#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
import math
from os.path import basename, dirname, join as joinpath
import glob
import datetime

import numpy as np

ROOT = dirname(__file__)
sys.path.insert(0, ROOT)  # Make sure sasmodels is first on the path


from . import core
from . import kerneldll
from . import models
from .data import plot_theory, empty_data1D, empty_data2D
from .direct_model import DirectModel
from .convert import revert_model
kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True

# List of available models
MODELS = [basename(f)[:-3]
          for f in sorted(glob.glob(joinpath(ROOT,"models","[a-zA-Z]*.py")))]

# CRUFT python 2.6
if not hasattr(datetime.timedelta, 'total_seconds'):
    def delay(dt):
        """Return number date-time delta as number seconds"""
        return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds
else:
    def delay(dt):
        """Return number date-time delta as number seconds"""
        return dt.total_seconds()


def tic():
    """
    Timer function.

    Use "toc=tic()" to start the clock and "toc()" to measure
    a time interval.
    """
    then = datetime.datetime.now()
    return lambda: delay(datetime.datetime.now() - then)


def set_beam_stop(data, radius, outer=None):
    """
    Add a beam stop of the given *radius*.  If *outer*, make an annulus.

    Note: this function does not use the sasview package
    """
    if hasattr(data, 'qx_data'):
        q = np.sqrt(data.qx_data**2 + data.qy_data**2)
        data.mask = (q < radius)
        if outer is not None:
            data.mask |= (q >= outer)
    else:
        data.mask = (data.x < radius)
        if outer is not None:
            data.mask |= (data.x >= outer)


def sasview_model(model_definition, **pars):
    """
    Load a sasview model given the model name.
    """
    # convert model parameters from sasmodel form to sasview form
    #print "old",sorted(pars.items())
    modelname, pars = revert_model(model_definition, pars)
    #print "new",sorted(pars.items())
    sas = __import__('sas.models.'+modelname)
    ModelClass = getattr(getattr(sas.models,modelname,None),modelname,None)
    if ModelClass is None:
        raise ValueError("could not find model %r in sas.models"%modelname)
    model = ModelClass()

    for k,v in pars.items():
        if k.endswith("_pd"):
            model.dispersion[k[:-3]]['width'] = v
        elif k.endswith("_pd_n"):
            model.dispersion[k[:-5]]['npts'] = v
        elif k.endswith("_pd_nsigma"):
            model.dispersion[k[:-10]]['nsigmas'] = v
        elif k.endswith("_pd_type"):
            model.dispersion[k[:-8]]['type'] = v
        else:
            model.setParam(k, v)
    return model

def randomize(p, v):
    """
    Randomizing parameter.

    Guess the parameter type from name.
    """
    if any(p.endswith(s) for s in ('_pd_n','_pd_nsigma','_pd_type')):
        return v
    elif any(s in p for s in ('theta','phi','psi')):
        # orientation in [-180,180], orientation pd in [0,45]
        if p.endswith('_pd'):
            return 45*np.random.rand()
        else:
            return 360*np.random.rand() - 180
    elif 'sld' in p:
        # sld in in [-0.5,10]
        return 10.5*np.random.rand() - 0.5
    elif p.endswith('_pd'):
        # length pd in [0,1]
        return np.random.rand()
    else:
        # values from 0 to 2*x for all other parameters
        return 2*np.random.rand()*(v if v != 0 else 1)

def randomize_model(name, pars, seed=None):
    if seed is None:
        seed = np.random.randint(1e9)
    np.random.seed(seed)
    # Note: the sort guarantees order of calls to random number generator
    pars = dict((p,randomize(p,v)) for p,v in sorted(pars.items()))
    # The capped cylinder model has a constraint on its parameters
    if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']:
        pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius']
    return pars, seed

def parlist(pars):
    return "\n".join("%s: %s"%(p,v) for p,v in sorted(pars.items()))

def suppress_pd(pars):
    """
    Suppress theta_pd for now until the normalization is resolved.

