From ebf365dbe07ba443b7c32217f27db13bf356497f Mon Sep 17 00:00:00 2001
From: xaver <xaver.roos@t-online.de>
Date: Fri, 23 Oct 2020 17:42:53 +0200
Subject: [PATCH] 23.10
---
.../figure_apteronotus_gain_plot.py | 50 ++++++++-----------
...igure_apteronotus_gaincurve_cutofff_tau.py | 4 +-
.../figure_apteronotus_jar_plot.py | 20 ++++----
.../figure_apteronotus_rms_gaincurve.py | 10 ++--
.../{sin_all_uniform.py => sin_all.py} | 39 ++++++++++-----
.../{sin_all_normal.py => sin_all_model.py} | 26 +++++-----
eigenmannia_code/eigenmannia_jar_subplot.py | 4 +-
7 files changed, 80 insertions(+), 73 deletions(-)
rename apteronotus_code/{sin_all_uniform.py => sin_all.py} (72%)
rename apteronotus_code/{sin_all_normal.py => sin_all_model.py} (85%)
diff --git a/apteronotus_code/figure_apteronotus_gain_plot.py b/apteronotus_code/figure_apteronotus_gain_plot.py
index acdb8a5..7dfe595 100644
--- a/apteronotus_code/figure_apteronotus_gain_plot.py
+++ b/apteronotus_code/figure_apteronotus_gain_plot.py
@@ -7,6 +7,8 @@ from matplotlib.mlab import specgram
import os
from jar_functions import gain_curve_fit
+plt.rcParams.update({'font.size': 10})
+
identifier = ['2018lepto1',
#'2018lepto4',
'2018lepto5',
@@ -52,81 +54,73 @@ for ID in identifier:
sort = sorted(zip(f_cs, identifier))
print(sort)
+# order of plotting: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2019lepto24, 2020lepto06
# order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5
fig = plt.figure(figsize=(8.27,11.69))
ax0 = fig.add_subplot(321)
+fig.text(0.05, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
+fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
+
ax0.set_xlim(0.0007, 1.5)
+ax0.set_ylim(0.001, 10)
ax0.plot(amfs[4], gains[4],'o' , label = 'gain')
ax0.plot(amfs[4], predicts[4], label = 'fit')
ax0.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax0.set_xscale('log')
ax0.set_yscale('log')
-ax0.set_ylabel('gain [Hz/(mV/cm)]')
-#ax0.set_xlabel('envelope_frequency [Hz]')
-#ax0.set_title('gaincurve %s' %ID)
+ax0.axes.xaxis.set_ticklabels([])
ax1 = fig.add_subplot(322)
ax1.set_xlim(0.0007, 1.5)
-ax1.get_shared_y_axes().join(ax0, ax1)
-ax1.axes.yaxis.set_ticklabels([])
+ax1.set_ylim(0.001, 10)
ax1.plot(amfs[5], gains[5],'o' , label = 'gain')
ax1.plot(amfs[5], predicts[5], label = 'fit')
ax1.axvline(x=f_cs[5], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax1.set_xscale('log')
ax1.set_yscale('log')
-#ax1.set_ylabel('gain [Hz/(mV/cm)]')
-#ax1.set_xlabel('envelope_frequency [Hz]')
-#ax1.set_title('gaincurve %s' %ID)
+ax1.axes.yaxis.set_ticklabels([])
+ax1.axes.xaxis.set_ticklabels([])
ax2 = fig.add_subplot(323)
ax2.set_xlim(0.0007, 1.5)
-ax2.plot(amfs[4], gains[4],'o' , label = 'gain')
-ax2.plot(amfs[4], predicts[4], label = 'fit')
-ax2.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
+ax2.set_ylim(0.001, 10)
+ax2.plot(amfs[3], gains[3],'o' , label = 'gain')
+ax2.plot(amfs[3], predicts[3], label = 'fit')
+ax2.axvline(x=f_cs[3], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax2.set_xscale('log')
ax2.set_yscale('log')
-ax2.set_ylabel('gain [Hz/(mV/cm)]')
-#ax2.set_xlabel('envelope_frequency [Hz]')
-#ax2.set_title('gaincurve %s' %ID)
+ax2.axes.xaxis.set_ticklabels([])
