diff --git a/code/base_chirps.py b/code/base_chirps.py
index 1b26e6c..9ead914 100644
--- a/code/base_chirps.py
+++ b/code/base_chirps.py
@@ -8,7 +8,7 @@ from IPython import embed
data_dir = "../data"
-dataset = "2018-11-13-ah-invivo-1"
+dataset = "2018-11-13-ad-invivo-1"
data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1"]
@@ -41,7 +41,9 @@ for i in sort_df:
chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(chirp_spikes)
sort_df = sorted(df_map.keys())
-dct_phase = plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
+example = [-50, 200, 400]
+
+dct_phase = plot_std_chirp(example, df_map, chirp_spikes, chirp_mods)
plt.show()
plt.close('all')
diff --git a/code/base_eod.py b/code/base_eod.py
index 86a5ec2..8789b9e 100644
--- a/code/base_eod.py
+++ b/code/base_eod.py
@@ -14,10 +14,16 @@ time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
zeit = np.asarray(time)
-plt.plot(zeit[0:1000], eod[0:1000])
-plt.title('A.lepto EOD', fontsize = 18)#Plottitelk
-plt.xlabel('time [ms]', fontsize = 16)#Achsentitel
-plt.ylabel('amplitude[mv]', fontsize = 16)#Achsentitel
-plt.xticks(fontsize = 14)
-plt.yticks(fontsize = 14)
+
+inch_factor = 2.54
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+
+plt.plot(zeit[0:1000], eod[0:1000], color = 'darkblue')
+plt.title('A.lepto EOD', fontsize = 24)#Plottitel
+plt.xlabel('time [ms]', fontsize = 22)#Achsentitel
+plt.ylabel('amplitude[mv]', fontsize = 22)#Achsentitel
+plt.tick_params(axis='both', which='major', labelsize = 22)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+fig.tight_layout()
plt.show()
diff --git a/code/base_spikes.py b/code/base_spikes.py
index 32ffd68..014b7b8 100644
--- a/code/base_spikes.py
+++ b/code/base_spikes.py
@@ -12,25 +12,28 @@ data_base = ("2018-11-09-ab-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-
data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
dataset = "2018-11-13-ad-invivo-1"
-
+inch_factor = 2.54
#for dataset in data_base:
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
spike_iv = np.diff(spike_times)
x = np.arange(0.001, 0.01, 0.0001)
-plt.hist(spike_iv,x)
+
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.hist(spike_iv,x, color = 'darkblue')
mu = np.mean(spike_iv)
sigma = np.std(spike_iv)
cv = sigma/mu
-plt.title('A.lepto ISI Histogramm', fontsize = 18)
-plt.xlabel('duration ISI[ms]', fontsize = 16)
-plt.ylabel('number of ISI', fontsize = 16)
-
-plt.xticks(fontsize = 14)
-plt.yticks(fontsize = 14)
+plt.title('A.lepto ISI Histogramm', fontsize = 24)
+plt.xlabel('duration ISI[ms]', fontsize = 22)
+plt.ylabel('number of ISI', fontsize = 22)
+plt.tick_params(axis='both', which='major', labelsize = 22)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
plt.show()
@@ -47,6 +50,7 @@ sort_df = sorted(df_map.keys())
dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
plt.figure()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
ls_mean = plot_df_spikes(sort_df, dct_rate)
plt.show()
@@ -54,14 +58,17 @@ plt.show()
#mittlere Feuerrate einer Frequenz auf Frequenz:
-plt.figure()
-plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.plot(np.arange(0,len(ls_mean),1),ls_mean, color = 'darkblue')
plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r, color = 'green')
-plt.title('Mean firing rate of a cell for a range of frequency differences', fontsize = 18)
+plt.title('Mean firing rate of a cell for a range of frequency differences', fontsize = 24)
plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
-plt.xlabel('Range of frequency differences [Hz]', fontsize = 16)
-plt.ylabel('Mean firing rate of the cell', fontsize = 16)
-plt.tick_params(axis='both', which='major', labelsize = 14)
+plt.xlabel('Range of frequency differences [Hz]', fontsize = 22)
+plt.ylabel('Mean firing rate of the cell', fontsize = 22)
+plt.tick_params(axis='both', which='major', labelsize = 18)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
plt.show()
@@ -70,12 +77,15 @@ plt.show()
#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
adapt = adaptation_df(sort_df, dct_rate)
-plt.figure()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.boxplot(adapt)
-plt.title('Adaptation of cell firing rate during a trial', fontsize = 18)
-plt.xlabel('Cell', fontsize = 16)
-plt.ylabel('Adaptation size [Hz]', fontsize = 16)
-plt.tick_params(axis='both', which='major', labelsize = 14)
+plt.title('Adaptation of cell firing rate during a trial', fontsize = 24)
+plt.xlabel('Cell', fontsize = 22)
+plt.ylabel('Adaptation size [Hz]', fontsize = 22)
+plt.tick_params(axis='both', which='major', labelsize = 18)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
plt.show()
diff --git a/code/func_chirp.py b/code/func_chirp.py
index b358e3a..5e2bddb 100644
--- a/code/func_chirp.py
+++ b/code/func_chirp.py
@@ -2,8 +2,10 @@ from read_baseline_data import *
from read_chirp_data import *
from utility import *
import matplotlib.pyplot as plt
+import math
import numpy as np
+inch_factor = 2.54
def chirp_eod_plot(df_map, eod, times):
#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
@@ -11,7 +13,7 @@ def chirp_eod_plot(df_map, eod, times):
for i in df_map.keys():
freq = list(df_map[i])
- fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
+ fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
for idx, k in enumerate(freq):
ct = times[k]
