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cctbx.snip
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cctbx.snip
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snippet millerMtzImports
abbr millerMtzImports
alias millerMtzImports
from iotbx.reflection_file_reader import any_reflection_file
hkl_file = any_reflection_file("${1:3hz7.mtz}")
miller_arrays = hkl_file.as_miller_arrays(merge_equivalents=False)
$0
snippet millerArrayLabels
abbr millerArrayLabels
alias millerArrayLabels
[print("Miller Array %s: %s" % (i, miller_array.info().labels)) for i, miller_array in list(enumerate(miller_arrays))[:2]]
$0
snippet millerArrayWavelengths
abbr millerArrayWavelengths
alias millerArrayWavelengths
[print("Miller Array %s: %s" % (i, miller_array.info().wavelength)) for i, miller_array in list(enumerate(miller_arrays))]
$0
snippet millerArraySources
abbr millerArraySources
alias millerArraySources
print("Miller Array %s: %s" % (i, miller_array.info().source)) for i, miller_array in list(enumerate(miller_arrays))]
$0
snippet millerArrayLengths
abbr millerArrayLengths
alias millerArrayLengths
len(miller_arrays)
$0
snippet millerArraySymmetry
abbr millerArraySymmetry
alias millerArraySymmetry
[print("Miller Array %s: %s" % (i, miller_array.info().crystal_symmetry_from_file)) for i, miller_array in list(enumerate(miller_arrays))]
$0
snippet millerArrayHKLs
abbr millerArrayHKLs
alias millerArrayHKLs
[print(hkl) for hkl in miller_arrays[${1:0}].indices()]
$0
snippet millerArrayMethods
abbr millerArrayMethods
alias millerArrayMethods
dir(miller_arrays[${1:0}])
$0
snippet millerArraydstar
abbr millerArraydstar
alias millerArraydstar
miller_arrays[${1:0}].min_max_d_star_sq()
$0
snippet millerArrayDminDmax
abbr millerArrayDminDmax
alias millerArrayDminDmax
miller_arrays[${1:0}].d_max_min()
$0
snippet millerArrayIsigma
abbr millerArrayIsigma
alias millerArrayIsigma
miller_arrays[${1:0}].i_over_sig_i()
$0
snippet millerArrayCC
abbr millerArrayCC
alias millerArrayCC
miller_arrays[${1:0}].cc_one_half_sigma_tau()
$0
snippet millerArrayCConeHalf
abbr millerArrayCConeHalf
alias millerArrayCConeHalf
miller_arrays[${1:0}].cc_one_half()
$0
snippet millerArrayBijvoetRatios
abbr millerArrayBijvoetRatios
alias millerArrayBijvoetRatios
[print(i) for i in miller_arrays[${1:0}].bijvoet_ratios()]
$0
snippet millerArrayMeasurability
abbr millerArrayMeasurability
alias millerArrayMeasurability
miller_arrays[${1:0}].measurability()
$0
snippet millerArrayAnomalousSignal
abbr millerArrayAnomalousSignal
alias millerArrayAnomalousSignal
miller_arrays[${1:0}].anomalous_signal()
$0
snippet millerArrayComprehensiveSummary
abbr millerArrayComprehensiveSummary
alias millerArrayComprehensiveSummary
miller_arrays[${1:0}].show_comprehensive_summary()
$0
snippet millerArrayCountBijvoetPairs
abbr millerArrayCountBijvoetPairs
alias millerArrayCountBijvoetPairs
miller_arrays[${1:0}].n_bijvoet_pairs()
$0
snippet millerArrayWilsonRatio
abbr millerArrayWilsonRatio
alias millerArrayWilsonRatio
miller_arrays[${1:0}].wilson_ratio()
$0
snippet millerArrayUnpackIpIn
abbr millerArrayUnpackIpIn
alias millerArrayUnpackIpIn
Iobs = miller_arrays[${1:0}]
i_plus, i_minus = Iobs.hemispheres_acentrics()
ipd = i_plus.data()
ip=list(ipd)
imd = i_minus.data()
im = list(imd)
len(im)
Iobs.show_summary()
print(Iobs.info())
print(Iobs.observation_type())
$0
snippet millerArrayPrintSelectRows
abbr millerArrayPrintSelectRows
alias millerArrayPrintSelectRows
list(Iobs[${1:100:105}])
$0
snippet millerArrayExtractIntensities
abbr millerArrayExtractIntensities
alias millerArrayExtractIntensities
Iobs = miller_arrays[${1:0}]
iobsdata = Iobs.data()
list(iobsdata[${1:100:110}])
$0
snippet millerArrayPrintntensities
abbr millerArrayPrintntensities
alias millerArrayPrintntensities
[print(hkl) for hkl in miller_arrays[1].data()]
