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General zt plot #1

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617a76e
Initial commit with formatted scripts and general ZT plot script
adamrhyshill Jan 21, 2025
90a0922
updated system path to placeholder variable
adamrhyshill Jan 21, 2025
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8 changes: 8 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -144,6 +144,14 @@ dmypy.json
# Cython debug symbols
cython_debug/

# debug folder
debug/

# debug outputs
*.png
*.pdf
*.csv

# PyCharm
# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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160 changes: 92 additions & 68 deletions example/Bi2SeO2-Expt-Comparison/compare_calc_expt.py
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Expand Up @@ -27,124 +27,148 @@

import numpy as np

import sys ; sys.path.append(r"/mnt/d/Repositories/ZT-Calc-Workflow")
import sys

sys.path.append(r"/mnt/d/Repositories/ZT-Calc-Workflow")

from zt_calc_workflow.amset import read_amset_csv
from zt_calc_workflow.analysis import match_data
from zt_calc_workflow.dataset import dataset_to_2d


def read_expt_data_csv(file_path):
""" Read experimental data from a two-column CSV file. """
with open(file_path, 'r') as f:
"""Read experimental data from a two-column CSV file."""

with open(file_path, "r") as f:
f_csv = csv.reader(f)

return np.array(
[[float(val) for val in r] for r in f_csv], dtype=np.float64)

return np.array([[float(val) for val in r] for r in f_csv], dtype=np.float64)


if __name__ == "__main__":
# Load AMSET calculation and create 2D datasets.

data = read_amset_csv(r"Bi2SeO2-AMSET-n.csv")
calc_n, calc_t, calc_data_2d = dataset_to_2d(data)

# Experimental carrier concentrations (nominal).

expt_n = {'S1': 1.0e18, 'S2': 6.8e18, 'S3': 1.3e19, 'S4': 2.2e19,
'S5': 8.7e19}


expt_n = {"S1": 1.0e18, "S2": 6.8e18, "S3": 1.3e19, "S4": 2.2e19, "S5": 8.7e19}

# Load experimental data.

expt_data = {}

for s in "S1", "S2", "S3", "S4", "S5":
expt_sigma = read_expt_data_csv(r"5.0063091-2a_{0}.csv".format(s))

# Resistivity data -> sigma = 1 / rho.

expt_sigma = np.array([[t, 1. / rho] for t, rho in expt_sigma],
dtype=np.float64)


expt_sigma = np.array(
[[t, 1.0 / rho] for t, rho in expt_sigma], dtype=np.float64
)

expt_s = read_expt_data_csv(r"5.0063091-2b_{0}.csv".format(s))
expt_data[s] = {'sigma': expt_sigma, 's': expt_s}

expt_data[s] = {"sigma": expt_sigma, "s": expt_s}

# Match sigma and S for each of the five samples and print results.
for expt_k, calc_k in('sigma', 'sigma_ave'), ('s', 's_ave'):

for expt_k, calc_k in ("sigma", "sigma_ave"), ("s", "s_ave"):
for s in "S1", "S2", "S3", "S4", "S5":
# List of (n, t, val) to match to.

match_to = [(expt_n[s], t, v) for t, v in expt_data[s][expt_k]]

# Match to calculated data. Using mode='same_t' will return the
# calculated n that best match the experimental data at the
# measurement T.

res = match_data(calc_n, calc_t, calc_data_2d[calc_k],
match_to, mode='same_t', num_seeds=5)


res = match_data(
calc_n,
calc_t,
calc_data_2d[calc_k],
match_to,
mode="same_t",
num_seeds=5,
)

# Print results.

header = "Sample: '{0}', Data: '{1}'".format(s, expt_k)

print(header)
print('-' * len(header))
print("-" * len(header))
print("")

print("{0: <10} | {1: <10} | {2: <10} | {3: <10} | {4: <10} | "
"{5: <10} | {6: <10}".format("n", "T", "Expt.", "n", "T",
"Calc.", "Diff."))

