dut_hdev work (58a3a5f0) · Commits · DMT-development / DMT-core

DMT/Hdev/dut_hdev.py

+46 −36

Original line number	Diff line number	Diff line
		@@ -276,13 +276,14 @@ class DutHdev(DutTcad):
		# add frequency def
		if has_f_def:
		sub_sweep = f_def
		if sub_sweep.sweep_type == "CON":
		if sub_sweep.sweep_type == "CON" or sub_sweep.sweep_type == "LIST":
		bias_fun = "'TAB'"
		ac_info = {
		"port1": "'" + self.ac_ports[0] + "'",
		"port2": "'" + self.ac_ports[1] + "'",
		"sweep_type": bias_fun,
		"freq_val": sub_sweep.value_def[0],
		# "freq_val": sub_sweep.value_def[0],
		"freq_val": " ".join([f"{val:3.6e}" for val in sub_sweep.value_def]),
		}
		# add sweep definition to inp file
		bias_str = (
		@@ -509,7 +510,7 @@ class DutHdev(DutTcad):
		ac = False
		# read in the iv data
		pd.options.mode.chained_assignment = None # default='warn'
		if len(dfs_ac) > 1:
		if len(dfs_ac) > 0:
		# first we sort by n_op
		dfs_temp = []
		n = 0
		@@ -596,8 +597,8 @@ class DutHdev(DutTcad):
		except:
		continue
		df_inqu = self.data[key_inqu]
		qn[i_row] = np.trapz(df_inqu["N"], df_inqu["X"])
		qp[i_row] = np.trapz(df_inqu["P"], df_inqu["X"])
		qn[i_row] = np.trapezoid(df_inqu["N"], df_inqu["X"])
		qp[i_row] = np.trapezoid(df_inqu["P"], df_inqu["X"])

		df_iv["Q\|N"] = qn
		df_iv["Q\|P"] = qp
		@@ -609,6 +610,8 @@ class DutHdev(DutTcad):
		tau_b = np.ones(len(df_iv))
		tau_c = np.ones(len(df_iv))
		tau_bc = np.ones(len(df_iv))
		x_be = np.ones(len(df_iv))
		x_bc = np.ones(len(df_iv))

		try:
		for i_row, row in enumerate(df_iv.iterrows()):
		@@ -644,55 +647,59 @@ class DutHdev(DutTcad):
		)
		index_be = changes[np.argmin(np.abs(changes - junctions[0]))]
		index_bc = changes[np.argmin(np.abs(changes - junctions[1]))]
		xbe = x[index_be]
		xbc = x[index_bc]
		x_be[i_row] = x[index_be]
		x_bc[i_row] = x[index_bc]

		tau = np.zeros(len(x))
		taup = np.zeros(len(x))
		taun = np.zeros(len(x))
		taun2 = np.zeros(len(x))
		for j, x_ in enumerate(x):
		if x_ <= xbe:
		tau[j] += np.trapz(dp_dic[:j], x[:j])
		tau[j] += np.trapz(dn_dic[:j], x[:j]) - np.trapz(
		if x_ <= x_be[i_row]:
		tau[j] += np.trapezoid(dp_dic[:j], x[:j])
		tau[j] += np.trapezoid(dn_dic[:j], x[:j]) - np.trapezoid(
		dp_dic[:j], x[:j]
		)
		elif x_ <= xbc:
		tau[j] += np.trapz(dn_dic[:j], x[:j])
		elif x_ <= x_bc[i_row]:
		tau[j] += np.trapezoid(dn_dic[:j], x[:j])
		else:
		tau[j] += np.trapz(dp_dic[:j], x[:j])
		tau[j] += np.trapz(dn_dic[:j], x[:j]) - np.trapz(
		tau[j] += np.trapezoid(dp_dic[:j], x[:j])
		tau[j] += np.trapezoid(dn_dic[:j], x[:j]) - np.trapezoid(
		dp_dic[:j], x[:j]
		)

