"""
===================
Output files module
===================
Functions to store data in files
"""
from pathlib import Path
import numpy as np
import re
import copy
import math
import matplotlib.pyplot as plt
from . import auxiliar as aux
[docs]
def fom_to_json(path, fds1, fds2=None):
"""Stores the figures_of_merit to a Figure_of_merit.json.
By default it is generated with one fds, but exists the option
to include two fds, one for DD and other to MC matching Id's.
Parameters
----------
path: Path
Path to store the json file with the FoMs and statistics
fds1: MLFoMpyDataset
fds2: MLFoMpyDataset
"""
# If the folders are not in the desired format change [2:] for [0:] to have all the simulations folder id
id1 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds1.simulation_id]
fom1 = copy.deepcopy(fds1.figure_of_merit)
id2 = []
var1 = set(["_".join(x.split('_')[0:-1]) for x in id1])
if fds2:
id2 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds2.simulation_id]
var2 = set(["_".join(x.split('_')[0:-1]) for x in id2])
if len(var1) != 1 or len(var2) != 1 or var1 != var2:
aux.print_error(f'The DD and MC folders not correspond to the same variability parameters')
exit()
fom2 = copy.deepcopy(fds2.figure_of_merit)
ids = sorted(set(id1 + id2))
for id in ids:
if id not in id1:
for fom in fom1:
for method in fom1[fom]:
fom1[fom][method]['values'].insert(ids.index(id), np.nan)
fom1[fom][method]['is_anomalous'].insert(ids.index(id), True)
if fds2 and id not in id2:
for fom in fom2:
for method in fom2[fom]:
fom2[fom][method]['values'].insert(ids.index(id), np.nan)
fom2[fom][method]['is_anomalous'].insert(ids.index(id), True)
if fds2:
fom1.update(fom2)
fom = []
fom.append({
'device': '/'.join(str(fds1.device_path).split('/')[-2:]),
'id': ids,
'fom': fom1
})
aux.save_json(Path(path,f'fom_{str(var1)[2:-2]}.json'),fom)
[docs]
def ML_fom_to_json_ler(path, fds1, fds2=None, width=None, label=None, n=400):
"""Stores the figures_of_merit and variability ler profiles to a ml_maps.json.
By default it is generated with one fds, but exists the option
to include two fds, one for DD and other to MC matching Id's.
Parameters
----------
path: Path
Path to store the json file with the FoMs and statistics
fds1: MLFoMpyDataset
fds2: MLFoMpyDataset
width: float
Value of the width/2 to subtract to the left column and
add to the right column of the LER map: width=(Wch+2*Wox)/2
By default, if width is None, the value is extracted from the z4.out file
label: str
str of the device
n: int
Length required of the ler map, by default fixed to 400
"""
id1 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds1.simulation_id]
id2 = []
z4 = sorted(fds1.device_path.glob('**/z4.out'))[0]
if width is None:
try:
with open(z4,"r") as z4:
z4_read = z4.read()
width = float(re.findall(r"axis_width[ ]*length[ ]*max[ ]*:[ ]*([-\d+.e]*)",z4_read)[0])
except Exception as e:
aux.print_error(f'No z4.out file to read the width: {e}')
exit()
var1 = set(["_".join(x.split('_')[0:-1]) for x in id1])
if fds2:
id2 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds2.simulation_id]
var2 = set(["_".join(x.split('_')[0:-1]) for x in id2])
# if len(var1) != 1 or len(var2) != 1 or var1 != var2: # TODO: Comment for Pichel
# aux.print_error(f'The DD and MC folders not correspond to the same variability parameters')
# exit()
ids = sorted(set(id1 + id2))
ler_map = []
lista_channel = []
for i in ids:
temp_map = {}
if label:
temp_map['device'] = label
else:
try:
device = (str(fds1.device).split('/')[-4], str(fds1.device).split('/')[-3].split('_')[-1])
temp_map['device'] = '/'.join(device)
except:
aux.print_error(f'Device string can not be extracted from repository path and manual label has not been defined')
exit(1)
temp_map['id'] = i
data_temp = None
if i in id1:
profile_file = Path(fds1.dirs[id1.index(i)],'ler-profile.dat')
data_temp = np.loadtxt(profile_file, skiprows=10, unpack=True)
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds1.figure_of_merit[fom][method]['values'][id1.index(i)]
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds1.figure_of_merit[fom][method]['values'][id1.index(i)]
elif i not in id1:
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
