From 2d942f468c71d90ee5dd8500ad780fa094629297 Mon Sep 17 00:00:00 2001 From: Xavier Olive Date: Wed, 30 Oct 2024 23:04:08 +0100 Subject: [PATCH] pyarrow fixes + typing for lazy.py remove libarchive and so6 from_file and indexation do not return None anymore remove sample_so6 silent tqdm for datasets --- .github/workflows/run-tests.yml | 6 +- .pre-commit-config.yaml | 2 +- docs/airac_usage.rst | 76 - docs/data_sources/flightplans.rst | 8 - docs/quickstart.rst | 8 +- docs/tutorial.rst | 10 +- docs/user_guide.rst | 2 +- poetry.lock | 3005 +++++++++-------- pyproject.toml | 10 +- src/traffic/algorithms/clustering.py | 4 +- src/traffic/algorithms/cpa.py | 6 +- src/traffic/algorithms/navigation.py | 13 +- src/traffic/algorithms/openap.py | 21 +- src/traffic/core/airspace.py | 1 + src/traffic/core/flight.py | 187 +- src/traffic/core/lazy.py | 92 +- src/traffic/core/mixins.py | 123 +- src/traffic/core/time.py | 8 +- src/traffic/core/traffic.py | 141 +- src/traffic/data/adsb/opensky.py | 21 - src/traffic/data/datasets/__init__.py | 15 +- src/traffic/data/datasets/default.py | 27 +- .../data/eurocontrol/aixm/navpoints.py | 10 +- src/traffic/data/eurocontrol/aixm/routes.py | 10 +- src/traffic/data/eurocontrol/ddr/allft.py | 41 +- src/traffic/data/eurocontrol/ddr/navpoints.py | 11 +- src/traffic/data/eurocontrol/ddr/so6.py | 899 ----- src/traffic/data/samples/__init__.py | 9 +- src/traffic/plugins/cesiumjs.py | 4 +- tests/test_flight.py | 8 +- tests/test_so6.py | 69 - tests/test_traffic.py | 2 - 32 files changed, 1983 insertions(+), 2866 deletions(-) delete mode 100644 docs/airac_usage.rst delete mode 100644 src/traffic/data/eurocontrol/ddr/so6.py delete mode 100644 tests/test_so6.py diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index 1d34b98d..7594b57b 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -112,16 +112,14 @@ jobs: - name: Ubuntu system dependencies run: | sudo apt update - sudo apt install -y libgdal-dev libgeos-dev libproj-dev proj-bin proj-data libarchive-dev - # Weird issue with libarchive-dev - sudo ln -s -f /usr/lib/x86_64-linux-gnu/libarchive.a /usr/lib/x86_64-linux-gnu/liblibarchive.a + sudo apt install -y libgdal-dev libgeos-dev libproj-dev proj-bin proj-data - name: Dynamic versioning run: poetry self add "poetry-dynamic-versioning[plugin]" || true - name: Install dependencies run: | - poetry install -E full -E libarchive + poetry install -E full - name: Style checking run: | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1e2e8226..99a1c283 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -28,7 +28,7 @@ repos: - id: ruff-format args: [--check] - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.11.2 + rev: v1.13.0 hooks: - id: mypy exclude: ^docs/conf.py diff --git a/docs/airac_usage.rst b/docs/airac_usage.rst deleted file mode 100644 index a6cbbb81..00000000 --- a/docs/airac_usage.rst +++ /dev/null @@ -1,76 +0,0 @@ -How to find all the airspaces crossed by a trajectory? ------------------------------------------------------- - -The provided infrastructure lets you find all elementary sectors crossed -by a trajectory (here from the so6 file) - -.. code:: python - - from traffic.data import SO6 - so6 = SO6.from_file('data/sample_m3.so6.7z') - - with plt.style.context('traffic'): - fig = plt.figure() - ax = plt.axes(projection=Lambert93()) - - ax.add_feature(countries()) - ax.gridlines() - ax.set_extent(nm_airspaces['LFFFUIR']) - - nm_airspaces['LFFFUIR'].plot(ax, lw=2, alpha=.5, linestyle='dashed') - so6['DAH1008'].plot(ax, marker='.') - - # display elementary sectors (ES) crossed by the trajectory - for airspace in nm_airspaces.search("LF.*/ES"): - if so6['DAH1008'].intersects(airspace): - airspace.plot(ax, alpha=.5, lw=2) - - - - -.. image:: _static/airac_traj_sectors.png - :scale: 70 % - :alt: Trajectories over Bordeaux AAC - :align: center - - -Another use case could be to plot all flights going through an airspace -at noon. - -.. code:: python - - # callsigns at noon inside LFBBBDX - bdx_noon = ( - so6.at("2018-01-01 12:00") - .inside_bbox(nm_airspaces['LFBBBDX']) - .intersects(nm_airspaces['LFBBBDX']) - ) - - # full so6 limited to flights hereabove - so6_bdx_noon = so6.select(bdx_noon) - -.. code:: python - - from cartes.crs import EuroPP - from cartes.utils.features import countries - - with plt.style.context('traffic'): - fig = plt.figure() - ax = plt.axes(projection=EuroPP()) - - ax.add_feature(countries()) - ax.gridlines() - ax.set_extent((-10, 15, 35, 55)) - - nm_airspaces['LFBBBDX'].plot(ax, lw=2, alpha=.5) - - for _, flight in so6_bdx_noon: - flight.plot(ax, color='#aa3a3a', lw=.4, alpha=.5) - - - -.. image:: _static/airac_traj_bdx.png - :scale: 70 % - :alt: Trajectories over Bordeaux AAC - :align: center - diff --git a/docs/data_sources/flightplans.rst b/docs/data_sources/flightplans.rst index 0139974c..accb0fed 100644 --- a/docs/data_sources/flightplans.rst +++ b/docs/data_sources/flightplans.rst @@ -5,14 +5,6 @@ A :class:`~traffic.core.flightplan.FlightPlan` is a structure designed to parse flight plans in the ICAO format. Access to such field may be complicated for non operational fellows, who may have access to different sources of data. -EUROCONTROL DDR SO6 files -------------------------- - -.. automodule:: traffic.data.eurocontrol.ddr.so6 - :members: SO6, SO6Flight - :no-undoc-members: - :show-inheritance: - EUROCONTROL B2B web services ---------------------------- diff --git a/docs/quickstart.rst b/docs/quickstart.rst index f2da697b..c958b6c0 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -400,6 +400,8 @@ colour based on the vertical rate average value. fig, ax = plt.subplots(subplot_kw=dict(projection=Lambert93())) for flight in quickstart: + if pd.isna(flight.vertical_rate_mean): + continue if flight.vertical_rate_mean < -500: flight.plot(ax, color="#4c78a8", alpha=0.5) # blue elif flight.vertical_rate_mean > 1000: @@ -426,6 +428,8 @@ intersects the shape of the airport. fig, ax = plt.subplots(subplot_kw=dict(projection=Lambert93())) for flight in quickstart: + if pd.isna(flight.vertical_rate_mean): + continue if flight.vertical_rate_mean < -500: if flight.last("5 min").intersects(airports["LFPO"]): flight.plot(ax, color="#4c78a8", alpha=0.5) @@ -547,7 +551,7 @@ an additional landmark: seine_river = ( Nominatim.search("Seine river, France") .shape.intersection( - paris_area.unary_union.buffer(0.1) + paris_area.union_all().buffer(0.1) ) ) @@ -638,7 +642,7 @@ There are several ways to collect trajectories: def select_landing(airport: "Airport"): for flight in quickstart: if low_alt := flight.query("altitude < 3000"): # Flight -> None or Flight - if low_alt.vertical_rate_mean < -500: # Flight -> bool + if not pd.isna(v_mean := low_alt.vertical_rate_mean) and v_mean < -500: # Flight -> bool if low_alt.intersects(airport): # Flight -> bool if low_alt.aligned_on_ils(airport).has(): # Flight -> bool yield low_alt.last("10 min") # Flight -> None or Flight diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 04fbf038..fd2e22b4 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -213,7 +213,15 @@ We can also estimate the total fuel consumed: .. jupyter-execute:: - g.emission().fuel_max + g.between(takeoff_time, landing_time).emission().fuel_max + +.. warning:: + + Note that it is not reasonable to consider these models on the ground and that it can result to big discrepancies. + + .. jupyter-execute:: + + g.emission().fuel_max .. hint:: diff --git a/docs/user_guide.rst b/docs/user_guide.rst index 99e9b454..19014251 100644 --- a/docs/user_guide.rst +++ b/docs/user_guide.rst @@ -69,9 +69,9 @@ Navigation events :maxdepth: 1 navigation/flight_phases - airac_usage navigation/go_around navigation/runway_changes + navigation/holding_pattern navigation/top_of_climb navigation/flight_plan navigation/fuel_burn diff --git a/poetry.lock b/poetry.lock index 1d08ad84..5fd89c84 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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altair = { version = ">=5.4.1", optional = true } ipywidgets = { version = ">=8.1.5", optional = true } ipyleaflet = { version = ">=0.18,<0.20", optional = true } @@ -102,7 +102,6 @@ altair = ["altair"] plotly = ["plotly"] leaflet = ["ipyleaflet", "ipywidgets"] lonboard = ["lonboard"] -libarchive = ["libarchive"] spark = ["pyspark"] [tool.poetry.group.dev.dependencies] @@ -161,6 +160,7 @@ warn_redundant_casts = true warn_unused_configs = true warn_unused_ignores = true + [tool.pytest.ini_options] addopts = "--log-level=INFO --color=yes --doctest-modules --doctest-report ndiff" testpaths = [ diff --git a/src/traffic/algorithms/clustering.py b/src/traffic/algorithms/clustering.py index 0551a938..b9ede1fc 100644 --- a/src/traffic/algorithms/clustering.py +++ b/src/traffic/algorithms/clustering.py @@ -64,7 +64,9 @@ def prepare_features( ) resampled = resampled.compute_xy(projection) - return np.stack(list(f.data[features].values.ravel() for f in resampled)) + return np.stack( + list(f.data[features].to_numpy().ravel() for f in resampled) + ) class Clustering: diff --git a/src/traffic/algorithms/cpa.py b/src/traffic/algorithms/cpa.py index 947b81d3..80db8326 100644 --- a/src/traffic/algorithms/cpa.py +++ b/src/traffic/algorithms/cpa.py @@ -204,10 +204,8 @@ def yield_pairs(t_chunk: "Traffic") -> Iterator[Tuple[Flight, Flight]]: if first_shape.intersects(second_shape): yield first, second - t_xyt = ( - traffic.airborne() - .compute_xy(projection) - .assign(round_t=lambda df: df.timestamp.dt.round(round_t)) + t_xyt = traffic.compute_xy(projection).assign( + round_t=lambda df: df.timestamp.dt.round(round_t) ) cumul = list() diff --git a/src/traffic/algorithms/navigation.py b/src/traffic/algorithms/navigation.py index 8e8f8c37..cde10fa6 100644 --- a/src/traffic/algorithms/navigation.py +++ b/src/traffic/algorithms/navigation.py @@ -368,7 +368,8 @@ def aligned_on_ils( for chunk in tentative.split("20s"): if ( chunk.longer_than(min_duration) - and chunk.altitude_min < 5000 + and not pd.isna(altmin := chunk.altitude_min) + and altmin < 5000 ): chunks.append( chunk.assign( @@ -848,7 +849,7 @@ def diversion(self) -> Optional["Flight"]: f_above: Optional["Flight"] = self.query("altitude > 15000") if ( - self.destination != self.destination + pd.isna(self.destination) or airports[self.destination] is None # type: ignore or f_above is None ): @@ -1068,7 +1069,7 @@ def holding_pattern( window = window.assign(flight_id=str(i)) resampled = window.resample(samples) - if resampled.data.eval("track != track").any(): + if resampled.data.eval("track.isnull()").any(): continue features = ( @@ -1077,9 +1078,7 @@ def holding_pattern( ).values.reshape(1, -1) if vertical_rate: - if resampled.data.eval( - "vertical_rate != vertical_rate" - ).any(): + if resampled.data.eval("vertical_rate.notnull()").any(): continue vertical_rates = ( resampled.data.vertical_rate.values.reshape(1, -1) @@ -1431,7 +1430,7 @@ def ground_trajectory( onground = airport_segment.query("onground") else: onground = airport_segment.query( - "altitude < 500 or altitude != altitude" + "altitude < 500 or altitude.isnull()" ) if onground is not None: yield onground diff --git a/src/traffic/algorithms/openap.py b/src/traffic/algorithms/openap.py index 9b9e990a..de567c80 