pyobjcryst

https://pyobjcryst.readthedocs.io/
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### SpaceGroupExplorer Output Example

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-multiprocessing.html

Example output showing the exploration process, goodness-of-fit metrics for various spacegroups, and the final selection.

```text
Beginning spacegroup exploration... 37 to go...
  (#  1) P 1           : Rwp=  6.80%  GoF=    14.98  nGoF=     3.24  (186 reflections,   0 extinct)
  (#  2) P -1          : Rwp=  6.80%  GoF=    14.98  nGoF=     3.24  (186 reflections,   0 extinct) [same extinctions as:P 1]
  (#  3) P 1 2 1       : Rwp=  6.69%  GoF=    14.46  nGoF=     1.84  (105 reflections,   0 extinct)
  (#  4) P 1 21 1      : Rwp=  6.61%  GoF=    14.11  nGoF=     1.65  (101 reflections,   2 extinct)
  (#  5) C 1 2 1       : Rwp= 62.70%  GoF=  1246.13  nGoF=   311.68  ( 52 reflections,  84 extinct)
  (#  5) A 1 2 1       : Rwp= 62.83%  GoF=  1253.74  nGoF=   313.87  ( 53 reflections,  85 extinct)
  (#  5) I 1 2 1       : Rwp= 60.91%  GoF=  1196.43  nGoF=   246.94  ( 52 reflections,  87 extinct)
  (#  6) P 1 m 1       : Rwp=  6.69%  GoF=    14.46  nGoF=     1.84  (105 reflections,   0 extinct) [same extinctions as:P 1 2 1]
  (#  7) P 1 c 1       : Rwp=  6.58%  GoF=    13.92  nGoF=     1.66  ( 96 reflections,  15 extinP 1 21/c 1    nGoF=   1.4866 GoF=  13.575 Rw= 6.50 [ 92 reflections, extinct446= 17]
P 1 21 1      nGoF=   1.6488 GoF=  14.108 Rw= 6.61 [101 reflections, extinct446=  2]
P 1 21/m 1    nGoF=   1.6488 GoF=  14.108 Rw= 6.61 [101 reflections, extinct446=  2]
P 1 c 1       nGoF=   1.6649 GoF=  13.922 Rw= 6.58 [ 96 reflections, extinct446= 15]
P 1 2/c 1     nGoF=   1.6649 GoF=  13.922 Rw= 6.58 [ 96 reflections, extinct446= 15]
P 1 2 1       nGoF=   1.8392 GoF=  14.458 Rw= 6.69 [105 reflections, extinct446=  0]
P 1 m 1       nGoF=   1.8392 GoF=  14.458 Rw= 6.69 [105 reflections, extinct446=  0]
P 1 2/m 1     nGoF=   1.8392 GoF=  14.458 Rw= 6.69 [105 reflections, extinct446=  0]
P 1           nGoF=   3.2428 GoF=  14.982 Rw= 6.80 [186 reflections, extinct446=  0]
P -1          nGoF=   3.2428 GoF=  14.982 Rw= 6.80 [186 reflections, extinct446=  0]
P 1 21/n 1    nGoF=   5.4155 GoF=  26.697 Rw= 9.11 [ 92 reflections, extinct446= 19]
P 1 2/n 1     nGoF=   5.7672 GoF=  27.063 Rw= 9.17 [ 96 reflections, extinct446= 17]
P 1 n 1       nGoF=   5.7672 GoF=  27.063 Rw= 9.17 [ 96 reflections, extinct446= 17]
Chosen spacegroup (smallest nGoF):  P 1 21/c 1
ct)
  (#  7) P 1 n 1       : Rwp=  9.17%  GoF=    27.06  nGoF=     5.77  ( 96 reflections,  17 extinct)
  (#  7) P 1 a 1       : Rwp=  9.26%  GoF=    27.58  nGoF=     5.97  ( 97 reflections,  14 extinct)
  (#  8) C 1 m 1       : Rwp= 62.70%  GoF=  1246.13  nGoF=   311.68  ( 52 reflections,  84 extinct) [same extinctions as:C 1 2 1]
  (#  8) A 1 m 1       : Rwp= 62.83%  GoF=  1253.74  nGoF=   313.87  ( 53 reflections,  85 extinct) [same extinctions as:A 1 2 1]
  (#  8) I 1 m 1       : Rwp= 60.91%  GoF=  1196.43  nGoF=   246.94  ( 52 reflections,  87 extinct) [same extinctions as:I 1 2 1]
  (#  9) C 1 c 1       : Rwp= 62.51%  GoF=  1236.15  nGoF=   280.76  ( 47 reflections,  93 extinct)
  (#  9) A 1 n 1       : Rwp= 62.99%  GoF=  1258.62  nGoF=   291.60  ( 49 reflections,  93 extinct)
  (#  9) I 1 a 1       : Rwp= 59.00%  GoF=  1120.91  nGoF=   221.05  ( 48 reflections,  93 extinct)
  (#  9) A 1 a 1       : Rwp= 62.99%  GoF=  1258.62  nGoF=   291.60  ( 49 reflections,  93 extinct) [same extinctions as:A 1 n 1]
  (#  9) C 1 n 1       : Rwp= 62.51%  GoF=  1236.15  nGoF=   280.76  ( 47 reflections,  93 extinct) [same extinctions as:C 1 c 1]
  (#  9) I 1 c 1       : Rwp= 59.00%  GoF=  1120.91  nGoF=   221.05  ( 48 reflections,  93 extinct) [same extinctions as:I 1 a 1]
  (# 10) P 1 2/m 1     : Rwp=  6.69%  GoF=    14.46  nGoF=     1.84  (105 reflections,   0 extinct) [same extinctions as:P 1 2 1]
  (# 11) P 1 21/m 1    : Rwp=  6.61%  GoF=    14.11  nGoF=     1.65  (101 reflections,   2 extinct) [same extinctions as:P 1 21 1]
  (# 12) C 1 2/m 1     : Rwp= 62.70%  GoF=  1246.13  nGoF=   311.68  ( 52 reflections,  84 extinct) [same extinctions as:C 1 2 1]
  (# 12) A 1 2/m 1     : Rwp= 62.83%  GoF=  1253.74  nGoF=   313.87  ( 53 reflections,  85 extinct) [same extinctions as:A 1 2 1]
  (# 12) I 1 2/m 1     : Rwp= 60.91%  GoF=  1196.43  nGoF=   246.94  ( 52 reflections,  87 extinct) [same extinctions as:I 1 2 1]
  (# 13) P 1 2/c 1     : Rwp=  6.58%  GoF=    13.92  nGoF=     1.66  ( 96 reflections,  15 extinct) [same extinctions as:P 1 c 1]
  (# 13) P 1 2/n 1     : Rwp=  9.17%  GoF=    27.06  nGoF=     5.77  ( 96 reflections,  17 extinct) [same extinctions as:P 1 n 1]
```

