Temporal convergence test

import json
import numpy as np
from collections import defaultdict
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import requests

pd.options.display.max_columns = 5

headers = {
    "Accept": "application/vnd.github+json",
    "X-GitHub-Api-Version": "2022-11-28",
}
print("Get existing data from gist")
response = requests.get(
    "https://api.github.com/gists/73fa6531f28da2b3633a7ddaca38a7cd",
    headers=headers,
)
data = json.loads(response.json()["files"]["convergence_test.json"]["content"])

Get existing data from gist

runs = set()
all_results = defaultdict(list)
spatial_convergence = []
for git_hash, d in data.items():
    runs |= set(d.keys())
    if "dx0.2_dt0.025" not in d:
        # We have not temporaø convergence data
        continue

    for key1, d1 in d.items():
        if "dx0.2" not in key1:
            continue  # Only select data with "dt0.05"
        for key, value in d1.items():
            all_results[key].append(value)

N = max(len(v) for v in all_results.values())
results = {}
for k, v in all_results.items():
    print(k, len(v))
    if len(v) == N:
        results[k] = v

df = pd.DataFrame(results)
df["timestamp"] = pd.to_datetime(df["timestamp"])
df

import_time 39
timestamp 39
simcardems_version 39
dt 39
dx 39
sha 39
coupling_type 21
num_cells_mechanics 39
num_vertices_mechanics 39
num_cells_ep 39
num_vertices_ep 39
create_runner_time 39
solve_time 39
APD40 39
APD50 39
APD90 39
triangulation 39
Vpeak 39
Vmin 39
dvdt_max 21
maxCa 39
ampCa 39
CaTD50 39
CaTD80 39
maxTa 39
ampTa 39
ttp_Ta 39
rt50_Ta 39
rt95_Ta 39
maxlmbda 39
minlmbda 39
ttplmbda 39
lmbdaD50 39
lmbdaD80 39
lmbdaD90 39
rt50_lmbda 39
rt95_lmbda 39
max_displacement_norm 39
min_displacement_norm 21
time_to_max_displacement_norm 39
time_to_min_displacement_norm 21
max_displacement_x 39
min_displacement_x 21
time_to_max_displacement_x 39
time_to_min_displacement_x 21
max_displacement_y 39
min_displacement_y 21
time_to_max_displacement_y 39
time_to_min_displacement_y 21
max_displacement_z 39
min_displacement_z 21
time_to_max_displacement_z 39
time_to_min_displacement_z 21
dvdt 18
CaTD90 18
rel_max_displacement_norm 18
max_displacement_perc_norm 18
rel_max_displacement_perc_norm 18
rel_max_displacement_x 18
max_displacement_perc_x 18
rel_max_displacement_perc_x 18
rel_max_displacement_y 18
max_displacement_perc_y 18
rel_max_displacement_perc_y 18
rel_max_displacement_z 18
max_displacement_perc_z 18
rel_max_displacement_perc_z 18

