3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj]))","function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =","stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching","os, json import numpy as np from ctypes import *","!= nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e,","in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" #","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\","c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def","idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] !=","value_): \"\"\" set numerical data in the cost module of","in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","out # Note: this function should not be used anymore,","is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np","Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO:","= nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,))","self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,","idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0]","'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't']","raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu","= {ns} = nsbx + nsbu + nsg + nsh","+ mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]","Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e =","field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set","or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e,","constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e,","'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal","with dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape))","!= sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path","opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes):","model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created","con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) #","constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.')","np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding","cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif","external function calls 'time_sim_la', # cpu time for integrator contribution","LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #","if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p","'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_","cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p,","(stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out","'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model,","\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_,","ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization","Exception('solver option {} must be of type float. You have","contribution of linear algebra 'time_qp', # cpu time qp solution","= np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,","discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf","nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng","nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] !=","dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e)","Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0 for","should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny","conversion issues value_ = value_.astype(float) field = field_ field =","the following conditions are met: # # 1. Redistributions of","# nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh >","this list of conditions and the following disclaimer in the","is not a valid argument.\\ \\n Possible values are {}.","cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape","+ '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout","values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\","model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e","= {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e =","only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type","'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir,","'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format(","c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]","constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension","= {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\","np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding","acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file","self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)","= cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e:","License # # Redistribution and use in source and binary","idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] !=","'{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex","= ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi',","in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps","= {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization","!= dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps =","vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 =","(you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double))","+ ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]","future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, #","templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make","Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise","inequality constraints \\n su: slack variables of soft upper inequality","lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e:","{nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}')","opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown","= dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0:","ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()","nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif","= c_int(value_) elif field_ in double_fields: if not isinstance(value_, float):","np from ctypes import * from casadi import CasadiMeta, Function,","!= nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu,","ctypes import * from casadi import CasadiMeta, Function, SX from","interact with the acados ocp solver C object \"\"\" def","not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0","cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)","values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m',","c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise","acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name =","{tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with","in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","size \"\"\" # cast value_ to avoid conversion issues value_","dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_,","D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh =","inequalities are internally organized in the following order: \\n [","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","= 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh:","set numerical data in the constraint module of the solver:","raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e","is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0]","self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1,",":param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of","Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush =","the solver: :param stage: integer corresponding to shooting node :param","# make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp)","'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name)","else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_","field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n","acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp,","= acados_ocp.model.name # setting up loader and environment json_path =","' for field \"{}\" with dimension {} (you have {})'.format(field_,","# Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict =","= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","= np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh,","ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] !=","'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field","type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if","byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib #","self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes =","for integrator contribution of linear algebra 'time_qp', # cpu time","self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp with","DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while","out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file,","= c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config =","if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {}","= '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on","os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so'","nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0]","ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) #","out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields =","self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m,","NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND","= np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,","dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints:","idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e):","casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or","ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] !=","'su'] field = field_ field = field.encode('utf-8') if (field_ not","+ nsh + nsg + nsphi wrong_field = \"\" if","in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] ..","def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =","1. Redistributions of source code must retain the above copyright","= dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0","= 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data,","PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE","== 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if","elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,","'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True)","a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields","idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] !=","+ \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and","json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure","stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj])))","0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr):","terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0]","appropriate size \"\"\" # cast value_ to avoid conversion issues","= ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e =","field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option","# path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if","Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1]","idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu","self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype","dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny","= dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else:","'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name #","!= ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e,","render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact","['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data,","from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp","field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p,","get the information of the last solver call: :param field_:","generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from","dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip","nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0]","type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_)","render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type ==","ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent","of soft upper inequality constraints \\n \"\"\" out_fields = ['x',","You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_","dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e:","string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\","terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e","= nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0","time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either","or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf","= '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not","= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file,","convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e","json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code')","ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e","stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def","nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif","nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else:","= '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost","regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0]","nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e =","Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values are","(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension',","template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e ==","of appropriate size \"\"\" # cast value_ to avoid conversion","-- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': #","discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not","else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0]","nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0]","!= ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field","cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name):","constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension","dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx","field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if"],"string":"[\n \"on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_,\",\n \"nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu\",\n \"self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self,\",\n \"is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent\",\n \"# and/or other materials provided with the distribution. # #\",\n \"ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model\",\n \"stage_, field_, value_): \\\"\\\"\\\" set numerical data in the cost\",\n \"integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else:\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_,\",\n \"elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator\",\n \"\\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes))\",\n \"nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif\",\n \"funciton with formatting for acados_struct, v in ocp_layout.items(): # skip\",\n \"\\\\n su: slack variables of soft upper inequality constraints \\\\n\",\n \"regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0]\",\n \"time previous call 'time_lin', # cpu time for linearization 'time_sim',\",\n \"out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def\",\n \"about last iteration 'stat_m', 'stat_n', ] field = field_ field\",\n \"'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast value_\",\n \"cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim =\",\n \"tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\"\",\n \"= '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost -\",\n \"json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() #\",\n \"nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr =\",\n \"the information of the last solver call: :param field_: string\",\n \"issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8')\",\n \"generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from\",\n \"if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise\",\n \"cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif\",\n \"self.nlp_solver, field, out_data) return out # Note: this function should\",\n \"string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type\",\n \"can ' 'take only values 0, 1, 2 for SQP-RTI-type\",\n \"AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty,\",\n \"'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p)\",\n \"regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension\",\n \"'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file =\",\n \"dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 ==\",\n \"integer corresponding to shooting node :param field_: string in ['x',\",\n \"# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\",\n \"f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS':\",\n \"= casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e\",\n \"structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object\",\n \"nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg\",\n \"acados_ocp.dims.nphi > 0: # constraints on outer function template_dir =\",\n \"'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0]\",\n \"self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp\",\n \"shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param\",\n \"model.p and parameter_values.') ## cost # path if cost.cost_type ==\",\n \"sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data\",\n \"in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"time regularization 'sqp_iter', # number of SQP iterations 'statistics', #\",\n \"else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent\",\n \"nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e =\",\n \"cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"!= ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny,\",\n \"if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e:\",\n \"EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE\",\n \"'pi', 'lam', 't'] # cast value_ to avoid conversion issues\",\n \"acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims =\",\n \"if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h'\",\n \"= 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file\",\n \"if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype =\",\n \"json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir =\",\n \"def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in\",\n \"idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e\",\n \"terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"list of conditions and the following disclaimer. # # 2.\",\n \"field_): \\\"\\\"\\\" get the information of the last solver call:\",\n \"elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,\",\n \"0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\\",\n \"over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e >\",\n \"constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have\",\n \"Exception('solver option {} must be of type str. You have\",\n \"c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\"\",\n \"'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear\",\n \"self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_,\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in\",\n \"self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in\",\n \"for inequalities \\\\n t: slack variables corresponding to evaluation of\",\n \"# number of SQP iterations 'statistics', # table with info\",\n \"\\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field =\",\n \"generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx ==\",\n \"field = field_ field = field.encode('utf-8') if (field_ not in\",\n \"is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0]\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims =\",\n \"cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0]\",\n \"= CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts\",\n \"0 for SQP-type solvers') field = field_ field = field.encode('utf-8')\",\n \"self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data,\",\n \"# terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or\",\n \"f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure\",\n \"ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0]\",\n \"raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost\",\n \"have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:\",\n \"+ nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] !=\",\n \"!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx\",\n \"acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format(\",\n \"field_ field = field.encode('utf-8') if (field_ not in out_fields +\",\n \"c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes\",\n \"= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)),\",\n \"should be column vector!') # nu if is_empty(model.u): dims.nu =\",\n \"if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0]\",\n \"# Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx =\",\n \"= {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e =\",\n \"constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr)\",\n \"constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref']\",\n \"{wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e}\",\n \"json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in'\",\n \"and binary forms, with or without # modification, are permitted\",\n \"cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e\",\n \"or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can '\",\n \"nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding\",\n \"# <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file\",\n \"np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage,\",\n \"ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],\",\n \"= 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file\",\n \"should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent\",\n \"\\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj],\",\n \"iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8')\",\n \":param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't',\",\n \"have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx\",\n \"int_fields: if not isinstance(value_, int): raise Exception('solver option {} must\",\n \"# Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) #\",\n \"SQP iterations 'statistics', # table with info about last iteration\",\n \"of source code must retain the above copyright notice, #\",\n \"nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\"\",\n \"ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver\",\n \"else: value_ctypes = c_int(value_) elif field_ in double_fields: if not\",\n \"raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1]\",\n \"self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see\",\n \"Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i\",\n \"if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C\",\n \"stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\")\",\n \"'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi\",\n \"Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.')\",\n \"== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model,\",\n \"print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if\",\n \"have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:\",\n \"not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N,\",\n \"if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent\",\n \"dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton\",\n \"if not isinstance(value_, float): raise Exception('solver option {} must be\",\n \"if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]),\",\n \"dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e =\",\n \"GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\",\n \"WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\",\n \"import sys, os, json import numpy as np from ctypes\",\n \"= constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape:\",\n \"elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver\",\n \"is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 =\",\n \"dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension\",\n \"dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu\",\n \"def options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver:\",\n \"0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding\",\n \"] field = field_ field = field.encode('utf-8') if (field_ not\",\n \".generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import\",\n \"BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\",\n \"= 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir\",\n \"stage_, field_): \\\"\\\"\\\" get the last solution of the solver:\",\n \"raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif\",\n \"!= dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx\",\n \"= ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl',\",\n \"= field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if\",\n \"mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension {}\",\n \"!= 0: raise Exception('inconsistent dimension: Vu should have nu columns.')\",\n \"\\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding\",\n \"node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_:\",\n \"generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from\",\n \"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\",\n \"= 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file\",\n \"size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected\",\n \"and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have\",\n \"' for field \\\"{}\\\" with dimension {} (you have {})'.format(\",\n \"= casadi_length(model.x) else: raise Exception('model.x should be column vector!') #\",\n \"if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent\",\n \"acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to\",\n \"of external function calls 'time_sim_la', # cpu time for integrator\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u',\",\n \"int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields =\",\n \"TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\",\n \"ny != 0: raise Exception('inconsistent dimension: Vx should have nx\",\n \"Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e =\",\n \"# nz if is_empty(model.z): dims.nz = 0 else: dims.nz =\",\n \"get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict\",\n \"dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else:\",\n \"'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir =\",\n \"# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy\",\n \"!= nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx,\",\n \"in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid\",\n \"can ' 'take only value 0 for SQP-type solvers') field\",\n \"return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE:\",\n \"in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"last iteration 'stat_m', 'stat_n', ] field = field_ field =\",\n \"regarding lam, t: \\\\n the inequalities are internally organized in\",\n \"W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if\",\n \"== 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model)\",\n \"template_dir, json_path) # terminal constraints on convex over nonlinear function\",\n \"or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg,\",\n \"type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8')\",\n \"acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints,\",\n \"{nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e\",\n \"= \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim\",\n \"elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver\",\n \"if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e ==\",\n \"[] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape\",\n \"acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict\",\n \"!= \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension\",\n \"raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e\",\n \"constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.')\",\n \"self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def\",\n \"the last solution of the solver: :param stage: integer corresponding\",\n \"column vector!') # nu if is_empty(model.u): dims.nu = 0 else:\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f'\",\n \"# set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate\",\n \"= cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e =\",\n \"ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load\",\n \"Redistributions of source code must retain the above copyright notice,\",\n \"tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf\",\n \"Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode\",\n \"\\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type solvers')\",\n \"out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file\",\n \"elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny\",\n \"<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>,\",\n \"object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ =\",\n \"'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields\",\n \"if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) #\",\n \"HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\",\n \"# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\",\n \"ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) #\",\n \"tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)\",\n \"t: \\\\n the inequalities are internally organized in the following\",\n \"ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json =\",\n \"ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with\",\n \"f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load\",\n \"cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim =\",\n \"constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.')\",\n \"outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file =\",\n \"Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if\",\n \"np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif\",\n \"node :param field_: string, e.g. 'lbx' :param value_: of appropriate\",\n \"if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be\",\n \"model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e >\",\n \"opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model)\",\n \"self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function\",\n \"os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout =\",\n \"not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\\",\n \"'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with\",\n \"from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode\",\n \"dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4,\",\n \"+ nsbu + nsh + nsg + nsphi wrong_field =\",\n \"(you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,\",\n \"value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if\",\n \"\\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field =\",\n \"if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise\",\n \"c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this\",\n \"in the constraint module of the solver: Parameters: :param stage_:\",\n \"constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension\",\n \"out_file, template_dir, json_path) # constraints on convex over nonlinear function\",\n \"field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate\",\n \"Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n')\",\n \"open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def\",\n \"be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0):\",\n \"clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name +\",\n \"POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data,\",\n \"if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise\",\n \"= {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh =\",\n \"raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or\",\n \"OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE\",\n \"+ out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\\",\n \"self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config,\",\n \"# Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class\",\n \"c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\\",\n \"= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"!= ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] !=\",\n \"json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp\",\n \"Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0:\",\n \"if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields:\",\n \"np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"\\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free()\",\n \"field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option\",\n \"> 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts)\",\n \"constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if\",\n \"!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e\",\n \"f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): #\",\n \"regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0]\",\n \"'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx\",\n \"Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file,\",\n \"= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching\",\n \"value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical\",\n \"of the solver: Parameters: :param stage_: integer corresponding to shooting\",\n \"in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"# TODO: maybe make one funciton with formatting for acados_struct,\",\n \"lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0]\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}],\",\n \"constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu:\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int\",\n \"if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj\",\n \"\\\"\\\"\\\" get the information of the last solver call: :param\",\n \"constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.')\",\n \"# strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes',\",\n \"wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns:\",\n \"f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate\",\n \"'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver:\",\n \"Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise\",\n \"Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] !=\",\n \"\\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"!= ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension\",\n \"= nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field =\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_\",\n \"if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0]\",\n \"Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh\",\n \"form must reproduce the above copyright notice, # this list\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes\",\n \"# 1. Redistributions of source code must retain the above\",\n \"= cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny:\",\n \"c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if\",\n \"{})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics',\",\n \"{} must be of type int. You have {}.'.format(field_, type(value_)))\",\n \"!= ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field\",\n \"acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict,\",\n \"\\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if\",\n \"'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time previous\",\n \"f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg},\",\n \"Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else:\",\n \"nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx):\",\n \"generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting\",\n \"dimension' \\\\ ' for field \\\"{}\\\" with dimension {} (you\",\n \"stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj],\",\n \"dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise\",\n \"be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\",\n \"OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\",\n \"Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost #\",\n \"self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def\",\n \"= '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints\",\n \"elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,\",\n \"= np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,\",\n \"from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint\",\n \"Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =\",\n \"regarding model.p and parameter_values.') ## cost # path if cost.cost_type\",\n \"code must retain the above copyright notice, # this list\",\n \"{wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns}\",\n \"- opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not\",\n \"acados. # # The 2-Clause BSD License # # Redistribution\",\n \"are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields +\",\n \"dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if\",\n \"\\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims,\",\n \"value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take\",\n \"dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise\",\n \"value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical\",\n \"for field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\\",\n \"generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif\",\n \"== 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension\",\n \"explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type ==\",\n \"argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields))\",\n \"# cpu time for integrator contribution of linear algebra 'time_qp',\",\n \"json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh\",\n \"np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding\",\n \"inequalities \\\\n t: slack variables corresponding to evaluation of all\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is\",\n \"# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\",\n \"shooting node :param field_: string, e.g. 'lbx' :param value_: of\",\n \"distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT\",\n \"field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options\",\n \"Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi:\",\n \"!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if\",\n \"uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi =\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"if not isinstance(value_, int): raise Exception('solver option {} must be\",\n \"stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type\",\n \"['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_\",\n \"if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or\",\n \"Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension\",\n \"<NAME> # # This file is part of acados. #\",\n \"template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file,\",\n \"= {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e =\",\n \"Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts =\",\n \"ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp =\",\n \"def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields\",\n \"terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir =\",\n \"dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng\",\n \"out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES\",\n \"* np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise\",\n \"for dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n\",\n \"!= 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif\",\n \"nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0]\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)\",\n \"'.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True\",\n \"# terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e\",\n \"usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e\",\n \"# Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,\",\n \"stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3:\",\n \"<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,\",\n \"dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype =\",\n \"acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'):\",\n \"model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit\",\n \"nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0]\",\n \"0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if\",\n \"'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\\",\n \"elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0]\",\n \"nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e):\",\n \"\\\"\\\"\\\" set options of the solver: Parameters: :param field_: string,\",\n \"the cost module of the solver: :param stage_: integer corresponding\",\n \"ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if\",\n \"wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\":\",\n \"casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but\",\n \"constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension\",\n \"the following disclaimer in the documentation # and/or other materials\",\n \"range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj],\",\n \"cpu time for linearization 'time_sim', # cpu time for integrator\",\n \"dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi =\",\n \"Exception('solver option {} must be of type int. You have\",\n \"out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear\",\n \"* from casadi import CasadiMeta, Function, SX from copy import\",\n \"dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu\",\n \"= ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if\",\n \"function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name)\",\n \"formatting for acados_struct, v in ocp_layout.items(): # skip non dict\",\n \"= ['time_tot', # total cpu time previous call 'time_lin', #\",\n \"lam, t: \\\\n the inequalities are internally organized in the\",\n \"attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items():\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims\",\n \"external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"= np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ ==\",\n \"self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib\",\n \"acados_struct, v in ocp_layout.items(): # skip non dict attributes if\",\n \"Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi =\",\n \"with formatting for acados_struct, v in ocp_layout.items(): # skip non\",\n \"(without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization\",\n \"IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; #\",\n \"PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\",\n \"ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] !=\",\n \"ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp\",\n \"'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0\",\n \"constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension\",\n \"call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad',\",\n \"ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] !=\",\n \"folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file =\",\n \"nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding\",\n \"acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\")\",\n \"matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx))\",\n \"if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ '\",\n \"c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config,\",\n \"raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else:\",\n \"else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape\",\n \"nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0]\",\n \"{dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e +\",\n \"over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi >\",\n \"out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP':\",\n \"is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes\",\n \"nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns\",\n \"dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,))\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields:\",\n \"ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status\",\n \"\\\"\\\"\\\" get the last solution of the solver: :param stage:\",\n \"del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! #\",\n \"isinstance(value_, int): raise Exception('solver option {} must be of type\",\n \"= np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,\",\n \"= 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex\",\n \"c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out\",\n \"else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if\",\n \"'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name)\",\n \"> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file =\",\n \"== 'IRK': # implicit model -- generate C code opts\",\n \"Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi\",\n \"'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\"\",\n \"!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh\",\n \"from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils\",\n \"return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data\",\n \"'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of\",\n \"total cpu time previous call 'time_lin', # cpu time for\",\n \"cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny\",\n \"this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if\",\n \"multipliers for dynamics equality constraints \\\\n lam: multipliers for inequalities\",\n \"= np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] -\",\n \"corresponding to evaluation of all inequalities (at the solution) \\\\n\",\n \"constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h'\",\n \"0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over\",\n \"0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np\",\n \"dims.nsg = nsg ns = nsbx + nsbu + nsh\",\n \"acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over\",\n \"+ f'Detected ns = {ns} = nsbx + nsbu +\",\n \"self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del\",\n \"raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg\",\n \"the solution) \\\\n sl: slack variables of soft lower inequality\",\n \"constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\\",\n \"usphi] .. note:: pi: multipliers for dynamics equality constraints \\\\n\",\n \"W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}],\",\n \"and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path):\",\n \"nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if\",\n \"dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): #\",\n \"+= 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg)\",\n \"LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS\",\n \"regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr):\",\n \"constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e,\",\n \"out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute)\",\n \"= cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim\",\n \"= acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict,\",\n \"internally organized in the following order: \\\\n [ lbu lbx\",\n \"= 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int\",\n \"dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\"\",\n \"json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\\",\n \"## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0\",\n \"!= 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\'\",\n \"is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent\",\n \"nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e):\",\n \"in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e:\",\n \"= 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"float): raise Exception('solver option {} must be of type float.\",\n \"conditions and the following disclaimer. # # 2. Redistributions in\",\n \"!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if\",\n \"continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] !=\",\n \"out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear\",\n \"'stat_n', ] field = field_ field = field.encode('utf-8') if (field_\",\n \"in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p,\",\n \"raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ##\",\n \"if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): #\",\n \"> 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\\",\n \"= '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function\",\n \"node :param field_: string in ['x', 'u', 'z', 'pi', 'lam',\",\n \"mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension\",\n \"int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj],\",\n \"self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config,\",\n \"!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.'\",\n \"import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost\",\n \"acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if\",\n \"DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,\",\n \"dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu =\",\n \"dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1:\",\n \"constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.')\",\n \"is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz\",\n \"OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT\",\n \"organized in the following order: \\\\n [ lbu lbx lg\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes =\",\n \"dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data\",\n \"cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension\",\n \"constraint module of the solver: Parameters: :param stage_: integer corresponding\",\n \"only value 0 for SQP-type solvers') field = field_ field\",\n \"## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file\",\n \"0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name)\",\n \"\\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes)\",\n \"!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if\",\n \"yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS':\",\n \"constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension\",\n \"\\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim},\",\n \"'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0]\",\n \"'IRK': # implicit model -- generate C code opts =\",\n \"c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \"0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over\",\n \"get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype =\",\n \"np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i]\",\n \"that the following conditions are met: # # 1. Redistributions\",\n \"render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if\",\n \"{} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"= value_.astype(float) field = field_ field = field.encode('utf-8') stage =\",\n \"invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields +\",\n \"1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise\",\n \"'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder\",\n \"cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the\",\n \"= 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z):\",\n \"cpu time for integrator 'time_sim_ad', # cpu time for integrator\",\n \"if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal\",\n \"= cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim\",\n \"elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0]\",\n \"be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #\",\n \"['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter']\",\n \"regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if\",\n \"== 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS':\",\n \"Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ +\",\n \"field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_,\",\n \"'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data =\",\n \"is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent\",\n \"= c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if\",\n \"import numpy as np from ctypes import * from casadi\",\n \"not isinstance(value_, float): raise Exception('solver option {} must be of\",\n \"= {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns =\",\n \"out_data) return out # Note: this function should not be\",\n \"function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: #\",\n \"self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily\",\n \"OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE\",\n \"= np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15:\",\n \"idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0]\",\n \"INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF\",\n \"regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],\",\n \"0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if\",\n \"= self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size =\",\n \"'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp,\",\n \"ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict\",\n \"cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise\",\n \"constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\\",\n \"'time_sim_la', # cpu time for integrator contribution of linear algebra\",\n \"!= ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.')\",\n \"the distribution. # # THIS SOFTWARE IS PROVIDED BY THE\",\n \"= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e\",\n \"casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp):\",\n \"ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns\",\n \"'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file =\",\n \"{nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes):\",\n \"\\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields +\",\n \"last solution of the solver: :param stage: integer corresponding to\",\n \"if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent\",\n \"generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from\",\n \"\\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =\",\n \"!= nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.')\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype\",\n \"template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file,\",\n \"= cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\\",\n \"print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self,\",\n \"OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER\",\n \"stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7:\",\n \"2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #\",\n \"Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh =\",\n \"ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e\",\n \"= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)\",\n \"json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL':\",\n \"== []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return\",\n \"should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!')\",\n \"= 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg =\",\n \"# setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(),\",\n \"else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr\",\n \"from ctypes import * from casadi import CasadiMeta, Function, SX\",\n \"in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file,\",\n \"for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ >\",\n \"np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding\",\n \"!= ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0]\",\n \"json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h'\",\n \"are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']:\",\n \"of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0:\",\n \"stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx',\",\n \"solver: :param stage_: integer corresponding to shooting node :param field_:\",\n \"a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields))\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes =\",\n \"== 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny !=\",\n \"ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.'