From 7077c5b8e6dd244e777ad1288f57d6f555f79062 Mon Sep 17 00:00:00 2001
From: "a.ott" <a.ott@student.uni-tuebingen.de>
Date: Thu, 4 Jun 2020 17:12:15 +0200
Subject: [PATCH] add prototype to plot sam pdf comparisions cell-model

---
 sam_experiments.py | 31 ++++++++++++++++++++++++++++++-
 1 file changed, 30 insertions(+), 1 deletion(-)

diff --git a/sam_experiments.py b/sam_experiments.py
index fcad28c..fb1e244 100644
--- a/sam_experiments.py
+++ b/sam_experiments.py
@@ -4,6 +4,7 @@ from models.LIFACnoise import LifacNoiseModel
 import numpy as np
 import matplotlib.pyplot as plt
 import helperFunctions as hF
+from CellData import CellData
 
 
 def main():
@@ -12,8 +13,23 @@ def main():
                   'v_base': 0, 'step_size': 5e-05, 'dend_tau': 0.0008667253013050408, 'v_zero': 0, 'v_offset': -6.25,
                   'noise_strength': 0.03337309379328535, 'a_zero': 2, 'threshold': 1, 'delta_a': 0.0726267312975076}
     eod_freq = 658
-
+    cell_data = CellData("./data/2012-12-13-ao-invivo-1/")
     model = LifacNoiseModel(parameters)
+    mean_duration = np.mean(cell_data.get_sam_durations())
+    contrasts = cell_data.get_sam_contrasts()
+    spiketimes = cell_data.get_sam_spiketimes()
+    for i, m_freq in enumerate(cell_data.get_sam_delta_frequencies()):
+
+        stimulus = SAM(eod_freq, contrasts[i], m_freq)
+        prob_desnity_function_model = generate_pdf(model, stimulus, sim_length=mean_duration)
+        for spikes in spiketimes[i]:
+            prob_density_cell = spiketimes_calculate_pdf(spikes, cell_data.get_sampling_interval())
+
+            plt.plot(prob_density_cell)
+            plt.plot(prob_desnity_function_model)
+            plt.show()
+            plt.close()
+
 
     # __init__(carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1)
     mod_freqs = np.arange(-60, eod_freq*4 + 61, 10)
@@ -58,6 +74,19 @@ def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
     return mean_rate
 
 
+def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
+    length = int(spikes[-1] / step_size)+1
+    binary = np.zeros(length)
+    spikes = [int(s / step_size) for s in spikes]
+    for s_idx in spikes:
+        binary[s_idx] = 1
+
+    kernel = gaussian_kernel(kernel_width, step_size)
+    rate = np.convolve(binary, kernel, mode='same')
+
+    return rate
+
+
 def gaussian_kernel(sigma, dt):
     x = np.arange(-4. * sigma, 4. * sigma, dt)
     y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma