Source code for salishsea_tools.visualisations

# Copyright 2016 The Salish Sea NEMO Project and
# The University of British Columbia

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#    http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Functions for common model visualisations
"""
import datetime

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from salishsea_tools import geo_tools, nc_tools


[docs]def contour_thalweg( axes, var, bathy, mesh_mask, clevels=None, mesh_mask_depth_var='gdept_0', cmap='hsv', land_colour='burlywood', xcoord_distance=True, thalweg_file='/home/sallen/MEOPAR/Tools/bathymetry/thalweg_working.txt', cbar_args=None, mesh_args=None, method='contourf' ): """Contour the data stored in var along the domain thalweg. :arg axes: Axes instance to plot thalweg contour on. :type axes: :py:class:`matplotlib.axes.Axes` :arg var: Salish Sea NEMO model results variable to be contoured :type var: :py:class:`numpy.ndarray` :arg bathy: Salish Sea NEMO model bathymetry dataset :type bathy: :py:class:`netCDF4.Dataset` :arg mesh_mask: Salish Sea NEMO model mesh_mask dataset :type mesh_mask: :py:class:`netCDF4.Dataset` :arg clevels: argument for determining contour levels. Choices are 1. 'salinity' or 'temperature' for pre-determined levels used in nowcast. 2. an integer N, for N automatically determined levels. 3. a sequence V of contour levels, which must be in increasing order. :type clevels: str or int or iterable :arg str mesh_mask_depth_var: name of depth variable in :kbd:`mesh_mask` that is appropriate for :kbd:`var`; defaults to :kbd:`gdept_0` for NEMO-3.6 tracer variables. :arg str cmap: matplotlib colormap :arg str land_colour: matplotlib colour for land :arg xcoord_distance: plot along thalweg distance (True) or index (False) :type xcoord_distance: boolean :arg thalweg_pts_file: Path and file name to read the array of thalweg grid points from. :type thalweg_pts_file: str :arg dict cbar_args: Additional arguments to be passed to the cbar function (fraction, pad, etc.) :arg dict mesh_args: Additional arguments to be passed to the contourf or pcolormesh function : arg string method: method to use for data display: defaults to 'contourf' but 'pcolormesh' is also accepted :returns: matplotlib colorbar object """ thalweg_pts = np.loadtxt(thalweg_file, delimiter=' ', dtype=int) depth = mesh_mask.variables[mesh_mask_depth_var][:] dep_thal, distance, var_thal = load_thalweg( depth[0, ...], var, bathy['nav_lon'][:], bathy['nav_lat'][:], thalweg_pts) if xcoord_distance: xx_thal = distance axes.set_xlabel('Distance along thalweg [km]') else: xx_thal, _ = np.meshgrid(np.arange(var_thal.shape[-1]), dep_thal[:, 0]) axes.set_xlabel('Thalweg index') # Determine contour levels clevels_default = { 'salinity': [ 26, 27, 28, 29, 30, 30.2, 30.4, 30.6, 30.8, 31, 32, 33, 34 ], 'temperature': [ 6.9, 7, 7.5, 8, 8.5, 9, 9.8, 9.9, 10.3, 10.5, 11, 11.5, 12, 13, 14, 15, 16, 17, 18, 19 ] } if isinstance(clevels, str): try: clevels = clevels_default[clevels] except KeyError: raise KeyError('no default clevels defined for {}'.format(clevels)) # Prepare for plotting by filling in grid points just above bathymetry var_plot = _fill_in_bathy(var_thal, mesh_mask, thalweg_pts) if method == 'pcolormesh': if mesh_args is None: mesh = axes.pcolormesh(xx_thal, dep_thal, var_plot, cmap=cmap) else: mesh = axes.pcolormesh(xx_thal, dep_thal, var_plot, cmap=cmap, **mesh_args) axes.set_xlim((np.min(xx_thal),np.max(xx_thal))) else: if mesh_args is None: mesh = axes.contourf(xx_thal, dep_thal, var_plot, clevels, cmap=cmap, extend='both') else: mesh = axes.contourf(xx_thal, dep_thal, var_plot, clevels, cmap=cmap, extend='both', **mesh_args) _add_bathy_patch( xx_thal, bathy['Bathymetry'][:], thalweg_pts, axes, color=land_colour) if cbar_args is None: cbar = plt.colorbar(mesh, ax=axes) else: cbar = plt.colorbar(mesh, ax=axes, **cbar_args) axes.invert_yaxis() axes.set_ylabel('Depth [m]') return cbar
def _add_bathy_patch(xcoord, bathy, thalweg_pts, ax, color, zmin=450): """Add a polygon shaped as the land in the thalweg section :arg xcoord: x grid along thalweg :type xcoord: 2D numpy array :arg bathy: Salish Sea NEMO model bathymetry data :type bathy: :py:class:`numpy.ndarray` :arg thalweg_pts: Salish Sea NEMO model grid indices along thalweg :type thalweg_pts: 2D numpy array :arg ax: Axes instance to plot thalweg contour on. :type ax: :py:class:`matplotlib.axes.Axes` :arg str color: color of bathymetry patch :arg zmin: minimum depth for plot in meters :type zmin: float """ # Look up bottom bathymetry along thalweg thalweg_bottom = bathy[thalweg_pts[:, 0], thalweg_pts[:, 1]] # Construct bathy polygon poly = np.zeros((thalweg_bottom.shape[0]+2, 2)) poly[0, :] = 0, zmin poly[1:-1, 0] = xcoord[0, :] poly[1:-1:, 1] = thalweg_bottom poly[-1, :] = xcoord[0, -1], zmin ax.add_patch(patches.Polygon(poly, facecolor=color, edgecolor=color))
[docs]def load_thalweg(depths, var, lons, lats, thalweg_pts): """Returns depths, cumulative distance and variable along thalweg. :arg depths: depth array for variable. Can be 1D or 3D. :type depths: :py:class:`numpy.ndarray` :arg var: 3D Salish Sea NEMO model results variable :type var: :py:class:`numpy.ndarray` :arg lons: Salish Sea NEMO model longitude grid data :type lons: :py:class:`numpy.ndarray` :arg lats: Salish Sea NEMO model latitude grid data :type lats: :py:class:`numpy.ndarray` :arg thalweg_pts: Salish Sea NEMO model grid indices along thalweg :type thalweg_pts: 2D numpy array :returns: dep_thal, xx_thal, var_thal, all the same shape (depth, thalweg length) """ lons_thal = lons[thalweg_pts[:, 0], thalweg_pts[:, 1]] lats_thal = lats[thalweg_pts[:, 0], thalweg_pts[:, 1]] var_thal = var[:, thalweg_pts[:, 0], thalweg_pts[:, 1]] xx_thal = geo_tools.distance_along_curve(lons_thal, lats_thal) xx_thal = xx_thal + np.zeros(var_thal.shape) if depths.ndim > 1: dep_thal = depths[:, thalweg_pts[:, 0], thalweg_pts[:, 1]] else: _, dep_thal = np.meshgrid(xx_thal[0, :], depths) return dep_thal, xx_thal, var_thal
def _fill_in_bathy(variable, mesh_mask, thalweg_pts): """For each horizontal point in variable, fill in first vertically masked point with the value just above. Use mbathy in mesh_mask file to determine level of vertical masking :arg variable: the variable to be filled :type variable: 2D numpy array :arg mesh_mask: Salish Sea NEMO model mesh_mask data :type mesh_mask: :py:class:`netCDF4.Dataset` :arg thalweg_pts: Salish Sea NEMO model grid indices along thalweg :type thalweg_pts: 2D numpy array :returns: newvar, the filled numpy array """ mbathy = mesh_mask.variables['mbathy'][0, :, :] newvar = np.copy(variable) mbathy = mbathy[thalweg_pts[:, 0], thalweg_pts[:, 1]] for i, level in enumerate(mbathy): newvar[level, i] = variable[level-1, i] return newvar
