#########################################
### Really important parameters
## to be set to discover what the geometry conventions
## really are


orientation_codes=5  # 8 possibilities look at analyse_maxima_fourier.py
theta =  0.0
theta_offset = 0.0

alpha = 50.0
beta = 0.0
angular_step = +0.1


dist =  244.31059
pixel_size = 0.172
kappa = 2.25396653702
omega = 89.57298

det_origin_X=731.071873845
det_origin_Y=1665.81842237


# lmbda is not so important in the desperate phase. It just expand the size of the lattice
lmbda = 0.68894

#####################################################
### Other parameters that are generally small or that can
##  be refined later like r1,r2,r3
beam_tilt_angle = 0.13396

d1 = -0.146084537931
d2 =  0.171515179405

omega_offset =  0
phi = 0


##################################
# AA,BB,CC r1,r2,r3 : we will find them later 
############################"
## aAA, aBB, aCC... later


################################################
###### peaks positions are read from this file
# maxima_file = "tagged_maxima.p"


###########################################################################
## we are going to visualise the spots distribution in reciprocal 3D space
##


#############
## ESTIMATION by FOURIER 
AA=4.255178e+00
BB=4.255178e+00
CC=4.255178e+00
aAA=90.0
aBB=90.0   ## set by hand
aCC=90.0
r3=1.654992e+02
r2=8.877376e+01
r1=-8.946453e+01 
#############
## FOLLOWING PARAMETERS ARE QUATERNIONS COEFFICIENTS. 
#############
## TOBEUSED WITH USEQUAT=True 
USEQUAT=True 
r1=4.294775e-01
r2=5.616485e-01
r3=4.414863e-01 
 ############################ 

r1 = 0.430043234958
r2 = 0.561156286782
r3 = 0.441641465046
AA = 4.20909540045
BB = 4.20909540045
CC = 4.20909540045


################################################################
## which spot to harvest and fit are read from this file
## alternatively you can set collectSpots variable

# file_maxima = "tagged_maxima.p"

#######################################
### OPTIONS FOR EASY EDITING
DO_HARVESTING = 0
DO_CENTERED_HARVESTING = 1
DRY_RUN = 2
FIT = 3


## Select one of these operations
# TODO = DRY_RUN
# TODO = DO_HARVESTING
# TODO = DO_CENTERED_HARVESTING
# TODO = FIT

TODO = DO_HARVESTING
TODO = FIT

###########################
#
#  Temperature and density
#  just in case
#
#   At room temperature you can simultaneously scale
#   frequencies and intensity, our fit
#   so far is insensitive to absolute scale
#   because the intensity factors are absorbed by
#   the fitting procedure. This because we dont have an absolute
#   scale of the absolute intensity : we dont know the exact geometry
#   of the sample and many other factors

Temperature = 92.5  # Kelvin, some average temperature,  the scan was not so flat in temperature
density = 2.71     # g/cc

ftol=1.0e-9
 


if TODO == FIT :
    show_hkls = False
    DO_ELASTIC=1
    load_from_file= "collection.h5"
    save_to_file  =  None
    fitSpots = True

    CGUESS={"A11":  [300.,300 , 300],
            "A44":  [100.,0 ,400 ],
            "A12":  [80.,-400,400 ]
            } 

    activ=0
    activSample=1
    A_variables = (
    		[ minimiser.Variable(0.0,-1.5*activSample  ,1.5*activSample ) for i in range(1) ]+                           # r1,
    		[ minimiser.Variable(0.0,-1.5*activSample ,1.5*activSample ) for i in range(1) ]+                            # r2
    		[ minimiser.Variable(0.0,-1.5*activSample ,1.5*activSample ) for i in range(1) ]+                            # r3,
    		[ minimiser.Variable(0.0,-0.1*activSample ,0.1*activSample ) ,-1,-1]+                                        #  AA, BB, CC
    		[ minimiser.Variable(0.0,-2.0*activ,2.0*activ) ,
		  minimiser.Variable(0.0,0,0) ,
		  minimiser.Variable(0.0,0,0) ,
		  minimiser.Variable(0.0,-2.0*activ,2.0*activ) ,
		  ]+                                                                                              # omega, alpha, beta, kappa
                      [         minimiser.Variable(0.0,-0.2*activ,+0.2*activ) for i in range(2)  ] +              # d1,d2,
                      [         minimiser.Variable(0.0,-5*activ,+5*activ) for i in range(2)  ]                    # det_origin_X, det_origin_Y
		      )			




