mirror of https://github.com/abinit/abipy.git
Import df_widgets
This commit is contained in:
Matteo Giantomassi
parent
7257f92fba
commit
6cf681b510
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@ -81,7 +81,7 @@ class DdbFile(TextFile, Has_Structure, NotebookWriter):
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self._structure = Structure.from_abivars(**self.header)
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# Add AbinitSpacegroup (needed in guessed_ngkpt)
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# FIXME: has_timerev is always True
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# FIXME: kptopt is not reported in the header --> has_timerev is always set to True
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spgid, has_timerev, h = 0, True, self.header
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self._structure.set_abi_spacegroup(AbinitSpaceGroup(spgid, h.symrel, h.tnons, h.symafm, has_timerev))
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@ -96,13 +96,12 @@ class DdbFile(TextFile, Has_Structure, NotebookWriter):
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"""String representation."""
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lines = []
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app, extend = lines.append, lines.extend
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#extend(super(DdbFile, self).__str__().splitlines())
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app(marquee("File Info", mark="="))
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app(self.filestat(as_string=True))
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app("")
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app(marquee("Structure", mark="="))
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app(str(self.structure))
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app(str(self.structure.to_string(verbose=verbose)))
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app(marquee("Q-points", mark="="))
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app(str(self.qpoints))
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@ -0,0 +1,44 @@
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# coding: utf-8
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"""Widgets for Pandas Dataframes."""
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from __future__ import print_function, division, unicode_literals, absolute_import
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import matplotlib.pyplot as plt
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import pandas as pd
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import ipywidgets as ipw
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import df_widgets.utils as ut
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from functools import wraps
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@wraps(pd.DataFrame.plot)
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def dfw_plot(data, **kwargs):
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def plot_dataframe(x, y, kind, sharex, sharey, subplots, grid, legend,
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logx, logy, loglog, colorbar, sort_columns):
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x, y = ut.widget2py(x, y)
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sharex, colorbar = ut.str2bool_or_none(sharex, colorbar)
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data.plot(x=x, y=y, kind=kind, subplots=subplots, sharex=None, sharey=sharey,
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layout=None, figsize=None, use_index=True, title=None, grid=grid, legend=legend, style=None,
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logx=logx, logy=logy, loglog=loglog, xticks=None, yticks=None, xlim=None, ylim=None,
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rot=None, fontsize=None, colormap=colorbar, table=False, yerr=None, xerr=None, secondary_y=False,
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sort_columns=sort_columns, **kwargs)
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# There's a typo in the documentation (colorbar/colormap!)
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return plt.gcf()
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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plot_dataframe,
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x=allcols,
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y=allcols,
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sharex=["None", "True", "False"],
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sharey=False,
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kind=["line", "bar", "barh", "hist", "box", "kde", "density", "area", "pie", "scatter", "hexbin"],
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subplots=False,
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grid=True,
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legend=True,
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logx=False,
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logy=False,
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loglog=False,
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colorbar=["None", "True", "False"],
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sort_columns=False,
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)
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@ -0,0 +1,472 @@
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# coding: utf-8
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"""
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Widgets for Pandas Dataframes based on seaborn API.
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API reference
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Distribution plots
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jointplot(x, y[, data, kind, stat_func, ...]) Draw a plot of two variables with bivariate and univariate graphs.
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pairplot(data[, hue, hue_order, palette, ...]) Plot pairwise relationships in a dataset.
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distplot(a[, bins, hist, kde, rug, fit, ...]) Flexibly plot a univariate distribution of observations.
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kdeplot(data[, data2, shade, vertical, ...]) Fit and plot a univariate or bivariate kernel density estimate.
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rugplot(a[, height, axis, ax]) Plot datapoints in an array as sticks on an axis.
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Regression plots
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lmplot(x, y, data[, hue, col, row, palette, ...]) Plot data and regression model fits across a FacetGrid.
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regplot(x, y[, data, x_estimator, x_bins, ...]) Plot data and a linear regression model fit.
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residplot(x, y[, data, lowess, x_partial, ...]) Plot the residuals of a linear regression.
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interactplot(x1, x2, y[, data, filled, ...]) Visualize a continuous two-way interaction with a contour plot.
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coefplot(formula, data[, groupby, ...]) Plot the coefficients from a linear model.
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# Categorical plots
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factorplot([x, y, hue, data, row, col, ...]) Draw a categorical plot onto a FacetGrid.
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boxplot([x, y, hue, data, order, hue_order, ...]) Draw a box plot to show distributions with respect to categories.
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violinplot([x, y, hue, data, order, ...]) Draw a combination of boxplot and kernel density estimate.
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stripplot([x, y, hue, data, order, ...]) Draw a scatterplot where one variable is categorical.
