#initial parameters

# Function to make a dataset for histogram based on a list of carriers

# a minimum delay, maximum delay, and histogram bin widt

def make_dataset(gender_list=list(["Male","Female"]),

age_start=2,

age_end=26,

weight_start=10,

weight_end=218,

height_start=80,

height_end=200,

bmi_start=11.2,

bmi_end=67.3,

bin_width = 25

):

hist_population = pd.DataFrame(columns=['proportion', 'left', 'right',

'f_proportion', 'f_interval','gender','color'])

for i, gender_name in enumerate(gender_list):

if gender_name == 'All':

subset = population

else:

subset = population[population['GENDER2'] == gender_name]

subset = subset[(subset.AGE > age_start)

& (subset.AGE < age_end)

& (subset.WEIGHT > weight_start)

& (subset.WEIGHT < weight_end)

& (subset.HEIGHT > height_start)

& (subset.HEIGHT < height_end)

& (subset.BMI > bmi_start)

& (subset.BMI < bmi_end)

]

arr_hist, edges = np.histogram(subset['FVC_MAX'], bins = np.arange(np.min(subset['FVC_MAX']), np.max(subset['FVC_MAX']) + bin_width, bin_width))

# Divide the counts by the total to get a proportion

arr_df = pd.DataFrame({'proportion': arr_hist , 'left': edges[:-1], 'right': edges[1:] })

# Format the proportion

arr_df['f_proportion'] = ['%0.5f' % proportion for proportion in arr_df['proportion']]

# Format the interval

arr_df['f_interval'] = ['%d to %d ml' % (left, right) for left, right in zip(arr_df['left'], arr_df['right'])]

#assign the carrier for labels

arr_df['gender'] = gender_name

#color each carrier differently

arr_df['color'] = Category20_16[i]

# Add to the overall dataframe

hist_population = hist_population.append(arr_df)

# Overall dataframe

hist_population = hist_population.sort_values(['gender', 'left'])

#Add to the overall dataframe

return ColumnDataSource(hist_population)

def style(p):

# Title

p.title.align = 'center'

p.title.text_font_size = '20pt'

p.title.text_font = 'serif'

# Axis titles

p.xaxis.axis_label_text_font_size = '14pt'

p.xaxis.axis_label_text_font_style = 'bold'

p.yaxis.axis_label_text_font_size = '14pt'

p.yaxis.axis_label_text_font_style = 'bold'

# Tick labels

p.xaxis.major_label_text_font_size = '12pt'

p.yaxis.major_label_text_font_size = '12pt'

return p

def make_plot(src):

# Blank plot with correct labels

p = figure(plot_width = 700, plot_height = 700,

title = 'Force Vital Capacity',

x_axis_label = 'FVC (ml)', y_axis_label = 'Count',background_fill_color="#2E3332")

# Quad glyphs to create a histogram

p.quad(source = src, bottom = 0, top = 'proportion', left = 'left', right='right',

color = 'color', fill_alpha = 0.7,hover_fill_color = 'color', legend = 'gender',

hover_fill_alpha = 1.0, line_color = 'black')

#pdf line

# Hover tool with vline mode

hover = HoverTool(tooltips=[('Gender', '@gender'),

('ml', '@f_interval'),

('Proportion', '@f_proportion')],

mode='vline')

p.add_tools(hover)

p = style(p)

return p

def update(attr,old,new):

genders_to_plot = [select_gender.labels[i] for i in select_gender.active]

new_src = make_dataset(genders_to_plot,

age_start = age_select.value[0],

age_end = age_select.value[1],

weight_start = weight_select.value[0],

weight_end = weight_select.value[1],

height_start = height_select.value[0],

height_end = height_select.value[1],

bmi_start = bmi_select.value[0],

bmi_end = bmi_select.value[1],

bin_width = binwidth_select.value)

src.data.update(new_src.data)

available_gender = list(['Male','Female','All'])

available_gender.sort()

gender_colors = Category20_16

gender_colors.sort()

select_gender = CheckboxGroup(labels=list(['Male','Female','All']),active = [0,1,2])

select_gender.on_change('active', update)