100644 --- a/src/traffic/algorithms/openap.py +++ b/src/traffic/algorithms/openap.py @@ -32,7 +32,7 @@ def phases(self, twindow: int = 60) -> "Flight": fp = FlightPhase() fp.set_trajectory( - (self.data.timestamp.astype(int) // 1_000_000_000).values, + self.data.timestamp.dt.as_unit("s").astype(int).values, altitude, groundspeed, vertical_rate, @@ -124,16 +124,20 @@ def fuelflow( ) TAS: tt.speed_array = self.data.get("TAS", None) - altitude: tt.altitude_array = self.data.altitude - vertical_rate: tt.vertical_rate_array = self.data.vertical_rate + altitude: tt.altitude_array = self.data.altitude.to_numpy() + vertical_rate: tt.vertical_rate_array = ( + self.data.vertical_rate.to_numpy() + ) if TAS is None: CAS: tt.speed_array = self.data.get("CAS", None) if CAS is not None: - TAS = aero.cas2tas(CAS, altitude) + TAS = aero.cas2tas(CAS.to_numpy(), altitude) + else: + TAS = TAS.to_numpy() # type: ignore if TAS is None: - TAS = self.data.groundspeed # unit: knots + TAS = self.data.groundspeed.to_numpy() # unit: knots FF = [] Fuel = [] @@ -175,15 +179,18 @@ def emission( dt: tt.seconds_array = self.data.dt TAS: tt.speed_array = self.data.get("TAS", None) - altitude: tt.altitude_array = self.data.altitude + altitude: tt.altitude_array = self.data.altitude.to_numpy() if TAS is None: CAS: tt.speed_array = self.data.get("CAS", None) if CAS is not None: + CAS = CAS.to_numpy() TAS = aero.cas2tas(CAS, altitude) + else: + TAS = TAS.to_numpy() # type: ignore if TAS is None: - TAS = self.data.groundspeed + TAS = self.data.groundspeed.to_numpy() emission = openap.Emission(ac=actype, eng=engine, use_synonym=True) diff --git a/src/traffic/core/airspace.py b/src/traffic/core/airspace.py index 8c83a6e3..3cb6df00 100644 --- a/src/traffic/core/airspace.py +++ b/src/traffic/core/airspace.py @@ -437,6 +437,7 @@ def unary_union_with_alt(polyalt: AirspaceList) -> AirspaceList: def inside_bbox( geography: T, + /, bounds: Union[ ShapelyMixin, base.BaseGeometry, Tuple[float, float, float, float] ], diff --git a/src/traffic/core/flight.py b/src/traffic/core/flight.py index 5d301fed..020553fb 100644 --- a/src/traffic/core/flight.py +++ b/src/traffic/core/flight.py @@ -22,9 +22,7 @@ Optional, Set, Tuple, - Type, TypedDict, - TypeVar, Union, cast, overload, @@ -35,6 +33,7 @@ from impunity import impunity from pitot import geodesy as geo from rich.console import Console, ConsoleOptions, RenderResult +from typing_extensions import Self import numpy as np import pandas as pd @@ -84,30 +83,15 @@ class Entry(TypedDict, total=False): name: str -T = TypeVar("T", bound="Flight") +def _tz_interpolate( + data: DatetimeTZBlock, *args: Any, **kwargs: Any +) -> DatetimeTZBlock: + coerced = data.coerce_to_target_dtype("int64") + interpolated, *_ = coerced.interpolate(*args, **kwargs) + return interpolated -if str(pd.__version__) < "1.3": - def _tz_interpolate( - data: DatetimeTZBlock, *args: Any, **kwargs: Any - ) -> DatetimeTZBlock: - return data.astype(int).interpolate(*args, **kwargs).astype(data.dtype) - - DatetimeTZBlock.interpolate = _tz_interpolate - -else: - # - with version 1.3.0, interpolate returns a list - # - Windows require "int64" as "int" may be interpreted as "int32" and raise - # an error (was not raised before 1.3.0) - - def _tz_interpolate( - data: DatetimeTZBlock, *args: Any, **kwargs: Any - ) -> DatetimeTZBlock: - coerced = data.coerce_to_target_dtype("int64") - interpolated, *_ = coerced.interpolate(*args, **kwargs) - return interpolated - - DatetimeTZBlock.interpolate = _tz_interpolate +DatetimeTZBlock.interpolate = _tz_interpolate def _split( @@ -118,9 +102,9 @@ def _split( return diff = data.timestamp.diff() if unit is None: - delta = pd.Timedelta(value).to_timedelta64() + delta = pd.Timedelta(value) else: - delta = np.timedelta64(value, unit) + delta = pd.Timedelta(np.timedelta64(value, unit)) # There seems to be a change with numpy >= 1.18 # max() now may return NaN, therefore the following fix max_ = diff.max() @@ -741,7 +725,7 @@ def label( value = value.format(i=i, segment=segment, self=self) result.data.loc[mask, key] = value - return result.convert_dtypes(convert_string=False) + return result def all( self, @@ -805,7 +789,7 @@ def final( >>> flight.final("runway_change") >>> flight.final(lambda f: f.aligned_on_ils("LFBO")) """ - fun = ( + fun: Callable[[Flight], Iterator[Flight]] = ( getattr(self.__class__, method) if isinstance(method, str) else method @@ -813,7 +797,7 @@ def final( segment = None for segment in fun(self): continue - return segment # type: ignore + return segment # --- Iterators --- @@ -823,7 +807,7 @@ def timestamp(self) -> Iterator[pd.Timestamp]: @property def coords(self) -> Iterator[Tuple[float, float, float]]: - data = self.data.query("longitude == longitude") + data = self.data.query("longitude.notnull()") if "altitude" not in data.columns: data = data.assign(altitude=0) yield from zip( @@ -834,7 +818,7 @@ def coords(self) -> Iterator[Tuple[float, float, float]]: ) def coords4d(self, delta_t: bool = False) -> Iterator[Entry]: - data = self.data.query("longitude == longitude") + data = self.data.query("longitude.notnull()") if delta_t: time = (data.timestamp - data.timestamp.min()).dt.total_seconds() else: @@ -860,7 +844,7 @@ def coords4d(self, delta_t: bool = False) -> Iterator[Entry]: @property def xy_time(self) -> Iterator[Tuple[float, float, float]]: - self_filtered = self.query("longitude == longitude") + self_filtered = self.query("longitude.notnull()") if self_filtered is None: return None iterator = iter(zip(self_filtered.coords, self_filtered.timestamp)) @@ -921,6 +905,8 @@ def feature_gt( if isinstance(feature, str): feature = attrgetter(feature) attribute = feature(self) + if pd.isna(attribute): + return False if strict: return attribute > value # type: ignore return attribute >= value # type: ignore @@ -949,6 +935,8 @@ def feature_lt( if isinstance(feature, str): feature = attrgetter(feature) attribute = feature(self) + if pd.isna(attribute): + return False if strict: return attribute < value # type: ignore return attribute <= value # type: ignore @@ -1042,6 +1030,9 @@ def _get_unique( if field not in self.data.columns: return None tmp = self.data[field].unique() + tmp = list(elt for elt in tmp if not pd.isna(elt)) + if len(tmp) == 0: + return None if len(tmp) == 1: return tmp[0] # type: ignore if warn: @@ -1057,8 +1048,6 @@ def callsign(self) -> Union[str, Set[str], None]: with a route for a commercial aircraft. """ callsign = self._get_unique("callsign") - if callsign != callsign: - raise ValueError("NaN appearing in callsign field") return callsign @property @@ -1653,7 +1642,7 @@ def flatten( data.columns = ( [ how(filter(None, map(str, levels))) - for levels in data.columns.values + for levels in data.columns.to_numpy() ] if isinstance(data.columns, pd.MultiIndex) else data.columns @@ -1693,13 +1682,14 @@ def handle_last_position(self) -> Flight: if "last_position" in self.data.columns: data = ( data.assign( - _mark=lambda df: df.last_position - != df.shift(1).last_position + _mark=lambda df: ( + df.last_position != df.shift(1).last_position + ).astype(float) ) .assign( - latitude=lambda df: df.latitude * df._mark / df._mark, - longitude=lambda df: df.longitude * df._mark / df._mark, - altitude=lambda df: df.altitude * df._mark / df._mark, + latitude=lambda df: df.latitude * (df._mark / df._mark), + longitude=lambda df: df.longitude * (df._mark / df._mark), + altitude=lambda df: df.altitude * (df._mark / df._mark), ) # keeping last_position causes more problems (= Nan) than # anything. Safer to just remove it for now. Like it or not! @@ -1777,7 +1767,7 @@ def resample( .data.set_index("timestamp") .resample(rule) .first() - .reset_index() + .reset_index(names="timestamp") ) data = data.infer_objects(copy=False) @@ -1787,10 +1777,15 @@ def resample( if isinstance(how, str): if how == "interpolate": - interpolable = data.dtypes[data.dtypes != object].index # noqa: E721 - other = data.dtypes[data.dtypes == object].index # noqa: E721 - how = {how: set(interpolable) - {"timestamp"}} - how["ffill"] = set(other) + interpolable = data.select_dtypes(["float", "int"]) + how = { + "interpolate": set(interpolable), + "ffill": set( + data.select_dtypes( + exclude=["float", "int", "bool", "datetime"] + ) + ), + } else: how = {how: set(data.columns) - {"timestamp"}} @@ -1798,25 +1793,18 @@ def resample( if meth is not None: idx = data.columns.get_indexer(columns) kwargs = interpolate_kw if meth == "interpolate" else {} - value = getattr(data.iloc[:, idx], meth)(**kwargs) + value = getattr(data[list(columns)], meth)(**kwargs) data[data.columns[idx]] = value elif isinstance(rule, int): - # ./site-packages/pandas/core/indexes/base.py:2820: FutureWarning: - # Converting timezone-aware DatetimeArray to timezone-naive ndarray - # with 'datetime64[ns]' dtype. In the future, this will return an - # ndarray with 'object' dtype where each element is a - # 'pandas.Timestamp' with the correct 'tz'. - # To accept the future behavior, pass 'dtype=object'. - # To keep the old behavior, pass 'dtype="datetime64[ns]"'. + # use pd.date_range to compute the best freq + new_index = pd.date_range(self.start, self.stop, periods=rule) data = ( self.handle_last_position() .unwrap() # avoid filled gaps in track and heading - .assign(tz_naive=lambda d: d.timestamp.dt.tz_localize(None)) - .data.set_index("tz_naive") - .asfreq((self.stop - self.start) / (rule - 1), method="nearest") - .reset_index() - .drop(columns="tz_naive") + .data.set_index("timestamp") + .reindex(new_index, method="nearest") + .reset_index(names="timestamp") ) else: raise TypeError("rule must be a str or an int") @@ -1883,9 +1871,6 @@ def filter( if strategy is not None: new_data = strategy(new_data) - if "onground" in new_data.columns: - new_data = new_data.assign(onground=new_data.onground.astype(bool)) - postprocess = self.