solver wrapper API#

openalea.hydroroot.solver_wrapper.pure_hydraulic_model(parameter=<openalea.hydroroot.init_parameter.Parameters object>, df_archi=None, df_law=None, df_exp=None, Data_to_Optim=None, output=None, Flag_verbose=False, Flag_radius=False, Flag_Constraint=True, dK_constraint=-0.03, dk_max=0.1)[source]#

Perform direct simulations or parameters adjustment to fit data of cut and flow experiment. Water transport only, electrical network analogy

Parameters:

  • parameter – Parameter - (see :func: Parameters)

  • df_archi – DataFrame (None) - DataFrame with the architecture data (see below structure description)

  • df_law – DataFrame list (None) - DataFrame with the length law data (see below structure description)

  • df_exp – DataFrame (None) - data to fit

  • Data_to_Optim – string list (None) - list of parameters to adjust, if None perform direct simulation, [‘K’, ‘k’]

  • output – string (None) - if not None output filename

  • Flag_verbose – boolean (False) - if True print intermediary results

  • Flag_radius – boolean (False) - if True use diameter recorded in architecture file if present, otherwise use ref_radius

  • Flag_Constraint – boolean (True) - if True apply constraint on axial conductance 1st derivative

  • dK_constraint – float (-3.0e-2) - lower bound of the axial conductance 1st derivative if Flag_Constraint = True

  • dk_max – float (0.1) - the convergence criteria on

Returns:

  • df: DataFrame with results

  • g_cut: dictionary with MTG at each cut

df_archi column names

  • distance_from_base_(mm), lateral_root_length_(mm), order

df_law

  • list of 2 dataframe with the length law data: the first for the 1st order laterals on the primary root, the 2nd for the laterals on laterals whatever their order (2nd, 3rd, …)

  • column names: LR_length_mm , relative_distance_to_tip

The adjustment is performed as follows

  1. pre-optimization with the adjustment of axfold and radfold, K and k factor, if only k adjustment is asked then step 1 is not performed

  2. loop of two successive adjustments: 1st K adjustment then k adjustment. The loop stop when change of k is below dk_max

Data_to_Optim list of string

  • ‘K’: optimize axial conductance K only

  • ‘k’: optimize radial conductivity k only

  • [ ] empty list or [‘K’, ‘k’]: optimize K and k

df_exp column names

  • arch: sample name that must be contained in the ‘input_file’ of the yaml file

  • J0, …, Jn: columns containing the flux values of the full root, 1st cut, 2d cut, etc.

  • lcut1, …., lcutn: columns containing the maximum length to the base after each cut, 1st cut, 2d cut, etc. (the primary length of the full root is calculated from the architecture)

outputfile

  • column names: ‘plant’, ‘cut length (m)’, ‘max_length’, ‘k (10-8 m/s/MPa)’, ‘length (m)’, ‘surface (m2)’, ‘Jv (uL/s)’, ‘Jexp (uL/s)’

  • if ‘K’ in Data_to_Optim add the following: ‘x’, ‘K 1st’, ‘K optimized’ the initial and adjusted K(x)

Remark

The routine is designed to work with a single value (float) for parameter.hydro[‘k0’].

example

>>> parameter = Parameters()
>>> filename='parameters_fig-2-B.yml'
>>> parameter.read_file(filename)
>>> fn = 'data/arabido_cnf_data.csv'
>>> df_exp = pd.read_csv(fn, sep = ',', keep_default_na = True)
>>> df = pure_hydraulic_model(parameter,df_exp=df_exp, Flag_verbose=True, Data_to_Optim = ['k', 'K'])
openalea.hydroroot.solver_wrapper.water_solute_model(parameter, df_archi=None, df_law=None, df_cnf=None, df_JvP=None, Data_to_Optim=None, Flag_verbose=False, data_to_use='all', output=None, optim_method='COBYLA', Flag_debug=False, Flag_radius=True, Flag_Constraint=True, dK_constraint=-0.03, Flag_w_Lpr=False, Flag_w_cnf=False)[source]#

Perform direct simulations or parameters adjustment to fit data of Jv(P) and/or cut and flow experiments. Water and solute transport. Works with constant radial conductivity.

Parameters:

  • parameter – Parameter - (see :func: Parameters)

  • df_archi – DataFrame (None) - DataFrame with the architecture data (see below structure description)

  • df_law – DataFrame list (None) - DataFrame with the length law data (see below structure description)

  • df_cnf – DataFrame (None) - cut and flow data to fit (see below structure description)

  • df_JvP – DataFrame (None) - Jv(P) data to fit (see below structure description)

  • Data_to_Optim – string list (None) - list of parameters to adjust, if None perform direct simulation, empty list is equivalent to [‘K’, ‘k’, ‘Js’, ‘Ps’]

  • Flag_verbose – boolean (False) - if True print intermediary results, optimization details, final simulation outputs, etc.