#binwidth select

binwidth_select = Slider(start=1,end=600,step = 1,value=60,title='Bin Width')

binwidth_select.on_change('value', update)

#age range select

age_select = RangeSlider(start=1,end=25,value=(1, 28),step=1,title='Range of Age')

age_select.on_change('value', update)

#weight range

weight_select =RangeSlider(start=10,end=200,value=(10, 200),step=1,title='Range of Weight')

weight_select.on_change('value', update)

#height range select

height_select = RangeSlider(start=80,end=200,value=(80, 200),step=1,title='Range of Height')

height_select.on_change('value', update)

#BMI range select

bmi_select = RangeSlider(start= 10,end=60,value=(10, 60),step=0.5,title='Range of Body Mass Index')

bmi_select.on_change('value', update)

# Initial carriers and data source

initial_gender = [select_gender.labels[i] for i in select_gender.active]

src = make_dataset(initial_gender,

age_start = age_select.value[0],

age_end = age_select.value[1],

weight_start = weight_select.value[0],

weight_end = weight_select.value[1],

height_start = height_select.value[0],

height_end = height_select.value[1],

bmi_start = bmi_select.value[0],

bmi_end = bmi_select.value[1],

bin_width = binwidth_select.value)

p = make_plot(src)

# Put controls in a single element

controls = WidgetBox(select_gender,binwidth_select, age_select, weight_select, bmi_select, height_select)

# Create a row layout

layout = row(controls, p)

# Make a tab with the layout

tab = Panel(child=layout, title = 'Histogram')

def make_dataset(gender_list=list(['Male']),

age_start=2,

age_end=26,

weight_start=10,

weight_end=218,

height_start=80,

height_end=200,

bmi_start=11.2,

bmi_end=67.3,

bin_width = 25,

x_axis ='SESSION_IQR',

y_axis ='SESSION_STD'

):

gender_list = sorted(gender_list)

plot_population = pd.DataFrame(columns=[x_axis , y_axis ,'gender','color'])

if len(gender_list) == 0:

plot_population = pd.DataFrame(columns=[x_axis , y_axis ,'gender','color'])

else:

for i, gender_name in enumerate(gender_list):

if gender_name == 'All':

subset = population

else:

subset = population[population['GENDER2'] == gender_name]

subset = subset[(subset.AGE > age_start)

& (subset.AGE < age_end)

& (subset.WEIGHT > weight_start)

& (subset.WEIGHT < weight_end)

& (subset.HEIGHT > height_start)

& (subset.HEIGHT < height_end)

& (subset.BMI > bmi_start)

& (subset.BMI < bmi_end)

]

arr_df = pd.DataFrame({'SEQN': subset['SEQN'], x_axis : subset[x_axis] , y_axis : subset[y_axis] })

arr_df['gender'] = gender_name

arr_df['color'] = Category20_16[i]

arr_df = arr_df[['SEQN',x_axis,y_axis,'gender','color']]

if gender_name == "Male":

arr_df1 = arr_df

elif gender_name == "Female":

arr_df2 = arr_df

else:

arr_df3 = arr_df

if len(gender_list) == 1:

plot_population = arr_df

plot_population = plot_population.sort_values(['gender','SEQN'])

plot_population = plot_population[['SEQN',x_axis,y_axis,'gender','color']]

plot_population = plot_population.reset_index(drop=True)

elif len(gender_list) == 2:

if gender_list[0] == "Female":

plot_population = arr_df1.append(arr_df2, ignore_index=True)

plot_population = plot_population.sort_values(['gender','SEQN'])

plot_population = plot_population.reset_index(drop=True)

elif gender_list[1] == "Female":

plot_population = arr_df3.append(arr_df2, ignore_index=True)

