def get_idx_from_plot_pick(deskew_row,plot_pick,flip=False,dist_e=None):
data_file_path = os.path.join(deskew_row["data_dir"],deskew_row["comp_name"])
data_df = utl.open_mag_file(data_file_path)
if flip: projected_distances = utl.calc_projected_distance(deskew_row['inter_lon'],deskew_row['inter_lat'],data_df['lon'].tolist(),data_df['lat'].tolist(),(180+deskew_row['strike'])%360)
else: projected_distances = utl.calc_projected_distance(deskew_row['inter_lon'],deskew_row['inter_lat'],data_df['lon'].tolist(),data_df['lat'].tolist(),deskew_row['strike'])
min_idx = (projected_distances["dist"]-plot_pick).abs().idxmin()
return min_idx,projected_distances["dist"][min_idx]
def on_sub_btn(self, event):
if self.parent.user_warning(
"WARNING: This is a non-reversable function please be sure this is what you want to do. Also if you are not looking at the 0 phase profile this may have unexpected results, please view the 0 phase profile before hitting OK."
):
try:
self.deg = int(self.deg_box.GetValue())
except (ValueError, TypeError) as e:
self.parent.user_warning(
"Degree of polynomial must be a natural number")
try:
mag_path = os.path.join(self.parent.dsk_row["data_dir"],
self.parent.dsk_row["comp_name"])
mag_df = utl.open_mag_file(mag_path)
self.projected_distances = utl.calc_projected_distance(
self.parent.dsk_row["inter_lon"],
self.parent.dsk_row["inter_lat"], mag_df["lon"],
mag_df["lat"], self.parent.dsk_row["strike"])["dist"]
shifted_mag = mag_df['mag'].tolist()
self.pols = np.polyfit(self.projected_distances, shifted_mag,
self.deg)
self.poly = np.polyval(self.pols, self.projected_distances)
mag_df["mag"] = mag_df["mag"] - self.poly
utl.write_mag_file_df(mag_df, mag_path)
except AttributeError:
return
self.on_plot_btn(event)
def plot_tracer_on_self_and_parent(self, dsk_row, lonlat):
proj_locs = utl.calc_projected_distance(dsk_row['inter_lon'],
dsk_row['inter_lat'],
[lonlat[0]], [lonlat[1]],
dsk_row['strike'])
self.plot_tracer_point(dsk_row,
proj_locs.iloc[0]["dist"],
color="red",
marker="o",
s=10)
self.parent.plot_tracer_point(proj_locs.iloc[0]["dist"],
linestyle='--',
color='red',
alpha=.5)
def on_select_dleft_click(self, event):
try:
dsk_row = self.parent.dsk_row
except AttributeError:
event.Skip()
return
pos = event.GetPosition()
width, height = self.canvas.get_width_height()
pos = [pos[0], height - pos[1]]
pos = self.ax.transData.inverted().transform(pos)
lonlat = ccrs.PlateCarree().transform_point(*pos, self.proj)
proj_locs = utl.calc_projected_distance(dsk_row['inter_lon'],
dsk_row['inter_lat'],
[lonlat[0]], [lonlat[1]],
dsk_row['strike'])
self.parent.set_new_intercept(proj_locs.iloc[0]["dist"])
self.parent.update(event)
self.update()
def fit_poly(self):
try:
self.deg = int(self.deg_box.GetValue())
except (ValueError, TypeError) as e:
self.parent.user_warning(
"Degree of polynomial must be a natural number")
try:
mag_path = os.path.join(self.parent.dsk_row["data_dir"],
