization procedures. The objective of standardization is to avoid the dependence on the variable type (chosen among symbolic, ordinal, numeric and circular) and, for numeric variables, on the choice of the measurement units by converting the original variables to unitless variables.

Parameters:

type1, type2, … (string): variable types (“NUMERIC” (“N”), “ORDINAL” (“O”) or “SYMBOLIC” (“S”)),
weight1, weight2, … (float): weights of variables,
file_name (string).

Keywords:

Distance (string): distance type: “ABSOLUTE_VALUE” (default) or “QUADRATIC”. This optional argument is only relevant in the multivariate case.

Returns:

If the construction succeeds, an object of type vector_distance is returned.

Examples:

>>> VectorDistance(type1, type2,..., Distance="QUADRATIC")
>>> VectorDistance(weight1, type1, weight2, type2,..., Distance="QUADRATIC")
>>> VectorDistance(file_name)

See also

openalea.stat_tool.vectors.Vectors(*args, **kargs)[source]#

Construction of a set of vectors from a multidimensional array, from a set of sequences or from an ASCII file.

The data structure of type list(list(int)) should be constituted at the most internal level of arrays of constant size.

Parameters:

list (list(list(int))) :
seq (sequences, discrete_sequences, markov_data, semi-markov_data)
file_name (string) :

Keywords:

Identifiers (list(int)): explicit identifiers of vectors. This optional argument can only be used if the first mandatory argument is of type list(list(int)).
IndexVariable (bool): taking into account of the implicit index parameter as a supplementary variable (default value: False). This optional argument can only be used if the first mandatory argument is of type sequences, discrete_sequences, markov_data or semi-markov_data.

Returns:

If the construction succeeds, an object of type vectors is returned, otherwise no object is returned.

Examples:

>>> Vectors(list, Identifiers=[1, 8, 12])
>>> Vectors(seq, IndexVariable=True)
>>> Vectors(file_name)

See also

Save(), ExtractHistogram(), Cluster(), Merge(), MergeVariable(), SelectIndividual(), SelectVariable(), Shift(), Transcode(), ValueSelect(), Compare(), ComputeRankCorrelation(), ContingencyTable(), Regression(), VarianceAnalysis()

`openalea.stat_tool.multivariate_mixture` module#

Multivariate mixture

Functionalities #

`openalea.stat_tool.regression` module#

Regression functions

openalea.stat_tool.regression.Regression(vec, utype, explanatory, response, *args, **kargs)[source]#

Simple regression (with a single explanatory variable).

Parameters:

vec : vectors vectors
type : string “Linear” or “MovingAverage” or “NearestNeighbors”
explanatory_variable : int index of the explanatory variable
response_variable : int index of the response variable
filter : list of float filter values on the half width i.e. from one extremity to the central value (with the constraint sum(filter) = 1),
frequencies : list of float frequencies defining the filter,
dist : distribution, mixture, convolution, compound symmetric distribution, whose size of the support is even, defining the filter (for instance Distribution(“BINOMIAL”,0,4,0.5)),
span : float proportion of individuals in each neighbourhood.

Keywords:

Algorithmstring
- “Averaging” (default)
- “LeastSquares”
This optional argument can only be used if the second mandatory argument specifying the regression type is “MovingAverage”.
Weighting : bool weighting or not of the neighbors according to their distance to the computed point (default value: True). This optional argument can only be used if the second mandatory argument specifying the regression type is “NearestNeighbors”.

Returns:

An object of type regression is returned.

Examples:

>>> Regression(vec, "Linear", explanatory_variable, response_variable)
>>> Regression(vec, "MovingAverage", explanatory_variable,
...    response_variable, filter, Algorithm="LeastSquares")
>>> Regression(vec, "MovingAverage", explanatory_variable,
..     response_variable, frequencies, Algorithm="LeastSquares")
>>> Regression(vec, "MovingAverage", explanatory_variable,
...    response_variable, dist, Algorithm="LeastSquares")
>>> Regression(vec, "NearestNeighbors", explanatory_variable,
...    response_variable, span, Weighting=False)

See also

Plot()

`openalea.stat_tool.simulate` module#

Simulate functions

openalea.stat_tool.simulate.Simulate(obj, *args)[source]#

Generation of a random sample from a distribution.

Parameters:

dist (distribution),
mixt (mixture)
convol (convolution)
compound (compound),
size (int): sample size.

Returns:

If the first argument is of type distribution and if 0 < size < 1000000, an object of type HISTOGRAM is returned, otherwise no object is returned. If the first argument is of type mixture and if 0 < size < 1000000, an object of type mixture_data is returned, otherwise no object is returned. If the first argument is of type convolution and if 0 < size < 1000000, an object of type convolution_data is returned, otherwise no object is returned. If the first argument is of type compound and if 0 < size < 1000000, an object of type compound_data is returned, otherwise no object is returned. The returned object of type HISTOGRAM, mixture_data, convolution_data or compound_data contains both the simulated sample and the model used for simulation.

Example:

>>> Simulate(dist, size)
>>> Simulate(mixt, size)
>>> Simulate(convol, size)
>>> Simulate(compound, size)

See Also:: Distribution, Mixture, Convolution, Compound, ExtractHistogram.

`openalea.stat_tool.cluster` module#

Cluster functions and classes

openalea.stat_tool.cluster.Cluster(obj, utype, *args, **kargs)[source]#

Clustering of values.

In the case of the clustering of values of a frequency distribution on the basis of an information measure criterion (argument Information), both the information measure ratio and the selected optimal step are given in the shell window.

