def loadFile(path, sr=16000, time=3.0):
spectrogram = Spectrogram(input_shape=(int(time * sr), 1), sr=sr)
X = []
curr = 0 # indicates 1st and last file of speaker
speaker_dict = {}
print('Total speakers:', len(os.listdir(path)))
c = 0
for speaker in os.listdir(path):
if c % 10 == 0:
print('Speakers done:', c)
c += 1
print('Loading speaker: ' + speaker)
speaker_path = os.path.join(path, speaker)
speaker_dict[speaker] = [curr, None]
X_speaker = []
for file_path in os.listdir(speaker_path):
files = os.path.join(speaker_path, file_path)
audio, rate = librosa.load(files, sr=sr)
audio = np.expand_dims(audio, axis=1)
spec = spectrogram.spectrogram(audio)
X_speaker.append(spec)
curr += 1
speaker_dict[speaker][1] = curr - 1
X.append(X_speaker)
return X, speaker_dict
def saveBaselineIntensityImages(files,
saveLoc: str = None,
imageExt: str = "png"):
"""
Save a image file of the baseline as a function of time to the folder specified by saveLoc.
"""
if saveLoc == None:
saveLoc = r"../baselineIntensityMaps"
for i in tqdm.trange(len(files)):
filename = f"../dig/{files[i]}"
MySpect = Spectrogram(filename)
peaks, _, heights = baselines_by_squash(MySpect)
plt.plot(
np.array(MySpect.time) * 1e6,
MySpect.intensity[MySpect._velocity_to_index(peaks[0])])
plt.xlabel("Time ($\mu$s)")
plt.ylabel("Intensity (db)")
plt.title(f"{MySpect.data.filename}")
path = os.path.join(
saveLoc,
files[i].replace(".dig", "") + f"_baselineIntensity.{imageExt}")
if not os.path.exists(os.path.split(path)[0]):
os.makedirs(os.path.split(path)[0])
plt.savefig(path)
# Reset the state for the next data file.
plt.clf()
del MySpect
def create_plot(self):
if self.plot is not None:
self.plot.close()
self.toolbar.set_auto(True)
if self.settings.display == Display.PLOT:
self.plot = Plotter(self.notify, self.figure, self.settings)
elif self.settings.display == Display.SPECT:
self.plot = Spectrogram(self.notify, self.figure, self.settings)
else:
self.plot = Plotter3d(self.notify, self.figure, self.settings)
self.set_fonts()
self.toolbar.set_plot(self.plot)
self.toolbar.set_type(self.settings.display)
self.measureTable.set_type(self.settings.display)
self.set_plot_title()
self.figure.tight_layout()
self.figure.subplots_adjust(top=0.85)
self.redraw_plot()
self.plot.scale_plot(True)
self.mouseZoom = MouseZoom(self.plot, self.toolbar, self.hide_overlay)
self.mouseSelect = MouseSelect(self.plot, self.on_select,
self.on_selected)
self.measureTable.show(self.settings.showMeasure)
self.panel.SetFocus()
def createGallery(digsToLookAt: list = None,
colormap="blue-orange-div",
fileext="jpeg",
transformData=False):
if type(digsToLookAt) == type(None):
digsToLookAt = DigFile.inventory(justSegments=True)['file']
digDir = DigFile.dig_dir()
imageDir, _ = os.path.split(digDir)
imageFold = "ImageGallery"
imageDir = os.path.join(imageDir, imageFold)
print(digsToLookAt)
for i in range(len(digsToLookAt)):
filename = digsToLookAt[i]
print(filename)
spec = Spectrogram(os.path.join(digDir, filename))
pcms, lastAxes = spec.plot(transformData, cmap=colormap)
fileloc, filename = os.path.split(
os.path.splitext(os.path.join(imageDir, filename))[0])
fileloc = os.path.join(fileloc, filename)
for key in pcms.keys():
if "complex" in key:
continue # There is not a graph with a name that contains complex.
if not os.path.exists(fileloc):
os.makedirs(fileloc)
plt.figure(num=key) # Get to the appropriate figure.
plt.savefig(
os.path.join(fileloc, filename) +
f' {key} spectrogram.{fileext}')
plt.clf()
del spec
def baselineTracking(spectrogram: Spectrogram,
baselineVel,
changeThreshold,
skipUntilTime: float = 12e-6):
"""
Return the first time in microseconds that baseline's intensity value changes outside the changeThreshold.
Use the average baseline value as the estimate
"""
baselineInd = spectrogram._velocity_to_index(baselineVel)
runningAverage = spectrogram.intensity[baselineInd][
spectrogram._time_to_index(spectrogram.t_start + skipUntilTime)]
startInd = spectrogram._time_to_index(spectrogram.t_start + skipUntilTime)
for ind in range(startInd, spectrogram.intensity.shape[-1]):
currentIntensity = spectrogram.intensity[baselineInd][ind]
if (np.abs(currentIntensity) >= np.abs(
(1 + changeThreshold) * runningAverage)) or (
np.abs(currentIntensity) <= np.abs(
(1 - changeThreshold) * runningAverage)):
# print(f"{changeThreshold}: {spectrogram.time[ind]*1e6}")
return spectrogram.time[ind] * 1e6
runningAverage += 1 / (ind + 1 - startInd) * \
(currentIntensity - runningAverage)
return spectrogram.time[ind] * 1e6
def __init__(self, weight_file, params_file):
"""Performs detection on an audio file.
The structure of the network is hard coded to a network with 2
convolution layers with pooling, 1 or 2 fully connected layers, and a
final softmax layer.
weight_file is the path to the numpy weights to the network
params_file is the path to the network parameters
"""
self.weights = np.load(weight_file, encoding='bytes', allow_pickle=True)
if not all([weight.dtype == np.float32 for weight in self.weights]):
for i in range(self.weights.shape[0]):
self.weights[i] = self.weights[i].astype(np.float32)
with open(params_file, 'rb') as fp:
params = pickle.load(fp)
self.chunk_size = 4.0 # seconds
self.win_size = params['win_size']
self.max_freq = params['max_freq']
self.min_freq = params['min_freq']
self.slice_scale = params['slice_scale']
self.overlap = params['overlap']
self.crop_spec = params['crop_spec']
self.denoise = params['denoise']
self.smooth_spec = params['smooth_spec']
self.nms_win_size = int(params['nms_win_size'])
self.smooth_op_prediction_sigma = params['smooth_op_prediction_sigma']
self.sp = Spectrogram()
def __init__(self, file_pth: str, train=True, random_state=None):
super(Dataset, self).__init__()
self.sp = Spectrogram()
self.file_pth = file_pth
self.train = train
self.random_state = None
self.train = self.read_data()
def __init__(self, file_pth: str, train=True, random_state=None):
super(Dataset, self).__init__()
self.sp = Spectrogram()
self.file_pth = file_pth
self.train = train
self.random_state = None
self.data = self.read_data()
datasets = train_test_split(self.data)
if train:
self.train = datasets[0]
else:
# actually this is test dataset
self.train = datasets[1]
def make_spectrogram(pipe: PNSPipe, **kwargs):
"""
Compute a spectrogram using the passed kwargs or
built-in defaults and set the pipe.spectrogram field.
