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added publish script for PIP

tags/1.6.1
Trevor Irons 5 anos atrás
pai
commit
e715317d6b
5 arquivos alterados com 40 adições e 561 exclusões
  1. 14
    3
      README.md
  2. 4
    4
      akvo/gui/main.ui
  3. 3
    549
      akvo/tressel/decay.py
  4. 4
    0
      publish.sh
  5. 15
    5
      setup.py

+ 14
- 3
README.md Ver arquivo

@@ -1,5 +1,6 @@
1 1
 # Akvo 
2
- Akvo provides processing of surface NMR data. It aims to be simple to use yet flexible for accommodating changes to processing flow. Akvo is written primarily in Python 3 with a small amount of R as well. The application is written around a Qt GUI with plotting provided by Matplotlib.
2
+
3
+Akvo provides processing of surface NMR data. It aims to be simple to use yet flexible for accommodating changes to processing flow. Akvo is written primarily in Python 3 with a small amount of R as well. The application is written around a Qt GUI with plotting provided by Matplotlib.
3 4
 
4 5
 The bleeding-edge code may be accesed using the git client 
5 6
 ```
@@ -11,22 +12,32 @@ git clone https://github.com/LemmaSoftware/akvo.git
11 12
 ```
12 13
 
13 14
 ## Installation 
14
-Installation is straightforward. The only prerequisite that is sometimes not properly handled is PyQt5 which needs to be manually installed. 
15 15
 
16
+Installation is straightforward. The only prerequisite that is sometimes not properly handled is PyQt5 which sometimes needs to be manually installed. 
16 17
 ```
17 18
 python3 setup.py build 
18 19
 python3 setup.py install
19 20
 ```
20 21
 
22
+Alternatively, release versions can be installed via pip
23
+```
24
+pip install akvo
25
+```
26
+
27
+
21 28
 ## Team 
29
+
22 30
 Akvo is developed by several teams including the University of Utah.
23 31
 
24 32
 ## Capabilities 
33
+
25 34
 Akvo currently has preprocessing capabilities for VistaClara GMR data. 
26 35
 
27 36
 ## Benefits 
37
+
28 38
 Processing steps are retained and logged in the processed file header, which is written in YAML. 
29 39
 This allows data processing to be repeatible, which is a major benefit. 
30 40
 
31 41
 ## Languages
32
-Akvo is written primarily in Python 3, but has components written in R as well. The graphical user unterface is written in PyQt5.  An interface to modelling software written in C++ (Lemma and Merlin) is in development. 
42
+
43
+Akvo is written primarily in Python 3. The graphical user unterface is written in PyQt5.  An interface to modelling software written in C++ (Lemma and Merlin) is in development. 

+ 4
- 4
akvo/gui/main.ui Ver arquivo

@@ -2159,8 +2159,8 @@ background: dark grey;
2159 2159
               <rect>
2160 2160
                <x>0</x>
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                <y>0</y>
2162
-               <width>100</width>
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-               <height>30</height>
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+               <width>96</width>
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+               <height>26</height>
2164 2164
               </rect>
2165 2165
              </property>
2166 2166
              <attribute name="label">
@@ -2172,8 +2172,8 @@ background: dark grey;
2172 2172
               <rect>
2173 2173
                <x>0</x>
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                <y>0</y>
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-               <width>411</width>
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-               <height>77</height>
2175
+               <width>96</width>
2176
+               <height>26</height>
2177 2177
               </rect>
2178 2178
              </property>
2179 2179
              <attribute name="label">

+ 3
- 549
akvo/tressel/decay.py Ver arquivo

@@ -2,10 +2,10 @@ import numpy, array #,rpy2
2 2
 from matplotlib import pyplot as plt
3 3
 import numpy as np
4 4
 from scipy.optimize import least_squares
5
-from rpy2.robjects.packages import importr
6 5
 
7
-import rpy2.robjects as robjects
8
-import rpy2.robjects.numpy2ri
6
+#from rpy2.robjects.packages import importr
7
+#import rpy2.robjects as robjects
8
+#import rpy2.robjects.numpy2ri
9 9
 
10 10
 #import notch
11 11
 from numpy.fft import fft, fftfreq
@@ -52,215 +52,6 @@ def peakPicker(data, omega, dt):
52 52
         indices           
53 53
 
