def main(ifile, n=''):
# Message to terminal
print 'processing file:', ifile, '...'
# Check for empty file
if os.stat(ifile).st_size == 0:
print 'input file is empty!'
return
print 'loading data ...'
# Determine input file type
if not ifile.endswith(('.h5', '.H5', '.hdf', '.hdf5')):
print "input file must be in hdf5-format"
return
# Input variables names
xvar, yvar, tvar, zvar, svar, ivar, ovar = icol
# Load all 1d variables needed
with h5py.File(ifile, 'r') as fi:
# Read in needed variables
lon = fi[xvar][:] # Longitude (deg)
lat = fi[yvar][:] # Latitude (deg)
time = fi[tvar][:] # Time (yrs)
elev = fi[zvar][:] # Height (meters)
sigma = fi[svar][:] # RMSE (meters)
mode = fi[ivar][:] # Mission (int)
oind = fi[ovar][:] if ovar in fi else np.ones(
lon.shape) # Outliers (int)
# Check for NaN-values
inan = ~np.isnan(elev) & ~np.isnan(oind)
# Remove NaN values from arrays
lon, lat, time, elev, sigma, mode = lon[inan], lat[inan], time[inan], \
elev[inan], sigma[inan], mode[inan]
# Select only observations inside time interval
itime = (time > t1lim) & (time < t2lim)
# Select wanted time span
lon, lat, time, elev, sigma, mode = lon[itime], lat[itime], time[itime], \
elev[itime], sigma[itime], mode[itime]
# Select only wanted missions - not mission 4
imode = (mode != 4)
# Select wanted modes
lon, lat, time, elev, sigma, mode = lon[imode], lat[imode], time[imode], \
elev[imode], sigma[imode], mode[imode]
# EPSG number for lon/lat proj
projGeo = '4326'
# EPSG number for grid proj
projGrd = proj
print 'converting lon/lat to x/y ...'
# Get geographic boundaries + max search radius
if bbox:
# Extract bounding box
(xmin, xmax, ymin, ymax) = bbox
# Transform coordinates
(x, y) = transform_coord(projGeo, projGrd, lon, lat)
# Select data inside bounding box
ig = (x >= xmin - dmax) & (x <= xmax + dmax) & \
(y >= ymin - dmax) & (y <= ymax + dmax)
# Check bbox for obs.
if len(x[ig]) == 0:
print 'no data points inside bounding box!'
return
# Cut data to bounding box limits
lon, lat, time, elev, sigma, mode = lon[ig], lat[ig], time[ig], \
elev[g], sigma[ig], mode[ig]
else:
# Convert into stereographic coordinates
(x, y) = transform_coord(projGeo, projGrd, lon, lat)
# Get bbox from data
(xmin, xmax, ymin, ymax) = x.min(), x.max(), y.min(), y.max()
# Construct solution grid - add border to grid
(Xi, Yi) = make_grid(xmin - 10e3, xmax + 10e3, ymin - 10e3, ymax + 10e3,
dx, dy)
# Convert to geographical coordinates
(LON, LAT) = transform_coord(projGrd, projGeo, Xi, Yi)
# Flatten prediction grid
xi = Xi.ravel()
yi = Yi.ravel()
# Zip data to vector
coord = zip(x.ravel(), y.ravel())
print 'building the k-d tree ...'
# Construct KD-Tree
Tree = cKDTree(coord)
# Number of months of time series
months = len(np.arange(t1lim, t2lim + tstep, tstep))
# Total number of columns
ntot = months + 4
# Create output array
OFILE0 = np.ones((len(xi), 23)) * 9999
OFILE1 = np.ones((len(xi), ntot)) * 9999
OFILE2 = np.ones((len(xi), ntot)) * 9999
OFILE3 = np.ones((len(xi), ntot)) * 9999
OFILE4 = np.ones((len(xi), ntot)) * 9999
# Save corrected rate
b_rate = np.ones((len(xi), 1)) * np.nan
# Set up search cap
dr = np.arange(dmin, dmax + 2e3, 2e3)
# Enter prediction loop
for i in xrange(len(xi)):
# Number of observations
nobs = 0
# Time difference
dt = 0
# Temporal sampling
npct = 1
# Number of sensors
nsen = 0
# Meet data constraints
for ii in xrange(len(dr)):
# Query the Tree with data coordinates
idx = Tree.query_ball_point((xi[i], yi[i]), dr[ii])
# Check for empty arrays
if len(time[idx]) == 0:
continue
# Constraints parameters
dt = np.max(time[idx]) - np.min(time[idx])
nobs = len(time[idx])
nsen = len(np.unique(mode[idx]))
# Bin time vector
t_sample = binning(time[idx], time[idx], t1lim, t2lim, 1.0 / 12.,
5, 5)[1]
# Test for null vector
if len(t_sample) == 0: continue
# Sampling fraction
npct = np.float(len(t_sample[~np.isnan(t_sample)])) / len(t_sample)
# Constraints
if nobs > nlim:
if dt > dtlim:
if nsen >= nmlim:
if npct > 0.70:
break
# Final test of data coverage
if (nobs < nlim) or (dt < dtlim): continue
# Parameters for model-solution
xcap = x[idx]
ycap = y[idx]
tcap = time[idx]
hcap = elev[idx]
scap = sigma[idx]
mcap = mode[idx]
# Centroid of all data
xc = np.median(xcap)
yc = np.median(ycap)
# Get reference
mref = mref_
# Reference to specific mission
if len(hcap[mcap == mref]) > 0:
# Tie to laser surface
hcap -= np.median(hcap[mcap == mref])
elif len(hcap[mcap == (mref + 1)]) > 0:
# Tie to SARin surface
hcap -= np.median(hcap[mcap == (mref + 1)])
# Change mission tie index
mref += 1
else:
# Tie to mean surface
hcap -= np.median(hcap)
#
# Least-Squares Adjustment
# ---------------------------------
#
# h = x_t + x_j + x_s
# x = (A' W A)^(-1) A' W y
# r = y - Ax
#
# ---------------------------------
#
# Threshold for outliers in each bin
alpha = 5.0
# Convergence tolerance (%)
tol = 3.0
# Times series binning of each mission
(tcap, hcap, scap, ncap, mcap) = bin_mission(tcap, hcap, mcap, scap,
t1lim, t2lim, tstep, tol,
alpha)
# Size of original observational matrix
(n, m) = hcap.T.shape
# Unravel array to vectors
tcap = tcap.T.ravel()
hcap = hcap.T.ravel()
scap = scap.T.ravel()
mcap = mcap.T.ravel()
# Additional outlier editing
inan = np.isnan(
binfilt(tcap.copy(), hcap.copy(), tcap.min(), tcap.max(), 3.0,
3. / 12.))
# Set outliers to NaN
hcap[inan] = np.nan
scap[inan] = np.nan
mcap[inan] = np.nan
# Trend component
dti = tcap - tref
# Compute new statistics
(nobs, tspan) = len(hcap[~np.isnan(hcap)]), tcap.max() - tcap.min()
# Reject grid node if true
if (nobs < nlim) & (tspan < dtlim): continue
# Four-term fourier series for seasonality
cos1 = np.cos(2 * np.pi * dti)
sin1 = np.sin(2 * np.pi * dti)
cos2 = np.cos(4 * np.pi * dti)
sin2 = np.sin(4 * np.pi * dti)
# Construct bias parameters
b_ice1 = np.zeros(hcap.shape)
b_csin = np.zeros(hcap.shape)
b_clrm = np.zeros(hcap.shape)
b_ra21 = np.zeros(hcap.shape)
b_ra22 = np.zeros(hcap.shape)
b_ers1 = np.zeros(hcap.shape)
b_ers2 = np.zeros(hcap.shape)
# Set unit-step functions (0/1)
b_ers1[mcap == 6] = 1.
b_ers2[mcap == 5] = 1.
b_ice1[mcap == 0] = 1.
b_ra21[mcap == 3] = 1.
b_ra22[mcap == 4] = 1.
b_csin[mcap == 1] = 1.
b_clrm[mcap == 2] = 1.
# Design matrix for adjustment procedure
Acap = np.vstack((dti, 0.5*dti**2, cos1, sin1, cos2, sin2, b_ice1, \
b_csin, b_clrm, b_ra21, b_ra22, b_ers2, b_ers1)).T
# Try to solve least-squares system
try:
# Least-squares bias adjustment
linear_model = sm.RLM(hcap, Acap, missing='drop')
# Fit the model to the data
linear_model_fit = linear_model.fit(maxiter=10)
# If not possible continue
except:
continue
# Length post editing
nsol = len(hcap)
# Coefficients and standard errors
Cm = linear_model_fit.params
Ce = linear_model_fit.bse
# Amplitude of annual seasoanl signal
amp = np.sqrt(Cm[2]**2 + Cm[3]**2)
# Phase of annual seasoanl signal
phi = np.arctan2(Cm[3], Cm[2]) / (2.0 * np.pi)
# Compute model residuals
dh = hcap - np.dot(Acap, Cm)
# Identify outliers
inan = np.isnan(iterfilt(dh.copy(), -slim, slim, 5, 3.0))
# Set outliers to NaN
hcap[inan] = np.nan
scap[inan] = np.nan
mcap[inan] = np.nan
# Compute RMSE of corrected residuals
rmse = mad_std(dh[~inan])
# Bias correction
h_bias = np.dot(Acap[:, [-7, -6, -5, -4, -3, -2, -1]],
Cm[[-7, -6, -5, -4, -3, -2, -1]])
# Save original uncorrected time series
horg = hcap.copy()
# Remove inter mission biases
hcap -= h_bias
# Initiate residual cross-calibration flag
flag = 0
# Apply post-fit cross-calibration in overlapping areas
hcap, flag = cross_calibrate(tcap.copy(), hcap.copy(), dh.copy(),
mcap.copy(), 1.0)
# Binned time for plotting
tbin = np.arange(t1lim, t2lim, tstep) + 0.5 * tstep
# Re-format back to arrays
hbo = horg.reshape(n, m).T
hbi = hcap.reshape(n, m).T
tbi = tcap.reshape(n, m).T
ebi = scap.reshape(n, m).T
mbi = mcap.reshape(n, m).T
# Copy original vector
hcor = np.copy(hbi)
# Take the weighted average of all mission in each bin
(hbi_w, ebi_w) = np.ma.average(np.ma.array(hbi, mask=np.isnan(hbi)), \
weights=np.ma.array(ebi, mask=np.isnan(ebi)), \
axis=0, returned=True)
# Convert back to original array, with nan's
hbi_w = np.ma.filled(hbi_w, np.nan)
ebi_w = np.ma.filled(ebi_w, np.nan)
# Number of rows to add
n_add = 6 - len(hbi)
# Construct binary mask
binary = hbi_w.copy()
# Set to zeros (data) and ones (nan)
binary[~np.isnan(binary)] = 0
binary[np.isnan(binary)] = 1
# Apply distance transform
bwd = distance_transform_edt(binary)
# Set these values to nan's
inoip = bwd >= 12
# Pad by adding rows
for kx in xrange(n_add):
# Add rows to obs. matrix
hbo = np.vstack((hbo, np.ones(hbi_w.shape) * np.nan))
