Changeset 841753c in sasmodels


Ignore:
Timestamp:
Jan 28, 2016 3:42:34 PM (8 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
master, core_shell_microgels, costrafo411, magnetic_model, release_v0.94, release_v0.95, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
d4666ca
Parents:
69ec80f
Message:

delint

Files:
6 edited

Legend:

Unmodified
Added
Removed

  • extra/pylint.rc

    r37a7252 r841753c  
    6464    star-args, 
    6565    unbalanced-tuple-unpacking, 
     66    locally-enabled, 
    6667    locally-disabled, 
    6768 
    … …  
    107108good-names=_, 
    108109    input, 
    109     i,j,k,n,x,y,z, 
     110    h,i,j,k,n,w,x,y,z, 
    110111    q,qx,qy,qz, 
    111112    dt,dx,dy,dz,id, 
    112     Iq,dIq,Qx,Qy,Qz, 
     113    Iq,dIq,Qx,Qy,Qz,Iqxy, 
    113114    p, 
    114115    ER, call_ER, VR, call_VR, 
     116    Rmax, SElength, 
    115117 
    116118# Bad variable names which should always be refused, separated by a comma 
    … …  
    335337 
    336338# Maximum number of arguments for function / method 
    337 max-args=5 
     339max-args=15 
    338340 
    339341# Argument names that match this expression will be ignored. Default to name 
    … …  
    342344 
    343345# Maximum number of locals for function / method body 
    344 max-locals=15 
     346max-locals=25 
    345347 
    346348# Maximum number of return / yield for function / method body 

  • sasmodels/compare.py

    r4f2478e r841753c  
    533533    if Nbase > 0: 
    534534        if Ncomp > 0: plt.subplot(131) 
    535         plot_theory(data, base_value, view=view, plot_data=False, limits=limits) 
     535        plot_theory(data, base_value, view=view, use_data=False, limits=limits) 
    536536        plt.title("%s t=%.1f ms"%(base.engine, base_time)) 
    537537        #cbar_title = "log I" 
    538538    if Ncomp > 0: 
    539539        if Nbase > 0: plt.subplot(132) 
    540         plot_theory(data, comp_value, view=view, plot_data=False, limits=limits) 
     540        plot_theory(data, comp_value, view=view, use_data=False, limits=limits) 
    541541        plt.title("%s t=%.1f ms"%(comp.engine, comp_time)) 
    542542        #cbar_title = "log I" 
    … …  
    548548            err, errstr, errview = abs(relerr), "rel err", "log" 
    549549        #err,errstr = base/comp,"ratio" 
    550         plot_theory(data, None, resid=err, view=errview, plot_data=False) 
     550        plot_theory(data, None, resid=err, view=errview, use_data=False) 
    551551        plt.title("max %s = %.3g"%(errstr, max(abs(err)))) 
    552552        #cbar_title = errstr if errview=="linear" else "log "+errstr 

  • sasmodels/models/be_polyelectrolyte.py

    r07a6700 r841753c  
    5050""" 
    5151 
    52 from numpy import inf, power, pi, sqrt 
     52from numpy import inf, pi, sqrt 
    5353 
    5454name = "be_polyelectrolyte" 
    5555title = "Polyelectrolyte with the RPA expression derived by Borue and Erukhimovich" 
    5656description = """ 
    57             Evaluate 
    58             F(x) = K 1/(4 pi Lb (alpha)^(2)) (q^(2)+k2)/(1+(r02)^(2)) 
    59                  (q^(2)+k2) (q^(2)-(12 h C/b^(2))) 
     57    Evaluate 
     58    F(x) = K 1/(4 pi Lb (alpha)^(2)) (q^(2)+k2)/(1+(r02)^(2)) 
     59         (q^(2)+k2) (q^(2)-(12 h C/b^(2))) 
    6060 
    61             has 3 internal parameters : 
    62                    The inverse Debye Length: K2 = 4 pi Lb (2 Cs+alpha C) 
    63                    r02 =1/alpha/Ca^(0.5) (B/(48 pi Lb)^(0.5)) 
    64                    Ca = 6.022136e-4 C 
    65             """ 
     61    has 3 internal parameters : 
     62           The inverse Debye Length: K2 = 4 pi Lb (2 Cs+alpha C) 
     63           r02 =1/alpha/Ca^(0.5) (B/(48 pi Lb)^(0.5)) 
     64           Ca = 6.022136e-4 C 
     65    """ 
    6666category = "shape-independent" 
    6767 
    68 #            ["name", "units", default, [lower, upper], "type", "description"], 
    69 parameters = [["contrast_factor",       "barns",   10.0,  [-inf, inf], "", "Contrast factor of the polymer"], 
    70               ["bjerrum_length",        "Ang",      7.1,  [0, inf],    "", "Bjerrum length"], 
    71               ["virial_param",          "1/Ang^2", 12.0,  [-inf, inf], "", "Virial parameter"], 
    72               ["monomer_length",        "Ang",     10.0,  [0, inf],    "", "Monomer length"], 
    73               ["salt_concentration",    "mol/L",    0.0,  [-inf, inf], "", "Concentration of monovalent salt"], 
    74               ["ionization_degree",     "",         0.05, [0, inf],    "", "Degree of ionization"], 
    75               ["polymer_concentration", "mol/L",    0.7,  [0, inf],    "", "Polymer molar concentration"], 
    76               ] 
     68# pylint: disable=bad-whitespace,line-too-long 
     69#   ["name",                  "units", default, [lower, upper], "type", "description"], 
     70parameters = [ 
     71    ["contrast_factor",       "barns",    10.0,  [-inf, inf], "", "Contrast factor of the polymer"], 
     72    ["bjerrum_length",        "Ang",       7.1,  [0, inf],    "", "Bjerrum length"], 
     73    ["virial_param",          "1/Ang^2",  12.0,  [-inf, inf], "", "Virial parameter"], 
     74    ["monomer_length",        "Ang",      10.0,  [0, inf],    "", "Monomer length"], 
     75    ["salt_concentration",    "mol/L",     0.0,  [-inf, inf], "", "Concentration of monovalent salt"], 
     76    ["ionization_degree",     "",          0.05, [0, inf],    "", "Degree of ionization"], 
     77    ["polymer_concentration", "mol/L",     0.7,  [0, inf],    "", "Polymer molar concentration"], 
     78    ] 
     79# pylint: enable=bad-whitespace,line-too-long 
    7780 
    7881 
    … …  
    106109 
    107110def Iqxy(qx, qy, *args): 
    108         iq = Iq(sqrt(qx**2 + qy**2), *args) 
    109  
    110         return iq 
     111    return Iq(sqrt(qx**2 + qy**2), *args) 
    111112 
    112113Iqxy.vectorized = True  # Iqxy accepts an array of qx, qy values 
    … …  
    133134               polymer_concentration='c') 
    134135 
     136# pylint: disable=bad-whitespace 
    135137tests = [ 
    136          # Accuracy tests based on content in test/utest_other_models.py 
    137          [{'contrast_factor':       10.0, 
    138            'bjerrum_length':         7.1, 
    139            'virial_param':          12.0, 
    140            'monomer_length':        10.0, 
    141            'salt_concentration':     0.0, 
    142            'ionization_degree':      0.05, 
    143            'polymer_concentration':  0.7, 
    144            'background':             0.001, 
    145            }, 0.001, 0.0948379], 
     138    # Accuracy tests based on content in test/utest_other_models.py 
     139    [{'contrast_factor':       10.0, 
     140      'bjerrum_length':         7.1, 
     141      'virial_param':          12.0, 
     142      'monomer_length':        10.0, 
     143      'salt_concentration':     0.0, 
     144      'ionization_degree':      0.05, 
     145      'polymer_concentration':  0.7, 
     146      'background':             0.001, 
     147     }, 0.001, 0.0948379], 
    146148 
    147          # Additional tests with larger range of parameters 
    148          [{'contrast_factor':       10.0, 
    149            'bjerrum_length':       100.0, 
    150            'virial_param':           3.0, 
    151            'monomer_length':         1.0, 
    152            'salt_concentration':    10.0, 
    153            'ionization_degree':      2.0, 
    154            'polymer_concentration': 10.0, 
    155            }, 0.1, -3.75693800588], 
     149    # Additional tests with larger range of parameters 
     150    [{'contrast_factor':       10.0, 
     151      'bjerrum_length':       100.0, 
     152      'virial_param':           3.0, 
     153      'monomer_length':         1.0, 
     154      'salt_concentration':    10.0, 
     155      'ionization_degree':      2.0, 
     156      'polymer_concentration': 10.0, 
     157     }, 0.1, -3.75693800588], 
    156158 
    157          [{'contrast_factor':       10.0, 
    158            'bjerrum_length':       100.0, 
    159            'virial_param':           3.0, 
    160            'monomer_length':         1.0, 
    161            'salt_concentration':    10.0, 
    162            'ionization_degree':      2.0, 
    163            'polymer_concentration': 10.0, 
    164            'background':           100.0 
    165            }, 5.0, 100.029142149], 
     159    [{'contrast_factor':       10.0, 
     160      'bjerrum_length':       100.0, 
     161      'virial_param':           3.0, 
     162      'monomer_length':         1.0, 
     163      'salt_concentration':    10.0, 
     164      'ionization_degree':      2.0, 
     165      'polymer_concentration': 10.0, 
     166      'background':           100.0 
     167     }, 5.0, 100.029142149], 
    166168 
    167          [{'contrast_factor':     100.0, 
    168            'bjerrum_length':       10.0, 
    169            'virial_param':        180.0, 
    170            'monomer_length':        1.0, 
    171            'salt_concentration':    0.1, 
    172            'ionization_degree':     0.5, 
    173            'polymer_concentration': 0.1, 
    174            }, 200., 1.80664667511e-06], 
    175          ] 
     169    [{'contrast_factor':     100.0, 
     170      'bjerrum_length':       10.0, 
     171      'virial_param':        180.0, 
     172      'monomer_length':        1.0, 
     173      'salt_concentration':    0.1, 
     174      'ionization_degree':     0.5, 
     175      'polymer_concentration': 0.1, 
     176     }, 200., 1.80664667511e-06], 
     177    ] 
     178# pylint: enable=bad-whitespace 