$0
snippet millerArrayconvert2mtz
abbr millerArrayconvert2mtz
alias millerArrayconvert2mtz
# Convert the miller array into a mtz_dataset and write out as a mtz file.
mtz_dataset = Iobs.as_mtz_dataset(column_root_label="${1:I}")
mtz_dataset.mtz_object().write("${2:3hz7intensities}.mtz")
$0
snippet millerArrayReadMtz
abbr millerArrayReadMtz
alias millerArrayReadMtz
mtz_filename2 = "${1:3hz7intensities}.mtz"
mtz_file2 = mtz.object(mtz_filename2)
mtz_file2.column_labels()
$0
snippet millerArray2Dictionary
abbr millerArray2Dictionary
alias millerArray2Dictionary
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:3nd4}.mtz")
# Only works with mtz.object.
# Does not work if mtz is read in with iotbx.file_reader.
miller_arrays_dict = mtz_obj.as_miller_arrays_dict()
$0
snippet millerArrayDictionaryKeys
abbr millerArrayDictionaryKeys
alias millerArrayDictionaryKeys
miller_arrays_dict.keys()
$0
snippet millerArrayDictPrintColumns
abbr millerArrayDictPrintColumns
alias millerArrayDictPrintColumns
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:/Users/blaine/3nd4.mtz}")
# Only works with mtz.object. Does not work if mtz is read in with iotbx.file_reader.
miller_arrays_dict = mtz_obj.as_miller_arrays_dict()
[print(f"Column label: {key[2]}") for key in miller_arrays_dict.keys()]
$0
snippet millerArrayBuildFromUnitCell
abbr millerArrayBuildFromUnitCell
alias millerArrayBuildFromUnitCell
from cctbx import miller
import cctbx
from cctbx import crystal
ms = miller.build_set(
crystal_symmetry=crystal.symmetry(
space_group_symbol="${1:Fd-3m}",
unit_cell=(${2:5.4307,5.4307,5.4307,90.00,90.0,90.00})),
anomalous_flag=${3:True},
d_min=${4:0.4})
[print(hkl) for hkl in ms.indices()]
$0
snippet millerArrayMapToASU
abbr millerArrayMapToASU
alias millerArrayMapToASU
from cctbx import miller
import cctbx
from cctbx import crystal
ms = miller.build_set(
crystal_symmetry=crystal.symmetry(
space_group_symbol="${1:Fd-3m}",
unit_cell=(${2:5.4307,5.4307,5.4307,90.00,90.0,90.00})),
anomalous_flag=${3:True},
d_min=${4:0.4})
msu = ms.map_to_asu()
[print(hkl2) for hkl2 in msu.indices()]
$0
snippet millerArrayPrintSummary
abbr millerArrayPrintSummary
alias millerArrayPrintSummary
from iotbx.reflection_file_reader import any_reflection_file
hkl_in = any_reflection_file(file_name="${1:3nd4}.mtz")
miller_arrays = hkl_in.as_miller_arrays()
f_obs = miller_arrays[0]
f_obs.show_summary()
$0
snippet mtzObjectSummary
abbr mtzObjectSummary
alias mtzObjectSummary
from iotbx import mtz
mtz_obj = mtz.object(file_name="/Users/blaine/${1:3nd4}.mtz")
mtz_obj.show_summary()
$0
snippet millerArrayFromMtz
abbr millerArrayFromMtz
alias millerArrayFromMtz
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:3nd4}.mtz")
miller_arrays = mtz_obj.as_miller_arrays()
$0
snippet millerArrayTruncate
abbr millerArrayTruncate
alias millerArrayTruncate
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:3nd4}.mtz")
miller_arrays = mtz_obj.as_miller_arrays()
miller_array_truncated = miller_arrays[0].resolution_filter(d_min=${2:2}, d_max=${3:5})
print(miller_array_truncated)
miller_array_truncated.show_summary()
$0
snippet millerArrayDictColumnLabels
abbr millerArrayDictColumnLabels
alias millerArrayDictColumnLabels
[print(f"Column label: {key[2]}") for key in miller_arrays_dict.keys()]
$0
snippet condaInstall
abbr condaInstall
alias condaInstall
conda create --name ${1:cctbx37} python=${2:3.7}
conda activate ${1:cctbx37}
conda install -c conda-forge cctbx-base jupyter pandas xarray
$0
snippet condaRemove
abbr condaRemove
alias condaRemove
conda remove --name ${1:cctbx37}
snippet fetchPDB
abbr fetchPDB
alias fetchPDB
from iotbx.pdb.fetch import get_pdb
import sys
get_pdb(id="${1:3nd4}",data_type="pdb", mirror="rcsb", format="pdb", log=sys.stdout)