__class__(new_data) if filter.projection is not None: postprocess = postprocess.compute_latlon_from_xy(filter.projection) @@ -1994,9 +1979,9 @@ def onground(self) -> Optional[Flight]: if "altitude" not in self.data.columns: return self if "onground" in self.data.columns and self.data.onground.dtype == bool: - return self.query("onground or altitude != altitude") + return self.query("onground or altitude.isnull()") else: - return self.query("altitude != altitude") + return self.query("altitude.isnull()") def airborne(self) -> Optional[Flight]: """Returns the airborne part of the Flight. @@ -2007,9 +1992,9 @@ def airborne(self) -> Optional[Flight]: if "altitude" not in self.data.columns: return None if "onground" in self.data.columns and self.data.onground.dtype == bool: - return self.query("not onground and altitude == altitude") + return self.query("not onground and altitude.notnull()") else: - return self.query("altitude == altitude") + return self.query("altitude.notnull()") def unwrap(self, features: Union[None, str, List[str]] = None) -> Flight: """Unwraps angles in the DataFrame. @@ -2182,10 +2167,10 @@ def plot_wind( ) return ax.barbs( # type: ignore - data.longitude.values, - data.latitude.values, - data.wind_u.values, - data.wind_v.values, + data.longitude.to_numpy(), + data.latitude.to_numpy(), + data.wind_u.to_numpy(), + data.wind_v.to_numpy(), **kwargs, ) @@ -2199,8 +2184,8 @@ def bearing( return self.assign( **{ column_name: geo.bearing( - self.data.latitude.values, - self.data.longitude.values, + self.data.latitude.to_numpy(), + self.data.longitude.to_numpy(), other.latitude * np.ones(size), other.longitude * np.ones(size), ) @@ -2260,8 +2245,11 @@ def distance( """ if other is None: - first = self.at_ratio(0) - last = self.at_ratio(1) + with_position = self.query("latitude.notnull()") + if with_position is None: + return 0 + first = with_position.at_ratio(0) + last = with_position.at_ratio(1) if first is None or last is None: return 0 result: tt.distance = geo.distance( @@ -2277,8 +2265,8 @@ def distance( if isinstance(other, PointMixin): size = self.data.shape[0] distance_vec = geo.distance( - self.data.latitude.values, - self.data.longitude.values, + self.data.latitude.to_numpy(), + self.data.longitude.to_numpy(), other.latitude * np.ones(size), other.longitude * np.ones(size), ) @@ -2333,10 +2321,10 @@ def distance( table = f1.data[cols].merge(f2.data[cols], on="timestamp") distance_vec = geo.distance( - table.latitude_x.values, - table.longitude_x.values, - table.latitude_y.values, - table.longitude_y.values, + table.latitude_x.to_numpy(), + table.longitude_x.to_numpy(), + table.latitude_y.to_numpy(), + table.longitude_y.to_numpy(), ) return table.assign( lateral=distance_vec, @@ -2487,10 +2475,10 @@ def cumulative_distance( delta_1 = delta.iloc[1:] distance_nm: tt.distance_array distance_nm = geo.distance( - (delta_1.latitude - delta_1.latitude_1).values, - (delta_1.longitude - delta_1.longitude_1).values, - delta_1.latitude.values, - delta_1.longitude.values, + (delta_1.latitude - delta_1.latitude_1).to_numpy(), + (delta_1.longitude - delta_1.longitude_1).to_numpy(), + delta_1.latitude.to_numpy(), + delta_1.longitude.to_numpy(), ) res = cur_sorted.assign( @@ -2506,10 +2494,10 @@ def cumulative_distance( if compute_track: track = geo.bearing( - (delta_1.latitude - delta_1.latitude_1).values, - (delta_1.longitude - delta_1.longitude_1).values, - delta_1.latitude.values, - delta_1.longitude.values, + (delta_1.latitude - delta_1.latitude_1).to_numpy(), + (delta_1.longitude - delta_1.longitude_1).to_numpy(), + delta_1.latitude.to_numpy(), + delta_1.longitude.to_numpy(), ) track = np.where(track > 0, track, 360 + track) res = res.assign( @@ -2794,19 +2782,14 @@ def fail_silent() -> Flight: if df is None or df.shape[0] == 0: return failure() - timestamped_df = ( - df.sort_values("mintime") - .assign(timestamp=lambda df: df.mintime) - # TODO shouldn't be necessary after pyopensky 2.10 - .convert_dtypes(dtype_backend="pyarrow") + timestamped_df = df.sort_values("mintime").assign( + timestamp=lambda df: df.mintime ) + timestamp_s = self.data.timestamp.dt.as_unit("s").astype(int) referenced_df = ( timestamped_df.merge( - # TODO shouldn't be necessary after pyopensky 2.10 - self.data.convert_dtypes(dtype_backend="pyarrow").assign( - timestamp=lambda df: df.timestamp.astype("int64") * 1e-9 - ), + self.data.assign(timestamp=timestamp_s), on="timestamp", how="outer", ) @@ -2841,9 +2824,9 @@ def fail_silent() -> Flight: pd.DataFrame.from_records(d) for d in rs1090.batched(decoded, 5000) ) - df = df.assign( + df = df.convert_dtypes(dtype_backend="pyarrow").assign( timestamp=pd.to_datetime(df.timestamp, unit="s", utc=True) - ).convert_dtypes(dtype_backend="pyarrow") + ) extended = Flight(df) # fix for https://stackoverflow.com/q/53657210/1595335 @@ -3076,7 +3059,7 @@ def plot_time( secondary_y=column if column in secondary_y else "", ), } - subtab = self.data.query(f"{column} == {column}") + subtab = self.data.query(f"{column}.notnull()") if localized: ( @@ -3100,9 +3083,7 @@ def from_fr24(cls, filename: Union[Path, str]) -> Flight: return FlightRadar24.from_file(filename) @classmethod - def from_file( - cls: Type[T], filename: Union[Path, str], **kwargs: Any - ) -> Optional[T]: + def from_file(cls, filename: Union[Path, str], **kwargs: Any) -> Self: """Read data from various formats. This class method dispatches the loading of data in various format to @@ -3126,8 +3107,6 @@ def from_file( """ tentative = super().from_file(filename, **kwargs) - if tentative is None: - return None # Special treatment for flights to download from flightradar24 cols_fr24 = { diff --git a/src/traffic/core/lazy.py b/src/traffic/core/lazy.py index aa986a2d..3d847825 100644 --- a/src/traffic/core/lazy.py +++ b/src/traffic/core/lazy.py @@ -14,10 +14,13 @@ TYPE_CHECKING, Any, Callable, + Concatenate, Dict, List, Literal, Optional, + ParamSpec, + TypeVar, Union, cast, overload, @@ -37,6 +40,17 @@ _log = logging.getLogger(__name__) +P = ParamSpec("P") +R = TypeVar("R") + +TrafficMethod = Callable[Concatenate["Traffic", P], R] + +# TODO PEP 563 (Postponed Evaluation of Annotations) from Python 3.11 + +# This syntax from Python 3.12 +# type TrafficMethod[P] = +# Callable[Concatenate[Traffic, P], bool | None | Traffic] + class FaultCatcher: flag: bool = False @@ -283,20 +297,25 @@ def __getattr__(self, name: str) -> Any: def lazy_evaluation( default: "Literal[None, False]" = False, idx_name: Optional[str] = None, -) -> Callable[..., Callable[..., LazyTraffic]]: ... +) -> Callable[ + [TrafficMethod[P, R]], Callable[Concatenate["Traffic", P], LazyTraffic] +]: ... @overload def lazy_evaluation( default: "Literal[True]", idx_name: Optional[str] = None, -) -> Callable[..., Callable[..., "Traffic"]]: ... +) -> Callable[[TrafficMethod[P, R]], TrafficMethod[P, R]]: ... def lazy_evaluation( default: None | bool = False, idx_name: None | str = None, -) -> Callable[..., Callable[..., "Traffic" | LazyTraffic]]: +) -> Callable[ + [TrafficMethod[P, R]], + Callable[Concatenate["Traffic", P], R | LazyTraffic], +]: """A decorator to delegate methods to :class:`~traffic.core.Flight` in a lazy manner. @@ -324,8 +343,8 @@ def lazy_evaluation( """ def wrapper( - f: Callable[..., "Traffic"], - ) -> Callable[..., Union["Traffic", LazyTraffic]]: + f: TrafficMethod[P, R], + ) -> Callable[Concatenate["Traffic", P], R | LazyTraffic]: # Check parameters passed (esp. filter_if) are not lambda because those # are not serializable therefore **silently** fail when multiprocessed. msg = """ @@ -367,24 +386,22 @@ def stringify_lambda(lambda_func: Callable[..., Any]) -> str: return cast(str, astor.to_source(ast_lambda)) def lazy_λf( - lazy: LazyTraffic, - *args: str | Callable[..., Union["Traffic", LazyTraffic]], - **kwargs: str | Callable[..., Union["Traffic", LazyTraffic]], + self: LazyTraffic, /, *args: P.args, **kwargs: P.kwargs ) -> LazyTraffic: if any(is_lambda(arg) for arg in args): _log.warning(msg.format(method=f.__name__)) - args = tuple( - stringify_lambda(arg) + args = tuple( # type: ignore + stringify_lambda(arg) # type: ignore if not isinstance(arg, str) and is_lambda(arg) else arg for arg in args ) if any(is_lambda(arg) for arg in kwargs.values()): _log.warning(msg.format(method=f.__name__)) - kwargs = dict( + kwargs = dict( # type: ignore ( key, - stringify_lambda(arg) + stringify_lambda(arg) # type: ignore if not isinstance(arg, str) and is_lambda(arg) else arg, ) @@ -394,16 +411,15 @@ def lazy_λf( op_idx = LazyLambda(f.__name__, idx_name, *args, **kwargs) return LazyTraffic( - lazy.wrapped_t, - [*lazy.stacked_ops, op_idx], - lazy.iterate_kw, - lazy.tqdm_kw, + self.wrapped_t, + [*self.stacked_ops, op_idx], + self.iterate_kw, + self.tqdm_kw, ) lazy_λf.__annotations__ = dict( # make a copy!! getattr(Flight, f.__name__).__annotations__ ) - lazy_λf.__annotations__["self"] = LazyTraffic lazy_λf.__annotations__["return"] = LazyTraffic # Attach the method to LazyCollection for further chaining @@ -422,8 +438,9 @@ def lazy_λf( # Take the method in Flight and create a LazyCollection def λf( wrapped_t: "Traffic", - *args: Callable[..., Union["Traffic", LazyTraffic]], - **kwargs: Callable[..., Union["Traffic", LazyTraffic]], + /, + *args: P.args, + **kwargs: P.kwargs, ) -> LazyTraffic: op_idx = LazyLambda(f.__name__, idx_name, *args, **kwargs) @@ -466,34 +483,37 @@ def λf( GeographyMixin, predicate=inspect.isfunction ): annots = handle.__annotations__ - if name.startswith("_") or "self" not in annots or "return" not in annots: + if name.startswith("_") or "return" not in annots: continue - if ( # includes .query() - annots["return"] == annots["self"] - or annots["return"] == Optional[annots["self"]] # noqa: F821 - or annots["return"] == f"Optional[{annots['self']}]" - or annots["return"] == f"None | {annots['self']}" + # includes .query(), etc. + if ( + annots["return"] == "Self" + or annots["return"] == "Optional[Self]" + or annots["return"] == "None | Self" ): - def make_lambda(name: str) -> Callable[..., LazyTraffic]: + def make_lambda( + handle: Callable[Concatenate[GeographyMixin, P], Any], + ) -> Callable[Concatenate[LazyTraffic, P], LazyTraffic]: def lazy_λf( - lazy: LazyTraffic, - *args: Callable[..., Union["Traffic", LazyTraffic]], - **kwargs: Callable[..., Union["Traffic", LazyTraffic]], + self: LazyTraffic, /, *args: P.args, **kwargs: P.kwargs ) -> LazyTraffic: - op_idx = LazyLambda(name, None, *args, **kwargs) + op_idx = LazyLambda(handle.__name__, None, *args, **kwargs) return LazyTraffic( - lazy.wrapped_t, - [*lazy.stacked_ops, op_idx], - lazy.iterate_kw, - lazy.tqdm_kw, + self.wrapped_t, + [*self.stacked_ops, op_idx], + self.iterate_kw, + self.tqdm_kw, ) + lazy_λf.__name__ = handle.__name__ + lazy_λf.__qualname__ = f"LazyTraffic.{handle.__name__}" lazy_λf.__doc__ = handle.__doc__ - lazy_λf.__annotations__ = handle.__annotations__ + lazy_λf.__annotations__ = handle.__annotations__.copy() + lazy_λf.__annotations__["return"] = "Self" return lazy_λf # Attach the method to LazyCollection for further chaining - setattr(LazyTraffic, name, make_lambda(name)) + setattr(LazyTraffic, name, make_lambda(handle)) diff --git a/src/traffic/core/mixins.py b/src/traffic/core/mixins.py index f9e970bb..69025b0a 100644 --- a/src/traffic/core/mixins.py +++ b/src/traffic/core/mixins.py @@ -1,8 +1,6 @@ # ruff: noqa: E501 from __future__ import annotations -import gzip -import json import logging import re from functools import lru_cache @@ -14,14 +12,14 @@ ClassVar, Mapping, Sequence, - Type, TypedDict, - TypeVar, ) +from py7zr import SevenZipFile from rich.box import SIMPLE_HEAVY from rich.console import Console, ConsoleOptions, RenderResult from rich.table import Table +from typing_extensions import Self import numpy as np import pandas as pd @@ -40,8 +38,8 @@ from matplotlib.artist import Artist -T = TypeVar("T", bound="DataFrameMixin") -G = TypeVar("G", bound="GeoDBMixin") +# T = TypeVar("T", bound="DataFrameMixin") +# G = TypeVar("G", bound="GeoDBMixin") _log = logging.getLogger(__name__) @@ -74,9 +72,7 @@ def __sizeof__(self) -> int: return int(self.data.memory_usage().sum()) @classmethod - def from_file( - cls: Type[T], filename: str | Path, **kwargs: Any - ) -> None | T: + def from_file(cls, filename: str | Path, **kwargs: Any) -> Self: """Read data from various formats. This class method dispatches the loading of data in various format to @@ -101,6 +97,16 @@ def from_file( >>> t = Traffic.from_file(filename) """ path = Path(filename) + + if path.suffix == (".7z"): + with SevenZipFile(path) as archive: + if (files := archive.readall()) is None: + raise FileNotFoundError(f"Empty archive {path}") + for name, io in files.items(): + if name.endswith(".jsonl"): + return cls(pd.read_json(io, lines=True, **kwargs)) + raise FileNotFoundError(f"Empty archive {path}") + if ".pkl" in path.suffixes or ".pickle" in path.suffixes: return cls(pd.read_pickle(path, **kwargs)) if ".parquet" in path.suffixes: @@ -109,29 +115,14 @@ def from_file( return cls(pd.read_feather(path, **kwargs)) if ".json" in path.suffixes: return cls(pd.read_json(path, **kwargs)) - if path.suffix == ".jsonl": - df = pd.json_normalize( - json.loads(elt) for elt in path.read_text().split("\n")[:-1] - ) - df = df.assign( - timestamp=pd.to_datetime(df.timestamp, unit="s", utc=True) - ) - return cls(df) - if ".jsonl" in path.suffixes and ".gz" in path.suffixes: - with gzip.open(path) as fh: - df = pd.json_normalize( - json.loads(elt) for elt in fh.readlines() - ) - df = df.assign( - timestamp=pd.to_datetime(df.timestamp, unit="s", utc=True) - ) - return cls(df) - + if ".jsonl" in path.suffixes: + return cls(pd.read_json(path, lines=True, **kwargs)) if ".csv" in path.suffixes: return cls(pd.read_csv(path, **kwargs)) if ".h5" == path.suffixes[-1]: # coverage: ignore return cls(pd.read_hdf(path, **kwargs)) - return None + + raise FileNotFoundError(path) # --- Special methods --- @@ -189,7 +180,7 @@ def __rich_console__( # --- Redirected to pandas.DataFrame --- - def assign(self: T, *args: Any, **kwargs: Any) -> T: + def assign(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.assign` method to the underlying pandas DataFrame and get the result back in the same @@ -197,7 +188,7 @@ def assign(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.assign(*args, **kwargs)) - def convert_dtypes(self: T, *args: Any, **kwargs: Any) -> T: + def convert_dtypes(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.convert_dtypes` method to the underlying pandas DataFrame and get the result back in the same @@ -205,7 +196,7 @@ def convert_dtypes(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.convert_dtypes(*args, **kwargs)) - def drop(self: T, *args: Any, **kwargs: Any) -> T: + def drop(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.drop` method to the underlying pandas DataFrame and get the result back in the same @@ -213,7 +204,7 @@ def drop(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.drop(*args, **kwargs)) - def drop_duplicates(self: T, *args: Any, **kwargs: Any) -> T: + def drop_duplicates(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.drop_duplicates` method to the underlying pandas DataFrame and get the result back in the same @@ -221,7 +212,7 @@ def drop_duplicates(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.drop_duplicates(*args, **kwargs)) - def fillna(self: T, *args: Any, **kwargs: Any) -> T: + def fillna(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.fillna` method to the underlying pandas DataFrame and get the result back in the same @@ -238,7 +229,7 @@ def groupby( """ return self.data.groupby(*args, **kwargs) - def merge(self: T, *args: Any, **kwargs: Any) -> T: + def merge(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.merge` method to the underlying pandas DataFrame and get the result back in the same @@ -246,7 +237,7 @@ def merge(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.merge(*args, **kwargs)) - def query(self: T, query_str: str, *args: Any, **kwargs: Any) -> None | T: + def query(self, query_str: str, *args: Any, **kwargs: Any) -> None | Self: """ Applies the Pandas :meth:`~pandas.DataFrame.query` method to the underlying pandas DataFrame and get the result back in the same @@ -257,7 +248,7 @@ def query(self: T, query_str: str, *args: Any, **kwargs: Any) -> None | T: return None return self.__class__(df) - def rename(self: T, *args: Any, **kwargs: Any) -> T: + def rename(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.rename` method to the underlying pandas DataFrame and get the result back in the same @@ -265,7 +256,7 @@ def rename(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.rename(*args, **kwargs)) - def replace(self: T, *args: Any, **kwargs: Any) -> T: + def replace(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.replace` method to the underlying pandas DataFrame and get the result back in the same @@ -273,7 +264,7 @@ def replace(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.replace(*args, **kwargs)) - def reset_index(self: T, *args: Any, **kwargs: Any) -> T: + def reset_index(self, *args: Any, **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.reset_index` method to the underlying pandas DataFrame and get the result back in the same @@ -281,7 +272,7 @@ def reset_index(self: T, *args: Any, **kwargs: Any) -> T: """ return self.__class__(self.data.reset_index(*args, **kwargs)) - def sort_values(self: T, by: str | Sequence[str], **kwargs: Any) -> T: + def sort_values(self, by: str | Sequence[str], **kwargs: Any) -> Self: """ Applies the Pandas :meth:`~pandas.DataFrame.sort_values` method to the underlying pandas DataFrame and get the result back in the same @@ -516,7 +507,7 @@ class GeographyMixin(DataFrameMixin): __slots__ = () - def projection(self: T, proj: str = "lcc") -> pyproj.Proj: + def projection(self, proj: str = "lcc") -> pyproj.Proj: return pyproj.Proj( proj=proj, ellps="WGS84", @@ -527,8 +518,8 @@ def projection(self: T, proj: str = "lcc") -> pyproj.Proj: ) def compute_xy( - self: T, projection: None | pyproj.Proj | "crs.Projection" = None - ) -> T: + self, projection: None | pyproj.Proj | "crs.Projection" = None + ) -> Self: """Enrich the structure with new x and y columns computed through a projection of the latitude and longitude columns. @@ -547,21 +538,21 @@ def compute_xy( projection = pyproj.Proj(projection.proj4_init) if projection is None: - projection = self.projection(proj="lcc") # type: ignore + projection = self.projection(proj="lcc") transformer = pyproj.Transformer.from_proj( pyproj.Proj("epsg:4326"), projection, always_xy=True ) x, y = transformer.transform( - self.data.longitude.values, - self.data.latitude.values, + self.data.longitude.to_numpy(), + self.data.latitude.to_numpy(), ) return self.__class__(self.data.assign(x=x, y=y)) def compute_latlon_from_xy( - self: T, projection: pyproj.Proj | crs.Projection - ) -> T: + self, projection: pyproj.Proj | crs.Projection + ) -> Self: """Enrich a DataFrame with new longitude and latitude columns computed from x and y columns. @@ -583,8 +574,8 @@ def compute_latlon_from_xy( projection, pyproj.Proj("epsg:4326"), always_xy=True ) lon, lat = transformer.transform( - self.data.x.values, - self.data.y.values, + self.data.x.to_numpy(), + self.data.y.to_numpy(), ) return self.assign(latitude=lat, longitude=lon) @@ -666,29 +657,27 @@ def geoencode(self, **kwargs: Any) -> "alt.Chart": # coverage: ignore return ( alt.Chart( - self.data.query( - "latitude == latitude and longitude == longitude" - ) + self.data.query("latitude.notnull() and longitude.notnull()") ) .encode(latitude="latitude", longitude="longitude") .mark_line(**kwargs) ) def interpolate_grib( - self: T, wind: "xarray.Dataset", features: list[str] = ["u", "v"] - ) -> T: + self, wind: "xarray.Dataset", features: list[str] = ["u", "v"] + ) -> Self: from openap import aero from sklearn.linear_model import Ridge from sklearn.pipeline import make_pipeline from sklearn.preprocessing import PolynomialFeatures - projection: pyproj.Proj = self.projection("lcc") # type: ignore + projection: pyproj.Proj = self.projection("lcc") transformer = pyproj.Transformer.from_proj( pyproj.Proj("epsg:4326"), projection, always_xy=True ) west, east = self.data.longitude.min(), self.data.longitude.max() - longitude_index = wind.longitude.values + longitude_index = wind.longitude.to_numpy() margin = np.diff(longitude_index).max() longitude_index = longitude_index[ np.where( @@ -698,7 +687,7 @@ def interpolate_grib( ] south, north = self.data.latitude.min(), self.data.latitude.max() - latitude_index = wind.latitude.values + latitude_index = wind.latitude.to_numpy() margin = np.diff(latitude_index).max() latitude_index = latitude_index[ np.where( @@ -709,7 +698,7 @@ def interpolate_grib( timestamp = self.data.timestamp.dt.tz_convert("utc") start, stop = timestamp.min(), timestamp.max() - time_index = wind.time.values + time_index = wind.time.to_numpy() margin = np.diff(time_index).max() time_index = time_index[ np.where( @@ -719,10 +708,10 @@ def interpolate_grib( ] idx_max = 1 + np.sum( - aero.h_isa(wind.isobaricInhPa.values * 100) + aero.h_isa(wind.isobaricInhPa.to_numpy() * 100) < self.data.altitude.max() * aero.ft ) - isobaric_index = wind.isobaricInhPa.values[:idx_max] + isobaric_index = wind.isobaricInhPa.to_numpy()[:idx_max] wind_df = ( wind.sel( @@ -737,8 +726,8 @@ def interpolate_grib( ) wind_x, wind_y = transformer.transform( - wind_df.longitude.values, - wind_df.latitude.values, + wind_df.longitude.to_numpy(), + wind_df.latitude.to_numpy(), ) wind_xy = wind_df.assign(x=wind_x, y=wind_y) @@ -752,10 +741,10 @@ def interpolate_grib( ridges = model["ridge"].coef_ x, y = transformer.transform( - self.data.longitude.values, - self.data.latitude.values, + self.data.longitude.to_numpy(), + self.data.latitude.to_numpy(), ) - h = self.data.altitude.values * aero.ft + h = self.data.altitude.to_numpy() * aero.ft return self.assign( **dict( @@ -778,10 +767,10 @@ class GeoDBMixin(DataFrameMixin): __slots__ = () def extent( - self: G, + self, extent: str | ShapelyMixin | tuple[float, float, float, float], buffer: float = 0.5, - ) -> None | G: + ) -> None | Self: """ Selects the subset of data inside the given extent. diff --git a/src/traffic/core/time.py b/src/traffic/core/time.py index c4e06530..db7de920 100644 --- a/src/traffic/core/time.py +++ b/src/traffic/core/time.py @@ -1,5 +1,5 @@ import logging -from datetime import datetime, timezone +from datetime import datetime, timezone # noqa: F401 from numbers import Real from pyopensky.time import ( @@ -25,9 +25,7 @@ def to_datetime(time: timelike) -> pd.Timestamp: - """Facility to convert anything to a datetime. - - This function will soon be replaced by pd.to_datetime. + """Facility to convert anything to a pd.Timestamp. >>> f"{to_datetime('2017-01-14')}" '2017-01-14 00:00:00+00:00' @@ -53,4 +51,4 @@ def to_datetime(time: timelike) -> pd.Timestamp: "automatically, look at the tzinfo (resp. tz) argument of the " "datetime (resp. pd.Timestamp) constructor." ) - return time # type: ignore + return time diff --git a/src/traffic/core/traffic.py b/src/traffic/core/traffic.py index 6220ca86..2a929c03 100644 --- a/src/traffic/core/traffic.py +++ b/src/traffic/core/traffic.py @@ -17,14 +17,13 @@ Optional, Sequence, Set, - Type, - TypeVar, Union, cast, overload, ) from rich.console import Console, ConsoleOptions, RenderResult +from typing_extensions import Self import numpy as np import pandas as pd @@ -57,7 +56,7 @@ # https://github.com/python/mypy/issues/2511 -TrafficTypeVar = TypeVar("TrafficTypeVar", bound="Traffic") +# TrafficTypeVar = TypeVar("TrafficTypeVar", bound="Traffic") # The thing is that Iterable[str] causes issue sometimes... IterStr = Union[List[str], Set[str]] @@ -120,12 +119,18 @@ def from_flights( return cls(pd.concat(cumul, sort=False)) @classmethod - def from_file( - cls: Type[TrafficTypeVar], filename: Union[Path, str], **kwargs: Any - ) -> Optional[TrafficTypeVar]: + def from_file(cls, filename: Union[Path, str], **kwargs: Any) -> Self: tentative = super().from_file(filename, **kwargs) if tentative is not None: + tentative = tentative.convert_dtypes(dtype_backend="pyarrow") + + for column in tentative.data.select_dtypes(include=["datetime"]): + if tentative.data[column].dtype.pyarrow_dtype.tz is None: + tentative = tentative.assign( + **{column: tentative.data[column].dt.tz_localize("UTC")} + ) + rename_columns = { "time": "timestamp", "lat": "latitude", @@ -431,24 +436,24 @@ def _getSeries(self, index: pd.Series) -> None | Flight: return None @overload - def __getitem__(self, key: int) -> None | Flight: ... + def __getitem__(self, key: int) -> Flight: ... @overload - def __getitem__(self, key: str) -> None | Flight: ... + def __getitem__(self, key: str) -> Flight: ... @overload - def __getitem__(self, key: slice) -> None | Traffic: ... + def __getitem__(self, key: slice) -> Traffic: ... @overload - def __getitem__(self, key: IterStr) -> None | Traffic: ... + def __getitem__(self, key: IterStr) -> Traffic: ... @overload - def __getitem__(self, key: Traffic) -> None | Traffic: ... + def __getitem__(self, key: Traffic) -> Traffic: ... def __getitem__( self, key: int | slice | str | IterStr | pd.Series | pd.DataFrame | Traffic, - ) -> None | Flight | Traffic: + ) -> Flight | Traffic: """Indexation of collections. :param key: @@ -482,27 +487,36 @@ def __getitem__( """ if isinstance(key, pd.Series): - return self._getSeries(key) + flight = self._getSeries(key) + if flight is not None: + return flight + raise KeyError(f"Indexing with {key} returns an empty result") if isinstance(key, pd.DataFrame): - return self.__class__.from_flights( + traffic = self.__class__.from_flights( flight for flight in self.iterate(by=key) ) + if traffic is not None: + return traffic + raise KeyError(f"Indexing with {key} returns an empty result") if isinstance(key, int): for i, flight in enumerate(self.iterate()): if i == key: return flight - return None + raise KeyError(f"Indexing with {key} returns an empty result") if isinstance(key, slice): max_size = key.stop if key.stop is not None else len(self) indices = list(range(max_size)[key]) - return self.__class__.from_flights( + traffic = self.__class__.from_flights( flight for i, flight in enumerate(self.iterate()) if i in indices ) + if traffic is not None: + return traffic + raise KeyError(f"Indexing with {key} returns an empty result") if isinstance(key, Traffic): if (flight_ids := key.flight_ids) is not None: @@ -532,20 +546,24 @@ def __getitem__( ) else: cumul.append(flight) - if len(cumul) == 0: - return None - return Traffic.from_flights(cumul) + traffic = Traffic.from_flights(cumul) + if traffic is not None: + return traffic + raise KeyError(f"Indexing with {key} returns an empty result") if not isinstance(key, str): # List[str], Set[str], Iterable[str] _log.debug("Selecting flights from a list of identifiers") subset = repr(list(key)) query_str = f"callsign in {subset} or icao24 in {subset}" if "flight_id" in self.data.columns: - return self.query(f"flight_id in {subset} or " + query_str) + traffic = self.query(f"flight_id in {subset} or " + query_str) elif "callsign" in self.data.columns: - return self.query(query_str) + traffic = self.query(query_str) else: - return self.query(f"icao24 in {subset}") + traffic = self.query(f"icao24 in {subset}") + if traffic is not None: + return traffic + raise KeyError(f"Indexing with {key} returns an empty result") query_str = f"callsign == '{key}' or icao24 == '{key}'" if "callsign" not in self.data.columns: @@ -558,12 +576,12 @@ def __getitem__( if df.shape[0] > 0: return Flight(df) - return None + raise KeyError(f"Key '{key}' not found") def _ipython_key_completions_(self) -> Optional[List[str]]: if self.flight_ids is not None: return self.flight_ids # type: ignore - return list({*self.aircraft, *self.callsigns}) + return list({*self.icao24, *self.callsigns}) def sample( self, @@ -730,53 +748,51 @@ def aircraft_data(self) -> "Traffic": ) # -- Methods for lazy evaluation, delegated to Flight -- - # 'type: ignore' annotations are fine here, since everything will be - # overridden by the decorator anyway @lazy_evaluation() - def filter_if(self, *args, **kwargs): # type: ignore + def filter_if(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def pipe(self, *args, **kwargs): # type: ignore + def pipe(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def has(self, *args, **kwargs): # type: ignore + def has(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def all(self, *args, **kwargs): # type: ignore + def all(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def next(self, *args, **kwargs): # type: ignore + def next(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def final(self, *args, **kwargs): # type: ignore + def final(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def resample(self, rule: Union[str, int] = "1s"): # type: ignore + def resample(self, /, rule: Union[str, int] = "1s"): # type: ignore ... @lazy_evaluation() - def filter(self, *args, **kwargs): # type: ignore + def filter(self, /, *args, **kwargs): # type: ignore ... @lazy_evaluation() - def filter_position(self, cascades: int = 2): # type: ignore + def filter_position(self, /, cascades: int = 2): # type: ignore ... @lazy_evaluation() def unwrap( # type: ignore - self, features: Union[None, str, List[str]] = None + self, /, features: Union[None, str, List[str]] = None ): ... @lazy_evaluation(idx_name="idx") def assign_id( # type: ignore - self, name: str = "{self.callsign}_{idx:>03}", idx: int = 0 + self, /, name: str = "{self.callsign}_{idx:>03}", idx: int = 0 ): """Assigns a `flight_id` to trajectories present in the structure. @@ -790,18 +806,20 @@ def assign_id( # type: ignore ... @lazy_evaluation() - def clip(self, shape: Union["Airspace", base.BaseGeometry]): # type: ignore + def clip(self, /, shape: Union["Airspace", base.BaseGeometry]): # type: ignore ... @lazy_evaluation() def intersects( # type: ignore self, + /, shape: Union["Airspace", base.BaseGeometry], ) -> bool: ... @lazy_evaluation() def simplify( # type: ignore self, + /, tolerance: float, altitude: Optional[str] = None, z_factor: float = 3.048, @@ -812,20 +830,21 @@ def query_opensky(self): # type: ignore ... @lazy_evaluation() - def query_ehs(self, data, failure_mode, propressbar): # type: ignore + def query_ehs(self, /, data, failure_mode, propressbar): # type: ignore ... @lazy_evaluation() - def first(self, **kwargs): # type: ignore + def first(self, /, **kwargs): # type: ignore ... @lazy_evaluation() - def last(self, **kwargs): # type: ignore + def last(self, /, **kwargs): # type: ignore ... @lazy_evaluation() def feature_gt( # type: ignore self, + /, feature: Union[str, Callable[["Flight"], Any]], value: Any, strict: bool = True, @@ -834,6 +853,7 @@ def feature_gt( # type: ignore @lazy_evaluation() def feature_lt( # type: ignore self, + /, feature: Union[str, Callable[["Flight"], Any]], value: Any, strict: bool = True, @@ -841,42 +861,43 @@ def feature_lt( # type: ignore @lazy_evaluation() def shorter_than( # type: ignore - self, value: Union[str, timedelta, pd.Timedelta], strict: bool = True + self, /, value: Union[str, timedelta, pd.Timedelta], strict: bool = True ): ... @lazy_evaluation() def longer_than( # type: ignore - self, value: Union[str, timedelta, pd.Timedelta], strict: bool = True + self, /, value: Union[str, timedelta, pd.Timedelta], strict: bool = True ): ... @lazy_evaluation() def max_split( # type: ignore self, + /, value: Union[int, str] = "10T", unit: Optional[str] = None, key: str = "duration", ): ... @lazy_evaluation() - def diff(self, features: Union[str, List[str]], **kwargs): # type: ignore + def diff(self, /, features: Union[str, List[str]], **kwargs): # type: ignore ... @lazy_evaluation() def apply_segments( # type: ignore - self, fun: Callable[..., "LazyTraffic"], name: str, *args, **kwargs + self, /, fun: Callable[..., "LazyTraffic"], name: str, *args, **kwargs ): ... @lazy_evaluation() - def apply_time(self, freq="1T", merge=True, **kwargs): # type: ignore + def apply_time(self, /, freq="1T", merge=True, **kwargs): # type: ignore ... @lazy_evaluation() - def agg_time(self, freq="1T", merge=True, **kwargs): # type: ignore + def agg_time(self, /, freq="1T", merge=True, **kwargs): # type: ignore ... @lazy_evaluation() def cumulative_distance( # type: ignore - self, compute_gs: bool = True, compute_track: bool = True, **kwargs + self, /, compute_gs: bool = True, compute_track: bool = True, **kwargs ): ... @lazy_evaluation() @@ -957,18 +978,18 @@ def airborne(self) -> Optional["Traffic"]: in the altitude column. """ if "onground" in self.data.columns and self.data.onground.dtype == bool: - return self.query("not onground and altitude == altitude") + return self.query("not onground and altitude.notnull()") else: - return self.query("altitude == altitude") + return self.query("altitude.notnull()") @lazy_evaluation(default=True) def onground(self) -> Optional["Traffic"]: if "altitude" not in self.data.columns: return self if "onground" in self.data.columns and self.data.onground.dtype == bool: - return self.query("onground or altitude != altitude") + return self.query("onground or altitude.isnull()") else: - return self.query("altitude != altitude") + return self.query("altitude.isnull()") # --- Properties --- @@ -987,17 +1008,11 @@ def callsigns(self) -> Set[str]: """Return all the different callsigns in the DataFrame""" if "callsign" not in self.data.columns: return set() - sub = self.data.query("callsign == callsign") + sub = self.data.query("callsign.notnull()") if sub.shape[0] == 0: return set() return set(sub.callsign) - @property_cache - def aircraft(self) -> Set[str]: - """Return all the different icao24 aircraft ids in the DataFrame""" - _log.warning("Use .icao24", DeprecationWarning) - return set(self.data.icao24) - @property_cache def icao24(self) -> Set[str]: """Return all the different icao24 aircraft ids in the DataFrame""" @@ -1271,10 +1286,10 @@ def plot_wind( ) return ax.barbs( # type: ignore - windfield.longitude.values, - windfield.latitude.values, - windfield.wind_u.values, - windfield.wind_v.values, + windfield.longitude.to_numpy(), + windfield.latitude.to_numpy(), + windfield.wind_u.to_numpy(), + windfield.wind_v.to_numpy(), **kwargs, ) diff --git a/src/traffic/data/adsb/opensky.py b/src/traffic/data/adsb/opensky.py index 87ceb03a..24a511ab 100644 --- a/src/traffic/data/adsb/opensky.py +++ b/src/traffic/data/adsb/opensky.py @@ -129,27 +129,6 @@ def format_history( """ - if "lastcontact" in df.columns: - df = df.drop(["lastcontact"], axis=1) - - if "lat" in df.columns and df.lat.dtype == object: - df = df[df.lat != "lat"] # header is regularly repeated - - # restore all types - for column_name in [ - "lat", - "lon", - "velocity", - "heading", - "vertrate", - "baroaltitude", - "geoaltitude", - # "lastposupdate", - # "lastcontact", - ]: - if column_name in df.columns: - df[column_name] = df[column_name].astype(float) - # better (to me) formalism about columns df = df.rename( columns={ diff --git a/src/traffic/data/datasets/__init__.py b/src/traffic/data/datasets/__init__.py index 8f2c5a9f..3b9d22de 100644 --- a/src/traffic/data/datasets/__init__.py +++ b/src/traffic/data/datasets/__init__.py @@ -7,6 +7,16 @@ from ._squawk7700 import Squawk7700Dataset from .default import Default, Entry +airspace_bordeaux_2017: Traffic +landing_amsterdam_2019: Traffic +landing_cdg_2019: Traffic +landing_dublin_2019: Traffic +landing_heathrow_2019: Traffic +landing_londoncity_2019: Traffic +landing_toulouse_2017: Traffic +landing_zurich_2019: Traffic +paris_toulouse_2017: Traffic + datasets: dict[str, Entry] = dict( paris_toulouse_2017=dict( url="https://ndownloader.figshare.com/files/20291055", @@ -55,7 +65,7 @@ ), ) -squawk7700 = Squawk7700Dataset().traffic +squawk7700: Traffic __all__ = [*list(datasets.keys()), "squawk7700"] @@ -67,6 +77,9 @@ def __getattr__(name: str) -> Any: if on_disk := config.get("datasets", name, fallback=None): return Traffic.from_file(on_disk) + if name == "squawk7700": + return Squawk7700Dataset().traffic + if name not in datasets: raise AttributeError(f"No such dataset: {name}") diff --git a/src/traffic/data/datasets/default.py b/src/traffic/data/datasets/default.py index 2b67eb83..63587083 100644 --- a/src/traffic/data/datasets/default.py +++ b/src/traffic/data/datasets/default.py @@ -4,7 +4,7 @@ import httpx -from ... import cache_dir +from ... import cache_dir, tqdm_style from ...core import tqdm client = httpx.Client(follow_redirects=True) @@ -35,20 +35,25 @@ def get_data(self, entry: Entry) -> Path: file_handle.write(content) md5_hash.update(content) else: - with tqdm( # type: ignore - total=int(content_length), - unit_scale=True, - unit_divisor=1024, - unit="B", - ) as progress: - n_bytes = response.num_bytes_downloaded + if tqdm_style == "silent": for chunk in response.iter_bytes(): file_handle.write(chunk) md5_hash.update(chunk) - progress.update( - response.num_bytes_downloaded - n_bytes - ) + else: + with tqdm( # type: ignore + total=int(content_length), + unit_scale=True, + unit_divisor=1024, + unit="B", + ) as progress: n_bytes = response.num_bytes_downloaded + for chunk in response.iter_bytes(): + file_handle.write(chunk) + md5_hash.update(chunk) + progress.update( + response.num_bytes_downloaded - n_bytes + ) + n_bytes = response.num_bytes_downloaded if (digest := md5_hash.hexdigest()) != entry["md5sum"]: filename.unlink() diff --git a/src/traffic/data/eurocontrol/aixm/navpoints.py b/src/traffic/data/eurocontrol/aixm/navpoints.py index b2a0565c..cc5aa5ac 100644 --- a/src/traffic/data/eurocontrol/aixm/navpoints.py +++ b/src/traffic/data/eurocontrol/aixm/navpoints.py @@ -3,17 +3,15 @@ import logging import zipfile from pathlib import Path -from typing import Any, Dict, List, Optional, Type, TypeVar, Union +from typing import Any, Dict, List, Optional, Union from lxml import etree +from typing_extensions import Self import pandas as pd from ...basic.navaid import Navaids -# https://github.com/python/mypy/issues/2511 -T = TypeVar("T", bound="AIXMNavaidParser") - _log = logging.getLogger(__name__) @@ -79,9 +77,7 @@ def data(self) -> pd.DataFrame: return self._data @classmethod - def from_file( - cls: Type[T], filename: Union[Path, str], **kwargs: Any - ) -> Optional[T]: + def from_file(cls, filename: Union[Path, str], **kwargs: Any) -> Self: instance = cls(None) instance.filename = Path(filename) return instance diff --git a/src/traffic/data/eurocontrol/aixm/routes.py b/src/traffic/data/eurocontrol/aixm/routes.py index daa46a3f..30bca0eb 100644 --- a/src/traffic/data/eurocontrol/aixm/routes.py +++ b/src/traffic/data/eurocontrol/aixm/routes.py @@ -4,9 +4,10 @@ import zipfile from functools import lru_cache from pathlib import Path -from typing import Any, Iterator, Type, TypeVar +from typing import Any, Iterator from lxml import etree +from typing_extensions import Self import pandas as pd @@ -14,9 +15,6 @@ from ... import aixm_navaids from ...basic.airways import Airways -# https://github.com/python/mypy/issues/2511 -T = TypeVar("T", bound="AIXMRoutesParser") - _log = logging.getLogger(__name__) @@ -27,9 +25,7 @@ class AIXMRoutesParser(Airways): cache_dir: Path @classmethod - def from_file( - cls: Type[T], filename: str | Path, **kwargs: Any - ) -> None | T: + def from_file(cls, filename: str | Path, **kwargs: Any) -> Self: instance = cls(None) instance.filename = Path(filename) return instance diff --git a/src/traffic/data/eurocontrol/ddr/allft.py b/src/traffic/data/eurocontrol/ddr/allft.py index 6cae12e1..109d0fab 100644 --- a/src/traffic/data/eurocontrol/ddr/allft.py +++ b/src/traffic/data/eurocontrol/ddr/allft.py @@ -9,22 +9,18 @@ Optional, Set, Tuple, - Type, - TypeVar, Union, cast, ) +from py7zr import SevenZipFile from tqdm.rich import tqdm +from typing_extensions import Self import pandas as pd from ....core import Flight from ....core.mixins import DataFrameMixin, _HBox -from .so6 import _prepare_libarchive - -AllFTTypeVar = TypeVar("AllFTTypeVar", bound="AllFT") - allft_fields = list( x @@ -165,9 +161,7 @@ class AllFT(DataFrameMixin): __slots__ = ("data",) @classmethod - def from_allft( - cls: Type[AllFTTypeVar], filename: Union[str, Path, BytesIO] - ) -> AllFTTypeVar: + def from_allft(cls, filename: Union[str, Path, BytesIO]) -> Self: allft = ( pd.read_csv( filename, @@ -207,19 +201,12 @@ def from_allft( return cls(allft.sort_values("EOBT")) @classmethod - def from_allft_7z( - cls: Type[AllFTTypeVar], filename: Union[str, Path] - ) -> AllFTTypeVar: - from libarchive.public import memory_reader - - _prepare_libarchive() - - with open(filename, "rb") as fh: - with memory_reader(fh.read()) as entries: - b = BytesIO() - for file in entries: - for block in file.get_blocks(): - b.write(block) + def from_allft_7z(cls, filename: Union[str, Path]) -> Self: + with SevenZipFile(filename, "rb") as fh: + b = BytesIO() + for file in fh.readall(): + for block in file.get_blocks(): + b.write(block) cumul = list() max_, current, previous = b.tell(), 0, 0 @@ -274,20 +261,14 @@ def from_allft_7z( @classmethod def from_file( - cls: Type[AllFTTypeVar], filename: Union[Path, str], **kwargs: Any - ) -> Optional[AllFTTypeVar]: # coverage: ignore + cls, filename: Union[Path, str], **kwargs: Any + ) -> Self: # coverage: ignore """ In addition to `usual formats `_, you can parse so6 files as text files (.ALL_FT+ extension) or as 7-zipped text files (.ALL_FT+.7z extension). - .. warning:: - - You will need the `libarchive - `_ library to be able - to parse .ALL_FT+.7z files on the fly. - """ path = Path(filename) if path.suffixes == [".ALL_FT+", ".7z"]: diff --git a/src/traffic/data/eurocontrol/ddr/navpoints.py b/src/traffic/data/eurocontrol/ddr/navpoints.py index 2c26b618..6ce8f2c1 100644 --- a/src/traffic/data/eurocontrol/ddr/navpoints.py +++ b/src/traffic/data/eurocontrol/ddr/navpoints.py @@ -1,13 +1,12 @@ from pathlib import Path -from typing import Any, Optional, Type, TypeVar, Union +from typing import Any, Optional, Union + +from typing_extensions import Self import pandas as pd from ...basic.navaid import Navaids -# https://github.com/python/mypy/issues/2511 -T = TypeVar("T", bound="NMNavaids") - class NMNavaids(Navaids): name: str = "nm_navaids" @@ -30,9 +29,7 @@ def data(self) -> pd.DataFrame: raise RuntimeError(msg) @classmethod - def from_file( - cls: Type[T], filename: Union[Path, str], **kwargs: Any - ) -> Optional[T]: + def from_file(cls, filename: Union[Path, str], **kwargs: Any) -> Self: if filename == "": return cls(None) diff --git a/src/traffic/data/eurocontrol/ddr/so6.py b/src/traffic/data/eurocontrol/ddr/so6.py deleted file mode 100644 index 6e2864d1..00000000 --- a/src/traffic/data/eurocontrol/ddr/so6.py +++ /dev/null @@ -1,899 +0,0 @@ -import json -import logging -import os -import subprocess -import sys -from calendar import timegm -from datetime import datetime, timedelta, timezone -from functools import lru_cache -from io import StringIO -from pathlib import Path -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Dict, - Iterable, - Iterator, - List, - Literal, - NoReturn, - Optional, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, - overload, -) - -import numpy as np -import numpy.typing as npt -import pandas as pd -import pyproj -from shapely.geometry import LineString, base - -from ....core import Airspace, Flight -from ....core.flight import Entry, Position -from ....core.mixins import DataFrameMixin -from ....core.time import time_or_delta, timelike, to_datetime - -if TYPE_CHECKING: - from cartopy.crs import Projection - -_log = logging.getLogger(__name__) - - -def _prepare_libarchive() -> None: # coverage: ignore - """ - There are some well documented issues in MacOS about libarchive. - Let's try to do things ourselves... - - https://github.com/dsoprea/PyEasyArchive - """ - - if sys.platform != "darwin": - return - - if "LA_LIBRARY_FILEPATH" in os.environ: - return - - command = ["brew", "info", "--json=v1", "libarchive"] - - try: - result = subprocess.check_output(command) - except Exception as e: - _log.error("Could not lookup 'libarchive' package info", e) - - info = json.loads(result) - installed_versions = info[0]["installed"] - if len(installed_versions) == 0: - _log.warning("libarchive is not currently installed via Brew") - return - - version = installed_versions[0]["version"] - - command = ["brew", "--cellar", "libarchive"] - package_path = subprocess.check_output(command)[:-1] - - library_path = os.path.join(package_path.decode(), version, "lib") - os.environ["LA_LIBRARY_FILEPATH"] = os.path.join( - library_path, "libarchive.dylib" - ) - return - - -# https://github.com/python/mypy/issues/2511 -SO6TypeVar = TypeVar("SO6TypeVar", bound="SO6") - - -def time(int_: int) -> datetime: - ts = timegm((2000 + int_ // 10000, int_ // 100 % 100, int_ % 100, 0, 0, 0)) - return datetime.fromtimestamp(ts, timezone.utc) - - -def hour(int_: int) -> timedelta: - return timedelta( - hours=int_ // 10000, minutes=int_ // 100 % 100, seconds=int_ % 100 - ) - - -class SO6Flight(Flight): - """ - - SO6 Flight inherit from `traffic.core.Flight `_ - and implement specificities of the SO6 format. - - .. code:: python - - so6['HOP36PP'].data.drop( - columns=['alt1', 'alt2', 'aircraft', 'callsign', 'flight_id'] - ) - - - .. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
origindestinationlat1lon1lat2lon2time1time2
65794LFMLLFBD43.6083984.52732544.5435551.1781502018-01-01 18:15:40+00:002018-01-01 18:44:50+00:00
65795LFMLLFBD44.5435551.17815044.7268980.4608372018-01-01 18:44:50+00:002018-01-01 18:52:10+00:00
65796LFMLLFBD44.7268980.46083744.751343-0.0914222018-01-01 18:52:10+00:002018-01-01 18:58:00+00:00
-
