Loading src/tgreft/utils/data/data.json 0 → 100644 +58 −0 Original line number Diff line number Diff line { "parameters": { "q_min": 0.008, "q_max": 0.2, "step_size": 0.015, "FINAL_SEI_SLD": 6.13, "SIMULATE_ERRORS": true, "ERR": 0.15, "total_time": 50, "transition_time": 3, "transition_midpoint": 15 }, "materials": [ { "name": "Si", "rho": 2.07, "irho": 0, "thickness": 0, "roughness": 0 }, { "name": "THF", "rho": 6.13, "irho": 0, "thickness": 0, "roughness": 43.77 }, { "name": "Ti", "rho": -1.238, "irho": 0, "thickness": 52.91, "roughness": 12.7 }, { "name": "Cu", "rho": 6.446, "irho": 0, "thickness": 566.1, "roughness": 9.736 }, { "name": "material", "rho": -1.648, "irho": 0, "thickness": 21.73, "roughness": 18.22 }, { "name": "SEI", "rho": 4.581, "irho": 0, "thickness": 177.7, "roughness": 23.04 } ] } src/tgreft/utils/data/dataloader.py 0 → 100644 +71 −0 Original line number Diff line number Diff line from refl1d.names import * class DataLoader: def __init__(self, json_dict: dict): """Initialize the class. Parameters ---------- json_dict : json dictionary json dictionary of all parameters and materials to stack in a sample. """ self.json_dict = json_dict self.q_min = json_dict["parameters"]["q_min"] self.q_max = json_dict["parameters"]["q_max"] self.step_size = json_dict["parameters"]["step"] self.final_sei_sld = json_dict["parameters"]["FINAL_SEI_SLD"] self.simulate_errors = json_dict["parameters"]["SIMULATE_ERRORS"] self.err = json_dict["parameters"]["ERR"] self.total_time = json_dict["parameters"]["total_time"] self.transition_time = json_dict["parameters"]["transition_time"] self.transition_midpoint = json_dict["parameters"]["transition_midpoint"] self.sample = self.create_sample_from_json(json_dict["materials"]) def create_slab_chunk(self, material : dict) -> "Slab": """Return a slab chunk built from given configuration. Parameters ---------- material : dictionary Single material dictionary to turn into slab chunk. Returns ------- Slab The slab chunk instance. """ slab = Slab( material=SLD( name=material["name"], rho=material["rho"], irho=material["irho"], ), thickness=material["thickness"], interface=material["roughness"], ) return slab def create_sample_from_json(self, json_object : list) -> "Slab | Stack": """Return a slab instance built from given configuration. Parameters ---------- json_object : list The sample configuration dictionary list. Returns ------- Slab The slab instance. """ sample = self.create_slab_chunk(json_object[0]) for i in range(1, len(json_object)): sample = sample | self.create_slab_chunk(json_object[i]) return sample No newline at end of file src/tgreft/utils/data/r_curve_generator.py 0 → 100644 +210 −0 Original line number Diff line number Diff line import json import numpy as np import math from refl1d.names import * from dataloader import DataLoader class RCurveGenerator: def __init__(self, data : DataLoader): """Initialize the class. Parameters ---------- data : DataLoader object Object of parsed data to use with RCurveGenerator and related functions """ self.data = data self.probe, self.tr_probe = self.create_probes(data.q_min, data.q_max, res=data.step) self.expt = Experiment(probe=self.probe, sample=self.data.sample) self.r_final, self.r_init = self.get_states(self.expt, self.data.final_sei_sld, self.data.simulate_errors, self.data.err) self.tr_expt = Experiment(probe=self.tr_probe, sample=self.data.sample) self.tr_data, self.rho_data = self.generate_tNR(self.tr_expt, self.data.total_time, self.data.transition_time, self.data.transition_midpoint, self.data.final_sei_sld, self.data.simulate_errors, self.data.err) def create_probes(self, q_min : float, q_max : float, step_size : float, resolution : float = 0.028) -> tuple: """Returns two probes, one using the full data and one with a slice, in the form of a tuple. Parameters ---------- q_min : float minimum of the q range q_max : float maximum of the q range res : float step size between min and max Returns ------- tuple tuple containing both relevant probes """ q = np.arange(np.log(q_min), np.log(q_max), step_size) q = np.exp(q) dq = resolution*q # Time-resolved Q range q_tr = q[50:130] dq_tr = dq[50:130] probe = QProbe(q, dq, data=None) # If using data for fits, replace data by the data to fit: # probe = QProbe(q, dq, data=(data, errors)) tr_probe = QProbe(q_tr, dq_tr, data=None) return probe, tr_probe def get_states(self, expt, final_sei_sld, simulate_errors, err) -> tuple: """Returns a tuple containing both the initial and final r states. Parameters ---------- expt : Experiment Experiment object - full range final_sei_sld : float final sei sld contant simulate_errors : boolean boolean indicating whether you want to include error or not in simulation err : float error constant Returns ------- tuple Tuple of both r_final and r_init lists """ # Final state ################################################################## _, r_final = expt.reflectivity() if simulate_errors: r_final = np.random.normal(r_final, err*r_final) # Initial state ################################################################ expt.sample['SEI'].material.rho.value = final_sei_sld expt.update() _, r_init = expt.reflectivity() if simulate_errors: r_init = np.random.normal(r_init, err*r_init) return r_final, r_init def generate_tNR(self, tr_expt, total_time, transition_time, transition_midpoint, final_sei_sld, simulate_errors, err) -> tuple: """Return a tuple built containing tr_data and rho_data. Parameters ---------- tr_expt : Experiment Subset of experiment total_time : int transition_time : int transition_midpoint : int final_sei_sld : float simulate_errors : bool err : float Returns ------- tuple tuple of both tr_data and rho_data lists """ tr_data = [] rho_data = [] for i in range(total_time): # This is a simple ERF transition, but it could be any function! rho_sei = final_sei_sld - (final_sei_sld-4.581) * (1 + math.erf((i-transition_midpoint)/transition_time)) / 2 tr_expt.sample['SEI'].material.rho.value = rho_sei tr_expt.update() _, _r = tr_expt.reflectivity() if simulate_errors: _r = np.random.normal(_r, err*_r) rho_data.append(rho_sei) tr_data.append(_r) tr_data = np.asarray(tr_data) return tr_data, rho_data def get_r_final(self) -> list: """Returns a list from the RCurveGenerator object instance. Returns ------- list r_final list """ return self.r_final def get_r_init(self) -> list: """Returns a list from the object instance. Returns ------- list r_init list """ return self.r_init def get_tr_data(self) -> list: """Returns a list from the object instance. Returns ------- list tr_data list """ return self.tr_data def get_rho_data(self) -> list: """Returns a list from the object instance. Returns ------- list rho_data list """ return self.rho_data """ USE EXAMPLE if __name__ == "__main__": f = open("data.json","r") data = json.load(f) dataloader = DataLoader(data) rc = RCurveGenerator(dataloader) print(rc.get_r_init()) #gen = RCurveGenerator(0.008, 0.2) #print(gen.q) """ Loading
src/tgreft/utils/data/data.json 0 → 100644 +58 −0 Original line number Diff line number Diff line { "parameters": { "q_min": 0.008, "q_max": 0.2, "step_size": 0.015, "FINAL_SEI_SLD": 6.13, "SIMULATE_ERRORS": true, "ERR": 0.15, "total_time": 50, "transition_time": 3, "transition_midpoint": 15 }, "materials": [ { "name": "Si", "rho": 2.07, "irho": 0, "thickness": 0, "roughness": 0 }, { "name": "THF", "rho": 6.13, "irho": 0, "thickness": 0, "roughness": 43.77 }, { "name": "Ti", "rho": -1.238, "irho": 0, "thickness": 52.91, "roughness": 12.7 }, { "name": "Cu", "rho": 6.446, "irho": 0, "thickness": 566.1, "roughness": 9.736 }, { "name": "material", "rho": -1.648, "irho": 0, "thickness": 21.73, "roughness": 18.22 }, { "name": "SEI", "rho": 4.581, "irho": 0, "thickness": 177.7, "roughness": 23.04 } ] }
src/tgreft/utils/data/dataloader.py 0 → 100644 +71 −0 Original line number Diff line number Diff line from refl1d.names import * class DataLoader: def __init__(self, json_dict: dict): """Initialize the class. Parameters ---------- json_dict : json dictionary json dictionary of all parameters and materials to stack in a sample. """ self.json_dict = json_dict self.q_min = json_dict["parameters"]["q_min"] self.q_max = json_dict["parameters"]["q_max"] self.step_size = json_dict["parameters"]["step"] self.final_sei_sld = json_dict["parameters"]["FINAL_SEI_SLD"] self.simulate_errors = json_dict["parameters"]["SIMULATE_ERRORS"] self.err = json_dict["parameters"]["ERR"] self.total_time = json_dict["parameters"]["total_time"] self.transition_time = json_dict["parameters"]["transition_time"] self.transition_midpoint = json_dict["parameters"]["transition_midpoint"] self.sample = self.create_sample_from_json(json_dict["materials"]) def create_slab_chunk(self, material : dict) -> "Slab": """Return a slab chunk built from given configuration. Parameters ---------- material : dictionary Single material dictionary to turn into slab chunk. Returns ------- Slab The slab chunk instance. """ slab = Slab( material=SLD( name=material["name"], rho=material["rho"], irho=material["irho"], ), thickness=material["thickness"], interface=material["roughness"], ) return slab def create_sample_from_json(self, json_object : list) -> "Slab | Stack": """Return a slab instance built from given configuration. Parameters ---------- json_object : list The sample configuration dictionary list. Returns ------- Slab The slab instance. """ sample = self.create_slab_chunk(json_object[0]) for i in range(1, len(json_object)): sample = sample | self.create_slab_chunk(json_object[i]) return sample No newline at end of file
