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add f_0_curve error image to master plot

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a.ott 2020-07-28 09:30:38 +02:00
parent 3803628749
commit 51d8f72c06
1 changed files with 66 additions and 10 deletions
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76
ModelFit.py
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@ -1,20 +1,25 @@
import os
from models.LIFACnoise import LifacNoiseModel
from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
from Baseline import get_baseline_class
from FiCurve import get_fi_curve_class
from CellData import CellData
import helperFunctions as hF
import numpy as np
import functions as fu
import matplotlib.pyplot as plt
def get_best_fit(folder_path):
def get_best_fit(folder_path, use_comparable_error=True):
min_err = np.inf
min_item = ""
for item in os.listdir(folder_path):
item_path = os.path.join(folder_path, item)
err = ModelFit(item_path).comparable_error()
if use_comparable_error:
err = ModelFit(item_path).comparable_error()
else:
err = ModelFit(item_path).get_fit_routine_error()
if err < min_err:
min_err = err
min_item = item
@ -118,6 +123,11 @@ class ModelFit:
path = os.path.join(self.path, self.cell_f_zero_file)
return np.load(path)
def get_fit_routine_error(self):
foldername = os.path.basename(self.path)
parts = foldername.split("_")
return float(parts[-1])
def comparable_error(self):
cell_values, model_values = self.get_behaviour_values()
@ -165,12 +175,12 @@ class ModelFit:
model = self.get_model()
cell = self.get_cell_data()
fig, axes = plt.subplots(3, 1, figsize=(8, 10))
fig, axes = plt.subplots(4, 1, figsize=(8, 12))
# isi histogram:
axes[0].set_title("ISI-Histogram")
axes[0].set_xlim((0, 50))
bins = np.arange(0, 50, 0.1)
for data, name in zip((model, cell), ("model", "cell")):
for data, name in zip((cell, model), ("cell", "model")):
base = get_baseline_class(data, cell.get_eod_frequency(), trials=5)
isis = np.array(base.get_interspike_intervals()) * 1000
axes[0].hist(isis, bins=bins, label=name, alpha=0.5, density=True)
@ -178,8 +188,18 @@ class ModelFit:
axes[0].legend()
# fi_curve
fi_curve_cell = get_fi_curve_class(cell, cell.get_fi_contrasts(), eod_freq=cell.get_eod_frequency(), trials=15)
fi_curve_model = get_fi_curve_class(model, cell.get_fi_contrasts(), eod_freq=cell.get_eod_frequency(), trials=15)
fi_curve = get_fi_curve_class(cell, cell.get_fi_contrasts(), save_dir=cell.get_data_path())
f_inf_slope = fi_curve.get_f_inf_slope()
contrasts = np.array(cell.get_fi_contrasts())
if f_inf_slope < 0:
contrasts = contrasts * -1
# print("old contrasts:", cell_data.get_fi_contrasts())
# print("new contrasts:", contrasts)
contrasts = sorted(contrasts)
fi_curve_cell = get_fi_curve_class(cell, contrasts, eod_freq=cell.get_eod_frequency(), trials=15)
fi_curve_model = get_fi_curve_class(model, contrasts, eod_freq=cell.get_eod_frequency(), trials=15)
axes[1].set_title("Fi-Curve")
min_x = min(min(fi_curve_cell.stimulus_values), min(fi_curve_model.stimulus_values))
@ -210,7 +230,42 @@ class ModelFit:
axes[1].set_title("cell model comparision")
axes[1].set_xlabel("Stimulus value - contrast")
axes[1].legend()
# comparision of f_zero_curve:
max_contrast = max(contrasts)
test_contrast = 0.5 * max_contrast
diff_contrasts = np.abs(contrasts - test_contrast)
f_zero_curve_contrast_idx = np.argmin(diff_contrasts)
# model:
stimulus = SinusoidalStepStimulus(cell.get_eod_frequency(), contrasts[f_zero_curve_contrast_idx],
start_time=0, duration=cell.get_stimulus_duration())
freq_traces = []
time_traces = []
for i in range(10):
v1, spikes = model.simulate_fast(stimulus, cell.get_time_end() - cell.get_time_start(), cell.get_time_start())
time, freq = hF.calculate_time_and_frequency_trace(spikes, model.get_sampling_interval())
freq_traces.append(freq)
time_traces.append(time)
time, freq = hF.calculate_mean_of_frequency_traces(time_traces, freq_traces, model.get_sampling_interval())
cell_times, cell_freqs = fi_curve_cell.get_mean_time_and_freq_traces()
axes[2].plot(cell_times[f_zero_curve_contrast_idx], cell_freqs[f_zero_curve_contrast_idx])
axes[2].plot(time, freq)
axes[2].set_title("blue: cell, orange: model")
axes[2].set_xlim(-0.15, 0.35)
start_idx = -1
end_idx = -1
for idx in range(len(cell_times[f_zero_curve_contrast_idx])):
if cell_times[f_zero_curve_contrast_idx][idx] < -0.15:
start_idx = idx
elif cell_times[f_zero_curve_contrast_idx][idx] > 0.35:
end_idx = idx
break
axes[2].set_ylim(0.9*min(cell_freqs[f_zero_curve_contrast_idx][start_idx:end_idx]),
1.1*max(cell_freqs[f_zero_curve_contrast_idx][start_idx:end_idx]))
# Value table:
cell_values, model_values = self.get_behaviour_values()
@ -220,10 +275,11 @@ class ModelFit:
clust_data[0].append(cell_values[k])
clust_data[1].append(model_values[k])
axes[2].axis('tight')
axes[2].axis('off')
table = axes[2].table(cellText=clust_data, colLabels=collabel, rowLabels=("cell", "model"), loc='center')
axes[3].axis('tight')
axes[3].axis('off')
table = axes[3].table(cellText=clust_data, colLabels=collabel, rowLabels=("cell", "model"), loc='center')
fig.suptitle(cell.get_cell_name() + "_comp_err: {:.2f}".format(self.comparable_error()))
plt.tight_layout()
if save_path is None:
plt.show()
else: