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Import matplotlib only when plotting #38

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Import matplotlib only when plotting #38

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32 changes: 22 additions & 10 deletions lyopronto/high_level.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,25 @@
from warnings import warn
import numpy as np
import csv
import matplotlib.pyplot as plt
from matplotlib import rc as matplotlibrc
from scipy.optimize import curve_fit, brentq
from ruamel.yaml import YAML

yaml = YAML()


def _load_matplotlib():
"""Import matplotlib on demand.

Only the plotting helpers need it, so importing it at module scope makes
every ``import lyopronto`` pay for a backend initialization that a headless
or simulation-only caller never uses.
"""
import matplotlib.pyplot as plt
from matplotlib import rc as matplotlibrc

return plt, matplotlibrc


def execute_simulation(inputs):
"""
Run the selected simulation tool with the provided inputs.
Expand Down Expand Up @@ -419,6 +430,7 @@ def generate_visualizations(output_data, inputs, timestamp):
"""
Create and save publication-quality plots based on simulation results.
"""
plt, matplotlibrc = _load_matplotlib()

# TODO: move these to kwargs for the function
figure_props = {
Expand All @@ -433,19 +445,19 @@ def generate_visualizations(output_data, inputs, timestamp):
plt.rcParams["font.family"] = "Arial"

if tool == "Freezing Calculator":
_plot_freezing_results(output_data, figure_props, timestamp)
_plot_freezing_results(output_data, figure_props, timestamp, plt)
elif tool in ["Primary Drying Calculator", "Optimizer"]:
if tool == "Primary Drying Calculator" and not inputs["sim"]["Rp_known"]:
_plot_rp_results(output_data, figure_props, timestamp)
_plot_rp_results(output_data, figure_props, timestamp, plt)
data = output_data[0] # There are extra returns for Rp fitting
else:
data = output_data # for all but unknown Rp, output_data is the only return
_plot_drying_results(data, figure_props, timestamp)
_plot_drying_results(data, figure_props, timestamp, plt)
elif tool == "Design Space Generator":
_plot_design_space(output_data, inputs, figure_props, timestamp)
_plot_design_space(output_data, inputs, figure_props, timestamp, plt)


def _plot_freezing_results(data, props, timestamp):
def _plot_freezing_results(data, props, timestamp, plt):
"""Generate freezing process visualization."""
fig, ax = plt.subplots(figsize=(props["figwidth"], props["figheight"]))
ax.plot(
Expand All @@ -469,7 +481,7 @@ def _plot_freezing_results(data, props, timestamp):
plt.close()


def _plot_drying_results(data, props, timestamp):
def _plot_drying_results(data, props, timestamp, plt):
"""Generate primary drying process visualizations."""

figwidth = props["figwidth"]
Expand Down Expand Up @@ -551,7 +563,7 @@ def _plot_drying_results(data, props, timestamp):
plt.close()


def _plot_rp_results(data, props, timestamp):
def _plot_rp_results(data, props, timestamp, plt):
product_res = data[1]
params = data[2]
figwidth = props["figwidth"]
Expand Down Expand Up @@ -583,7 +595,7 @@ def _plot_rp_results(data, props, timestamp):
plt.close()


def _plot_design_space(data, inputs, props, timestamp):
def _plot_design_space(data, inputs, props, timestamp, plt):
"""Generate design space boundary visualization."""
# Implementation for design space plotting

Expand Down
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