ax3 = fig.add_subplot(324)
ax3.set_xlim(0.0007, 1.5)
-ax3.get_shared_y_axes().join(ax2, ax3)
-ax3.axes.yaxis.set_ticklabels([])
+ax3.set_ylim(0.001, 10)
ax3.plot(amfs[2], gains[2],'o' , label = 'gain')
ax3.plot(amfs[2], predicts[2], label = 'fit')
ax3.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax3.set_xscale('log')
ax3.set_yscale('log')
-#ax3.set_ylabel('gain [Hz/(mV/cm)]')
-#ax3.set_xlabel('envelope_frequency [Hz]')
-#ax3.set_title('gaincurve %s' %ID)
+ax3.axes.yaxis.set_ticklabels([])
+ax3.axes.xaxis.set_ticklabels([])
ax4 = fig.add_subplot(325)
ax4.set_xlim(0.0007, 1.5)
-#ax4.get_shared_y_axes().join(ax0, ax4)
+ax4.set_ylim(0.001, 10)
ax4.plot(amfs[0], gains[0],'o' , label = 'gain')
ax4.plot(amfs[0], predicts[0], label = 'fit')
ax4.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax4.set_xscale('log')
ax4.set_yscale('log')
-ax4.set_ylabel('gain [Hz/(mV/cm)]')
-#ax4.set_xlabel('envelope_frequency [Hz]')
-#ax4.set_title('gaincurve %s' %ID)
ax5 = fig.add_subplot(326)
ax5.set_xlim(0.0007, 1.5)
-ax5.axes.yaxis.set_ticklabels([])
-ax5.get_shared_y_axes().join(ax4, ax5)
+ax5.set_ylim(0.001, 10)
ax5.plot(amfs[1], gains[1],'o' , label = 'gain')
ax5.plot(amfs[1], predicts[1], label = 'fit')
ax5.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax5.set_xscale('log')
ax5.set_yscale('log')
-#ax5.set_ylabel('gain [Hz/(mV/cm)]')
-#ax5.set_xlabel('envelope_frequency [Hz]')
-#ax5.set_title('gaincurve %s' %ID)
+ax5.axes.yaxis.set_ticklabels([])
#plt.legend(loc = 'lower left')
plt.show()
diff --git a/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py b/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
index a5e69e5..b04b2dc 100644
--- a/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
+++ b/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
@@ -35,8 +35,8 @@ for ID in identifier:
fig = plt.figure()
ax = fig.add_subplot()
ax.plot(amf, gain,'o' , label = 'gain')
- #ax.plot(amf, predict, label = 'fit')
- #ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
+ ax.plot(amf, predict, label = 'fit')
+ ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylabel('gain [Hz/(mV/cm)]')
diff --git a/apteronotus_code/figure_apteronotus_jar_plot.py b/apteronotus_code/figure_apteronotus_jar_plot.py
index 1b0441f..4324f6e 100644
--- a/apteronotus_code/figure_apteronotus_jar_plot.py
+++ b/apteronotus_code/figure_apteronotus_jar_plot.py
@@ -13,22 +13,22 @@ from jar_functions import sin_response
from jar_functions import mean_noise_cut
from jar_functions import gain_curve_fit
-plt.rcParams.update({'font.size': 10})
+#plt.rcParams.update({'font.size': 10})
def take_second(elem): # function for taking the names out of files
return elem[1]
-identifier = [#'2018lepto1',
- #'2018lepto4',
- #'2018lepto5',
- #'2018lepto76',
+identifier = ['2018lepto1',
+ '2018lepto4',
+ '2018lepto5',
+ '2018lepto76',
'2018lepto98',
'2019lepto03',
- #'2019lepto24',
- #'2019lepto27',
- #'2019lepto30',
- #'2020lepto04',
- #'2020lepto06',
+ '2019lepto24',
+ '2019lepto27',
+ '2019lepto30',
+ '2020lepto04',
+ '2020lepto06',
'2020lepto16',
'2020lepto19',
'2020lepto20'
diff --git a/apteronotus_code/figure_apteronotus_rms_gaincurve.py b/apteronotus_code/figure_apteronotus_rms_gaincurve.py