The clustering mode Step (and its variant Information) is naturally adapted to numeric variables while the clustering mode Limit applies to both symbolic (nominal) and numeric variables. In the case of a symbolic variable, the function Cluster with the mode Limit can be seen as a dedicated interface of the more general function Transcode.

Parameters:

histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
step (int) - step for the clustering of values
information_ratio (float) - proportion of the information measure of the original sample for determining the clustering step,
limits (list(int)) - first values corresponding to the new classes classes 1, …, nb_class - 1. By convention, the first value corresponding to the first class is 0,
vec1 (_Vector) - values,
vecn (_Vectors) - vectors,
variable (int) - variable index,
seq1 (_Sequences) - univariate sequences,
seqn (_Sequences) - multivariate sequences,
discrete_seq1 (_DiscreteSequences, _Markov, _SemiMarkovData) - discrete univariate sequences,
discrete_seqn (_DiscreteSequences, _Markov, _SemiMarkovData) - discrete multivariate sequences.

Keywords:

AddVariable (bool) : addition (instead of simple replacement) of the variable corresponding to the clustering of values (default value: False). This optional argument can only be used if the first argument is of type _DiscreteSequences, _Markov or _SemiMarkovData. The addition of the clustered variable is particularly useful if one wants to evaluate a lumpability hypothesis.

Returns:

If step > 0, or if 0 < information_ratio < 1, or if 0 < limits[1] < limits[2] < … < limits[nb_class - 1] < (maximum possible value of histo), an object of type _FrequencyDistribution is returned.
If variable is a valid index of a variable and if step > 0, or if 0 < limits[1] < limits[2] < … < limits[nb_class - 1] < (maximum possible value taken by the selected variable of vec1 or vecn), an object of type _Vectors is returned.
If variable is a valid index of a variable of type STATE and if step > 0, or if 0 < limits[1] < limits[2] < … < limits[nb_class - 1] < (maximum possible value taken by the selected variable of seq1, seqn, discrete_seq1 or discrete_seqn), an object of type _Sequences or _DiscreteSequences is returned.
In the case of a first argument of type _Sequences, an object of type _DiscreteSequences is returned if all the variables are of type STATE, if the possible values taken by each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> Cluster(histo, "Step", step)
>>> Cluster(histo, "Information", information_ratio)
>>> Cluster(histo, "Limit", limits)
>>> Cluster(vec1, "Step", step)
>>> Cluster(vecn, "Step", variable, step)
>>> Cluster(vec1, "Limit", limits)
>>> Cluster(vecn, "Limit", variable, limits)
>>> Cluster(seq1, "Step", step)
>>> Cluster(seqn, "Step", variable, step)
>>> Cluster(discrete_seq1, "Step", step, AddVariable=True)
>>> Cluster(discrete_seqn, "Step", variable, step, AddVariable=True)
>>> Cluster(seq1, "Limit", limits)
>>> Cluster(seqn, "Limit", variable, limits)
>>> Cluster(discrete_seq1, "Limit", limits, AddVariable=True)
>>> Cluster(discrete_seqn, "Limit", variable, limits, AddVariable=True)

See also

Merge(), Shift(), ValueSelect(), MergeVariable(), SelectIndividual(), SelectVariable(), Transcode(), AddAbsorbingRun(), Cumulate(), Difference(), IndexExtract(), LengthSelect(), MovingAverage(), RecurrenceTimeSequences(), Removerun(), Reverse(), SegmentationExtract(), VariableScaling().

openalea.stat_tool.cluster.Clustering(matrix, utype, *args, **kargs)[source]#

Application of clustering methods (either partitioning methods or hierarchical methods) to dissimilarity matrices between patterns.

In the case where the composition of clusters is a priori fixed, the function Clustering simply performs an evaluation of the a priori fixed partition.

Parameters:

dissimilarity_matrix (distance_matrix) - dissimilarity matrix between patterns,
nb_cluster (int) - number of clusters,
clusters (list(list(int))) - cluster composition.

Keywords:

Prototypes (list(int)): cluster prototypes.
Algorithm (string): “Agglomerative”, “Divisive” or “Ordering”
Criterion (string): “FarthestNeighbor” or “Averaging”
Filename (string): filename
Format (string) : “ASCII” or “SpreadSheet”

Returns:

If the second mandatory argument is “Partitioning” and if 2 < nb_cluster < (number of patterns), an object of type clusters is returned

Examples:

>>> Clustering(dissimilarity_matrix, "Partition", nb_cluster, Prototypes=[1, 3, 12])
>>> Clustering(dissimilarity_matrix, "Partition", clusters)
>>> Clustering(dissimilarity_matrix, "Hierarchy", Algorithm="Agglomerative")
>>> Clustering(dissimilarity_matrix, "Hierarchy", Algorithm="Divisive")

Note

if type=Partition, Algorthim must be 1 (divisive) or 2 (ordering).

Note

if type!=Divisive criterion must be provided

openalea.stat_tool.cluster.ToDistanceMatrix(distance_matrix)[source]#

Cast and object of type CLUSTER into an object of type DISTANCE_MATRIX.

Parameters:

distance_matrix

Returns:

An object of type distance_matrix is returned.

Examples:

>>> ToDistanceMatrix(distance_matrix)

See also

Clustering(),

openalea.stat_tool.cluster.Transcode(obj, *args, **kargs)[source]#

Transcoding of values.

The function Cluster with the mode “Limit” can be seen as a dedicated interface of the more general function Transcode.

