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" "Equation:
1/("{{=round(cf[0][0],2)}}"+"{{=round(cf[0][1],2)}}"*EXP("{{=round(cf[0][2],2)}}"*x))"
</li>
{{pass}}
Thanks all
--
Resources:
- http://web2py.com
- http://web2py.com/book (Documentation)
- http://github.com/web2py/web2py (Source code)
- https://code.google.com/p/web2py/issues/list (Report Issues)
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salinity,N2,chla
-0.8,0.6,7.9
-0.38,0.37,4.1
-0.23,-0.01,3.2
-0.27,-0.09,5
-0.13,-0.14,2.6
0.03,-0.13,2.2
0.11,0.07,1.5
0.1,-0.06,1
0.15,-0.08,0.7
0.29,-0.08,0.7
0.16,-0.09,0.9
0.07,-0.02,3.5
0.05,-0.05,1.9
0,-0.17,2.4
-0.49,0.44,7.8
-0.17,-0.13,3.7
-0.33,-0.14,3.7
-0.58,0.62,9.1
-0.17,-0.09,3.1
-0.07,-0.14,3.1
-0.5,0.09,6.2
-0.09,-0.12,3.8
-0.12,-0.07,2.5
-0.29,0.01,5.2
-0.12,-0.08,3
-0.1,-0.11,3.9
-0.11,-0.14,5.6
-0.14,-0.14,8.1
-0.49,0.32,6.6
-0.11,-0.19,5.3
-0.48,0.18,8.9
-0.28,-0.17,3.4
-0.16,-0.22,2.5
-0.13,-0.18,2.7
-0.03,-0.18,2.8
-0.17,-0.02,3.4
0.02,-0.12,3.6
-0.19,0.56,8.6
-0.04,0.09,1.9
-0.03,0.12,2.4
-0.07,0.03,2
-0.27,0.3,7.1
-0.27,0.09,2
0.04,-0.08,1.8
0.04,0.02,1.4
-0.1,0.1,4.8
-0.3,-0.08,2.9
-0.39,0.01,5.7
0.09,-0.03,1.8
-0.03,0.18,3.2
-0.11,-0.05,2.6
-0.23,-0.15,1.8
-0.23,-0.18,1.4
0.22,-0.21,1.5
-0.21,-0.2,1.6
0.26,-0.2,1.4
-0.23,-0.14,1.4
-0.41,0.36,3.6
-0.66,0.42,4.1
-0.3,0.05,1.5
-0.36,0.09,2.2
-1.38,0.43,7.8
-0.14,0.41,1.6
-1.02,0.39,5.9
-0.19,0.26,1.9
-0.58,0.66,3.2
0.16,-0.11,3.6
0.26,-0.14,2.6
0.14,-0.12,1.9
0.28,-0.19,2.3
0.38,-0.16,1.3
0.34,-0.18,1.6
0.25,-0.19,2.2
0.36,-0.17,1
0.36,-0.13,0.6
0.41,-0.19,1.1
0.27,-0.04,1.2
0.32,-0.14,0.7
0.26,-0.07,0.8
-0.26,1.31,3.5
0.21,-0.08,1
0.33,-0.16,0.9
0.08,0.14,2.2
0.15,-0.07,0.4
0.16,-0.08,0.9
0.15,-0.03,1
-0.01,0.36,4.5
0.14,0.08,1.7
0.08,0.03,0.9
-0.07,0.3,0.5
-0.24,0.02,2.9
0.1,0.21,1.1
-0.05,0.34,2.5
-0.35,0.46,1.5
0.02,0.22,1.5
-0.08,0.35,0.7
-0.03,0.2,2.3
-0.31,1.05,2.1
-0.05,0.57,3
-0.03,0.73,4.5
-0.01,0.18,8.4
-0.1,0.39,12.2
0.55,-0.15,3.1