plot_population = plot_population.sort_values(['gender','SEQN'])

plot_population = plot_population.reset_index(drop=True)

else:

plot_population = arr_df1.append(arr_df3, ignore_index=True)

plot_population = plot_population.sort_values(['gender','SEQN'])

plot_population = plot_population.reset_index(drop=True)

elif len(gender_list) == 3:

plot_population = arr_df1.append(arr_df2, ignore_index=True)

plot_population = plot_population.append(arr_df3, ignore_index=True)

plot_population = plot_population.sort_values(['gender','SEQN'])

plot_population = plot_population.reset_index(drop=True)

return ColumnDataSource(plot_population)

def make_plot(src):

p=figure()

menu = [('Raw Curve Sequence Number','RAW_CURVE',),

('Force Vital Capacity (ml)','FVC_MAX'),

('Force Vital Capacity (ml) at 1sec','FEV1'),

('Force Vital Capacity (ml) at 3sec','FEV3'),

('Force Vital Capacity (ml) at 6sec','FEV6'),

('Peak expiratory flow (ml)','PEAK_EXPIRATORY'),

('Max-Mid expiratory flow (ml)','MAX_MID_EXPIRATORY'),

('Pseudo PSU','PSEUDO_PSU'),

('Age (years)','AGE'),

('Gender','GENDER2'),

('Height (cm)', 'HEIGHT'),

('Weight (kg)','WEIGHT'),

('Body mass index (kg/m2)','BMI'),

('Session best FVC','SESSION_BEST'),

('Session mean FVC','SESSION_MEAN'),

('Session std FVC','SESSION_STD'),

('Session median FVC','SESSION_MEDIAN'),

('Session iqr FVC','SESSION_IQR'),

('Session minimum FVC','SESSION_MINIMUM'),

('Session maximum FVC','SESSION_MAXIMUM'),

('Session max distance FVC','SESSION_MAX_DISTANCE'),

('Session median distance FVC','SESSION_MEDIAN_DISTANCE')]

if len(src.column_names) == 5:

for i in range(22):

if menu[i][1] == src.column_names[1]:

titulo_x = menu[i][0]

titulo_y = menu[i][0]

break

xs=src.column_names[1]

ys=src.column_names[1]

gs=src.column_names[2]

zs=src.column_names[3]

x_title = titulo_x.title()

y_title = titulo_y.title()

kw = dict()

kw['title'] = "%s vs %s" % (x_title, y_title)

# Blank plot with correct labels

p = figure(plot_width = 700, plot_height = 700,background_fill_color="#2E3332",**kw)

p.xaxis.axis_label = x_title

p.yaxis.axis_label = y_title

# Quad glyphs to create a histogram

p.circle(source=src,x=xs , y=ys , size=7, color=zs,legend=gs,fill_alpha = 0.7)

#

if len(src.column_names) == 6:

for i in range(22):

if menu[i][1] == src.column_names[1]:

titulo_x = menu[i][0]

if menu[i][1] == src.column_names[2]:

titulo_y = menu[i][0]

xs=src.column_names[1]

ys=src.column_names[2]

gs=src.column_names[3]

zs=src.column_names[4]

x_title = titulo_x.title()

y_title = titulo_y.title()

kw = dict()

kw['title'] = "%s vs %s" % (x_title, y_title)

# Blank plot with correct labels

p = figure(plot_width = 700, plot_height = 700,background_fill_color="#2E3332",**kw)

p.xaxis.axis_label = x_title

p.yaxis.axis_label = y_title

# Quad glyphs to create a histogram

p.circle(source=src,x=xs , y=ys , size=7, color=zs,legend=gs,fill_alpha = 0.7)

return p

def update():

genders_to_plot = [select_gender.labels[i] for i in select_gender.active]

new_src = make_dataset(genders_to_plot,

age_start = age_select.value[0],

age_end = age_select.value[1],

weight_start = weight_select.value[0],

weight_end = weight_select.value[1],

height_start = height_select.value[0],

height_end = height_select.value[1],

bmi_start = bmi_select.value[0],

bmi_end = bmi_select.value[1],

x_axis = select_x.value,

y_axis = select_y.value)

layout.children[1] = make_plot(new_src)