self.parent.dsk_row["comp_name"])
mag_df = utl.open_mag_file(mag_path)
self.projected_distances = utl.calc_projected_distance(
self.parent.dsk_row["inter_lon"],
self.parent.dsk_row["inter_lat"], mag_df["lon"], mag_df["lat"],
(180 + self.parent.dsk_row["strike"]) % 360)["dist"]
shifted_mag = sk.phase_shift_data(
mag_df['mag'].tolist(), self.parent.dsk_row["phase_shift"])
self.pols = np.polyfit(self.projected_distances, shifted_mag,
self.deg)
self.poly = np.polyval(self.pols, self.projected_distances)
except AttributeError:
return
def old_get_idx_from_plot_pick(deskew_row,plot_pick,dist_e=.01):
drow,correction=deskew_row,plot_pick
data_file_path = os.path.join(drow["data_dir"],drow["comp_name"])
data_df = pd.read_csv(data_file_path,names=["dist","idk","mag","lat","lon"],delim_whitespace=True)
projected_distances = utl.calc_projected_distance(drow['inter_lon'],drow['inter_lat'],data_df['lon'].tolist(),data_df['lat'].tolist(),drow['strike'])
found_dist=False
for j,row in projected_distances.iterrows():
if row['dist']>=correction-dist_e and row['dist']<=correction+dist_e:
picked_idx = j
picked_distance = row['dist']
found_dist=True
break
if found_dist:
print("found lat lon of %s at a distance %.3f"%(drow["comp_name"],picked_distance))
else:
print("couldn't find picked distance in datafile to calculate lat and lon for %s"%drow["comp_name"]); return (drow['inter_lon'],drow['inter_lat'],0)
return picked_idx,picked_distance
def draw_figures(self):
####################################################Get Values
dsk_row = self.parent.dsk_row
track = self.parent.track
ddis = float(self.parent.samp_dis_box.GetValue())
if ddis==0: self.parent.user_warning("Synthetic is required for comparision of phase, start by initalilzing a synthetic"); return
synth_dis = self.parent.dis_synth
synth_mag = self.parent.synth
filter_type = self.filter_type_box.GetValue()
lowcut = float(self.lowcut_box.GetValue())
highcut = float(self.highcut_box.GetValue())
order = int(self.order_box.GetValue())
left_bound = float(self.low_bound_box.GetValue())
right_bound = float(self.high_bound_box.GetValue())
aero_diff = float(self.aero_diff_box.GetValue())
left_idx = np.argmin(np.abs(synth_dis-left_bound))
right_idx = np.argmin(np.abs(synth_dis-right_bound))
left_idx,right_idx = min([left_idx,right_idx]),max([left_idx,right_idx])
bin_range,bin_num = (-180,180),120
###################################################Filter Data
data_path = os.path.join(dsk_row["data_dir"],dsk_row["comp_name"])
data_df = utl.open_mag_file(data_path)
projected_distances = utl.calc_projected_distance(dsk_row['inter_lon'],dsk_row['inter_lat'],data_df['lon'].tolist(),data_df['lat'].tolist(),(180+dsk_row['strike'])%360)
shifted_mag = sk.phase_shift_data(data_df["mag"],dsk_row["phase_shift"])
if np.any(np.diff(projected_distances["dist"])<0): itshifted_mag = np.interp(-synth_dis,-projected_distances["dist"],shifted_mag)
else: itshifted_mag = np.interp(synth_dis,projected_distances["dist"],shifted_mag)
