def plot_contour(ds: xr.Dataset, var: VarName.TYPE, time: TimeLike.TYPE = None, indexers: DictLike.TYPE = None, title: str = None, filled: bool = True, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a contour plot of a variable given by dataset *ds* and variable name *var*. :param ds: the dataset containing the variable to plot :param var: the variable's name :param time: time slice index to plot, can be a string "YYYY-MM-DD" or an integer number :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "layer=4". :param title: an optional title :param filled: whether the regions between two contours shall be filled :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name = VarName.convert(var) if not var_name: raise ValidationError("Missing name for 'var'") var = ds[var_name] time = TimeLike.convert(time) indexers = DictLike.convert(indexers) or {} properties = DictLike.convert(properties) or {} figure = plt.figure(figsize=(8, 4)) ax = figure.add_subplot(111) var_data = get_var_data(var, indexers, time=time) if filled: var_data.plot.contourf(ax=ax, **properties) else: var_data.plot.contour(ax=ax, **properties) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_hist(ds: xr.Dataset, var: VarName.TYPE, indexers: DictLike.TYPE = None, title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Plot a variable, optionally save the figure in a file. The plot can either be shown using pyplot functionality, or saved, if a path is given. The following file formats for saving the plot are supported: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff :param ds: Dataset that contains the variable named by *var*. :param var: The name of the variable to plot :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lon=12.6, layer=3, time='2012-05-02'". :param title: an optional title :param properties: optional histogram plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.hist.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name = VarName.convert(var) if not var_name: raise ValidationError("Missing name for 'var'") var = ds[var] indexers = DictLike.convert(indexers) properties = DictLike.convert(properties) or {} figure = plt.figure(figsize=(8, 4)) ax = figure.add_subplot(111) figure.tight_layout() var_data = get_var_data(var, indexers) var_data.plot.hist(ax=ax, **properties) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_hist(ds: xr.Dataset, var: VarName.TYPE, indexers: DictLike.TYPE = None, title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Plot a variable, optionally save the figure in a file. The plot can either be shown using pyplot functionality, or saved, if a path is given. The following file formats for saving the plot are supported: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff :param ds: Dataset that contains the variable named by *var*. :param var: The name of the variable to plot :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lon=12.6, layer=3, time='2012-05-02'". :param title: an optional title :param properties: optional histogram plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.hist.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name = VarName.convert(var) if not var_name: raise ValidationError("Missing name for 'var'") var = ds[var] indexers = DictLike.convert(indexers) properties = DictLike.convert(properties) or {} figure = plt.figure(figsize=(8, 4)) ax = figure.add_subplot(111) figure.tight_layout() var_data = get_var_data(var, indexers) var_data.plot.hist(ax=ax, **properties) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_contour(ds: xr.Dataset, var: VarName.TYPE, indexers: DictLike.TYPE = None, title: str = None, filled: bool = True, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a contour plot of a variable given by dataset *ds* and variable name *var*. :param ds: the dataset containing the variable to plot :param var: the variable's name :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "layer=4". :param title: an optional title :param filled: whether the regions between two contours shall be filled :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name = VarName.convert(var) if not var_name: raise ValidationError("Missing name for 'var'") var = ds[var_name] indexers = DictLike.convert(indexers) or {} properties = DictLike.convert(properties) or {} figure = plt.figure(figsize=(8, 4)) ax = figure.add_subplot(111) var_data = get_var_data(var, indexers) if filled: var_data.plot.contourf(ax=ax, **properties) else: var_data.plot.contour(ax=ax, **properties) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot(ds: DatasetLike.TYPE, var: VarName.TYPE, indexers: DictLike.TYPE = None, title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a 1D/line or 2D/image plot of a variable given by dataset *ds* and variable name *var*. :param ds: Dataset or Dataframe that contains the variable named by *var*. :param var: The name of the variable to plot :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param title: an optional plot title :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ ds = DatasetLike.convert(ds) var_name = VarName.convert(var) if not var_name: raise ValidationError("Missing name for 'var'") var = ds[var_name] indexers = DictLike.convert(indexers) properties = DictLike.convert(properties) or {} figure = plt.figure() ax = figure.add_subplot(111) var_data = get_var_data(var, indexers) var_data.plot(ax=ax, **properties) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_data_frame(df: pd.DataFrame, plot_type: str = 'line', file: str = None, **kwargs) -> Figure: """ Plot a data frame. This is a wrapper of pandas.DataFrame.plot() function. For further documentation please see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html :param df: A pandas dataframe to plot :param plot_type: Plot type :param file: path to a file in which to save the plot :param kwargs: Keyword arguments to pass to the underlying pandas.DataFrame.plot function """ if not isinstance(df, pd.DataFrame): raise ValidationError('"df" must be of type "pandas.DataFrame"') ax = df.plot(kind=plot_type, figsize=(8, 4), **kwargs) figure = ax.get_figure() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_data_frame(df: pd.DataFrame, plot_type: str = 'line', file: str = None, **kwargs) -> Figure: """ Plot a data frame. This is a wrapper of pandas.DataFrame.plot() function. For further documentation please see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html :param df: A pandas dataframe to plot :param plot_type: Plot type :param file: path to a file in which to save the plot :param kwargs: Keyword arguments to pass to the underlying pandas.DataFrame.plot function """ if not isinstance(df, pd.DataFrame): raise ValidationError('"df" must be of type "pandas.DataFrame"') ax = df.plot(kind=plot_type, figsize=(8, 4), **kwargs) figure = ax.get_figure() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_hovmoeller(ds: xr.Dataset, var: VarName.TYPE = None, x_axis: DimName.TYPE = None, y_axis: DimName.TYPE = None, method: str = 'mean', contour: bool = True, title: str = None, file: str = None, monitor: Monitor = Monitor.NONE, **kwargs) -> Figure: """ Create a Hovmoeller plot of the given dataset. Dimensions other than the ones defined as x and y axis will be aggregated using the given method to produce the plot. :param ds: Dataset to plot :param var: Name of the variable to plot :param x_axis: Dimension to show on x axis :param y_axis: Dimension to show on y axis :param method: Aggregation method :param contour: Whether to produce a contour plot :param title: Plot title :param file: path to a file in which to save the plot :param monitor: A progress monitor :param kwargs: Keyword arguments to pass to underlying xarray plotting fuction """ var_name = None if not var: for key in ds.data_vars.keys(): var_name = key break else: var_name = VarName.convert(var) var = ds[var_name] if not x_axis: x_axis = var.dims[0] else: x_axis = DimName.convert(x_axis) if not y_axis: try: y_axis = var.dims[1] except IndexError: raise ValidationError('Given dataset variable should have at least two dimensions.') else: y_axis = DimName.convert(y_axis) if x_axis == y_axis: raise ValidationError('Dimensions should differ between plot axis.') dims = list(var.dims) try: dims.remove(x_axis) dims.remove(y_axis) except ValueError: raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\ {}, {}.'.format(var_name, x_axis, y_axis)) ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean, 'median': np.nanmedian, 'sum': np.nansum} with monitor.starting("Plot Hovmoeller", total_work=100): monitor.progress(5) with monitor.child(90).observing("Aggregate"): var = var.reduce(ufuncs[method], dim=dims) monitor.progress(5) figure = plt.figure() ax = figure.add_subplot(111) if x_axis == 'time': figure.autofmt_xdate() if contour: var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs) else: var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_scatter(ds1: xr.Dataset, ds2: xr.Dataset, var1: VarName.TYPE, var2: VarName.TYPE, indexers1: DictLike.TYPE = None, indexers2: DictLike.TYPE = None, type: str = '2D Histogram', title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a scatter plot of two variables of two variables given by datasets *ds1*, *ds2* and the variable names *var1*, *var2*. :param ds1: Dataset that contains the variable named by *var1*. :param ds2: Dataset that contains the variable named by *var2*. :param var1: The name of the first variable to plot :param var2: The name of the second variable to plot :param indexers1: Optional indexers into data array *var1*. The *indexers1* is a dictionary or comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param indexers2: Optional indexers into data array *var2*. :param type: The plot type. :param title: optional plot title :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name1 = VarName.convert(var1) var_name2 = VarName.convert(var2) indexers1 = DictLike.convert(indexers1) or {} indexers2 = DictLike.convert(indexers2) or {} properties = DictLike.convert(properties) or {} datasets = ds1, ds2 var_names = var_name1, var_name2 vars = [None, None] for i in (0, 1): try: vars[i] = datasets[i][var_names[i]] except KeyError as e: raise ValidationError(f'"{var_names[i]}" is not a variable in dataset given by "ds{i+1}"') from e var_dim_names = set(vars[0].dims), set(vars[1].dims) indexer_dim_names = set(indexers1.keys()), set(indexers2.keys()) if set(var_dim_names[0]).isdisjoint(var_dim_names[1]): raise ValidationError('"var1" and "var2" have no dimensions in common:' f' {var_dim_names[0]} and {var_dim_names[1]}.') for i in (0, 1): if indexer_dim_names[i] and not (indexer_dim_names[i] < var_dim_names[i]): raise ValidationError(f'"indexers{i+1}" must be a subset of the dimensions of "var{i+1}",' f' but {indexer_dim_names[i]} is not a subset of {var_dim_names[i]}.') rem_dim_names1 = var_dim_names[0] - indexer_dim_names[0] rem_dim_names2 = var_dim_names[1] - indexer_dim_names[1] if rem_dim_names1 != rem_dim_names2: raise ValidationError('Remaining dimensions of data from "var1" must be equal to' f' remaining dimensions of data from "var2",' f' but {rem_dim_names1} is not equal to {rem_dim_names2}.' ' You may need to use the indexers correctly.') indexers = indexers1, indexers2 labels = [None, None] for i in (0, 1): # Note, long_name can be really long, too long. # name = vars[i].attrs.get('long_name', var_names[i]) name = var_names[i] units = vars[i].attrs.get('units', '-') labels[i] = f'{name} ({units})' if indexers[i]: try: vars[i] = vars[i].sel(method='nearest', **indexers[i]) except (KeyError, ValueError, TypeError) as e: raise ValidationError(f'"indexers{i+1}" is not valid for "var{i+1}": {e}') from e labels[i] += " at " + ",".join(f"{key} = {value}" for key, value in indexers[i].items()) shape1 = vars[0].shape shape2 = vars[1].shape if shape1 != shape2: raise ValidationError('Remaining shape of data from "var1" must be equal to' ' remaining shape of data from "var2",' f' but {shape1} is not equal to {shape2}.' ' You may need to use the "coregister" operation first.') figure = plt.figure(figsize=(8, 8)) ax = figure.add_subplot(111) try: x = vars[0].values.flatten() y = vars[1].values.flatten() except MemoryError as e: raise ValidationError('Out of memory. Try using a data subset' ' or specify indexers to reduce number of dimensions.') from e default_cmap = 'Reds' if type == 'Point': ax.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5) if 'alpha' not in properties: properties['alpha'] = 0.25 if 'markerfacecolor' not in properties: properties['markerfacecolor'] = '#880000' if 'markeredgewidth' not in properties: properties['markeredgewidth'] = 0.0 if 'markersize' not in properties: properties['markersize'] = 5.0 ax.plot(x, y, '.', **properties) elif type == '2D Histogram': if 'cmap' not in properties: properties['cmap'] = default_cmap if 'bins' not in properties: properties['bins'] = (256, 256) if 'norm' not in properties: properties['norm'] = matplotlib.colors.LogNorm() if 'range' not in properties: xrange = np.nanpercentile(x, [0, 100]) yrange = np.nanpercentile(y, [0, 100]) properties['range'] = [xrange, yrange] h, xedges, yedges, pc = ax.hist2d(x, y, **properties) figure.colorbar(pc, ax=ax, cmap=properties['cmap']) elif type == 'Hexbin': if 'cmap' not in properties: properties['cmap'] = default_cmap if 'gridsize' not in properties: properties['gridsize'] = (64, 64) if 'norm' not in properties: properties['norm'] = matplotlib.colors.LogNorm() x = np.ma.masked_invalid(x, copy=False) y = np.ma.masked_invalid(y, copy=False) collection = ax.hexbin(x, y, **properties) figure.colorbar(collection, ax=ax, cmap=properties['cmap']) ax.set_xlabel(labels[0]) ax.set_ylabel(labels[1]) if title: ax.set_title(title) # see https://matplotlib.org/tutorials/intermediate/tight_layout_guide.html figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_line(ds: DatasetLike.TYPE, var_names: VarNamesLike.TYPE, fmt: str = None, label: DimName.TYPE = None, indexers: DictLike.TYPE = None, title: str = None, file: str = None) -> Figure: """ Create a 1D/line plot of variable(s) given by dataset *ds* and variable name(s) *var_names*. :param ds: Dataset or Dataframe that contains the variable(s) named by *var_names*. :param var_names: The name of the variable(s) to plot :param fmt: optional semicolon-separated matplotlib formats, e.g. 1 variable - "b.-" 2 variables - "b.-;r+:" If the number of properties is less than the number of selected variables, the next non-corresponding variable will repeat the first style on the list, and so on. For full reference on matplotlib plot() function, refer to https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html :param file: path to a file in which to save the plot :param label: dimension name to be selected as the x-axis of the plot :param indexers: Optional indexers into data array of *var_names*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param title: an optional plot title :return: a matplotlib figure object or None if in IPython mode """ ds = DatasetLike.convert(ds) fmt_count = 0 fmt_list = [] if fmt: fmt_list = fmt.split(";") fmt_count = len(fmt_list) if not var_names: raise ValidationError("Missing name for 'vars'") figure = plt.figure() ax = figure.add_subplot(111) figure.subplots_adjust(right=0.65) var_names = VarNamesLike.convert(var_names) if not title: if label: title = ','.join(var_names) + ' over ' + label else: title = ','.join(var_names) if indexers: title = title + '\n' + ' at ' + json.dumps(indexers).strip('"') ax.set_title(title) indexers = DictLike.convert(indexers) ax_var = {} var_count = len(var_names) predefined_fmt = ['r', 'g', 'b', 'c', 'm', 'y', 'k'] if label: ds = get_vars_data(ds, indexers, remaining_dims=[label]) else: ds = get_vars_data(ds, indexers) for i in range(var_count): var_name = var_names[i] var = ds[var_name] if len(var.dims) > 1: raise ValidationError(f'Unable to plot because variable {var_name} has more than one dimension: {var.dims}.' f' To specify value(s) of these dimension(s), please use the indexers.') var_label = var_name + ' (' + var.attrs['units'] + ')' if 'units' in var.attrs else var_name properties_dict = {} indexers = DictLike.convert(indexers) if fmt is None: selected_fmt = predefined_fmt[i % len(predefined_fmt)] else: selected_fmt = fmt_list[i % fmt_count] if label: x_axis = var[label] elif 'time' in var: x_axis = var.time else: x_axis = [] # to differentiate the creation of y-axis of the first and the nth variable if i == 0: if len(x_axis) > 0: ax.plot(x_axis, var, selected_fmt, **properties_dict) else: ax.plot(var, selected_fmt, **properties_dict) ax.set_ylabel(var_label, wrap=True) ax.yaxis.label.set_color(selected_fmt[0]) ax.tick_params(axis='y', colors=selected_fmt[0]) else: ax_var[var_name] = ax.twinx() if len(ax_var) > 1: ax_var[var_name].spines["right"].set_position(("axes", 1 + ((i - 1) * 0.2))) ax_var[var_name].set_frame_on(True) ax_var[var_name].patch.set_visible(False) if len(x_axis) > 0: ax_var[var_name].plot(x_axis, var, selected_fmt, **properties_dict) else: ax_var[var_name].plot(var, selected_fmt, **properties_dict) ax_var[var_name].set_ylabel(var_label, wrap=True) ax_var[var_name].yaxis.label.set_color(selected_fmt[0]) ax_var[var_name].tick_params(axis='y', colors=selected_fmt[0]) ax.tick_params(axis='x', rotation=45) if label in ds and 'long_name' in ds[label].attrs: ax.set_xlabel(ds[label].attrs['long_name']) figure.tight_layout() if file: figure.savefig(file, dpi=600) return figure if not in_notebook() else None
def plot_scatter(ds1: xr.Dataset, ds2: xr.Dataset, var1: VarName.TYPE, var2: VarName.TYPE, indexers1: DictLike.TYPE = None, indexers2: DictLike.TYPE = None, title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a scatter plot of two variables of two variables given by datasets *ds1*, *ds2* and the variable names *var1*, *var2*. :param ds1: Dataset that contains the variable named by *var1*. :param ds2: Dataset that contains the variable named by *var2*. :param var1: The name of the first variable to plot :param var2: The name of the second variable to plot :param indexers1: Optional indexers into data array *var1*. The *indexers1* is a dictionary or comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param indexers2: Optional indexers into data array *var2*. :param title: optional plot title :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name1 = VarName.convert(var1) var_name2 = VarName.convert(var2) if not var_name1: raise ValidationError("Missing name for 'var1'") if not var_name2: raise ValidationError("Missing name for 'var2'") var1 = ds1[var_name1] var2 = ds2[var_name2] indexers1 = DictLike.convert(indexers1) or {} indexers2 = DictLike.convert(indexers2) or {} properties = DictLike.convert(properties) or {} try: if indexers1: var_data1 = var1.sel(method='nearest', **indexers1) if not indexers2: indexers2 = indexers1 var_data2 = var2.sel(method='nearest', **indexers2) remaining_dims = list(set(var1.dims) ^ set(indexers1.keys())) min_dim = max(var_data1[remaining_dims[0]].min(), var_data2[remaining_dims[0]].min()) max_dim = min(var_data1[remaining_dims[0]].max(), var_data2[remaining_dims[0]].max()) var_data1 = var_data1.where((var_data1[remaining_dims[0]] >= min_dim) & (var_data1[remaining_dims[0]] <= max_dim), drop=True) var_data2 = var_data2.where( (var_data2[remaining_dims[0]] >= min_dim) & (var_data2[remaining_dims[0]] <= max_dim), drop=True) if len(remaining_dims) is 1: indexer3 = {remaining_dims[0]: var_data1[remaining_dims[0]].data} var_data2.reindex(method='nearest', **indexer3) else: raise ValidationError('Please use indexers to reduce the dimensionality of each variable to one.') else: var_data1 = var1 var_data2 = var2 except ValueError: var_data1 = var1 var_data2 = var2 figure = plt.figure(figsize=(12, 8)) ax = figure.add_subplot(111) # var_data1.plot(ax = ax, **properties) ax.plot(var_data1.values, var_data2.values, '.', **properties) # var_data1.plot(ax=ax, **properties) xlabel_txt = "".join(", " + str(key) + " = " + str(value) for key, value in indexers1.items()) xlabel_txt = var_name1 + xlabel_txt ylabel_txt = "".join(", " + str(key) + " = " + str(value) for key, value in indexers2.items()) ylabel_txt = var_name2 + ylabel_txt ax.set_xlabel(xlabel_txt) ax.set_ylabel(ylabel_txt) figure.tight_layout() if title: ax.set_title(title) if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_hovmoeller(ds: xr.Dataset, var: VarName.TYPE = None, x_axis: DimName.TYPE = None, y_axis: DimName.TYPE = None, method: str = 'mean', contour: bool = True, title: str = None, file: str = None, monitor: Monitor = Monitor.NONE, **kwargs) -> Figure: """ Create a Hovmoeller plot of the given dataset. Dimensions other than the ones defined as x and y axis will be aggregated using the given method to produce the plot. :param ds: Dataset to plot :param var: Name of the variable to plot :param x_axis: Dimension to show on x axis :param y_axis: Dimension to show on y axis :param method: Aggregation method :param contour: Whether to produce a contour plot :param title: Plot title :param file: path to a file in which to save the plot :param monitor: A progress monitor :param kwargs: Keyword arguments to pass to underlying xarray plotting fuction """ var_name = None if not var: for key in ds.data_vars.keys(): var_name = key break else: var_name = VarName.convert(var) var = ds[var_name] if not x_axis: x_axis = var.dims[0] else: x_axis = DimName.convert(x_axis) if not y_axis: try: y_axis = var.dims[1] except IndexError: raise ValidationError( 'Given dataset variable should have at least two dimensions.') else: y_axis = DimName.convert(y_axis) if x_axis == y_axis: raise ValidationError('Dimensions should differ between plot axis.') dims = list(var.dims) try: dims.remove(x_axis) dims.remove(y_axis) except ValueError: raise ValidationError( 'Given dataset variable: {} does not feature requested dimensions:\ {}, {}.'.format(var_name, x_axis, y_axis)) ufuncs = { 'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean, 'median': np.nanmedian, 'sum': np.nansum } with monitor.starting("Plot Hovmoeller", total_work=100): monitor.progress(5) with monitor.child(90).observing("Aggregate"): var = var.reduce(ufuncs[method], dim=dims) monitor.progress(5) figure = plt.figure() ax = figure.add_subplot(111) if x_axis == 'time': figure.autofmt_xdate() if contour: var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs) else: var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs) if title: ax.set_title(title) figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_scatter(ds1: xr.Dataset, ds2: xr.Dataset, var1: VarName.TYPE, var2: VarName.TYPE, indexers1: DictLike.TYPE = None, indexers2: DictLike.TYPE = None, type: str = '2D Histogram', title: str = None, properties: DictLike.TYPE = None, file: str = None) -> Figure: """ Create a scatter plot of two variables of two variables given by datasets *ds1*, *ds2* and the variable names *var1*, *var2*. :param ds1: Dataset that contains the variable named by *var1*. :param ds2: Dataset that contains the variable named by *var2*. :param var1: The name of the first variable to plot :param var2: The name of the second variable to plot :param indexers1: Optional indexers into data array *var1*. The *indexers1* is a dictionary or comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param indexers2: Optional indexers into data array *var2*. :param type: The plot type. :param title: optional plot title :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5), label='Sea Surface Temperature'" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/devdocs/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ var_name1 = VarName.convert(var1) var_name2 = VarName.convert(var2) indexers1 = DictLike.convert(indexers1) or {} indexers2 = DictLike.convert(indexers2) or {} properties = DictLike.convert(properties) or {} datasets = ds1, ds2 var_names = var_name1, var_name2 vars = [None, None] for i in (0, 1): try: vars[i] = datasets[i][var_names[i]] except KeyError as e: raise ValidationError( f'"{var_names[i]}" is not a variable in dataset given by "ds{i+1}"' ) from e var_dim_names = set(vars[0].dims), set(vars[1].dims) indexer_dim_names = set(indexers1.keys()), set(indexers2.keys()) if set(var_dim_names[0]).isdisjoint(var_dim_names[1]): raise ValidationError('"var1" and "var2" have no dimensions in common:' f' {var_dim_names[0]} and {var_dim_names[1]}.') for i in (0, 1): if indexer_dim_names[i] and not (indexer_dim_names[i] < var_dim_names[i]): raise ValidationError( f'"indexers{i+1}" must be a subset of the dimensions of "var{i+1}",' f' but {indexer_dim_names[i]} is not a subset of {var_dim_names[i]}.' ) rem_dim_names1 = var_dim_names[0] - indexer_dim_names[0] rem_dim_names2 = var_dim_names[1] - indexer_dim_names[1] if rem_dim_names1 != rem_dim_names2: raise ValidationError( 'Remaining dimensions of data from "var1" must be equal to' f' remaining dimensions of data from "var2",' f' but {rem_dim_names1} is not equal to {rem_dim_names2}.' ' You may need to use the indexers correctly.') indexers = indexers1, indexers2 labels = [None, None] for i in (0, 1): # Note, long_name can be really long, too long. # name = vars[i].attrs.get('long_name', var_names[i]) name = var_names[i] units = vars[i].attrs.get('units', '-') labels[i] = f'{name} ({units})' if indexers[i]: try: vars[i] = vars[i].sel(method='nearest', **indexers[i]) except (KeyError, ValueError, TypeError) as e: raise ValidationError( f'"indexers{i+1}" is not valid for "var{i+1}": {e}') from e labels[i] += " at " + ",".join( f"{key} = {value}" for key, value in indexers[i].items()) shape1 = vars[0].shape shape2 = vars[1].shape if shape1 != shape2: raise ValidationError( 'Remaining shape of data from "var1" must be equal to' ' remaining shape of data from "var2",' f' but {shape1} is not equal to {shape2}.' ' You may need to use the "coregister" operation first.') figure = plt.figure(figsize=(8, 8)) ax = figure.add_subplot(111) try: x = vars[0].values.flatten() y = vars[1].values.flatten() except MemoryError as e: raise ValidationError( 'Out of memory. Try using a data subset' ' or specify indexers to reduce number of dimensions.') from e default_cmap = 'Reds' if type == 'Point': ax.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5) if 'alpha' not in properties: properties['alpha'] = 0.25 if 'markerfacecolor' not in properties: properties['markerfacecolor'] = '#880000' if 'markeredgewidth' not in properties: properties['markeredgewidth'] = 0.0 if 'markersize' not in properties: properties['markersize'] = 5.0 ax.plot(x, y, '.', **properties) elif type == '2D Histogram': if 'cmap' not in properties: properties['cmap'] = default_cmap if 'bins' not in properties: properties['bins'] = (256, 256) if 'norm' not in properties: properties['norm'] = matplotlib.colors.LogNorm() if 'range' not in properties: xrange = np.nanpercentile(x, [0, 100]) yrange = np.nanpercentile(y, [0, 100]) properties['range'] = [xrange, yrange] h, xedges, yedges, pc = ax.hist2d(x, y, **properties) figure.colorbar(pc, ax=ax, cmap=properties['cmap']) elif type == 'Hexbin': if 'cmap' not in properties: properties['cmap'] = default_cmap if 'gridsize' not in properties: properties['gridsize'] = (64, 64) if 'norm' not in properties: properties['norm'] = matplotlib.colors.LogNorm() x = np.ma.masked_invalid(x, copy=False) y = np.ma.masked_invalid(y, copy=False) collection = ax.hexbin(x, y, **properties) figure.colorbar(collection, ax=ax, cmap=properties['cmap']) ax.set_xlabel(labels[0]) ax.set_ylabel(labels[1]) if title: ax.set_title(title) # see https://matplotlib.org/tutorials/intermediate/tight_layout_guide.html figure.tight_layout() if file: figure.savefig(file) return figure if not in_notebook() else None
def plot_line(ds: DatasetLike.TYPE, var_names: VarNamesLike.TYPE, fmt: str = None, label: DimName.TYPE = None, indexers: DictLike.TYPE = None, title: str = None, file: str = None) -> Figure: """ Create a 1D/line plot of variable(s) given by dataset *ds* and variable name(s) *var_names*. :param ds: Dataset or Dataframe that contains the variable(s) named by *var_names*. :param var_names: The name of the variable(s) to plot :param fmt: optional semicolon-separated matplotlib formats, e.g. 1 variable - "b.-" 2 variables - "b.-;r+:" If the number of properties is less than the number of selected variables, the next non-corresponding variable will repeat the first style on the list, and so on. For full reference on matplotlib plot() function, refer to https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html :param file: path to a file in which to save the plot :param label: dimension name to be selected as the x-axis of the plot :param indexers: Optional indexers into data array of *var_names*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "lat=12.4, time='2012-05-02'". :param title: an optional plot title :return: a matplotlib figure object or None if in IPython mode """ ds = DatasetLike.convert(ds) fmt_count = 0 fmt_list = [] if fmt: fmt_list = fmt.split(";") fmt_count = len(fmt_list) if not var_names: raise ValidationError("Missing name for 'vars'") figure = plt.figure() ax = figure.add_subplot(111) figure.subplots_adjust(right=0.65) var_names = VarNamesLike.convert(var_names) if not title: if label: title = ','.join(var_names) + ' over ' + label else: title = ','.join(var_names) if indexers: title = title + '\n' + ' at ' + json.dumps(indexers).strip('"') ax.set_title(title) indexers = DictLike.convert(indexers) ax_var = {} var_count = len(var_names) predefined_fmt = ['r', 'g', 'b', 'c', 'm', 'y', 'k'] if label: ds = get_vars_data(ds, indexers, remaining_dims=[label]) else: ds = get_vars_data(ds, indexers) for i in range(var_count): var_name = var_names[i] var = ds[var_name] if len(var.dims) > 1: raise ValidationError( f'Unable to plot because variable {var_name} has more than one dimension: {var.dims}.' f' To specify value(s) of these dimension(s), please use the indexers.' ) var_label = var_name + ' (' + var.attrs[ 'units'] + ')' if 'units' in var.attrs else var_name properties_dict = {} indexers = DictLike.convert(indexers) if fmt is None: selected_fmt = predefined_fmt[i % len(predefined_fmt)] else: selected_fmt = fmt_list[i % fmt_count] if label: x_axis = var[label] elif 