$0
snippet updateCLT
abbr updateCLT
alias updateCLT
sudo touch /tmp/.com.apple.dt.CommandLineTools.installondemand.in-progress
softwareupdate -l
# Update command line tools via software update.
sudo rm /tmp/.com.apple.dt.CommandLineTools.installondemand.in-progress
snippet extractCrystalSymmetry
abbr extractCrystalSymmetry
alias extractCrystalSymmetry
from __future__ import absolute_import, division, print_function
from iotbx import mtz
from cctbx import crystal
def extract_from(file_name):
mtz_object = mtz.object(file_name=file_name)
assert mtz_object.n_symmetry_matrices() > 0
return mtz_object.crystals()[0].crystal_symmetry()
extract_from(file_name="${1:3nd4}.mtz")
$0
snippet plotDstarsLogMeans
abbr plotDstarsLogMeans
alias plotDstarsLogMeans
'''Generate the list of dstars and logMeans as lists
for plotting by matplotlib.'''
used = list(binner.range_used())
selections = [binner.selection(i) for i in used]
# make means of the intensities by bin
means = [Iobs.select(sel).mean() for sel in selections]
from math import log
lnmeans = [log(y) for y in means]
# meansBR = [Iobs.bijvoet_ratios().select(sel).mean() for sel in selections]
# make d_centers
d_star_power = 1.618034
centers = binner.bin_centers(d_star_power)
d_centers = list(centers**(-1 / d_star_power))
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams["savefig.dpi"] = 600
mpl.rcParams["figure.dpi"] = 600
fig, ax = plt.subplots(figsize=[3.25, 2.])
ax.scatter(d_centers,lnmeans,c="k",alpha=0.3,s=5.5)
ax.set_xlim(${1:8, 1.5}) # decreasing time
ax.set_xlabel(r"$d^*$ in $\AA$",fontsize=12)
ax.set_ylabel("ln(I)",fontsize=12)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
ax.grid(False)
plt.savefig("${2:3hz7}iobsvsdstar.pdf",bbox_inches="tight")
plt.show()
$0
snippet plotDstarsMeasurability
abbr plotDstarsMeasurability
alias plotDstarsMeasurability
from iotbx.reflection_file_reader import any_reflection_file
hkl_file = any_reflection_file("${1:3hz7}.mtz")
miller_arrays = hkl_file.as_miller_arrays(merge_equivalents=False)
Iobs = miller_arrays[1]
# Set up the bins
n_bins = 50
binner = Iobs.setup_binner(n_bins=n_bins)
# binner.show_summary()
used = list(binner.range_used())
selections = [binner.selection(i) for i in used]
# make d_centers for the x-axis
d_star_power = 1.618034
centers = binner.bin_centers(d_star_power)
d_centers = list(centers**(-1 / d_star_power))
# make list of the measurabilities by resolution bin
meas = [Iobs.select(sel).measurability() for sel in selections]
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams["savefig.dpi"] = 600
mpl.rcParams["figure.dpi"] = 600
fig, ax = plt.subplots(figsize=[3.25, 2.])