Default values are:
wavelength = 1.55e-6
points_per_spectrum = 4096
overlap = 7/8
window_function = None
form = 'power'
"""
from spectrogram import Spectrogram
defaults = dict(
t_start=kwargs.get('t_start'),
ending=kwargs.get('ending'),
wavelength=kwargs.get('wavelength', 1.55e-6),
points_per_spectrum=kwargs.get('points_per_spectrum', 4096),
overlap=kwargs.get('overlap', 7 / 8),
window_function=kwargs.get('window_function'),
form=kwargs.get('form', 'power'),
)
allargs = dict({**defaults, **kwargs})
# list the arguments we use in the log
for k, v in allargs.items():
pipe.log(f"+ {k} = {v}")
pipe.spectrogram = Spectrogram(pipe.df, **allargs)
def mainTest(SpectrogramObject:Spectrogram, startTime:int, stopTime:int, signalJump:int=50, bottomIndex:int=0, topIndex:int=None, vStartInd: int=None):
widths = [1,3,5,11,21]
orders = [1,2,10]
seam = []
velocities = SpectrogramObject.velocity
time = SpectrogramObject.time
for width in widths:
for order in orders:
signal, p_table, dp_table, botVel, topVel = seamExtraction(SpectrogramObject, startTime, stopTime, width, signalJump, bottomIndex, topIndex, order)
plt.plot(velocities[botVel:topVel+1], dp_table[0])
plt.title("Total Minkowski" + str(order) +" Order Cost diagram with a window size of " +str(width) +" raw")
plt.xlabel("Starting velocity of the trace (m/s)")
plt.ylabel("Minimum value of the sum (|p_i - p_{i-1}|^" + str(order) + ") along the path")
plt.show()
print("Here is a graph of the signal trace across time")
plt.figure(figsize=(10, 6))
myplot = SpectrogramObject.plot()
plt.xlim((SpectrogramObject.time[startTime], SpectrogramObject.time[stopTime]))
myplot.plot(time[startTime: stopTime+1], velocities[signal], 'b-', alpha = 0.4, label="Minimum Cost")
if vStartInd != None:
# Compute the signal seam for assuming this is the start point.
seam = reconstruction(p_table, vStartInd-botVel, botVel)
myplot.plot(time[startTime: stopTime+1], velocities[seam], 'r--', alpha = 0.4, label="Expected Start Point")
myplot.title("Velocity as a function of time for the minimum cost seam with Minkowski" + str(order)+ " and a window size of " + str(width) + " raw")
myplot.legend()
myplot.show()
def calc_spectrogram(self, **kwargs):
if not hasattr(self, '_spectrogram'):
self._spectrogram = Spectrogram.from_waveform(
self.signal,
**kwargs
)
return self._spectrogram
class SampleDataset(Dataset):
def __init__(self, file_pth: str, train=True, random_state=None):
super(Dataset, self).__init__()
self.sp = Spectrogram()
self.file_pth = file_pth
self.train = train
self.random_state = None
self.train = self.read_data()
def __getitem__(self, index):
# datasets[0][index][0]
stage_layer, degc_layer = self.feature_layer(self.train[index][2])
img = self.sp.spec_array(self.train[index][0])
img = np.concatenate((img, stage_layer), axis=0)
img = np.concatenate((img, degc_layer), axis=0)
img = torch.tensor(img, dtype=torch.float32)
target = self.train[index][1]
target = torch.tensor(target)
return img, target
def __len__(self):
return len(self.train)
def feature_layer(self, features):
stage = int(re.findall("\d+", features[0])[0])
degc = float(features[1])
stage_layer = np.full((224, 224), stage, dtype=np.int8)
degc_layer = np.full((224, 224), degc, dtype=np.float)
stage_layer = stage_layer[np.newaxis, :, :]
degc_layer = degc_layer[np.newaxis, :, :]
return stage_layer, degc_layer
def read_data(self):
datas = []
#self.file = "./sample_data.txt"
with open(self.file_pth, "r") as f:
header = f.readline()
while 1:
line = f.readline()
if not line:
break
tmp = line.strip().split('\t')
freq = list(map(float, tmp[4:]))
features = tmp[2:4]
# freq = list(map(float, tmp[1:]))
# print(freq[-1])
label = int(tmp[0])
datas.append([freq, label, features])
return datas
def generate_graphs(directory):
for file in os.listdir(directory):
print(directory + file)
if file.endswith(".dig"):
# Then we have the right file.
Spectrogram_Object = Spectrogram(directory + file)
sampleSize = 256
full_length_spectra = Spectrogram_Object.spectrogram(
0, Spectrogram_Object.samples, sampleSize)
colormaps = ["gist_stern", "tab20c", "tab20b", "terrain"]
for colormap in colormaps:
Spectrogram_Object.plot(full_length_spectra,
sample_window=sampleSize,
cmap=colormap)
def signal_finder(spectrogram: Spectrogram,
baseline: np.ndarray,
t_start: float,
dt: int = 2,
min_separation=200): # m/s
"""
Look above the baseline for a signal corresponding to a surface velocity.
"""
from scipy.signal import find_peaks
t_index = spectrogram._time_to_index(t_start)
spectra = np.sum(spectrogram.intensity[:, t_index - dt:t_index + dt],
axis=1)
try:
# limit the region we look at to points above the baseline
# get the index of the baseline
blo = spectrogram._velocity_to_index(baseline[0]) + 5
if len(baseline) > 1:
bhi = spectrogram._velocity_to_index(baseline[1]) - 5
else:
bhi = len(spectrogram.velocity) - 1
velocity = spectrogram.velocity[blo:bhi]
spectrum = spectra[blo:bhi]
smax = spectrum.max()
min_sep = int(min_separation / spectrogram.dv)
peaks, properties = find_peaks(spectrum,
height=0.05 * smax,
distance=min_sep)
heights = properties['peak_heights']
# produce an ordering of the peaks from high to low
ordering = np.flip(np.argsort(heights))
peak_index = peaks[ordering]
peaks = velocity[peak_index]
hts = heights[ordering]
# normalize to largest peak of 1 (e.g., the baseline)
hts = hts / hts[0]
return peaks[0]
except Exception as eeps:
print(eeps)
return None
def analyze_region(spectrogram: Spectrogram, roi: dict):
"""
"""
time, velocity, intensity = spectrogram.slice(roi['time'], roi['velocity'])
# Do we want to convert to power?
power = spectrogram.power(intensity)
# let's look at the peak power at each time
peaks = np.max(power, axis=0)
speaks = sorted(peaks)
threshold = speaks[int(len(peaks) * 0.04)]
amax = np.argmax(power, axis=0)
power[power < threshold] = 0.0
span = 30
nmax = len(velocity) - 1
times, centers, widths, amps = [], [], [], []
def peg(x):
if x < 0:
return 0
if x > nmax:
return nmax
return x
for t in range(len(time) - 1):
if power[amax[t], t] > threshold:
acenter = amax[t]
vfrom, vto = peg(acenter - span), peg(acenter + span)
gus = Gaussian(velocity[vfrom:vto], power[vfrom:vto, t])
if gus.valid:
times.append(time[t])
centers.append(gus.center)
widths.append(gus.width)
amps.append(gus.amplitude)
else:
print(f"[{t}] {gus.error}")
#power[power>=threshold] = 1
plt.pcolormesh(time * 1e6, velocity, power)
plt.plot(np.array(times) * 1e6, centers, 'r.', alpha=0.5)
plt.show()
def calc_spectrogram(self,
step_size=None,
fft_resolution=None,
scale_factors=None):
self._spectrogram = Spectrogram.from_waveform(
self.signal,
step_size=step_size,
fft_resolution=fft_resolution,
)
if scale_factors is not None:
self._spectrogram.data = self._spectrogram.scale(scale_factors)
return self._spectrogram
def __init__(self,
spectrogram: Spectrogram,
start_point: tuple,
span: int = 80,
rotate: bool = False,
weightingVector: dict = {},
debug=False):
"""
Input:
spectrogram: The object that we would like to trace a signal in.
start_point: (t, v) The actual time and velocity values (not the indices)
span: The number of velocity values up and down that you will check at the next time step.
rotate: whether or not you want to use linear extrapolation and rotation at each time step.
weightingVector: the weighting vector dictionary used to combine the available data into one matrix.
Values must sum to one. The default is {intensity: 1} meaning to use only the intensity data.
It will be in the same order as the availableData.
If all the modes have been computed availableData will be
['magnitude', 'angle', 'phase', 'real', 'imaginary', 'intensity']
debug: Do you want to print debugging statements?
"""
self.spectrogram = spectrogram
self.t_start, self.v_start = start_point
self.span = span
self.rotate = rotate
self.weightingVector = weightingVector if (
weightingVector != {}) else {
"intensity": 1
}
self.debug = debug
if self.rotate:
raise NotImplementedError(
"The rotate capacity for peak follower is not implemented.")
# Now establish storage for intermediate results and
# state. time_index is the index into the spectrogram.intensity
# along the time axis.
self.time_index = spectrogram._time_to_index(self.t_start)
self.results = dict(
velocity_index_spans=[], # the range of points used for the fit
times=[], # the times of the fits
time_index=[], # the corresponding point index in the time dimension
velocities=[], # the fitted center velocities
intensities=[] # the peak intensity
)
# for convenience, install pointers to useful fields in spectrogram
self.velocity = self.spectrogram.velocity
self.time = self.spectrogram.time
self.intensity = self.spectrogram.intensity
self.dV = self.velocity[1] - self.velocity[0]
self.dT = self.time[1] - self.time[0]
def get_data(self):
print('Reading Data...')
file_list = os.listdir(self.params['data_path'])
file_list = [
os.path.join(self.params['data_path'], i) for i in file_list[:10]
]
x_data = Spectrogram(filenames=file_list)
self.x_train, self.x_test = train_test_split(x_data.spectrogram,
test_size=0.3)
def __init__(self, user_dir, contains_vocals, n_samples):
os.chdir(user_dir)
# The line below makes a list of all the filenames in the specified directory,
# while excluding names of subdirectories/folders
self.filename_list = [name for name in os.listdir('.') if os.path.isfile(name)]
print('Processing songs from:' + user_dir)
# For test set, n_samples = 24505
# For training set, n_samples = 46200
freq_size = 513
time_size = 23
self.n_samples = n_samples
# These are dimensions of the matrix [X, Y]
self.X = np.zeros((self.n_samples, freq_size, time_size,1), dtype=int)
self.Y = np.zeros((self.n_samples,1), dtype=int)
# This will ensure we are indexing correctly to put the x for our sample into our X matrix of all songs' data
n_samples_so_far = 0
for i in range(len(self.filename_list)):
filename = self.filename_list[i] # Iterates through each file name in the list
print(filename)
self.contains_vocals = contains_vocals
spect = Spectrogram(filename, contains_vocals)
x_is = spect.get_X()
y_is = spect.get_Y()
#print("x_is"+str(x_is.shape))
#print("y_is"+str(y_is.shape))
#print("samples so far "+str(n_samples_so_far))
n_song_samples = np.shape(y_is)[0] # I.e., how many samples did we make from the song
# (song length (sec)*2, since 500ms segments used)
upper_index = n_samples_so_far + n_song_samples # Each sample is a row in our array/matrix
#print("upper index "+str(upper_index))
self.X[n_samples_so_far:upper_index, :, :,:] = x_is
self.Y[n_samples_so_far:upper_index,:] = y_is
n_samples_so_far = n_samples_so_far + n_song_samples # Updates how many samples we've done so far
class TestDataset(Dataset):
def __init__(self, file_pth: str, train=True, random_state=None):
super(Dataset, self).__init__()
self.sp = Spectrogram()
self.file_pth = file_pth
self.train = train
self.random_state = None
self.data = self.read_data()
datasets = train_test_split(self.data)
if train:
self.train = datasets[0]
else:
# actually this is test dataset
self.train = datasets[1]
def __getitem__(self, index):
# datasets[0][index][0]
img = self.sp.spec_array(self.train[index][0])
img = torch.tensor(img, dtype=torch.float32)
target = self.train[index][1]
target = torch.tensor(target)
return img, target
def __len__(self):
return len(self.train)
def read_data(self):
datas = []
#self.file = "./sample_data.txt"
with open(self.file_pth, "r") as f:
header = f.readline()
while 1:
line = f.readline()
if not line:
break
tmp = line.strip().split('\t')
# freq = list(map(float, tmp[4:]))
freq = list(map(float, tmp[1:]))
# print(freq[-1])
label = int(tmp[0])
datas.append([freq,label])
return datas
def test(self, filenames):
filenames = filenames[5:15]
for filename in filenames:
print(filename)
test_data = Spectrogram(filenames=[filename])
decoded_spectrogram = self.network.model.predict(
test_data.spectrogram)
#print_summary(self.network.model, line_length=80)
test_data.spectrogram_to_wav(
spectrogram=copy.deepcopy(decoded_spectrogram),
filename=filename.split("/")[-1])
test_data.visualize(filename=filename,
spectrogram=decoded_spectrogram)
def __init__(
self,
spectrogram: Spectrogram,
start_point, # either a tuple or a list of tuples
max_acceleration=10000,
smoothing=4, # average this many points on each side
max_hop=500, # require peaks at successive time steps
# to be within this many m/s
direction=1, # go forward in time (1), backward (-1)
):
span = max_acceleration * spectrogram.dt * 1e6 / spectrogram.dv
super().__init__(spectrogram, start_point, int(span))
self.smoothing = smoothing
self.max_hop = max_hop
assert direction in (-1, 1), "Direction must be +1 or -1"
self.direction = direction
peaks, dv, heights = bline(spectrogram)
# Let's take only the peaks greater than 10% of the strongest peak
self.baselines = peaks[heights > 0.1]
self.noise_level = spectrogram.noise_level() * 3
def runAgainstTestSet(testSetFilename,
guessAtOptimalThres,
guessAtOptimalSkipUntil,
errorPower: int = 1):
data = pd.read_excel(testSetFilename)
# Ignore the samples that we do not have the full data for.
data = data.dropna()
data.reset_index() # Reset so that it can be easily indexed.
