def test_difference_2():
_dset1 = dataset.Dataset(3)
_dset1.add_float("numbers", [1, 2, 3], unit="meter")
_dset1.add_time(
"t",
[datetime(2009, 1, 1),
datetime(2009, 1, 2),
datetime(2009, 2, 2)],
scale="utc",
fmt="datetime")
_dset1.add_bool("flag", [True, False, True])
_dset1.add_text("letters", list("abc"))
_dset1.add_position("pos", [[1, 2, 3], [3, 2, 1], [2, 2, 2]], system="trs")
_dset1.add_float("group.more", [5, 5, 5])
_dset1.add_text("group.even_more", list("fgh"))
_dset2 = dataset.Dataset(4)
_dset2.add_float("numbers", [3, 4, 5, 6], unit="meter")
_dset2.add_time(
"t",
[
datetime(2009, 1, 1),
datetime(2009, 2, 1),
datetime(2009, 2, 2),
datetime(2009, 3, 3)
],
scale="utc",
fmt="datetime",
)
_dset2.add_bool("flag", [False, False, True, True])
_dset2.add_text("letters", list("cdef"))
_dset2.add_position("pos", [[4, 5, 6], [6, 5, 4], [7, 8, 9], [2, 3, 4]],
system="trs")
_dset2.add_float("group.more", [4, 3, 4, 3])
_dset2.add_text("group.even_more", list("jilh"))
_dset3 = _dset1.difference(_dset2, index_by="t")
assert np.equal(_dset3.numbers, [-2, -2]).all()
assert np.equal(np.asarray(_dset3.pos), [[-3, -3, -3], [-5, -6, -7]]).all()
assert np.equal(_dset3.group.more, [1, 1]).all()
assert np.equal(
_dset3.t,
[datetime(2009, 1, 1), datetime(2009, 2, 2)]).all()
_dset4 = _dset1.difference(_dset2, index_by="t, flag")
assert np.equal(_dset4.numbers, [-2]).all()
assert np.equal(np.asarray(_dset4.pos), [[-5, -6, -7]]).all()
assert np.equal(_dset4.group.more, [1]).all()
assert np.equal(_dset4.t, [datetime(2009, 2, 2)]).all()
assert np.equal(_dset4.flag, [True]).all()
_dset5 = _dset1.difference(_dset2,
index_by="t, flag",
copy_self_on_error=True,
copy_other_on_error=True)
assert np.char.equal(_dset5.letters_self, list("c")).all()
assert np.char.equal(_dset5.letters_other, list("e")).all()
assert np.char.equal(_dset5.group.even_more_self, list("h")).all()
assert np.char.equal(_dset5.group.even_more_other, list("l")).all()
def test_difference_1():
_dset1 = dataset.Dataset(2)
_dset1.add_float("numbers", [1, 2], unit="meter")
_dset1.add_time(
"t", [datetime(2009, 1, 1), datetime(2009, 2, 1)],
scale="utc",
fmt="datetime")
_dset1.add_bool("flag", [True, False])
_dset1.add_text("letters", list("ab"))
_dset1.add_position("pos", [[1, 2, 3], [3, 2, 1]], system="trs")
_dset1.add_float("group.more", [5, 5])
_dset2 = dataset.Dataset(2)
_dset2.add_float("numbers", [4, 5], unit="meter")
_dset2.add_time(
"t", [datetime(2009, 1, 1), datetime(2009, 2, 2)],
scale="utc",
fmt="datetime")
_dset2.add_bool("flag", [False, True])
_dset2.add_text("letters", list("cd"))
_dset2.add_position("pos", [[4, 5, 6], [6, 5, 4]], system="trs")
_dset2.add_float("group.more", [4, 3])
_dset3 = _dset1.difference(_dset2)
assert np.equal(_dset3.numbers, [-3, -3]).all()
assert np.equal(np.asarray(_dset3.pos), [[-3, -3, -3], [-3, -3, -3]]).all()
assert np.equal(_dset3.group.more, [1, 2]).all()
def test_unit():
_dset1 = dataset.Dataset(3)
_dset1.add_float("numbers_1", [1, 2, 3], unit="meter")
_dset1.add_float("numbers_2", [1, 2, 3], unit="second")
_dset1.add_bool("idx", [True, False, True])
_dset1.add_float("group.numbers", [1, 2, 3], unit="watt")
_dset1.add_sigma("sigma", [1, 2, 3], sigma=[0.1, 0.2, 0.3], unit="meter")
assert _dset1.unit("numbers_1") == ("meter", )
assert _dset1.unit("numbers_2") == ("second", )
assert _dset1.unit("group.numbers") == ("watt", )
with pytest.raises(exceptions.UnitError):
_dset1.unit("idx")
assert _dset1.unit_short("numbers_1") == ("m", )
assert _dset1.unit_short("numbers_2") == ("s", )
with pytest.raises(exceptions.UnitError):
_dset1.unit_short("idx")
assert _dset1.unit_short("group.numbers") == ("W", )
assert _dset1.unit("sigma") == ("meter", )
assert _dset1.unit("sigma.sigma") == ("meter", )
assert _dset1.unit_short("sigma") == ("m", )
assert _dset1.unit_short("sigma.sigma") == ("m", )
_dset1.set_unit("sigma", "seconds")
assert _dset1.unit("sigma") == ("seconds", )
assert _dset1.unit("sigma.sigma") == ("seconds", )
assert _dset1.unit_short("sigma") == ("s", )
assert _dset1.unit_short("sigma.sigma") == ("s", )
def dset_meta_error_msg(type_):
# Too complex datatype is being saved to meta and should give error message
_dset = dataset.Dataset()
_dset.meta.add("a", type_)
with pytest.raises(TypeError):
_dset.write("test.hdf5")
def as_dataset(self):
"""Store GNSS data in a dataset
Returns:
Midgard Dataset where data are stored.
"""
dset = dataset.Dataset(num_obs=len(self.data["pseudorange"]))
dset.add_float("C1C", val=self.data["pseudorange"])
import IPython
IPython.embed()