Parameters:

histo (_FrequencyDistribution, _MixtureData, _ConvolutionData, _CompoundData),
new_values (array(int)) - new values replacing the old ones min, min + 1, …, max.
vec1 (_Vectors) - values,
vecn (_Vectors) - vectors,
variable (int) - variable index,
seq1 (_Sequences) - univariate sequences,
seqn (_Ssequences) - multivariate sequences,
discrete_seq1 (_DiscreteSequences, _MarkovData, _SemiMarkovData) - discrete univariate sequences,
discrete_seqn (_DiscreteSequences, _MarkovData, _SemiMarkovData) - discrete multivariate sequences.

Keywords:

AddVariable (bool): addition (instead of simple replacement) of the variable to which the transcoding is applied (default value: False). This optional argument can only be used if the first argument is of type (_DiscreteSequences, _MarkovData, _SemiMarkovData).

Returns:

If the new values are in same number as the old values and are consecutive from 0, an object of type _FrequencyDistribution is returned (respectively _Vectors, _Sequences or _DiscreteSequences). In the case of a first argument of type _Sequences, the returned object is of type _DiscreteSequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> Transcode(histo, new_values)
>>> Transcode(vec1, new_values)
>>> Transcode(vecn, variable, new_values)
>>> Transcode(seq1, new_values)
>>> Transcode(seqn, variable, new_values)
>>> Transcode(discrete_seq1, new_values, AddVariable=True)
>>> Transcode(discrete_seqn, variable, new_values, AddVariable=True)

See also

Clustering(), Merge(), Shift(), ValueSelect(), MergeVariable(), SelectIndividual(), SelectVariable(), Cumulate(), AddAbsorbingRun(), Cumulate(), Difference(), IndexExtract(), LengthSelect(), MovingAverage(), RecurrenceTimeSequences(), Removerun(), Reverse(), SegmentationExtract(), VariableScaling().

`openalea.stat_tool.comparison` module#

Comparison

openalea.stat_tool.comparison.Compare(arg1, *args, **kargs)[source]#

Comparison functions factory

Parameters:

arg1 should be in :
- compare_histo : Histograms comparison
- compare_vectors : Vectors comparison
- compare_seq : Sequences comparison
- compare_markov : Markovian models comparison

See also

compare_histo(), compare_vectors() compare_seq() compare_markov()

openalea.stat_tool.comparison.ComparisonTest(utype, histo1, histo2)[source]#

Test of comparaison of frequency distributions.

The objective is to compare two independent random samples in order to decide if they have been drawn from the same population or not. In the case of samples from normal populations, the Fisher-Snedecor (“F”) test enables to test is the two variances are not significantly different. The normal distribution assumption should be checked for instance by the exam of the shape coefficients (skewness and kurtosis coefficients). The test statistic is:

$F_{n_1-1,n_2-1} = \frac { \frac{\displaystyle\sum_{i=1}^{n_1}\left( x_{1i}-m_1 \right)^2}{n_1-1} } { \frac{\displaystyle\sum_{i=1}^{n_2}\left( x_{2i}-m_2 \right)^2}{n_2-1} }$

where $m_1$ and $m_2$ are the means of the samples.

The Fisher-Snedecor variable $F_{n_1-1,n_2-1}$ with $n_1-1$ degrees of freedom and $n_2-1$ degrees of freedom can be interpreted as the ratio of the variance estimators of the two samples. In practice, the larger estimated variance is put at the denominator. Hence $F_{n_1-1,n_2-1} \geq 1$ . The critical region is of the form $F_{n_1-1,n_2-1} > f$ (one-sided test).

In the case of samples from normal populations with equal variances, the Student (“T”) test enables to test if the two means are not significantly different. The test statistic is:

$T_{n_1+n_2 - 2} = \frac{m_1 - m_2}{ \sqrt{\left( \displaystyle\sum_{i=1}^{n_1}\left( x_{1i}-m_1 \right)^2{n_1-1} + \displaystyle\sum_{i=1}^{n_2}\left( x_{2i}-m_1 \right)^2{n_2-1} \right) \left( \frac{1}{n_1} + \frac{1}{n_2}\right) } } \sqrt{n_1 + n_2 - 2}$

The critical region is of the form $\left| T_{n_1+n_2-2}\right| > t$ (two-sided test). For sufficiently large sample sizes, this test of sample mean comparison can be used for samples from non-normal populations with unequal variances. This test is said to be robust.

The Wilcoxon-Mann-Whitney (“W”) test is a distribution-free test relying on the homogeneity of the ranking of the two sample (ranks of one sample should not cluster at either or both ends of the range). It can be seen as the non-parametric analog of the Student’s t test and can be applied to compare two sets of observations measures on an interval scale when it is supposed that the data are non-normally distributed, or to compare two sets of observations measured on an ordinal scale.

Parameters:

type(string) : type of test “F” (Fisher-Snedecor), “T” (Student) or “W” (Wilcoxon-Mann-Whitney)
histo1, histo2 (Histogram, MixtureData, ConvolutionData, CompoundData)

Returns:

A string containing the result of the tests

Examples:

>>> ComparisonTest(type, histo1, histo2)

`openalea.stat_tool.data_transform` module#

Data transformation functions

Warning

sequence analysis package also contain a data transform module

openalea.stat_tool.data_transform.Extract(obj, *args, **kargs)[source]#: Common method to redirect extract function call See`ExtractHistogram` or ExtractDistribution

openalea.stat_tool.data_transform.ExtractData(model)[source]#

Extraction of the ‘data’ part of an object of type ‘model’.