fitshifted_mag = self.filters[filter_type](itshifted_mag,lowcut,highcut,fs=1/ddis,order=order)
###################################################Actual Plotting
outer = gridspec.GridSpec(4, 1)
###################################################Axis 0: Magnetic profiles
self.ax0 = self.fig.add_subplot(outer[0])
if self.parent.show_other_comp: #Handle Other Aeromag Component
if dsk_row["track_type"]=="aero":
if "Ed.lp" in track:
other_track = track.replace("Ed.lp","Vd.lp")
total_track = track.replace("Ed.lp","Td.lp")
other_phase = dsk_row["phase_shift"]-90
elif "Hd.lp" in track:
other_track = track.replace("Hd.lp","Vd.lp")
total_track = track.replace("Hd.lp","Td.lp")
other_phase = dsk_row["phase_shift"]-90
elif "Vd.lp" in track:
other_track = track.replace("Vd.lp","Ed.lp")
total_track = track.replace("Vd.lp","Td.lp")
if other_track not in self.parent.deskew_df["comp_name"].tolist(): other_track = track.replace("Vd.lp","Hd.lp")
other_phase = dsk_row["phase_shift"]+90
else: self.parent.user_warning("Improperly named component files should have either Ed.lp, Hd.lp, or Vd.lp got: %s"%track); return
oth_row = self.parent.deskew_df[self.parent.deskew_df["comp_name"]==other_track].iloc[0]
oth_data_path = os.path.join(oth_row["data_dir"],oth_row["comp_name"])
tot_data_path = os.path.join(oth_row["data_dir"],total_track) #Should be in same place
oth_data_df = utl.open_mag_file(oth_data_path)
oth_shifted_mag = sk.phase_shift_data(oth_data_df["mag"],other_phase)
if np.any(np.diff(projected_distances["dist"])<0): oth_itshifted_mag = np.interp(-synth_dis,-projected_distances["dist"],oth_shifted_mag)
else: oth_itshifted_mag = np.interp(synth_dis,projected_distances["dist"],oth_data_df)
oth_fitshifted_mag = self.filters[filter_type](oth_itshifted_mag,lowcut,highcut,fs=1/ddis,order=order)
if filter_type=="None": psk.plot_skewness_data(oth_row,other_phase,self.ax0,xlims=[None,None],color='darkgreen',zorder=2,picker=True,alpha=.7,return_objects=True,flip=True)
else: self.ax0.plot(synth_dis,oth_fitshifted_mag,color="#299C29",zorder=3,alpha=.6)
tot_data_df = utl.open_mag_file(tot_data_path)
if np.any(np.diff(projected_distances["dist"])<0): tot_imag = np.interp(-synth_dis,-projected_distances["dist"],tot_data_df["mag"])
else: tot_imag = np.interp(synth_dis,projected_distances["dist"],tot_data_df["mag"])
tot_fimag = self.filters[filter_type](tot_imag,lowcut,highcut,fs=1/ddis,order=order)
if filter_type=="None": psk.plot_skewness_data(dsk_row,dsk_row["phase_shift"],self.ax0,xlims=[None,None],zorder=3,picker=True,return_objects=True,flip=True)
else: self.ax0.plot(synth_dis,fitshifted_mag,color="#7F7D7D",zorder=3,alpha=.6)
self.ax0.plot(self.parent.dis_synth,self.parent.synth,'r-',alpha=.4,zorder=1)
self.ax0.set_ylabel("Magnetic Profiles")
# self.ax0.get_xaxis().set_ticklabels([])
###################################################Axis 1/2: Phase Angles and Differences
