Development Status:

This is my attempt on adding a searchbox. I got the searchbox to appear but it does not seem to work.

The goal is to add a text box to select items containing a particular string.

- For example, to search all instances where the "Shrt_Desc" field contains the string "Kayle"



In [1]:

    
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
from bokeh.layouts import row, widgetbox
from bokeh.models import CustomJS, Slider, HoverTool, BoxAnnotation
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from collections import OrderedDict

#from plotly.offline import download_plotlyjs, init_notebook_mode, plot
#from plotly.graph_objs import *


#%matplotlib inline
#init_notebook_mode()



In [2]:

    
dffile = "./Data/sr28abxl/ABBREV_FoodDatabase.xlsx"
dvfile = "./Playdata.xlsx"
#df = pd.read_excel(file, sheetname = 'Sheet1', header = 0, index_col = 0, convert_float = True)
#dv = pd.read_excel(file, sheetname = 'Sheet3', header = 0, index_col = 0, convert_float = True)
df = pd.read_excel(dffile, sheetname = 'ABBREV', header = 0, convert_float = True)
dv = pd.read_excel(dvfile, sheetname = 'Sheet3', header = 0, index_col = 0, convert_float = True)
df.columns = df.dtypes.index.str.replace("(", "").str.replace(")", "").str.replace("µ", "p")

Searchstring = "SHAKE|BEVERAGES|LIQUOR|FORMULA|BABY|SALTED|UNSWTND|PDR|FREEZE|GENERAL MILLS|FRUIT-FLAV|CEREALS|SNACKFO|OIL,|LARD|CANDIES|YEAST|SNACKS|BACKFAT|SHORTENING|COOKIES|BASIL,DRIED|SYRUP"
df = df[df.Shrt_Desc.str.contains(Searchstring) == False]
#dffout = dff[dff.Shrt_Desc.str.contains(Searchstring) == True]

#foodgroups = ['Bread','Candy', 'Dessert','Dressing','Fruit','Meat','Pasta','Pizza','Spread','Vegetable','fruit'}
#  This file can be used to calculate nutrient values for food portions from the
#values provided per 100 g of food. The following formula is used to calculate the nutrient
#content per household measure:
#N = (V*W)/100
#where:
#N = nutrient value per household measure,
#V = nutrient value per 100 g (Nutr_Val in the Nutrient Data file), and
#W = g weight of portion (Gm_Wgt in the Weight file).

