def do_example(datafile, outfile, invert=False, bins=100, ignore=0.0):
xyvv = clx.calc_load_xyvv(datafile, invert=invert)
(bins, xyvv) = cgsx.calc_grid_sum_xyvs(xyvv)
xyz = cxp.calc_xyvv_percentage(xyvv)
xyz = cix.calc_ignore_xyz(xyz, ignore=ignore)
pgx.plot_gmt_xyz(xyz, outfile, title='%loss sum binned values',
np_posn='NE', s_posn='SE',
cb_label='% loss')
def do_example(datafile, outfile, invert=False):
annotations = [('text', (144.5, -37.40),
'@;red;example_calc_plot_mean_xyz_map@;;')]
(bins_x, xyz) = cmx.calc_mean_xyz(datafile, scale=1e3, invert=invert)
pgx.plot_gmt_xyz(xyz, outfile, bins_x,
title='Loss mean binned values',
np_posn='NE', s_posn='SE',
cb_label='$ loss x 1,000',
annotate=annotations)
def do_example(datafile, outfile, invert=False):
xyz = csx.calc_sum_xyz(datafile, scale=1e6, invert=invert)
pgx.plot_gmt_xyz(
xyz,
outfile, #bins_x,
title='Loss sum binned values',
np_posn='NE',
s_posn='SE',
cb_label='$ loss x 1,000,000')
def do_example(datafile, outfile, invert=False, bins=100, ignore=0.0):
xyvv = clx.calc_load_xyvv(datafile, invert=invert)
(bins, xyvv) = cgsx.calc_grid_sum_xyvs(xyvv)
xyz = cxp.calc_xyvv_percentage(xyvv)
xyz = cix.calc_ignore_xyz(xyz, ignore=ignore)
pgx.plot_gmt_xyz(xyz,
outfile,
title='%loss sum binned values',
np_posn='NE',
s_posn='SE',
cb_label='% loss')
def obsolete_plot_loss_map(indir,
data,
out_dir,
output_file,
save_file=None,
title=None,
np_posn=None,
s_posn=None,
cb_label=None,
bins=100,
scale=1.0,
ignore=None,
invert=False,
colourmap=None):
"""Plot a loss map from randomly sampled data.
indir input directory
data name of the data file to plot (in indir)
out_dir general output directory
output_file name of map output file to create in 'out_dir' directory
save_file name of map data output file to create in 'out_dir' directory
title title to put on the graph
np_posn place to put a north pointer symbol at
s_posn place to put a length scale at
cb_label label text to put on the colour bar
bins either scalar for bins in each direction or (bin_x, bin_y)
scale amount to scale the data by
ignore the value to ignore if value <= ignore
colourmap the base colourmap to use
"""
# read in and scale XYZ data
xyz = csx.calc_sum_xyz(data, scale=scale, bins=bins, invert=invert)
# ignore everything less than an 'ignore' value?
if ignore is not None:
xyz = cix.calc_ignore_xyz(xyz, ignore)
# if we want a save of actual plotted data, do it here
if save_file:
save_outfile = os.path.join(out_dir, save_file)
# write 'xyz' data to 'save_outfile' here
# generate the plot output required
plot_outfile = os.path.join(out_dir, output_file)
pgx.plot_gmt_xyz(xyz,
plot_outfile,
bins=bins,
title=title,
cb_label=cb_label,
np_posn=np_posn,
s_posn=s_posn,
colourmap=colourmap)
def do_example(datafile, outfile, invert=False):
annotations = [('text', (144.5, -37.40),
'@;red;example_calc_plot_mean_xyz_map@;;')]
(bins_x, xyz) = cmx.calc_mean_xyz(datafile, scale=1e3, invert=invert)
pgx.plot_gmt_xyz(xyz,
outfile,
bins_x,
title='Loss mean binned values',
np_posn='NE',
s_posn='SE',
cb_label='$ loss x 1,000',
annotate=annotations)
def do_example(datafile, outfile, invert=False):
annotations = [('text', (144.0, -37.10),
'@;blue;cb_steps = (0.01,0.05,0.1,0.2,0.3,0.4), bins = 200@;;')]
bins = 200
#cb_steps = None
#cb_steps = ()
cb_steps = (0.01,0.05,0.1,0.2,0.3,0.4)
data = clx.calc_load_xyz(datafile, invert=invert)
pgx.plot_gmt_xyz(data, outfile, bins=bins,
title='example_plot_xyz_map.py',
np_posn='NE', s_posn='SE',
cb_label='$ loss x 1,000',
colourmap='hazmap',
cb_steps=cb_steps,
annotate=annotations)
def obsolete_plot_loss_map(indir, data, out_dir, output_file, save_file=None,
title=None, np_posn=None, s_posn=None, cb_label=None,
bins=100, scale=1.0, ignore=None, invert=False,
colourmap=None):
"""Plot a loss map from randomly sampled data.
indir input directory
data name of the data file to plot (in indir)
out_dir general output directory
output_file name of map output file to create in 'out_dir' directory
save_file name of map data output file to create in 'out_dir' directory
title title to put on the graph
np_posn place to put a north pointer symbol at
s_posn place to put a length scale at
cb_label label text to put on the colour bar
bins either scalar for bins in each direction or (bin_x, bin_y)
scale amount to scale the data by
ignore the value to ignore if value <= ignore
colourmap the base colourmap to use
"""
# read in and scale XYZ data
xyz = csx.calc_sum_xyz(data, scale=scale, bins=bins, invert=invert)
# ignore everything less than an 'ignore' value?
if ignore is not None:
xyz = cix.calc_ignore_xyz(xyz, ignore)
# if we want a save of actual plotted data, do it here
if save_file:
save_outfile = os.path.join(out_dir, save_file)
# write 'xyz' data to 'save_outfile' here
# generate the plot output required
plot_outfile = os.path.join(out_dir, output_file)
pgx.plot_gmt_xyz(xyz, plot_outfile, bins=bins,
title=title, cb_label=cb_label,
np_posn=np_posn, s_posn=s_posn,
colourmap=colourmap)
def do_example(datafile, outfile, invert=False):
xyz = csx.calc_sum_xyz(datafile, scale=1e6, invert=invert)
pgx.plot_gmt_xyz(xyz, outfile, #bins_x,
title='Loss sum binned values',
np_posn='NE', s_posn='SE',
cb_label='$ loss x 1,000,000')