  • sasmodels/models/pearl_necklace.py

    rcf404cb r841753c  
    11r""" 
    2 This model provides the form factor for a pearl necklace composed of two  
    3 elements: *N* pearls (homogeneous spheres of radius *R*) freely jointed by *M*  
     2This model provides the form factor for a pearl necklace composed of two 
     3elements: *N* pearls (homogeneous spheres of radius *R*) freely jointed by *M* 
    44rods (like strings - with a total mass *Mw* = *M* \* *m*\ :sub:`r` + *N* \* *m*\ 
    5 :sub:`s`, and the string segment length (or edge separation) *l*  
     5:sub:`s`, and the string segment length (or edge separation) *l* 
    66(= *A* - 2\ *R*)). *A* is the center-to-center pearl separation distance. 
    77 
    … …  
    1313---------- 
    1414 
    15 The output of the scattering intensity function for the PearlNecklaceModel is  
     15The output of the scattering intensity function for the PearlNecklaceModel is 
    1616given by (Schweins, 2004) 
    1717 
    … …  
    3737    \beta(q) &= \frac{\int_{qR}^{q(A-R)}\frac{sin(t)}{t}dt}{ql} 
    3838 
    39 where the mass *m*\ :sub:`i` is (SLD\ :sub:`i` - SLD\ :sub:`solvent`) \*  
     39where the mass *m*\ :sub:`i` is (SLD\ :sub:`i` - SLD\ :sub:`solvent`) \* 
    4040(volume of the *N* pearls/rods). *V* is the total volume of the necklace. 
    4141 
    42 The 2D scattering intensity is the same as $P(q)$ above, regardless of the  
     42The 2D scattering intensity is the same as $P(q)$ above, regardless of the 
    4343orientation of the *q* vector. 
    4444 
    … …  
    5454REFERENCE 
    5555 
    56 R Schweins and K Huber, *Particle Scattering Factor of Pearl Necklace Chains*,  
     56R Schweins and K Huber, *Particle Scattering Factor of Pearl Necklace Chains*, 
    5757*Macromol. Symp.* 211 (2004) 25-42 2004 
    5858""" 
    … …  
    7979 
    8080#             ["name", "units", default, [lower, upper], "type","description"], 
    81 parameters = [["radius", "Angstrom", 80.0, [0, inf], "volume",  
     81parameters = [["radius", "Angstrom", 80.0, [0, inf], "volume", 
    8282               "Mean radius of the chained spheres"], 
    83               ["edge_separation", "Angstrom", 350.0, [0, inf], "volume",  
     83              ["edge_separation", "Angstrom", 350.0, [0, inf], "volume", 
    8484               "Mean separation of chained particles"], 
    85               ["string_thickness", "Angstrom", 2.5, [0, inf], "volume",  
     85              ["string_thickness", "Angstrom", 2.5, [0, inf], "volume", 
    8686               "Thickness of the chain linkage"], 
    87               ["number_of_pearls", "none", 3, [0, inf], "volume",  
     87              ["number_of_pearls", "none", 3, [0, inf], "volume", 
    8888               "Mean number of pearls in each necklace"], 
    89               ["sld", "Angstrom^2", 1.0, [-inf, inf], "",  
     89              ["sld", "Angstrom^2", 1.0, [-inf, inf], "", 
    9090               "Scattering length density of the chained spheres"], 
    91               ["string_sld", "Angstrom^2", 1.0, [-inf, inf], "",  
     91              ["string_sld", "Angstrom^2", 1.0, [-inf, inf], "", 
    9292               "Scattering length density of the chain linkage"], 
    93               ["solvent_sld", "Angstrom^2", 6.3, [-inf, inf], "",  
     93              ["solvent_sld", "Angstrom^2", 6.3, [-inf, inf], "", 
    9494               "Scattering length density of the solvent"], 
    95               ] 
     95             ] 
    9696 
    9797source = ["lib/Si.c", "pearl_necklace.c"] 
    … …  
    110110# names and the target sasview model name. 
    111111oldname = 'PearlNecklaceModel' 
    112 oldpars = dict(scale='scale',background='background',radius='radius', 
     112oldpars = dict(scale='scale', background='background', radius='radius', 
    113113               number_of_pearls='num_pearls', solvent_sld='sld_solv', 
    114114               string_thickness='thick_string', sld='sld_pearl', 