ax.scatter(d_centers,lnmeans,c="k",alpha=0.3,s=5.5)
ax.set_xlim(8, 1.5) # decreasing time
ax.set_xlabel(r"$d^*$ in $\AA$",fontsize=12)
ax.set_ylabel("ln(I)",fontsize=12)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
ax.grid(False)
plt.savefig("${1:3hz7}measureability.pdf",bbox_inches="tight")
plt.show()
$0
snippet computeAllMillerIndices
abbr computeAllMillerIndices
alias computeAllMillerIndices
from cctbx import miller
def generate_reflection_indices(uc, dmin):
maxh, maxk, maxl = uc.max_miller_indices(dmin)
indices = []
for h in range(-maxh, maxh + 1):
for k in range(-maxk, maxk + 1):
for l in range(-maxl, maxl + 1):
if h == 0 and k == 0 and l == 0:
continue
if uc.d((h, k, l)) < dmin:
continue
indices.append((h, k, l))
return indices
uc=(${1:5.4307,5.4307,5.4307,90.00,90.0,90.00})
dmin=${2:1.0}
$0
snippet computeAllMillerIndicesASU
abbr computeAllMillerIndicesASU
alias computeAllMillerIndicesASU
from cctbx import miller
import cctbx
from cctbx import crystal
ms = miller.build_set(
crystal_symmetry=crystal.symmetry(
space_group_symbol="${1:Fd-3m}",
unit_cell=("${2:5.4307,5.4307,5.4307,90.00,90.0,90.00}") ),
anomalous_flag=${3:False},
d_min=${4:0.4})
for hkl in ms.indices():
print(hkl)
# map the reflections to the asu and print
msu = ms.map_to_asu()
[print(hkl2) for hkl2 in msu.indices()]
$0
snippet computeAllMillerIndicesUnitCell
abbr computeAllMillerIndicesUnitCell
alias computeAllMillerIndicesUnitCell
from cctbx import crystal
from cctbx import miller
ms = miller.build_set(
crystal_symmetry=crystal.symmetry(
space_group_symbol="${1:P4}",
unit_cell=(${2:150.8,150.8,250.4,90.0,90.0,90.0})),
anomalous_flag=${3:False},
d_min=${4:1.4})
msu = ms.map_to_asu()
[print(hkl) for hkl in msu.indices()]
print(msu.show_comprehensive_summary())
$0
snippet extractReflectionMtzFile
abbr extractReflectionMtzFile
alias extractReflectionMtzFile
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:Users/blaine/manuscripts/RETkinaseLoxo/ret_blu.mtz}")
mtz_obj.show_summary()
$0
snippet extractReflectionInShell
abbr extractReflectionInShell
alias extractReflectionInShell
from iotbx import mtz
mtz_obj = mtz.object(file_name="${1:2V89}.mtz")
miller_arrays = mtz_obj.as_miller_arrays()
for miller_array in miller_arrays:
miller_array_truncated = miller_array.resolution_filter(d_min=${2:2}, d_max=${3:5})
print(miller_array_truncated)
$0
snippet plotRfactorResolutionBin
abbr plotRfactorResolutionBin
alias plotRfactorResolutionBin
#!/usr/bin/env python
# coding: utf-8
'''
This script reads in a phenix.refine mtz file.
It plots the R-factor by resolution bin.
The plots are made with matplotlib using miller arrays.
It also plots the correlation coefficients.
The plots were made with matplotlib.
This script was adapted from an example script in iotbx:
Source: https://github.com/cctbx/cctbx_project/blob/master/
iotbx/examples/recalculate_phenix_refine_r_factors.py
'''
# get_ipython().run_line_magic("matplotlib", "inline")
from __future__ import absolute_import, division, print_function
from iotbx.reflection_file_utils import get_r_free_flags_scores
from iotbx.file_reader import any_file
import matplotlib
import matplotlib.pyplot as plt
def compute_r_factors(fobs, fmodel, flags):
fmodel, fobs = fmodel.common_sets(other=fobs)
fmodel, flags = fmodel.common_sets(other=flags)
fc_work = fmodel.select(~(flags.data()))
fo_work = fobs.select(~(flags.data()))
fc_test = fmodel.select(flags.data())
fo_test = fobs.select(flags.data())
r_work = fo_work.r1_factor(fc_work)
r_free = fo_test.r1_factor(fc_test)
print("r_work = %.4f" % r_work)
print("r_free = %.4f" % r_free)
print("")
binner = flags.setup_binner(n_bins=20)
d_star_power = 1.618034
centers = binner.bin_centers(d_star_power)
d_centers = list(centers**(-1 / d_star_power))
# for i in d_centers:
# print(i)
fo_work.use_binning_of(flags)
fc_work.use_binner_of(fo_work)
fo_test.use_binning_of(fo_work)
fc_test.use_binning_of(fo_work)
r_work_list = []
r_free_list = []
cc_work_list = []
cc_free_list = []
for i_bin in fo_work.binner().range_all():
sel_work = fo_work.binner().selection(i_bin)
sel_test = fo_test.binner().selection(i_bin)
fo_work_bin = fo_work.select(sel_work)
fc_work_bin = fc_work.select(sel_work)
fo_test_bin = fo_test.select(sel_test)
fc_test_bin = fc_test.select(sel_test)
if fc_test_bin.size() == 0 : continue
r_work_bin = fo_work_bin.r1_factor(other=fc_work_bin,
assume_index_matching=True)
r_work_list.append(r_work_bin)
r_free_bin = fo_test_bin.r1_factor(other=fc_test_bin,
assume_index_matching=True)
r_free_list.append(r_free_bin)
cc_work_bin = fo_work_bin.correlation(fc_work_bin).coefficient()
cc_work_list.append(cc_work_bin)
cc_free_bin = fo_test_bin.correlation(fc_test_bin).coefficient()
cc_free_list.append(cc_free_bin)
legend = flags.binner().bin_legend(i_bin, show_counts=False)
print("%s %8d %8d %.4f %.4f %.3f %.3f" % (legend, fo_work_bin.size(),
fo_test_bin.size(), r_work_bin, r_free_bin, cc_work_bin, cc_free_bin))
return d_centers, r_work_list, r_free_list, cc_work_list, cc_free_list
def plot_r_factors(d_centers, r_work_list, r_free_list):
plt.scatter(d_centers, r_work_list, label=r"$\mathit{R_{work}}$")
plt.scatter(d_centers, r_free_list, label=r"$\mathit{R_{free}}$")
plt.xlabel(r"Resolution ($\mathrm{\AA}$)")
plt.ylabel(r"R-factor (%)")
plt.legend(loc="upper right")
plt.savefig("Rs.pdf")
plt.close()
def plot_cc(d_centers, cc_work_list, cc_free_list):
plt.scatter(d_centers, cc_work_list, label=r"$\mathit{CC_{work}}$")
plt.scatter(d_centers, cc_free_list, label=r"$\mathit{CC_{free}}$")
plt.xlabel(r"Resolution ($\mathrm{\AA}$)")
plt.ylabel(r"Correlation Coefficeint Fo vs Fc (%)")
plt.legend(loc="lower right")
plt.savefig("CCs.pdf")
def run(input_mtz):
mtz_in = any_file(input_mtz)
ma = mtz_in.file_server.miller_arrays
flags = fmodel = fobs = None
# select the output arrays from phenix.refine. This could easily be modified
# to handle MTZ files from other programs.
for array in ma :
labels = array.info().label_string()
if labels.startswith("R-free-flags"):
flags = array
elif labels.startswith("F-model"):
fmodel = abs(array)
elif labels.startswith("F-obs-filtered"):
fobs = array
if (None in [flags, fobs, fmodel]):
raise RuntimeError("Not a valid phenix.refine output file")
scores = get_r_free_flags_scores([flags], None)
test_flag_value = scores.test_flag_values[0]
flags = flags.customized_copy(data=flags.data()==test_flag_value)
(d_centers,
r_work_list,
r_free_list,
cc_work_list,
cc_free_list) = compute_r_factors(fobs, fmodel, flags)
plot_r_factors(d_centers, r_work_list, r_free_list)
plot_cc(d_centers, cc_work_list, cc_free_list)
if (__name__ == "__main__"):
run(input_mtz="${1:28molrepEdited_5_refine_001}.mtz")
$0
snippet plotFcalcsrResolutionBin
abbr plotFcalcsrResolutionBin
alias plotFcalcsrResolutionBin
'''
This script reads in a phenix.refine mtz file.
It plots the R-factor by resolution bin.
The plots are made with matplotlib using miller arrays.
It also plots the correlation coefficients.
The plots were made with matplotlib.