self.ax1 = self.fig.add_subplot(outer[1], sharex=self.ax0)
self.ax2 = self.fig.add_subplot(outer[2], sharex=self.ax0)
###################################################Calculate: Phase Differences
trimmed_dis = synth_dis[left_idx:right_idx]
trimmed_synth = synth_mag[left_idx:right_idx]
trimmed_fitshifted_mag = fitshifted_mag[left_idx:right_idx]
al_data = np.angle(hilbert(fitshifted_mag),deg=False)[left_idx:right_idx]
al_synth = np.angle(hilbert(np.real(synth_mag)),deg=False)[left_idx:right_idx]
data_synth_diff = phase_diff_func(al_synth,al_data)
if self.parent.show_other_comp and dsk_row["track_type"]=="aero":
trimmed_oth_fitshifted_mag = oth_fitshifted_mag[left_idx:right_idx]
al_oth = np.angle(hilbert(oth_fitshifted_mag),deg=False)[left_idx:right_idx]
oth_synth_diff = phase_diff_func(al_synth,al_oth)
oth_data_diff = phase_diff_func(al_oth,al_data)
if abs(aero_diff) > 0:
idx = ma.array(np.abs(oth_data_diff)<aero_diff)
self.ax1.plot((trimmed_dis[~idx]),(np.rad2deg(al_oth[~idx])),color="darkgreen",linestyle=":")
self.ax2.plot((trimmed_dis[~idx]),(oth_synth_diff[~idx]),color="tab:pink",alpha=.8,linestyle=":")
self.ax2.plot((trimmed_dis[~idx]),(oth_data_diff[~idx]),color="tab:grey",alpha=.8,linestyle=":")
self.ax1.plot((trimmed_dis[~idx]),(np.rad2deg(al_data[~idx])),color="k",linestyle=":")
self.ax1.plot((trimmed_dis[~idx]),(np.rad2deg(al_synth[~idx])),color="r",linestyle=":")
self.ax2.plot((trimmed_dis[~idx]),(data_synth_diff[~idx]),color="tab:red",alpha=.8,linestyle=":")
# import pdb; pdb.set_trace()
# not_trimmed_dis = (trimmed_dis[~idx])
# not_trimmed_dis[np.diff(~idx,prepend=[0])] = ma.masked
# not_al_data = (al_data[~idx])
# not_al_data[np.diff(~idx)] = ma.masked
# not_al_synth = (al_synth[~idx])
# not_al_synth[np.diff(~idx)] = ma.masked
# not_al_oth = (al_oth[~idx])
# not_al_oth[np.diff(~idx)] = ma.masked
# not_data_synth_diff = (data_synth_diff[~idx])
# not_data_synth_diff[np.diff(~idx)] = ma.masked
# not_oth_synth_diff = (oth_synth_diff[~idx])
# not_oth_synth_diff[np.diff(~idx)] = ma.masked
# not_oth_data_diff = (oth_data_diff[~idx])
# not_oth_data_diff[np.diff(~idx)] = ma.masked
trimmed_dis = (trimmed_dis[idx])
al_data = (al_data[idx])
al_synth = (al_synth[idx])
al_oth = (al_oth[idx])
data_synth_diff = (data_synth_diff[idx])
oth_synth_diff = (oth_synth_diff[idx])
oth_data_diff = (oth_data_diff[idx])
self.ax1.plot(trimmed_dis,np.rad2deg(al_oth),color="darkgreen")
self.ax2.plot(trimmed_dis,oth_synth_diff,color="tab:pink",alpha=.8)
self.ax2.plot(trimmed_dis,oth_data_diff,color="tab:grey",alpha=.8)
self.ax1.plot(trimmed_dis,np.rad2deg(al_data),color="k")
self.ax1.plot(trimmed_dis,np.rad2deg(al_synth),color="r")
self.ax2.plot(trimmed_dis,data_synth_diff,color="tab:red",alpha=.8)
# self.ax2.get_xaxis.set_ticklabels
self.ax0.set_xlim(*self.parent.ax.get_xlim())
self.ax0.set_ylim(*self.parent.ax.get_ylim())
self.ax1.set_ylabel("Phase Angles")
self.ax2.set_ylabel("Phase Differences")