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swarmplot([x, y, hue, data, order, ...]) Draw a categorical scatterplot with non-overlapping points.
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pointplot([x, y, hue, data, order, ...]) Show point estimates and confidence intervals using scatter plot glyphs.
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barplot([x, y, hue, data, order, hue_order, ...]) Show point estimates and confidence intervals as rectangular bars.
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countplot([x, y, hue, data, order, ...]) Show the counts of observations in each categorical bin using bars.
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Matrix plots
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heatmap(data[, vmin, vmax, cmap, center, ...]) Plot rectangular data as a color-encoded matrix.
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clustermap(data[, pivot_kws, method, ...]) Plot a hierarchically clustered heatmap of a pandas DataFrame
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Timeseries plots
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tsplot(data[, time, unit, condition, value, ...]) Plot one or more timeseries with flexible representation of uncertainty.
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Miscellaneous plots
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palplot(pal[, size]) Plot the values in a color palette as a horizontal array.
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Axis grids
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FacetGrid(data[, row, col, hue, col_wrap, ...]) Subplot grid for plotting conditional relationships.
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PairGrid(data[, hue, hue_order, palette, ...]) Subplot grid for plotting pairwise relationships in a dataset.
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JointGrid(x, y[, data, size, ratio, space, ...]) Grid for drawing a bivariate plot with marginal univariate plots.
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"""
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from __future__ import print_function, division, unicode_literals, absolute_import
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import sys
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import ipywidgets as ipw
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import seaborn as sns
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import df_widgets.utils as ut
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from functools import wraps
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from collections import OrderedDict
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from IPython.display import display, clear_output
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__all__ = [
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"api_selector",
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# Distribution plots
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"jointplot",
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"pairplot",
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#"distplot",
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#"kdeplot",
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#"rugplot",
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# Regression plots
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"lmplot",
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#"regplot",
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#"residplot",
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#"interactplot",
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#"coefplot",
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# Categorical plots
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"factorplot",
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"boxplot",
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"violinplot",
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"stripplot",
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"swarmplot",
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"pointplot",
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"barplot",
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"countplot",
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# Matrix plots
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#"heatmap",
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#"clustermap",
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# Timeseries plots
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#"tsplot",
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# Miscellaneous plots
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#"palplot",
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]
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def api_selector(data, funcname="countplot"):
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"""
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A widgets with ToogleButtons that allow the user to select and display
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the widget associated to the different seaborn functions.
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"""
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this_module = sys.modules[__name__]
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name2wfunc = OrderedDict()
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for a in __all__:
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if a == "api_selector": continue
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func = this_module.__dict__.get(a)
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if not callable(func): continue
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name2wfunc[func.__name__] = func
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w1 = ipw.ToggleButtons(description='seaborn API', options=list(name2wfunc.keys()))
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w1.value = funcname
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w2 = name2wfunc[funcname](data)
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box = ipw.VBox(children=[w1, w2])
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def on_value_change(change):
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#print(change)
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box.close()
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clear_output()
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api_selector(data, funcname=change["new"])
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w1.observe(on_value_change, names='value')
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return display(box)
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@wraps(sns.jointplot)
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def joinplot(data, joint_kws=None, marginal_kws=None, annot_kws=None, **kwargs):
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def sns_joinplot(x, y, kind, color):
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x, y, color = ut.widget2py(x, y, color)
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# TODO: stat_func
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return sns.jointplot(x, y, data=data, kind=kind, # stat_func=<function pearsonr>,
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color=color, size=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None,
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joint_kws=joint_kws, marginal_kws=marginal_kws, annot_kws=annot_kws, **kwargs)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_joinplot,
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x=allcols,
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y=allcols,
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kind=["scatter", "reg", "resid", "kde", "hex"],
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color=ut.colors_dropdow(),
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)
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@wraps(sns.pairplot)
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def pairplot(data, plot_kws=None, diag_kws=None, grid_kws=None):
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# TODO: Write widget with multiple checkboxes to implement lists.
