pydune.data_processing.meteorological.wind_plot.netcdf_to_flux_rose#

netcdf_to_flux_rose(file, ax, fig, netcdflonlatinds=(0, 0), z=10, z_0=0.001, rho_g=2650.0, rho_f=1, g=9.81, d=0.00018, shield_th=0.0035, Kappa=0.4, mu=0.63, cm=1.7, bin_edges=array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153., 154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164., 165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175., 176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186., 187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197., 198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208., 209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219., 220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230., 231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241., 242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252., 253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263., 264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274., 275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285., 286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296., 297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307., 308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318., 319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329., 330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340., 341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351., 352., 353., 354., 355., 356., 357., 358., 359., 360.]), nsector=20, label_flux=False, label_angle=False, label=None, props={'boxstyle': 'round', 'edgecolor': (1, 1, 1, 1), 'facecolor': (1, 1, 1, 0.9), 'pad': 0}, blowfrom=False, **kwargs)[source]#

This function loads and concatenate (along the time axis) several NETCDF files from a list of filenames, calcuates the sand flux from a location in the NETCDF wind data using the quartic_transport_law, and plots a sand flux angular distribution on the given axe of the given figure.

Parameters:

  • files_list (list, str) – file name or list of downloaded file names.

  • ax (matplotlib.Axes) – axe of the figure that will be replaced by the flux rose.

  • fig (matplotlib.figure) – figure on which the flux rose is plotted.

  • netcdflonlatinds (tuple) – the longitude and latatitude indicies of the netcdf file the flux rose should be calculated for. (default is (0,0)).

  • z (scalar, numpy array) – elevation of the wind velocity (the default is 10). units: m.

  • z_0 (scalar, numpy array) – roughness length of surface (the default is 10^-3). units: m.

  • rho_g (scalar, numpy array) – density of sediment (the default is 2650). units: kg/m^3.

  • rho_f (scalar, numpy array) – density of fluid (the default is 1). units: kg/m^3.

  • g (scalar, numpy array) – gravity acceleration (the default is 9.81). units: m/s^2.

  • d (scalar, numpy array) – sediment grain diameter (the default is 180*10^-6). units: m.

  • shield_th (scalar, numpy array) – threshold shields number for transport initiation (the default is 0.0035). units: dimensionless.

  • Kappa (scalar, numpy array) – von Kármán constant (the default is 0.4). units: dimensionless.

  • mu (scalar, numpy array) – friction coefficient (the default is 0.63). units: dimensionless.

  • cm (scalar, numpy array) – transport law coefficient (the default is 1.7). units: dimensionless.

  • bin_edges (numpy array) – edges of the bins for finding the angular distribution of flux (the default is np.linspace(0, 360, 361)). units: degrees.

  • nsector (int) – number of angular bins for the flux rose (the default is 20).

  • label_flux (bool) – if True, labels the radial axis (the default is False).

  • label_angle (bool) – if True, label the angles (the default is False).

  • label (str, None) – if provided, labels the flux rose with the given string (the default is None).

  • props (dict) – Bbox properties used around the label (the default is dict(boxstyle=’round’, facecolor=(1, 1, 1, 0.9), edgecolor=(1, 1, 1, 1), pad=0)).

  • blowfrom (bool) – If blow from, the rose will be \(\pi\)-rotated, to show where the fluxes come from (the default is False).

  • **kwargs (other kwargs) – any other parameter supported by windrose.WindroseAxes.bar

Returns:

return the axe on which the wind rose is plotted. Can be used for further modifications.

Return type:

WindroseAxes