This function enables to extract the ‘data’ part of an object of type ‘model’ when the estimation of model parameters from data gives rise to the construction of pseudo-data. This situation is notably exemplified by the computation of optimal state sequences from estimated hidden Markovian processes (optional argument StateSequences of the function Estimate set at “ForwardBackward” or “Viterbi”).

Parameters:

mixt (_DiscreteMixture),
convol (_Convolution),
compound (_Compound),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov).

Returns:

If mixt contains a ‘data’ part, an object of type _DiscreteMixtureData is returned.
If convol contains a ‘data’ part, an object of type _ConvolutionData is returned.
If compound contains a ‘data’ part, an object of type _CompoundData is returned.
If hmc contains a ‘data’ part, an object of type _MarkovData is returned.
If hsmc contains a ‘data’ part, an object of type _SemiMarkovData is returned.

Examples:

>>> ExtractData(mixt)
>>> ExtractData(convol)
>>> ExtractData(compound)
>>> ExtractData(hmc)
>>> ExtractData(hsmc)

See also

Estimate()

openalea.stat_tool.data_transform.ExtractDistribution(model, *args)[source]#

Extraction of a distribution from an object of type ‘model’.

Parameters:

mixt (_DiscreteMixture),
mixt_type (string): type of distribution: “Weight” or “Mixture”,
index (int): index of the elementary distribution,
convol (_Convolution),
compound (_Compound),
compound_type (string): type of distribution: “Sum”, “Elementary” or “Compound”,
renew (renewal),
renew_type (string): type of distribution: “InterEvent”, “Backward”, “Forward”, “LengthBias” or “Mixture”,
time (int): observation period,
markov (markov, semi-markov, hidden_markov, hidden_semi-markov),
markov_type (string): type of distribution: “Observation”, “FirstOccurrence”, “Recurrence”, “Sojourn”, “NbRun” or “NbOccurrence”,
state (int): state,
variable (int): variable index,
output (int): output,
top_param (top_parameters),
position (int): position.

Returns:

If the arguments (mixt_type, index, compound_type, renew_type, time, markov_type, state, variable, output, position) defined an existing distribution, an object of type _Distribution is returned.

Examples:

>>> ExtractDistribution(mixt, mixt_type)
>>> ExtractDistribution(mixt, "Component", index)
>>> ExtractDistribution(convol, "Elementary", index)
>>> ExtractDistribution(convol, "Convolution")
>>> ExtractDistribution(compound, compound_type)
>>> ExtractDistribution(renew, renew_type)
>>> ExtractDistribution(renew, "NbEvent", time)
>>> ExtractDistribution(markov, markov_type, state)
>>> ExtractDistribution(markov, markov_type, variable, output)
>>> ExtractDistribution(top_param, position)

See also

Plot(), Fit(), Simulate().

openalea.stat_tool.data_transform.ExtractHistogram(data, *args, **kargs)[source]#

Extraction of a frequency distribution from an object of type ‘data’.

Parameters:

mixt_histo (_DiscreteMixtureData),
mixt_type (string): type of distribution: “Weight” or “Mixture”,
index (int): index of the elementary distribution,
convol_histo (_ConvolutionData),
compound_histo (_CompoundData),
compound_type (string): type of distribution: “Sum”, “Elementary” or “Compound”,
vec1 (_Vectors) : values,
vecn (_Vectors) : vectors,
variable (int) : variable index
timev (_TimeEvents, _RenewalData)
timev_type (string):
time (int) : observation period

Returns:

If the arguments (mixt_type, index, compound_type, renew_type, time, markov_type, state, variable, output, position) defined an existing frequency distribution, an object of type _Histogram is returned.

Examples:

>>> ExtractHistogram(mixt_histo, mixt_type)
>>> ExtractHistogram(mixt_histo, "Component", index)
>>> ExtractHistogram(mixt_histo, "Mixture")
>>> ExtractHistogram(mixt_histo, "Weight")
>>> ExtractHistogram(convol_histo, "Elementary", index)
>>> ExtractHistogram(convol_histo, "Convolution")
>>> ExtractHistogram(compound_histo, compound_type)
>>> ExtractHistogram(compound_histo, "Sum")
>>> ExtractHistogram(compound_histo, "Elementary")
>>> ExtractHistogram(vec1)
>>> ExtractHistogram(vecn, variable)
>>> ExtractHistogram(renewval_data, renew_type)
>>> ExtractHistogram(timev, timev_type)
>>> ExtractHistogram(timev, "NbEvent", time)
>>> ExtractHistogram(seq, "Length")
>>> ExtractHistogram(seq, "Value")
>>> ExtractHistogram(seq, "Value", variable)
>>> ExtractHistogram(discrete_seq1, seq_type, value)
>>> ExtractHistogram(discrete_seqn, seq_type, variable, value)
>>> ExtractHistogram(simul_seq1, "Observation", value)
>>> ExtractHistogram(simul_seq1, "Observation", variable, value)
>>> ExtractHistogram(tops, "Main")
>>> ExtractHistogram(top, "NbAxillary", position)

See also

Plot(), Fit(), Simulate().

openalea.stat_tool.data_transform.Fit(histo, dist)[source]#

Fit of a frequency distribution by a theoretical distribution.

The result is displayed in the shell window (characteristics of the frequency and theoretical distributions, log-likelihood of the data for the theoretical distribution, information - maximum possible log-likelihood of the data -, c2 goodness of fit test).

