pibronic.plotting package

Scripts involved in plotting information

pibronic.plotting.prepare_mpl_rc_file(pretty_but_slow=False)

TODO - this needs to be refactored and cleaned up (it is sufficiently functional for the moment)

pibronic.plotting.load_latex_module_on_server(version='2017')

load the texlive module so that we can make plots with latex this function will only work on our local server TODO - there should be a replacement for local execution and execution on other servers

Submodules

pibronic.plotting.grid_plots module

analysis plotting - intensity grid of the parameters of a model

class pibronic.plotting.grid_plots.plotGrids(input_FS)

Bases: object

annotate_intensity_grid(fig, ax, data, title)

commonly shared code for plotting

generate_sign_array(array)

load_coupled_models()

x

load_data()

x

load_original_models()

x

plot_coupled_energy_grid()

x

plot_coupled_linear_grids(data)

x

plot_coupled_quadratic_grids(data)

x

plot_energy()

x

plot_linear_coupling()

x

plot_original_energy_grid()

x

plot_original_linear_grids(data)

x

plot_original_quadratic_grids(data)

x

plot_quadratic_coupling()

x

plot_single_energy_grid(energy, filename, title)

x

plot_single_linear_grid(energy, filename, title)

x

plot_single_quadratic_grid(energy, filename, title)

x

prepare_data()

optionally - if we want to modify the data before plotting

pibronic.plotting.plotting module

first pass at creating plotting module

pibronic.plotting.plotting.generate_tau_values(self, temperature, idx_FS)

returns a numpy array of the same length as lst_P takes in one temperature and an array of P values

pibronic.plotting.plotting.load_analytical_original(self)

generic loading function that can be used by multiple plotting classes

pibronic.plotting.plotting.load_analytical_sampling(self)

generic loading function that can be used by multiple plotting classes

pibronic.plotting.plotting.load_sos(self, basis_size=80)

generic loading function that can be used by multiple plotting classes

pibronic.plotting.plotting.load_trotter_coupled(self, basis_size=80)

generic loading function that can be used by multiple plotting classes

class pibronic.plotting.plotting.plot_Z_multiple_FS(list_of_FileStructure_objects)

Bases: pibronic.plotting.virtual.plotVirtual

this is just an empty template at the moment, will be completed later

generate_file_lists()

x

generate_parameter_lists()

x

load_data()

x

load_pimc_data()

x

class pibronic.plotting.plotting.plot_Z_test(list_of_FileStructure_objects)

Bases: pibronic.plotting.virtual.plotVirtual

plotting when we only provide 1 FS object

generate_file_lists()

create a list of lists of parameters specific to each FileStructure object

generate_parameter_lists()

x

load_data()

x

plot()

x

plot_Cv()

plot_E()

plot_Z()

class pibronic.plotting.plotting.plot_original_Z_test(list_of_FileStructure_objects)

Bases: pibronic.plotting.virtual.plotVirtual

plotting pimc results against analytical results of original model when we only provide 1 FS object

generate_file_lists()

create a list of lists of parameters specific to each FileStructure object

generate_parameter_lists()

x

generate_tau_values(temperature)

returns a numpy array of the same length as lst_P takes in one temperature and an array of P values

load_analytical_data()

x

load_data()

x

load_pimc_data()

x

plot()

x

plot_Z()

prepare_data()

class pibronic.plotting.plotting.plot_original_Z_vs_diagonal_test(list_of_FileStructure_objects)

Bases: pibronic.plotting.plotting.plot_Z_multiple_FS

plotting pimc results drawn from the original_coupled_model.json vs the pimc results drawn from the diagonal of the coupled_model.json vs analytical results of the original model when we provide 2 FS objects

generate_tau_values(temperature, idx_FS)

returns a numpy array of the same length as lst_P takes in one temperature and an array of P values

load_analytical_data()

x

load_data()

x

plot()

x

plot_Z()

prepare_data()

class pibronic.plotting.plotting.plot_rectangle(list_of_FileStructure_objects)

Bases: pibronic.plotting.plotting.plot_Z_multiple_FS

generate_tau_values(temperature, idx_FS)

returns a numpy array of the same length as lst_P takes in one temperature and an array of P values

load_data()

x

plot()

x

plot_Z()

prepare_data()

x

class pibronic.plotting.plotting.plot_sos_Z_vs_rho_n(list_of_FileStructure_objects)

Bases: pibronic.plotting.plotting.plot_Z_multiple_FS

plotting pimc results drawn from the coupled_model.json vs the pimc results drawn from a rho vs sos results of the coupled model when we provide 2 FS objects

load_data()

x

plot()

x

plot_Z()

prepare_data()

pibronic.plotting.prepare module

functions which prepare the environment for executing matplotlib on the server

pibronic.plotting.prepare.load_latex_module_on_server(version='2017')

load the texlive module so that we can make plots with latex this function will only work on our local server TODO - there should be a replacement for local execution and execution on other servers

pibronic.plotting.prepare.prepare_mpl_rc_file(pretty_but_slow=False)

TODO - this needs to be refactored and cleaned up (it is sufficiently functional for the moment)

pibronic.plotting.virtual module

class pibronic.plotting.virtual.plotVirtual(list_of_FileStructure_objects)

Bases: object

outline the basic flow of plotting most members are designed to be overloaded actual plotting functions should be added to children classes

generate_file_lists()

create lists of paths to all data files to be used for plotting

generate_parameter_lists()

create lists of all possible unique valid parameters that are to be plotted for example:

• a list of all possible bead values might be [12, 20, 50],
• a list of all possible temperature values might be [250.00, 275.00, 300.00]

which could arise from 3 data files with the following parameters:

• [12, 250.00], [20, 275.00], [50, 300.00]

or 5 data files with the following parameters:

• [12, 250.00], [12, 275.00], [12, 300.00], [22, 300.00], [50, 300.00]

the purposes of this function is to generate lists which allow for modifying the range of each parameter separately using intersections

validate_data()