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4. CORE

4.1. Base for all pipelines

class CNOBase(pknmodel, data, tag=None, verbose=False, use_cnodata=False, config=None)[source]

Abstract Base Class common class to all formalisms

cnograph
create_report()[source]
create_report_images()[source]
data

get the data (MIDAS)

debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

level

alias to debugLevel (Read-only access)

midas

get the data (MIDAS)

onweb(show=True)[source]
pknmodel

get the prior knowledge network

plot_fitness(show=True, save=False)[source]
plot_mapback_model()[source]
plot_midas(xkcd=False)[source]

Plot the MIDAS data

See also

full documentation about MIDAS in cellnopt.core.midas.plot()

plot_model()[source]
plot_optimised_model(filename=None, show=True, show_pruned_edges=True)[source]
plot_pknmodel()[source]

Plot the original PKN model

See also

full documentation about MIDAS in cellnopt.core.cnograph

preprocessing(expansion=True, compression=True, cutnonc=True, maxInputsPerGate=2)[source]

Apply preprocessing on the PKN model

report(show=True)[source]
report_directory
save_config_file(filename=None)[source]
class CNORBase(verboseR)[source]

A base class to handle the R session and its verbosity

verboseR

4.2. Models

class BooleanModels(data, reacID=None, index_col=None)[source]

Class to read and plot models as exported by CASPO or CellNOptR

Models contains dataframe with reactions as columns and models as rows. For each reaction, we can then obtain the average paramters for a reaction. In a boolean case, a Model stores a value made of 0/1

scores may be available. No sizes are stored. Sizes could be extracted easily as sum over rows.

>>> from cno.core.models import Models
>>> m = Models()
>>> m.plot() # average model, whcih can be obtained with  m.get_average_model()
>>> m.plot(model_number=0)  # indices are m.df.index
>>> m.plot(model_number=0)  # indices are m.df.index

Note

One difficulty is the way ANDs are coded in different software. In CASPO, the AND gate is coded as “A+B=C”. Note that internally we use ^ especially in CNOGraph. Then, an AND edge is splitted in sub edges. so, A+B=C is made of 3 edges A -> A+B=C , B -> A+B=C and A+B=C -> C. This explains the wierd code in cno.io.cnograph.plot().

  • plots average models with edges on/off
  • plot of errobars on edges sorted by average presence
  • plots heatmap of the models

if you have a first column, whihc is not a reaction, set index_col to 0

Todo

values are 0/1 since we have bit strings but could be anything in other formalisms (e.g., ODE) how to handle those cases ?

Parameters:dta – a filename with columns as the reacitons and rowss as parameters for each reactions. Each row is therefore a model.
compute_average(model_number=None, tolerance=None)[source]

Compute the average and update the cnograph accordingly

Parameters:model_number (int) – model_number as shown by df.index if not provided, the average is taken
copy()[source]
drop_duplicates()[source]
drop_scores_above(tolerance=None)
errorbar(tolerance=100000000.0, errorbar=True)[source]

Plot the average presence of reactions over all models

get_average_model(max_score=None)

Returns the average model (on each reaction)

get_consensus_model(threshold=0.5)[source]
get_cv_model()[source]

Returns the average coefficient of variation on each reaction

get_jaccard(progress=True)[source]
get_main_reactions(threshold=0.5)[source]
heatmap(num=1, transpose=False, cmap='gist_heat_r', heatmap_attr={})[source]
plot(model_number=None, cmap='gist_heat_r', colorbar=True, tolerance=None, filename=None, **kargs)[source]

Plot the average model

sizes
to_csv(filename, index=False)

Exports the dataframe to a CSV file

to_sif(filename=None)

Exports 2 SIF using the “and” convention

can read the results with CellNOptR for instance:

library(CellNOptR)
plotModel(readSIF("test.sif"))
class CompareTwoModels(m1, m2)[source]
Parameters:
  • m1 – first model as a Pandas time series e.g. row of BooleanModels
  • m2 – first model as a Pandas time series e.g. row of BooleanModels
Returns:

from a models, m1 = pd.TimeSeries(models.df.ix[0], dtype=int) m2 = pd.TimeSeries(models.df.ix[1], dtype=int)

get_both()[source]
get_both_off()[source]
get_intersection()[source]
get_m1_only()[source]
get_m2_only()[source]
get_union()[source]
plot_multigraph(cmap='jet')[source]
class DTModels(data, reacID=None, index_col=None)[source]
compute_average(model_number=None, tolerance=None)