    May also suppress complete polydispersity of the model to test
    models more quickly.
    """
    for p in pars:
        if p.endswith("_pd"): pars[p] = 0

def eval_sasview(model_definition, pars, data, Nevals=1):
    from sas.models.qsmearing import smear_selection
    model = sasview_model(model_definition, **pars)
    smearer = smear_selection(data, model=model)
    value = None  # silence the linter
    toc = tic()
    for _ in range(max(Nevals, 1)):  # make sure there is at least one eval
        if hasattr(data, 'qx_data'):
            q = np.sqrt(data.qx_data**2 + data.qy_data**2)
            index = ((~data.mask) & (~np.isnan(data.data))
                     & (q >= data.qmin) & (q <= data.qmax))
            if smearer is not None:
                smearer.model = model  # because smear_selection has a bug
                smearer.accuracy = data.accuracy
                smearer.set_index(index)
                value = smearer.get_value()
            else:
                value = model.evalDistribution([data.qx_data[index], data.qy_data[index]])
        else:
            value = model.evalDistribution(data.x)
            if smearer is not None:
                value = smearer(value)
    average_time = toc()*1000./Nevals
    return value, average_time

def eval_opencl(model_definition, pars, data, dtype='single', Nevals=1, cutoff=0.):
    try:
        model = core.load_model(model_definition, dtype=dtype, platform="ocl")
    except Exception,exc:
        print exc
        print "... trying again with single precision"
        model = core.load_model(model_definition, dtype='single', platform="ocl")
    calculator = DirectModel(data, model, cutoff=cutoff)
    value = None  # silence the linter
    toc = tic()
    for _ in range(max(Nevals, 1)):  # force at least one eval
        value = calculator(**pars)
    average_time = toc()*1000./Nevals
    return value, average_time


def eval_ctypes(model_definition, pars, data, dtype='double', Nevals=1, cutoff=0.):
    model = core.load_model(model_definition, dtype=dtype, platform="dll")
    calculator = DirectModel(data, model, cutoff=cutoff)
    value = None  # silence the linter
    toc = tic()
    for _ in range(max(Nevals, 1)):  # force at least one eval
        value = calculator(**pars)
    average_time = toc()*1000./Nevals
    return value, average_time


def make_data(qmax, is2D, Nq=128, resolution=0.0, accuracy='Low', view='log'):
    if is2D:
        data = empty_data2D(np.linspace(-qmax, qmax, Nq), resolution=resolution)
        data.accuracy = accuracy
        set_beam_stop(data, 0.004)
        index = ~data.mask
    else:
        if view == 'log':
            qmax = math.log10(qmax)
            q = np.logspace(qmax-3, qmax, Nq)
        else:
            q = np.linspace(0.001*qmax, qmax, Nq)
        data = empty_data1D(q, resolution=resolution)
        index = slice(None, None)
    return data, index

def compare(name, pars, Ncpu, Nocl, opts, set_pars):
    view = 'linear' if '-linear' in opts else 'log' if '-log' in opts else 'q4' if '-q4' in opts else 'log'

    opt_values = dict(split
                      for s in opts for split in ((s.split('='),))
                      if len(split) == 2)
    # Sort out data
    qmax = 10.0 if '-exq' in opts else 1.0 if '-highq' in opts else 0.2 if '-midq' in opts else 0.05
    Nq = int(opt_values.get('-Nq', '128'))
    res = float(opt_values.get('-res', '0'))
    accuracy = opt_values.get('-accuracy', 'Low')
    is2D = "-2d" in opts
    data, index = make_data(qmax, is2D, Nq, res, accuracy, view=view)


    # modelling accuracy is determined by dtype and cutoff
    dtype = 'double' if '-double' in opts else 'single'
    cutoff = float(opt_values.get('-cutoff','1e-5'))

    # randomize parameters
    #pars.update(set_pars)  # set value before random to control range
    if '-random' in opts or '-random' in opt_values:
        seed = int(opt_values['-random']) if '-random' in opt_values else None
        pars, seed = randomize_model(name, pars, seed=seed)
        print "Randomize using -random=%i"%seed
    pars.update(set_pars)  # set value after random to control value

