def _track_process(self, group_number):
data_name = (str(group_number) + '.hdf5'
if self.multiprocess else self.dataname)
with dataframes.DataStore(data_name, load=False) as data:
data.add_metadata('num_frames', self.num_frames)
data.add_metadata('video_filename', self.vidname)
data.add_metadata('crop', self.manager.preprocessor.crop)
start = self.frame_div * group_number
vid = video.ReadVideo(self.vidname)
vid.set_frame(start)
if group_number == 3:
missing = self.num_frames - self.num_processes * (
self.frame_div)
frame_div = self.frame_div + missing
else:
frame_div = self.frame_div
# Iterate over frames
for f in tqdm(range(frame_div), 'Tracking'):
frame = vid.read_next_frame()
info, boundary, info_headings = self.manager.analyse_frame(
frame)
data.add_tracking_data(start + f, info,
col_names=info_headings)
if f == 0:
data.add_metadata('boundary', boundary)
def extra_steps(self, filename):
vid_name = filename + ".MP4"
data_name = filename + ".hdf5"
with dataframes.DataStore(data_name, True) as d:
f = d.metadata['num_frames']
duty_cycle = read_audio_file(vid_name, f)
d.add_frame_property('Duty', duty_cycle)
def calculate_and_save_data(files):
for file in tqdm(files):
filename = os.path.splitext(os.path.split(file)[1])[0]
rate, direction, trial = filename.split('_')
trial = int(trial)
df = dataframes.DataStore(file).df
df.Duty = df.Duty.round()
df = filter_box(df, 750, 1250, 750, 1250)
result = df.groupby('frame')[PARAMETER].mean()
result.to_csv(f'{save_direc}/{rate}_{direction}_{trial}')
def create_csv(file):
filename = os.path.splitext(os.path.split(file)[1])[0]
rate, direction, trial = filename.split('_')
trial = int(trial)
with dataframes.DataStore(file) as data:
df = data.df
df.Duty = df.Duty.round()
df = filter_hexagon(df, data.metadata['boundary'], 0.8)
df = df.groupby('Duty')[PARAMETER].mean()
df.to_csv(f'{save_direc}/{rate}_{direction}_{trial}')
def check(filename):
core_name = os.path.splitext(filename)[0]
vid_name = core_name + '.MP4'
data_name = core_name + '.hdf5'
out_name = core_name + '_check.png'
data = dataframes.DataStore(data_name)
crop = data.metadata['crop']
vid = video.ReadVideo(vid_name)
print(vid_name)
frames = np.arange(4)*vid.num_frames//4
ims = [images.crop(vid.read_frame(f), crop) for f in frames]
circles = [data.get_info(f, ['x', 'y', 'r']) for f in frames]
new_ims = [images.draw_circles(im, c) for im, c in zip(ims, circles)]
out = images.vstack(images.hstack(new_ims[0], new_ims[1]),
images.hstack(new_ims[2], new_ims[3]))
images.save(out, out_name)
import filehandling
from particletracking import dataframes
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
directory = filehandling.open_directory('/media/data/Data')
files = filehandling.get_directory_filenames(directory + '/*.hdf5')
duties = np.arange(750, 525, 25)
numbers = np.arange(1700, 2400, 50)
counts = {n: {d: 0 for d in duties} for n in numbers}
for file in files:
filename = os.path.splitext(os.path.split(file)[1])[0]
number, duty = filename.split('_')
number = int(number)
duty = int(duty)
data = dataframes.DataStore(file)
order = data.df.order
count = np.count_nonzero(order > 0.95) / len(order)
counts[number][duty] = count
# print(count)
counts = pd.DataFrame(counts)
counts.plot()
plt.show()
def run():
direc = "/media/data/Data/FirstOrder/Interfaces/RecordFluctuatingInterfaceJanuary2020/Quick/first_frames"
savename = f"{direc}/data_new.hdf5"
files = filehandling.get_directory_filenames(direc + '/*.png')
ims = [images.load(f, 0) for f in tqdm(files, 'Loading images')]
ims = [images.bgr_to_gray(im) for im in ims]
circles = [images.find_circles(im, 27, 200, 7, 16, 16)
for im in tqdm(ims, 'Finding Circles')]
data = dataframes.DataStore(savename, load=False)
for f, info in tqdm(enumerate(circles), 'Adding Circles'):
data.add_tracking_data(f, info, ['x', 'y', 'r'])
calc = statistics.PropertyCalculator(data)
calc.order()
lattice_spacing = 10
x = np.arange(0, ims[0].shape[1], lattice_spacing)
y = np.arange(0, ims[0].shape[0], lattice_spacing)
x, y = np.meshgrid(x, y)
# cgw = get_cgw(data.df.loc[0], 1.85) # k=1.85 looked the best
cgw = get_cgw(data.df.loc[0], 1.85)
fields = [coarse_order_field(data.df.loc[f], cgw, x, y)
for f in tqdm(range(len(ims)), 'Calculating Fields')]
field_threshold = get_field_threshold(fields, lattice_spacing, ims[0])
contours = [find_contours(f, field_threshold)
for f in tqdm(fields, 'Calculating contours')]
# Multiply the contours by the lattice spacing and squeeze
contours = [c.squeeze() * lattice_spacing for c in contours]
# Close contours
contours = [close_contour(c) for c in contours]
# Convert to LineString
contours = [LineString(c) for c in contours]
# Select the line to query the points across
print("Select the line to query points")