files = data["Filename"].to_list()
bestGuessTimes = (data[data.columns[1]] * 1e6).to_list()
# This will be the same times for the probe destruction dataset. It is generally ignored currently.
bestGuessVels = data[data.columns[-1]].to_list()
d = {"Filename": files}
d["ProbeDestructionEstimateTime"] = np.zeros(len(files))
d["Error Versus Label"] = np.zeros(len(files))
for i in tqdm.trange(len(files)):
filename = os.path.join(os.path.join("..", "dig"), f"{files[i]}")
MySpect = Spectrogram(filename, overlap=0)
peaks, _, heights = baselines_by_squash(MySpect, min_percent=0.75)
timeEstimate = baselineTracking(MySpect, peaks[0], guessAtOptimalThres,
guessAtOptimalSkipUntil)
d["ProbeDestructionEstimateTime"][i] = timeEstimate
d["Error Versus Label"][i] = np.power(bestGuessTimes[i] - timeEstimate,
errorPower)
errors = d["Error Versus Label"]
print(f"The statistics for the test set specified by {testSetFilename}\n")
print(
f"Avg: {np.mean(errors)} Std: {np.std(errors)} Median: {np.median(errors)} Max: {np.max(errors)} Min: {np.min(errors)}"
)
return d
def __init__(self, spec: Spectrogram, time_chunk: int = 16, velocity_chunk: int = 16):
self.spectrogram = spec
self.time_chunk = time_chunk
self.velocity_chunk = velocity_chunk
powers = spec.power(spec.intensity)
nrows, ncols = powers.shape
nrows = nrows // velocity_chunk
ncols = ncols // time_chunk
self.intensity = ar = np.zeros((nrows, ncols))
rows = np.linspace(0, nrows * velocity_chunk,
num=nrows + 1, dtype=np.uint32)
cols = np.linspace(0, ncols * time_chunk,
num=ncols + 1, dtype=np.uint32)
for row in range(nrows):
rfrom, rto = rows[row], rows[row + 1]
for col in range(ncols):
ar[row, col] = np.mean(
powers[rfrom:rto, cols[col]:cols[col + 1]])
self.time = spec.time[0:len(spec.time):time_chunk]
self.velocity = spec.velocity[0:len(spec.velocity):velocity_chunk]
vals = ar.flatten()
vals.sort()
self.spower = vals
self.threshold = vals[int(len(vals) * 0.8)]
def setupForDocumentation():
# Set up
filename = "../dig/CH_4_009.dig"
t1 = 14.2389/1e6
t2 = 31.796/1e6
MySpect = Spectrogram(filename, mode = "complex")
sTime = MySpect._time_to_index(t1)
eTime = MySpect._time_to_index(t2)
bottomVel = 1906.38
botInd = MySpect._velocity_to_index(bottomVel)
topVel = 5000
topVelInd = MySpect._velocity_to_index(topVel)
visualSignalStart = 2652.21
visSigIndex = MySpect._velocity_to_index(visualSignalStart)
return MySpect, sTime, eTime, botInd, topVelInd, visSigIndex
def setupForDocumentation():
# Set up
filename = "../See_if_this_is_enough_to_get_started/CH_4_009.dig"
t1 = 14.2389/1e6
t2 = 31.796/1e6
MySpect = Spectrogram(filename)
sTime = MySpect._time_to_index(t1)
eTime = MySpect._time_to_index(t2)
bottomVel = 1906.38
botInd = MySpect._velocity_to_index(bottomVel)
topVel = 5000
topVelInd = MySpect._velocity_to_index(topVel)
signalJump = 25
visualSignalStart = 2652.21
visSigIndex = MySpect._velocity_to_index(visualSignalStart)
return MySpect, sTime, eTime, signalJump, botInd, topVelInd, visSigIndex
def plot_trace(self, spectrogram, trace_info, clim = [0.5, 1.5], color = 'r'):
''' plot traced coordinates in the dynamic spectrum
'''
base0 = Spectrogram()
base0.spectrogram = spectrogram
fig0, ax0 = base0.plot_spectrogram(figsize = [12,8], subplot = 211, clim = clim, cmap = 'Greys_r', aspect = 0.2)
base1 = Spectrogram()
base1.spectrogram = spectrogram
fig1, ax1 = base1.plot_spectrogram(fig = fig0, subplot = 212, clim = clim, cmap = 'Greys_r', aspect = 0.2)
trace_info = self.idx2map(spectrogram, trace_info)
idplt = trace_info["ID"]
xplt= trace_info["indy"]
yplt = trace_info["frequency"]
# overplot traced drift bursts
for i in np.arange(idplt.min(), idplt.max()+1):
idx = np.where(idplt == i)[0]
if len(idx) > 1:
plt.plot(xplt[idx],yplt[idx], color = color)
ylim = ax0.get_ylim()
ax1.set_ylim(ylim)
plt.show()
# -*- coding: utf-8 -*- """ Created on Thu Jun 02 23:35:05 2016 @author: Zhitao """ from trace import Trace from spectrogram import Spectrogram ########################## Import raw data. ################################### file_path = "C:/Users/Zhitao/Desktop/pywork/" # modify YOUR_PATH here sample = Spectrogram(file_path = file_path) sample.read_spectrogram() data = sample.spectrogram ########### Apply tracing over specific range in time and frequency. ########## t = Trace() trace_info = t.trace(data, mask = None) # Visualize traced structures. t.plot_trace(data, trace_info)
from tables import *
import config
import utils
from spectrogram import Spectrogram
conf = config.get_config()
winsize = int(conf.get('Spectrogram', 'WindowSize'))
nframes = int(conf.get('Tracks', 'LengthInSeconds')) * \
int(conf.get('Tracks', 'SampleRate'))
stepsize = int(conf.get('Spectrogram', 'StepSize'))
fftres = int(conf.get('Spectrogram', 'FFTResolution'))
audio_folder = os.path.expanduser(conf.get('Input', 'AudioFolder'))
numberofgenres = len(utils.list_subdirs(audio_folder))
wins = Spectrogram.wins(winsize, nframes, stepsize)
bins = Spectrogram.bins(fftres)
shape = Spectrogram.shape(wins, bins)
class Track(IsDescription):
"""Description of a track in HDF5"""
idnumber = Int32Col()
name = StringCol(64)
path = StringCol(512)
genre = StringCol(32)
# target = BoolCol(shape=(numberofgenres,))
target = Int8Col()
spectrogram = Float32Col(dflt=0.0, shape=shape)
def seamExtraction(SpectrogramObject:Spectrogram, startTime:int, stopTime:int, width:int, bottomIndex:int=0, topIndex:int=None, order:int = None, verbose:bool = False, theta = None):
"""
Input:
intensityMatrix: a 2d array [velocity][time] and each cell
corresponds to the intensity at that point.
startTime: index corresponding to the start time in the intensity
matrix of the signal being considered.
stopTime: index corresponding to the stop time in the intensity
matrix of the signal being considered.
width:
How wide of a window that is being considered at
each time step. The window will be split evenly up and down
bottomIndex: Upper bound on the velocity.
Assumed to be a valid index for sgram's intensity array.
Defaults to zero
order: the order of the minkowski distance function that will be minimized
to determine the most well connected signal.