###################################################Axis 2.1: Power Spectrum
# inner = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=outer[2])#, hspace=0.)
# self.ax2 = self.fig.add_subplot(inner[0])
###################################################Axis 2.2: Phase Statistics
self.ax3 = self.fig.add_subplot(outer[3])
if self.parent.show_other_comp and dsk_row["track_type"]=="aero":
self.ax3.hist(oth_synth_diff,range=bin_range,bins=bin_num,color="tab:pink",alpha=.5,zorder=2)
self.ax3.hist(oth_data_diff,range=bin_range,bins=bin_num,color="tab:grey",alpha=.5,zorder=1)
self.ax3.hist(data_synth_diff,range=bin_range,bins=bin_num,color="tab:red",alpha=.5,zorder=3)
self.ax3.axvline(np.median(data_synth_diff),color="k",alpha=.5,zorder=5,linestyle=":")
self.ax3.axvline(np.mean(data_synth_diff),color="k",alpha=.5,zorder=5)
self.ax3.axvspan(np.mean(data_synth_diff)-np.std(data_synth_diff),np.mean(data_synth_diff)+np.std(data_synth_diff),color="tab:grey",alpha=.3,zorder=0)
self.ax3.annotate(r"$\theta_{mean}$ = $%.1f^\circ \pm %.1f^\circ$"%(np.mean(data_synth_diff),np.std(data_synth_diff)) + "\n" + r"$\theta_{median}$ = %.1f$^\circ$"%np.median(data_synth_diff),xy=(0.02,1-0.02),xycoords="axes fraction",bbox=dict(boxstyle="round", fc="w",alpha=.5),fontsize=self.fontsize,va='top',ha='left')
self.ax3.set_ylabel(r"$\Delta \theta$ Count")
self.fig.suptitle("%s\n%s\n"%(dsk_row["sz_name"],track))
###################################################Power Figure
N = (right_idx-left_idx) #Length of signal in distance domain
NW = 3 #following Parker and O'brien '97 and HJ-Gordon '03 we use a time-bandwith product of 6 (Nw is half)
Ns = 5 #Number of points to use in running average smoothing
#Handle Distance Domain
# import pdb; pdb.set_trace()
Sk_complex, weights, eigenvalues=pmtm(itshifted_mag[left_idx:right_idx], NW=NW, NFFT=N, show=False)
Sk = np.abs(Sk_complex)**2
smoothed_tshifted_freq = (np.mean(Sk * np.transpose(weights), axis=0) * ddis)[N//2:][::-1]
# smoothed_tshifted_freq = np.convolve(smoothed_tshifted_freq, np.ones((Ns,))/Ns, mode='same') #10 point running average smoothing
tdata_freqs = np.linspace(0.0, 1.0/(2.0*ddis), N-N//2) #0 to Nyquest
self.power_ax = self.power_fig.add_subplot(111)
if self.parent.show_other_comp and dsk_row["track_type"]=="aero":
Sk_complex, weights, eigenvalues=pmtm(oth_itshifted_mag[left_idx:right_idx], NW=NW, NFFT=N, show=False)
Sk = np.abs(Sk_complex)**2
oth_smoothed_tshifted_freq = (np.mean(Sk * np.transpose(weights), axis=0) * ddis)[N//2:][::-1]
# oth_smoothed_tshifted_freq = np.convolve(oth_smoothed_tshifted_freq, np.ones((Ns,))/Ns, mode='same') #10 point running average smoothing
self.power_ax.semilogy(tdata_freqs, oth_smoothed_tshifted_freq, color="darkgreen")
# self.power_ax.semilogy(tdata_freqs, oth_smoothed_tshifted_freq+smoothed_tshifted_freq, color="grey")
Sk_complex, weights, eigenvalues=pmtm(tot_imag[left_idx:right_idx], NW=NW, NFFT=N, show=False)
Sk = np.abs(Sk_complex)**2
tot_smoothed_tshifted_freq = (np.mean(Sk * np.transpose(weights), axis=0) * ddis)[N//2:][::-1]
# tot_smoothed_tshifted_freq = np.convolve(tot_smoothed_tshifted_freq, np.ones((Ns,))/Ns, mode='same') #10 point running average smoothing
self.power_ax.semilogy(tdata_freqs, tot_smoothed_tshifted_freq, color="tab:orange")
#Old Numpy Method
# synth_freqs = np.fft.fftfreq(len(synth_dis[left_idx:right_idx]),ddis)
# tdata_freqs = np.fft.fftfreq(len(shifted_mag[left_idx:right_idx]),ddis)
# tshifted_freq = np.fft.fft(shifted_mag[left_idx:right_idx])
# fitshifted_freq = np.fft.fft(fitshifted_mag[left_idx:right_idx])
# tsynth_freq = np.fft.fft(synth_mag[left_idx:right_idx])
self.power_ax.semilogy(tdata_freqs, smoothed_tshifted_freq, color="k",zorder=100)
# self.power_ax.semilogy(tdata_freqs, np.abs(tshifted_freq), color="k")
# self.power_ax.plot(synth_freqs, np.abs(fitshifted_freq), color="#7F7D7D")
# self.power_ax.plot(synth_freqs, np.abs(tsynth_freq), color="r")
self.power_ax.set_xlim(0.0,0.4)
self.power_ax.set_ylim(1e-1,1e6)
def auto_dsk(dsk_row,synth,bounds,conv_limit=0,conv_bounds=[None,None],phase_args=(0.,360.,1.),highcut=0.,order=3):
"""
Returns the maximum likelihood phase shift to deskew the data to match a provided synthetic given a bounds
on a window to match.