#gender selection

available_gender = list(['Male','Female','All'])

available_gender.sort()

gender_colors = Category20_16

gender_colors.sort()

select_gender = CheckboxGroup(labels=list(['Male','Female','All']),active = [0,1,2])

select_gender.on_change('active', lambda attr, old, new: update())

#age range select

age_select = RangeSlider(start=1,end=25,value=(1, 28),step=1,title='Range of Age')

age_select.on_change('value', lambda attr, old, new: update())

#weight range

weight_select =RangeSlider(start=10,end=200,value=(10, 200),step=1,title='Range of Weight')

weight_select.on_change('value', lambda attr, old, new: update())

#height range select

height_select = RangeSlider(start=80,end=200,value=(80, 200),step=1,title='Range of Height')

height_select.on_change('value', lambda attr, old, new: update())

#BMI range select

bmi_select = RangeSlider(start= 10,end=60,value=(10, 60),step=0.5,title='Range of Body Mass Index')

bmi_select.on_change('value', lambda attr, old, new: update())

menu = [('Raw Curve Sequence Number','RAW_CURVE',),

('Force Vital Capacity (ml)','FVC_MAX'),

('Force Vital Capacity (ml) at 1sec','FEV1'),

('Force Vital Capacity (ml) at 3sec','FEV3'),

('Force Vital Capacity (ml) at 6sec','FEV6'),

('Peak expiratory flow (ml)','PEAK_EXPIRATORY'),

('Max-Mid expiratory flow (ml)','MAX_MID_EXPIRATORY'),

('Pseudo PSU','PSEUDO_PSU'),

('Age (years)','AGE'),

('Gender','GENDER2'),

('Height (cm)', 'HEIGHT'),

('Weight (kg)','WEIGHT'),

('Body mass index (kg/m2)','BMI'),

('Session best FVC','SESSION_BEST'),

('Session mean FVC','SESSION_MEAN'),

('Session std FVC','SESSION_STD'),

('Session median FVC','SESSION_MEDIAN'),

('Session iqr FVC','SESSION_IQR'),

('Session minimum FVC','SESSION_MINIMUM'),

('Session maximum FVC','SESSION_MAXIMUM'),

('Session max distance FVC','SESSION_MAX_DISTANCE'),

('Session median distance FVC','SESSION_MEDIAN_DISTANCE')]

#select x axis

select_x = Dropdown(label='X Axis', button_type="warning", value='SESSION_IQR', menu=menu)

select_x.on_change('value', lambda attr, old, new: update())

#select y axis

select_y = Dropdown(label='Y Axis', button_type="warning", value='SESSION_STD', menu=menu)

select_y.on_change('value', lambda attr, old, new: update())

# Initial carriers and data source

initial_gender = [select_gender.labels[i] for i in select_gender.active]

src = make_dataset(initial_gender,

age_start = age_select.value[0],

age_end = age_select.value[1],

weight_start = weight_select.value[0],

weight_end = weight_select.value[1],

height_start = height_select.value[0],

height_end = height_select.value[1],

bmi_start = bmi_select.value[0],

bmi_end = bmi_select.value[1],

x_axis = select_x.value,

y_axis= select_y.value

)

# p = make_plot(src)

# Put controls in a single element

controls = WidgetBox(select_gender, age_select, weight_select, bmi_select, height_select, select_x, select_y)

# Create a row layout

layout = row(controls, make_plot(src))

# Make a tab with the layout

tab2 = Panel(child=layout, title = 'Scatter')

update()

source = ColumnDataSource(data=dict())

def update():

current = df

if select_gender.value == "Male":

current = df[(df['GENDER2'] == 'Male')]

elif select_gender.value == "Female":

current = df[(df['GENDER2'] == 'Female')]

elif select_gender.value == "All":

current = df

current = current[ (current['HEIGHT'] >= height_select.value[0]) & (current['HEIGHT'] <= height_select.value[1])