The difference between the information measure and the log-likelihood is the Kullback-Leibler divergence from the observed distribution to the theoretical distribution. It is also one-half the deviance of the theoretical distribution.

Assume that a sample of size n is generated by a given random variable. The statistic measures the random deviation between the observed frequencies fi and the theoretical frequencies npi:

$D^2 = \sum_{i=0}^k \frac{\left(f_i - n p_i\right)^2}{n p_i} \textrm{with} \sum_{i=0}^k f_i = n$

If each theoretical frequency npi is greater than a given threshold (between 1 and 5 according to the authors), has a c2 with k - 1 degrees of freedom.

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
dist (distribution, mixture, convolution, compound).

Returns:

Distribution

Examples:

>>> Fit(histo, dist)

openalea.stat_tool.data_transform.Merge(obj, *args)[source]#

Merging of objects of the same ‘data’ type or merging of sample correlation functions.

Parameters:

histo1, histo2, … (_Histogram, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
vec1, vec2, … (_Vectors),
timev1, timev2, … (_TimeEvents, _RenewalData),
seq1, seq2, … (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData),
top1, top2, … (_Tops),
correl1, correl2, … (_Correlation).

Returns:

If the arguments are of type _Histogram, _DiscreteMixtureData, _ConvolutionData, _CompoundData an object of type _Histogram is returned.

If the arguments are of type _Vectors and if the vectors have the same number of variables, an object of type vectors is returned, otherwise no object is returned.

If the arguments are of type _TimeEvents, _RenewalData, an object of type _TimeEvents is returned.

If the arguments are of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData and if the sequences have the same number of variables, an object of type _Sequences is returned.

If the arguments are of type _Tops, an object of type _Tops is returned. If the arguments are of type correlation, an object of type correlation is returned.

Examples:

>>> Merge(histo1, histo2,...)
>>> Merge(vec1, vec2,...)
>>> Merge(timev1, timev2,...)
>>> Merge(seq1, seq2,...)
>>> Merge(discrete_seq1, discrete_seq2,...)
>>> Merge(top1, top2,...)
>>> Merge(correl1, correl2,...)

See also

Cluster(), Shift(), Transcode(), ValueSelect(), MergeVariable(), SelectIndividual(), SelectVariable(), NbEventSelect(), TimeScaling(), TimeSelect(), AddAbsorbingRun(), Cumulate(), Difference(), IndexExtract(), LengthSelect(), MovingAverage(), RecurrenceTimeSequences(), RemoveRun(), Reverse(), SegmentationExtract(), VariableScaling(), RemoveApicalInternodes()

openalea.stat_tool.data_transform.MergeVariable(obj, *args, **kargs)[source]#

Merging of variables.

Parameters:

vec1, vec2, … (_Vectors),
seq1, seq2, … (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData).

Keywords:

RefSample (int): reference sample to define individual identifiers (the default: no reference sample).

Returns:

If the arguments are of type _Vectors and if the number of vectors is the same for each sample, an object of type _Vectors is returned.

If the arguments are of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, if all the variables are of type STATE, and if the number and the lengths of sequences are the same for each sample, an object of type _Sequences or _DiscreteSequences is returned.

The returned object is of type _DiscreteSequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> MergeVariable(histo1, histo2)
>>> MergeVariable(vec1, vec2,..., RefSample=2)
>>> MergeVariable(seq1, seq2,..., RefSample=2)

See also

Cluster(), Shift(), Transcode(), ValueSelect(), Merge(), SelectIndividual(), SelectVariable(), AddAbsorbingRun(), Cumulate(), Difference(), IndexExtract(), LengthSelect(), MovingAverage(), RecurrenceTimeSequences(), RemoveRun(), Reverse(), SegmentationExtract(), VariableScaling(),

openalea.stat_tool.data_transform.SelectIndividual(obj, identifiers, Mode='Keep')[source]#

Selection of vectors, sequences, tops or patterns (in a dissimilarity matrix).

Parameters:

vec (vectors),
seq (sequences, discrete_sequences, markov_data, semi-markov_data),
top (tops),
dist_matrix (distance_matrix),
identifiers (array(int)): identifiers.

Keywords:

Mode (string): conservation or rejection of the selected individuals: “Keep” (default) or “Reject”.

Returns:

If identifiers[1], …, identifiers[n] are valid identifiers of vectors (respectively sequences, tops or patterns compared in a dissimilarity matrix), an object of type vectors (respectively sequences or discrete_sequences, tops or distance_matrix) is returned, otherwise no object is returned. In the case of a first argument of type sequences, discrete_sequences, markov_data, semi-markov_data, the returned object is of type discrete_sequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> SelectIndividual(vec, identifiers, Mode="Reject")
>>> SelectIndividual(seq, identifiers, Mode="Reject")
>>> SelectIndividual(top, identifiers, Mode="Reject")
>>> SelectIndividual(dist_matrix, identifiers, Mode="Reject")

See also

Cluster(), Merge(), Shift(), Transcode(), ValueSelect(), MergeVariable(), SelectVariable() AddAbsorbingRun, Cumulate, Difference, IndexExtract, LengthSelect, MovingAverage, RecurrenceTimeSequences, RemoveSeries, Reverse, SegmentationExtract, VariableScaling, RemoveApicalInternodes, Symmetrize.

openalea.stat_tool.data_transform.SelectStep(obj, *args)[source]#

Change the internal step of a vector or a sequence

Parameters:

obj – the vector or sequence objet
1 (argument) – the new step

Example:

>>> seq = Sequences([])
>>> SelectStep(seq, 100)
>>> Plot(seq)

openalea.stat_tool.data_transform.SelectVariable(obj, variables, Mode='Keep')[source]#

Selection of variables.