src/tgreft/utils/data/r_curve_generator.py 0 → 100644 +210 −0 Original line number Diff line number Diff line import json import numpy as np import math from refl1d.names import * from dataloader import DataLoader class RCurveGenerator: def __init__(self, data : DataLoader): """Initialize the class. Parameters ---------- data : DataLoader object Object of parsed data to use with RCurveGenerator and related functions """ self.data = data self.probe, self.tr_probe = self.create_probes(data.q_min, data.q_max, res=data.step) self.expt = Experiment(probe=self.probe, sample=self.data.sample) self.r_final, self.r_init = self.get_states(self.expt, self.data.final_sei_sld, self.data.simulate_errors, self.data.err) self.tr_expt = Experiment(probe=self.tr_probe, sample=self.data.sample) self.tr_data, self.rho_data = self.generate_tNR(self.tr_expt, self.data.total_time, self.data.transition_time, self.data.transition_midpoint, self.data.final_sei_sld, self.data.simulate_errors, self.data.err) def create_probes(self, q_min : float, q_max : float, step_size : float, resolution : float = 0.028) -> tuple: """Returns two probes, one using the full data and one with a slice, in the form of a tuple. Parameters ---------- q_min : float minimum of the q range q_max : float maximum of the q range res : float step size between min and max Returns ------- tuple tuple containing both relevant probes """ q = np.arange(np.log(q_min), np.log(q_max), step_size) q = np.exp(q) dq = resolution*q # Time-resolved Q range q_tr = q[50:130] dq_tr = dq[50:130] probe = QProbe(q, dq, data=None) # If using data for fits, replace data by the data to fit: # probe = QProbe(q, dq, data=(data, errors)) tr_probe = QProbe(q_tr, dq_tr, data=None) return probe, tr_probe def get_states(self, expt, final_sei_sld, simulate_errors, err) -> tuple: """Returns a tuple containing both the initial and final r states. Parameters ---------- expt : Experiment Experiment object - full range final_sei_sld : float final sei sld contant simulate_errors : boolean boolean indicating whether you want to include error or not in simulation err : float error constant Returns ------- tuple Tuple of both r_final and r_init lists """ # Final state ################################################################## _, r_final = expt.reflectivity() if simulate_errors: r_final = np.random.normal(r_final, err*r_final) # Initial state ################################################################ expt.sample['SEI'].material.rho.value = final_sei_sld expt.update() _, r_init = expt.reflectivity() if simulate_errors: r_init = np.random.normal(r_init, err*r_init) return r_final, r_init def generate_tNR(self, tr_expt, total_time, transition_time, transition_midpoint, final_sei_sld, simulate_errors, err) -> tuple: """Return a tuple built containing tr_data and rho_data. Parameters ---------- tr_expt : Experiment Subset of experiment total_time : int transition_time : int transition_midpoint : int final_sei_sld : float simulate_errors : bool err : float Returns ------- tuple tuple of both tr_data and rho_data lists """ tr_data = [] rho_data = [] for i in range(total_time): # This is a simple ERF transition, but it could be any function! rho_sei = final_sei_sld - (final_sei_sld-4.581) * (1 + math.erf((i-transition_midpoint)/transition_time)) / 2 tr_expt.sample['SEI'].material.rho.value = rho_sei tr_expt.update() _, _r = tr_expt.reflectivity() if simulate_errors: _r = np.random.normal(_r, err*_r) rho_data.append(rho_sei) tr_data.append(_r) tr_data = np.asarray(tr_data) return tr_data, rho_data def get_r_final(self) -> list: """Returns a list from the RCurveGenerator object instance. Returns ------- list r_final list """ return self.r_final def get_r_init(self) -> list: """Returns a list from the object instance. Returns ------- list r_init list """ return self.r_init def get_tr_data(self) -> list: """Returns a list from the object instance. Returns ------- list tr_data list """ return self.tr_data def get_rho_data(self) -> list: """Returns a list from the object instance. Returns ------- list rho_data list """ return self.rho_data """ USE EXAMPLE if __name__ == "__main__": f = open("data.json","r") data = json.load(f) dataloader = DataLoader(data) rc = RCurveGenerator(dataloader) print(rc.get_r_init()) #gen = RCurveGenerator(0.008, 0.2) #print(gen.q) """
