Commit 866105b7 authored by Carl Schaffer's avatar Carl Schaffer
Browse files

Merge branch 'headers_gris_coords' into 'master'

Headers gris coords

See merge request !166
parents a2d943bd 94d958ab
include kis_tools/generic/field_list/*.csv
include kis_tools/gris/resources/*
include kis_tools/gris/gris_coordinate_study/*.gz
include kis_tools/util/*.pkl
global-include *.yaml *.yml
include kis_tools/bbi/*.csv
......
import logging
from datetime import datetime, timedelta
import sqlalchemy as sa
_logger = logging.getLogger(__name__)
_gdbs_url = 'postgresql://readuser:logit@dockertest:4321/gregor'
try:
gdbs_engine = sa.create_engine(_gdbs_url)
gdbs_engine.connect()
except:
_logger.warning(
f'Could not connect to GREGOR database through {_gdbs_url}, are you on the KIS network and is the ssh tunnel running?')
raise IOError('GDBS is not online')
def query_gdbs(table, datetime, engine=gdbs_engine):
query = f"""
SELECT value, timestamp, ttl from {table} where timestamp < '{str(datetime)}' order by timestamp desc limit 1
"""
res = engine.execute(query)
value, timestamp, ttl = res.fetchall()[0]
age = datetime - timestamp
is_valid = age < timedelta(seconds=ttl)
return value, is_valid, age
if __name__ == '__main__':
engine = gdbs_engine
print(query_gdbs('xcen', datetime.now(), engine=engine))
print(query_gdbs('ycen', datetime.now(), engine=engine))
......@@ -5,11 +5,11 @@ Created on 2018-04-09
"""
import datetime # date handling
import logging
import os # path and file manipulations
import sys # error handling
from glob import glob
from os.path import basename, exists, join, dirname
from warnings import warn
import astropy.io.fits as fitsio # fits reading and writing
import astropy.units as u
......@@ -18,9 +18,14 @@ import numpy as np
from astropy.coordinates import SkyCoord, Angle
from astropy.wcs import WCS
from kis_tools.gris.coord_correction_ml import get_coords_ml
from kis_tools.gris.util import gregor_parallactic_angle, telescope_to_hpc
from kis_tools.util.calculations import estimate_telescope_drift
from ..generic.fits import FitsFile
from ..util.util import date_from_fn, gris_obs_mode
logger = logging.getLogger(__name__)
class GrisFitsFile(FitsFile):
"""Class for handling fits files for GRIS db
......@@ -88,6 +93,41 @@ class GrisFitsFile(FitsFile):
outstring += f".{int(self.runnumber):03d}"
return outstring
@property
def telescope_centering(self):
"""
Determine state of telescope centering at the time of observation, queries GDBS
for centering data
Returns:
outstring: x_offset, y_offset, x_uncertainty, y_uncertainty all in arcseconds
"""
# Query centering from GDBS
try:
from kis_tools.gdbs.query_gdbs import query_gdbs
gdbs_usable = True
x_telcenter, x_valid, x_age = query_gdbs('xcen', self.obs_time)
y_telcenter, y_valid, y_age = query_gdbs('ycen', self.obs_time)
# Determine uncertainties for centering values
centering_age = max(x_age, y_age)
drift = estimate_telescope_drift(centering_age.seconds / 3600)
x_std, y_std = drift, drift
# if the centering is older than 24 hours, discard
if centering_age > datetime.timedelta(hours=24):
gdbs_usable = False
except IOError:
gdbs_usable = False
if not gdbs_usable:
# Determined from offset between uncentered and cross_correlated data
# STD-DEV of distances in X and Y again see gris coord study for details
uncertainty_no_center_x = 117.1
uncertainty_no_center_y = 182.4
x_telcenter, y_telcenter, x_std, y_std = 0, 0, uncertainty_no_center_x, uncertainty_no_center_y
return x_telcenter, y_telcenter, x_std, y_std
@property
def _coords_from_simple_header(self):
"""coordinates for slit, the Keywords 'SLITPOSX' and 'SLITPOSY' are assumed to be the center of the slit
......@@ -104,13 +144,24 @@ class GrisFitsFile(FitsFile):
"""
h = self.header
slit_length_pixels = h["NAXIS2"]
slit_length_pixels = h["NAXIS2"] if self.old_header else h['NAXIS1']
center_pixel = slit_length_pixels // 2
# Slit center in telescope coordinates
x_slitcenter = h["SLITPOSX"] if h["SLITPOSX"] != "" else 0