Parameters:

vec (vectors),
seq (sequences, discrete_sequences, markov_data, semi-markov_data),
variable (int): variable index.
variables (array(int)): variable indices.

Keywords:

Mode (string): conservation or rejection of the selected variables: “Keep” (default) or “Reject”.

Returns:

If either variable or variables[1], …, variables[n] are valid indices of variables, an object of type vectors (respectively sequences or discrete_sequences) is returned, otherwise no object is returned. In the case of a first argument of type sequences, the returned object is of type discrete_sequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> SelectVariable(vec, variable, Mode="Reject")
>>> SelectVariable(vec, variables, Mode="Reject")
>>> SelectVariable(seq, variable, Mode="Reject")
>>> SelectVariable(seq, variables, Mode="Reject")

See also

AddAbsorbingRun, Cluster(), Cumulate(), Difference, IndexExtract, LengthSelect, Merge(), MergeVariable(), MovingAverage, RecurrenceTimeSequences, RemoveRun, Reverse, SelectIndividual(), Shift(), Transcode(), ValueSelect(), SegmentationExtract, VariableScaling.

openalea.stat_tool.data_transform.Shift(obj, *args)[source]#

Shifting of values

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
param (int): shifting parameter,
vec1 (vectors): values,
vecn (vectors): vectors,
variable (int): variable index,
seq1 (sequences): univariate sequences,
seqn (sequences): multivariate sequences.

Returns:

If the shifting makes that the lower bound to the possible values is positive, an object of type HISTOGRAM (respectively _Vectors, _Sequences) is returned. In the case of a first argument of type sequences, the returned object is of type discrete_sequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is 15.

Examples:

>>> Shift(histo, param)
>>> Shift(vec1, param)
>>> Shift(vecn, variable, param)
>>> Shift(seq1, param)
>>> Shift(seqn, variable, param)

See also

Cluster(), Merge(), Transcode(), SelectIndividual(), MergeVariable(), SelectVariable() AddAbsorbingRun(), Cumulate(), Difference(), Lengthselect(), MovingAverage(), IndexExtract(), RecurrenceTimeSequences(), RemoveRun(), Reverse(), SegmentationExtract(), ValueSelect(), VariableScaling().

openalea.stat_tool.data_transform.Symmetrize(obj)[source]#

openalea.stat_tool.data_transform.TruncateDistribution(obj, variable)[source]#

openalea.stat_tool.data_transform.Unnormalize()[source]#

Parameters:

Returns:: An object of type distance_matrix is returned.

Examples:

>>>  Unnormalize(dist_matrix)

openalea.stat_tool.data_transform.ValueSelect(obj, *args, **kargs)[source]#

Selection of individuals according to the values taken by a variable

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
value (int): value,
min_value (int): minimum value,
max_value (int): maximum value,
vec1 (vectors): values,
vecn (vectors): vectors,
variable (int): variable index,
seq1 (sequences, discrete_sequences, markov_data, semi-markov_data): univariate sequences,
seqn (sequences, discrete_sequences, markov_data, semi-markov_data): multivariate sequences.

Keywords:

Mode (string): conservation or rejection of selected individuals: “Keep” (the default) or “Reject”.

Returns:

If either value 0 or if 0 < min_value < max_value and if the range of values defined either by value or by min_value and max_value enables to select individuals, an object of type HISTOGRAM is returned (respectively vectors, sequences or discrete_sequences), otherwise no object is returned. In the case of a first argument of type sequences, discrete_sequences, markov_data or semi-markov_data, the returned object is of type discrete_sequences if all the variables are of type STATE, if the possible values for each variable are consecutive from 0 and if the number of possible values for each variable is < 15.

Examples:

>>> ValueSelect(histo, value, Mode="Reject")
>>> ValueSelect(histo, min_value, max_value, Mode="Reject")
>>> ValueSelect(vec1, value, Mode="Reject")
>>> ValueSelect(vec1, min_value, max_value, Mode="Reject")
>>> ValueSelect(vecn, variable, value, Mode="Reject")
>>> ValueSelect(vecn, variable, min_value, max_value, Mode="Reject")
>>> ValueSelect(seq1, value, Mode="Reject")
>>> ValueSelect(seq1, min_value, max_value, Mode="Reject")
>>> ValueSelect(seqn, variable, value, Mode="Reject")
>>> ValueSelect(seqn, variable, min_value, max_value, Mode="Reject")

See also

Cluster(), Merge(), Shift(), Transcode(), SelectIndividual(), MergeVariable(), SelectVariable() Cumulate` Difference` IndexExtract` LengthSelect`, MovingAverage`, RecurrenceTimeSequences` RemoveRun`, Reverse`, SegmentationExtract`, VariableScaling`.

`openalea.stat_tool.estimate` module#

Estimate functions

Warning

sequence analysis package also contains an estimate module and function

openalea.stat_tool.estimate.Estimate(histo, itype, *args, **kargs)[source]#

Estimate function

This function is a dispatcher to several estimate functions depending on the first argument and the type.