- - - """ - - def __init__(self, data: pd.DataFrame) -> None: - super().__init__(data) - self.interpolator: Dict[ - "Projection", - Callable[[npt.NDArray[np.datetime64]], npt.NDArray[np.float64]], - ] = dict() - - def __add__( # type: ignore - self, other: Union[Literal[0], "SO6Flight", "SO6"] - ) -> "SO6": - # TODO fix mypy error: Return type "SO6" of "__add__" incompatible with - # return type "Traffic" in supertype "Flight" - if other == 0: - # useful for compatibility with sum() function - return SO6(self.data) - - return SO6(pd.concat([self.data, other.data], sort=False)) - - @property - def timestamp(self) -> Iterator[pd.Timestamp]: - if self.data.shape[0] == 0: - return - for _, s in self.data.iterrows(): - yield s.time1 - yield s.time2 - - @property - def typecode(self) -> str: - return cast(str, self.data.iloc[0].aircraft) - - @property - def start(self) -> pd.Timestamp: - return min(self.timestamp) - - @property - def stop(self) -> pd.Timestamp: - return max(self.timestamp) - - @property - def registration(self) -> None: - return None - - @property - def flight(self) -> Flight: - return Flight( - pd.DataFrame.from_records(self.coords4d()).assign( - callsign=self.callsign, - flight_id=str(self.flight_id), - typecode=self.typecode, - ) - ) - - def coords4d(self, delta_t: bool = False) -> Iterator[Entry]: - t = 0 - if self.data.shape[0] == 0: - return - for _, s in self.data.assign( - segment=lambda df: df.segment.str.replace("NO_POINT", "NOPOINT") - ).iterrows(): - if delta_t: - yield { - "timedelta": t, - "longitude": s.lon1, - "latitude": s.lat1, - "altitude": s.alt1, - "name": s.segment.split("_")[0], - } - t += (s.time2 - s.time1).total_seconds() - else: - yield { - "timestamp": s.time1, - "longitude": s.lon1, - "latitude": s.lat1, - "altitude": s.alt1, - "name": s.segment.split("_")[0], - } - if delta_t: - yield { - "timedelta": t, - "longitude": s.lon2, - "latitude": s.lat2, - "altitude": s.alt2, - "name": s.segment.split("_")[1], - } - else: - yield { - "timestamp": s.time2, - "longitude": s.lon2, - "latitude": s.lat2, - "altitude": s.alt2, - "name": s.segment.split("_")[1], - } - - @property - def coords(self) -> Iterator[Tuple[float, float, float]]: - if self.data.shape[0] == 0: - return - for _, s in self.data.iterrows(): - yield s.lon1, s.lat1, s.alt1 - yield s.lon2, s.lat2, s.alt2 - - @property - def xy_time(self) -> Iterator[Tuple[float, float, float]]: - if self.data.shape[0] == 0: - return - for _, s in self.data.iterrows(): - yield s.lon1, s.lat1, s.time1.to_pydatetime().timestamp() - yield s.lon2, s.lat2, s.time2.to_pydatetime().timestamp() - - @property - def linestring(self) -> LineString: - return LineString(list(self.coords)) - - @property - def shape(self) -> LineString: - return self.linestring - - def airborne(self) -> "SO6Flight": - """Identity method, available for consistency""" - return self - - def interpolate( - self, - times: npt.NDArray[np.datetime64], - proj: Optional["Projection"] = None, - ) -> npt.NDArray[np.float64]: - from cartopy.crs import PlateCarree - from scipy.interpolate import interp1d - - if proj is None: - proj = PlateCarree() - if proj not in self.interpolator: - self.interpolator[proj] = interp1d( - np.stack( - [t.to_pydatetime().timestamp() for t in self.timestamp] - ), - proj.transform_points( - PlateCarree(), *np.stack(list(self.coords)).T - ).T, - ) - return cast( - npt.NDArray[np.float64], - PlateCarree().transform_points( - proj, *self.interpolator[proj](times) - ), - ) - - def at(self, time: Optional[timelike] = None) -> Position: - """ - Since DDR files contain few points on their trajectory, interpolation - functions are provided: - - .. code:: python - - >>> so6['HOP36PP'].at("2018/01/01 18:40") - - longitude 1.733156 - latitude 44.388586 - altitude 26638.857143 - dtype: float64 - - """ - if time is None: - raise NotImplementedError() - - time = to_datetime(time) - timearray = np.array([time.timestamp()]) - res = self.interpolate(timearray) - - return Position( - pd.Series(res[0], index=["longitude", "latitude", "altitude"]) - ) - - def between( - self, - start: timelike, - stop: time_or_delta, - strict: bool = True, - ) -> "SO6Flight": - """ - .. danger:: - - strict: bool = True is not taken into account yet. - """ - start = to_datetime(start) - if isinstance(stop, timedelta): - stop = start + stop - else: - stop = to_datetime(stop) - - t = np.stack(list(self.timestamp)) - index = np.where((start < t) & (t < stop)) - - new_data = np.stack(list(self.coords))[index] - time1: List[datetime] = [start, *t[index]] - time2: List[datetime] = [*t[index], stop] - - if start > t[0]: - new_data = np.vstack([self.at(start), new_data]) - else: - time1, time2 = time1[1:], time2[1:] - if stop < t[-1]: - new_data = np.vstack([new_data, self.at(stop)]) - else: - time1, time2 = time1[:-1], time2[:-1] - - df: pd.DataFrame = pd.DataFrame.from_records( - np.c_[new_data[:-1, :], new_data[1:, :]], - columns=["lon1", "lat1", "alt1", "lon2", "lat2", "alt2"], - ).assign( - time1=time1, - time2=time2, - origin=self.origin, - destination=self.destination, - aircraft=self.aircraft, - flight_id=self.flight_id, - callsign=self.callsign, - ) - - return SO6Flight(df) - - def clip( - self, shape: base.BaseGeometry, strict: bool = True - ) -> Optional["SO6Flight"]: - linestring = LineString(list(self.xy_time)) - intersection = linestring.intersection(shape) - begin: Optional[datetime] = None - - if intersection.is_empty: - return None - - if isinstance(intersection, LineString): - begin, *_, end = list( - datetime.fromtimestamp(t, timezone.utc) - for t in np.stack(intersection.coords)[:, 2] - ) - - else: - for x in LineString(list(self.xy_time)).intersection(shape).geoms: - begin_, *_, end = list( - datetime.fromtimestamp(t, timezone.utc) - for t in np.stack(x.coords)[:, 2] - ) - if begin is None: - begin = begin_ - - return self.between(begin, end, strict=strict) - - def clip_altitude(self, min_: int, max_: int) -> Iterator["SO6Flight"]: - """ - Splits a Flight in several segments comprised between altitudes - `min_` and `max_`, with proper interpolations where needed. - """ - - def buffer_to_iter( - proj: pyproj.Proj, buffer: List[pd.Series] - ) -> Iterator["SO6Flight"]: - df = pd.DataFrame.from_records(buffer) - transformer = pyproj.Transformer.from_proj( - proj, pyproj.Proj("epsg:4326"), always_xy=True - ) - df["lon1"], df["lat1"] = transformer.transform( - df.x1.values, df.y1.values - ) - - df["lon2"], df["lat2"] = transformer.transform( - df.x2.values, df.y2.values - ) - - yield self.__class__(df.drop(["x1", "x2", "y1", "y2"], axis=1)) - - data = self.data.copy() - - proj = pyproj.Proj( - proj="lcc", - ellps="WGS84", - lat_0=data.lat1.mean(), - lon_0=data.lon1.mean(), - lat_1=data.lat1.min(), - lat_2=data.lat1.max(), - ) - transformer = pyproj.Transformer.from_proj( - pyproj.Proj("epsg:4326"), proj, always_xy=True - ) - - data["x1"], data["y1"] = transformer.transform( - data.lon1.values, - data.lat1.values, - ) - - data["x2"], data["y2"] = transformer.transform( - data.lon2.values, - data.lat2.values, - ) - - buffer = [] - for _, line in data.iterrows(): - if (line.alt1 < max_ or line.alt2 < max_) and ( - line.alt1 > min_ or line.alt2 > min_ - ): - if line.alt1 != line.alt2: - f_x = (line.x1 - line.x2) / (line.alt1 - line.alt2) - f_y = (line.y1 - line.y2) / (line.alt1 - line.alt2) - - if line.alt1 > max_: - line.x1 = line.x2 + (max_ - line.alt2) * f_x - line.y1 = line.y2 + (max_ - line.alt2) * f_y - line.alt1 = max_ - if line.alt1 < min_: - line.x1 = line.x2 + (min_ - line.alt2) * f_x - line.y1 = line.y2 + (min_ - line.alt2) * f_y - line.alt1 = min_ - if line.alt2 > max_: - line.x2 = line.x1 + (max_ - line.alt1) * f_x - line.y2 = line.y1 + (max_ - line.alt1) * f_y - line.alt2 = max_ - if line.alt2 < min_: - line.x2 = line.x1 + (min_ - line.alt1) * f_x - line.y2 = line.y1 + (min_ - line.alt1) * f_y - line.alt2 = min_ - - buffer.append(line) - - else: - if len(buffer) > 0: - yield from buffer_to_iter(proj, buffer) - buffer = [] - - if len(buffer) > 0: - yield from buffer_to_iter(proj, buffer) - - def resample(self) -> NoReturn: # type: ignore - """ - The resampling method is not available. - """ - raise NotImplementedError - - -class SO6(DataFrameMixin): - """ - Eurocontrol DDR files provide flight intentions of aircraft for a full - day of traffic across Europe. This data cannot be shared so the file - included in the repository has actually been generated from OpenSky - ADS-B data to match so6 format. - - `SO6 <#traffic.data.SO6>`_ represent a collection of trajectories, the - bracket notation yields a `Flight <#traffic.data.so6.so6.Flight>`_ - structure adapted to the specificities of the SO6 format. - - .. code:: python - - from traffic.data import SO6 - so6 = SO6.from_file("./data/sample_m3.so6.7z") - so6.to_pickle("./data/sample_m3.pkl") - - If you are going to work a lot with data for one day, it is recommended - to serialize the data so that it loads faster. The structure holds a - DataFrame in the data attribute. - - You can then access data from the so6 file, by flight, with the bracket - notation. Interactive environments (IPython, Jupyter notebooks) provide - completion on the flight names. - - Callsigns may not be enough to discriminate flights because of same - callsigns assigned to a trip with many legs. In general, you can access a - Flight from its unique ID or from its callsign - - .. code:: python - - so6[332206265] - # here equivalent to: so6['HOP36PP'] - - .. raw:: html - - Flight HOP36PP (332206265)
    -
  • aircraft: A319
    • -
  • origin: LFML (2018-01-01 18:15:40+00:00)
    • -
  • destination: LFBD (2018-01-01 18:58:00+00:00)
    • -
-
- - - -
- - """ - - __slots__ = ("data",) - - identifier = Union[int, str] - - @overload - def __getitem__(self, index: identifier) -> SO6Flight: ... - - @overload - def __getitem__( - self, index: Union["SO6", Set["str"], Set[int]] - ) -> Optional["SO6"]: ... - - def __getitem__( - self, index: Union[identifier, "SO6", Set["str"], Set[int]] - ) -> Union[SO6Flight, "SO6", None]: - if isinstance(index, int): - return SO6Flight(self.data.groupby("flight_id").get_group(index)) - if isinstance(index, str): - return SO6Flight(self.data.groupby("callsign").get_group(index)) - - if isinstance(index, SO6): - # not very natural, but why not... - index = index.flight_ids - list_query = list(index) - - if len(list_query) == 0: - return None - - if isinstance(list_query[0], str): - select = self.data.callsign.isin(list_query) - else: - select = self.data.flight_id.isin(list_query) - - return SO6(self.data[select]) - - def __iter__(self) -> Iterator[SO6Flight]: - for _, flight in self.data.groupby("flight_id"): - yield SO6Flight(flight) - - def __len__(self) -> int: - return len(self.flight_ids) - - def _ipython_key_completions_(self) -> Set[Union[int, str]]: - return {*self.flight_ids, *self.callsigns} - - def __add__(self, other: Union[Literal[0], SO6Flight, "SO6"]) -> "SO6": - # useful for compatibility with sum() function - if other == 0: - return self - return self.__class__(pd.concat([self.data, other.data], sort=False)) - - def __radd__(self, other: Union[SO6Flight, "SO6"]) -> "SO6": - return self + other - - def get(self, callsign: str) -> Iterable[Tuple[int, SO6Flight]]: - all_flights = self.data.groupby("callsign").get_group(callsign) - for flight_id, flight in all_flights.groupby("flight_id"): - yield flight_id, SO6Flight(flight) - - @property - def start_time(self) -> pd.Timestamp: - return min(self.data.time1) - - @property - def end_time(self) -> pd.Timestamp: - return max(self.data.time2) - - @property - def callsigns(self) -> Set[str]: - return set(self.data.callsign) - - @property - def flight_ids(self) -> Set[int]: - return set(self.data.flight_id) - - @lru_cache() - def stats(self) -> pd.DataFrame: # coverage: ignore - cumul = [] - for f in self: - info = { - "flight_id": f.flight_id, - "callsign": f.callsign, - "origin": f.origin, - "destination": f.destination, - "duration": f.stop - f.start, - "start": f.start, - } - cumul.append(info) - - return ( - pd.DataFrame.from_records(cumul) - .set_index("flight_id") - .sort_values("start", ascending=True) - ) - - def __repr__(self) -> str: - stats = self.stats() - return cast(str, stats.