BUGFIX for autocorrelation function (#140)

* track.ncReadTrackData returns true datetime objects - track.ncReadTrackData previously returned cftime.DatetimeGregorian objects, which caused newer versions of matplotlib.dates.num2date to fail. This is because we write the tracks with units of 'hours since 1900-01-01 00:00', but matplotlib.dates uses 1970-01-01 as the epoch, and works in units of days (with no way to specify units in the num2date function).
* Read netcdf-format track file from GA SST - GA's Scenario selection tool allows users to download a selected track file, but due to technical constraints this is in a different format. This change allows TCRM to read the modified format.
* Move definition statements to separate file
* Move queries to separate file
* Add script to run through all permutations of wind parameters
* NHIRS 148 fix thread (#119)
* assertDictEqual doesn't like complex values (like arrays)
* NHIRS-148: Fixed issue with FlushCache failing. (#121)
* update wind field model to get direction correct, and update documentation
* Enhancement/linear rmax (#125)
* fix double calculation of t3 - it was previously L3 / (L2 * L2) instead of L3 / L2
* only call Kepert wind field fortran code when a Holland pressure profile is used
* Working version of cartopy-based scalebar
* Pycxml compatability (#133)
* gdal multi-threading for reprojection
* Use expected DataProcess-InputFile
* Create daily LTM MSLP file from ERA5
* Low resolution wind multipliers (#136) - Adds in a script to quickly downscale the wind multipliers and apply them to many wind fields quickly. If the wind multipliers have already been extracted this process takes minutes instead of weeks.
* Bugfix for ACF function (#139)

---------

Co-authored-by: mahmudulhasanGA <66761046+mahmudulhasanGA@users.noreply.github.com>
Co-authored-by: Kieran Ricardo <kieranricardo@hotmail.com>

Author: 3 people

.github/workflows/tcrm-pylint.yml

@@ -11,13 +11,17 @@ jobs:
 
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python env
+    - name: Set up environment
       uses: conda-incubator/setup-miniconda@v2.0.0
       with: 
+        python-version: 3.7
+        mamba-version: "*"
+        channels: conda-forge,defaults
+        channel-priority: true
         activate-environment: tcrm
         environment-file: tcrmenv.yml
-        python-version: 3.7
         auto-activate-base: false
+        use-only-tar-bz2: true
 
     - name: Install dependencies
       run: |

.github/workflows/tcrm-tests.yml

@@ -13,20 +13,26 @@ jobs:
   TCRM:
     name: Test TCRM
     runs-on: ubuntu-latest
-    strategy: 
+    strategy:
       matrix:
         python-version: [3.7, 3.8, 3.9]
     steps:
     - uses: actions/checkout@v2
     - name: Set up environment
       uses: conda-incubator/setup-miniconda@v2.0.0
       with:
+        python-version: ${{ matrix.python-version }}
+        mamba-version: "*"
+        channels: conda-forge,defaults
+        channel-priority: true
         activate-environment: tcrm
         environment-file: tcrmenv.yml
-        python-version: ${{ matrix.python-version }}
         auto-activate-base: false
+        use-only-tar-bz2: true
 
-    - name: Test with nose
+    - name: Test with pytest
+      env:
+        PYTHONPATH: ~/tcrm;~/tcrm/Utilities
       shell: bash -l {0}
       run: |
-        python tests/run.py
+        pytest -x --cov=. --cov-report xml

DataProcess/DataProcess.py

@@ -171,13 +171,17 @@ def processData(self, restrictToWindfieldDomain=False):
         if config.has_option('DataProcess', 'InputFile'):
             inputFile = config.get('DataProcess', 'InputFile')
             self.logger.info(f"Input file from DataProcess: {inputFile}")
+        else:
+            inputFile = None
 
         if config.has_option('DataProcess', 'Source'):
             source = config.get('DataProcess', 'Source')
             self.logger.info(f"Loading {source} dataset")
-            fn = config.get(source, 'Filename')
-            path = config.get(source, 'Path')
-            inputFile = pjoin(path, fn)
+            if inputFile is None:
+                # Use this as alternate source of input file (downloaded?)
+                fn = config.get(source, 'Filename')
+                path = config.get(source, 'Path')
+                inputFile = pjoin(path, fn)
             self.logger.info(f"Input file set to {inputFile}")
 
         # If input file has no path information, default to tcrm input folder

Evaluate/interpolateTracks.py

@@ -9,6 +9,9 @@
 from Utilities.maputils import latLon2Azi
 from Utilities.loadData import loadTrackFile, maxWindSpeed
 from Utilities.track import Track, ncSaveTracks
+from Utilities.parallel import attemptParallel
+import pandas as pd
+
 
 LOG = logging.getLogger(__name__)
 LOG.addHandler(logging.NullHandler())
@@ -139,7 +142,9 @@ def interpolate(track, delta, interpolation_type=None):
         if interpolation_type == 'akima':
             # Use the Akima interpolation method:
             try:
-                import akima
+                from Utilities import akima
+                nLon = akima.interpolate(timestep, track.Longitude, newtime)
+                nLat = akima.interpolate(timestep, track.Latitude, newtime)
             except ImportError:
                 LOG.exception(("Akima interpolation module unavailable "
                                " - default to scipy.interpolate"))
@@ -148,10 +153,6 @@ def interpolate(track, delta, interpolation_type=None):
                 nLat = splev(newtime, splrep(timestep, track.Latitude, s=0),
                              der=0)
 
-            else:
-                nLon = akima.interpolate(timestep, track.Longitude, newtime)
-                nLat = akima.interpolate(timestep, track.Latitude, newtime)
-
         elif interpolation_type == 'linear':
             nLon = interp1d(timestep, track.Longitude, kind='linear')(newtime)
             nLat = interp1d(timestep, track.Latitude, kind='linear')(newtime)
@@ -299,21 +300,48 @@ def parseTracks(configFile, trackFile, source, delta, outputFile=None,
     if trackFile.endswith("nc"):
         from Utilities.track import ncReadTrackData
         tracks = ncReadTrackData(trackFile)
+    elif trackFile.endswith("xml"):
+        from pycxml.pycxml import loadfile
+        dfs = loadfile(trackFile)
+        tracks = [bom2tcrm(df, i) for i, df in enumerate(dfs)]
     else:
         tracks = loadTrackFile(configFile, trackFile, source)
 
     results = []
 
-    for track in tracks:
-        if len(track.data) == 1:
-            results.append(track)
-        else:
-            newtrack = interpolate(track, delta, interpolation_type)
-            results.append(newtrack)
+    # interpolating is memory intensive - only use a single process
+    MPI = attemptParallel()
+    if MPI.COMM_WORLD.rank == 0:
 
-    if outputFile:
-        # Save data to file:
-        ncSaveTracks(outputFile, results)
+        for track in tracks:
+            if len(track.data) == 1:
+                results.append(track)
+            else:
+                newtrack = interpolate(track, delta, interpolation_type)
+                results.append(newtrack)
 
+        if outputFile:
+            # Save data to file:
+            ncSaveTracks(outputFile, results)
+    MPI.COMM_WORLD.barrier()
 
     return results
+
+
+def bom2tcrm(df, trackId):
+    """
+    Transforms a dataframe in BoM format into a tcrm track.
+
+    """
+    df['Datetime'] = pd.to_datetime(df.validtime)
+    df['Speed'] = df.translation_speed
+    df['CentralPressure'] = df.pcentre
+    df['Longitude'] = df.longitude
+    df['Latitude'] = df.latitude
+    df['EnvPressure'] = df.poci
+    df['rMax'] = df.rmax
+    df['trackId'] = df.disturbance.values
+
+    track = Track(df)
+    track.trackId = [trackId, trackId]
+    return track

PlotInterface/AutoPlotHazard.py

@@ -36,6 +36,8 @@
 from Utilities import pathLocator
 from Utilities import metutils
 
+from database.queries import locationRecords
+
 from PlotInterface.maps import saveHazardMap
 from PlotInterface.curves import saveHazardCurve
 

PlotInterface/curves.py

@@ -149,7 +149,7 @@ def subplot(self, axes, subfigure):
 
         xdata, ydata, xlabel, ylabel, title = subfigure
 
-        axes.semilogx(xdata, ydata, '-', subsx=xdata)
+        axes.semilogx(xdata, ydata, '-', subs=xdata)
         axes.set_xlabel(xlabel)
         axes.set_ylabel(ylabel)
         axes.set_title(title)
@@ -328,7 +328,7 @@ def subplot(self, axes, subfigure):
         """
         xdata, ymean, ymax, ymin, xlabel, ylabel, title = subfigure
 
-        axes.semilogx(xdata, ymean, lw=2, subsx=xdata)
+        axes.semilogx(xdata, ymean, lw=2, subs=xdata)
         if (ymin[0] > 0) and (ymax[0] > 0):
             self.addRange(axes, xdata, ymin, ymax)
 
@@ -390,7 +390,7 @@ def subplot(self, axes, subfigure):
         log.debug("xvalues = {0} length".format(len(emprp)))
         log.debug("xvalues = {0}".format(emprp))
 
-        axes.semilogx(xdata, ymean, lw=2, subsx=xdata, 
+        axes.semilogx(xdata, ymean, lw=2, subs=xdata, 
                       label = 'Fitted hazard curve ({0})'.format(fit))
         axes.scatter(emprp[emprp > 1], events[emprp > 1], s=100,
                 color='r', label = 'Empirical ARI')
@@ -478,8 +478,8 @@ def subplot(self, axes, subfigure):
         """
         xdata, y1, y2, y2max, y2min, xlabel, ylabel, title = subfigure
 
-        axes.semilogx(xdata, y1, color='r', lw=2, label="", subsx=xdata)
-        axes.semilogx(xdata, y2, color='k', lw=2, label="", subsx=xdata)
+        axes.semilogx(xdata, y1, color='r', lw=2, label="", subs=xdata)
+        axes.semilogx(xdata, y2, color='k', lw=2, label="", subs=xdata)
         self.addRange(axes, xdata, y2min, y2max)
         ylim = (0., np.max([100, np.ceil(y2.max()/10.)*10.]))
         axes.set_ylim(ylim)

PlotInterface/maps.py

@@ -24,6 +24,8 @@
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
 import seaborn as sns
 
+from .scalebar import scale_bar
+
 def levels(maxval, minval=0):
     """
     Calculate a nice number of levels between `minval` and `maxval`
@@ -230,35 +232,20 @@ def maskOceans(self, mapobj, fillcolor="#66ccff"):
 
         mapobj.add_feature(cartopy.feature.OCEAN, color=fillcolor)
 
-    def addMapScale(self, mapobj):
+    def addMapScale(self, axes):
         """
-        Add a map scale to the curent `Basemap` instance. This
-        automatically determines a 'nice' length forthe scale bar -
+        Add a map scale to the curent `cartopy.mpl.geoaxes.GeoAxes` instance.
+        This automatically determines a 'nice' length forthe scale bar -
         chosen to be approximately 20% of the map width at the centre
         of the map.
 
-        :param mapobj: Current `Basemap` instance to add the scale bar to.
+        :param ax: Current `cartopy.mpl.geoaxes.GeoAxes` instance to add the
+        scale bar to.
 
+        see https://stackoverflow.com/questions/32333870/
         """
-        return # TODO: migrate to cartopy - see https://stackoverflow.com/questions/32333870/
-
-        midlon = (mapobj.lonmax - mapobj.lonmin) / 2.
-        midlat = (mapobj.latmax - mapobj.latmin) / 2.
-
-        xmin = mapobj.llcrnrx
-        xmax = mapobj.urcrnrx
-        ymin = mapobj.llcrnry
-        ymax = mapobj.urcrnry
-
-        xloc = xmin + 0.15 * abs(xmax - xmin)
-        yloc = ymin + 0.1 * abs(ymax - ymin)
+        axes = scale_bar(axes, (0.05, 0.1))
 
-        lonloc, latloc = mapobj(xloc, yloc, inverse=True)
-
-        # Set scale length to nearest 100-km for 20% of map width
-        scale_length = 100*int((0.2 * (xmax - xmin) / 1000.)/100)
-        mapobj.drawmapscale(lonloc, latloc, midlon, midlat, scale_length,
-                            barstyle='fancy', zorder=10)
 
     def createMap(self, axes, xgrid, ygrid, map_kwargs):
         """
@@ -296,7 +283,7 @@ def subplot(self, axes, subfigure):
         self.addGraticule(axes, mapobj)
         self.addCoastline(mapobj)
         self.fillContinents(mapobj)
-        self.addMapScale(mapobj)
+        self.addMapScale(axes)
 
     def plot(self):
         """
@@ -380,7 +367,7 @@ def subplot(self, axes, subfigure):
         cmap = selectColormap(lvls)
         CS = mapobj.contourf(mx, my, data, levels=lvls,
                              extend='both', cmap=cmap)
-        CB = self.colorbar(CS, ticks=lvls[::2], ax=axes, extend='both')
+        CB = self.colorbar(CS, ticks=lvls[::2], ax=axes)
         CB.set_label(cbarlab)
         axes.set_title(title)
         self.addGraticule(axes, mapobj)

PlotInterface/plotTimeseries.py

@@ -49,7 +49,7 @@ def loadTimeseriesData(datafile):
                              comments='#', delimiter=',', skip_header=1,
                              converters=INPUT_CNVT)
     except ValueError:
-        logging.warn("Timeseries data file is empty - returning empty array")
+        logging.warning("Timeseries data file is empty - returning empty array")
         return np.empty(0, dtype={
                         'names': INPUT_COLS,
                         'formats': INPUT_FMTS})