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def sns_pairplot(x_vars, y_vars, hue, kind, diag_kind):
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x_vars, y_vars, hue = ut.widget2py(x_vars, y_vars, hue)
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return sns.pairplot(data, hue=hue, hue_order=None, palette=None, vars=None, x_vars=x_vars, y_vars=y_vars,
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kind=kind, diag_kind=diag_kind, markers=None, size=2.5, aspect=1, dropna=True,
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plot_kws=plot_kws, diag_kws=diag_kws, grid_kws=grid_kws)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_pairplot,
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x_vars=allcols,
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y_vars=allcols,
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hue=allcols,
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kind=["scatter", "ref"],
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diag_kind=["hist", "kde"],
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)
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"""
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@wraps(sns.distplot)
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def distplot(data, fit=None, hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None):
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def sns_distplot(hist, kde, rug, color, vertical, norm_hist):
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color = ut.widget2py(color)
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ax, fig, _ = ut.get_ax_fig_plt()
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return sns.distplot(a, bins=None, hist=hist, kde=kde, rug=rug, fit=fit,
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hist_kws=hist_kws, kde_kws=kde_kws, rug_kws=rug_kws, fit_kws=fit_kws,
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color=clor, vertical=vertical, norm_hist=norm_hist, axlabel=None, label=None, ax=ax)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_distplot,
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hist=True,
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kde=True,
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rug=False,
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color=ut.colors_dropdow(),
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vertical=False,
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norm_hist=False,
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)
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@wraps(sns.kdeplot)
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def kdeplot(data, **kwargs):
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def sns_kdeplot()
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color = ut.widget2py(color)
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ax, fig, _ = ut.get_ax_fig_plt()
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return sns.kdeplot(data, data2=None, shade=False, vertical=False, kernel='gau', bw='scott',
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gridsize=100, cut=3, clip=None, legend=True, cumulative=False, shade_lowest=True, ax=ax, **kwargs)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_kdeplot,
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color=ut.colors_dropdow(),
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)
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"""
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####################
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# Regression plots #
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####################
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@wraps(sns.lmplot)
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def lmplot(data, scatter_kws=None, line_kws=None):
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def sns_lmplot(x, y, hue, col, row, legend, size):
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x, y, hue, col, row = ut.widget2py(x, y, hue, col, row)
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return sns.lmplot(x, y, data, hue=hue, col=col, row=row, palette=None, col_wrap=None,
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size=size, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None,
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col_order=None, row_order=None, legend=legend, legend_out=True,
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x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True,
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ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False,
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logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None,
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scatter_kws=scatter_kws, line_kws=line_kws)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_lmplot,
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x=allcols,
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y=allcols,
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hue=allcols,
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col=allcols,
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row=allcols,
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legend=True,
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size=ut.size_slider(default=5),
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)
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@wraps(sns.interactplot)
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def interactplot(data, contour_kws=None, scatter_kws=None, **kwargs):
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def sns_interactplot(x1, x2, y, filled, colorbar, logistic):
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ax, fig, _ = ut.get_ax_fig_plt()
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return sns.interactplot(x1, x2, y, data=data, filled=filled, cmap='RdBu_r', colorbar=colorbar,
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levels=30, logistic=logistic, contour_kws=contour_kws, scatter_kws=scatter_kws,
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ax=ax, **kwargs)
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allcols = list(data.keys())
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return ipw.interact_manual(
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sns_interactplot,
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x1=allcols,
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x2=allcols,
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y=allcols,
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filled=False,
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colorbar=True,
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logistic=False,
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)
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#####################
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# Categorical plots #
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#####################
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@wraps(sns.factorplot)
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def factorplot(data, facet_kws=None, **kwargs):
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def sns_factorplot(x, y, hue, color, kind, size, legend):
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x, y, hue, color = ut.widget2py(x, y, hue, color)
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return sns.factorplot(x=x, y=y, hue=hue, data=data, row=None, col=None, col_wrap=None, # estimator=<function mean>,
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ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None,
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kind=kind, size=size, aspect=1, orient=None, color=color, palette=None,
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legend=legend, legend_out=True, sharex=True, sharey=True, margin_titles=False,
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facet_kws=facet_kws, **kwargs)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_factorplot,
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x=allcols,
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y=allcols,
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hue=allcols,
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color=ut.colors_dropdow(),
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kind=["point", "bar", "count", "box", "violin", "strip"],
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size=ut.size_slider(default=4),
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legend=True,
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)
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@wraps(sns.boxplot)
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def boxplot(data, **kwargs):
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def sns_boxplot(x, y, hue, orient, color, saturation, notch):
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x, y, hue, orient, color = ut.widget2py(x, y, hue, orient, color)
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ax, fig, _ = ut.get_ax_fig_plt()
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return sns.boxplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None, orient=orient,
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color=color, palette=None, saturation=saturation, width=0.8, fliersize=5, linewidth=None,
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whis=1.5, notch=notch, ax=ax, **kwargs)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_boxplot,
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x=allcols,
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y=allcols,
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hue=allcols,
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orient=["None", "v", "h"],
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color=ut.colors_dropdow(),
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saturation=ut.saturation_slider(default=0.75),
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notch=False,
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)
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@wraps(sns.violinplot)
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def violinplot(data, **kwargs):
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def sns_violinplot(x, y, hue, bw, scale, inner, split, orient, color, saturation):
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x, y, hue, inner, orient, color = ut.widget2py(x, y, hue, inner, orient, color)
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ax, fig, _ = ut.get_ax_fig_plt()
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sns.violinplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None,
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bw=bw, cut=2, scale=scale, scale_hue=True,
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gridsize=100, width=0.8, inner=inner, split=split, orient=orient,
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linewidth=None, color=color, palette=None, saturation=saturation, ax=ax, **kwargs)
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allcols = ["None"] + list(data.keys())
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return ipw.interact_manual(
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sns_violinplot,
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x=allcols,
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y=allcols,
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hue=allcols,
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bw=["scott", "silverman", "float"],
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scale=["area", "count", "width"],
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inner=["box", "quartile", "point", "stick", "None"],
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split=False,
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orient=["None", "v", "h"],
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color=ut.colors_dropdow(),
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saturation=ut.saturation_slider(default=0.75),
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)
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@wraps(sns.stripplot)
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def stripplot(data, **kwargs):
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def sns_stripplot(x, y, hue, split, orient, color, size, linewidth):
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x, y, hue, orient, color = ut.widget2py(x, y, hue, orient, color)
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ax, fig, _ = ut.get_ax_fig_plt()
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return sns.stripplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None, jitter=False,
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split=split, orient=orient, color=color, palette=None, size=size, edgecolor='gray',
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linewidth=linewidth, ax=ax, **kwargs)
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|
||||
allcols = ["None"] + list(data.keys())
|
||||
return ipw.interact_manual(
|
||||
sns_stripplot,
|
||||
x=allcols,
|
||||
y=allcols,
|
||||
hue=allcols,
|
||||
split=False,
|
||||
orient=["None", "v", "h"],
|
||||
color=ut.colors_dropdow(),
|
||||
size=ut.size_slider(default=5),
|
||||
linewidth=ut.linewidth_slider(default=0),
|
||||
)
|
||||
|
||||
|
||||
@wraps(sns.swarmplot)
|
||||
def swarmplot(data, **kwargs):
|
||||
|
||||
def sns_swarmplot(x, y, hue, split, orient, color, size, linewidth):
|
||||
x, y, hue, orient, color = ut.widget2py(x, y, hue, orient, color)
|
||||
ax, fig, _ = ut.get_ax_fig_plt()
|
||||
return sns.swarmplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None,
|
||||
split=split, orient=orient, color=color, palette=None, size=size,
|
||||
edgecolor='gray', linewidth=linewidth, ax=ax, **kwargs)
|
||||
|
||||
allcols = ["None"] + list(data.keys())
|
||||
return ipw.interact_manual(
|
||||
sns_swarmplot,
|
||||
x=allcols,
|
||||
y=allcols,
|
||||
hue=allcols,
|
||||
split=False,
|
||||
orient=["None", "v", "h"],
|
||||
color=ut.colors_dropdow(),
|
||||
size=ut.size_slider(default=5),
|
||||
linewidth=ut.linewidth_slider(default=0),
|
||||
)
|
||||
|
||||
|
||||
@wraps(sns.pointplot)
|
||||
def pointplot(data, **kwargs):
|
||||
|
||||
def sns_pointplot(x, y, hue, split, join, orient, color, linewidth):
|
||||
x, y, hue, orient, color = ut.widget2py(x, y, hue, orient, color)
|
||||
ax, fig, _ = ut.get_ax_fig_plt()
|
||||
return sns.pointplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None, # estimator=<function mean>,
|
||||
ci=95, n_boot=1000, units=None, markers='o', linestyles='-', dodge=False, join=join, scale=1,
|
||||
orient=orient, color=color, palette=None, ax=ax, errwidth=None, capsize=None, **kwargs)
|
||||
|
||||
allcols = ["None"] + list(data.keys())
|
||||
return ipw.interact_manual(
|
||||
sns_pointplot,
|
||||
x=allcols,
|
||||
y=allcols,
|
||||
hue=allcols,
|
||||
split=False,
|
||||
join=True,
|
||||
orient=["None", "v", "h"],
|
||||
color=ut.colors_dropdow(),
|
||||
linewidth=ut.linewidth_slider(default=0),
|
||||
)
|
||||
|
||||
|
||||
@wraps(sns.barplot)
|
||||
def barplot(data, **kwargs):
|
||||
|
||||
def sns_barplot(x, y, hue, orient, color, saturation):
|
||||
x, y, hue, orient, color = ut.widget2py(x, y, hue, orient, color)
|
||||
ax, fig, _ = ut.get_ax_fig_plt()
|
||||
return sns.barplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None, # estimator=<function mean>,
|
||||
ci=95, n_boot=1000, units=None, orient=orient, color=color, palette=None,
|
||||
saturation=saturation, errcolor='.26', ax=ax, **kwargs) # errwidth=None, capsize=None, # New args added in ??
|
||||
|
||||
allcols = ["None"] + list(data.keys())
|
||||
return ipw.interact_manual(
|
||||
sns_barplot,
|
||||
x=allcols,
|
||||
y=allcols,
|
||||
hue=allcols,
|
||||
orient=["None", "v", "h"],
|
||||
color=ut.colors_dropdow(),
|
||||
saturation=ut.saturation_slider(default=0.75),
|
||||
)
|
||||
|
||||
|
||||
@wraps(sns.countplot)
|
||||
def countplot(data, **kwargs):
|
||||
|
||||
def sns_countplot(x, y, hue, color, saturation):
|
||||
x, y, hue, color = ut.widget2py(x, y, hue, color)
|
||||
ax, fig, _ = ut.get_ax_fig_plt()
|
||||
return sns.countplot(x=x, y=y, hue=hue, data=data, order=None, hue_order=None, orient=None,
|
||||
color=color, palette=None, saturation=saturation, ax=ax, **kwargs)
|
||||
|
||||
allcols = ["None"] + list(data.keys())
|
||||
return ipw.interact_manual(
|
||||
sns_countplot,
|
||||
x=allcols,
|
||||
y=allcols,
|
||||
hue=allcols,
|
||||
color=ut.colors_dropdow(),
|
||||
saturation=ut.saturation_slider(default=0.75),
|
||||
)
|
||||
|
||||
################
|
||||
# Matrix plots #
|
||||
################
|
||||
|
||||
@wraps(sns.heatmap)
|
||||
def heatmap(data, annot_kws=None, cbar_kws=None, **kwargs):
|
||||
|
||||
def sns_heatmap():
|
||||
ax, fig, _ = ut.get_ax_fig_plt()
|
||||
return sns.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None,
|
||||
fmt='.2g', annot_kws=annot_kws, linewidths=0, linecolor='white', cbar=True,
|
||||
cbar_kws=cbar_kws, cbar_ax=None, square=False, ax=ax,
|
||||
xticklabels=True, yticklabels=True, mask=None, **kwargs)
|
||||
|
||||
return ipw.interact_manual(
|
||||
sns_heatmap,
|
||||
)
|
||||
|
||||
|
||||
@wraps(sns.clustermap)
|
||||
def clustermap(data, pivot_kws=None, cbar_kws=None, **kwargs):
|
||||
|
||||
def sns_clustermap():
|
||||
return sns.clustermap(data, pivot_kws=pivot_kws, method='average', metric='euclidean',
|
||||
z_score=None, standard_scale=None, figsize=None, cbar_kws=cbar_kws,
|
||||
row_cluster=True, col_cluster=True, row_linkage=None, col_linkage=None,
|
||||
row_colors=None, col_colors=None, mask=None, **kwargs)
|
||||
|
||||
return ipw.interact_manual(
|
||||
sns_clustermap,
|
||||
)
|
||||
|
|
@ -0,0 +1,236 @@
|
|||
# coding: utf-8
|
||||
"""Widgets for Pandas Dataframes."""
|
||||
from __future__ import print_function, division, unicode_literals, absolute_import
|
||||
|
||||
import ipywidgets as ipw
|
||||
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
def add_docstrings(*tuples):
|
||||
"""
|
||||
This decorator adds to the docstring the documentation for functions.
|
||||
When writing high-level API, it's quite common to call thirdy-party functions
|
||||
with a restricted set of arguments while optional keyword arguments are
|
||||
collected in an optional dictionary.
|
||||
|
||||
The first item of the tuple contains the function (python object) wrapped by the code.
|
||||
The second item is list of strings with the name of the actual arguments passed to function.
|
||||
"""
|
||||
from functools import wraps
|
||||
def wrapper(func):
|
||||
@wraps(func)
|
||||
def wrapped_func(*args, **kwargs):
|
||||
return func(*args, **kwargs)
|
||||
|
||||
# Add docstrings for the functions that will be called by func.
|
||||
lines = []
|
||||
app = lines.append
|
||||
for t in tuples:
|
||||
fname = t[0].__name__
|
||||
# List of strings or string.
|
||||
if isinstance(t[1], (list, tuple)):
|
||||
fargs = ",".join("`%s`" % a for a in t[1])
|
||||
else:
|
||||
fargs = "`%s`" % t[1]
|
||||
app("\n%s are passed to function :func:`%s` in module :mod:`%s`" % (fargs, fname, t[0].__module__))
|
||||
app("Docstring of `%s`:" % fname)
|
||||
app(t[0].__doc__)
|
||||
s = "\n".join(lines)
|
||||
|
||||
if wrapped_func.__doc__ is not None:
|
||||
# Add s at the end of the docstring.
|
||||
wrapped_func.__doc__ += "\n" + s
|
||||
else:
|
||||
# Use s
|
||||
wrapped_func.__doc__ = s
|
||||
return wrapped_func
|
||||
return wrapper
|
||||
|
||||
|
||||
def widget2py(*args):
|
||||
l = [None if a == "None" else a for a in args]
|
||||
return l[0] if len(l) == 1 else l
|
||||
|
||||
|
||||
def str2bool_or_none(*args):
|
||||
d = {"None": None, "True": True, "False": False}
|
||||
l = [d[a] for a in args]
|
||||
return l[0] if len(l) == 1 else l
|
||||
|
||||
|
||||
def get_ax_fig_plt(ax=None):
|
||||
"""
|
||||
Helper function used in plot functions supporting an optional Axes argument.
|
||||
If ax is None, we build the `matplotlib` figure and create the Axes else
|
||||
we return the current active figure.
|
||||
|
||||
Returns:
|
||||
ax: :class:`Axes` object
|
||||
figure: matplotlib figure
|
||||
plt: matplotlib pyplot module.
|
||||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
if ax is None:
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1,1,1)
|
||||
else:
|
||||
fig = plt.gcf()
|
||||
|
||||
return ax, fig, plt
|
||||
|
||||
|
||||
# Taken from matplotlib.markers.MarkerStyle (replaced dict with OrderedDict).
|
||||
_mpl_markers = OrderedDict([
|
||||
('.', 'point'),
|
||||
(',', 'pixel'),
|
||||
('o', 'circle'),
|
||||
('v', 'triangle_down'),
|
||||
('^', 'triangle_up'),
|
||||
('<', 'triangle_left'),
|
||||
('>', 'triangle_right'),
|
||||
('1', 'tri_down'),
|
||||
('2', 'tri_up'),
|
||||
('3', 'tri_left'),
|
||||
('4', 'tri_right'),
|
||||
('8', 'octagon'),
|
||||
('s', 'square'),
|
||||
('p', 'pentagon'),
|
||||
('*', 'star'),
|
||||
('h', 'hexagon1'),
|
||||
('H', 'hexagon2'),
|
||||
('+', 'plus'),
|
||||
('x', 'x'),
|
||||
('D', 'diamond'),
|
||||
('d', 'thin_diamond'),
|
||||
('|', 'vline'),
|
||||
('_', 'hline'),
|
||||
#(TICKLEFT: 'tickleft',
|
||||
#(TICKRIGHT: 'tickright',
|
||||
#(TICKUP: 'tickup',
|
||||
#(TICKDOWN: 'tickdown',
|
||||
#(CARETLEFT: 'caretleft',
|
||||
#(CARETRIGHT: 'caretright',
|
||||
#(CARETUP: 'caretup',
|
||||
#(CARETDOWN: 'caretdown',
|
||||
("None", 'nothing'),
|
||||
(None, 'nothing'),
|
||||
(' ', 'nothing'),
|
||||
('', 'nothing'),
|
||||
])
|
||||
|
||||
|
||||
def markers_dropdown(default="o"):
|
||||
return ipw.Dropdown(
|
||||
options={name: key for key, name in _mpl_markers.items()},
|
||||
value=default,
|
||||
description='marker',
|
||||
)
|
||||
|
||||
|
||||
_mpl_colors = OrderedDict([
|
||||
("None", "None"),
|
||||
("blue", "b"),
|
||||
("green", "g"),
|
||||
("red", "r"),
|
||||
("cyan", "c"),
|
||||
("magenta", "m"),
|
||||
("yellow", "y"),
|
||||
("black", "k"),
|
||||
("white", "w"),
|
||||
])
|
||||
|
||||
|
||||
def colors_dropdow(default="None"):
|
||||
return ipw.Dropdown(
|
||||
options=_mpl_colors,
|
||||
value=default,
|
||||
description='color',
|
||||
)
|
||||
|
||||
|
||||
def linewidth_slider(default=1, orientation="horizontal"):
|
||||
return ipw.FloatSlider(
|
||||
value=default,
|
||||
min=0,
|
||||
max=10,
|
||||
step=0.5,
|
||||
description='linewidth',
|
||||
orientation=orientation,
|
||||
readout_format='.1f'
|
||||
)
|
||||
|
||||
|
||||
def size_slider(default=5, orientation="horizontal"):
|
||||
return ipw.FloatSlider(
|
||||
value=default,
|
||||
min=0,
|
||||
max=20,
|
||||
step=0.5,
|
||||
description='size',
|
||||
orientation=orientation,
|
||||