54 54
 
55
-#################################################
56
-# Regress for T2 using rpy2 interface
57
-def regressCurve(peaks,times,sigma2=1,intercept=True):
58
-
59
-    # TODO, if regression fails, it might be because there is no exponential
60
-    # term, maybe do a second regression then on a linear model. 
61
-    b1  = 0                  # Bias
62
-    b2  = 0                  # Linear 
63
-    rT2 = 0.3                # T2 regressed
64
-    r   = robjects.r         
65
-
66
-    # Variable shared between R and Python
67
-    robjects.globalenv['b1'] = b1
68
-    robjects.globalenv['b2'] = b2
69
-    robjects.globalenv['rT2'] = rT2
70
-    robjects.globalenv['sigma2'] = sigma2
71
-    value = robjects.FloatVector(peaks)
72
-    times = robjects.FloatVector(numpy.array(times))
73
-    
74
-#    my_weights = robjects.RVector(value/sigma2)
75
-#    robjects.globalenv['my_weigts'] = my_weights
76
-
77
-#    if sigma2 != 0:
78
-#        print "weighting"
79
-#        tw = numpy.array(peaks)/sigma2 
80
-#        my_weights = robjects.RVector( tw/numpy.max(tw) )
81
-#    else:
82
-#        my_weights = robjects.RVector(numpy.ones(len(peaks))) 
83
-
84
-#    robjects.globalenv['my_weights'] = my_weights
85
-    
86
-    if (intercept):
87
-        my_list = robjects.r('list(b1=50, b2=1e2, rT2=0.03)')
88
-        my_lower = robjects.r('list(b1=0, b2=0, rT2=.005)')
89
-        my_upper = robjects.r('list(b1=20000, b2=2000, rT2=.700)')
90
-    else:
91
-        my_list = robjects.r('list(b2=1e2, rT2=0.3)')
92
-        my_lower = robjects.r('list(b2=0, rT2=.005)')
93
-        my_upper = robjects.r('list(b2=2000, rT2=.700)')
94
-
95
-    my_cont = robjects.r('nls.control(maxiter=1000, warnOnly=TRUE, printEval=FALSE)')
96
-
97
-    
98
-    if (intercept):
99
-        #fmla = robjects.RFormula('value ~ b1 + exp(-times/rT2)')
100
-        fmla = robjects.Formula('value ~ b1 + b2*exp(-times/rT2)')
101
-        #fmla = robjects.RFormula('value ~ b1 + b2*times + exp(-times/rT2)')
102
-    else:
103
-        fmla = robjects.Formula('value ~ b2*exp(-times/rT2)')
104
-
105
-    env = fmla.getenvironment()
106
-    env['value'] = value
107
-    env['times'] = times
108
-    
109
-    # ugly, but I get errors with everything else I've tried
110
-    my_weights = robjects.r('rep(1,length(value))')
111
-    for ii in range(len(my_weights)):
112
-        my_weights[ii] *= peaks[ii]/sigma2
113
-    Error = False
114
-    #fit = robjects.r.nls(fmla,start=my_list,control=my_cont,weights=my_weights)
115
-    if (sigma2 != 1):
116
-        print("SIGMA 2")
117
-        #fit = robjects.r.tryCatch(robjects.r.suppressWarnings(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port", \
118
-        #                     weights=my_weights)), 'silent=TRUE')
119
-        fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont))#, \
120
-                            # weights=my_weights))
121
-    else:
122
-        try:
123
-            fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port"))#,lower=my_lower,upper=my_upper))
124
-        except:
125
-            print("regression issue pass")
126
-            Error = True
127
-    # If failure fall back on zero regression values   
128
-    if not Error:
129
-        #Error = fit[3][0]
130
-        report =  r.summary(fit)
131
-    b1 = 0
132
-    b2 = 0 
133
-    rT2 = 1
134
-    if (intercept):
135
-        if not Error:
136
-            b1  =  r['$'](report,'par')[0]
137
-            b2  =  r['$'](report,'par')[1]
138
-            rT2 =  r['$'](report,'par')[2]
139
-            #print  report
140
-            #print  r['$'](report,'convergence')
141
-            #print  r['convergence'] #(report,'convergence')
142
-            #print  r['$'](report,'par')[13]
143
-            #print  r['$'](report,'par')[14]
144
-        else:
145
-            print("ERROR DETECTED, regressed values set to default")
146
-            b1 = 1e1
147
-            b2 = 1e-2
148
-            rT2 = 1e-2
149
-            #print r['$'](report,'par')[0]
150
-            #print r['$'](report,'par')[1]
151
-            #print r['$'](report,'par')[2]
152
-        return [b1,b2,rT2] 
153
-    else:
154
-        if not Error:
155
-            rT2 =  r['$'](report,'par')[1]
156
-            b2  =  r['$'](report,'par')[0]
157
-        else:
158
-            print("ERROR DETECTED, regressed values set to default")
159
-        return [b2, rT2] 
160
-
161
-#################################################
162
-# Regress for T2 using rpy2 interface
163
-def regressCurve2(peaks,times,sigma2=[None],intercept=True):
164
-
165
-    if sigma2[0] != None:
166
-        my_weights = robjects.FloatVector( sigma2 )
167
-
168
-    # TODO, if regression fails, it might be because there is no exponential
169
-    # term, maybe do a second regression then on a linear model. 
170
-    b1  = 0                  # Bias
171
-    b2  = 0                  # Linear 
172
-    bb2  = 0                 # Linear 
173
-    rT2 = 0.3                # T2 regressed
174
-    rrT2 = 1.3               # T2 regressed
175
-    r   = robjects.r         
176
-
177
-    # Variable shared between R and Python
178
-    robjects.globalenv['b1'] = b1
179
-    robjects.globalenv['b2'] = b2
180
-    robjects.globalenv['rT2'] = rT2
181
-    
182
-    robjects.globalenv['bb2'] = b2
183
-    robjects.globalenv['rrT2'] = rT2
184
-    
185
-    #robjects.globalenv['sigma2'] = sigma2
186
-    value = robjects.FloatVector(peaks)
187
-    times = robjects.FloatVector(numpy.array(times))
188
-    
189
-    
190
-    if (intercept):
191
-        my_list = robjects.r('list(b1=.50, b2=1e2, rT2=0.03, bb2=1e1, rrT2=1.3)')
192
-        my_lower = robjects.r('list(b1=0, b2=0, rT2=.005, bb2=0, rrT2=.005 )')
193
-        my_upper = robjects.r('list(b1=2000, b2=2000, rT2=.700, bb2=2000, rrT2=1.3 )')
194
-    else:
195
-        my_list  = robjects.r('list(b2=.5, rT2=0.3,  bb2=.5, rrT2=1.3)')
196
-        my_lower = robjects.r('list(b2=0,  rT2=.005, bb2=0,  rrT2=.005)')
197
-        my_upper = robjects.r('list(b2=1,  rT2=2.6,    bb2=1,  rrT2=2.6)')
198
-
199
-    my_cont = robjects.r('nls.control(maxiter=1000, warnOnly=TRUE, printEval=FALSE)')
200
-
201
-    
202
-    if (intercept):
203
-        #fmla = robjects.RFormula('value ~ b1 + exp(-times/rT2)')
204
-        fmla = robjects.Formula('value ~ b1 + b2*exp(-times/rT2) + bb2*exp(-times/rrT2)')
205
-        #fmla = robjects.RFormula('value ~ b1 + b2*times + exp(-times/rT2)')
206
-    else:
207
-        fmla = robjects.Formula('value ~ b2*exp(-times/rT2) + bb2*exp(-times/rrT2)')
208
-
209
-    env = fmla.getenvironment()
210
-    env['value'] = value
211
-    env['times'] = times
212
-    
213
-    # ugly, but I get errors with everything else I've tried
214
-    Error = False
215
-    #fit = robjects.r.nls(fmla,start=my_list,control=my_cont,weights=my_weights)
216
-    if (sigma2[0] != None):
217
-        #print("SIGMA 2")
218
-        #fit = robjects.r.tryCatch(robjects.r.suppressWarnings(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port", \
219
-        #                     weights=my_weights)), 'silent=TRUE')
220
-        fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm='port',weights=my_weights,lower=my_lower,upper=my_upper))#, \
221
-                            # weights=my_weights))
222
-    else:
223
-        try:
224
-            fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port"))#,lower=my_lower,upper=my_upper))
225
-        except:
226
-            print("regression issue pass")
227
-            Error = True
228
-    # If failure fall back on zero regression values   
229
-    if not Error:
230
-        #Error = fit[3][0]
231
-        report =  r.summary(fit)
232
-    b1 = 0
233
-    b2 = 0 
234
-    rT2 = 1
235
-    if (intercept):
236
-        if not Error:
237
-            b1  =  r['$'](report,'par')[0]
238
-            b2  =  r['$'](report,'par')[1]
239
-            rT2 =  r['$'](report,'par')[2]
240
-            #print  report
241
-            #print  r['$'](report,'convergence')
242
-            #print  r['convergence'] #(report,'convergence')
243
-            #print  r['$'](report,'par')[13]
244
-            #print  r['$'](report,'par')[14]
245
-        else:
246
-            print("ERROR DETECTED, regressed values set to default")
247
-            b1 = 1e1
248
-            b2 = 1e-2
249
-            rT2 = 1e-2
250
-            #print r['$'](report,'par')[0]
251
-            #print r['$'](report,'par')[1]
252
-            #print r['$'](report,'par')[2]
253
-        return [b1,b2,rT2, bb2, rrT2] 
254
-    else:
255
-        if not Error:
256
-            rT2 =  r['$'](report,'par')[1]
257
-            b2  =  r['$'](report,'par')[0]
258
-            rrT2 =  r['$'](report,'par')[3]
259
-            bb2  =  r['$'](report,'par')[2]
260
-        else:
261
-            print("ERROR DETECTED, regressed values set to default")
262
-        return [b2, rT2, bb2, rrT2] 
263
-
264 55
 def fun(x, t, y):
265 56
     """ Cost function for regression, single exponential, no DC term 
266 57
         x[0] = A0
@@ -322,343 +113,6 @@ def quadratureDetect2(X, Y, tt, x0="None"):
322 113
     #return conv, E0,df,phi,T2
323 114
     #return res_lsq.success, x[0], 0, x[1], x[2]
324 115
 
325
-def quadratureDetect(X, Y, tt, CorrectFreq=False, BiExp=False, CorrectDC=False):
326
- 
327
-    r   = robjects.r        
328
-
329
-    if CorrectDC:
330
-        robjects.r(''' 
331
-             Xc1 <- function(E01, df, tt, phi, T2_1, DC) {
332
-	                DC + E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1)
333
-            }
334
-    
335
-            Yc1 <- function(E01, df, tt, phi, T2_1, DC) {
336
-	                DC - E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1)
337
-            } 
338
-            ''')
339
-    else:   
340
-        robjects.r(''' 
341
-             Xc1 <- function(E01, df, tt, phi, T2_1) {
342
-	                E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1)
343
-            }
344
-    
345
-            Yc1 <- function(E01, df, tt, phi, T2_1) {
346
-	                -E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1)
347
-            } 
348
-            ''')
349
-
350
-    # bi-exponential 
351
-    if CorrectDC:
352
-        robjects.r(''' 
353
-             Xc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2, DC) {
354
-	               DC + E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
355
-	                DC + E02*cos(2*pi*df*tt + phi) * exp(-tt/T2_2)
356
-            }
357
-
358
-            Yc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2, DC) {
359
-	                DC - E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
360
-	                DC - E02*sin(2*pi*df*tt + phi) * exp(-tt/T2_2)
361
-            } 
362
-            ''')
363
-    else:   
364
-        robjects.r(''' 
365
-             Xc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2) {
366
-	               E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
367
-	               E02*cos(2*pi*df*tt + phi) * exp(-tt/T2_2)
368
-            }
369
-
370
-            Yc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2) {
371
-	                -E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
372
-	                -E02*sin(2*pi*df*tt + phi) * exp(-tt/T2_2)
373
-            } 
374
-            ''')
375
-
376
-    # Make 0 vector 
377
-    Zero = robjects.FloatVector(numpy.zeros(len(X)))
378
-    
379
-    # Fitted Parameters
380
-    E01 = 0.
381
-    E02 = 0.
382
-    df = 0.
383
-    phi = 0.
384
-    T2_1 = 0.
385
-    T2_2 = 0.
386
-    DC = 0.
387
-    robjects.globalenv['DC'] = DC
388
-    robjects.globalenv['E01'] = E01
389
-    robjects.globalenv['E02'] = E02
390
-    robjects.globalenv['df'] = df
391
-    robjects.globalenv['phi'] = phi
392
-    robjects.globalenv['T2_1'] = T2_1
393
-    robjects.globalenv['T2_2'] = T2_2
394
-    XY = robjects.FloatVector(numpy.concatenate((X,Y)))
395
-    
396
-    # Arrays
397
-    tt = robjects.FloatVector(numpy.array(tt))
398
-    X = robjects.FloatVector(numpy.array(X))
399
-    Y = robjects.FloatVector(numpy.array(Y))
400
-    Zero = robjects.FloatVector(numpy.array(Zero))
401
-
402
-    
403
-
404
-    if BiExp:
405
-        if CorrectDC:
406
-            fmla = robjects.Formula('XY ~ c(Xc2( E01, E02, df, tt, phi, T2_1, T2_2, DC ), Yc2( E01, E02, df, tt, phi, T2_1, T2_2, DC ))')
407
-            if CorrectFreq:    
408
-                start = robjects.r('list(E01=.100, E02=.01,   df=0,    phi=0.    ,  T2_1=.100, T2_2=.01, DC=0.0)')
409
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  df=-50,  phi=-3.14 ,  T2_1=.001, T2_2=.001, DC=0.0)')
410
-                upper = robjects.r('list(E01=1.00, E02=1.0,   df=50,   phi=3.14  ,  T2_1=.800, T2_2=.8, DC=0.5)')
411
-            else:
412
-                start = robjects.r('list(E01=.100, E02=.01,   phi=0.9   ,  T2_1=.100, T2_2=.01,  DC=0.0)')
413
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  phi=-3.14 ,  T2_1=.001, T2_2=.001, DC=0.0)')
414
-                upper = robjects.r('list(E01=1.00, E02=1.0,   phi=3.14  ,  T2_1=.800, T2_2=.8,   DC=0.5)')
