how to upload csv data and then use the data for calculation

[wangfen...@gmail.com]

I am a nood in coding and web development. I want to use web2py to establish a small APP for my research. But I have some problems to import the csv data and then use the data for calculation (or model establishment). I search for some examples in manual book, web2pyslices.com and some other websites, but still could not solve it.

Here is my controllers and views for my app

#controller data.py (import csv data which contains 3 columns salinity, N2 and chla)

def data(): 
    import numpy as np
    import pandas as pd
    dat = pd.read_csv('C:\Users\wangfeng\Desktop\TEST.csv')
    sal = dat['salinity']
    N2 = dat['N2']
    chl = dat['chla']
    return dict(sal=sal, N2=N2, chl=chl)

# controller csv.py (creat a form to upload data, this code is copied from some other websites)

def import_table():

    form = SQLFORM.factory(Field('table',requires=IS_IN_SET(db.tables)),

                           Field('csvfile','upload',uploadfield=False))

    form.process()

    if form.accepted:

        try:

            db[form.vars.table].import_from_csv_file(request.vars.csvfile.file)

        except: form.errors.csvfile = 'invalid file'

    return dict(form = form)

#data/data.html (for calculation of my research)

#show the data

<h1>这是 This is raw data</h1>

<h5>sal={{=sal}}</h5>

<h5>N2={{=N2}}</h5>

<h5>chl={{=chl}}</h5>

{{import numpy as np}}

{{from scipy.optimize import leastsq}}

#calculation

#data standization

{{s_sal=((chl-np.mean(sal))/np.std(sal))}}

{{s_N2=((chl-np.mean(N2))/np.std(N2))}}

#model fitting

{{def func1(t,x,y):}}

    {{return 1/(t[0]+t[1]*np.exp(t[2]*x))-y}}

{{t0=np.array([1.0,1.0,1.0])}}

{{for i in np.arange(0,2.0,0.05):}}

    {{index = i*s_sal + (2-i)*s_N2}}

    {{cf = leastsq(func1,t0,args=(index,chl))}}

#show the results

<li>

"Model: "{{=i}}"*salinity+"{{=2-i}}"*N2" &nbsp;&nbsp; "Equation: 1/("{{=round(cf[0][0],2)}}"+"{{=round(cf[0][1],2)}}"*EXP("{{=round(cf[0][2],2)}}"*x))"

</li>

{{pass}}

Thanks all