if fds2 and i in id2: # TODO: For pichel
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
if fds2 and i in id2: # TODO: For pichel
profile_file = Path(fds2.dirs[id2.index(i)],'ler-profile.dat')
data_temp2 = np.loadtxt(profile_file, skiprows=10, unpack=True)
# if data_temp is not None and not (data_temp == data_temp2).all(): # (data_temp != data_temp2).any() # Comment if MC profile desired
# aux.print_error(f'For ID={i}, DD and MC profiles do not match')
# exit()
# data_temp = data_temp2 # TODO: LINE FOR FIX THE PROFILE TO THE MC
# Loop for the values of the foms
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds2.figure_of_merit[fom][method]['values'][id2.index(i)]
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds2.figure_of_merit[fom][method]['values'][id2.index(i)]
elif fds2 and i not in id2:
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
# Concatenating two ler columns
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
# Concatenating two ler columns
left, right = np.round(data_temp[0]-width,7), np.round(data_temp[1]+width,7)
t_ler_map = np.concatenate((left, right))
# Minimun area of channel
nw_channel_diameter = []
left_filter = left[1400:-1400] # Filtering to extract only the area in the channel under the gate
right_filter = right[1400:-1400] # Filtering to extract only the area in the channel under the gate
for point_index in range(len(left_filter)):
nw_channel_diameter.append((float(-left_filter[point_index])+float(right_filter[point_index])))
min_nw_channel_diameter = min(nw_channel_diameter)
temp_map['min_channel_diameter [nm]'] = round(min_nw_channel_diameter, 2)
# Formating the ler profiles
if len(t_ler_map) < 2*n:
t_ler_map = aux.complete_maps_with_zeros(t_ler_map,n)
elif len(t_ler_map) > 2*n:
step = math.ceil(len(t_ler_map)/(2*n))
t_ler_map = t_ler_map[0::step]
if len(t_ler_map) < 2*n:
t_ler_map = aux.complete_maps_with_zeros(t_ler_map,n)
temp_map['ler_profile [nm]'] = list(t_ler_map)
ler_map.append(temp_map)
aux.save_json(Path(path,f'ml_{str(var1)[2:-2]}.json'),ler_map)
[docs]
def ML_fom_to_json_mgg(path, fds1, fds2=None, label=None):
"""Stores the figures_of_merit and variability mgg profiles to a ml_maps.json.
By default it is generated with one fds, but exists the option
to include two fds, one for DD and other to MC matching Id's.
Parameters
----------
path: Path
Path to store the json file with the FoMs and statistics
fds1: MLFoMpyDataset
fds2: MLFoMpyDataset
"""
id1 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds1.simulation_id]
id2 = []
var1 = set(["_".join(x.split('_')[0:-1]) for x in id1])
if fds2:
id2 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds2.simulation_id]
var2 = set(["_".join(x.split('_')[0:-1]) for x in id2])
ids = sorted(set(id1 + id2))
mgg_map = []
for i in ids:
temp_map = {}
temp_map['id'] = i
data_temp = None
if i in id1:
profile_file = Path(fds1.dirs[id1.index(i)],'mgg-profile.dat')
data_temp = np.loadtxt(profile_file, skiprows=11, unpack=True)
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds1.figure_of_merit[fom][method]['values'][id1.index(i)]
elif i not in id1:
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
if fds2 and i in id2:
profile_file = Path(fds2.dirs[id2.index(i)],'mgg-profile.dat')
data_temp2 = np.loadtxt(profile_file, skiprows=10, unpack=True)
# data_temp = data_temp2 # TODO: LINE FOR FIX THE PROFILE TO THE MC
# Loop for the values of the foms
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds2.figure_of_merit[fom][method]['values'][id2.index(i)]
elif fds2 and i not in id2:
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
# Concatenating two mgg columns
t_mgg_map = np.hstack(data_temp).tolist()
temp_map['mean_wf [eV]'] = round(np.mean(t_mgg_map), 2)
temp_map['mgg_profile [eV]'] = t_mgg_map
mgg_map.append(temp_map)
aux.save_json(Path(path,f'ml_{str(var1)[2:-2]}.json'),mgg_map)
[docs]
def ML_fom_iv_to_json_mgg(path, fds1, fds2=None, label=None):
"""Stores the figures_of_merit, iv_curve and variability mgg profiles to a ml_maps.json.
By default it is generated with one fds, but exists the option
to include two fds, one for DD and other to MC matching Id's.