'time' in var: x_axis = var.time else: x_axis = [] # to differentiate the creation of y-axis of the first and the nth variable if i == 0: if len(x_axis) > 0: ax.plot(x_axis, var, selected_fmt, **properties_dict) else: ax.plot(var, selected_fmt, **properties_dict) ax.set_ylabel(var_label, wrap=True) ax.yaxis.label.set_color(selected_fmt[0]) ax.tick_params(axis='y', colors=selected_fmt[0]) else: ax_var[var_name] = ax.twinx() if len(ax_var) > 1: ax_var[var_name].spines["right"].set_position( ("axes", 1 + ((i - 1) * 0.2))) ax_var[var_name].set_frame_on(True) ax_var[var_name].patch.set_visible(False) if len(x_axis) > 0: ax_var[var_name].plot(x_axis, var, selected_fmt, **properties_dict) else: ax_var[var_name].plot(var, selected_fmt, **properties_dict) ax_var[var_name].set_ylabel(var_label, wrap=True) ax_var[var_name].yaxis.label.set_color(selected_fmt[0]) ax_var[var_name].tick_params(axis='y', colors=selected_fmt[0]) ax.tick_params(axis='x', rotation=45) if label in ds and 'long_name' in ds[label].attrs: ax.set_xlabel(ds[label].attrs['long_name']) figure.tight_layout() if file: figure.savefig(file, dpi=600) return figure if not in_notebook() else None
def plot_map(ds: xr.Dataset, var: VarName.TYPE = None, indexers: DictLike.TYPE = None, region: PolygonLike.TYPE = None, projection: str = 'PlateCarree', central_lon: float = 0.0, title: str = None, contour_plot: bool = False, properties: DictLike.TYPE = None, file: str = None) -> object: """ Create a geographic map plot for the variable given by dataset *ds* and variable name *var*. Plots the given variable from the given dataset on a map with coastal lines. In case no variable name is given, the first encountered variable in the dataset is plotted. In case no *time* is given, the first time slice is taken. It is also possible to set extents of the plot. If no extents are given, a global plot is created. The plot can either be shown using pyplot functionality, or saved, if a path is given. The following file formats for saving the plot are supported: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff :param ds: the dataset containing the variable to plot :param var: the variable's name :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "layer=4". :param region: Region to plot :param projection: name of a global projection, see http://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html :param central_lon: central longitude of the projection in degrees :param title: an optional title :param contour_plot: If true plot a filled contour plot of data, otherwise plots a pixelated colormesh :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5)" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.contourf.html :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ if not isinstance(ds, xr.Dataset): raise ValidationError('Only gridded datasets are currently supported.') var_name = None if not var: for key in ds.data_vars.keys(): var_name = key break else: var_name = VarName.convert(var) var = ds[var_name] indexers = DictLike.convert(indexers) or {} properties = DictLike.convert(properties) or {} extents = None bounds = handle_plot_polygon(region) if bounds: lon_min, lat_min, lon_max, lat_max = bounds extents = [lon_min, lon_max, lat_min, lat_max] if len(ds.lat) < 2 or len(ds.lon) < 2: # Matplotlib can not plot datasets with less than these dimensions with # contourf and pcolormesh methods raise ValidationError( 'The minimum dataset spatial dimensions to create a map' ' plot are (2,2)') # See http://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html# if projection == 'PlateCarree': proj = ccrs.PlateCarree(central_longitude=central_lon) elif projection == 'LambertCylindrical': proj = ccrs.LambertCylindrical(central_longitude=central_lon) elif projection == 'Mercator': proj = ccrs.Mercator(central_longitude=central_lon) elif projection == 'Miller': proj = ccrs.Miller(central_longitude=central_lon) elif projection == 'Mollweide': proj = ccrs.Mollweide(central_longitude=central_lon) elif projection == 'Orthographic': proj = ccrs.Orthographic(central_longitude=central_lon) elif projection == 'Robinson': proj = ccrs.Robinson(central_longitude=central_lon) elif projection == 'Sinusoidal': proj = ccrs.Sinusoidal(central_longitude=central_lon) elif projection == 'NorthPolarStereo': proj = ccrs.NorthPolarStereo(central_longitude=central_lon) elif projection == 'SouthPolarStereo': proj = ccrs.SouthPolarStereo(central_longitude=central_lon) else: raise ValidationError('illegal projection: "%s"' % projection) figure = plt.figure(figsize=(8, 4)) ax = plt.axes(projection=proj) if extents: ax.set_extent(extents, ccrs.PlateCarree()) else: ax.set_global() ax.coastlines() var_data = get_var_data(var, indexers, remaining_dims=('lon', 'lat')) # transform keyword is for the coordinate our data is in, which in case of a # 'normal' lat/lon dataset is PlateCarree. if contour_plot: var_data.plot.contourf(ax=ax, transform=ccrs.PlateCarree(), subplot_kws={'projection': proj}, **properties) else: var_data.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), subplot_kws={'projection': proj}, **properties) if title: ax.set_title(title) figure.tight_layout() if file: try: figure.savefig(file) except MemoryError: raise MemoryError( 'Not enough memory to save the plot. Try using a different file format' ' or enabling contour_plot.') return figure if not in_notebook() else ax
