File size: 2,517 Bytes
5686f5b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | """StudyJSON plotting helpers."""
from __future__ import annotations
from typing import Tuple
from sim_priors_pk.data.data_empirical.json_schema import StudyJSON
try: # pragma: no cover - plotting is optional in CI environments
import matplotlib.pyplot as plt
except ImportError: # pragma: no cover - fallback handled at runtime
plt = None
def plot_studyjson_ensemble(
study_list: list[StudyJSON],
num_rows: int,
num_cols: int,
figsize: Tuple[int, int] = (12, 8),
) -> None:
"""Visualise a list of studies as observation curves.
Parameters
----------
study_list:
Collection of :class:`StudyJSON` records to render.
num_rows, num_cols:
Dimensions of the subplot grid. The product must be greater or equal to
``len(study_list)`` so that each study receives its own axis.
figsize:
Matplotlib ``figure`` size argument applied when creating the subplot
grid.
"""
if plt is None: # pragma: no cover - only triggered when matplotlib missing
raise ImportError(
"matplotlib is required for plot_studyjson_ensemble but is not installed"
)
if not study_list:
raise ValueError("study_list must contain at least one StudyJSON entry")
total_plots = num_rows * num_cols
if len(study_list) > total_plots:
raise ValueError(
"The subplot grid is too small for the provided study list: "
f"{len(study_list)} studies for {total_plots} subplots."
)
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize, squeeze=False)
for idx, study in enumerate(study_list):
row = idx // num_cols
col = idx % num_cols
ax = axes[row][col]
context = study.get("context", [])
for individual in context:
times = individual.get("observation_times", [])
observations = individual.get("observations", [])
if times and observations:
ax.plot(times, observations, marker="o", linestyle="-")
study_meta = study.get("meta_data", {})
study_name = study_meta.get("study_name", f"study_{idx}")
ax.set_title(study_name)
ax.set_xlabel("Time")
ax.set_ylabel("Observation")
ax.grid(True, alpha=0.3)
for remaining_idx in range(len(study_list), total_plots):
row = remaining_idx // num_cols
col = remaining_idx % num_cols
axes[row][col].axis("off")
fig.tight_layout()
plt.show()
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