How to use the cameo.flux_analysis.analysis.phenotypic_phase_plane function in cameo

def test_one_variable_sequential(self, core_model):
        ppp = phenotypic_phase_plane(core_model, ['EX_o2_LPAREN_e_RPAREN_'], view=SequentialView())
        assert_data_frames_equal(ppp, REFERENCE_PPP_o2_EcoliCore, sort_by=['EX_o2_LPAREN_e_RPAREN_'])
        ppp = phenotypic_phase_plane(core_model, 'EX_o2_LPAREN_e_RPAREN_', view=SequentialView())
        assert_data_frames_equal(ppp, REFERENCE_PPP_o2_EcoliCore, sort_by=['EX_o2_LPAREN_e_RPAREN_'])

def test_one_variable_parallel(self):
        ppp = phenotypic_phase_plane(self.model, ['EX_o2_LPAREN_e_RPAREN_'], view=MultiprocessingView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)
        ppp = phenotypic_phase_plane(self.model, 'EX_o2_LPAREN_e_RPAREN_', view=MultiprocessingView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)

def test_one_variable_parallel(self, core_model):
        ppp = phenotypic_phase_plane(core_model, ['EX_o2_LPAREN_e_RPAREN_'], view=MultiprocessingView())
        assert_data_frames_equal(ppp, REFERENCE_PPP_o2_EcoliCore, sort_by=['EX_o2_LPAREN_e_RPAREN_'])
        ppp = phenotypic_phase_plane(core_model, 'EX_o2_LPAREN_e_RPAREN_', view=MultiprocessingView())
        assert_data_frames_equal(ppp, REFERENCE_PPP_o2_EcoliCore, sort_by=['EX_o2_LPAREN_e_RPAREN_'])

def test_one_variable_sequential(self):
        ppp = phenotypic_phase_plane(self.model, ['EX_o2_LPAREN_e_RPAREN_'], view=SequentialView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)
        ppp = phenotypic_phase_plane(self.model, 'EX_o2_LPAREN_e_RPAREN_', view=SequentialView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)

def test_two_variables_sequential(self, core_model):
        ppp2d = phenotypic_phase_plane(core_model, ['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'],
                                       view=SequentialView())
        assert_data_frames_equal(ppp2d, REFERENCE_PPP_o2_glc_EcoliCore,
                                 sort_by=['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'])

def test_one_variable_sequential(self):
        ppp = phenotypic_phase_plane(self.model, ['EX_o2_LPAREN_e_RPAREN_'], view=SequentialView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)
        ppp = phenotypic_phase_plane(self.model, 'EX_o2_LPAREN_e_RPAREN_', view=SequentialView())
        assert_dataframes_equal(ppp, REFERENCE_PPP_o2_EcoliCore)

def test_two_variables_sequential(self):
        ppp2d = phenotypic_phase_plane(self.model, ['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'],
                                       view=SequentialView())
        assert_dataframes_equal(ppp2d, REFERENCE_PPP_o2_glc_EcoliCore)

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 self._model:
            for ko in self.data_frame.loc[index, "reactions"]:
                swap_cofactors(self._model.reactions.get_by_id(ko), self._model, self._swap_pairs)
            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)

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 self._model:
            for ko in self.data_frame.loc[index, "reactions"]:
                self._model.reactions.get_by_id(ko).knock_out()
            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)

def _init_search_grid(self, surface_only=False, improvements_only=True):
        """Initialize the grid of points to be scanned within the production envelope."""
        self.envelope = phenotypic_phase_plane(
            self.design_space_model, self.variables, objective=self.objective, points=self.points)
        intervals = self.envelope[['objective_lower_bound', 'objective_upper_bound']].copy()
        intervals['objective_lower_bound'] = float_floor(intervals.objective_lower_bound, ndecimals)
        intervals['objective_upper_bound'] = float_ceil(intervals.objective_upper_bound, ndecimals)
        max_distance = 0.
        max_interval = None
        for i, (lb, ub) in intervals.iterrows():
            distance = abs(ub - lb)
            if distance > max_distance:
                max_distance = distance
                max_interval = (lb, ub)
        step_size = (max_interval[1] - max_interval[0]) / (self.points - 1)
        grid = list()
        minimal_reference_production = self.reference_flux_ranges['lower_bound'][self.objective]
        for i, row in self.envelope.iterrows():
            variables = row[self.variables]