Parameters
----------
path: Path
Path to store the json file with the FoMs and statistics
fds1: MLFoMpyDataset
fds2: MLFoMpyDataset
method: str
- Extraction method used for vth, accepted values: ['SD','CC','LE']
- Extraction method used for ioff: ['VG']
- Extraction method used for ss: ['VGI']
- Extraction method used for ion_dd: ['VG']
- Extraction method used for ion_mc: ['VG']
"""
id1 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds1.simulation_id]
id2 = []
var1 = set(["_".join(x.split('_')[0:-1]) for x in id1])
if fds2:
id2 = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds2.simulation_id]
var2 = set(["_".join(x.split('_')[0:-1]) for x in id2])
ids = sorted(set(id1 + id2))
mgg_map = []
iv_data = []
for i in ids:
temp_map = {}
temp_map['id'] = i
v_gate, i_drain = aux.iv_curve_dd_filter(fds1, ids.index(i))
data_temp = None
if i in id1:
profile_file = Path(fds1.dirs[id1.index(i)],'mgg-profile.dat')
data_temp = np.loadtxt(profile_file, skiprows=11, unpack=True)
temp_map['iv_curve'] = {}
temp_map['iv_curve']['i_drain [A]'], temp_map['iv_curve']['v_gate [V]'] = list(i_drain), list(v_gate)
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds1.figure_of_merit[fom][method]['values'][id1.index(i)]
elif i not in id1:
temp_map[f'{fom}_{method} [{units}]'] = np.nan
temp_map['iv_curve']['i_drain [A]'], temp_map['iv_curve']['v_gate [V]'] = np.nan, np.nan
for fom in fds1.figure_of_merit:
for method in fds1.figure_of_merit[fom]:
units = fds1.figure_of_merit[fom][method]['units']
if fds2 and i in id2:
profile_file = Path(fds2.dirs[id2.index(i)],'mgg-profile.dat')
data_temp2 = np.loadtxt(profile_file, skiprows=10, unpack=True)
# data_temp = data_temp2 # TODO: LINE FOR FIX THE PROFILE TO THE MC
# Loop for the values of the foms
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = fds2.figure_of_merit[fom][method]['values'][id2.index(i)]
elif fds2 and i not in id2:
for fom in fds2.figure_of_merit:
for method in fds2.figure_of_merit[fom]:
units = fds2.figure_of_merit[fom][method]['units']
temp_map[f'{fom}_{method} [{units}]'] = np.nan
# Concatenating the mgg profile columns
t_mgg_map = np.hstack(data_temp).tolist()
temp_map['mean_wf [eV]'] = round(np.mean(t_mgg_map), 2)
temp_map['mgg_profile [eV]'] = t_mgg_map
mgg_map.append(temp_map)
aux.save_json(Path(path,f'ml_iv_fom_{str(var1)[2:-2]}.json'),mgg_map)
[docs]
def iv_fom_to_json(path, fds, fom=None, method=None):
"""Stores the figures_of_merit and iv_curves from drift diffussion to a iv_fom_method_variability.json
Parameters
----------
path: Path
Path to store the json file with the FoMs and statistics
fds: MLFoMpyDataset
fom: str
Figure of merit to plot, accepted values: ['vth','ioff','ss','ion_dd', 'ion_mc']
method: str
- Extraction method used for vth, accepted values: ['SD','CC','LE']
- Extraction method used for ioff: ['VG']
- Extraction method used for ss: ['VGI']
- Extraction method used for ion_dd: ['VG']
- Extraction method used for ion_mc: ['VG']
"""
# If the folders are not in the desired format change [2:] for [0:] to have all the simulations folder id
id = ["_".join(id.split('/')[-2].split('_')[2:]) for id in fds.simulation_id]
var = set(["_".join(x.split('_')[0:-1]) for x in id])
data = []
for i in id:
t_data = {}
t_data['device'], t_data['id'] = ('/').join(str(fds.device).split('/')[6:]), i
v_gate, i_drain = aux.iv_curve_dd_filter(fds, id.index(i))
if fom != None and method is None:
for method in fds.figure_of_merit[fom]:
units = fds.figure_of_merit[f'{fom}'][f'{method}']['units']
t_data[f'{fom}_{method} [{units}]'] = fds.figure_of_merit[f'{fom}'][f'{method}']['values'][id.index(i)]
elif fom == None:
aux.print_error('Please add the fom argument')
else:
units = fds.figure_of_merit[f'{fom}'][f'{method}']['units']
t_data[f'{fom}_{method} [{units}]'] = fds.figure_of_merit[f'{fom}'][f'{method}']['values'][id.index(i)]
t_data['iv_curve'] = {}
t_data['iv_curve']['i_drain [A]'], t_data['iv_curve']['v_gate [V]'] = list(i_drain), list(v_gate)
data.append(t_data)
if fom == None and method == None:
aux.save_json(Path(path,f'iv_fom_{str(var)[2:-2]}.json'),data)
else:
aux.save_json(Path(path,f'iv_{fom}_{method}_{str(var)[2:-2]}.json'),data)
# TODO: fom_to_csv
# TODO: fom_to_excel
