def plot(self,
index=None,
grid=None,
width=None,
height=None,
title=None,
palette=None,
**kwargs):
if index is None:
index = self.data_frame.index[0:10]
fva_result = self.data_frame.loc[index]
if title is None:
title = "Flux Variability Analysis"
dataframe = pandas.DataFrame(
columns=["lb", "ub", "strain", "reaction"])
for reaction_id, row in fva_result.iterrows():
_df = pandas.DataFrame(
[[row['lower_bound'], row['upper_bound'], "WT", reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.flux_variability_analysis(dataframe,
grid=grid,
width=width,
height=height,
title=title,
y_axis_label="Reactions",
x_axis_label="Flux limits",
palette=palette)
if grid is None:
plotter.display(plot)
def _plot_flux_variability_analysis(self, index, variables=None, title=None,
width=None, height=None, palette=None, grid=None):
if variables is None:
variables = self.reference_fva.index[0:10]
title = "Compare WT solution %i" % index if title is None else title
wt_fva_res = self.reference_fva.loc[variables]
strain_fva_res = self.nth_panel(index).loc[variables]
dataframe = pandas.DataFrame(columns=["lb", "ub", "strain", "reaction"])
for reaction_id, row in wt_fva_res.iterrows():
_df = pandas.DataFrame([[row['lower_bound'], row['upper_bound'], "WT", reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
for reaction_id, row in strain_fva_res.iterrows():
_df = pandas.DataFrame([[row['lower_bound'], row['upper_bound'], "Strain %i" % index, reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.flux_variability_analysis(dataframe, grid=grid, width=width, height=height,
title=title, x_axis_label="Reactions", y_axis_label="Flux limits",
palette=palette)
plotter.display(plot)
def _plot_flux_variability_analysis(self, index, variables=None, title=None,
width=None, height=None, palette=None, grid=None):
if variables is None:
variables = self.reference_fva.index[0:10]
title = "Compare WT solution %i" % index if title is None else title
wt_fva_res = self.reference_fva.loc[variables]
strain_fva_res = self.nth_panel(index).loc[variables]
dataframe = pandas.DataFrame(columns=["lb", "ub", "strain", "reaction"])
for reaction_id, row in wt_fva_res.iterrows():
_df = pandas.DataFrame([[row['lower_bound'], row['upper_bound'], "WT", reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
for reaction_id, row in strain_fva_res.iterrows():
_df = pandas.DataFrame([[row['lower_bound'], row['upper_bound'], "Strain %i" % index, reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.flux_variability_analysis(dataframe, grid=grid, width=width, height=height,
title=title, x_axis_label="Reactions", y_axis_label="Flux limits",
palette=palette)
plotter.display(plot)
def plot(self, grid=None, width=None, height=None, title=None, *args, **kwargs):
if title is None:
title = "FSEOF fluxes"
plot = plotter.line(self.data_frame, grid=grid, width=width, height=height, title=title, **kwargs)
if grid is None:
plotter.display(plot)
def plot(self, grid=None, width=None, height=None, title=None, *args, **kwargs):
if title is None:
title = "FSEOF fluxes"
plot = plotter.line(self.data_frame, grid=grid, width=width, height=height, title=title, **kwargs)
if grid is None:
plotter.display(plot)
def plot(self,
index=0,
grid=None,
width=None,
height=None,
title=None,
palette=None,
**kwargs):
wt_production = phenotypic_phase_plane(self._model,
objective=self._target,
variables=[self._biomass])
with TimeMachine() as tm:
for ko in self.data_frame.loc[index, "reactions"]:
self._model.reactions.get_by_id(ko).swap_cofactors(
self._swap_pairs, tm)
mt_production = phenotypic_phase_plane(self._model,
objective=self._target,
variables=[self._biomass])
if title is None:
title = "Production Envelope"
dataframe = DataFrame(columns=["ub", "lb", "value", "strain"])
for _, row in wt_production.iterrows():
_df = DataFrame([[
row['objective_upper_bound'], row['objective_lower_bound'],
row[self._biomass.id], "WT"
]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
for _, row in mt_production.iterrows():
_df = DataFrame([[
row['objective_upper_bound'], row['objective_lower_bound'],
row[self._biomass.id], "MT"
]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope(dataframe,
grid=grid,
width=width,
height=height,
title=title,
x_axis_label=self._biomass.id,
y_axis_label=self._target.id,
palette=palette)
plotter.display(plot)
def plot(self, grid=None, width=None, height=None, title=None, axis_font_size=None, palette=None,
points=None, points_colors=None, **kwargs):
if title is None:
title = "Phenotypic Phase Plane"
if len(self.variable_ids) == 1:
variable = self.variable_ids[0]
x_axis_label = self.nice_variable_ids[0]
y_axis_label = self.nice_objective_id
dataframe = pandas.DataFrame(columns=["ub", "lb", "value", "strain"])
for _, row in self.iterrows():
_df = pandas.DataFrame([[row['objective_upper_bound'], row['objective_lower_bound'], row[variable], "WT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope(dataframe, grid=grid, width=width, height=height,
title=title, y_axis_label=y_axis_label, x_axis_label=x_axis_label,
palette=palette, points=points, points_colors=points_colors)
elif len(self.variable_ids) == 2:
var_1 = self.variable_ids[0]
var_2 = self.variable_ids[1]
x_axis_label = self.nice_variable_ids[0]
y_axis_label = self.nice_variable_ids[1]
z_axis_label = self.nice_objective_id
dataframe = pandas.DataFrame(columns=["ub", "lb", "value1", "value2", "strain"])