[docs]def contour_layer_grid(axes,data,mask,clevels=10,lat=None,lon=None,cmap=None,var_name=None, land_colour='burlywood',is_depth_avg=False,is_pcolmesh=False,title='',cbar_args=None, ): """Contour 2d data at an arbitrary klevel on the model grid :arg axes: Axes instance to plot thalweg contour on. :type axes: :py:class:`matplotlib.axes.Axes` :arg data: 2D array to be contoured at level k :type data: :py:class:`numpy.ndarray` :arg klev: Index of k-level along which to contour :type klev: int :arg mask: Mask array with same dimensions as data :type mask: :py:class:`numpy.ndarray` :arg clevels: argument for determining contour levels. Choices are 1. an integer N, for N automatically determined levels. 2. a sequence V of contour levels, which must be in increasing order. :type clevels: str or int or iterable :arg lon: Array of longitudes with same length as x dimension of data. :type lon: :py:class:`numpy.ndarray` :arg lat: Array of longitudes with same length as x dimension of data. :type lat: :py:class:`numpy.ndarray` :arg str cmap: matplotlib colormap :arg str var_name: Name of variable to plot. Necesssary if cmap=None. :arg str land_colour: matplotlib colour for land :arg is_depth_avg: True if data is a depth averaged field (default is False). :type is_depth_avg: boolean :arg is_pcolmesh: plot a pcolormesh (True) instead of a contourf (default). :type is_pcolmesh: boolean :arg str title: Title string :arg dict cbar_args: Additional arguments to be passed to the cbar function (fraction, pad, etc.) :returns: matplotlib colorbar object """ mdata = np.ma.masked_where(mask==0,data) viz_tools.set_aspect(axes) if cmap == None: cbMIN, cbMAX, cmap = visualisations.retrieve_cmap(var_name,is_depth_avg) cmap = plt.get_cmap(cmocean.cm.algae) if is_pcolmesh: mesh = axes.pcolormesh(mdata, cmap=cmap) else: mesh= axes.contourf(mdata,clevels,cmap=cmap) axes.set_xlabel('X index') axes.set_ylabel('Y index') axes.set_title(title) axes.set_axis_bgcolor(land_colour) if cbar_args is None: cbar = plt.colorbar(mesh, ax=axes) else: cbar = plt.colorbar(mesh, ax=axes, **cbar_args) return cbar
[docs]def plot_drifters(ax, DATA, DRIFT_OBJS=None, color='red', cutoff=24, zorder=15): """Plot a drifter track from ODL Drifter observations. :arg time_ind: Time index (current drifter position, track will be visible up until this point, ex. 'YYYY-mmm-dd HH:MM:SS', format is flexible) :type time_ind: str or :py:class:`datetime.datetime` :arg ax: Axis object :type ax: :py:class:`matplotlib.pyplot.axes` :arg DATA: Drifter track dataset :type DATA: :py:class:`xarray.Dataset` :arg color: Drifter track color :type color: str :arg cutoff: Time threshold for color plotting (hours) :type cutoff: integer :arg zorder: Plotting layer specifier :type zorder: integer :returns: Dictionary of line objects :rtype: dict > :py:class:`matplotlib.lines.Line2D` """ if DATA.time.shape[0] > 0: # Convert time boundaries to datetime.datetime to allow operations/slicing starttime = nc_tools.xarraytime_to_datetime(DATA.time[ 0]) endtime = nc_tools.xarraytime_to_datetime(DATA.time[-1]) # Color plot cutoff time_cutoff = endtime - datetime.timedelta(hours=cutoff) if DRIFT_OBJS is not None: # --- Update line objects only # Plot drifter track (gray) DRIFT_OBJS['L_old'][0].set_data( DATA.lon.sel(time=slice(starttime, time_cutoff)), DATA.lat.sel(time=slice(starttime, time_cutoff))) # Plot drifter track (color) DRIFT_OBJS['L_new'][0].set_data( DATA.lon.sel(time=slice(time_cutoff, endtime)), DATA.lat.sel(time=slice(time_cutoff, endtime))) # Plot drifter position DRIFT_OBJS['P'][0].set_data( DATA.lon.sel(time=endtime, method='nearest'), DATA.lat.sel(time=endtime, method='nearest')) else: # ------------------------ Plot new line objects instances # Define drifter objects dict DRIFT_OBJS = {} # Plot drifter track (gray) DRIFT_OBJS['L_old'] = ax.plot( DATA.lon.sel(time=slice(starttime, time_cutoff)), DATA.lat.sel(time=slice(starttime, time_cutoff)), '-', linewidth=2, color='gray', zorder=zorder) # Plot drifter track (color) DRIFT_OBJS['L_new'] = ax.plot( DATA.lon.sel(time=slice(time_cutoff, endtime)), DATA.lat.sel(time=slice(time_cutoff, endtime)), '-', linewidth=2, color=color, zorder=zorder+1) # Plot drifter position DRIFT_OBJS['P'] = ax.plot( DATA.lon.sel(time=endtime, method='nearest'), DATA.lat.sel(time=endtime, method='nearest'), 'o', color=color, zorder=zorder+2) else: if DRIFT_OBJS is not None: # --- Update line objects only # Update drifter tracks DRIFT_OBJS['L_old'][0].set_data([], []) # gray DRIFT_OBJS['L_new'][0].set_data([], []) # color DRIFT_OBJS['P' ][0].set_data([], []) # position else: DRIFT_OBJS = {} DRIFT_OBJS['L_old'] = ax.plot([], [], '-', linewidth=2, color='gray', zorder=zorder) # Plot drifter track (color) DRIFT_OBJS['L_new'] = ax.plot([], [], '-', linewidth=2, color=color, zorder=zorder+1) # Plot drifter position DRIFT_OBJS['P'] = ax.plot([], [], 'o', color=color, zorder=zorder+2) return DRIFT_OBJS
[docs]def create_figure(ax, DATA, coords='map', window=[-125, -122.5, 48, 50]): """ Boilerplate figure code like coastline, aspect ratio, axis lims, etc. .. note:: This function is deprecated. Call plot formatting functions individually instead. """ raise DeprecationWarning( 'create_figure has been deprecated. Call plot formatting functions ' 'individually instead.')
[docs]def plot_tracers( ax, qty, DATA, C=None, coords='map', clim=[0, 35, 1], cmap='jet', zorder=0 ): """Plot a horizontal slice of NEMO tracers as filled contours. .. note:: This function is deprecated. Plot NEMO results directly using `matplotlib.pyplot.contourf` or equivalent instead. """ raise DeprecationWarning( 'plot_tracers has been deprecated. Plot NEMO results directly using ' 'matplotlib.pyplot.contourf or equivalent instead.')
[docs]def plot_velocity( ax, model, DATA, Q=None, coords='map', processed=False, spacing=5, mask=True, color='black', scale=20, headwidth=3, linewidth=0, zorder=5 ): """Plot a horizontal slice of NEMO or GEM velocities as quiver objects. Accepts subsampled u and v fields via the **processed** keyword argument. .. note:: This function is deprecated. Plot NEMO results directly using `matplotlib.pyplot.quiver` or equivalent instead. """ raise DeprecationWarning( 'plot_velocity has been deprecated. Plot NEMO results directly using ' 'matplotlib.pyplot.quiver or equivalent instead.')
