Exemplo n.º 1
0
def demo_arctic_proj():
    rll = RotatedLatLon(lon1=60, lat1=90, lon2=-30, lat2=0)

    print(rll.get_north_pole_coords(),
          rll.get_true_pole_coords_in_rotated_system())

    pass
Exemplo n.º 2
0
def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
                                     vname="STBM",
                                     region_of_interest_shp=None,
                                     **bmp_kwargs):
    """
    :param path:
    :param vname:
    :return: get basemap object for the variable in the given file
    """
    with RPN(str(path)) as r:
        _ = r.variables[vname][:]

        proj_params = r.get_proj_parameters_for_the_last_read_rec()
        lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()

        rll = RotatedLatLon(**proj_params)

    if region_of_interest_shp is not None:
        mask = get_mask(lons, lats, region_of_interest_shp)
        delta_points = 10
        i_arr, j_arr = np.where(mask >= 0.5)
        i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
        j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points

        slices = (slice(i_min, i_max + 1), slice(j_min, j_max + 1))

        bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices],
                                                   lats2d=lats[slices],
                                                   **bmp_kwargs)
    else:
        bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
                                                   lats2d=lats,
                                                   **bmp_kwargs)

    return lons, lats, bmp
Exemplo n.º 3
0
def plot_only_vegetation_fractions(
        data_path="/RESCUE/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS",
        canopy_name="VF", label="QC_10km"):
    r = RPN(data_path)

    veg_fractions = r.get_2D_field_on_all_levels(name=canopy_name)
    print(list(veg_fractions.keys()))

    proj_params = r.get_proj_parameters_for_the_last_read_rec()

    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    print(lons.shape)

    rll = RotatedLatLon(lon1=proj_params["lon1"], lat1=proj_params["lat1"],
                        lon2=proj_params["lon2"], lat2=proj_params["lat2"])

    lon0, lat0 = rll.get_true_pole_coords_in_rotated_system()
    plon, _ = rll.get_north_pole_coords()

    b = Basemap(projection="rotpole", llcrnrlon=lons[0, 0], llcrnrlat=lats[0, 0],
                urcrnrlon=lons[-1, -1], urcrnrlat=lats[-1, -1], lon_0=lon0 - 180,
                o_lon_p=lon0, o_lat_p=lat0)

    lons[lons > 180] -= 360
    for lev in list(veg_fractions.keys()):
        veg_fractions[lev] = maskoceans(lons, lats, veg_fractions[lev], inlands=False)

    x, y = b(lons, lats)
    plot_veg_fractions(x, y, b, veg_fractions, out_image=os.path.join(os.path.dirname(data_path),
                                                                      "veg_fractions_{0}.png".format(label)))
def get_lons_lats_basemap(rpnfile_path="", varname=None, index_subset=None):
    """
    Get longitudes, latitudes and the basemap object corresponding to the rpn file
    :param rpnfile_path:
    :param varname:
    :return:
    """
    with RPN(rpnfile_path) as r:

        assert isinstance(r, RPN)

        if varname is None:
            varname = next(v for v in r.get_list_of_varnames()
                           if v not in [">>", "^^", "HY"])

        r.get_first_record_for_name(varname)

        lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()

        nx, ny = lons.shape

        if index_subset is None:
            index_subset = IndexSubspace(i_start=0,
                                         i_end=nx - 1,
                                         j_start=0,
                                         j_end=ny - 1)

        rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
        bmp = rll.get_basemap_object_for_lons_lats(
            lons2d=lons[index_subset.get_islice(),
                        index_subset.get_jslice()],
            lats2d=lats[index_subset.get_islice(),
                        index_subset.get_jslice()])

        return lons, lats, bmp
Exemplo n.º 5
0
def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
                           vname="STBM", region_of_interest_shp=None, **bmp_kwargs):

    """
    :param path:
    :param vname:
    :return: get basemap object for the variable in the given file
    """
    with RPN(str(path)) as r:
        _ = r.variables[vname][:]

        proj_params = r.get_proj_parameters_for_the_last_read_rec()
        lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()

        rll = RotatedLatLon(**proj_params)

    if region_of_interest_shp is not None:
        mask = get_mask(lons, lats, region_of_interest_shp)
        delta_points = 10
        i_arr, j_arr = np.where(mask >= 0.5)
        i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
        j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points

        slices = (slice(i_min,i_max + 1), slice(j_min,j_max + 1))

        bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices], lats2d=lats[slices], **bmp_kwargs)
    else:
        bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, **bmp_kwargs)


    return lons, lats, bmp
Exemplo n.º 6
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def main(
    path="/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS"
):
    r = RPN(path)

    varnames = ["ITFS"]

    ncols = 3
    nrows = len(varnames) // 3

    fig = plt.figure()
    varname_to_field = {}
    for vname in varnames:

        data = r.get_first_record_for_name(vname)
        varname_to_field[vname] = data
        data = np.ma.masked_where(data < 0, data)
        lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
        params = r.get_proj_parameters_for_the_last_read_rec()
        print(params)
        rll = RotatedLatLon(**params)
        b = rll.get_basemap_object_for_lons_lats(lons2d, lats2d)
        x, y = b(lons2d, lats2d)
        b.drawcoastlines()
        img = b.pcolormesh(x, y, data)
        b.colorbar()

    fig = plt.figure()
    itfs = varname_to_field["ITFS"]
    plt.hist(itfs[itfs >= 0], bins=100)

    plt.show()

    r.close()
    pass
Exemplo n.º 7
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    def __init__(self, **kwargs):
        self.dx = self.dy = kwargs.get("dx", -1)
        self.iref, self.jref = kwargs.get("iref", -1), kwargs.get("jref", -1)

        self.xref, self.yref = kwargs.get("xref", -1), kwargs.get("yref", -1)
        self.ni, self.nj = kwargs.get("ni", -1), kwargs.get("nj", -1)

        # interpolated driving data (width of the outer band in number of gridpoints)
        self.halo = 10

        # size of the blending zone in grid points
        self.blendig = 10

        self.rll = None
        if "rll" not in kwargs:
            self.lon1, self.lat1 = kwargs.get("lon1",
                                              None), kwargs.get("lat1", None)
            self.lon2, self.lat2 = kwargs.get("lon2",
                                              None), kwargs.get("lat2", None)
            if None not in (self.lon1, self.lon2, self.lat1, self.lat2):
                self.rll = RotatedLatLon(lon1=self.lon1,
                                         lon2=self.lon2,
                                         lat1=self.lat1,
                                         lat2=self.lat2)
        else:
            self.rll = kwargs.get("rll")

        # private fields
        self._center_lons_2d = None
        self._center_lats_2d = None
Exemplo n.º 8
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def get_lons_lats_basemap(rpnfile_path="", varname=None, index_subset=None):


    """
    Get longitudes, latitudes and the basemap object corresponding to the rpn file
    :param rpnfile_path:
    :param varname:
    :return:
    """
    with RPN(rpnfile_path) as r:

        assert isinstance(r, RPN)

        if varname is None:
            varname = next(v for v in r.get_list_of_varnames() if v not in [">>", "^^", "HY"])

        r.get_first_record_for_name(varname)

        lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()

        nx, ny = lons.shape

        if index_subset is None:
            index_subset = IndexSubspace(i_start=0, i_end=nx - 1, j_start=0, j_end=ny - 1)






        rll = RotatedLatLon(**r.get_proj_parameters_for_the_last_read_rec())
        bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[index_subset.get_islice(), index_subset.get_jslice()],
                                                   lats2d=lats[index_subset.get_islice(), index_subset.get_jslice()])

        return lons, lats, bmp
Exemplo n.º 9
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def main(base_folder="/skynet3_rech1/huziy/veg_fractions/",
         fname="pm1983120100_00000000p", canopy_name="Y2C", label="USGS",
         depth_to_bedrock_name="8L"
         ):
    data_path = os.path.join(base_folder, fname)
    r = RPN(data_path)

    veg_fractions = r.get_2D_field_on_all_levels(name=canopy_name)
    print(list(veg_fractions.keys()))
    sand = r.get_first_record_for_name("SAND")
    clay = r.get_first_record_for_name("CLAY")

    dpth_to_bedrock = r.get_first_record_for_name(depth_to_bedrock_name)

    proj_params = r.get_proj_parameters_for_the_last_read_rec()

    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    print(lons.shape)

    rll = RotatedLatLon(lon1=proj_params["lon1"], lat1=proj_params["lat1"],
                        lon2=proj_params["lon2"], lat2=proj_params["lat2"])

    lon0, lat0 = rll.get_true_pole_coords_in_rotated_system()
    plon, _ = rll.get_north_pole_coords()

    b = Basemap(projection="rotpole", llcrnrlon=lons[0, 0], llcrnrlat=lats[0, 0],
                urcrnrlon=lons[-1, -1], urcrnrlat=lats[-1, -1], lon_0=lon0 - 180,
                o_lon_p=lon0, o_lat_p=lat0)

    lons[lons > 180] -= 360
    for lev in list(veg_fractions.keys()):
        veg_fractions[lev] = maskoceans(lons, lats, veg_fractions[lev], inlands=False)

    sand = maskoceans(lons, lats, sand)
    clay = maskoceans(lons, lats, clay)
    dpth_to_bedrock = maskoceans(lons, lats, dpth_to_bedrock)

    x, y = b(lons, lats)
    plot_veg_fractions(x, y, b, veg_fractions, out_image=os.path.join(base_folder,
                                                                      "veg_fractions_{0}.jpeg".format(label)))
    plot_sand_and_clay(x, y, b, sand, clay, out_image=os.path.join(base_folder,
                                                                   "sand_clay_{0}.jpeg".format(label)))

    # set relation between vegetation frsction fields and names
    veg_fract_dict = {}
    for lev, the_field in veg_fractions.items():
        lev = int(lev)
        if lev not in y2c_level_to_title:
            continue
        veg_fract_dict[y2c_level_to_title[lev]] = the_field

    data = {
        "SAND": sand, "CLAY": clay, "BDRCK_DEPTH": dpth_to_bedrock
    }
    data.update(veg_fract_dict)

    return b, lons, lats, data, label
Exemplo n.º 10
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def get_default_basemap_for_glk(lons, lats, resolution = "c"):
    rll = RotatedLatLon(lon1=domprops.lon1, lat1 = domprops.lat1, lon2=domprops.lon2, lat2=domprops.lat2)
    lonp, latp = rll.get_north_pole_coords()
    lon0, _ = rll.get_true_pole_coords_in_rotated_system()
    return Basemap(
        projection="rotpole", llcrnrlon=lons[0, 0], llcrnrlat=lats[0, 0],
        urcrnrlon=lons[-1, -1], urcrnrlat=lats[-1, -1],
        lon_0=lon0 - 180, o_lon_p = lonp, o_lat_p = latp, resolution=resolution
    )
Exemplo n.º 11
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def get_arctic_basemap(lons2d, lats2d, lon1=60, lat1=90, lon2=-30, lat2=0, resolution="l"):
    rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
    rplon, rplat = rll.get_north_pole_coords()
    lon_0, lat_0 = rll.get_true_pole_coords_in_rotated_system()

    basemap = Basemap(projection="rotpole", o_lon_p=rplon, o_lat_p=rplat,
                      lon_0=lon_0 - 180,
                      llcrnrlon=lons2d[0, 0], llcrnrlat=lats2d[0, 0],
                      urcrnrlon=lons2d[-1, -1], urcrnrlat=lats2d[-1, -1],
                      resolution=resolution, round=True)
    return basemap
Exemplo n.º 12
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    def __init__(self, **kwargs):
        self.dx = self.dy = kwargs.get("dx", -1)
        self.iref, self.jref = kwargs.get("iref", -1), kwargs.get("jref", -1)

        self.xref, self.yref = kwargs.get("xref", -1), kwargs.get("yref", -1)
        self.ni, self.nj = kwargs.get("ni", -1), kwargs.get("nj", -1)

        # interpolated driving data (width of the outer band in number of gridpoints)
        self.halo = 10

        # size of the blending zone in grid points
        self.blendig = 10

        self.rll = None
        if "rll" not in kwargs:
            self.lon1, self.lat1 = kwargs.get("lon1", None), kwargs.get("lat1", None)
            self.lon2, self.lat2 = kwargs.get("lon2", None), kwargs.get("lat2", None)
            if None not in (self.lon1, self.lon2, self.lat1, self.lat2):
                self.rll = RotatedLatLon(lon1=self.lon1, lon2=self.lon2, lat1=self.lat1, lat2=self.lat2)
        else:
            self.rll = kwargs.get("rll")


        # private fields
        self._center_lons_2d = None
        self._center_lats_2d = None
Exemplo n.º 13
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def get_basemap_glaciers_nw_america():
    r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/Depth_to_bedrock_WestNA_0.25")
    r.get_first_record_for_name("8L")
    proj_params = r.get_proj_parameters_for_the_last_read_rec()
    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    bsmp = RotatedLatLon(**proj_params).get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)
    return bsmp
Exemplo n.º 14
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def gridconfig_from_grid_nml(nml_str):
    """
    Parse the copy-pasted string nml_str and construct the gridconfig object
    :param nml_str:
    """

    import re

    nml_str = nml_str.lower()

    gc = GridConfig()

    def get_val_of(par_name, parser_func=float):
        return parser_func(
            re.search("grd_{}".format(par_name) + "\s*=\s*(-?\s*\d*\.?\d*)",
                      nml_str).group(1))

    gc.dx = get_val_of("dx")
    gc.dy = get_val_of("dy")
    gc.ni = get_val_of("ni", int)
    gc.nj = get_val_of("nj", int)
    gc.iref = get_val_of("iref", int)
    gc.jref = get_val_of("jref", int)

    gc.xref = get_val_of("lonr")
    gc.yref = get_val_of("latr")

    parnames = ["xlat1", "xlat2", "xlon1", "xlon2"]
    pardict = {pn[1:]: get_val_of(pn) for pn in parnames}
    gc.rll = RotatedLatLon(**pardict)
    return gc
Exemplo n.º 15
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def main(inout_paths):
    tiff_path, rpn_path = inout_paths
    print("tif path = {0}".format(tiff_path))
    print("rpn path = {0}".format(rpn_path))

    outGrid = RotatedLatLon(lon1=-90.0, lat1=50.0, lon2=0.0, lat2=0.0)
    Grd_dx = 0.5
    Grd_dy = 0.5
    Grd_ni = 170
    Grd_nj = 158
    Grd_iref = 11
    Grd_jref = 11
    Grd_latr = -33.5
    Grd_lonr = 140.5

    lons1d = np.array(
        [Grd_lonr + (i - Grd_iref + 1) * Grd_dx for i in range(Grd_ni)])
    lats1d = np.array(
        [Grd_latr + (j - Grd_jref + 1) * Grd_dy for j in range(Grd_nj)])

    lats2d, lons2d = np.meshgrid(lats1d, lons1d)

    lonlats = np.array(
        list(
            map(lambda x, y: outGrid.toGeographicLonLat(x, y),
                lons2d.flatten(), lats2d.flatten())))
    print(lonlats.shape)

    rObj = RPN(rpn_path, mode="w")
    data = convert(tiff_path, lonlats)
    print("interpolated data")
    data.shape = lons2d.shape

    fieldName = os.path.basename(tiff_path).split("_")[0].lower()

    #write coordinates
    ig = outGrid.write_coords_to_rpn(rObj, lons1d, lats1d)

    rObj.write_2D_field(name=fieldName,
                        data=data,
                        grid_type="Z",
                        ig=ig,
                        label=fieldName)
    rObj.close()
    return 0

    pass
Exemplo n.º 16
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def plot_only_vegetation_fractions(
        data_path="/RESCUE/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS",
        canopy_name="VF",
        label="QC_10km"):
    r = RPN(data_path)

    veg_fractions = r.get_2D_field_on_all_levels(name=canopy_name)
    print(list(veg_fractions.keys()))

    proj_params = r.get_proj_parameters_for_the_last_read_rec()

    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    print(lons.shape)

    rll = RotatedLatLon(lon1=proj_params["lon1"],
                        lat1=proj_params["lat1"],
                        lon2=proj_params["lon2"],
                        lat2=proj_params["lat2"])

    lon0, lat0 = rll.get_true_pole_coords_in_rotated_system()
    plon, _ = rll.get_north_pole_coords()

    b = Basemap(projection="rotpole",
                llcrnrlon=lons[0, 0],
                llcrnrlat=lats[0, 0],
                urcrnrlon=lons[-1, -1],
                urcrnrlat=lats[-1, -1],
                lon_0=lon0 - 180,
                o_lon_p=lon0,
                o_lat_p=lat0)

    lons[lons > 180] -= 360
    for lev in list(veg_fractions.keys()):
        veg_fractions[lev] = maskoceans(lons,
                                        lats,
                                        veg_fractions[lev],
                                        inlands=False)

    x, y = b(lons, lats)
    plot_veg_fractions(x,
                       y,
                       b,
                       veg_fractions,
                       out_image=os.path.join(
                           os.path.dirname(data_path),
                           "veg_fractions_{0}.png".format(label)))
Exemplo n.º 17
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def get_basemap_and_coords_improved(
        file_path="data/CORDEX/NorthAmerica_0.44deg_CanHistoE1/Samples/NorthAmerica_0.44deg_CanHistoE1_198101/pm1950010100_00816912p",
        field_name="PR"):
    rpnobj = RPN(file_path)
    the_mask = rpnobj.get_first_record_for_name(field_name)

    # plt.figure()
    # plt.pcolormesh(the_mask.transpose())
    # plt.show()

    proj_params = rpnobj.get_proj_parameters_for_the_last_read_rec()
    rll = RotatedLatLon(**proj_params)

    lons2d, lats2d = rpnobj.get_longitudes_and_latitudes_for_the_last_read_rec()
    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d)
    rpnobj.close()
    return basemap, lons2d, lats2d
Exemplo n.º 18
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def main(path="/skynet3_rech1/huziy/gemclim_settings.nml"):
    params = _parse_parameters(path)
    print(params)

    ni, nj = 140, 140  # params[Grd_ni_name], params[Grd_nj_name]
    dx, dy = params[Grd_dx_name], params[Grd_dy_name]
    iRef, jRef = params[Grd_iref_name] - 1, params[Grd_jref_name] - 1
    lonRef, latRef = params[Grd_lonr_name], params[Grd_latr_name]

    lon1, lat1 = params[Grd_xlon1_name], params[Grd_xlat1_name]
    lon2, lat2 = params[Grd_xlon2_name], params[Grd_xlat2_name]

    lons_rot = np.arange(lonRef + (0 - iRef) * dx, lonRef + (ni - iRef) * dx, dx)
    lats_rot = np.arange(latRef + (0 - jRef) * dy, latRef + (nj - jRef) * dy, dy)

    lats_rot, lons_rot = np.meshgrid(lats_rot, lons_rot)
    print(lats_rot.shape)
    # lons_rot[lons_rot > 180] -= 360

    rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)

    truepole_lonr, truepole_latr = rll.get_true_pole_coords_in_rotated_system()
    rotpole_lon, rotpole_lat = rll.get_north_pole_coords()

    llcrnrlon, llcrnrlat = rll.toGeographicLonLat(lons_rot[0, 0], lats_rot[0, 0])
    urcrnrlon, urcrnrlat = rll.toGeographicLonLat(lons_rot[-1, -1], lats_rot[-1, -1])

    b = Basemap(
        projection="rotpole",
        lon_0=truepole_lonr - 180,
        o_lat_p=rotpole_lat,
        o_lon_p=rotpole_lon,
        llcrnrlon=llcrnrlon,
        llcrnrlat=llcrnrlat,
        urcrnrlon=urcrnrlon,
        urcrnrlat=urcrnrlat,
    )
    print(lons_rot[0, 0], lats_rot[0, 0], lons_rot[-1, -1], lats_rot[-1, -1])
    b.contourf(lons_rot, lats_rot, lons_rot)
    b.colorbar()

    b.drawcoastlines()
    # b.drawmeridians(np.arange(160, 200, 10))
    plt.show()
Exemplo n.º 19
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def main(inout_paths):
    tiff_path, rpn_path = inout_paths
    print("tif path = {0}".format(tiff_path))
    print("rpn path = {0}".format(rpn_path))

    outGrid = RotatedLatLon(lon1=-90.0, lat1=50.0, lon2=0.0, lat2=0.0)
    Grd_dx  = 0.5
    Grd_dy  = 0.5
    Grd_ni  = 170
    Grd_nj  = 158
    Grd_iref = 11
    Grd_jref = 11
    Grd_latr = -33.5
    Grd_lonr = 140.5


    lons1d = np.array([Grd_lonr + (i - Grd_iref + 1) * Grd_dx for i in range(Grd_ni)])
    lats1d = np.array([Grd_latr + (j - Grd_jref + 1) * Grd_dy for j in range(Grd_nj)])


    lats2d, lons2d = np.meshgrid(lats1d, lons1d)

    lonlats = np.array( list(map( lambda x, y: outGrid.toGeographicLonLat(x, y), lons2d.flatten(), lats2d.flatten() )) )
    print(lonlats.shape)


    rObj = RPN(rpn_path, mode = "w")
    data = convert(tiff_path, lonlats)
    print("interpolated data")
    data.shape = lons2d.shape

    fieldName = os.path.basename(tiff_path).split("_")[0].lower()

    #write coordinates
    ig = outGrid.write_coords_to_rpn(rObj, lons1d, lats1d)

    rObj.write_2D_field(name = fieldName, data=data, grid_type="Z", ig = ig, label = fieldName)
    rObj.close()
    return 0

    pass
Exemplo n.º 20
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    def get_cartopy_proj_and_coords(self):
        """
        :return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
        """
        from cartopy import crs
        # Read longitudes and latitudes and create the basemap only if they are not initialized
        if self.ccrs is None:

            with Dataset(os.path.join(self.data_folder,
                                      self.bathymetry_file)) as ds:
                self.lons, self.lats = ds.variables["nav_lon"][:].transpose(
                ), ds.variables["nav_lat"][:].transpose()

            import re

            lon1, lat1 = None, None
            lon2, lat2 = None, None
            with open(os.path.join(self.data_folder, self.proj_file)) as f:
                for line in f:
                    if "Grd_xlat1" in line and "Grd_xlon1" in line:
                        groups = re.findall(r"-?\b\d+.?\d*\b", line)
                        lat1, lon1 = [float(s) for s in groups]

                    if "Grd_xlat2" in line and "Grd_xlon2" in line:
                        groups = re.findall(r"-?\b\d+.?\d*\b", line)
                        lat2, lon2 = [float(s) for s in groups]

            rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
            # self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons, lats2d=self.lats)

            lon0, _ = rll.get_true_pole_coords_in_rotated_system()
            o_lon_p, o_lat_p = rll.get_north_pole_coords()
            print(lon0, o_lat_p)
            self.ccrs = crs.RotatedPole(pole_longitude=lon0,
                                        pole_latitude=o_lat_p)

        self.lons[self.lons > 180] -= 360

        return self.lons, self.lats, self.ccrs
Exemplo n.º 21
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def get_rotpole_for_na_glaciers():
    """
    Glacier grid
      Grd_typ_S     = 'LU'     ,
      Grd_ni        =  196     ,  Grd_nj          =  140     ,
      Grd_dx        =    0.1375,  Grd_dy          =    0.1375,
      Grd_iref      =  106     ,  Grd_jref        =   70     ,
      Grd_latr      =    0.0   ,  Grd_lonr        =  180.0   ,
      Grd_xlat1     =   57.5   ,  Grd_xlon1       = -130.    ,
      Grd_xlat2     =    0.    ,  Grd_xlon2       =  -40.    ,
    :return:
    """
    params = dict(lon1=-130, lat1=57.5, lon2=-40.0, lat2=0.0)
    return RotatedLatLon(**params)
Exemplo n.º 22
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    def get_coords_and_basemap(self):
        """
        :return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
        """

        # Read longitudes and latitudes and create the basemap only if they are not initialized
        if self.lons is None:
            with Dataset(os.path.join(self.data_folder, self.bathymetry_file)) as ds:
                self.lons, self.lats = ds.variables["nav_lon"][:].transpose(), ds.variables["nav_lat"][:].transpose()

                import re

                lon1, lat1 = None, None
                lon2, lat2 = None, None
                with open(os.path.join(self.data_folder, self.proj_file)) as f:
                    for line in f:
                        if "Grd_xlat1" in line and "Grd_xlon1" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat1, lon1 = [float(s) for s in groups]

                        if "Grd_xlat2" in line and "Grd_xlon2" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat2, lon2 = [float(s) for s in groups]

                rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
                self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons, lats2d=self.lats)
                print(lon1, lat1, lon2, lat2)


        # self.basemap.drawcoastlines()
        # xx, yy = self.basemap(self.lons, self.lats)
        # self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
        # plt.show()

        self.lons[self.lons > 180] -= 360

        return self.lons, self.lats, self.basemap
Exemplo n.º 23
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    def get_cartopy_proj_and_coords(self):
        """
        :return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
        """
        from cartopy import crs
        # Read longitudes and latitudes and create the basemap only if they are not initialized
        if self.ccrs is None:

            with Dataset(os.path.join(self.data_folder, self.bathymetry_file)) as ds:
                self.lons, self.lats = ds.variables["nav_lon"][:].transpose(), ds.variables["nav_lat"][:].transpose()

            import re

            lon1, lat1 = None, None
            lon2, lat2 = None, None
            with open(os.path.join(self.data_folder, self.proj_file)) as f:
                for line in f:
                    if "Grd_xlat1" in line and "Grd_xlon1" in line:
                        groups = re.findall(r"-?\b\d+.?\d*\b", line)
                        lat1, lon1 = [float(s) for s in groups]

                    if "Grd_xlat2" in line and "Grd_xlon2" in line:
                        groups = re.findall(r"-?\b\d+.?\d*\b", line)
                        lat2, lon2 = [float(s) for s in groups]

            rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)
            # self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons, lats2d=self.lats)


            lon0, _ = rll.get_true_pole_coords_in_rotated_system()
            o_lon_p, o_lat_p = rll.get_north_pole_coords()
            print(lon0, o_lat_p)
            self.ccrs = crs.RotatedPole(pole_longitude=lon0, pole_latitude=o_lat_p)

        self.lons[self.lons > 180] -= 360

        return self.lons, self.lats, self.ccrs
Exemplo n.º 24
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def main(path = "/skynet3_rech1/huziy/geof_lake_infl_exp/geophys_Quebec_0.1deg_260x260_with_dd_v6_with_ITFS"):
    r = RPN(path)

    varnames = ["ITFS"]


    ncols = 3
    nrows = len(varnames) // 3

    fig = plt.figure()
    varname_to_field = {}
    for vname in varnames:

        data = r.get_first_record_for_name(vname)
        varname_to_field[vname] = data
        data = np.ma.masked_where(data < 0, data)
        lons2d, lats2d = r.get_longitudes_and_latitudes_for_the_last_read_rec()
        params = r.get_proj_parameters_for_the_last_read_rec()
        print(params)
        rll = RotatedLatLon(**params)
        b = rll.get_basemap_object_for_lons_lats(lons2d, lats2d)
        x, y = b(lons2d, lats2d)
        b.drawcoastlines()
        img = b.pcolormesh(x, y, data)
        b.colorbar()



    fig = plt.figure()
    itfs = varname_to_field["ITFS"]
    plt.hist(itfs[itfs >= 0], bins = 100)


    plt.show()

    r.close()
    pass
Exemplo n.º 25
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def gridconfig_from_dict(param_dict):
    gc = GridConfig()

    gc.dx = param_dict["dx"]
    gc.dy = param_dict["dy"]
    gc.ni = param_dict["ni"]
    gc.nj = param_dict["nj"]
    gc.iref = param_dict["iref"]
    gc.jref = param_dict["jref"]

    gc.xref = param_dict["lonr"]
    gc.yref = param_dict["latr"]

    parnames = ["xlat1", "xlat2", "xlon1", "xlon2"]
    proj_pardict = {pn[1:]: param_dict[pn] for pn in parnames}
    gc.rll = RotatedLatLon(**proj_pardict)
    return gc
Exemplo n.º 26
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def main(path="/skynet3_rech1/huziy/gemclim_settings.nml"):
    params = _parse_parameters(path)
    print(params)

    ni, nj = 140, 140  # params[Grd_ni_name], params[Grd_nj_name]
    dx, dy = params[Grd_dx_name], params[Grd_dy_name]
    iRef, jRef = params[Grd_iref_name] - 1, params[Grd_jref_name] - 1
    lonRef, latRef = params[Grd_lonr_name], params[Grd_latr_name]

    lon1, lat1 = params[Grd_xlon1_name], params[Grd_xlat1_name]
    lon2, lat2 = params[Grd_xlon2_name], params[Grd_xlat2_name]

    lons_rot = np.arange(lonRef + (0 - iRef) * dx, lonRef + (ni - iRef) * dx,
                         dx)
    lats_rot = np.arange(latRef + (0 - jRef) * dy, latRef + (nj - jRef) * dy,
                         dy)

    lats_rot, lons_rot = np.meshgrid(lats_rot, lons_rot)
    print(lats_rot.shape)
    # lons_rot[lons_rot > 180] -= 360

    rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)

    truepole_lonr, truepole_latr = rll.get_true_pole_coords_in_rotated_system()
    rotpole_lon, rotpole_lat = rll.get_north_pole_coords()

    llcrnrlon, llcrnrlat = rll.toGeographicLonLat(lons_rot[0, 0], lats_rot[0,
                                                                           0])
    urcrnrlon, urcrnrlat = rll.toGeographicLonLat(lons_rot[-1, -1],
                                                  lats_rot[-1, -1])

    b = Basemap(projection="rotpole",
                lon_0=truepole_lonr - 180,
                o_lat_p=rotpole_lat,
                o_lon_p=rotpole_lon,
                llcrnrlon=llcrnrlon,
                llcrnrlat=llcrnrlat,
                urcrnrlon=urcrnrlon,
                urcrnrlat=urcrnrlat)
    print(lons_rot[0, 0], lats_rot[0, 0], lons_rot[-1, -1], lats_rot[-1, -1])
    b.contourf(lons_rot, lats_rot, lons_rot)
    b.colorbar()

    b.drawcoastlines()
    # b.drawmeridians(np.arange(160, 200, 10))
    plt.show()
Exemplo n.º 27
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    def get_coords_and_basemap(self,
                               subregion=None,
                               reload=True,
                               **basemap_kwargs):
        """
        :return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
        if reload is True, do not use cached arrays even if they are available
        """

        # Read longitudes and latitudes and create the basemap only if they are not initialized
        if self.basemap is None or reload:
            with Dataset(os.path.join(self.data_folder,
                                      self.bathymetry_file)) as ds:

                if "nav_lon" in ds.variables:
                    self.lons, self.lats = ds.variables[
                        "nav_lon"][:].transpose(
                        ), ds.variables["nav_lat"][:].transpose()
                else:
                    for vname, v in ds.variables.items():
                        if "lon" in vname.lower():
                            self.lons = v[:].T
                            continue

                        if "lat" in vname.lower():
                            self.lats = v[:].T
                            continue

                        if self.lons is not None and self.lats is not None:
                            break

                import re

                lon1, lat1 = None, None
                lon2, lat2 = None, None
                with open(os.path.join(self.data_folder, self.proj_file)) as f:
                    for line in f:
                        if "Grd_xlat1" in line and "Grd_xlon1" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat1, lon1 = [float(s) for s in groups]

                        if "Grd_xlat2" in line and "Grd_xlon2" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat2, lon2 = [float(s) for s in groups]

                rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)

                nx, ny = self.lons.shape
                if subregion is not None:
                    ill, iur, jll, jur = int(nx * subregion[0]), int(
                        nx * subregion[1]), int(ny * subregion[2]), int(
                            ny * subregion[3])
                else:
                    ill, iur, jll, jur = 0, self.lons.shape[
                        0], 0, self.lons.shape[1]

                self.basemap = rll.get_basemap_object_for_lons_lats(
                    lons2d=self.lons[ill:iur, jll:jur],
                    lats2d=self.lats[ill:iur, jll:jur],
                    **basemap_kwargs)
                print(lon1, lat1, lon2, lat2)

        # self.basemap.drawcoastlines()
        # xx, yy = self.basemap(self.lons, self.lats)
        # self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
        # plt.show()

        self.lons[self.lons > 180] -= 360

        return self.lons, self.lats, self.basemap
def plot_acc_area_with_glaciers(
        gmask_vname: str = "VF",
        gmask_level=2,
        gmask_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85",
        route_data_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_north_america_0.25deg_glaciers.nc",
        lons_target=None,
        lats_target=None,
        basin_shape_files=None):

    plot_scatter = False

    # stab reading of the glacier mask
    with RPN(gmask_path) as r:
        gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
                                                      level=gmask_level)

        # r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
        r.get_first_record_for_name(
            "VF")  # Because I almost sure that PR is there
        proj_params = r.get_proj_parameters_for_the_last_read_rec()
        rll = RotatedLatLon(**proj_params)
        lons_gmask, lats_gmask = r.get_longitudes_and_latitudes_for_the_last_read_rec(
        )

        gl_fraction_limit = 0.01

        gmask = np.ma.masked_where(gmask < gl_fraction_limit, gmask)
        mask_value = 0.25
        gmask[~gmask.mask] = mask_value

    if str(route_data_path).endswith(".nc"):
        print("route_data_path ends with .nc => assuming netcdf format: {}".
              format(route_data_path))
        with Dataset(route_data_path) as ds:
            var_name = "accumulation_area"
            data = ds.variables[var_name][:]
            # flow directions
            fldr = ds.variables["flow_direction_value"][:]

            coord_names = ["lon", "lat"] if "lon" in ds.variables else [
                "longitudes", "latitudes"
            ]
            lons_route, lats_route = [ds.variables[k] for k in coord_names]

    else:
        print(
            "route_data_path does not end with .nc => assuming rpn format: {}".
            format(route_data_path))
        with RPN(route_data_path) as r:
            data = r.get_first_record_for_name("FACC")
            fldr = r.get_first_record_for_name("FLDR")

            lons_route, lats_route = r.get_longitudes_and_latitudes_for_the_last_read_rec(
            )

    # do the spatial interpolation if required
    xg, yg, zg = lat_lon.lon_lat_to_cartesian(lons_gmask.flatten(),
                                              lats_gmask.flatten())
    xr, yr, zr = lat_lon.lon_lat_to_cartesian(lons_route.flatten(),
                                              lats_route.flatten())

    if lons_target is None or lats_target is None:
        lons_target, lats_target = lons_route, lats_route
        xt, yt, zt = xr, yr, zr
    else:
        xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_target.flatten(),
                                                  lats_target.flatten())

    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons_target,
                                                   lats2d=lats_target,
                                                   resolution="i")

    ktree_route = KDTree(list(zip(xr, yr, zr)))
    dists_route, inds_route = ktree_route.query(list(zip(xt, yt, zt)))
    data = data.flatten()[inds_route].reshape(lons_target.shape)
    fldr = fldr.flatten()[inds_route].reshape(lons_target.shape)

    ktree_gmask = KDTree(list(zip(xg, yg, zg)))
    dists_gmask, inds_gmask = ktree_gmask.query(list(zip(xt, yt, zt)))
    gmask = gmask.flatten()[inds_gmask].reshape(lons_target.shape)

    data = np.ma.masked_where(data <= 0, data)

    i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)

    xx, yy = basemap(lons_target, lats_target)
    fig = plt.figure(figsize=(15, 15))

    dx = (xx[-1, -1] - xx[0, 0]) / float(xx.shape[0])
    dy = (yy[-1, -1] - yy[0, 0]) / float(yy.shape[1])

    x1 = xx - dx / 2.0
    y1 = yy - dy / 2.0

    # Uncomment to plot the accumulation areas
    im = basemap.pcolormesh(x1,
                            y1,
                            data,
                            norm=LogNorm(vmin=1e3, vmax=1e7),
                            cmap=cm.get_cmap("jet", 12))
    cb = basemap.colorbar(im)

    cmap = cm.get_cmap("gray_r", 10)

    basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)

    nx, ny = xx.shape
    inds_j, inds_i = np.meshgrid(range(ny), range(nx))
    inds_i_next = inds_i + i_shifts
    inds_j_next = inds_j + j_shifts

    inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
                                     inds_i_next)
    inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
                                     inds_j_next)

    u = np.ma.masked_all_like(xx)
    v = np.ma.masked_all_like(xx)

    good = (~inds_i_next.mask) & (~inds_j_next.mask)
    u[good] = xx[inds_i_next[good], inds_j_next[good]] - xx[inds_i[good],
                                                            inds_j[good]]
    v[good] = yy[inds_i_next[good], inds_j_next[good]] - yy[inds_i[good],
                                                            inds_j[good]]

    basemap.quiver(xx,
                   yy,
                   u,
                   v,
                   pivot="tail",
                   width=0.0005,
                   scale_units="xy",
                   headlength=20,
                   headwidth=15,
                   scale=1)

    basemap.drawcoastlines(linewidth=0.5, zorder=5)

    basemap.drawrivers(color="lightcoral", zorder=5, linewidth=3)

    plt.legend([
        Rectangle((0, 0), 5, 5, fc=cmap(mask_value)),
    ], [
        r"Glacier ($\geq {}\%$)".format(gl_fraction_limit * 100),
    ],
               loc=3)

    watershed_bndry_width = 4

    if basin_shape_files is not None:
        for i, the_shp in enumerate(basin_shape_files):
            basemap.readshapefile(the_shp[:-4],
                                  "basin_{}".format(i),
                                  zorder=2,
                                  color="m",
                                  linewidth=watershed_bndry_width)

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
    #                       zorder=2, linewidth=watershed_bndry_width, color="m")
    #
    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
    #                       zorder=2, linewidth=watershed_bndry_width, color="m")
    #
    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
    #                       zorder=2, linewidth=watershed_bndry_width, color="m")
    #
    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
    #                       zorder=2, linewidth=watershed_bndry_width, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
    #                      "upsas",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
    #                      zorder=2, linewidth=0.5, color="b")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
    #                      zorder=2, linewidth=0.5, color="b")

    if plot_scatter:
        points_lat = [51.54, 49.2476]
        points_lon = [-122.277, -122.784]

        point_x, point_y = basemap(points_lon, points_lat)
        basemap.scatter(point_x, point_y, c="g", s=20, zorder=3)

    plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_only.png",
                bbox_inches="tight",
                dpi=300)
    # plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")

    plt.show()
Exemplo n.º 29
0
def plot_domain_using_coords_from_file(path=""):
    fig = plt.figure()
    assert isinstance(fig, Figure)
    rpnObj = RPN(path)

    lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()

    basemap = Basemap(projection="omerc", lon_1=-68, lat_1=52,
                      lon_2=16.65, lat_2=0.0, llcrnrlon=lons2d[0, 0], llcrnrlat=lats2d[0, 0],
                      urcrnrlon=lons2d[-1, -1], urcrnrlat=lats2d[-1, -1], no_rot=True)

    # basemap.drawcoastlines()



    rot_lat_lon_proj = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)

    g_params = GridParams(lonr=180, latr=0, iref=45, jref=41, dx=0.5, dy=0.5, nx=86, ny=86)

    lons2d_1, lats2d_1 = get_lons_lats_using_grid_params(g_params, rot_lat_lon_proj)

    basemap = Basemap(projection="omerc", lon_1=-68, lat_1=52,
                      lon_2=16.65, lat_2=0.0, llcrnrlon=lons2d_1[18, 18], llcrnrlat=lats2d_1[18, 18],
                      urcrnrlon=lons2d_1[-1, -1], urcrnrlat=lats2d_1[-1, -1], no_rot=True, resolution="i")

    basemap.drawcoastlines(linewidth=0.4)
    basemap.drawrivers()
    # basemap.drawmeridians(np.arange(-180, 0, 20))


    x, y = basemap(lons2d, lats2d)
    basemap.scatter(x, y, c="r", linewidths=0, s=1.0)
    print(x.shape)

    xll_big, yll_big = g_params.get_ll_point(marginx=20, marginy=20)
    xll_big -= g_params.dx / 2.0
    yll_big -= g_params.dy / 2.0
    xll_big, yll_big = rot_lat_lon_proj.toGeographicLonLat(xll_big, yll_big)
    xll_big, yll_big = basemap(xll_big, yll_big)

    xur_big, yur_big = g_params.get_ur_point(marginx=20, marginy=20)
    xur_big += g_params.dx / 2.0
    yur_big += g_params.dy / 2.0
    xur_big, yur_big = rot_lat_lon_proj.toGeographicLonLat(xur_big, yur_big)
    xur_big, yur_big = basemap(xur_big, yur_big)

    margin = 20

    # plot 0.25 degree grid
    g_params = GridParams(lonr=180, latr=0, iref=71, jref=63, dx=0.25, dy=0.25, nx=133, ny=133)
    lons2d_2, lats2d_2 = get_lons_lats_using_grid_params(g_params, rot_lat_lon_proj)
    x2, y2 = basemap(lons2d_2[margin:-margin, margin:-margin], lats2d_2[margin:-margin, margin:-margin])
    basemap.scatter(x2, y2, c="g", linewidth=0, marker="s", s=7.5)

    # plot 0.5 degree grid using the output file
    # debug
    rObj1 = RPN("/home/huziy/skynet3_exec1/from_guillimin/quebec_86x86_0.5deg_without_lakes/pm1985010100_00000000p")
    lons2d_1, lats2d_1 = rObj1.get_longitudes_and_latitudes()

    # x1, y1 = basemap(lons2d_1[margin:-margin,margin:-margin], lats2d_1[margin:-margin,margin:-margin])
    x1, y1 = basemap(lons2d_1, lats2d_1)

    print(x1.shape, lons2d_1[0, 0], lats2d_1[0, 0])

    basemap.scatter(x1, y1, c="b", linewidths=0, s=10)

    dx1 = (x1[1, 0] - x1[0, 0]) / 2.0
    dy1 = (y1[0, 1] - y1[0, 0]) / 2.0

    rbig = Rectangle((xll_big, yll_big), xur_big - xll_big,
                     yur_big - yll_big, linewidth=2, edgecolor="b",
                     facecolor="none"
                     )

    ax = plt.gca()
    assert isinstance(ax, Axes)
    # ax.add_patch(rsmall)
    ax.add_patch(rbig)

    # draw north arrow
    plot_north_cross(-45, 45, basemap, ax=ax)

    # zoom to a region
    axins = zoomed_inset_axes(ax, 4, loc=1)  # zoom = 6
    basemap.drawcoastlines(ax=axins)
    basemap.drawrivers(ax=axins)
    basemap.scatter(x, y, c="r", linewidths=0, s=5, ax=axins)
    basemap.scatter(x2, y2, c="g", marker="s", linewidth=0, s=15, ax=axins)
    basemap.scatter(x1, y1, c="b", linewidths=0, s=25, ax=axins)

    # subregion to zoom in
    nx, ny = lons2d.shape
    part = 3
    xins_ll = lons2d[nx / part, ny / part]
    yins_ll = lats2d[nx / part, ny / part]
    xins_ur = lons2d[nx / part + 40, ny / part + 40]
    yins_ur = lats2d[nx / part + 40, ny / part + 40]

    xins_ll, yins_ll = basemap(xins_ll, yins_ll)
    xins_ur, yins_ur = basemap(xins_ur, yins_ur)

    axins.set_xlim(xins_ll, xins_ur)
    axins.set_ylim(yins_ll, yins_ur)

    # draw a bbox of the region of the inset axes in the parent axes and
    # connecting lines between the bbox and the inset axes area
    mark_inset(ax, axins, loc1=2, loc2=4, fc="none", linewidth=2)

    fig.tight_layout(pad=0.8)
    fig.savefig("high_low_res_domains.png")

    pass
Exemplo n.º 30
0
def main():

    swe_obs_manager = SweDataManager(var_name="SWE")

    data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_86x86_0.5deg_without_lakes_v3"
    coord_file = os.path.join(data_path, "pm1985050100_00000000p")
    managerLowRes = Crcm5ModelDataManager(samples_folder_path=data_path,
                                          file_name_prefix="pm",
                                          all_files_in_samples_folder=True,
                                          need_cell_manager=True)

    data_path = "/home/huziy/skynet3_exec1/from_guillimin/quebec_highres_spinup_12_month_without_lakes_v3"
    coord_file = os.path.join(data_path, "pm1985050100_00000000p")
    managerHighRes = Crcm5ModelDataManager(samples_folder_path=data_path,
                                           file_name_prefix="pm",
                                           all_files_in_samples_folder=True,
                                           need_cell_manager=True)

    start_year = 1987
    end_year = 1987
    months = [1, 2, 12]
    rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)

    basemap = Basemap(projection="omerc",
                      llcrnrlon=managerHighRes.lons2D[0, 0],
                      llcrnrlat=managerHighRes.lats2D[0, 0],
                      urcrnrlon=managerHighRes.lons2D[-1, -1],
                      urcrnrlat=managerHighRes.lats2D[-1, -1],
                      lat_1=rot_lat_lon.lat1,
                      lat_2=rot_lat_lon.lat2,
                      lon_1=rot_lat_lon.lon1,
                      lon_2=rot_lat_lon.lon2,
                      no_rot=True)

    swe_obs = swe_obs_manager.get_mean(start_year, end_year, months=months)

    obs_ihr = swe_obs_manager.interpolate_data_to(swe_obs,
                                                  managerHighRes.lons2D,
                                                  managerHighRes.lats2D,
                                                  nneighbours=1)

    obs_ilr = swe_obs_manager.interpolate_data_to(swe_obs,
                                                  managerLowRes.lons2D,
                                                  managerLowRes.lats2D,
                                                  nneighbours=1)

    lowResSwe = managerLowRes.get_mean_field(start_year,
                                             end_year,
                                             months=months,
                                             var_name="I5")

    lowResErr = (lowResSwe - obs_ilr)
    lowResErr[obs_ilr > 0] /= obs_ilr[obs_ilr > 0]
    lowResErr = np.ma.masked_where(obs_ilr <= 0, lowResErr)

    highResSwe = managerHighRes.get_mean_field(start_year,
                                               end_year,
                                               months=months,
                                               var_name="I5")
    highResErr = (highResSwe - obs_ihr)
    highResErr[obs_ihr > 0] /= obs_ihr[obs_ihr > 0]
    highResErr = np.ma.masked_where(obs_ihr <= 0, highResErr)

    upscaledHighResSwe = upscale(managerHighRes, managerLowRes, highResSwe)
    upscaledHighResErr = upscaledHighResSwe - obs_ilr
    good_points = obs_ilr > 0
    upscaledHighResErr[good_points] /= obs_ilr[good_points]
    upscaledHighResErr = np.ma.masked_where(~good_points, upscaledHighResErr)

    plot_and_compare_2fields(lowResSwe,
                             "low res",
                             upscaledHighResSwe,
                             "high res (upscaled)",
                             basemap=basemap,
                             manager1=managerLowRes,
                             manager2=managerLowRes)

    plot_and_compare_2fields(lowResErr,
                             "low res err",
                             upscaledHighResErr,
                             "high res (upscaled) err",
                             basemap=basemap,
                             manager1=managerLowRes,
                             manager2=managerLowRes,
                             clevs=np.arange(-1, 1.2, 0.2))

    plot_and_compare_2fields(lowResSwe,
                             "low res",
                             highResSwe,
                             "high res",
                             basemap=basemap,
                             manager1=managerLowRes,
                             manager2=managerHighRes)

    plot_and_compare_2fields(lowResErr,
                             "low res err",
                             highResErr,
                             "high res err",
                             basemap=basemap,
                             manager1=managerLowRes,
                             manager2=managerHighRes,
                             clevs=np.arange(-1, 1.2, 0.2))

    plt.show()
Exemplo n.º 31
0
def main():
    #path = "/RECH/data/Simulations/CRCM5/North_America/NorthAmerica_0.44deg_ERA40-Int_B1/Diagnostics/NorthAmerica_0.44deg_ERA40-Int_B1_2007{:02d}"
    path = "/RESCUE/skynet3_rech1/huziy/from_guillimin/new_outputs/current_climate_30_yr_sims/quebec_0.1_crcm5-hcd-rl-intfl_ITFS/Samples/quebec_crcm5-hcd-rl-intfl_1988{:02d}"

    months = [6, 7, 8]

    pm_list = []
    dm_list = []
    for m in months:
        print(path.format(m))

        month_folder = path.format(m)
        for fn in os.listdir(month_folder):

            # if not fn.endswith("moyenne"):
            #    continue

            if fn.startswith("pm"):
                pm_list.append(os.path.join(month_folder, fn))
            elif fn.startswith("dm"):
                dm_list.append(os.path.join(month_folder, fn))

    pm = MultiRPN(pm_list)
    dm = MultiRPN(dm_list)

    tsurf_mean = np.mean([
        field for field in pm.get_all_time_records_for_name_and_level(
            varname="J8").values()
    ],
                         axis=0)
    tair_mean = np.mean([
        field for field in dm.get_all_time_records_for_name_and_level(
            varname="TT", level=1, level_kind=level_kinds.HYBRID).values()
    ],
                        axis=0)

    lons, lats = pm.get_longitudes_and_latitudes_of_the_last_read_rec()

    projparams = pm.linked_robj_list[
        0].get_proj_parameters_for_the_last_read_rec()

    rll = RotatedLatLon(**projparams)
    bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)

    xx, yy = bmp(lons, lats)

    plt.figure()
    cs = bmp.contourf(xx, yy, tsurf_mean - 273.15, 40)
    bmp.drawcoastlines()
    plt.title("Tsurf")
    plt.colorbar()

    plt.figure()
    bmp.contourf(xx,
                 yy,
                 tair_mean,
                 levels=cs.levels,
                 norm=cs.norm,
                 cmap=cs.cmap)
    bmp.drawcoastlines()
    plt.title("Tair")
    plt.colorbar()

    plt.figure()
    bmp.contourf(xx,
                 yy,
                 tsurf_mean - 273.15 - tair_mean,
                 levels=np.arange(-2, 2.2, 0.2),
                 cmap=cs.cmap)
    bmp.drawcoastlines()
    plt.title("Tsurf - Tair")
    plt.colorbar()

    pm.close()
    dm.close()

    plt.show()
Exemplo n.º 32
0
def plot_acc_area_with_glaciers():
    gmask_vname = "VF"
    gmask_level = 2
    # gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
    gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
    
    # stab reading of the glacier mask
    # r = RPN(gmask_path)
    # gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
    #                                              level=gmask_level)
    
    r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
    r.get_first_record_for_name("PR") # Because I almost sure that PR is there
    proj_params = r.get_proj_parameters_for_the_last_read_rec()
    rll = RotatedLatLon(**proj_params)
    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="i")
    # gmask = np.ma.masked_where(gmask < 0.01, gmask)
    gmask = np.ma.masked_all(lons.shape)
    mask_value = 0.25
    gmask[~gmask.mask] = mask_value

    path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
    # path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
    ds = Dataset(path)


    margin = 20

    var_name = "accumulation_area"
    data = ds.variables[var_name][margin:-margin, margin:-margin]

    data = np.ma.masked_where(data <= 0, data)

    # flow directions
    fldr = ds.variables["flow_direction_value"][:][margin:-margin, margin:-margin]

    i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)



    x, y = basemap(lons, lats)
    fig = plt.figure(figsize=(15, 15))

    dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
    dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])

    x1 = x - dx / 2.0
    y1 = y - dy / 2.0

    # im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
    # cb = basemap.colorbar(im)
    # cb.ax.tick_params(labelsize=25)

    cmap = cm.get_cmap("gray_r", 10)

    basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)

    nx, ny = x.shape
    inds_j, inds_i = np.meshgrid(range(ny), range(nx))
    inds_i_next = inds_i + i_shifts
    inds_j_next = inds_j + j_shifts

    inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
    inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)


    u = np.ma.masked_all_like(x)
    v = np.ma.masked_all_like(x)

    good = (~inds_i_next.mask) & (~inds_j_next.mask)
    u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
    v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]

    basemap.quiver(x, y, u, v,
                   pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1)

    basemap.drawcoastlines(linewidth=0.5)

    basemap.drawrivers(color="b")

    # plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
    #                      "upsas",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
    #                      zorder=2, linewidth=0.5, color="b")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
    #                      zorder=2, linewidth=0.5, color="b")


    basemap.readshapefile("/skynet3_exec2/aganji/NE_can/bow_river/bow_projected", "basin", color="m", linewidth=2)

    # plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
    plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")
    
    plt.show()
Exemplo n.º 33
0
def main():
    """

    """

    # s_lons = [-3.38, 3.40, -12.45, -11.04,-2.92, 0.1,32.55,30.48,23.6,31.27,15.31,23.91,17.51,21.08]
    # s_lats = [16.26,11.88,14.9,13.91,10.57,6.2,15.61,19.18,-14.02,-21.13,-4.26,4.97,-28.71,-28.69]

    # assert len(s_lons) == len(s_lats)
    #path = "data/directions_Africa_Bessam_0.44/infocell_Africa.nc"
    #path = "/home/huziy/skynet3_exec1/for_offline_routing/directions_africa_dx0.44deg_2.nc"
    path = "/skynet3_rech1/huziy/for_Arman_routing_data/infocell_na_0.44deg_arman.nc"
    ds = Dataset(path)

    dirs = ds.variables["flow_direction_value"][:]
    acc_area = ds.variables["accumulation_area"][:]

    lons2d = ds.variables["lon"][:]
    lats2d = ds.variables["lat"][:]

    lon_1, lat_1 = -97, 47.5
    lon_2, lat_2 = -7, 0

    lons2d[lons2d >= 180] -= 360

    #min_lon = lons2d[0,0]
    #max_lon = lons2d[-1,-1]
    #min_lat = lats2d[0, 0]
    #max_lat = lats2d[-1,-1]

    #plot_utils.apply_plot_params(width_pt=None, width_cm=80)
    #print max_lon

    fig = plt.figure(dpi=800)
    #TODO: change projection to rotpole (it will require params)
    #b = Basemap(projection="rotpole", llcrnrlon=min_lon,
    #    llcrnrlat=min_lat,
    #    urcrnrlon=max_lon, urcrnrlat=max_lat, resolution="i")
    myproj = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
    print(lons2d.shape)
    b = myproj.get_basemap_object_for_lons_lats(lons2d[100:150, 70:132],
                                                lats2d[100:150, 70:132])

    x, y = b(lons2d, lats2d)
    b.drawcoastlines(linewidth=0.2, color="0.5")

    #    b.pcolormesh(x, y, np.ma.masked_where(dirs <= 0, dirs ))
    #    plt.colorbar()

    di_list = np.array([1, 1, 0, -1, -1, -1, 0, 1])
    dj_list = np.array([0, -1, -1, -1, 0, 1, 1, 1])

    delta_indices = np.log2(dirs[dirs > 0])
    delta_indices = delta_indices.astype(int)

    di = di_list[delta_indices]
    dj = dj_list[delta_indices]

    acc_area = np.ma.masked_where(acc_area < 0, acc_area)
    #img = b.pcolormesh(x, y, np.ma.log(acc_area))

    ax = plt.gca()
    #divider = make_axes_locatable(ax)
    #cax = divider.append_axes("right", "5%", pad="3%")

    #plt.colorbar(img, cax = cax)

    i_indices_1d = list(range(dirs.shape[0]))
    j_indices_1d = list(range(dirs.shape[1]))

    j_indices_2d, i_indices_2d = np.meshgrid(j_indices_1d, i_indices_1d)

    i_indices_2d_next = np.zeros_like(i_indices_2d)
    j_indices_2d_next = np.zeros_like(i_indices_2d)

    i_indices_2d_next[dirs > 0] = i_indices_2d[dirs > 0] + di
    j_indices_2d_next[dirs > 0] = j_indices_2d[dirs > 0] + dj

    for i, j, i_next, j_next in zip(i_indices_2d[dirs > 0],
                                    j_indices_2d[dirs > 0],
                                    i_indices_2d_next[dirs > 0],
                                    j_indices_2d_next[dirs > 0]):
        # ax.add_line(Line2D([x[i,j], x[i_next, j_next]], [y[i,j], y[i_next, j_next]], linewidth=0.5))
        if i == i_next and j == j_next:
            continue
        p1 = [x[i, j], y[i, j]]
        p2 = [x[i_next, j_next], y[i_next, j_next]]
        dr = [p2[0] - p1[0], p2[1] - p1[1]]
        ax.add_patch(
            FancyArrow(p1[0],
                       p1[1],
                       dr[0],
                       dr[1],
                       linewidth=0.5,
                       fc="k",
                       head_width=0.1,
                       length_includes_head=True))

    # x1, y1 = b(s_lons, s_lats)
    # b.scatter(x1, y1, c="r", linewidth=0, zorder = 7, ax = ax)
    # b.drawrivers(linewidth=0.5, color="#0cf5f8", zorder=8, ax=ax)
    # b.drawmeridians(np.arange(-10, 90,30))
    # b.drawparallels(np.arange(-50, 40, 5), labels=[1,1,1,1], linewidth=0.1)
    plt.tight_layout()
    # plt.show()

    #b.readshapefile("/home/huziy/skynet3_exec1/other_shape/af_major_basins/af_basins", "basin",
    #    linewidth=3, zorder=9, ax=ax
    #)
    plt.savefig("with_station_riv_af_dirs_basin_1.0.eps")

    pass
Exemplo n.º 34
0
def demo_arctic_proj():
    rll = RotatedLatLon(lon1=60, lat1=90, lon2=-30, lat2=0)

    print(rll.get_north_pole_coords(), rll.get_true_pole_coords_in_rotated_system())

    pass
Exemplo n.º 35
0
def plot_lake_fraction_field():
    folder = "/home/huziy/skynet3_rech1/geof_lake_infl_exp"
    fName = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
    path = os.path.join(folder, fName)

    rObj = RPN(path)

    lkf = rObj.get_first_record_for_name_and_level(varname="VF", level=3, level_kind=level_kinds.ARBITRARY)

    proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
    lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()

    lons2d[lons2d >= 180] -= 360
    rObj.close()

    rll = RotatedLatLon(**proj_params)

    margin = 20
    lons2d = lons2d[margin:-margin, margin:-margin]
    lats2d = lats2d[margin:-margin, margin:-margin]
    lkf = lkf[margin:-margin, margin:-margin]

    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d, resolution="l")
    x, y = basemap(lons2d, lats2d)

    fig = plt.figure()
    gs = GridSpec(1, 2, width_ratios=[1, 1])

    ax = fig.add_subplot(gs[0, 0])
    df = 0.1
    levels = np.arange(0, 1.1, df)
    cMap = get_cmap("gist_ncar_r", len(levels) - 1)
    bn = BoundaryNorm(levels, cMap.N)

    basemap.drawmapboundary(fill_color="0.75")
    lkf_plot = maskoceans(lons2d, lats2d, lkf, inlands=False)
    print("Percentage of lakes in the sim domain: {}".format(lkf_plot.mean() * 100))

    img = basemap.pcolormesh(x, y, lkf_plot, norm=bn, cmap=cMap)
    basemap.drawcoastlines()

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("bottom", "5%", pad="3%")
    cb = fig.colorbar(img, cax=cax, ticks=levels, orientation="horizontal")

    ax = fig.add_subplot(gs[0, 1])
    df1 = df
    levels1 = np.arange(0, 1.1, df1)
    cell_numms = np.zeros((len(levels1) - 1,))

    left = levels[0]
    right = levels[1]

    lefts = []
    rights = []
    lkf_land = lkf[lkf > 0.01]
    for i in range(len(cell_numms)):
        cell_numms[i] = ((lkf_land > left) & (lkf_land <= right)).astype(int).sum()
        lefts.append(left)
        rights.append(right)
        left += df1
        right += df1

    assert isinstance(ax, Axes)
    ax.bar(lefts, cell_numms, width=df1)

    # ax.semilogy(rights, cell_numms)
    ax.xaxis.set_ticks(levels)
    ax.yaxis.set_ticks(np.arange(1000, 10000, 1000))
    sf = ScalarFormatter(useMathText=True)
    sf.set_powerlimits([-2, 1])
    ax.yaxis.set_major_formatter(sf)

    ax.grid("on")
    ax.set_xlabel("fraction")
    ax.set_ylabel("# gridcells")

    plt.show()
    fig.tight_layout()
    fig.savefig("lake_fractions_220x220_0.1deg.jpeg")
    plt.show()

    pass
Exemplo n.º 36
0
def plot_acc_area_with_glaciers():
    gmask_vname = "VF"
    gmask_level = 2
    # gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
    gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"

    # stab reading of the glacier mask
    # r = RPN(gmask_path)
    # gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
    #                                              level=gmask_level)

    r = RPN(
        "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p"
    )
    r.get_first_record_for_name("PR")  # Because I almost sure that PR is there
    proj_params = r.get_proj_parameters_for_the_last_read_rec()
    rll = RotatedLatLon(**proj_params)
    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons,
                                                   lats2d=lats,
                                                   resolution="i")
    # gmask = np.ma.masked_where(gmask < 0.01, gmask)
    gmask = np.ma.masked_all(lons.shape)
    mask_value = 0.25
    gmask[~gmask.mask] = mask_value

    path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
    # path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
    ds = Dataset(path)

    margin = 20

    var_name = "accumulation_area"
    data = ds.variables[var_name][margin:-margin, margin:-margin]

    data = np.ma.masked_where(data <= 0, data)

    # flow directions
    fldr = ds.variables["flow_direction_value"][:][margin:-margin,
                                                   margin:-margin]

    i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)

    x, y = basemap(lons, lats)
    fig = plt.figure(figsize=(15, 15))

    dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
    dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])

    x1 = x - dx / 2.0
    y1 = y - dy / 2.0

    # im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
    # cb = basemap.colorbar(im)
    # cb.ax.tick_params(labelsize=25)

    cmap = cm.get_cmap("gray_r", 10)

    basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)

    nx, ny = x.shape
    inds_j, inds_i = np.meshgrid(range(ny), range(nx))
    inds_i_next = inds_i + i_shifts
    inds_j_next = inds_j + j_shifts

    inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
                                     inds_i_next)
    inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
                                     inds_j_next)

    u = np.ma.masked_all_like(x)
    v = np.ma.masked_all_like(x)

    good = (~inds_i_next.mask) & (~inds_j_next.mask)
    u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good],
                                                          inds_j[good]]
    v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good],
                                                          inds_j[good]]

    basemap.quiver(x,
                   y,
                   u,
                   v,
                   pivot="tail",
                   width=0.0005,
                   scale_units="xy",
                   headlength=20,
                   headwidth=15,
                   scale=1)

    basemap.drawcoastlines(linewidth=0.5)

    basemap.drawrivers(color="b")

    # plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
    #                      "upsas",
    #                      zorder=2, linewidth=3, color="m")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
    #                      zorder=2, linewidth=0.5, color="b")

    # basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
    #                      zorder=2, linewidth=0.5, color="b")

    basemap.readshapefile(
        "/skynet3_exec2/aganji/NE_can/bow_river/bow_projected",
        "basin",
        color="m",
        linewidth=2)

    # plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
    plt.savefig(
        "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png",
        bbox_inches="tight")

    plt.show()
Exemplo n.º 37
0
class GridConfig(object):
    projection = "rotpole"

    def __init__(self, **kwargs):
        self.dx = self.dy = kwargs.get("dx", -1)
        self.iref, self.jref = kwargs.get("iref", -1), kwargs.get("jref", -1)

        self.xref, self.yref = kwargs.get("xref", -1), kwargs.get("yref", -1)
        self.ni, self.nj = kwargs.get("ni", -1), kwargs.get("nj", -1)

        # interpolated driving data (width of the outer band in number of gridpoints)
        self.halo = 10

        # size of the blending zone in grid points
        self.blendig = 10

        self.rll = None
        if "rll" not in kwargs:
            self.lon1, self.lat1 = kwargs.get("lon1",
                                              None), kwargs.get("lat1", None)
            self.lon2, self.lat2 = kwargs.get("lon2",
                                              None), kwargs.get("lat2", None)
            if None not in (self.lon1, self.lon2, self.lat1, self.lat2):
                self.rll = RotatedLatLon(lon1=self.lon1,
                                         lon2=self.lon2,
                                         lat1=self.lat1,
                                         lat2=self.lat2)
        else:
            self.rll = kwargs.get("rll")

        # private fields
        self._center_lons_2d = None
        self._center_lats_2d = None

    @classmethod
    def get_default_for_resolution(cls, res=0.5):
        """
        :param res:
        :rtype GridConfig
        """
        obj = GridConfig()
        obj.dx = obj.dy = res
        if res == 0.5:
            obj.iref = 46  # starts from 1 not 0!!
            obj.jref = 42  # starts from 1 not 0!!
            obj.ni = 86
            obj.nj = 86
        elif res == 0.1:
            obj.iref = 142  # no need to do -1, doing it later in the formulas
            obj.jref = 122
            obj.xref = 180  # rotated longitude
            obj.yref = 0  # rotated latitude

            # projection parameters
            obj.lon1 = -68.0
            obj.lat1 = 52.0

            obj.lon2 = 16.65
            obj.lat2 = 0.0

            obj.ni = 260
            obj.nj = 260

        return obj

    def export_to_shape(self,
                        shp_folder="",
                        shp_filename="",
                        free_zone_only=True,
                        export_mask=None,
                        shape_fields=None):
        """
        export the grid to the shape file
        :param export_mask: Mask to specify exactly which gridcells should be exported
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        import shapefile as shp

        w = shp.Writer(shp.POLYGON)

        w.field("i", fieldType="I")
        w.field("j", fieldType="I")

        field_names_in_order = ["i", "j"]

        if shape_fields is not None:
            for field_name, field in shape_fields.items():
                w.field(field_name, *field.type_of_shp_field)

                field_names_in_order.append(field_name)

        if isinstance(shp_folder, str):
            folder = Path(shp_folder)
        else:
            folder = shp_folder

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        if export_mask is None:
            export_mask = np.ones((self.ni, self.nj), dtype=bool)

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                if not export_mask[i, j]:
                    continue

                p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                  y - self.dy / 2.0)
                p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                  y + self.dy / 2.0)
                p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                  y + self.dy / 2.0)
                p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                  y - self.dy / 2.0)

                w.poly(parts=[[p00, p01, p11, p10]])

                if shape_fields is None:
                    w.record(i + 1, j + 1)
                else:
                    record_fields = {}
                    record_fields["i"] = i + 1
                    record_fields["j"] = j + 1
                    for field_name, field in shape_fields.items():
                        record_fields[field_name] = field[i, j]

                    w.record(
                        *[record_fields[key] for key in field_names_in_order])

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        w.save(str(folder.joinpath(shp_filename)))

    def export_to_shape_fiona(self,
                              shp_folder="",
                              shp_filename="",
                              free_zone_only=True,
                              export_mask=None,
                              shape_fields=None):
        """
        export the grid to the shape file
        using fiona since pyshp was not compatible with arcgis
        :param export_mask: Mask to specify exactly which gridcells should be exported
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """
        from fiona.crs import from_epsg, from_string
        import fiona

        proj = from_epsg(4326)
        # proj = from_epsg(4269)
        # proj = from_string("+units=m +lon_0=-97.0 +o_lon_p=180.0 +R=6370997.0 +o_proj=longlat +proj=ob_tran +o_lat_p=42.5")
        print(proj)
        print(dir(proj))
        # proj = from_epsg(900913)

        if isinstance(shp_folder, str):
            folder = Path(shp_folder)
        else:
            folder = shp_folder

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        schema = {
            "geometry": "Polygon",
            "properties": OrderedDict([("i", "int"), ("j", "int")])
        }

        if shape_fields is not None:
            # additional fields
            for field_name, field in shape_fields.items():
                schema["properties"][field_name] = field.type_of_shp_field

        with fiona.open(str(folder.joinpath(shp_filename)),
                        mode="w",
                        driver="ESRI Shapefile",
                        crs=proj,
                        schema=schema) as output:

            lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                    for i in range(self.ni)]
            latr = [(j - (self.jref - 1)) * self.dy + self.yref
                    for j in range(self.nj)]

            margin = 0
            if free_zone_only:
                margin = self.blendig + self.halo

            start_i = margin
            start_j = margin

            end_i = self.ni - margin - 1
            end_j = self.nj - margin - 1

            if export_mask is None:
                export_mask = np.ones((self.ni, self.nj), dtype=bool)

            polygons = []
            lake_fractions = []

            for i in range(start_i, end_i + 1):
                x = lonr[i]

                for j in range(start_j, end_j + 1):
                    y = latr[j]

                    if not export_mask[i, j]:
                        continue

                    p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                      y - self.dy / 2.0)
                    p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                      y + self.dy / 2.0)
                    p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                      y + self.dy / 2.0)
                    p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                      y - self.dy / 2.0)

                    # p00 = (x - self.dx / 2.0, y - self.dy / 2.0)
                    # p01 = (x - self.dx / 2.0, y + self.dy / 2.0)
                    # p11 = (x + self.dx / 2.0, y + self.dy / 2.0)
                    # p10 = (x + self.dx / 2.0, y - self.dy / 2.0)

                    poly = Polygon(shell=[p00, p01, p11, p10, p00])
                    props = OrderedDict([("i", i + 1), ("j", j + 1)])

                    polygons.append(PolygonPatch(poly))

                    if shape_fields is not None:

                        lake_fractions.append(shape_fields["lkfr"][i, j])
                        for field_name, field in shape_fields.items():
                            converter = float if field.type_of_shp_field.startswith(
                                "float") else int
                            props[field_name] = converter(field[i, j])

                    output.write({
                        "geometry": mapping(poly),
                        "properties": props
                    })

            # plot the gridcells with basemap
            # pcol = PatchCollection(polygons, cmap="bone_r")
            # pcol.set_array(np.array(lake_fractions))
            # import matplotlib.pyplot as plt
            # bmp = self.get_basemap_for_free_zone(resolution="l")
            # fig = plt.figure()
            # ax = fig.add_subplot(111)
            # bmp.ax = ax
            # ax.add_collection(pcol)
            # bmp.drawcoastlines(ax=ax)
            # plt.show()

    def export_to_shape_ogr(self,
                            shp_folder="",
                            shp_filename="",
                            free_zone_only=True):
        """
        export the grid to the shape file
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        from osgeo import ogr, osr

        folder = Path(shp_folder)

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        # set up the shapefile driver
        driver = ogr.GetDriverByName("ESRI Shapefile")

        if not shp_filename.lower().endswith(".shp"):
            shp_filename += ".shp"

        # create the data source
        data_source = driver.CreateDataSource(
            str(folder.joinpath(shp_filename)))

        srs = osr.SpatialReference()
        srs.ImportFromWkt(osr.SRS_WKT_WGS84)
        print(srs)
        print(srs.ExportToPrettyWkt())

        # create the layer
        layer = data_source.CreateLayer("grid", srs, ogr.wkbPolygon)
        layer.CreateField(ogr.FieldDefn("i", ogr.OFTInteger))
        layer.CreateField(ogr.FieldDefn("j", ogr.OFTInteger))

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                # create the feature
                feature = ogr.Feature(layer.GetLayerDefn())

                p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                  y - self.dy / 2.0)
                p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0,
                                                  y + self.dy / 2.0)
                p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                  y + self.dy / 2.0)
                p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0,
                                                  y - self.dy / 2.0)

                ring = ogr.Geometry(ogr.wkbLinearRing)
                ring.AddPoint(*p00)
                ring.AddPoint(*p01)
                ring.AddPoint(*p11)
                ring.AddPoint(*p10)

                poly = ogr.Geometry(ogr.wkbPolygon)
                poly.AddGeometry(ring)

                feature.SetField("i", i + 1)
                feature.SetField("j", j + 1)

                feature.SetGeometry(poly)

                layer.CreateFeature(feature)
                feature.Destroy()

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        data_source.Destroy()

    def export_to_shape_native_grid(self,
                                    shp_folder="",
                                    shp_filename="",
                                    free_zone_only=True):
        """
        export the grid to the shape file
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        from osgeo import ogr, osr

        folder = Path(shp_folder)

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        # set up the shapefile driver
        driver = ogr.GetDriverByName("ESRI Shapefile")

        if not shp_filename.lower().endswith(".shp"):
            shp_filename += ".shp"

        # create the data source
        data_source = driver.CreateDataSource(
            str(folder.joinpath(shp_filename)))

        # Projection
        srs = osr.SpatialReference()

        bmp = self.get_basemap_for_free_zone()
        srs.ImportFromProj4(bmp.proj4string)
        print(srs)
        print(srs.ExportToPrettyWkt())

        # create the layer
        layer = data_source.CreateLayer("grid", srs, ogr.wkbPolygon)
        layer.CreateField(ogr.FieldDefn("i", ogr.OFTInteger))
        layer.CreateField(ogr.FieldDefn("j", ogr.OFTInteger))

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                # create the feature
                feature = ogr.Feature(layer.GetLayerDefn())

                p00 = (x - self.dx / 2.0, y - self.dy / 2.0)
                p01 = (x - self.dx / 2.0, y + self.dy / 2.0)
                p11 = (x + self.dx / 2.0, y + self.dy / 2.0)
                p10 = (x + self.dx / 2.0, y - self.dy / 2.0)

                ring = ogr.Geometry(ogr.wkbLinearRing)
                ring.AddPoint(*p00)
                ring.AddPoint(*p01)
                ring.AddPoint(*p11)
                ring.AddPoint(*p10)

                poly = ogr.Geometry(ogr.wkbPolygon)
                poly.AddGeometry(ring)

                feature.SetField("i", i + 1)
                feature.SetField("j", j + 1)

                feature.SetGeometry(poly)

                layer.CreateFeature(feature)
                feature.Destroy()

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        data_source.Destroy()

    def get_basemap_for_free_zone(self, halo=None, blending=None, **kwargs):
        if halo is None:
            halo = self.halo

        if blending is None:
            blending = self.blendig

        lons_c, lats_c = self.get_free_zone_corners(halo=halo,
                                                    blending=blending)
        return self.get_basemap(lons=lons_c, lats=lats_c, **kwargs)

    def get_basemap_using_shape_with_polygons_of_interest(
            self, lons, lats, shp_path=None, mask_margin=5, **kwargs):

        if shp_path is None:
            return self.get_basemap(lons=lons, lats=lats, **kwargs)

        reg_of_interest = get_mask(lons, lats, shp_path=shp_path) > 0

        i_list, j_list = np.where(reg_of_interest)

        i_min = min(i_list) - mask_margin
        i_max = max(i_list) + mask_margin

        j_min = min(j_list) - mask_margin
        j_max = max(j_list) + mask_margin

        bsmap = self.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1],
                                 lats=lats[i_min:i_max + 1, j_min:j_max + 1],
                                 **kwargs)
        return bsmap, reg_of_interest

    def get_basemap(self, lons=None, lats=None, **kwargs):

        if lons is None:

            lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                    for i in [0, self.ni - 1]]
            latr = [(j - (self.jref - 1)) * self.dy + self.yref
                    for j in [0, self.nj - 1]]

            latr, lonr = np.meshgrid(latr, lonr)

            lons = np.zeros((2, 2))
            lats = np.zeros((2, 2))

            for i in [-1, 0]:
                for j in [-1, 0]:
                    shiftx = self.dx / 2.0
                    shifty = self.dy / 2.0

                    shiftx = -shiftx if i == 0 else shiftx
                    shifty = -shifty if i == 0 else shifty

                    lons[i, j], lats[i, j] = self.rll.toGeographicLonLat(
                        lonr[i, j] + shiftx, latr[i, j] + shifty)

        return self.get_rot_latlon_proj_obj().get_basemap_object_for_lons_lats(
            lons2d=lons, lats2d=lats, **kwargs)

    def get_corners_in_proj_coords(self):
        """
        :return: xxcorners, yycorners

             xxcorners = [[xll, xur], [xll, xur]]
             yycorners = [[yll, yll], [yur, yur]]

        """

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in [0, self.ni - 1]]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in [0, self.nj - 1]]
        latr, lonr = np.meshgrid(latr, lonr)

        return lonr, latr

    def get_free_zone_corners(self, halo=10, blending=10):

        margin = halo + blending
        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in [margin, self.ni - margin - 1]]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in [margin, self.nj - margin - 1]]

        latr, lonr = np.meshgrid(latr, lonr)

        lons = np.zeros((2, 2))
        lats = np.zeros((2, 2))

        for i in [-1, 0]:
            mulx = -1 if i >= 0 else 1
            shiftx = mulx * self.dx / 2.0

            for j in [-1, 0]:
                muly = -1 if j >= 0 else 1
                shifty = muly * self.dy / 2.0

                lons[i, j], lats[i, j] = self.rll.toGeographicLonLat(
                    lonr[i, j] + shiftx, latr[i, j] + shifty)

        return lons, lats

    def get_rot_latlon_proj_obj(self):
        return self.rll

    def subgrid(self, i0, j0, di=-1, dj=-1):
        """

        :param i0: 0-based i-index of the lower left corner of the domain
        :param j0:
        :param di: number of grid points in i direction
        :param dj: number of grid points in j direction
        """

        subgr = GridConfig(rll=self.rll,
                           dx=self.dx,
                           dy=self.dy,
                           xref=self.xref,
                           yref=self.yref)

        if di > 0:
            subgr.ni = di
        else:
            subgr.ni = self.ni - i0

        if dj > 0:
            subgr.nj = dj
        else:
            subgr.nj = self.nj - j0

        subgr.iref = self.iref - i0
        subgr.jref = self.jref - j0

        return subgr

    def copy(self):
        return self.subgrid(0, 0, di=self.ni, dj=self.nj)

    def double_resolution(self):
        gc = GridConfig(rll=self.rll,
                        dx=self.dx / 2.0,
                        dy=self.dy / 2.0,
                        xref=self.xref,
                        yref=self.yref)
        gc.ni = 2 * self.ni
        gc.nj = 2 * self.nj

        gc.iref = 2 * self.iref
        gc.jref = 2 * self.jref

        gc.xref -= gc.dx / 2.0
        gc.yref -= gc.dy / 2.0

        return gc

    def double_resolution_keep_free_domain_same(self,
                                                halo_pts=10,
                                                blending_pts=10):
        gc = GridConfig(rll=self.rll,
                        dx=self.dx / 2.0,
                        dy=self.dy / 2.0,
                        xref=self.xref,
                        yref=self.yref)
        margin_pts = halo_pts + blending_pts
        gc.ni = 2 * (self.ni - 2 * margin_pts) + 2 * margin_pts
        gc.nj = 2 * (self.nj - 2 * margin_pts) + 2 * margin_pts

        gc.iref = 2 * (self.iref - margin_pts) + margin_pts
        gc.jref = 2 * (self.jref - margin_pts) + margin_pts

        gc.xref -= gc.dx / 2.0
        gc.yref -= gc.dy / 2.0

        return gc

    def decrease_resolution_keep_free_domain_same(self,
                                                  factor,
                                                  halo_pts=10,
                                                  blending_pts=10):
        gc = GridConfig(rll=self.rll,
                        dx=self.dx * factor,
                        dy=self.dy * factor,
                        xref=self.xref,
                        yref=self.yref)
        margin_pts = halo_pts + blending_pts
        gc.ni = (self.ni - 2 * margin_pts) / factor + 2 * margin_pts
        gc.nj = (self.nj - 2 * margin_pts) / factor + 2 * margin_pts

        # Change the reference point if the new iref and jref cannot be the same

        new_iref = self.iref - margin_pts
        new_jref = self.jref - margin_pts

        new_iref = new_iref // factor + (new_iref % factor != 0)
        x00 = self.xref + self.dx * (margin_pts + 1 -
                                     self.iref) - self.dx / 2.0
        new_xref = x00 + new_iref * self.dx * factor - self.dx * factor / 2.0

        new_jref = new_jref // factor + (new_jref % factor != 0)
        y00 = self.yref + self.dy * (margin_pts + 1 -
                                     self.jref) - self.dy / 2.0
        new_yref = y00 + new_jref * self.dy * factor - self.dy * factor / 2.0

        gc.iref = new_iref + margin_pts
        gc.jref = new_jref + margin_pts

        gc.xref = new_xref
        gc.yref = new_yref

        gc.ni = int(gc.ni)
        gc.nj = int(gc.nj)

        return gc

    def move(self, di=0, dj=0):
        gc = GridConfig(rll=self.rll,
                        dx=self.dx,
                        dy=self.dy,
                        xref=self.xref,
                        yref=self.yref,
                        ni=self.ni,
                        nj=self.nj)
        gc.iref -= di
        gc.jref -= dj
        return gc

    def expand(self, di=0, dj=0):
        gc = GridConfig(rll=self.rll,
                        dx=self.dx,
                        dy=self.dy,
                        xref=self.xref,
                        yref=self.yref,
                        ni=self.ni + di,
                        nj=self.nj + dj)
        gc.iref = self.iref
        gc.jref = self.jref
        return gc

    def __str__(self):
        s = """
              Grd_ni        =  {ni}     , Grd_nj         =    {nj}     ,
              Grd_dx        =  {dx}     , Grd_dy         =    {dy},
              Grd_iref      =  {iref}     ,  Grd_jref       =   {jref}     ,
              Grd_latr      =    {latref}   ,  Grd_lonr       =  {lonref}   ,
              Grd_xlat1     =   {lat1}   ,  Grd_xlon1       = {lon1}    ,
              Grd_xlat2     =    {lat2}    ,  Grd_xlon2       =  {lon2}    ,
        """.format(ni=self.ni,
                   nj=self.nj,
                   dx=self.dx,
                   dy=self.dy,
                   iref=self.iref,
                   jref=self.jref,
                   latref=self.yref,
                   lonref=self.xref,
                   lat1=self.rll.lat1,
                   lon1=self.rll.lon1,
                   lat2=self.rll.lat2,
                   lon2=self.rll.lon2)

        return s

    def __hash__(self):
        return hash(str(self).lower())

    def __eq__(self, other):
        if other is None:
            return False

        return str(self).lower() == str(other).lower()

    def get_lons_and_lats_of_gridpoint_centers(self):
        """
        :return: lons and lats corresponding to the gridpoint centers
        """

        if self._center_lons_2d is not None:
            return self._center_lons_2d, self._center_lats_2d

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref
                for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref
                for j in range(self.nj)]

        lons = np.zeros((self.ni, self.nj))
        lats = np.zeros_like(lons)

        for i, lonri in enumerate(lonr):
            for j, latrj in enumerate(latr):
                lons[i,
                     j], lats[i,
                              j] = self.rll.toGeographicLonLat(lonri, latrj)

        # Save for re-use
        self._center_lons_2d = lons
        self._center_lats_2d = lats

        return lons, lats
Exemplo n.º 38
0
    def get_coords_and_basemap(self, subregion=None, reload=True, **basemap_kwargs):
        """
        :return: lons2d, lats2d, basemap [based on the bathymetry file and gemclim_settings.nml]
        if reload is True, do not use cached arrays even if they are available
        """

        # Read longitudes and latitudes and create the basemap only if they are not initialized
        if self.basemap is None or reload:
            with Dataset(os.path.join(self.data_folder, self.bathymetry_file)) as ds:

                if "nav_lon" in ds.variables:
                    self.lons, self.lats = ds.variables["nav_lon"][:].transpose(), ds.variables["nav_lat"][:].transpose()
                else:
                    for vname, v in ds.variables.items():
                        if "lon" in vname.lower():
                            self.lons = v[:].T
                            continue

                        if "lat" in vname.lower():
                            self.lats = v[:].T
                            continue

                        if self.lons is not None and self.lats is not None:
                            break


                import re

                lon1, lat1 = None, None
                lon2, lat2 = None, None
                with open(os.path.join(self.data_folder, self.proj_file)) as f:
                    for line in f:
                        if "Grd_xlat1" in line and "Grd_xlon1" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat1, lon1 = [float(s) for s in groups]

                        if "Grd_xlat2" in line and "Grd_xlon2" in line:
                            groups = re.findall(r"-?\b\d+.?\d*\b", line)
                            lat2, lon2 = [float(s) for s in groups]

                rll = RotatedLatLon(lon1=lon1, lat1=lat1, lon2=lon2, lat2=lat2)


                nx, ny = self.lons.shape
                if subregion is not None:
                    ill, iur, jll, jur = int(nx * subregion[0]), int(nx * subregion[1]), int(ny * subregion[2]), int(ny * subregion[3])
                else:
                    ill, iur, jll, jur = 0, self.lons.shape[0], 0, self.lons.shape[1]

                self.basemap = rll.get_basemap_object_for_lons_lats(lons2d=self.lons[ill:iur, jll:jur], lats2d=self.lats[ill:iur, jll:jur],
                                                                    **basemap_kwargs)
                print(lon1, lat1, lon2, lat2)


        # self.basemap.drawcoastlines()
        # xx, yy = self.basemap(self.lons, self.lats)
        # self.basemap.pcolormesh(xx, yy, ds.variables["Bathymetry"][:].transpose())
        # plt.show()

        self.lons[self.lons > 180] -= 360

        return self.lons, self.lats, self.basemap
Exemplo n.º 39
0
def main():
    # plot_utils.apply_plot_params(20)
    folder = "/home/huziy/skynet3_rech1/from_guillimin"
    fname = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
    path = os.path.join(folder, fname)

    rObj = RPN(path)

    mg = rObj.get_first_record_for_name_and_level("MG", level=0, level_kind=level_kinds.PRESSURE)
    # j2 = rObj.get_first_record_for_name("J2")

    levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
    norm = BoundaryNorm(levs, len(levs) - 1)

    me = rObj.get_first_record_for_name_and_level("ME", level=0, level_kind=level_kinds.ARBITRARY)

    proj_params = rObj.get_proj_parameters_for_the_last_read_rec()

    print(me.shape)
    lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()

    lons2d[lons2d > 180] -= 360

    # me_to_plot = np.ma.masked_where(mg < 0.4, me)
    me_to_plot = me
    # print me_to_plot.min(), me_to_plot.max()

    rll = RotatedLatLon(**proj_params)
    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d)
    rObj.close()

    x, y = basemap(lons2d, lats2d)

    plt.figure()
    ax = plt.gca()
    # the_cmap = cm.get_cmap(name = "gist_earth", lut=len(levs) -1)
    # the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(path="colormap_files/topo_15lev.rgb")
    the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
        path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1
    )

    # new_cm = matplotlib.colors.LinearSegmentedColormap('colormap',new_dict,len(levs) - 1)

    me_to_plot = maskoceans(lons2d, lats2d, me_to_plot, resolution="l")
    basemap.contourf(x, y, me_to_plot, cmap=the_cmap, levels=levs, norm=norm)

    # basemap.fillcontinents(color = "none", lake_color="aqua")
    basemap.drawmapboundary(fill_color="#479BF9")
    basemap.drawcoastlines(linewidth=0.5)
    basemap.drawmeridians(np.arange(-180, 180, 20), labels=[1, 0, 0, 1])
    basemap.drawparallels(np.arange(45, 75, 15), labels=[1, 0, 0, 1])

    basemap.readshapefile("data/shape/contour_bv_MRCC/Bassins_MRCC_latlon", name="basin", linewidth=1)

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", "5%", pad="3%")
    plt.colorbar(ticks=levs, cax=cax)

    # basemap.scatter(x, y, color="k", s=1, linewidths=0, ax=ax, zorder=2)

    margin = 20
    x1 = x[margin, margin]
    x2 = x[-margin, margin]
    y1 = y[margin, margin]
    y2 = y[margin, -margin]
    pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
    ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="dashed", lw=3))

    # plt.tight_layout()
    # plt.show()
    plt.savefig("free_domain_260x260.png", dpi=cpp.FIG_SAVE_DPI)

    pass
Exemplo n.º 40
0
def main():
    """

    """

    # s_lons = [-3.38, 3.40, -12.45, -11.04,-2.92, 0.1,32.55,30.48,23.6,31.27,15.31,23.91,17.51,21.08]
    # s_lats = [16.26,11.88,14.9,13.91,10.57,6.2,15.61,19.18,-14.02,-21.13,-4.26,4.97,-28.71,-28.69]

    # assert len(s_lons) == len(s_lats)
    #path = "data/directions_Africa_Bessam_0.44/infocell_Africa.nc"
    #path = "/home/huziy/skynet3_exec1/for_offline_routing/directions_africa_dx0.44deg_2.nc"
    path = "/skynet3_rech1/huziy/for_Arman_routing_data/infocell_na_0.44deg_arman.nc"
    ds = Dataset(path)

    dirs = ds.variables["flow_direction_value"][:]
    acc_area = ds.variables["accumulation_area"][:]

    lons2d = ds.variables["lon"][:]
    lats2d = ds.variables["lat"][:]

    lon_1, lat_1 = -97, 47.5
    lon_2, lat_2 = -7, 0

    lons2d[lons2d >= 180] -= 360

    #min_lon = lons2d[0,0]
    #max_lon = lons2d[-1,-1]
    #min_lat = lats2d[0, 0]
    #max_lat = lats2d[-1,-1]

    #plot_utils.apply_plot_params(width_pt=None, width_cm=80)
    #print max_lon

    fig = plt.figure(dpi=800)
    #TODO: change projection to rotpole (it will require params)
    #b = Basemap(projection="rotpole", llcrnrlon=min_lon,
    #    llcrnrlat=min_lat,
    #    urcrnrlon=max_lon, urcrnrlat=max_lat, resolution="i")
    myproj = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
    print(lons2d.shape)
    b = myproj.get_basemap_object_for_lons_lats(lons2d[100:150, 70:132], lats2d[100:150, 70:132])

    x, y = b(lons2d, lats2d)
    b.drawcoastlines(linewidth=0.2, color="0.5")


    #    b.pcolormesh(x, y, np.ma.masked_where(dirs <= 0, dirs ))
    #    plt.colorbar()


    di_list = np.array([1, 1, 0, -1, -1, -1, 0, 1])
    dj_list = np.array([0, -1, -1, -1, 0, 1, 1, 1])

    delta_indices = np.log2(dirs[dirs > 0])
    delta_indices = delta_indices.astype(int)

    di = di_list[delta_indices]
    dj = dj_list[delta_indices]

    acc_area = np.ma.masked_where(acc_area < 0, acc_area)
    #img = b.pcolormesh(x, y, np.ma.log(acc_area))

    ax = plt.gca()
    #divider = make_axes_locatable(ax)
    #cax = divider.append_axes("right", "5%", pad="3%")

    #plt.colorbar(img, cax = cax)



    i_indices_1d = list(range(dirs.shape[0]))
    j_indices_1d = list(range(dirs.shape[1]))

    j_indices_2d, i_indices_2d = np.meshgrid(j_indices_1d, i_indices_1d)

    i_indices_2d_next = np.zeros_like(i_indices_2d)
    j_indices_2d_next = np.zeros_like(i_indices_2d)

    i_indices_2d_next[dirs > 0] = i_indices_2d[dirs > 0] + di
    j_indices_2d_next[dirs > 0] = j_indices_2d[dirs > 0] + dj

    for i, j, i_next, j_next in zip(i_indices_2d[dirs > 0], j_indices_2d[dirs > 0],
                                    i_indices_2d_next[dirs > 0], j_indices_2d_next[dirs > 0]):
        # ax.add_line(Line2D([x[i,j], x[i_next, j_next]], [y[i,j], y[i_next, j_next]], linewidth=0.5))
        if i == i_next and j == j_next:
            continue
        p1 = [x[i, j], y[i, j]]
        p2 = [x[i_next, j_next], y[i_next, j_next]]
        dr = [p2[0] - p1[0], p2[1] - p1[1]]
        ax.add_patch(FancyArrow(p1[0], p1[1], dr[0], dr[1], linewidth=0.5,
                                fc="k", head_width=0.1,
                                length_includes_head=True))




    # x1, y1 = b(s_lons, s_lats)
    # b.scatter(x1, y1, c="r", linewidth=0, zorder = 7, ax = ax)
    # b.drawrivers(linewidth=0.5, color="#0cf5f8", zorder=8, ax=ax)
    # b.drawmeridians(np.arange(-10, 90,30))
    # b.drawparallels(np.arange(-50, 40, 5), labels=[1,1,1,1], linewidth=0.1)
    plt.tight_layout()
    # plt.show()

    #b.readshapefile("/home/huziy/skynet3_exec1/other_shape/af_major_basins/af_basins", "basin",
    #    linewidth=3, zorder=9, ax=ax
    #)
    plt.savefig("with_station_riv_af_dirs_basin_1.0.eps")

    pass
Exemplo n.º 41
0
def main(base_folder="/skynet3_rech1/huziy/veg_fractions/",
         fname="pm1983120100_00000000p",
         canopy_name="Y2C",
         label="USGS",
         depth_to_bedrock_name="8L"):
    data_path = os.path.join(base_folder, fname)
    r = RPN(data_path)

    veg_fractions = r.get_2D_field_on_all_levels(name=canopy_name)
    print(list(veg_fractions.keys()))
    sand = r.get_first_record_for_name("SAND")
    clay = r.get_first_record_for_name("CLAY")

    dpth_to_bedrock = r.get_first_record_for_name(depth_to_bedrock_name)

    proj_params = r.get_proj_parameters_for_the_last_read_rec()

    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    print(lons.shape)

    rll = RotatedLatLon(lon1=proj_params["lon1"],
                        lat1=proj_params["lat1"],
                        lon2=proj_params["lon2"],
                        lat2=proj_params["lat2"])

    lon0, lat0 = rll.get_true_pole_coords_in_rotated_system()
    plon, _ = rll.get_north_pole_coords()

    b = Basemap(projection="rotpole",
                llcrnrlon=lons[0, 0],
                llcrnrlat=lats[0, 0],
                urcrnrlon=lons[-1, -1],
                urcrnrlat=lats[-1, -1],
                lon_0=lon0 - 180,
                o_lon_p=lon0,
                o_lat_p=lat0)

    lons[lons > 180] -= 360
    for lev in list(veg_fractions.keys()):
        veg_fractions[lev] = maskoceans(lons,
                                        lats,
                                        veg_fractions[lev],
                                        inlands=False)

    sand = maskoceans(lons, lats, sand)
    clay = maskoceans(lons, lats, clay)
    dpth_to_bedrock = maskoceans(lons, lats, dpth_to_bedrock)

    x, y = b(lons, lats)
    plot_veg_fractions(x,
                       y,
                       b,
                       veg_fractions,
                       out_image=os.path.join(
                           base_folder,
                           "veg_fractions_{0}.jpeg".format(label)))
    plot_sand_and_clay(x,
                       y,
                       b,
                       sand,
                       clay,
                       out_image=os.path.join(
                           base_folder, "sand_clay_{0}.jpeg".format(label)))

    # set relation between vegetation frsction fields and names
    veg_fract_dict = {}
    for lev, the_field in veg_fractions.items():
        lev = int(lev)
        if lev not in y2c_level_to_title:
            continue
        veg_fract_dict[y2c_level_to_title[lev]] = the_field

    data = {"SAND": sand, "CLAY": clay, "BDRCK_DEPTH": dpth_to_bedrock}
    data.update(veg_fract_dict)

    return b, lons, lats, data, label
Exemplo n.º 42
0
class GridConfig(object):
    projection = "rotpole"

    def __init__(self, **kwargs):
        self.dx = self.dy = kwargs.get("dx", -1)
        self.iref, self.jref = kwargs.get("iref", -1), kwargs.get("jref", -1)

        self.xref, self.yref = kwargs.get("xref", -1), kwargs.get("yref", -1)
        self.ni, self.nj = kwargs.get("ni", -1), kwargs.get("nj", -1)

        # interpolated driving data (width of the outer band in number of gridpoints)
        self.halo = 10

        # size of the blending zone in grid points
        self.blendig = 10

        self.rll = None
        if "rll" not in kwargs:
            self.lon1, self.lat1 = kwargs.get("lon1", None), kwargs.get("lat1", None)
            self.lon2, self.lat2 = kwargs.get("lon2", None), kwargs.get("lat2", None)
            if None not in (self.lon1, self.lon2, self.lat1, self.lat2):
                self.rll = RotatedLatLon(lon1=self.lon1, lon2=self.lon2, lat1=self.lat1, lat2=self.lat2)
        else:
            self.rll = kwargs.get("rll")

    @classmethod
    def get_default_for_resolution(cls, res=0.5):
        """
        :param res:
        :rtype GridConfig
        """
        obj = GridConfig()
        obj.dx = obj.dy = res
        if res == 0.5:
            obj.iref = 46  # starts from 1 not 0!!
            obj.jref = 42  # starts from 1 not 0!!
            obj.ni = 86
            obj.nj = 86
        elif res == 0.1:
            obj.iref = 142  # no need to do -1, doing it later in the formulas
            obj.jref = 122
            obj.xref = 180  # rotated longitude
            obj.yref = 0  # rotated latitude

            # projection parameters
            obj.lon1 = -68.0
            obj.lat1 = 52.0

            obj.lon2 = 16.65
            obj.lat2 = 0.0

            obj.ni = 260
            obj.nj = 260

        return obj

    def export_to_shape(self, shp_folder="", shp_filename="", free_zone_only=True,
                        export_mask=None, shape_fields=None):
        """
        export the grid to the shape file
        :param export_mask: Mask to specify exactly which gridcells should be exported
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        import shapefile as shp

        w = shp.Writer(shp.POLYGON)

        w.field("i", fieldType="I")
        w.field("j", fieldType="I")

        field_names_in_order = ["i", "j"]

        if shape_fields is not None:
            for field_name, field in shape_fields.items():
                w.field(field_name, *field.type_of_shp_field)

                field_names_in_order.append(field_name)

        if isinstance(shp_folder, str):
            folder = Path(shp_folder)
        else:
            folder = shp_folder

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        if export_mask is None:
            export_mask = np.ones((self.ni, self.nj), dtype=bool)

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                if not export_mask[i, j]:
                    continue

                p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y - self.dy / 2.0)
                p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y + self.dy / 2.0)
                p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y + self.dy / 2.0)
                p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y - self.dy / 2.0)

                w.poly(parts=[
                    [p00, p01, p11, p10]
                ])

                if shape_fields is None:
                    w.record(i + 1, j + 1)
                else:
                    record_fields = {}
                    record_fields["i"] = i + 1
                    record_fields["j"] = j + 1
                    for field_name, field in shape_fields.items():
                        record_fields[field_name] = field[i, j]

                    w.record(*[record_fields[key] for key in field_names_in_order])

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        w.save(str(folder.joinpath(shp_filename)))

    def export_to_shape_fiona(self, shp_folder="", shp_filename="", free_zone_only=True,
                              export_mask=None, shape_fields=None):
        """
        export the grid to the shape file
        using fiona since pyshp was not compatible with arcgis
        :param export_mask: Mask to specify exactly which gridcells should be exported
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """
        from fiona.crs import from_epsg, from_string
        import fiona

        proj = from_epsg(4326)
        # proj = from_epsg(4269)
        # proj = from_string("+units=m +lon_0=-97.0 +o_lon_p=180.0 +R=6370997.0 +o_proj=longlat +proj=ob_tran +o_lat_p=42.5")
        print(proj)
        print(dir(proj))
        # proj = from_epsg(900913)

        if isinstance(shp_folder, str):
            folder = Path(shp_folder)
        else:
            folder = shp_folder

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        schema = {
            "geometry": "Polygon",
            "properties": OrderedDict(
                [("i", "int"), ("j", "int")]
            )
        }

        if shape_fields is not None:
            # additional fields
            for field_name, field in shape_fields.items():
                schema["properties"][field_name] = field.type_of_shp_field

        with fiona.open(str(folder.joinpath(shp_filename)), mode="w", driver="ESRI Shapefile", crs=proj,
                        schema=schema) as output:

            lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in range(self.ni)]
            latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in range(self.nj)]

            margin = 0
            if free_zone_only:
                margin = self.blendig + self.halo

            start_i = margin
            start_j = margin

            end_i = self.ni - margin - 1
            end_j = self.nj - margin - 1

            if export_mask is None:
                export_mask = np.ones((self.ni, self.nj), dtype=bool)

            polygons = []
            lake_fractions = []

            for i in range(start_i, end_i + 1):
                x = lonr[i]

                for j in range(start_j, end_j + 1):
                    y = latr[j]

                    if not export_mask[i, j]:
                        continue

                    p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y - self.dy / 2.0)
                    p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y + self.dy / 2.0)
                    p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y + self.dy / 2.0)
                    p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y - self.dy / 2.0)

                    # p00 = (x - self.dx / 2.0, y - self.dy / 2.0)
                    # p01 = (x - self.dx / 2.0, y + self.dy / 2.0)
                    # p11 = (x + self.dx / 2.0, y + self.dy / 2.0)
                    # p10 = (x + self.dx / 2.0, y - self.dy / 2.0)

                    poly = Polygon(shell=[p00, p01, p11, p10, p00])
                    props = OrderedDict([("i", i + 1), ("j", j + 1)])

                    polygons.append(PolygonPatch(poly))


                    if shape_fields is not None:

                        lake_fractions.append(shape_fields["lkfr"][i, j])
                        for field_name, field in shape_fields.items():
                            converter = float if field.type_of_shp_field in ["float"] else int
                            props[field_name] = converter(field[i, j])

                    output.write({"geometry": mapping(poly), "properties": props})



            # plot the gridcells with basemap
            # pcol = PatchCollection(polygons, cmap="bone_r")
            # pcol.set_array(np.array(lake_fractions))
            # import matplotlib.pyplot as plt
            # bmp = self.get_basemap_for_free_zone(resolution="l")
            # fig = plt.figure()
            # ax = fig.add_subplot(111)
            # bmp.ax = ax
            # ax.add_collection(pcol)
            # bmp.drawcoastlines(ax=ax)
            # plt.show()




    def export_to_shape_ogr(self, shp_folder="", shp_filename="", free_zone_only=True):
        """
        export the grid to the shape file
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        from osgeo import ogr, osr

        folder = Path(shp_folder)

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        # set up the shapefile driver
        driver = ogr.GetDriverByName("ESRI Shapefile")

        if not shp_filename.lower().endswith(".shp"):
            shp_filename += ".shp"

        # create the data source
        data_source = driver.CreateDataSource(str(folder.joinpath(shp_filename)))

        srs = osr.SpatialReference()
        srs.ImportFromWkt(osr.SRS_WKT_WGS84)
        print(srs)
        print(srs.ExportToPrettyWkt())

        # create the layer
        layer = data_source.CreateLayer("grid", srs, ogr.wkbPolygon)
        layer.CreateField(ogr.FieldDefn("i", ogr.OFTInteger))
        layer.CreateField(ogr.FieldDefn("j", ogr.OFTInteger))

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                # create the feature
                feature = ogr.Feature(layer.GetLayerDefn())

                p00 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y - self.dy / 2.0)
                p01 = self.rll.toGeographicLonLat(x - self.dx / 2.0, y + self.dy / 2.0)
                p11 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y + self.dy / 2.0)
                p10 = self.rll.toGeographicLonLat(x + self.dx / 2.0, y - self.dy / 2.0)

                ring = ogr.Geometry(ogr.wkbLinearRing)
                ring.AddPoint(*p00)
                ring.AddPoint(*p01)
                ring.AddPoint(*p11)
                ring.AddPoint(*p10)

                poly = ogr.Geometry(ogr.wkbPolygon)
                poly.AddGeometry(ring)

                feature.SetField("i", i + 1)
                feature.SetField("j", j + 1)

                feature.SetGeometry(poly)

                layer.CreateFeature(feature)
                feature.Destroy()

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        data_source.Destroy()

    def export_to_shape_native_grid(self, shp_folder="", shp_filename="", free_zone_only=True):
        """
        export the grid to the shape file
        :param free_zone_only:
        :param shp_folder:
        :param shp_filename:
        """

        from osgeo import ogr, osr

        folder = Path(shp_folder)

        # create the directory if does not exist
        if not folder.is_dir():
            folder.mkdir()

        # set up the shapefile driver
        driver = ogr.GetDriverByName("ESRI Shapefile")

        if not shp_filename.lower().endswith(".shp"):
            shp_filename += ".shp"

        # create the data source
        data_source = driver.CreateDataSource(str(folder.joinpath(shp_filename)))

        # Projection
        srs = osr.SpatialReference()

        bmp = self.get_basemap_for_free_zone()
        srs.ImportFromProj4(bmp.proj4string)
        print(srs)
        print(srs.ExportToPrettyWkt())

        # create the layer
        layer = data_source.CreateLayer("grid", srs, ogr.wkbPolygon)
        layer.CreateField(ogr.FieldDefn("i", ogr.OFTInteger))
        layer.CreateField(ogr.FieldDefn("j", ogr.OFTInteger))

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in range(self.ni)]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in range(self.nj)]

        margin = 0
        if free_zone_only:
            margin = self.blendig + self.halo

        start_i = margin
        start_j = margin

        end_i = self.ni - margin - 1
        end_j = self.nj - margin - 1

        for i in range(start_i, end_i + 1):
            x = lonr[i]

            for j in range(start_j, end_j + 1):
                y = latr[j]

                # create the feature
                feature = ogr.Feature(layer.GetLayerDefn())

                p00 = (x - self.dx / 2.0, y - self.dy / 2.0)
                p01 = (x - self.dx / 2.0, y + self.dy / 2.0)
                p11 = (x + self.dx / 2.0, y + self.dy / 2.0)
                p10 = (x + self.dx / 2.0, y - self.dy / 2.0)

                ring = ogr.Geometry(ogr.wkbLinearRing)
                ring.AddPoint(*p00)
                ring.AddPoint(*p01)
                ring.AddPoint(*p11)
                ring.AddPoint(*p10)

                poly = ogr.Geometry(ogr.wkbPolygon)
                poly.AddGeometry(ring)

                feature.SetField("i", i + 1)
                feature.SetField("j", j + 1)

                feature.SetGeometry(poly)

                layer.CreateFeature(feature)
                feature.Destroy()

        # w.poly(parts=[[[-20, -20], [-20, 20], [20, 20], [20, -20]],])
        # w.record(1, 1)

        data_source.Destroy()

    def get_basemap_for_free_zone(self, halo=None, blending=None, **kwargs):
        if halo is None:
            halo = self.halo

        if blending is None:
            blending = self.blendig

        lons_c, lats_c = self.get_free_zone_corners(halo=halo, blending=blending)
        return self.get_basemap(lons=lons_c, lats=lats_c, **kwargs)

    def get_basemap_using_shape_with_polygons_of_interest(self, lons, lats, shp_path=None, mask_margin=5, **kwargs):

        if shp_path is None:
            return self.get_basemap(lons=lons, lats=lats, **kwargs)

        reg_of_interest = get_mask(lons, lats, shp_path=shp_path) > 0

        i_list, j_list = np.where(reg_of_interest)

        i_min = min(i_list) - mask_margin
        i_max = max(i_list) + mask_margin

        j_min = min(j_list) - mask_margin
        j_max = max(j_list) + mask_margin

        bsmap = self.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1],
                                 lats=lats[i_min:i_max + 1, j_min:j_max + 1])
        return bsmap, reg_of_interest

    def get_basemap(self, lons=None, lats=None, **kwargs):

        if lons is None:

            lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in [0, self.ni - 1]]
            latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in [0, self.nj - 1]]

            latr, lonr = np.meshgrid(latr, lonr)

            lons = np.zeros((2, 2))
            lats = np.zeros((2, 2))

            for i in [-1, 0]:
                for j in [-1, 0]:
                    shiftx = self.dx / 2.0
                    shifty = self.dy / 2.0

                    shiftx = -shiftx if i == 0 else shiftx
                    shifty = -shifty if i == 0 else shifty

                    lons[i, j], lats[i, j] = self.rll.toGeographicLonLat(lonr[i, j] + shiftx, latr[i, j] + shifty)

        return self.get_rot_latlon_proj_obj().get_basemap_object_for_lons_lats(lons2d=lons,
                                                                               lats2d=lats,
                                                                               **kwargs)

    def get_corners_in_proj_coords(self):

        """
        :return: xxcorners, yycorners

             xxcorners = [[xll, xur], [xll, xur]]
             yycorners = [[yll, yll], [yur, yur]]

        """

        lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in [0, self.ni - 1]]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in [0, self.nj - 1]]
        latr, lonr = np.meshgrid(latr, lonr)

        return lonr, latr

    def get_free_zone_corners(self, halo=10, blending=10):

        margin = halo + blending
        lonr = [(i - (self.iref - 1)) * self.dx + self.xref for i in [margin, self.ni - margin - 1]]
        latr = [(j - (self.jref - 1)) * self.dy + self.yref for j in [margin, self.nj - margin - 1]]

        latr, lonr = np.meshgrid(latr, lonr)

        lons = np.zeros((2, 2))
        lats = np.zeros((2, 2))

        for i in [-1, 0]:
            mulx = -1 if i >= 0 else 1
            shiftx = mulx * self.dx / 2.0

            for j in [-1, 0]:
                muly = -1 if j >= 0 else 1
                shifty = muly * self.dy / 2.0

                lons[i, j], lats[i, j] = self.rll.toGeographicLonLat(lonr[i, j] + shiftx, latr[i, j] + shifty)

        return lons, lats

    def get_rot_latlon_proj_obj(self):
        return self.rll

    def subgrid(self, i0, j0, di=-1, dj=-1):

        """

        :param i0: 0-based i-index of the lower left corner of the domain
        :param j0:
        :param di: number of grid points in i direction
        :param dj: number of grid points in j direction
        """

        subgr = GridConfig(rll=self.rll, dx=self.dx, dy=self.dy, xref=self.xref, yref=self.yref)

        if di > 0:
            subgr.ni = di
        else:
            subgr.ni = self.ni - i0

        if dj > 0:
            subgr.nj = dj
        else:
            subgr.nj = self.nj - j0

        subgr.iref = self.iref - i0
        subgr.jref = self.jref - j0

        return subgr

    def copy(self):
        return self.subgrid(0, 0, di=self.ni, dj=self.nj)

    def double_resolution(self):
        gc = GridConfig(rll=self.rll, dx=self.dx / 2.0, dy=self.dy / 2.0, xref=self.xref, yref=self.yref)
        gc.ni = 2 * self.ni
        gc.nj = 2 * self.nj

        gc.iref = 2 * self.iref
        gc.jref = 2 * self.jref

        gc.xref -= gc.dx / 2.0
        gc.yref -= gc.dy / 2.0

        return gc

    def double_resolution_keep_free_domain_same(self, halo_pts=10, blending_pts=10):
        gc = GridConfig(rll=self.rll, dx=self.dx / 2.0, dy=self.dy / 2.0, xref=self.xref, yref=self.yref)
        margin_pts = halo_pts + blending_pts
        gc.ni = 2 * (self.ni - 2 * margin_pts) + 2 * margin_pts
        gc.nj = 2 * (self.nj - 2 * margin_pts) + 2 * margin_pts

        gc.iref = 2 * (self.iref - margin_pts) + margin_pts
        gc.jref = 2 * (self.jref - margin_pts) + margin_pts

        gc.xref -= gc.dx / 2.0
        gc.yref -= gc.dy / 2.0

        return gc

    def decrease_resolution_keep_free_domain_same(self, factor, halo_pts=10, blending_pts=10):
        gc = GridConfig(rll=self.rll, dx=self.dx * factor, dy=self.dy * factor, xref=self.xref, yref=self.yref)
        margin_pts = halo_pts + blending_pts
        gc.ni = (self.ni - 2 * margin_pts) / factor + 2 * margin_pts
        gc.nj = (self.nj - 2 * margin_pts) / factor + 2 * margin_pts

        # Change the reference point if the new iref and jref cannot be the same

        new_iref = self.iref - margin_pts
        new_jref = self.jref - margin_pts

        new_iref = new_iref // factor + (new_iref % factor != 0)
        x00 = self.xref + self.dx * (margin_pts + 1 - self.iref) - self.dx / 2.0
        new_xref = x00 + new_iref * self.dx * factor - self.dx * factor / 2.0

        new_jref = new_jref // factor + (new_jref % factor != 0)
        y00 = self.yref + self.dy * (margin_pts + 1 - self.jref) - self.dy / 2.0
        new_yref = y00 + new_jref * self.dy * factor - self.dy * factor / 2.0

        gc.iref = new_iref + margin_pts
        gc.jref = new_jref + margin_pts

        gc.xref = new_xref
        gc.yref = new_yref

        gc.ni = int(gc.ni)
        gc.nj = int(gc.nj)

        return gc

    def move(self, di=0, dj=0):
        gc = GridConfig(rll=self.rll, dx=self.dx, dy=self.dy, xref=self.xref, yref=self.yref, ni=self.ni, nj=self.nj)
        gc.iref -= di
        gc.jref -= dj
        return gc

    def expand(self, di=0, dj=0):
        gc = GridConfig(rll=self.rll, dx=self.dx, dy=self.dy, xref=self.xref, yref=self.yref, ni=self.ni + di,
                        nj=self.nj + dj)
        gc.iref = self.iref
        gc.jref = self.jref
        return gc

    def __str__(self):
        s = """
              Grd_ni        =  {ni}     , Grd_nj         =    {nj}     ,
              Grd_dx        =  {dx}     , Grd_dy         =    {dy},
              Grd_iref      =  {iref}     ,  Grd_jref       =   {jref}     ,
              Grd_latr      =    {latref}   ,  Grd_lonr       =  {lonref}   ,
              Grd_xlat1     =   {lat1}   ,  Grd_xlon1       = {lon1}    ,
              Grd_xlat2     =    {lat2}    ,  Grd_xlon2       =  {lon2}    ,
        """.format(ni=self.ni, nj=self.nj, dx=self.dx, dy=self.dy, iref=self.iref, jref=self.jref,
                   latref=self.yref, lonref=self.xref, lat1=self.rll.lat1, lon1=self.rll.lon1,
                   lat2=self.rll.lat2, lon2=self.rll.lon2)

        return s
Exemplo n.º 43
0
def plot_lake_fraction_field():
    folder = "/home/huziy/skynet3_rech1/geof_lake_infl_exp"
    fName = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
    path = os.path.join(folder, fName)

    rObj = RPN(path)

    lkf = rObj.get_first_record_for_name_and_level(
        varname="VF", level=3, level_kind=level_kinds.ARBITRARY)

    proj_params = rObj.get_proj_parameters_for_the_last_read_rec()
    lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()

    lons2d[lons2d >= 180] -= 360
    rObj.close()

    rll = RotatedLatLon(**proj_params)

    margin = 20
    lons2d = lons2d[margin:-margin, margin:-margin]
    lats2d = lats2d[margin:-margin, margin:-margin]
    lkf = lkf[margin:-margin, margin:-margin]

    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
                                                   lats2d=lats2d,
                                                   resolution="l")
    x, y = basemap(lons2d, lats2d)

    fig = plt.figure()
    gs = GridSpec(1, 2, width_ratios=[1, 1])

    ax = fig.add_subplot(gs[0, 0])
    df = 0.1
    levels = np.arange(0, 1.1, df)
    cMap = get_cmap("gist_ncar_r", len(levels) - 1)
    bn = BoundaryNorm(levels, cMap.N)

    basemap.drawmapboundary(fill_color="0.75")
    lkf_plot = maskoceans(lons2d, lats2d, lkf, inlands=False)
    print("Percentage of lakes in the sim domain: {}".format(lkf_plot.mean() *
                                                             100))

    img = basemap.pcolormesh(x, y, lkf_plot, norm=bn, cmap=cMap)
    basemap.drawcoastlines()

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("bottom", "5%", pad="3%")
    cb = fig.colorbar(img, cax=cax, ticks=levels, orientation="horizontal")

    ax = fig.add_subplot(gs[0, 1])
    df1 = df
    levels1 = np.arange(0, 1.1, df1)
    cell_numms = np.zeros((len(levels1) - 1, ))

    left = levels[0]
    right = levels[1]

    lefts = []
    rights = []
    lkf_land = lkf[lkf > 0.01]
    for i in range(len(cell_numms)):
        cell_numms[i] = ((lkf_land > left) &
                         (lkf_land <= right)).astype(int).sum()
        lefts.append(left)
        rights.append(right)
        left += df1
        right += df1

    assert isinstance(ax, Axes)
    ax.bar(lefts, cell_numms, width=df1)

    # ax.semilogy(rights, cell_numms)
    ax.xaxis.set_ticks(levels)
    ax.yaxis.set_ticks(np.arange(1000, 10000, 1000))
    sf = ScalarFormatter(useMathText=True)
    sf.set_powerlimits([-2, 1])
    ax.yaxis.set_major_formatter(sf)

    ax.grid("on")
    ax.set_xlabel("fraction")
    ax.set_ylabel("# gridcells")

    plt.show()
    fig.tight_layout()
    fig.savefig("lake_fractions_220x220_0.1deg.jpeg")
    plt.show()

    pass
Exemplo n.º 44
0
def diagnose(station_ids=None, model_data_path=None):

    manager = Crcm5ModelDataManager(samples_folder_path=model_data_path,
                                    file_name_prefix="pm",
                                    all_files_in_samples_folder=True,
                                    need_cell_manager=True)

    nx, ny = manager.lons2D.shape

    rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)

    x00, y00 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 0],
                                          manager.lats2D[0, 0])
    x10, y10 = rot_lat_lon.toProjectionXY(manager.lons2D[1, 0],
                                          manager.lats2D[1, 0])
    x01, y01 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 1],
                                          manager.lats2D[0, 1])

    dx = x10 - x00
    dy = y01 - y00

    print("dx, dy = {0}, {1}".format(dx, dy))
    areas = rot_lat_lon.get_areas_of_gridcells(
        dx, dy, nx, ny, y00, 1)  #1 -since the index is starting from 1
    print(areas[0, 0])

    start_date = datetime(1986, 1, 1)
    end_date = datetime(1986, 12, 31)

    stations = cehq_station.read_station_data(selected_ids=station_ids,
                                              start_date=start_date,
                                              end_date=end_date)

    stations.sort(key=lambda x: x.latitude, reverse=True)

    for i, s in enumerate(stations):

        fig = plt.figure()
        #3 columns
        gs = GridSpec(5,
                      3,
                      hspace=0.2,
                      wspace=0.2,
                      right=0.98,
                      left=0.1,
                      top=0.98)

        model_ts = manager.get_streamflow_timeseries_for_station(
            s, start_date=start_date, end_date=end_date, nneighbours=9)

        print(model_ts.time[0], model_ts.time[-1])

        i_model0, j_model0 = model_ts.metadata["ix"], model_ts.metadata["jy"]
        mask = manager.get_mask_for_cells_upstream(i_model0, j_model0)

        #hydrographs
        ax = fig.add_subplot(gs[0, 0])
        plot_streamflows(ax, s, model_ts)

        #relative error
        ax = fig.add_subplot(gs[1, 0])
        plot_streamflow_re(ax, s, model_ts)

        #directions
        plot_directions_and_positions(fig.add_subplot(gs[:2, 1]),
                                      s,
                                      model_ts,
                                      manager,
                                      rot_lat_lon,
                                      mask=mask)

        #runoff
        ax = fig.add_subplot(gs[2, 0])
        plot_runoff(ax, manager, areas, model_ts, mask=mask)

        #runoff from gldas
        ax = fig.add_subplot(gs[2, 1])
        #plot_gldas_runoff(ax, manager, areas, model_ts, mask = mask)

        #temperature
        ax_temp = fig.add_subplot(gs[3, 0])
        ax_prec = fig.add_subplot(gs[4, 0])

        plot_total_precip_and_temp_re_1d(ax_prec,
                                         ax_temp,
                                         manager,
                                         rot_lat_lon,
                                         areas,
                                         model_ts,
                                         mask=mask)

        #swe timeseries
        ax = fig.add_subplot(gs[3, 1])
        plot_swe_timeseries(ax, manager, areas, model_ts, mask=mask)

        #print np.where(mask == 1)
        print("(i, j) = ({0}, {1})".format(model_ts.metadata["ix"],
                                           model_ts.metadata["jy"]))

        fig.savefig("diagnose_{0}_{1:.2f}deg.pdf".format(s.id, dx))
Exemplo n.º 45
0
def main():
    # plot_utils.apply_plot_params(20)
    folder = "/home/huziy/skynet3_rech1/from_guillimin"
    fname = "geophys_Quebec_0.1deg_260x260_with_dd_v6"
    path = os.path.join(folder, fname)

    rObj = RPN(path)

    mg = rObj.get_first_record_for_name_and_level(
        "MG", level=0, level_kind=level_kinds.PRESSURE)
    # j2 = rObj.get_first_record_for_name("J2")

    levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
    norm = BoundaryNorm(levs, len(levs) - 1)

    me = rObj.get_first_record_for_name_and_level(
        "ME", level=0, level_kind=level_kinds.ARBITRARY)

    proj_params = rObj.get_proj_parameters_for_the_last_read_rec()

    print(me.shape)
    lons2d, lats2d = rObj.get_longitudes_and_latitudes_for_the_last_read_rec()

    lons2d[lons2d > 180] -= 360

    # me_to_plot = np.ma.masked_where(mg < 0.4, me)
    me_to_plot = me
    # print me_to_plot.min(), me_to_plot.max()

    rll = RotatedLatLon(**proj_params)
    basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
                                                   lats2d=lats2d)
    rObj.close()

    x, y = basemap(lons2d, lats2d)

    plt.figure()
    ax = plt.gca()
    # the_cmap = cm.get_cmap(name = "gist_earth", lut=len(levs) -1)
    # the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(path="colormap_files/topo_15lev.rgb")
    the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
        path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1)

    # new_cm = matplotlib.colors.LinearSegmentedColormap('colormap',new_dict,len(levs) - 1)

    me_to_plot = maskoceans(lons2d, lats2d, me_to_plot, resolution="l")
    basemap.contourf(x, y, me_to_plot, cmap=the_cmap, levels=levs, norm=norm)

    # basemap.fillcontinents(color = "none", lake_color="aqua")
    basemap.drawmapboundary(fill_color='#479BF9')
    basemap.drawcoastlines(linewidth=0.5)
    basemap.drawmeridians(np.arange(-180, 180, 20), labels=[1, 0, 0, 1])
    basemap.drawparallels(np.arange(45, 75, 15), labels=[1, 0, 0, 1])

    basemap.readshapefile("data/shape/contour_bv_MRCC/Bassins_MRCC_latlon",
                          name="basin",
                          linewidth=1)

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", "5%", pad="3%")
    plt.colorbar(ticks=levs, cax=cax)

    # basemap.scatter(x, y, color="k", s=1, linewidths=0, ax=ax, zorder=2)

    margin = 20
    x1 = x[margin, margin]
    x2 = x[-margin, margin]
    y1 = y[margin, margin]
    y2 = y[margin, -margin]
    pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
    ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="dashed", lw=3))

    # plt.tight_layout()
    # plt.show()
    plt.savefig("free_domain_260x260.png", dpi=cpp.FIG_SAVE_DPI)

    pass
Exemplo n.º 46
0
from mpl_toolkits.basemap import Basemap

from domains.grid_config import GridConfig, gridconfig_from_grid_nml
from domains.rotated_lat_lon import RotatedLatLon

default_projection = RotatedLatLon(lon1=-97.0, lat1=47.5, lon2=-7.0, lat2=0.)


# iref and jref are 1-based indices coming from gemclim_settings.nml

gc_cordex_na_011 = GridConfig(rll=default_projection, dx=0.11, dy=0.11, ni=695, nj=680, iref=21, jref=580, xref=145.955, yref=28.525)
gc_cordex_na_022 = GridConfig(rll=default_projection, dx=0.22, dy=0.22, ni=380, nj=360, iref=21, jref=300, xref=146.01, yref=28.47)
gc_cordex_na_044 = GridConfig(rll=default_projection, dx=0.44, dy=0.44, ni=212, nj=200, iref=21, jref=160, xref=146.12, yref=28.36)




gc_cordex_caio_subdomain = GridConfig(rll=default_projection, dx=0.22, dy=0.22,
                                      ni=276, nj=212, iref=35, jref=153, xref=151.51, yref=28.25)


cordex_arctic_proj = RotatedLatLon(lon1=180, lat1=83.45, lon2=270, lat2=0.)
gc_cordex_Arctic_044 = GridConfig(
    rll=cordex_arctic_proj, dx=0.44, dy=0.44, ni=164, nj=180, iref=21, jref=153, xref=157.12, yref=33.88
)

cordex_na_proj = RotatedLatLon(lon1=-97, lat1=47.5, lon2=-7.0, lat2=0.0)


gc_panarctic_05 = gridconfig_from_grid_nml(
    """
Exemplo n.º 47
0
def plot_domain_for_different_margins(path, margins=None):
    if not margins: margins = [20, 40, 60]
    rpnObj = RPN(path)

    lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()

    # projection parameters
    lon_1 = -68
    lat_1 = 52
    lon_2 = 16.65
    lat_2 = 0.0

    rot_lat_lon = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
    xll, yll = rot_lat_lon.toProjectionXY(lons2d[0, 0], lats2d[0, 0])
    xur, yur = rot_lat_lon.toProjectionXY(lons2d[-1, -1], lats2d[-1, -1])

    if xll < 0: xll += 360.0
    if xur < 0: xur += 360.0

    nx, ny = lons2d.shape

    dx = (xur - xll) / float(nx - 1)
    dy = (yur - yll) / float(ny - 1)

    print(dx, dy)
    print(xur, yur, xll, yll)

    x1 = xll - dx / 2.0
    y1 = yll - dy / 2.0
    x2 = xur + dx / 2.0
    y2 = yur + dy / 2.0

    x1lon, y1lat = rot_lat_lon.toGeographicLonLat(x1, y1)
    x2lon, y2lat = rot_lat_lon.toGeographicLonLat(x2, y2)

    llcrnrlon, llcrnrlat = rot_lat_lon.toGeographicLonLat(x1 - dx, y1 - dx)
    urcrnrlon, urcrnrlat = rot_lat_lon.toGeographicLonLat(x2 + dx, y2 + dx)

    basemap = Basemap(projection="omerc",
                      lon_1=lon_1, lat_1=lat_1,
                      lon_2=lon_2, lat_2=lat_2,
                      llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat,
                      urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat, no_rot=True, resolution="l")

    basemap.drawcoastlines()
    basemap.drawrivers()

    x1, y1 = basemap(x1lon, y1lat)
    x2, y2 = basemap(x2lon, y2lat)

    # add rectangle for the grid 220x220
    #    r1 = Rectangle((x1, y1), x2-x1, y2-y1, facecolor="none", edgecolor="r",  linewidth=5  )

    ax = plt.gca()
    assert isinstance(ax, Axes)
    #    xr1_label, yr1_label = rot_lat_lon.toGeographicLonLat(xur - 2 * dx, yll + 2 * dy)
    #    xr1_label, yr1_label = basemap( xr1_label, yr1_label )
    #    ax.annotate("{0}x{1}".format(nx, ny), xy = (xr1_label, yr1_label), va = "bottom", ha = "right", color = "r")
    #    assert isinstance(ax, Axes)
    #    ax.add_patch(r1)

    margins_all = [0] + margins

    for margin in margins_all:
        # mfree = margin - 20
        xlli = xll + margin * dx
        ylli = yll + margin * dy
        xuri = xur - margin * dx
        yuri = yur - margin * dy

        x1lon, y1lat = rot_lat_lon.toGeographicLonLat(xlli, ylli)
        x2lon, y2lat = rot_lat_lon.toGeographicLonLat(xuri, yuri)

        x1, y1 = basemap(x1lon, y1lat)
        x2, y2 = basemap(x2lon, y2lat)

        ri = Rectangle((x1, y1), x2 - x1, y2 - y1, facecolor="none", edgecolor="r", linewidth=5)
        ax.add_patch(ri)

        xri_label, yri_label = rot_lat_lon.toGeographicLonLat(xlli + 2 * dx, yuri - 2 * dy)
        xri_label, yri_label = basemap(xri_label, yri_label)
        ax.annotate("{0}x{1}\nmarg. = {2}".format(nx - margin * 2, ny - margin * 2, margin + 20),
                    xy=(xri_label, yri_label),
                    va="top", ha="left", color="k", backgroundcolor="w")

    plt.show()
Exemplo n.º 48
0
def main():
    #path = "/RECH/data/Simulations/CRCM5/North_America/NorthAmerica_0.44deg_ERA40-Int_B1/Diagnostics/NorthAmerica_0.44deg_ERA40-Int_B1_2007{:02d}"
    path = "/RESCUE/skynet3_rech1/huziy/from_guillimin/new_outputs/current_climate_30_yr_sims/quebec_0.1_crcm5-hcd-rl-intfl_ITFS/Samples/quebec_crcm5-hcd-rl-intfl_1988{:02d}"

    months = [6, 7, 8]

    pm_list = []
    dm_list = []
    for m in months:
        print(path.format(m))

        month_folder = path.format(m)
        for fn in os.listdir(month_folder):

            # if not fn.endswith("moyenne"):
            #    continue

            if fn.startswith("pm"):
                pm_list.append(os.path.join(month_folder, fn))
            elif fn.startswith("dm"):
                dm_list.append(os.path.join(month_folder, fn))



    pm = MultiRPN(pm_list)
    dm = MultiRPN(dm_list)

    tsurf_mean = np.mean([field for field in pm.get_all_time_records_for_name_and_level(varname="J8").values()], axis=0)
    tair_mean = np.mean([field for field in dm.get_all_time_records_for_name_and_level(varname="TT", level=1, level_kind=level_kinds.HYBRID).values()], axis=0)


    lons, lats = pm.get_longitudes_and_latitudes_of_the_last_read_rec()

    projparams = pm.linked_robj_list[0].get_proj_parameters_for_the_last_read_rec()

    rll = RotatedLatLon(**projparams)
    bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats)

    xx, yy = bmp(lons, lats)


    plt.figure()
    cs = bmp.contourf(xx, yy, tsurf_mean - 273.15, 40)
    bmp.drawcoastlines()
    plt.title("Tsurf")
    plt.colorbar()

    plt.figure()
    bmp.contourf(xx, yy, tair_mean, levels=cs.levels, norm=cs.norm, cmap=cs.cmap)
    bmp.drawcoastlines()
    plt.title("Tair")
    plt.colorbar()

    plt.figure()
    bmp.contourf(xx, yy, tsurf_mean - 273.15 - tair_mean, levels=np.arange(-2, 2.2, 0.2), cmap=cs.cmap)
    bmp.drawcoastlines()
    plt.title("Tsurf - Tair")
    plt.colorbar()



    pm.close()
    dm.close()

    plt.show()
Exemplo n.º 49
0
def plot_domain_using_coords_from_file(path=""):
    fig = plt.figure()
    assert isinstance(fig, Figure)
    rpnObj = RPN(path)

    lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()

    basemap = Basemap(projection="omerc",
                      lon_1=-68,
                      lat_1=52,
                      lon_2=16.65,
                      lat_2=0.0,
                      llcrnrlon=lons2d[0, 0],
                      llcrnrlat=lats2d[0, 0],
                      urcrnrlon=lons2d[-1, -1],
                      urcrnrlat=lats2d[-1, -1],
                      no_rot=True)

    # basemap.drawcoastlines()

    rot_lat_lon_proj = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)

    g_params = GridParams(lonr=180,
                          latr=0,
                          iref=45,
                          jref=41,
                          dx=0.5,
                          dy=0.5,
                          nx=86,
                          ny=86)

    lons2d_1, lats2d_1 = get_lons_lats_using_grid_params(
        g_params, rot_lat_lon_proj)

    basemap = Basemap(projection="omerc",
                      lon_1=-68,
                      lat_1=52,
                      lon_2=16.65,
                      lat_2=0.0,
                      llcrnrlon=lons2d_1[18, 18],
                      llcrnrlat=lats2d_1[18, 18],
                      urcrnrlon=lons2d_1[-1, -1],
                      urcrnrlat=lats2d_1[-1, -1],
                      no_rot=True,
                      resolution="i")

    basemap.drawcoastlines(linewidth=0.4)
    basemap.drawrivers()
    # basemap.drawmeridians(np.arange(-180, 0, 20))

    x, y = basemap(lons2d, lats2d)
    basemap.scatter(x, y, c="r", linewidths=0, s=1.0)
    print(x.shape)

    xll_big, yll_big = g_params.get_ll_point(marginx=20, marginy=20)
    xll_big -= g_params.dx / 2.0
    yll_big -= g_params.dy / 2.0
    xll_big, yll_big = rot_lat_lon_proj.toGeographicLonLat(xll_big, yll_big)
    xll_big, yll_big = basemap(xll_big, yll_big)

    xur_big, yur_big = g_params.get_ur_point(marginx=20, marginy=20)
    xur_big += g_params.dx / 2.0
    yur_big += g_params.dy / 2.0
    xur_big, yur_big = rot_lat_lon_proj.toGeographicLonLat(xur_big, yur_big)
    xur_big, yur_big = basemap(xur_big, yur_big)

    margin = 20

    # plot 0.25 degree grid
    g_params = GridParams(lonr=180,
                          latr=0,
                          iref=71,
                          jref=63,
                          dx=0.25,
                          dy=0.25,
                          nx=133,
                          ny=133)
    lons2d_2, lats2d_2 = get_lons_lats_using_grid_params(
        g_params, rot_lat_lon_proj)
    x2, y2 = basemap(lons2d_2[margin:-margin, margin:-margin],
                     lats2d_2[margin:-margin, margin:-margin])
    basemap.scatter(x2, y2, c="g", linewidth=0, marker="s", s=7.5)

    # plot 0.5 degree grid using the output file
    # debug
    rObj1 = RPN(
        "/home/huziy/skynet3_exec1/from_guillimin/quebec_86x86_0.5deg_without_lakes/pm1985010100_00000000p"
    )
    lons2d_1, lats2d_1 = rObj1.get_longitudes_and_latitudes()

    # x1, y1 = basemap(lons2d_1[margin:-margin,margin:-margin], lats2d_1[margin:-margin,margin:-margin])
    x1, y1 = basemap(lons2d_1, lats2d_1)

    print(x1.shape, lons2d_1[0, 0], lats2d_1[0, 0])

    basemap.scatter(x1, y1, c="b", linewidths=0, s=10)

    dx1 = (x1[1, 0] - x1[0, 0]) / 2.0
    dy1 = (y1[0, 1] - y1[0, 0]) / 2.0

    rbig = Rectangle((xll_big, yll_big),
                     xur_big - xll_big,
                     yur_big - yll_big,
                     linewidth=2,
                     edgecolor="b",
                     facecolor="none")

    ax = plt.gca()
    assert isinstance(ax, Axes)
    # ax.add_patch(rsmall)
    ax.add_patch(rbig)

    # draw north arrow
    plot_north_cross(-45, 45, basemap, ax=ax)

    # zoom to a region
    axins = zoomed_inset_axes(ax, 4, loc=1)  # zoom = 6
    basemap.drawcoastlines(ax=axins)
    basemap.drawrivers(ax=axins)
    basemap.scatter(x, y, c="r", linewidths=0, s=5, ax=axins)
    basemap.scatter(x2, y2, c="g", marker="s", linewidth=0, s=15, ax=axins)
    basemap.scatter(x1, y1, c="b", linewidths=0, s=25, ax=axins)

    # subregion to zoom in
    nx, ny = lons2d.shape
    part = 3
    xins_ll = lons2d[nx / part, ny / part]
    yins_ll = lats2d[nx / part, ny / part]
    xins_ur = lons2d[nx / part + 40, ny / part + 40]
    yins_ur = lats2d[nx / part + 40, ny / part + 40]

    xins_ll, yins_ll = basemap(xins_ll, yins_ll)
    xins_ur, yins_ur = basemap(xins_ur, yins_ur)

    axins.set_xlim(xins_ll, xins_ur)
    axins.set_ylim(yins_ll, yins_ur)

    # draw a bbox of the region of the inset axes in the parent axes and
    # connecting lines between the bbox and the inset axes area
    mark_inset(ax, axins, loc1=2, loc2=4, fc="none", linewidth=2)

    fig.tight_layout(pad=0.8)
    fig.savefig("high_low_res_domains.png")

    pass
def main():
    # stations = cehq_station.read_grdc_stations(st_id_list=["2903430", "2909150", "2912600", "4208025"])

    selected_ids = [
        "08MH001", "08NE074", "08NG065", "08NJ013", "08NK002", "08NK016",
        "08NL004", "08NL007", "08NL024", "08NL038", "08NN002"
    ]
    stations = cehq_station.load_from_hydat_db(natural=True,
                                               province="BC",
                                               selected_ids=selected_ids)

    stations_to_mp = None

    import matplotlib.pyplot as plt

    # labels = ["CanESM", "MPI"]
    # paths = ["/skynet3_rech1/huziy/offline_stfl/canesm/discharge_1958_01_01_00_00.nc",
    # "/skynet3_rech1/huziy/offline_stfl/mpi/discharge_1958_01_01_00_00.nc"]
    #
    # colors = ["r", "b"]

    # labels = ["ERA", ]
    # colors = ["r", ]
    # paths = ["/skynet3_rech1/huziy/arctic_routing/era40/discharge_1958_01_01_00_00.nc"]

    labels = ["Glacier-only", "All"]
    colors = ["r", "b"]
    paths = [
        "/skynet3_exec2/aganji/glacier_katja/watroute_gemera/discharge_stat_glac_00_99_2000_01_01_00_00.nc",
        "/skynet3_exec2/aganji/glacier_katja/watroute_gemera/discharge_stat_both_00_992000_01_01_00_00.nc"
    ]

    start_year_current = 2000
    end_year_current = 2013

    plot_future = False
    start_year_future = 2071  # ignored when plot future is false
    end_year_future = 2100

    if not plot_future:
        start_year = start_year_current
        end_year = end_year_current
    else:
        start_year = start_year_future
        end_year = end_year_future

    stations_filtered = []
    for s in stations:
        # Also filter out stations with small accumulation areas
        if s.drainage_km2 < 1000:
            continue

        if s.latitude > 49.4:
            continue

        # Filter stations with data out of the required time frame
        year_list = s.get_list_of_complete_years()
        if max(year_list) < start_year or min(year_list) > end_year:
            continue

        stations_filtered.append(s)

    stations = stations_filtered

    min_lon = min(s.longitude for s in stations)
    stations = [s for s in stations if s.longitude == min_lon]

    print("Retained {} stations.".format(len(stations)))

    sim_to_time = {}

    monthly_dates = [datetime(2001, m, 15) for m in range(1, 13)]
    fmt = FuncFormatter(lambda x, pos: num2date(x).strftime("%b")[0])
    locator = MonthLocator()

    fig = plt.figure()

    axes = []
    row_indices = []
    col_indices = []

    ncols = 1
    shiftrow = 0 if len(stations) % ncols == 0 else 1
    nrows = len(stations) // ncols + shiftrow
    shared_ax = None
    gs = gridspec.GridSpec(ncols=ncols, nrows=nrows)

    for i, s in enumerate(stations):
        row = i // ncols
        col = i % ncols

        row_indices.append(row)
        col_indices.append(col)

        if shared_ax is None:
            ax = fig.add_subplot(gs[row, col])
            shared_ax = ax
            assert isinstance(shared_ax, Axes)

        else:
            ax = fig.add_subplot(gs[row, col])

        ax.xaxis.set_major_locator(locator)
        ax.yaxis.set_major_locator(MaxNLocator(nbins=4))

        ax.xaxis.set_major_formatter(fmt)
        sfmt = ScalarFormatter(useMathText=True)
        sfmt.set_powerlimits((-3, 4))
        ax.yaxis.set_major_formatter(sfmt)
        assert isinstance(ax, Axes)

        axes.append(ax)

    # generate daily stamp dates
    d0 = datetime(2001, 1, 1)
    stamp_dates = [d0 + timedelta(days=i) for i in range(365)]

    # plot a panel for each station
    for s, ax, row, col in zip(stations, axes, row_indices, col_indices):

        assert isinstance(s, Station)
        assert isinstance(ax, Axes)
        if s.grdc_monthly_clim_max is not None:
            ax.fill_between(monthly_dates,
                            s.grdc_monthly_clim_min,
                            s.grdc_monthly_clim_max,
                            color="0.6",
                            alpha=0.5)

        avail_years = s.get_list_of_complete_years()
        print("{}: {}".format(s.id, ",".join([str(y) for y in avail_years])))
        years = [y for y in avail_years if start_year <= y <= end_year]
        _, obs_clim_stfl = s.get_daily_climatology_for_complete_years_with_pandas(
            stamp_dates=stamp_dates, years=years)

        if obs_clim_stfl is None:
            continue

        ax.plot(stamp_dates, obs_clim_stfl, "k", lw=3, label="Obs")

        if s.river_name is not None and s.river_name != "":
            ax.set_title(s.river_name)
        else:
            ax.set_title(s.id)

        for path, sim_label, color in zip(paths, labels, colors):
            ds = Dataset(path)

            if stations_to_mp is None:
                acc_area_2d = ds.variables["accumulation_area"][:]
                lons2d, lats2d = ds.variables["longitude"][:], ds.variables[
                    "latitude"][:]
                x_index, y_index = ds.variables["x_index"][:], ds.variables[
                    "y_index"][:]
                stations_to_mp = get_dataless_model_points_for_stations(
                    stations, acc_area_2d, lons2d, lats2d, x_index, y_index)

            # read dates only once for a given simulation
            if sim_label not in sim_to_time:
                time_str = ds.variables["time"][:].astype(str)
                times = [
                    datetime.strptime("".join(t_s), TIME_FORMAT)
                    for t_s in time_str
                ]
                sim_to_time[sim_label] = times

            mp = stations_to_mp[s]
            data = ds.variables["water_discharge_accumulated"][:,
                                                               mp.cell_index]
            print(path)
            df = DataFrame(data=data,
                           index=sim_to_time[sim_label],
                           columns=["value"])
            df["year"] = df.index.map(lambda d: d.year)
            df = df.ix[df.year.isin(years), :]
            df = df.select(lambda d: not (d.month == 2 and d.day == 29))
            df = df.groupby(lambda d: datetime(stamp_dates[0].year, d.month, d.
                                               day)).mean()

            daily_model_data = [df.ix[d, "value"] for d in stamp_dates]

            # print np.mean( monthly_model ), s.river_name, sim_label
            ax.plot(stamp_dates,
                    daily_model_data,
                    color,
                    lw=3,
                    label=sim_label + "(C)")

            if plot_future:
                ax.plot(stamp_dates,
                        daily_model_data,
                        color + "--",
                        lw=3,
                        label=sim_label + "(F2)")

            ds.close()

        if row < nrows - 1:
            ax.set_xticklabels([])

    axes[0].legend(fontsize=17, loc=2)
    plt.tight_layout()
    plt.savefig("offline_validation.png", dpi=400)
    plt.close(fig)

    r = RPN(
        "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/Depth_to_bedrock_WestNA_0.25")
    r.get_first_record_for_name("8L")
    proj_params = r.get_proj_parameters_for_the_last_read_rec()
    lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
    bsmp = RotatedLatLon(**proj_params).get_basemap_object_for_lons_lats(
        lons2d=lons, lats2d=lats)
    plot_utils.apply_plot_params(width_pt=None,
                                 width_cm=19,
                                 height_cm=19,
                                 font_size=12)
    plot_station_positions(manager=None, station_list=stations, bsmp=bsmp)
Exemplo n.º 51
0
def plot_domain_for_different_margins(path, margins=None):
    if not margins: margins = [20, 40, 60]
    rpnObj = RPN(path)

    lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()

    # projection parameters
    lon_1 = -68
    lat_1 = 52
    lon_2 = 16.65
    lat_2 = 0.0

    rot_lat_lon = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
    xll, yll = rot_lat_lon.toProjectionXY(lons2d[0, 0], lats2d[0, 0])
    xur, yur = rot_lat_lon.toProjectionXY(lons2d[-1, -1], lats2d[-1, -1])

    if xll < 0: xll += 360.0
    if xur < 0: xur += 360.0

    nx, ny = lons2d.shape

    dx = (xur - xll) / float(nx - 1)
    dy = (yur - yll) / float(ny - 1)

    print(dx, dy)
    print(xur, yur, xll, yll)

    x1 = xll - dx / 2.0
    y1 = yll - dy / 2.0
    x2 = xur + dx / 2.0
    y2 = yur + dy / 2.0

    x1lon, y1lat = rot_lat_lon.toGeographicLonLat(x1, y1)
    x2lon, y2lat = rot_lat_lon.toGeographicLonLat(x2, y2)

    llcrnrlon, llcrnrlat = rot_lat_lon.toGeographicLonLat(x1 - dx, y1 - dx)
    urcrnrlon, urcrnrlat = rot_lat_lon.toGeographicLonLat(x2 + dx, y2 + dx)

    basemap = Basemap(projection="omerc",
                      lon_1=lon_1,
                      lat_1=lat_1,
                      lon_2=lon_2,
                      lat_2=lat_2,
                      llcrnrlon=llcrnrlon,
                      llcrnrlat=llcrnrlat,
                      urcrnrlon=urcrnrlon,
                      urcrnrlat=urcrnrlat,
                      no_rot=True,
                      resolution="l")

    basemap.drawcoastlines()
    basemap.drawrivers()

    x1, y1 = basemap(x1lon, y1lat)
    x2, y2 = basemap(x2lon, y2lat)

    # add rectangle for the grid 220x220
    #    r1 = Rectangle((x1, y1), x2-x1, y2-y1, facecolor="none", edgecolor="r",  linewidth=5  )

    ax = plt.gca()
    assert isinstance(ax, Axes)
    #    xr1_label, yr1_label = rot_lat_lon.toGeographicLonLat(xur - 2 * dx, yll + 2 * dy)
    #    xr1_label, yr1_label = basemap( xr1_label, yr1_label )
    #    ax.annotate("{0}x{1}".format(nx, ny), xy = (xr1_label, yr1_label), va = "bottom", ha = "right", color = "r")
    #    assert isinstance(ax, Axes)
    #    ax.add_patch(r1)

    margins_all = [0] + margins

    for margin in margins_all:
        # mfree = margin - 20
        xlli = xll + margin * dx
        ylli = yll + margin * dy
        xuri = xur - margin * dx
        yuri = yur - margin * dy

        x1lon, y1lat = rot_lat_lon.toGeographicLonLat(xlli, ylli)
        x2lon, y2lat = rot_lat_lon.toGeographicLonLat(xuri, yuri)

        x1, y1 = basemap(x1lon, y1lat)
        x2, y2 = basemap(x2lon, y2lat)

        ri = Rectangle((x1, y1),
                       x2 - x1,
                       y2 - y1,
                       facecolor="none",
                       edgecolor="r",
                       linewidth=5)
        ax.add_patch(ri)

        xri_label, yri_label = rot_lat_lon.toGeographicLonLat(
            xlli + 2 * dx, yuri - 2 * dy)
        xri_label, yri_label = basemap(xri_label, yri_label)
        ax.annotate("{0}x{1}\nmarg. = {2}".format(nx - margin * 2,
                                                  ny - margin * 2,
                                                  margin + 20),
                    xy=(xri_label, yri_label),
                    va="top",
                    ha="left",
                    color="k",
                    backgroundcolor="w")

    plt.show()