credit.visualization_tools

credit.visualization_tools#

Functions:

  • cmap_combine(cmap1, cmap2)

  • get_projection(proj_name)

  • get_colormap(cmap_strings)

  • get_colormap_extend(var_range)

  • get_variable_range_with_rounding(data)

  • get_variable_range(var_name, conf, level=level, method=’mean_std’)

  • figure_panel_planner(var_num, proj)

  • cartopy_single_panel(figsize=(13, 6.5), proj=ccrs.EckertIII())

  • cartopy_panel2(figsize=(13, 8), proj=ccrs.EckertIII())

  • cartopy_panel4(var_num, figsize=(13, 6.5), proj=ccrs.EckertIII())

  • cartopy_panel6(var_num, figsize=(13, 9.75), proj=ccrs.EckertIII())

  • map_gridline_opt(AX)

  • colorbar_opt(fig, ax, cbar, cbar_extend)

  • draw_sigma_level(data, conf=None, times=None, forecast_count=None, save_location=None)

  • draw_diagnostics(data, conf=None, times=None, forecast_count=None, save_location=None)

  • draw_surface(data, conf=None, times=None, forecast_count=None, save_location=None)

Attributes#

logger

Functions#

cmap_combine(cmap1, cmap2)

combine two matplotlib colormaps as one.

get_projection(proj_name)

returns a cartopy projection obj

get_colormap(cmap_strings)

returns a list of colormaps from input strings.

get_colormap_extend(var_range)

return colorbar extend options based on the given value range.

get_variable_range_with_rounding(data)

Estimate pcolor value ranges based on the input data.

get_variable_range(var_name, conf[, level, method])

figure_panel_planner(var_num, proj)

Choose a figure layout based on the number of variables to plot.

cartopy_single_panel([figsize, proj])

Single panel figure layout

cartopy_panel2([figsize, proj])

Two-panel figure layout

cartopy_panel4(var_num[, figsize, proj])

Four-panel figure layout

cartopy_panel6(var_num[, figsize, proj])

Six-panel figure layout

map_gridline_opt(AX)

Customize cartopy map gridlines

colorbar_opt(fig, ax, cbar, cbar_extend)

Customize the colorbar

shared_mem_draw_wrapper(shm, level, step, ...)

draw_variables(pred, level, step, visualization_key[, ...])

This function produces figures for given variables.

Module Contents#

credit.visualization_tools.logger#
credit.visualization_tools.cmap_combine(cmap1, cmap2)#

combine two matplotlib colormaps as one.

credit.visualization_tools.get_projection(proj_name)#

returns a cartopy projection obj

credit.visualization_tools.get_colormap(cmap_strings)#

returns a list of colormaps from input strings.

credit.visualization_tools.get_colormap_extend(var_range)#

return colorbar extend options based on the given value range.

credit.visualization_tools.get_variable_range_with_rounding(data)#

Estimate pcolor value ranges based on the input data.

credit.visualization_tools.get_variable_range(var_name, conf, level=-1, method='mean_std')#
credit.visualization_tools.figure_panel_planner(var_num, proj)#

Choose a figure layout based on the number of variables to plot. ! Handles up to 6 variables

credit.visualization_tools.cartopy_single_panel(figsize=(13, 6.5), proj=ccrs.EckertIII())#

Single panel figure layout

credit.visualization_tools.cartopy_panel2(figsize=(13, 8), proj=ccrs.EckertIII())#

Two-panel figure layout

credit.visualization_tools.cartopy_panel4(var_num, figsize=(13, 6.5), proj=ccrs.EckertIII())#

Four-panel figure layout

credit.visualization_tools.cartopy_panel6(var_num, figsize=(13, 9.75), proj=ccrs.EckertIII())#

Six-panel figure layout

credit.visualization_tools.map_gridline_opt(AX)#

Customize cartopy map gridlines

credit.visualization_tools.colorbar_opt(fig, ax, cbar, cbar_extend)#

Customize the colorbar

credit.visualization_tools.shared_mem_draw_wrapper(shm, level, step, visualization_key, conf, save_location)#
credit.visualization_tools.draw_variables(pred, level, step, visualization_key, conf=None, save_location=None)#

This function produces figures for given variables.