hbi = np.vstack((hbi, np.ones(hbi_w.shape) * np.nan))
ebi = np.vstack((ebi, np.ones(hbi_w.shape) * np.nan))
mbi = np.vstack((mbi, np.ones(hbi_w.shape) * np.nan))
tbi = np.vstack((tbi, tbin))
# Padd mission arrays using weighted averages
hbi = fill(hbi, hbi_w)
ebi = fill(ebi, ebi_w)
# Reject grid node if true
if len(hbi_w[~np.isnan(hbi_w)]) <= 2: continue
#
# Kalman state-space model
# ------------------------
#
# z_t = H * z_t + v_t
# x_t = A * x_t-1 + w_t-1
#
# ------------------------
#
# Create observational matrix
Ht = np.eye(4)
# Determine the number of rows to add
n_add = len(hbi) - 4
# Rows to observational matrix
for ky in xrange(n_add):
# Add rows to obs. matrix
Ht = np.vstack((Ht, [0, 0, 0, 0]))
# Populate observational matrix
Ht[:, [0, 2]] = 1
# Seasonal signal
ck = np.cos(np.pi / 6)
sk = np.sin(np.pi / 6)
# Transition matrix
At = [[1.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, +ck, +sk],
[0.0, 0.0, -sk, +ck]]
# Observational noise
Rt = np.diag(np.nanmean(ebi**2, 1))
# Initial start value of filter
y0 = np.median(hbi_w[~np.isnan(hbi_w)][0:3])
# Constrain only transition covariance
params = ['transition_covariance']
# Estimating transition covaraiance from individual missions
if len(hcap[(mcap <= 3) & (~np.isnan(mcap))]) > 1:
# Only good missions
Ct = KalmanFilter(em_vars=params). \
em(hcap[mcap <= 3], n_iter=2).transition_covariance
else:
# All missions
Ct = KalmanFilter(em_vars=params). \
em(hcap[~np.isnan(hcap)], n_iter=2).transition_covariance
# Transition covariance
Qt = np.diag([0.0, 1.0, 0.5, 0.5]) * tstep * Ct
# Initial state vector
m0 = [y0, Cm[0], Cm[2], Cm[3]]
# Initial state covariance
q0 = np.diag([0, Ce[0], Ce[2], Ce[3]])**2
# Create kalman filter
kf = KalmanFilter(initial_state_mean=m0,
initial_state_covariance=q0,
transition_matrices=At,
observation_matrices=Ht,
observation_covariance=Rt,
transition_covariance=Qt)
# Estimate number percentage of interpolated data
n_per = 100 * float(len(hbi_w[np.isnan(hbi_w)])) / len(hbi_w)
# Mask and transpose array
hbi_masked = ma.masked_array(hbi, mask=np.isnan(hbi)).T
# Apply Kalman filter with parameter learning on residuals
(dh_ts, dh_es) = kf.smooth(hbi_masked)
# Compute the total RSS of all model parameters
dh_es = [dh_es[k, 0, 0] for k in xrange(len(dh_es))]
# Sum all parameters for time series
dh_ts = dh_ts[:, 0]
# Compute standard deviation
dh_es = np.sqrt(dh_es)
# Mask output array
dh_ts[inoip] = np.nan
dh_es[inoip] = np.nan
# Rename weighted solution
dh_ws = hbi_w
dh_ew = ebi_w
# Converte back to georaphical coordinates
(lon_c, lat_c) = transform_coord(projGrd, projGeo, xc, yc)
# Final search radius
radius = dr[ii]
# Compute new elevation change rate after post-fit residuals
b_rate = np.polyfit(tbin[~np.isnan(dh_ws)] -
tbin[~np.isnan(dh_ws)].mean(),
dh_ws[~np.isnan(dh_ws)],
2,
w=1.0 / dh_ew[[~np.isnan(dh_ws)]]**2)[1]
# Save data to output files
OFILE0[i, :] = np.hstack(
(lat_c, lon_c, Cm[0], Ce[0], Cm[1], Ce[1], rmse, dt, amp, phi,
n_per, Cm[[-7, -6, -5, -4, -3, -2,
-1]], nobs, nsol, radius, flag, b_rate))
OFILE1[i, :] = np.hstack(
(lat_c, lon_c, t1lim, t2lim, len(hbi.T), dh_ts))
OFILE2[i, :] = np.hstack(
(lat_c, lon_c, t1lim, t2lim, len(hbi.T), dh_es))
OFILE3[i, :] = np.hstack(
(lat_c, lon_c, t1lim, t2lim, len(hbi.T), dh_ws))
OFILE4[i, :] = np.hstack(
(lat_c, lon_c, t1lim, t2lim, len(hbi.T), dh_es))
# Print progress
print str(i) + "/" + str(len(xi))+" Radius: "+ str(np.around(dr[ii], 0)) +" Rate: "+str(np.around(Cm[0]*100,2))+\
" (cm/yr)"+' Interp: '+str(np.around(n_per,0))+' Rate_adj: '+str(np.around(b_rate*100,2))+" (cm/yr)"
# Identify unwanted data
I0 = OFILE0[:, 0] != 9999
I1 = OFILE1[:, 0] != 9999
I2 = OFILE2[:, 0] != 9999
I3 = OFILE3[:, 0] != 9999
I4 = OFILE4[:, 0] != 9999
# Remove unwnated data
OFILE0 = OFILE0[I0, :]
OFILE1 = OFILE1[I1, :]
OFILE2 = OFILE2[I2, :]
OFILE3 = OFILE3[I3, :]
OFILE4 = OFILE4[I4, :]
# Check if we have any data
if len(OFILE0[:, 0]) == 0:
# Print message
print " No data to save! "
return
# Save solution to disk
with h5py.File(ifile.replace('.h5', '_sf.h5'), 'w') as f0:
# Save meta data
f0['sf'] = OFILE0
with h5py.File(ifile.replace('.h5', '_ts.h5'), 'w') as f1:
# Save adjusted and merged time series
f1['ts'] = OFILE1
with h5py.File(ifile.replace('.h5', '_es.h5'), 'w') as f2:
# Save error estimate for time series
f2['es'] = OFILE2
with h5py.File(ifile.replace('.h5', '_tw.h5'), 'w') as f3:
# Save error estimate for time series
f3['tw'] = OFILE3
with h5py.File(ifile.replace('.h5', '_ew.h5'), 'w') as f4:
# Save error estimate for time series
f4['ew'] = OFILE4
def _extract_sdm_params(ee, tc, iph, io, rs, rsh, n, u, specs, const,
model):
# Get single diode model parameters from five parameters iph, io, rs, rsh
# and n vs. effective irradiance and temperature
try:
import statsmodels.api as sm
except ImportError:
raise ImportError(
'Parameter extraction using Sandia method requires statsmodels')
tck = tc + 273.15
tok = const['T0'] + 273.15 # convert to to K
params = {}
if model == 'pvsyst':
# Estimate I_o_ref and EgRef
x_for_io = const['q'] / const['k'] * (1. / tok - 1. / tck[u]) / n[u]
# Estimate R_sh_0, R_sh_ref and R_sh_exp
# Initial guesses. R_sh_0 is value at ee=0.
nans = np.isnan(rsh)
if any(ee < 400):
grsh0 = np.mean(rsh[np.logical_and(~nans, ee < 400)])
else:
grsh0 = np.max(rsh)
# Rsh_ref is value at Ee = 1000
if any(ee > 400):
grshref = np.mean(rsh[np.logical_and(~nans, ee > 400)])
else:
grshref = np.min(rsh)
# PVsyst default for Rshexp is 5.5
R_sh_exp = 5.5
# Find parameters for Rsh equation
def fun_rsh(x, rshexp, ee, e0, rsh):
tf = np.log10(_rsh_pvsyst(x, R_sh_exp, ee, e0)) - np.log10(rsh)
return tf
x0 = np.array([grsh0, grshref])
beta = optimize.least_squares(
fun_rsh, x0, args=(R_sh_exp, ee[u], const['E0'], rsh[u]),
bounds=np.array([[1., 1.], [1.e7, 1.e6]]), verbose=2)
# Extract PVsyst parameter values
R_sh_0 = beta.x[0]
R_sh_ref = beta.x[1]
# parameters unique to PVsyst
params['R_sh_0'] = R_sh_0
params['R_sh_exp'] = R_sh_exp
elif model == 'desoto':
dEgdT = 0.0002677
x_for_io = const['q'] / const['k'] * (
1. / tok - 1. / tck[u] + dEgdT * (tc[u] - const['T0']) / tck[u])
# Estimate R_sh_ref
nans = np.isnan(rsh)
x = const['E0'] / ee[np.logical_and(u, ee > 400, ~nans)]
y = rsh[np.logical_and(u, ee > 400, ~nans)]
new_x = sm.add_constant(x)
beta = sm.RLM(y, new_x).fit()
R_sh_ref = beta.params[1]
params['dEgdT'] = dEgdT
# Estimate I_o_ref and EgRef
y = np.log(io[u]) - 3. * np.log(tck[u] / tok)
new_x = sm.add_constant(x_for_io)
res = sm.RLM(y, new_x).fit()
beta = res.params
I_o_ref = np.exp(beta[0])
EgRef = beta[1]
# Estimate I_L_ref
x = tc[u] - const['T0']
y = iph[u] * (const['E0'] / ee[u])
# average over non-NaN values of Y and X
nans = np.isnan(y - specs['alpha_sc'] * x)
I_L_ref = np.mean(y[~nans] - specs['alpha_sc'] * x[~nans])
# Estimate R_s
nans = np.isnan(rs)
R_s = np.mean(rs[np.logical_and(u, ee > 400, ~nans)])
params['I_L_ref'] = I_L_ref
params['I_o_ref'] = I_o_ref
params['EgRef'] = EgRef
params['R_sh_ref'] = R_sh_ref
params['R_s'] = R_s
# save values for each IV curve
params['iph'] = iph
params['io'] = io
params['rsh'] = rsh
params['rs'] = rs
params['u'] = u
return params
def setup(self):
#fit for each test, because results will be changed by test
x = self.exog
np.random.seed(987689)
y = x.sum(1) + np.random.randn(x.shape[0])
self.results = sm.RLM(y, self.exog).fit()
hue='SITE_ID',
data=df_pheno_morpho)
plt.show()
# and assess whether the effects of each factor
results = smf.ols(
'surface_S_C_left ~ AGE_AT_SCAN + C(SITE_ID)+ AGE_AT_SCAN * C(SITE_ID)',
data=df_pheno_morpho).fit()
print(results.summary())
# comparison between OLS and RLM
y2 = df_pheno_morpho['surface_S_C_left']
x1 = df_pheno_morpho['AGE_AT_SCAN']
X = sm.add_constant(x1)
ols_model = sm.OLS(y2, X).fit()
print(ols_model.summary())
rlm_model = sm.RLM(y2, X).fit()
print(rlm_model.summary())
# nice figure with confidence intervals
prstd, iv_l, iv_u = wls_prediction_std(ols_model)
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(x1, y2, 'o', label="data")
ax.plot(x1, ols_model.fittedvalues, 'r-', label="OLS")
ax.plot(x1, iv_u, 'r--')
ax.plot(x1, iv_l, 'r--')
ax.plot(x1, rlm_model.fittedvalues, 'g.-', label="RLM")
legend = ax.legend(loc="best")
plt.show()
# influence analysis for outliers detection
def solve_iteratively(science, reference, mask_tolerance=10e-5, gain_tolerance=10e-6, max_iterations=5,
sigma_cut=5., use_pixels=False, show=False, percent=99, use_mask=True, size_cut=True,
pixstack_limit=None):
"""Solve for linear fit iteratively"""
gain = 1.