This script was adapted from an example script in iotbx:
Source: https://github.com/cctbx/cctbx_project/blob/master/
iotbx/examples/recalculate_phenix_refine_r_factors.py
'''
# get_ipython().run_line_magic("matplotlib", "inline")
from __future__ import absolute_import, division, print_function
from iotbx.reflection_file_utils import get_r_free_flags_scores
from iotbx.file_reader import any_file
import matplotlib
import matplotlib.pyplot as plt
def compute_r_factors(fobs, fmodel, flags):
fmodel, fobs = fmodel.common_sets(other=fobs)
fmodel, flags = fmodel.common_sets(other=flags)
fc_work = fmodel.select(~(flags.data()))
fo_work = fobs.select(~(flags.data()))
fc_test = fmodel.select(flags.data())
fo_test = fobs.select(flags.data())
r_work = fo_work.r1_factor(fc_work)
r_free = fo_test.r1_factor(fc_test)
print("r_work = %.4f" % r_work)
print("r_free = %.4f" % r_free)
print("")
binner = flags.setup_binner(n_bins=20)
d_star_power = 1.618034
centers = binner.bin_centers(d_star_power)
d_centers = list(centers**(-1 / d_star_power))
# for i in d_centers:
# print(i)
fo_work.use_binning_of(flags)
fc_work.use_binner_of(fo_work)
fo_test.use_binning_of(fo_work)
fc_test.use_binning_of(fo_work)
r_work_list = []
r_free_list = []
cc_work_list = []
cc_free_list = []
for i_bin in fo_work.binner().range_all():
sel_work = fo_work.binner().selection(i_bin)
sel_test = fo_test.binner().selection(i_bin)
fo_work_bin = fo_work.select(sel_work)
fc_work_bin = fc_work.select(sel_work)
fo_test_bin = fo_test.select(sel_test)
fc_test_bin = fc_test.select(sel_test)
if fc_test_bin.size() == 0 : continue
r_work_bin = fo_work_bin.r1_factor(other=fc_work_bin,
assume_index_matching=True)
r_work_list.append(r_work_bin)
r_free_bin = fo_test_bin.r1_factor(other=fc_test_bin,
assume_index_matching=True)
r_free_list.append(r_free_bin)
cc_work_bin = fo_work_bin.correlation(fc_work_bin).coefficient()
cc_work_list.append(cc_work_bin)
cc_free_bin = fo_test_bin.correlation(fc_test_bin).coefficient()
cc_free_list.append(cc_free_bin)
legend = flags.binner().bin_legend(i_bin, show_counts=False)
print("%s %8d %8d %.4f %.4f %.3f %.3f" % (legend, fo_work_bin.size(),
fo_test_bin.size(), r_work_bin, r_free_bin, cc_work_bin, cc_free_bin))
return d_centers, r_work_list, r_free_list, cc_work_list, cc_free_list
def plot_r_factors(d_centers, r_work_list, r_free_list):
plt.scatter(d_centers, r_work_list, label=r"$\mathit{R_{work}}$")
plt.scatter(d_centers, r_free_list, label=r"$\mathit{R_{free}}$")
plt.xlabel(r"Resolution ($\mathrm{\AA}$)")
plt.ylabel(r"R-factor (%)")
plt.legend(loc="upper right")
plt.savefig("Rs.pdf")
plt.close()
def plot_cc(d_centers, cc_work_list, cc_free_list):
plt.scatter(d_centers, cc_work_list, label=r"$\mathit{CC_{work}}$")
plt.scatter(d_centers, cc_free_list, label=r"$\mathit{CC_{free}}$")
plt.xlabel(r"Resolution ($\mathrm{\AA}$)")
plt.ylabel(r"Correlation Coefficeint Fo vs Fc (%)")
plt.legend(loc="lower right")
plt.savefig("CCs.pdf")
def run(input_mtz):
mtz_in = any_file(input_mtz)
ma = mtz_in.file_server.miller_arrays
flags = fmodel = fobs = None
# select the output arrays from phenix.refine. This could easily be modified
# to handle MTZ files from other programs.
for array in ma :
labels = array.info().label_string()
if labels.startswith("R-free-flags"):
flags = array
elif labels.startswith("F-model"):
fmodel = abs(array)
elif labels.startswith("F-obs-filtered"):
fobs = array
if (None in [flags, fobs, fmodel]):
raise RuntimeError("Not a valid phenix.refine output file")
scores = get_r_free_flags_scores([flags], None)
test_flag_value = scores.test_flag_values[0]
flags = flags.customized_copy(data=flags.data()==test_flag_value)
(d_centers,
r_work_list,
r_free_list,
cc_work_list,
cc_free_list) = compute_r_factors(fobs, fmodel, flags)