\",\n \"used anymore, better use cost_set, constraints_set def set(self, stage_, field_,\",\n \"value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields\",\n \"= 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p):\",\n \"generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes =\",\n \"cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir\",\n \"the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', #\",\n \"= casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx):\",\n \"constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam',\",\n \"!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' +\",\n \"' 'take only value 0 for SQP-type solvers') field =\",\n \"[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return\",\n \"= nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or\",\n \"argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_\",\n \"os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render\",\n \"in string_fields: if not isinstance(value_, str): raise Exception('solver option {}\",\n \"ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr):\",\n \"'take only value 0 for SQP-type solvers') field = field_\",\n \"!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg\",\n \"'{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if\",\n \"'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can\",\n \"or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e\",\n \"AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN\",\n \"dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\"\",\n \"POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =\",\n \"self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts,\",\n \"is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent\",\n \"Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e =\",\n \"raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values\",\n \"yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif\",\n \"solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length'\",\n \"ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,\",\n \"nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding\",\n \"model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model\",\n \"with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def\",\n \"np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e,\",\n \"ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT\",\n \"the acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp,\",\n \"np, regarding model.p and parameter_values.') ## cost # path if\",\n \"uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif\",\n \"columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,\",\n \"def solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\"\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not\",\n \"Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu =\",\n \"nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu =\",\n \"> 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only\",\n \"dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out =\",\n \"True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up\",\n \"cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise\",\n \"== other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not\",\n \"'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', #\",\n \"np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding\",\n \"dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,))\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx\",\n \"but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack\",\n \"< 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\'\",\n \"c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"= 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if\",\n \"current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self,\",\n \"self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created:\",\n \"THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os,\",\n \"IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\"\",\n \"if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims:\",\n \"but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal\",\n \"['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su']\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile'\",\n \"not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an\",\n \"value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options of\",\n \"have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must\",\n \"dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\"\",\n \"raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with\",\n \"getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp\",\n \"self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype\",\n \"as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'):\",\n \"elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,\",\n \"lg, ug, C, D.') else: dims.ng = ng if not\",\n \"terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"+ nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With\",\n \"'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field\",\n \"raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e\",\n \"POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =\",\n \"convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi\",\n \"SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from\",\n \"string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_,\",\n \"{nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns\",\n \"nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0]\",\n \"solver: Parameters: :param stage_: integer corresponding to shooting node :param\",\n \"+ nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx =\",\n \"os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get\",\n \"BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY\",\n \"'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external\",\n \"if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0\",\n \"ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' #\",\n \"!= ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0]\",\n \"SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS\",\n \"of acados. # # The 2-Clause BSD License # #\",\n \"# path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or\",\n \"SQP-type solvers') field = field_ field = field.encode('utf-8') if field_\",\n \"(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT\",\n \"nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should\",\n \"cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver,\",\n \"ny_e ## constraints # initial if (constraints.lbx_0 == [] and\",\n \"dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape))\",\n \"cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny\",\n \"2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values\",\n \"THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\",\n \"casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')\",\n \"ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi\",\n \"out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints\",\n \"Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e\",\n \"is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding\",\n \"acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True,\",\n \"cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif\",\n \"constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.')\",\n \"!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if\",\n \"dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,))\",\n \"sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path =\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file,\",\n \"BSD License # # Redistribution and use in source and\",\n \"elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name\",\n \"int): raise Exception('solver option {} must be of type int.\",\n \"if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise\",\n \"= nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,))\",\n \"elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,\",\n \"def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK':\",\n \"'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total\",\n \"acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type ==\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #\",\n \"regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if\",\n \"'time_sim_ad', # cpu time for integrator contribution of external function\",\n \"is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e =\",\n \"if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return\",\n \"OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON\",\n \"if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.')\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ ==\",\n \"= nsbx + nsbu + nsh + nsg + nsphi\",\n \"found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c'\",\n \"double_fields: if not isinstance(value_, float): raise Exception('solver option {} must\",\n \"if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf\",\n \"{nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e\",\n \"acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f:\",\n \"= tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module =\",\n \"must retain the above copyright notice, # this list of\",\n \"= c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out =\",\n \"= {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__)\",\n \"regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny =\",\n \"if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise\",\n \"cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding\",\n \"return status def get(self, stage_, field_): \\\"\\\"\\\" get the last\",\n \"== 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name\",\n \"constraints \\\\n lam: multipliers for inequalities \\\\n t: slack variables\",\n \"'take only values 0, 1, 2 for SQP-RTI-type solvers') if\",\n \"dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0]\",\n \"# terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e\",\n \"acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if\",\n \"in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,\",\n \"make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints\",\n \"value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_,\",\n \"materials provided with the distribution. # # THIS SOFTWARE IS\",\n \"dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints #\",\n \"consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator\",\n \"columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise\",\n \"cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,\",\n \"must reproduce the above copyright notice, # this list of\",\n \"nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx\",\n \"acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny,\",\n \"= ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if\",\n \"automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model)\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ <\",\n \"nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else:\",\n \"dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh =\",\n \"shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file,\",\n \"stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\\",\n \"dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny,\",\n \"stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1:\",\n \"!= dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.')\",\n \"up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if\",\n \"Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] !=\",\n \"= 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"!= tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field\",\n \"dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or\",\n \"lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0]\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file,\",\n \"json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims\",\n \"dimension ng, regarding lg, ug, C, D.') else: dims.ng =\",\n \"{nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}')\",\n \"of the solver: :param stage_: integer corresponding to shooting node\",\n \"# This file is part of acados. # # The\",\n \"!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if\",\n \"acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx))\",\n \"value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"== constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0]\",\n \"raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0,\",\n \"print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type ==\",\n \"SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0:\",\n \"'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if\",\n \"c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =\",\n \"field, out_data) return out # Note: this function should not\",\n \"= np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_,\",\n \"attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp,\",\n \"wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns:\",\n \"!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if\",\n \"render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type ==\",\n \"c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise\",\n \"dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0\",\n \"idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh):\",\n \"# load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class\",\n \"C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e =\",\n \"idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e):\",\n \"the solver: :param stage_: integer corresponding to shooting node :param\",\n \"in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e =\",\n \"= 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"cpu time previous call 'time_lin', # cpu time for linearization\",\n \"is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent\",\n \"con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0\",\n \"\\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model =\",\n \"\\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent\",\n \"value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file\",\n \"!= ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\\",\n \"shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] =\",\n \"if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,\",\n \"dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e =\",\n \"if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name,\",\n \"of the solver: :param stage: integer corresponding to shooting node\",\n \"solution 'time_qp_solver_call', # cpu time inside qp solver (without converting\",\n \"if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) #\",\n \"POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =\",\n \"argument \\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type\",\n \"= acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else:\",\n \"import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost\",\n \"skip non dict attributes if not isinstance(v, dict): continue #\",\n \"> 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only\",\n \"value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or\",\n \"= nsg ns = nsbx + nsbu + nsh +\",\n \"make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields:\",\n \"def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader\",\n \"np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field\",\n \"AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED\",\n \"out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file,\",\n \"value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes\",\n \"nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e:\",\n \"acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type\",\n \"raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts\",\n \"default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description\",\n \"if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64)\",\n \"option {} must be of type float. You have {}.'.format(field_,\",\n \"slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields\",\n \":param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_:\",\n \"sl: slack variables of soft lower inequality constraints \\\\n su:\",\n \"if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve():\",\n \"constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension\",\n \"casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding\",\n \"nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e =\",\n \"in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return\",\n \"field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\\",\n \"constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg:\",\n \"the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return\",\n \"stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get\",\n \"# strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict)\",\n \"nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u)\",\n \"self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config\",\n \"acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and\",\n \"= 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file\",\n \"c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out\",\n \"is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization')\",\n \"out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type ==\",\n \"out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_\",\n \"nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] !=\",\n \"if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny:\",\n \"Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name =\",\n \"value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set():\",\n \"solvers') field = field_ field = field.encode('utf-8') if field_ in\",\n \"nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx},\",\n \"= c_int(stage_) # treat parameters separately if field_ is 'p':\",\n \"{} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data,\",\n \"# cpu time for integrator 'time_sim_ad', # cpu time for\",\n \"fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n\",\n \"import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode\",\n \"cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu']\",\n \"ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.')\",\n \"= constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx:\",\n \"raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi\",\n \"lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e:\",\n \"if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h'\",\n \"== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file =\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if\",\n \"json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':\",\n \"shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column\",\n \"json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir =\",\n \"\\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,\",\n \"nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0]\",\n \"0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not\",\n \"# total cpu time previous call 'time_lin', # cpu time\",\n \"cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' +\",\n \"W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding\",\n \"regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0]\",\n \"out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model\",\n \"inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', #\",\n \"CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,\",\n \"time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1]\",\n \"stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\\",\n \"field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data =\",\n \"string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',]\",\n \"ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx\",\n \"\\\\n lam: multipliers for inequalities \\\\n t: slack variables corresponding\",\n \"Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if\",\n \"= nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or\",\n \"'rti_phase': if value_ < 0 or value_ > 2: raise\",\n \"nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0]\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise\",\n \"corresponding to shooting node :param field_: string in ['x', 'u',\",\n \"object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip\",\n \"FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL\",\n \"elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0]\",\n \"# cast value_ to avoid conversion issues value_ = value_.astype(float)\",\n \"== 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]):\",\n \"# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\",\n \"raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx\",\n \"type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts\",\n \"field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields +\",\n \"= ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields\",\n \"value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take\",\n \"'time_reg', # cpu time regularization 'sqp_iter', # number of SQP\",\n \"ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e =\",\n \"over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e\",\n \"stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj],\",\n \"= '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if\",\n \"print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj])))\",\n \"ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if\",\n \"TODO: maybe make one funciton with formatting for acados_struct, v\",\n \"= casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr\",\n \"constraints, etc for acados_struct, v in ocp_layout.items(): # skip non\",\n \"ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise\",\n \"not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file =\",\n \"= 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e:\",\n \"solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise\",\n \"dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr)\",\n \"C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False\",\n \"= value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0\",\n \"for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\\",\n \"= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \":param value_: of appropriate size \\\"\\\"\\\" # cast value_ to\",\n \"acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current input\",\n \"have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file,\",\n \"are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0:\",\n \"with info about last iteration 'stat_m', 'stat_n', ] field =\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type ==\",\n \"from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost\",\n \"or without # modification, are permitted provided that the following\",\n \"IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\",\n \"symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict\",\n \"is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0]\",\n \"field = field.encode('utf-8') if (field_ not in out_fields + mem_fields):\",\n \"dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0]\",\n \"regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:\",\n \"dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension\",\n \"cpu time for integrator contribution of external function calls 'time_sim_la',\",\n \"acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file\",\n \"field_): \\\"\\\"\\\" get the last solution of the solver: :param\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims,\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters\",\n \"time qp solution 'time_qp_solver_call', # cpu time inside qp solver\",\n \"'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'):\",\n \"dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,))\",\n \"integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions\",\n \".acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import\",\n \"'{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if\",\n \"constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension\",\n \"= 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file,\",\n \"value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for\",\n \"value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase',\",\n \"is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model\",\n \"elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and\",\n \"nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0]\",\n \"USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED\",\n \"(tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization:\",\n \"regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e =\",\n \"raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates\",\n \"wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns:\",\n \"cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim =\",\n \"regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1]\",\n \"self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out =\",\n \"cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"== 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n =\",\n \"nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding\",\n \"constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension\",\n \"copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import\",\n \"return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description\",\n \"values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\\",\n \"ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr)\",\n \"'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion\",\n \"dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e =\",\n \"'{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over\",\n \"better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields\",\n \"Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out =\",\n \"!= ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0]\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) !=\",\n \"nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0:\",\n \"cpu time inside qp solver (without converting the QP) 'time_qp_xcond',\",\n \"OF SUCH DAMAGE.; # import sys, os, json import numpy\",\n \"Note: this function should not be used anymore, better use\",\n \"module of the solver: Parameters: :param stage_: integer corresponding to\",\n \"info about last iteration 'stat_m', 'stat_n', ] field = field_\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields:\",\n \"if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise\",\n \"> 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf !=\",\n \"\\\"\\\"\\\" set numerical data in the constraint module of the\",\n \"= '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage,\",\n \"dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model\",\n \"generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp,\",\n \"\\\"\\\"\\\" class to interact with the acados ocp solver C\",\n \"string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" #\",\n \"np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding\",\n \"model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type ==\",\n \"not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if\",\n \"ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created\",\n \"i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps =\",\n \"\\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_, tuple(dims),\",\n \"'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external\",\n \"json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\",\n \"nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx =\",\n \"stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg):\",\n \"{} must be of type float. You have {}.'.format(field_, type(value_)))\",\n \"np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding\",\n \"np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding\",\n \"== 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on\",\n \"is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi =\",\n \"= ['sl', 'su'] field = field_ field = field.encode('utf-8') if\",\n \"acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type ==\",\n \"opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and\",\n \"regularization 'sqp_iter', # number of SQP iterations 'statistics', # table\",\n \"# treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes =\",\n \"+ '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created =\",\n \"wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\"\",\n \"regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:\",\n \"field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have\",\n \"json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir,\",\n \"must be of type str. You have {}.'.format(field_, type(value_))) else:\",\n \"field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of\",\n \"path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0]\",\n \"c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out()\",\n \"environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise\",\n \"0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or\",\n \"json_file): name = acados_ocp.model.name # setting up loader and environment\",\n \"ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] !=\",\n \"this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape ==\",\n \"use in source and binary forms, with or without #\",\n \"0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr)\",\n \"value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0],\",\n \"if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different\",\n \"= ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform\",\n \"POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field,\",\n \"ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension\",\n \"0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise\",\n \"template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file,\",\n \"field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if\",\n \"= self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype =\",\n \"c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in()\",\n \"cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim =\",\n \"'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if\",\n \"STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING\",\n \"acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices\",\n \"dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n t:\",\n \"constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.')\",\n \"sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =\",\n \"raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values\",\n \"constraints \\\\n su: slack variables of soft upper inequality constraints\",\n \"acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp,\",\n \"self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape)\",\n \"if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts =\",\n \"integer corresponding to shooting node :param field_: string, e.g. 'lbx'\",\n \"= ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model):\",\n \"elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,\",\n \"from casadi import CasadiMeta, Function, SX from copy import deepcopy\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes\",\n \"template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH'\",\n \"Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e =\",\n \"'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields =\",\n \"ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0\",\n \"ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES\",\n \"cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field\",\n \"out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"= \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise\",\n \"def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path\",\n \"json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir,\",\n \"self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp\",\n \"## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file,\",\n \"= 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if\",\n \"value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes =\",\n \"ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict =\",\n \"stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj],\",\n \"out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"= 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"= casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np,\",\n \"constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.')\",\n \"usbu usbx usg ush usphi] .. note:: pi: multipliers for\",\n \"from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout,\",\n \"cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m\",\n \"in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is\",\n \"dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] !=\",\n \"dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!')\",\n \"OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\",\n \"format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims\",\n \"/ tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' =\",\n \"if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically\",\n \"= \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field\",\n \"\\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e\",\n \"ns = nsbx + nsbu + nsh + nsg +\",\n \"!= 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny,\",\n \"= json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp\",\n \"evaluation of all inequalities (at the solution) \\\\n sl: slack\",\n \"mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension {}\",\n \"class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp\",\n \"dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): #\",\n \"nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif\",\n \"= np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,\",\n \"outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file =\",\n \"stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\"\",\n \"dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"/ dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] !=\",\n \"'{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function\",\n \"if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h'\",\n \"(you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"\\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields\",\n \"model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates\",\n \"opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please\",\n \"dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e\",\n \"raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\\",\n \"= json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp()\",\n \"'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"= acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current\",\n \"jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format(\",\n \"con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) #\",\n \"constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension\",\n \"Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and\",\n \"is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding\",\n \"with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} =\",\n \"or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e,\",\n \"np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W,\",\n \"ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')\",\n \"EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.;\",\n \"acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False,\",\n \"or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu,\",\n \"['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields =\",\n \"tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\"\",\n \"DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND\",\n \"<NAME>, <NAME>, <NAME>, <NAME> # # This file is part\",\n \"ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e +\",\n \"time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp,\",\n \"nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi =\",\n \"np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e ==\",\n \"[c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage,\",\n \"self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes =\",\n \"shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf -\",\n \"in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"be column vector!') # nu if is_empty(model.u): dims.nu = 0\",\n \"DAMAGE.; # import sys, os, json import numpy as np\",\n \"self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data =\",\n \"!= ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C,\",\n \"False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e ==\",\n \"!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes\",\n \"setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries\",\n \"HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR\",\n \".acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template,\",\n \"= \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim\",\n \"== 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim =\",\n \"= os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout\",\n \"out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints\",\n \"generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type\",\n \"\\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny =\",\n \"is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent\",\n \"self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve\",\n \"ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w')\",\n \"in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length']\",\n \"field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape\",\n \"Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu =\",\n \"in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj],\",\n \"dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu =\",\n \"= c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver =\",\n \"self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out\",\n \"= self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp')\",\n \"Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e\",\n \"columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,\",\n \"if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.'\",\n \"= np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,\",\n \"out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with\",\n \"if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent\",\n \"dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,))\",\n \"= ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization']\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p]\",\n \"= c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self):\",\n \"= c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims =\",\n \"<NAME>, <NAME>, <NAME> # # This file is part of\",\n \"= np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,\",\n \"of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase',\",\n \"else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] !=\",\n \"lsh lsphi usbu usbx usg ush usphi] .. note:: pi:\",\n \"!= dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\"\",\n \"nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif\",\n \"dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e =\",\n \"elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"= constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu:\",\n \"= nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0\",\n \"'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model,\",\n \"constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h'\",\n \"regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0]\",\n \"SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR\",\n \"dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh =\",\n \"field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl',\",\n \"np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file,\",\n \"constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e:\",\n \"QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number\",\n \"nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif\",\n \"constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e:\",\n \"value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage\",\n \"calls 'time_sim_la', # cpu time for integrator contribution of linear\",\n \"string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size\",\n \"ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')\",\n \"# explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type\",\n \"None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as\",\n \"sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as\",\n \"nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes =\",\n \"__del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot\",\n \"print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if\",\n \"generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate\",\n \"ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0]\",\n \"dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__)\",\n \"# initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []):\",\n \"opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh >\",\n \"constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi:\",\n \"not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type\",\n \"range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]),\",\n \"on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file\",\n \"# # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,\",\n \"if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise\",\n \"for integrator contribution of external function calls 'time_sim_la', # cpu\",\n \"# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\",\n \"constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not\",\n \"'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format(\",\n \"function calls 'time_sim_la', # cpu time for integrator contribution of\",\n \"= \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise\",\n \"= nsphi_e # terminal ns_e = nsbx_e + nsh_e +\",\n \"\\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't']\",\n \"= cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"= dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting\",\n \"terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e >\",\n \"C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type ==\",\n \"nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] !=\",\n \"get(self, stage_, field_): \\\"\\\"\\\" get the last solution of the\",\n \"to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess'\",\n \"= cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for\",\n \"ARISING IN ANY WAY OUT OF THE USE OF THIS\",\n \"and the following disclaimer. # # 2. Redistributions in binary\",\n \"elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C\",\n \".utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list,\",\n \"print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\\",\n \"ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN\",\n \"W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny\",\n \"+ model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create()\",\n \"Redistributions in binary form must reproduce the above copyright notice,\",\n \"if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p,\",\n \"is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent\",\n \"= constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] !=\",\n \"raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for\",\n \"idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu):\",\n \"f'Detected ns = {ns} = nsbx + nsbu + nsg\",\n \"open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json,\",\n \"import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode\",\n \"OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys,\",\n \"= constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e:\",\n \"nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) /\",\n \"os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' +\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)\",\n \"str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if\",\n \"cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"template_dir, json_path) # constraints on convex over nonlinear function if\",\n \"is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent\",\n \"!= dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e\",\n \"constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise\",\n \"cost module of the solver: :param stage_: integer corresponding to\",\n \"constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e:\",\n \"lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers\",\n \"else: value_ctypes = c_double(value_) elif field_ in string_fields: if not\",\n \"self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument\",\n \"and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir\",\n \"out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH'\",\n \"!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e\",\n \":param stage: integer corresponding to shooting node :param field_: string\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name)\",\n \"'.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0])\",\n \"int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif\",\n \"constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension\",\n \"f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0]\",\n \"raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size\",\n \"{dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx +\",\n \"template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file,\",\n \"0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) #\",\n \"\\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field =\",\n \"self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat =\",\n \"and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function\",\n \"self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config,\",\n \"+ out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e >\",\n \".generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import\",\n \"dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu\",\n \"LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\",\n \"self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p,\",\n \"of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): # dlclose(handle)\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return\",\n \"raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0]\",\n \"1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps)\",\n \"constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension\",\n \"nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e):\",\n \"ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for\",\n \"field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e:\",\n \"uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif\",\n \"os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name\",\n \"constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension\",\n \"ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list,\",\n \"= self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the\",\n \"= \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field\",\n \"copyright notice, # this list of conditions and the following\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] !=\",\n \"nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif\",\n \"set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] #\",\n \"else: if field_ not in constraints_fields + cost_fields + out_fields:\",\n \"== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file =\",\n \"are internally organized in the following order: \\\\n [ lbu\",\n \"= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching\",\n \"value 0 for SQP-type solvers') field = field_ field =\",\n \"values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type !=\",\n \"self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver\",\n \"Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file\",\n \"c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims =\",\n \"# table with info about last iteration 'stat_m', 'stat_n', ]\",\n \"field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately\",\n \"nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns:\",\n \"self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_):\",\n \"str): raise Exception('solver option {} must be of type str.\",\n \"= ocp_nlp_dict # laod class attributes dict, dims, constraints, etc\",\n \"cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim =\",\n \"out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on\",\n \"with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict =\",\n \"return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data\",\n \"of SQP iterations 'statistics', # table with info about last\",\n \".generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import\",\n \"raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx\",\n \"wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\"\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes\",\n \"in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0]\",\n \"value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_,\",\n \"= 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file\",\n \"value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,\",\n \"stage = c_int(stage_) # treat parameters separately if field_ is\",\n \"set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints\",\n \"implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model,\",\n \"raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush\",\n \"{})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"lg lh lphi ubu ubx ug uh uphi; \\\\n lsbu\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in,\",\n \"stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\"\",\n \"to shooting node :param field_: string, e.g. 'lbx' :param value_:\",\n \"c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),\",\n \"= self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros(\",\n \"self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_):\",\n \"out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c'\",\n \"= 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0]\",\n \"LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\",\n \"this list of conditions and the following disclaimer. # #\",\n \"self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\"\",\n \"' 'take only values 0, 1, 2 for SQP-RTI-type solvers')\",\n \"solution of the solver: :param stage: integer corresponding to shooting\",\n \"IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\",\n \"other materials provided with the distribution. # # THIS SOFTWARE\",\n \"for field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you\",\n \"field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"{nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0]\",\n \"double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields:\",\n \"without # modification, are permitted provided that the following conditions\",\n \"raise Exception('solver option {} must be of type float. You\",\n \"elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,\",\n \"acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict\",\n \"out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h'\",\n \"raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty')\",\n \"sys, os, json import numpy as np from ctypes import\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self):\",\n \"lsg lsh lsphi usbu usbx usg ush usphi] .. note::\",\n \"raise Exception('solver option {} must be of type str. You\",\n \"= 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file\",\n \"dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,))\",\n \"PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\",\n \"self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype\",\n \"if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent\",\n \"POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m =\",\n \"dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else:\",\n \"import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp\",\n \"time for integrator 'time_sim_ad', # cpu time for integrator contribution\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) # treat\",\n \"self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in\",\n \"== 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if\",\n \"= self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out\",\n \"\\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2 for\",\n \"dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if\",\n \"dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,\",\n \"nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e\",\n \"dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e\",\n \"model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts)\",\n \"module of the solver: :param stage_: integer corresponding to shooting\",\n \"stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray(\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape\",\n \"{} is not a valid argument.\\\\ \\\\nPossible values are {}.\",\n \"Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg\",\n \"self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set():\",\n \"get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__)\",\n \"<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This\",\n \"casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu\",\n \"template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS':\",\n \"Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1,\",\n \"cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e:\",\n \"self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp\",\n \"== 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims):\",\n \"out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter =\",\n \"render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims\",\n \"nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ +\",\n \"acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) #\",\n \"to avoid conversion issues value_ = value_.astype(float) field = field_\",\n \"'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True)\",\n \"the inequalities are internally organized in the following order: \\\\n\",\n \"= 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr)\",\n \"# external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes\",\n \"set numerical data in the cost module of the solver:\",\n \"{} must be of type str. You have {}.'.format(field_, type(value_)))\",\n \"is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent\",\n \"casadi import CasadiMeta, Function, SX from copy import deepcopy from\",\n \"if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/'\",\n \"or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy\",\n \"'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e\",\n \"soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u',\",\n \"e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast\",\n \"if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise\",\n \"the constraint module of the solver: Parameters: :param stage_: integer\",\n \"# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS\",\n \"if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve():\",\n \"integer corresponding to shooting node :param field_: string, e.g. 