def read_combi_sum_full(sum_full_file,
RMS_lines_output=True,
set_PRN_as_index=True):
Vals_stk = []
for l in open(sum_full_file):
F = l.split()
if "|" in F:
F.remove("|")
### Find date line
if "MJD:" in l:
date_line = l
### Skip useless lines
if not "|" in l or "------" in l:
continue
### Find AC list
if "PRN" in l:
ACs_list = F
ACs_list.append("RMS_sat")
ACs_list.append("PRN_str")
ACs_list.append("CONST")
elif F[0].isnumeric():
Fout = [float(f) for f in F]
Fout[0] = int(Fout[0])
#Add the PRN string and the constellation
Fout.append(prn_int_2_prn_str(int(Fout[0])))
Fout.append(Fout[-1][0])
Vals_stk.append(Fout)
elif "RMS" in F[0] and RMS_lines_output:
Fout = [float(f) for f in F[1:]]
Fout.append(np.nan)
Fout.insert(0, F[0])
#Add FAKE the PRN string and the constellation
Fout.append(F[0])
Fout.append(None)
Vals_stk.append(Fout)
DF = pd.DataFrame(Vals_stk, columns=ACs_list)
### Date management
mjd = float(date_line.split("MJD:")[1].split()[0])
date_dt = conv.MJD2dt(mjd)
DF.date_mjd = mjd
DF.date_dt = date_dt
DF.date_gps = utils.join_improved("", conv.dt2gpstime(date_dt))
if set_PRN_as_index:
DF.set_index("PRN_str", inplace=True)
return DF
def compar_orbit(Data_inp_1,
Data_inp_2,
step_data=900,
sats_used_list=['G'],
name1='',
name2='',
use_name_1_2_for_table_name=False,
RTNoutput=True,
convert_ECEF_ECI=True,
clean_null_values=True,
conv_coef=10**3,
return_satNull=False):
"""
Compares 2 GNSS orbits files (SP3), and gives a summary plot and a
statistics table
Parameters
----------
Data_inp_1 & Data_inp_2 : str or Pandas DataFrame
contains the orbits or path (string) to the sp3
step_data : int
per default data sampling
sats_used_list : list of str
used constellation or satellite : G E R C ... E01 , G02 ...
Individuals satellites are prioritary on whole constellations
e.g. ['G',"E04"]
RTNoutput : bool
select output, Radial Transverse Normal or XYZ
convert_ECEF_ECI : bool
convert sp3 ECEF => ECI, must be True in operational !
name1 & name2 : str (optionals)
optional custom names for the 2 orbits
use_name_1_2_for_table_name : bool
False : use name 1 and 2 for table name, use datafile instead
clean_null_values : bool or str
if True or "all" remove sat position in all X,Y,Z values
are null (0.000000)
if "any", remove sat position if X or Y or Z is null
if False, keep everything
conv_coef : int
conversion coefficient, km to m 10**3, km to mm 10**6
Returns
-------
Diff_sat_all : Pandas DataFrame
contains differences b/w Data_inp_1 & Data_inp_2
in Radial Transverse Normal OR XYZ frame
Attributes of Diff_sat_all :
Diff_sat_all.name : title of the table
Note
----
clean_null_values if useful (and necessary) only if
convert_ECEF_ECI = False
if convert_ECEF_ECI = True, the cleaning will be done by
a side effect trick : the convertion ECEF => ECI will generate NaN
for a zero-valued position
But, nevertheless, activating clean_null_values = True is better
This Note is in fact usefull if you want to see bad positions on a plot
=> Then convert_ECEF_ECI = False and clean_null_values = False
Source
------
"Coordinate Systems", ASEN 3200 1/24/06 George H. Born
"""
# selection of both used Constellations AND satellites
const_used_list = []
sv_used_list = []
for sat in sats_used_list:
if len(sat) == 1:
const_used_list.append(sat)
elif len(sat) == 3:
sv_used_list.append(sat)
if not sat[0] in const_used_list:
const_used_list.append(sat[0])
