def main():
args = get_input_args()
df = io_lib.df_from_input(args)
# extract parameters from arg parser
nbins = args.nbins[0]
range_tup = args.range
layout_tup = args.layout
alpha = args.alpha[0]
do_density = args.density
sharex = args.sharex
sharey = args.sharey
cols = args.cols if args.cols else [df.columns[0]]
validate_args(args, cols, df)
plot_lib.set_plot_styling(args)
# no plotting if output requested
if args.quiet:
counts, edges = np.histogram(
df[cols[0]], bins=nbins, range=range_tup, density=do_density)
centers = edges[:-1] + 0.5 * np.diff(edges)
df_out = pd.DataFrame({'bins': centers, 'counts': counts})
io_lib.df_to_output(args, df_out)
# otherwise do plotting
else:
df.hist(cols, bins=nbins, range=range_tup,
alpha=alpha, sharex=sharex, sharey=sharey, layout=layout_tup,
normed=do_density)
plot_lib.refine_plot(args)
plot_lib.show(args)
def main():
msg = textwrap.dedent("""
Creates interactive xy plots. Loosely based around matplotlib's
pyplot.plot command.
-----------------------------------------------------------------------
Examples:
* Really simple plot
p.linspace 1 10 7 | p.plot -x c0 -y c0
* Plot two traces
p.linspace 0 6.28 100\\
| p.df 'df["cos"]=np.cos(df.t)' 'df["sin"]=np.sin(df.t)'\\
--names t\\
| p.plot -x t -y sin cos\\
--style '.-' 'o-' --alpha 1 .2 --legend best
* Plot sea-level time series
p.example_data -d sealevel\\
| p.plot -x year -y sealevel_mm --style '.'\\
--xlabel year --ylabel 'relative sea level (mm)'\\
--title 'Sea Level Rise' --legend best --xlim 1995 2015
-----------------------------------------------------------------------
""")
# read command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=msg)
arg_lib.add_args(parser, 'io_in', 'xy_plotting', 'decorating')
parser.add_argument("-a",
"--alpha",
help="Set opacity level(s)",
nargs='+',
default=[1.],
type=float,
metavar='alpha')
# parse arguments
args = parser.parse_args()
# get the input dataframe
df = io_lib.df_from_input(args)
# set the appropriate theme
plot_lib.set_plot_styling(args)
# draw the plot
plot_lib.draw_xy_plot(args, df)
def test_set_plot_styling(self):
"""set_plot_styling() alters mpl.rcParams
"""
args = MagicMock(plot_context=["talk"], plot_theme=["darkgrid"], plot_palette=["muted"])
mpl.rcParams["axes.color_cycle"] = ["m", "c"]
mpl.rcParams["axes.labelsize"] = 1
mpl.rcParams["axes.titlesize"] = 1
rc_pre = dict(mpl.rcParams)
plot_lib.set_plot_styling(args)
rc_post = dict(mpl.rcParams)
self.assertNotEqual(rc_pre["axes.color_cycle"], rc_post["axes.color_cycle"])
self.assertNotEqual(rc_pre["axes.labelsize"], rc_post["axes.labelsize"])
self.assertNotEqual(rc_pre["axes.titlesize"], rc_post["axes.titlesize"])
def main():
msg = textwrap.dedent(
"""
Creates interactive xy plots. Loosely based around matplotlib's
pyplot.plot command.