index af0dafd..bfa4d67 100644
--- a/apteronotus_code/figure_apteronotus_rms_gaincurve.py
+++ b/apteronotus_code/figure_apteronotus_rms_gaincurve.py
@@ -19,7 +19,7 @@ plt.rcParams.update({'font.size': 12})
def take_second(elem): # function for taking the names out of files
return elem[1]
-identifier = ['2020lepto16']
+identifier = ['2020lepto19']
for ident in identifier:
predict = []
@@ -37,16 +37,16 @@ for ident in identifier:
currf = None
idxlist = []
- data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
+ data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
for i, d in enumerate(data):
dd = list(d)
- jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
+ jar = np.load('%s.npy' %dd) # load data for every file name
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
print(dd)
- time = np.load('5Hz_%s time.npy' %dd) # time file
+ time = np.load('%s time.npy' %dd) # time file
dt = time[1] - time[0]
n = int(1/float(d[1])/dt)
@@ -127,7 +127,7 @@ for ident in identifier:
fig = plt.figure(figsize = (8,14))
fig.suptitle('gaincurve and RMS %s' %ident)
ax0 = fig.add_subplot(2, 1, 1)
- ax0.plot(amfreq_arr, mgain_arr, 'o')
+ ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
ax0.set_yscale('log')
ax0.set_xscale('log')
ax0.set_ylabel('gain [Hz/(mV/cm)]')
diff --git a/apteronotus_code/sin_all_uniform.py b/apteronotus_code/sin_all.py
similarity index 72%
rename from apteronotus_code/sin_all_uniform.py
rename to apteronotus_code/sin_all.py
index 98cfca8..be66ef9 100644
--- a/apteronotus_code/sin_all_uniform.py
+++ b/apteronotus_code/sin_all.py
@@ -8,6 +8,8 @@ from jar_functions import avgNestedLists
import matplotlib as mpl
from matplotlib import cm
+#plt.rcParams.update({'font.size': 18})
+
identifier_uniform = ['2018lepto1',
#'2018lepto4',
'2018lepto5',
@@ -32,14 +34,14 @@ identifier = ['2018lepto1',
'2019lepto24',
'2019lepto27',
'2019lepto30',
- #'2020lepto04',
+ '2020lepto04',
'2020lepto06',
'2020lepto16',
- #'2020lepto19',
+ '2020lepto19',
'2020lepto20'
]
-amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
+custom_amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
#colors = ['dimgray', 'dimgrey', 'gray', 'grey', 'darkgray', 'darkgrey', 'silver', 'lightgray', 'lightgrey', 'gainsboro', 'whitesmoke']
colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k']
all = []
@@ -54,10 +56,16 @@ for ident in identifier_uniform:
av = avgNestedLists(all)
new_av = avgNestedLists(new_all)
-fig = plt.figure()
+fig = plt.figure(figsize=(8.27, 11.69/2))
ax = fig.add_subplot(111)
-ax.plot(amf, av, 'o', color = 'darkorange', label = 'normal')
-ax.plot(amf, new_av, 'o', label = 'uniform')
+ax.plot(custom_amf, av, 'o', label = 'normal')
+#ax.plot(amf, new_av, 'o', label = 'uniform')
+
+sinv, sinc = curve_fit(gain_curve_fit, custom_amf, av, [2, 3])
+predict = []
+for f in custom_amf:
+ G = np.max(av) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
+ predict.append(G)
tau = []
f_c = []
@@ -76,8 +84,10 @@ for ID in identifier:
f_c.append(f_cutoff)
fit.append(gain_curve_fit(amf, *sinv))
fit_amf.append(amf)
+ ax.axvline(x=f_cutoff, ymin=0, ymax=5, color='C0', ls='-', alpha=0.5, label='cutoff frequency')
-# uniformed: 2018lepto5, 2018lepto1, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto4
