{
“cells”: [
{

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## Store the data to HDF5 file for rapid analysis and calculationn”, “n”, “* This tutorial discuss the analyses that can be performed using the [dnaMD Python module](http://do-x3dna.readthedocs.io/en/latest/api_summary.html) included in the _do\_x3dna_ package. The tutorial is prepared using [Jupyter Notebook](https://jupyter.org/) and this notebook tutorial file could be downloaded from this [link](http://rjdkmr.github.io/do_x3dna/tut_notebook/hdf5_tutorial.ipynb).n”, “n”, “n”, “* Download the input files that are used in the tutorial from this [link](http://rjdkmr.github.io/do_x3dna/tutorial_data.tar.gz).n”, “n”, “n”, “* Two following input files are required in this tutorialn”, ” * L-BP_cdna.dat n”, ” * L-BPS_cdna.datn”, ” * L-BPH_cdna.datn”, ” * HelAxis_cdna.datn”, ” * MGroove_cdna.datn”, ” * BackBoneCHiDihedrals_cdna.datn”, ” n”, ” These files should be present inside tutorial_data of the current/present working directory.n”, ” n”, ” n”, “* The Python APIs should be only used when do_x3dna is executed with -ref option.n”, “n”, “n”, “* Detailed documentation is provided [here](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html).”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Importing Python Modulesn”, “n”, “* [numpy](http://www.numpy.org/): Required for the calculations involving large arraysn”, “n”, “n”, “* [matplotlib](http://matplotlib.org/): Required to plot the resultsn”, “n”, “n”, “* [dnaMD](http://do-x3dna.readthedocs.io/en/latest/api_summary.html): Python module to analyze DNA/RNA structures from the do_x3dna output files.n”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “metadata”: {

“collapsed”: true

}, “outputs”: [], “source”: [

“import osn”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “import dnaMDn”, “%matplotlib inlinen”, “n”, “n”, “try:n”, ” os.remove(‘cdna.h5’)n”, “except:n”, ” passn”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Initializing DNA object with HDF5 filen”, “n”, “* [DNA object](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA) is initialized by using the total number of base-pairsn”, “n”, “n”, “To store the data in HDF5 file, just initialize the class with the filename as shown below. Here, we named the HDF5 file as cdna.h5.n”, “n”, “NOTE: Except initialization, all other methods and functions can be used in similar ways.”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “metadata”: {

“collapsed”: true

}, “outputs”: [], “source”: [

“# Initializationn”, “dna = dnaMD.DNA(60, filename=’cdna.h5’) #Initialization for 60 base-pairs DNA bound with the proteinn”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Store/Save data to HDF5 filen”, “n”, “No extra step neccessary to store the data in HDF5 file. Just read the parameters from do_x3dna output files as described in previous tutorials.n”, “n”, “n”, “* Local base-pair parameters as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/base_pairs_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).n”, “* Local base-step parameters as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/base_steps_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).n”, “* Local helical base-step parameters as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/helical_steps_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).n”, “* Helical axis as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/helical_axis_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).n”, “* Major and minor grooves as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/major_minor_groove_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).n”, “* Backbone dihedrals as shown [preveiosly here](http://do-x3dna.readthedocs.io/en/latest/notebooks/backbone_torsion_wheel_tutorial.html#Initializing-DNA-object-and-storing-data-to-it).”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“n”, “Reading file : tutorial_data/L-BP_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”, “n”, “Reading file : tutorial_data/L-BPS_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”, “n”, “Reading file : tutorial_data/L-BPH_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”, “n”, “Reading file : tutorial_data/HelAxis_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”, “|frame: 526| WARNING: Bending angle [-1-0-1] = 20.14 is more than cut-off angle 20;n”, ” Four maximum distances between three adjacent axis positions = (16.4, 14.4, 13.9, 13.5);n”, ” Deleting [45, 46] original helical axis positions to remove possible fitting artifact…n”, “|frame: 549| WARNING: Bending angle [-1-0-1] = 27.93 is more than cut-off angle 20;n”, ” Four maximum distances between three adjacent axis positions = (21.0, 19.1, 15.9, 15.3);n”, ” Deleting [2, 3] original helical axis positions to remove possible fitting artifact…n”, “|frame: 636| WARNING: Bending angle [-1-0-1] = 33.38 is more than cut-off angle 20;n”, ” Four maximum distances between three adjacent axis positions = (14.9, 14.6, 14.2, 13.8);n”, ” Deleting [5, 6] original helical axis positions to remove possible fitting artifact…n”, “|frame: 640| WARNING: Bending angle [-1-0-1] = 28.03 is more than cut-off angle 20;n”, ” Four maximum distances between three adjacent axis positions = (16.9, 14.0, 13.4, 13.4);n”, ” Deleting [2, 3] original helical axis positions to remove possible fitting artifact…n”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