0.57,-0.17,1.2
0.61,-0.19,1.4
0.52,-0.16,1.2
0.62,-0.15,0.8
0.54,-0.19,1.3
0.51,-0.21,1.7
0.59,-0.2,1.4
0.61,-0.19,1.7
0.46,-0.22,1
0.17,0,2.5
0.14,0.11,2.5
0.51,-0.1,1.4
0.5,-0.08,1.3
0.53,-0.1,0.8
-0.18,0.13,1.6
-0.11,0.01,1.5
-0.3,0.13,3
0.06,-0.16,1.7
-0.23,0.17,3.3
0.15,-0.19,2.2
0.06,-0.06,4.3
0.12,-0.15,3
0.19,-0.22,2
0.21,-0.19,1.9
0.18,-0.22,1.7
0.17,-0.22,2.1
0.14,-0.23,1.8
0.11,-0.23,1.9
0.02,-0.07,1.1
0.26,-0.22,2
0.22,-0.22,2.1
0.21,-0.23,1.7
-0.8,0.49,6.5
-0.33,0.02,2.9
-0.22,-0.13,3
-0.19,0.15,2.6
-0.19,-0.09,2
-0.13,-0.11,1.8
0.04,-0.18,4
-0.02,-0.14,1.9
-0.12,-0.2,1.6
-0.15,-0.2,1.4
-0.31,0.21,3.6
-0.2,-0.14,1
-0.14,-0.19,1.4
-0.28,-0.04,1.6
-0.14,-0.16,1
-0.08,-0.15,0.8
-0.41,0.37,4.5
-0.17,-0.11,1.1
-0.11,-0.16,0.9
-0.06,-0.17,1.2
-0.23,-0.12,2.6
-0.12,-0.12,1.2
-0.07,-0.13,1.2
-0.12,-0.12,2
-0.21,-0.11,1.7
-0.16,-0.13,1
-1.24,1.77,13.7
-0.64,0.58,5.1
-0.06,-0.14,2.6
-0.28,0,2
0.32,-0.16,1
0.29,-0.12,1.4
0.28,-0.19,1.2
0.21,-0.23,1.3
0.27,-0.23,1.6
0.24,-0.19,2.2
0.31,-0.17,0.5
0.36,-0.16,0.7
0.34,-0.14,0.4
0.15,-0.19,1.6
0.1,-0.16,1.2
0.03,0.12,1.5
0.08,-0.11,0.6
0.06,-0.13,0.9
0.01,0.04,1.2
0.05,0,0.7
0,0.12,1.1
0.28,-0.2,2.2
0.12,0.04,2.3
-0.02,0.14,1.5
0.08,0.26,0.8
0.14,0.09,0.8
0.1,0.27,0.9
0.11,0.02,0.6
0.13,0.16,0.5
0,0.33,0.9
0.19,0.02,0.3
0.09,0.22,0.7
0.19,0.08,0.3
0.16,0.07,0.6
0.17,0.3,1
0.19,0.01,0.4
0.13,0.03,0.9
0.25,-0.05,0.7
0.23,-0.09,0.6
0.28,-0.06,0.3
0.28,-0.1,0.5
0.33,-0.13,0.9
-0.19,-0.15,3.5
-0.08,-0.17,2.5
-0.16,-0.14,5.2
-0.04,-0.17,2.1
-0.18,-0.24,2.4
-0.33,-0.16,5.2
-0.38,-0.14,5.4
-0.21,-0.18,2.5
-0.33,0.03,3.9
-0.19,-0.17,2.2
-0.19,-0.18,3.5
-0.1,-0.07,2.7
-0.03,-0.09,1
-0.14,-0.23,2.4
-0.07,-0.04,1.1
-0.01,-0.08,0.5
-0.01,-0.02,0.7
-0.07,0.06,0.9
-0.07,0.09,1.2
0.15,-0.12,1.5
0.19,-0.12,0.9
0.16,-0.11,2.4
0.04,-0.15,1.7
0.21,-0.1,0.8
0.22,-0.04,1.2
-0.05,-0.06,2.7
0.35,-0.09,0.6
0.23,-0.11,1.3
0.06,-0.06,2.4
0.37,-0.09,0.7
0.2,-0.07,1.2
0.4,-0.1,0.6
-0.1,-0.07,2.3