gain0 = 10e5
i = 1
# pad image to power of two to speed fft
old_size = science.shape
science_image = pad_to_power2(science)
reference_image = pad_to_power2(reference)
science_psf = center_psf(resize_psf(science.raw_psf, science_image.shape))
science_psf /= np.sum(science.raw_psf)
reference_psf = center_psf(resize_psf(reference.raw_psf, reference_image.shape))
reference_psf /= np.sum(reference.raw_psf)
science_std = pad_to_power2(science.background_std)
reference_std = pad_to_power2(reference.background_std)
science_mask = pad_to_power2(science.mask, value='bool')
reference_mask = pad_to_power2(reference.mask, value='bool')
# fft arrays
science_image_fft = np.fft.fft2(science_image)
reference_image_fft = np.fft.fft2(reference_image)
science_psf_fft = np.fft.fft2(science_psf)
reference_psf_fft = np.fft.fft2(reference_psf)
while abs(gain - gain0) > gain_tolerance:
# calculate the psf in the difference image to convolve masks
# not a simple convolution of the two PSF's; see the paper for details
difference_zero_point = gain / np.sqrt(science_std ** 2 + reference_std ** 2 * gain ** 2)
denominator = science_std ** 2 * abs(reference_psf_fft) ** 2
denominator += reference_std ** 2 * gain ** 2 * abs(science_psf_fft) ** 2
difference_psf_fft = gain * science_psf_fft * reference_psf_fft / (difference_zero_point * np.sqrt(denominator))
if use_mask:
# convolve masks with difference psf to mask all pixels within a psf radius
# this is important to prevent convolutions of saturated pixels from affecting the fit
science_mask_convolved = np.fft.ifft2(difference_psf_fft * np.fft.fft2(science_mask))
science_mask_convolved[science_mask_convolved > mask_tolerance] = 1
science_mask_convolved = np.real(science_mask_convolved).astype(int)
reference_mask_convolved = np.fft.ifft2(difference_psf_fft * np.fft.fft2(reference_mask))
reference_mask_convolved[reference_mask_convolved > mask_tolerance] = 1
reference_mask_convolved = np.real(reference_mask_convolved).astype(int)
# do the convolutions on the images
denominator = science_std ** 2 * abs(reference_psf_fft) ** 2
denominator += gain ** 2 * reference_std ** 2 * abs(science_psf_fft) ** 2
science_convolved_image_fft = reference_psf_fft * science_image_fft / np.sqrt(denominator)
reference_convolved_image_fft = science_psf_fft * reference_image_fft / np.sqrt(denominator)
science_convolved_image = np.real(np.fft.ifft2(science_convolved_image_fft))
reference_convolved_image = np.real(np.fft.ifft2(reference_convolved_image_fft))
# remove power of 2 padding
science_convolved_image = science_convolved_image[: old_size[0], : old_size[1]]
reference_convolved_image = reference_convolved_image[: old_size[0], : old_size[1]]
if use_mask:
science_mask_convolved = science_mask_convolved[: old_size[0], : old_size[1]]
reference_mask_convolved = reference_mask_convolved[: old_size[0], : old_size[1]]
else:
science_mask_convolved = None
reference_mask_convolved = None
# do a linear robust regression between convolved image
x, y = join_images(science_convolved_image, science_mask_convolved, reference_convolved_image,
reference_mask_convolved, sigma_cut, use_pixels, show, percent, size_cut, pixstack_limit)
robust_fit = stats.RLM(y, stats.add_constant(x), stats.robust.norms.TukeyBiweight()).fit()
parameters = robust_fit.params
gain0 = gain
gain = parameters[-1]
if show:
import matplotlib.pyplot as plt
xfit = np.logspace(np.log10(np.min(x)), np.log10(np.max(x)))
plt.plot(xfit, robust_fit.predict(stats.add_constant(xfit)))
plt.pause(0.1)
logging.info('Iteration {0}: Gain = {1}'.format(i, gain))
if i == max_iterations:
logging.warning('Maximum regression ({0}) iterations reached'.format(max_iterations))
break
i += 1
logging.info('Fit done in {0} iterations'.format(i))
return gain
def dealData(bk, begd, endd, adjustPeriods, factorsInfo, FactorName, Path):
#数据库连接引擎
tableName = factorsInfo.get("tableName")
direction = factorsInfo.get("direction") #因子方向1为正序，0位逆序
reciprocal = factorsInfo.get("reciprocal") #因子值是否取倒数
isLogDeal = factorsInfo.get("isLogDeal") #因子是否进行ln处理
#engine = create_engine('mysql://*****:*****@172.16.158.142/dwlh?charset=utf8')
store = pd.HDFStore(tableName + '.h5', "r", complevel=9)
periedValues = []
#循环每次调仓日期
for i in adjustPeriods.index[:-1]:
adjustDay = adjustPeriods.ix[i, "date"]
nextAdjustDay = adjustPeriods.ix[i, "nextAdjustDay"]
logging.warning(u"处理第" + adjustDay + u"天数据!")
factor = store.select(
'/' + tableName + '/' + FactorName,
where=["Date='{date}'".format(date=adjustDay.replace('-', ''))])
#将日期字段设置为日期类型
factor.con_date = pd.to_datetime(factor.Date)
factor['stock_code'] = factor["stockID"].apply(lambda x: x[2:])
#按照调仓日期取板块信息，天软函数getbkByName，会剔除调仓日一字涨跌停、停牌以及上市时间小于120日的股票
BKStocks = TSLPy2.RemoteCallFunc('getbkByName2', [
bk,
TSLPy2.EncodeDate(int(adjustDay[:4]), int(adjustDay[5:7]),
int(adjustDay[8:10]))
], {})
BKStocks = pd.DataFrame(BKStocks[1])
BKStocks["SWNAME"] = BKStocks["SWNAME"].apply(
lambda x: x.decode('gbk'))
BKStocks["stock_code"] = BKStocks["id"].apply(lambda x: x[2:])
BKStocks["TotalValue"] = BKStocks["TotalValue"].apply(np.log)
#对因子值和板块合并
factor = factor.merge(BKStocks, on="stock_code")
#判断是否对因子值进行倒序处理
if reciprocal == 1:
factor[FactorName] = factor[FactorName].apply(lambda x: 1 / x
if x <> 0 else x)
if isLogDeal == 1:
factor[FactorName] = factor[FactorName].apply(np.log)
#对因子值进行方向处理
factor[FactorName] = factor[FactorName] * direction
#替换异常值
factorMedia = factor[FactorName].median()
MAD = (factor[FactorName] - factorMedia).apply(abs).median()
factor.loc[factor[FactorName] > (factorMedia + 3 * 1.4826 * MAD),
FactorName] = factorMedia + 3 * 1.4826 * MAD
factor.loc[factor[FactorName] < (factorMedia - 3 * 1.4826 * MAD),
FactorName] = factorMedia - 3 * 1.4826 * MAD
#zscore标准化
factorMean = factor[FactorName].mean()
factorStd = factor[FactorName].std()
factor[FactorName] = factor[FactorName].apply(
lambda x: (x - factorMean) / factorStd
if factorStd <> 0 else (x - factorMean))
#下期收益序列：
stokzf = pd.DataFrame(
TSLPy2.RemoteCallFunc('getStockZF', [
bk,
TSLPy2.EncodeDate(int(adjustDay[:4]), int(adjustDay[5:7]),
int(adjustDay[8:10])),
TSLPy2.EncodeDate(int(nextAdjustDay[:4]),
int(nextAdjustDay[5:7]),
int(nextAdjustDay[8:10]))
], {})[1])
factor = factor.merge(stokzf, on="stock_code")
factor.set_index("stock_code", inplace=True)
#通过回归对因子进行行业和市值中性化处理
factor = factor.dropna()
#方法1
#y, X = dmatrices('{factorName} ~ SWNAME + TotalValue'.format(factorName=FactorName), data=factor, return_type='dataframe')
#方法2
y = factor[FactorName]
X = pd.get_dummies(factor['SWNAME'])
if FactorName <> 'CAP':
X['TotalValue'] = factor['TotalValue']
X = sm.add_constant(X)
#res = sm.OLS(y, X).fit() #通过OLS进行回归
res2 = sm.RLM(y, X).fit() #通过RLM进行回归
#res3= sm.WLS(y, X).fit() #通过WLS进行回归
#factorParam = res2.params[FactorName]
#factorT = res2.tvalues[FactorName]
#tinyedFactor列，为回归后的残差项，看做新的因子值
factor["tinyedFactor2"] = factor[FactorName] - res2.fittedvalues
factor["tinyedFactor"] = res2.resid
#对新的因子值和下期涨幅进行T检验，得到T值和P值
factorT, factorP = ttest_rel(factor["tinyedFactor"], factor["zf"])
#计算IC值和RANKIC值
IC = factor["zf"].corr(factor["tinyedFactor"])
rankIC = factor["zf"].corr(factor["tinyedFactor"], method="spearman")
periedValues.append(
pd.DataFrame(
{
"FactorName": FactorName,
"adjustDay": adjustDay,
"IC": IC,
"rankIC": rankIC,
"factorP": factorP,
"factorT": factorT
},
index=[0]))
"""
fig, ax = plt.subplots(figsize=(8,6))
ax.plot(factor["con_roe"], y, 'o', label="Data")
#ax.plot(x["con_roe"], y_true, 'b-', label="True")
ax.plot(factor["con_roe"], res2.fittedvalues, 'r--.', label="RLMPredicted")
ax.plot(factor["con_roe"], res.fittedvalues, 'b--.', label="OLSPredicted")
legend = ax.legend(loc="best")
"""
store.close()
return periedValues
def fit_linreg_robust(x,
y,
mask=None,
intercept=False,
r2=True,
est_method="rlm"):
"""Apply robust linear regression of y w.r.t x.
Arguments
---------
x: :class:`~numpy.ndarray` or sparse `csr_matrix`
A vector of independent variables.
y: :class:`~numpy.ndarray` or sparse `csr_matrix`
A vector of dependent variables.
intercept: bool
If using steady state assumption for fitting, then:
True -- the linear regression is performed with an unfixed intercept;
False -- the linear regresssion is performed with a fixed zero intercept.
est_method: str (default: `rlm`)
The linear regression estimation method that will be used.
Returns
-------
k: float
The estimated slope.
b: float
The estimated intercept.
r2: float
Coefficient of determination or r square calculated with the extreme data points.
all_r2: float
The r2 calculated using all data points.
"""
x = x.A if issparse(x) else x
y = y.A if issparse(y) else y
_mask = np.logical_and(~np.isnan(x), ~np.isnan(y))
if mask is not None:
_mask &= mask
xx = x[_mask]
yy = y[_mask]
try:
if est_method.lower() == "rlm":
xx_ = sm.add_constant(xx) if intercept else xx
res = sm.RLM(yy, xx_).fit()
k, b = res.params[::-1] if intercept else (res.params[0], 0)
elif est_method.lower() == "ransac":
reg = RANSACRegressor(LinearRegression(fit_intercept=intercept),
random_state=0)
reg.fit(xx.reshape(-1, 1), yy.reshape(-1, 1))
k, b = reg.estimator_.coef_[0, 0], (reg.estimator_.intercept_[0]
if intercept else 0)
else:
raise ImportError(
f"estimation method {est_method} is not implemented. "
f"Currently supported linear regression methods include `rlm` and `ransac`."
)
except:
if intercept:
ym = np.mean(yy)
xm = np.mean(xx)
cov = np.mean(xx * yy) - xm * ym
var_x = np.mean(xx * xx) - xm * xm
k = cov / var_x
b = ym - k * xm
# # assume b is always positive
# if b < 0:
# k, b = np.mean(xx * yy) / np.mean(xx * xx), 0
else:
# use uncentered cov and var_x
cov = np.mean(xx * yy)
var_x = np.mean(xx * xx)
k = cov / var_x
b = 0
if r2:
SS_tot_n, all_SS_tot_n = np.var(yy), np.var(y)
SS_res_n, all_SS_res_n = (
np.mean((yy - k * xx - b)**2),
np.mean((y - k * x - b)**2),
)
r2, all_r2 = 1 - SS_res_n / SS_tot_n, 1 - all_SS_res_n / all_SS_tot_n
return k, b, r2, all_r2
else:
return k, b
# Переменных x может быть одна или две, и они должны быть записаны
# столбцами в соответствующих степенях.
# Следующий набор команд это и делает:
x_var1 = create_var1(x_cols[0], centering)
x_array = x_var1
for i in range(2, x_degs[0] + 1):
x_array = np.column_stack((x_array, x_var1**i))
if len(x_cols) > 1:
x_var2 = create_var2(x_cols[1], centering)
x_array = np.column_stack((x_array, x_var2))
for i in range(2, x_degs[1] + 1):
x_array = np.column_stack((x_array, x_var2**i))
x_array = sm.add_constant(x_array, prepend=True)
rlm_model = sm.RLM(y_array, x_array, M=sm.robust.norms.TukeyBiweight())
results = rlm_model.fit()
# -----------------------------------------------
def centering_back(params, degs):
A, B1, C1, B2, C2, D2 = 0., 0., 0., 0., 0., 0.