# TODO: why does dset.add_time("time", val=self.data["time"]) not work?
dset.add_time("time",
val=self.data["time"].gps.datetime,
scale="gps",
fmt="datetime")
# dset.add_time('sat_time', val=self.data["sat_time"].gps.datetime, scale="gps", fmt="datetime")
dset.add_text("station",
val=np.full(dset.num_obs, "android", dtype=str))
dset.add_text("satellite", val=self.data["satellite"])
dset.add_text("satnum", val=self.data["Svid"])
dset.add_text("system", val=self.data["system"])
dset.add_position("site_pos",
time=dset.time,
val=self.data["site_pos"])
return dset
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
dset (Dataset): The Dataset where water level observation are stored with following fields:
| Field | Type | Description |
|----------------------|-------------------|-------------------------------------------------------------------|
| flag | numpy.ndarray | Data flag (obs: observation, pre: prediction, weather: weather |
| | | effect, forecast: forecast) |
| time | TimeTable | Observation time given as TimeTable object |
| water_level | numpy.ndarray | Water level in [m] |
and following Dataset `meta` data:
| Entry | Type | Description |
|----------------------|-------------------|-------------------------------------------------------------------|
| __data_path__ | str | File path |
| __url__ | str | URL of water level API |
"""
dset = dataset.Dataset(num_obs=len(self.data["time"]))
time = [
d.astimezone(tz=pytz.utc).replace(tzinfo=None)
for d in self.data["time"]
] # Convert time to UTC
dset.add_time("time", val=time, scale="utc", fmt="datetime")
dset.add_float("water_level",
val=np.array(self.data["value"]) *
Unit.centimeter2meter)
dset.add_text("flag", val=self.data["flag"])
dset.meta.update(self.meta)
return dset
def test_filter():
_dset = dataset.Dataset(7)
_dset.add_text("text_1", list("abcdefg"))
_dset.add_text("text_2", list("gfedcba"))
_dset.add_time("time", [datetime(2015, 1, 1, i) for i in range(0, 7)],
fmt="datetime",
scale="utc")
_dset.add_text("group.text_1", list("hijklmn"))
_dset.add_text("group.text_2", list("nmlkjih"))
# Normal filter with and without group
idx1 = _dset.filter(text_1="a")
idx2 = _dset.filter(text_1="h", collection="group")
idx3 = _dset.filter(time=datetime(2015, 1, 1, 0))
idx = np.array([True, False, False, False, False, False, False],
dtype=bool)
assert np.equal(idx1, idx).all()
assert np.equal(idx2, idx).all()
assert np.equal(idx3, idx).all()
# Underscore filter with and without group
idx1 = _dset.filter(text="a")
idx2 = _dset.filter(text="h", collection="group")
idx = np.array([True, False, False, False, False, False, True], dtype=bool)
assert np.equal(idx1, idx).all()
assert np.equal(idx2, idx).all()
# Wrong field
with pytest.raises(AttributeError):
_dset.filter(tull="a")
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
A dataset containing the data.
"""
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["year"])
# Add time
epochs = list()
for year, doy, seconds in zip(self.data["year"], self.data["doy"],
self.data["seconds"]):
epochs.append(
datetime.strptime("{:.0f} {:.0f}".format(year, doy), "%Y %j") +
timedelta(seconds=seconds))
dset.add_time(name="time",
val=epochs,
scale="gps",
fmt="datetime",
write_level="operational")
# Add system field
if "system" in self.data.keys():
systems = []
for system in self.data["system"]:
systems.append(enums.gnss_name_to_id[system.lower()].value)
dset.add_text("system", val=systems)
# Add system field
if "satellite" in self.data.keys():
satellites = []
for system, satellite in zip(dset.system, self.data["satellite"]):
satellites.append(system + str(satellite).zfill(2))
dset.add_text("satellite", val=satellites)
# Add text and float fields
fields = set(self.data.keys()) - {
"year", "doy", "seconds", "system", "satellite"
}
for field in fields:
if self.data[field].dtype.kind in {
"U", "S"
}: # Check if numpy type is string
dset.add_text(field, val=self.data[field])
continue
dset.add_float(field, val=self.data[field])
return dset
def test_nested_collections():
_dset = dataset.Dataset(2)
_dset.add_float("group.group.tall", [5, 6])
assert np.equal(_dset.group.group.tall, [5, 6]).all()
idx = np.array([True, False], dtype=bool)
_dset.subset(idx)
assert np.equal(_dset.group.group.tall, [5]).all()
_dset1 = dataset.Dataset(2)
_dset1.add_float("group.group.tall", [5, 6])
_dset2 = dataset.Dataset(3)
_dset2.add_float("group.group.tall", [1, 2, 3])
_dset1.extend(_dset2)
assert np.equal(_dset1.group.group.tall, [5, 6, 1, 2, 3]).all()
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
Midgard Dataset where troposphere observation are stored with following fields:
| Field | Type | Description |
|--------------------------|----------------|------------------------------------------------------------------|
| flag | numpy.ndarray | Station flag (see section 24.7.1 of Bernese GNSS software |
| | | version 5.2, November 2015) |
| gradients_ew | numpy.ndarray | Gradients in East/West in [m] |
| gradients_ns | numpy.ndarray | Gradients in North/South in [m] |
| sigma_gradients_ew | numpy.ndarray | Standard deviation of gradients in East/West in [m] |
| sigma_gradients_ns | numpy.ndarray | Standard deviation of gradients in North/South in [m] |
| sigma_zwd | numpy.ndarray | Standard devivation of ZWD in [m] |
| station | numpy.ndarray | Station name |
| time | TimeTable | Observation time given as TimeTable object |
| zhd | numpy.ndarray | Zenith Hydrostatic Delay (ZHD) from a-priori model in [m] |
| ztd | numpy.ndarray | Zenith Total Delay (ZTD) in [m] |
| zwd | numpy.ndarray | Zenith Wet Delay (ZWD) in [m] |
"""
skip_fields = ["to_time"]
text_fields = ["flag", "station"]
# Generate dataset
dset = dataset.Dataset(num_obs=len(self.data["time"]))
dset.meta = self.meta
for field in self.data.keys():
if field in skip_fields:
continue
if field in text_fields:
if field == "station":
dset.add_text(field,
val=[v.lower() for v in self.data[field]])
else:
dset.add_text(field, val=self.data[field])
elif field == "time":
dset.add_time(field,
val=self.data[field],
scale="gps",
fmt="datetime")
else:
dset.add_float(field, val=self.data[field], unit="meter")
return dset
def test_suffix():
_dset = dataset.Dataset(2)
_dset.add_float("numbers_1", [1, 1], multiplier=10)
_dset.add_float("numbers_2", [2, 2], multiplier=-10)
_dset.add_float("group.numbers_1", [3, 3], multiplier=10)
_dset.add_float("group.numbers_2", [4, 4], multiplier=-10)
answer = np.array([[10, 10], [-20, -20]])
for i, multiplier in enumerate(_dset.for_each_suffix("numbers")):
assert np.equal(answer[i], multiplier * _dset.numbers).all()
answer2 = np.array([[30, 30], [-40, -40]])
for i, multiplier in enumerate(_dset.for_each_suffix("group.numbers")):
assert np.equal(answer2[i], multiplier * _dset.group.numbers).all()
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
Midgard Dataset where station coordinates and belonging information are stored with following fields:
| Field | Type | Description |
|--------------------------|----------------|------------------------------------------------------------------|
| domes | numpy.ndarray | Domes number |
| flag | numpy.ndarray | Station flag (see section 24.7.1 of Bernese GNSS software |
| | | version 5.2, November 2015) |
| station | numpy.ndarray | Station name |
| site_pos | PositionTable | Station coordinates given as PositionTable object |
and following Dataset `meta` data:
| Entry | Type | Description |
|---------------------|-------|--------------------------------------------------------------------------------|
| \__data_path__ | str | File path |
"""
# Generate dataset
dset = dataset.Dataset(num_obs=len(self.data["station"]))
dset.meta = self.meta.copy()
# Remove unnecessary fields in meta
for key in ["__params__", "__parser_name__"]:
del dset.meta[key]
# Add fields to dataset
for field in ["domes", "flag", "station"]:
if field == "station":
dset.add_text(field, val=[v.lower() for v in self.data[field]])
else:
dset.add_text(field, val=self.data[field])
dset.add_position(
"site_pos",
time=Time([dset.meta["epoch"] for ii in range(0, dset.num_obs)],
scale="gps",
fmt="datetime"),
system="trs",
val=np.stack(
(np.array(self.data["pos_x"]), np.array(
self.data["pos_y"]), np.array(self.data["pos_z"])),
axis=1),
)
return dset
def test_difference_3():
_dset1 = dataset.Dataset(3)
_dset1.add_float("numbers", [1, 2, 3], unit="meter")
_dset2 = dataset.Dataset(3)
_dset2.add_float("numbers", [1, 2, 3], unit="cm")
_dset3 = _dset1.difference(_dset2)
assert np.equal(_dset3.numbers, [0.99, 1.98, 2.97]).all()
assert _dset3.unit("numbers") == ("meter", )
_dset4 = dataset.Dataset(3)
_dset4.add_float("numbers", [1, 2, 3], unit="second")
# Incompatible units
with pytest.raises(ValueError):
_dset1.difference(_dset4)
_dset5 = dataset.Dataset(1)
_dset5.add_float("numbers", [1], unit="meter")
# Different length datasets (without specifying index_by)
with pytest.raises(ValueError):
_dset1.difference(_dset5)
def as_dataset(self, ref_pos: Union[np.ndarray, List[float]]) -> "Dataset":
"""Return the parsed data as a Dataset
Args:
ref_pos: Reference position given in terrestrial reference system and meters
Returns:
A dataset containing the data.
"""
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["date"])
# Add position
ref_pos = position.Position(np.repeat(np.array([ref_pos]),
dset.num_obs,
axis=0),
system="trs")
dset.add_position_delta(
name="pos",
val=np.stack(
(self.data["east"], self.data["north"], self.data["vertical"]),
axis=1) * Unit.millimeter2meter,
system="enu",
ref_pos=ref_pos,
)
# Add position sigma
sigma = np.stack((self.data["east_sigma"], self.data["north_sigma"],
self.data["vertical_sigma"]),
axis=1)
dset.add_sigma(name="pos_sigma",
val=dset.pos.val,
sigma=sigma * Unit.millimeter2meter,
unit="meter")
# Add time
dset.add_time(name="time",
val=self.data["year"],
scale="utc",
fmt="decimalyear",
write_level="operational")
return dset
def test_read_write_3():
"""Test references after write and then read when attributes are not dataset fields"""
_dset = dataset.Dataset(2)
ref_pos = position.Position([[1, 2, 3], [1, 2, 3]], system="trs")
ref_pos2 = position.PosVel([[1, 2, 3, 1, 1, 1], [1, 2, 3, 2, 2, 2]],
system="trs")
other = position.Position([[7, 8, 9], [7, 8, 9]], system="trs")
other2 = position.PosVel([[1, 2, 3, 1, 2, 3], [1, 2, 3, 4, 5, 6]],
system="trs")
_dset.add_position("testpos", [[4, 5, 6], [4, 5, 6]],
system="trs",
other=other)
_dset.add_position_delta("testposdelta",
[[0.1, 0.1, 0.1], [0.2, 0.2, 0.2]],
system="trs",
ref_pos=ref_pos)
_dset.add_posvel("testposvel", [[1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4]],
system="trs",
other=other2)
_dset.add_posvel_delta("testposveldelta",
[[4, 4, 4, 1, 1, 1], [5, 5, 5, 2, 2, 2]],
system="trs",
ref_pos=ref_pos2)
file_name = "test.hdf5"
_dset.write(file_name)
_dset_new = dataset.Dataset.read(file_name)
def test_field(field, new_field):
try:
if field.data.dtype.type is np.str_:
assert np.char.equal(field.data, new_field.data).all()
else:
assert np.equal(np.asarray(field.data),
np.asarray(new_field.data)).all()
except AttributeError:
# field is a collection
for collection_field_name, collection_field in field.data._fields.items(
):
new_collection_field = new_field.data._fields[
collection_field_name]
test_field(collection_field, new_collection_field)
for field_name, field in _dset._fields.items():
print(f"Testing {field_name}")
new_field = _dset_new._fields[field_name]
test_field(field, new_field)
os.remove(file_name)
def test_unique():
_dset = dataset.Dataset(10)
_dset.add_text("text", list("abcabcabca"))
_dset.add_time("time", [58000] * 2 + [58001] * 6 + [58002] * 2,
fmt="mjd",
scale="utc")
_dset.add_text("group.text", list("defdefdefd"))
_dset.add_float("group.numbers", range(0, 10))
assert np.char.equal(_dset.unique("text"), np.array(list("abc"))).all()
assert np.equal(_dset.unique("time"), np.array([58000, 58001,
58002])).all()
assert np.char.equal(_dset.unique("text", collection="group"),
np.array(list("def"))).all()
assert np.equal(_dset.unique("group.numbers"), np.arange(0, 10)).all()
assert np.equal(_dset.unique("numbers", collection="group"),
np.arange(0, 10)).all()
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
GipsyX summary results are added to Dataset 'meta' variable.
Args:
dset: Dataset.
Returns:
A dataset containing the data.
"""
dset = dataset.Dataset(num_obs=0)
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.meta["summary"] = self.data
return dset
def dset_no_collection():
"""Contains all available fieldstypes"""
_dset = dataset.Dataset(5)
_dset.add_bool("idx", val=[0, 1, 1, 0, 1])
_dset.add_float("numbers", val=[1, 2, 3, 4, 5])
_dset.add_position("sat_pos", val=np.ones((5, 3)), system="trs")
_dset.add_position("site_pos",
val=np.ones((5, 3)) * 2,
system="trs",
other=_dset.sat_pos)
_dset.add_position_delta("site_delta",
val=np.ones((5, 3)) * 0.5,
system="trs",
ref_pos=_dset.site_pos)
_dset.add_posvel("sat_posvel", val=np.ones((5, 6)), system="trs")
_dset.add_posvel("site_posvel",
val=np.ones((5, 6)) * 2,
system="trs",
other=_dset.sat_posvel)
_dset.add_posvel_delta("site_posvel_delta",
val=np.ones((5, 6)) * 0.5,
system="trs",
ref_pos=_dset.site_posvel)
_dset.add_sigma("numbers2",
val=[3, 3, 3, 3, 3],
sigma=[0.2, 0.2, 0.2, 0.2, 0.2])
_dset.add_text("text", val=["aaa", "aaa", "aaa", "aaa", "aaa"])
_dset.add_time("time",
val=[datetime(2015, 1, i) for i in range(5, 10)],
scale="utc",
fmt="datetime")
_dset.add_time_delta("time_delta",
val=[timedelta(seconds=i) for i in range(20, 25)],
scale="utc",
fmt="timedelta")
return _dset
def as_dataset(self) -> "Dataset":
"""Store GNSS data in a dataset
Returns:
Midgard Dataset where GNSS observation are stored with following fields:
| Field | Type | Description |
|----------------------|-------------------|-----------------------------------------------------------------------|
| <observation type> | numpy.ndarray | GNSS observation type data (e.g. C1, P2, L1, L2, ...) given |
| | | in meters. Only observation types are kept, which are defined in |
| | | configuration file. Observation types are rejected, which include |
| | | only blank or 0.0 entries. |
| epoch_flag | numpy.ndarray | Epoch flag |
| rcv_clk_offset | numpy.ndarray | Receiver clock offset in seconds given for each epoch |
| satellite | numpy.ndarray | Satellite PRN number together with GNSS system identifier |
| | | (e.g. G07). Only satellites are kept, which are defined in |
| | | configuration file. |
| satnum | numpy.ndarray | Satellite PRN number (e.g. 07). Only satellites are kept, which are |
| | | defined in configuration file. |
| site_pos | PositionTable | PositionTable object with given station coordinates (read from |
| | | RINEX header) |
| station | numpy.ndarray | Station name list |
| system | numpy.ndarray | GNSS system identifier. Only satellite system are kept, which are |
| | | defined in configuration file. |
| time | TimeTable | Observation time given as TimeTable object |
and following Dataset `meta` data:
| Entry | Type | Description |
|---------------------|-------|------------------------------------------------------------------------------------|
| agency | str | Name of agency from observer |
| antenna_east | float | East component of vector between marker and antenna reference point in meters |
| antenna_height | float | Height component of vector between marker and antenna reference point in meters |
| antenna_north | float | North component of vector between marker and antenna reference point in meters |
| antenna_number | str | Antenna serial number |
| antenna_type | str | Antenna type |
| comment | list | List with RINEX header comment lines |
| file_created | str | Date and time of file creation |
| file_type | str | File type (e.g. 'O' for observation data) |
| interval | float | Observation interval in seconds |
| l1_wave_fact_default| str | Default wavelength factors for L1 (GPS only) |
| l1_wave_fact_prn | str | Wavelength factors for L1 (GPS only) valid for a list of satellite PRNs (see |
| | | wave_fact_prn) |
| l2_wave_fact_default| str | Default wavelength factors for L2 (GPS only) |
| l2_wave_fact_prn | str | Wavelength factors for L2 (GPS only) valid for a list of satellite PRNs (see |
| | | wave_fact_prn) |
| leap_seconds | dict | Dictionary with information related to leap seconds |
| marker_name | str | Name of antenna marker |
| marker_number | str | Number of antenna marker |
| num_satellites | int | Number of satellites, for which observations are stored in the RINEX file |
| observer | str | Name of observer |
| obstypes | dict | Observation types given for each GNSS, whereby observation types and GNSS are |
| | | rejected, which are empty (blank or zero entries). |
| program | str | Name of program creating current file |
| rcv_clk_offset_flag | str | Flag (1=yes, 0=no) indicating if realtime-derived receiver clock offset is |
| | | applied for epoch, code, and phase |
| receiver_number | str | Receiver serial number |
| receiver_type | str | Receiver type |
| receiver_version | str | Receiver firmware version |
| run_by | str | Name of agency creating current file |
| sat_sys | str | Satellite system given in observation file (G, R, E, S or M) |
| time_first_obs | str | Time of first observation record |
| time_last_obs | str | Time of last observation record |
| time_sys | str | Time system used for GNSS observations (GPS, GLO or GAL) |
| version | str | Format version |
| wave_fact_prn | list | List of satellite PRNs for which the wavelength factors l1_wave_fact_prn and |
| | | l2_wave_fact_prn are valid |
"""
# Time
# TODO: Handling of different time systems needed!!!
dset = dataset.Dataset(num_obs=len(self.data["time"]))
# TODO workaround: "isot" does not work for initialization of time field (only 5 decimals for seconds are
# allowed). Therefore self.data["time"] is converted to datetime object.
from datetime import datetime, timedelta
date = []
millisec = []
for v in self.data["time"]:
val, val2 = v.split(".")
date.append(datetime.strptime(val, "%Y-%m-%dT%H:%M:%S"))
millisec.append(timedelta(milliseconds=int(val2)))
dset.add_time("time",
val=date,
val2=millisec,
scale=self.time_scale,
fmt="datetime")
dset.add_float("epoch_flag", val=np.array(self.data["epoch_flag"]))
dset.add_float("rcv_clk_offset",
val=np.array(self.data["rcv_clk_offset"]))
dset.meta.update(self.meta)
for field, value in self.data["text"].items():
dset.add_text(field, val=value)
# Observations
for obs_type in self.data["obs"]:
dset.add_float(f"obs.{obs_type}",
val=np.array(self.data["obs"][obs_type]),
unit="meter")
dset.add_float(f"lli.{obs_type}",
val=np.array(self.data["cycle_slip"][obs_type]))
dset.add_float(f"snr.{obs_type}",
val=np.array(
self.data["signal_strength"][obs_type]))
# Positions
dset.add_position("site_pos",
time=dset.time,
system="trs",
val=np.repeat(self.data["pos"][None, :],
dset.num_obs,
axis=0))
return dset
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
A dataset containing the data.
"""
# Spring constellation definition
system_def = {
"0": "", # Unknown
"1": "G", # GPS
"2": "R", # GLONASS
"3": "S", # SBAS
"4": "E", # Galileo
"5": "C", # BeiDou
"6": "J", # QZSS
}
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["GPSEpoch"])
# Add time
dset.add_time(
name="time",
val=[
dateutil.parser.parse(v.replace("UTC", ""))
for v in self.data["UTCDateTime"]
],
scale="utc",
fmt="datetime",
write_level="operational",
)
# Add system field based on Constellation column
if "Constellation" in self.data.keys():
dset.add_text("system",
val=[
system_def[str(value)]
for value in self.data["Constellation"]
])
# Add satellite field based on PRN column
if "PRN" in self.data.keys():
prn_data = []
for prn in self.data["PRN"]:
if prn >= 71 and prn <= 140: # Handling of Galileo satellites
prn_data.append("E" + str(prn - 70).zfill(2))
else:
log.fatal(f"Spring PRN number '{prn}' is unknown.")
dset.add_text("satellite", val=prn_data)
# Define fields to save in dataset
remove_time_fields = {
"Constellation", "GPSEpoch", "GPSWeek", "GPSSecond", "PRN", "",
"UTCDateTime"
}
fields = set(self.data.keys()) - remove_time_fields
# Add text and float fields
for field in fields:
if self.data[field].dtype.kind in {
"U", "S"
}: # Check if numpy type is string
dset.add_text(field.lower(), val=self.data[field])
continue
dset.add_float(field.lower(), val=self.data[field])
return dset
def as_dataset(self) -> "Dataset":
"""Store Gipsy time dependent parameter data in a dataset
Returns:
Midgard Dataset where time dependent parameter data are stored with following fields:
| Field | Type | Description |
|---------------------|-------------------|--------------------------------------------------------------------|
| receiver_clock | numpy.ndarray | Receiver clock parameter |
| satellite | numpy.ndarray | Satellite PRN number together with GNSS identifier (e.g. G07) |
| satellite_clock | numpy.ndarray | Satellite clock parameter |
| satellite_ant_pco | PositionTable | Satellite antenna phase center offset |
| site_posvel | PosVel | Station coordinates and velocities |
| source_id | numpy.ndarray | Source ID |
| station | numpy.ndarray | Station name list |
| system | numpy.ndarray | GNSS identifier (e.g. G or E) |
| time | Time | Parameter time given as TimeTable object |
| troposphere_zhd | numpy.ndarray | Zenith hydrostatic troposphere delay parameter |
| troposphere_zwd | numpy.ndarray | Zenith hydrostatic troposphere delay parameter |
| troposphere_ge | numpy.ndarray | Horizontal delay gradient in the East direction |
| troposphere_gn | numpy.ndarray | Horizontal delay gradient in the North direction |
The fields above are given for 'apriori', 'value' and 'sigma' Dataset collections.