Defaults to 2:
minimizing the Euclidean distance between neighboring pixels.
verbose: Boolean that indicates if you want there to be debugging print statements.
Defaults to False:
theta: The relative importance of the phase array to the amplitude array if applicable.
It should be between 0 and 1.
Output:
list of indices that should be plotted as the signal as an array of indices
indicating the intensity values that correspond to the most
connected signal as defined by the metric.
"""
if stopTime <= startTime:
raise ValueError("Stop Time is assumed to be greater than start time.")
else:
try:
theta = float(theta)
except TypeError:
raise TypeError("Theta needs to be able to be cast to a float.")
if 0 > theta:
theta = np.exp(theta) # This will get between 0 and 1.
if theta > 1:
theta = np.exp(-1*theta) # To get between 0 and 1.
if width % 2 == 1:
width += 1
tableHeight = stopTime - startTime + 1
velocities = np.zeros(tableHeight, dtype = np.int64)
# This will hold the answers that we are looking for to return. It is a list of indices.
halfFan = width//2
bottomDP = bottomIndex + 1
if topIndex == None:
topIndex = SpectrogramObject.velocity.shape[0] # I just want the maximum velocity index
elif topIndex <= bottomIndex:
topIndex = SpectrogramObject.velocity.shape[0]
t, vel, real_raw_vals, ovals = SpectrogramObject.slice((SpectrogramObject.time[startTime], SpectrogramObject.time[stopTime]), (SpectrogramObject.velocity[bottomDP], SpectrogramObject.velocity[topIndex]))
amplitudeArray = [] # This is will be the magnitude of the complex data.
phaseArray = [] # This will be the phase of the complex data.
if SpectrogramObject.computeMode == "complex":
# We have the complex data.
amplitudeArray = real_raw_vals # It will be the appropriate item in this case.
phaseArray = np.arctan2(np.real(ovals)/np.imag(ovals))
elif SpectrogramObject.computeMode == "psd" or SpectrogramObject.computeMode == "magnitude":
amplitudeArray = real_raw_vals # This is all that we will have as we cannot get the phase info.
elif SpectrogramObject.computeMode == "angle" or SpectrogramObject.computeMode == "phase":
phaseArray = ovals
if verbose:
print("new time shape", t.shape, "vel shape:", vel.shape)
print("Before the transpose: real_raw_vals.shape", real_raw_vals.shape)
print("real_raw_vals.shape = ",real_raw_vals.shape)
print("bottomDP", bottomDP)
print()
print("t2-t1", stopTime-startTime)
print("(t2-t1)*halfan", (stopTime-startTime)*halfFan)
print()
print(topIndex)
print(bottomIndex+1)
print()
amplitudeArray = np.transpose(amplitudeArray)
phaseArray = np.transpose(phaseArray)
tableHeight, tableWidth = np.transpose(real_raw_vals).shape
DPTable = np.zeros((tableHeight, tableWidth))
parentTable = np.zeros(DPTable.shape, dtype = np.int64)
for timeIndex in range(2, tableHeight + 1):
dpTime = tableHeight - timeIndex
for velocityIndex in range(tableWidth):
bestSoFar = np.Infinity
bestPointer = None
for testIndex in range(-halfFan, halfFan+1): # The + 1 is so that you get a balanced window.
if velocityIndex + testIndex >= 0 and velocityIndex + testIndex < tableWidth:
# then we have a valid index to test from.
ampAddition = 0
phaseAddition = 0
if len(amplitudeArray) != 0: # Then it has been initialized.
ampAddition = np.power(np.abs(amplitudeArray[dpTime+1][testIndex+velocityIndex]-amplitudeArray[dpTime][velocityIndex]), order)
if len(phaseArray) != 0: # Then it has been initialized.
phaseAddition = np.power(np.abs(phaseArray[dpTime+1][testIndex+velocityIndex]-phaseArray[dpTime][velocityIndex]), order)
current = (1-theta)*ampAddition + theta*phaseAddition + DPTable[dpTime+1][velocityIndex+testIndex]
if current < bestSoFar:
bestSoFar = current
bestPointer = velocityIndex + testIndex
if verbose:
print("The bestValue has been updated for time", t[dpTime], "and velocity", vel[velocityIndex], \
"to", bestSoFar, "with a bestPointer of ", vel[velocityIndex+testIndex], "at the next timeslice.")
DPTable[dpTime][velocityIndex] = bestSoFar
parentTable[dpTime][velocityIndex] = bestPointer
# Now for the reconstruction.
currentPointer = np.argmin(DPTable[0])
if verbose:
print("The value of the current pointer is", currentPointer)
print("The minimum cost is", DPTable[0][currentPointer])
velocities = reconstruction(parentTable, currentPointer, bottomDP)
return velocities, parentTable, DPTable, bottomDP, topIndex
WHITE_CH4_SHOT/seg00.dig -- ??? opencv_long_start_pattern4 span=200
BLUE_CH1_SHOT/seg00.dig -- opencv_long_start_pattern4 span=150
BLUE_CH2_SHOT/seg00.dig -- ??? opencv_long_start_pattern3 span=200
BLUE_CH3_SHOT/seg00.dig -- opencv_long_start_pattern4 span=200
CH_1_009/seg00.dig -- opencv_long_start_pattern2 span=200
CH_3_009/seg00.dig -- opencv_long_start_pattern2 span=200
CH_4_009/seg01.dig -- opencv_long_start_pattern2 span=200
CH_4_009/seg02.dig -- opencv_long_start_pattern4 span=200
"""
from ProcessingAlgorithms.preprocess.digfile import DigFile
path = "../dig/BLUE_CH1_SHOT/seg00.dig"
df = DigFile(path)
spec = Spectrogram(df, 0.0, 60.0e-6, overlap_shift_factor= 1/8, form='db')
spec.availableData = ['intensity']
# print(spec.time[200])
# gives user the option to click, by default it searches from (0,0)
template_matcher = TemplateMatcher(spec,template=Templates.opencv_long_start_pattern5.value,
span=200,
k=20,
methods=['cv2.TM_CCOEFF_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'])
# masks the baselines to try and avoid matching with baselines or echoed signals
template_matcher.mask_baselines()
# get the times and velocities from matching
def mainTest(SpectrogramObject:Spectrogram, startTime:int, stopTime:int, bottomIndex:int=0, topIndex:int=None, vStartInd: int=None,verbose:bool = False):
widths = [1,3,5,11,21]
orders = [1,2,10]
precision = 10
thetas = np.arange(precision+1)/precision # Get in the range of zero to one inclusive.