Parameters
----------
dsk_row : Pandas.Series
Single row of a deskew file with valid path to data file
synth : list
This should be the output of the make synthetic function it needs to contain three elements
0) an array of the synthetic magnetic anomalies
1) an array of the distance coordinates of the points in 0, should be of equal length to 0, MUST be in
the same coordinate system as the profile provided in dsk_row!!! Which it may not by default.
2) the distance resolution of the synthetic in 0 and 1
bounds : list of floats
Has two elements which corespond to the left and right bounds of the window
conv_limit : float, optional
Weather or not to realign the anomaly each phase shift using a time lagged convolution method which
increases runtime significantly but can also increase accuracy. This argument should be a positve
float which corresponds to the amount of +- shift the anomaly is allowed to move used otherwise it should
be 0 to not use the shift method (Default: 0, which implies not to use method).
conv_bounds : list of 2 floats, optional
The left and right boundary in the distance domain to use to time lag convolve the synthetic and the filtered
data signal. Thus 300 km of signal can be convolved but only the 10 km of motion allowed to pin down the
crossing location. (Default: [None,None], which implies conv_bounds=bounds)
phase_args : tuple or other unpackable sequence, optional
Arguments to np.arange which define the phases searched in the minimization. (Default: (0.,360.,1.) which
implies a search of the entire parameter space of phases at 1 degree resolution)
highcut : float, optional
The upper cutoff frequency to filter the data by in order to remove any topographic anomalies in the data.
This value should be between 0 and Nyquest of the synthetic which MUST be regularly sampled like those
returned by make_synthetic. The data is up or down sampled to the synthetic before filtering. (Default:
0 which implies not to filter the data)
order : int, optional
The order of the lowpass butterworth filter to apply to the data.