readout_format='.1f'
|
||||
)
|
||||
|
||||
def saturation_slider(default=0.75, orientation="horizontal"):
|
||||
return ipw.FloatSlider(
|
||||
value=default,
|
||||
min=0,
|
||||
max=1,
|
||||
step=0.05,
|
||||
description='saturation',
|
||||
orientation=orientation,
|
||||
readout_format='.1f'
|
||||
)
|
||||
|
||||
# Have colormaps separated into categories:
|
||||
# http://matplotlib.org/examples/color/colormaps_reference.html
|
||||
|
||||
_mpl_categ_cmaps = OrderedDict([
|
||||
#('Perceptually Uniform Sequential',
|
||||
('Uniform', ['viridis', 'inferno', 'plasma', 'magma']),
|
||||
('Sequential', ['Blues', 'BuGn', 'BuPu',
|
||||
'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
|
||||
'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
|
||||
'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
|
||||
('Sequential(2)', ['afmhot', 'autumn', 'bone', 'cool',
|
||||
'copper', 'gist_heat', 'gray', 'hot',
|
||||
'pink', 'spring', 'summer', 'winter']),
|
||||
('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
|
||||
'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
|
||||
'seismic']),
|
||||
('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',
|
||||
'Pastel2', 'Set1', 'Set2', 'Set3']),
|
||||
('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',
|
||||
'brg', 'CMRmap', 'cubehelix',
|
||||
'gnuplot', 'gnuplot2', 'gist_ncar',
|
||||
'nipy_spectral', 'jet', 'rainbow',
|
||||
'gist_rainbow', 'hsv', 'flag', 'prism'])
|
||||
])
|
||||
|
||||
# flat list.
|
||||
_mpl_cmaps = [cm for sublist in _mpl_categ_cmaps.values() for cm in sublist]
|
||||
|
||||
def colormap_widget(default=None):
|
||||
options = _mpl_cmaps
|
||||
value = options[0]
|
||||
if default is not None:
|
||||
value = default
|
||||
if default not in _mpl_cmaps: options[:].insert(0, value)
|
||||
return ipw.Dropdown(options=options, value=value, description='colormap')
|
||||
|
||||
|
||||
#def colormap_widget():
|
||||
# from IPython.display import display, clear_output
|
||||
# w_type = ipw.Dropdown(options=list(_mpl_categ_cmaps.keys()), description='colormap category')
|
||||
# w_cmap = ipw.Dropdown(options=_mpl_categ_cmaps["Uniform"], description='colormap name')
|
||||
#
|
||||
# def on_value_change(change):
|
||||
# print(change)
|
||||
# print(w_cmap.value)
|
||||
# w_cmap.options = _mpl_categ_cmaps[w_type.value]
|
||||
# print(w_cmap.value)
|
||||
#
|
||||
# w_type.observe(on_value_change, names='value')
|
||||
# box = ipw.HBox(children=[w_type, w_cmap])
|
||||
# return display(box)
|
||||
|
|
@ -56,6 +56,7 @@ class HistFile(AbinitNcFile, NotebookWriter):
|
|||
app("")
|
||||
app(marquee("Initial structure", mark="="))
|
||||
app(str(self.initial_structure))
|
||||
app("")
|
||||
app(marquee("Final structure", mark="="))
|
||||
app("Number of relaxation steps performed: %d" % self.num_steps)
|
||||
app(str(self.final_structure))
|
||||
|
|
@ -201,30 +202,31 @@ class HistFile(AbinitNcFile, NotebookWriter):
|
|||
|
||||
return fig
|
||||
|
||||
def mvplot_trajectories(self, colormap="hot", figure=None, show=True, with_forces=True, **kwargs):
|
||||
def mvplot_trajectories(self, colormap="hot", sampling=1, figure=None, show=True, with_forces=True, **kwargs):
|
||||
"""
|
||||
Call mayavi to plot atomic trajectories and variation of the unit cell.
|
||||
Call mayavi to plot atomic trajectories and the variation of the unit cell.