		taun[j] = np.trapz(dn_dic[:j], x[:j])
		taup[j] = np.trapz(dp_dic[:j], x[:j])
		taun2[j] = np.trapz(dn2_dic[:j], x[:j])
		taun[j] = np.trapezoid(dn_dic[:j], x[:j])
		taup[j] = np.trapezoid(dp_dic[:j], x[:j])
		taun2[j] = np.trapezoid(dn2_dic[:j], x[:j])

		tau = tau * constants.P_Q
		taun = taun * constants.P_Q
		taun2 = taun2 * constants.P_Q
		taup = taup * constants.P_Q

		tau_e[i_row] = np.trapz(dp_dic[:index_be], x[:index_be]) * constants.P_Q
		tau_e[i_row] = (
		np.trapezoid(dp_dic[:index_be], x[:index_be]) * constants.P_Q
		)
		tau_be[i_row] = (
		np.trapz(dn_dic[:index_be], x[:index_be]) * constants.P_Q
		np.trapezoid(dn_dic[:index_be], x[:index_be]) * constants.P_Q
		- tau_e[i_row]
		)
		tau_b[i_row] = (
		np.trapz(dn_dic[index_be:index_bc], x[index_be:index_bc])
		np.trapezoid(dn_dic[index_be:index_bc], x[index_be:index_bc])
		* constants.P_Q
		)
		tau_c[i_row] = np.trapz(dp_dic[index_bc:], x[index_bc:]) * constants.P_Q
		tau_c[i_row] = (
		np.trapezoid(dp_dic[index_bc:], x[index_bc:]) * constants.P_Q
		)
		tau_bc[i_row] = (
		np.trapz(dn_dic[index_bc:], x[index_bc:]) * constants.P_Q
		np.trapezoid(dn_dic[index_bc:], x[index_bc:]) * constants.P_Q
		- tau_c[i_row]
		)

		dm_dic = np.where(dn_dic < dp_dic, dn_dic, dn_dic)

		# for j in range(len(tau)):
		# tau[j] = constants.P_Q * np.trapz(dm_dic[:j], x[:j])
		# tau[j] = constants.P_Q * np.trapezoid(dm_dic[:j], x[:j])

		self.data[key_inqu][specifiers.TRANSIT_TIME] = tau
		self.data[key_inqu]["TAUP"] = taup
		@@ -705,6 +712,9 @@ class DutHdev(DutTcad):
		df_iv["tau_bc"] = tau_bc
		df_iv["tau_c"] = tau_c

		df_iv["x_be"] = x_be
		df_iv["x_bc"] = x_bc

		except Exception as err:
		print(err)
		# pass
		@@ -750,8 +760,8 @@ class DutHdev(DutTcad):
		# except:
		# continue
		# df_inqu = self.data[key_inqu]
		# qn[i_row] = np.trapz(df_inqu["N"], df_inqu["X"])
		# qp[i_row] = np.trapz(df_inqu["P"], df_inqu["X"])
		# qn[i_row] = np.trapezoid(df_inqu["N"], df_inqu["X"])
		# qp[i_row] = np.trapezoid(df_inqu["P"], df_inqu["X"])

		# df_iv["Q\|N"] = qn
		# df_iv["Q\|P"] = qp
		@@ -793,33 +803,33 @@ class DutHdev(DutTcad):
		# tau = np.zeros(len(x))
		# for j, x_ in enumerate(x):
		# if x_ <= xbe:
		# tau[j] += np.trapz(dp_dic[:j], x[:j])
		# tau[j] += np.trapz(dn_dic[:j], x[:j]) - np.trapz(dp_dic[:j], x[:j])
		# tau[j] += np.trapezoid(dp_dic[:j], x[:j])
		# tau[j] += np.trapezoid(dn_dic[:j], x[:j]) - np.trapezoid(dp_dic[:j], x[:j])
		# elif x_ <= xbc:
		# tau[j] += np.trapz(dn_dic[:j], x[:j])
		# tau[j] += np.trapezoid(dn_dic[:j], x[:j])
		# else:
		# tau[j] += np.trapz(dp_dic[:j], x[:j])
		# tau[j] += np.trapz(dn_dic[:j], x[:j]) - np.trapz(dp_dic[:j], x[:j])
		# tau[j] += np.trapezoid(dp_dic[:j], x[:j])
		# tau[j] += np.trapezoid(dn_dic[:j], x[:j]) - np.trapezoid(dp_dic[:j], x[:j])

