Get thermal data#
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from __future__ import annotations
from collections.abc import Iterable
from boilercore.fits import fit_from_params
from boilercore.models.geometry import GEOMETRY
from boilercore.paths import ISOLIKE, dt_fromisolike
from boilercv_pipeline.models.column import Col, convert, rename
from boilercv_pipeline.stages.find_objects import FindObjects
from boilercv_pipeline.stages.get_thermal_data import GetThermalData as Params
from boilercv_pipeline.units import U
from dev.docs.nbs import get_mode, init
from devtools import pprint
from matplotlib.figure import Figure
from matplotlib.pyplot import subplots
from pandas import DataFrame, Series, concat, read_csv, read_hdf
from seaborn import lineplot, scatterplot
PARAMS = None
"""Notebook stage parameters."""
MODE = get_mode()
"""Notebook execution mode."""
Params.hide()
Show code cell source
context = init(mode=MODE)
if isinstance(PARAMS, str):
params = Params.model_validate_json(PARAMS)
context = params.context
else:
params = Params(context=context)
params.set_display_options()
data = params.data
C = params.cols
contours = FindObjects(context=context).contours
HIGHLIGHTS = ["2024-07-18T17-07-13", "2024-07-18T18-04-05", "2024-07-18T18-49-55"]
"""Chosen datasets to higlight in the dissertation."""
WIDTH_SCALE = 1.257
"""Width to scale plots by."""
HEIGHT_SCALE = 1.000
"""Width to scale plots by."""
def scale_figure(fig: Figure, width: float = WIDTH_SCALE, height: float = HEIGHT_SCALE):
"""Scale up figure size."""
fig.set_figwidth(width * fig.get_figwidth())
fig.set_figheight(height * fig.get_figheight())
def fit(df: DataFrame, flux: Col, sample_temps: Iterable[Col]) -> DataFrame:
"""Fit model function across sample temperatures."""
models = params.fit.get_models(params.deps.modelfunctions)[0]
def apply_fit(df: DataFrame) -> Series[float]:
return df.loc[:, [c() for c in sample_temps]].apply(
lambda ser: fit(ser)[0]["q_s"], axis="columns"
)
def fit(ser: Series[float]) -> tuple[dict[str, float], dict[str, float]]:
return fit_from_params(models, params.fit, GEOMETRY.rods["R"], ser)
return df.assign(**{flux(): apply_fit})
pprint(params)
Show code cell source
sources = [c.source.raw for c in C.sources]
data.dfs.src = params.preview(
cols=C.sources,
df=concat([
read_csv(
p,
usecols=[C.time.source.raw, *sources],
parse_dates=[C.time.source.raw],
index_col=C.time.source.raw,
)
for p in params.deps.thermal_paths
])
.reset_index()
.pipe(rename, C.sources)
.assign(**{
C.time_elapsed(): lambda df: (
(df[C.index()] - df[C.index()][0]).dt.total_seconds()
)
}),
)
\(\mathsf{Time}\) |
\(\mathsf{T_{w3}\ \left(°C\right)}\) |
\(\mathsf{T_{w4}\ \left(°C\right)}\) |
\(\mathsf{T_{1}\ \left(°C\right)}\) |
\(\mathsf{T_{2}\ \left(°C\right)}\) |
\(\mathsf{T_{3}\ \left(°C\right)}\) |
\(\mathsf{T_{4}\ \left(°C\right)}\) |
\(\mathsf{T_{5}\ \left(°C\right)}\) |
\(\mathsf{T_{s}\ \left(°C\right)}\) |
|---|---|---|---|---|---|---|---|---|
2024-07-18 16:57:59.835356 |
98.0 |
97.8 |
128. |
124. |
121. |
117. |
108. |
102. |
2024-07-18 16:58:02.729356 |
98.0 |
97.8 |
128. |
124. |