--------------------------------

### Activate environment and launch Jupyter

Source: https://pyobjcryst.readthedocs.io/en/latest/index.html

Activate the created environment and start the Jupyter Lab interface.

```bash
conda activate pyobjcryst
jupyter-lab
```

--------------------------------

### Install pyobjcryst via diffpy channel

Source: https://pyobjcryst.readthedocs.io/en/latest/index.html

Install the library using the diffpy channel.

```bash
conda install -c diffpy libobjcryst pyobjcryst
```

--------------------------------

### Setup and import libraries for parallel structure solution

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-multiprocessing.html

Imports required packages including multiprocess (instead of standard multiprocessing due to IPython issues), pyobjcryst, numpy, matplotlib, and optional ipywidgets. It also calculates the number of processor cores available, using psutil if available, and prints the count. This setup is necessary before running the meta-structure solution workflow.

```python
%matplotlib widget

import os
import sys
import io
import timeit

# Note: we need 'multiprocess' instead of standard multiprocessing because of the
# following issue (specific to ipython or notebooks):
# https://bugs.python.org/issue25053
# https://stackoverflow.com/questions/41385708/multiprocessing-example-giving-attributeerror
#
# In a python script (not in ipython or a notebook) multiprocessing could be used instead
try:
    from multiprocess import Pool, current_process
except ImportError:
    print("Please install `multiprocess` using 'pip', 'conda' or 'mamba' to run this notebook")
    print()
    sys.exit()

import pyobjcryst
import numpy as np
import matplotlib.pyplot as plt
from pyobjcryst.crystal import *
from pyobjcryst.powderpattern import *
from pyobjcryst.indexing import *
from pyobjcryst.molecule import *
from pyobjcryst.globaloptim import MonteCarlo
from pyobjcryst.io import xml_cryst_file_save_global
try:
    import ipywidgets as widgets
except ImportError:
    widgets = None

try:
    # Get the real number of processor cores available - requires psutil
    # os.sched_getaffinity is only available on some *nix platforms
    import psutil
    nproc = len(os.sched_getaffinity(0)) * psutil.cpu_count(logical=False) // psutil.cpu_count(logical=True)
except:
    nproc = os.cpu_count()

print("Number of processors or cores available: ", nproc)
  
```

```text
Number of processors or cores available:  8

```

--------------------------------

### XML Loading Output

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-multiprocessing.html

Example output showing the loading of a crystal structure from XML.

```text
XML: Loading Crystal:
XML: Loading Crystal:(spg:P 1 21/c 1)
Input ScatteringPowerAtom:C(C)
Input ScatteringPowerAtom:N(N)
Input ScatteringPowerAtom:S(S)
```

--------------------------------

### Importing pyobjcryst and dependencies

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-cimetidine.html

Initial setup for the notebook, including matplotlib configuration for interactive plotting and necessary pyobjcryst modules.

```python
# 'widget' allows live update and works in both classical notebooks and jupyter-lab.
# Otherwise 'notebook', 'ipympl', 'inline' can be used
%matplotlib widget

import os
import pyobjcryst
import numpy as np
import matplotlib.pyplot as plt
from pyobjcryst.crystal import *
from pyobjcryst.powderpattern import *
from pyobjcryst.indexing import *
from pyobjcryst.molecule import *
from pyobjcryst.globaloptim import MonteCarlo
from pyobjcryst.io import xml_cryst_file_save_global
```

--------------------------------

### Imports and matplotlib backend setup

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-pbso4.html

Sets up the matplotlib backend for live updates in notebooks and imports the necessary pyobjcryst modules and standard libraries. The '%matplotlib widget' line enables interactive plots; alternatives include 'notebook', 'ipympl', or 'inline'.

```python
# 'widget' allows live update and works in both classical notebooks and jupyter-lab.
# Otherwise 'notebook', 'ipympl', 'inline' can be used
%matplotlib widget

import os
import pyobjcryst
import numpy as np
import matplotlib.pyplot as plt
from pyobjcryst.crystal import *
from pyobjcryst.scatteringpower import ScatteringPowerAtom
from pyobjcryst.atom import Atom
from pyobjcryst.polyhedron import MakeTetrahedron
from pyobjcryst.powderpattern import *
from pyobjcryst.radiation import RadiationType
from pyobjcryst.indexing import *
from pyobjcryst.molecule import *
from pyobjcryst.globaloptim import MonteCarlo
from pyobjcryst.io import xml_cryst_file_save_global
```

--------------------------------

### Run multiple optimizations with automatic LSQ and live widgets

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-cimetidine.html

Configures automatic least squares refinement every 150k trials, displays the 3D structure and progress widgets, then runs 3 parallel tempering runs of 1 million trials each. Each run starts from a randomized configuration. The output shows the LSQ option and final log-likelihood.

```python
mc.GetOption("Automatic Least Squares Refinement").SetChoice(2)
print("LSQ option: ", mc.GetOption("Automatic Least Squares Refinement").GetChoiceName(2))

# 3D structure view which will be live-updated with the best
# configuration of the current run
display(c.widget_3d())

# Small widget to see the progress of the optimisation, with the current run
# best log-likelihood, the run number and remaining number of trials.
display(mc.widget())

# The powder pattern plot a few cells above should also be updated for each run best solution
mc.MultiRunOptimize(nb_run=3, nb_step=1e6)
print("Final LLK: %.2f" % mc.GetLogLikelihood())
```

```text
LSQ option:  Every 150000 trials, and at the end of each run
```

```text
Final LLK: 18608.95
```