[docs]def retrieve_cmap(varname,deep_bool): """takes 2 args: string varname - name of a variable from nowcast-green output boolean deep_bool - indicates whether the variable is depth-integrated or not returns 2 ints(min and max value of range), and string identifying cmap""" var_namemap ={'Fraser_tracer': {'varname':'Fraser_tracer'}, 'ammonium': {'varname':'NH4'}, 'NH4': {'varname':'NH4'}, 'biogenic_silicon': {'varname':'bSi'}, 'bSi': {'varname':'bSi'}, 'ciliates': {'varname':'MYRI'}, 'MYRI': {'varname':'MYRI'}, 'diatoms': {'varname':'PHY2'}, 'PHY2': {'varname':'PHY2'}, 'dissolved_organic_nitrogen': {'varname':'dissolved_organic_nitrogen'}, 'flagellates': {'varname':'PHY'}, 'PHY': {'varname':'PHY'}, 'mesozooplankton': {'varname':'MESZ'}, 'MESZ': {'varname':'MESZ'}, 'microzooplankton': {'varname':'MICZ'}, 'MICZ': {'varname':'MICZ'}, 'nitrate': {'varname':'NO3'}, 'NO3': {'varname':'NO3'}, 'particulate_organic_nitrogen': {'varname':'PON'}, 'POC': {'varname':'PON'}, 'PON': {'varname':'PON'}, 'dissolved_organic_nitrogen': {'varname':'DON'}, 'DOC': {'varname':'DON'}, 'DON': {'varname':'DON'}, 'silicon': {'varname':'Si'}, 'Si': {'varname':'Si'}} #dictionary of colour ranges var_colour_ranges = { 'Fraser_tracer':{'colorBarMinimum': 0.0, 'colorBarMaximum': 140.0,'cmap': 'turbid'}, 'MESZ': {'colorBarMinimum': 0.0, 'colorBarMaximum': 3.0,'cmap': 'algae'}, 'MICZ': {'colorBarMinimum': 0.0, 'colorBarMaximum': 4.0,'cmap': 'algae'}, 'MYRI': {'colorBarMinimum': 0.0, 'colorBarMaximum': 5.0,'cmap': 'algae'}, 'NH4': {'colorBarMinimum': 0.0, 'colorBarMaximum': 10.0,'cmap': 'matter'}, 'NO3': {'colorBarMinimum': 0.0, 'colorBarMaximum': 40.0,'cmap': 'tempo'}, 'PON': {'colorBarMinimum': 0.0, 'colorBarMaximum': 2.0,'cmap': 'amp'}, 'DON': {'colorBarMinimum': 0.0, 'colorBarMaximum': 20.0,'cmap': 'amp'}, 'O2': {'colorBarMinimum': 0.0, 'colorBarMaximum': 140.0,'cmap': 'turbid'}, 'PHY': {'colorBarMinimum': 0.0, 'colorBarMaximum': 6.0, 'cmap': 'algae'}, 'PHY2': {'colorBarMinimum': 0.0, 'colorBarMaximum': 15.0,'cmap': 'algae'}, 'Si': {'colorBarMinimum': 0.0, 'colorBarMaximum': 70.0,'cmap': 'turbid'}, 'bSi': {'colorBarMinimum': 0.0, 'colorBarMaximum': 70.0,'cmap': 'turbid'}, 'Fraser_tracer_int':{'colorBarMinimum': 0.0, 'colorBarMaximum': 6500,'cmap': 'turbid'}, 'MESZ_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 140,'cmap': 'algae'}, 'MICZ_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 350,'cmap': 'algae'}, 'MYRI_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 75,'cmap': 'algae'}, 'NH4_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 1500,'cmap': 'matter'}, 'NO3_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 24000,'cmap': 'tempo'}, 'PON_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 600,'cmap': 'amp'}, 'DON_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 2500,'cmap': 'amp'}, 'O2_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 1000,'cmap': 'turbid'}, 'PHY_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 100,'cmap': 'algae'}, 'PHY2_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 350,'cmap': 'algae'}, 'Si_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 40000,'cmap': 'turbid'}, 'bSi_int': {'colorBarMinimum': 0.0, 'colorBarMaximum': 40000,'cmap': 'turbid'}, } dp = var_namemap[varname] vn = dp['varname'] if (deep_bool == True): vn = vn + '_int' dict_pull = var_colour_ranges[vn] cbMIN = dict_pull['colorBarMinimum'] print() cbMAX = dict_pull['colorBarMaximum'] cmap_name = dict_pull['cmap'] return cbMIN, cbMAX, cmap_name