# Read the files or DataFrames
# metadata attributes are not copied
# Thus, manual copy ...
# (Dirty way, should be impoved without so many lines ...)
if type(Data_inp_1) is str:
D1orig = files_rw.read_sp3(Data_inp_1, epoch_as_pd_index=True)
else:
D1orig = Data_inp_1.copy(True)
try:
D1orig.name = Data_inp_1.name
except:
D1orig.name = "no_name"
try:
D1orig.path = Data_inp_1.path
except:
D1orig.path = "no_path"
try:
D1orig.filename = Data_inp_1.filename
except:
D1orig.filename = "no_filename"
if type(Data_inp_2) is str:
D2orig = files_rw.read_sp3(Data_inp_2, epoch_as_pd_index=True)
else:
D2orig = Data_inp_2.copy(True)
try:
D2orig.name = Data_inp_2.name
except:
D2orig.name = "no_name"
try:
D2orig.path = Data_inp_2.path
except:
D2orig.path = "no_path"
try:
D2orig.filename = Data_inp_2.filename
except:
D2orig.filename = "no_filename"
#### NB : It has been decided with GM that the index of a SP3 dataframe
#### will be integers, not epoch datetime anymore
#### BUT here, for legacy reasons, the index has to be datetime
if isinstance(D1orig.index[0], (int, np.integer)):
D1orig.set_index("epoch", inplace=True)
if isinstance(D2orig.index[0], (int, np.integer)):
D2orig.set_index("epoch", inplace=True)
Diff_sat_stk = []
# This block is for removing null values
if clean_null_values:
if clean_null_values == "all":
all_or_any = np.all
elif clean_null_values == "any":
all_or_any = np.any
else:
all_or_any = np.all
xyz_lst = ['x', 'y', 'z']
D1_null_bool = all_or_any(np.isclose(D1orig[xyz_lst], 0.), axis=1)
D2_null_bool = all_or_any(np.isclose(D2orig[xyz_lst], 0.), axis=1)
D1 = D1orig[np.logical_not(D1_null_bool)]
D2 = D2orig[np.logical_not(D2_null_bool)]
if np.any(D1_null_bool) or np.any(D2_null_bool):
sat_nul = utils.join_improved(
" ", *list(set(D1orig[D1_null_bool]["sat"])))
print("WARN : Null values contained in SP3 files : ")
print(
"f1:", np.sum(D1_null_bool),
utils.join_improved(" ",
*list(set(D1orig[D1_null_bool]["sat"]))))
print(
"f2:", np.sum(D2_null_bool),
utils.join_improved(" ",
*list(set(D2orig[D2_null_bool]["sat"]))))
else:
sat_nul = []
else:
D1 = D1orig.copy()
D2 = D2orig.copy()
for constuse in const_used_list:
D1const = D1[D1['const'] == constuse]
D2const = D2[D2['const'] == constuse]
# checking if the data correspond to the step
bool_step1 = np.mod((D1const.index - np.min(D1.index)).seconds,
step_data) == 0
bool_step2 = np.mod((D2const.index - np.min(D2.index)).seconds,
step_data) == 0
D1window = D1const[bool_step1]
D2window = D2const[bool_step2]
# find common sats and common epochs
sv_set = sorted(
list(set(D1window['sv']).intersection(set(D2window['sv']))))
epoc_set = sorted(
list(set(D1window.index).intersection(set(D2window.index))))
# if special selection of sats, then apply it
# (it is late and this selection is incredibely complicated ...)
if np.any([True if constuse in e else False for e in sv_used_list]):
# first find the selected sats for the good constellation
sv_used_select_list = [
int(e[1:]) for e in sv_used_list if constuse in e
]
#and apply it
sv_set = sorted(
list(set(sv_set).intersection(set(sv_used_select_list))))
for svv in sv_set:
# First research : find corresponding epoch for the SV
# this one is sufficent if there is no gaps (e.g. with 0.00000) i.e.
# same nb of obs in the 2 files
# NB : .reindex() is smart, it fills the DataFrame
# with NaN
try:
D1sv_orig = D1window[D1window['sv'] == svv].reindex(epoc_set)
D2sv_orig = D2window[D2window['sv'] == svv].reindex(epoc_set)
except Exception as exce:
print("ERR : Unable to re-index with an unique epoch")
print(
" are you sure there is no multiple-defined epochs for the same sat ?"
)
print(
" it happens e.g. when multiple ACs are in the same DataFrame "
)
print(
"TIP : Filter the input Dataframe before calling this fct with"
)
print(" DF = DF[DF['AC'] == 'gbm']")
raise exce
# Second research, it is a security in case of gap
# This step is useless, because .reindex() will fill the DataFrame
# with NaN
if len(D1sv_orig) != len(D2sv_orig):
print("INFO : different epochs nbr for SV", svv,
len(D1sv_orig), len(D2sv_orig))
epoc_sv_set = sorted(
list(
set(D1sv_orig.index).intersection(set(
D2sv_orig.index))))
D1sv = D1sv_orig.loc[epoc_sv_set]
D2sv = D2sv_orig.loc[epoc_sv_set]
else:
D1sv = D1sv_orig
D2sv = D2sv_orig
P1 = D1sv[['x', 'y', 'z']]
P2 = D2sv[['x', 'y', 'z']]
# Start ECEF => ECI
if convert_ECEF_ECI:
# Backup because the columns xyz will be reaffected
#D1sv_bkp = D1sv.copy()
#D2sv_bkp = D2sv.copy()
P1b = conv.ECEF2ECI(
np.array(P1),
conv.dt_gpstime2dt_utc(P1.index.to_pydatetime(),
out_array=True))
P2b = conv.ECEF2ECI(
np.array(P2),
conv.dt_gpstime2dt_utc(P2.index.to_pydatetime(),
out_array=True))
D1sv[['x', 'y', 'z']] = P1b
D2sv[['x', 'y', 'z']] = P2b
P1 = D1sv[['x', 'y', 'z']]
P2 = D2sv[['x', 'y', 'z']]
# End ECEF => ECI
if not RTNoutput:
# Compatible with the documentation +
# empirically tested with OV software
# it is P1 - P2 (and not P2 - P1)
Delta_P = P1 - P2
Diff_sat = Delta_P.copy()
Diff_sat.columns = ['dx', 'dy', 'dz']
else:
rnorm = np.linalg.norm(P1, axis=1)
Vx = utils.diff_pandas(D1sv, 'x')
Vy = utils.diff_pandas(D1sv, 'y')
Vz = utils.diff_pandas(D1sv, 'z')
V = pd.concat((Vx, Vy, Vz), axis=1)
V.columns = ['vx', 'vy', 'vz']
R = P1.divide(rnorm, axis=0)
R.columns = ['xnorm', 'ynorm', 'znorm']
H = pd.DataFrame(np.cross(R, V), columns=['hx', 'hy', 'hz'])
hnorm = np.linalg.norm(H, axis=1)
C = H.divide(hnorm, axis=0)
C.columns = ['hxnorm', 'hynorm', 'hznorm']
I = pd.DataFrame(np.cross(C, R), columns=['ix', 'iy', 'iz'])
R_ar = np.array(R)
I_ar = np.array(I)
C_ar = np.array(C)
#R_ar[1]
Beta = np.stack((R_ar, I_ar, C_ar), axis=1)
# Compatible with the documentation +
# empirically tested with OV software
# it is P1 - P2 (and not P2 - P1)
Delta_P = P1 - P2
# Final determination
Astk = []
for i in range(len(Delta_P)):
A = np.dot(Beta[i, :, :], np.array(Delta_P)[i])
Astk.append(A)
Diff_sat = pd.DataFrame(np.vstack(Astk),
index=P1.index,
columns=['dr', 'dt', 'dn'])
Diff_sat = Diff_sat * conv_coef # metrer conversion
Diff_sat['const'] = [constuse] * len(Diff_sat.index)
Diff_sat['sv'] = [svv] * len(Diff_sat.index)
Diff_sat['sat'] = [constuse + str(svv).zfill(2)] * len(
Diff_sat.index)
Diff_sat_stk.append(Diff_sat)
Diff_sat_all = pd.concat(Diff_sat_stk)
Date = Diff_sat.index[0]
# Attribute definition
if RTNoutput:
Diff_sat_all.frame_type = 'RTN'
# Pandas donesn't manage well iterable as attribute
# So, it is separated
Diff_sat_all.frame_col_name1 = 'dr'
Diff_sat_all.frame_col_name2 = 'dt'
Diff_sat_all.frame_col_name3 = 'dn'
else:
# Pandas donesn't manage well iterable as attribute
# So, it is separated
Diff_sat_all.frame_col_name1 = 'dx'
Diff_sat_all.frame_col_name2 = 'dy'
Diff_sat_all.frame_col_name3 = 'dz'
if convert_ECEF_ECI:
Diff_sat_all.frame_type = 'ECI'
else:
Diff_sat_all.frame_type = 'ECEF'
# Name definitions
if name1:
Diff_sat_all.name1 = name1
else:
Diff_sat_all.name1 = D1orig.name
if name2:
Diff_sat_all.name2 = name2
else:
Diff_sat_all.name2 = D2orig.name
Diff_sat_all.filename1 = D1orig.filename
Diff_sat_all.filename2 = D2orig.filename
Diff_sat_all.path1 = D1orig.path
Diff_sat_all.path2 = D2orig.path
Diff_sat_all.name = ' '.join(
('Orbits comparison (' + Diff_sat_all.frame_type + ') b/w',
Diff_sat_all.name1, '(ref.) and', Diff_sat_all.name2, ',',
Date.strftime("%Y-%m-%d"), ', doy', str(conv.dt2doy(Date))))
if return_satNull:
return Diff_sat_all, sat_nul
else:
return Diff_sat_all
def regex_OR_from_list(listin):
return "(" + utils.join_improved("|", *listin) + ")"
def sp3_overlap_creator(ac_list,
dir_in,
dir_out,
suffix_out_input=None,
overlap_size=7200,
force=False,
manage_missing_sats='common_sats_only',
eliminate_null_sat=True,
severe=False,
separated_systems_export=False,
first_date=None):
"""
Generate an SP3 Orbit file with overlap based on the SP3s of the
days before and after
Parameters
----------
ac_list : list
3-character codes of the ACs.
dir_in : str
where the input sp3 are.
dir_out : str
where the output sp3 will be outputed.
suffix_out_input : str, optional
last char of the 3-char. code. if None, then it is the same as input.
overlap_size : int, optional
Overlapsize. The default is 7200.
force : True, optional
force overwrite. The default is False.
manage_missing_sats : str, optional
'exclude' : generate a file with only the common sat
between the 3 days. Thus, exclude the missing sats
'extrapolate' : extrapolate the missing sats based on the first/last epoch
The default is 'common_sats_only'.
eliminate_null_sat : bool, optional
eliminate null sat. The default is True.
severe : bool, optional
raise an exception if problem. The default is False.
separated_systems_export : bool, optional
export different sp3 for different system. The default is False.
first_date : datetime, optional
exclude SP3 before this epoch
Returns
-------
None.
Note
----
start/end date are not implemented
the force option skips existing files
"""
Dict_Lfiles_ac = dict()
for ac in ac_list:
Dict_Lfiles_ac[ac] = []
Lfile = Dict_Lfiles_ac[ac]
Extlist = ["sp3", "SP3", "sp3.gz", "SP3.gz"]
for ext in Extlist:
Lfile = Lfile + utils.find_recursive(dir_in, "*" + ac + "*" + ext)
print("Nb of SP3 found for", ac, len(Lfile))
if not suffix_out_input:
suffix_out = ac
else:
suffix_out = ac[:2] + suffix_out_input
D = []
WWWWD = []
for sp3 in Lfile:
#wwwwd_str = os.path.basename(sp3)[3:8]
#D.append(conv.gpstime2dt(int(wwwwd_str[:4]),int(wwwwd_str[4:])))
dat = conv.sp3name2dt(sp3)
D.append(dat)
for dat in D[1:-1]: ####if selection manuel, zip > 2lists !!!