def test_nominal(self): """ Nominal test """ self.assertFalse(in_notebook())
def test_nominal(self): """ Nominal test """ self.assertFalse(in_notebook())
def plot_map(ds: xr.Dataset, var: VarName.TYPE = None, indexers: DictLike.TYPE = None, region: PolygonLike.TYPE = None, projection: str = 'PlateCarree', central_lon: float = 0.0, title: str = None, contour_plot: bool = False, properties: DictLike.TYPE = None, file: str = None) -> object: """ Create a geographic map plot for the variable given by dataset *ds* and variable name *var*. Plots the given variable from the given dataset on a map with coastal lines. In case no variable name is given, the first encountered variable in the dataset is plotted. In case no *time* is given, the first time slice is taken. It is also possible to set extents of the plot. If no extents are given, a global plot is created. The plot can either be shown using pyplot functionality, or saved, if a path is given. The following file formats for saving the plot are supported: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff :param ds: the dataset containing the variable to plot :param var: the variable's name :param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary or a comma-separated string of key-value pairs that maps the variable's dimension names to constant labels. e.g. "layer=4". :param region: Region to plot :param projection: name of a global projection, see http://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html :param central_lon: central longitude of the projection in degrees :param title: an optional title :param contour_plot: If true plot a filled contour plot of data, otherwise plots a pixelated colormesh :param properties: optional plot properties for Python matplotlib, e.g. "bins=512, range=(-1.5, +1.5)" For full reference refer to https://matplotlib.org/api/lines_api.html and https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.contourf.html :param file: path to a file in which to save the plot :return: a matplotlib figure object or None if in IPython mode """ if not isinstance(ds, xr.Dataset): raise ValidationError('Only gridded datasets are currently supported.') var_name = None if not var: for key in ds.data_vars.keys(): var_name = key break else: var_name = VarName.convert(var) var = ds[var_name] indexers = DictLike.convert(indexers) or {} properties = DictLike.convert(properties) or {} extents = None bounds = handle_plot_polygon(region) if bounds: lon_min, lat_min, lon_max, lat_max = bounds extents = [lon_min, lon_max, lat_min, lat_max] if len(ds.lat) < 2 or len(ds.lon) < 2: # Matplotlib can not plot datasets with less than these dimensions with # contourf and pcolormesh methods raise ValidationError('The minimum dataset spatial dimensions to create a map' ' plot are (2,2)') # See http://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html# if projection == 'PlateCarree': proj = ccrs.PlateCarree(central_longitude=central_lon) elif projection == 'LambertCylindrical': proj = ccrs.LambertCylindrical(central_longitude=central_lon) elif projection == 'Mercator': proj = ccrs.Mercator(central_longitude=central_lon) elif projection == 'Miller': proj = ccrs.Miller(central_longitude=central_lon) elif projection == 'Mollweide': proj = ccrs.Mollweide(central_longitude=central_lon) elif projection == 'Orthographic': proj = ccrs.Orthographic(central_longitude=central_lon) elif projection == 'Robinson': proj = ccrs.Robinson(central_longitude=central_lon) elif projection == 'Sinusoidal': proj = ccrs.Sinusoidal(central_longitude=central_lon) elif projection == 'NorthPolarStereo': proj = ccrs.NorthPolarStereo(central_longitude=central_lon) elif projection == 'SouthPolarStereo': proj = ccrs.SouthPolarStereo(central_longitude=central_lon) else: raise ValidationError('illegal projection: "%s"' % projection) figure = plt.figure(figsize=(8, 4)) ax = plt.axes(projection=proj) if extents: ax.set_extent(extents, ccrs.PlateCarree()) else: ax.set_global() ax.coastlines() var_data = get_var_data(var, indexers, remaining_dims=('lon', 'lat')) # transform keyword is for the coordinate our data is in, which in case of a # 'normal' lat/lon dataset is PlateCarree. if contour_plot: var_data.plot.contourf(ax=ax, transform=ccrs.PlateCarree(), subplot_kws={'projection': proj}, **properties) else: var_data.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), subplot_kws={'projection': proj}, **properties) if title: ax.set_title(title) figure.tight_layout() if file: try: figure.savefig(file) except MemoryError: raise MemoryError('Not enough memory to save the plot. Try using a different file format' ' or enabling contour_plot.') return figure if not in_notebook() else ax