-----------------------------------------------------------------------
Examples:
* Really simple plot
p.linspace 1 10 7 | p.plot -x c0 -y c0
* Plot two traces
p.linspace 0 6.28 100\\
| p.df 'df["cos"]=np.cos(df.t)' 'df["sin"]=np.sin(df.t)'\\
--names t\\
| p.plot -x t -y sin cos\\
--style '.-' 'o-' --alpha 1 .2 --legend best
* Plot sea-level time series
p.example_data -d sealevel\\
| p.plot -x year -y sealevel_mm --style '.'\\
--xlabel year --ylabel 'relative sea level (mm)'\\
--title 'Sea Level Rise' --legend best --xlim 1995 2015
-----------------------------------------------------------------------
"""
)
# read command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=msg)
arg_lib.add_args(parser, 'io_in', 'xy_plotting', 'decorating')
parser.add_argument(
"-a", "--alpha", help="Set opacity level(s)", nargs='+', default=[1.],
type=float, metavar='alpha')
# parse arguments
args = parser.parse_args()
# get the input dataframe
df = io_lib.df_from_input(args)
# set the appropriate theme
plot_lib.set_plot_styling(args)
# draw the plot
plot_lib.draw_xy_plot(args, df)
def test_set_plot_styling(self):
"""set_plot_styling() alters mpl.rcParams
"""
args = MagicMock(
plot_context=['talk'],
plot_theme=['darkgrid'],
plot_palette=['muted'],
)
mpl.rcParams['axes.labelsize'] = 1
mpl.rcParams['axes.titlesize'] = 1
rc_pre = dict(mpl.rcParams)
plot_lib.set_plot_styling(args)
rc_post = dict(mpl.rcParams)
self.assertNotEqual(
rc_pre['axes.labelsize'], rc_post['axes.labelsize'])
self.assertNotEqual(
rc_pre['axes.titlesize'], rc_post['axes.titlesize'])
def test_set_plot_styling(self):
"""set_plot_styling() alters mpl.rcParams
"""
args = MagicMock(
plot_context=['talk'],
plot_theme=['darkgrid'],
plot_palette=['muted'],
)
mpl.rcParams['axes.labelsize'] = 1
mpl.rcParams['axes.titlesize'] = 1
rc_pre = dict(mpl.rcParams)
plot_lib.set_plot_styling(args)
rc_post = dict(mpl.rcParams)
self.assertNotEqual(rc_pre['axes.labelsize'],
rc_post['axes.labelsize'])
self.assertNotEqual(rc_pre['axes.titlesize'],
rc_post['axes.titlesize'])
def main():
args = get_input_args()
df = io_lib.df_from_input(args)
# extract parameters from arg parser
nbins = args.nbins[0]
range_tup = args.range
layout_tup = args.layout
alpha = args.alpha[0]
do_density = args.density
sharex = args.sharex
sharey = args.sharey
cols = args.cols if args.cols else [df.columns[0]]
validate_args(args, cols, df)
plot_lib.set_plot_styling(args)
# no plotting if output requested
if args.quiet:
counts, edges = np.histogram(df[cols[0]],
bins=nbins,
range=range_tup,
density=do_density)
centers = edges[:-1] + 0.5 * np.diff(edges)
df_out = pd.DataFrame({'bins': centers, 'counts': counts})
io_lib.df_to_output(args, df_out)
# otherwise do plotting
else:
df.hist(cols,
bins=nbins,
range=range_tup,
alpha=alpha,
sharex=sharex,
sharey=sharey,
layout=layout_tup,
normed=do_density)
plot_lib.refine_plot(args)
plot_lib.show(args)
def exec_plot_command(args, cmd, df): # pragma: no cover
from pandashells.lib import plot_lib
plot_lib.set_plot_styling(args)
execute(cmd, scope_entries={'df': df})
plot_lib.refine_plot(args)
plot_lib.show(args)
def main():
msg = textwrap.dedent(
"""
Plots the emperical cumulative distribution function (ECDF).
-----------------------------------------------------------------------
Examples:
* Plot ECDF for 10k samples from the standard normal distribution.
p.rand -t normal -n 10000 | p.cdf -c c0
* Instead of plotting, send ECDF values to stdout
p.rand -t normal -n 10000 | p.cdf -c c0 -q | head
-----------------------------------------------------------------------
"""
)
# read command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=msg)
# specify column to use
parser.add_argument(
"-c", "--col", required=True, nargs=1,
help="Column to plot distribution")
parser.add_argument(
'-n', '--n_points', nargs=1, type=int,
help='Number of output points (default is twice input len)')
parser.add_argument(
'-q', '--quiet', action='store_true', default=False,
help='Quiet mean no plots. Send numeric output to stdout instead')
# parse arguments
arg_lib.add_args(parser, 'decorating', 'io_in', 'io_out',)
args = parser.parse_args()
# get the input dataframe and extract column
df = io_lib.df_from_input(args)
x = df[args.col[0]].values
# create the output distribution
n_out = 2 * len(x) if args.n_points is None else args.n_points[0]
x_out = np.linspace(min(x), max(x), n_out)
y_out = ECDF(x)(x_out)
# send values to stdout if quiet specified
if args.quiet:
df_out = pd.DataFrame(
{'x': x_out, 'p_less': y_out, 'p_greater': 1 - y_out})
df_out = df_out[['x', 'p_less', 'p_greater']]
io_lib.df_to_output(args, df_out)
return
# set the appropriate theme ad make plot
plot_lib.set_plot_styling(args)
pl.plot(x_out, y_out, label='P({} < x)'.format(args.col[0]))
pl.plot(x_out, 1. - y_out, label='P({} > x)'.format(args.col[0]))
pl.xlabel('x')
pl.legend(loc='best')
plot_lib.refine_plot(args)
plot_lib.show(args)
def exec_plot_command(args, cmd, df): # pragma: no cover
from pandashells.lib import plot_lib
plot_lib.set_plot_styling(args)
execute(cmd, scope_entries={'df': df})
plot_lib.refine_plot(args)
plot_lib.show(args)