Parameters:

obj – the input object (may be histogram, sequence, compound, …)
itype – string.

class openalea.stat_tool.estimate.EstimateFunctions[source]#

Bases: object

Class containing histogram estimation functions This class must not be used alone, but through an histogram object

estimate_DiscreteMixture(*args, **kargs)[source]#

Estimate a finite mixture of discrete distributions

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
distributions (list)a list of distribution object
or distribution label(string) : ‘B’, ‘NB’, ‘U’, ‘P’, …
unknown (string): type of unknown distribution: “Sum” or “Elementary”.

Keywords:

MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). This optional argument cannot be used in conjunction with the optional argument InitialDistribution.
InfBoundStatus (string): shifting or not of the distribution: “Free” (default value) or “Fixed”.
DistInfBoundStatus (string): shifting or not of the subsequent components of the mixture: “Free” (default value) or “Fixed”.
NbComponent (string): estimation of the number of components of the mixture: “Fixed” (default value) or “Estimated”. Le number of estimated components is comprised between 1 and a maximum number which is given by the number of specified parametric distributions in the mandatory arguments (all of these distributions are assumed to be unknown).
Penalty (string): type of Penalty function for model selection: “AIC” (Akaike Information Criterion), “AICc” (corrected Akaike Information Criterion) “BIC” (Bayesian Information Criterion - default value). “BICc” (corrected Bayesian Information Criterion).
This optional argument can only be used if the optional argument NbComponent is set at “Estimated”.

Examples:

>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
                 MinInfBound=1, InfBoundStatus="Fixed",
                 DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
                   MinInfBound=1, InfBoundStatus="Fixed",
                   DistInfBoundStatus="Fixed",
                   NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
        MinInfBound=1, InfBoundStatus="Fixed",
        DistInfBoundStatus="Fixed",
        NbComponent="Estimated", Penalty="AIC")

estimate_compound(*args, **kargs)[source]#

estimate a compound

Usage:

>>> Estimate(histo, "COMPOUND", dist, unknown,
        Parametric=False, MinInfBound=0)
        Estimate(histo, "COMPOUND", dist, unknown,
        InitialDistribution=initial_dist, Parametric=False)

estimate_convolution(*args, **kargs)[source]#

Estimate a convolution

Usage:

>>> Estimate(histo, "CONVOLUTION", dist,
        MinInfBound=1, Parametric=False)
        Estimate(histo, "CONVOLUTION", dist,
        InitialDistribution=initial_dist, Parametric=False)

estimate_nonparametric()[source]#

Estimate a non parametric distribution

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data)

Usage:

>>> Estimate(histo, "NON-PARAMETRIC")
>>> estimate_nonparametric(histo)

estimate_parametric(ident, MinInfBound=0, InfBoundStatus='Free')[source]#

Estimate a parametric discrete distribution (binomial, Poisson or negative binomial distribution with an additional shift parameter)

Parameters:

histo (histogram, mixture_data, convolution_data, compound_data),
ident (“BINOMIAL”, “POISSON”, “NEGATIVE_BINOMIAL”, “UNIFORM”)
MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
InfBoundStatus (string): shifting or not of the distribution:
“Free” (default value) or “Fixed”. T

Usage:

>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")

Display and Plot functionalities #

`openalea.stat_tool.output` module#

Output functions

openalea.stat_tool.output.Display(obj, *args, **kargs)[source]#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

openalea.stat_tool.output.Plot(obj, *args, **kargs)[source]#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

openalea.stat_tool.output.Save(obj, *args, **kargs)[source]#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also

class openalea.stat_tool.output.StatInterface[source]#

Bases: object

Abstract base class for stat_tool objects

display(*args, **kargs)[source]#

ASCII output of an object of the STAT module

Parameters:

obj - object to display,
vec (_Vectors),
seq (_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops),
dist (_Distribution, _MixtureDist, _Convolution, _Compound),
histo (_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),
hmc (_HiddenMarkov),
hsmc (_HiddenSemiMarkov),
identifier (int) - identifier of a sequence.

Keywords:

ViewPoint (string): point of view on the object (“Survival” or “Data” or “StateProfile”). This optional argument can be set at
- “Data” only if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- “Survival” only if the first argument is of type _Distribution, _MixtureDist, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- “StateProfile” only if the first argument is of type _HiddenMarkov or _HiddenSemiMarkov.
Detail (int): level of detail: 1 (default value) or 2. This optional argument cannot be used if the optional argument ViewPoint is set at “Survival” or “StateProfile”.
Format (string): format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.

Returns:

A string

Examples:

>>> from openalea.stat_tool.output import Display
>>> Display(obj, Detail=2)
>>> Display(vec, ViewPoint="Data", Detail=2)
>>> Display(seq, ViewPoint="Data", Format="Line", Detail=2)
>>> Display(dist, ViewPoint="Survival")
>>> Display(histo, ViewPoint="Survival")
>>> Display(hmc, identifier, ViewPoint="StateProfile")
>>> Display(hsmc, identifier, ViewPoint="StateProfile")

See also

old_plot(*args, **kargs)[source]#: Old AML style plot

plot(*args, **kargs)[source]#

Graphical output of an object of the STAT module using the GNUPLOT software.

In the case of Markovian models or sequences, the graphical outputs are grouped as follows:

Parameters:

obj1 ((_Distribution, _Mixture, _Convolution, _Compound,) – _DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData, _CompoundData,`_Renewal`, _TimeEvents, _RenewalData, _Sequences, _DistanceMatrix, ` _TopParameters`, _Tops),
vec1 ((_Vectors) values,)
vecn ((_Vectors) vectors,)
variable ((int) variable index,)
obj2 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete univariate sequences or discrete univariate sequences,
obj3 ((_Markov, _SemiMarkov, _HiddenMarkov, _HiddenSemiMarkov,) – _DiscreteSequences, _MarkovData, _SemiMarkovData): Markovian model for discrete multivariate sequences or discrete multivariate sequences,
(string) (type) – or sequences: “SelfTransition”, “Observation”, “Intensity”, “FirstOccurrence”, “Recurrence”, “Sojourn” or “Counting”,
dist1 ((_Distribution, _Mixture, _Convolution, _Compound),)
dist2 ((_Distribution, _Mixture, _Convolution, _Compound),)
... ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,)
histo1 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
histo2 ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
... – _CompoundData),
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData,) – _Tops),
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
histo ((_DiscreteDistributionData, _DiscreteMixtureData, _ConvolutionData,) – _CompoundData),
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov),)
identifier ((int) identifier of a sequence.)
Keywords
--------
ViewPoint ((string) point of view on the object ("Data" or "Survival") –
or “StateProfile”). This optional argument can be set at :
- ”Data” only if the first mandatory argument is of type sequences, discrete_sequences, markov_data, semi-markov_data or tops,
- ”Survival” only if the first mandatory argument is of type distribution, mixture, convolution, compound, histogram, mixture_data, convolution_data or compound_data
- ”StateProfile” only if the first mandatory argument is of type hidden_markov or hidden_semi-markov.
Title ((string)) – graphic title (the default: no title).
nbcol ((int)) – number of columns in the output figure
Show ((dict)) –
Display options
- legend_size: 10
- legend_nbcol: 2
- legend_loc: best
- legend: True/False

Return type:

Nothing.

Examples

>>> from openalea.stat_tool.output import Display
>>> Plot(obj1, Title="Distribution")
>>> Plot(vec1, Title="Values")
>>> Plot(vecn, variable, Title="Vectors")
>>> Plot(variable)
>>> Plot(obj2, type, Title="Sequences")
>>> Plot(type)
>>> Plot(obj3, type, variable, Title="Multivariate sequences")
>>> Plot(type, variable)
>>> Plot(dist1, dist2,..., Title="Family of distributions")
>>> Plot(histo1, histo2,..., Title="Family of frequency distributions")
>>> Plot(seq, ViewPoint="Data")
>>> Plot(dist, ViewPoint="Survival", Title="Survival rates")
>>> Plot(histo, ViewPoint="Survival", Title="Survival rates")
>>> Plot(hsmc, identifier, ViewPoint="StateProfile", Title="Smoothed probabilities")

See also

plot_print(*args, **kargs)[source]#: Old AML style print into .ps file

save(filename, Detail=2, ViewPoint='', Format='ASCII')[source]#

Saving of an object of the STAT module in a file.

Note

The persistence mechanism is implemented by the Save function.

Parameters:

obj (object of the STAT module (except objects of type vector_distance),)
file_name ((string),)
histo ((_FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData, _CompoundData),)
vec ((_Vectors),)
timev ((_TimeEvents, _RenewalData),)
seq ((_Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData, _Tops).)
dist ((_Distribution, _Mixture, _Convolution, _Compound),)
hmc ((_HiddenMarkov),)
hsmc ((_HiddenSemiMarkov).)
Keywords
--------
ViewPoint ((string)) –
Point of view on the object (“Data” or “Survival” or “StateProfile”).

This optional argument can be set at :
- ”Data” only if the first argument is of type _Sequences,
  _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops,
- ”Survival” only if the first argument is of type _Distribution,
  _Mixture, _Convolution, _Compound, _FrequencyDistribution, _DiscreteMixtureData, _ConvolutionData or _CompoundData
- ”StateProfile” only if the first argument is of type `_HiddenMarkov or
  _HiddenSemiMarkov.
Detail ((int)) – level of detail: 1 (default value) or 2. This optional argument can only be used if the optional argument ViewPoint is not set, or if the optional argument ViewPoint is set at “Data” and if the first mandatory argument is of type _Vectors, _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops.
format (file) – These file formats cannot be specified if the optional argument ViewPoint is set at “Data”. The optional argument Format can only be set at “Binary” if the optional argument ViewPoint is not set.
Format ((string)) – format of sequences (only relevant for multivariate sequences): “Column” (default value) or “Line”. This optional argument can only be used if the optional argument ViewPoint is set at “Data”, and hence, if the first argument is of type _Sequences, _DiscreteSequences, _MarkovData, _SemiMarkovData or _Tops. If the first argument is of type _Vectors, use Format=”Data” to actually save the data rather than their summary.
Sequence ((int)) – identifier of a sequence. This optional argument can only be used if the optional argument ViewPoint is set at “StateProfile”, and hence, if the first mandatory argument is of type _HiddenMarkov or _HiddenSemiMarkov.

Return type:

No object returned.

Examples

>>> Save(obj, file_name, Format="ASCII", Detail=2)
>>> Save(histo, file_name, ViewPoint="Data")
>>> Save(vec, file_name, ViewPoint="Data", Detail=2)
>>> Save(vec, file_name, Format="Data")
>>> Save(timev, file_name, ViewPoint="Data")
>>> Save(seq, file_name, ViewPoint="Data", Format="Line", Detail=2)
>>> Save(dist, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(histo, file_name, ViewPoint="Survival", Format="SpreadSheet")
>>> Save(hmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")
>>> Save(hsmc, ViewPoint="StateProfile", Sequence=1, Format="SpreadSheet")

See also