  • data_to_use – string (‘all’) - data to fit either ‘JvP’ (Jv(P)), ‘cnf’ (cut and flow), or ‘all’ both

  • output – string (None) - if not None output filename

  • optim_method – string (‘COBYLA’) - solver method used in scipy.optimize.minimize see docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

  • Flag_debug – boolean (False) - if True print intermediary values of parameters adjustment

  • Flag_radius – boolean (True) - if True use diameter recorded in architecture file if present, otherwise use ref_radius

  • Flag_Constraint – boolean (True) - if True apply constraint on axial conductance 1st derivative (with COBYLA constraint may not be respected)

  • dK_constraint – float (-3.0e-2) - lower bound of the axial conductance 1st derivative if Flag_Constraint = True (with COBYLA constraint may not be respected)

  • Flag_w_Lpr – boolean (False) - set weight to 1.0 / len(list_DP) on the JvP objective function

  • Flag_w_cnf – boolean (False) - set weight to len(cut_n_flow_length) on the cut and flow objective function

Returns:

  • d: DataFrame with results

  • g_cut: dictionary with MTG at each cut

Data_to_Optim list

  • ‘K’: optimize axial conductance K

  • ‘k’: optimize radial conductivity k

  • ‘Js’: optimize pumping rate Js

  • ‘Ps’: optimize permeability

  • ‘Klr’: optimize axial conductance of laterals Klr if <> than PR

  • ‘klr’: optimize radial conductivity of laterals klr if <> than PR

  • ‘sigma’: optimize the reflection coefficient

[‘K’, ‘k’, ‘Js’, ‘Ps’] will optimize the four parameters in the list. But, having too many parameters with this routine using local optimizer from scipy may not lead to any results.

df_archi column names

  • distance_from_base_(mm), lateral_root_length_(mm), order

df_law

  • list of 2 dataframe with the length law data: the first for the 1st order laterals on the primary root, the 2nd for the laterals on laterals whatever their order (2nd, 3rd, …)

  • column names: LR_length_mm , relative_distance_to_tip

df_cnf column names

  • arch: sample name that must be contained in the ‘input_file’ of the yaml file

  • dP_Mpa: column with the working cut and flow pressure (in relative to the base) if constant, may be empty see below

  • J0, J1, …, Jn: columns that start with ‘J’ containing the flux values, 1st the for the full root, then 1st cut, 2d cut, etc.

  • lcut1, …., lcutn: columns starting with ‘lcut’ containing the maximum length to the base after each cut, 1st cut, 2d cut, etc. (not the for full root)

  • dP0, dP1,.., dPn: column starting with ‘dP’ containing the working pressure (in relative to the base) of each steps (if not constant): full root, 1st cut, 2d cut, etc.

df_JvP column names

  • arch: sample name that must be contained in the ‘input_file’ of the yaml file

  • J0, J1, …, Jn: columns that start with ‘J’ containing the flux values of each pressure steps

  • dP0, dP1,.., dPn: column starting with ‘dP’ containing the working pressure (in relative to the base) of each steps

outputfile (csv)

  • column names: ‘max_length’, ‘Jexp cnf (uL/s)’, ‘Jv cnf (uL/s)’, ‘surface (m2)’, ‘length (m)’, ‘dp’, ‘Jexp(P)’, ‘Jv(P)’, ‘Cbase’, ‘kpr’, ‘klr’, ‘Js’, ‘Ps’, ‘F cnf’,’F Lpr’, ‘x pr’, ‘K1st pr’, ‘x lr’, ‘K1st lr’, ‘K pr’, ‘K lr’

i.e.: max length from the cut to the base, J cnf exp, J cnf sim, root surface, total root length, pressure (Jv(P), J exp Jv(P), J sim Jv(P), solute concentration at the base (Jv(P)), radial conductivity PR, radial conductivity LR, pumping rate, permeability, objective fct cnf, objective fct Jv(P), x pr distance to tip for PR, K 1st guess for PR, the same for laterals if different, then axial conductances for PR and LR.

Remark

  • radial conductivity: single value or list of 2 values [kpr, klr]: 1st value for PR and 2nd for LR

  • axial conductance: it is possible to add K for LR

Download the source file ../../src/openalea/hydroroot/solver_wrapper.py.