    collectSpots = np.array([
        [ -0,  2,-4] ,[ -2,  2,-4] ,[  2,  2,-4] ,[ -0, -0,-2] ,[ -1,  1,-5] ,[ -2, -0,-2] ,[  0, -0,-4] ,[  2,  0,-2] ,[ -2,  0,-4] ,
        [  2, -0,-4] ,[ -1, -1,-5] ,[  0, -2,-4] ,[ -2, -2,-4] ,[  2, -2,-4] ,[ -1, -1,-1] ,[  1, -1,-1] ,[ -0, -2,-2] ,[ -2, -2,-2] ,
        [  2, -2,-2] ,[ -1, -3,-3] ,[  1, -3,-3] ,[ -0, -4,-2] ,[ -2, -4,-2] ,[  2, -4,-2] ,[ -0, -2,-0] ,[ -1, -5,-1] ,[ -2, -2, 0] ,
        [  0, -4, 0] ,[  2, -2,-0] ,[ -2, -4, 0] ,[  2, -4,-0] ,[ -1, -5, 1] ,[  1, -5, 1] ,[  0, -4, 2] ,[ -2, -4, 2] ,[  2, -4, 2] ,
        [  1, -1, 1] ,[  0, -2, 2] ,[ -2, -2, 2] ,[  2, -2, 2] ,[ -1, -3, 3] ,[  1, -3, 3] ,[  0, -2, 4] ,[ -1, -1, 3] ,[ -2, -2, 4] ,
        [  2, -2, 4] ,[ -0, -0, 2] ,[  1, -1, 5] ,[ -2,  0, 2] ,[  0, -0, 4] ,[  2, -0, 2] ,[  0, 4, -2] ,[  2, -0, 4] ,[  1,  1, 3] ,
        [  1,  1, 5] ,[  0,  2, 4] ,[ -2,  2, 4] ,              [  2,  2, 4] ,[ -1,  1, 1] ,[ -0,  2, 2] ,[ -2,  2, 2] ,[ -1,  3, 3] ,
        [  2,  2, 2] ,[  1,  3, 3] ,[ -0,  4, 2] ,[ -2,  4, 2] ,              [  2,  4, 2] ,[ -0,  2, 0] ,[  1,  5, 1] ,[ -2,  2,-0] ,
        [  0,  4, 0] ,[  2,  2, 0] ,[ -2,  4, 0] ,[  2,  4, 0] ,[  1,  5,-1] ,[ -0,  4,-2] ,[ -2,  4,-2] ,[  2,  4,-2] 
    ])
    collectSpots = np.array(
	[[-2, -4, -2], [-2, -4, 0], [-2, -4, 2], [-2, -2, -4], [-2, -2, -2], [-2, -2, 0], [-2, -2, 2], [-2, -2, 4], [-2, 0, -4],
	 [-2, 0, -2], [-2, 0, 2], [-2, 0, 4], [-2, 2, -4], [-2, 2, -2], [-2, 2, 0], [-2, 2, 2], [-2, 2, 4], [-2, 4, -2], [-2, 4, 0], 
	 [-2, 4, 2], [-1, -5, -1], [-1, -5, 1], [-1, -3, -3], [-1, -3, -1], [-1, -3, 1], [-1, -3, 3], [-1, -1, -5], [-1, -1, -3], 
	 [-1, -1, -1], [-1, -1, 1], [-1, -1, 3], [-1, -1, 5], [-1, 1, -5], [-1, 1, -3], [-1, 1, -1], [-1, 1, 1], [-1, 1, 3], [-1, 1, 5], 
	 [-1, 3, -3], [-1, 3, -1], [-1, 3, 1], [-1, 3, 3], [-1, 5, -1], [-1, 5, 1], [0, -4, -2], [0, -4, 0], [0, -4, 2], [0, -2, -4], 
	 [0, -2, -2], [0, -2, 0], [0, -2, 2], [0, -2, 4], [0, 0, -4], [0, 0, -2], [0, 0, 2], [0, 0, 4], [0, 2, -4], [0, 2, -2],
         [0, 2, 0], [0, 2, 2], [0, 2, 4], [0, 4, -2], [0, 4, 0], [0, 4, 2], [1, -5, -1], [1, -5, 1], [1, -3, -3], [1, -3, -1], [1, -3, 1],
         [1, -3, 3], [1, -1, -5], [1, -1, -3], [1, -1, -1], [1, -1, 1], [1, -1, 3], [1, -1, 5], [1, 1, -5], [1, 1, -3], [1, 1, -1], [1, 1, 1],
         [1, 1, 3], [1, 1, 5], [1, 3, -3], [1, 3, -1], [1, 3, 1], [1, 3, 3], [1, 5, -1], [1, 5, 1], [2, -4, -2], [2, -4, 0], [2, -4, 2], [2, -2, -4], 
         [2, -2, -2], [2, -2, 0], [2, -2, 2], [2, -2, 4], [2, 0, -4], [2, 0, -2], [2, 0, 2], [2, 0, 4], [2, 2, -4], [2, 2, -2], [2, 2, 0], [2, 2, 2],
        [2, 2, 4], [2, 4, -2], [2,  4, 0], [2, 4, 2]]
        )
    
    doMinimisation=1
    visSim=1
    
    testSpot_beta = 0.06
    testSpot_Niter = 80
    testSpot_N = 128

if TODO == DRY_RUN:
    DO_ELASTIC=1
    load_from_file= "collection_centered.h5"
    save_to_file  =  None
    fitSpots = True


if(TODO==DO_CENTERED_HARVESTING):
    centering_file ="collection4c.h5"
    threshold = 1000.0
    badradius = 0
    DeltaQ_max = 0.15
    DeltaQ_min = 0.06
    load_from_file= None
    save_to_file  =  "collection_centered.h5"
    DO_ELASTIC=1
    REPLICA_COLLECTION = 0
    badradius=0

if(TODO==DO_HARVESTING):
    threshold = 1000.0
    badradius = 0	
    DeltaQ_max = 0.15
    DeltaQ_min = 0.06
    load_from_file= None
    save_to_file  =  "collection.h5"
    DO_ELASTIC=1
    badradius = 0    		   
    collectSpots = [ [ 3,1,3] ,[0,0,4], [ 4,2,2],[ 2,2,2],[2,0,2],[4,0,2],[0,0, 2] ,	
                     [ 1,1,1],[5,1,1],[3,1,1] ]    
    REPLICA_COLLECTION = 1


import numpy as np

r1+= -0.0001586012452
r2+=   0.00012127225
r3+= -5.070206146644e-05
AA+= 0.0011324056
BB+= 0.0011324056
CC+= 0.0011324056

DELTA={
"r1":-0.00000000001,
}