"""
# TODO: Handling of unit. Should be added to dataset fields.
field = {
"Clk Bias":
DatasetField(
None, None,
"float"), # can be either receiver or satellite clock bias
"Antennas Antenna1 MapCenterOffset All Z":
DatasetField("satellite_ant_pco", "Satellite", "position"),
"State Pos Z":
DatasetField("site_posvel", "Station", "posvel"),
"Source":
DatasetField("source_id", "Source", "float"),
"Trop GradEast":
DatasetField("troposphere_ge", "Station", "float"),
"Trop GradNorth":
DatasetField("troposphere_gn", "Station", "float"),
"Trop DryZ":
DatasetField("troposphere_zhd", "Station", "float"),
"Trop WetZ":
DatasetField("troposphere_zwd", "Station", "float"),
}
not_used_parameter = [
"Antennas Antenna1 MapCenterOffset All X",
"Antennas Antenna1 MapCenterOffset All Y",
"State Pos X",
"State Pos Y",
"State Vel X",
"State Vel Y",
"State Vel Z",
]
dset = dataset.Dataset(num_obs=len(self.data["time_past_j2000"]))
dset.meta.update(self.meta)
# Note: GipsyX uses continuous seconds past Jan. 1, 2000 11:59:47 UTC time format in TDP files. That means,
# GipsyX does not follow convention of J2000:
# 1.01.2000 12:00:00 TT (TT = GipsyX(t) + 13s)
# 1.01.2000 11:59:27.816 TAI (TAI = TT - 32.184s)
# 1.01.2000 11:58:55.816 UTC (UTC = TAI + leap_seconds = TAI - 32s)
# 1.01.2000 11:59:08.816 GPS (GPS = TAI - 19s)
#
# Therefore Time object initialized with TT time scale has to be corrected about 13 seconds.
#
# TODO: Introduce j2000 = 2451545.0 as constant or unit?
dset.add_time(
"time",
val=Time((self.data["time_past_j2000"] + 13.0) * Unit.second2day +
2451545.0,
scale="tt",
fmt="jd").gps,
)
keep_idx = np.ones(dset.num_obs, dtype=bool)
collections = ["apriori", "value", "sigma"]
# Loop over all existing parameter names
for name in set(self.data["name"]):
category, identifier, parameter = name.replace(
".", " ").split(maxsplit=2)
if parameter in not_used_parameter:
continue
# Add station and satellite field to Dataset by first occurence
if "Satellite" in category:
if "satellite" not in dset.fields:
dset.add_text("satellite",
val=np.repeat(None, dset.num_obs))
dset.add_text("system", val=np.repeat(None, dset.num_obs))
if "Station" in category:
if "station" not in dset.fields:
dset.add_text("station",
val=np.repeat(identifier.lower(),
dset.num_obs))
if "Source" in category:
idx = name == self.data["name"]
for collection in collections:
field_name = f"{collection}.{field['Source'].name}"
dset.add_float(field_name,
val=np.full(dset.num_obs, np.NaN))
dset[field_name][idx] = self.data["value"][idx]
continue
# Add parameter solution to Dataset
if parameter in field.keys():
idx = name == self.data["name"]
if category == "Satellite":
sys = enums.get_value("gnss_3digit_id_to_id",
identifier[0:3])
dset.system[idx] = sys
dset.satellite[idx] = sys + identifier[3:5]
# Loop over 'apriori', 'value' and 'sigma' solutions, which are saved in separated Dataset collections
for collection in collections:
field_name = f"{collection}.{field[parameter].name}"
log.debug(
f"Add dataset field '{field_name}' for parameter '{parameter}' and identifier '{identifier}'."
)
# Add float fields to Dataset
if field[parameter].dtype == "float":
# Note: "Clk Bias" parameter exists for receiver and satellite, therefore it has to be
# distinguished based on the length of the 'identifier' (e.g. USNO or GPS64).
if parameter == "Clk Bias":
field_name = (f"{collection}.satellite_clock"
if len(identifier) == 5 else
f"{collection}.receiver_clock")
if field_name not in dset.fields:
dset.add_float(field_name,
val=np.full(dset.num_obs, np.NaN))
dset[field_name][idx] = self.data[collection][idx]
# Add position fields to Dataset
elif field[parameter].dtype == "position":
if field_name not in dset.fields:
dset.add_position(field_name,
time=dset.time,
system="trs",
val=np.full((dset.num_obs, 3),
np.NaN))
# Fill position field with data
tmp_sol = dict()
for item in [".X", ".Y", ".Z"]:
idx_item = name.replace(".Z",
item) == self.data["name"]
tmp_sol[item] = self.data["value"][idx_item]
# Note: Only .Z dataset indices are used for saving position field in Dataset. .X and .Y are
# not necessary anymore and are removed from Dataset by using "keep_idx" variable.
if not item == ".Z":
keep_idx[idx_item] = False
dset[field_name][idx] = np.vstack(
(tmp_sol[".X"], tmp_sol[".Y"], tmp_sol[".Z"])).T
# Add posvel fields to Dataset
elif field[parameter].dtype == "posvel":
if field_name not in dset.fields:
dset.add_posvel(field_name,
time=dset.time,
system="trs",
val=np.full((dset.num_obs, 6),
np.NaN))
# Fill position field with data
tmp_sol = dict()
for item in [
"State.Pos.X",
"State.Pos.Y",
"State.Pos.Z",
"State.Vel.X",
"State.Vel.Y",
"State.Vel.Z",
]:
idx_item = name.replace("State.Pos.Z",
item) == self.data["name"]
tmp_sol[item] = self.data["value"][idx_item]
if not item == "State.Pos.Z":
keep_idx[idx_item] = False
dset[field_name][idx] = np.vstack((
tmp_sol["State.Pos.X"],
tmp_sol["State.Pos.Y"],
tmp_sol["State.Pos.Z"],
tmp_sol["State.Vel.X"],
tmp_sol["State.Vel.Y"],
tmp_sol["State.Vel.Z"],
)).T
else:
log.fatal(f"Parameter {parameter} is not defined.")
dset.subset(
keep_idx) # Remove unnecessary entries (e.g. '.X' and '.Y' )
return dset
# Pakken støtter mange operasjoner inkludert **transformasjoner**, **kombinasjon** av data, **maskeringer** osv.
#
# Se [rasterio.readthedocs.io](https://rasterio.readthedocs.io/) for mer informasjon.
# %% [markdown] slideshow={"slide_type": "slide"}
# # Midgard
#
# **Midgard** er en pakke utviklet av **Kartverket**. Midgard startet som et støttebibliotek til **Where**, Kartverkets programvare for å beregne referanserammer basert på VLBI, SLR, og GNSS.