df['protein_rda'] = df['Protein_g']/dv['DV']['Protein'] * (df['GmWt_1']/100)
df['Calories_rda'] = df['Energ_Kcal']/dv['DV']['Caloriesm'] * (df['GmWt_1']/100)
df['Calories'] = df['Energ_Kcal'] * (df['GmWt_1']/100)
#df['foodtext'] = df.Shrt_Desc 
df['Totfat_rda'] = df['Lipid_Tot_g']/dv['DV']['Total Fat']  * (df['GmWt_1']/100)
df['Satfat_rda'] = df['FA_Sat_g']/dv['DV']['Saturated Fat']  * (df['GmWt_1']/100)
df['Colesterol_rda'] = df['Cholestrl_mg']/dv['DV']['Cholesterol']  * (df['GmWt_1']/100)
df['Potassium_rda'] = df['Potassium_mg']/dv['DV']['Potassium'] * (df['GmWt_1']/100)
df['Sodium_rda'] = df['Sodium_mg']/dv['DV']['Sodium']  * (df['GmWt_1']/100)
#df['TotCarbs_rda'] = df['Lipid_Tot_g']/dv['DV']['Total Carbohydrate']  * (df['GmWt_1']/100)
df['Dietfiber_rda'] = df['Fiber_TD_g']/dv['DV']['Dietary Fiber']  * (df['GmWt_1']/100)
df['VitaminA_rda'] = df['Vit_A_IU']/dv['DV']['Vitamin A']  * (df['GmWt_1']/100)
df['VitaminC_rda'] = df['Vit_C_mg']/dv['DV']['Vitamin C'] * (df['GmWt_1']/100)
df['Calcium_rda'] = df['Calcium_mg']/dv['DV']['Calcium']  * (df['GmWt_1']/100)
df['Iron_rda'] = df['Iron_mg']/dv['DV']['Iron']  * (df['GmWt_1']/100)
df['VitaminD_rda'] = df['Vit_D_pg']/dv['DV']['Vitamin D']  * (df['GmWt_1']/100)
df['VitaminE_rda'] = df['Vit_E_mg']/dv['DV']['Vitamin E']  * (df['GmWt_1']/100)
df['VitaminK_rda'] = df['Vit_K_pg']/dv['DV']['Vitamin K']  * (df['GmWt_1']/100)
df['Thiamin_rda'] = df['Thiamin_mg']/dv['DV']['Thiamin']  * (df['GmWt_1']/100)
df['Riboflavin_rda'] = df['Riboflavin_mg']/dv['DV']['Riboflavin']  * (df['GmWt_1']/100)
df['Niacin_rda'] = df['Niacin_mg']/dv['DV']['Niacin']  * (df['GmWt_1']/100)
df['VitaminB6_rda'] = df['Vit_B6_mg']/dv['DV']['Vitamin B6']  * (df['GmWt_1']/100)
df['Folate_rda'] = df['Folate_Tot_pg']/dv['DV']['Folate']  * (df['GmWt_1']/100)
df['VitaminB12_rda'] = df['Vit_B12_pg']/dv['DV']['Vitamin B12']  * (df['GmWt_1']/100)
df['PantothenicAcid_rda'] = df['Panto_Acid_mg']/dv['DV']['Pantothenic acid']  * (df['GmWt_1']/100)
df['Phosphorus_rda'] = df['Phosphorus_mg']/dv['DV']['Phosphorus']  * (df['GmWt_1']/100)
df['Magnesium_rda'] = df['Magnesium_mg']/dv['DV']['Magnesium']  * (df['GmWt_1']/100)
df['Zinc_rda'] = df['Zinc_mg']/dv['DV']['Zinc']  * (df['GmWt_1']/100)
df['Selenium_rda'] = df['Selenium_pg']/dv['DV']['Selenium']  * (df['GmWt_1']/100)
df['Copper_rda'] = df['Copper_mg']/dv['DV']['Copper']  * (df['GmWt_1']/100)
df['Manganese_rda'] = df['Manganese_mg']/dv['DV']['Manganese']  * (df['GmWt_1']/100)
df['Molybdenum_rda'] = df['Energ_Kcal']/dv['DV']['Molybdenum']  * (df['GmWt_1']/100)
##df["Quantity"] = df["GmWt_1"].map(str) + " @ " + df["GmWt_Desc1"] 
df["Quantity"] = df["GmWt_Desc1"] 
 
df['Healthy'] = df['protein_rda']+ df['Dietfiber_rda']+ df['Thiamin_rda'] + df['Riboflavin_rda'] + df['Niacin_rda']  + df['PantothenicAcid_rda'] + df['Phosphorus_rda'] + df['Magnesium_rda'] + df['Selenium_rda'] + df['Copper_rda'] + df['Manganese_rda'] + df['Molybdenum_rda']  + (df['VitaminA_rda'] + df['VitaminB6_rda'] + df['VitaminC_rda'] + df['Calcium_rda'] + df['Iron_rda'] + df['VitaminD_rda'] + df['VitaminE_rda'] + df['VitaminK_rda'] + df['Folate_rda'] + df['VitaminB12_rda'] + df['Zinc_rda'])*2 - (10*df['Sodium_rda']) 


#df['Calories_rda']+ df['Totfat_rda']+df['Satfat_rda']+df['Colesterol_rda'] +df['Sodium_rda'] +df['TotCarbs_rda']+