try:
print("***********", ac, dat)
if first_date:
if dat < first_date:
print("INFO: SKIP date", dat)
continue
wwwwd_str = conv.dt_2_sp3_datestr(dat).zfill(5)
dat_bef = dat - dt.timedelta(days=1)
dat_aft = dat + dt.timedelta(days=1)
wwwwd_str_bef = utils.join_improved(
"", *conv.dt2gpstime(dat_bef)).zfill(5)
wwwwd_str_aft = utils.join_improved(
"", *conv.dt2gpstime(dat_aft)).zfill(5)
###### check if exists
dir_out_wk = os.path.join(dir_out, "wk" + str(wwwwd_str)[:4])
utils.create_dir(dir_out_wk)
fil_out = dir_out_wk + "/" + suffix_out + wwwwd_str + ".sp3"
if not force and os.path.isfile(fil_out):
print("0))", fil_out, "exists, skipping...")
continue
### *************** STEP 1 ***************
print("1)) Search for the days before/after")
print("1))", dat_bef, dat_aft)
p1 = utils.find_regex_in_list(wwwwd_str + ".sp3", Lfile, True)
p_bef = utils.find_regex_in_list(wwwwd_str_bef + ".sp3", Lfile,
True)
p_aft = utils.find_regex_in_list(wwwwd_str_aft + ".sp3", Lfile,
True)
print("1)) Files found for the days before/after")
print("0b)", p_bef)
print("01)", p1)
print("0a)", p_aft)
if not p1 or not p_bef or not p_aft:
print("ERROR with day", dat)
continue
SP3 = files_rw.read_sp3(p1)
SP3_bef = files_rw.read_sp3(p_bef)
SP3_aft = files_rw.read_sp3(p_aft)
### Filtering to keep P only
SP3 = SP3[SP3.type == "P"]
SP3_bef = SP3_bef[SP3_bef.type == "P"]
SP3_aft = SP3_aft[SP3_aft.type == "P"]
SP3_bef = SP3_bef[SP3_bef["epoch"] < SP3["epoch"].min()]
SP3_aft = SP3_aft[SP3_aft["epoch"] > SP3["epoch"].max()]
SP3concat = pd.concat((SP3_bef, SP3, SP3_aft))
dat_filter_bef = dat - dt.timedelta(seconds=overlap_size)
dat_filter_aft = dat + dt.timedelta(
seconds=overlap_size) + dt.timedelta(days=1)
### *************** STEP 2 ***************
print("2)) dates of the overlap period before/after")
print("2))", dat_filter_bef, dat_filter_aft)
### *************** STEP 3 ***************
print("3)) Dates of: SP3 concatenated, before, current, after")
print("3))", SP3concat["epoch"].min(),
SP3concat["epoch"].max())
print("3b)", SP3_bef["epoch"].min(), SP3_bef["epoch"].max())
print("31)", SP3["epoch"].min(), SP3["epoch"].max())
print("3a)", SP3_aft["epoch"].min(), SP3_aft["epoch"].max())
SP3concat = SP3concat[(SP3concat["epoch"] >= dat_filter_bef)
& (SP3concat["epoch"] <= dat_filter_aft)]
########## HERE WE MANAGE THE MISSING SATS
if manage_missing_sats == "exclude":
print("4))", "remove missing sats ")
common_sats = set(SP3_bef["sat"]).intersection(
set(SP3["sat"])).intersection(set(SP3_aft["sat"]))
SP3concat = SP3concat[SP3concat["sat"].isin(common_sats)]
elif manage_missing_sats == "extrapolate":
print("4))", "extrapolate missing sats ")
for iovl, SP3_ovl in enumerate((SP3_bef, SP3_aft)):
if iovl == 0:
backward = True
forward = False
backfor = "backward"
elif iovl == 1:
backward = False
forward = True
backfor = "forward"
Sats = set(SP3["sat"])
Sats_ovl = set(SP3_ovl["sat"])
Sats_miss = Sats.difference(Sats_ovl)
if not Sats_miss:
continue
print("4a)", "extrapolate missing sats", backfor,
Sats_miss)
SP3extrapo_in = SP3concat[SP3concat["sat"].isin(
Sats_miss)]
#step = utils.most_common(SP3concat["epoch"].diff().dropna())
#step = step.astype('timedelta64[s]').astype(np.int32)
step = 900
#print(step)
#print("SP3extrapo_in",SP3extrapo_in)
SP3extrapo = reffram.extrapolate_sp3_DataFrame(
SP3extrapo_in,
step=step,
n_step=int(overlap_size / step),
backward=backward,
