{
“cells”: [
{

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

“## Analysis of local base-pairs parametersn”, “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/base_pairs_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 (do_x3dna output from the trajectory, which contains the DNA bound with the protein)n”, ” * L-BP_odna.dat (do_x3dna output from the trajectory, which only contains the free DNA)n”, ” n”, ” These two file 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 numpy as npn”, “import matplotlib.pyplot as pltn”, “import dnaMDn”, “n”, “n”, “%matplotlib inlinen”

]

}, {

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

“### Initializing DNA object and storing data to itn”, “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”, “* Six base-pair parameters (shear, stretch, stagger, buckle, propeller and opening) can be read and stored in DNA object from the input file using function [set_base_pair_parameters(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.set_base_pair_parameters).n”, “n”, “n”, “* To speed up processing and analysis, data can be stored in a HDF5 file by including HDF5 file name as a argument during initialization. Same file can be used to store and retrieve all other parameters.”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “metadata”: {}, “outputs”: [

{

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

“n”, “Reading file : tutorial_data/L-BP_cdna.datn”, “Reading frame 1000n”, “Finished reading…. Total number of frame read = 1001n”, “n”, “Reading file : tutorial_data/L-BP_odna.datn”, “Reading frame 1000n”, “Finished reading…. Total number of frame read = 1001n”

]

}

], “source”: [

“# Initializationn”, “pdna = dnaMD.DNA(60) #Initialization for 60 base-pairs DNA bound with the proteinn”, “fdna = dnaMD.DNA(60) #Initialization for 60 base-pairs free DNAn”, “n”, “## If HDF5 file is used to store/save data use these:n”, “# pdna = dnaMD.DNA(60, filename=’cdna.h5’) #Initialization for 60 base-pairs DNA bound with the proteinn”, “# fdna = dnaMD.DNA(60, filename=’odna.h5’) #Initialization for 60 base-pairs free DNAn”, “n”, “# Loading data from input files in respective DNA objectn”, “# "bp=[1, 60]" will load local base-pair parameters of 1 to 60 base-pairsn”, “# It will load all six parameters (shear, stretch, stagger, buckle, propeller and opening)n”, “parameters=[‘shear’, ‘stretch’, ‘stagger’, ‘buckle’, ‘propeller’, ‘opening’]n”, “pdna.set_base_pair_parameters(‘tutorial_data/L-BP_cdna.dat’, bp=[1, 60], parameters=parameters, bp_range=True)n”, “fdna.set_base_pair_parameters(‘tutorial_data/L-BP_odna.dat’, bp=[1, 60], parameters=parameters, bp_range=True)”

]

}, {

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

“### Local base-pair parameter of a base-pair directly from dictionaryn”, “n”, “* The DNA.data is a python dictionary which contains all the data. For a base-pair, parameter as a function of time can be directly extracted.n”

]

}, {

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

{
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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732e5b5cf8>”

]

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

}

], “source”: [

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

]

}, {

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

“### Local base-pair parameters as a function of time (manually)n”, “n”, “* A specific local base-pair parameters for the given base-pairs range can be extracted from the DNA obejct using function [dnaMD.DNA.get_parameters(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.get_parameters).n”, “n”, “n”, “* The extracted parameters of the given base-pair can be plotted as a function of timen”, “n”, “n”, “* The extracted parameters (average) for the DNA segment can be plotted as a function of timen”, “n”, “n”, “Following example shows Shear vs Time plots. These example also shows that how to extract the parameters value from the DNA object. Other properties could be extracted and plotted using similar steps. “

]

}, {

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

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf0c630>”

]

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

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf42668>”

]

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

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf314e0>”

]

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

}

], “source”: [

“# Extracting "Shear" of 20 to 30 bpn”, “shear_20to30, bp_nums = pdna.get_parameters(‘shear’,[20,30], bp_range=True)n”, “n”, “# Shear vs Time for 22nd bpn”, “plt.title(‘22nd bp’)n”, “plt.plot(pdna.time, shear_20to30[2]) # index is 2 for 22nd bp: (20 + 2)n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Shear ($\AA$)’)n”, “plt.show()n”, “n”, “# Average Shear vs Time for segment 20-30 bpn”, “avg_shear_20to30 = np.mean(shear_20to30, axis=0) # Calculation of mean using mean function of numpyn”, “plt.title(‘20-30 bp segment’)n”, “plt.plot(pdna.time, avg_shear_20to30)n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Shear ($\AA$)’)n”, “plt.show()n”, “n”, “# Average Shear vs Time for segment 24-28 bpn”, “avg_shear_24to28 = np.mean(shear_20to30[4:8], axis=0) # index of 24th bp is 4 (20 + 4). index of 28th bp is 8 (20 + 8)n”, “plt.title(‘24-28 bp segment’)n”, “plt.plot(pdna.time, avg_shear_24to28)n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Shear ($\AA$)’)n”, “plt.show()”

]

