137 lines
3.8 KiB
Python
137 lines
3.8 KiB
Python
import h5py
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import pandas as pd
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import numpy as np
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import re
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import matplotlib as plt
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from Model import Model
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from fitter import Fitter
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from Data import Data
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file = "ltcc_current.h5"
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dfs_by_sex_tag_spid = {}
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def print_attrs(name, obj):
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# print(f"\nAttributes for {name}:")
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# for key, val in obj.attrs.items():
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# print(f" {key}: {val}")
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pass
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with h5py.File(file, "r") as h5_file:
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for eid in h5_file.keys():
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attributes = h5_file[eid].attrs
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sex = attributes.get("sex")
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tag = attributes.get("tag")
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spid = attributes.get("spid")
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key = f"{sex}_{tag}_{spid}"
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if key not in dfs_by_sex_tag_spid:
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dfs_by_sex_tag_spid[key] = pd.DataFrame()
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row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
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temp_df = pd.DataFrame([row_data])
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dfs_by_sex_tag_spid[key] = pd.concat(
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[dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
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)
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# for key, df in dfs_by_sex_tag_spid.items():
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# print(f"DataFrame for {key}:")
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# print(df)
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# print()
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def fit_and_plot_dataframes(dfs_by_sex_tag_spid):
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for key, df in dfs_by_sex_tag_spid.items():
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print(f"Fitting and plotting data for {key}...")
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combined_current = []
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combined_time = []
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for eid in df["experiment_id"].tolist():
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data = Data(file, group_key=eid)
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combined_current.append(data.current)
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combined_time.append(data.current_t)
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combined_current = np.concatenate(combined_current)
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combined_time = np.concatenate(combined_time)
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# Sort by time for consistency - inspired by data
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sorted_indices = np.argsort(combined_time)
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combined_time = combined_time[sorted_indices]
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combined_current = combined_current[sorted_indices]
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combined_data = Data(file, group_key=eid)
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combined_data.current = combined_current
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combined_data.current_t = combined_time
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fit = Fitter(Model, combined_data)
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fit.optimize()
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res, fig = fit.optimize()
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plt.figure()
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plt.title(f"Fit results for {key}")
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for i, eid in enumerate(df["experiment_id"].tolist()):
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plt.plot(combined_time, combined_current,
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label=f"Experiment {eid}")
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plt.plot(
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combined_data.current_t, combined_data.current, "k-",
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label="Combined Fit"
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)
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plt.legend()
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plt.xlabel("Time")
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plt.ylabel("Current")
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key_cleaned = re.sub(r"[^\w.-]", "", key)
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plt.savefig(f"combined_plot_{key_cleaned}.png")
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plt.savefig(f"combined_plot_{key_cleaned}.pdf")
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plt.close()
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fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
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fit_hist.index.name = "Iterations"
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res_filename = f"combined_fit_results_{key}.csv"
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res_filename = res_filename.replace(" ", "_").replace(":", "-")
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fit_hist.to_csv(res_filename, index=True)
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print(f"Finished fitting for {key}.")
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fit_and_plot_dataframes(dfs_by_sex_tag_spid)
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"""
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def fit_data():
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filename = "ltcc_current.h5"
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with h5py.File(filename, "r") as h5:
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eids = list(h5.keys())
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for eid in eids:
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data = Data(filename, group_key=eid)
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fit = Fitter(Model, data)
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fit.optimize()
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res, fig = fit.optimize()
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fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
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fit_hist.index.name = "Iterations"
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res_filename = f"fit_results_{eid}.csv"
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res_filename = res_filename.replace(" ", "_").replace(":", "-")
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fit_hist.to_csv(res_filename, index=True)
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eid_cleaned = re.sub(r"[^w.-]", "", eid) # Eemaldab kõik eritähed
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fig.savefig(f"plot_{eid_cleaned}.png")
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fig.savefig(f"plot_{eid_cleaned}.pdf")
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fit_data()
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"""
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