--------------------------------

### Setup and Spacegroup Optimization Function

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-multiprocessing.html

This code downloads the cimetidine z-matrix, configures the Monte-Carlo optimization, and defines a function to test specific spacegroups by adjusting the number of independent molecules and running parallel optimizations.

```python
# Get the cimetidine z-matrix
if not os.path.exists("cime.fhz"):
    os.system("curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.fhz")

# Disable dynamical occupancy correction (no special position expected, usual in organic structures)
c.GetOption(1).SetChoice(0)

# Create the Monte-Carlo object with the crystal and the powder pattern
mc = MonteCarlo()
mc.AddRefinableObj(c)
mc.AddRefinableObj(p)
mc.GetOption("Automatic Least Squares Refinement").SetChoice(2)

nb_step = 1e6  # Increase this for more complex problems - it's ok if the spacegroup is correct (1 independent molecule)

# Disable profile fitting for the powder pattern (or nothing gets optimised !)
pdiff.SetExtractionMode(False)


def solve_for_spacegroup(spgname):
    # Set spacegroup
    spg = c.GetSpaceGroup()
    spg.ChangeSpaceGroup(spgname)

    # Multiplicity
    mult = spg.GetNbSymmetrics()

    # Empty Crystal of existing scatterers (should not be necessary here)
    for i in range(c.GetNbScatterer()):
        c.RemoveScatterer(c.GetScatterer(0))

    # Add 4/mult independent cimetidine molecules
    nb_mol = 4//mult
    for i in range(nb_mol):
        m = ImportFenskeHallZMatrix(c,"cime.fhz")

    # Disable all display update if not in the Main process-or strange things happen !
    if current_process().name != 'MainProcess':
        # we must do that for the crystal, monte-carlo and powder pattern object
        for o in (c, mc, p):
            o.disable_display_update()

    # Run the parallel tempering optimisation
    # We could run multiple times the optimisation (using nb_run), but it is more
    # efficient to distribute that using the multiprocessing pool
    nb_run = 1
    t0 = timeit.default_timer()
    for i in range(nb_run):
        mc.MultiRunOptimize(nb_run=1, nb_step=nb_step)
        dt = timeit.default_timer() - t0
        print("Spacegroup: %12s LLK: %12.2f dt=%3.0fs" %  # /run (%3.0fs remaining)
              (spgname,mc.GetLogLikelihood(), dt,)) #  dt/(i+1) * (nb_run-i-1)

    # Extract all solutions as the XML output of the crystal
    vsol = []
    for i in range(mc.GetNbParamSet()-nb_run, mc.GetNbParamSet()):
        mc.RestoreParamSet(i, update_display=False)
        s = c.xml()
        llk = mc.GetLogLikelihood()
        vsol.append({'xml': s, 'llk': llk, 'spg': spgname, 'nb_mol': nb_mol})

    return vsol
```

--------------------------------

### Create powder pattern and download data (pbso4-x.dat)

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-pbso4.html

Initializes a PowderPattern, downloads the X-ray data file 'pbso4-x.dat' if not present, imports it with ImportPowderPatternFullprof, sets the wavelength to 'Cu', prints the radiation, and plots the pattern.

```python
px = PowderPattern()
if not os.path.exists("pbso4-x.dat"):
    os.system("curl -o pbso4-x.dat https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-pbso4/xray.dat")
px.ImportPowderPatternFullprof("pbso4-x.dat")
px.SetWavelength("Cu")  # Valid strings for X-ray tubes are "Cu", "CuA1",...
print(px.GetRadiation()) # Better check the string was understood
px.plot()
```

