Spatial 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 "dx0.1_dt0.05" not in d:
        # We have not spatial convergence data
        continue

    for key1, d1 in d.items():
        if "dt0.05" 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 36
timestamp 36
simcardems_version 36
dt 36
dx 36
sha 36
coupling_type 18
num_cells_mechanics 36
num_vertices_mechanics 36
num_cells_ep 36
num_vertices_ep 36
create_runner_time 36
solve_time 36
APD40 36
APD50 36
APD90 36
triangulation 36
Vpeak 36
Vmin 36
dvdt_max 18
maxCa 36
ampCa 36
CaTD50 36
CaTD80 36
maxTa 36
ampTa 36
ttp_Ta 36
rt50_Ta 36
rt95_Ta 36
maxlmbda 36
minlmbda 36
ttplmbda 36
lmbdaD50 36
lmbdaD80 36
lmbdaD90 36
rt50_lmbda 36
rt95_lmbda 36
max_displacement_norm 36
min_displacement_norm 18
time_to_max_displacement_norm 36
time_to_min_displacement_norm 18
max_displacement_x 36
min_displacement_x 18
time_to_max_displacement_x 36
time_to_min_displacement_x 18
max_displacement_y 36
min_displacement_y 18
time_to_max_displacement_y 36
time_to_min_displacement_y 18
max_displacement_z 36
min_displacement_z 18
time_to_max_displacement_z 36
time_to_min_displacement_z 18
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	1.000000e-06	2023-10-26 09:55:43.116594	...	0.104235	137.05
1	7.000000e-07	2023-10-26 09:55:25.319508	...	0.104234	136.05
2	7.600000e-06	2023-10-26 09:55:49.558780	...	0.104241	137.05
3	8.000000e-07	2023-09-24 20:53:35.963792	...	0.097217	138.05
4	1.000000e-06	2023-09-24 20:53:48.833177	...	0.097159	138.05
5	9.000000e-07	2023-09-24 20:54:02.914676	...	0.096905	139.05
6	9.000000e-07	2023-09-11 12:49:33.990384	...	0.097217	138.05
7	9.000000e-07	2023-09-11 12:49:36.888188	...	0.097159	138.05
8	9.000000e-07	2023-09-11 12:49:33.033569	...	0.096905	139.05
9	8.000000e-07	2023-06-27 12:42:45.253835	...	0.097159	138.05
10	1.100000e-06	2023-06-27 12:42:49.266930	...	0.097217	138.05
11	1.100000e-06	2023-06-27 12:42:43.474076	...	0.096905	139.05
12	1.600000e-06	2023-06-05 10:35:21.889433	...	0.096905	139.05
13	1.000000e-06	2023-06-05 10:34:58.182515	...	0.097159	138.05
14	8.000000e-07	2023-06-05 10:34:56.082409	...	0.097217	138.05
15	1.000000e-06	2023-05-23 19:54:03.785756	...	0.097159	138.05
16	1.000000e-06	2023-05-23 19:54:26.746227	...	0.097217	138.05
17	1.700000e-06	2023-05-23 19:54:21.676974	...	0.096905	139.05
18	1.200000e-06	2023-05-22 18:51:32.688650	...	0.097159	0.05
19	7.000000e-07	2023-05-22 18:51:29.002098	...	0.097217	0.05
20	1.000000e-06	2023-05-22 18:51:20.047152	...	0.096905	0.05
21	1.300000e-06	2023-05-16 08:33:54.547115	...	0.097159	0.05
22	1.000000e-06	2023-05-16 08:34:13.875302	...	0.097217	0.05
23	1.000000e-06	2023-05-16 08:33:56.392320	...	0.096905	0.05
24	1.200000e-06	2023-05-10 07:52:46.302512	...	0.096905	0.05
25	1.000000e-06	2023-05-10 07:52:54.342434	...	0.097159	0.05
26	9.000000e-07	2023-05-10 07:52:36.195420	...	0.097217	0.05
27	9.000000e-07	2023-04-12 20:33:25.203391	...	0.096905	0.05
28	1.001000e-06	2023-04-12 20:33:25.502552	...	0.097217	0.05
29	1.000000e-06	2023-04-12 20:33:32.537703	...	0.097159	0.05
30	1.100000e-06	2023-04-12 17:17:24.396818	...	0.096905	0.05
31	1.000000e-06	2023-04-12 17:17:24.948740	...	0.097217	0.05
32	1.000000e-06	2023-04-12 17:17:26.820219	...	0.097159	0.05
33	1.300000e-06	2023-04-12 09:53:34.139318	...	0.096905	0.05
34	8.000000e-07	2023-04-12 09:53:30.436212	...	0.097217	0.05
35	1.000000e-06	2023-04-12 09:53:31.010237	...	0.097159	0.05

36 rows × 43 columns

df["timestamp"]
df.keys()
df["dx"].unique()

array([0.4, 0.1, 0.2])

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)

dxs = df["dx"].unique()
DX = np.median(dxs)  # Choose the dx in the middle as the one to compare with
dxs

array([0.4, 0.1, 0.2])

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"]
dxs = df["dx"].unique()
dates = [t.date() for t in df["timestamp"]]

df_DX = df[df["dx"] == DX]
df_DX.sort_values(by="timestamp")

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

yranges = []
colors = ["red", "green", "blue"]
for color, dx in zip(colors, sorted(dxs)):
    df_dx = df[df["dx"] == dx]
    df_dx.sort_values(by="timestamp")

    git_hash = df_dx["sha"]
    versions = df_dx["simcardems_version"]
    dates = [t.date() for t in df_dx["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_dx[c].to_numpy() / df_DX[c].to_numpy()

        row = i + 1
        col = 1
        showlegend = i == 0
        fig.add_trace(
            go.Scatter(
                x=df_dx["timestamp"],
                y=y,
                text=text,
                hovertemplate="%{text}",
                name=f"dx={dx}",
                legendgroup=str(dx),
                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=5000, showlegend=True)
fig.show()