#foodgrp_key= list(set(df['foodgroup']))
#grp_val = list(map(int,range(len(foodgrp_key))))
#grpdict = dict(zip(foodgrp_key,grp_val))
#df.head()

#Get rid of the NaNs
dffin = df[np.isfinite(df['Healthy'])]
#FiFORMULAstrings in dffin and dFORMULA

#Searchstring = "SHAKE|BEVERAGES|LIQUOR|FORMULA|BABY|SALTED|UNSWTND|PDR|FREEZE|GENERAL MILLS|FRUIT-FLAV|CEREALS|SNACKFO|OIL,|LARD|CANDIES|YEAST|SNACKS|BACKFAT|SHORTENING|COOKIES|BASIL,DRIED|SYRUP"
#dffin = dff[dff.Shrt_Desc.str.contains(Searchstring) == False]
#dffout = dff[dff.Shrt_Desc.str.contains(Searchstring) == True]



In [3]:

    
# Create a dictionary out of the dataframe so we can select them later
#dffin.set_index("Shrt_Desc", drop=True, inplace=True)
#dictionary = df.to_dict(orient="index")



In [28]:

    
#keep only the top 500 healthy items
dffin = dffin.sort_values('Healthy', ascending=False)
dffin = dffin.head(500)
tinydffin = dffin.head(10)

## Modifying Max calories  to fit my whims
## will loop in nutritionist later
Maxgoodcal = 400
Redboxhigh = 600
Yellowboxlow = 400

# Setup the plot
high_box = BoxAnnotation( left=Redboxhigh, fill_alpha=0.1, fill_color='red')
mid_box = BoxAnnotation( left=Yellowboxlow, right=Redboxhigh, fill_alpha=0.1, fill_color='yellow')
low_box = BoxAnnotation( right=Yellowboxlow, fill_alpha=0.1, fill_color='green')

x= dffin.Calories.values.tolist()
h= dffin.Healthy.values.tolist()
y= dffin.Healthy.values.tolist()
n=dffin.Shrt_Desc.values.tolist()
s=dffin.Sodium_mg.values.tolist()
k=dffin.Vit_K_pg.values.tolist()
phos=dffin.Phosphorus_mg.values.tolist()
por=dffin.Quantity.values.tolist()
pro=dffin.Protein_g.values.tolist()
pot=dffin.Potassium_mg.values.tolist()

ylim = max(h)+50
ds1 = ColumnDataSource(data=dict(x=x,y=y,n=n,s=s,h=h,pro=pro,phos=phos, por=por,k=k, pot=pot))

###########Testing

x= tinydffin.Calories.values.tolist()
h= tinydffin.Healthy.values.tolist()
y= tinydffin.Healthy.values.tolist()
n=tinydffin.Shrt_Desc.values.tolist()
s=tinydffin.Sodium_mg.values.tolist()
k=tinydffin.Vit_K_pg.values.tolist()
phos=tinydffin.Phosphorus_mg.values.tolist()
por=tinydffin.Quantity.values.tolist()
pro=tinydffin.Protein_g.values.tolist()
pot=tinydffin.Potassium_mg.values.tolist()

tinyds1= ColumnDataSource(data=dict(x=x,y=y,n=n,s=s,h=h,pro=pro,phos=phos, por=por,k=k, pot=pot))
#################


TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,tap,previewsave,box_select,poly_select,lasso_select,hover"
plot = figure(title= "Nutrition Filter Test",tools = TOOLS)
plot.scatter('x', 'y', source=tinyds1, radius= dffin.Healthy ,line_color=None)
plot.add_layout(low_box)
plot.add_layout(mid_box)
plot.add_layout(high_box)
#plot.title.text = "Nutrition Filter"
#p.xgrid[0].grid_line_color=None
#p.ygrid[0].grid_line_alpha=0.5
plot.xaxis.axis_label = 'Calories(lower tends to be better)'
plot.yaxis.axis_label = 'Healthy (Higher is Better)'