--------------------------------

### Install pyobjcryst via conda-forge

Source: https://pyobjcryst.readthedocs.io/en/latest/index.html

Install the library using the conda-forge channel, which is recommended for frequent updates.

```bash
conda install -c conda-forge libobjcryst pyobjcryst
```

--------------------------------

### Fit profile and background with quick_fit_profile

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-pbso4.html

Fits the profile and background using a maximum sin(theta)/lambda of 0.3. The first call adds an automatic background and initializes the profile; the second refines asymmetry and displacement. The output shows the fit result and a reminder to disable profile fitting before structure optimization.

```python
px.SetMaxSinThetaOvLambda(0.3)
px.quick_fit_profile(auto_background=True,plot=False, init_profile=True,verbose=True)
px.quick_fit_profile(plot=False, init_profile=False, asym=True, displ_transl=True, verbose=False)

# Plot in new figure
px.plot(diff=True, fig=None, hkl=True)
print("Fit result: Rw=%6.2f%% Chi2=%10.2f  GoF=%8.2f  LLK=%10.3f" %
      (px.rw * 100, px.chi2, px.chi2/px.GetNbPointUsed(), px.llk))
```

```text
No background, adding one automatically
Selected PowderPatternDiffraction:    with Crystal:
Profile fitting finished.
Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object
to disable profile fitting and optimise the structure.
Fit result: Rw=  8.06% Chi2=   7704.09  GoF=    1.28  LLK=  1194.686
```

--------------------------------

### Create powder pattern, index, and fit profile

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-multiprocessing.html

This snippet imports a powder pattern from 'cime.dat' (downloaded if missing), sets the wavelength, finds peaks, indexes the unit cell, creates a crystal, adds a powder pattern diffraction, fits the profile, and plots the result. It prints the indexed unit cell and fit statistics (Rw, Chi2, GoF, LLK).

```python
p = PowderPattern()
if not os.path.exists("cime.dat"):
    os.system("curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.dat")
p.ImportPowderPatternFullprof("cime.dat")
p.SetWavelength(1.52904)

# Index
pl = p.FindPeaks(1.5, -1, 1000, verbose=False)
if len(pl) > 20:
    pl.resize(20)  # Only keep 20 peaks

ex = quick_index(pl, verbose=False)

print("Indexed unit cell:")
for s in ex.GetSolutions():
    print(s)

# Use solution to create a crystal
uc = ex.GetSolutions()[0][0].DirectUnitCell()
c = Crystal(uc[0], uc[1], uc[2], uc[3], uc[4], uc[5], "P1")
pdiff = p.AddPowderPatternDiffraction(c)

# Fit profile
p.SetMaxSinThetaOvLambda(0.3)
p.quick_fit_profile(auto_background=True,plot=False, init_profile=True,verbose=True)
p.quick_fit_profile(plot=False, init_profile=False, asym=True, displ_transl=True, verbose=False)

# Plot
p.plot(diff=True, fig=None, hkl=True)
print("Fit result: Rw=%6.2f%% Chi2=%10.2f  GoF=%8.2f  LLK=%10.3f" %
      (p.rw * 100, p.chi2, p.chi2/p.GetNbPointUsed(), p.llk))
```

```text
Imported powder pattern: 7699 points, 2theta=  8.010 ->  84.990, step= 0.010
Indexed unit cell:
( 6.83 18.82 10.39  90.0 106.4  90.0 V=1281 MONOCLINIC P, 130.0296630859375)
No background, adding one automatically
Selected PowderPatternDiffraction:    with Crystal:
Profile fitting finished.
Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object
to disable profile fitting and optimise the structure.
Fit result: Rw=  5.51% Chi2=  34095.69  GoF=    4.43  LLK=  6397.991
```

--------------------------------

### Set up notebook environment and import pyobjcryst modules

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/Quantitative-phase-analysis.html