	import_time	timestamp	...	max_displacement_z	time_to_max_displacement_z
0	6.810000e-07	2025-02-14 17:45:30.415965	...	0.104658	137.03
1	5.910000e-07	2025-02-14 17:45:23.848380	...	0.104101	138.05
2	6.110000e-07	2025-02-14 17:45:30.405582	...	0.102929	141.10
3	7.000000e-07	2023-10-26 09:55:30.404286	...	0.103191	139.10
4	7.000000e-07	2023-10-26 09:55:29.253581	...	0.104727	136.03
5	7.600000e-06	2023-10-26 09:55:49.558780	...	0.104241	137.05
6	9.000000e-07	2023-09-24 20:53:40.724515	...	0.095933	140.10
7	1.300000e-06	2023-09-24 20:53:48.670951	...	0.097361	138.03
8	9.000000e-07	2023-09-24 20:54:02.914676	...	0.096905	139.05
9	8.000000e-07	2023-09-11 12:49:36.181244	...	0.097361	138.03
10	8.000000e-07	2023-09-11 12:49:34.416894	...	0.095933	140.10
11	9.000000e-07	2023-09-11 12:49:33.033569	...	0.096905	139.05
12	9.000000e-07	2023-06-27 12:42:47.509674	...	0.097361	138.03
13	8.000000e-07	2023-06-27 12:43:10.218629	...	0.095933	140.10
14	1.100000e-06	2023-06-27 12:42:43.474076	...	0.096905	139.05
15	1.600000e-06	2023-06-05 10:35:21.889433	...	0.096905	139.05
16	1.100000e-06	2023-06-05 10:34:55.025194	...	0.097361	138.03
17	9.000000e-07	2023-06-05 10:35:11.636501	...	0.095933	140.10
18	1.200000e-06	2023-05-23 19:54:22.684955	...	0.095933	140.10
19	9.000000e-07	2023-05-23 19:54:33.438331	...	0.097361	138.03
20	1.700000e-06	2023-05-23 19:54:21.676974	...	0.096905	139.05
21	1.200000e-06	2023-05-22 18:51:19.153618	...	0.095933	0.10
22	1.000000e-06	2023-05-22 18:51:19.693717	...	0.097361	0.02
23	1.000000e-06	2023-05-22 18:51:20.047152	...	0.096905	0.05
24	8.000000e-07	2023-05-16 08:34:00.664103	...	0.095933	0.10
25	7.000000e-07	2023-05-16 08:33:55.188781	...	0.097361	0.02
26	1.000000e-06	2023-05-16 08:33:56.392320	...	0.096905	0.05
27	1.200000e-06	2023-05-10 07:52:46.302512	...	0.096905	0.05
28	7.000000e-07	2023-05-10 07:52:37.970327	...	0.095933	0.10
29	1.000000e-06	2023-05-10 07:52:39.150154	...	0.097361	0.02
30	8.000000e-07	2023-04-12 20:33:24.760391	...	0.097361	0.02
31	9.000000e-07	2023-04-12 20:33:25.203391	...	0.096905	0.05
32	1.400000e-06	2023-04-12 20:33:25.396738	...	0.095933	0.10
33	1.400000e-06	2023-04-12 17:17:47.733858	...	0.097361	0.02
34	1.100000e-06	2023-04-12 17:17:24.396818	...	0.096905	0.05
35	1.100000e-06	2023-04-12 17:17:23.977668	...	0.095933	0.10
36	1.300000e-06	2023-04-12 09:53:44.010221	...	0.097361	0.02
37	1.300000e-06	2023-04-12 09:53:34.139318	...	0.096905	0.05
38	1.000000e-06	2023-04-12 09:53:31.939260	...	0.095933	0.10

39 rows × 43 columns

dts = df["dt"].unique()
DT = np.median(dts)  # Choose the dt in the middle as the one to compare with
dts

array([0.025, 0.05 , 0.1  ])

def get_ylim(values):
    if np.isclose(values, 1).all():
        return (0.99, 1.01)
    y_max = np.max(values)
    y_min = np.min(values)
    d_max = y_max - 1
    d_min = 1 - y_min

    d = max(d_min, d_max) + 0.025
    return (1 - d, 1 + d)

columns = [c for c in df.columns if c not in ["timestamp", "simcardems_version", "sha", "dx", "dt"]]
git_hash = df["sha"]
versions = df["simcardems_version"]
dates = [t.date() for t in df["timestamp"]]

df_DT = df[df["dt"] == DT]
df_DT.sort_values(by="timestamp")

fig = make_subplots(
    rows=len(columns),
    cols=1,
    y_title="Percentage deviation from median case",
    subplot_titles=columns,
    shared_xaxes=True,
)

yranges = []
colors = ["red", "green", "blue"]
for color, dt in zip(colors, sorted(dts)):
    df_dt = df[df["dt"] == dt]
    df_dt.sort_values(by="timestamp")

    git_hash = df_dt["sha"]
    versions = df_dt["simcardems_version"]
    dates = [t.date() for t in df_dt["timestamp"]]

    text = []
    for h, v, t in zip(git_hash, versions, dates):
        text.append(
            "\n".join(
                [
                    f"<br>Git Hash: {h}</br>",
                    f"<br>Version: {v}</br>",
                    f"<br>Timestamp {t}</br>",
                ]
            )
        )

    for i, c in enumerate(columns):
        y = df_dt[c].to_numpy() / df_DT[c].to_numpy()

        row = i + 1
        col = 1
        showlegend = i == 0
        fig.add_trace(
            go.Scatter(
                x=df_dt["timestamp"],
                y=y,
                text=text,
                hovertemplate="%{text}",
                name=f"dt={dt}",
                legendgroup=str(dt),
                showlegend=i == 0,
                marker_color=color,
            ),
            row=row,
            col=col,
        )
        fig.update_yaxes(range=get_ylim(y), row=row, col=col)

fig.update_layout(height=7000, showlegend=True)
fig.show()