y_slitcenter = h["SLITPOSY"] if h["SLITPOSY"] != "" else 0
angle = h["SLITORIE"] if h["SLITORIE"] != "" else 0
# Transform to HPC (shift by telescope centering, rotate by p0 angle)
delta_x, delta_y, std_delta_x, std_delta_y = self.telescope_centering
# If uncertainties are too large, use ML model to correct the coordinates
from kis_tools.gris.coord_correction_ml import coord_model_stdx, coord_model_stdy
if std_delta_x > coord_model_stdx or std_delta_y > coord_model_stdy:
x_hpc, y_hpc, std_delta_x, std_delta_y = get_coords_ml(self)
else:
x_hpc, y_hpc = telescope_to_hpc(x_slitcenter, y_slitcenter, self.p0_angle, delta_x, delta_y)
# Setup grid of pixels
delta_pix = [i - center_pixel for i in range(slit_length_pixels)]
delta_pix = np.array(delta_pix)
arcsec_per_pixel_x = h["STEPSIZE"] # assuming square pixels
......@@ -118,19 +169,18 @@ class GrisFitsFile(FitsFile):
h["STEPSIZE"] if h["STEPSIZE"] else 0.135
) # assuming square pixels
X = x_slitcenter - np.sin(angle) * delta_pix * arcsec_per_pixel_x
Y = y_slitcenter - np.cos(angle) * delta_pix * arcsec_per_pixel_y
angle = self.slit_orientation
message = (
"Using FITS file coordinates, they are probably wrong by at least 100 arcsec!"
" Also the slit angle is definitely off!"
)
warn(message)
# generate coordinates for full grid of slit pixels
X = x_hpc - np.sin(angle) * delta_pix * arcsec_per_pixel_x
Y = y_hpc - np.cos(angle) * delta_pix * arcsec_per_pixel_y
coord_array = np.array([X, Y]) * u.arcsec
# reorder axes for consistency with other coordinate retrieval methods
coord_array = np.swapaxes(coord_array, 0, 1)
# track coordinate uncertainty within class
self.coord_uncertainty = (std_delta_x, std_delta_y)
return coord_array
@property
......@@ -149,17 +199,37 @@ class GrisFitsFile(FitsFile):
old_header = False
return old_header
@property
def zenit(self):
"""
Get Zenit angle (90° - elevation)
Returns: zenit float in degrees
"""
zenit = 90 - self._get_value(['ELEV_ANG', 'ELEVATIO'])
return zenit
@property
def slit_orientation(self):
"""Get slit_orientation, defaults to 0. Not sure what this angle means...
"""Get slitorie:
clockwise angle from the western section of the solar equator
Attempts to correct for derotator and image rotation, usually correct within a couple 10s of degrees.
Returns:
slit_orientation: float
slitorie: float (degrees)
"""
slit_orientation = self._get_value(["SLITORIE"])
slit_orientation = slit_orientation if slit_orientation != "" else 0
return slit_orientation
p0_angle = self.p0_angle
azimut = self._get_value(['AZIMUT'])
zenit = self.zenit
slitorie = self.SLITORIE
const_term = -130 # determined by trial and error to match results from cross correlation with HMI
alpha = -1 * gregor_parallactic_angle(self.obs_time) + azimut - zenit - p0_angle + const_term
if self.derotator_in:
rotangle = self.ROTANGLE
alpha = -2 * rotangle + (const_term / 2)
angle = slitorie - alpha
return angle
@property
def number_of_maps(self):
......@@ -223,6 +293,15 @@ class GrisFitsFile(FitsFile):
"""
return self._get_value(["ISTEP"])
@property
def p0_angle(self):
"""
Returns:
p0_angle: angle between telescope and solar north
"""
return self._get_value(["P0ANGLE", 'SOLAR_P0'])
@property
def step_angle(self):
"""
......@@ -361,6 +440,9 @@ class GrisFitsFile(FitsFile):
degrees = Angle(u.degree * converted[:, :2]).wrap_at(180 * u.degree)
arcsec = degrees.to(u.arcsec)
# Track coord uncertainty within instance
self.coord_uncertainty = (self.CSYER1, self.CSYER2)
return arcsec
def plot_slit(self):
......
import re
from pathlib import Path
import joblib
import numpy as np
_coord_model_path = Path(__file__).parent / 'gris_coordinate_study' / 'multi_tree_stdx38_stdy36.gz'
_coord_model = joblib.load(_coord_model_path)
coord_model_stdx, coord_model_stdy = (int(i) for i in
re.search(r'stdx(\d+)_stdy(\d+)', _coord_model_path.name).groups())
def get_coords_ml(gris_fitsfile):
"""
Determine the coordinates of the observation from a pre-trained statistical model.