Use this snippet to enable live plotting with %matplotlib widget and import the required pyobjcryst, numpy, and matplotlib modules. The widget backend allows live updates in classical notebooks and Jupyter Lab; alternatives include 'notebook', 'ipympl', and 'inline'.

```python
# 'widget' allows live update and works in both classical notebooks and jupyter-lab.
# Otherwise 'notebook', 'ipympl', 'inline' can be used
%matplotlib widget

import os
import pyobjcryst
import numpy as np
import matplotlib.pyplot as plt
from pyobjcryst.crystal import *
from pyobjcryst.powderpattern import *
from pyobjcryst.indexing import *
from pyobjcryst.molecule import *
from pyobjcryst.globaloptim import MonteCarlo
from pyobjcryst.io import xml_cryst_file_save_global
from pyobjcryst.lsq import LSQ
from pyobjcryst.refinableobj import refpartype_scattdata_scale

```

--------------------------------

### Refine profile and lattice parameters with LSQ

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/Quantitative-phase-analysis.html

Sets up an LSQ refinement, fixes all parameters, then unfixes the scale, W, Zero, Eta0, a, b, and c parameters. Uses SafeRefine with Levenberg-Marquardt for 10 cycles. The comment notes that parameter names are unique and get renamed with tildes for multiple phases.

```python
lsq = LSQ()
lsq.SetRefinedObj(p, 0, True, True)
lsq.PrepareRefParList(True)
# lsq.GetCompiledRefinedObj().Print()
lsqr = lsq.GetCompiledRefinedObj()

lsqr.FixAllPar()
# lsqr.Print()
# print(lsq.ChiSquare())
lsq.SetParIsFixed(refpartype_scattdata_scale, False)
for par in ["W", "Zero", "Eta0", "a", "b", "c"]:
    for i in range(p.GetNbPowderPatternComponent()):
        # This is a KLUDGE - we need this because parameter names are
        # unique, and thus "U" gets renamed to "U~", "U~~" in case of 2,3 phases..
        lsq.SetParIsFixed(par + "~"*i, False)
lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True)

p.UpdateDisplay()
  
```

--------------------------------

### Create PowderPattern and import cimetidine data

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-cimetidine.html

Creates a PowderPattern object, downloads the cimetidine data file if it does not exist, imports it using ImportPowderPatternFullprof, sets the wavelength to 1.52904, and plots the pattern.

```python
p = PowderPattern()
if not os.path.exists("cime.dat"):
    os.system("curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.dat")
p.ImportPowderPatternFullprof("cime.dat")
p.SetWavelength(1.52904)

p.plot()
```

--------------------------------

### Display 3D crystal structure widget

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-cimetidine.html

Displays the 3D crystal structure widget in a Jupyter notebook. Requires installing ipywidgets and py3Dmol via pip (conda-forge version is obsolete as of 2021/05). The widget updates live during optimization and when RestoreParamSet() is called. The display() call is only needed if c.widget_3d() is not the last command in the cell.

```python
display(c.widget_3d())
```

--------------------------------

### Create Powder Pattern and Plot

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/Quantitative-phase-analysis.html

Creates a PowderPattern object, downloads the data file if needed, imports the observed pattern, sets the wavelength to Cu K-alpha1+alpha2, prints the wavelength, and plots with hkl markers.

```python
p = PowderPattern()
if not os.path.exists(data_file):
    os.system("curl -O https://www.iucr.org/__data/iucr/powder/QARR/col/%s" % data_file)

p.ImportPowderPattern2ThetaObs(data_file)
# Copper K-alpha1+alpha2. Use "Cua1" for Cu-alpha1 only
p.SetWavelength("Cu")
print(p.GetWavelength())

p.plot(hkl=True)
```

```text
1.5418366591135662
Imported powder pattern: 7251 points, 2theta=  5.000 -> 150.000, step= 0.020
```

--------------------------------

### Activate environment and launch Jupyter Lab

Source: https://pyobjcryst.readthedocs.io

Activates the pyobjcryst environment and launches Jupyter Lab. Run these commands after creating the environment with mamba.

```bash
conda activate pyobjcryst
jupyter-lab