415
-        else:
416
-            fmla = robjects.Formula('XY ~ c(Xc2( E01, E02, df, tt, phi, T2_1, T2_2 ), Yc2( E01, E02, df, tt, phi, T2_1, T2_2))')
417
-            if CorrectFreq:    
418
-                start = robjects.r('list(E01=.100, E02=.01,   df=0,    phi=0.    ,  T2_1=.100, T2_2=.01)')
419
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  df=-50,  phi=-3.14 ,  T2_1=.001, T2_2=.001)')
420
-                upper = robjects.r('list(E01=1.00, E02=1.0,   df=50,   phi=3.14  ,  T2_1=.800, T2_2=.8)')
421
-            else:
422
-                start = robjects.r('list(E01=.100, E02=.01,   phi=0.9   ,  T2_1=.100, T2_2=.01)')
423
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  phi=-3.14 ,  T2_1=.001, T2_2=.001)')
424
-                upper = robjects.r('list(E01=1.00, E02=1.0,   phi=3.14  ,  T2_1=.800, T2_2=.8)')
425
-    else: 
426
-        if CorrectDC:
427
-            fmla = robjects.Formula('XY ~ c(Xc1( E01, df, tt, phi, T2_1, DC), Yc1( E01, df, tt, phi, T2_1,DC))')
428
-            if CorrectFreq:    
429
-                start = robjects.r('list(E01=.100, df=0   , phi=0.   , T2_1=.100, DC=0.0)')
430
-                lower = robjects.r('list(E01=1e-6, df=-50., phi=-3.14, T2_1=.001, DC=0.0)')
431
-                upper = robjects.r('list(E01=1.00, df=50. , phi=3.14 , T2_1=.800, DC=0.5)')
432
-            else:
433
-                start = robjects.r('list(E01=.100, phi= 0.  , T2_1=.100, DC=0.0)')
434
-                lower = robjects.r('list(E01=1e-6, phi=-3.13, T2_1=.001, DC=0.0)')
435
-                upper = robjects.r('list(E01=1.00, phi= 3.13, T2_1=.800, DC=0.5)')
436
-        else:
437
-            fmla = robjects.Formula('XY ~ c(Xc1( E01, df, tt, phi, T2_1), Yc1( E01, df, tt, phi, T2_1))')
438
-            if CorrectFreq:    
439
-                start = robjects.r('list(E01=.100, df=0     , phi=0.   ,  T2_1=.100)')
440
-                lower = robjects.r('list(E01=1e-6, df=-50. , phi=-3.14 ,  T2_1=.001)')
441
-                upper = robjects.r('list(E01=1.00, df=50.  , phi=3.14  ,  T2_1=.800)')
442
-            else:
443
-                start = robjects.r('list(E01=.100, phi= 0.  , T2_1=.100)')
444
-                lower = robjects.r('list(E01=1e-6, phi=-3.13, T2_1=.001)')
445
-                upper = robjects.r('list(E01=1.00, phi= 3.13, T2_1=.800)')
446
-
447
-    env = fmla.getenvironment()
448
-    env['Zero'] = Zero
449
-    env['X'] = X
450
-    env['Y'] = Y
451
-    env['XY'] = XY 
452
-    env['tt'] = tt
453
-
454
-    cont = robjects.r('nls.control(maxiter=10000, warnOnly=TRUE, printEval=FALSE)')
455
-    
456
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=start, control=cont, lower=lower, upper=upper, algorithm='port')) #, \
457
-    #fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=start, control=cont)) #, \
458
-    report =  r.summary(fit)
459
-
460
-    conv = r['$'](fit,'convergence')[0]
461
-    #if conv:
462
-    #    print (report)
463
-    #    print ("conv", conv)
464
-    print ("Conv",  r['$'](fit,'convergence'))  # T2
465
-    print (report)
466
-    
467
-    if BiExp:
468
-        if CorrectFreq:    
469
-            E0   =  r['$'](report,'par')[0]   # E01
470
-            E0  +=  r['$'](report,'par')[1]   # E02
471
-            df  =  r['$'](report,'par')[2]   # offset
472
-            phi =  r['$'](report,'par')[3]   # phase 
473
-            T2  =  r['$'](report,'par')[4]   # T2
474
-        else:
475
-            E0   =  r['$'](report,'par')[0]   # E01
476
-            E0  +=  r['$'](report,'par')[1]   # E02
477
-            phi =  r['$'](report,'par')[2]   # phase 
478
-            T2  =  r['$'](report,'par')[3]   # T2
479
-    else:
480
-        if CorrectFreq:    
481
-            E0   =  r['$'](report,'par')[0]   # E01
482
-            df  =  r['$'](report,'par')[1]   # offset
483
-            phi =  r['$'](report,'par')[2]   # phase 
484