A = params[0]
if len(degs) == 1:
global m_x1, m_x2
# Потому что при построении x1 было получено m_x1, а
# m_x2 так и осталось нулем. Но тут я работаю с одной
# переменной как со второй (это подтверждают двойки
# в названии используемых в этом блоке if переменных
m_x1, m_x2 = m_x2, m_x1
Robust Linear Models Notes ----- The syntax for the arguments will be shortened to accept string arguments in the future. """ import statsmodels.api as sm ###Example for using Huber's T norm with the default ###median absolute deviation scaling data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) hub_results = huber_t.fit() print hub_results.params print hub_results.bse ###Or with the 'H2' covariance matrix hub_results2 = huber_t.fit(cov="H2") print hub_results2.params print hub_results2.bse ###Example for using Andrew's Wave norm with ###Huber's Proposal 2 scaling and 'H3' covariance matrix andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave()) andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov="H3") print andrew_results.params
def fit(self, y, X):
model = sm.RLM(y, X, M=sm.robust.norms.HuberT())
return model.fit()
def xr_regression_resid(y):
date = y.date
model = sm.RLM(y.values, X.loc[date].values, M=sm.robust.norms.HuberT())
results = model.fit()
return xr.DataArray(results.resid)
dt_pos = idx_date.get_loc(cur_date)
if dt_pos == 0:
continue
dt_pre_pos = dt_pos - 1
# symbols having valid value(not nan)
s = X[:, dt_pre_pos].notnull().all(axis=0)
valid_x = X[:, dt_pre_pos, s].symbol.values
w = y.loc[cur_date].notnull()
valid_y = y.loc[cur_date, w].symbol.values
valid_symbol = np.intersect1d(valid_x, valid_y)
try:
model = sm.RLM(
y.loc[cur_date, valid_symbol].values,
X.isel(date=dt_pre_pos,
symbol=idx_symbol.get_indexer(valid_symbol)).values.T,
M=sm.robust.norms.HuberT())
results = model.fit()
except ValueError:
continue
params.loc[cur_date] = results.params
residuals.loc[cur_date, valid_symbol] = results.resid
class RLMModel:
""" create RLM regression module
"""
def __init__(self):
pass
def main(files, n=''):
# Input variables names
xvar, yvar, tvar, zvar, evar, ivar = icol
# If cubes for each mission are in separate files,
# concatenate them and generate a single cube.
# Each mission (on individual file) will be given a unique identifier.
for nf, ifile in enumerate(files):
print 'processing file:', ifile, '...'
if nf == 0:
with h5py.File(ifile, 'r') as fi:
x = fi[xvar][:] # 1d
y = fi[yvar][:] # 1d
time = fi[tvar][:] # 1d
elev = fi[zvar][:] # 3d
mode = fi[ivar][:] if ivar in fi \
else np.full_like(time, nf) # 1d
sigma = fi[evar][:] if evar in fi \
else np.full_like(elev, np.nan) # 3d
else:
with h5py.File(ifile, 'r') as fi:
time = np.hstack((time, fi[tvar][:])) # 1d
elev = np.dstack((elev, fi[zvar][:])) # 3d
mode = np.hstack((mode, fi[ivar][:] if ivar in fi \
else np.full_like(fi[tvar][:], nf))) # 1d
sigma = np.dstack((sigma, fi[evar][:] if evar in fi \
else np.full_like(fi[zvar][:], np.nan))) # 3d
if len(np.unique(mode)) < 2:
print 'it seems there is only one mission!'
return
t1, t2 = np.nanmin(time), np.nanmax(time) ##TODO: Rethink this
# Output containers
zi = np.full_like(elev, np.nan)
ei = np.full_like(elev, np.nan)
ni = np.full_like(elev, np.nan)
# Temporal coverage
t_pct = np.zeros(elev.shape)
# Minimum sampling for all mission < 81.5 deg
nsam = 0.60
# Enter prediction loop
for i in xrange(elev.shape[0]):
for j in xrange(elev.shape[1]):
# Number of observations
nobs = 0
# Time difference
dt = 0
# Temporal sampling
npct = 1
# Number of sensors
nsen = 0
# Final test of data coverage
#if (nobs < nlim) or (npct < 0.70): continue
# Parameters for model-solution
tcap = time[:]
mcap = mode[:]
hcap = elev[i, j, :]
scap = sigma[i, j, :]
torg = tcap.copy()
morg = mcap.copy()
horg = hcap.copy()
sorg = scap.copy()
# Least-Squares Adjustment
# ---------------------------------
#
# h = x_t + x_j + x_s
# x = (A' A)^(-1) A' y
# r = y - Ax
#
# ---------------------------------
# Need to think of a smarth way to filter out outliears.
# In particular those at the end of each mission-record!!!
# Also, need to plot and see how the model fit compares to the data.
##FIXME ############################################################
# compute median series
##NOTE: Not needed for calibrating cube series (they are clean)
if 0:
hcap = binfilter(tcap,
hcap,
mcap,
window=3,
n_abs=5,
interp=False)
##FIXME ############################################################
if sum(~np.isnan(hcap)) < nlim: continue
#plt.figure()
ii = mcap == np.unique(mcap)[0]
jj = mcap == np.unique(mcap)[1]
plt.plot(tcap[ii], hcap[ii])
plt.plot(tcap[jj], hcap[jj])
dt = tcap - tref # trend component
# Create design matrix for alignment
Acap, cols = design_matrix(dt, mcap)
try:
# Least-squares bias adjustment
linear_model = sm.RLM(hcap, Acap, missing='drop')
linear_model_fit = linear_model.fit(maxiter=niter)
except:
print "Solution invalid!"
continue
# Coefficients and standard errors
Cm = linear_model_fit.params
Ce = linear_model_fit.bse
# Compute model residuals
dh = hcap - np.dot(Acap, Cm)
# Compute RMSE of corrected residuals (fit)
rms_fit = mad_std(dh)
# Bias correction (mission offsets)
h_cal_fit = np.dot(Acap[:, cols], Cm[cols])
# Remove inter satellite biases
horg -= h_cal_fit
# Plot
if 1:
plt.figure()
plt.plot(torg[ii], horg[ii])
plt.plot(torg[jj], horg[jj])
plt.show()
##FIXME: This doesn't work. Think of a better strategy!!!!!!!!!!!!
##TODO: How/Where to do this??? <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# Bin full calibrated record
if 0:
tmed, hmed, emed, nmed = binning(torg,
horg,
xmin=t1,
xmax=t2,
dx=1 / 12.,
window=3 / 12.,
median=True,
interp=False)[:4]
# Interpolate
'''
try:
i_valid = ~np.isnan(hmed)
i_inval = np.isnan(hmed)
hmed[i_inval] = np.interp(tmed[i_inval], tmed[i_valid], hmed[i_valid])
except:
continue
'''
# Reference final solution
'''
if 1:
# To original discrete time step
idx = find_nearest(tmed, tref)
hmed -= hmed[idx]
else:
# To exact given time epoch
href = np.interp(tref, tmed[~np.isnan(hmed)], hmed[~np.isnan(hmed)])
'''
"""
zi[i,j,:] = hmed
ei[i,j,:] = emed
ni[i,j,:] = nmed
"""
# Plot crosscal time series
if 1:
horg[np.abs(horg) > mad_std(horg) * 5] = np.nan
plt.figure(figsize=(12, 4))
plt.scatter(tcap, horg, s=10, c=mcap, alpha=0.7, cmap='tab10')
plt.scatter(tcap, hcap, s=10, c=mcap, cmap='gray')
try:
plt.figure(figsize=(12, 3.5))
plt.plot(tmed, hmed, '-', linewidth=2)
plt.ylim(np.nanmin(hmed), np.nanmax(hmed))
plt.xlim(t1, t2)
except:
pass
plt.show()
continue
'''
# Transform coordinates
(lon_i, lat_i) = transform_coord(projGrd, projGeo, xcap, ycap)
(lon_0, lat_0) = transform_coord(projGrd, projGeo, xi[i], yi[i])
# ********************** #
# Apply calibration to original data points
horg -= h_cal_fit
# Save output variables to list for each solution
lats.append(lat_i)
lons.append(lon_i)
lat0.append(lat_0)
lon0.append(lon_0)
dxy0.append(dxy)
h_ts.append(horg)
e_ts.append(sorg)
m_id.append(morg)
h_cf.append(h_cal_fit)
f_cr.append(flag)
tobs.append(torg)
rmse.append(rms_fit)
'''
# Transform coordinates
# Print meta data to terminal
if (i % 1) == 0:
print 'Progress:',str(i),'/',str(len(xi)), \
'Rate:', np.around(Cm[1],2), \
'Acceleration:', np.around(Cm[2],2)
# Saveing the data to file
print 'Saving data to file ...'
'''
ofile = ifile.replace('.h5', '_XCAL_FUSED.h5')
with h5py.File(ofile, 'w') as f:
f['h_res'] = zi.reshape(Xi.shape[0], Xi.shape[1], ti.shape[0])
f['h_err'] = ei.reshape(Xi.shape[0], Xi.shape[1], ti.shape[0])
f['n_obs'] = ni.reshape(Xi.shape[0], Xi.shape[1], ti.shape[0])
f['x'] = Xi[0,:]
f['y'] = Yi[:,0]
f['t'] = tmed
print 'out ->', ofile
'''
return
def main(ifile, n='', robust_fit=True, n_iter=niter):
# Check for empty file
if is_empty(ifile):
print 'SKIP FILE: EMPTY OR CORRUPTED FILE:', ifile
return
# Start timing of script
startTime = datetime.now()
print 'loading data ...'
xvar, yvar, tvar, zvar, svar, ivar, cvar = names
with h5py.File(ifile, 'r') as fi:
lon = fi[xvar][:]
lat = fi[yvar][:]
time = fi[tvar][:]
height = fi[zvar][:]
sigma = fi[svar][:] if svar in fi else np.ones(lon.shape)
id = fi[ivar][:] if ivar in fi else np.ones(lon.shape) * nmidx
cal = fi[cvar][:] if cvar in fi else np.zeros(lon.shape)
# Filter in time
if 1:
i_time, = np.where(
(time > 1993.972) & (time < 1995.222)) ##NOTE: To remove ERS-1 GM
if len(i_time) > 0: height[i_time] = np.nan
##NOTE: Filter data based on 'cal' but DO NOT REMOVE NANs!
if sum(cal) != 0:
cal[np.isnan(cal)] = 0. # keep values w/o correction
height -= cal # correct absolute H for bs
# Filter NaNs
if 1:
i_valid = ~np.isnan(height)
lon = lon[i_valid]
lat = lat[i_valid]
time = time[i_valid]
height = height[i_valid]
sigma = sigma[i_valid]
id = id[i_valid]
cal = cal[i_valid]
projGeo = '4326' # EPSG number for lon/lat proj
projGrd = projo # EPSG number for grid proj
print 'converting lon/lat to x/y ...'
# If no bbox was given
if bbox_ is None:
try:
bbox = get_bbox(ifile) # Try reading bbox from file name
except:
bbox = None
else:
bbox = bbox_
# Get geographic boundaries + max search radius
if bbox:
# Extract bounding box
xmin, xmax, ymin, ymax = bbox
# Transform coordinates
x, y = transform_coord(projGeo, projGrd, lon, lat)
# Select data inside bounding box
Ig = (x >= xmin - dmax) & (x <= xmax + dmax) & (y >= ymin - dmax) & (
y <= ymax + dmax)
# Check bbox for obs.
if len(x[Ig]) == 0:
print 'SKIP FILE: NO DATA POINTS INSIDE BBOX:', ifile
return
print 'Number of obs. edited by bbox!', 'before:', len(
x), 'after:', len(x[Ig])
# Only select wanted data
x = x[Ig]
y = y[Ig]
id = id[Ig]
time = time[Ig]
height = height[Ig]
sigma = sigma[Ig]
else:
# Convert into stereographic coordinates
x, y = transform_coord(projGeo, projGrd, lon, lat)
# Get bbox from data
xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
# Apply transformation to time
if expr: time = eval(expr.replace('t', 'time'))
# Define time interval of solution
if tspan:
# Time interval = given time span
t1lim, t2lim = tspan
# Select only observations inside time interval
Itime = (time > t1lim) & (time < t2lim)
# Keep only data inside time span
x = x[Itime]
y = y[Itime]
id = id[Itime]
time = time[Itime]
height = height[Itime]
sigma = sigma[Itime]
else:
# Time interval = all data
t1lim, t2lim = time.min(), time.max()
if mode == 'p':
# Point solution - all points
xi, yi = np.copy(x), np.copy(y)
else:
# Grid solution - defined by nodes
Xi, Yi = make_grid(xmin, xmax, ymin, ymax, dx, dy)
xi, yi = Xi.ravel(), Yi.ravel()
coord = zip(x.ravel(), y.ravel())
print 'building the k-d tree ...'