Returns
----------
best_phase : float
The maximum liklehood phase shift to match the data to the synthetic
best_shift : float
the maximum likelihood shift for the best_phase which aligned the two anomalies
phase_func : Numpy.NdArray
The summed phase asynchrony between the data and the synthetic as a function of phase shift (best_phase is
the global minimum of this function)
best_shifts : Numpy.NdArray
the maximum likelihood shift as a function of the phase shift
"""
#Unpack Arguments
dage = dsk_row["age_max"]-dsk_row["age_min"]
phases = np.arange(*phase_args)
left_bound,right_bound = bounds
synth_mag = np.array(synth[0])
synth_dis = np.array(synth[1])
ddis = synth[2]
data_path = os.path.join(dsk_row["data_dir"],dsk_row["comp_name"])
data_df = utl.open_mag_file(data_path)
projected_distances = utl.calc_projected_distance(dsk_row['inter_lon'],dsk_row['inter_lat'],data_df['lon'].tolist(),data_df['lat'].tolist(),(180+dsk_row['strike'])%360)
if conv_limit: #create the fully interpolated profile for convolution
#create the shortened synthetic for the time lagged convolution
if isinstance(conv_bounds[0],type(None)): conv_bounds[0] = bounds[0]
if isinstance(conv_bounds[1],type(None)): conv_bounds[1] = bounds[1]
left_idx = np.argmin(np.abs(synth_dis - conv_bounds[0]))
right_idx = np.argmin(np.abs(synth_dis - conv_bounds[1]))
right_idx,left_idx = max([right_idx,left_idx]),min([right_idx,left_idx])
conv_synth,conv_synth_dis = synth_mag[left_idx:right_idx],synth_dis[left_idx:right_idx]
if np.any(np.diff(projected_distances["dist"])<0): #redefine to the right because interp dumbs
mag = data_df["mag"].to_numpy()[::-1]
mag_dis = projected_distances["dist"].to_numpy()[::-1]
full_imag = np.interp(conv_synth_dis,mag_dis,mag)
if highcut: full_fimag = butter_lowpass_filter(full_imag,highcut=highcut,fs=1/ddis,order=order)
else: full_fimag = full_imag
#trim to only window of relivence
left_idx = np.argmin(np.abs(synth_dis - left_bound))
right_idx = np.argmin(np.abs(synth_dis - right_bound))
right_idx,left_idx = max([right_idx,left_idx]),min([right_idx,left_idx])
tsynth_mag = synth_mag[left_idx:right_idx]
tsynth_dis = synth_dis[left_idx:right_idx]
N = len(tsynth_mag) #because this is easier and regularly sampled plus the user can set it simply
al2 = np.angle(hilbert(np.real(tsynth_mag),N),deg=False)
best_shifts = [] #record best shifts as function of phase shift
phase_async_func = [] #record summed phase asynchrony as a function of phase shift
for i,phase in enumerate(phases):
shifted_mag = phase_shift_data(data_df["mag"],phase)
if conv_limit: #DON'T YOU KNOW WE'RE GONNAAAA DOOOOOOO THE COOONVOLUTIOOOON!!!
shifted_full_fimag = phase_shift_data(full_fimag,phase)
correlation_func = np.abs(np.convolve(shifted_full_fimag,conv_synth,"full"))
correlation_func = correlation_func[int(len(conv_synth)-conv_limit/ddis+.5):int(len(conv_synth)+conv_limit/ddis+.5)]
best_shift = ddis*(len(correlation_func)/2-np.argmax(correlation_func))/2
else: best_shift = 0.
#trim the data to the right segments
left_idx = np.argmin(np.abs(projected_distances["dist"] - left_bound + best_shift))
right_idx = np.argmin(np.abs(projected_distances["dist"]- right_bound + best_shift))
right_idx,left_idx = max([right_idx,left_idx]),min([right_idx,left_idx])
tproj_dist = projected_distances["dist"][left_idx:right_idx] + best_shift
tshifted_mag = shifted_mag[left_idx:right_idx]
#numpy.interp only works for monotonic increasing independent variable data
if np.any(np.diff(tproj_dist)<0): itshifted_mag = np.interp(-tsynth_dis,-tproj_dist,tshifted_mag)
else: itshifted_mag = np.interp(tsynth_dis,tproj_dist,tshifted_mag)
if highcut: fitshifted_mag = butter_lowpass_filter(itshifted_mag,highcut=highcut,fs=1/ddis,order=order)
else: fitshifted_mag = itshifted_mag
al1 = np.angle(hilbert(fitshifted_mag,N),deg=False)
phase_asynchrony = np.sin((al1-al2)/2) #shouldn't go negative but...just in case
best_shifts.append(best_shift)
phase_async_func.append(phase_asynchrony.sum())
best_idx = np.argmin(phase_async_func)
return phases[best_idx],best_shifts[best_idx],phase_async_func,best_shifts