From 68e7b75768c0c21a02f1d811ca1fab7ad13cb97b Mon Sep 17 00:00:00 2001
From: "a.ott" <a.ott@student.uni-tuebingen.de>
Date: Wed, 19 Aug 2020 17:16:57 +0200
Subject: [PATCH] better figures

---
 Figures_Model.py   | 25 +++++++++++++----------
 Figures_Stimuli.py | 50 +++++++++++++++++++---------------------------
 2 files changed, 34 insertions(+), 41 deletions(-)

diff --git a/Figures_Model.py b/Figures_Model.py
index cf64c4b..5f2817c 100644
--- a/Figures_Model.py
+++ b/Figures_Model.py
@@ -7,10 +7,11 @@ import matplotlib.pyplot as plt
 import numpy as np
 import models.smallModels as sM
 
+
 def main():
     # stimulus_development()
-    # model_adaption_example()
-    model_comparison()
+    model_adaption_example()
+    # model_comparison()
     pass
 
 
@@ -81,18 +82,19 @@ def model_comparison():
 
 
 def stimulus_development():
-    time_start = 0
-    time_duration = 0.025
+    time_start = -0.020
+    time_duration = 0.080
 
     stimulus = SinusoidalStepStimulus(745, 0.2, 0.1, 0.1)
-    step_size = 0.00005
+    step_size = 0.000005
     stim_array = stimulus.as_array(time_start, time_duration, step_size)
 
     rectified = hF.rectify_stimulus_array(stim_array)
-    filtered = dendritic_lowpass(rectified, 0.0014, step_size)
+    filtered = dendritic_lowpass(rectified, 0.001, step_size)
 
     fig, axes = plt.subplots(3, 1, figsize=(6, 6), sharex="col")
-    time = np.arange(time_start, time_duration, step_size)
+    time = np.arange(time_start, time_start+time_duration, step_size)
+
     axes[0].plot(time, stim_array)
     axes[0].set_title("stimulus")
 
@@ -102,13 +104,14 @@ def stimulus_development():
     axes[2].plot(time, filtered)
     axes[2].set_title("rectified with dendritic filter")
 
-    axes[0].set_ylim((-1.05, 1.05))
-    axes[1].set_ylim((-1.05, 1.05))
+    axes[0].set_ylim((-1.15, 1.15))
+    axes[1].set_ylim((-1.15, 1.15))
+    axes[2].set_ylim((-1.15, 1.15))
 
     for ax in axes:
         ax.set_ylabel("Amplitude [mV]")
     axes[2].set_xlabel("Time [s]")
-    axes[0].set_xlim((time_start-0.0005, time_duration))
+    axes[0].set_xlim((0, 0.05))
     # axes[2].set_ylim((0, 1.05))
 
     plt.tight_layout()
@@ -162,7 +165,7 @@ def model_adaption_example():
     axes[0].plot(time_part, v1[start_idx:end_idx])
     axes[0].set_ylabel("Membrane voltage [mV]")
     # axes[1].plot(time[start_idx:end_idx], adaption[start_idx:end_idx])
-    axes[1].plot(time_part, adaption[start_idx:end_idx])
+    axes[1].plot(time_part, -1*np.array(adaption[start_idx:end_idx]))
     axes[1].set_ylabel("Adaption current [mV]")
     axes[1].set_xlabel("Time [ms]")
     axes[1].set_xlim((start, end))
diff --git a/Figures_Stimuli.py b/Figures_Stimuli.py
index aeef33c..8b442a0 100644
--- a/Figures_Stimuli.py
+++ b/Figures_Stimuli.py
@@ -6,12 +6,14 @@ import matplotlib.pyplot as plt
 
 
 def main():
-    plot_step_stimulus()
-    plot_sam_stimulus()
+    stimuli_protocol_examples()
     pass
 
 
-def plot_step_stimulus():
+def stimuli_protocol_examples():
+
+    fig, axes = plt.subplots(2, 1)
+
     start = 0
     end = 1
 
@@ -19,47 +21,35 @@ def plot_step_stimulus():
     time_end = 1.2
     step_size = 0.00005
 
-    frequency = 20
+    frequency = 55
     contrast = 0.5
+
+    # Sinusoidal STEP stimulus
     # frequency, contrast, start_time=0, duration=np.inf, amplitude=1
-    step_stim= SinusoidalStepStimulus(frequency, contrast, start, end-start)
+    step_stim = SinusoidalStepStimulus(frequency, contrast, start, end - start)
 
-    values = step_stim.as_array(time_start, time_end-time_start, step_size)
+    values = step_stim.as_array(time_start, time_end - time_start, step_size)
     time = np.arange(time_start, time_end, step_size)
 
-    plt.plot(time, values)
-    plt.xlabel("Time [s]")
-    plt.ylabel("Voltage [mV]")
-    plt.savefig("thesis/figures/sin_step_stim_example.pdf")
-    plt.close()
-
-
-def plot_sam_stimulus():
-    start = 0
-    end = 1
-
-    time_start = -0.2
-    time_end = 1.2
-    step_size = 0.00005
+    axes[0].plot(time, values)
+    axes[0].set_ylabel("Voltage [mV]")
 
-    contrast = 0.5
+    # SAM Stimulus:
     mod_freq = 10
-    carrier_freq = 53
-    # carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1
-    step_stim = SinusAmplitudeModulationStimulus(carrier_freq, contrast, mod_freq, start, end - start)
+    step_stim = SinusAmplitudeModulationStimulus(frequency, contrast, mod_freq, start, end - start)
 
     values = step_stim.as_array(time_start, time_end - time_start, step_size)
     time = np.arange(time_start, time_end, step_size)
     plt.plot(time, values)
 
     beat_time = np.arange(start, end, step_size)
-    beat_values = np.sin(beat_time*2*np.pi*mod_freq) * contrast + 1
-    plt.plot(beat_time, beat_values)
+    beat_values = np.sin(beat_time * 2 * np.pi * mod_freq) * contrast + 1
+    axes[1].plot(beat_time, beat_values)
+
+    axes[1].set_xlabel("Time [s]")
+    axes[1].set_ylabel("Voltage [mV]")
 
-    plt.xlabel("Time [s]")
-    plt.ylabel("Voltage [mV]")
-    # plt.show()
-    plt.savefig("thesis/figures/sam_stim_example.pdf")
+    plt.savefig("thesis/figures/stimuliExamples.pdf")
     plt.close()