Compute the average and update the cnograph accordingly

Parameters:model_number (int) – model_number as shown by df.index if not provided, the average is taken
copy()[source]
drop_duplicates()
drop_scores_above(tolerance=None)
errorbar(tolerance=100000000.0, errorbar=True)

Plot the average presence of reactions over all models

get_average_model(max_score=None)

Returns the average model (on each reaction)

get_consensus_model(threshold=0.5)
get_cv_model()

Returns the average coefficient of variation on each reaction

get_jaccard(progress=True)
get_main_reactions(threshold=0.5)
heatmap(num=1, transpose=False, cmap='gist_heat_r', heatmap_attr={})
plot(model_number=None, cmap='gist_heat_r', colorbar=True, tolerance=None, filename=None, **kargs)

Plot the average model

sizes
to_csv(filename, index=False)

Exports the dataframe to a CSV file

to_sif(filename=None)

Exports 2 SIF using the “and” convention

can read the results with CellNOptR for instance:

library(CellNOptR)
plotModel(readSIF("test.sif"))
class FuzzyModels(data, reacID=None, index_col=None)[source]
copy()[source]
drop_scores_above(tolerance=None)
get_average_model(max_score=None)

Returns the average model (on each reaction)

to_csv(filename, index=False)

Exports the dataframe to a CSV file

to_sif(filename=None)

Exports 2 SIF using the “and” convention

can read the results with CellNOptR for instance:

library(CellNOptR)
plotModel(readSIF("test.sif"))
class Models(data, reacID=None, index_col=None, verbose=True)[source]

Data structure to store models.

Models are stored in dataframes. Columns will hold the reactions.

constructor

Parameters:
  • data – could be a string to read a file (CSV). The CSV header should contain the reaction name. First column is not expected to be found. Note, however that :param:`index_col` may be used. The input can also be a dataframe (column names being the reactions) set to 0/1. The input can also be an instance of Models.
  • reacID (list) – if provided, columns are renamed using this list
  • index_col
  • verbose (bool) –

Reaction names may contain a symbol indicating the logical ANDs. This should be “^” character.

drop_scores_above(tolerance=None)[source]
get_average_model(max_score=None)[source]

Returns the average model (on each reaction)

to_csv(filename, index=False)[source]

Exports the dataframe to a CSV file

to_sif(filename=None)[source]

Exports 2 SIF using the “and” convention

can read the results with CellNOptR for instance:

library(CellNOptR)
plotModel(readSIF("test.sif"))
class MultiModels[source]

4.3. Parameters for the pipelines

members:
undoc-members:
inherited-members:
 
synopsis:

4.4. Reports

class Report(formalism='base', directory='report', tag=None, filename='index.html', overwrite=True, verbose=True, dependencies=True)[source]
add_section(content, title, references=, []position=None)[source]
close_body()[source]
close_html()[source]
debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

get_header()[source]

a possible common header ?

get_html_reproduce()[source]
get_table_dependencies()[source]

Returns dependencies of the pipeline into a HTML/XML table

dependencies are the python dependencies as returned by pkg_resource. additionally, r dependencies added in dependencies are also added.

get_time_now()[source]
get_toc()[source]
level

alias to debugLevel (Read-only access)

plot_fitness(show=True, save=False, loglog=True)[source]
report(browse=True)[source]
savefig(filename)[source]
show()[source]
write(filename)[source]
class ReportBool[source]
add_section(content, title, references=, []position=None)
close_body()
close_html()
debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

get_header()

a possible common header ?

get_html_reproduce()
get_table_dependencies()

Returns dependencies of the pipeline into a HTML/XML table

dependencies are the python dependencies as returned by pkg_resource. additionally, r dependencies added in dependencies are also added.

get_time_now()
get_toc()
level

alias to debugLevel (Read-only access)

plot_fitness(show=True, save=False, loglog=True)
report(browse=True)
savefig(filename)
show()
write(filename)
class ReportODE[source]
add_section(content, title, references=, []position=None)
close_body()
close_html()
debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

get_header()

a possible common header ?

get_html_reproduce()
get_table_dependencies()

Returns dependencies of the pipeline into a HTML/XML table

dependencies are the python dependencies as returned by pkg_resource. additionally, r dependencies added in dependencies are also added.

get_time_now()
get_toc()
level

alias to debugLevel (Read-only access)

plot_fitness(show=True, save=False, loglog=True)
report(browse=True)
savefig(filename)
show()
write(filename)
class ReportFuzzy[source]
add_section(content, title, references=, []position=None)
close_body()
close_html()
debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

get_header()

a possible common header ?

get_html_reproduce()
get_table_dependencies()

Returns dependencies of the pipeline into a HTML/XML table

dependencies are the python dependencies as returned by pkg_resource. additionally, r dependencies added in dependencies are also added.

get_time_now()
get_toc()
level

alias to debugLevel (Read-only access)

plot_fitness(show=True, save=False, loglog=True)
report(browse=True)
savefig(filename)
show()
write(filename)
class ReportDT[source]
add_section(content, title, references=, []position=None)
close_body()
close_html()
debugLevel

Read/Write access to the debug level. Must be one of INFO, DEBUG, WARNING, CRITICAL, ERROR

get_header()

a possible common header ?

get_html_reproduce()
get_table_dependencies()

Returns dependencies of the pipeline into a HTML/XML table

dependencies are the python dependencies as returned by pkg_resource. additionally, r dependencies added in dependencies are also added.

get_time_now()
get_toc()
level

alias to debugLevel (Read-only access)

plot_fitness(show=True, save=False, loglog=True)
report(browse=True)
savefig(filename)
show()
write(filename)

4.5. Data structure to store results

class BooleanResults[source]
best_score
hist2d_scores_vs_model_size(bins=None, cmap='gist_heat_r', fontsize=16, contour=False, Nlevels=10)[source]
hist_scores(fontsize=16, **kargs)[source]

Plot histogram of the MSEs

>>> from cno.boolean.cnorbool import CNORbool
>>> from cno import cnodata
>>> a = CNORbool(cnodata("PKN-ToyMMB.sif"), cnodata("MD-ToyMMB.csv"))
>>> a.optimise()
>>> a.results.hist_scores()

(Source code, png, hires.png, pdf)

_images/ref_core-1.png
models
plot_fit()[source]
plot_mse_grouped_by_size()[source]
results
scatter_scores_vs_model_size()[source]

Scatter plot of the model size and scores

scores
sizes
class FuzzyResults[source]
best_score
models
results
scores
class ODEResults[source]
best_score
models
plot_fit(ymin=None, ymax=None)[source]
results
scores
class DTResults[source]
best_score
hist2d_scores_vs_model_size(bins=None, cmap='gist_heat_r', fontsize=16, contour=False, Nlevels=10)
hist_scores(fontsize=16, **kargs)

Plot histogram of the MSEs

>>> from cno.boolean.cnorbool import CNORbool
>>> from cno import cnodata
>>> a = CNORbool(cnodata("PKN-ToyMMB.sif"), cnodata("MD-ToyMMB.csv"))
>>> a.optimise()
>>> a.results.hist_scores()

(Source code, png, hires.png, pdf)

_images/ref_core-2.png
models
plot_fit()
plot_mse_grouped_by_size()
results
scatter_scores_vs_model_size()

Scatter plot of the model size and scores

scores
sizes

4.6. Tools to install r packages

Module to install the R packages required by CellNOptR and other related packages

install_dependencies_cellnoptr(verbose='INFO')

Install the CellNOptr dependencies

install_dependencies_ode()

Install the dependencies for the ODE package

4.7. GTT

class GTTBool(simulator)[source]

API could be simplified.

# here there are duplicate, should be called automatically ?
s = steady.Steady(cnodata("PKN-ToyMMB.sif"), cnodata("MD-ToyMMB.csv"))
s.preprocessing()
s.optimise()
s.results.models.drop_duplicates()


g = gtt.GTTBool(s)
g.analyse()
# here indices are same s in models, which may not be contiguous
g.truth_tables[189]
analyse()[source]