    # parameter selection
    if '-mono' in opts:
        suppress_pd(pars)
    if '-pars' in opts:
        print "pars",parlist(pars)

    model_definition = core.load_model_definition(name)
    # OpenCl calculation
    if Nocl > 0:
        ocl, ocl_time = eval_opencl(model_definition, pars, data,
                                    dtype=dtype, cutoff=cutoff, Nevals=Nocl)
        print "opencl t=%.1f ms, intensity=%.0f"%(ocl_time, sum(ocl))
        #print max(ocl), min(ocl)

    # ctypes/sasview calculation
    if Ncpu > 0 and "-ctypes" in opts:
        cpu, cpu_time = eval_ctypes(model_definition, pars, data,
                                    dtype=dtype, cutoff=cutoff, Nevals=Ncpu)
        comp = "ctypes"
        print "ctypes t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu))
    elif Ncpu > 0:
        try:
            cpu, cpu_time = eval_sasview(model_definition, pars, data, Ncpu)
            comp = "sasview"
            #print "ocl/sasview", (ocl-pars['background'])/(cpu-pars['background'])
            print "sasview t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu))
        except ImportError:
            Ncpu = 0

    # Compare, but only if computing both forms
    if Nocl > 0 and Ncpu > 0:
        #print "speedup %.2g"%(cpu_time/ocl_time)
        #print "max |ocl/cpu|", max(abs(ocl/cpu)), "%.15g"%max(abs(ocl)), "%.15g"%max(abs(cpu))
        #cpu *= max(ocl/cpu)
        resid = (ocl - cpu)
        relerr = resid/cpu
        #bad = (relerr>1e-4)
        #print relerr[bad],cpu[bad],ocl[bad],data.qx_data[bad],data.qy_data[bad]
        _print_stats("|ocl-%s|"%comp+(" "*(3+len(comp))), resid)
        _print_stats("|(ocl-%s)/%s|"%(comp,comp), relerr)

    # Plot if requested
    if '-noplot' in opts: return
    import matplotlib.pyplot as plt
    if Ncpu > 0:
        if Nocl > 0: plt.subplot(131)
        plot_theory(data, cpu, view=view, plot_data=False)
        plt.title("%s t=%.1f ms"%(comp,cpu_time))
        #cbar_title = "log I"
    if Nocl > 0:
        if Ncpu > 0: plt.subplot(132)
        plot_theory(data, ocl, view=view, plot_data=False)
        plt.title("opencl t=%.1f ms"%ocl_time)
        #cbar_title = "log I"
    if Ncpu > 0 and Nocl > 0:
        plt.subplot(133)
        if '-abs' in opts:
            err,errstr,errview = resid, "abs err", "linear"
        else:
            err,errstr,errview = abs(relerr), "rel err", "log"
        #err,errstr = ocl/cpu,"ratio"
        plot_theory(data, None, resid=err, view=errview, plot_data=False)
        plt.title("max %s = %.3g"%(errstr, max(abs(err))))
        #cbar_title = errstr if errview=="linear" else "log "+errstr
    #if is2D:
    #    h = plt.colorbar()
    #    h.ax.set_title(cbar_title)

    if Ncpu > 0 and Nocl > 0 and '-hist' in opts:
        plt.figure()
        v = relerr
        v[v==0] = 0.5*np.min(np.abs(v[v!=0]))
        plt.hist(np.log10(np.abs(v)), normed=1, bins=50);
        plt.xlabel('log10(err), err = | F(q) single - F(q) double| / | F(q) double |');
        plt.ylabel('P(err)')
        plt.title('Comparison of single and double precision models for %s'%name)

    plt.show()

def _print_stats(label, err):
    sorted_err = np.sort(abs(err))
    p50 = int((len(err)-1)*0.50)
    p98 = int((len(err)-1)*0.98)
    data = [
        "max:%.3e"%sorted_err[-1],
        "median:%.3e"%sorted_err[p50],
        "98%%:%.3e"%sorted_err[p98],
        "rms:%.3e"%np.sqrt(np.mean(err**2)),
        "zero-offset:%+.3e"%np.mean(err),
        ]
    print label,"  ".join(data)



# ===========================================================================
#
USAGE="""
usage: compare.py model [Nopencl] [Nsasview] [options...] [key=val]

Compare the speed and value for a model between the SasView original and the
OpenCL rewrite.