print('-' * (7 * 10 + 6 * 3))


print(
"{0: <10} | {1: <10} | {2: <10} | {3: <10} | {4: <10} | "
"{5: <10} | {6: <10}".format(
"n", "T", "Expt.", "n", "T", "Calc.", "Diff."
)
)

print("-" * (7 * 10 + 6 * 3))

for (e_n, e_t, e_v), (c_n, c_t, c_v) in zip(match_to, res):
print(
"{0: >10.2e} | {1: >10.0f} | {2: >10.2f} | {3: >10.2e} | "
"{4: >10.0f} | {5: >10.2f} | {6: >10.2e}".format(
e_n, e_t, e_v, c_n, c_t, c_v, c_v - e_v))

e_n, e_t, e_v, c_n, c_t, c_v, c_v - e_v
)
)

print("")

print("")

# Try one sample with all four modes.

s = "S3"
for expt_k, calc_k in ('sigma', 'sigma_ave'), ('s', 's_ave'):
for mode in 'same', 'same_t', 'same_n', 'best_match':

for expt_k, calc_k in ("sigma", "sigma_ave"), ("s", "s_ave"):
for mode in "same", "same_t", "same_n", "best_match":
match_to = [(expt_n[s], t, v) for t, v in expt_data[s][expt_k]]

# num_seeds gives dubious results with mode = 'best_match' (and
# will issue a RuntimeWarning to this effect).

num_seeds = 1 if mode == 'best_match' else 5

res = match_data(calc_n, calc_t, calc_data_2d[calc_k],
match_to, mode=mode, num_seeds=num_seeds)

header = ("Sample: '{0}', Data: '{1}', Mode: "
"'{2}'".format(s, expt_k, mode))

num_seeds = 1 if mode == "best_match" else 5

res = match_data(
calc_n,
calc_t,
calc_data_2d[calc_k],
match_to,
mode=mode,
num_seeds=num_seeds,
)

header = "Sample: '{0}', Data: '{1}', Mode: " "'{2}'".format(
s, expt_k, mode
)

print(header)
print('-' * len(header))
print("-" * len(header))
print("")

print("{0: <10} | {1: <10} | {2: <10} | {3: <10} | {4: <10} | "
"{5: <10} | {6: <10}".format("n", "T", "Expt.", "n", "T",
"Calc.", "Diff."))

print('-' * (7 * 10 + 6 * 3))


print(
"{0: <10} | {1: <10} | {2: <10} | {3: <10} | {4: <10} | "
"{5: <10} | {6: <10}".format(
"n", "T", "Expt.", "n", "T", "Calc.", "Diff."
)
)

print("-" * (7 * 10 + 6 * 3))

for (e_n, e_t, e_v), (c_n, c_t, c_v) in zip(match_to, res):
print(
"{0: >10.2e} | {1: >10.0f} | {2: >10.2f} | {3: >10.2e} | "
"{4: >10.0f} | {5: >10.2f} | {6: >10.2e}".format(
e_n, e_t, e_v, c_n, c_t, c_v, c_v - e_v))

e_n, e_t, e_v, c_n, c_t, c_v, c_v - e_v
)
)

print("")

print("")
81 changes: 45 additions & 36 deletions example/SnS-SnSe-ZT/elec_n_plot.py
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Expand Up @@ -30,64 +30,72 @@
# Read input data.

amset_data = {
'SnS' : (read_amset_csv("SnS-Pnma-AMSET-p.csv"),
read_amset_csv("SnS-Pnma-AMSET-n.csv")),
'SnSe' : (read_amset_csv("SnSe-Pnma-AMSET-p.csv"),
read_amset_csv("SnSe-Pnma-AMSET-n.csv"))}
"SnS": (
read_amset_csv("SnS-Pnma-AMSET-p.csv"),
read_amset_csv("SnS-Pnma-AMSET-n.csv"),
),
"SnSe": (
read_amset_csv("SnSe-Pnma-AMSET-p.csv"),
read_amset_csv("SnSe-Pnma-AMSET-n.csv"),
),
}

# Setup Matplotlib.

setup_matplotlib()

