def corrVert(f1, f2=None, i_ref=0, j_ref=0, norm=True):
'''
Do the two point vertical correlation from a reference point
pt(i_ref,j_ref). The field has dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Vertical location of the horizontal line. Default=0
*j_ref*: python int.
Vertical location of the horizontal line. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*lags_l*: numpy array.
Array of left/negative lags.
*res_l*: numpy array.
Array of left/negative results.
*lags_r*: numpy array.
Array of right/positive lags.
*res_r*: numpy array.
Array of right/positive results.
'''
f1sum = np.sum(f1, axis=0)
if f2 == None:
f2 = f1
f2sum = f1sum
else:
f2sum = np.sum(f2, axis=0)
res = np.empty(f1[0].shape[0], dtype=float)
res[:] = np.nan
k, dx, dy = f1.shape
for i in range(dx):
if np.isnan(f1sum[i_ref, j_ref]) or np.isnan(f2sum[i_ref, j_ref]):
res[i] = np.nan
else:
res[i] = tt.twoPointCorr(x=f1[:, i_ref, j_ref],
y=f2[:, i, j_ref],
norm=True)
if norm == True:
res = res / res[i_ref]
res_l = res[:i_ref + 1][::-1]
res_r = res[i_ref:]
lags_l = np.arange(len(res_l))
lags_r = np.arange(len(res_r))
return lags_l, res_l, lags_r, res_r
def corrVert(f1,f2=None,i_ref=0,j_ref=0,norm=True):
'''
Do the two point vertical correlation from a reference point
pt(i_ref,j_ref). The field has dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Vertical location of the horizontal line. Default=0
*j_ref*: python int.
Vertical location of the horizontal line. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*lags_l*: numpy array.
Array of left/negative lags.
*res_l*: numpy array.
Array of left/negative results.
*lags_r*: numpy array.
Array of right/positive lags.
*res_r*: numpy array.
Array of right/positive results.
'''
f1sum=np.sum(f1,axis=0)
if f2==None:
f2=f1
f2sum=f1sum
else:
f2sum=np.sum(f2,axis=0)
res=np.empty(f1[0].shape[0], dtype=float)
res[:] = np.nan
k,dx,dy=f1.shape
for i in range(dx):
if np.isnan(f1sum[i_ref,j_ref]) or np.isnan(f2sum[i_ref,j_ref]):
res[i]=np.nan
else:
res[i]=tt.twoPointCorr(x=f1[:,i_ref,j_ref],y=f2[:,i,j_ref],norm=True)
if norm==True:
res=res/res[i_ref]
res_l=res[:i_ref+1][::-1]
res_r=res[i_ref:]
lags_l=np.arange(len(res_l))
lags_r=np.arange(len(res_r))
return lags_l,res_l,lags_r,res_r
def corrField(f1, f2=None, i_ref=0, j_ref=0, norm=True):
'''
Two point correlation of an entire field with the point pt(i_ref,j_ref) as
reference. The field has the dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Horizontal location of the reference point. Default=0
*j_ref*: python int.
Vertical location of the reference point. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*res*: np.array of shape (N,M).
the two point horizontal correlation.
'''
f1sum = np.sum(f1, axis=0)
if f2 == None:
f2 = f1
f2sum = f1sum
else:
f2sum = np.sum(f2, axis=0)
res = np.empty(f1[0].shape, dtype=float)
res[:] = np.nan
k, dx, dy = f1.shape
if np.isnan(f1sum[i_ref, j_ref]):
res[i_ref, j_ref] = np.nan
else:
for i in range(dx):
for j in range(dy):
if np.isnan(f2sum[i, j]):
res[i, j] = np.nan
else:
res[i, j] = tt.twoPointCorr(x=f1[:, i_ref, j_ref],
y=f2[:, i, j],
norm=True)
if norm == True:
return res / res[i_ref, j_ref]
else:
return res
def corrField(f1,f2=None,i_ref=0,j_ref=0,norm=True):
'''
Two point correlation of an entire field with the point pt(i_ref,j_ref) as
reference. The field has the dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Horizontal location of the reference point. Default=0
*j_ref*: python int.
Vertical location of the reference point. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*res*: np.array of shape (N,M).
the two point horizontal correlation.
'''
f1sum=np.sum(f1,axis=0)
if f2==None:
f2=f1
f2sum=f1sum
else:
f2sum=np.sum(f2,axis=0)
res=np.empty(f1[0].shape, dtype=float)
res[:] = np.nan
k,dx,dy=f1.shape
if np.isnan(f1sum[i_ref,j_ref]):
res[i_ref,j_ref] = np.nan
else:
for i in range(dx):
for j in range(dy):
if np.isnan(f2sum[i,j]):
res[i,j]=np.nan
else:
res[i,j]=tt.twoPointCorr(x=f1[:,i_ref,j_ref],y=f2[:,i,j],norm=True)
if norm==True:
return res/res[i_ref,j_ref]
else:
return res