		# tau = tau * constants.P_Q

		# tau_e[i_row] = np.trapz(dp_dic[:index_be], x[:index_be]) * constants.P_Q
		# tau_e[i_row] = np.trapezoid(dp_dic[:index_be], x[:index_be]) * constants.P_Q
		# tau_be[i_row] = (
		# np.trapz(dn_dic[:index_be], x[:index_be]) * constants.P_Q - tau_e[i_row]
		# np.trapezoid(dn_dic[:index_be], x[:index_be]) * constants.P_Q - tau_e[i_row]
		# )
		# tau_b[i_row] = (
		# np.trapz(dn_dic[index_be:index_bc], x[index_be:index_bc])
		# np.trapezoid(dn_dic[index_be:index_bc], x[index_be:index_bc])
		# * constants.P_Q
		# )
		# tau_c[i_row] = np.trapz(dp_dic[index_bc:], x[index_bc:]) * constants.P_Q
		# tau_c[i_row] = np.trapezoid(dp_dic[index_bc:], x[index_bc:]) * constants.P_Q
		# tau_bc[i_row] = (
		# np.trapz(dn_dic[index_bc:], x[index_bc:]) * constants.P_Q - tau_c[i_row]
		# np.trapezoid(dn_dic[index_bc:], x[index_bc:]) * constants.P_Q - tau_c[i_row]
		# )

		# dm_dic = np.where(dn_dic < dp_dic, dn_dic, dn_dic)

		# for j in range(len(tau)):
		# tau[j] = constants.P_Q * np.trapz(dm_dic[:j], x[:j])
		# tau[j] = constants.P_Q * np.trapezoid(dm_dic[:j], x[:j])

		# self.data[key_inqu][specifiers.TRANSIT_TIME] = tau

DMT/core/init.py

+8 −1

Original line number	Diff line number	Diff line
		@@ -88,7 +88,14 @@ from .plot_2yaxis import Plot2YAxis
		from .data_processor import is_iterable, flatten, strictly_increasing, DataProcessor

		from .data_frame import DataFrame
		from .sweep_def import SweepDef, SweepDefConst, SweepDefLinear, SweepDefLog, SweepDefSync
		from .sweep_def import (
		SweepDef,
		SweepDefConst,
		SweepDefLinear,
		SweepDefLog,
		SweepDefSync,
		SweepDefList,
		)
		from .sweep import Sweep, get_sweepdef
		from .database_manager import DatabaseManager
		from .data_reader import (

DMT/core/data_reader.py

+4 −2

Original line number	Diff line number	Diff line
		@@ -750,9 +750,11 @@ def save_elpa(fname, ELPA, cols, firstline):
		for ii in range(ELPA.shape[1]):
		for i in range(ELPA.shape[0]):
		if np.isnan(ELPA[i][ii]):
		myfile.write("{0:17.9e} ".format(0))
		myfile.write("0.000000000 ")
		elif np.isreal(ELPA[i][ii]):
		myfile.write(f"{np.real(ELPA[i][ii]):17.9e} ")
		else:
		myfile.write("{0:17.9e} ".format(ELPA[i][ii]))
		myfile.write(f"{ELPA[i][ii]:17.9e} ")

		myfile.write("\r\n")