}, {

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

“### Local base-pair parameters as a function of time (using provided functions)n”, “n”, “Above examples show the step to extract the values from the DNA object. However, [dnaMD.DNA.time_vs_parameter(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.time_vs_parameter) function could be use to get parameter values as a function of time for the given base-pairs/step or segmentn”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [

{
“data”: {

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”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf42e10>”

]

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

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf2d1d0>”

]

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

}

], “source”: [

“# Stagger vs Time for 22nd bpn”, “plt.title(‘Stagger for 22nd bp’)n”, “time, value = pdna.time_vs_parameter(‘stagger’, [22])n”, “plt.plot(time, value)n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Stagger ($\AA$)’)n”, “plt.show()n”, “n”, “# Propeller vs Time for 30-35 bp segmentn”, “plt.title(‘Propeller for 24-28 bp segment’)n”, “# Bound DNAn”, “time, value = pdna.time_vs_parameter(‘propeller’, [30, 35], merge=True, merge_method=’mean’)n”, “plt.plot(time, value, label=’bound DNA’, c=’k’) # balck color => bound DNAn”, “# Free DNAn”, “time, value = fdna.time_vs_parameter(‘propeller’, [30, 35], merge=True, merge_method=’mean’)n”, “plt.plot(time, value, label=’free DNA’, c=’r’) # red color => free DNAn”, “n”, “plt.xlabel(‘Time (ps)’)n”, “plt.ylabel(‘Propeller ( $^o$)’)n”, “plt.legend()n”, “plt.show()”

]

}, {

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

“### Distribution of local base-pairs parameters during MD simulationsn”, “n”, “* As shown in above plot of time vs propeller, comparison between bound and free DNA is very difficult. Therefore, to compare the parameters of either different DNAs or same DNAs in different environment or different segment of same DNAs, the distribution of parameters over the MD trajectory are sometime useful.n”, “n”, “n”, “* The distribution could be calculated using the function [dnaMD.DNA.parameter_distribution(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.parameter_distribution) as shown in the following examples.n”, “n”, “n”, “* The normalized distribution is calculated using [numpy.histogram(…)](http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html).n

]

}, {

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

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732be40908>”

]

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

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732be670f0>”

]

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

}

], “source”: [

“#### Propeller distribution for 30-35 bp segmentn”, “plt.title(‘Propeller distribution for 30-35 bp segment’)n”, “n”, “### Bound DNA ###n”, “n”, “## calculation of parameter distribution for the segmentn”, “values, density = pdna.parameter_distribution(‘propeller’, [30, 35], bins=20, merge=True, merge_method=’mean’)n”, “n”, “## plot distributionn”, “plt.plot(values, density, label=’bound DNA’, c=’k’) # balck color => bound DNAn”, “n”, “### Free DNA ###n”, “n”, “## calculation of parameter distribution for the segmentn”, “values, density = fdna.parameter_distribution(‘propeller’, [30, 35], bins=20, merge=True, merge_method=’mean’)n”, “n”, “## plot distributionn”, “plt.plot(values, density, label=’free DNA’, c=’r’) # red color => free DNAn”, “n”, “plt.xlabel(‘Propeller ( $^o$)’)n”, “plt.ylabel(‘Density’)n”, “plt.legend()n”, “plt.show()n”, “n”, “n”, “#### Buckle distribution for 40-45 bp segmentn”, “plt.title(‘Buckle distribution for 40-45 bp segment’)n”, “n”, “### Bound DNA ###n”, “n”, “## calculation of parameter distribution for the segmentn”, “values, density = pdna.parameter_distribution(‘buckle’, [40, 45], bins=20, merge=True, merge_method=’mean’)n”, “n”, “## plot distributionn”, “plt.plot(values, density, label=’bound DNA’, c=’k’) # balck color => bound DNAn”, “n”, “### Free DNA ###n”, “n”, “## calculation of parameter distribution for the segmentn”, “values, density = fdna.parameter_distribution(‘buckle’, [40, 45], bins=20, merge=True, merge_method=’mean’)n”, “n”, “## plot distributionn”, “plt.plot(values, density, label=’free DNA’, c=’r’) # red color => free DNAn”, “n”, “plt.xlabel(‘Buckle ( $^o$)’)n”, “plt.ylabel(‘Density’)n”, “plt.legend()n”, “plt.show()”

]

}, {

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

“### Local base-pair parameters as a function of base-pairsn”, “n”, “* What is the average values of a given parameter for either each base-pair or a DNA segment?n”, “n”, “n”, “* To address this question, average values of a given parameter with its error could be calculated for either each base-pair or a DNA segment using a function [dnaMD.DNA.get_mean_error(…)](http://do-x3dna.readthedocs.io/en/latest/dna_class_api.html#dnaMD.dnaMD.DNA.get_mean_error).n”, “n”, “n”, “* This average values could be also use to compare two DNA.n”, “n”, “n”, “* Standard error could be calculated using block averaging method as derived in this [publication](http://scitation.aip.org/content/aip/journal/jcp/116/1/10.1063/1.1421362). To use this method, [gmx analyze](http://manual.gromacs.org/programs/gmx-analyze.html) of GROMACS package should be present in $PATH environment variable.n”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732beb30f0>”