for _, row in self.iterrows():
_df = pandas.DataFrame([[row['objective_upper_bound'], row['objective_lower_bound'],
row[var_1], row[var_2], "WT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope_3d(dataframe, grid=grid, width=width, height=height,
title=title, y_axis_label=y_axis_label, x_axis_label=x_axis_label,
z_axis_label=z_axis_label, palette=palette, points=points,
points_colors=points_colors)
else:
notice("Multi-dimensional plotting is not supported")
return
if grid is None:
plotter.display(plot)
def plot(self, index=None, grid=None, width=None, height=None, title=None, palette=None, **kwargs):
if index is None:
index = self.data_frame.index[0:10]
fva_result = self.data_frame.loc[index]
if title is None:
title = "Flux Variability Analysis"
dataframe = pandas.DataFrame(columns=["lb", "ub", "strain", "reaction"])
for reaction_id, row in fva_result.iterrows():
_df = pandas.DataFrame([[row['lower_bound'], row['upper_bound'], "WT", reaction_id]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.flux_variability_analysis(dataframe, grid=grid, width=width, height=height,
title=title, y_axis_label="Reactions", x_axis_label="Flux limits",
palette=palette)
if grid is None:
plotter.display(plot)
def plot(self, index=0, grid=None, width=None, height=None, title=None, palette=None, **kwargs):
wt_production = phenotypic_phase_plane(self._model, objective=self._target, variables=[self._biomass.id])
with TimeMachine() as tm:
for ko in self.data_frame.loc[index, "reactions"]:
self._model.reactions.get_by_id(ko).knock_out(tm)
mt_production = phenotypic_phase_plane(self._model, objective=self._target, variables=[self._biomass.id])
if title is None:
title = "Production Envelope"
dataframe = DataFrame(columns=["ub", "lb", "value", "strain"])
for _, row in wt_production.iterrows():
_df = DataFrame([[row['objective_upper_bound'], row['objective_lower_bound'], row[self._biomass.id], "WT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
for _, row in mt_production.iterrows():
_df = DataFrame([[row['objective_upper_bound'], row['objective_lower_bound'], row[self._biomass.id], "MT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope(dataframe, grid=grid, width=width, height=height, title=title,
x_axis_label=self._biomass.id, y_axis_label=self._target, palette=palette)
plotter.display(plot)
def plot(self,
grid=None,
width=None,
height=None,
title=None,
axis_font_size=None,
palette=None,
points=None,
points_colors=None,
estimate='flux',
**kwargs):
"""plot phenotypic phase plane result
create a plot of a phenotypic phase plane analysis
Parameters
----------
grid: plotting grid
the grid for plotting
width: int
the width of the plot
height: int
the height of the plot
title: string
the height of the plot
axis_font_size: int
the font sizes for the axis
palette: string
name of color palette to use, e.g. RdYlBlu
points: iterable of points
additional points to plot as x, y iterable
points_colors: iterable of strings
iterable with colors for the points
estimate: string
either flux, mass_yield (g output / g output) or c_yield (mol carbon output / mol carbon input)
"""
possible_estimates = {
'flux': ('objective_upper_bound', 'objective_lower_bound', 'flux',
'[mmol gDW^-1 h^-1]'),
'mass_yield': ('mass_yield_upper_bound', 'mass_yield_lower_bound',
'mass yield, src={}'.format(self.source_reaction),
'[g/g(src) h^-1]'),
'c_yield': ('c_yield_upper_bound', 'c_yield_lower_bound',
'carbon yield, src={}'.format(self.source_reaction),
'[mmol(C)/mmol(C(src)) h^-1]')
}
if estimate not in possible_estimates:
raise Exception('estimate must be one of %s' %
', '.join(possible_estimates.keys()))
upper, lower, description, unit = possible_estimates[estimate]
if title is None:
title = "Phenotypic Phase Plane ({})".format(description)
if len(self.variable_ids) == 1:
variable = self.variable_ids[0]
y_axis_label = self._axis_label(self.objective,
self.nice_objective_id, unit)
x_axis_label = self._axis_label(variable,
self.nice_variable_ids[0],
'[mmol gDW^-1 h^-1]')
dataframe = pandas.DataFrame(
columns=["ub", "lb", "value", "strain"])
for _, row in self.iterrows():
_df = pandas.DataFrame(
[[row[upper], row[lower], row[variable], "WT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope(dataframe,
grid=grid,
width=width,
height=height,
title=title,
y_axis_label=y_axis_label,
x_axis_label=x_axis_label,
palette=palette,
points=points,
points_colors=points_colors)
elif len(self.variable_ids) == 2:
var_1 = self.variable_ids[0]
var_2 = self.variable_ids[1]
x_axis_label = self._axis_label(var_1, self.nice_variable_ids[0],
'[mmol gDW^-1 h^-1]')
y_axis_label = self._axis_label(var_2, self.nice_variable_ids[1],
'[mmol gDW^-1 h^-1]')
z_axis_label = self._axis_label(self.objective,
self.nice_objective_id, unit)
dataframe = pandas.DataFrame(
columns=["ub", "lb", "value1", "value2", "strain"])
for _, row in self.iterrows():
_df = pandas.DataFrame(
[[row[upper], row[lower], row[var_1], row[var_2], "WT"]],
columns=dataframe.columns)
dataframe = dataframe.append(_df)
plot = plotter.production_envelope_3d(dataframe,
grid=grid,
width=width,
height=height,
title=title,
y_axis_label=y_axis_label,
x_axis_label=x_axis_label,
z_axis_label=z_axis_label,
palette=palette,
points=points,
points_colors=points_colors)
else:
notice("Multi-dimensional plotting is not supported")
return
if grid is None:
plotter.display(plot)