seam = []
velocities = SpectrogramObject.velocity
time = SpectrogramObject.time
signalData = time*1e6
headers = ["Time ($\mu$s)"]
basefilePath = "../DocumentationImages/DP/ComplexSpectra/"
digfileUsed = SpectrogramObject.data.filename
for width in widths:
for order in orders:
for theta in thetas:
hyperParamNames = "w " + str(width) + " ord " + str(order) + " Minkowski dist theta " + str(theta).replace(".", "_")
signal, p_table, dp_table, botVel, topVel = seamExtraction(SpectrogramObject, startTime, stopTime, width, bottomIndex, topIndex, order, theta=theta)
fname = "DP_Cost_Table time by vel " +hyperParamNames + ".csv"
filename = basefilePath + fname
np.savetxt(filename,dp_table,delimiter=",")
fname = "Parent_Table time by vel " + hyperParamNames + ".csv"
filename = basefilePath + fname
np.savetxt(filename,p_table,delimiter=",")
fig = plt.figure(num=1)
plt.plot(velocities[botVel:topVel+1], dp_table[0])
plt.title("Total Minkowski" + str(order) +" Order Cost diagram with a window size of " +str(width) +" PercPhase:" + str(theta))
plt.xlabel("Starting velocity of the trace (m/s)")
plt.ylabel("Minimum value of the sum ($\theta$|$\phi_i$ - $\phi_{i-1}$|^" + str(order) + "(1-$\theta$)|$Amp_i$ - $Amp_{i-1}$|^" + str(order) +") along the path")
# manager = plt.get_current_fig_manager()
# manager.window.Maximized()
if verbose:
fig.show()
print("Here is a graph of the signal trace across time")
# fig.show()
extension = "svg"
fname = "DP_Start_Cost " + hyperParamNames + extension
filename = basefilePath + fname
fig.savefig(filename, bbox_inches = "tight")
fig2 = plt.figure(num = 2, figsize=(10, 6))
ax = fig2.add_subplot(1,1,1)
fig2Axes = fig2.axes[0]
print(type(fig2Axes))
print(fig2Axes)
SpectrogramObject.plot(axes=fig2Axes)
plt.xlim((SpectrogramObject.time[startTime], SpectrogramObject.time[stopTime]))
fig2Axes.plot(time[startTime: stopTime+1], velocities[signal], 'b-', alpha = 0.4, label="Minimum Cost")
if vStartInd != None:
# Compute the signal seam for assuming this is the start point.
seam = reconstruction(p_table, vStartInd-botVel, botVel)
fig2Axes.plot(time[startTime: stopTime+1], velocities[seam], 'r--', alpha = 0.4, label="Expected Start Point")
fig2Axes.set_title("Velocity as a function of time for the minimum cost seam with Minkowski" + str(order)+ " and a window size of " + str(width) + " raw")
fig2Axes.legend()
# manager = plt.get_current_fig_manager()
# manager.window.showMaximized()
if verbose:
fig2.show()
fname = "Overlay Spectra " + hyperParamNames + extension
filename = basefilePath + fname
plt.savefig(filename, bbox_inches = "tight")
# Build the reconstruction of every possible starting velocity. Then, save them in a csv file.
extension = "csv"
fname = "Velocity Traces " + hyperParamNames + extension
filename = basefilePath + fname
for velInd in range(0, topVel-botVel+1):
trace = reconstruction(p_table, velInd, botVel)
header = "Starting Velocity " + str(velocities[velInd+botVel]) + "(m/s)"
headers.append(header)
meaured = velocities[trace]
signalData = np.hstack((signalData, meaured))
signalData = signalData.reshape((topVel-botVel + 2, len(time))).transpose() # I want each column to be a velocity trace.
# np.savetxt(filename, signalData, delimiter=",")
df = pd.DataFrame(data=signalData, index=time*1e6, columns=headers)
df.to_csv(filename)
print("Completed the documentation for ", hyperParamNames)
tr = table.row
i = 0
for filename, genre in gt.ground_truth.iteritems():
# Read file
f = Sndfile(filename, mode='r')
# Check against specs
check_audio_file_specs(f, samplerate, encoding, channels)
# Read
logging.debug("Reading %s" % filename)
frames = f.read_frames(lengthinseconds*samplerate)
# Calculate Spectrogram
logging.debug("Calculating spectrogram of %s" % filename)
sg = Spectrogram.from_waveform(frames, windowsize, stepsize, windowtype, fftres)
# Save in feature file
tr['idnumber'] = i
tr['name'] = os.path.basename(filename)
tr['path'] = filename
tr['genre'] = genre
tr['target'] = gt.genres.index(genre)
tr['spectrogram'] = sg.spectrogram
logging.debug("Saving %s in HDF5 file." % filename)
tr.append()
i = i + 1
h5file.close()
logging.info("Feature Extraction: spectrograms saved in %s." % features_path)
else:
if x < 0:
return 0
if x > nmax:
return nmax
return x
for t in range(len(time) - 1):
if power[amax[t], t] > threshold:
acenter = amax[t]
vfrom, vto = peg(acenter - span), peg(acenter + span)
gus = Gaussian(velocity[vfrom:vto], power[vfrom:vto, t])
if gus.valid:
times.append(time[t])
centers.append(gus.center)
widths.append(gus.width)
amps.append(gus.amplitude)
else:
print(f"[{t}] {gus.error}")
#power[power>=threshold] = 1
plt.pcolormesh(time * 1e6, velocity, power)
plt.plot(np.array(times) * 1e6, centers, 'r.', alpha=0.5)
plt.show()
if __name__ == "__main__":
sp = Spectrogram('../dig/sample')
roi = dict(time=(13.6636e-6, 35.1402e-6), velocity=(2393.33, 4500.377))
analyze_region(sp, roi)
print("Hi!")