```

--------------------------------

### Create neutron powder pattern and fit profile

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-pbso4.html

This snippet imports the neutron data, sets the radiation to neutron and wavelength to 1.909, adds the powder pattern diffraction, fixes the W profile parameter, and performs profile fitting. It prints the final fit results (Rw, Chi2, GoF, LLK). The output shows the download progress and the imported pattern details.

```python
pn = PowderPattern()
if not os.path.exists("pbso4-n.dat"):
    os.system("curl -o pbso4-n.dat https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-pbso4/neutron.dat")
pn.ImportPowderPatternFullprof("pbso4-n.dat")
pn.GetRadiation().SetRadiationType(RadiationType.RAD_NEUTRON)
pn.SetWavelength(1.909)
pn.SetMaxSinThetaOvLambda(0.3)
pdiffn = pn.AddPowderPatternDiffraction(c)

#
pdiffn.GetProfile().GetPar("W").SetValue(0.001)


pn.FitScaleFactorForIntegratedRw()

# Plot
pn.plot(diff=True, fig=None, hkl=True)

# Fit profile - we keep the unit cell fixed as it was already refined
pn.quick_fit_profile(auto_background=True,plot=True, init_profile=True, cell=False, verbose=True)
pn.quick_fit_profile(plot=False, init_profile=False, cell=False, asym=True, displ_transl=True,
                     backgd=False, verbose=False)

print("Fit result: Rw=%6.2f%% Chi2=%10.2f  GoF=%8.2f  LLK=%10.3f" %
      (pn.rw * 100, pn.chi2, pn.chi2/pn.GetNbPointUsed(), pn.llk))
```

```text
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 17902  100 17902    0     0  45463      0 --:--:-- --:--:-- --:--:-- 45552
```

```text
Imported powder pattern: 2920 points, 2theta= 10.000 -> 155.950, step= 0.050
No background, adding one automatically
Selected PowderPatternDiffraction:    with Crystal:
Profile fitting finished.
Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object
to disable profile fitting and optimise the structure.
Fit result: Rw=  4.51% Chi2=    874.61  GoF=    0.30  LLK=   219.850
```

--------------------------------

### Create pyobjcryst environment and launch jupyter-lab

Source: https://pyobjcryst.readthedocs.io/en/latest/_sources/index.rst.txt

Create a new conda environment named pyobjcryst with all useful dependencies, then activate it and launch jupyter-lab. This is the fastest way to use pyobjcryst.

```bash
mamba create -n pyobjcryst python=3.11 pyobjcryst matplotlib py3dmol jupyterlab ipympl multiprocess
```

```bash
conda activate pyobjcryst
jupyter-lab
```

--------------------------------

### Find peaks and index reflections with quick_index

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/structure-solution-powder-cimetidine.html

Finds peaks using FindPeaks with a threshold of 1.5, keeps only the first 20 peaks, prints each peak, and then indexes the unit cell using quick_index, printing the solutions.

```python
# Index
pl = p.FindPeaks(1.5, -1, 1000)
if len(pl) > 20:
    pl.resize(20)  # Only keep 20 peaks
for peak in pl:
    print(peak)

ex = quick_index(pl)

print("Solutions:")
for s in ex.GetSolutions():
    print(s)
```

--------------------------------

### Add Crystalline Phases from CIF Files

Source: https://pyobjcryst.readthedocs.io/en/latest/examples/Quantitative-phase-analysis.html

Loops over COD IDs, creates crystal structures from CIF URLs, adds them as diffraction components, fits scale factors, and updates the display.

```python
for cod_id in cod_phases:
    c = CreateCrystalFromCIF("http://crystallography.net/cod/%d.cif" % cod_id)
    #print(c,"\n")
    p.AddPowderPatternDiffraction(c)

p.FitScaleFactorForRw()
p.UpdateDisplay()
```

```text
========================== WARNING =========================
   In ScatteringPowerAtom::GetTemperatureFactor():
   Anisotropic Displacement Parameters are not currently properly handled
   for Debye-Waller calculations (no symmetry handling for ADPs).
   =>The Debye-Waller calculations will instead use only isotropic DPs
```
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