ls = LineSelector(ims[0])
p1, p2 = ls.points
centre_line = get_extended_centre_line(p1, p2)
# Distance between query points that determines one end of the frequency
dL = data.df.loc[0].r.mean() / 10
L = np.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)
N_query = int(L/dL)
xq, yq = np.linspace(p1[0], p2[0], N_query), np.linspace(p1[1], p2[1],
N_query)
dL = np.sqrt((xq[1] - xq[0]) ** 2 + (yq[1] - yq[0]) ** 2)
dists, crosses = zip(
*[get_dists(xq, yq, c, centre_line) for c in tqdm(contours)])
# plot_crosses(crosses, ims)
# Select points from inside red edge to inside red edge across the centre
# of the tray which is 200mm to convert pixels to mm
PIX_2_mm = get_pix_2_mm(ims[0])
plot_fft(dists, dL, PIX_2_mm, data.df.loc[0].r.mean(), cgw)
'0.6': ['16870001', '16870002'],
'0.2': ['16870005', '16870006'],
'0.1': ['16870007', '16870008'],
'0.05': ['16870009', '16870010'],
'0.8': ['16870011', '16870012'],
'1': ['16870013', '16870014'],
'2': ['16870015', '16870016']
}
files = {r: [direc + d, direc + u] for r, (d, u) in files.items()}
for i, (rate, (down, up)) in enumerate(files.items()):
print(i)
print(rate)
fig, ax = plt.subplots()
ax.set_title(rate)
down_data = dataframes.DataStore(down + '.hdf5')
up_data = dataframes.DataStore(up + '.hdf5')
if i == 0:
up_video = video.ReadVideo(up + '.MP4')
frame = up_video.read_next_frame()
frame = images.crop(frame, up_data.metadata['crop'])
result = images.crop_polygon(frame)
bbox = result.bbox
with open(save_direc + 'bbox.txt', 'w') as f:
f.writelines([
'BBOX xmin, xmax, ymin, ymax',
str(bbox.xmin),
str(bbox.xmax),
str(bbox.ymin),
str(bbox.ymax)
def run(direc, lattice_spacing=5):
files = filehandling.get_directory_filenames(direc + '/*.png')
print(files)
savename = direc + '/data.hdf5'
N = len(files)
# load images
ims = [images.load(f, 0) for f in tqdm(files, 'Loading images')]
images.display(ims[0])
# Find Circles
ims = [images.bgr_to_gray(im) for im in ims]
circles = [
images.find_circles(im, 27, 200, 7, 16, 16)
for im in tqdm(ims, 'Finding Circles')
]
# Save data
data = dataframes.DataStore(savename, load=False)
for f, info in tqdm(enumerate(circles), 'Adding Circles'):
data.add_tracking_data(f, info, ['x', 'y', 'r'])
# Calculate order parameter
calc = statistics.PropertyCalculator(data)
calc.order()
# Get the course graining width
cgw = get_cgw(data.df.loc[0]) / 2
# Create the lattice points
x = np.arange(0, max(data.df.x), lattice_spacing)
y = np.arange(0, max(data.df.y), lattice_spacing)
x, y = np.meshgrid(x, y)
# Calculate the coarse order fields
fields = [
coarse_order_field(data.df.loc[f], cgw, x, y)
for f in tqdm(range(N), 'Calculating Fields')
]
# Calculate the field threshold
field_threshold = get_field_threshold(fields, lattice_spacing, ims[0])
# Find the contours representing the boundary in each frame
contours = [
find_contours(f, field_threshold)
for f in tqdm(fields, 'Calculating contours')
]
# Multiply the contours by the lattice spacing
contours = [c * lattice_spacing for c in contours]
# Find the angle of the image to rotate the boundary to the x-axis
a, c, p1, p2 = get_angle(ims[0])
print(p1)
print(p2)
# Rotate the selection points and the contours by the angle
p1 = rotate_points(np.array(p1), c, a)
p2 = rotate_points(np.array(p2), c, a)
contours = [rotate_points(contour.squeeze(), c, a) for contour in contours]
xmin = int(p1[0])
xmax = int(p2[0])
h = int(p1[1])
# Get the heights of the fluctuations from the straight boundary
hs = [
get_h(contour, ims[0].shape, xmin, xmax, h)
for contour in tqdm(contours, 'Calculating heights')
]
# Calculate the fourier transforms for all the frames
L = xmax - xmin
pixels_to_mms = 195 / L
print("pixels_to_mms = ", pixels_to_mms)
# convert to mm
hs = [h * pixels_to_mms for h in hs]
L = L * pixels_to_mms
x = np.linspace(0, L, len(hs[0]))
np.savetxt(direc + '/x.txt', x)
np.savetxt(direc + '/hs.txt', hs)
# k, yplot = get_fourier(hs, L)
return k, yplot
from particletracking import dataframes
data = dataframes.DataStore("/media/NAS/share/James/HighSpeed/test.hdf5")
print(data.df.head())
import filehandling
from particletracking import dataframes
import trackpy as tp
# direc1 = filehandling.open_directory(initialdir='/media/data/Data')
# files1 = filehandling.get_directory_filenames(direc1+'/*.hdf5')
#
# direc2 = filehandling.open_directory(initialdir='/media/data/Data')
# files2 = filehandling.get_directory_filenames(direc1+'/*.hdf5')
#
# files = files1 + files2
files = ["/media/data/Data/FirstOrder/PhaseDiagram/FlatPlate2Feb2021/2000.hdf5",]
for file in files:
with dataframes.DataStore(file) as data:
d = data.df.copy()
print(d.head())
d = d.reset_index()
print(d.head())
d = tp.link(d, d.r.mean())