121. |
117. |
108. |
103. |
2024-07-18 16:58:04.732189 |
98.1 |
97.8 |
128. |
124. |
121. |
117. |
108. |
103. |
2024-07-18 16:58:06.724565 |
98.1 |
97.8 |
128. |
124. |
121. |
117. |
108. |
103. |
2024-07-18 16:58:08.734172 |
98.1 |
97.8 |
128. |
124. |
121. |
117. |
108. |
103. |
Show code cell source
only_dests = [C.time, *[c for c in C.dests if c not in C.sources]]
data.dfs.dst = params.preview(
cols=only_dests,
df=(
DataFrame(read_hdf(params.outs.df))
if params.load_src_from_outs and params.outs.df.exists()
else (
data.dfs.src.set_index(C.index())
.resample("s")
.median()
.assign(**{
C.video(): lambda df: df.index.isin(
df.index[
df.index.get_indexer(
[
dt_fromisolike(match)
for p in contours
if (match := ISOLIKE.search(p.stem))
],
method="nearest",
)
]
)
})
.assign(**{
C.highlight(): lambda df: df.index.isin(
df.index[
df.index.get_indexer(
[
dt_fromisolike(match)
for highlight in HIGHLIGHTS
if (match := ISOLIKE.search(highlight))
],
method="nearest",
)
]
)
})
.reset_index()
.ffill()
.assign(**{
C.water_temp(): lambda df: df[[c() for c in C.water_temps]].mean(
axis="columns"
),
C.boiling(): lambda df: df[C.water_temp()].max(),
C.superheat(): lambda df: df[C.surface_temp()] - df[C.boiling()],
C.subcool(): lambda df: df[C.boiling()] - df[C.water_temp()],
})
.pipe(fit, flux=C.flux.source, sample_temps=C.sample_temps)
.pipe(convert, cols=[C.time_elapsed_min, C.flux], ureg=U)
)
)[[c() for c in C.dests]],
)
\(\mathsf{Time}\) |
\(\mathsf{t\ \left(s\right)}\) |
\(\mathsf{t\ \left(min\right)}\) |
\(\mathsf{T_{w}\ \left(°C\right)}\) |
\(\mathsf{ΔT_{super}\ \left(K\right)}\) |
\(\mathsf{ΔT_{sub}\ \left(K\right)}\) |
\(\mathsf{q''\ \left(W/cm^2\right)}\) |
\(\mathsf{T_{sat}\ \left(°C\right)}\) |
\(\mathsf{Video}\) |
\(\mathsf{Highlight}\) |
|---|---|---|---|---|---|---|---|---|---|
2024-07-18 16:57:59 |
0.00 |
0.00 |
97.9 |
4.03 |
0.378 |
7.91 |
98.3 |
False |
False |
2024-07-18 16:58:00 |
0.00 |
0.00 |
97.9 |
4.03 |
0.378 |
7.91 |
98.3 |
False |
False |
2024-07-18 16:58:01 |
0.00 |
0.00 |
97.9 |
4.03 |
0.378 |
7.91 |
98.3 |
False |
False |
2024-07-18 16:58:02 |
2.89 |
0.0482 |
97.9 |
4.30 |
0.346 |
7.87 |
98.3 |
False |
False |
2024-07-18 16:58:03 |
2.89 |
0.0482 |
97.9 |
4.30 |
0.346 |
7.87 |
98.3 |
False |
False |
Show code cell source
data.dfs.resampled = params.preview(
cols=only_dests,
df=(
data.dfs.dst.set_index(C.index())
.resample("20s")
.agg({
**dict.fromkeys([c() for c in C.dests if c != C.index], "median"),
C.video(): "max",
C.highlight(): "max",
})
.reset_index()
),
)
\(\mathsf{Time}\) |
\(\mathsf{t\ \left(s\right)}\) |
\(\mathsf{t\ \left(min\right)}\) |
\(\mathsf{T_{w}\ \left(°C\right)}\) |
\(\mathsf{ΔT_{super}\ \left(K\right)}\) |
\(\mathsf{ΔT_{sub}\ \left(K\right)}\) |
\(\mathsf{q''\ \left(W/cm^2\right)}\) |
\(\mathsf{T_{sat}\ \left(°C\right)}\) |
\(\mathsf{Video}\) |
\(\mathsf{Highlight}\) |
|---|---|---|---|---|---|---|---|---|---|
2024-07-18 16:57:40 |
0.00 |
0.00 |
97.9 |
4.03 |
0.378 |
7.91 |
98.3 |
False |
False |
2024-07-18 16:58:00 |
9.90 |
0.165 |
97.9 |
4.52 |
0.325 |
7.85 |
98.3 |
False |
False |
2024-07-18 16:58:20 |
29.9 |
0.499 |