-            T2  =  r['$'](report,'par')[3]   # T2
485
-        else:
486
-            E0   =  r['$'](report,'par')[0]   # E01
487
-            phi =  r['$'](report,'par')[1]   # phase 
488
-            T2  =  r['$'](report,'par')[2]   # T2
489
-    #phi = 0.907655876627
490
-    #phi = 0
491
-    #print ("df", df)# = 0
492
-    return conv, E0,df,phi,T2
493
-    
494
-
495
-#################################################
496
-# Regress for T2 using rpy2 interface
497
-def regressSpec(w, wL, X): #,sigma2=1,intercept=True):
498
-
499
-    # compute s
500
-    s = -1j*w
501
-
502
-    # TODO, if regression fails, it might be because there is no exponential
503
-    # term, maybe do a second regression then on a linear model. 
504
-    a   = 0                  # Linear 
505
-    rT2 = 0.1                # T2 regressed
506
-    r   = robjects.r         
507
-
508
-    # Variable shared between R and Python
509
-    robjects.globalenv['a'] = a
510
-    robjects.globalenv['rT2'] = rT2
511
-    robjects.globalenv['wL'] = wL
512
-    robjects.globalenv['nb'] = 0
513
-
514
-    s = robjects.ComplexVector(numpy.array(s))
515
-    XX = robjects.ComplexVector(X)
516
-    Xr = robjects.FloatVector(numpy.real(X))
517
-    Xi = robjects.FloatVector(numpy.imag(X))
518
-    Xa = robjects.FloatVector(numpy.abs(X))
519
-    Xri = robjects.FloatVector(numpy.concatenate((Xr,Xi)))
520
-    
521
-    #my_lower = robjects.r('list(a=.001, rT2=.001, nb=.0001)')
522
-    my_lower = robjects.r('list(a=.001, rT2=.001)')
523
-    #my_upper = robjects.r('list(a=1.5, rT2=.300, nb =100.)')
524
-    my_upper = robjects.r('list(a=1.5, rT2=.300)')
525
-     
526
-    #my_list = robjects.r('list(a=.2, rT2=0.03, nb=.1)')
527
-    my_list = robjects.r('list(a=.2, rT2=0.03)')
528
-    my_cont = robjects.r('nls.control(maxiter=5000, warnOnly=TRUE, printEval=FALSE)')
529
-    
530
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
531
-    ##fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
532
-    #fmla = robjects.Formula('XX ~ a*(wL) / (wL^2 + (s+1/rT2)^2 )') # complex
533
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 )) + nb') # complex
534
-    fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 ))') # complex
535
- 
536
-    env = fmla.getenvironment()
537
-    env['s'] = s
538
-    env['Xr'] = Xr
539
-    env['Xa'] = Xa
540
-    env['Xi'] = Xi
541
-    env['Xri'] = Xri
542
-    env['XX'] = XX
543
-     
544
-    #fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list, control=my_cont)) #, lower=my_lower, algorithm='port')) #, \
545
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
546
-    report =  r.summary(fit)
547
-    #print report 
548
-    #print  r.warnings()
549
- 
550
-    a  =  r['$'](report,'par')[0]
551
-    rT2 =  r['$'](report,'par')[1]
552
-    nb =  r['$'](report,'par')[2]
553
-    
554
-    return a, rT2, nb
555
-
556
-#################################################
557
-# Regress for T2 using rpy2 interface
558
-def regressSpecComplex(w, wL, X): #,sigma2=1,intercept=True):
559
-
560
-    # compute s
561
-    s = -1j*w
562
-
563
-    # TODO, if regression fails, it might be because there is no exponential
564
-    # term, maybe do a second regression then on a linear model. 
565
-    a   = 1                  # Linear 
566
-    rT2 = 0.1                # T2 regressed
567
-    r   = robjects.r         
568
-    phi2 = 0                 # phase
569
-    wL2 = wL
570
-
571
-    # Variable shared between R and Python
572
-    robjects.globalenv['a'] = a
573
-    robjects.globalenv['rT2'] = rT2
574
-    robjects.globalenv['wL'] = wL
575
-    robjects.globalenv['wL2'] = 0
576
-    robjects.globalenv['nb'] = 0
577
-    robjects.globalenv['phi2'] = phi2
578
-
579
-    s = robjects.ComplexVector(numpy.array(s))
580
-    XX = robjects.ComplexVector(X)
581
-    Xr = robjects.FloatVector(numpy.real(X))
582
-    Xi = robjects.FloatVector(numpy.imag(X))