# %% [markdown] slideshow={"slide_type": "fragment"}
# Midgard gir tilgang til en datastruktur for sammenstilling av **posisjon**, **hastighet** og **tid** med geodetisk nøyaktighet.
# %% slideshow={"slide_type": "-"}
from midgard.data import dataset
dset = dataset.Dataset(num_obs=4)
dset.add_text("navn", val=list(punkter.keys()))
dset.add_time("tidspunkt", val=["2020-11-25T11:30:00"] * 4, scale="utc", fmt="isot")
dset.add_position("posisjon", val=np.array([[pkt[0] for pkt in punkter.values()], [pkt[1] for pkt in punkter.values()], [10, 20, 40, 25]]).T, system="llh", time="tidspunkt")
print(dset)
# %% [markdown] slideshow={"slide_type": "subslide"}
# ## Midgard
#
# Posisjoner og hastigheter støtter forskjellige koordinatsystemer:
#
# - **llh**: Breddegrad, Lengdegrad, Høyde
# - **trs**: XYZ
# - **enu**: Lokalt Øst, Nord, Opp
# %% slideshow={"slide_type": "-"}
def dset_empty():
_dset = dataset.Dataset(5)
return _dset
def as_dataset(
self,
ref_pos: Union[np.ndarray, List[float], None] = None) -> "Dataset":
"""Return the parsed data as a Dataset
Args:
ref_pos: Reference position given in terrestrial reference system and meters
Returns:
Midgard Dataset where GALAT result data are stored with following fields:
| Field | Type | Description |
|--------------------------|-------------------|-----------------------------------------------------------|
| hpe | np.ndarray | Horizontal Position Error of site position vs. reference |
| | | position |
| num_satellite_available | np.ndarray | Number of available satellites |
| num_satellite_used | np.ndarray | Number of used satellites |
| pdop | np.ndarray | Position dilution of precision |
| site_pos | Position | Site position |
| site_pos_vs_ref | PositionDelta | Site position versus reference coordinate |
| site_vel_3d | np.ndarray | 3D site velocity |
| time | Time | Parameter time given as TimeTable object |
| vpe | np.ndarray | Vertical Position Error of site position vs. reference |
| | | position |
"""
fields = {
#"hpe": "meter", # Recalculated based on site position and given reference coordinate
#"vpe": "meter", # Recalculated based on site position and given reference coordinate
"site_vel_3d": "meter/second",
"pdop": "",
"num_satellite_available": "",
"num_satellite_used": "",
}
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["time"])
# Add time field
dset.add_time(
"time",
val=self.data["time"],
scale="gps",
fmt="datetime",
)
# Add float fields
for field in fields.keys():
dset.add_float(name=field,
val=self.data[field],
unit=fields[field])
# Add site position field
dset.add_position(
"site_pos",
val=np.stack((
self.data["latitude"] * Unit.deg2rad,
self.data["longitude"] * Unit.deg2rad,
self.data["height"],
),
axis=1),
system="llh",
)
# Use either reference position from RINEX header or given argument as reference position
if ref_pos is None:
ref_pos = position.Position(
np.repeat(
np.array([[
self.meta["pos_x"], self.meta["pos_y"],
self.meta["pos_z"]
]]),
dset.num_obs,
axis=0,
),
system="trs",
)
else:
ref_pos = position.Position(np.repeat(np.array([ref_pos]),
dset.num_obs,
axis=0),
system="trs")
# Add relative position
dset.add_position_delta(
name="site_pos_vs_ref",
val=(dset.site_pos.trs - ref_pos.trs).val,
system="trs",
ref_pos=ref_pos,
)
# Add HPE and VPE to dataset
dset.add_float(
"hpe",
val=np.sqrt(dset.site_pos_vs_ref.enu.east**2 +
dset.site_pos_vs_ref.enu.north**2),
unit="meter",
)
dset.add_float("vpe",
val=np.absolute(dset.site_pos_vs_ref.enu.up),
unit="meter")
return dset
def dset_float():
_dset = dataset.Dataset(5)
_dset.add_float("numbers", val=[0.1, 0.2, 0.3, 0.4, 0.5], unit="seconds")
return _dset
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
Midgard Dataset where station coordinates and belonging information are stored with following fields:
| Field | Type | Description |
|-------------------------|---------------|-------------------------------------------------------------------|
| coord_comp_east_day<x> | numpy.ndarray | Station coordinate comparison results for East component in [m] |
| | | for day X (X=[1|2|...|7]) |
| coord_comp_north_day<x> | numpy.ndarray | Station coordinate comparison results for North component in [m] |
| | | for day X (X=[1|2|...|7]) |
| coord_comp_up_day<x> | numpy.ndarray | Station coordinate comparison results for Up component in [m] |
| | | for day X (X=[1|2|...|7]) |
| coord_comp_rms_east | numpy.ndarray | List with daily station coordinate comparison results for East |
| | | component in [m] |
| coord_comp_rms_north | numpy.ndarray | List with daily station coordinate comparison results for North |
| | | component in [m] |
| coord_comp_rms_up | numpy.ndarray | List with daily station coordinate comparison results for Up |
| | | component in [m] |
| num_of_days | numpy.ndarray | Number of days used for analysis |
| pos_mean_x | numpy.ndarray | X-coordinate of mean station coordinate position in [m] |
| pos_mean_x_rms1 | numpy.ndarray | RMS1 of X-coordinate of mean station coordinate position in [m] |
| pos_mean_x_rms2 | numpy.ndarray | RMS2 of X-coordinate of mean station coordinate position in [m] |
| pos_mean_y | numpy.ndarray | Y-coordinate of mean station coordinate position in [m] |
| pos_mean_y_rms1 | numpy.ndarray | RMS1 of Y-coordinate of mean station coordinate position in [m] |
| pos_mean_y_rms2 | numpy.ndarray | RMS2 of Y-coordinate of mean station coordinate position in [m] |
| pos_mean_z | numpy.ndarray | Z-coordinate of mean station coordinate position in [m] |
| pos_mean_z_rms1 | numpy.ndarray | RMS1 of Z-coordinate of mean station coordinate position in [m] |
| pos_mean_z_rms2 | numpy.ndarray | RMS2 of Z-coordinate of mean station coordinate position in [m] |
| repeatability_east | numpy.ndarray | Station coordinate repeatability for East component in [m] |
| repeatability_north | numpy.ndarray | Station coordinate repeatability for North component in [m] |
| repeatability_up | numpy.ndarray | Station coordinate repeatability for Up component in [m] |
| residual_east | numpy.ndarray | Station residuals for East component in [m] |
| residual_north | numpy.ndarray | Station residuals for North component in [m] |
| residual_up | numpy.ndarray | Station residuals for Up component in [m] |
| station | numpy.ndarray | Station names |
| time | TimeTable | Date of analysis session |
and following Dataset `meta` data:
| Entry | Type | Description |
|---------------------|-------|--------------------------------------------------------------------------------|
| num_coord_files | int | Number of coordinate files used for analysis |
| \__data_path__ | str | File path |
"""
data = dict()
# Generate dataset
dset = dataset.Dataset(num_obs=len(self.data.keys()))
dset.meta = self.meta.copy()
# Remove unnecessary fields in meta
for key in ["__parser_name__"]:
del dset.meta[key]
# Prepare data for adding to dataset
for sta in sorted(self.data.keys()):
for field in self.fields:
if field in [
"coord_comp_east", "coord_comp_north", "coord_comp_up"
]:
for idx in range(0, self.meta["num_coord_files"]):
if field in self.data[sta]:
data.setdefault(f"{field}_day{idx+1}",
list()).append(
self.data[sta][field][idx])
else:
data.setdefault(f"{field}_day{idx+1}",
list()).append(float('nan'))
continue
if field in self.data[sta]:
data.setdefault(field,
list()).append(self.data[sta][field])
else:
# Field does not exist for station 'sta', therefore it is initialized with NaN.
data.setdefault(field, list()).append(float('nan'))
# Add fields to dataset
dset.add_text("station", val=sorted(self.data.keys()))
for field in data:
unit = "" if field == "num_of_days" else "meter"
dset.add_float(field, val=data[field], unit=unit)
dset.add_time(
"time",
val=[dset.meta["time"] for ii in range(0, dset.num_obs)],
scale="utc",
fmt="datetime",
)
return dset
def dset_full():
"""Contains all available fieldstypes"""
_dset = dataset.Dataset(5)
_dset.add_bool("idx", val=[0, 1, 1, 0, 1])
_dset.add_float("numbers", val=[1, 2, 3, 4, 5])
_dset.add_float("numbers_1", val=[2, 2, 2, 2, 2])
_dset.add_float("numbers_2", val=[3, 3, 3, 3, 3])
_dset.add_position("sat_pos", val=np.ones((5, 3)), system="trs")
_dset.add_position("site_pos",
val=np.ones((5, 3)) * 2,
system="trs",
other=_dset.sat_pos)
_dset.add_position_delta("site_delta",
val=np.ones((5, 3)) * 0.5,
system="trs",
ref_pos=_dset.site_pos)
_dset.add_posvel("sat_posvel", val=np.ones((5, 6)), system="trs")
_dset.add_posvel("site_posvel",
val=np.ones((5, 6)) * 2,
system="trs",
other=_dset.sat_posvel)
_dset.add_posvel_delta("site_posvel_delta",
val=np.ones((5, 6)) * 0.5,
system="trs",
ref_pos=_dset.site_posvel)
_dset.add_sigma("numbers2",
val=[3, 3, 3, 3, 3],
sigma=[0.2, 0.2, 0.2, 0.2, 0.2])
_dset.add_text("text", val=["aaa", "aaa", "aaa", "aaa", "aaa"])
_dset.add_time("time",
val=[datetime(2015, 1, i) for i in range(5, 10)],
scale="utc",
fmt="datetime")
_dset.add_time_delta("time_delta",
val=[timedelta(seconds=i) for i in range(20, 25)],
scale="utc",
fmt="timedelta")
# Collections
_dset.add_bool("group.idx", val=[0, 0, 0, 0, 0])
_dset.add_float("group.numbers", val=[6, 7, 8, 9, 10])
_dset.add_position("group.sat_pos", val=np.ones((5, 3)) * 7, system="trs")
_dset.add_position("group.site_pos",
val=np.ones((5, 3)) * 8,
system="trs",
other=_dset.group.sat_pos)
_dset.add_position_delta("group.site_delta",
val=np.ones((5, 3)) * 9.5,
system="trs",
ref_pos=_dset.group.site_pos)
_dset.add_posvel("group.sat_posvel", val=np.ones((5, 6)) * 6, system="trs")
_dset.add_posvel("group.site_posvel",
val=np.ones((5, 6)) * 5,
system="trs",
other=_dset.group.sat_posvel)
_dset.add_posvel_delta("group.site_posvel_delta",
val=np.ones((5, 6)) * 1.5,
system="trs",
ref_pos=_dset.group.site_posvel)
_dset.add_sigma("group.numbers2",
val=[1.2, 1.2, 1.2, 1.2, 1.2],
sigma=[3.2, 3.2, 3.2, 3.2, 3.2])
_dset.add_text("group.text", val=["bbb", "bbb", "bbb", "bbb", "bbb"])
_dset.add_time("group.time",
val=[datetime(2015, 1, i) for i in range(10, 15)],
scale="utc",
fmt="datetime")
_dset.add_time_delta("group.time_delta",
val=[timedelta(seconds=i) for i in range(0, 5)],
scale="utc",
fmt="timedelta")
# Nested collections
_dset.add_bool("group.anothergroup.idx", val=[0, 0, 0, 0, 0])
_dset.add_float("group.anothergroup.numbers", val=[6, 7, 8, 9, 10])
_dset.add_position("group.anothergroup.sat_pos",
val=np.ones((5, 3)) * 7,
system="trs")
_dset.add_position("group.anothergroup.site_pos",
val=np.ones((5, 3)) * 8,
system="trs",
other=_dset.group.anothergroup.sat_pos)
_dset.add_position_delta(
"group.anothergroup.site_delta",
val=np.ones((5, 3)) * 9.5,
system="trs",
ref_pos=_dset.group.anothergroup.site_pos,
)
_dset.add_posvel("group.anothergroup.sat_posvel",
val=np.ones((5, 6)) * 6,
system="trs")
_dset.add_posvel(
"group.anothergroup.site_posvel",
val=np.ones((5, 6)) * 5,
system="trs",
other=_dset.group.anothergroup.sat_posvel,
)
_dset.add_posvel_delta(
"group.anothergroup.site_posvel_delta",
val=np.ones((5, 6)) * 1.5,
system="trs",
ref_pos=_dset.group.anothergroup.site_posvel,
)
_dset.add_sigma("group.anothergroup.numbers2",
val=[1.2, 1.2, 1.2, 1.2, 1.2],
sigma=[3.2, 3.2, 3.2, 3.2, 3.2])
_dset.add_text("group.anothergroup.text",
val=["bbb", "bbb", "bbb", "bbb", "bbb"])
_dset.add_time("group.anothergroup.time",
val=[datetime(2015, 1, i) for i in range(10, 15)],
scale="utc",
fmt="datetime")
_dset.add_time_delta("group.anothergroup.time_delta",
val=[timedelta(seconds=i) for i in range(0, 5)],
scale="utc",
fmt="timedelta")
_dset.meta.add("dummy", "something")
_dset.meta.add("testlist", [1, 2])
_dset.meta.add("testdict", {"a": 2, "b": 3})
_dset.meta.add("testtuple", ("c", "d"))
_dset.meta.add("testset", {1, 2})
_dset.meta.add("testlist2", list())
_dset.meta.add("testdict2", dict())
_dset.meta.add("testtuple2", tuple())
_dset.meta.add("testset2", set())
_dset.meta.add_event(_dset.time[0], "jump", "something happened")
return _dset
def as_dataset(self) -> "Dataset":
"""Return the parsed data as a Dataset
Returns:
Midgard Dataset where timeseries data are stored with following fields:
| Field | Type | Description |
|-----------------------|-------------------|--------------------------------------------------------------|
| amplitude | numpy.array | Amplitude |
| azimuth | numpy.array | Azimuth in [rad] |
| frequency | numpy.array | GNSS frequency identifier |
| peak2noise | numpy.array | Peak to noise |
| satellite | numpy.array | Satellite number |
| reflection_height | numpy.array | Reflection height in [m] |
| time | Time | Time |
"""
freq_def = {
1: "L1", # G
2: "L2", # G
5: "L5", # G
20: "L2C", # G
101: "L1", # R
102: "L2", # R
201: "E1", # E
205: "E5a", # E
206: "E6", # E
207: "E5b", # E
208: "E5", # E
302: "B1_2", # C
306: "B3", # C
307: "B2b", # C
}
float_fields = {
"amplitude": None,
"azimuth": "radian",
"peak2noise": None,
"reflection_height": "meter",
}
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["time"])
# Add text fields
satellite = list()
system = list()
for sat in self.data["satellite"]:
if sat >= 1 and sat < 100: # GPS satellites
system.append("G")
satellite.append("G" + str(int(sat)).zfill(2))
elif sat >= 101 and sat < 200: # GLONASS satellites
system.append("R")
satellite.append("R" + str(int(sat))[1:3])
elif sat >= 201 and sat < 300: # Galileo satellites
system.append("E")
satellite.append("E" + str(int(sat))[1:3])
elif sat >= 301 and sat < 400: # BeiDou satellites
system.append("C")
satellite.append("C" + str(int(sat))[1:3])
else:
log.fatal(
"GNSSREFL satellite number {sat} is not defined. Valid satellite numbers are between [1-399]."