@@ -19,22 +21,28 @@ def chirp_eod_plot(df_map, eod, times):
zeit = e1[0]
eods = e1[1]
- if idx <= 3:
- axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
- axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
- elif 4<= idx <= 7:
- axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
- axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
- elif 8<= idx <= 11:
- axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
- axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
- else:
- axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
- axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+ if idx <= 1:
+ ax.plot(zeit, eods, color= 'darkblue')
+ ax.scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+ #elif 4<= idx <= 7:
+ # axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+ # axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+ #elif 8<= idx <= 11:
+ # axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+ # axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+ else:
+ continue
+ #axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+ #axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+
+ ax.set_ylabel('Amplitude [mV]', fontsize = 22)
+ ax.set_xlabel('Time [ms]', fontsize = 22)
+ ax.tick_params(axis='both', which='major', labelsize = 18)
+ ax.spines['right'].set_visible(False)
+ ax.spines['top'].set_visible(False)
+ fig.suptitle('EOD for chirps', fontsize = 24)
+ fig.tight_layout()
- axs[0,1].set_ylabel('Amplitude [mV]')
- axs[1,0].set_xlabel('Time [ms]')
- fig.suptitle('EOD for chirps', fontsize = 16)
@@ -69,7 +77,7 @@ def cut_chirps(freq, eod, times):
def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
- plt.figure()
+ fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
dct_phase = {}
num_bin = 12
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
@@ -82,10 +90,13 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
dct_phase[i].append(phase[1])
for i in sort_df:
- plt.scatter(dct_phase[i], chirp_mods[i], label = i)
- plt.title('Change of std depending on the phase where the chirp occured')
- plt.xlabel('Phase')
- plt.ylabel('Standard deviation of the amplitude modulation')
+ norm = np.asarray(dct_phase[i]) *2*math.pi
+ plt.scatter(norm, chirp_mods[i], label = i, s = 22)
+ plt.title('Change of std depending on the phase where the chirp occured', fontsize = 24)
+ plt.xlabel('Phase', fontsize = 22)
+ plt.ylabel('Standard deviation of the amplitude modulation', fontsize = 22)
+ plt.xticks([0, math.pi/2, math.pi, math.pi*1.5, math.pi*2], ('0', '$\pi$ /2', '$\pi$', '1.5 $\pi$', '2$\pi$'))
+ plt.tick_params(axis='both', which='major', labelsize = 18)
plt.legend()
return(dct_phase)
diff --git a/code/func_spike.py b/code/func_spike.py
index ac47c8f..458f9d5 100644
--- a/code/func_spike.py
+++ b/code/func_spike.py
@@ -43,6 +43,8 @@ def spike_rates(sort_df, df_map, chirp_spikes):
def plot_df_spikes(sort_df, dct_rate):
#gibt die Feuerrate gegen die Frequenz aufgetragen
+ inch_factor = 2.54
+ fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
ls_mean = []
for h in sort_df:
mean = np.mean(dct_rate[h])
@@ -50,10 +52,13 @@ def plot_df_spikes(sort_df, dct_rate):
plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
plt.legend()
- plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 18)
- plt.xlabel('# of trials', fontsize = 16)
- plt.ylabel('Instant firing rate of the cell', fontsize = 16)
- plt.tick_params(axis='both', which='major', labelsize = 14)
+ plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 24)
+ plt.xlabel('# of trials', fontsize = 22)
+ plt.ylabel('Instant firing rate of the cell', fontsize = 22)
+ plt.tick_params(axis='both', which='major', labelsize = 18)
+ ax.spines['right'].set_visible(False)
+ ax.spines['top'].set_visible(False)
+ plt.tight_layout()
return(ls_mean)
diff --git a/code/order_eff.py b/code/order_eff.py
index 35f14e1..1657ef3 100644
--- a/code/order_eff.py
+++ b/code/order_eff.py
@@ -12,6 +12,7 @@ data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-a
+inch_factor = 2.54
data_rate_dict = {}
for dataset in data_chirps:
@@ -31,14 +32,17 @@ for dataset in data_chirps:
rate = len(spikes)/ 1.2
data_rate_dict[dataset].append(rate)
-
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
for dataset in data_rate_dict:
plt.plot(data_rate_dict[dataset])
-plt.title('Test for sequence effects', fontsize = 20)
-plt.xlabel('Number of stimulus presentations', fontsize = 18)
-plt.ylabel('Firing rates of cells', fontsize = 18)
-plt.tick_params(axis='both', which='major', labelsize = 16)
+plt.title('Test for sequence effects', fontsize = 24)
+plt.xlabel('Number of stimulus presentations', fontsize = 22)
+plt.ylabel('Firing rates of cells', fontsize = 22)
+plt.tick_params(axis='both', which='major', labelsize = 22)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+fig.tight_layout()
plt.show()
diff --git a/code/plot_spikesduringbaselineactivity.py b/code/plot_spikesduringbaselineactivity.py
index a13c16a..d9c2e49 100644
--- a/code/plot_spikesduringbaselineactivity.py
+++ b/code/plot_spikesduringbaselineactivity.py
@@ -17,6 +17,7 @@ interspikeintervals = np.diff(spikes)*1000
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
+#Titel fehlt!!
plt.xlabel("time [ms]", fontsize = 22)
plt.xticks(fontsize = 18)
plt.ylabel("Number of \n Interspikeinterval", fontsize = 22)