def errcon(n, ievt, se, prax):
#n - number of phases used to calculate error ellipse (??)
#ievt - boolean whether depth was fixed or free (do I know this?)
#se - std error from origin quality
#prax - Matrix with values:
#[minorazimuth minorplunge minorlength;
# interazimuth interplunge interlength;
# majorazimuth majorplunge majorlength]
#output - 2x2
# /* initialize some values */
nFree = 8
tol = 1.0e-15
tol2 = 2.0e-15
m = 3
m1 = 2
m2 = 4
a = np.zeros((2, 2))
s2 = (nFree + (n - m2) * se * se) / (nFree + n - m2)
f10 = Fisher10(m, nFree + n - m2)
fac = 1.0 / (m * s2 * f10)
x = np.zeros((2, 1))
for k in range(0, m):
ce = np.cos(DEG2RAD * prax[k][1])
x[0] = -ce * np.cos(DEG2RAD * prax[k][0])
x[1] = ce * np.sin(DEG2RAD * prax[k][0])
ce = fac * prax[k][2] * prax[k][2]
for j in range(0, m1):
for i in range(0, m1):
a[j][i] = a[j][i] + ce * x[i] * x[j]
ellipse2d = np.zeros((3, 3))
#we're running into a problem with jacobi - namely, the diagonal values aren't exactly
#equal (differ at 15th decimal). We'll round them both to nearest billionth.
a[0][1] = text.roundToNearest(a[0][1], 1e-9)
a[1][0] = text.roundToNearest(a[0][1], 1e-9)
eigenvalue, eigenvector, sweeps = jacobi.jacobi(a)
idx = eigenvalue.argsort()[::-1]
eigenvalue = eigenvalue[idx]
eigenvector = eigenvector[:, idx]
for i in range(0, m1):
ce = 1.0
if np.fabs(eigenvector[0][i]) + np.fabs(eigenvector[1][i]) > tol2:
ellipse2d[i][0] = RAD2DEG * np.arctan2(ce * eigenvector[1][i],
-ce * eigenvector[0][i])
if (ellipse2d[i][0] < 0.0):
ellipse2d[i][0] += 360.0
ellipse2d[i][2] = np.sqrt(fac * max(eigenvalue[i], 0.0))
return ellipse2d
def errcon(n,ievt,se,prax):
#n - number of phases used to calculate error ellipse (??)
#ievt - boolean whether depth was fixed or free (do I know this?)
#se - std error from origin quality
#prax - Matrix with values:
#[minorazimuth minorplunge minorlength;
# interazimuth interplunge interlength;
# majorazimuth majorplunge majorlength]
#output - 2x2
# /* initialize some values */
nFree = 8
tol = 1.0e-15
tol2 = 2.0e-15
m = 3
m1 = 2
m2 = 4
a = np.zeros((2,2))
s2 = (nFree + (n-m2)*se*se)/(nFree + n - m2)
f10 = Fisher10(m, nFree + n - m2)
fac = 1.0/(m * s2 * f10)
x = np.zeros((2,1))
for k in range(0,m):
ce = np.cos(DEG2RAD * prax[k][1])
x[0] = -ce * np.cos(DEG2RAD * prax[k][0])
x[1] = ce * np.sin(DEG2RAD * prax[k][0])
ce = fac * prax[k][2] * prax[k][2]
for j in range(0,m1):
for i in range(0,m1):
a[j][i] = a[j][i] + ce * x[i] * x[j]
ellipse2d = np.zeros((3,3))
#we're running into a problem with jacobi - namely, the diagonal values aren't exactly
#equal (differ at 15th decimal). We'll round them both to nearest billionth.
a[0][1] = text.roundToNearest(a[0][1],1e-9)
a[1][0] = text.roundToNearest(a[0][1],1e-9)
eigenvalue,eigenvector,sweeps = jacobi.jacobi(a)
idx = eigenvalue.argsort()[::-1]
eigenvalue = eigenvalue[idx]
eigenvector = eigenvector[:,idx]
for i in range(0,m1):
ce = 1.0
if np.fabs(eigenvector[0][i]) + np.fabs(eigenvector[1][i]) > tol2:
ellipse2d[i][0] = RAD2DEG * np.arctan2(ce * eigenvector[1][i], -ce * eigenvector[0][i])
if (ellipse2d[i][0] < 0.0):
ellipse2d[i][0] += 360.0
ellipse2d[i][2] = np.sqrt(fac * max(eigenvalue[i], 0.0))
return ellipse2d
def getMapLines(dmin,dmax):
NLINES = 4
drange = dmax-dmin
if drange > 4:
near = 1
else:
if drange >= 0.5:
near = 0.25
else:
near = 0.125
inc = roundToNearest(drange/NLINES,near)
if inc == 0:
near = pow(10,round(log10(drange))) #make the increment the closest power of 10
inc = ceilToNearest(drange/NLINES,near)
newdmin = floorToNearest(dmin,near)
newdmax = ceilToNearest(dmax,near)
else:
newdmin = ceilToNearest(dmin,near)
newdmax = floorToNearest(dmax,near)
darray = np.arange(newdmin,newdmax,inc)
return darray
def getMapLines(dmin, dmax, nlines):
drange = dmax-dmin
if drange > 4:
near = 1
else:
if drange >= 0.5:
near = 0.25
else:
near = 0.125
inc = roundToNearest(drange/nlines, near)
if inc == 0:
near = np.power(10, round(math.log10(drange))) # make the increment the closest power of 10
inc = ceilToNearest(drange/nlines, near)
newdmin = floorToNearest(dmin, near)
newdmax = ceilToNearest(dmax, near)
else:
newdmin = ceilToNearest(dmin, near)
newdmax = floorToNearest(dmax, near)
darray = np.arange(newdmin, newdmax+inc, inc)
if darray[-1] > dmax:
darray = darray[0:-1]
return darray