forward=forward,
until_backward=dat_filter_bef,
until_forward=dat_filter_aft,
return_all=False)
SP3concat = pd.concat((SP3concat, SP3extrapo))
print(SP3extrapo)
else:
print("ERR: check manage_missing_sats value")
raise Exception
if eliminate_null_sat:
GoodSats = []
for sat in SP3concat["sat"].unique():
XYZvals = SP3concat[SP3concat["sat"] == sat][[
"x", "y", "z"
]].sum(axis=1)
V = np.sum(np.isclose(XYZvals, 0)) / len(XYZvals)
if V < 0.50:
GoodSats.append(sat)
else:
print("6) eliminate because null position", sat)
SP3concat = SP3concat[SP3concat["sat"].isin(GoodSats)]
### *************** STEP 7 ***************
print("7))", "Start/End Epoch of the concatenated file ")
print("7))", SP3concat["epoch"].min(),
SP3concat["epoch"].max())
#### All systems
print("8)) outputed file")
print(fil_out)
write_sp3(SP3concat, fil_out)
#### system separated
if False:
for sys in SP3concat["const"].unique():
try:
SP3concat_sys = SP3concat[SP3concat["const"] ==
sys]
fil_out_sys = dir_out_wk + "/" + suffix_out[:2] + sys.lower(
) + wwwwd_str.zfill(5) + ".sp3"
print("9)) outputed file")
print(fil_out_sys)
write_sp3(SP3concat_sys, fil_out_sys)
except:
continue
except KeyboardInterrupt:
raise KeyboardInterrupt
except Exception as e:
if severe:
print("WARN:", e)
raise e
else:
print("WARN: Error", e, "but no severe mode, continue...")
continue
"""
def sp3_overlap_creator(ac_list,
dir_in,
dir_out,
suffix_out_input=None,
overlap_size=7200,
force=False,
common_sats_only=True,
eliminate_null_sat=True,
severe=False,
separated_systems_export=False):
"""
Generate an SP3 Orbit file with overlap based on the SP3s of the
days before and after
Parameters
----------
ac_list : list
3-character codes of the ACs.
dir_in : str
where the input sp3 are.
dir_out : str
where the output sp3 will be outputed.
suffix_out_input : str, optional
last char of the 3-char. code. if None, then it is the same as input.
overlap_size : int, optional
Overlapsize. The default is 7200.
force : True, optional
force overwrite. The default is False.
common_sats_only : True, optional
generate a file with only the common sat between the 3 days.
The default is True.
eliminate_null_sat : bool, optional
eliminate null sat. The default is True.
severe : bool, optional
raise an exception if problem. The default is False.
separated_systems_export : bool, optional
export different sp3 for different system. The default is False.
Returns
-------
None.
"""
for ac in ac_list:
Lfile = utils.find_recursive(dir_in, "*" + ac + "*sp3")
if not suffix_out_input:
suffix_out = ac
else:
suffix_out = ac[:2] + suffix_out_input
D = []
WWWWD = []
for sp3 in Lfile:
wwwwd_str = os.path.basename(sp3)[3:8]
D.append(conv.gpstime2dt(int(wwwwd_str[:4]), int(wwwwd_str[4:])))
for dat in D[1:-1]: ####if selection manuel, zip > 2lists !!!
try:
print("******", ac, dat)
if conv.dt2gpstime(dat)[0] < 1800:
print("SKIP", dat)
continue
wwwwd_str = conv.dt_2_sp3_datestr(dat)
dat_bef = dat - dt.timedelta(days=1)
dat_aft = dat + dt.timedelta(days=1)
wwwwd_str_bef = utils.join_improved("",
*conv.dt2gpstime(dat_bef))
wwwwd_str_aft = utils.join_improved("",
*conv.dt2gpstime(dat_aft))
###### check if exsists
dir_out_wk = os.path.join(dir_out, "wk" + str(wwwwd_str)[:4])
utils.create_dir(dir_out_wk)
fil_out = dir_out_wk + "/" + suffix_out + wwwwd_str + ".sp3"
if not force and os.path.isfile(fil_out):
print("0))", fil_out, "exsists, skipping...")