'yref',\",\n \"nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0]\",\n \"idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e):\",\n \"dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\\",\n \"= casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh\",\n \"c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data\",\n \"time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg',\",\n \"ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e\",\n \"c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def\",\n \"['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid\",\n \"ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' +\",\n \"one funciton with formatting for acados_struct, v in ocp_layout.items(): #\",\n \"is an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_,\",\n \"# -*- coding: future_fstrings -*- # # Copyright 2019 <NAME>,\",\n \"ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if\",\n \"casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np\",\n \"out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type\",\n \"solver: :param stage: integer corresponding to shooting node :param field_:\",\n \"Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg =\",\n \"\\\"\\\"\\\" set numerical data in the cost module of the\",\n \"constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.')\",\n \"c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config,\",\n \"{nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh},\",\n \"elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,\",\n \"raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise\",\n \"treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int,\",\n \"= 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype =\",\n \"\\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim =\",\n \"constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.')\",\n \"= json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object\",\n \"dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e =\",\n \"dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims,\",\n \"FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT\",\n \"is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is\",\n \"nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif\",\n \"= ['x', 'u', 'pi', 'lam', 't'] # cast value_ to\",\n \"acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on\",\n \"POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =\",\n \"template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e ==\",\n \"external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file)\",\n \"or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,\",\n \"= ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e\",\n \"out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file,\",\n \"FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO\",\n \"Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny:\",\n \"ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field !=\",\n \"stage_, field_, value_): \\\"\\\"\\\" set numerical data in the constraint\",\n \"ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and\",\n \"elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,\",\n \"function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in\",\n \"constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP'\",\n \"contribution of external function calls 'time_sim_la', # cpu time for\",\n \"print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]),\",\n \"IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT\",\n \"argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2\",\n \"raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu\",\n \"con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e\",\n \"+ nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu\",\n \"> 0: # terminal constraints on outer function template_dir =\",\n \"value_.shape[0]) else: if field_ not in constraints_fields + cost_fields +\",\n \"= np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,\",\n \"be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi\",\n \"dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr =\",\n \"stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the\",\n \"c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if\",\n \"ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh =\",\n \"= c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int))\",\n \"in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\\",\n \"value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument\",\n \"value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields\",\n \"else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e\",\n \"elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0]\",\n \"= [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field,\",\n \"'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file =\",\n \"field_, value_): \\\"\\\"\\\" set numerical data in the cost module\",\n \"cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim =\",\n \"'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot',\",\n \"constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh,\",\n \"acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code\",\n \"if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__))\",\n \"if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ '\",\n \"'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float\",\n \"cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.'\",\n \"nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e):\",\n \"or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise\",\n \"= 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial\",\n \"else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] !=\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if\",\n \"def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints =\",\n \"idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] !=\",\n \"nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field =\",\n \"'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name)\",\n \"following order: \\\\n [ lbu lbx lg lh lphi ubu\",\n \"= 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg +=\",\n \"status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\"\",\n \"\\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e\",\n \"nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else:\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"# nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes =\",\n \"constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.')\",\n \"2. Redistributions in binary form must reproduce the above copyright\",\n \"AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp solver\",\n \"json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type\",\n \"= cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config,\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims,\",\n \"dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e\",\n \"MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.\",\n \"constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent\",\n \"OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\",\n \"constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP'\",\n \"'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function\",\n \"= opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if\",\n \"option {} must be of type int. You have {}.'.format(field_,\",\n \"(field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is\",\n \"+ nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field\",\n \"constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension\",\n \"import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\\",\n \"the last solver call: :param field_: string in ['statistics', 'time_tot',\",\n \"Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values are\",\n \"nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg =\",\n \"is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent\",\n \"\\\"\\\"\\\" fields = ['time_tot', # total cpu time previous call\",\n \"# Note: this function should not be used anymore, better\",\n \"if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if\",\n \"is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_):\",\n \"msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise\",\n \"W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding\",\n \"lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]\",\n \"class attributes dict, dims, constraints, etc for acados_struct, v in\",\n \"else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT':\",\n \"TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF\",\n \"as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) #\",\n \"= c_double(value_) elif field_ in string_fields: if not isinstance(value_, str):\",\n \"BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND\",\n \"dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e =\",\n \"# 2. Redistributions in binary form must reproduce the above\",\n \"out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c'\",\n \"AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod\",\n \"W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e\",\n \"out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost\",\n \"min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64)\",\n \"is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is\",\n \"= cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim\",\n \"regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension\",\n \"not be used anymore, better use cost_set, constraints_set def set(self,\",\n \"an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields\",\n \"if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] !=\",\n \"value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes\",\n \"# # 1. Redistributions of source code must retain the\",\n \"above copyright notice, # this list of conditions and the\",\n \"not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) #\",\n \"and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,\",\n \"note:: pi: multipliers for dynamics equality constraints \\\\n lam: multipliers\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data\",\n \"# # Redistribution and use in source and binary forms,\",\n \"if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise\",\n \"terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e ==\",\n \"json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile\",\n \"dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ##\",\n \"True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p\",\n \"np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding\",\n \"field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set\",\n \"lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg:\",\n \"\\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME>\",\n \"'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"!= nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.')\",\n \"dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should\",\n \"skip non dict attributes if not isinstance(v, dict): continue acados_attribute\",\n \"class to interact with the acados ocp solver C object\",\n \"f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and\",\n \"value_ to avoid conversion issues value_ = value_.astype(float) field =\",\n \"c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),\",\n \"vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu\",\n \"field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p,\",\n \"cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding\",\n \"nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else:\",\n \"= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"part of acados. # # The 2-Clause BSD License #\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes\",\n \"int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n')\",\n \"ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': #\",\n \"self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) ==\",\n \"opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi >\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data)\",\n \"field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension\",\n \"for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps\",\n \"def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()\",\n \"cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"= True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype =\",\n \"variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields =\",\n \"\\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_):\",\n \"THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR\",\n \"usg ush usphi] .. note:: pi: multipliers for dynamics equality\",\n \".. note:: regarding lam, t: \\\\n the inequalities are internally\",\n \"the following disclaimer. # # 2. Redistributions in binary form\",\n \"dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e\",\n \"= np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,\",\n \"np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding\",\n \"+ f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e =\",\n \"self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p\",\n \"\\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush\",\n \"lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu:\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and\",\n \"{ns} = nsbx + nsbu + nsg + nsh +\",\n \"!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e\",\n \"source and binary forms, with or without # modification, are\",\n \"c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_\",\n \"separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype\",\n \"False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e\",\n \"code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model --\",\n \"unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1)\",\n \"['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype\",\n \"'t', 'sl', 'su',] .. note:: regarding lam, t: \\\\n the\",\n \"model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x):\",\n \"regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0]\",\n \"INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,\",\n \"'/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def\",\n \"regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng =\",\n \"CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\",\n \"is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent\",\n \"nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e\",\n \"self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims\",\n \"with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} =\",\n \"out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\"\",\n \"pi: multipliers for dynamics equality constraints \\\\n lam: multipliers for\",\n \"nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx\",\n \"print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj],\",\n \"indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field,\",\n \"is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf /\",\n \"0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e):\",\n \"= 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex\",\n \"qp solution 'time_qp_solver_call', # cpu time inside qp solver (without\",\n \"if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent\",\n \"print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information of\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes\",\n \"Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param\",\n \"and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for\",\n \"nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e =\",\n \"if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent\",\n \"# dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx =\",\n \"C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh\",\n \"regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu =\",\n \"if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu\",\n \"= 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np:\",\n \"idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] !=\",\n \"POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields\",\n \"ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi\",\n \"else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear\",\n \"0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value\",\n \"constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension\",\n \"dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should\",\n \"dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter\",\n \"in ocp_layout.items(): # skip non dict attributes if not isinstance(v,\",\n \"ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import\",\n \"tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__]\",\n \"f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e !=\",\n \"'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_,\",\n \"discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def\",\n \"= 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ##\",\n \"dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\"\",\n \"and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h'\",\n \"usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e +\",\n \"int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n')\",\n \"dimension np, regarding model.p and parameter_values.') ## cost # path\",\n \"not isinstance(value_, str): raise Exception('solver option {} must be of\",\n \"raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg\",\n \"N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N):\",\n \"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\",\n \"import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\\",\n \"'{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to\",\n \"return def get_stats(self, field_): \\\"\\\"\\\" get the information of the\",\n \"['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam',\",\n \"0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng:\",\n \"= constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] !=\",\n \"mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"# this list of conditions and the following disclaimer. #\",\n \"corresponding to shooting node :param field_: string, e.g. 'lbx' :param\",\n \"# render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make\",\n \"notice, # this list of conditions and the following disclaimer\",\n \"dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp,\",\n \"if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.')\",\n \"= acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx\",\n \"self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes =\",\n \"nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif\",\n \"self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self,\",\n \"'sqp_iter', # number of SQP iterations 'statistics', # table with\",\n \"0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise\",\n \".generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import\",\n \"is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent\",\n \"C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model\",\n \"field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p,\",\n \"if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__\",\n \"else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0]\",\n \"else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\\",\n \"= nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,))\",\n \"elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,\",\n \"open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return\",\n \"usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu\",\n \"= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] !=\",\n \"disclaimer in the documentation # and/or other materials provided with\",\n \"external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir =\",\n \"in int_fields: if not isinstance(value_, int): raise Exception('solver option {}\",\n \"nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi\",\n \"regarding lg, ug, C, D.') else: dims.ng = ng if\",\n \"f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e\",\n \"# laod class attributes dict, dims, constraints, etc for acados_struct,\",\n \"= c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else:\",\n \"BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR\",\n \"= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e =\",\n \"Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny !=\",\n \"in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0\",\n \"constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension\",\n \"lbu lbx lg lh lphi ubu ubx ug uh uphi;\",\n \"lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi\",\n \"= nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or\",\n \"self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p\",\n \"constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\\",\n \"if field_ not in constraints_fields + cost_fields + out_fields: raise\",\n \"= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \"raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' +\",\n \"and ny != 0: raise Exception('inconsistent dimension ny: regarding W,\",\n \"ns = {ns} = nsbx + nsbu + nsg +\",\n \"cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,\",\n \"\\\"\\\"\\\" # cast value_ to avoid conversion issues value_ =\",\n \"= self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype =\",\n \"fields = ['time_tot', # total cpu time previous call 'time_lin',\",\n \"nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh =\",\n \"idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg):\",\n \"cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise\",\n \".generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N\",\n \"nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if\",\n \"setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model =\",\n \"not a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields,\",\n \"= get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict =\",\n \"to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ##\",\n \"elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension\",\n \"nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e\",\n \"dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e\",\n \"!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu\",\n \"nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e #\",\n \"field_ == 'rti_phase': if value_ < 0 or value_ >\",\n \"cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension:\",\n \"return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type ==\",\n \"idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi):\",\n \"+ nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e},\",\n \"stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp')\",\n \"regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n')\",\n \"dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size\",\n \"Parameters: :param stage_: integer corresponding to shooting node :param field_:\",\n \"value_): \\\"\\\"\\\" set numerical data in the constraint module of\",\n \"c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config()\",\n \"# cpu time inside qp solver (without converting the QP)\",\n \"ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else:\",\n \"np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps\",\n \"if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise\",\n \"nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0]\",\n \"!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if\",\n \"acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,\",\n \"option {} must be of type str. You have {}.'.format(field_,\",\n \"time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if\",\n \"int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7:\",\n \"Redistribution and use in source and binary forms, with or\",\n \"path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0]\",\n \"nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding\",\n \"\\\\ ' for field \\\"{}\\\" with dimension {} (you have\",\n \"= c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type ==\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64)\",\n \"wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e:\",\n \"variables of soft lower inequality constraints \\\\n su: slack variables\",\n \"int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields =\",\n \"dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0\",\n \"nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] !=\",\n \"import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf\",\n \"wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e:\",\n \"acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim\",\n \"stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\"\",\n \"['time_tot', # total cpu time previous call 'time_lin', # cpu\",\n \"value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set():\",\n \"= acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model =\",\n \"jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj],\",\n \"= self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp =\",\n \"from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model\",\n \"if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise\",\n \"laod class attributes dict, dims, constraints, etc for acados_struct, v\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver,\",\n \"out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\\",\n \"'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_\",\n \"Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e =\",\n \"!= 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif\",\n \"This file is part of acados. # # The 2-Clause\",\n \"!= nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.')\",\n \"nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e\",\n \"acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid\",\n \"f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,\",\n \"c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)\",\n \"binary form must reproduce the above copyright notice, # this\",\n \"# dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp,\",\n \"= constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e:\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field,\",\n \"Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz\",\n \"cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"= 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class\",\n \"valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if\",\n \".generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import\",\n \"raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with\",\n \"function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name)\",\n \"acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f,\",\n \"INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\",\n \"CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #\",\n \"= constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] !=\",\n \"OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\",\n \"to interact with the acados ocp solver C object \\\"\\\"\\\"\",\n \"Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot\",\n \"{ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ +\",\n \"-- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif\",\n \"min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out =\",\n \"dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e\",\n \"nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh\",\n \"generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def\",\n \"cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p,\",\n \"file is part of acados. # # The 2-Clause BSD\",\n \"set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external\",\n \"is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p,\",\n \"ny != 0: raise Exception('inconsistent dimension: Vu should have nu\",\n \"'{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type\",\n \"== 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e !=\",\n \"is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N *\",\n \"# discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps\",\n \"either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps)\",\n \"ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] =\",\n \"solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status\",\n \"su: slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\"\",\n \"!= ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0]\",\n \"of conditions and the following disclaimer in the documentation #\",\n \"ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with\",\n \"if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]),\",\n \"value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu',\",\n \"dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0\",\n \"'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear\",\n \"raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty')\",\n \"cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,\",\n \"model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type\",\n \"[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out\",\n \"range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif\",\n \"Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0,\",\n \"if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent\",\n \"json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and\",\n \"if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir =\",\n \"= self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype =\",\n \"of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call',\",\n \"yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny #\",\n \"elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,\",\n \"lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0]\",\n \"nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z)\",\n \"ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise\",\n \"\\\\n [ lbu lbx lg lh lphi ubu ubx ug\",\n \"\\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim =\",\n \"is not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field,\",\n \"usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e\",\n \"= {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e):\",\n \"'{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal\",\n \"ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost\",\n \"def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL\",\n \"t: slack variables corresponding to evaluation of all inequalities (at\",\n \"USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #\",\n \"dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\"\",\n \"'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file =\",\n \"type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if\",\n \"regarding idxsg, usg.') dims.nsg = nsg ns = nsbx +\",\n \"'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif\",\n \"= field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {}\",\n \"is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def\",\n \"is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column\",\n \"(constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0\",\n \"+ nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e =\",\n \"constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"# # The 2-Clause BSD License # # Redistribution and\",\n \"= casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else:\",\n \"nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else:\",\n \"nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0]\",\n \"{nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if\",\n \"'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False)\",\n \"description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary\",\n \"usbx usg ush usphi] .. note:: pi: multipliers for dynamics\",\n \"elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,\",\n \"# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER\",\n \"field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib\",\n \"input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe\",\n \"field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is\",\n \"of the last solver call: :param field_: string in ['statistics',\",\n \"== 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer\",\n \"Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p]\",\n \"\\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot\",\n \"0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not\",\n \"type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if\",\n \"acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not\",\n \"raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"= make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model --\",\n \"field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp',\",\n \"template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file,\",\n \"value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"of all inequalities (at the solution) \\\\n sl: slack variables\",\n \"not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {}\",\n \"# nu if is_empty(model.u): dims.nu = 0 else: dims.nu =\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m'\",\n \"Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r')\",\n \"= field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise\",\n \"different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be\",\n \"\\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field =\",\n \"out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c'\",\n \"OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\",\n \"the following order: \\\\n [ lbu lbx lg lh lphi\",\n \"out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data =\",\n \"c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if\",\n \"), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)),\",\n \"print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj],\",\n \"LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)\",\n \"integrator contribution of linear algebra 'time_qp', # cpu time qp\",\n \"+ cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"in the cost module of the solver: :param stage_: integer\",\n \"type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields\",\n \"= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if\",\n \"cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif\",\n \"nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif\",\n \"acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints\",\n \"= np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,\",\n \"Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny:\",\n \"# import sys, os, json import numpy as np from\",\n \"THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY\",\n \"== [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else:\",\n \"in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"\\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent\",\n \"and ny != 0: raise Exception('inconsistent dimension: Vu should have\",\n \"'t'] mem_fields = ['sl', 'su'] field = field_ field =\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims\",\n \"nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif\",\n \"cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_\",\n \"0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise\",\n \"of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks']\",\n \"= {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and\",\n \"NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;\",\n \"acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx =\",\n \"terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0]\",\n \"mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8')\",\n \"constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension\",\n \"c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\\",\n \"stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information\",\n \"= constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] !=\",\n \"'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0]\",\n \"casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz\",\n \"cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension:\",\n \"dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model']\",\n \"= getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return\",\n \"in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path)\",\n \"model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp,\",\n \"out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_\",\n \"field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e =\",\n \"qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu\",\n \"np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics':\",\n \"['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not\",\n \"class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict,\",\n \"nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"field_ field = field.encode('utf-8') if (field_ not in fields): raise\",\n \"value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for\",\n \"BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\",\n \"raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e\",\n \"else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear\",\n \"== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file =\",\n \"acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code\",\n \"acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file\",\n \"is part of acados. # # The 2-Clause BSD License\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file,\",\n \"dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise\",\n \"# np if is_empty(model.p): dims.np = 0 else: dims.np =\",\n \"W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if\",\n \"# <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,\",\n \"W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e ==\",\n \"function should not be used anymore, better use cost_set, constraints_set\",\n \"and use in source and binary forms, with or without\",\n \"Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny:\",\n \"slack variables corresponding to evaluation of all inequalities (at the\",\n \"acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model)\",\n \"order: \\\\n [ lbu lbx lg lh lphi ubu ubx\",\n \"in the following order: \\\\n [ lbu lbx lg lh\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data)\",\n \"+ nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] !=\",\n \"cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts\",\n \"import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics\",\n \"c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file,\",\n \"IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\",\n \"strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None)\",\n \"if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx\",\n \"= nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\\",\n \"'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time\",\n \"isinstance(value_, float): raise Exception('solver option {} must be of type\",\n \"functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) #\",\n \"__init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model #\",\n \"constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension\",\n \"print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for\",\n \"should not be used anymore, better use cost_set, constraints_set def\",\n \"else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e)\",\n \"W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] !=\",\n \"= False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp)\",\n \"conditions and the following disclaimer in the documentation # and/or\",\n \"dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns =\",\n \"0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can\",\n \"!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e\",\n \"v in ocp_layout.items(): # skip non dict attributes if not\",\n \"= constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0\",\n \"out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h'\",\n \"constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.')\",\n \"tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf >\",\n \"= np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in\",\n \"dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0]\",\n \"reproduce the above copyright notice, # this list of conditions\",\n \"dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif\",\n \"is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent\",\n \"nsbu + nsh + nsg + nsphi wrong_field = \\\"\\\"\",\n \"if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e\",\n \"idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] !=\",\n \"'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding\",\n \"options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver: Parameters:\",\n \"attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct)\",\n \"variables corresponding to evaluation of all inequalities (at the solution)\",\n \"dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size\",\n \"self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p\",\n \"dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_)\",\n \"forms, with or without # modification, are permitted provided that\",\n \"field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"## cost # path if cost.cost_type == 'LINEAR_LS': ny =\",\n \"c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_\",\n \"dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise\",\n \"dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file)\",\n \"= self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get\",\n \"and/or other materials provided with the distribution. # # THIS\",\n \"have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions\",\n \"c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes\",\n \"the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks',\",\n \"dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in\",\n \"[ lbu lbx lg lh lphi ubu ubx ug uh\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg\",\n \"np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding\",\n \"if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints\",\n \"ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if\",\n \"stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\\",\n \"dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg =\",\n \"raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file,\",\n \"the documentation # and/or other materials provided with the distribution.\",\n \"nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif\",\n \"def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model\",\n \"setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file)\",\n \"np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding\",\n \"self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p\",\n \"elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,\",\n \"raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file,\",\n \"<NAME>, <NAME> # # This file is part of acados.\",\n \"raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0\",\n \"are permitted provided that the following conditions are met: #\",\n \"constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0]\",\n \"+ f'Detected ns_e = {ns_e} = nsbx_e + nsg_e +\",\n \"nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0:\",\n \"# generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json\",\n \"> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file =\",\n \"self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts()\",\n \"dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data =\",\n \"acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict,\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\\",\n \"# terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir\",\n \"return def options_set(self, field_, value_): \\\"\\\"\\\" set options of the\",\n \"WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #\",\n \"met: # # 1. Redistributions of source code must retain\",\n \"'{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path))\",\n \"if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir =\",\n \"def get(self, stage_, field_): \\\"\\\"\\\" get the last solution of\",\n \"all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx =\",\n \"non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp,\",\n \"of soft lower inequality constraints \\\\n su: slack variables of\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"provide either time_steps or shooting_nodes for nonuniform discretization') tf =\",\n \"constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the\",\n \"raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e\",\n \"if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent\",\n \"ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\",\n \"nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0]\",\n \"Vu should have nu columns.') if cost.yref.shape[0] != ny: raise\",\n \"in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg',\",\n \"# Redistribution and use in source and binary forms, with\",\n \"discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps =\",\n \"- terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"to evaluation of all inequalities (at the solution) \\\\n sl:\",\n \"table with info about last iteration 'stat_m', 'stat_n', ] field\",\n \"dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e ==\",\n \"the above copyright notice, # this list of conditions and\",\n \"nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0:\",\n \"'time_qp_solver_call', # cpu time inside qp solver (without converting the\",\n \"cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding\",\n \"raise Exception('model.x should be column vector!') # nu if is_empty(model.u):\",\n \"def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in\",\n \"dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp\",\n \"acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL':\",\n \"in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path)\",\n \"mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype\",\n \"field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data\",\n \"elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"provided with the distribution. # # THIS SOFTWARE IS PROVIDED\",\n \"ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng,\",\n \"= ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh\",\n \"Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e\",\n \"ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] !=\",\n \"== 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu))\",\n \"Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter',\",\n \"and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN\",\n \"cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx\",\n \"slack variables of soft lower inequality constraints \\\\n su: slack\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file,\",\n \"= self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype =\",\n \"is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent\",\n \"nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0]\",\n \"W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e:\",\n \"'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in\",\n \"cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension\",\n \"= cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data =\",\n \"casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx\",\n \"# constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"'sl', 'su',] .. note:: regarding lam, t: \\\\n the inequalities\",\n \"nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else:\",\n \"and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can '\",\n \"DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS\",\n \"OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,\",\n \"object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model\",\n \"out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost\",\n \"\\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field =\",\n \"= \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field\",\n \"anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_):\",\n \"= constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 #\",\n \"template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file =\",\n \"= time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide\",\n \"'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time\",\n \"templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,\",\n \"(not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform\",\n \"\\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':\",\n \"json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados\",\n \"in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim\",\n \"regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not\",\n \"generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if\",\n \"self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get the\",\n \"IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED\",\n \"wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with\",\n \"\\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_):\",\n \"= 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise\",\n \"out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e\",\n \"\\\\n the inequalities are internally organized in the following order:\",\n \"if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent\",\n \"if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi\",\n \"over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims\",\n \"in double_fields: if not isinstance(value_, float): raise Exception('solver option {}\",\n \"self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields +\",\n \"import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint\",\n \"self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat\",\n \"min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0])\",\n \"acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model,\",\n \"raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh\",\n \"'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field =\",\n \"SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH\",\n \"import * from casadi import CasadiMeta, Function, SX from copy\",\n \"and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape =\",\n \"shooting node :param field_: string in ['x', 'u', 'z', 'pi',\",\n \"'time_lin', # cpu time for linearization 'time_sim', # cpu time\",\n \"field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats():\",\n \"if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!')\",\n \"dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx\",\n \"= ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if\",\n \"cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif\",\n \"'lam', 't'] # cast value_ to avoid conversion issues value_\",\n \"and the following disclaimer in the documentation # and/or other\",\n \"else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh\",\n \"# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\",\n \"= \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field\",\n \"acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0)\",\n \"lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e):\",\n \"field_, value_): \\\"\\\"\\\" set numerical data in the constraint module\",\n \"!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if\",\n \"of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0):\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file,\",\n \"c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims()\",\n \"regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if\",\n \"= np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note:\",\n \"dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,))\",\n \"if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] !=\",\n \"json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and\",\n \"cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in\",\n \"# # 2. Redistributions in binary form must reproduce the\",\n \"c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def\",\n \"= \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field\",\n \"= constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] !=\",\n \"make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p,\",\n \"regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e =\",\n \"Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict\",\n \"disclaimer. # # 2. Redistributions in binary form must reproduce\",\n \"0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name)\",\n \"= np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p,\",\n \"string_fields: if not isinstance(value_, str): raise Exception('solver option {} must\",\n \"# terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi =\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]')\",\n \"= \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field\",\n \"nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e\",\n \"c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver()\",\n \"NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python #\",\n \"stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format(\",\n \"if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif\",\n \"= nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,))\",\n \"retain the above copyright notice, # this list of conditions\",\n \"get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path +\",\n \"cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif\",\n \"acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"\\\\n sl: slack variables of soft lower inequality constraints \\\\n\",\n \"[]): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape =\",\n \"non dict attributes if not isinstance(v, dict): continue acados_attribute =\",\n \":param field_: string, e.g. 'lbx' :param value_: of appropriate size\",\n \"out_file, template_dir, json_path) # terminal constraints on convex over nonlinear\",\n \"OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY\",\n \"template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and\",\n \"= nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,))\",\n \"self.solver_created = False model = acados_ocp.model # make dims consistent\",\n \"nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu):\",\n \"+ mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n\",\n \"field_, value_): \\\"\\\"\\\" set options of the solver: Parameters: :param\",\n \"= casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e\",\n \"WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE\",\n \"nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif\",\n \"= AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict #\",\n \"= constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] !=\",\n \"field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p]\",\n \"if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise\",\n \"dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\"\",\n \"modification, are permitted provided that the following conditions are met:\",\n \"raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0]\",\n \"json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp\",\n \"ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot\",\n \"cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension:\",\n \"= field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p,\",\n \"!= ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0]\",\n \"field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get():\",\n \"for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3:\",\n \"np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p)\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file,\",\n \"# ARISING IN ANY WAY OUT OF THE USE OF\",\n \"time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not\",\n \"for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', #\",\n \"solve the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve()\",\n \"(field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a\",\n \"if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e\",\n \"with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f)\",\n \"wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\":\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,))\",\n \"wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e:\",\n \"# uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,))\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name)\",\n \"'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\\\n\",\n \"np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields):\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype\",\n \"else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0]\",\n \"elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and\",\n \"upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z',\",\n \"self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype\",\n \"not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid\",\n \"if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h'\",\n \"regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e):\",\n \"dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny:\",\n \"self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self,\",\n \"nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu =\",\n \"= \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field,\",\n \"+ nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu},\",\n \"for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns\",\n \"template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file,\",\n \"'t'] # cast value_ to avoid conversion issues value_ =\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage,\",\n \"model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK':\",\n \"+ nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field\",\n \"in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set():\",\n \"Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise\",\n \"- opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\\",\n \"get_stats(self, field_): \\\"\\\"\\\" get the information of the last solver\",\n \"on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and\",\n \"'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name)\",\n \"or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh,\",\n \"OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #\",\n \"'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file =\",\n \"field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"raise Exception('solver option {} must be of type int. You\",\n \"+ nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e\",\n \"Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict)\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase':\",\n \"SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\",\n \"input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_,\",\n \"= {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi =\",\n \"'su',] .. note:: regarding lam, t: \\\\n the inequalities are\",\n \"def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter')\",\n \"cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,\",\n \"structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f:\",\n \"int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj]))\",\n \"# # This file is part of acados. # #\",\n \"all inequalities (at the solution) \\\\n sl: slack variables of\",\n \"yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e =\",\n \"nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e},\",\n \"'statistics', # table with info about last iteration 'stat_m', 'stat_n',\",\n \"raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) =\",\n \"NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE #\",\n \"fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)),\",\n \"= np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,\",\n \"== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file =\",\n \"if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver\",\n \"ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] !=\",\n \"constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug,\",\n \"dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0]\",\n \"template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file\",\n \"soft lower inequality constraints \\\\n su: slack variables of soft\",\n \"acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp\",\n \"for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e\",\n \"'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64))\",\n \"constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path\",\n \"= nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With\",\n \"np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding\",\n \"== 'rti_phase': if value_ < 0 or value_ > 2:\",\n \"SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\",\n \"constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh:\",\n \"stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter')\",\n \"out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL':\",\n \"acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir\",\n \"{}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p']))\",\n \"lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh:\",\n \"self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts,\",\n \"or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e,\",\n \"-*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>,\",\n \"nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if\",\n \"W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny\",\n \"if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise\",\n \"0: raise Exception('inconsistent dimension: Vx should have nx columns.') if\",\n \"= np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,\",\n \"set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields =\",\n \"data in the cost module of the solver: :param stage_:\",\n \"OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY\",\n \"Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e =\",\n \"number of SQP iterations 'statistics', # table with info about\",\n \"constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.')\",\n \"COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS\",\n \"= np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,\",\n \"if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise\",\n \"stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if\",\n \"W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny:\",\n \"== 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,\",\n \"ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"# external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir\",\n \"cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT\",\n \"!= 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.')\",\n \"ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif\",\n \"\\\\n t: slack variables corresponding to evaluation of all inequalities\",\n \"raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e\",\n \"ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make\",\n \"# cpu time for integrator contribution of external function calls\",\n \"cast value_ to avoid conversion issues value_ = value_.astype(float) field\",\n \"CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts =\",\n \"False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if\",\n \"Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf\",\n \"= 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL':\",\n \"nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0]\",\n \"acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise\",\n \"ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] !=\",\n \"c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_\",\n \"avoid conversion issues value_ = value_.astype(float) field = field_ field\",\n \"field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0]\",\n \"\\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in\",\n \"constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir\",\n \"opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)):\",\n \"'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam,\",\n \"acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file\",\n \"# cpu time for linearization 'time_sim', # cpu time for\",\n \"Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension\",\n \"corresponding to shooting node :param field_: string, e.g. 'yref', 'W',\",\n \"nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif\",\n \"if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for\",\n \"= cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent\",\n \"regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0]\",\n \"# constraints on convex over nonlinear function if acados_ocp.constraints.constr_type ==\",\n \"Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n')\",\n \"value_): \\\"\\\"\\\" set options of the solver: Parameters: :param field_:\",\n \"if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype =\",\n \"cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e\",\n \"THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY\",\n \"model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name)\",\n \"= constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] !=\",\n \"{}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out\",\n \":param value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level',\",\n \"must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 ==\",\n \"!= nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh,\",\n \"constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.')\",\n \"acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS':\",\n \"np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else:\",\n \"generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from\",\n \"ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\",\n \"constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise\",\n \"> 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear\",\n \"numerical data in the cost module of the solver: :param\",\n \"self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p\",\n \"raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e\",\n \"+ nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if\",\n \"value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"!= ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if\",\n \"and ny != 0: raise Exception('inconsistent dimension: Vx should have\",\n \"notice, # this list of conditions and the following disclaimer.\",\n \"generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from\",\n \"dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise\",\n \"\\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields\",\n \"self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in\",\n \"0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz\",\n \"np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise\",\n \"acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file\",\n \"out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e\",\n \"raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\\",\n \"0: raise Exception('inconsistent dimension: Vu should have nu columns.') if\",\n \"= cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent\",\n \"elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps\",\n \"self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg =\",\n \"'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"# terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if\",\n \"with the acados ocp solver C object \\\"\\\"\\\" def __init__(self,\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_\",\n \"idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] !=\",\n \"regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if\",\n \"ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib')\",\n \"constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 ==\",\n \"column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0\",\n \"= cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for\",\n \"\\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field =\",\n \"!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu,\",\n \"out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file,\",\n \"Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\\\\",\n \"raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible\",\n \"for field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims),\",\n \"regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0]\",\n \"NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF\",\n \"= 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx\",\n \"set options of the solver: Parameters: :param field_: string, e.g.\",\n \"CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import\",\n \"> 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0\",\n \"= opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps))\",\n \"= dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] !=\",\n \"{} is an invalid argument.\\\\ \\\\n Possible values are {}.\",\n \"in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data =\",\n \"['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note::\",\n \"json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() #\",\n \"= np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF':\",\n \"dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if\",\n \"import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column,\",\n \"2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_\",\n \"'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file =\",\n \"c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out #\",\n \"solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_'\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\\",\n \"= nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,))\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) #\",\n \"dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should\",\n \"if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not\",\n \"dimension ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot\",\n \"ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load\",\n \"function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: #\",\n \"solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created =\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif\",\n \"ush usphi] .. note:: pi: multipliers for dynamics equality constraints\",\n \"last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin',\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\\",\n \"np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost =\",\n \"if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise\",\n \"idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] !=\",\n \"DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE\",\n \"solution) \\\\n sl: slack variables of soft lower inequality constraints\",\n \"regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i]\",\n \"= acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims)\",\n \"raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu\",\n \"f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file,\",\n \"# The 2-Clause BSD License # # Redistribution and use\",\n \"# get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype\",\n \"# cpu time qp solution 'time_qp_solver_call', # cpu time inside\",\n \"= get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f)\",\n \"out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file,\",\n \"= field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes\",\n \"ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent\",\n \"template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir\",\n \"= c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in =\",\n \"ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field !=\",\n \"-*- coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>,\",\n \"field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx',\",\n \"nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0]\",\n \"nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif\",\n \"ng, regarding lg, ug, C, D.') else: dims.ng = ng\",\n \"use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields =\",\n \"constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx:\",\n \"'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer\",\n \"= min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ),\",\n \"# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file,\",\n \"call 'time_lin', # cpu time for linearization 'time_sim', # cpu\",\n \"have nx columns.') if cost.Vu.shape[1] != dims.nu and ny !=\",\n \"if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr\",\n \"stage_: integer corresponding to shooting node :param field_: string, e.g.\",\n \"dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape =\",\n \"render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib')\",\n \"ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding\",\n \"= cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim\",\n \"constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.')\",\n \"= constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] !=\",\n \"nsbx + nsbu + nsh + nsg + nsphi wrong_field\",\n \"ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name)\",\n \"self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in\",\n \"self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes\",\n \"value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_)\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0\",\n \"dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f:\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field,\",\n \"converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter',\",\n \"0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI'\",\n \"c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)\",\n \"format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes\",\n \"'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif\",\n \"np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding\",\n \"nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0]\",\n \"'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast\",\n \"# implicit model -- generate C code opts = dict(generate_hess=1)\",\n \"= ny_e ## constraints # initial if (constraints.lbx_0 == []\",\n \"= sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r')\",\n \"deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from\",\n \"+ f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg =\",\n \"with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True)\",\n \"json_path) # terminal constraints on convex over nonlinear function if\",\n \"lh lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx\",\n \"raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e\",\n \"if not isinstance(value_, str): raise Exception('solver option {} must be\",\n \"lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e:\",\n \"sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size\",\n \"Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz !=\",\n \"generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit\",\n \"Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] !=\",\n \"dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension {}\",\n \"cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape\",\n \"nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx =\",\n \"solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time\",\n \"following conditions are met: # # 1. Redistributions of source\",\n \"'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] #\",\n \"Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else:\",\n \"'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name)\",\n \"<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> #\",\n \"acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model\",\n \"np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent\",\n \"(not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or\",\n \"> 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts)\",\n \"out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_\",\n \"acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as\",\n \"== 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64)\",\n \"nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field =\",\n \"elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\")\",\n \"must be of type int. You have {}.'.format(field_, type(value_))) else:\",\n \"from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost\",\n \"nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e =\",\n \"if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be\",\n \"lam: multipliers for inequalities \\\\n t: slack variables corresponding to\",\n \"'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\")\",\n \"constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options #\",\n \"POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent\",\n \"opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0]\",\n \"CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #\",\n \"casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')\",\n \"e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\"\",\n \"True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS':\",\n \"dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct,\",\n \"msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg\",\n \"constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension\",\n \"AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,\",\n \"is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent\",\n \"parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)]\",\n \"constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e,\",\n \"value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field\",\n \"ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0\",\n \"elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"= format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip\",\n \"as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): #\",\n \"\\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self):\",\n \"self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype\",\n \":param stage_: integer corresponding to shooting node :param field_: string,\",\n \"!= nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e,\",\n \"ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] !=\",\n \"'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims'])\",\n \"if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type ==\",\n \"dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,\",\n \"regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS':\",\n \"out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e ==\",\n \"!= nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.')\",\n \"generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type\",\n \"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #\",\n \"documentation # and/or other materials provided with the distribution. #\",\n \"Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e =\",\n \"not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu =\",\n \"template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the\",\n \"cpu time qp solution 'time_qp_solver_call', # cpu time inside qp\",\n \"'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields =\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if\",\n \"if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name,\",\n \"lsbx lsg lsh lsphi usbu usbx usg ush usphi] ..\",\n \"source code must retain the above copyright notice, # this\",\n \".. note:: pi: multipliers for dynamics equality constraints \\\\n lam:\",\n \"attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics\",\n \"generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions:\",\n \"field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_\",\n \"f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e},\",\n \"== 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or\",\n \"= 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =\",\n \"idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] !=\",\n \"elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':\",\n \"else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz =\",\n \"nsg ns = nsbx + nsbu + nsh + nsg\",\n \"this function should not be used anymore, better use cost_set,\",\n \"= np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh,\",\n \"inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi',\",\n \"is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data\",\n \"field_: string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\"\",\n \"W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] !=\",\n \"= value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0)\",\n \"numpy as np from ctypes import * from casadi import\",\n \"Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx =\",\n \"field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file,\",\n \"['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise\",\n \"if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx =\",\n \"= casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] !=\",\n \"nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush):\",\n \"0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e)\",\n \"for acados_struct, v in ocp_layout.items(): # skip non dict attributes\",\n \"if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if\",\n \"self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in\",\n \"= field_ field = field.encode('utf-8') if (field_ not in fields):\",\n \"for SQP-type solvers') field = field_ field = field.encode('utf-8') if\",\n \"The 2-Clause BSD License # # Redistribution and use in\",\n \"nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0:\",\n \"strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with\",\n \"IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\",\n \"<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # #\",\n \"of conditions and the following disclaimer. # # 2. Redistributions\",\n \"lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx:\",\n \"raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh\",\n \"string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call',\",\n \"c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims =\",\n \"continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct,\",\n \"= 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file\",\n \"get the last solution of the solver: :param stage: integer\",\n \"constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e:\",\n \"with the distribution. # # THIS SOFTWARE IS PROVIDED BY\",\n \"constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.')\",\n \"con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0\",\n \"nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding\",\n \"in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\\",\n \"= cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\")\",\n \"PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" #\",\n \"(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension'\",\n \"0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\\",\n \"and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h'\",\n \"nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps)\",\n \"'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast\",\n \"'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0:\",\n \"elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,\",\n \"PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY\",\n \"TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY\",\n \"constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape\",\n \"!= 0: raise Exception('inconsistent dimension: Vx should have nx columns.')\",\n \"regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if\",\n \"in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"if field_ == 'rti_phase': if value_ < 0 or value_\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape\",\n \"options of the solver: Parameters: :param field_: string, e.g. 'print_level',\",\n \"constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension\",\n \"time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad',\",\n \"constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx,\",\n \"in source and binary forms, with or without # modification,\",\n \"W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny\",\n \"# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\",\n \"raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] !=\",\n \"A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL\",\n \"and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS':\",\n \"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\",\n \"current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json',\",\n \"permitted provided that the following conditions are met: # #\",\n \"field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns =\",\n \"lbx lg lh lphi ubu ubx ug uh uphi; \\\\n\",\n \"\\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status =\",\n \"previous call 'time_lin', # cpu time for linearization 'time_sim', #\",\n \"name = acados_ocp.model.name # setting up loader and environment json_path\",\n \"ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"!= tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field\",\n \"self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!!\",\n \"from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length,\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"in the documentation # and/or other materials provided with the\",\n \"for integrator 'time_sim_ad', # cpu time for integrator contribution of\",\n \"nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e:\",\n \"dimension', \\\\ ' for field \\\"{}\\\" with dimension {} (you\",\n \"constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.')\",\n \"casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but\",\n \"Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if\",\n \"field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data\",\n \"{opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout():\",\n \"AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__\",\n \"information of the last solver call: :param field_: string in\",\n \"0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e\",\n \"else: raise Exception('model.x should be column vector!') # nu if\",\n \"isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct]\",\n \"acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make\",\n \"algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu\",\n \"render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name)\",\n \"= 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib =\",\n \"constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension\",\n \"== 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]):\",\n \"uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx\",\n \"acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def\",\n \"mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible\",\n \"# nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir =\",\n \"Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise\",\n \"= np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False)\",\n \"np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding\",\n \"out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes =\",\n \"# NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python\",\n \"lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg\",\n \"code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF':\",\n \"= acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx\",\n \"ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh\",\n \"2-Clause BSD License # # Redistribution and use in source\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields:\",\n \"stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1])\",\n \"np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p,\",\n \"constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension\",\n \"(field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF\",\n \"make one funciton with formatting for acados_struct, v in ocp_layout.items():\",\n \"json import numpy as np from ctypes import * from\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return\",\n \"make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) #\",\n \"must be of type float. You have {}.'.format(field_, type(value_))) else:\",\n \"constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e)\",\n \"Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu\",\n \"nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0:\",\n \"load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes\",\n \"is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent\",\n \"else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes\",\n \"usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh\",\n \"nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] !=\",\n \"ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render\",\n \"raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_\",\n \"with or without # modification, are permitted provided that the\",\n \"= '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if\",\n \"elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0]\",\n \"json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir\",\n \"'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t:\",\n \"{} is not a valid argument.\\\\ \\\\n Possible values are\",\n \"etc for acados_struct, v in ocp_layout.items(): # skip non dict\",\n \"cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref',\",\n \"numerical data in the constraint module of the solver: Parameters:\",\n \"elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj\",\n \"return out # Note: this function should not be used\",\n \"{})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"iterations 'statistics', # table with info about last iteration 'stat_m',\",\n \"True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file):\",\n \"'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data\",\n \"HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,\",\n \"1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and\",\n \"= [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return\",\n \"linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu\",\n \"dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh\",\n \"# terminal ns_e = nsbx_e + nsh_e + nsg_e +\",\n \"'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\"\",\n \"nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0]\",\n \"type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_)\",\n \"stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set\",\n \"= ['globalization'] if field_ in int_fields: if not isinstance(value_, int):\",\n \"not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if\",\n \"if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field},\",\n \"# cpu time regularization 'sqp_iter', # number of SQP iterations\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in,\",\n \"raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) !=\",\n \"# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\",\n \"# this list of conditions and the following disclaimer in\",\n \"status def get(self, stage_, field_): \\\"\\\"\\\" get the last solution\",\n \"constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension\",\n \"coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>,\",\n \"W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0:\",\n \"ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if\",\n \"loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not\",\n \"cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension\",\n \"ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0]\",\n \"other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0,\",\n \"<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,\",\n \"cpu time for integrator contribution of linear algebra 'time_qp', #\",\n \"elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,\",\n \"note:: regarding lam, t: \\\\n the inequalities are internally organized\",\n \"nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] !=\",\n \"== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model,\",\n \"elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,\",\n \":param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la',\",\n \"is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent\",\n \"['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if\",\n \"regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if\",\n \"'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"time for integrator contribution of linear algebra 'time_qp', # cpu\",\n \"integrator contribution of external function calls 'time_sim_la', # cpu time\",\n \"self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config,\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name)\",\n \"else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] !=\",\n \"POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import\",\n \"'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time\",\n \"dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e\",\n \"Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible values\",\n \"self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0])\",\n \"not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ##\",\n \"def get_stats(self, field_): \\\"\\\"\\\" get the information of the last\",\n \"OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE\",\n \"= 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file =\",\n \"ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict):\",\n \"out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m'\",\n \"as np from ctypes import * from casadi import CasadiMeta,\",\n \"isinstance(value_, str): raise Exception('solver option {} must be of type\",\n \"usg.') dims.nsg = nsg ns = nsbx + nsbu +\",\n \"lower inequality constraints \\\\n su: slack variables of soft upper\",\n \"dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp,\",\n \"return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type\",\n \"{}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast value_\",\n \"field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data\",\n \"are met: # # 1. Redistributions of source code must\",\n \"OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\",\n \"else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np =\",\n \"ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\\",\n \"nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding\",\n \"if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0\",\n \"if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise\",\n \"ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field\",\n \"argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields\",\n \"= constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng\",\n \"inequalities (at the solution) \\\\n sl: slack variables of soft\",\n \"time for integrator contribution of external function calls 'time_sim_la', #\",\n \"# POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json\",\n \"be used anymore, better use cost_set, constraints_set def set(self, stage_,\",\n \"in binary form must reproduce the above copyright notice, #\",\n \"= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)\",\n \"'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x',\",\n \"= casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else:\",\n \"solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim',\",\n \"len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape !=\",\n \"binary forms, with or without # modification, are permitted provided\",\n \"self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option\",\n \"dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e =\",\n \"make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time\",\n \"!= nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.')\",\n \"= ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,))\",\n \"uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e =\",\n \"= acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump\",\n \"ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu\",\n \"provided that the following conditions are met: # # 1.\",\n \"'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file =\",\n \"multipliers for inequalities \\\\n t: slack variables corresponding to evaluation\",\n \"in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in\",\n \"nsh + nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0]\",\n \"= '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class\",\n \"= nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,))\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\\",\n \"(at the solution) \\\\n sl: slack variables of soft lower\",\n \"'{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type\",\n \"if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 =\",\n \"dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e =\",\n \"'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e\",\n \"description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi):\",\n \"data in the constraint module of the solver: Parameters: :param\",\n \"or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx,\",\n \"== 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise\",\n \"dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,))\",\n \"\\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx + nsbu\",\n \"render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type\",\n \"= \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field,\",\n \"stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else:\",\n \"from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf\",\n \"c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"{} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data =\",\n \"= constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] !=\",\n \"field = field_ field = field.encode('utf-8') if field_ in string_fields:\",\n \"Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu\",\n \"+ cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a\",\n \"isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy\",\n \"SUCH DAMAGE.; # import sys, os, json import numpy as\",\n \"template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file,\",\n \"Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields:\",\n \"equality constraints \\\\n lam: multipliers for inequalities \\\\n t: slack\",\n \"constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.')\",\n \"nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e\",\n \"the solver: Parameters: :param stage_: integer corresponding to shooting node\",\n \"json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{}\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal\",\n \"opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x)\",\n \"not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ =\",\n \"ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout\",\n \"nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0]\",\n \"conditions are met: # # 1. Redistributions of source code\",\n \"self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for\",\n \"dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints:\",\n \"is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent\",\n \"not isinstance(value_, int): raise Exception('solver option {} must be of\",\n \"opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model,\",\n \"maybe make one funciton with formatting for acados_struct, v in\",\n \"integrator 'time_sim_ad', # cpu time for integrator contribution of external\",\n \"## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name\",\n \"constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu,\",\n \"stage: integer corresponding to shooting node :param field_: string in\",\n \"!= dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu\",\n \"dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set\",\n \"list of conditions and the following disclaimer in the documentation\",\n \"out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost\",\n \"[c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else:\",\n \"casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e\",\n \"= c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field)\",\n \"nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] !=\",\n \"# skip non dict attributes if not isinstance(v, dict): continue\",\n \"constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise\",\n \"Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e #\",\n \"Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints\",\n \"# modification, are permitted provided that the following conditions are\",\n \"following disclaimer. # # 2. Redistributions in binary form must\",\n \"e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int,\",\n \"usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg\",\n \"following disclaimer in the documentation # and/or other materials provided\",\n \"(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int\",\n \"for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf)\",\n \"dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,))\",\n \"object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one\",\n \"= acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts =\",\n \"= 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if\",\n \"'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name)\",\n \"!= ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0]\",\n \"linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', #\",\n \"dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for\",\n \"# setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes\",\n \"if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise\",\n \"= np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p,\",\n \"= field_ field = field.encode('utf-8') if (field_ not in out_fields\",\n \"dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0]\",\n \"ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e)\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name)\",\n \"c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config,\",\n \"json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..')\",\n \"Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,))\",\n \"nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e\",\n \"dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal\",\n \"nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e\",\n \"'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in\",\n \"to shooting node :param field_: string in ['x', 'u', 'z',\",\n \"!= ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e =\",\n \"acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options\",\n \"acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model'])\",\n \"opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx\",\n \"int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj]))\",\n \"function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching\",\n \"os, json import numpy as np from ctypes import *\",\n \"!= nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e,\",\n \"in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" #\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\\",\n \"c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def\",\n \"idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] !=\",\n \"value_): \\\"\\\"\\\" set numerical data in the cost module of\",\n \"in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"out # Note: this function should not be used anymore,\",\n \"is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np\",\n \"Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO:\",\n \"= nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,))\",\n \"self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,\",\n \"idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0]\",\n \"'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't']\",\n \"raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu\",\n \"= {ns} = nsbx + nsbu + nsg + nsh\",\n \"+ mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]\",\n \"Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e =\",\n \"field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set\",\n \"or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e,\",\n \"constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e,\",\n \"'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"with dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape))\",\n \"!= sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path\",\n \"opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes):\",\n \"model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created\",\n \"con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) #\",\n \"constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.')\",\n \"np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding\",\n \"cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif\",\n \"external function calls 'time_sim_la', # cpu time for integrator contribution\",\n \"LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #\",\n \"if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p\",\n \"'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_\",\n \"cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p,\",\n \"(stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out\",\n \"'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model,\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_,\",\n \"ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization\",\n \"Exception('solver option {} must be of type float. You have\",\n \"contribution of linear algebra 'time_qp', # cpu time qp solution\",\n \"= np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,\",\n \"discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf\",\n \"nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng\",\n \"nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] !=\",\n \"dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e)\",\n \"Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0 for\",\n \"should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny\",\n \"conversion issues value_ = value_.astype(float) field = field_ field =\",\n \"the following conditions are met: # # 1. Redistributions of\",\n \"# nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh >\",\n \"this list of conditions and the following disclaimer in the\",\n \"is not a valid argument.\\\\ \\\\n Possible values are {}.\",\n \"cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape\",\n \"+ '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout\",\n \"values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\\",\n \"model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e\",\n \"= {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e =\",\n \"only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type\",\n \"'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format(\",\n \"c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]\",\n \"constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension\",\n \"= {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\\",\n \"np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding\",\n \"acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file\",\n \"self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)\",\n \"= cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e:\",\n \"License # # Redistribution and use in source and binary\",\n \"idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] !=\",\n \"'{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex\",\n \"= ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi',\",\n \"in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps\",\n \"= {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization\",\n \"!= dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps =\",\n \"vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 =\",\n \"(you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"+ ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]\",\n \"future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, #\",\n \"templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make\",\n \"Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise\",\n \"inequality constraints \\\\n su: slack variables of soft upper inequality\",\n \"lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e:\",\n \"{nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}')\",\n \"opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown\",\n \"= dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0:\",\n \"ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()\",\n \"nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif\",\n \"= c_int(value_) elif field_ in double_fields: if not isinstance(value_, float):\",\n \"np from ctypes import * from casadi import CasadiMeta, Function,\",\n \"!= nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu,\",\n \"ctypes import * from casadi import CasadiMeta, Function, SX from\",\n \"interact with the acados ocp solver C object \\\"\\\"\\\" def\",\n \"not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0\",\n \"cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m',\",\n \"c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise\",\n \"acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name =\",\n \"{tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with\",\n \"in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"size \\\"\\\"\\\" # cast value_ to avoid conversion issues value_\",\n \"dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh =\",\n \"inequalities are internally organized in the following order: \\\\n [\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"= 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh:\",\n \"set numerical data in the constraint module of the solver:\",\n \"raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e\",\n \"is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0]\",\n \"self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1,\",\n \":param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of\",\n \"Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush =\",\n \"the solver: :param stage: integer corresponding to shooting node :param\",\n \"# make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp)\",\n \"'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name)\",\n \"else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_\",\n \"field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n\",\n \"acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp,\",\n \"= acados_ocp.model.name # setting up loader and environment json_path =\",\n \"' for field \\\"{}\\\" with dimension {} (you have {})'.format(field_,\",\n \"# Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict =\",\n \"= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"= np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh,\",\n \"ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] !