In [1]:

    

#general imports 
import pygslib


    

In [2]:

    

#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')


    

In [3]:

    

parameters = {
        'hmin' : None,                          #in/output rank-0 array(float,'d')
        'hmax' : None,                         #in/output rank-0 array(float,'d')
        'ncl'  : 30,                            #int, number of bins
        'iwt'  : 1,                             #int, 1 use declustering weight
        'ilog' : 1,                             #int, 1 use logscale
        'icum' : 0,                             #int, 1 use cumulative
        'va'   : mydata['Primary'],             # array('d') with bounds (nd)
        'wt'   : None,                          # array('d') with bounds (nd), wight variable (obtained with declust?)
        'figure' : None ,                       # a bokeh figure object (Optional: new figure created if None). Set none or undefined if creating a new figure. 
        'title'  : 'A test',                    # string. Figure title
        'xlabel' : 'Grade', # string. X axis label 
        'ylabel' : 'f(%)', # string. Y axis label
        # visual parameter for the histogram
        'color' : 'red', # string with valid CSS colour (https://www.w3schools.com/colors/colors_names.asp), or an RGB(A) hex value, or tuple of integers (r,g,b), or tuple of (r,g,b,a) 
        'legend': 'Non - Declustered', # string (Optional, default "NA")
        'alpha' :  0.5, # float [0-1]. Transparency of the fill colour 
        'lwidth': 1, # float. Line width
        # legend 
        'legendloc': 'top_left'}


    

In [4]:

    

res, fig = pygslib.plothtml.histgplt(parameters)


    

In [5]:

    

# show the figure
pygslib.plothtml.show(fig)


    

In [6]:

    

# note that parameters was updated
parameters['figure']


    

    
Out[6]:




Figure(
id = 'eb4ee984-a6c9-4f21-80cf-705ae4ed111d', …)
above = [],
aspect_scale = 1,
background_fill_alpha = {'value': 1.0},
background_fill_color = {'value': '#ffffff'},
below = [LogAxis(id='e53722cb-b74c-4424-a52e-84796f1a9028', ...)],
border_fill_alpha = {'value': 1.0},
border_fill_color = {'value': '#ffffff'},
css_classes = [],
disabled = False,
extra_x_ranges = {},
extra_y_ranges = {},
h_symmetry = True,
height = None,
hidpi = True,
js_event_callbacks = {},
js_property_callbacks = {},
left = [LinearAxis(id='6754f275-a602-4798-88bd-19519e61cdf3', ...)],
lod_factor = 10,
lod_interval = 300,
lod_threshold = 2000,
lod_timeout = 500,
match_aspect = False,
min_border = 5,
min_border_bottom = None,
min_border_left = None,
min_border_right = None,
min_border_top = None,
name = None,
outline_line_alpha = {'value': 1.0},
outline_line_cap = 'butt',
outline_line_color = {'value': '#e5e5e5'},
outline_line_dash = [],
outline_line_dash_offset = 0,
outline_line_join = 'miter',
outline_line_width = {'value': 1},
output_backend = 'canvas',
plot_height = 600,
plot_width = 600,
renderers = [LogAxis(id='e53722cb-b74c-4424-a52e-84796f1a9028', ...), Grid(id='77ed800d-04e9-49f7-a41a-b15647ed54ed', ...), LinearAxis(id='6754f275-a602-4798-88bd-19519e61cdf3', ...), Grid(id='cbf46d46-4f77-4381-86e6-88805e826319', ...), BoxAnnotation(id='66d70840-5728-4e68-b792-43a3eb498537', ...), Legend(id='3f6202d6-ff7e-4391-873a-54bcaf377c06', ...), GlyphRenderer(id='3346d60e-0553-4aa4-9b4d-3cad197fce46', ...)],
right = [],
sizing_mode = 'fixed',
subscribed_events = [],
tags = [],
title = Title(id='a15ec902-129e-49c6-91cb-905b92502cb8', ...),
title_location = 'above',
toolbar = Toolbar(id='78031736-cbd2-46f3-aa13-7a8eeb59f462', ...),
toolbar_location = 'above',
toolbar_sticky = True,
v_symmetry = False,
width = None,
x_range = Range1d(id='1dc6d76d-0f5f-46d8-ae86-d33d0b803aa5', ...),
x_scale = LogScale(id='6b11f304-dfae-4568-a038-410730f782e3', ...),
y_range = DataRange1d(id='57da6557-bec3-485b-a21c-8470f1ab190c', ...),
y_scale = LinearScale(id='53fa7f91-b940-4b74-b816-14bf3bd8565d', ...))

In [7]:

    

#append another histogram but declustered
parameters['wt']= mydata['Declustering Weight']
parameters['color']= 'blue'
parameters['legend'] = 'Declustered'
res, fig = pygslib.plothtml.histgplt(parameters)
pygslib.plothtml.show(fig)


    

In [8]:

    

# you can do some edits
fig.title.text = "@@@@@@ new name  @@@@@@"
pygslib.plothtml.show(fig)


    

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