  • sasmodels/models/power_law.py

    reb69cce r841753c  
    1212    I(q) = \text{scale} \cdot q^{-\text{power}} + \text{background} 
    1313 
    14 Note the minus sign in front of the exponent. The exponent *power*  
     14Note the minus sign in front of the exponent. The exponent *power* 
    1515should therefore be entered as a **positive** number for fitting. 
    1616 
    17 Also note that unlike many other models, *scale* in this model  
    18 is NOT explicitly related to a volume fraction. Be careful if  
     17Also note that unlike many other models, *scale* in this model 
     18is NOT explicitly related to a volume fraction. Be careful if 
    1919combining this model with other models. 
    2020 
    … …  
    3131from numpy import inf, sqrt 
    3232 
    33 name =  "power_law" 
     33name = "power_law" 
    3434title = "Simple power law with a flat background" 
    3535 
    36 description = """\ 
    37         Evaluates the function 
    38         I(q) = scale * q^(-power) + background 
    39         NB: enter power as a positive number! 
    40         """ 
     36description = """ 
     37    Evaluates the function 
     38    I(q) = scale * q^(-power) + background 
     39    NB: enter power as a positive number! 
     40    """ 
    4141category = "shape-independent" 
    4242 
    … …  
    4545 
    4646# NB: Scale and Background are implicit parameters on every model 
    47 def Iq(q,power): 
     47def Iq(q, power): 
     48    # pylint: disable=missing-docstring 
    4849    inten = (q**-power) 
    4950    return inten 
    … …  
    5152 
    5253def Iqxy(qx, qy, *args): 
     54    # pylint: disable=missing-docstring 
    5355    return Iq(sqrt(qx ** 2 + qy ** 2), *args) 
    5456Iqxy.vectorized = True # Iqxy accepts an array of qx, qy values 
    … …  
    6466 
    6567tests = [ 
    66         [ {'scale': 1.0, 'power': 4.0, 'background' : 0.0}, [0.0106939, 0.469418], [7.64644e+07, 20.5949]] 
    67         ] 
     68    [{'scale': 1.0, 'power': 4.0, 'background' : 0.0}, 
     69     [0.0106939, 0.469418], [7.64644e+07, 20.5949]], 
     70    ] 