=\",\n \"'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field\",\n \"type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if\",\n \"byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib #\",\n \"self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes =\",\n \"for integrator contribution of linear algebra 'time_qp', # cpu time\",\n \"self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with\",\n \"DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file,\",\n \"= c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config =\",\n \"if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {}\",\n \"= '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on\",\n \"os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so'\",\n \"nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0]\",\n \"ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) #\",\n \"out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields =\",\n \"self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m,\",\n \"NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND\",\n \"= np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,\",\n \"dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints:\",\n \"idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e):\",\n \"casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or\",\n \"ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] !=\",\n \"'su'] field = field_ field = field.encode('utf-8') if (field_ not\",\n \"+ nsh + nsg + nsphi wrong_field = \\\"\\\" if\",\n \"in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] ..\",\n \"def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =\",\n \"1. Redistributions of source code must retain the above copyright\",\n \"= dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0\",\n \"= 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE\",\n \"== 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if\",\n \"elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,\",\n \"'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True)\",\n \"a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields\",\n \"idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] !=\",\n \"+ \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and\",\n \"json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure\",\n \"stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj])))\",\n \"0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr):\",\n \"terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0]\",\n \"appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion issues\",\n \"= ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e =\",\n \"field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option\",\n \"# path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if\",\n \"Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1]\",\n \"idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu\",\n \"self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype\",\n \"dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny\",\n \"= dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else:\",\n \"'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name #\",\n \"!= ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e,\",\n \"render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact\",\n \"['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data,\",\n \"from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp\",\n \"field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p,\",\n \"get the information of the last solver call: :param field_:\",\n \"generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from\",\n \"dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip\",\n \"nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0]\",\n \"type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_)\",\n \"render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type ==\",\n \"ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent\",\n \"of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x',\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_\",\n \"dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e:\",\n \"string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\\",\n \"terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e\",\n \"= nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0\",\n \"time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either\",\n \"or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf\",\n \"= '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file,\",\n \"convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e\",\n \"json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code')\",\n \"ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e\",\n \"stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def\",\n \"nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif\",\n \"nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else:\",\n \"= '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost\",\n \"regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0]\",\n \"nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e =\",\n \"Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values are\",\n \"(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension',\",\n \"template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e ==\",\n \"of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion\",\n \"-- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': #\",\n \"discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not\",\n \"else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0]\",\n \"nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0]\",\n \"!= ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field\",\n \"cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name):\",\n \"constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension\",\n \"dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx\",\n \"field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if\"\n]","isConversational":false}}},{"rowIdx":2598,"cells":{"ngram":{"kind":"list like","value":["/ 255.0 h, w = img.shape psize = min(min(patchSize, h),","h_, start_x : start_x + w_] = sigma if saveResults:","isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input","Internal number of sub-image-patches. :param dirOut: Directory where to save","num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch),","pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise","sys import os import glob def im2patch(im, pch_size, stride=1): '''","w: end_x = w end_x = shift_factor * ((end_x) //","start_x = end_x - psize if end_y > h: end_y",":start_y + h_, start_x : start_x + w_] = sigma","by patch. Based on: \"An Efficient Statistical Method for Image","start_x : start_x + w_] = sigma if saveResults: if","pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch","of the following paper: <NAME> , <NAME> , <NAME> .","= psize shift_factor = 2 # Result array estimatedNoiseMap =","Example # if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\"","W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the","rangey: end_x = start_x + psize end_y = start_y +","C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H))","* ((end_y) // shift_factor) start_y = end_y - psize tileM","os import glob def im2patch(im, pch_size, stride=1): ''' Transform image","noise of image patches. The noise is estimated patch by","x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch))","start_x in rangex: for start_y in rangey: end_x = start_x","elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The","len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch =","Level Estimation\" (2015) :param imgPath: Path to the input image.","+ psize end_y = start_y + psize if end_x >","== 3: im = im.transpose((2, 0, 1)) else: im =","= num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)","kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C,","W x 3 or H x W numpy tensor, range","img / 255.0 h, w = img.shape psize = min(min(patchSize,","np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img /","patch_step) for start_x in rangex: for start_y in rangey: end_x","h: end_y = h end_y = shift_factor * ((end_y) //","kk = 0 for ii in range(pch_H): for jj in","''' if im.ndim == 3: im = im.transpose((2, 0, 1))","x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level:","# dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for","input of pch_size must be a integer or a int","isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input","Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV).","ii in range(pch_H): for jj in range(pch_W): temp = im[:,","image, H x W x 3 or H x W","num_pch)) # d x num_pch matrix d = pch.shape[0] mu","matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d","shift_factor * ((end_x) // shift_factor) start_x = end_x - psize","= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w","range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]","\"\"\" Estimates the standard deviation of (additive white gaussian) noise","estimated patch by patch. Based on: \"An Efficient Statistical Method","in rangex: for start_y in rangey: end_x = start_x +","if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath,","Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision","integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif","psize if end_y > h: end_y = h end_y =","psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma","Method for Image Noise Level Estimation\" (2015) :param imgPath: Path","Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param","H x W image, numpy format pch_size: (int, int) tuple","Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input:","Based on: \"An Efficient Statistical Method for Image Noise Level","pch_size must be a integer or a int tuple!') if","tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H =",":param patchSize: Image patch size. :param internalNumPatches: Internal number of","3 x H x W or 1 X H x","# image to patch pch = im2patch(im, pch_size, 3) #","im = np.expand_dims(im, axis=0) # image to patch pch =","level ''' if im.ndim == 3: im = im.transpose((2, 0,","3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im,","os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName +","+ w_] = sigma if saveResults: if dirOut is not","input of stride must be a integer or a int","the noise image, H x W x 3 or H","* 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x +","W or 1 X H x W image, numpy format","// shift_factor) start_y = end_y - psize tileM = img[start_y:end_y,","H x W numpy tensor, range [0,1] pch_size: patch_size Output:","= pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch",":param saveResults: Whether to save the estimation results or not.","pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return","noise is estimated patch by patch. Based on: \"An Efficient","= os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName","* ((end_x) // shift_factor) start_x = end_x - psize if","pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H):","format pch_size: (int, int) tuple or integer stride: (int, int)","range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii]","\"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img =","image to patch pch = im2patch(im, pch_size, 3) # C","= end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_","0 for ii in range(pch_H): for jj in range(pch_W): temp","white gaussian) noise of image patches. The noise is estimated","''' Transform image to patches. Input: im: 3 x H","num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W))","= 0 for ii in range(pch_H): for jj in range(pch_W):","saveResults=True): \"\"\" Estimates the standard deviation of (additive white gaussian)","stride elif isinstance(stride, int): stride_H = stride_W = stride else:","for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128,","((end_x) // shift_factor) start_x = end_x - psize if end_y","end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ =","# Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img,","stride_H = stride_W = stride else: sys.exit('The input of stride","dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h,","im: the noise image, H x W x 3 or","= r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\"","dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding =","noise level estimation of the following paper: <NAME> , <NAME>","if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut","tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise",", <NAME> , <NAME> . An Efficient Statistical Method for","estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if","temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C,","for ii in range(pch_H): for jj in range(pch_W): temp =","num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W","imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath =","im: 3 x H x W or 1 X H","shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w],","x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True)","a integer or a int tuple!') if isinstance(stride, tuple): stride_H,","temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch))","stride_W = stride elif isinstance(stride, int): stride_H = stride_W =","or H x W numpy tensor, range [0,1] pch_size: patch_size","to patches. Input: im: 3 x H x W or","as np import cv2 import sys import os import glob","1 X H x W image, numpy format pch_size: (int,","<NAME> , <NAME> , <NAME> . An Efficient Statistical Method","of pch_size must be a integer or a int tuple!')","glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches.","255.0 h, w = img.shape psize = min(min(patchSize, h), w)","+ \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except:","following paper: <NAME> , <NAME> , <NAME> . An Efficient","Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the","input image. :param patchSize: Image patch size. :param internalNumPatches: Internal","np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey =","# Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex =","sys.exit('The input of pch_size must be a integer or a","Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International","noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the","w end_x = shift_factor * ((end_x) // shift_factor) start_x =","internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x",":param imgPath: Path to the input image. :param patchSize: Image","= np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau)","start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_,","w) psize -= psize % 2 patch_step = psize shift_factor","pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level","noise estimation results. :param saveResults: Whether to save the estimation","try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0","return None # Example # if __name__ == '__main__': #","= pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1","end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x","import os import glob def im2patch(im, pch_size, stride=1): ''' Transform","pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix","except: return None # Example # if __name__ == '__main__':","noise image, H x W x 3 or H x","def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input:","= sigma if saveResults: if dirOut is not None: imgName","Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE","keepdims=True) # d x 1 X = pch - mu","# C x pch_size x pch_size x num_pch tensor num_pch","H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W","// shift_factor) start_x = end_x - psize if end_y >","import glob def im2patch(im, pch_size, stride=1): ''' Transform image to","pch_size: patch_size Output: noise_level: the estimated noise level ''' if","or 1 X H x W image, numpy format pch_size:","pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W","if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\")","estimated noise level ''' if im.ndim == 3: im =","tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W =","where to save the noise estimation results. :param saveResults: Whether","patch_step) rangey = range(0, h, patch_step) for start_x in rangex:","# d x 1 X = pch - mu sigma_X","or not. :return: None \"\"\" # Load image img =","numpy as np import cv2 import sys import os import","= np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau","np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for","range(0, h, patch_step) for start_x in rangex: for start_y in","W image, numpy format pch_size: (int, int) tuple or integer","is estimated patch by patch. Based on: \"An Efficient Statistical","np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the","= np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img","array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w,","of (additive white gaussian) noise of image patches. The noise","psize = min(min(patchSize, h), w) psize -= psize % 2","= np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort()","> h: end_y = h end_y = shift_factor * ((end_y)","for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if","import cv2 import sys import os import glob def im2patch(im,","results. :param saveResults: Whether to save the estimation results or","deviation of (additive white gaussian) noise of image patches. The","save the noise estimation results. :param saveResults: Whether to save","W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C,","C x pch_size x pch_size x num_pch tensor num_pch =","psize % 2 patch_step = psize shift_factor = 2 #","a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride","is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if","_ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1):","im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0)","noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x :","(ICCV). IEEE Computer Society, 2015. Input: im: the noise image,","def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard","in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) ==","X = pch - mu sigma_X = np.matmul(X, X.transpose()) /","= noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x","= tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y","stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W,","# imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\"","= shift_factor * ((end_y) // shift_factor) start_y = end_y -","= imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath","or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size","IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society,","the following paper: <NAME> , <NAME> , <NAME> . An","= stride else: sys.exit('The input of stride must be a","pch = im2patch(im, pch_size, 3) # C x pch_size x","tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride,","for start_x in rangex: for start_y in rangey: end_x =","Society, 2015. Input: im: the noise image, H x W","the noise estimation results. :param saveResults: Whether to save the","Efficient Statistical Method for Image Noise Level Estimation\" (2015) :param","3 or H x W numpy tensor, range [0,1] pch_size:","jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1","end_x > w: end_x = w end_x = shift_factor *",". An Efficient Statistical Method for Image Noise Level Estimation[C]//","1)) else: im = np.expand_dims(im, axis=0) # image to patch","(int, int) tuple or integer stride: (int, int) tuple or","else: im = np.expand_dims(im, axis=0) # image to patch pch","dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut)","if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int):","h, w = img.shape psize = min(min(patchSize, h), w) psize","end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y","noise_level: the estimated noise level ''' if im.ndim == 3:","np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut,","np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def","= os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap)","cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w =","level estimation of the following paper: <NAME> , <NAME> ,","range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level","w_] = sigma if saveResults: if dirOut is not None:","x pch_size x num_pch tensor num_pch = pch.shape[3] pch =","0, 1)) else: im = np.expand_dims(im, axis=0) # image to","for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:,","pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1,","to save the estimation results or not. :return: None \"\"\"","sys.exit('The input of stride must be a integer or a","tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return","Estimates the standard deviation of (additive white gaussian) noise of","the standard deviation of (additive white gaussian) noise of image","= 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8)","= np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey","int): pch_H = pch_W = pch_size else: sys.exit('The input of","end_x = w end_x = shift_factor * ((end_x) // shift_factor)","255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_]",": start_x + w_] = sigma if saveResults: if dirOut","Image Noise Level Estimation\" (2015) :param imgPath: Path to the","Noise Level Estimation\" (2015) :param imgPath: Path to the input","Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)","* num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk =","to the input image. :param patchSize: Image patch size. :param","im.ndim == 3: im = im.transpose((2, 0, 1)) else: im","mu = pch.mean(axis=1, keepdims=True) # d x 1 X =","os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath):","w, patch_step) rangey = range(0, h, patch_step) for start_x in","(additive white gaussian) noise of image patches. The noise is","''' Implement of noise level estimation of the following paper:","(int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H,","in rangey: end_x = start_x + psize end_y = start_y","for start_y in rangey: end_x = start_x + psize end_y","im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk","be a integer or a int tuple!') if isinstance(stride, tuple):","standard deviation of (additive white gaussian) noise of image patches.","or a int tuple!') C, H, W = im.shape num_H","for Image Noise Level Estimation\" (2015) :param imgPath: Path to","jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk,","= end_x - psize if end_y > h: end_y =","gaussian) noise of image patches. The noise is estimated patch","image patches. The noise is estimated patch by patch. Based","Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE","2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex","= img.shape psize = min(min(patchSize, h), w) psize -= psize","in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128, 8, dirOut)","internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to","paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical","> w: end_x = w end_x = shift_factor * ((end_x)",":param dirOut: Directory where to save the noise estimation results.","if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None","Implement of noise level estimation of the following paper: <NAME>","kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im,","= stride elif isinstance(stride, int): stride_H = stride_W = stride","psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h,","= h end_y = shift_factor * ((end_y) // shift_factor) start_y","not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not","of noise level estimation of the following paper: <NAME> ,","Output: noise_level: the estimated noise level ''' if im.ndim ==","the input image. :param patchSize: Image patch size. :param internalNumPatches:","results or not. :return: None \"\"\" # Load image img","pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d","cv2 import sys import os import glob def im2patch(im, pch_size,","saveResults: Whether to save the estimation results or not. :return:","stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H *","d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x","d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1,","psize if end_x > w: end_x = w end_x =","np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example #","or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W =","Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference","(2015) :param imgPath: Path to the input image. :param patchSize:","The noise is estimated patch by patch. Based on: \"An","of image patches. The noise is estimated patch by patch.","start_y + psize if end_x > w: end_x = w","# Example # if __name__ == '__main__': # dirIn =","h), w) psize -= psize % 2 patch_step = psize","stride=1): ''' Transform image to patches. Input: im: 3 x","return estimatedNoiseMap except: return None # Example # if __name__","isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H","estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] =","h, patch_step) for start_x in rangex: for start_y in rangey:","end_y = h end_y = shift_factor * ((end_y) // shift_factor)","- psize if end_y > h: end_y = h end_y","pch_size, 3) # C x pch_size x pch_size x num_pch","tuple or integer stride: (int, int) tuple or integer '''","3) # C x pch_size x pch_size x num_pch tensor","+= 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8):","num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1,","H x W or 1 X H x W image,","patch. Based on: \"An Efficient Statistical Method for Image Noise","numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated","np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size,","the estimation results or not. :return: None \"\"\" # Load","end_y > h: end_y = h end_y = shift_factor *","pch.mean(axis=1, keepdims=True) # d x 1 X = pch -","psize -= psize % 2 patch_step = psize shift_factor =","os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example","os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return","tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d","not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None #","= pch.mean(axis=1, keepdims=True) # d x 1 X = pch","''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size,","imgPath: Path to the input image. :param patchSize: Image patch","save the estimation results or not. :return: None \"\"\" #","pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0]","patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of","num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for","dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut,","patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches.","np import cv2 import sys import os import glob def","run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation","if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut =","im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1,","= len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch","estimation results or not. :return: None \"\"\" # Load image","sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_,","or integer stride: (int, int) tuple or integer ''' if","min(min(patchSize, h), w) psize -= psize % 2 patch_step =","Input: im: the noise image, H x W x 3","psize end_y = start_y + psize if end_x > w:","Estimation\" (2015) :param imgPath: Path to the input image. :param","= range(0, h, patch_step) for start_x in rangex: for start_y","img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches)","sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1,","internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive","start_x + psize end_y = start_y + psize if end_x","= pch.reshape((-1, num_pch)) # d x num_pch matrix d =","for Image Noise Level Estimation[C]// 2015 IEEE International Conference on","imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128, 8,","= pch_W = pch_size else: sys.exit('The input of pch_size must","\".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return","= range(0, w, patch_step) rangey = range(0, h, patch_step) for","in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :]","h end_y = shift_factor * ((end_y) // shift_factor) start_y =","if im.ndim == 3: im = im.transpose((2, 0, 1)) else:","patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut:","International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015.","os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not","\".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): #","start_x + w_] = sigma if saveResults: if dirOut is","integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W","= \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)):","image to patches. Input: im: 3 x H x W","% 2 patch_step = psize shift_factor = 2 # Result","+ psize if end_x > w: end_x = w end_x","= im2patch(im, pch_size, 3) # C x pch_size x pch_size","return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates","range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] =","integer stride: (int, int) tuple or integer ''' if isinstance(pch_size,","not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if","-d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] <","pch_W = pch_size else: sys.exit('The input of pch_size must be","on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im:","elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The","int): stride_H = stride_W = stride else: sys.exit('The input of","dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive white","img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h,","imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap","__name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut =","int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif","sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X)","number of sub-image-patches. :param dirOut: Directory where to save the","not. :return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath))","H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H","patch by patch. Based on: \"An Efficient Statistical Method for","\"An Efficient Statistical Method for Image Noise Level Estimation\" (2015)","= pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch","np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in","Input: im: 3 x H x W or 1 X",":param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where","'__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" #","else: sys.exit('The input of pch_size must be a integer or","start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) *","patch pch = im2patch(im, pch_size, 3) # C x pch_size","num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def","int tuple!') C, H, W = im.shape num_H = len(range(0,","tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\"","to patch pch = im2patch(im, pch_size, 3) # C x","if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int):","# for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath,","im2patch(im, pch_size, 3) # C x pch_size x pch_size x","imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" +","if end_x > w: end_x = w end_x = shift_factor","integer or a int tuple!') C, H, W = im.shape","estimation of the following paper: <NAME> , <NAME> , <NAME>","<NAME> , <NAME> . An Efficient Statistical Method for Image","image. :param patchSize: Image patch size. :param internalNumPatches: Internal number","must be a integer or a int tuple!') if isinstance(stride,","pch_size else: sys.exit('The input of pch_size must be a integer","must be a integer or a int tuple!') C, H,","# if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" #","= stride_W = stride else: sys.exit('The input of stride must","im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im:","shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x]","[0,1] pch_size: patch_size Output: noise_level: the estimated noise level '''","/ num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in","-1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):","d x 1 X = pch - mu sigma_X =","stride must be a integer or a int tuple!') C,","= img / 255.0 h, w = img.shape psize =","= len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch","Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0,","= shift_factor * ((end_x) // shift_factor) start_x = end_x -","= img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM,","ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau)","pch_size, stride=1): ''' Transform image to patches. Input: im: 3","end_x = start_x + psize end_y = start_y + psize","Computer Society, 2015. Input: im: the noise image, H x","import numpy as np import cv2 import sys import os","image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img","= im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0,","Whether to save the estimation results or not. :return: None","= start_y + psize if end_x > w: end_x =","dirOut: Directory where to save the noise estimation results. :param","pch_size: (int, int) tuple or integer stride: (int, int) tuple","= pch_size else: sys.exit('The input of pch_size must be a","stride else: sys.exit('The input of stride must be a integer","x 3 or H x W numpy tensor, range [0,1]","im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image","= w end_x = shift_factor * ((end_x) // shift_factor) start_x","tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H =","-= psize % 2 patch_step = psize shift_factor = 2","x W image, numpy format pch_size: (int, int) tuple or","None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img","to save the noise estimation results. :param saveResults: Whether to","rangex: for start_y in rangey: end_x = start_x + psize",":] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H,","size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory","x H x W or 1 X H x W","be a integer or a int tuple!') C, H, W","dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath","1 X = pch - mu sigma_X = np.matmul(X, X.transpose())","of sub-image-patches. :param dirOut: Directory where to save the noise","stride_W = stride else: sys.exit('The input of stride must be","num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation","estimatedNoiseMap except: return None # Example # if __name__ ==","num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) #","end_x - psize if end_y > h: end_y = h","import sys import os import glob def im2patch(im, pch_size, stride=1):","start_y in rangey: end_x = start_x + psize end_y =","= np.expand_dims(im, axis=0) # image to patch pch = im2patch(im,","Transform image to patches. Input: im: 3 x H x","int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W","w = img.shape psize = min(min(patchSize, h), w) psize -=","# dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding","x 1 X = pch - mu sigma_X = np.matmul(X,","estimation results. :param saveResults: Whether to save the estimation results","- mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _","end_y = start_y + psize if end_x > w: end_x","- psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape","pch_size=8): ''' Implement of noise level estimation of the following","isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H","num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk","img = img / 255.0 h, w = img.shape psize","num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x","pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for","sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii])","An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015","x W or 1 X H x W image, numpy","pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W =","rangey = range(0, h, patch_step) for start_x in rangex: for","stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W","mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ =","Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer","pch_H = pch_W = pch_size else: sys.exit('The input of pch_size","2015. Input: im: the noise image, H x W x","imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut)","else: sys.exit('The input of stride must be a integer or","len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch =","tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma =","= temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W,","range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x","shift_factor * ((end_y) // shift_factor) start_y = end_y - psize","patches. The noise is estimated patch by patch. Based on:","if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0]","int) tuple or integer stride: (int, int) tuple or integer","tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1,","<reponame>MaikWischow/Camera-Condition-Monitoring<gh_stars>1-10 import numpy as np import cv2 import sys import","pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value,","= im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) #","= np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii","= r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in",", <NAME> . An Efficient Statistical Method for Image Noise","# d x num_pch matrix d = pch.shape[0] mu =","h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0",":return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try:","patch_size Output: noise_level: the estimated noise level ''' if im.ndim","w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y","< tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True):","cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape","img.shape psize = min(min(patchSize, h), w) psize -= psize %","num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) #","noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath,","IEEE Computer Society, 2015. Input: im: the noise image, H","on: \"An Efficient Statistical Method for Image Noise Level Estimation\"","= pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size","return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement","np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize,","= start_x + psize end_y = start_y + psize if","img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img =","sigma if saveResults: if dirOut is not None: imgName =","2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer","tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y +","= im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch))","((end_y) // shift_factor) start_y = end_y - psize tileM =","2 patch_step = psize shift_factor = 2 # Result array","== np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches,","X H x W image, numpy format pch_size: (int, int)","a int tuple!') C, H, W = im.shape num_H =","H x W x 3 or H x W numpy","the estimated noise level ''' if im.ndim == 3: im","== '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\"","Statistical Method for Image Noise Level Estimation\" (2015) :param imgPath:","in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H,","stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple):","def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of","patches. Input: im: 3 x H x W or 1","dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj","Directory where to save the noise estimation results. :param saveResults:","None # Example # if __name__ == '__main__': # dirIn","<NAME> . An Efficient Statistical Method for Image Noise Level","x W x 3 or H x W numpy tensor,","= min(min(patchSize, h), w) psize -= psize % 2 patch_step","if end_y > h: end_y = h end_y = shift_factor","a integer or a int tuple!') C, H, W =","num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0","patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap","Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise","image, numpy format pch_size: (int, int) tuple or integer stride:","noise level ''' if im.ndim == 3: im = im.transpose((2,","ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk +=","pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of","r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" #","saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut","of stride must be a integer or a int tuple!')","x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3]","pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X","Path to the input image. :param patchSize: Image patch size.","W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W =","1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): '''","sub-image-patches. :param dirOut: Directory where to save the noise estimation","rangex = range(0, w, patch_step) rangey = range(0, h, patch_step)","r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn,","+ h_, start_x : start_x + w_] = sigma if","np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau =","pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else:","estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step)","shift_factor) start_x = end_x - psize if end_y > h:","w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0,","None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut):","X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii","axis=0) # image to patch pch = im2patch(im, pch_size, 3)","numpy format pch_size: (int, int) tuple or integer stride: (int,"],"string":"[\n \"/ 255.0 h, w = img.shape psize = min(min(patchSize, h),\",\n \"h_, start_x : start_x + w_] = sigma if saveResults:\",\n \"isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input\",\n \"Internal number of sub-image-patches. :param dirOut: Directory where to save\",\n \"num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch),\",\n \"pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise\",\n \"sys import os import glob def im2patch(im, pch_size, stride=1): '''\",\n \"w: end_x = w end_x = shift_factor * ((end_x) //\",\n \"start_x = end_x - psize if end_y > h: end_y\",\n \":start_y + h_, start_x : start_x + w_] = sigma\",\n \"by patch. Based on: \\\"An Efficient Statistical Method for Image\",\n \"start_x : start_x + w_] = sigma if saveResults: if\",\n \"pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch\",\n \"of the following paper: <NAME> , <NAME> , <NAME> .\",\n \"= psize shift_factor = 2 # Result array estimatedNoiseMap =\",\n \"Example # if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\"\",\n \"W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the\",\n \"rangey: end_x = start_x + psize end_y = start_y +\",\n \"C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H))\",\n \"* ((end_y) // shift_factor) start_y = end_y - psize tileM\",\n \"os import glob def im2patch(im, pch_size, stride=1): ''' Transform image\",\n \"noise of image patches. The noise is estimated patch by\",\n \"x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch))\",\n \"start_x in rangex: for start_y in rangey: end_x = start_x\",\n \"elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The\",\n \"len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch =\",\n \"Level Estimation\\\" (2015) :param imgPath: Path to the input image.\",\n \"+ psize end_y = start_y + psize if end_x >\",\n \"== 3: im = im.transpose((2, 0, 1)) else: im =\",\n \"= num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)\",\n \"kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C,\",\n \"W x 3 or H x W numpy tensor, range\",\n \"img / 255.0 h, w = img.shape psize = min(min(patchSize,\",\n \"np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img /\",\n \"patch_step) for start_x in rangex: for start_y in rangey: end_x\",\n \"h: end_y = h end_y = shift_factor * ((end_y) //\",\n \"kk = 0 for ii in range(pch_H): for jj in\",\n \"''' if im.ndim == 3: im = im.transpose((2, 0, 1))\",\n \"x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level:\",\n \"# dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for\",\n \"input of pch_size must be a integer or a int\",\n \"isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input\",\n \"Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV).\",\n \"ii in range(pch_H): for jj in range(pch_W): temp = im[:,\",\n \"image, H x W x 3 or H x W\",\n \"num_pch)) # d x num_pch matrix d = pch.shape[0] mu\",\n \"matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d\",\n \"shift_factor * ((end_x) // shift_factor) start_x = end_x - psize\",\n \"= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w\",\n \"range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]\",\n \"\\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian) noise\",\n \"estimated patch by patch. Based on: \\\"An Efficient Statistical Method\",\n \"in rangex: for start_y in rangey: end_x = start_x +\",\n \"if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath,\",\n \"Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision\",\n \"integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif\",\n \"psize if end_y > h: end_y = h end_y =\",\n \"psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma\",\n \"Method for Image Noise Level Estimation\\\" (2015) :param imgPath: Path\",\n \"Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param\",\n \"H x W image, numpy format pch_size: (int, int) tuple\",\n \"Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input:\",\n \"Based on: \\\"An Efficient Statistical Method for Image Noise Level\",\n \"pch_size must be a integer or a int tuple!') if\",\n \"tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H =\",\n \":param patchSize: Image patch size. :param internalNumPatches: Internal number of\",\n \"3 x H x W or 1 X H x\",\n \"# image to patch pch = im2patch(im, pch_size, 3) #\",\n \"im = np.expand_dims(im, axis=0) # image to patch pch =\",\n \"level ''' if im.ndim == 3: im = im.transpose((2, 0,\",\n \"3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im,\",\n \"os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName +\",\n \"+ w_] = sigma if saveResults: if dirOut is not\",\n \"input of stride must be a integer or a int\",\n \"the noise image, H x W x 3 or H\",\n \"* 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x +\",\n \"W or 1 X H x W image, numpy format\",\n \"// shift_factor) start_y = end_y - psize tileM = img[start_y:end_y,\",\n \"H x W numpy tensor, range [0,1] pch_size: patch_size Output:\",\n \"= pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch\",\n \":param saveResults: Whether to save the estimation results or not.\",\n \"pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return\",\n \"noise is estimated patch by patch. Based on: \\\"An Efficient\",\n \"= os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName\",\n \"* ((end_x) // shift_factor) start_x = end_x - psize if\",\n \"pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H):\",\n \"format pch_size: (int, int) tuple or integer stride: (int, int)\",\n \"range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii]\",\n \"\\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img =\",\n \"image to patch pch = im2patch(im, pch_size, 3) # C\",\n \"= end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_\",\n \"0 for ii in range(pch_H): for jj in range(pch_W): temp\",\n \"white gaussian) noise of image patches. The noise is estimated\",\n \"''' Transform image to patches. Input: im: 3 x H\",\n \"num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W))\",\n \"= 0 for ii in range(pch_H): for jj in range(pch_W):\",\n \"saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian)\",\n \"stride elif isinstance(stride, int): stride_H = stride_W = stride else:\",\n \"for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128,\",\n \"((end_x) // shift_factor) start_x = end_x - psize if end_y\",\n \"end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ =\",\n \"# Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img,\",\n \"stride_H = stride_W = stride else: sys.exit('The input of stride\",\n \"dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h,\",\n \"im: the noise image, H x W x 3 or\",\n \"= r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\"\",\n \"dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding =\",\n \"noise level estimation of the following paper: <NAME> , <NAME>\",\n \"if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut\",\n \"tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise\",\n \", <NAME> , <NAME> . An Efficient Statistical Method for\",\n \"estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if\",\n \"temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C,\",\n \"for ii in range(pch_H): for jj in range(pch_W): temp =\",\n \"num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W\",\n \"imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath =\",\n \"im: 3 x H x W or 1 X H\",\n \"shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w],\",\n \"x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True)\",\n \"a integer or a int tuple!') if isinstance(stride, tuple): stride_H,\",\n \"temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch))\",\n \"stride_W = stride elif isinstance(stride, int): stride_H = stride_W =\",\n \"or H x W numpy tensor, range [0,1] pch_size: patch_size\",\n \"to patches. Input: im: 3 x H x W or\",\n \"as np import cv2 import sys import os import glob\",\n \"1 X H x W image, numpy format pch_size: (int,\",\n \"<NAME> , <NAME> , <NAME> . An Efficient Statistical Method\",\n \"of pch_size must be a integer or a int tuple!')\",\n \"glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches.\",\n \"255.0 h, w = img.shape psize = min(min(patchSize, h), w)\",\n \"+ \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except:\",\n \"following paper: <NAME> , <NAME> , <NAME> . An Efficient\",\n \"Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the\",\n \"input image. :param patchSize: Image patch size. :param internalNumPatches: Internal\",\n \"np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey =\",\n \"# Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex =\",\n \"sys.exit('The input of pch_size must be a integer or a\",\n \"Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International\",\n \"noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the\",\n \"w end_x = shift_factor * ((end_x) // shift_factor) start_x =\",\n \"internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x\",\n \":param imgPath: Path to the input image. :param patchSize: Image\",\n \"= np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau)\",\n \"start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_,\",\n \"w) psize -= psize % 2 patch_step = psize shift_factor\",\n \"pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level\",\n \"noise estimation results. :param saveResults: Whether to save the estimation\",\n \"try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0\",\n \"return None # Example # if __name__ == '__main__': #\",\n \"= pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1\",\n \"end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x\",\n \"import os import glob def im2patch(im, pch_size, stride=1): ''' Transform\",\n \"pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix\",\n \"except: return None # Example # if __name__ == '__main__':\",\n \"noise image, H x W x 3 or H x\",\n \"def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input:\",\n \"= sigma if saveResults: if dirOut is not None: imgName\",\n \"Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE\",\n \"keepdims=True) # d x 1 X = pch - mu\",\n \"# C x pch_size x pch_size x num_pch tensor num_pch\",\n \"H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W\",\n \"// shift_factor) start_x = end_x - psize if end_y >\",\n \"import glob def im2patch(im, pch_size, stride=1): ''' Transform image to\",\n \"pch_size: patch_size Output: noise_level: the estimated noise level ''' if\",\n \"or 1 X H x W image, numpy format pch_size:\",\n \"pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W\",\n \"if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\")\",\n \"estimated noise level ''' if im.ndim == 3: im =\",\n \"tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W =\",\n \"where to save the noise estimation results. :param saveResults: Whether\",\n \"patch_step) rangey = range(0, h, patch_step) for start_x in rangex:\",\n \"# d x 1 X = pch - mu sigma_X\",\n \"or not. :return: None \\\"\\\"\\\" # Load image img =\",\n \"numpy as np import cv2 import sys import os import\",\n \"= np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau\",\n \"np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for\",\n \"range(0, h, patch_step) for start_x in rangex: for start_y in\",\n \"W image, numpy format pch_size: (int, int) tuple or integer\",\n \"is estimated patch by patch. Based on: \\\"An Efficient Statistical\",\n \"np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the\",\n \"= np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img\",\n \"array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w,\",\n \"of (additive white gaussian) noise of image patches. The noise\",\n \"psize = min(min(patchSize, h), w) psize -= psize % 2\",\n \"= np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort()\",\n \"> h: end_y = h end_y = shift_factor * ((end_y)\",\n \"for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if\",\n \"import cv2 import sys import os import glob def im2patch(im,\",\n \"results. :param saveResults: Whether to save the estimation results or\",\n \"deviation of (additive white gaussian) noise of image patches. The\",\n \"save the noise estimation results. :param saveResults: Whether to save\",\n \"W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C,\",\n \"C x pch_size x pch_size x num_pch tensor num_pch =\",\n \"psize % 2 patch_step = psize shift_factor = 2 #\",\n \"a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride\",\n \"is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if\",\n \"_ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1):\",\n \"im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0)\",\n \"noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x :\",\n \"(ICCV). IEEE Computer Society, 2015. Input: im: the noise image,\",\n \"def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard\",\n \"in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) ==\",\n \"X = pch - mu sigma_X = np.matmul(X, X.transpose()) /\",\n \"= noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x\",\n \"= tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y\",\n \"stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W,\",\n \"# imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\"\",\n \"= shift_factor * ((end_y) // shift_factor) start_y = end_y -\",\n \"= imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath\",\n \"or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size\",\n \"IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society,\",\n \"the following paper: <NAME> , <NAME> , <NAME> . An\",\n \"= stride else: sys.exit('The input of stride must be a\",\n \"pch = im2patch(im, pch_size, 3) # C x pch_size x\",\n \"tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride,\",\n \"for start_x in rangex: for start_y in rangey: end_x =\",\n \"Society, 2015. Input: im: the noise image, H x W\",\n \"the noise estimation results. :param saveResults: Whether to save the\",\n \"Efficient Statistical Method for Image Noise Level Estimation\\\" (2015) :param\",\n \"3 or H x W numpy tensor, range [0,1] pch_size:\",\n \"jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1\",\n \"end_x > w: end_x = w end_x = shift_factor *\",\n \". An Efficient Statistical Method for Image Noise Level Estimation[C]//\",\n \"1)) else: im = np.expand_dims(im, axis=0) # image to patch\",\n \"(int, int) tuple or integer stride: (int, int) tuple or\",\n \"else: im = np.expand_dims(im, axis=0) # image to patch pch\",\n \"dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut)\",\n \"if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int):\",\n \"h, w = img.shape psize = min(min(patchSize, h), w) psize\",\n \"end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y\",\n \"noise_level: the estimated noise level ''' if im.ndim == 3:\",\n \"np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut,\",\n \"np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def\",\n \"= os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap)\",\n \"cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w =\",\n \"level estimation of the following paper: <NAME> , <NAME> ,\",\n \"range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level\",\n \"w_] = sigma if saveResults: if dirOut is not None:\",\n \"x pch_size x num_pch tensor num_pch = pch.shape[3] pch =\",\n \"0, 1)) else: im = np.expand_dims(im, axis=0) # image to\",\n \"for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:,\",\n \"pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1,\",\n \"to save the estimation results or not. :return: None \\\"\\\"\\\"\",\n \"sys.exit('The input of stride must be a integer or a\",\n \"tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return\",\n \"Estimates the standard deviation of (additive white gaussian) noise of\",\n \"the standard deviation of (additive white gaussian) noise of image\",\n \"= 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8)\",\n \"= np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey\",\n \"int): pch_H = pch_W = pch_size else: sys.exit('The input of\",\n \"end_x = w end_x = shift_factor * ((end_x) // shift_factor)\",\n \"255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_]\",\n \": start_x + w_] = sigma if saveResults: if dirOut\",\n \"Image Noise Level Estimation\\\" (2015) :param imgPath: Path to the\",\n \"Noise Level Estimation\\\" (2015) :param imgPath: Path to the input\",\n \"Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\",\n \"* num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk =\",\n \"to the input image. :param patchSize: Image patch size. :param\",\n \"im.ndim == 3: im = im.transpose((2, 0, 1)) else: im\",\n \"mu = pch.mean(axis=1, keepdims=True) # d x 1 X =\",\n \"os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath):\",\n \"w, patch_step) rangey = range(0, h, patch_step) for start_x in\",\n \"(additive white gaussian) noise of image patches. The noise is\",\n \"''' Implement of noise level estimation of the following paper:\",\n \"(int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H,\",\n \"in rangey: end_x = start_x + psize end_y = start_y\",\n \"for start_y in rangey: end_x = start_x + psize end_y\",\n \"im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk\",\n \"be a integer or a int tuple!') if isinstance(stride, tuple):\",\n \"standard deviation of (additive white gaussian) noise of image patches.