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Merged
kaelyndunnell merged 2 commits into from
Dec 3, 2024
Merged

Export data to JSON #56

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432c685
Add JSON data processing and storage in all_bin_scenario.py
RemDelaporteMathurin Dec 2, 2024
ed66c5f
changed structure
RemDelaporteMathurin Dec 2, 2024
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61 changes: 42 additions & 19 deletions examples/all_bin_scenario.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import numpy as np
import matplotlib.pyplot as plt
import json

from numpy.typing import NDArray

Expand Down Expand Up @@ -50,6 +51,7 @@
if __name__ == "__main__":
############# CREATE EMPTY NP ARRAYS TO STORE ALL DATA #############
global_data = {}
processed_data = []

def max_stepsize(t: float) -> float:
pulse = my_scenario.get_pulse(t)
Expand Down Expand Up @@ -139,6 +141,9 @@ def which_model(subbin: SubBin | DivBin):
############# RUN FW BIN SIMUS #############
# TODO: adjust to run monoblocks in parallel
for fw_bin in FW_bins.bins[:3]: # only running 3 fw_bins to demonstrate capability
global_data[fw_bin] = {}
fw_bin_data = {"bin_index": fw_bin.index, "sub_bins": []}

for sub_bin in fw_bin.sub_bins:
my_model, quantities = which_model(sub_bin)

Expand All @@ -157,7 +162,16 @@ def which_model(subbin: SubBin | DivBin):
my_model.initialise()
my_model.run()
my_model.progress_bar.close()
global_data.update(quantities)

global_data[fw_bin][sub_bin] = quantities
subbin_data = {
"mode": sub_bin.mode,
"parent_bin_index": sub_bin.parent_bin_index,
}
for key, value in quantities.items():
subbin_data[key] = {"t": value.t, "data": value.data}
fw_bin_data["sub_bins"].append(subbin_data)
processed_data.append(fw_bin_data)

############# RUN DIV BIN SIMUS #############
# for div_bin in Div_bins.bins:
Expand All @@ -181,30 +195,39 @@ def which_model(subbin: SubBin | DivBin):
my_model.initialise()
my_model.run()
my_model.progress_bar.close()
global_data.update(quantities)

global_data[div_bin] = quantities
bin_data = {"bin_index": div_bin.index}
for key, value in quantities.items():
bin_data[key] = {"t": value.t, "data": value.data}
processed_data.append(bin_data)

# write the processed data to JSON

with open("processed_data.json", "w+") as f:
json.dump(processed_data, f, indent=4)
############# Results Plotting #############
# TODO: add a graph that computes grams

for name, quantity in global_data.items():
plt.plot(quantity.t, quantity.data, label=name, marker="o")
# for name, quantity in global_data.items():
# plt.plot(quantity.t, quantity.data, label=name, marker="o")

plt.xlabel("Time (s)")
plt.ylabel("Total quantity (atoms/m2)")
plt.legend()
plt.yscale("log")
# plt.xlabel("Time (s)")
# plt.ylabel("Total quantity (atoms/m2)")
# plt.legend()
# plt.yscale("log")

plt.show()
# plt.show()

fig, ax = plt.subplots()
# fig, ax = plt.subplots()

ax.stackplot(
quantity.t,
[quantity.data for quantity in global_data.values()],
labels=global_data.keys(),
)
# ax.stackplot(
# quantity.t,
# [quantity.data for quantity in global_data.values()],
# labels=global_data.keys(),
# )

plt.xlabel("Time (s)")
plt.ylabel("Total quantity (atoms/m2)")
plt.legend()
plt.show()
# plt.xlabel("Time (s)")
# plt.ylabel("Total quantity (atoms/m2)")
# plt.legend()
# plt.show()