############## more test

text_input = TextInput(title="title", value='my sine wave')

plot.title.text = text_input.value

text_input.callback = CustomJS(args=dict(source=text_input.value), code="""
    
    text_input = TextInput(title="title", value='something else')
    plot.title.text = text_input.value

    source.trigger('change');
""")

#title.callback = CustomJS(update_title('value'))
################ End Test

ds1.callback = CustomJS(args=dict(source=tinyds1), code="""
    var data = source.data;
    var S = salt.value;
    var Prot = protein.value;
    var Phos = phosphorus.value;
    var K = vit_k.value;
    var Pot = pot.value;
    
    h = data['h']
    x = data['x']
    y = data['y']
    s = data['s']
    k = data['k']
    pot = data['pot']
    pro = data['pro']
    phos = data['phos']
    por = data['por']
    


for (i = 0; i < x.length; i++) {
        y[i] =  h[i]-((s[i]-100)*S)- ((pro[i]- 10)*Prot *3) - ((phos[i]- 100)*Phos *2) - ((k[i] - 200)* K *1 ) - ((pot[i] - 200)* Pot *1 );
        
        if (y[i] < 0) {
            y[i] = 0
            }   
        }
        
    
    source.trigger('change');
""")


salt_slider = Slider(start=0, end=1, value=0, step=.005, title="Salt", callback=ds1.callback)
ds1.callback.args["salt"] = salt_slider

protein_slider = Slider(start=0, end=1, value=0, step=.005, title="Protein", callback=ds1.callback)
ds1.callback.args["protein"] = protein_slider

phos_slider = Slider(start=0, end=1, value=0, step=.005, title="Phosphorus", callback=ds1.callback)
ds1.callback.args["phosphorus"] = phos_slider

k_slider = Slider(start=0, end=1, value=0, step=.005, title="Vitamin K", callback=ds1.callback)
ds1.callback.args["vit_k"] = k_slider

pot_slider = Slider(start=0, end=1, value=0, step=.005, title="Potassium", callback=ds1.callback)
ds1.callback.args["pot"] = pot_slider

hover = plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([('Food', '@n'),('Nutrition', '@y'),('Sodium', '@s'),('Phosphorus', '@phos'),('Calories', '@x'),('Protein', '@pro'),('Potassium', '@pot'),('Vitamin K', '@k'),('Quantity','@por')])


layout = row(
    plot,
    widgetbox(text_input, salt_slider,protein_slider, phos_slider, k_slider, pot_slider),
)


output_file("ffslidertext.html", title="Food Filters")


show(layout)









    



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-28-020f8f89bcbd> in <module>()
     71 
     72     source.trigger('change');
---> 73 """)
     74 
     75 #title.callback = CustomJS(update_title('value'))

C:\Program Files\Anaconda3\lib\site-packages\bokeh\model.py in __init__(self, **kwargs)
     83         self._id = kwargs.pop("id", make_id())
     84         self._document = None
---> 85         super(Model, self).__init__(**kwargs)
     86         default_theme.apply_to_model(self)
     87 

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in __init__(self, **properties)
    701 
    702         for name, value in properties.items():
--> 703             setattr(self, name, value)
    704 
    705     def __setattr__(self, name, value):

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in __setattr__(self, name, value)
    714 
    715         if name in props or name in deprecated:
--> 716             super(HasProps, self).__setattr__(name, value)
    717         else:
    718             matches, text = difflib.get_close_matches(name.lower(), props), "similar"

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in __set__(self, obj, value)
    453             raise RuntimeError("Cannot set a property value '%s' on a %s instance before HasProps.__init__" %
    454                                (self.name, obj.__class__.__name__))
--> 455         value = self.descriptor.prepare_value(obj.__class__, self.name, value)
    456 
    457         old = self.__get__(obj)

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in prepare_value(self, cls, name, value)
    270                     break
    271             else:
--> 272                 raise e
    273         else:
    274             value = self.transform(value)