# Plot parameters.

t_plot = 700.
colours = ['b', 'r']
t_plot = 700.0

colours = ["b", "r"]

xlim = (1.e16, 1.e20)
ylim_sets = [(0., 750.), (0., 750.), (0., 6.), (0., 1.)]
xlim = (1.0e16, 1.0e20)
ylim_sets = [(0.0, 750.0), (0.0, 750.0), (0.0, 6.0), (0.0, 1.0)]

# Plot.

plt.figure(figsize=(16.2 / 2.54, 13.5 / 2.54))

subplot_axes = [plt.subplot(2, 2, i + 1) for i in range(4)]

for data_k, axes in zip(
['sigma_ave', 's_ave', 'pf_ave', 'kappa_el_ave'], subplot_axes):
["sigma_ave", "s_ave", "pf_ave", "kappa_el_ave"], subplot_axes
):

for sys_k, c in zip(['SnS', 'SnSe'], colours):
for sys_k, c in zip(["SnS", "SnSe"], colours):
data_p, data_n = amset_data[sys_k]

# Extract data at plot temperature.

data_p = data_p[data_p['t'] == t_plot]
data_n = data_n[data_n['t'] == t_plot]

for data, l, d in [(data_p, sys_k, (None, None)),
(data_n, None, (3., 1.))]:

data_p = data_p[data_p["t"] == t_plot]
data_n = data_n[data_n["t"] == t_plot]

for data, l, d in [
(data_p, sys_k, (None, None)),
(data_n, None, (3.0, 1.0)),
]:
# Plot |S| rather than S.

y = (np.abs(data[data_k]) if data_k == 's_ave'
else data[data_k])

axes.plot(data['n'], y, label=l, color=c, dashes=d)

y = np.abs(data[data_k]) if data_k == "s_ave" else data[data_k]

axes.plot(data["n"], y, label=l, color=c, dashes=d)

# "Fake" line to add a single entry for n-type doping.

axes.plot([-0.75, -0.25], [-0.75, -0.25], label="n-type", color='k',
dashes=(3., 1.))
axes.plot(
[-0.75, -0.25], [-0.75, -0.25], label="n-type", color="k", dashes=(3.0, 1.0)
)

# Adjust axis scales/ranges and labels.

for axes in subplot_axes:
axes.set_xscale('log')
axes.set_xscale("log")
axes.set_xlim(xlim)

for axes, ylim in zip(subplot_axes, ylim_sets):
axes.set_ylim(ylim)

y_min, y_max = ylim
axes.set_yticks(np.linspace(y_min, y_max, 6))

Expand All @@ -101,27 +109,28 @@

# Add legend.

legend = subplot_axes[0].legend(loc='upper left')
legend.get_frame().set_edgecolor('k')
legend.get_frame().set_facecolor((1., 1., 1., 0.5))
legend = subplot_axes[0].legend(loc="upper left")
legend.get_frame().set_edgecolor("k")
legend.get_frame().set_facecolor((1.0, 1.0, 1.0, 0.5))

# Add subplot labels.

locs = ['lower left', 'upper right', 'upper left', 'upper left']
locs = ["lower left", "upper right", "upper left", "upper left"]

for i, (axes, loc) in enumerate(zip(subplot_axes, locs)):
subplot_label = AnchoredText(
r"({0})".format(chr(97 + i)), loc=loc, frameon=True)
r"({0})".format(chr(97 + i)), loc=loc, frameon=True
)

subplot_label.patch.set_edgecolor('k')
subplot_label.patch.set_facecolor((1., 1., 1., 0.5))
subplot_label.patch.set_edgecolor("k")
subplot_label.patch.set_facecolor((1.0, 1.0, 1.0, 0.5))

axes.add_artist(subplot_label)

# Add gridlines.

for axes in subplot_axes:
axes.grid(color=(0.9, 0.9, 0.9), dashes=(3., 1.), linewidth=0.5)
axes.grid(color=(0.9, 0.9, 0.9), dashes=(3.0, 1.0), linewidth=0.5)
axes.set_axisbelow(True)

plt.tight_layout()
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