& (current['WEIGHT'] >= weight_select.value[0]) & (current['WEIGHT'] <= weight_select.value[1])

& (current['AGE'] >= age_select.value[0]) & (current['AGE'] <= age_select.value[1])

& (current['BMI'] >= bmi_select.value[0]) & (current['BMI'] <= bmi_select.value[1])

].dropna()

source.data = {

'SEQN' : current.SEQN,

'RAW_CURVE' : current.RAW_CURVE,

'FVC_MAX' : current.FVC_MAX,

'FEV1' : current.FEV1,

'FEV3' : current.FEV3,

'FEV6' : current.FEV6,

'PEAK_EXPIRATORY' : current.PEAK_EXPIRATORY,

'MAX_MID_EXPIRATORY' : current.MAX_MID_EXPIRATORY,

'PSEUDO_PSU' : current.PSEUDO_PSU,

'AGE' : current.AGE,

'GENDER2' : current.GENDER2,

'GENDER' : current.GENDER,

'HEIGHT' : current.HEIGHT,

'WEIGHT' : current.WEIGHT,

'BMI' : current.BMI,

'SESSION_BEST' : current.SESSION_BEST,

'SESSION_MEAN' : current.SESSION_MEAN,

'SESSION_STD' : current.SESSION_STD,

'SESSION_MEDIAN' : current.SESSION_MEDIAN,

'SESSION_IQR' : current.SESSION_IQR,

'SESSION_MINIMUM' : current.SESSION_MINIMUM,

'SESSION_MAXIMUM' : current.SESSION_MAXIMUM,

'SESSION_MAX_DISTANCE' : current.SESSION_MAX_DISTANCE,

'SESSION_MEDIAN_DISTANCE' : current.SESSION_MEDIAN_DISTANCE

}

menu = [("All", "All"), ("Male", "Male"), ("Female", "Female")]

select_gender = Dropdown(label="Gender Selection", button_type="warning", menu=menu)

select_gender.on_change('value', lambda attr, old, new: update())

#height range select

height_select = RangeSlider(title="Range of Height", start=80, end=200, value=(80, 200), step=0.5)

height_select.on_change('value', lambda attr, old, new: update())

#height range select

age_select = RangeSlider(title="Range of Age", start=1, end=28, value=(1, 28), step=1)

age_select.on_change('value', lambda attr, old, new: update())

#weight range select

weight_select = RangeSlider(title="Range of Weight", start=10, end=200, value=(10, 200), step=1)

weight_select.on_change('value', lambda attr, old, new: update())

#height range select

bmi_select = RangeSlider(title="Range of Body Mass Index", start=10, end=60, value=(10, 60), step=0.5)

bmi_select.on_change('value', lambda attr, old, new: update())

button = Button(label="Download", button_type="success")

button.callback = CustomJS(args=dict(source=source),code=open(join(dirname(__file__), "download.js")).read())

columns = [

TableColumn(field='SEQN', title='SEQN'),

TableColumn(field='FVC_MAX', title='FVC (ml)'),

TableColumn(field='AGE', title='AGE (years)'),

TableColumn(field='GENDER2', title='GENDER'),

TableColumn(field='HEIGHT', title='HEIGHT (cm)'),

TableColumn(field='BMI', title='BMI'),

TableColumn(field="SESSION_BEST", title='SESSION MAXIMUM (ml)')

]

data_table = DataTable(source=source, columns=columns, width=1000)

# Put controls in a single element

controls = WidgetBox(select_gender, age_select, weight_select, bmi_select, height_select,button)

# Create a row layout

layout = row(controls,data_table)

# Make a tab with the layout

tab = Panel(child=layout, title = 'Summary Table')

update()