\",\n \"or a int tuple!') C, H, W = im.shape num_H\",\n \"for Image Noise Level Estimation\\\" (2015) :param imgPath: Path to\",\n \"jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk,\",\n \"= end_x - psize if end_y > h: end_y =\",\n \"gaussian) noise of image patches. The noise is estimated patch\",\n \"image patches. The noise is estimated patch by patch. Based\",\n \"Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE\",\n \"2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex\",\n \"= img.shape psize = min(min(patchSize, h), w) psize -= psize\",\n \"in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128, 8, dirOut)\",\n \"internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to\",\n \"paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical\",\n \"> w: end_x = w end_x = shift_factor * ((end_x)\",\n \":param dirOut: Directory where to save the noise estimation results.\",\n \"if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None\",\n \"Implement of noise level estimation of the following paper: <NAME>\",\n \"kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im,\",\n \"= stride elif isinstance(stride, int): stride_H = stride_W = stride\",\n \"psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h,\",\n \"= h end_y = shift_factor * ((end_y) // shift_factor) start_y\",\n \"not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not\",\n \"of noise level estimation of the following paper: <NAME> ,\",\n \"Output: noise_level: the estimated noise level ''' if im.ndim ==\",\n \"the input image. :param patchSize: Image patch size. :param internalNumPatches:\",\n \"results or not. :return: None \\\"\\\"\\\" # Load image img\",\n \"pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d\",\n \"cv2 import sys import os import glob def im2patch(im, pch_size,\",\n \"saveResults: Whether to save the estimation results or not. :return:\",\n \"stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H *\",\n \"d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x\",\n \"d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1,\",\n \"psize if end_x > w: end_x = w end_x =\",\n \"np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example #\",\n \"or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W =\",\n \"Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference\",\n \"(2015) :param imgPath: Path to the input image. :param patchSize:\",\n \"The noise is estimated patch by patch. Based on: \\\"An\",\n \"of image patches. The noise is estimated patch by patch.\",\n \"start_y + psize if end_x > w: end_x = w\",\n \"# Example # if __name__ == '__main__': # dirIn =\",\n \"h), w) psize -= psize % 2 patch_step = psize\",\n \"stride=1): ''' Transform image to patches. Input: im: 3 x\",\n \"return estimatedNoiseMap except: return None # Example # if __name__\",\n \"isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H\",\n \"estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] =\",\n \"h, patch_step) for start_x in rangex: for start_y in rangey:\",\n \"end_y = h end_y = shift_factor * ((end_y) // shift_factor)\",\n \"- psize if end_y > h: end_y = h end_y\",\n \"pch_size, 3) # C x pch_size x pch_size x num_pch\",\n \"tuple or integer stride: (int, int) tuple or integer '''\",\n \"3) # C x pch_size x pch_size x num_pch tensor\",\n \"+= 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8):\",\n \"num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1,\",\n \"H x W or 1 X H x W image,\",\n \"patch. Based on: \\\"An Efficient Statistical Method for Image Noise\",\n \"numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated\",\n \"np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size,\",\n \"the estimation results or not. :return: None \\\"\\\"\\\" # Load\",\n \"end_y > h: end_y = h end_y = shift_factor *\",\n \"pch.mean(axis=1, keepdims=True) # d x 1 X = pch -\",\n \"psize -= psize % 2 patch_step = psize shift_factor =\",\n \"os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example\",\n \"os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return\",\n \"tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d\",\n \"not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None #\",\n \"= pch.mean(axis=1, keepdims=True) # d x 1 X = pch\",\n \"''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size,\",\n \"imgPath: Path to the input image. :param patchSize: Image patch\",\n \"save the estimation results or not. :return: None \\\"\\\"\\\" #\",\n \"pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0]\",\n \"patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of\",\n \"num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for\",\n \"dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut,\",\n \"patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches.\",\n \"np import cv2 import sys import os import glob def\",\n \"run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation\",\n \"if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut =\",\n \"im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1,\",\n \"= len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch\",\n \"estimation results or not. :return: None \\\"\\\"\\\" # Load image\",\n \"sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_,\",\n \"or integer stride: (int, int) tuple or integer ''' if\",\n \"min(min(patchSize, h), w) psize -= psize % 2 patch_step =\",\n \"Input: im: the noise image, H x W x 3\",\n \"psize end_y = start_y + psize if end_x > w:\",\n \"Estimation\\\" (2015) :param imgPath: Path to the input image. :param\",\n \"= range(0, h, patch_step) for start_x in rangex: for start_y\",\n \"img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches)\",\n \"sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1,\",\n \"internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive\",\n \"start_x + psize end_y = start_y + psize if end_x\",\n \"= pch.reshape((-1, num_pch)) # d x num_pch matrix d =\",\n \"for Image Noise Level Estimation[C]// 2015 IEEE International Conference on\",\n \"imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128, 8,\",\n \"= pch_W = pch_size else: sys.exit('The input of pch_size must\",\n \"\\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return\",\n \"= range(0, w, patch_step) rangey = range(0, h, patch_step) for\",\n \"in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :]\",\n \"h end_y = shift_factor * ((end_y) // shift_factor) start_y =\",\n \"if im.ndim == 3: im = im.transpose((2, 0, 1)) else:\",\n \"patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut:\",\n \"International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015.\",\n \"os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not\",\n \"\\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): #\",\n \"start_x + w_] = sigma if saveResults: if dirOut is\",\n \"integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W\",\n \"= \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)):\",\n \"image to patches. Input: im: 3 x H x W\",\n \"% 2 patch_step = psize shift_factor = 2 # Result\",\n \"+ psize if end_x > w: end_x = w end_x\",\n \"= im2patch(im, pch_size, 3) # C x pch_size x pch_size\",\n \"return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates\",\n \"range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] =\",\n \"integer stride: (int, int) tuple or integer ''' if isinstance(pch_size,\",\n \"not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if\",\n \"-d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] <\",\n \"pch_W = pch_size else: sys.exit('The input of pch_size must be\",\n \"on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im:\",\n \"elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The\",\n \"int): stride_H = stride_W = stride else: sys.exit('The input of\",\n \"dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white\",\n \"img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h,\",\n \"imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap\",\n \"__name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut =\",\n \"int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif\",\n \"sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X)\",\n \"number of sub-image-patches. :param dirOut: Directory where to save the\",\n \"not. :return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath))\",\n \"H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H\",\n \"patch by patch. Based on: \\\"An Efficient Statistical Method for\",\n \"\\\"An Efficient Statistical Method for Image Noise Level Estimation\\\" (2015)\",\n \"= pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch\",\n \"np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in\",\n \"Input: im: 3 x H x W or 1 X\",\n \":param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where\",\n \"'__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" #\",\n \"else: sys.exit('The input of pch_size must be a integer or\",\n \"start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) *\",\n \"patch pch = im2patch(im, pch_size, 3) # C x pch_size\",\n \"num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def\",\n \"int tuple!') C, H, W = im.shape num_H = len(range(0,\",\n \"tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\"\",\n \"to patch pch = im2patch(im, pch_size, 3) # C x\",\n \"if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int):\",\n \"# for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath,\",\n \"im2patch(im, pch_size, 3) # C x pch_size x pch_size x\",\n \"imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" +\",\n \"if end_x > w: end_x = w end_x = shift_factor\",\n \"integer or a int tuple!') C, H, W = im.shape\",\n \"estimation of the following paper: <NAME> , <NAME> , <NAME>\",\n \"<NAME> , <NAME> . An Efficient Statistical Method for Image\",\n \"image. :param patchSize: Image patch size. :param internalNumPatches: Internal number\",\n \"must be a integer or a int tuple!') if isinstance(stride,\",\n \"pch_size else: sys.exit('The input of pch_size must be a integer\",\n \"must be a integer or a int tuple!') C, H,\",\n \"# if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" #\",\n \"= stride_W = stride else: sys.exit('The input of stride must\",\n \"im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im:\",\n \"shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x]\",\n \"[0,1] pch_size: patch_size Output: noise_level: the estimated noise level '''\",\n \"/ num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in\",\n \"-1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):\",\n \"d x 1 X = pch - mu sigma_X =\",\n \"stride must be a integer or a int tuple!') C,\",\n \"= img / 255.0 h, w = img.shape psize =\",\n \"= len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch\",\n \"Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0,\",\n \"= shift_factor * ((end_x) // shift_factor) start_x = end_x -\",\n \"= img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM,\",\n \"ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau)\",\n \"pch_size, stride=1): ''' Transform image to patches. Input: im: 3\",\n \"end_x = start_x + psize end_y = start_y + psize\",\n \"Computer Society, 2015. Input: im: the noise image, H x\",\n \"import numpy as np import cv2 import sys import os\",\n \"image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img\",\n \"= im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0,\",\n \"Whether to save the estimation results or not. :return: None\",\n \"= start_y + psize if end_x > w: end_x =\",\n \"dirOut: Directory where to save the noise estimation results. :param\",\n \"pch_size: (int, int) tuple or integer stride: (int, int) tuple\",\n \"= pch_size else: sys.exit('The input of pch_size must be a\",\n \"stride else: sys.exit('The input of stride must be a integer\",\n \"x 3 or H x W numpy tensor, range [0,1]\",\n \"im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image\",\n \"= w end_x = shift_factor * ((end_x) // shift_factor) start_x\",\n \"tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H =\",\n \"-= psize % 2 patch_step = psize shift_factor = 2\",\n \"x W image, numpy format pch_size: (int, int) tuple or\",\n \"None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img\",\n \"to save the noise estimation results. :param saveResults: Whether to\",\n \"rangex: for start_y in rangey: end_x = start_x + psize\",\n \":] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H,\",\n \"size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory\",\n \"x H x W or 1 X H x W\",\n \"be a integer or a int tuple!') C, H, W\",\n \"dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath\",\n \"1 X = pch - mu sigma_X = np.matmul(X, X.transpose())\",\n \"of sub-image-patches. :param dirOut: Directory where to save the noise\",\n \"stride_W = stride else: sys.exit('The input of stride must be\",\n \"num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation\",\n \"estimatedNoiseMap except: return None # Example # if __name__ ==\",\n \"num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) #\",\n \"end_x - psize if end_y > h: end_y = h\",\n \"import sys import os import glob def im2patch(im, pch_size, stride=1):\",\n \"start_y in rangey: end_x = start_x + psize end_y =\",\n \"= np.expand_dims(im, axis=0) # image to patch pch = im2patch(im,\",\n \"Transform image to patches. Input: im: 3 x H x\",\n \"int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W\",\n \"w = img.shape psize = min(min(patchSize, h), w) psize -=\",\n \"# dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding\",\n \"x 1 X = pch - mu sigma_X = np.matmul(X,\",\n \"estimation results. :param saveResults: Whether to save the estimation results\",\n \"- mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _\",\n \"end_y = start_y + psize if end_x > w: end_x\",\n \"- psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape\",\n \"pch_size=8): ''' Implement of noise level estimation of the following\",\n \"isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H\",\n \"num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk\",\n \"img = img / 255.0 h, w = img.shape psize\",\n \"num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x\",\n \"pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for\",\n \"sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii])\",\n \"An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015\",\n \"x W or 1 X H x W image, numpy\",\n \"pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W =\",\n \"rangey = range(0, h, patch_step) for start_x in rangex: for\",\n \"stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W\",\n \"mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ =\",\n \"Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer\",\n \"pch_H = pch_W = pch_size else: sys.exit('The input of pch_size\",\n \"2015. Input: im: the noise image, H x W x\",\n \"imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut)\",\n \"else: sys.exit('The input of stride must be a integer or\",\n \"len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch =\",\n \"tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma =\",\n \"= temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W,\",\n \"range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x\",\n \"shift_factor * ((end_y) // shift_factor) start_y = end_y - psize\",\n \"patches. The noise is estimated patch by patch. Based on:\",\n \"if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0]\",\n \"int) tuple or integer stride: (int, int) tuple or integer\",\n \"tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1,\",\n \"<reponame>MaikWischow/Camera-Condition-Monitoring<gh_stars>1-10 import numpy as np import cv2 import sys import\",\n \"pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value,\",\n \"= im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) #\",\n \"= np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii\",\n \"= r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in\",\n \", <NAME> . An Efficient Statistical Method for Image Noise\",\n \"# d x num_pch matrix d = pch.shape[0] mu =\",\n \"h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0\",\n \":return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try:\",\n \"patch_size Output: noise_level: the estimated noise level ''' if im.ndim\",\n \"w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y\",\n \"< tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True):\",\n \"cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape\",\n \"img.shape psize = min(min(patchSize, h), w) psize -= psize %\",\n \"num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) #\",\n \"noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath,\",\n \"IEEE Computer Society, 2015. Input: im: the noise image, H\",\n \"on: \\\"An Efficient Statistical Method for Image Noise Level Estimation\\\"\",\n \"= pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size\",\n \"return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement\",\n \"np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize,\",\n \"= start_x + psize end_y = start_y + psize if\",\n \"img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img =\",\n \"sigma if saveResults: if dirOut is not None: imgName =\",\n \"2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer\",\n \"tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y +\",\n \"= im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch))\",\n \"((end_y) // shift_factor) start_y = end_y - psize tileM =\",\n \"2 patch_step = psize shift_factor = 2 # Result array\",\n \"== np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches,\",\n \"X H x W image, numpy format pch_size: (int, int)\",\n \"a int tuple!') C, H, W = im.shape num_H =\",\n \"H x W x 3 or H x W numpy\",\n \"the estimated noise level ''' if im.ndim == 3: im\",\n \"== '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\"\",\n \"Statistical Method for Image Noise Level Estimation\\\" (2015) :param imgPath:\",\n \"in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H,\",\n \"stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple):\",\n \"def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of\",\n \"patches. Input: im: 3 x H x W or 1\",\n \"dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj\",\n \"Directory where to save the noise estimation results. :param saveResults:\",\n \"None # Example # if __name__ == '__main__': # dirIn\",\n \"<NAME> . An Efficient Statistical Method for Image Noise Level\",\n \"x W x 3 or H x W numpy tensor,\",\n \"= min(min(patchSize, h), w) psize -= psize % 2 patch_step\",\n \"if end_y > h: end_y = h end_y = shift_factor\",\n \"a integer or a int tuple!') C, H, W =\",\n \"num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0\",\n \"patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap\",\n \"Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise\",\n \"image, numpy format pch_size: (int, int) tuple or integer stride:\",\n \"noise level ''' if im.ndim == 3: im = im.transpose((2,\",\n \"ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk +=\",\n \"pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of\",\n \"r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" #\",\n \"saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut\",\n \"of stride must be a integer or a int tuple!')\",\n \"x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3]\",\n \"pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X\",\n \"Path to the input image. :param patchSize: Image patch size.\",\n \"W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W =\",\n \"1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): '''\",\n \"sub-image-patches. :param dirOut: Directory where to save the noise estimation\",\n \"rangex = range(0, w, patch_step) rangey = range(0, h, patch_step)\",\n \"r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn,\",\n \"+ h_, start_x : start_x + w_] = sigma if\",\n \"np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau =\",\n \"pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else:\",\n \"estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step)\",\n \"shift_factor) start_x = end_x - psize if end_y > h:\",\n \"w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0,\",\n \"None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut):\",\n \"X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii\",\n \"axis=0) # image to patch pch = im2patch(im, pch_size, 3)\",\n \"numpy format pch_size: (int, int) tuple or integer stride: (int,\"\n]","isConversational":false}}},{"rowIdx":2599,"cells":{"ngram":{"kind":"list like","value":["the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self):","AppState changes to *FINISHED*. This is checked via the :class:`Application`","self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\"","self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application","*JOINED*. This can only be done from the *RUNNING* or","an external piece of runnable software in any sense. Subclasses","\"\"\" Check if the application has finished. PROTECTED: Override when","= auto() FINISHED = auto() JOINED = auto() CANCELLED =","the application has finished. PROTECTED: Override when inheriting. Returns -------","for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in","interval : float Time (in seconds) between calls of :func:`is_finished()`","= AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING |","otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval","PROTECTED: Override when inheriting. Returns ------- finished : bool True","[\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time","self.cancel() raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f}","AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func): @wraps(func)","wraps from enum import Flag, auto class AppState(Flag): \"\"\" This","is *RUNNING*, this method waits until the application is *FINISHED*.","or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self):","Parameters ---------- timeout : float, optional If this parameter is","enum type represents the app states of an application. \"\"\"","terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception):","the current app state. Returns ------- app_state : AppState The","current app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished():","def is_finished(self): \"\"\" Check if the application has finished. PROTECTED:","timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise","when inheriting. Returns ------- finished : bool True of the",":class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ----------","finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The","Indicate that the application lifecycle was violated. \"\"\" pass class","\"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools","this abstract base class specify the respective kind of software","raised and the application is cancelled. Raises ------ TimeoutError If","def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED:","AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app state.","self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence","and :func:`join()` other Python code can be executed, while the","the application run and set its state to *RUNNING*. This","AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run and","has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\"","raises an :class:`AppStateError` in case the method is called, when","termination: Directly after its instantiation the app is in the",":class:`AppStateError` is called. The application run behaves like an additional","state is required\" ) return func(*args, **kwargs) return wrapper return","kind of software and the way of interacting with it.","PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\"","to *FINISHED*. This is checked via the :class:`Application` type specific","'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__ =","call the :func:`join()` method, concluding the application in the *JOINED*","the joining process. PROTECTED: Override when inheriting. Returns ------- interval","until this value (in seconds) runs out. After this time","run and set its state to *RUNNING*. This can only","is specified, the :class:`Application` only waits for finishing until this","the application finishes the AppState changes to *FINISHED*. This is","= AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the","abstract base class specify the respective kind of software and","that raises an :class:`AppStateError` in case the method is called,","the AppState changes to *FINISHED*. This is checked via the","Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an external","def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses","is in {instance.get_app_state()} state, \" f\"but {app_state} state is required\"","A decorator for methods of :class:`Application` subclasses that raises an","when in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED","Returns ------- interval : float Time (in seconds) between calls","method, too, but there are no accessible results. If a","in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED)","the 3-Clause BSD License. Please see 'LICENSE.rst' for further #","the application is *FINISHED* the joining process happens immediately, if","*FINISHED* the joining process happens immediately, if otherwise the application","Indicate that the application's timeout expired. \"\"\" pass class VersionError(Exception):","is checked via the :class:`Application` type specific :func:`is_finished()` method. The","no accessible results. If a method is called in an","If the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval())","finished. PROTECTED: Override when inheriting. Returns ------- finished : bool","app state. Examples -------- Raises :class:`AppStateError` when `function` is called,","if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return","state to *JOINED*. This can only be done from the","the joining process happens immediately, if otherwise the application is","self.clean_up() def get_app_state(self): \"\"\" Get the current app state. Returns","Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def","\"\"\" This class is a wrapper around an external piece","Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod",":class:`AppStateError` in case the method is called, when the :class:`Application`","be done from the *CREATED* state. \"\"\" self.run() self._start_time =","run. After the user calls the :func:`start()` method, the app",") return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta):","after its instantiation the app is in the *CREATED* state.","*FINISHED*. Parameters ---------- timeout : float, optional If this parameter","def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of","time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED","has finished. PROTECTED: Override when inheriting. Returns ------- finished :","unsuitable app state, an :class:`AppStateError` is called. The application run","decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError`","app state. Returns ------- app_state : AppState The current app","After this time is exceeded a :class:`TimeoutError` is raised and","run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass","to *RUNNING* and the :class:`Application` type specific :func:`run()` method is","the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\"","time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None):","the application when in *RUNNING* or *FINISHED* state. \"\"\" self._state","wrapper(*args, **kwargs): # First parameter of method is always 'self'","function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs):","parameters can be set for the application run. After the","into the *JOINED* state as soon as the application reaches","in case the method is called, when the :class:`Application` is","method is always 'self' instance = args[0] if not instance._state","is called, if :class:`Application` is not in one of the","when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up","RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED","else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self):",":class:`TimeoutError` is raised and the application is cancelled. Raises ------","the way of interacting with it. Every :class:`Application` runs through","time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired its","see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__","in the *RUNNING* state: This will constantly check :func:`is_finished()` and","not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state :","the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state =","in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too,","different app states (instances of enum :class:`AppState`) from its creation","of the application has finished, false otherwise \"\"\" pass @abc.abstractmethod","__name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\",","this parameter is specified, the :class:`Application` only waits for finishing","Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not","---------- app_state : AppState The required app state. Examples --------","AppState The current app state. \"\"\" if self._state == AppState.RUNNING:","be called in the *RUNNING* state: This will constantly check","app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state","is always 'self' instance = args[0] if not instance._state &","parameter of method is always 'self' instance = args[0] if","the :func:`start()` method, the app state is set to *RUNNING*","\"\"\" Conclude the application run and set its state to","def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args,","self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED)","app state, an :class:`AppStateError` is called. The application run behaves","its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try:","executed, while the application runs in the background. \"\"\" def","application expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval())","AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and","Raises ------ TimeoutError If the joining process exceeds the `timeout`","else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state","Commence the application run. Called in :func:`start()`. PROTECTED: Override when","AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application","even be called in the *RUNNING* state: This will constantly","application is in {instance.get_app_state()} state, \" f\"but {app_state} state is","return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run.","& app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state,","*RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state.","\"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not","timeout : float, optional If this parameter is specified, the","of :func:`start()` and :func:`join()` other Python code can be executed,","When the application finishes the AppState changes to *FINISHED*. This","is required\" ) return func(*args, **kwargs) return wrapper return decorator","the app state is set to *RUNNING* and the :class:`Application`","type represents the app states of an application. \"\"\" CREATED","specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific","of :class:`Application` subclasses that raises an :class:`AppStateError` in case the","an :class:`AppStateError` is called. The application run behaves like an",": AppState The current app state. \"\"\" if self._state ==","__init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the","if timeout is not None and time.time()-self._start_time > timeout: self.cancel()","the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def","After the user calls the :func:`start()` method, the app state","the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but","inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up work","\"\"\" Indicate that the application's version is invalid. \"\"\" pass","application is *FINISHED* the joining process happens immediately, if otherwise","its creation until its termination: Directly after its instantiation the","------- interval : float Time (in seconds) between calls of","the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The","First parameter of method is always 'self' instance = args[0]","inheriting. Returns ------- interval : float Time (in seconds) between",":func:`is_finished()` and will directly go into the *JOINED* state as","states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass","interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override","of enum :class:`AppState`) from its creation until its termination: Directly","from the *RUNNING* or *FINISHED* state. If the application is","app_state : AppState The current app state. \"\"\" if self._state","FINISHED = auto() JOINED = auto() CANCELLED = auto() def","Parameters ---------- app_state : AppState The required app state. Examples","3-Clause BSD License. Please see 'LICENSE.rst' for further # information.","the *RUNNING* state: This will constantly check :func:`is_finished()` and will","around an external piece of runnable software in any sense.","external piece of runnable software in any sense. Subclasses of","timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout \"","def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in","= [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import",":func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves","an additional thread: Between the call of :func:`start()` and :func:`join()`","additional thread: Between the call of :func:`start()` and :func:`join()` other","self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application","information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\",","The required app state. Examples -------- Raises :class:`AppStateError` when `function`","or *FINISHED* leaves the application in the *CANCELLED* state. This","import abc import time from functools import wraps from enum","calls in the joining process. PROTECTED: Override when inheriting. Returns","run(self): \"\"\" Commence the application run. Called in :func:`start()`. PROTECTED:","state. Examples -------- Raises :class:`AppStateError` when `function` is called, if","raise AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but","can even be called in the *RUNNING* state: This will","def wrapper(*args, **kwargs): # First parameter of method is always","from enum import Flag, auto class AppState(Flag): \"\"\" This enum","specify the respective kind of software and the way of","VersionError(Exception): \"\"\" Indicate that the application's version is invalid. \"\"\"","sense. Subclasses of this abstract base class specify the respective","This class is a wrapper around an external piece of","state to *RUNNING*. This can only be done from the","@abc.abstractmethod def is_finished(self): \"\"\" Check if the application has finished.","of an application. \"\"\" CREATED = auto() RUNNING = auto()","while the application is *RUNNING* or *FINISHED* leaves the application","*RUNNING* state: This will constantly check :func:`is_finished()` and will directly","\" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except","\"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of","application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that","expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval())","making the results of the application accessible by executing the","def run(self): \"\"\" Commence the application run. Called in :func:`start()`.",":func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate","of runnable software in any sense. Subclasses of this abstract","Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean","{instance.get_app_state()} state, \" f\"but {app_state} state is required\" ) return","In this state further parameters can be set for the",":func:`join()` method, concluding the application in the *JOINED* state and","this value (in seconds) runs out. After this time is","the application is cancelled. Raises ------ TimeoutError If the joining","and is distributed # under the 3-Clause BSD License. Please",": float, optional If this parameter is specified, the :class:`Application`",":class:`Application` type specific :func:`run()` method is called. When the application","self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\"","Override when inheriting. Returns ------- finished : bool True of","when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the","application runs in the background. \"\"\" def __init__(self): self._state =","not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The","the application in the *JOINED* state and making the results","AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state}","method. :func:`join()` can even be called in the *RUNNING* state:","runs in the background. \"\"\" def __init__(self): self._state = AppState.CREATED","AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state =",":func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\"","of this abstract base class specify the respective kind of","joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state()","concluding the application in the *JOINED* state and making the","runnable software in any sense. Subclasses of this abstract base","\"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\",","application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED =","clean up work after the application terminates. PROTECTED: Optionally override","violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's","AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in *RUNNING*","if not instance._state & app_state: raise AppStateError( f\"The application is","soon as the application reaches the *FINISHED* state. Calling the","wrapper around an external piece of runnable software in any","application reaches the *FINISHED* state. Calling the :func:`cancel()` method while","its termination: Directly after its instantiation the app is in","raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED","\"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED","class VersionError(Exception): \"\"\" Indicate that the application's version is invalid.","any sense. Subclasses of this abstract base class specify the","seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod","of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def","state and making the results of the application accessible by","immediately, if otherwise the application is *RUNNING*, this method waits","Between the call of :func:`start()` and :func:`join()` other Python code","done from the *RUNNING* or *FINISHED* state. If the application","base class specify the respective kind of software and the","TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\" pass","finished : bool True of the application has finished, false","in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState","after the application terminates. PROTECTED: Optionally override when inheriting. \"\"\"","class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an","is *FINISHED*. Parameters ---------- timeout : float, optional If this","app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state, \"","lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that","run behaves like an additional thread: Between the call of","runs through a different app states (instances of enum :class:`AppState`)","set to *RUNNING* and the :class:`Application` type specific :func:`run()` method","through a different app states (instances of enum :class:`AppState`) from","the application runs in the background. \"\"\" def __init__(self): self._state","until the application is *FINISHED*. Parameters ---------- timeout : float,","self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current","of interacting with it. Every :class:`Application` runs through a different","\"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import","the app states of an application. \"\"\" CREATED = auto()","time from functools import wraps from enum import Flag, auto","changes to *FINISHED*. This is checked via the :class:`Application` type","the application is *RUNNING*, this method waits until the application","__author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\",","when inheriting. Returns ------- interval : float Time (in seconds)","but there are no accessible results. If a method is","and set its state to *JOINED*. This can only be",":func:`join()` can even be called in the *RUNNING* state: This","the user calls the :func:`start()` method, the app state is","clean_up(self): \"\"\" Do clean up work after the application terminates.","called. The application run behaves like an additional thread: Between",": bool True of the application has finished, false otherwise","called. When the application finishes the AppState changes to *FINISHED*.","can only be done from the *CREATED* state. \"\"\" self.run()","functools import wraps from enum import Flag, auto class AppState(Flag):",":class:`Application` type specific :func:`is_finished()` method. The user can now call","The current app state. \"\"\" if self._state == AppState.RUNNING: if","application has finished. PROTECTED: Override when inheriting. Returns ------- finished","AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout:","in an unsuitable app state, an :class:`AppStateError` is called. The","if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self):","state: This will constantly check :func:`is_finished()` and will directly go","specific :func:`clean_up()` method. :func:`join()` can even be called in the","application has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self):",":func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()`",":class:`Application` runs through a different app states (instances of enum","that the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\"","accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore","value (in seconds) runs out. After this time is exceeded","decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around","timeout=None): \"\"\" Conclude the application run and set its state","will directly go into the *JOINED* state as soon as","seconds) runs out. After this time is exceeded a :class:`TimeoutError`","set its state to *JOINED*. This can only be done","auto() RUNNING = auto() FINISHED = auto() JOINED = auto()","*FINISHED* state. Calling the :func:`cancel()` method while the application is","between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def","only be done from the *CREATED* state. \"\"\" self.run() self._start_time","== AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod",":func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self):","source code is part of the Biotite package and is","state. This triggers the :func:`clean_up()` method, too, but there are","specific :func:`run()` method is called. When the application finishes the","True of the application has finished, false otherwise \"\"\" pass","Get the current app state. Returns ------- app_state : AppState","\"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle","called, when the :class:`Application` is not in the specified :class:`AppState`","the application run. After the user calls the :func:`start()` method,",":func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING*","in the *CREATED* state. In this state further parameters can",":class:`Application` is not in one of the specified states: >>>","def cancel(self): \"\"\" Cancel the application when in *RUNNING* or",": float Time (in seconds) between calls of :func:`is_finished()` in","timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the","License. Please see 'LICENSE.rst' for further # information. __name__ =","s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise","in the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED)","while the application runs in the background. \"\"\" def __init__(self):","\"\"\" Do clean up work after the application terminates. PROTECTED:","= AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude","only be done from the *RUNNING* or *FINISHED* state. If","start(self): \"\"\" Start the application run and set its state","*RUNNING* or *FINISHED* state. If the application is *FINISHED* the","\"requires_state\"] import abc import time from functools import wraps from","process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() !=","This source code is part of the Biotite package and","= auto() RUNNING = auto() FINISHED = auto() JOINED =","further parameters can be set for the application run. After","is *FINISHED* the joining process happens immediately, if otherwise the","set its state to *RUNNING*. This can only be done","the *JOINED* state as soon as the application reaches the","timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate()","def join(self, timeout=None): \"\"\" Conclude the application run and set","*FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()`","in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self):","under the 3-Clause BSD License. Please see 'LICENSE.rst' for further","application run. After the user calls the :func:`start()` method, the","only waits for finishing until this value (in seconds) runs","TimeoutError If the joining process exceeds the `timeout` value. \"\"\"","*FINISHED* leaves the application in the *CANCELLED* state. This triggers","the *FINISHED* state. Calling the :func:`cancel()` method while the application","class AppState(Flag): \"\"\" This enum type represents the app states","app is in the *CREATED* state. In this state further","and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired","inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application","specific :func:`is_finished()` method. The user can now call the :func:`join()`","waits for finishing until this value (in seconds) runs out.","in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def","thread: Between the call of :func:`start()` and :func:`join()` other Python","Conclude the application run and set its state to *JOINED*.","\"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from","| AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func):","= auto() def requires_state(app_state): \"\"\" A decorator for methods of","happens immediately, if otherwise the application is *RUNNING*, this method","(instances of enum :class:`AppState`) from its creation until its termination:",":func:`join()` other Python code can be executed, while the application","*JOINED* state and making the results of the application accessible","import wraps from enum import Flag, auto class AppState(Flag): \"\"\"","-------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is","application in the *CANCELLED* state. This triggers the :func:`clean_up()` method,","exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED:","self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else:","respective kind of software and the way of interacting with","app state is set to *RUNNING* and the :class:`Application` type","otherwise the application is *RUNNING*, this method waits until the","evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override","up work after the application terminates. PROTECTED: Optionally override when","\"\"\" Indicate that the application's timeout expired. \"\"\" pass class","Cancel the application when in *RUNNING* or *FINISHED* state. \"\"\"","class is a wrapper around an external piece of runnable","cancel(self): \"\"\" Cancel the application when in *RUNNING* or *FINISHED*","!= AppState.FINISHED: if timeout is not None and time.time()-self._start_time >","the application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass","app_state : AppState The required app state. Examples -------- Raises","is part of the Biotite package and is distributed #","is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state","func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This","If a method is called in an unsuitable app state,","and the way of interacting with it. Every :class:`Application` runs","calls the :func:`start()` method, the app state is set to","states of an application. \"\"\" CREATED = auto() RUNNING =","application accessible by executing the :class:`Application` type specific :func:`evaluate()` method.",">>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\"","part of the Biotite package and is distributed # under","pass def clean_up(self): \"\"\" Do clean up work after the","the :func:`join()` method, concluding the application in the *JOINED* state","an application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED","check :func:`is_finished()` and will directly go into the *JOINED* state","as soon as the application reaches the *FINISHED* state. Calling","(in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass","joining process. PROTECTED: Override when inheriting. Returns ------- interval :","with it. Every :class:`Application` runs through a different app states",":func:`run()` method is called. When the application finishes the AppState","If the application is *FINISHED* the joining process happens immediately,","the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if","wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a","from its creation until its termination: Directly after its instantiation","AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the","override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that","@requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\" def","\"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\"","@requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application","or *FINISHED* state. If the application is *FINISHED* the joining","\"\"\" pass def clean_up(self): \"\"\" Do clean up work after","can be set for the application run. After the user","method, the app state is set to *RUNNING* and the","self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\"","states (instances of enum :class:`AppState`) from its creation until its","state as soon as the application reaches the *FINISHED* state.","AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def","= \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\",","optional If this parameter is specified, the :class:`Application` only waits","method waits until the application is *FINISHED*. Parameters ---------- timeout","pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First","... def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def","a wrapper around an external piece of runnable software in",") else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except:","specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required","\"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when","application run and set its state to *JOINED*. This can","application is *RUNNING* or *FINISHED* leaves the application in the","f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state} state","*CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there","application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\"","\"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called","the application has finished, false otherwise \"\"\" pass @abc.abstractmethod def","type specific :func:`clean_up()` method. :func:`join()` can even be called in","of the application accessible by executing the :class:`Application` type specific","@wraps(func) def wrapper(*args, **kwargs): # First parameter of method is","is not in one of the specified states: >>> @requires_state(AppState.RUNNING","pass class VersionError(Exception): \"\"\" Indicate that the application's version is","too, but there are no accessible results. If a method","value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is","directly go into the *JOINED* state as soon as the","def get_app_state(self): \"\"\" Get the current app state. Returns -------","state is set to *RUNNING* and the :class:`Application` type specific","if the application has finished. PROTECTED: Override when inheriting. Returns","this method waits until the application is *FINISHED*. Parameters ----------","done from the *CREATED* state. \"\"\" self.run() self._start_time = time.time()","is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED*","\" f\"but {app_state} state is required\" ) return func(*args, **kwargs)","for further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\"","the call of :func:`start()` and :func:`join()` other Python code can","*RUNNING*, this method waits until the application is *FINISHED*. Parameters","def clean_up(self): \"\"\" Do clean up work after the application","accessible results. If a method is called in an unsuitable","one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ...","= \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"]","(in seconds) runs out. After this time is exceeded a","------ TimeoutError If the joining process exceeds the `timeout` value.","f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError:","# This source code is part of the Biotite package","the :class:`Application` type specific :func:`is_finished()` method. The user can now","self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run. Called",":class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called","and making the results of the application accessible by executing","*JOINED* state as soon as the application reaches the *FINISHED*","run and set its state to *JOINED*. This can only","and the application is cancelled. Raises ------ TimeoutError If the","an unsuitable app state, an :class:`AppStateError` is called. The application","None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application","the *CREATED* state. In this state further parameters can be","time is exceeded a :class:`TimeoutError` is raised and the application","*CREATED* state. In this state further parameters can be set","return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper",":class:`AppStateError` when `function` is called, if :class:`Application` is not in","pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired.","PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do","pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in","| AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in","\"\"\" CREATED = auto() RUNNING = auto() FINISHED = auto()","This is checked via the :class:`Application` type specific :func:`is_finished()` method.","= AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel","can be executed, while the application runs in the background.","application is cancelled. Raises ------ TimeoutError If the joining process","\"\"\" Get the current app state. Returns ------- app_state :","------- finished : bool True of the application has finished,","an :class:`AppStateError` in case the method is called, when the","raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def","can now call the :func:`join()` method, concluding the application in","of software and the way of interacting with it. Every","this time is exceeded a :class:`TimeoutError` is raised and the","app states (instances of enum :class:`AppState`) from its creation until","method, concluding the application in the *JOINED* state and making","= args[0] if not instance._state & app_state: raise AppStateError( f\"The","self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def","application run and set its state to *RUNNING*. This can","background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self):","state. Calling the :func:`cancel()` method while the application is *RUNNING*","**kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class","\"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application","the Biotite package and is distributed # under the 3-Clause","The application run behaves like an additional thread: Between the",":func:`is_finished()` method. The user can now call the :func:`join()` method,","is cancelled. Raises ------ TimeoutError If the joining process exceeds","the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate","subclasses that raises an :class:`AppStateError` in case the method is","piece of runnable software in any sense. Subclasses of this","results of the application accessible by executing the :class:`Application` type","decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method","auto() def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application`","method is called, when the :class:`Application` is not in the","it. Every :class:`Application` runs through a different app states (instances","for finishing until this value (in seconds) runs out. After","def start(self): \"\"\" Start the application run and set its","float, optional If this parameter is specified, the :class:`Application` only","creation until its termination: Directly after its instantiation the app","*CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING","is exceeded a :class:`TimeoutError` is raised and the application is","Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate","in {instance.get_app_state()} state, \" f\"but {app_state} state is required\" )","the application reaches the *FINISHED* state. Calling the :func:`cancel()` method","f\"The application expired its timeout \" f\"({timeout:.1f} s)\" ) else:","class specify the respective kind of software and the way","executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even","`app_state`. Parameters ---------- app_state : AppState The required app state.","instantiation the app is in the *CREATED* state. In this","\"\"\" Start the application run and set its state to","the *RUNNING* or *FINISHED* state. If the application is *FINISHED*","{app_state} state is required\" ) return func(*args, **kwargs) return wrapper","AppState The required app state. Examples -------- Raises :class:`AppStateError` when","JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A","is distributed # under the 3-Clause BSD License. Please see","other Python code can be executed, while the application runs","called in the *RUNNING* state: This will constantly check :func:`is_finished()`","AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED)","Calling the :func:`cancel()` method while the application is *RUNNING* or","state. Returns ------- app_state : AppState The current app state.","AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated. \"\"\"","a :class:`TimeoutError` is raised and the application is cancelled. Raises","pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application has","required app state. Examples -------- Raises :class:`AppStateError` when `function` is","joining process happens immediately, if otherwise the application is *RUNNING*,",": AppState The required app state. Examples -------- Raises :class:`AppStateError`",":func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results.","Every :class:`Application` runs through a different app states (instances of","... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): #",":class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application`","is called in an unsuitable app state, an :class:`AppStateError` is","bool True of the application has finished, false otherwise \"\"\"","expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's","user can now call the :func:`join()` method, concluding the application","__all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc","auto class AppState(Flag): \"\"\" This enum type represents the app","the application accessible by executing the :class:`Application` type specific :func:`evaluate()`","def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start","raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\"","code is part of the Biotite package and is distributed","can only be done from the *RUNNING* or *FINISHED* state.","method while the application is *RUNNING* or *FINISHED* leaves the","Returns ------- app_state : AppState The current app state. \"\"\"","enum :class:`AppState`) from its creation until its termination: Directly after",":func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting.","the :class:`Application` type specific :func:`run()` method is called. When the","is called. The application run behaves like an additional thread:","of :func:`is_finished()` calls in the joining process. PROTECTED: Override when","its instantiation the app is in the *CREATED* state. In","class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated.","\"AppState\", \"requires_state\"] import abc import time from functools import wraps","for the application run. After the user calls the :func:`start()`","way of interacting with it. Every :class:`Application` runs through a","now call the :func:`join()` method, concluding the application in the","via the :class:`Application` type specific :func:`is_finished()` method. The user can","Directly after its instantiation the app is in the *CREATED*","while self.get_app_state() != AppState.FINISHED: if timeout is not None and","AppState(Flag): \"\"\" This enum type represents the app states of","checked via the :class:`Application` type specific :func:`is_finished()` method. The user","Returns ------- finished : bool True of the application has","= AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run","are no accessible results. If a method is called in","\"\"\" Commence the application run. Called in :func:`start()`. PROTECTED: Override","Do clean up work after the application terminates. PROTECTED: Optionally","code can be executed, while the application runs in the","@requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when",":class:`AppState`) from its creation until its termination: Directly after its","methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case","Python code can be executed, while the application runs in",":func:`clean_up()` method, too, but there are no accessible results. If","out. After this time is exceeded a :class:`TimeoutError` is raised","user calls the :func:`start()` method, the app state is set","= time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self,","runs out. After this time is exceeded a :class:`TimeoutError` is","the *JOINED* state and making the results of the application","pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was","and the :class:`Application` type specific :func:`run()` method is called. When","\"\"\" The time interval of :func:`is_finished()` calls in the joining","time interval of :func:`is_finished()` calls in the joining process. PROTECTED:","called, if :class:`Application` is not in one of the specified","return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\"",":class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app","self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state","requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses that","Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check","state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING","*RUNNING*. This can only be done from the *CREATED* state.","BSD License. Please see 'LICENSE.rst' for further # information. __name__","state. If the application is *FINISHED* the joining process happens","further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__","inheriting. Returns ------- finished : bool True of the application","be executed, while the application runs in the background. \"\"\"","\"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter","auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\"","If this parameter is specified, the :class:`Application` only waits for",":class:`Application` subclasses that raises an :class:`AppStateError` in case the method","a different app states (instances of enum :class:`AppState`) from its","= auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator","TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\" )","\"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the","the respective kind of software and the way of interacting","and will directly go into the *JOINED* state as soon","> timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout","type specific :func:`is_finished()` method. The user can now call the","required\" ) return func(*args, **kwargs) return wrapper return decorator class","type specific :func:`run()` method is called. When the application finishes","Start the application run and set its state to *RUNNING*.","is_finished(self): \"\"\" Check if the application has finished. PROTECTED: Override","of method is always 'self' instance = args[0] if not","*FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\"","self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time","not instance._state & app_state: raise AppStateError( f\"The application is in","@abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`.","*RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def","except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state","\"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout","reaches the *FINISHED* state. Calling the :func:`cancel()` method while the","abc import time from functools import wraps from enum import","= AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app","= auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state):","instance._state & app_state: raise AppStateError( f\"The application is in {instance.get_app_state()}","PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\"","auto() FINISHED = auto() JOINED = auto() CANCELLED = auto()","application when in *RUNNING* or *FINISHED* state. \"\"\" self._state =","like an additional thread: Between the call of :func:`start()` and","pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()`","of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\"","application run behaves like an additional thread: Between the call","this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can",":class:`Application` only waits for finishing until this value (in seconds)","time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state =","wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in the","Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting.","state, \" f\"but {app_state} state is required\" ) return func(*args,","finishes the AppState changes to *FINISHED*. This is checked via","calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self):","Flag, auto class AppState(Flag): \"\"\" This enum type represents the","application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class","results. If a method is called in an unsuitable app","app states of an application. \"\"\" CREATED = auto() RUNNING","the :func:`cancel()` method while the application is *RUNNING* or *FINISHED*","f\"but {app_state} state is required\" ) return func(*args, **kwargs) return","false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time","**kwargs): # First parameter of method is always 'self' instance","state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get","state, an :class:`AppStateError` is called. The application run behaves like","is set to *RUNNING* and the :class:`Application` type specific :func:`run()`","set for the application run. After the user calls the","import Flag, auto class AppState(Flag): \"\"\" This enum type represents","self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING","*RUNNING* and the :class:`Application` type specific :func:`run()` method is called.","its state to *JOINED*. This can only be done from","as the application reaches the *FINISHED* state. Calling the :func:`cancel()`","until its termination: Directly after its instantiation the app is","enum import Flag, auto class AppState(Flag): \"\"\" This enum type","of the Biotite package and is distributed # under the","except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up()","distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst'",":func:`start()` and :func:`join()` other Python code can be executed, while","by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this","application is *FINISHED*. Parameters ---------- timeout : float, optional If","@abc.abstractmethod def run(self): \"\"\" Commence the application run. Called in","that the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception):","import time from functools import wraps from enum import Flag,","parameter is specified, the :class:`Application` only waits for finishing until","if :class:`Application` is not in one of the specified states:","return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is","software and the way of interacting with it. Every :class:`Application`","constantly check :func:`is_finished()` and will directly go into the *JOINED*","the application is *RUNNING* or *FINISHED* leaves the application in","there are no accessible results. If a method is called","Check if the application has finished. PROTECTED: Override when inheriting.","This triggers the :func:`clean_up()` method, too, but there are no","a method is called in an unsuitable app state, an","Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\"","cancelled. Raises ------ TimeoutError If the joining process exceeds the","software in any sense. Subclasses of this abstract base class","finishing until this value (in seconds) runs out. After this","This will constantly check :func:`is_finished()` and will directly go into","the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the","\"\"\" This enum type represents the app states of an","process happens immediately, if otherwise the application is *RUNNING*, this","current app state. Returns ------- app_state : AppState The current","method. The user can now call the :func:`join()` method, concluding","executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes","time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None","'self' instance = args[0] if not instance._state & app_state: raise","application finishes the AppState changes to *FINISHED*. This is checked","application in the *JOINED* state and making the results of","the application run. Called in :func:`start()`. PROTECTED: Override when inheriting.","from functools import wraps from enum import Flag, auto class","the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters","inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the","method is called. When the application finishes the AppState changes","args[0] if not instance._state & app_state: raise AppStateError( f\"The application","AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the","the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while","# information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ =","PROTECTED: Override when inheriting. Returns ------- interval : float Time","method is called in an unsuitable app state, an :class:`AppStateError`","The user can now call the :func:`join()` method, concluding the","is called, when the :class:`Application` is not in the specified","This can only be done from the *RUNNING* or *FINISHED*","in the *JOINED* state and making the results of the","---------- timeout : float, optional If this parameter is specified,","application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\"","the application in the *CANCELLED* state. This triggers the :func:`clean_up()`","`timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout","@requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and set","Please see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\"","`function` is called, if :class:`Application` is not in one of","always 'self' instance = args[0] if not instance._state & app_state:","to *RUNNING*. This can only be done from the *CREATED*","Override when inheriting. Returns ------- interval : float Time (in","will constantly check :func:`is_finished()` and will directly go into the","work after the application terminates. PROTECTED: Optionally override when inheriting.","This enum type represents the app states of an application.","get_app_state(self): \"\"\" Get the current app state. Returns ------- app_state","if otherwise the application is *RUNNING*, this method waits until","its state to *RUNNING*. This can only be done from","in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application","\"\"\" Cancel the application when in *RUNNING* or *FINISHED* state.","specified, the :class:`Application` only waits for finishing until this value","was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the","triggers the :func:`clean_up()` method, too, but there are no accessible","and set its state to *RUNNING*. This can only be","is raised and the application is cancelled. Raises ------ TimeoutError","the results of the application accessible by executing the :class:`Application`","when `function` is called, if :class:`Application` is not in one","\"\"\" Indicate that the application lifecycle was violated. \"\"\" pass","is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError(","CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED","specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ...",":func:`start()` method, the app state is set to *RUNNING* and","when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if","waits until the application is *FINISHED*. Parameters ---------- timeout :","the application is *FINISHED*. Parameters ---------- timeout : float, optional","interacting with it. Every :class:`Application` runs through a different app","in any sense. Subclasses of this abstract base class specify","is called. When the application finishes the AppState changes to","the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be","method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method.","\"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING |","not in one of the specified states: >>> @requires_state(AppState.RUNNING |","leaves the application in the *CANCELLED* state. This triggers the","the :class:`Application` only waits for finishing until this value (in","class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\"","is in the *CREATED* state. In this state further parameters","| AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run","Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application`","try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise","exceeded a :class:`TimeoutError` is raised and the application is cancelled.","the app is in the *CREATED* state. In this state","\"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's version","represents the app states of an application. \"\"\" CREATED =","Biotite package and is distributed # under the 3-Clause BSD","@abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls","type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type","CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for methods","when the :class:`Application` is not in the specified :class:`AppState` `app_state`.","in the joining process. PROTECTED: Override when inheriting. Returns -------","process. PROTECTED: Override when inheriting. Returns ------- interval : float","This can only be done from the *CREATED* state. \"\"\"","case the method is called, when the :class:`Application` is not","called in an unsuitable app state, an :class:`AppStateError` is called.","package and is distributed # under the 3-Clause BSD License.","float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()`","to *JOINED*. This can only be done from the *RUNNING*","\"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools import","the application run and set its state to *JOINED*. This","application is *RUNNING*, this method waits until the application is","Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()`","be done from the *RUNNING* or *FINISHED* state. If the","state further parameters can be set for the application run.","be set for the application run. After the user calls","is a wrapper around an external piece of runnable software","state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state =","in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up()","this state further parameters can be set for the application","call of :func:`start()` and :func:`join()` other Python code can be","*FINISHED* state. If the application is *FINISHED* the joining process","the :func:`clean_up()` method, too, but there are no accessible results.","instance = args[0] if not instance._state & app_state: raise AppStateError(","auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for","the method is called, when the :class:`Application` is not in","------- app_state : AppState The current app state. \"\"\" if","application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate","The time interval of :func:`is_finished()` calls in the joining process.","from the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state","results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass","# under the 3-Clause BSD License. Please see 'LICENSE.rst' for","Subclasses of this abstract base class specify the respective kind","\"\"\" A decorator for methods of :class:`Application` subclasses that raises","# First parameter of method is always 'self' instance =","state. In this state further parameters can be set for","go into the *JOINED* state as soon as the application","behaves like an additional thread: Between the call of :func:`start()`","join(self, timeout=None): \"\"\" Conclude the application run and set its"],"string":"[\n \"the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self):\",\n \"AppState changes to *FINISHED*. This is checked via the :class:`Application`\",\n \"self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\"\",\n \"self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application\",\n \"*JOINED*. This can only be done from the *RUNNING* or\",\n \"an external piece of runnable software in any sense. Subclasses\",\n \"\\\"\\\"\\\" Check if the application has finished. PROTECTED: Override when\",\n \"= auto() FINISHED = auto() JOINED = auto() CANCELLED =\",\n \"the application has finished. PROTECTED: Override when inheriting. Returns -------\",\n \"for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in\",\n \"interval : float Time (in seconds) between calls of :func:`is_finished()`\",\n \"= AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING |\",\n \"otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval\",\n \"PROTECTED: Override when inheriting. Returns ------- finished : bool True\",\n \"[\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time\",\n \"self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f}\",\n \"AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func)\",\n \"wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This\",\n \"is *RUNNING*, this method waits until the application is *FINISHED*.\",\n \"or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self):\",\n \"Parameters ---------- timeout : float, optional If this parameter is\",\n \"enum type represents the app states of an application. \\\"\\\"\\\"\",\n \"terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception):\",\n \"the current app state. Returns ------- app_state : AppState The\",\n \"current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished():\",\n \"def is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED:\",\n \"timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise\",\n \"when inheriting. Returns ------- finished : bool True of the\",\n \":class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ----------\",\n \"finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The\",\n \"Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass class\",\n \"\\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools\",\n \"this abstract base class specify the respective kind of software\",\n \"raised and the application is cancelled. Raises ------ TimeoutError If\",\n \"def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED:\",\n \"AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state.\",\n \"self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence\",\n \"and :func:`join()` other Python code can be executed, while the\",\n \"the application run and set its state to *RUNNING*. This\",\n \"AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and\",\n \"has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\"\",\n \"raises an :class:`AppStateError` in case the method is called, when\",\n \"termination: Directly after its instantiation the app is in the\",\n \":class:`AppStateError` is called. The application run behaves like an additional\",\n \"state is required\\\" ) return func(*args, **kwargs) return wrapper return\",\n \"kind of software and the way of interacting with it.\",\n \"PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\"\",\n \"to *FINISHED*. This is checked via the :class:`Application` type specific\",\n \"'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__ =\",\n \"call the :func:`join()` method, concluding the application in the *JOINED*\",\n \"the joining process. PROTECTED: Override when inheriting. Returns ------- interval\",\n \"until this value (in seconds) runs out. After this time\",\n \"run and set its state to *RUNNING*. This can only\",\n \"is specified, the :class:`Application` only waits for finishing until this\",\n \"the application finishes the AppState changes to *FINISHED*. This is\",\n \"= AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the\",\n \"abstract base class specify the respective kind of software and\",\n \"that raises an :class:`AppStateError` in case the method is called,\",\n \"the AppState changes to *FINISHED*. This is checked via the\",\n \"Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an external\",\n \"def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses\",\n \"is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\"\",\n \"A decorator for methods of :class:`Application` subclasses that raises an\",\n \"when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED\",\n \"Returns ------- interval : float Time (in seconds) between calls\",\n \"method, too, but there are no accessible results. If a\",\n \"in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"the 3-Clause BSD License. Please see 'LICENSE.rst' for further #\",\n \"the application is *FINISHED* the joining process happens immediately, if\",\n \"*FINISHED* the joining process happens immediately, if otherwise the application\",\n \"Indicate that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception):\",\n \"is checked via the :class:`Application` type specific :func:`is_finished()` method. The\",\n \"no accessible results. If a method is called in an\",\n \"If the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval())\",\n \"finished. PROTECTED: Override when inheriting. Returns ------- finished : bool\",\n \"app state. Examples -------- Raises :class:`AppStateError` when `function` is called,\",\n \"if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return\",\n \"state to *JOINED*. This can only be done from the\",\n \"the joining process happens immediately, if otherwise the application is\",\n \"self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns\",\n \"Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def\",\n \"\\\"\\\"\\\" This class is a wrapper around an external piece\",\n \"Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod\",\n \":class:`AppStateError` in case the method is called, when the :class:`Application`\",\n \"be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time =\",\n \"run. After the user calls the :func:`start()` method, the app\",\n \") return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta):\",\n \"after its instantiation the app is in the *CREATED* state.\",\n \"*FINISHED*. Parameters ---------- timeout : float, optional If this parameter\",\n \"def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of\",\n \"time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED\",\n \"has finished. PROTECTED: Override when inheriting. Returns ------- finished :\",\n \"unsuitable app state, an :class:`AppStateError` is called. The application run\",\n \"decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError`\",\n \"app state. Returns ------- app_state : AppState The current app\",\n \"After this time is exceeded a :class:`TimeoutError` is raised and\",\n \"run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass\",\n \"to *RUNNING* and the :class:`Application` type specific :func:`run()` method is\",\n \"the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\"\",\n \"time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None):\",\n \"the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state\",\n \"wrapper(*args, **kwargs): # First parameter of method is always 'self'\",\n \"function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs):\",\n \"parameters can be set for the application run. After the\",\n \"into the *JOINED* state as soon as the application reaches\",\n \"in case the method is called, when the :class:`Application` is\",\n \"method is always 'self' instance = args[0] if not instance._state\",\n \"is called, if :class:`Application` is not in one of the\",\n \"when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up\",\n \"RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED\",\n \"else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self):\",\n \":class:`TimeoutError` is raised and the application is cancelled. Raises ------\",\n \"the way of interacting with it. Every :class:`Application` runs through\",\n \"time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired its\",\n \"see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__\",\n \"in the *RUNNING* state: This will constantly check :func:`is_finished()` and\",\n \"not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state :\",\n \"the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state =\",\n \"in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too,\",\n \"different app states (instances of enum :class:`AppState`) from its creation\",\n \"of the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod\",\n \"__name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\",\",\n \"this parameter is specified, the :class:`Application` only waits for finishing\",\n \"Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not\",\n \"---------- app_state : AppState The required app state. Examples --------\",\n \"AppState The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING:\",\n \"be called in the *RUNNING* state: This will constantly check\",\n \"app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state\",\n \"is always 'self' instance = args[0] if not instance._state &\",\n \"parameter of method is always 'self' instance = args[0] if\",\n \"the :func:`start()` method, the app state is set to *RUNNING*\",\n \"\\\"\\\"\\\" Conclude the application run and set its state to\",\n \"def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args,\",\n \"self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"app state, an :class:`AppStateError` is called. The application run behaves\",\n \"its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try:\",\n \"executed, while the application runs in the background. \\\"\\\"\\\" def\",\n \"application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval())\",\n \"AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and\",\n \"Raises ------ TimeoutError If the joining process exceeds the `timeout`\",\n \"else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state\",\n \"Commence the application run. Called in :func:`start()`. PROTECTED: Override when\",\n \"AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application\",\n \"even be called in the *RUNNING* state: This will constantly\",\n \"application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is\",\n \"return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run.\",\n \"& app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state,\",\n \"*RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state.\",\n \"\\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not\",\n \"timeout : float, optional If this parameter is specified, the\",\n \"of :func:`start()` and :func:`join()` other Python code can be executed,\",\n \"When the application finishes the AppState changes to *FINISHED*. This\",\n \"is required\\\" ) return func(*args, **kwargs) return wrapper return decorator\",\n \"the app state is set to *RUNNING* and the :class:`Application`\",\n \"type represents the app states of an application. \\\"\\\"\\\" CREATED\",\n \"specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific\",\n \"of :class:`Application` subclasses that raises an :class:`AppStateError` in case the\",\n \"an :class:`AppStateError` is called. The application run behaves like an\",\n \": AppState The current app state. \\\"\\\"\\\" if self._state ==\",\n \"__init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the\",\n \"if timeout is not None and time.time()-self._start_time > timeout: self.cancel()\",\n \"the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def\",\n \"After the user calls the :func:`start()` method, the app state\",\n \"the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but\",\n \"inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work\",\n \"\\\"\\\"\\\" Indicate that the application's version is invalid. \\\"\\\"\\\" pass\",\n \"application is *FINISHED* the joining process happens immediately, if otherwise\",\n \"its creation until its termination: Directly after its instantiation the\",\n \"------- interval : float Time (in seconds) between calls of\",\n \"the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The\",\n \"First parameter of method is always 'self' instance = args[0]\",\n \"inheriting. Returns ------- interval : float Time (in seconds) between\",\n \":func:`is_finished()` and will directly go into the *JOINED* state as\",\n \"states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass\",\n \"interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override\",\n \"of enum :class:`AppState`) from its creation until its termination: Directly\",\n \"from the *RUNNING* or *FINISHED* state. If the application is\",\n \"app_state : AppState The current app state. \\\"\\\"\\\" if self._state\",\n \"FINISHED = auto() JOINED = auto() CANCELLED = auto() def\",\n \"Parameters ---------- app_state : AppState The required app state. Examples\",\n \"3-Clause BSD License. Please see 'LICENSE.rst' for further # information.\",\n \"the *RUNNING* state: This will constantly check :func:`is_finished()` and will\",\n \"around an external piece of runnable software in any sense.\",\n \"external piece of runnable software in any sense. Subclasses of\",\n \"timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\"\",\n \"def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in\",\n \"= [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import\",\n \":func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves\",\n \"an additional thread: Between the call of :func:`start()` and :func:`join()`\",\n \"additional thread: Between the call of :func:`start()` and :func:`join()` other\",\n \"self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application\",\n \"information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\",\",\n \"The required app state. Examples -------- Raises :class:`AppStateError` when `function`\",\n \"or *FINISHED* leaves the application in the *CANCELLED* state. This\",\n \"import abc import time from functools import wraps from enum\",\n \"calls in the joining process. PROTECTED: Override when inheriting. Returns\",\n \"run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED:\",\n \"state. Examples -------- Raises :class:`AppStateError` when `function` is called, if\",\n \"raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but\",\n \"can even be called in the *RUNNING* state: This will\",\n \"def wrapper(*args, **kwargs): # First parameter of method is always\",\n \"from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum\",\n \"specify the respective kind of software and the way of\",\n \"VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is invalid. \\\"\\\"\\\"\",\n \"sense. Subclasses of this abstract base class specify the respective\",\n \"This class is a wrapper around an external piece of\",\n \"state to *RUNNING*. This can only be done from the\",\n \"@abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has finished.\",\n \"of an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto()\",\n \"while the application is *RUNNING* or *FINISHED* leaves the application\",\n \"*RUNNING* state: This will constantly check :func:`is_finished()` and will directly\",\n \"\\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of\",\n \"application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that\",\n \"expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval())\",\n \"making the results of the application accessible by executing the\",\n \"def run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`.\",\n \":func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate\",\n \"of runnable software in any sense. Subclasses of this abstract\",\n \"Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean\",\n \"{instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" ) return\",\n \"In this state further parameters can be set for the\",\n \":func:`join()` method, concluding the application in the *JOINED* state and\",\n \"this value (in seconds) runs out. After this time is\",\n \"the application is cancelled. Raises ------ TimeoutError If the joining\",\n \"and is distributed # under the 3-Clause BSD License. Please\",\n \": float, optional If this parameter is specified, the :class:`Application`\",\n \":class:`Application` type specific :func:`run()` method is called. When the application\",\n \"self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\"\",\n \"Override when inheriting. Returns ------- finished : bool True of\",\n \"when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"application runs in the background. \\\"\\\"\\\" def __init__(self): self._state =\",\n \"not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The\",\n \"the application in the *JOINED* state and making the results\",\n \"AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state}\",\n \"method. :func:`join()` can even be called in the *RUNNING* state:\",\n \"runs in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED\",\n \"AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state =\",\n \":func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\"\",\n \"of this abstract base class specify the respective kind of\",\n \"joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state()\",\n \"concluding the application in the *JOINED* state and making the\",\n \"runnable software in any sense. Subclasses of this abstract base\",\n \"\\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\",\",\n \"application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED =\",\n \"clean up work after the application terminates. PROTECTED: Optionally override\",\n \"violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's\",\n \"AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING*\",\n \"if not instance._state & app_state: raise AppStateError( f\\\"The application is\",\n \"soon as the application reaches the *FINISHED* state. Calling the\",\n \"wrapper around an external piece of runnable software in any\",\n \"application reaches the *FINISHED* state. Calling the :func:`cancel()` method while\",\n \"its termination: Directly after its instantiation the app is in\",\n \"raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED\",\n \"\\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED\",\n \"class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is invalid.\",\n \"any sense. Subclasses of this abstract base class specify the\",\n \"seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod\",\n \"of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def\",\n \"state and making the results of the application accessible by\",\n \"immediately, if otherwise the application is *RUNNING*, this method waits\",\n \"Between the call of :func:`start()` and :func:`join()` other Python code\",\n \"done from the *RUNNING* or *FINISHED* state. If the application\",\n \"base class specify the respective kind of software and the\",\n \"TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass\",\n \"finished : bool True of the application has finished, false\",\n \"in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState\",\n \"after the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\"\",\n \"class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an\",\n \"is *FINISHED*. Parameters ---------- timeout : float, optional If this\",\n \"app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\"\",\n \"lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that\",\n \"run behaves like an additional thread: Between the call of\",\n \"runs through a different app states (instances of enum :class:`AppState`)\",\n \"set to *RUNNING* and the :class:`Application` type specific :func:`run()` method\",\n \"through a different app states (instances of enum :class:`AppState`) from\",\n \"the application runs in the background. \\\"\\\"\\\" def __init__(self): self._state\",\n \"until the application is *FINISHED*. Parameters ---------- timeout : float,\",\n \"self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current\",\n \"of interacting with it. Every :class:`Application` runs through a different\",\n \"\\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import\",\n \"the app states of an application. \\\"\\\"\\\" CREATED = auto()\",\n \"time from functools import wraps from enum import Flag, auto\",\n \"changes to *FINISHED*. This is checked via the :class:`Application` type\",\n \"the application is *RUNNING*, this method waits until the application\",\n \"__author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\",\",\n \"when inheriting. Returns ------- interval : float Time (in seconds)\",\n \"but there are no accessible results. If a method is\",\n \"and set its state to *JOINED*. This can only be\",\n \":func:`join()` can even be called in the *RUNNING* state: This\",\n \"the user calls the :func:`start()` method, the app state is\",\n \"clean_up(self): \\\"\\\"\\\" Do clean up work after the application terminates.\",\n \"called. The application run behaves like an additional thread: Between\",\n \": bool True of the application has finished, false otherwise\",\n \"called. When the application finishes the AppState changes to *FINISHED*.\",\n \"can only be done from the *CREATED* state. \\\"\\\"\\\" self.run()\",\n \"functools import wraps from enum import Flag, auto class AppState(Flag):\",\n \":class:`Application` type specific :func:`is_finished()` method. The user can now call\",\n \"The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if\",\n \"application has finished. PROTECTED: Override when inheriting. Returns ------- finished\",\n \"AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout:\",\n \"in an unsuitable app state, an :class:`AppStateError` is called. The\",\n \"if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self):\",\n \"state: This will constantly check :func:`is_finished()` and will directly go\",\n \"specific :func:`clean_up()` method. :func:`join()` can even be called in the\",\n \"application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self):\",\n \":func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()`\",\n \":class:`Application` runs through a different app states (instances of enum\",\n \"that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\"\",\n \"accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore\",\n \"value (in seconds) runs out. After this time is exceeded\",\n \"decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around\",\n \"timeout=None): \\\"\\\"\\\" Conclude the application run and set its state\",\n \"will directly go into the *JOINED* state as soon as\",\n \"seconds) runs out. After this time is exceeded a :class:`TimeoutError`\",\n \"set its state to *JOINED*. This can only be done\",\n \"auto() RUNNING = auto() FINISHED = auto() JOINED = auto()\",\n \"*FINISHED* state. Calling the :func:`cancel()` method while the application is\",\n \"between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"only be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time\",\n \"== AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod\",\n \":func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self):\",\n \"source code is part of the Biotite package and is\",\n \"state. This triggers the :func:`clean_up()` method, too, but there are\",\n \"specific :func:`run()` method is called. When the application finishes the\",\n \"True of the application has finished, false otherwise \\\"\\\"\\\" pass\",\n \"Get the current app state. Returns ------- app_state : AppState\",\n \"\\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle\",\n \"called, when the :class:`Application` is not in the specified :class:`AppState`\",\n \"the application run. After the user calls the :func:`start()` method,\",\n \":func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING*\",\n \"in the *CREATED* state. In this state further parameters can\",\n \":class:`Application` is not in one of the specified states: >>>\",\n \"def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or\",\n \": float Time (in seconds) between calls of :func:`is_finished()` in\",\n \"timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"License. Please see 'LICENSE.rst' for further # information. __name__ =\",\n \"s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise\",\n \"in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED)\",\n \"while the application runs in the background. \\\"\\\"\\\" def __init__(self):\",\n \"\\\"\\\"\\\" Do clean up work after the application terminates. PROTECTED:\",\n \"= AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude\",\n \"only be done from the *RUNNING* or *FINISHED* state. If\",\n \"start(self): \\\"\\\"\\\" Start the application run and set its state\",\n \"*RUNNING* or *FINISHED* state. If the application is *FINISHED* the\",\n \"\\\"requires_state\\\"] import abc import time from functools import wraps from\",\n \"process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() !=\",\n \"This source code is part of the Biotite package and\",\n \"= auto() RUNNING = auto() FINISHED = auto() JOINED =\",\n \"further parameters can be set for the application run. After\",\n \"is *FINISHED* the joining process happens immediately, if otherwise the\",\n \"set its state to *RUNNING*. This can only be done\",\n \"the *JOINED* state as soon as the application reaches the\",\n \"timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate()\",\n \"def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set\",\n \"*FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()`\",\n \"in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self):\",\n \"under the 3-Clause BSD License. Please see 'LICENSE.rst' for further\",\n \"application run. After the user calls the :func:`start()` method, the\",\n \"only waits for finishing until this value (in seconds) runs\",\n \"TimeoutError If the joining process exceeds the `timeout` value. \\\"\\\"\\\"\",\n \"*FINISHED* leaves the application in the *CANCELLED* state. This triggers\",\n \"the *FINISHED* state. Calling the :func:`cancel()` method while the application\",\n \"class AppState(Flag): \\\"\\\"\\\" This enum type represents the app states\",\n \"app is in the *CREATED* state. In this state further\",\n \"and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired\",\n \"inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application\",\n \"specific :func:`is_finished()` method. The user can now call the :func:`join()`\",\n \"waits for finishing until this value (in seconds) runs out.\",\n \"in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"thread: Between the call of :func:`start()` and :func:`join()` other Python\",\n \"Conclude the application run and set its state to *JOINED*.\",\n \"\\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from\",\n \"| AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func):\",\n \"= auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of\",\n \"happens immediately, if otherwise the application is *RUNNING*, this method\",\n \"(instances of enum :class:`AppState`) from its creation until its termination:\",\n \":func:`join()` other Python code can be executed, while the application\",\n \"*JOINED* state and making the results of the application accessible\",\n \"import wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\"\",\n \"-------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is\",\n \"application in the *CANCELLED* state. This triggers the :func:`clean_up()` method,\",\n \"exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED:\",\n \"self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else:\",\n \"respective kind of software and the way of interacting with\",\n \"app state is set to *RUNNING* and the :class:`Application` type\",\n \"otherwise the application is *RUNNING*, this method waits until the\",\n \"evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override\",\n \"up work after the application terminates. PROTECTED: Optionally override when\",\n \"\\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass class\",\n \"Cancel the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\"\",\n \"class is a wrapper around an external piece of runnable\",\n \"cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED*\",\n \"!= AppState.FINISHED: if timeout is not None and time.time()-self._start_time >\",\n \"the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass\",\n \"app_state : AppState The required app state. Examples -------- Raises\",\n \"is part of the Biotite package and is distributed #\",\n \"is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state\",\n \"func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This\",\n \"If a method is called in an unsuitable app state,\",\n \"and the way of interacting with it. Every :class:`Application` runs\",\n \"calls the :func:`start()` method, the app state is set to\",\n \"states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING =\",\n \"application accessible by executing the :class:`Application` type specific :func:`evaluate()` method.\",\n \">>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\"\",\n \"part of the Biotite package and is distributed # under\",\n \"pass def clean_up(self): \\\"\\\"\\\" Do clean up work after the\",\n \"the :func:`join()` method, concluding the application in the *JOINED* state\",\n \"an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED\",\n \"check :func:`is_finished()` and will directly go into the *JOINED* state\",\n \"as soon as the application reaches the *FINISHED* state. Calling\",\n \"(in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass\",\n \"joining process. PROTECTED: Override when inheriting. Returns ------- interval :\",\n \"with it. Every :class:`Application` runs through a different app states\",\n \":func:`run()` method is called. When the application finishes the AppState\",\n \"If the application is *FINISHED* the joining process happens immediately,\",\n \"the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if\",\n \"wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a\",\n \"from its creation until its termination: Directly after its instantiation\",\n \"AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the\",\n \"override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def\",\n \"\\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\"\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application\",\n \"or *FINISHED* state. If the application is *FINISHED* the joining\",\n \"\\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work after\",\n \"can be set for the application run. After the user\",\n \"method, the app state is set to *RUNNING* and the\",\n \"self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\"\",\n \"states (instances of enum :class:`AppState`) from its creation until its\",\n \"state as soon as the application reaches the *FINISHED* state.\",\n \"AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def\",\n \"= \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\",\",\n \"optional If this parameter is specified, the :class:`Application` only waits\",\n \"method waits until the application is *FINISHED*. Parameters ---------- timeout\",\n \"pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First\",\n \"... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def\",\n \"a wrapper around an external piece of runnable software in\",\n \") else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except:\",\n \"specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required\",\n \"\\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when\",\n \"application run and set its state to *JOINED*. This can\",\n \"application is *RUNNING* or *FINISHED* leaves the application in the\",\n \"f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state\",\n \"*CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there\",\n \"application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called\",\n \"the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"type specific :func:`clean_up()` method. :func:`join()` can even be called in\",\n \"of the application accessible by executing the :class:`Application` type specific\",\n \"@wraps(func) def wrapper(*args, **kwargs): # First parameter of method is\",\n \"is not in one of the specified states: >>> @requires_state(AppState.RUNNING\",\n \"pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is\",\n \"too, but there are no accessible results. If a method\",\n \"value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is\",\n \"directly go into the *JOINED* state as soon as the\",\n \"def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns -------\",\n \"state is set to *RUNNING* and the :class:`Application` type specific\",\n \"if the application has finished. PROTECTED: Override when inheriting. Returns\",\n \"this method waits until the application is *FINISHED*. Parameters ----------\",\n \"done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time()\",\n \"is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED*\",\n \"\\\" f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs)\",\n \"for further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\"\",\n \"the call of :func:`start()` and :func:`join()` other Python code can\",\n \"*RUNNING*, this method waits until the application is *FINISHED*. Parameters\",\n \"def clean_up(self): \\\"\\\"\\\" Do clean up work after the application\",\n \"accessible results. If a method is called in an unsuitable\",\n \"one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ...\",\n \"= \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"]\",\n \"(in seconds) runs out. After this time is exceeded a\",\n \"------ TimeoutError If the joining process exceeds the `timeout` value.\",\n \"f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError:\",\n \"# This source code is part of the Biotite package\",\n \"the :class:`Application` type specific :func:`is_finished()` method. The user can now\",\n \"self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called\",\n \":class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called\",\n \"and making the results of the application accessible by executing\",\n \"*JOINED* state as soon as the application reaches the *FINISHED*\",\n \"run and set its state to *JOINED*. This can only\",\n \"and the application is cancelled. Raises ------ TimeoutError If the\",\n \"an unsuitable app state, an :class:`AppStateError` is called. The application\",\n \"None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application\",\n \"the *CREATED* state. In this state further parameters can be\",\n \"time is exceeded a :class:`TimeoutError` is raised and the application\",\n \"*CREATED* state. In this state further parameters can be set\",\n \"return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper\",\n \":class:`AppStateError` when `function` is called, if :class:`Application` is not in\",\n \"pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired.\",\n \"PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do\",\n \"pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in\",\n \"| AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in\",\n \"\\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED = auto()\",\n \"This is checked via the :class:`Application` type specific :func:`is_finished()` method.\",\n \"= AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel\",\n \"can be executed, while the application runs in the background.\",\n \"application is cancelled. Raises ------ TimeoutError If the joining process\",\n \"\\\"\\\"\\\" Get the current app state. Returns ------- app_state :\",\n \"------- finished : bool True of the application has finished,\",\n \"an :class:`AppStateError` in case the method is called, when the\",\n \"raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def\",\n \"can now call the :func:`join()` method, concluding the application in\",\n \"of software and the way of interacting with it. Every\",\n \"this time is exceeded a :class:`TimeoutError` is raised and the\",\n \"app states (instances of enum :class:`AppState`) from its creation until\",\n \"method, concluding the application in the *JOINED* state and making\",\n \"= args[0] if not instance._state & app_state: raise AppStateError( f\\\"The\",\n \"self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def\",\n \"application run and set its state to *RUNNING*. This can\",\n \"background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self):\",\n \"state. Calling the :func:`cancel()` method while the application is *RUNNING*\",\n \"**kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application\",\n \"the Biotite package and is distributed # under the 3-Clause\",\n \"The application run behaves like an additional thread: Between the\",\n \":func:`is_finished()` method. The user can now call the :func:`join()` method,\",\n \"is cancelled. Raises ------ TimeoutError If the joining process exceeds\",\n \"the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate\",\n \"subclasses that raises an :class:`AppStateError` in case the method is\",\n \"piece of runnable software in any sense. Subclasses of this\",\n \"results of the application accessible by executing the :class:`Application` type\",\n \"decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method\",\n \"auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application`\",\n \"method is called, when the :class:`Application` is not in the\",\n \"it. Every :class:`Application` runs through a different app states (instances\",\n \"for finishing until this value (in seconds) runs out. After\",\n \"def start(self): \\\"\\\"\\\" Start the application run and set its\",\n \"float, optional If this parameter is specified, the :class:`Application` only\",\n \"creation until its termination: Directly after its instantiation the app\",\n \"*CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING\",\n \"is exceeded a :class:`TimeoutError` is raised and the application is\",\n \"Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate\",\n \"in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" )\",\n \"the application reaches the *FINISHED* state. Calling the :func:`cancel()` method\",\n \"f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else:\",\n \"class specify the respective kind of software and the way\",\n \"executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even\",\n \"`app_state`. Parameters ---------- app_state : AppState The required app state.\",\n \"instantiation the app is in the *CREATED* state. In this\",\n \"\\\"\\\"\\\" Start the application run and set its state to\",\n \"the *RUNNING* or *FINISHED* state. If the application is *FINISHED*\",\n \"{app_state} state is required\\\" ) return func(*args, **kwargs) return wrapper\",\n \"AppState The required app state. Examples -------- Raises :class:`AppStateError` when\",\n \"JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A\",\n \"is distributed # under the 3-Clause BSD License. Please see\",\n \"other Python code can be executed, while the application runs\",\n \"called in the *RUNNING* state: This will constantly check :func:`is_finished()`\",\n \"AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"Calling the :func:`cancel()` method while the application is *RUNNING* or\",\n \"state. Returns ------- app_state : AppState The current app state.\",\n \"AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\"\",\n \"a :class:`TimeoutError` is raised and the application is cancelled. Raises\",\n \"pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has\",\n \"required app state. Examples -------- Raises :class:`AppStateError` when `function` is\",\n \"joining process happens immediately, if otherwise the application is *RUNNING*,\",\n \": AppState The required app state. Examples -------- Raises :class:`AppStateError`\",\n \":func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results.\",\n \"Every :class:`Application` runs through a different app states (instances of\",\n \"... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): #\",\n \":class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application`\",\n \"is called in an unsuitable app state, an :class:`AppStateError` is\",\n \"bool True of the application has finished, false otherwise \\\"\\\"\\\"\",\n \"expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's\",\n \"user can now call the :func:`join()` method, concluding the application\",\n \"__all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc\",\n \"auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the app\",\n \"the application accessible by executing the :class:`Application` type specific :func:`evaluate()`\",\n \"def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start\",\n \"raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\"\",\n \"code is part of the Biotite package and is distributed\",\n \"can only be done from the *RUNNING* or *FINISHED* state.\",\n \"method while the application is *RUNNING* or *FINISHED* leaves the\",\n \"Returns ------- app_state : AppState The current app state. \\\"\\\"\\\"\",\n \"enum :class:`AppState`) from its creation until its termination: Directly after\",\n \":func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting.\",\n \"the :class:`Application` type specific :func:`run()` method is called. When the\",\n \"is called. The application run behaves like an additional thread:\",\n \"of :func:`is_finished()` calls in the joining process. PROTECTED: Override when\",\n \"its instantiation the app is in the *CREATED* state. In\",\n \"class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated.\",\n \"\\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import wraps\",\n \"for the application run. After the user calls the :func:`start()`\",\n \"way of interacting with it. Every :class:`Application` runs through a\",\n \"now call the :func:`join()` method, concluding the application in the\",\n \"via the :class:`Application` type specific :func:`is_finished()` method. The user can\",\n \"Directly after its instantiation the app is in the *CREATED*\",\n \"while self.get_app_state() != AppState.FINISHED: if timeout is not None and\",\n \"AppState(Flag): \\\"\\\"\\\" This enum type represents the app states of\",\n \"checked via the :class:`Application` type specific :func:`is_finished()` method. The user\",\n \"Returns ------- finished : bool True of the application has\",\n \"= AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run\",\n \"are no accessible results. If a method is called in\",\n \"\\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED: Override\",\n \"Do clean up work after the application terminates. PROTECTED: Optionally\",\n \"code can be executed, while the application runs in the\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when\",\n \":class:`AppState`) from its creation until its termination: Directly after its\",\n \"methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case\",\n \"Python code can be executed, while the application runs in\",\n \":func:`clean_up()` method, too, but there are no accessible results. If\",\n \"out. After this time is exceeded a :class:`TimeoutError` is raised\",\n \"user calls the :func:`start()` method, the app state is set\",\n \"= time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self,\",\n \"runs out. After this time is exceeded a :class:`TimeoutError` is\",\n \"the *JOINED* state and making the results of the application\",\n \"pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was\",\n \"and the :class:`Application` type specific :func:`run()` method is called. When\",\n \"\\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the joining\",\n \"time interval of :func:`is_finished()` calls in the joining process. PROTECTED:\",\n \"called, if :class:`Application` is not in one of the specified\",\n \"return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\"\",\n \":class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app\",\n \"self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state\",\n \"requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that\",\n \"Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check\",\n \"state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING\",\n \"*RUNNING*. This can only be done from the *CREATED* state.\",\n \"BSD License. Please see 'LICENSE.rst' for further # information. __name__\",\n \"state. If the application is *FINISHED* the joining process happens\",\n \"further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__\",\n \"inheriting. Returns ------- finished : bool True of the application\",\n \"be executed, while the application runs in the background. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter\",\n \"auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\"\",\n \"If this parameter is specified, the :class:`Application` only waits for\",\n \":class:`Application` subclasses that raises an :class:`AppStateError` in case the method\",\n \"a different app states (instances of enum :class:`AppState`) from its\",\n \"= auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator\",\n \"TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" )\",\n \"\\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the\",\n \"the respective kind of software and the way of interacting\",\n \"and will directly go into the *JOINED* state as soon\",\n \"> timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout\",\n \"type specific :func:`is_finished()` method. The user can now call the\",\n \"required\\\" ) return func(*args, **kwargs) return wrapper return decorator class\",\n \"type specific :func:`run()` method is called. When the application finishes\",\n \"Start the application run and set its state to *RUNNING*.\",\n \"is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED: Override\",\n \"of method is always 'self' instance = args[0] if not\",\n \"*FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\"\",\n \"self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time\",\n \"not instance._state & app_state: raise AppStateError( f\\\"The application is in\",\n \"@abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`.\",\n \"*RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def\",\n \"except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state\",\n \"\\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout\",\n \"reaches the *FINISHED* state. Calling the :func:`cancel()` method while the\",\n \"abc import time from functools import wraps from enum import\",\n \"= AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app\",\n \"= auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state):\",\n \"instance._state & app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()}\",\n \"PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\"\",\n \"auto() FINISHED = auto() JOINED = auto() CANCELLED = auto()\",\n \"application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state =\",\n \"like an additional thread: Between the call of :func:`start()` and\",\n \"pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()`\",\n \"of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\"\",\n \"application run behaves like an additional thread: Between the call\",\n \"this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can\",\n \":class:`Application` only waits for finishing until this value (in seconds)\",\n \"time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state =\",\n \"wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the\",\n \"Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting.\",\n \"state, \\\" f\\\"but {app_state} state is required\\\" ) return func(*args,\",\n \"finishes the AppState changes to *FINISHED*. This is checked via\",\n \"calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self):\",\n \"Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the\",\n \"application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class\",\n \"results. If a method is called in an unsuitable app\",\n \"app states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING\",\n \"the :func:`cancel()` method while the application is *RUNNING* or *FINISHED*\",\n \"f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs) return\",\n \"false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time\",\n \"**kwargs): # First parameter of method is always 'self' instance\",\n \"state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get\",\n \"state, an :class:`AppStateError` is called. The application run behaves like\",\n \"is set to *RUNNING* and the :class:`Application` type specific :func:`run()`\",\n \"set for the application run. After the user calls the\",\n \"import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents\",\n \"self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING\",\n \"*RUNNING* and the :class:`Application` type specific :func:`run()` method is called.\",\n \"its state to *JOINED*. This can only be done from\",\n \"as the application reaches the *FINISHED* state. Calling the :func:`cancel()`\",\n \"until its termination: Directly after its instantiation the app is\",\n \"enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type\",\n \"of the Biotite package and is distributed # under the\",\n \"except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up()\",\n \"distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst'\",\n \":func:`start()` and :func:`join()` other Python code can be executed, while\",\n \"by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this\",\n \"application is *FINISHED*. Parameters ---------- timeout : float, optional If\",\n \"@abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called in\",\n \"that the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception):\",\n \"import time from functools import wraps from enum import Flag,\",\n \"parameter is specified, the :class:`Application` only waits for finishing until\",\n \"if :class:`Application` is not in one of the specified states:\",\n \"return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is\",\n \"software and the way of interacting with it. Every :class:`Application`\",\n \"constantly check :func:`is_finished()` and will directly go into the *JOINED*\",\n \"the application is *RUNNING* or *FINISHED* leaves the application in\",\n \"there are no accessible results. If a method is called\",\n \"Check if the application has finished. PROTECTED: Override when inheriting.\",\n \"This triggers the :func:`clean_up()` method, too, but there are no\",\n \"a method is called in an unsuitable app state, an\",\n \"Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\"\",\n \"cancelled. Raises ------ TimeoutError If the joining process exceeds the\",\n \"software in any sense. Subclasses of this abstract base class\",\n \"finishing until this value (in seconds) runs out. After this\",\n \"This will constantly check :func:`is_finished()` and will directly go into\",\n \"the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the\",\n \"\\\"\\\"\\\" This enum type represents the app states of an\",\n \"process happens immediately, if otherwise the application is *RUNNING*, this\",\n \"current app state. Returns ------- app_state : AppState The current\",\n \"method. The user can now call the :func:`join()` method, concluding\",\n \"executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes\",\n \"time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None\",\n \"'self' instance = args[0] if not instance._state & app_state: raise\",\n \"application finishes the AppState changes to *FINISHED*. This is checked\",\n \"application in the *JOINED* state and making the results of\",\n \"the application run. Called in :func:`start()`. PROTECTED: Override when inheriting.\",\n \"from functools import wraps from enum import Flag, auto class\",\n \"the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters\",\n \"inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the\",\n \"method is called. When the application finishes the AppState changes\",\n \"args[0] if not instance._state & app_state: raise AppStateError( f\\\"The application\",\n \"AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the\",\n \"the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while\",\n \"# information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ =\",\n \"PROTECTED: Override when inheriting. Returns ------- interval : float Time\",\n \"method is called in an unsuitable app state, an :class:`AppStateError`\",\n \"The user can now call the :func:`join()` method, concluding the\",\n \"is called, when the :class:`Application` is not in the specified\",\n \"This can only be done from the *RUNNING* or *FINISHED*\",\n \"in the *JOINED* state and making the results of the\",\n \"---------- timeout : float, optional If this parameter is specified,\",\n \"application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\"\",\n \"the application in the *CANCELLED* state. This triggers the :func:`clean_up()`\",\n \"`timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout\",\n \"@requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and set\",\n \"Please see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\"\",\n \"`function` is called, if :class:`Application` is not in one of\",\n \"always 'self' instance = args[0] if not instance._state & app_state:\",\n \"to *RUNNING*. This can only be done from the *CREATED*\",\n \"Override when inheriting. Returns ------- interval : float Time (in\",\n \"will constantly check :func:`is_finished()` and will directly go into the\",\n \"work after the application terminates. PROTECTED: Optionally override when inheriting.\",\n \"This enum type represents the app states of an application.\",\n \"get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns ------- app_state\",\n \"if otherwise the application is *RUNNING*, this method waits until\",\n \"its state to *RUNNING*. This can only be done from\",\n \"in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application\",\n \"\\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED* state.\",\n \"specified, the :class:`Application` only waits for finishing until this value\",\n \"was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"triggers the :func:`clean_up()` method, too, but there are no accessible\",\n \"and set its state to *RUNNING*. This can only be\",\n \"is raised and the application is cancelled. Raises ------ TimeoutError\",\n \"the results of the application accessible by executing the :class:`Application`\",\n \"when `function` is called, if :class:`Application` is not in one\",\n \"\\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass\",\n \"is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError(\",\n \"CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED\",\n \"specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ...\",\n \":func:`start()` method, the app state is set to *RUNNING* and\",\n \"when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if\",\n \"waits until the application is *FINISHED*. Parameters ---------- timeout :\",\n \"the application is *FINISHED*. Parameters ---------- timeout : float, optional\",\n \"interacting with it. Every :class:`Application` runs through a different app\",\n \"in any sense. Subclasses of this abstract base class specify\",\n \"is called. When the application finishes the AppState changes to\",\n \"the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be\",\n \"method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method.\",\n \"\\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING |\",\n \"not in one of the specified states: >>> @requires_state(AppState.RUNNING |\",\n \"leaves the application in the *CANCELLED* state. This triggers the\",\n \"the :class:`Application` only waits for finishing until this value (in\",\n \"class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\"\",\n \"is in the *CREATED* state. In this state further parameters\",\n \"| AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run\",\n \"Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application`\",\n \"try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise\",\n \"exceeded a :class:`TimeoutError` is raised and the application is cancelled.\",\n \"the app is in the *CREATED* state. In this state\",\n \"\\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version\",\n \"represents the app states of an application. \\\"\\\"\\\" CREATED =\",\n \"Biotite package and is distributed # under the 3-Clause BSD\",\n \"@abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls\",\n \"type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type\",\n \"CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods\",\n \"when the :class:`Application` is not in the specified :class:`AppState` `app_state`.\",\n \"in the joining process. PROTECTED: Override when inheriting. Returns -------\",\n \"process. PROTECTED: Override when inheriting. Returns ------- interval : float\",\n \"This can only be done from the *CREATED* state. \\\"\\\"\\\"\",\n \"case the method is called, when the :class:`Application` is not\",\n \"called in an unsuitable app state, an :class:`AppStateError` is called.\",\n \"package and is distributed # under the 3-Clause BSD License.\",\n \"float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()`\",\n \"to *JOINED*. This can only be done from the *RUNNING*\",\n \"\\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import\",\n \"the application run and set its state to *JOINED*. This\",\n \"application is *RUNNING*, this method waits until the application is\",\n \"Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()`\",\n \"be done from the *RUNNING* or *FINISHED* state. If the\",\n \"state further parameters can be set for the application run.\",\n \"be set for the application run. After the user calls\",\n \"is a wrapper around an external piece of runnable software\",\n \"state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state =\",\n \"in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up()\",\n \"this state further parameters can be set for the application\",\n \"call of :func:`start()` and :func:`join()` other Python code can be\",\n \"*FINISHED* state. If the application is *FINISHED* the joining process\",\n \"the :func:`clean_up()` method, too, but there are no accessible results.\",\n \"instance = args[0] if not instance._state & app_state: raise AppStateError(\",\n \"auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for\",\n \"the method is called, when the :class:`Application` is not in\",\n \"------- app_state : AppState The current app state. \\\"\\\"\\\" if\",\n \"application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate\",\n \"The time interval of :func:`is_finished()` calls in the joining process.\",\n \"from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state\",\n \"results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass\",\n \"# under the 3-Clause BSD License. Please see 'LICENSE.rst' for\",\n \"Subclasses of this abstract base class specify the respective kind\",\n \"\\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that raises\",\n \"# First parameter of method is always 'self' instance =\",\n \"state. In this state further parameters can be set for\",\n \"go into the *JOINED* state as soon as the application\",\n \"behaves like an additional thread: Between the call of :func:`start()`\",\n \"join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set its\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":25,"numItemsPerPage":100,"numTotalItems":12866649,"offset":2500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NjM3OTE2NCwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMCIsImV4cCI6MTc3NjM4Mjc2NCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.ebvDamCnSX6gHvneltezoapkaqUtpkeXgowaIiTf0KuBr7T_QC5ZKUn5EpyRiGFCr5c2AOLuO-pExqKsOKzABg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":126831957853}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 82k |
|---|
[
"-*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EMAIL>' __version__ = '0.1.0'",
"utf-8 -*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EMAIL>' __version__ =",
"-*- coding: utf-8 -*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EMAIL>'",
"coding: utf-8 -*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EM... |
[
"path) def save_to_s3(self, bucket, access_key, secret_key, **kwargs): \"\"\"Save the backup",
"\"\"\"A new directory which is subsequently added to the backup\"\"\"",
"file %s\" % self._path) def include_directory(self, path, preserve_paths=False, name=None): \"\"\"Add",
"filename = filename.replace(director... |
[
"'邀请面试'), ('WJ', '婉拒')], default='DD', max_length=2, verbose_name='投递状态')), ], options={ 'verbose_name': '面试',",
"13:03 from django.db import migrations, models class Migration(migrations.Migration): initial =",
"'待定'), ('YQ', '邀请面试'), ('WJ', '婉拒')], default='DD', max_length=2, verbose_name='投递状态')), ], options... |
[
"hour, minute = int(time[11:13]), int(time[14:16]) return f\"{int(time[0:2])} {months[int(time[3:5])-1]} {time[6:10]} year",
"minute!=1 else ''}\" if __name__ == '__main__': print(date_time(\"01.01.2018 00:00\")) assert",
"15 minutes\" assert date_time(\"17.12.1990 07:42\") == \"17 December 1990 year",
"8 hou... |
[
"port as an integer.\"\"\" return self._port('ssl_port') @cachedproperty def tcp_port(self): \"\"\"Returns",
"in-place.\"\"\" try: tmp = Peer(self.host, features) except Exception: pass else:",
"pruning > 0: return pruning return None def _protocol_version_string(self, key):",
"A PARTICULAR PURPOSE AND # NONI... |
[
"[], }, \"events\": {}, \"alarm_systems\": {}, \"groups\": {}, \"lights\": {},",
"\"home_assistant\": {\"get_system_info\": \"fake data\"}, \"config_entry\": dict(config_entry.data), \"deconz_config\": DECONZ_CONFIG, \"websocket_state\": STATE_RUNNING,",
"unittest.mock import patch from pydeconz.websocket impor... |
[
"function `cell_list_fn(R, **kwargs)` that partitions positions, `R`, and side data",
"maximum number of neighbors (along with additional space specified by",
"axis=-2) cell_idx = cell_idx[..., jnp.newaxis, :, :] cell_idx = jnp.broadcast_to(cell_idx,",
"2.0 (the \"License\"); # you may not use this file",
"... |
[
"rhucrl_experiments.evaluate.utilities import ENVIRONMENTS RARL_DIR = \"../../runs/RARLAgent\" ZERO_SUM_DIR = \"../../runs/ZeroSumAgent\" SCRIPT",
"\"RHUCRL\", \"base-dir\": ZERO_SUM_DIR}, \"lazy\": {\"algorithm\": \"HUCRL\", \"base-dir\": RARL_DIR}, }.get(socket.gethostname(), {\"algorithm\":",
"RARL_DIR}, }.g... |
[
"TagKeyStatus class ProjectTagsEndpoint(ProjectEndpoint): def get(self, request, project): tag_keys = TagKey.objects.filter(",
") data = [] for tag_key in tag_keys: data.append({ 'id':",
"status=TagKeyStatus.VISIBLE, ) data = [] for tag_key in tag_keys: data.append({",
"six from rest_framework.response import... |
[
"try: async_eel.init('web') await async_eel.start('callbacks.html', size=(400, 300)) # Call Javascript function,",
"function, and pass explicit callback function await async_eel.js_random()(print_num) # Do",
"asyncio loop = asyncio.get_event_loop() @async_eel.expose async def py_random(): return random.random()... |
[
"is available. :param application_name: A short, alphanumeric name to identify",
"logging from datacube.config import LocalConfig from datacube.drivers import index_driver_by_name, index_drivers",
"%r. %s available: %s\" % ( driver_name, len(index_drivers()), ', '.join(index_drivers())",
"for indexing dataset... |
[
"specified\") optparser.print_usage() sys.exit(1) if opt.directory and opt.mbox: print(\"Can't specify both",
"{ 'list': opt.list }) r = curs.fetchall() if len(r) !=",
"reads it into the database. # import os import sys",
"lib.exception import IgnorableException from lib.log import log, opstatus from lib.varn... |
[
"models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('shop', '0008_auto_20200310_1134'),",
"from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies",
"-*- coding: utf-8 -*- # Generated by Django 1.... |
[
"sample_class = np.random.choice(np.arange(self.num_classes), p=self.progress_p) sample_indexes = self.class_dict[sample_class] index = random.choice(sample_indexes)",
"super(iNaturalist, self).__init__(mode, cfg, transform) random.seed(0) self.class_dict = self._get_class_dict() def __getitem__(self,",
"class ... |
[
"parsed_second) def create_fake_conference_nodes(n, conference): nodes = [] for i in",
"else '', conference.endpoint, ) res = self.app.post( api_url_for('meeting_hook'), { 'X-Mailgun-Sscore':",
"# Create conference nodes n_conference_nodes = 3 create_fake_conference_nodes( n_conference_nodes, conference,",
"n... |
[
"class SendHandler(Handler): filemode = \"w\" class Server(object): def __init__(self, handlers):",
"SendHandler(Handler): filemode = \"w\" class Server(object): def __init__(self, handlers): self.handlers",
"key in to_destroy: server = self.servers.pop(key) server.stop() def connection_key(self, connection):",... |
[
"binary char\") if currName == \"\": break name.append(currName) mmax.append( data[OnOffstart",
"#print(\"increment params\") numParameters = numParameters + 1 except: pass #print(\"Found",
"+ 1 except: pass #print(\"Found {} parameters.\".format(numParameters)) tD['Parameters'] = []",
"!= -1: fxName=\"\" OnO... |
[
"in range(ord('a'), ord('z') + 1)] upper_char = [chr(x) for x",
"x in range(ord('A'), ord('Z') + 1)] number_char = [chr(x) for",
"source += \"\".join(lower_char) source += \"\".join(upper_char) source += \"\".join(number_char) source",
"= [chr(x) for x in range(ord('0'), ord('9') + 1)] source",
"random sour... |
[
"from django.test import TestCase from rest_framework.test import APIRequestFactory from .models",
"TestCase from rest_framework.test import APIRequestFactory from .models import GBIC, GBICType",
"gbic_test = GBIC.objects.create( serial='showdomilhao', patrimony_number='777', gbic_type=gbic_type_test ) gbic_tes... |
[
"branch: \"FruitBranch\" = None): \"\"\"Adds a new branch to the",
"\"\\n\\t \".join(lines[1:]) return summary def copy(self) -> \"FruitBranch\": \"\"\"Returns a",
"# transform the dataset X_train_transformed = fruit.transform(X_train) X_test_tranformed = fruit.transform(X_test)",
"self.fork() self._branches[... |
[
"not os.path.isdir(path): os.mkdir(path) class ParseDict(argparse.Action): def __call__(self, parser, namespace, values,",
"= output[output.find('LATEST'):].split('\\\\n')[0].split(' ')[-1] return latest_version else: return False def _setup_rpi(vendor_id:",
"and systemd service file\\n\" f\"to your system to e... |
[
"['\"T\"', '\"5\"']} def trim_adapters(fastq_files, dirs, config): QUALITY_CUTOFF = 5 to_trim",
"trim_sequences += [str(Seq(sequence).reverse_complement()) for sequence in v] return trim_sequences def",
"reverse complement of the adapters for _, v in builtin_adapters.items():",
"\"standard\").lower() if quali... |
[
"= request.query_params.get('category') list_id = int(request.query_params.get('id')) parentConfig = AnalysisConfig.objects.get(project=project) jsonHelper =",
"get_object_or_404(projects, id=kwargs['project_id']) if (SolverProgress.objects.filter(project=project).exists()): progress = json.loads(get_object_or_40... |
[
"unicode_literals from .base import Opener from .errors import OpenerError from",
"'sitedata': appfs.SiteDataFS, 'siteconf': appfs.SiteConfigFS, 'usercache': appfs.UserCacheFS, 'userlog': appfs.UserLogFS } def",
"len(tokens) == 2: appname, author = tokens version = None",
"<appname>:<author> ' 'or <appname>:<... |
[
"'svm', 'auc': area, 'f_beta': fb, 'projection': 'PCA'}, ignore_index=True) # Fit",
"{}'.format(autoencoder.evaluate(x_test, x_test, batch_size=8, verbose=False))) print('Mean loss on attacks: {}'.format(autoencoder.evaluate(x_attacks, x_attacks,",
"tpr = supervised_evaluation(classifier, x_test, y_test) xg_boo... |
[
"msg=\"foo test\") self.assertEqual(2201, res[\"response_code\"]) def test_message_banned(self): res = send_message(self.banned, self.banned_token,",
"test\") self.assertEqual(403, res[\"response_code\"]) def test_message_provider(self): res = send_message(self.provider, self.provider_token, to=self.user,",
"cl... |
[
"for xyP in XY: Win.append(0) for xyE in zip(x,y): Dis",
"= CU.Polygon() P.Load(Wkt) # Unwrapping coordinates if Unwrap: x =",
"Unwrap=Unwrap) else: XY = P.CartGrid(Dx=Delta, Dy=Delta, Bounds=Box) Win = []",
"Dis = CU.WgsDistance(xyP[1], xyP[0], xyE[1], xyE[0]) Win[-1] += Window(Dis, Par)",
".Exploration as... |
[
"each of them for start_offset, length in terms_offsets: result =",
"the Automaton offsets and create Recognizer result for each of",
"lexicon :param text: text for recognition :param entities: supported entities",
"means prepositions are not allowed) is the default \"\"\" super().__init__(name=name,",
"sta... |
[
"Local::in_line { 'req': ( 'goto.cc', 24, 26 ), 'res': (",
"default;' ), 'range': has_entries( { 'start': has_entries( { 'line_num': 54,",
"def FixIt_Check_RawStringReplace_Resolved( results ): assert_that( results, has_entries( { 'fixits': contains(",
"( 'goto.cc', 36, 25 ), 'res': ( 'goto.cc', 32, 54",
"2... |
[
"#Elasticsearch settings self.elasticsearch_host = \"\" self.elasticsearch_verify_certs = False self.elasticsearch_index_name =",
"of the JSON file. \"\"\" with open(filepath, \"r\") as file:",
"\"\"\"Loads the config from a JSON file. Args: filepath: path",
"JSON file. Args: filepath: path of the JSON file. ... |
[
"Invert Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_invert = [C.Decode(), C.Resize(size=[224,",
"range(num_samples): mse[i] = diff_mse(images_p_invert[i], images_c_invert[i]) logger.info(\"MSE= {}\".format(str(np.mean(mse)))) if plot: visualize_list(images_c_invert,",
... |
[
"[(1, 0), (0, 1)]], [(4, 4), [(3, 4), (4, 3)]]]:",
"0), [(1, 0), (0, 1)]], [(4, 4), [(3, 4), (4,",
"1)]], [(4, 4), [(3, 4), (4, 3)]]]: self.assertListEqual( collector.get_next_positions(pos), expected",
"m, t, candies) for pos, expected in [[(1, 1), [(0,",
"1, 1, 1, 1], [2, 2, 1, 1, 1], [1,",
"(1, 0), (1,... |
[
"os.uname()[0].startswith(\"Darw\"): import pygame pygame.mixer.init() # Plays a song def playSong(filename):",
"\"play song\" if not os.uname()[0].startswith(\"Darw\"): pygame.mixer.music.fadeout(1000) #fadeout current music over",
"music over 1 sec. pygame.mixer.music.load(\"music/\" + filename) pygame.mixer.... |
[
"this is a failure self.assertTrue(False, err_msg) except Exception as e:",
"of `get_item`.\"\"\" self.handler.table.get_item.return_value = { \"TheWrongKey\": dict(field1=\"1\", field2=\"2\") } self.assertIsNone(self.handler.get_item(\"key\"))",
"mock.MagicMock() def setUp(self): self.handler.table.reset_mock(... |
[
"\"Amount\": [4, 1, 2, 2, 4, 5], \"City\": [\"SF\", \"SF\",",
"as pd app = Dash(__name__) server = app.server # assume",
"2, 4, 5], \"City\": [\"SF\", \"SF\", \"SF\", \"Montreal\", \"Montreal\", \"Montreal\"]",
"# see https://plotly.com/python/px-arguments/ for more options df = pd.DataFrame({ \"Fruit\":",
... |
[
"= self.formStash # ------------------- # do a quick parse... requirements_either_or",
"None le_pkey_jsons = None le_reg_jsons = None private_key_cycle_id = None",
"we use `jsonS` to indicate a string self.le_meta_jsons = getcreate_args[",
"tracked acme_account_provider_id = None account_key_pem = None le_met... |
[
"(NStreams == 1): #Creates list of filenames for Phi in",
"'Dirac'): vr = 0 vi = 0 if(k%151 == 1):",
"table to associate data to streams NSamplesIn128bits = int(128/NBitsData )",
"2.0 (the \"License\"); # you may not use this file",
"and SequenceType != 'Random' and SequenceType != 'Dirac'): print ('Unknown... |
[
"5 item1_price_total = item1_price * item1_quantity print(type(item1)) # str print(type(item1_price))",
"item1='phone' item1_price = 100 item1_quantity = 5 item1_price_total = item1_price",
"print(type(item1_price)) # int print(type(item1_quantity)) # int print(type(item1_price_total)) # int #",
"output: # <c... |
[
"<gh_stars>10-100 _base_ = \"./FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_01_02MasterChefCan.py\" OUTPUT_DIR = \"output/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/16_36WoodBlock\" DATASETS =... |
[
"import get_page, select_page, process_args from .results import serialize_bookmark, unserialize_bookmark, Page,",
"import serialize_bookmark, unserialize_bookmark, Page, Paging __all__ = [ 'OC', 'get_page',",
".paging import get_page, select_page, process_args from .results import serialize_bookmark, unseriali... |
[
"if pass_is_training else layer(net) endpoints['layer%d' % i] = net tf.add_to_collection(tf.GraphKeys.UPDATE_OPS,",
"padding=padding, activation=None, use_bias=True, # not self.config.batch_norm, name=os.path.join(self.variable_scope, 'layer%d' %i, layer_type),",
"= layers.LowRankLocallyConnected2D( filters=cho... |
[
"Adafruit Industries # SPDX-License-Identifier: MIT import time import board import",
"time.sleep(2) for i in range(cycles): print(icm.gyro) icm.gyro_data_rate_divisor = 255 #",
"# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT",
"busio.I2C(board.SCL, board.SDA) ic... |
[
"* (a-x-2) + '*' print(out.center(2*a-1)) print('*' * (2 * a",
"* a - 1)) for x in range(a-1): out =",
"* (2 * a - 1)) for x in range(a-1):",
"in range(a-1): out = '*' + ' ' * x",
"' ' * x + '*' + ' ' *",
"'*' + ' ' * x + '*' print(out.center(2*a-1)) a",
"1)) for x in range(a-1): out = '*' + '",
"prin... |
[
"raise ValueError( f\"expected to find 1 high_res_video_path from PathDetails; instead",
"Optional[int] timestamp: datetime.datetime camera_name: str is_image: bool is_lowres: bool class",
"low_res_video_paths[0] # in Go: # eventId := uuid.NewSHA1( # uuid.NameSpaceDNS,",
"= relate_path_details(some_path_detai... |
[
"2019-09-27 14:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies",
"model_name='student_assessment', name='assessment_score', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='student_assessment', name='understanding_level', field... |
[
"self def __next__(self): if self._idx >= len(self._data): raise StopIteration else:",
"self._data is None: raise Exception('Dataset is not loaded') def __iter__(self):",
"None _label_col = 'label' def __init__(self): self._idx = 0 if",
"else: return item[self._first_text_col], int(item[self._label_col]) def ... |
[
"True/False \"\"\" try: # pass the hashed password req =",
"req.reset_identity() # make a request now morphed_identity = req.get('http://ipecho.net/plain') #",
"the tor service Parameters ---------- arg1 [OPTIONAL]| text: bool A",
"import literal_eval from fp.fp import FreeProxy from torrequest import TorRequ... |
[
"c.endswith(' m') or c.endswith(' re'), 'path must start with a",
"height): \"\"\"adds an ellipse to the path\"\"\" self._curves(pdfgeom.bezierArc(x, y, x",
"not long, so we pack onto one line the code",
"\"\"\" def __init__(self,code=None): self._code = (code,[])[code is None] self._code_append =",
"an eff... |
[
"wikipedia import re import TCPclient as client WORDS = [\"WIKIPEDIA\",\"SEARCH\",\"INFORMATION\"]",
"as client WORDS = [\"WIKIPEDIA\",\"SEARCH\",\"INFORMATION\"] def handle(text,mic,profile): # SEARCH ON",
"to search about\") #text = mic.activeListen() print \"entering wiki term\"",
"= client.grab_input() wh... |
[
"all of the saturated \" , random.choice(plural) , \" be",
"in range(4): n = input(\"Enter noun : \") noun.append(n) plural",
"school cafeteria.\") print(\"The lunch special, prepared by our \" ,",
"be eliminated.\") print(\"He rated the cafeteria a \" + letter",
"of either a \" , random.choice(noun) , \" s... |
[
"numpy g = open('/home/srallaba/mgc/transposed/arctic_a0404.mgc','w') x = numpy.loadtxt('/home/srallaba/mgc_spaces/arctic_a0404.mgc') numpy.savetxt(g, numpy.transpose(x)) g.close()",
"import numpy g = open('/home/srallaba/mgc/transposed/arctic_a0404.mgc','w') x = numpy.loadtxt('/home/srallaba/mgc_spaces/arctic_a0... |
[
"from zone_api.core.devices.motion_sensor import MotionSensor from zone_api.core.actions.turn_on_switch import TurnOnSwitch from zone_api_test.core.device_test",
"8 INVALID_ITEM_NAME = 'invalid item name' class ZoneManagerTest(DeviceTest): \"\"\" Unit",
"Zone from zone_api.core.zone_event import ZoneEvent from ... |
[
"conn = get_redis_connection('cart') # 获取hash数据sku_id ,count sku_id_count = conn.hgetall('cart_%s' %user.id)",
"= self.kwargs['sku_id'] # 获取商品信息 orders = OrderGoods.objects.filter(sku_id=sku_id, is_commented = True)",
"decimal import Decimal from rest_framework.generics import CreateAPIView,ListAPIView from res... |
[
"other file output options.\") if args.output: if args.output == \"-\":",
"+ \"[{name}][{levelname}] {message}\", datefmt=\"%H:%M:%S\" + (\".%f\" if level == \"trace\"",
"= platform.platform() log.debug(f\"OS: {os_version}\") log.debug(f\"Python: {platform.python_version()}\") log.debug(f\"Streamlink: {streamli... |
[
"\"Fortune Summit\": { \"id\": \"B2\", \"division\": { \"TMSC\": { \"id\":",
"} } } } } } } } #Create and",
"+\"-\" +str(end_date.month)+\"-\" + str(end_date.year) if(autoThrashed): end_date = scan_date if( not",
"\"tmc-1\":{ \"id\":\"DEP1\", \"floor\":{\"0\":\"0\", \"1\":\"1\", \"2\":\"2\", \"3\":\"3\", \"4\... |
[
"[t.task_id for t in test_dag.tasks] self.assertListEqual(actual_task_ids, expected_task_ids) if __name__ ==",
"mock.Mock() self.build_impersonated_client_mock.start() self.mock_variable = mock.patch.object( variable, 'Variable', autospec=True).start() # `side_effect`",
"# # http://www.apache.org/licenses/LICEN... |
[
"Regional code setting is done automatically by DOS Webservices, so",
"\"differential_rate\", \"BIDPOSTS:BT_REST_RELAXATION_POINT_TEXT\": strip_extra_spaces(\"rest_relaxation_point\"), \"BIDPOSTS:BT_DANGER_PAY_NUM\": \"danger_pay\", \"BIDPOSTS:BT_CONSUMABLE_ALLOWANCE_FLG\": parse_boolean(\"has_consumable_allowanc... |
[
"str(uuid.uuid4()) + '.test') with recfile.open(filename, \"w\") as g: g.write(teststring) self.assertEqual(recfile.open(filename,",
"teststring = 'test%s' % k filename = os.path.join('tests', str(uuid.uuid4()), str(uuid.uuid4())",
"% k filename = os.path.join('tests', str(uuid.uuid4()), str(uuid.uuid4()) + '.t... |
[
"dotenv import load_dotenv from subprocess import check_output, Popen, PIPE load_dotenv()",
"BUCKET_NAME = os.environ.get('BUCKET_NAME') def get_lambda_functions(): function_dict = {} res =",
"lambda_functions: function_name = lambda_function['FunctionName'] subprocess.run([ \"aws\", \"lambda\", \"update-functi... |
[
"0 ) ) gbSizer1.Add( self.m_button20, wx.GBPosition( 2, 0 ), wx.GBSpan(",
"self, event ): event.Skip() def onButton41Click( self, event ): event.Skip()",
"1 ), wx.ALL, 5 ) self.m_button01 = wx.Button( self, wx.ID_ANY,",
"wx.Size( 50,50 ), 0 ) self.m_button11.SetBackgroundColour( wx.Colour( 255, 0, 0",
"gbSi... |
[
"task. :copyright: (c) 2016-2020 by dry-python team. :license: BSD, see",
"LICENSE for more details. \"\"\" from _dependencies.contrib.celery import shared_task from",
"\"\"\" from _dependencies.contrib.celery import shared_task from _dependencies.contrib.celery import task __all__",
"\"\"\" dependencies.cont... |
[
"f, g: lambda *a, **kw: g(f(*a, **kw)), funcs) else: raise",
"(w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image = new_image # flip",
"*= val x[x>1] = 1 x[x<0] = 0 image_data =",
"val) if rand()<.5 else 1/rand(1, val) x = rgb_to_hsv(np.array(image)/255.) x[...,",
"max(0, int(max_bbox[1] - random.u... |
[
"pathlib logging.basicConfig(level=logging.INFO) # Dirs ROOT_DIR = pathlib.Path(__file__).parent.absolute() DUMP_DIR = ROOT_DIR",
"logging.basicConfig(level=logging.INFO) # Dirs ROOT_DIR = pathlib.Path(__file__).parent.absolute() DUMP_DIR = ROOT_DIR /",
"import pathlib logging.basicConfig(level=logging.INFO) # ... |
[
"week. The results expose the current and previous rankings as",
"abbreviation, 'name': name, 'rank': int(rank), 'week': int(week), 'date': date, 'previous':",
"weekly_rankings @property def current_extended(self): \"\"\" Returns a ``list`` of ``dictionaries``",
"Each dictionary has the following structure:: ... |
[
"wrapper(*args, **kwargs): if ding.enable_hpc_rl: shape = shape_fn(args, kwargs) if is_cls_method:",
"... }, ... } ``` Besides, `per_fn_limit` means the max",
"'vtrace_error': ['hpc_rll.rl_utils.vtrace', 'VTrace'], } fn_str = fn_name_mapping[fn_name] cls = getattr(importlib.import_module(fn_str[0]),",
"is_cls... |
[
"the count of int(s) in passed array. def number_of_occurrences(s, xs):",
"#Return the count of int(s) in passed array. def number_of_occurrences(s,",
"count of int(s) in passed array. def number_of_occurrences(s, xs): return",
"of int(s) in passed array. def number_of_occurrences(s, xs): return xs.count(s)"
... |
[
"# https://github.com/telegramdesktop/tdesktop/blob/4bf66cb6e93f3965b40084771b595e93d0b11bcd/Telegram/SourceFiles/codegen/scheme/codegen_scheme.py#L57-L62 WHITELISTED_MISMATCHING_IDS = { # 0 represents any layer",
"fullname if '.' in fullname: self.namespace, self.name = fullname.split('.', maxsplit=1)",
"' ' +... |
[
"out == None: fail(\"either name or out must be set\")",
"name that is used to load the resource. The default",
"adds an action that compiles a single .resx file into",
"generate. src: The .resx source file that is transformed into",
"name of the output file (if name should not be",
"<reponame>purkhusid/r... |
[
"and intialized to a list. class Net(torch.nn.Linear): x: torch.jit.Final[int] def",
"that has __constants__ set to something that is not a",
"# Make the helper files in test/ importable pytorch_test_dir =",
"= os.path.dirname(os.path.dirname(os.path.realpath(__file__))) sys.path.append(pytorch_test_dir) if _... |
[
"import time import pybullet as p import pybullet_data import cv2",
"pybullet_data import cv2 if __name__ == \"__main__\": env = gym.make(\"pix_sample_arena-v0\")",
"as p import pybullet_data import cv2 if __name__ == \"__main__\":",
"__name__ == \"__main__\": env = gym.make(\"pix_sample_arena-v0\") x = 0 whi... |
[
"parse_time @pytest.mark.parametrize( \"time_input, expected\", [ (\"12:13\", time(hour=12, minute=13)), (\"12:13:14\", time(hour=12,",
") def test_manually_time_parsing(time_input: str, expected: time): assert __parse_time_manually(time_input) == expected,",
"\"manual time parsing has gone wrong\" @pytest.mark... |
[
"coefficients.coeff]) @generator def construct(mod): \"\"\"Implement this module for input 'x'.\"\"\"",
"for input 'x'.\"\"\" x = mod.x taps = list() for",
"-> (%y: i32) # CHECK: hw.constant 1 # CHECK: hw.constant",
"{coeff = [62, 42, 6]}, module_name = \"PolyComputeForCoeff_62_42_6\", unused_parameter =",
... |
[
"/usr/bin/env python import arc import sys import os def retrieve(uc,",
"for service in retriever]))) # Get all the ExecutionTargets on",
"we want to query the LDAP GLUE2 tree arc.Endpoint(\"piff.hep.lu.se\", arc.Endpoint.COMPUTINGINFO,",
"finish before we destroy the objects they may use import",
"discover... |
[
"tried to change private viewability of exploration %s ' 'but",
"if activity_rights is None: return False elif activity_rights.is_published(): return bool(",
"unpublish the activity. \"\"\" committer_id = committer.user_id activity_rights = _get_activity_rights(activity_type,",
"logging from constants import ... |
[
"# executed at runtime def cast(type_, value): # noqa return",
"be imported at runtime and used in that fashion -",
"used to remove the need for the types to be",
"to remove the need for the types to be accessible",
"TYPE_CHECKING = False # pragma: no cover # typing's cast",
"the False-y guard in a nicely... |
[
"== 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart,",
"if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert))",
"actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can ... |
[] |
[
"\"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error",
"SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\",",
"code_correction = SelectField('Error Correction'... |
[
"for a stock. l : float The 'lambda' parameter of",
"'lambda' parameter of the exponential moving average model. Making this",
"pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices,",
"adjusted closing ... |
[
"map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int,",
"1); res[0] = 0 prev = '$' for i in",
"and s[i] == 'C') & 1 prev = s[i] for",
"lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res =",
"'$' + sys.stdin.readline().rstrip() lr = zip(*[map(int... |
[
"trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size,",
"= [batch size, trg len, embed dim] for layer in",
"embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term",
"[batch size, src ... |
[
"salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__)",
"Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting",
"minions .. versionadded:: 2016.11.0 For documentation on setting up the",
"except NxosClientError as err:... |
[
"str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit,",
"None, mac: str = None, remote_custom_field: str = None, comment:",
"-> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=... |
[
"== 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim ==",
"def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim",
"from __future__ import print_function, division import numpy as np from",
"Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt =",
"self.di... |
[
"os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3 /opt/code/manage.py",
"= get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") ... |
[
"src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import",
"calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.... |
[
"space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event):",
"import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event",
"from dataclasses import dataclass from space_game.domain_names import KeyId from spa... |
[
"\"User: \" + message.get(\"user\") + \" Message: \" + message.get(\"body\")",
"\" + message.get(\"user\") + \" Message: \" + message.get(\"body\") try:",
"message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \" Message:",
"message): heading = message.get(\"subject\") final_message = ... |
[
"the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The",
"model after HPO. :param data: evaluation data, a pandas dataframe",
"seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale,",
"changepoint_prior_scale=No... |
[
"diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal",
"if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else:",
"= base_network(dim // 2, dim // 2, hidden_dim) self.s2 =",
"s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transf... |
[
"} fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan,",
"return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0",
"# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN",
"div expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr))",
"be # included in all copies or ... |
[
"((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP",
"Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP,",
"b; c ^= (b>>13) a -= b; a -= c;",
"(flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else:",
"- this list allows to keep scan information # upto",
... |
[
"b): return (a > b) - (a < b) def",
"visited[i] = True miggroup.append([i]) cnt += 1 for j in",
"= dist closetd = i return closetd def get_domains_on_2sides(target1, target2,",
"tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt +=",
"sort_by_strand(val): return val[0][0] def check_edge_i... |
[
"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law",
"adapt this script on your own question answering task. Pointers",
"# # Licensed under the Apache License, Version 2.0 (the",
"compliance with the License. # You may obtain a copy",
"an \"AS IS\" BASIS, # WITHOUT WARRANT... |
[
"{ 'description': ( 'This specifies whether to create the non-root",
"If True, a root volume is created during RAID configuration.",
"in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self,",
"is created during RAID c... |
[
"QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, )",
"for bootstrap parts of an extension. This should be imported",
"from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shut... |
[
"info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value =",
"in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [",
"[ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype)",
... |
[
"on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file",
"self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_,",
"nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu",
"self.shared_... |
[
"/ 255.0 h, w = img.shape psize = min(min(patchSize, h),",
"h_, start_x : start_x + w_] = sigma if saveResults:",
"isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input",
"Internal number of sub-image-patches. :param dirOut: Directory where to save",
"num_pch = num_H * num_W pch = np... |
[
"the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self):",
"AppState changes to *FINISHED*. This is checked via the :class:`Application`",
"self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\"... |