Tree = cKDTree(coord)
# Overall (fixed) mean time
t_mean = np.round(np.nanmean(time), 2)
# Number of nodes
nodes = len(xi)
# Initialize bias param
bias = np.ones(lon.shape) * np.nan
# Temporal resolution: months -> years
tstep = tstep_ / 12.0
# Expected max number of months in time series
months = len(np.arange(t1lim, t2lim + tstep, tstep))
M = 5
# Create output containers (data matrix)
DATA0 = np.full((nodes, 21), np.nan)
DATA1 = np.full((nodes, months + M), np.nan)
DATA2 = np.full((nodes, months + M), np.nan)
# Search radius array (dmax is slightly increased by 1e-4)
dr = np.arange(dmin, dmax, 500)
# Enter prediction loop
print 'predicting values ...'
for i in xrange(len(xi)):
xc, yc = xi[i], yi[i] # Center coordinates
# Loop through search radii
for rad in dr:
# Get indices of data within search radius (after relocation)
i_cell, reloc_dist = get_radius_idx(x,
y,
xc,
yc,
rad,
Tree,
n_reloc=nreloc)
if len(i_cell) < nlim: continue # use larger radius
tcap, hcap = time[i_cell], height[i_cell]
Nb = sum(~np.isnan(hcap)) # length before editing
# 3-sigma filter
if SIGMAFILT:
#hcap = sigma_filter(tcap, hcap, order=1, n_sigma=3, n_iter=3) ##NOTE: It removes too much!!!
hcap[np.abs(hcap - np.nanmedian(hcap)) > mad_std(hcap) *
3] = np.nan
hcap[np.abs(hcap - np.nanmedian(hcap)) > 300] = np.nan
Na = sum(~np.isnan(hcap)) # Length after editing
n_mon, t_span = n_months(tcap, hcap, tstep=tstep)
##NOTE: Not using n_mon and t_span to constrain the solution! <<<<<<<<<<<<<<<<<<<<<
# If enough data accept radius
#if Na >= nlim and n_mon >= MINMONTHS and t_span >= dtlim:
if Na >= nlim:
break
else:
i_cell = []
if not i_cell: continue
# Parameters for model-solution
xcap = x[i_cell]
ycap = y[i_cell]
tcap = time[i_cell]
hcap = height[i_cell]
mcap = id[i_cell]
scap = sigma[i_cell]
i_valid = ~np.isnan(hcap)
if sum(i_valid) < nlim: continue
xcap = xcap[i_valid]
ycap = ycap[i_valid]
tcap = tcap[i_valid]
hcap = hcap[i_valid]
mcap = mcap[i_valid]
scap = scap[i_valid]
if nreloc:
xc = np.median(xcap) # update inversion cell coords
yc = np.median(ycap)
# Define resolution param (a fraction of the accepted radius)
dres = dres_ * rad
# Estimate variance
vcap = scap * scap
# If reference time not given, use fixed or variable mean
if tref_ == 'fixed':
tref = t_mean
elif tref_ == 'variable':
tref = np.nanmean(tcap)
else:
tref = np.float(tref_)
# Design matrix elements
c0 = np.ones(len(xcap)) # intercept (0)
c1 = xcap - xc # dx (1)
c2 = ycap - yc # dy (2)
c3 = c1 * c2 # dx**2
c4 = c1 * c1 # dx**2
c5 = c2 * c2 # dy**2
c6 = tcap - tref # trend (6)
c7 = 0.5 * (c6 * c6) # acceleration (7)
c8 = np.sin(2 * np.pi * c6) # seasonal sin (8)
c9 = np.cos(2 * np.pi * c6) # seasonal cos (9)
# Compute distance from prediction point to data inside cap
dist = np.sqrt((xcap - xc) * (xcap - xc) + (ycap - yc) * (ycap - yc))
# Add small value to stabilize SVD solution
vcap += 1e-6
# Weighting factor: distance and error
Wcap = 1.0 / (vcap * (1.0 + (dist / dres) * (dist / dres)))
# Create some intermediate output variables
sx, sy, at, ae, bi = np.nan, np.nan, np.nan, np.nan, np.nan
# Setup design matrix
if model == 0:
# Trend and seasonal
Acap = np.vstack((c0, c8, c9, c6)).T
mcol = [1, 2, 3] # columns to add back
elif model == 1:
# Trend, acceleration and seasonal
Acap = np.vstack((c0, c7, c8, c9, c6)).T
mcol = [1, 2, 3, 4]
elif model == 2:
# Trend, acceleration, seasonal and bi-linear surface
Acap = np.vstack((c0, c1, c2, c7, c8, c9, c6)).T
mcol = [3, 4, 5, 6]
else:
# Trend, acceleration, seasonal and bi-quadratic surface (full model)
Acap = np.vstack((c0, c1, c2, c3, c4, c5, c7, c8, c9, c6)).T
mcol = [6, 7, 8, 9]
has_bias = False # bias flag
# Check if bias is needed
if len(np.unique(mcap)) > 1:
# Add bias to design matrix
Acap = np.vstack((Acap.T, mcap)).T
has_bias = True
##NOTE: Not using t_span to constrain solution! <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# Check constrains before solving model (min_pts and min_tspan)
#if len(hcap) < nlim or np.max(tcap)-np.min(tcap) < dtlim: continue
if len(hcap) < nlim: continue
""" Least-squares fit """
if robust_fit:
# Robust least squares
try:
model_fit = sm.RLM(hcap, Acap,
missing='drop').fit(maxiter=n_iter,
tol=0.001)
except:
print 'SOMETHING WRONG WITH THE FIT... SKIPPING CELL!!!'
continue
else:
# Weighted Least squares
model_fit = sm.WLS(hcap, Acap, weights=Wcap, missing='drop').fit()
Cm = model_fit.params # coeffs
Ce = model_fit.bse # std err
resid = model_fit.resid # data - model
# Check rate and error
if np.abs(Cm[-1]) > dhlim or np.isinf(Ce[-1]):
continue ##NOTE: Important for ICESat !!!
# Residuals dH = H - A * Cm (remove linear trend)
dh = hcap - np.dot(Acap, Cm)
if robust_fit:
chisq = chisquared(model_fit)
else:
chisq = rsquared(model_fit)
# Compute amplitude of seasonal signal
asea = np.sqrt(Cm[-2] * Cm[-2] + Cm[-3] * Cm[-3])
# Compute phase offset
psea = np.arctan2(Cm[-2], Cm[-3])
# Convert phase to decimal years ##FIXME: Convert phase to days !!!
psea /= (2 * np.pi)
# Compute root-mean-square of full model residuals
rms = mad_std(resid)
# Add back wanted model parameters
dh += np.dot(Acap[:, mcol], Cm[mcol])
# Simple binning of residuals
tb, hb, eb, nb = binning(
tcap.copy(), dh.copy(), t1lim, t2lim,
tstep)[:4] ##FIXME: Use Median to construct time series
# Convert centroid location to latitude and longitude
lon_c, lat_c = transform_coord(projGrd, projGeo, xc, yc)
# Position
DATA0[i, 0] = lat_c
DATA0[i, 1] = lon_c
# Elevation Change
DATA0[i, 2] = Cm[-1] # trend
DATA0[i, 3] = Ce[-1] # trend error
# Compute acceleration and error
if model > 0:
at, ae = Cm[-4], Ce[-4]
DATA0[i, 4] = at # acceleration
DATA0[i, 5] = ae # acceleration error
# Surface Elevation
DATA0[i, 6] = Cm[0]
DATA0[i, 7] = Ce[0]
# Model RMS
DATA0[i, 8] = rms
# Compute x,y slopes in degrees
if model > 1:
sx, sy = np.arctan(Cm[1]) * (180 / np.pi), np.arctan(
Cm[2]) * (180 / np.pi)
# Surface slope values
DATA0[i, 9] = sx
DATA0[i, 10] = sy
# Time span of data in cap
DATA0[i, 11] = t_span
DATA0[i, 12] = tref
# Seasonal signal
DATA0[i, 13] = asea
DATA0[i, 14] = psea
# Bias magnitude
if has_bias: bi = Cm[-1]
# Aux-data from solution
DATA0[i, 15] = len(hcap)
DATA0[i, 16] = dmin
DATA0[i, 17] = rad
DATA0[i, 18] = Nb - Na
DATA0[i, 19] = chisq
DATA0[i, 20] = bi
# Time series values
DATA1[i, :] = np.hstack((lat_c, lon_c, t1lim, t2lim, len(tb),
hb)) ##FIXME: Think how to do this better
DATA2[i, :] = np.hstack((lat_c, lon_c, t1lim, t2lim, len(tb), eb))
# Print progress (every N iterations)
if (i % 200) == 0:
print 'cell#', str(i) + "/" + str(len(xi)), \
'trend:', np.around(Cm[mcol[-1]],2), 'm/yr', 'n_months:', n_mon, \
'n_pts:', len( resid), 'radius:', rad, 'reloc_dist:', reloc_dist
# Remove invalid entries from data matrix
if mode == 'p':
i_nan = np.where(np.isnan(DATA0[:, 3]))
DATA0 = np.delete(DATA0.T, i_nan, 1).T
i_nan = np.where(np.isnan(DATA1[:, 3]))
DATA1 = np.delete(DATA1.T, i_nan, 1).T
i_nan = np.where(np.isnan(DATA2[:, 3]))
DATA2 = np.delete(DATA2.T, i_nan, 1).T
else:
##NOTE: NaNs are not removed in case a grid soluction (n_reloc=0) is selected.
if not nreloc:
grids = [d.reshape(Xi.shape) for d in DATA0.T] # 1d -> 2d (grids)
variables = [
'lat', 'lon', 'trend', 'trend_err', 'accel', 'accel_err', 'height',
'height_err', 'model_rms', 'slope_x', 'slope_y', 't_span', 't_ref',
'amp_seas', 'pha_seas', 'n_obs', 'd_min', 'd_ri', 'n_edited',
'chi2', 'bias'
]
# Check if output arrays are empty
if np.isnan(DATA0[:, 3]).all():
print 'SKIP FILE: NO PREDICTIONS TO SAVE:', ifile
return
# Define output file name
if ofile:
outfile = ofile
else:
outfile = ifile
# Output file names - strings
path, ext = os.path.splitext(outfile)
ofile0 = path + '_sf.h5'
ofile1 = path + '_ts.h5'
ofile2 = path + '_es.h5'
print 'saving data ...'
# Save surface fit parameters
with h5py.File(ofile0, 'w') as fo0:
if mode == 'p':
fo0['sf'] = DATA0 # data matrix
elif nreloc:
for v, a in zip(variables, DATA0.T):
fo0[v] = a # 1d arrays
else:
for v, g in zip(variables, grids):
fo0[v] = g # 2d arrays
fo0['x'], fo0['y'] = Xi[0, :], Yi[:, 0]
# Save binned time series values
with h5py.File(ofile1, 'w') as fo1:
fo1['ts'] = DATA1
# Save binned time series errors
with h5py.File(ofile2, 'w') as fo2:
fo2['es'] = DATA2
# Print some statistics
print '*' * 70
print('%s %.5f %s %.2f %s %.2f %s %.2f %s %s' %
('Mean:', np.nanmean(DATA0[:, 2]), 'Std:', np.nanstd(
DATA0[:, 2]), 'Min:', np.nanmin(DATA0[:, 2]), 'Max:',
np.nanmax(DATA0[:, 2]), 'Model:', model))
print '*' * 70
print 'Execution time: ' + str(datetime.now() - startTime)
print 'Surface fit results ->', ofile0
print 'Time series values -> ', ofile1
print 'Time series errors -> ', ofile2
# In[8]: dataset.corr() > 0.98 # In[17]: xtrain_dataframe = pd.DataFrame(xtrain) ytrain_dataframe = pd.DataFrame(ytrain) xtest_dataframe = pd.DataFrame(xtest) ytest_dataframe = pd.DataFrame(ytest) xtrain_dataframe.columns = [u'R1',u'R2',u'R3',u'R4',u'R5',u'R6',u'R7',u'R8',u'Temp.',u'Humidity'] ytrain_dataframe.columns = ['class'] xtest_dataframe.columns = [u'R1',u'R2',u'R3',u'R4',u'R5',u'R6',u'R7',u'R8',u'Temp.',u'Humidity'] ytest_dataframe.columns = ['class'] res = sm.RLM(ytrain_dataframe, xtrain_dataframe).fit() res.summary() # When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. Hypothesis tests are used to test the validity of a claim that is made about a population. This claim that’s on trial, in essence, is called the null hypothesis. # # The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue. The evidence in the trial is your data and the statistics that go along with it. All hypothesis tests ultimately use a p-value to weigh the strength of the evidence (what the data are telling you about the population). The p-value is a number between 0 and 1 and interpreted in the following way: # # 1. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. # # 2. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. # # 3. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way). Always report the p-value so your readers can draw their own conclusions. # # # #### P value of R1 is too much which means that this variable donot have affect on the model. Hence we can remove this variable from our model.
def OLS_plot(col_x,
col_y,
dat,
hue=None,
robust=False,
title=None,
color='blue',
aspect=3):
'''
create correlation plot between two columns in a dataframe; add r2 and kendal tau stats to plot
hue: name of column used to color the data points
'''
#Calculate correlation stats
#OLS regression
if robust == False:
res = sm.OLS(dat[col_y], sm.add_constant(dat[col_x]),
missing='drop').fit()
pval = res.pvalues[col_x]
r2 = res.rsquared_adj
slope = res.params[col_x]
if robust:
res = sm.RLM(dat[col_y], sm.add_constant(dat[col_x]),
missing='drop').fit()
pval = res.pvalues[col_x]
r2 = sm.OLS(
dat[col_y], dat[col_x],
missing='drop').fit().rsquared_adj #same r2 as for non-robust
slope = res.params[col_x]
#Kendal-Tau (non-parametric)
kt_dat = dat.dropna(subset=[col_x, col_y])
kendall_tau, kt_pval_num = scipy.stats.stats.kendalltau(kt_dat[col_y],
kt_dat[col_x],
nan_policy="omit")
kt_pval = pretty_p_val(kt_pval_num)
#Build plot
sns.lmplot(y=col_y,
x=col_x,
data=dat,
hue=hue,
robust=robust,
line_kws={
'color': 'red',
'lw': 1,
'alpha': 0.8
},
scatter_kws={
'color': color,
'alpha': 0.6
},
aspect=aspect)
#plt.xticks(rotation=-90)
summary_text = "$r^2$=" + str(r2)[0:4] + "; " + pretty_p_val(
pval) + ". Slope= " + str(slope.round(4))
plt.tight_layout(pad=2)
plt.figtext(0.93, 0.01, summary_text, horizontalalignment='right')
plt.figtext(0.02,
0.01,
r"K. $\tau$ = " + str(kendall_tau)[0:5] + "; " + kt_pval,
horizontalalignment='left')
ax = plt.gca()
ax.set_title(title)
#store results for function to return
d = {
'r2': [r2],
'r2_p': [pval],
'slope': [slope],
'kendall_tau': [kendall_tau],
'kt_pval': [kt_pval_num]
}
df = pd.DataFrame(data=d)
return (df)
def relative2abs(adata,
dilution,
volume,
from_layer=None,
to_layers=None,
mixture_type=1,
ERCC_controls=None,
ERCC_annotation=None):
"""Converts FPKM/TPM data to transcript counts using ERCC spike-in. This is based on the relative2abs function from
monocle 2 (Qiu, et. al, Nature Methods, 2017).
Parameters
----------
adata: :class:`~anndata.AnnData`
an Annodata object
dilution: `float`
the dilution of the spikein transcript in the lysis reaction mix. Default is 40, 000. The number of spike-in
transcripts per single-cell lysis reaction was calculated from.
volume: `float`
the approximate volume of the lysis chamber (nanoliters). Default is 10
from_layer: `str` or `None`
The layer in which the ERCC TPM values will be used as the covariate for the ERCC based linear regression.
to_layers: `str`, `None` or `list-like`
The layers that our ERCC based transformation will be applied to.
mixture_type:
the type of spikein transcripts from the spikein mixture added in the experiments. By default, it is mixture 1.
Note that m/c we inferred are also based on mixture 1.
ERCC_controls:
the FPKM/TPM matrix for each ERCC spike-in transcript in the cells if user wants to perform the transformation based
on their spike-in data. Note that the row and column names should match up with the ERCC_annotation and relative_
exprs_matrix respectively.
ERCC_annotation:
the ERCC_annotation matrix from illumina USE GUIDE which will be ued for calculating the ERCC transcript copy
number for performing the transformation.
Returns
-------
An adata object with the data specified in the to_layers transformed into absolute counts.
"""
if ERCC_annotation is None:
ERCC_annotation = pd.read_csv(
'https://www.dropbox.com/s/cmiuthdw5tt76o5/ERCC_specification.txt?dl=1',
sep='\t')
ERCC_id = ERCC_annotation['ERCC ID']
ERCC_id = adata.var_names.intersection(ERCC_id)
if len(ERCC_id) < 10 and ERCC_controls is None:
raise Exception(
f'The adata object you provided has less than 10 ERCC genes.')
if to_layers is not None:
to_layers = [to_layers] if to_layers is str else to_layers
to_layers = list(set(adata.layers.keys()).intersection(to_layers))
if len(to_layers) == 0:
raise Exception(
f"The layers {to_layers} that will be converted to absolute counts doesn't match any layers"
f"from the adata object.")
mixture_name = "concentration in Mix 1 (attomoles/ul)" if mixture_type == 1 else "concentration in Mix 2 (attomoles/ul)"
ERCC_annotation['numMolecules'] = ERCC_annotation.loc[:, mixture_name] * (
volume * 10**(-3) * 1 / dilution * 10**(-18) * 6.02214129 * 10**(23))
ERCC_annotation['rounded_numMolecules'] = ERCC_annotation[
'numMolecules'].astype(int)
if from_layer in [None, 'X']:
X, X_ercc = (adata.X, adata[:, ERCC_id].X
if ERCC_controls is None else ERCC_controls)
else:
X, X_ercc = (adata.layers[from_layer], adata[:, ERCC_id] \
if ERCC_controls is None else ERCC_controls)
logged = False if X.max() > 100 else True
if not logged:
X, X_ercc = (np.log1p(X.A) if issparse(X_ercc) else np.log1p(X), \
np.log1p(X_ercc.A) if issparse(X_ercc) else np.log1p(X_ercc))
else:
X, X_ercc = (X.A if issparse(X_ercc) else X,
X_ercc.A if issparse(X_ercc) else X_ercc)
y = np.log1p(ERCC_annotation['numMolecules'])
for i in range(adata.n_obs):
X_i, X_ercc_i = X[i, :], X_ercc[i, :]
X_i, X_ercc_i = sm.add_constant(X_i), sm.add_constant(X_ercc_i)
res = sm.RLM(y, X_ercc_i).fit()
k, b = res.params[::-1]
if to_layers is None:
X = adata.X
logged = False if X.max() > 100 else True
if not logged:
X_i = np.log1p(X[i, :].A) if issparse(X) else np.log1p(X[i, :])
else:
X_i = X[i, :].A if issparse(X) else X[i, :]
res = k * X_i + b
res = res if logged else np.expm1(res)
adata.X[i, :] = csr_matrix(res) if issparse(X) else res
else:
for cur_layer in to_layers:
X = adata.layers[cur_layer]
logged = False if X.max() > 100 else True
if not logged:
X_i = np.log1p(X[i, :].A) if issparse(X) else np.log1p(
X[i, :])
else:
X_i = X[i, :].A if issparse(X) else X[i, :]
res = k * X_i + b if logged else np.expm1(k * X_i + b)
adata.layers[cur_layer][i, :] = csr_matrix(res) if issparse(
X) else res
#Example: OLS
model = sm.OLS(Y, X)
results = model.fit()
print(results.summary())
print(results.params)
print(results.cov_params())
infl = results.get_influence()
print(infl.summary_table())
#raise
#Example RLM
huber_t = sm.RLM(Y, X, M=sm.robust.norms.HuberT())
hub_results = huber_t.fit()
print(hub_results.params)
print(hub_results.bcov_scaled)
print(hub_results.summary())
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
def plot_acf_multiple(ys, lags=20):
"""
"""
from statsmodels.tsa.stattools import acf
print('corrected rsquared')
print((wls_fit3.uncentered_tss - wls_fit3.ssr) / wls_fit3.uncentered_tss)
plt.figure()
plt.title('WLS dropping heteroscedasticity variable from regressors')
plt.plot(data.endog, wls_fit3.fittedvalues, 'o')
plt.xlim([0, 2000])
# @savefig wls_drop_het.png
plt.ylim([0, 2000])
print('raw correlation of endog and fittedvalues')
print(np.corrcoef(data.endog, wls_fit.fittedvalues))
print('raw correlation coefficient of endog and fittedvalues squared')
print(np.corrcoef(data.endog, wls_fit.fittedvalues)[0, 1]**2)
# compare with robust regression,
# heteroscedasticity correction downweights the outliers
rlm_fit = sm.RLM(data.endog, data.exog).fit()
plt.figure()
plt.title('using robust for comparison')
plt.plot(data.endog, rlm_fit.fittedvalues, 'o')
plt.xlim([0, 2000])
# @savefig wls_robust_compare.png
plt.ylim([0, 2000])
# What is going on? A more systematic look at the data
# ----------------------------------------------------
# two helper functions
def getrsq(fitresult):
'''calculates rsquared residual, total and explained sums of squares
def rlsq(x, y, n=1):
""" Fit a robust polynomial of n:th deg."""