model is the name of the model to compare (see below).
Nopencl is the number of times to run the OpenCL model (default=5)
Nsasview is the number of times to run the Sasview model (default=1)

Options (* for default):

    -plot*/-noplot plots or suppress the plot of the model
    -single*/-double uses double precision for comparison
    -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0
    -Nq=128 sets the number of Q points in the data set
    -1d*/-2d computes 1d or 2d data
    -preset*/-random[=seed] preset or random parameters
    -mono/-poly* force monodisperse/polydisperse
    -ctypes/-sasview* whether cpu is tested using sasview or ctypes
    -cutoff=1e-5* cutoff value for including a point in polydispersity
    -pars/-nopars* prints the parameter set or not
    -abs/-rel* plot relative or absolute error
    -linear/-log/-q4 intensity scaling
    -hist/-nohist* plot histogram of relative error
    -res=0 sets the resolution width dQ/Q if calculating with resolution
    -accuracy=Low resolution accuracy Low, Mid, High, Xhigh

Key=value pairs allow you to set specific values to any of the model
parameters.

Available models:
"""


NAME_OPTIONS = set([
    'plot','noplot',
    'single','double',
    'lowq','midq','highq','exq',
    '2d','1d',
    'preset','random',
    'poly','mono',
    'sasview','ctypes',
    'nopars','pars',
    'rel','abs',
    'linear', 'log', 'q4',
    'hist','nohist',
    ])
VALUE_OPTIONS = [
    # Note: random is both a name option and a value option
    'cutoff', 'random', 'Nq', 'res', 'accuracy',
    ]

def columnize(L, indent="", width=79):
    column_width = max(len(w) for w in L) + 1
    num_columns = (width - len(indent)) // column_width
    num_rows = len(L) // num_columns
    L = L + [""] * (num_rows*num_columns - len(L))
    columns = [L[k*num_rows:(k+1)*num_rows] for k in range(num_columns)]
    lines = [" ".join("%-*s"%(column_width, entry) for entry in row)
             for row in zip(*columns)]
    output = indent + ("\n"+indent).join(lines)
    return output


def get_demo_pars(name):
    __import__('sasmodels.models.'+name)
    model = getattr(models, name)
    pars = getattr(model, 'demo', None)
    if pars is None: pars = dict((p[0],p[2]) for p in model.parameters)
    return pars

def main():
    opts = [arg for arg in sys.argv[1:] if arg.startswith('-')]
    args = [arg for arg in sys.argv[1:] if not arg.startswith('-')]
    models = "\n    ".join("%-15s"%v for v in MODELS)
    if len(args) == 0:
        print(USAGE)
        print(columnize(MODELS, indent="  "))
        sys.exit(1)
    if args[0] not in MODELS:
        print "Model %r not available. Use one of:\n    %s"%(args[0],models)
        sys.exit(1)

    invalid = [o[1:] for o in opts
               if o[1:] not in NAME_OPTIONS
                  and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)]
    if invalid:
        print "Invalid options: %s"%(", ".join(invalid))
        sys.exit(1)

    # Get demo parameters from model definition, or use default parameters
    # if model does not define demo parameters
    name = args[0]
    pars = get_demo_pars(name)

    Nopencl = int(args[1]) if len(args) > 1 else 5
    Nsasview = int(args[2]) if len(args) > 2 else 1

    # Fill in default polydispersity parameters
    pds = set(p.split('_pd')[0] for p in pars if p.endswith('_pd'))
    for p in pds:
        if p+"_pd_nsigma" not in pars: pars[p+"_pd_nsigma"] = 3
        if p+"_pd_type" not in pars: pars[p+"_pd_type"] = "gaussian"

    # Fill in parameters given on the command line
    set_pars = {}
    for arg in args[3:]:
        k,v = arg.split('=')
        if k not in pars:
            # extract base name without distribution
            s = set(p.split('_pd')[0] for p in pars)
            print "%r invalid; parameters are: %s"%(k,", ".join(sorted(s)))
            sys.exit(1)
        set_pars[k] = float(v) if not v.endswith('type') else v

    compare(name, pars, Nsasview, Nopencl, opts, set_pars)

if __name__ == "__main__":
    main()