)
dset.add_text(
name="system",
val=system,
write_level="operational",
)
dset.add_text(
name="satellite",
val=satellite,
write_level="operational",
)
dset.add_text(
name="frequency",
val=[freq_def[v] for v in self.data["frequency"]],
write_level="operational",
)
# Add time field
dset.add_time(
name="time",
val=self.data["time"],
scale="utc",
fmt="datetime",
write_level="operational",
)
# Add float fields
for field in float_fields.keys():
if field not in self.data.keys():
log.warn(
f"Field '{field}' does not exist in file {self.meta['__data_path__']}."
)
continue
value = np.deg2rad(
self.data[field]) if field == "azimuth" else self.data[field]
unit = "" if float_fields[field] is None else float_fields[field]
dset.add_float(name=field,
val=value,
unit=unit,
write_level="operational")
return dset
def as_dataset(
self,
ref_pos: Union[np.ndarray, List[float]] = [0.0, 0.0,
0.0]) -> "Dataset":
"""Return the parsed data as a Dataset
Args:
ref_pos: Reference position given in terrestrial reference system and meters
Returns:
Midgard Dataset where timeseries data are stored with following fields:
| Field | Type | Description |
|---------------------|-------------------|----------------------------------------------------------------|
| obs.dpos | PositionDelta | Position delta object referred to a reference position |
| obs.dpos_sigma_east | numpy.array | Standard deviation of east position |
| obs.dpos_sigma_north| numpy.array | Standard deviation of north position |
| obs.dpos_sigma_up | numpy.array | Standard deviation of up position |
| time | Time | Parameter time given as TimeTable object |
"""
# Initialize dataset
dset = dataset.Dataset()
if not self.data:
log.warn("No data in {self.file_path}.")
return dset
dset.num_obs = len(self.data["decimalyear"])
dset.meta.update(self.meta)
# Add position
ref_pos = position.Position(np.repeat(np.array([ref_pos]),
dset.num_obs,
axis=0),
system="trs")
dset.add_position_delta(
name="obs.dpos",
val=np.stack(
(self.data["east"], self.data["north"], self.data["vertical"]),
axis=1),
system="enu",
ref_pos=ref_pos,
)
# TODO: sigma functionality has to be improved: dpos_sigma.enu.east, dpos_sigma.trs.x
## Add position sigma
# sigma = np.stack((self.data["east_sigma"], self.data["north_sigma"], self.data["vertical_sigma"]), axis=1)
# dset.add_sigma(name="dpos_sigma", val=dset.dpos.val, sigma=sigma, unit="meter")
dset.add_float(name="obs.dpos_sigma_east",
val=self.data["east_sigma"],
unit="meter")
dset.add_float(name="obs.dpos_sigma_north",
val=self.data["north_sigma"],
unit="meter")
dset.add_float(name="obs.dpos_sigma_up",
val=self.data["vertical_sigma"],
unit="meter")
# Add time
dset.add_time(name="time",
val=self.data["decimalyear"],
scale="utc",
fmt="decimalyear",
write_level="operational")
return dset
def as_dataset(self) -> "Dataset":
"""Store Gipsy residual data in a dataset
Returns:
Midgard Dataset where residual data are stored with following fields:
| Field | Type | Description |
|---------------------|-------------------|--------------------------------------------------------------------|
| azimuth | numpy.ndarray | Azimuth from receiver |
| azimuth_sat | numpy.ndarray | Azimuth from satellite |
| elevation | numpy.ndarray | Elevation from receiver |
| elevation_sat | numpy.ndarray | Elevation from satellite |
| data_type | numpy.ndarray | Data type (e.g. IonoFreeC_1P_2P, IonoFreeL_1P_2P) |
| residual | numpy.ndarray | Post-fit residual |
| satellite | numpy.ndarray | Satellite PRN number together with GNSS identifier (e.g. G07) |
| station | numpy.ndarray | Station name list |
| system | numpy.ndarray | GNSS identifier (e.g. G or E) |
| time | Time | Parameter time given as TimeTable object |
"""
# TODO: Handling of unit. Should be added to dataset fields.
dset = dataset.Dataset(num_obs=len(self.data["time_past_j2000"]))
dset.meta.update(self.meta)
# Note: GipsyX uses continuous seconds past Jan. 1, 2000 11:59:47 UTC time format in TDP files. That means,
# GipsyX does not follow convention of J2000:
# 1.01.2000 12:00:00 TT (TT = GipsyX(t) + 13s)
# 1.01.2000 11:59:27.816 TAI (TAI = TT - 32.184s)
# 1.01.2000 11:58:55.816 UTC (UTC = TAI + leap_seconds = TAI - 32s)
# 1.01.2000 11:59:08.816 GPS (GPS = TAI - 19s)
#
# Therefore Time object initialized with TT time scale has to be corrected about 13 seconds.
#
# TODO: Introduce j2000 = 2451545.0 as constant or unit?
dset.add_time(
"time",
val=Time((np.array(self.data["time_past_j2000"]) + 13.0) *
Unit.second2day + 2451545.0,
scale="tt",
fmt="jd").gps,
)
# Loop over Dataset fields
for field in self.data.keys():
if field == "time_past_j2000":
continue
if field in ["data_type", "satellite", "station"]:
if field == "satellite":
dset.add_text("satellite",
val=np.repeat(None, dset.num_obs))
dset.add_text("system", val=np.repeat(None, dset.num_obs))
for sat in set(self.data["satellite"]):
idx = sat == np.array(self.data["satellite"])
sys = enums.get_value("gnss_3digit_id_to_id", sat[0:3])
dset.system[idx] = sys
dset.satellite[idx] = sys + sat[3:5]
else:
dset.add_text(field, val=self.data[field])
elif field == "deleted":
dset.add_bool(field, val=self.data[field])
else:
dset.add_float(field, val=self.data[field])
return dset