# Test solution
if len(x[~np.isnan(y)]) <= (n + 1):
if n == 0:
p = np.nan
s = np.nan
else:
p = np.zeros((1, n)) * np.nan
s = np.nan
return p, s
# Empty array
A = np.empty((0, len(x)))
# Create counter
i = 0
# Determine if we need centering
if n > 1:
# Center x-axis
x -= np.nanmean(x)
# Special case
if n == 0:
# Mean offset
A = np.ones(len(x))
else:
# Make design matrix
while i <= n:
# Stack coefficients
A = np.vstack((A, x ** i))
# Update counter
i += 1
# Test to see if we can solve the system
try:
# Robust least squares fit
fit = sm.RLM(y, A.T, missing='drop').fit(maxiter=5, tol=0.001)
# polynomial coefficients
p = fit.params
# RMS of the residuals
s = mad_std(fit.resid)
except:
# Set output to NaN
if n == 0:
p = np.nan
s = np.nan
else:
p = np.zeros((1, n)) * np.nan
s = np.nan
return p[::-1], s
if (context.vlevel >= 2):
sys.stderr.write("%s\n" % fulldiv)
sys.stderr.write("guess_ra: %15.7f\n" % guess_ra)
sys.stderr.write("guess_de: %15.7f\n" % guess_de)
#afpars = [np.radians(guess_ra), np.radians(guess_de), ts_pmra_masyr/1e3, ts_pmde_masyr/1e3, 1.0]
afpars = [np.radians(guess_ra), np.radians(guess_de),
np.radians(ts_ra_model[1]), np.radians(ts_de_model[1]), 1.0]
appcoo = af.apparent_radec(use_epoch.tdb.jd, afpars, use_eph)
# proper fit:
design_matrix = np.column_stack((np.ones(syr.size), syr))
#de_design_matrix = np.column_stack((np.ones(syr.size), syr))
ra_ols_res = sm.OLS(sra, design_matrix).fit()
de_ols_res = sm.OLS(sde, design_matrix).fit()
ra_rlm_res = sm.RLM(sra, design_matrix).fit()
de_rlm_res = sm.RLM(sde, design_matrix).fit()
rlm_pmde_masyr = de_rlm_res.params[1] * 3.6e6
rlm_pmra_masyr = ra_rlm_res.params[1] * 3.6e6 \
* np.cos(np.radians(de_rlm_res.params[0]))
if (context.vlevel >= 1):
sys.stderr.write("%s\n" % fulldiv)
sys.stderr.write("\nTheil-Sen intercepts:\n")
sys.stderr.write("RA: %15.7f\n" % ts_ra_model[0])
sys.stderr.write("DE: %15.7f\n" % ts_de_model[0])
sys.stderr.write("\nTheil-Sen proper motions:\n")
sys.stderr.write("RA: %10.6f mas/yr\n" % ts_pmra_masyr)
sys.stderr.write("DE: %10.6f mas/yr\n" % ts_pmde_masyr)
def main(ifile, n=''):
# Check for empty file
if os.stat(ifile).st_size == 0:
print('input file is empty!')
return
# Start timing of script
startTime = datetime.now()
print('loading data ...')
# Determine input file type
if not ifile.endswith(('.h5', '.H5', '.hdf', '.hdf5')):
print("Input file must be in hdf5-format")
return
# Input variables
xvar, yvar, tvar, zvar = icol
# Load all 1d variables needed
with h5py.File(ifile, 'r') as fi:
lon = fi[xvar][:]
lat = fi[yvar][:]
time = fi[tvar][:]
height = fi[zvar][:]
# EPSG number for lon/lat proj
projGeo = '4326'
# EPSG number for grid proj
projGrd = proj
print('converting lon/lat to x/y ...')
# Convert into stereographic coordinates
(x, y) = transform_coord(projGeo, projGrd, lon, lat)
# Get bbox from data
(xmin, xmax, ymin, ymax) = x.min(), x.max(), y.min(), y.max()
# Apply transformation to time
if expr: time = eval(expr.replace('t', 'time'))
# Overall (fixed) mean time
t_mean = np.round(np.nanmean(time), 2)
# Grid solution - defined by nodes
(Xi, Yi) = make_grid(xmin, xmax, ymin, ymax, dx, dy)
# Flatten prediction grid
xi = Xi.ravel()
yi = Yi.ravel()
# Zip data to vector
coord = list(zip(x.ravel(), y.ravel()))
# Construct cKDTree
print('building the k-d tree ...')
Tree = cKDTree(coord)
# Create output containers
dh_topo = np.full(height.shape, np.nan)
de_topo = np.full(height.shape, 999999.)
mi_topo = np.full(height.shape, np.nan)
hm_topo = np.full(height.shape, np.nan)
sx_topo = np.full(height.shape, np.nan)
sy_topo = np.full(height.shape, np.nan)
tr_topo = np.full(height.shape, np.nan)
# Set slope limit
slp_lim = np.tan(np.deg2rad(slplim))
# Enter prediction loop
print('predicting values ...')
for i in range(len(xi)):
x0, y0 = xi[i], yi[i]
# Get indexes of data within search radius or cell bbox
idx = get_radius_idx(
x, y, x0, y0, dmax, Tree, n_reloc=nreloc,
min_months=18, max_reloc=3, time=None, height=None)
# Length of data in search cap
nobs = len(x[idx])
# Check data density
if (nobs < nlim): continue
# Parameters for model-solution
xcap = x[idx]
ycap = y[idx]
tcap = time[idx]
hcap = height[idx]
# Copy original height vector
h_org = hcap.copy()
# Centroid node
xc = np.median(xcap)
yc = np.median(ycap)
# If reference time not given, use fixed or variable mean
if tref_ == 'fixed':
tref = t_mean
elif tref_ == 'variable':
tref = np.nanmean(tcap)
else:
tref = np.float(tref_)
# Design matrix elements
c0 = np.ones(len(xcap))
c1 = xcap - xc
c2 = ycap - yc
c3 = c1 * c2
c4 = c1 * c1
c5 = c2 * c2
c6 = tcap - tref
# Length before editing
nb = len(hcap)
# Determine model order
if order == 2 and nb >= mlim * 2:
# Biquadratic surface and linear trend
Acap = np.vstack((c0, c1, c2, c3, c4, c5, c6)).T
# Model identifier
mi = 1
# Set model order
elif nb >= mlim:
# Bilinear surface and linear trend
Acap = np.vstack((c0, c1, c2, c6)).T
# Model identifier
mi = 2
else:
# Model identifier
mi = 3
# Modelled topography
if mi == 1:
# Construct model object
linear_model = sm.RLM(hcap, Acap, M=sm.robust.norms.HuberT(), missing='drop')
# Fit the model to the data,
linear_model_fit = linear_model.fit(maxiter=niter, tol=0.001)
# Coefficients
Cm = linear_model_fit.params
# Biquadratic surface
h_model = np.dot(np.vstack((c0, c1, c2, c3, c4, c5)).T, Cm[[0, 1, 2, 3, 4, 5]])
# Compute along and across track slope
sx = np.sign(Cm[1]) * slp_lim if np.abs(Cm[1]) > slp_lim else Cm[1]
sy = np.sign(Cm[2]) * slp_lim if np.abs(Cm[2]) > slp_lim else Cm[2]
# Mean height
h_avg = Cm[0]
elif mi == 2:
# Construct model object
linear_model = sm.RLM(hcap, Acap, M=sm.robust.norms.HuberT(), missing='drop')
# Fit the model to the data,
linear_model_fit = linear_model.fit(maxiter=niter, tol=0.001)
# Coefficients
Cm = linear_model_fit.params
# Bilinear surface
h_model = np.dot(np.vstack((c0, c1, c2)).T, Cm[[0, 1, 2]])
# Compute along and across track slope
sx = np.sign(Cm[1]) * slp_lim if np.abs(Cm[1]) > slp_lim else Cm[1]
sy = np.sign(Cm[2]) * slp_lim if np.abs(Cm[2]) > slp_lim else Cm[2]
# Mean height
h_avg = Cm[0]
else:
# Mean surface from median
h_avg = np.median(hcap)
# Compute distance estimates from centroid
s_dx = (xcap - xc) + 1e-3
s_dy = (ycap - yc) + 1e-3
# Center surface height
dh_i = h_org - h_avg
# Compute along-track slope
px, rms_x = rlsq(s_dx, dh_i, 1)
py, rms_x = rlsq(s_dy, dh_i, 1)
# Set along-track slope
s_x = 0 if np.isnan(px[0]) else px[0]
# Set across-track slope to zero
s_y = 0 if np.isnan(py[0]) else py[0]
# Compute along and across track slope
sx = np.sign(s_x) * slp_lim if np.abs(s_x) > slp_lim else s_x
sy = np.sign(s_y) * slp_lim if np.abs(s_y) > slp_lim else s_y
# Compute the surface height correction
h_model = h_avg + (sx * s_dx) + (sy * s_dy)
# Compute full slope
slope = np.arctan(np.sqrt(sx**2 + sy**2)) * (180 / np.pi)
# Compute residual
dh = h_org - h_model
# Number of observations
na = len(dh)
# RMSE of the residuals
RMSE = mad_std(dh)
# Overwrite errors
iup = RMSE < de_topo[idx]
# Create temporary variables
dh_cap = dh_topo[idx].copy()
de_cap = de_topo[idx].copy()
hm_cap = hm_topo[idx].copy()
mi_cap = mi_topo[idx].copy()
tr_cap = tr_topo[idx].copy()
# Update variables
dh_cap[iup] = dh[iup]
de_cap[iup] = RMSE
hm_cap[iup] = h_avg
mi_cap[iup] = mi
tr_cap[iup] = tref
# Update with current solution
dh_topo[idx] = dh_cap
de_topo[idx] = de_cap
hm_topo[idx] = hm_cap
mi_topo[idx] = mi_cap
tr_topo[idx] = tr_cap
sx_topo[idx] = np.arctan(sx) * (180 / np.pi)
sy_topo[idx] = np.arctan(sy) * (180 / np.pi)
# Print progress (every N iterations)
if (i % 100) == 0 and diag is True:
# Print message every i:th solution
print(('%s %i %s %2i %s %i %s %03d %s %.3f %s %.3f' % \
('#',i,'/',len(xi),'Model:',mi,'Nobs:',nb,'Slope:',\
np.around(slope,3),'Residual:',np.around(mad_std(dh),3))))
# Print percentage of not filled
print(('Total NaNs (percent): %.2f' % \
(100 * float(len(dh_topo[np.isnan(dh_topo)])) / float(len(dh_topo)))))
# Print percentage of each model
one = np.sum(mi_topo == 1)
two = np.sum(mi_topo == 2)
tre = np.sum(mi_topo == 3)
N = float(len(mi_topo))
print(('Model types (percent): 1 = %.2f, 2 = %.2f, 3 = %.2f' % \
(100 * one/N, 100 * two/N, 100 * tre/N)))
# Append new columns to original file
with h5py.File(ifile, 'a') as fi:
# Check if we have variables in file
try:
# Save variables
fi['h_res'] = dh_topo
fi['h_mod'] = hm_topo
fi['e_res'] = de_topo
fi['m_deg'] = mi_topo
fi['t_ref'] = tr_topo
fi['slp_x'] = sx_topo
fi['slp_y'] = sy_topo
except:
# Update variables
fi['h_res'][:] = dh_topo
fi['h_mod'][:] = hm_topo
fi['e_res'][:] = de_topo
fi['m_deg'][:] = mi_topo
fi['t_ref'][:] = tr_topo
fi['slp_x'][:] = sx_topo
fi['slp_y'][:] = sy_topo
# Rename file
if ifile.find('TOPO') < 0:
os.rename(ifile, ifile.replace('.h5', '_TOPO.h5'))
# Print some statistics
print(('*' * 75))
print(('%s %s %.5f %s %.2f %s %.2f %s %.2f %s %.2f' % \
('Statistics',
'Mean:', np.nanmedian(dh_topo),
'Std.dev:', mad_std(dh_topo),
'Min:', np.nanmin(dh_topo),
'Max:', np.nanmax(dh_topo),
'RMSE:', np.nanmedian(de_topo[dh_topo!=999999]),)))
print(('*' * 75))
print('')
# Print execution time of algorithm
print(('Execution time: '+ str(datetime.now()-startTime)))
def robust_linear(self, x, y):
rlm_model = sm.RLM(y, x, M=sm.robust.norms.HuberT())
rlm_results = rlm_model.fit()
print(rlm_results.summary())
print(rlm_results.params)
z = ['x2']
alpha = 0.05
size = 5000
x1 = np.random.normal(size=size)
x2 = np.random.normal(size=size) + x1
x3 = np.random.normal(size=size) + x2
X = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3})
test = MixedChiSquaredTest(y,
x,
z,
X,
alpha,
variable_types={
'x1': 'c',
'x2': 'c',
'x3': 'c'
})
print 'null', test.chi2_bound
print 'actual', test.chi2
print test.independent()
raise Exception
X_sampled = test.generate_ci_sample()
print X.corr()
print X_sampled.corr()
regression = sm.RLM(X[y], X[x + z])
result = regression.fit()
print result.summary()
regression = sm.RLM(X_sampled[y], X_sampled[x + z])
result = regression.fit()
print result.summary()
fontsize=16,
)
# annotate these with their index
for i, row in dta.loc[dta['log.Te'] < 3.8].iterrows():
ax.annotate(i, row, row + .01, fontsize=14)
xlim, ylim = ax.get_xlim(), ax.get_ylim()
from IPython.display import Image
Image(filename='star_diagram.png')
y = dta['log.light']
X = sm.add_constant(dta['log.Te'], prepend=True)
ols_model = sm.OLS(y, X).fit()
abline_plot(model_results=ols_model, ax=ax)
rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()
abline_plot(model_results=rlm_mod, ax=ax, color='red')