583
-    Xa = robjects.FloatVector(numpy.abs(X))
584
-    Xri = robjects.FloatVector(numpy.concatenate((X.real,X.imag)))
585
-
586
-    robjects.r(''' 
587
-        source('kernel.r')
588
-    ''')   
589
-    #Kw = robjects.globalenv['Kwri']
590
-     
591
-    #print (numpy.shape(X))
592
-    
593
-    #my_lower = robjects.r('list(a=.001, rT2=.001, nb=.0001)')
594
-    #my_lower = robjects.r('list(a=.001, rT2=.001)') # Working
595
-    my_lower = robjects.r('list(a=.001, rT2=.001, phi2=-3.14, wL2=wL-5)')
596
-    #my_upper = robjects.r('list(a=1.5, rT2=.300, nb =100.)')
597
-    my_upper = robjects.r('list(a=3.5, rT2=.300, phi2=3.14, wL2=wL+5)')
598
-     
599
-    #my_list = robjects.r('list(a=.2, rT2=0.03, nb=.1)')
600
-    my_list = robjects.r('list(a=.2, rT2=0.03, phi2=0, wL2=wL)')
601
-    my_cont = robjects.r('nls.control(maxiter=5000, warnOnly=TRUE, printEval=FALSE)')
602
-    
603
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
604
-    #fmla = robjects.Formula('Xi   ~   Im(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 ))') # envelope
605
-    #fmla = robjects.Formula('Xri ~ c(Re(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )), Im(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )))') # envelope
606
-    
607
-    #fmlar = robjects.Formula('Xr ~ (Kwr(a, phi2, s, rT2, wL)) ') # envelope
608
-    #fmlai = robjects.Formula('Xi ~ (Kwi(a, phi2, s, rT2, wL)) ') # envelope
609
-    fmla = robjects.Formula('Xri ~ c(Kwr(a, phi2, s, rT2, wL2), Kwi(a, phi2, s, rT2, wL2) ) ') # envelope
610
-    #fmla = robjects.Formula('Xri ~ (Kwri(a, phi2, s, rT2, wL)) ') # envelope
611
-    
612
-    #fmla = robjects.Formula('Xa ~ (abs(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )))') # envelope
613
-    #fmla = robjects.Formula('XX ~ a*(wL) / (wL^2 + (s+1/rT2)^2 )') # complex
614
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 )) + nb') # complex
615
-    
616
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
617
-    
618
-    #        self.Gw[iw, iT2] = ((np.sin(phi2) *  (alpha + 1j*self.w[iw]) + self.wL*np.cos(phi2)) / \
619
-    #                               (self.wL**2 + (alpha+1.j*self.w[iw])**2 ))
620
-    #        self.Gw[iw, iT2] = ds * self.sc*((np.sin(phi2)*( alpha + 1j*self.w[iw]) + self.wL*np.cos(phi2)) / \
621
-    #                               (self.wL**2 + (alpha+1.j*self.w[iw])**2 ))
622
-    
623
-    # Works Amplitude Only!
624
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 ))') # complex
625
- 
626
-    env = fmla.getenvironment()
627
-    env['s'] = s
628
-    env['Xr'] = Xr
629
-    env['Xa'] = Xa
630
-    env['Xi'] = Xi
631
-    env['Xri'] = Xri
632
-    env['XX'] = XX
633
-     
634
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list, control=my_cont)) #, lower=my_lower, algorithm='port')) #, \
635
-    #fitr = robjects.r.tryCatch(robjects.r.nls(fmlar, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
636
-    
637
-    #env = fmlai.getenvironment()
638
-    #fiti = robjects.r.tryCatch(robjects.r.nls(fmlai, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
639
-    
640
-    #reportr =  r.summary(fitr)
641
-    #reporti =  r.summary(fiti)
642
-    report =  r.summary(fit)
643
-    #print( report )
644
-    #exit()
645
-    #print( reportr )
646
-    #print( reporti  )
647
-    #exit()
648
-    #print  r.warnings()
649
- 
650
-    #a   =  (r['$'](reportr,'par')[0] + r['$'](reporti,'par')[0]) / 2.
651
-    #rT2 =  (r['$'](reportr,'par')[1] + r['$'](reporti,'par')[1]) / 2.
652
-    #nb  =  (r['$'](reportr,'par')[2] + r['$'](reporti,'par')[2]) / 2.
653
-    a   =  r['$'](report,'par')[0] 
654
-    rT2 =  r['$'](report,'par')[1] 
655
-    nb  =  r['$'](report,'par')[2] #phi2 
656
-
657
-    print ("Python wL2", r['$'](report,'par')[3] )   
658
-    print ("Python zeta", r['$'](report,'par')[2] )   
659
- 
660
-    return a, rT2, nb
661
-
662 116
 