continue
### *************** STEP 1 ***************
print("1)) Search for the days before/after")
print("1))", dat_bef, dat_aft)
p1 = utils.find_regex_in_list(wwwwd_str + ".sp3", Lfile, True)
p_bef = utils.find_regex_in_list(wwwwd_str_bef + ".sp3", Lfile,
True)
p_aft = utils.find_regex_in_list(wwwwd_str_aft + ".sp3", Lfile,
True)
print("1)) Files found for the days before/after")
print("0b)", p_bef)
print("01)", p1)
print("0a)", p_aft)
if not p1 or not p_bef or not p_aft:
print("ERROR with day", dat)
continue
SP3 = files_rw.read_sp3(p1)
SP3_bef = files_rw.read_sp3(p_bef)
SP3_aft = files_rw.read_sp3(p_aft)
SP3_bef = SP3_bef[SP3_bef["epoch"] < SP3["epoch"].min()]
SP3_aft = SP3_aft[SP3_aft["epoch"] > SP3["epoch"].max()]
SP3concat = pd.concat((SP3_bef, SP3, SP3_aft))
dat_filter_bef = dat - dt.timedelta(seconds=overlap_size)
dat_filter_aft = dat + dt.timedelta(
seconds=overlap_size) + dt.timedelta(days=1)
### *************** STEP 2 ***************
print("2)) dates of the overlap period before/after")
print("2))", dat_filter_bef, dat_filter_aft)
### *************** STEP 3 ***************
print("3)) Dates of: SP3 concatenated, before, current, after")
print("3))", SP3concat["epoch"].min(),
SP3concat["epoch"].max())
print("3b)", SP3_bef["epoch"].min(), SP3_bef["epoch"].max())
print("31)", SP3["epoch"].min(), SP3["epoch"].max())
print("3a)", SP3_aft["epoch"].min(), SP3_aft["epoch"].max())
SP3concat = SP3concat[(SP3concat["epoch"] >= dat_filter_bef)
& (SP3concat["epoch"] <= dat_filter_aft)]
if common_sats_only:
common_sats = set(SP3_bef["sat"]).intersection(
set(SP3["sat"])).intersection(set(SP3_aft["sat"]))
SP3concat = SP3concat[SP3concat["sat"].isin(common_sats)]
if eliminate_null_sat:
GoodSats = []
for sat in SP3concat["sat"].unique():
XYZvals = SP3concat[SP3concat["sat"] == sat][[
"x", "y", "z"
]].sum(axis=1)
V = np.sum(np.isclose(XYZvals, 0)) / len(XYZvals)
if V < 0.50:
GoodSats.append(sat)
else:
print("6) eliminate because null position", sat)
SP3concat = SP3concat[SP3concat["sat"].isin(GoodSats)]
### *************** STEP 7 ***************
print("7))", "Start/End Epoch of the concatenated file ")
print("7))", SP3concat["epoch"].min(),
SP3concat["epoch"].max())
#### All systems
dir_out_wk = os.path.join(dir_out, "wk" + str(wwwwd_str)[:4])
utils.create_dir(dir_out_wk)
fil_out = dir_out_wk + "/" + suffix_out + wwwwd_str + ".sp3"
print("8)) outputed file")
print(fil_out)
write_sp3(SP3concat, fil_out)
#### system separated
if False:
for sys in SP3concat["const"].unique():
try:
SP3concat_sys = SP3concat[SP3concat["const"] ==
sys]
fil_out_sys = dir_out_wk + "/" + suffix_out[:2] + sys.lower(
) + wwwwd_str + ".sp3"
print("9)) outputed file")
print(fil_out_sys)
write_sp3(SP3concat_sys, fil_out_sys)
except:
continue
except KeyboardInterrupt:
raise KeyboardInterrupt
except Exception as e:
if severe:
raise e
else:
print("ERR:", e)
raise e
"""
