75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
import h5py
|
|
import pandas as pd
|
|
import re
|
|
|
|
from Model import Model
|
|
from fitter import Fitter
|
|
from Data import Data
|
|
|
|
file = "ltcc_current.h5"
|
|
|
|
|
|
def print_attrs(name, obj):
|
|
print(f"\nAttributes for {name}:")
|
|
for key, val in obj.attrs.items():
|
|
print(f" {key}: {val}")
|
|
|
|
|
|
with h5py.File(file, "r") as h5_file:
|
|
h5_file.visititems(print_attrs)
|
|
|
|
# Dict to hold DFs 'sex', 'tag' & 'spid'
|
|
dfs_by_sex_tag_spid = {}
|
|
|
|
with h5py.File(file, "r") as h5_file:
|
|
for eid in h5_file.keys():
|
|
attributes = h5_file[eid].attrs
|
|
sex = attributes.get("sex")
|
|
tag = attributes.get("tag")
|
|
spid = attributes.get("spid")
|
|
|
|
key = f"{sex}_{tag}_{spid}"
|
|
|
|
if key not in dfs_by_sex_tag_spid:
|
|
dfs_by_sex_tag_spid[key] = pd.DataFrame()
|
|
|
|
row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
|
|
temp_df = pd.DataFrame([row_data])
|
|
|
|
dfs_by_sex_tag_spid[key] = pd.concat(
|
|
[dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
|
|
)
|
|
|
|
for key, df in dfs_by_sex_tag_spid.items():
|
|
print(f"DataFrame for {key}:")
|
|
print(df)
|
|
print()
|
|
|
|
|
|
def fit_data():
|
|
filename = "ltcc_current.h5"
|
|
with h5py.File(filename, "r") as h5:
|
|
eids = list(h5.keys())
|
|
|
|
for eid in eids:
|
|
data = Data(filename, group_key=eid)
|
|
fit = Fitter(Model, data)
|
|
fit.optimize()
|
|
res, fig = fit.optimize()
|
|
|
|
fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
|
|
fit_hist.index.name = "Iterations"
|
|
|
|
res_filename = f"fit_results_{eid}.csv"
|
|
res_filename = res_filename.replace(" ", "_").replace(":", "-")
|
|
fit_hist.to_csv(res_filename, index=True)
|
|
|
|
eid_cleaned = re.sub(
|
|
r"[^\w.-]", "", eid
|
|
) # Eemaldab kõik eritähed
|
|
fig.savefig(f"plot_{eid_cleaned}.png")
|
|
fig.savefig(f"plot_{eid_cleaned}.pdf")
|
|
|
|
|
|
fit_data()
|