# * Why? Because M-estimators are not robust to leverage points.
infl = ols_model.get_influence()
h_bar = 2 * (ols_model.df_model + 1) / ols_model.nobs
hat_diag = infl.summary_frame()['hat_diag']
hat_diag.loc[hat_diag > h_bar]
sidak2 = ols_model.outlier_test('sidak')
sidak2.sort_values('unadj_p', inplace=True)
print(sidak2)
fdr2 = ols_model.outlier_test('fdr_bh')
def linear_best_fit(data,
x_args,
y_args,
fillNaN=True,
robust=True,
printdata=False,
plot=False):
"""
--------------------------------------------------------------------------
Create linear line of best fit and get coefficients
--------------------------------------------------------------------------
Input:
--------------------------------------------------------------------------
Output:
intercept - float, intercept of the linear equation (y=slope*x+intercept)
slope - float, slope of the linear equation (y=slope*x+intercept)
--------------------------------------------------------------------------
WARNIGN:
Input data cannot be negative - see first part of the code
--------------------------------------------------------------------------
"""
divider = '------------------------------------------------------------'
#Filter data, get rid of NaN's
data = data[(data[x_args] >= -1)]
data = data[(data[y_args] >= -1)]
#Set bounds
x_min = data[x_args].min()
x_max = data[x_args].max()
#Use add_constants to get intercept
x2_args = sm.add_constant(data[x_args])
if robust:
model = sm.RLM(data[y_args], x2_args, M=sm.robust.norms.LeastSquares())
else:
model = sm.OLS(data[y_args], x2_args)
#Straight line equation coefficients
parameters = model.fit().params
intercept = parameters[0]
slope = parameters[1]
if printdata:
#Get bounds of y-values
y_min = data[y_args].min()
y_max = data[y_args].max()
print('Data for {} vs {}:'.format(y_args, x_args))
print(divider)
print('Range of x:{} - {}, y:{} - {}'.format(x_min, x_max, y_min,
y_max))
print(divider)
if robust:
#Calculate OLS as well in order to get R^2 value
model2 = sm.OLS(data[y_args], x2_args)
parameters2 = model2.fit().params
intercept2 = parameters2[0]
slope2 = parameters2[1]
#Calculate R^2
r2 = model2.fit().rsquared
print('OLS: Slope: {}, Intercept: {}'.format(slope2, intercept2))
print(divider)
print('R^2={:.3f}'.format(r2))
print(divider)
print('RLM: Slope: {}, Intercept: {}'.format(slope, intercept))
print(divider)
else:
print('OLS: Slope: {}, Intercept: {}'.format(slope, intercept))
print(divider)
print('R^2=')
print(divider)
print('Extreme points: ({},{:.2f})({},{:.2f})'.format(
x_min, (slope * x_min + intercept), x_max,
(slope * x_max + intercept)))
print(divider)
if plot:
ax = data.plot(x=x_args, y=y_args, kind='scatter')
#Plot regression line on the same axes, set values
x = [x_min, x_max]
ax.plot(x, [intercept + x_min * slope, intercept + x_max * slope])
ax.set_xlim([x_min, x_max])
return intercept, slope
func = getattr(wrapping, meth)
wrapper = make_wrapper(func, how)
setattr(klass, meth, wrapper)
if __name__ == '__main__':
import statsmodels.api as sm
from pandas import DataFrame
data = sm.datasets.longley.load(as_pandas=False)
df = DataFrame(data.exog, columns=data.exog_name)
y = data.endog
# data.exog = sm.add_constant(data.exog)
df['intercept'] = 1.
olsresult = sm.OLS(y, df).fit()
rlmresult = sm.RLM(y, df).fit()
# olswrap = RegressionResultsWrapper(olsresult)
# rlmwrap = RLMResultsWrapper(rlmresult)
data = sm.datasets.wfs.load(as_pandas=False)
# get offset
offset = np.log(data.exog[:, -1])
exog = data.exog[:, :-1]
# convert dur to dummy
exog = sm.tools.categorical(exog, col=0, drop=True)
# drop reference category
# convert res to dummy
exog = sm.tools.categorical(exog, col=0, drop=True)
# convert edu to dummy
k['rate'] = k['BUILDINGGARAGE'] / k['carnumbersum']
k = k.groupby('bc').agg({'rate': 'median', 'BUILDINGGARAGE': 'count'})
k = df[(df['carnumbersum'] > 0) & (df['GarageArea'] == 0) &
(df['BUILDINGGARAGE'] == 0) &
(df['parkinglots'] > 0)].reset_index(drop=True)
# 1428
k['rate'] = k['parkinglots'] / k['carnumbersum']
k = k.groupby('bc').agg({'rate': 'median', 'parkinglots': 'count'})
k = df[(df['carnumbersum'] > 0)
& ((df['GarageArea'] > 0) | (df['BUILDINGGARAGE'] > 0)
| (df['parkinglots'] > 0))].reset_index(drop=True)
X = k[['GarageArea', 'BUILDINGGARAGE', 'parkinglots']]
y = k['carnumbersum']
model = sm.RLM(y, X).fit()
model.summary()
k['predict'] = model.predict()
k.to_csv(path + 'k.csv', index=False)
k = df[(df['carnumbersum'] > 0) & (df['GarageArea'] == 0) &
(df['BUILDINGGARAGE'] == 0) &
(df['parkinglots'] == 0)].reset_index(drop=True)
k = k[(k['bc'] == 'A') | (k['bc'] == 'B') |
(k['BldgClass'] == 'C0')].reset_index(drop=True)
k.to_csv(path + 'k.csv', index=False)
X = df[['GarageArea', 'BUILDINGGARAGE', 'parkinglots', 'LotArea']]
y = df['carnumbersum']
model = sm.OLS(y, X).fit()
model.summary()
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
# ## Estimation
#
# Load data:
data = sm.datasets.stackloss.load()
data.exog = sm.add_constant(data.exog)
# Huber's T norm with the (default) median absolute deviation scaling
huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())
hub_results = huber_t.fit()
print(hub_results.params)
print(hub_results.bse)
print(
hub_results.summary(
yname='y',
xname=['var_%d' % i for i in range(len(hub_results.params))]))
# Huber's T norm with 'H2' covariance matrix
hub_results2 = huber_t.fit(cov="H2")
print(hub_results2.params)
print(hub_results2.bse)
# Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance
def regComb(self, dsReg, field='LSTM', opt=1, fTest=None):
statSigma = dsReg.statCalSigma(field=field)
# do regression
if opt == 1:
x1 = np.square(statSigma.sigmaMC_mat)
x2 = statSigma.sigmaMC_mat * statSigma.sigmaX_mat
y = np.square(dsReg.LSTM-dsReg.SMAP) - \
np.square(statSigma.sigmaX_mat)
xx = np.stack((x1.flatten(), x2.flatten()), axis=1)
yy = y.flatten().reshape(-1, 1)
elif opt == 2:
x1 = np.square(statSigma.sigmaMC_mat)
y = np.square(dsReg.LSTM-dsReg.SMAP) - \
np.square(statSigma.sigmaX_mat)
xx = x1.flatten().reshape(-1, 1)
yy = y.flatten().reshape(-1, 1)
elif opt == 3:
x1 = np.square(statSigma.sigmaMC_mat)
x2 = np.square(statSigma.sigmaX_mat)
x3 = statSigma.sigmaMC_mat * statSigma.sigmaX_mat
x4 = np.ones(x1.shape)
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = np.stack(
(x1.flatten(), x2.flatten(), x3.flatten(), x4.flatten()),
axis=1)
yy = y.flatten().reshape(-1, 1)
elif opt == 4:
x1 = np.square(statSigma.sigmaMC_mat)
x2 = np.square(statSigma.sigmaX_mat)
x3 = np.ones(x1.shape)
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = np.stack((x1.flatten(), x2.flatten(), x3.flatten()), axis=1)
yy = y.flatten().reshape(-1, 1)
elif opt == 5:
x1 = np.square(statSigma.sigmaMC_mat)
x2 = np.square(statSigma.sigmaX_mat)
x3 = statSigma.sigmaMC_mat * statSigma.sigmaX_mat
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = np.stack((x1.flatten(), x2.flatten(), x3.flatten()), axis=1)
yy = y.flatten().reshape(-1, 1)
elif opt == 6:
x1 = np.square(statSigma.sigmaMC_mat)
x2 = np.square(statSigma.sigmaX_mat)
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = np.stack((x1.flatten(), x2.flatten()), axis=1)
yy = y.flatten().reshape(-1, 1)
elif opt == 7:
x1 = np.square(statSigma.sigmaMC_mat)
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = x1.flatten().reshape(-1, 1)
yy = y.flatten().reshape(-1, 1)
elif opt == 8:
x1 = np.square(statSigma.sigmaX_mat)
y = np.square(dsReg.LSTM - dsReg.SMAP)
xx = x1.flatten().reshape(-1, 1)
yy = y.flatten().reshape(-1, 1)
elif opt == 9:
x1 = np.ones(statSigma.sigma_mat.shape)
y = np.square(dsReg.LSTM-dsReg.SMAP) - \
np.square(statSigma.sigma_mat)
xx = x1.flatten().reshape(-1, 1)
yy = y.flatten().reshape(-1, 1)
ind = np.where(~np.isnan(yy))[0]
xf = xx[ind, :]
yf = yy[ind]
# w, _, _, _ = np.linalg.lstsq(xf, yf)
# model = sm.OLS(yf, xf)
model = sm.RLM(yf, xf)
result = model.fit()
w = result.params
if fTest is not None:
ftestP = list()
ftestF = list()
for k in range(len(w)):
ww = w.copy()
ww[k] = fTest[k]
ff = result.f_test(ww)
ftestP.append(ff.pvalue)
ftestF.append(ff.fvalue)
if opt == 1:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
self.sigmaMC_mat * self.sigmaX_mat * w[1] +
np.square(self.sigmaX_mat))
k = -w[1] / 2
a = w[0] - k**2
out = [a, k]
elif opt == 2:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
np.square(self.sigmaX_mat))
x1 = np.square(statSigma.sigmaMC_mat)
x2 = np.ones(x1.shape)
y = np.square(statSigma.sigmaX_mat)
xx = np.stack((x1.flatten(), x2.flatten()), axis=1)
yy = y.flatten().reshape(-1, 1)
k, _, _, _ = np.linalg.lstsq(xx, yy)
k = k[0]
a = w[0] + k
out = [a, k]
elif opt == 3:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
np.square(self.sigmaX_mat) * w[1] +
self.sigmaMC_mat * self.sigmaX_mat * w[2] +
np.ones(self.sigmaX_mat.shape) * w[3])
out = w
elif opt == 4:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
np.square(self.sigmaX_mat) * w[1] +
np.ones(self.sigmaX_mat.shape) * w[2])
out = w
elif opt == 5:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
np.square(self.sigmaX_mat) * w[1] +
self.sigmaMC_mat * self.sigmaX_mat * w[2])
out = w
elif opt == 6:
self.sigmaReg_mat = np.sqrt(
np.square(self.sigmaMC_mat) * w[0] +
np.square(self.sigmaX_mat) * w[1])
out = w
elif opt == 7:
self.sigmaReg_mat = np.sqrt(np.square(self.sigmaMC_mat) * w[0])
out = w
elif opt == 8:
self.sigmaReg_mat = np.sqrt(np.square(self.sigmaX_mat) * w[0])
out = w
elif opt == 9:
self.sigmaReg_mat = np.sqrt(np.square(self.sigma_mat) + w[0])
out = w
self.sigmaReg = np.sqrt(np.mean(self.sigmaReg_mat**2, axis=1))
if fTest is None:
return result
else:
return (out, ftestP, ftestF)