663 117
 
664 118
 ###################################################################

+ 4
- 0
publish.sh Ver arquivo

@@ -0,0 +1,4 @@
1
+rm -rf dist
2
+python setup.py build
3
+python setup.py sdist bdist_wheel
4
+twine upload dist/*

+ 15
- 5
setup.py Ver arquivo

@@ -16,12 +16,17 @@ class custom_build_py(build_py):
16 16
         self.run_command('build_ui')
17 17
         build_py.run(self)
18 18
 
19
+
20
+with open("README.md", "r") as fh:
21
+    long_description = fh.read()
22
+
19 23
 setup(name='Akvo',
20
-      version='1.0.7',
24
+      version='1.0.12',
21 25
       description='Surface nuclear magnetic resonance workbench',
26
+      long_description=long_description,
22 27
       author='Trevor P. Irons',
23 28
       author_email='Trevor.Irons@lemmasoftware.org',
24
-      url='https://svn.lemmasofware.org/akvo',
29
+      url='https://akvo.lemmasoftware.org/',
25 30
       #setup_requires=['PyQt5'],
26 31
       setup_requires=[
27 32
         # Setuptools 18.0 properly handles Cython extensions.
@@ -33,12 +38,12 @@ setup(name='Akvo',
33 38
 #      ext_modules = cythonise("akvo/tressel/*.pyx"), 
34 39
 #      build_requires=['cython'],
35 40
       install_requires=[
36
-#          'cython',
37
-          'rpy2',
41
+#         'cython',
42
+#          'rpy2',
38 43
           'matplotlib',
39 44
           'scipy',
40 45
           'numpy',
41
-          'PyQt5',
46
+          'pyqt5',
42 47
           'pyyaml',
43 48
           'pandas',
44 49
           'pyqt-distutils',
@@ -62,6 +67,11 @@ setup(name='Akvo',
62 67
       package_data={
63 68
         'akvo.gui': ['*.png']  #All .r files 
64 69
       },
70
+      classifiers=[
71
+        "Programming Language :: Python :: 3",
72
+        "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
73
+        "Operating System :: OS Independent",
74
+      ],
65 75
     )
66 76
 
67 77
 

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