The model was trained trained and evaluated against a dataset of original data and
results of a manual cross correlation with hmi. Albeit not being totally accurate,
the model has been shown to systematically reduce the distance between the given
and the actual coordinates. The typical accuracy (stdev) is around 40"
Returns: x,y tuple of the estimated observation center in Helioprojective Coordinates
"""
features = ['SLITPOSX', 'SLITPOSY', 'AZIMUT']
vals = [gris_fitsfile.header[f] for f in features]
vals.append(gris_fitsfile.p0_angle)
# Insert bogus parallax angle, it is 0 for all cases I observed
# so far anyway plus is not used very strongly by the model
vals.insert(2, 0)
# Insert Zenit
vals.insert(4, gris_fitsfile.zenit)
# Insert date parameters
date = gris_fitsfile.obs_time
day_of_year = date.timetuple().tm_yday
vals.append(day_of_year)
vals.append(date.year)
x, y = _coord_model.predict(np.array(vals).reshape(-1, 1).T)[0]
return x, y, coord_model_stdx, coord_model_stdy
......@@ -345,30 +345,32 @@
axes[0].set_aspect('equal')
axes[0].set_xlim(-1500,1500)
axes[0].set_ylim(-1500,1500)
axes[0].legend()
get_uncertainty_estimates(y_test.iloc[:,0], y_pred_test[:,0],ax=axes[1])
sigma_x = get_uncertainty_estimates(y_test.iloc[:,0], y_pred_test[:,0],ax=axes[1])
axes[1].set_title('CDF of x-offsets produced by model')
get_uncertainty_estimates(y_test.iloc[:,1], y_pred_test[:,1],ax=axes[2])
sigma_y = get_uncertainty_estimates(y_test.iloc[:,1], y_pred_test[:,1],ax=axes[2])
axes[2].set_title('CDF of y-offsets produced by model')
from joblib import dump, load
dump(model,dirpath/f'multi_tree_stdx{int(sigma_x)}_stdy{int(sigma_y)}.gz')
```
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: execute_result
Text(0.5, 1.0, 'CDF of y-offsets produced by model')
['C:\\Users\\schaffer\\PycharmProjects\\kis_tools\\kis_tools\\gris\\gris_coordinate_study\\multi_tree_stdx38_stdy36.gz']
%%%% Output: display_data
![](data:image/png;base64,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)
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)
%% Cell type:code id: tags:
``` python
# Model inspection: Determine importance of different features
......
#!/usr/bin/env python3
import sys
from pathlib import Path
import pandas as pd
from kis_tools.util.util import gris_run_number, date_from_fn
fn = sys.argv[1]
assert Path(fn).exists(), f'Invalid input file {fn} not found!'
from scipy.io.idl import readsav
data = readsav(fn)
# rotation angle of the slit with respect to the solar equator
angle = data['slit_angle']
run = gris_run_number(fn)
date = date_from_fn(fn)
ser = pd.Series({'slit_angle': angle})
df = pd.DataFrame()
df[f'gris_{date.strftime("%Y%m%d")}_{run:03d}'] = ser
df = df.T
df.index.name = 'obs_name'
from sqlalchemy import create_engine
outfile = Path(__file__).parent / 'gris_coords.db'
db_name = 'sqlite:///' + str(outfile)
engine = create_engine(str(db_name))
df.to_sql('slit_angle', con=engine, if_exists='append', index_label='obs_name')
......@@ -55,6 +55,9 @@ class GrisWCSGenerator(object):
else:
self.coords = self.get_coords_from_header()
def get_coords_from_header(self):
"""coordinates for slit, the Keywords 'SLITPOSX' and 'SLITPOSY' are assumed to be the center of the slit
while the keyword 'SLITORIE' describes the rotation of the slit w.r.t. the helioprojective axes. The
......@@ -66,6 +69,7 @@ class GrisWCSGenerator(object):
"""
coords = self.infile.coords.value
self.coord_uncertainties = self.infile.coord_uncertainty
return coords[:, 0], coords[:, 1]
def get_coords_from_save(self):
......@@ -193,8 +197,8 @@ class GrisWCSGenerator(object):
# Uncertainties estimated from difference between cross correlated coordinates and coordinates
# from the telescope. They showed a significantly larger uncertainty for the Y coordinate. See documentation of
# https://gitlab.leibniz-kis.de/sdc/sdc_importer/issues/199
spatial_uncertainty_x = 5 if has_ccorr else 141
spatial_uncertainty_y = 5 if has_ccorr else 186
spatial_uncertainty_x = 5 if has_ccorr else self.coord_uncertainties[0]
spatial_uncertainty_y = 5 if has_ccorr else self.coord_uncertainties[1]
c_syer = [
spatial_uncertainty_x,
spatial_uncertainty_y,
......
