def construct_multi_fly_df(fly_nums,rootpath='/media/imager/FlyDataD/FlyDB/'):
#Input: simple list of fly numbers, opt fly directory
#Returns a combined dataframe with all flies
flys = [flylib.NetFly(fly_num,rootpath) for fly_num in fly_nums]
fly_dfs = [fly.construct_dataframe() for fly in flys]
combined_df = pd.concat(fly_dfs,ignore_index=True)
return combined_df
corr_window_size = 1
frames_per_step = 1
def render_svg_to_png(svg_data, filename):
# Render
svg = rsvg.Handle(data=svg_data)
img = cairo.ImageSurface(cairo.FORMAT_ARGB32, svg.props.width,
svg.props.height)
ctx = cairo.Context(img)
svg.render_cairo(ctx)
img.write_to_png(filename)
try:
fly = flb.NetFly(
fly_num, rootpath='/home/annie/imager/media/imager/FlyDataD/FlyDB/')
except (OSError):
fly = flb.NetFly(fly_num)
flydf = fly.construct_dataframe()
dt = frames_per_step * (flydf['t'][1] - flydf['t'][0])
corr_window_size = np.floor(corr_window_size / dt) * dt
disp_window_size = np.floor(disp_window_size / dt) * dt
steps_per_disp_window = int(disp_window_size / dt)
steps_per_corr_window = int(corr_window_size / dt)
filtered_muscle_cols = \
['iii1_left', 'iii3_left',
from matplotlib import pyplot as plt
import numpy as np
import scipy
import flylib as flb
from flylib import util
import pandas as pd
fly = flb.NetFly(1538,rootpath='/home/annie/imager/media/imager/FlyDataD/FlyDB/')
# fly = flb.NetFly(1538,rootpath='/home/annie/work/programming/fly_muscle_data/')
flydf = fly.construct_dataframe()
# flydf['ca_pixel_left'] = fly.ca_cam_left #this is really big (obvs)
print(flydf.head())
print(np.unique(flydf['stimulus']))
print(flydf.columns.values)
#Here is an example of how to filter for rows of a certain column value
filtered_df = flydf.loc[flydf['stimulus']=='cl_blocks, g_x=-1, g_y=0, b_x=-8, b_y=0, ch=True']
print(filtered_df.head())
#Or for a combination of column values
idx = (flydf['stimulus']=='cl_blocks, g_x=-1, g_y=0, b_x=-8, b_y=0, ch=True')& \
(flydf['amp_diff']>0.1)
double_filtered_df = flydf.loc[idx]
print(double_filtered_df.head())
import flylib as flb
from matplotlib import pyplot as plt
import numpy as np
import scipy
from flylib import util
import figurefirst as fifi
import scipy.signal
#import local_project_functions as lpf
from IPython.display import SVG, display
import networkx as nx
flynumbers = list(range(1389, 1390))
#flynumbers = list(range(1548,1549))
flylist = [
flb.NetFly(fnum, rootpath='/media/imager/FlyDataD/FlyDB/')
for fnum in flynumbers
]
l = [fly.open_signals() for fly in flylist]
#fly = flylist[4]
flydf = fly.construct_dataframe()
#flydf_=fly.construct_dataframe()
#Access calcium values for a specific muscle and specific stimulus
pretrial_stripe_fix_b2_right = flydf.loc[flydf['stimulus'] ==
'pretrial_stripe_fix', ['b2_right']]
print(np.shape(pretrial_stripe_fix_b2_right))
#https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/
#print(filtered_df[key+'_right'])
from matplotlib import pyplot as plt
import numpy as np
import scipy
import flylib as flb
from flylib import util
import pandas as pd
# fly = flb.NetFly(1540,rootpath='/home/annie/imager/media/imager/FlyDataD/FlyDB/')
fly = flb.NetFly(1548,
rootpath='/home/annie/work/programming/fly_muscle_data/')
fly.open_signals()
flydf = pd.DataFrame()
flydf['t'] = fly.time
flydf['stimulus'] = np.array(fly.experimental_block)
flydf['amp_diff'] = np.array(fly.left_amp) - np.array(fly.right_amp)
for (key, value) in fly.ca_cam_left_model_fits.items():
flydf[key + '_left'] = value
for (key, value) in fly.ca_cam_right_model_fits.items():
flydf[key + '_right'] = value
# flydf['ca_pixel_left'] = fly.ca_cam_left #this is really big (obvs)
print(flydf.head())
print(np.unique(flydf['stimulus']))
print(flydf.columns.values)
#Here is an example of how to filter for rows of a certain column value
filtered_df = flydf.loc[flydf['stimulus'] ==
#from thllib import flylib as flb
import flylib as flb
from matplotlib import pyplot as plt
import numpy as np
import scipy
from flylib import util
import figurefirst as fifi
import scipy.signal
#import local_project_functions as lpf
from IPython.display import SVG,display
import networkx as nx
flynumbers = list(range(1389,1402))
#flynumbers = list(range(1548,1549))
flylist = [flb.NetFly(fnum,rootpath='/media/imager/FlyDataD/FlyDB/') for fnum in flynumbers]
l = [fly.open_signals() for fly in flylist]
#fly = flylist[4]
#fly = flylist[0]
for fly in range(len(flylist)):
print("%s_%s" % (fly, 'flydf'))
name=("%s_%s" % (fly,'flydf'))
str(value(name))=fly.construct_dataframe()
#flydf_=fly.construct_dataframe()
#flydf_=fly.construct_dataframe()
#Access calcium values for a specific muscle and specific stimulus
pretrial_stripe_fix_b2_right = flydf.loc[
flydf['stimulus']=='pretrial_stripe_fix',['b2_right']]
print(np.shape(pretrial_stripe_fix_b2_right))
import sys
import os
import numpy as np
from matplotlib import pyplot as plt
import neo
from neo import AxonIO
from cv_bridge import CvBridge, CvBridgeError
import flylib as flb
import rosbag
FLYNUM = %s
fly = flb.NetFly(FLYNUM)
abfreader = AxonIO(fly.abfpaths[0])
abffile = abfreader.read()
block = abffile[0]
segment = block.segments[0]
analogsignals = segment.analogsignals
times = np.array(analogsignals[0].times)
signal_idxs = {'abf_electrode':0,
'abf_wba_left_amp':1,
'abf_wba_right_amp':2,
'abf_sync':3,
'abf_freq':4,
'abf_left_hutchen':5,
'abf_right_hutchen':6,
'abf_led_pulse':7,
'abf_kinefly_lmr':8,
'abf_kinefly_left':9,