def __init__(self, path, which_set,
feature="spectrogram",
space="conv2d",
axes=('b', 0, 1, 'c'),
preprocess=False,
seconds=None,
window_size=None,
window_type=None,
step_size=None,
tw_window_size=None,
tw_window_type=None,
tw_step_size=None,
fft_resolution=None,
seed=None,
n_folds=4,
run_n=0,
verbose=False,
print_params=True):
super(AudioDataset, self).__init__()
converter = space
# signal is 1D
if feature == "signal":
space = "vector"
converter = "signal"
# inverting a spectrogram if the space is a vector doesn't make sense
if space == "vector" and feature == "inv_spectrogram":
feature == "spectrogram"
feature_extractors = {
"spectrogram": self.get_spectrogram_data,
"inv_spectrogram": self.get_inv_spectrogram_data,
"texture_window": self.get_texture_window_data,
"signal": self.get_signal_data
}
spaces_converters = {
"conv2d": AudioDataset.twod_to_conv2dspaces,
"vector": AudioDataset.twod_to_vectorspaces,
"signal": lambda x: x
}
index_converters = {
"conv2d": lambda x: x,
"vector": self.track_ids_to_frame_ids,
"signal": lambda x: x
}
path = string_utils.preprocess(path)
# init dynamic params
tracks, genres = self.tracks_and_genres(path, seconds)
samplerate = tracks[0].samplerate
seconds = tracks[0].seconds
if feature != "signal":
spec_wins_per_track = len(
Spectrogram.wins(
seconds * samplerate,
window_size,
step_size
)
)
if feature == "texture_window":
tw_wins_per_track = len(
TextureWindow.wins(
spec_wins_per_track,
tw_window_size,
tw_step_size
)
)
wins_per_track = tw_wins_per_track
else:
wins_per_track = spec_wins_per_track
bins_per_track = Spectrogram.bins(fft_resolution)
view_converters = {
"conv2d": dense_design_matrix.DefaultViewConverter(
(bins_per_track, wins_per_track, 1), axes
),
"vector": None,
"signal": None
}
view_converter = view_converters[converter]
self.__dict__.update(locals())
del self.self
if print_params:
print(self)
add_row(next(loc), 0.9, 0.8, 0.5) # to faded yellow
add_row(next(loc), 0.5, 0, 0.5)
add_row(next(loc), 0, 0.5, 0)
add_row(next(loc), 1, 0.75, 0.2)
add_row(next(loc), 0.2, 0.2, 0.2)
add_row(next(loc), 0, 0, 0)
except:
pass
add_row(1, 0, 0, 0)
return cdict
roids = calculate_centroids()
bounds = normalize_bounds(roids)
cdict = build_cdict(bounds)
from matplotlib.colors import LinearSegmentedColormap
COLORMAPS[name] = LinearSegmentedColormap(name, cdict)
return dict(name=name, centroids=roids, cmap=COLORMAPS[name])
if __name__ == '__main__':
sg = Spectrogram('../dig/CH_4_009.dig')
roids = make_spectrogram_color_map(sg, 4, "Kitty")
sg.plot(cmap = roids["cmap"])
import matplotlib.pyplot as plt
plt.xlim((0, 50))
plt.ylim((1500, 5000))
plt.show()
class PanelGraph(wx.Panel):
def __init__(self, panel, notify, settings, callbackMotion):
self.panel = panel
self.notify = notify
self.plot = None
self.settings = settings
self.spectrum = None
self.isLimited = None
self.limit = None
self.extent = None
self.mouseSelect = None
self.mouseZoom = None
self.measureTable = None
self.background = None
self.selectStart = None
self.selectEnd = None
self.menuClearSelect = []
self.measure = None
self.show = None
self.doDraw = False
wx.Panel.__init__(self, panel)
self.figure = matplotlib.figure.Figure(facecolor='white')
self.canvas = FigureCanvas(self, -1, self.figure)
self.measureTable = PanelMeasure(self)
self.toolbar = NavigationToolbar(self.canvas, self, settings,
self.hide_overlay)
self.toolbar.Realize()
vbox = wx.BoxSizer(wx.VERTICAL)
vbox.Add(self.canvas, 1, wx.EXPAND)
vbox.Add(self.measureTable, 0, wx.EXPAND)
vbox.Add(self.toolbar, 0, wx.EXPAND)
self.SetSizer(vbox)
vbox.Fit(self)
self.create_plot()
self.canvas.mpl_connect('motion_notify_event', callbackMotion)
self.canvas.mpl_connect('draw_event', self.on_draw)
self.canvas.mpl_connect('idle_event', self.on_idle)
self.Bind(wx.EVT_SIZE, self.on_size)
self.timer = wx.Timer(self)
self.Bind(wx.EVT_TIMER, self.on_timer, self.timer)
def create_plot(self):
if self.plot is not None:
self.plot.close()
self.toolbar.set_auto(True)
if self.settings.display == Display.PLOT:
self.plot = Plotter(self.notify, self.figure, self.settings)
elif self.settings.display == Display.SPECT:
self.plot = Spectrogram(self.notify, self.figure, self.settings)
else:
self.plot = Plotter3d(self.notify, self.figure, self.settings)
self.set_fonts()
self.toolbar.set_plot(self.plot)
self.toolbar.set_type(self.settings.display)
self.measureTable.set_type(self.settings.display)
self.set_plot_title()
self.figure.tight_layout()
self.figure.subplots_adjust(top=0.85)
self.redraw_plot()
self.plot.scale_plot(True)
self.mouseZoom = MouseZoom(self.plot, self.toolbar, self.hide_overlay)
self.mouseSelect = MouseSelect(self.plot, self.on_select,
self.on_selected)
self.measureTable.show(self.settings.showMeasure)
self.panel.SetFocus()
def set_fonts(self):
axes = self.plot.get_axes()
axes.xaxis.label.set_size('small')
axes.yaxis.label.set_size('small')
if self.settings.display == Display.SURFACE:
axes.zaxis.label.set_size('small')
axes.tick_params(axis='both', which='major', labelsize='small')
axes = self.plot.get_axes_bar()
axes.tick_params(axis='both', which='major', labelsize='small')
def add_menu_clear_select(self, menu):
self.menuClearSelect.append(menu)
menu.Enable(False)
def enable_menu(self, state):
for menu in self.menuClearSelect:
menu.Enable(state)
def on_size(self, event):
ppi = wx.ScreenDC().GetPPI()
size = [float(v) for v in self.canvas.GetSize()]
width = size[0] / ppi[0]
height = size[1] / ppi[1]
self.figure.set_figwidth(width)
self.figure.set_figheight(height)
self.figure.set_dpi(ppi[0])
event.Skip()
def on_draw(self, _event):
axes = self.plot.get_axes()
self.background = self.canvas.copy_from_bbox(axes.bbox)
self.draw_overlay()
def on_select(self):
self.hide_measure()
def on_selected(self, start, end):
self.enable_menu(True)
self.selectStart = start
self.selectEnd = end
self.measureTable.set_selected(self.spectrum, start, end)
def on_idle(self, _event):
if self.doDraw and self.plot.get_plot_thread() is None:
self.hide_overlay()
self.canvas.draw()
self.doDraw = False
def on_timer(self, _event):
self.timer.Stop()
self.set_plot(None, None, None, None, self.annotate)
def draw(self):
self.doDraw = True
def show_measureTable(self, show):
self.measureTable.show(show)
self.Layout()
def set_plot(self, spectrum, isLimited, limit, extent, annotate=False):
if spectrum is not None and extent is not None:
if isLimited is not None and limit is not None:
self.spectrum = copy.copy(spectrum)
self.extent = extent
self.annotate = annotate
self.isLimited = isLimited
self.limit = limit
if self.plot.get_plot_thread() is None:
self.timer.Stop()
self.measureTable.set_selected(self.spectrum, self.selectStart,
self.selectEnd)
if isLimited:
spectrum = reduce_points(spectrum, limit)
self.plot.set_plot(self.spectrum, self.extent, annotate)
else:
self.timer.Start(200, oneShot=True)
def set_plot_title(self):
if len(self.settings.devices) > 0:
gain = self.settings.devices[self.settings.index].gain
else:
gain = 0
self.figure.suptitle("Frequency Spectrogram\n{0} - {1} MHz,"
" gain = {2}dB".format(self.settings.start,
self.settings.stop, gain))
def redraw_plot(self):
if self.spectrum is not None:
self.set_plot(self.spectrum,
self.settings.pointsLimit,
self.settings.pointsMax,
self.extent, self.settings.annotate)
def set_grid(self, on):
self.plot.set_grid(on)
def draw_overlay(self):
if self.background is not None:
self.canvas.restore_region(self.background)
self.draw_select()
self.draw_measure()
self.canvas.blit(self.plot.get_axes().bbox)
def draw_select(self):
if self.selectStart is not None and self.selectEnd is not None:
self.mouseSelect.draw(self.selectStart, self.selectEnd)
def hide_overlay(self):
if self.plot is not None:
self.plot.hide_measure()
self.hide_select()
def hide_measure(self):
if self.plot is not None:
self.plot.hide_measure()
def hide_select(self):
if self.mouseSelect is not None:
self.mouseSelect.hide()
def draw_measure(self):
if self.measure is not None and self.measure.is_valid():
self.plot.draw_measure(self.measure, self.show)
def update_measure(self, measure, show):
self.measure = measure
self.show = show
self.draw_overlay()
def get_figure(self):
return self.figure
def get_axes(self):
return self.plot.get_axes()
def get_canvas(self):
return self.canvas
def get_toolbar(self):
return self.toolbar
def scale_plot(self, force=False):
self.plot.scale_plot(force)
def clear_plots(self):
self.plot.clear_plots()
def clear_selection(self):
self.measure = None
self.measureTable.clear_measurement()
self.selectStart = None
self.selectEnd = None
self.mouseSelect.clear()
self.enable_menu(False)
def close(self):
close_modeless()
allDigs = DigFile.inventory()[
"file"] # Just the files that are not segments.