]

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

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf28400>”

]

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

}

], “source”: [

“######## Average Buckle values as a function of base-pairs ########n”, “n”, “plt.title(‘Average Buckle for each base-pairs’)n”, “n”, “### Calculating Average Buckle values for 1 to 60 base-pairs DNA bound with proteinn”, “bp, buckle, error = pdna.get_mean_error([1,60], ‘buckle’, err_type=’block’, bp_range=True)n”, “n”, “# plot these valuesn”, “plt.errorbar(bp, buckle,yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4, label=’bound DNA’ )n”, “n”, “### Calculating Average Buckle values for 1 to 60 base-pairs DNAn”, “bp, buckle, error = fdna.get_mean_error([1,60], ‘buckle’, err_type=’block’, bp_range=True)n”, “n”, “# plot these valuesn”, “plt.errorbar(bp, buckle,yerr=error, ecolor=’r’, elinewidth=1, color=’r’, lw=0, marker=’x’, mfc=’r’, mew=1, ms=4, label=’free DNA’ )n”, “n”, “plt.ylabel(‘Buckle ( $^o$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.ylim(-20, 20)n”, “plt.legend()n”, “plt.show()n”, “n”, “######## Average Buckle values as a function of DNA segments ########n”, “n”, “plt.title(‘Average Buckle for DNA segments’)n”, “n”, “### Calculating Average Buckle values for 1 to 60 base-pairs DNA bound with proteinn”, “### DNA segments are assumed to made up of 5 base-pairs (merge_bp=4)n”, “bp, buckle, error = pdna.get_mean_error([1,60], ‘buckle’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(bp, buckle,yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4, label=’bound DNA’ )n”, “n”, “### Calculating Average Buckle values for 1 to 60 base-pairs DNAn”, “### DNA segments are assumed to made up of 5 base-pairs (merge_bp=4)n”, “bp, buckle, error = fdna.get_mean_error([1,60], ‘buckle’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(bp, buckle,yerr=error, ecolor=’r’, elinewidth=1, color=’r’, lw=0, marker=’x’, mfc=’r’, mew=1, ms=4, label=’free DNA’ )n”, “n”, “plt.ylabel(‘Buckle ( $^o$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.ylim(-15, 15)n”, “plt.legend()n”, “plt.show()”

]

}, {

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

“### Deviation in parameters of bound DNA with respect to free DNAn”, “n”, “As discussed in the above section, average parameters with standard error can be calculated for both bound and free DNA. Additionally, deviation in bound DNA with respect to the free DNA could be calculated using function [dnaMD.localDeformationVsBPS(…)](http://do-x3dna.readthedocs.io/en/latest/dnaMD_api.html#dnaMD.dnaMD.localDeformationVsBPS) as shown in the following example.n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bdb6588>”

]

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

}, {

“data”: {

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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bd846a0>”

]

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

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bf29b70>”

]

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

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bd5d2b0>”

]

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

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<matplotlib.figure.Figure at 0x7f732bcf6208>”

]

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

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

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], “source”: [

“#### Deviation in shear, stretch, stagger, buckle, propeller and openingn”, “#### Deviation = Bound DNA(parameter) - Free DNA(parameter)n”, “n”, “### Deviation in Shearn”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘shear’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Shear ($\AA$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “#plt.ylim(-10, 10)n”, “plt.show()n”, “n”, “### Deviation in Stretchn”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘stretch’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Stretch ($\AA$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “#plt.ylim(-10, 10)n”, “plt.show()n”, “n”, “### Deviation in Staggern”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘stagger’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Stagger ($\AA$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.show()n”, “n”, “### Deviation in Bucklen”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘buckle’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Buckle ( $^o$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.show()n”, “n”, “### Deviation in Propellern”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘propeller’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Propeller ( $^o$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.show()n”, “n”, “### Deviation in Openingn”, “fdna_bp, pdna_bp, deviation, error = dnaMD.localDeformationVsBPS(fdna, [5,55], pdna, [5,55], n”, ” ‘opening’, err_type=’block’, bp_range=True, merge_bp=4)n”, “n”, “# plot these valuesn”, “plt.errorbar(pdna_bp, deviation, yerr=error, ecolor=’k’, elinewidth=1, color=’k’, lw=0, marker=’o’, mfc=’k’, mew=1, ms=4)n”, “n”, “# plot line at zeron”, “plt.plot([0,61], [0.0, 0.0], ‘–k’)n”, “n”, “plt.ylabel(‘Deviation in Opening ( $^o$)’)n”, “plt.xlabel(‘base-pair number’)n”, “plt.xlim(0,61)n”, “plt.show()”

]

}, {

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

“collapsed”: true

}, “outputs”: [], “source”: []

}

], “metadata”: {

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