|
||||
"""
|
||||
from abipy.display import mvtk
|
||||
figure, mlab = mvtk.get_fig_mlab(figure=figure)
|
||||
style = "labels"
|
||||
line_width = 2
|
||||
line_width = 100
|
||||
mvtk.plot_structure(self.initial_structure, style=style, unit_cell_color=(1, 0, 0), figure=figure)
|
||||
mvtk.plot_structure(self.final_structure, style=style, unit_cell_color=(0, 0, 0), figure=figure)
|
||||
|
||||
steps = np.arange(start=0, stop=self.num_steps, step=sampling)
|
||||
xcart_list = self.reader.read_value("xcart") * units.bohr_to_ang
|
||||
t = np.arange(self.num_steps)
|
||||
for iatom in range(self.reader.natom):
|
||||
x, y, z = xcart_list[:, iatom, :].T
|
||||
trajectory = mlab.plot3d(x, y, z, t, colormap=colormap, tube_radius=None,
|
||||
x, y, z = xcart_list[::sampling, iatom, :].T
|
||||
#for i in zip(x, y, z): print(i)
|
||||
trajectory = mlab.plot3d(x, y, z, steps, colormap=colormap, tube_radius=None,
|
||||
line_width=line_width, figure=figure)
|
||||
mlab.colorbar(trajectory, title='Iteration', orientation='vertical')
|
||||
|
||||
if with_forces:
|
||||
fcart_list = self.reader.read_cart_forces(unit="eV ang^-1")
|
||||
for iatom in range(self.reader.natom):
|
||||
x, y, z = xcart_list[:, iatom, :].T
|
||||
u, v, w = fcart_list[:, iatom, :].T
|
||||
x, y, z = xcart_list[::sampling, iatom, :].T
|
||||
u, v, w = fcart_list[::sampling, iatom, :].T
|
||||
q = mlab.quiver3d(x, y, z, u, v, w, figure=figure, colormap=colormap,
|
||||
line_width=line_width, scale_factor=10)
|
||||
#mlab.colorbar(q, title='Forces [eV/Ang]', orientation='vertical')
|
||||
|
|
@ -232,12 +234,12 @@ class HistFile(AbinitNcFile, NotebookWriter):
|
|||
if show: mlab.show()
|
||||
return figure
|
||||
|
||||
def mvanimate(self, to_unit_cell=False):
|
||||
def mvanimate(self, delay=500):
|
||||
from abipy.display import mvtk
|
||||
figure, mlab = mvtk.get_fig_mlab(figure=None)
|
||||
style = "points"
|
||||
#mvtk.plot_structure(self.initial_structure, to_unit_cell=to_unit_cell, style=style, figure=figure)
|
||||
#mvtk.plot_structure(self.final_structure, to_unit_cell=to_unit_cell, style=style, figure=figure)
|
||||
#mvtk.plot_structure(self.initial_structure, style=style, figure=figure)
|
||||
#mvtk.plot_structure(self.final_structure, style=style, figure=figure)
|
||||
|
||||
xcart_list = self.reader.read_value("xcart") * units.bohr_to_ang
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#t = np.arange(self.num_steps)
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@ -255,14 +257,14 @@ class HistFile(AbinitNcFile, NotebookWriter):
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#nodes.mlab_source.dataset.point_data.scalars = np.random.random((5000,))
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@mlab.show
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@mlab.animate(delay=1000, ui=True)
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@mlab.animate(delay=delay, ui=True)
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def anim():
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"""Animate."""
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for it, structure in enumerate(self.structures):
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#for it in range(self.num_steps):
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print('Updating scene for iteration:', it)
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#mlab.clf(figure=figure)
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mvtk.plot_structure(structure, to_unit_cell=to_unit_cell, style=style, figure=figure)
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mvtk.plot_structure(structure, style=style, figure=figure)
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#x, y, z = xcart_list[it, :, :].T
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#nodes.mlab_source.set(x=x, y=y, z=z)
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#figure.scene.render()
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|
|
@ -270,7 +272,6 @@ class HistFile(AbinitNcFile, NotebookWriter):
|
|||
yield
|
||||
|
||||
anim()
|
||||
#mlab.close(figure)
|
||||
|
||||
def write_notebook(self, nbpath=None):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -46,6 +46,9 @@ def abimovie_hist(options):
|
|||
for path in options.paths:
|
||||
with abilab.abiopen(path) as hist:
|
||||
print(hist)
|
||||
if options.trajectories:
|
||||
hist.mvplot_trajectories()
|
||||
else:
|
||||
hist.mvanimate()
|
||||
return 0
|
||||
|
||||
|
|
@ -153,6 +156,7 @@ Use `-v` to increase verbosity level (can be supplied multiple times e.g -vv).
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|||
|
||||
# Subparser for hist command.
|
||||
p_hist = subparsers.add_parser('hist', parents=[copts_parser], help=abimovie_hist.__doc__)
|
||||
p_hist.add_argument("-t", "--trajectories", default=False, action="store_true", help="Plot trajectories.")
|
||||
|
||||
# Subparser for ebands command.
|
||||
p_ebands = subparsers.add_parser('ebands', parents=[copts_parser], help=abimovie_ebands.__doc__)
|
||||
|
|
|
|||
Loading…
Reference in New Issue