“/home/rajendra/workspace/github/do_x3dna/dnaMD/dnaMD/dnaMD.py:2588: RuntimeWarning: Mean of empty slice.n”, ” xsmooth.append(xnew[start:end].mean())n”, “/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalarsn”, ” ret = ret.dtype.type(ret / rcount)n”, “/home/rajendra/workspace/github/do_x3dna/dnaMD/dnaMD/dnaMD.py:2589: RuntimeWarning: Mean of empty slice.n”, ” ysmooth.append(ynew[start:end].mean())n”, “/home/rajendra/workspace/github/do_x3dna/dnaMD/dnaMD/dnaMD.py:2590: RuntimeWarning: Mean of empty slice.n”, ” zsmooth.append(znew[start:end].mean())n”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Fitting spline curve on helcial axis of frame 1000 out of 1001 framesn”, “Finished spline curve fitting…n”, “n”, “Reading file : tutorial_data/MGroove_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”, “n”, “Reading file : tutorial_data/BackBoneCHiDihedrals_cdna.datn”, “Reading frame 1000n”, “Finishid reading…. Total number of frame read = 1001n”

]

}

], “source”: [

“# Read Local base-pair parametersn”, “dna.set_base_pair_parameters(‘tutorial_data/L-BP_cdna.dat’, bp=[1, 60], bp_range=True)n”, “n”, “# Read Local base-step parametersn”, “dna.set_base_step_parameters(‘tutorial_data/L-BPS_cdna.dat’, bp_step=[1, 59], parameters=’All’, step_range=True)n”, “n”, “# Read Local helical base-step parametersn”, “dna.set_base_step_parameters(‘tutorial_data/L-BPH_cdna.dat’, bp_step=[1, 59], parameters=’All’, step_range=True, helical=True)n”, “n”, “# Read Helical axisn”, “dna.set_helical_axis(‘tutorial_data/HelAxis_cdna.dat’)n”, “n”, “# Generate global axis by interpolation (smoothening)n”, “dna.generate_smooth_axis(smooth=500, spline=3, fill_point=6)n”, “n”, “# Calculate curvature and tangent along global helical axisn”, “dna.calculate_curvature_tangent(store_tangent=True)n”, “n”, “# Major and minor grooves n”, “parameters = [ ‘minor groove’, ‘minor groove refined’, ‘major groove’, ‘major groove refined’ ]n”, “dna.set_major_minor_groove(‘tutorial_data/MGroove_cdna.dat’, bp_step=[1, 59], parameters=parameters, step_range=True)n”, “n”, “#Backbone dihedralsn”, “dna.set_backbone_dihedrals(‘tutorial_data/BackBoneCHiDihedrals_cdna.dat’, bp=[2, 59], parameters=’All’, bp_range=True)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Example to extract a parametern”, “As shown [previously here](http://do-x3dna.readthedocs.io/en/latest/notebooks/base_pairs_tutorial.html#Local-base-pair-parameter-of-a-base-pair-directly-from-dictionary), data can be extracted from HDF5 by same way as shown in the following.n”, “n”, “Also, see that plot is similar.n”, “n”, “n”, “Note that in this case, data is read from the HDF5 file, while in the previous tutorial, data was stored in memory (RAM).”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [

{
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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f39d25deba8>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“# Extracting "Shear" of 22nd bpn”, “shear_20bp = dna.data[‘bp’][‘22’][‘shear’]n”, “n”, “#Shear vs Time for 22nd bpn”, “plt.title(‘22nd bp’)n”, “plt.plot(dna.time, shear_20bp)n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Shear ($\AA$)’)n”, “plt.show()n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Example to extract parameter as a function of timen”, “n”, “As shown [previously here](hhttp://do-x3dna.readthedocs.io/en/latest/notebooks/base_steps_tutorial.html#Local-base-step-parameters-as-a-function-of-time-(using-provided-functions)), smae method ([dnaMD.DNA.time_vs_parameter(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.time_vs_parameter)) can be used to get parameter values as a function of time.n”, “n”, “Also, see that plot is similar.n”, “n”, “n”, “Note that in this case, data is read from the HDF5 file, while in the previous tutorial, data was stored in memory (RAM).”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f39d0f477f0>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“# Rise vs Time for 25-40 bp segmentn”, “plt.title(‘Rise for 25-40 bp segment’)n”, “n”, “# Rise is the distance between two base-pairs, so for a given segment it is sum over the base-stepsn”, “time, value = dna.time_vs_parameter(‘rise’, [25, 40], merge=True, merge_method=’sum’)n”, “plt.plot(time, value, label=’bound DNA’, c=’k’)n”, “n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Rise ( $\AA$)’)n”, “plt.legend()n”, “plt.show()”

]

}, {

“cell_type”: “code”, “execution_count”: null, “metadata”: {

“collapsed”: true

}, “outputs”: [], “source”: []

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.5.2”

}

}, “nbformat”: 4, “nbformat_minor”: 2

}