+
+# uniformed: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto06
tau_uniform = []
f_c_uniform = []
@@ -98,6 +108,7 @@ for ID in identifier_uniform:
fit_uniform.append(gain_curve_fit(amf, *sinv))
fit_amf_uniform.append(amf)
+
colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
#for ff ,f in enumerate(fit_uniform):
@@ -106,13 +117,15 @@ colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
ax.set_xscale('log')
ax.set_yscale('log')
-ax.set_title('gain average all fish uniform')
ax.set_ylabel('gain [Hz/(mV/cm)]')
-ax.set_xlabel('envelope_frequency [Hz]')
-ax.set_ylim(0.0008, )
-ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'normal cutoff frequencies')
-ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies')
-ax.legend(loc = 'center left')
+ax.set_xlabel('envelope frequency [Hz]')
+ax.set_xlim(0.0007, 1.5)
+ax.set_ylim(0.001, 10)
+ax.plot(custom_amf, predict)
+#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', color = 'C0', alpha = 0.5, label = 'normal cutoff frequencies')
+#ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies')
+#ax.legend(loc = 'center left')
+
plt.show()
embed()
diff --git a/apteronotus_code/sin_all_normal.py b/apteronotus_code/sin_all_model.py
similarity index 85%
rename from apteronotus_code/sin_all_normal.py
rename to apteronotus_code/sin_all_model.py
index 60f9038..e86bbe7 100644
--- a/apteronotus_code/sin_all_normal.py
+++ b/apteronotus_code/sin_all_model.py
@@ -7,20 +7,20 @@ from jar_functions import gain_curve_fit
from jar_functions import avgNestedLists
-identifier = [#'2018lepto1',
- #'2018lepto4',
- #'2018lepto5',
- #'2018lepto76',
+identifier = ['2018lepto1',
+ '2018lepto4',
+ '2018lepto5',
+ '2018lepto76',
'2018lepto98',
- #'2019lepto03',
- #'2019lepto24',
- #'2019lepto27',
- #'2019lepto30',
- #'2020lepto04',
- #'2020lepto06',
- #'2020lepto16',
+ '2019lepto03',
+ '2019lepto24',
+ '2019lepto27',
+ '2019lepto30',
+ '2020lepto04',
+ '2020lepto06',
+ '2020lepto16',
'2020lepto19',
- #'2020lepto20'
+ '2020lepto20'
]
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
@@ -62,7 +62,7 @@ for ident in identifier:
av = avgNestedLists(all)
embed()
-fig = plt.figure()
+fig = plt.figure(figsize=(8.27,11.69/2))
ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', c = 'C0', label = 'gain')
diff --git a/eigenmannia_code/eigenmannia_jar_subplot.py b/eigenmannia_code/eigenmannia_jar_subplot.py
index 4e03d4c..4742f0e 100644
--- a/eigenmannia_code/eigenmannia_jar_subplot.py
+++ b/eigenmannia_code/eigenmannia_jar_subplot.py
@@ -12,7 +12,7 @@ from jar_functions import get_time_zeros
from jar_functions import import_data_eigen
from scipy.signal import savgol_filter
-plt.rcParams.update({'font.size': 18})
+#plt.rcParams.update({'font.size': 18})
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
@@ -99,7 +99,7 @@ for ID in identifier:
# plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
# plt.xlim(times[0],times[-1])
-fig = plt.figure(figsize = (20,20))
+fig = plt.figure(figsize = (8.27, 11.69/2))
ax0 = fig.add_subplot(221)
ax0.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[0]), aspect='auto', vmin=-80, vmax=-10)
#ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)