plot_r_factors(d_centers, r_work_list, r_free_list)
plot_cc(d_centers, cc_work_list, cc_free_list)
if (__name__ == "__main__"):
run(input_mtz="${1:28molrepEdited_5_refine_001}.mtz")
snippet plotIntensityResolutionBin
abbr plotIntensityResolutionBin
alias plotIntensityResolutionBin
from iotbx.file_reader import any_file
import matplotlib.pyplot as plt
f = any_file("${1:/Users/blaine/manuscripts/RETkinaseLoxo/ret_blu.mtz}")
print(f.file_type)
f.show_summary()
miller_arrays = f.file_server.miller_arrays
iobs = miller_arrays[3]
flags = miller_arrays[0]
iobs, flags = iobs.common_sets(other=flags)
iobsData = iobs.data()
list(iobsData[100:110])
iobs.show_comprehensive_summary()
# iobs.binner()
n_bins = ${2:20}
binner = iobs.setup_binner(n_bins=n_bins)
binner.show_summary()
used = list(binner.range_used())
selections = [binner.selection(i) for i in used]
means = [iobs.select(sel).mean() for sel in selections]
from math import log
lnmeans = [log(y) for y in means]
d_star_power = 1.618034
centers = binner.bin_centers(d_star_power)
d_centers = list(centers**(-1 / d_star_power))
d_centers
# plt.ylabel("Natural log of the amplitudes squared")
# plt.xlabel(r"$\textrm{d^*}$ in $\textrm{\AA}$")
# ax.set_xlim(35, 1.5)
# plt.scatter(d_centers,lnmeanss)
fig, ax = plt.subplots()
ax.scatter(d_centers,lnmeans)
ax.set_xlim(${3:8}, ${4:1.5}) # decreasing
ax.set_xlabel(r"$d^*$ in $\AA$")
ax.set_ylabel("Natural log of the intensities")
ax.grid(False)
plt.savefig("${5:iobsvsdstar}.pdf")
$0
snippet cns2mtz
abbr cns2mtz
alias cns2mtz
from iotbx import reflection_file_reader
import os
reflection_file = reflection_file_reader.any_reflection_file(file_name=os.path.expandvars("${1:\$CNS_SOLVE/doc/html/tutorial/data/pen/scale.hkl}"))
from cctbx import crystal
crystal_symmetry = crystal.symmetry( unit_cell=(${2:97.37, 46.64, 65.47, 90, 115.4, 90}), space_group_symbol="${3:C2}")
miller_arrays = reflection_file.as_miller_arrays( crystal_symmetry=crystal_symmetry)
mtz_dataset = None
for miller_array in miller_arrays:
if (mtz_dataset is None):
mtz_dataset = miller_array.as_mtz_dataset(
column_root_label=miller_array.info().labels[0])
else:
mtz_dataset.add_miller_array(
miller_array=miller_array,
column_root_label=miller_array.info().labels[0])
mtz_object = mtz_dataset.mtz_object()
mtz_object.show_summary()
$0
snippet FsigmaFbyabsLindex
abbr FsigmaFbyabsLindex
alias FsigmaFbyabsLindex
import pandas as pd
'''
Use pandas to read in a hkl file with whitespace separators into a dataframe.
Append to the dataframe a column with F/sigmaF values.
Append to the dataframe a column with the absolute value of the L indices.
Average F/sigmaF by absL index.
Write to absL and F/sigmaF to csv file.
'''
mtzdata = pd.read_csv("${1:1524start}.hkl", names=["H","K","L",${2:"F","SIGF"}], sep="\s+")
mtzdata["FovSigF"] = mtzdata.apply(lambda row: row["F"] / row["SIGF"], axis=1)
mtzdata["absL"] = mtzdata.apply(lambda row: abs(row["L"]), axis=1)
FovSigFabsL = mtzdata.groupby([mtzdata.absL]).FovSigF.mean()
FovSigFabsL.to_csv("${3:test2}.csv")
$0
snippet changeMtzColumns
abbr changeMtzColumns
alias changeMtzColumns
from iotbx.reflection_file_reader import any_reflection_file
hkl_in = any_reflection_file("${1:/Users/blaine/manuscripts/RETkinaseLoxo/ret_blu.mtz}")
miller_arrays = hkl_in.as_miller_arrays()
i_obs = miller_arrays[3]
r_free_flags = miller_arrays[0]
f_obs = i_obs.f_sq_as_f()
mtz_dataset = i_obs.as_mtz_dataset(column_root_label="I")
mtz_dataset.add_miller_array(f_obs, column_root_label="F")
mtz_dataset.add_miller_array(r_free_flags,column_root_label="${2:FreeR_flag}")
mtz_dataset.mtz_object().write("${3:loxodata.mtz}")
$0
snippet normalizedStructureFactors
abbr normalizedStructureFactors
alias normalizedStructureFactors
all_e_values = miller_array.quasi_normalize_structure_factors().sort(by_value="data")