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in prepare_value(self, cls, name, value)
    263     def prepare_value(self, cls, name, value):
    264         try:
--> 265             self.validate(value)
    266         except ValueError as e:
    267             for tp, converter in self.alternatives:

C:\Program Files\Anaconda3\lib\site-packages\bokeh\core\properties.py in validate(self, value)
   1128             if not (isinstance(value, dict) and \
   1129                     all(self.keys_type.is_valid(key) and self.values_type.is_valid(val) for key, val in iteritems(value))):
-> 1130                 raise ValueError("expected an element of %s, got %r" % (self, value))
   1131 
   1132     def __str__(self):

ValueError: expected an element of Dict(String, Instance(Model)), got {'source': 'my sine wave'}



In [ ]:

    
#df["Quantity"] = df["GmWt_1"].map(str) + " @ " + df["GmWt_Desc1"]

#dffin['Healthy'][dffin['Calories' > Maxgoodcal].map()



In [ ]:

    
df["Quantity"] = df["GmWt_1"].map(str)



In [ ]:

    
df.GmWt_Desc1.head()



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df.Quantity.head(25)



In [ ]:

    
dff['Phosphorus_mg']



In [ ]:

    
#print("rgb("+ str(random.randint(0,255)) + "," + str(random.randint(0,255)) + "," + str(random.randint(0,255)) + ")")
#df.columns
#df.Healthy
#df.head().totalcal
#for i in df["foodgroup"]:
  # df["grpnumbers"] = grphash[df["foodgroup"][i]]
    
#grpdict
#grpdict["Burger"]
#grpdict



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#len(dffin)



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x.length



In [ ]:



In [ ]:

    
#df.Shrt_Desc.head()
#df['foodgroup'] = df[df['Shrt_Desc'].str.contains("BUTTER|MILK")==TRUE
#df.foodgroup.head()

#converter = {}
#i=1
#for item in df["foodgroup"]:
#    if item not in converter:
        #converter[item] =i
#        converter[item]= "rgb("+ str(random.randint(0,255)) + "," + str(random.randint(0,255)) + "," + str(random.randint(0,255)) + ")"
#        i += 1
#df['foodgrpnum'] = [converter[i] for i in df["foodgroup"]]
#print("rgb("+ str(random.randint(0,255)) + "," + str(random.randint(0,255)) + "," + str(random.randint(0,255)) + ")")
#df.foodgrpnum



In [ ]:

    
#TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,tap,previewsave,box_select,poly_select,lasso_select"

#output_file("color_scatter.html", title="color_scatter.py example")

#p = figure(tools=TOOLS)
#p.scatter(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)

#x=dffin.Healthy.values.tolist()
#y = dffin.Calories.values.tolist()

#p.annulus(x, y, inner_radius=0, outer_radius=2,color="orange", alpha=0.6)

#p.annulus(x=dffin.Calories_rda.tolist(), y=dffin.Healthy, inner_radius=0.1, outer_radius=0.25, color="orange", alpha=0.6)
#p.scatter(x,y, radius= 1 , fill_alpha=0.6, line_color=None)
#show(p)



In [ ]:

    
# plot({
#    'data': [
#        Scatter(x=dffin['Calories_rda'],
#                y=dffin['Healthy'],
#                text=dffin.foodtext ,
##                #marker=Marker(size=df['calperserv'], sizemode='area'),   #, sizeref=131868,),
 #               marker = dict(size= dffin.Healthy ,
#                    line= dict(width=1),
#                    color= "rgb(178,16,243)" ,
#                    opacity= 0.8
#                   ),
#                #name= y[i],
#                #mode='markers'
##                mode="markers"
#               ) 
#    ],
#    'layout': Layout(title='Nutrition and Calorie Count',xaxis=XAxis(title='Calories per Serving'), yaxis=YAxis(title='Nutrition 1-10'))
#}, show_link=False)