saveLoc = os.path.join(
os.path.split(digFolder)[0], "TotalIntensityMaps\\Median\\")
if not os.path.exists(saveLoc):
os.makedirs(saveLoc)
fractions = np.arange(10)
fractionsL = len(fractions)
data = np.zeros((fractionsL, len(allDigs)))
data[:, 0] = fractions
for i in range(len(allDigs)):
filename = allDigs[i]
path = os.path.join(digFolder, filename)
spec = Spectrogram(path)
inTen = np.sum(spec.intensity, axis=0)
# Offset the values so that everything is non negative.
inTen = inTen - np.min(inTen)
for j in range(fractionsL):
frac = np.power(10.0, -1 * fractions[j])
data[j, i] = len(threshold(inTen, frac))
del spec
name = os.path.join(saveLoc, "NumberOfLowValuesVsFracOfMax.txt")
np.savetxt(name, data)
class PanelGraph(wx.Panel):
def __init__(self, panel, notify, settings, callbackMotion):
self.panel = panel
self.notify = notify
self.plot = None
self.settings = settings
self.spectrum = None
self.isLimited = None
self.limit = None
self.extent = None
self.background = None
self.selectStart = None
self.selectEnd = None
self.menuClearSelect = []
self.measure = None
self.show = None
self.doDraw = False
wx.Panel.__init__(self, panel)
self.figure = matplotlib.figure.Figure(facecolor='white')
self.canvas = FigureCanvas(self, -1, self.figure)
self.measureTable = PanelMeasure(self)
self.toolbar = NavigationToolbar(self.canvas, self, settings,
self.on_nav_changed)
self.toolbar.Realize()
vbox = wx.BoxSizer(wx.VERTICAL)
vbox.Add(self.canvas, 1, wx.EXPAND)
vbox.Add(self.measureTable, 0, wx.EXPAND)
vbox.Add(self.toolbar, 0, wx.EXPAND)
self.SetSizer(vbox)
vbox.Fit(self)
self.create_plot()
self.canvas.mpl_connect('motion_notify_event', callbackMotion)
self.canvas.mpl_connect('draw_event', self.on_draw)
self.canvas.mpl_connect('idle_event', self.on_idle)
self.timer = wx.Timer(self)
self.Bind(wx.EVT_TIMER, self.on_timer, self.timer)
def create_plot(self):
if self.plot is not None:
self.plot.close()
if self.settings.display == Display.PLOT:
self.plot = Plotter(self.notify, self.figure, self.settings)
elif self.settings.display == Display.SPECT:
self.plot = Spectrogram(self.notify, self.figure, self.settings)
else:
self.plot = Plotter3d(self.notify, self.figure, self.settings)
self.toolbar.set_plot(self.plot)
self.toolbar.set_type(self.settings.display)
self.measureTable.set_type(self.settings.display)
self.set_plot_title()
self.redraw_plot()
self.plot.scale_plot(True)
self.mouseZoom = MouseZoom(self.plot, self.toolbar, self.hide_measure,
self.draw_measure)
self.mouseSelect = MouseSelect(self.plot, self.on_select,
self.on_selected)
self.draw_select()
self.measureTable.show(self.settings.showMeasure)
self.panel.SetFocus()
def add_menu_clear_select(self, menu):
self.menuClearSelect.append(menu)
menu.Enable(False)
def enable_menu(self, state):
for menu in self.menuClearSelect:
menu.Enable(state)
def on_draw(self, _event):
axes = self.plot.get_axes()
self.background = self.canvas.copy_from_bbox(axes.bbox)
self.mouseSelect.set_background(self.background)
self.draw_measure()
def on_nav_changed(self, _event):
self.draw_measure()
def on_select(self):
self.hide_measure()
def on_selected(self, start, end):
self.enable_menu(True)
self.on_draw(None)
self.selectStart = start
self.selectEnd = end
self.measureTable.set_selected(self.spectrum, start, end)
self.draw_measure()
def on_idle(self, _event):
if self.doDraw and self.plot.get_plot_thread() is None:
self.canvas.draw()
self.doDraw = False
def on_timer(self, _event):
self.timer.Stop()
self.set_plot(None, None, None, None, self.annotate)
def draw(self):
self.doDraw = True
def show_measureTable(self, show):
self.measureTable.show(show)
self.Layout()
def set_plot(self, spectrum, isLimited, limit, extent, annotate=False):
if spectrum is not None and extent is not None:
if isLimited is not None and limit is not None:
self.spectrum = copy.copy(spectrum)
self.extent = extent
self.annotate = annotate
self.isLimited = isLimited
self.limit = limit
if self.plot.get_plot_thread() is None:
self.timer.Stop()
self.measureTable.set_selected(spectrum, self.selectStart,
self.selectEnd)
if isLimited:
spectrum = reduce_points(spectrum, limit)
self.plot.set_plot(spectrum, extent, annotate)
self.draw_select()
else:
self.timer.Start(200, oneShot=True)
def set_plot_title(self):
if len(self.settings.devices) > 0:
gain = self.settings.devices[self.settings.index].gain
else:
gain = 0
self.plot.set_title("Frequency Spectrogram\n{0} - {1} MHz,"
" gain = {2}dB".format(self.settings.start,
self.settings.stop, gain))
def redraw_plot(self):
if self.spectrum is not None:
self.set_plot(self.spectrum,
self.settings.pointsLimit,
self.settings.pointsMax,
self.extent, self.settings.annotate)
def draw_select(self):
if self.selectStart is not None and self.selectEnd is not None:
self.mouseSelect.draw(self.selectStart, self.selectEnd)
def hide_measure(self):
self.plot.hide_measure()
def draw_measure(self):
if self.measure is not None and self.background is not None:
self.plot.draw_measure(self.background, self.measure, self.show)
def update_measure(self, measure, show):
self.measure = measure
self.show = show
self.draw_measure()
def get_figure(self):
return self.figure
def get_axes(self):
return self.plot.get_axes()
def get_canvas(self):
return self.canvas
def get_toolbar(self):
return self.toolbar
def scale_plot(self, force=False):
self.plot.scale_plot(force)
def clear_plots(self):
self.plot.clear_plots()
def clear_selection(self):
self.measure = None
self.measureTable.clear_measurement()
self.selectStart = None
self.selectEnd = None
self.mouseSelect.clear()
self.enable_menu(False)
def close(self):
close_modeless()