$0
snippet readMtzFile
abbr readMtzFile
alias readMtzFile
from iotbx.file_reader import any_file
mtz_in = any_file("${1:data}.mtz", force_type="mtz")
miller_arrays = mtz_in.file_server.miller_arrays
$0
snippet pattersonContourMap
abbr pattersonContourMap
alias pattersonContourMap
import numpy
from matplotlib import pyplot
import gemmi
# https://gemmi.readthedocs.io/en/latest/grid.html
ccp4 = gemmi.read_ccp4_map("${1:/Users/blaine/4bqrPatterson.ccp4}")
ccp4.setup()
arr = numpy.array(ccp4.grid, copy=False)
x = numpy.linspace(0, ccp4.grid.unit_cell.a, num=arr.shape[0], endpoint=False)
y = numpy.linspace(0, ccp4.grid.unit_cell.b, num=arr.shape[1], endpoint=False)
X, Y = numpy.meshgrid(x, y, indexing="ij")
pyplot.rcParams["figure.figsize"] = (8.0, 8.0)
pyplot.contour(X, Y, arr[:,:,0],500, zorder=1,linestyles="solid")
pyplot.gca().set_aspect("equal", adjustable="box")
pyplot.show()
arr2 = numpy.array(ccp4.grid, copy=False)
x = numpy.linspace(0, ccp4.grid.unit_cell.a, num=arr2.shape[0], endpoint=False)
z = numpy.linspace(0, ccp4.grid.unit_cell.c, num=arr2.shape[1], endpoint=False)
X, Z = numpy.meshgrid(x, z, indexing="ij")
pyplot.rcParams["figure.figsize"] = (4.0, 20.5)
pyplot.contour(X, Z, arr[:,:,0],500, zorder=1, linestyles="solid")
pyplot.gca().set_aspect("equal", adjustable="box")
pyplot.savefig("patterson.png", dpi=600)
pyplot.show()
$0
snippet condaInstall2
abbr condaInstall2
alias condaInstall2
conda remove --name cctbx${1:37}
conda create -n ${2:cctbx38} -c conda-forge cctbx-base python=${4:3.8}
conda activate ${2:cctbx38}
conda install -c conda-forge cctbx-base
conda install -c anaconda ipykernel
python -m ipykernel install --user --name ${2:cctbx38} --display-name "${2:cctbx38}"
$0
snippet eigenvalues
abbr eigenvalues
alias eigenvalues
from scitbx.array_family import flex;
from scitbx.linalg import eigensystem;
m = flex.double(($1:-2, -4, 2, -2, 1, 2, 4, 2, 5}));
m.reshape(flex.grid(3,3));
es = eigensystem.real_symmetric(m);
list(es.values());
list(es.vectors());
$0
snippet IpIm
abbr IpIm
alias IpIm
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as ticker
from matplotlib.ticker import MultipleLocator #, FormatStrFormatter
from matplotlib.ticker import FuncFormatter
from iotbx.reflection_file_reader import any_reflection_file
# >>> change the mtz file name
hkl_file = any_reflection_file("${1:3hz7}.mtz")
miller_arrays = hkl_file.as_miller_arrays(merge_equivalents=False)
Iobs = miller_arrays[1]
i_plus, i_minus = Iobs.hemispheres_acentrics()
ipd = i_plus.data()
ip=list(ipd)
imd = i_minus.data()
im = list(imd)
len(im)
comma_fmt = FuncFormatter(lambda x, p: format(int(x), ","))
mpl.rcParams["savefig.dpi"] = 600
mpl.rcParams["figure.dpi"] = 600
# Set to width of a one column on a two-column page.
# May want to adjust settings for a slide.
fig, ax = plt.subplots(figsize=[3.25, 3.25])
ax.scatter(ip,im,c="k",alpha=0.3,s=5.5)
ax.set_xlabel(r"I(+)",fontsize=12)
ax.set_ylabel(r"I(-)",fontsize=12)
ax.xaxis.set_major_locator(MultipleLocator(50000.))
ax.yaxis.set_major_locator(MultipleLocator(50000.))
ax.get_xaxis().set_major_formatter(comma_fmt)
ax.get_yaxis().set_major_formatter(comma_fmt)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
ax.grid(False)
# >>> change name of the figure file
plt.savefig("${1:3hz7}IpIm.pdf",bbox_inches="tight")
$0
snippet fetchFASTA
abbr fetchFASTA
alias fetchFASTA
from iotbx.pdb.fetch import get_pdb
import sys
get_pdb(id="3nd4",data_type="pdb", mirror="rcsb", format="pdb", log=sys.stdout)
$0
snippet fetchXrayCif
abbr fetchXrayCif
alias fetchXrayCif
from iotbx.pdb.fetch import get_pdb
import sys
get_pdb(id="${1:3nd4}",data_type="xray", mirror="rcsb", format="cif", log=sys.stdout)
$0
snippet fetchAtomicCif
abbr fetchAtomicCif
alias fetchAtomicCif
from iotbx.pdb.fetch import get_pdb