  • sasmodels/resolution2d.py

    r9404dd3 r841753c  
    22#This software was developed by the University of Tennessee as part of the 
    33#Distributed Data Analysis of Neutron Scattering Experiments (DANSE) 
    4 #project funded by the US National Science Foundation.  
     4#project funded by the US National Science Foundation. 
    55#See the license text in license.txt 
    66""" 
    … …  
    1919## Defaults 
    2020NR = {'xhigh':10, 'high':5, 'med':5, 'low':3} 
    21 NPHI ={'xhigh':20, 'high':12, 'med':6, 'low':4} 
     21NPHI = {'xhigh':20, 'high':12, 'med':6, 'low':4} 
    2222 
    2323class Pinhole2D(Resolution): 
    … …  
    2525    Gaussian Q smearing class for SAS 2d data 
    2626    """ 
    27       
     27 
    2828    def __init__(self, data=None, index=None, 
    2929                 nsigma=NSIGMA, accuracy='Low', coords='polar'): 
    3030        """ 
    3131        Assumption: equally spaced bins in dq_r, dq_phi space. 
    32          
     32 
    3333        :param data: 2d data used to set the smearing parameters 
    3434        :param index: 1d array with len(data) to define the range 
    … …  
    4242        ## number of bins in r axis for over-sampling 
    4343        self.nr = NR[accuracy.lower()] 
    44         ## number of bins in phi axis for over-sampling  
     44        ## number of bins in phi axis for over-sampling 
    4545        self.nphi = NPHI[accuracy.lower()] 
    4646        ## maximum nsigmas 
    47         self.nsigma= nsigma 
     47        self.nsigma = nsigma 
    4848        self.coords = coords 
    4949        self._init_data(data, index) 
    … …  
    8585    def _calc_res(self): 
    8686        """ 
    87         Over sampling of r_nbins times phi_nbins, calculate Gaussian weights,  
     87        Over sampling of r_nbins times phi_nbins, calculate Gaussian weights, 
    8888        then find smeared intensity 
    89         """     
     89        """ 
    9090        nr, nphi = self.nr, self.nphi 
    9191        # Total number of bins = # of bins 
    … …  
    143143        if self.coords == 'polar': 
    144144            q_r = sqrt(qx**2 + qy**2) 
    145             qx_res = ( (dqx*cos(dphi) + q_r) * cos(-q_phi) + 
     145            qx_res = ((dqx*cos(dphi) + q_r) * cos(-q_phi) + 
    146146                           dqy*sin(dphi) * sin(-q_phi)) 
    147147            qy_res = (-(dqx*cos(dphi) + q_r) * sin(-q_phi) + 
    … …  
    167167        else: 
    168168            return theory 
    169  
    170 """ 
    171 if __name__ == '__main__': 
    172     ## Test w/ 2D linear function 
    173     x = 0.001*np.arange(1, 11) 
    174     dx = np.ones(len(x))*0.0003 
    175     y = 0.001*np.arange(1, 11) 
    176     dy = np.ones(len(x))*0.001 
    177     z = np.ones(10) 
    178     dz = sqrt(z) 
    179  
    180     from sas.dataloader import Data2D 
    181     #for i in range(10): print(i, 0.001 + i*0.008/9.0) 
    182     #for i in range(100): print(i, int(math.floor( (i/ (100/9.0)) ))) 
    183     out = Data2D() 
    184     out.data = z 
    185     out.qx_data = x 
    186     out.qy_data = y 
    187     out.dqx_data = dx 
    188     out.dqy_data = dy 
    189     out.q_data = sqrt(dx * dx + dy * dy) 
    190     index = np.ones(len(x), dtype = bool) 
    191     out.mask = index 
    192     from sas.models.LineModel import LineModel 
    193     model = LineModel() 
    194     model.setParam("A", 0) 
    195  
    196     smear = Smearer2D(out, model, index) 
    197     #smear.set_accuracy('Xhigh') 
    198     value = smear.get_value() 
    199     ## All data are ones, so the smeared should also be ones. 
    200     print("Data length =", len(value)) 
    201     print(" 2D linear function, I = 0 + 1*qy") 
    202     text = " Gaussian weighted averaging on a 2D linear function will " 
    203     text += "provides the results same as without the averaging." 
    204     print(text) 
    205     print("qx_data", "qy_data", "I_nonsmear", "I_smeared") 
    206     for ind in range(len(value)): 
    207         print(x[ind], y[ind], model.evalDistribution([x, y])[ind], value[ind]) 
    208  
    209  
    210 if __name__ == '__main__': 
    211     ## Another Test w/ constant function 
    212     x = 0.001*np.arange(1,11) 
    213     dx = np.ones(len(x))*0.001 
    214     y = 0.001*np.arange(1,11) 
    215     dy = np.ones(len(x))*0.001 
    216     z = np.ones(10) 
    217     dz = sqrt(z) 
    218  
    219     from DataLoader import Data2D 
    220     #for i in range(10): print(i, 0.001 + i*0.008/9.0) 
    221     #for i in range(100): print(i, int(math.floor( (i/ (100/9.0)) ))) 
    222     out = Data2D() 
    223     out.data = z 
    224     out.qx_data = x 
    225     out.qy_data = y 
    226     out.dqx_data = dx 
    227     out.dqy_data = dy 
    228     index = np.ones(len(x), dtype = bool) 
    229     out.mask = index 
    230     from sas.models.Constant import Constant 
    231     model = Constant() 
    232  
    233     value = Smearer2D(out,model,index).get_value() 
    234     ## All data are ones, so the smeared values should also be ones. 
    235     print("Data length =",len(value), ", Data=",value) 
    236 """ 
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