__repr__()) - - def _repr_html_(self) -> str: - stats = self.stats() - return cast(str, stats._repr_html_()) - - @classmethod - def from_so6( - cls: Type[SO6TypeVar], filename: Union[str, Path, StringIO] - ) -> SO6TypeVar: - so6 = pd.read_csv( - filename, - sep=" ", - header=None, - names=[ - "segment", - "origin", - "destination", - "aircraft", - "hour1", - "hour2", - "alt1", - "alt2", - "d2", - "callsign", - "date1", - "date2", - "lat1", - "lon1", - "lat2", - "lon2", - "flight_id", - "d3", - "d4", - "d5", - ], - ) - - so6 = so6.assign( - lat1=so6.lat1 / 60, - lat2=so6.lat2 / 60, - lon1=so6.lon1 / 60, - lon2=so6.lon2 / 60, - alt1=so6.alt1 * 100, - alt2=so6.alt2 * 100, - time1=so6.date1.apply(time) + so6.hour1.apply(hour), - time2=so6.date2.apply(time) + so6.hour2.apply(hour), - ) - - for col in ( - "d2", - "d3", - "d4", - "d5", - "date1", - "date2", - "hour1", - "hour2", - ): - del so6[col] - - return cls(so6) - - @classmethod - def from_so6_7z( - cls: Type[SO6TypeVar], filename: Union[str, Path] - ) -> SO6TypeVar: - _prepare_libarchive() - from libarchive.public import memory_reader - - with open(filename, "rb") as fh: - with memory_reader(fh.read()) as entries: - s = StringIO() - for file in entries: - for block in file.get_blocks(): - s.write(block.decode()) - s.seek(0) - so6 = cls.from_so6(s) - s.close() - return so6 - - @classmethod - def from_so6_gz( - cls: Type[SO6TypeVar], filename: Union[str, Path] - ) -> SO6TypeVar: - import gzip - from io import StringIO - - with gzip.open(filename) as fh: - s = StringIO() - s.write(fh.read().decode("utf-8")) - s.seek(0) - so6 = cls.from_so6(s) - return so6 - - @classmethod - def from_file( - cls: Type[SO6TypeVar], filename: Union[Path, str], **kwargs: Any - ) -> Optional[SO6TypeVar]: # coverage: ignore - """ - In addition to `usual formats - `_, you can parse so6 - files as text files (.so6 extension) or as 7-zipped text files (.so6.7z - extension). - - .. warning:: - - You will need the `libarchive - `_ library to be able - to parse .so6.7z files on the fly. - - """ - path = Path(filename) - if path.suffixes == [".so6", ".7z"]: - return cls.from_so6_7z(filename) - if path.suffixes == [".so6", ".gz"]: - return cls.from_so6_gz(filename) - if path.suffixes == [".so6"]: - return cls.from_so6(filename) - return super().from_file(filename) - - def at(self, time: timelike) -> "SO6": - """Selects all segments of the SO6 dataframe with ``time`` included - in the segment interval. - """ - time = to_datetime(time) - return SO6( - self.data[(self.data.time1 <= time) & (self.data.time2 > time)] - ) - - def between(self, start: timelike, stop: time_or_delta) -> "SO6": - """Selects all segments of the SO6 dataframe with intervals intersecting - [``start``, ``stop``]. - - The ``stop`` argument may be also be written as a - ``datetime.timedelta``. - """ - start = to_datetime(start) - if isinstance(stop, timedelta): - stop = start + stop - else: - stop = to_datetime(stop) - return SO6( - self.data[(self.data.time1 <= stop) & (self.data.time2 >= start)] - ) - - def intersects(self, sector: Airspace) -> "SO6": - """ - Selects all Flights intersecting the given airspace. - - .. tip:: - - See the impact of calling `.inside_bbox(sector) - <#traffic.data.SO6.inside_bbox>`_ to the bounding box - before intersecting the airspace. Note that this - chaining may not be safe for smaller airspaces. - - .. code:: python - - noon = so6.at("2018/01/01 12:00") - - .. code:: python - - %%time - bdx_flights = noon.intersects(nm_airspaces['LFBBBDX']) - - CPU times: user 3.9 s, sys: 0 ns, total: 3.9 s - Wall time: 3.9 s - - - .. code:: python - - %%time - bdx_flights = ( - noon - .inside_bbox(nm_airspaces["LFBBBDX"]) - .intersects(nm_airspaces["LFBBBDX"]) - ) - - CPU times: user 1.42 s, sys: 8.27 ms, total: 1.43 s - Wall time: 1.43 s - """ - return SO6( - self.data.groupby("flight_id").filter( - lambda flight: SO6Flight(flight).intersects(sector) - ) - ) - - def inside_bbox(self, bounds: Union[Airspace, Tuple[float, ...]]) -> "SO6": - """ - Selects all Flights intersecting the bounding box of the given airspace. - - A tuple (west, south, east, north) is also accepted as a parameter. - - .. code:: python - - >>> bdx_so6 = so6.inside_bbox(nm_airspaces["LFBBBDX"]) - >>> f"before: {len(so6)} flights, after: {len(bdx_so6)} flights" - before: 11043 flights, after: 1548 flights - - """ - - if isinstance(bounds, Airspace): - bounds = bounds.flatten().bounds - - if isinstance(bounds, base.BaseGeometry): - bounds = bounds.bounds - - west, south, east, north = bounds - - # the numexpr query is 10% faster than the regular - # data[data.lat1 >= ...] conjunctions of comparisons - query = "{0} <= lon1 <= {2} and {1} <= lat1 <= {3}" - query = query.format(west, south, east, north) - - data = self.data.query(query) - - callsigns: Set[str] = set(data.callsign) - - return SO6( - self.data.groupby("flight_id").filter( - lambda data: data.iloc[0].callsign in callsigns - ) - ) diff --git a/src/traffic/data/samples/__init__.py b/src/traffic/data/samples/__init__.py index 00869e67..d2f8a591 100644 --- a/src/traffic/data/samples/__init__.py +++ b/src/traffic/data/samples/__init__.py @@ -8,12 +8,10 @@ import pandas as pd from ...core import Airspace, Flight, Traffic -from ..eurocontrol.ddr.so6 import SO6 _current_dir = Path(__file__).parent __all__ = [ *sorted(f.stem[:-5] for f in _current_dir.glob("**/*.json.gz")), - "sample_so6", "sample_dump1090", ] @@ -41,9 +39,7 @@ def get_flight(filename: str, directory: Path) -> Flight | Traffic: df.last_position * 1e6 ).dt.tz_localize("utc") ) - return flight.assign( - timestamp=lambda df: df.timestamp.dt.tz_localize("utc") - ) + return flight def get_sample( @@ -68,7 +64,6 @@ def assign_id(t: Union[Traffic, Flight], name: str) -> Union[Traffic, Flight]: noisy: Flight quickstart: Traffic sample_dump1090: Path -sample_m3: SO6 switzerland: Traffic texas_longhorn: Flight zurich_airport: Traffic @@ -78,8 +73,6 @@ def assign_id(t: Union[Traffic, Flight], name: str) -> Union[Traffic, Flight]: def __getattr__(name: str) -> Any: if name == "sample_dump1090": return Path(_current_dir / "dump1090" / "sample_dump1090.bin") - if name == "sample_m3": - return SO6.from_file(_current_dir / "so6" / "sample_m3.so6.7z") if name == "lfbo_tma": return Airspace.from_file(_current_dir / "airspaces" / "LFBOTMA.json") filelist = list(_current_dir.glob(f"**/{name}.json.gz")) diff --git a/src/traffic/plugins/cesiumjs.py b/src/traffic/plugins/cesiumjs.py index 7299e914..526a2aa3 100644 --- a/src/traffic/plugins/cesiumjs.py +++ b/src/traffic/plugins/cesiumjs.py @@ -10,7 +10,6 @@ from ..core import Flight, Traffic from ..core.time import timelike, to_datetime -from ..data import SO6 _log = logging.getLogger(__name__) @@ -92,7 +91,7 @@ def export_flight(flight: Flight) -> Iterator[Dict[str, Any]]: def to_czml( - traffic: Union[None, Traffic, SO6], + traffic: Union[None, Traffic], filename: Union[str, Path], minimum_time: Optional[timelike] = None, ) -> None: @@ -142,4 +141,3 @@ def to_czml( def _onload() -> None: setattr(Traffic, "to_czml", to_czml) - setattr(SO6, "to_czml", to_czml) diff --git a/tests/test_flight.py b/tests/test_flight.py index 89293203..91d51ea5 100644 --- a/tests/test_flight.py +++ b/tests/test_flight.py @@ -351,11 +351,11 @@ def test_landing_ils() -> None: def test_compute_navpoints() -> None: from traffic.data.samples import switzerland - df = cast(Flight, switzerland["BAW585E"]).compute_navpoints() + df = switzerland["BAW585E"].compute_navpoints() assert df is not None assert df.navaid.to_list() == ["RESIA", "LUL", "IXILU"] - df = cast(Flight, switzerland["EZY24DP"]).compute_navpoints() + df = switzerland["EZY24DP"].compute_navpoints() assert df is not None assert df.navaid.to_list() == ["RESIA", "ZH367", "LUL"] @@ -385,12 +385,14 @@ def test_takeoff_goaround() -> None: go_arounds = landing_zurich_2019.has("go_around").eval( desc="go_around", max_workers=8 ) + assert go_arounds is not None for flight in go_arounds: for segment in flight.go_around(): aligned = segment.aligned_on_ils("LSZH").next() + assert aligned is not None takeoff = ( - segment.after(aligned.stop) + cast(Flight, segment.after(aligned.stop)) .takeoff_from_runway("LSZH", threshold_alt=5000) .next() ) diff --git a/tests/test_so6.py b/tests/test_so6.py deleted file mode 100644 index 3d8bcb27..00000000 --- a/tests/test_so6.py +++ /dev/null @@ -1,69 +0,0 @@ -from datetime import timedelta - -import pytest - -from traffic.data import eurofirs - -notfound = False -try: - from traffic.data.samples import sample_m3 -except ModuleNotFoundError: - notfound = True - - -@pytest.mark.skipif(notfound, reason="libarchive not available") -def test_so6() -> None: - assert len(sample_m3) == 11043 - - hop36pp = sample_m3["HOP36PP"] - - assert hop36pp.origin == "LFML" - assert hop36pp.destination == "LFBD" - assert hop36pp.typecode == "A319" - assert hop36pp.flight_id == 332206265 # type: ignore - assert sample_m3[332206265].callsign == "HOP36PP" - - LFBB = eurofirs["LFBB"] - LFEE = eurofirs["LFEE"] - - assert LFBB is not None - assert LFEE is not None - - assert hop36pp.intersects(LFBB) - assert not hop36pp.intersects(LFEE) - - clipped = sample_m3["HOP36PP"].clip(LFBB.flatten()) - assert clipped is not None - assert str(clipped.start)[:19] == "2018-01-01 18:30:20" - assert str(clipped.stop)[:19] == "2018-01-01 18:52:10" - - assert len(next(hop36pp.clip_altitude(15000, 20000))) == 2 - assert len(next(hop36pp.clip_altitude(18000, 20000))) == 1 - - # This flight is on holding pattern and causes issues with intersection - clipped = sample_m3["BAW3TV"].clip(LFBB.flatten()) - assert clipped is not None - assert str(clipped.start)[:19] == "2018-01-01 14:51:19" - assert str(clipped.stop)[:19] == "2018-01-01 16:30:30" - - assert ( - sum(1 for _ in sample_m3["HOP36PP"].coords4d()) - == len(sample_m3["HOP36PP"]) + 1 - ) - - assert 26638 < hop36pp.at("2018/01/01 18:40").altitude < 26639 - assert hop36pp.between( - "2018/01/01 18:25", timedelta(minutes=30) - ).intersects(LFBB) - assert not hop36pp.between( - "2018/01/01 18:25", timedelta(minutes=5) - ).intersects(LFBB) - - noon = sample_m3.at("2018/01/01 12:00") - bdx_flights = noon.inside_bbox(LFBB).intersects(LFBB) - assert len(bdx_flights) == 3 - - assert bdx_flights.data.shape[0] == 3 - select = sample_m3[bdx_flights] - assert select is not None - assert select.data.shape[0] == 28 diff --git a/tests/test_traffic.py b/tests/test_traffic.py index fbfbc39a..a030f1a7 100644 --- a/tests/test_traffic.py +++ b/tests/test_traffic.py @@ -66,8 +66,6 @@ def test_index() -> None: assert s_0 is not None assert s_0.callsign == "SAA260" - assert switzerland[2000] is None - subset = switzerland[:2] assert subset is not None assert subset.callsigns == {"SAA260", "SAA261"}