97.9 |
4.44 |
0.335 |
7.88 |
98.3 |
False |
False |
2024-07-18 16:58:40 |
49.9 |
0.832 |
97.9 |
4.59 |
0.405 |
7.89 |
98.3 |
False |
False |
2024-07-18 16:59:00 |
70.0 |
1.17 |
97.9 |
4.84 |
0.415 |
7.82 |
98.3 |
False |
False |
Show code cell source
data.plots.subcool_superheat, ax = subplots()
lineplot(
ax=ax,
zorder=1,
data=data.dfs.resampled.set_index(C.time_elapsed_min())[
[C.subcool(), C.superheat()]
],
dashes=False,
errorbar=None,
)
scatterplot(
ax=ax,
s=10 * params.marker_scale,
markers={C.video(): "*"},
palette={C.video(): "red"},
data=data.dfs.resampled.assign(**{
C.video(): lambda df: df[df[C.video()]][C.superheat()]
}).set_index(C.time_elapsed_min())[[C.video()]],
)
scatterplot(
ax=ax,
s=10 * params.marker_scale,
markers={C.video(): "*"},
palette={C.video(): "red"},
data=data.dfs.resampled.assign(**{
C.video(): lambda df: df[df[C.video()]][C.subcool()]
}).set_index(C.time_elapsed_min())[[C.video()]],
legend=False,
)
scatterplot(
ax=ax,
s=9 * params.marker_scale,
markers={C.highlight(): "d"},
palette={C.highlight(): "green"},
data=data.dfs.resampled.assign(**{
C.highlight(): lambda df: df[df[C.highlight()]][C.superheat()]
}).set_index(C.time_elapsed_min())[[C.highlight()]],
)
scatterplot(
ax=ax,
s=9 * params.marker_scale,
markers={C.highlight(): "d"},
palette={C.highlight(): "green"},
data=data.dfs.resampled.assign(**{
C.highlight(): lambda df: df[df[C.highlight()]][C.subcool()]
}).set_index(C.time_elapsed_min())[[C.highlight()]],
legend=False,
)
ax.set_ylabel(C.subcool.no_sub())
params.move_legend(ax, ncol=4)
scale_figure(ax.get_figure())
Show code cell source
data.plots.subcool, ax = subplots()
scatterplot(
ax=ax, data=data.dfs.resampled, x=C.subcool(), y=C.flux(), hue=C.time_elapsed_min()
)
scatterplot(
ax=ax,
s=10 * params.marker_scale,
markers={C.video(): "*"},
palette={C.video(): "red"},
data=data.dfs.resampled.assign(**{
C.video(): lambda df: df[df[C.video()]][C.flux()]
}).set_index(C.subcool())[[C.video()]],
)
scatterplot(
ax=ax,
s=9 * params.marker_scale,
markers={C.highlight(): "d"},
palette={C.highlight(): "green"},
data=data.dfs.resampled.assign(**{
C.highlight(): lambda df: df[df[C.highlight()]][C.flux()]
}).set_index(C.subcool())[[C.highlight()]],
)
ax.get_legend().set_title(C.time_elapsed_min()) # pyright: ignore[reportOptionalMemberAccess]
params.move_legend(ax, ncol=4)
Show code cell source
data.plots.superheat, ax = subplots()
scatterplot(
ax=ax,
data=data.dfs.resampled,
x=C.superheat(),
y=C.flux(),
hue=C.time_elapsed_min(),
)
scatterplot(
ax=ax,
s=10 * params.marker_scale,
markers={C.video(): "*"},
palette={C.video(): "red"},
data=data.dfs.resampled.assign(**{
C.video(): lambda df: df[df[C.video()]][C.flux()]
}).set_index(C.superheat())[[C.video()]],
)
scatterplot(
ax=ax,
s=9 * params.marker_scale,
markers={C.highlight(): "d"},
palette={C.highlight(): "green"},
data=data.dfs.resampled.assign(**{
C.highlight(): lambda df: df[df[C.highlight()]][C.flux()]
}).set_index(C.superheat())[[C.highlight()]],
)
ax.get_legend().set_title(C.time_elapsed_min()) # pyright: ignore[reportOptionalMemberAccess]
params.move_legend(ax, ncol=4)