......@@ -6,7 +6,8 @@ Created by schaffer at 11/26/19
"""
import pandas as pd
from .gris.study_gris_slit_orientation.get_data import get_raw_data
from kis_tools.gris.study_gris_slit_orientation.get_data import get_raw_data
def get_cleaned_data(*args, **kwargs):
......
......@@ -6,16 +6,17 @@ Created by schaffer at 11/26/19
"""
from glob import iglob, glob
from glob import glob, iglob
from math import atan, degrees
from os.path import abspath, dirname, basename, join
from astropy.io.fits.verify import VerifyError
from scipy.io import readsav
from ..GrisFitsFile import GrisFitsFile
from kis_tools.util.util import date_from_fn, gris_run_number, cache
from tqdm import tqdm
from kis_tools.util.util import date_from_fn, gris_run_number, cache
from ..GrisFitsFile import GrisFitsFile
def get_slit(f):
data = readsav(f)
......@@ -71,6 +72,7 @@ def angle_keywords_from_fits(save_filename):
@cache
def get_raw_data(force=False):
sav_files = iglob("/dat/sdc/gris/*/context_data/*coord_mu*")
# sav_files = (Path("Y:") / r'dat\sdc\gris').glob("*/context_data/*coord_mu*")
progress_bar = tqdm(list(sav_files))
raw_values = []
......
......@@ -15,10 +15,17 @@ from itertools import chain
from os.path import basename
from pathlib import Path
import astropy.units as u
import numpy as np
import pandas as pd
import pytz
from astroplan import Observer
from astropy.coordinates import SkyCoord, GCRS, get_sun
from astropy.io.fits.hdu.hdulist import fitsopen as fitsopen
from sunpy.coordinates import Helioprojective
from kis_tools.util.calculations import rotate_around_origin
from kis_tools.util.constants import gregor_coords
from kis_tools.util.util import gris_run_number
......@@ -175,3 +182,53 @@ def extract_position_kws_from_manolo(raw_gris_file):
)
return pos
def gregor_parallactic_angle(time, hpc_x=None, hpc_y=None):
"""
Calculates parallactic angle for GREGOR based on a time and helioprojective coordinates on the sun.
Args:
time: time of observation
hpc_x: helioprojective x (defaults to sun center)
hpc_y: helioprojective y (defaults to sun center)
Returns:
angle: parallactic angle in degrees
"""
# Define observer and add timezone (assume tenerife time)
gregor_observer = Observer(location=gregor_coords)
date_tz = pytz.utc.localize(time)
time = gregor_observer.datetime_to_astropy_time(date_tz)
# Build Sky Coord object, default to sun center at time
if hpc_x and hpc_y:
sun_coords = SkyCoord(hpc_x * u.arcsec, hpc_y * u.arcsec, frame=Helioprojective, observer='earth', obstime=time)
sun_coords.representation_type = 'spherical'
sun_coords = sun_coords.transform_to(GCRS)
else:
sun_coords = get_sun(time)
angle = gregor_observer.parallactic_angle(time, sun_coords)
angle = angle.to('degree').value
return angle
def telescope_to_hpc(slitposx=None, slitposy=None, p0_angle=None, xcenter=None, ycenter=None):
"""
Transform GRIS header coordinate data to HPC coordinates
Args:
slitposx: float SLITPOSX in arcsec
slitposy: float SLITPOSY in arcsec
p0_angle: float p0_angle in degrees
xcenter: float X_center in arcsec (stored in GDBS upon centering the telescope)
ycenter: float Y_center in arcsec (stored in GDBS upon centering the telescope)
Returns:
hpc_x, hpc_y tuple of helioprojective coordinates in arcseconds
"""
xi = slitposx - xcenter
yi = slitposy - ycenter
rotated = rotate_around_origin((xi, yi), np.radians(p0_angle))
return rotated
......@@ -3,7 +3,6 @@
import tempfile
import unittest
from itertools import combinations
from pathlib import Path
import numpy as np
from importer_test_data import gris_structure
......@@ -12,6 +11,7 @@ from scipy.io import readsav
from kis_tools.gris.GrisDay import GrisDay
from kis_tools.gris.GrisMapExtractor import GrisMapExtractor
from kis_tools.gris.GrisRun import GrisRun
from kis_tools.gris.headers.wcs import GrisWCSGenerator