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msund/gist:2ca48d4b1824ba58ae7e

Last active August 29, 2015 14:02
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gistfile1.txt
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{
"metadata": {
"name": "Untitled0"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": "$LaTeX$ PLots with <br>MATLAB, Python, R, and a GUI"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "[Plotly](https://plot.ly) is an online graphing and analytics project. It's like a GitHub for data and graphs. The goal is: collaboratively make beautiful, interactive plots, 3D plots, and streaming graphs. It's all in your browser, for free, and without downloads or software installations. \n\nSupporting scientific plotting and collaboration is key to Plotly's mission. You can use $LaTeX$ from any language and a GUI. Starting with Plotly's matplotlib support we'll make an example. For more, head over to our [APIs](https://plot.ly/api):\n\n<a href=\"http://imgur.com/VHU2X3x\"><img src=\"http://i.imgur.com/VHU2X3x.png\" title=\"Hosted by imgur.com\"/></a>"
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": "I. $LaTeX$ and matplotlib"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "You can follow along by copy and pasting this code right into your termainal. Before getting started, run `$ pip install plotly`, and if that didn't work, try `$ sudo pip install plotly`. I always feel like a boss installing with sudo. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "import plotly\nplotly.__version__",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": "'1.0.12'"
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": "You can use Plotly's public account and keys or [sign-up](https://plot.ly/ssu) for free."
},
{
"cell_type": "code",
"collapsed": false,
"input": "import matplotlib.pyplot as plt # side-stepping mpl's backend\nimport plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\n%matplotlib inline\npy.sign_in(\"IPython.Demo\", \"1fw3zw2o13\")",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Here is our first matplotlib figure. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig1 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace(-2.0, 2.0, 10000) # The x-values\nsigma = np.linspace(0.4, 1.0, 4) # Some different values of sigma\n\n# Here we evaluate a Gaussians for each sigma\ngaussians = [(2*np.pi*s**2)**-0.5 * np.exp(-0.5*x**2/s**2) for s in sigma]\n\nax = plt.axes()\n\nfor s,y in zip(sigma, gaussians):\n ax.plot(x, y, lw=1.25, label=r\"$\\sigma = %3.2f$\"%s)\n\nformula = r\"$y(x)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}e^{-\\frac{x^2}{2\\sigma^2}}$\"\n\nax.text(0.05, 0.80, formula, transform=ax.transAxes, fontsize=20)\nax.set_xlabel(r\"$x$\", fontsize=18)\nax.set_ylabel(r\"$y(x)$\", fontsize=18)\nax.legend()\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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eDh/WaJM6wVBfX627BE3RZOnY5151x/Hf8rKRkZE4OTlhY2MjS8fmVzE6FakA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\nRK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x7f9ef2dd8fd0>"
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": "To convert it to Plotly, I call Plotly (check out [our NB on matplotlib](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s6_matplotlylib/s6_matplotlylib.ipynb) support for more details)."
},
{
"cell_type": "code",
"collapsed": false,
"input": "py.iplot_mpl(fig1) ",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/4018\" height=\"525\" width=\"100%\"></iframe>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x7f9ef2bfb910>"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Now if I want I can print the figure or get the data. I can also call in-line help from Plotly."
},
{
"cell_type": "code",
"collapsed": false,
"input": "from plotly.graph_objs import Data, Layout, Figure",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "help(Figure)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Help on class Figure in module plotly.graph_objs.graph_objs:\n\nclass Figure(PlotlyDict)\n | A dictionary-like object representing a figure to be rendered in plotly.\n | \n | This is the container for all things to be rendered in a figure.\n | \n | For help with setting up subplots, run:\n | `help(plotly.tools.get_subplots)`\n | \n | \n | Quick method reference:\n | \n | Figure.update(changes)\n | Figure.strip_style()\n | Figure.get_data()\n | Figure.to_graph_objs()\n | Figure.validate()\n | Figure.to_string()\n | Figure.force_clean()\n | \n | Valid keys:\n | \n | data [required=False] (value=Data object | dictionary-like):\n | A list-like array of the data that is to be visualized.\n | \n | For more, run `help(plotly.graph_objs.Data)`\n | \n | layout [required=False] (value=Layout object | dictionary-like):\n | The layout dictionary-like object contains axes information, gobal\n | settings, and layout information related to the rendering of the\n | figure.\n | \n | For more, run `help(plotly.graph_objs.Layout)`\n | \n | Method resolution order:\n | Figure\n | PlotlyDict\n | __builtin__.dict\n | __builtin__.object\n | \n | Methods defined here:\n | \n | __init__(self, *args, **kwargs)\n | \n | ----------------------------------------------------------------------\n | Methods inherited from PlotlyDict:\n | \n | force_clean(self)\n | Attempts to convert to graph_objs and call force_clean() on values.\n | \n | Calling force_clean() on a PlotlyDict will ensure that the object is\n | valid and may be sent to plotly. This process will also remove any\n | entries that end up with a length == 0.\n | \n | Careful! This will delete any invalid entries *silently*.\n | \n | get_data(self)\n | Returns the JSON for the plot with non-data elements stripped.\n | \n | strip_style(self)\n | Strip style from the current representation.\n | \n | All PlotlyDicts and PlotlyLists are guaranteed to survive the\n | stripping process, though they made be left empty. This is allowable.\n | \n | Keys that will be stripped in this process are tagged with\n | `'type': 'style'` in the INFO dictionary listed in graph_objs_meta.py.\n | \n | This process first attempts to convert nested collections from dicts\n | or lists to subclasses of PlotlyList/PlotlyDict. This process forces\n | a validation, which may throw exceptions.\n | \n | Then, each of these objects call `strip_style` on themselves and so\n | on, recursively until the entire structure has been validated and\n | stripped.\n | \n | to_graph_objs(self)\n | Walk obj, convert dicts and lists to plotly graph objs.\n | \n | For each key in the object, if it corresponds to a special key that\n | should be associated with a graph object, the ordinary dict or list\n | will be reinitialized as a special PlotlyDict or PlotlyList of the\n | appropriate `kind`.\n | \n | to_string(self, level=0, indent=4, eol='\\n', pretty=True, max_chars=80)\n | Returns a formatted string showing graph_obj constructors.\n | \n | Example:\n | \n | print obj.to_string()\n | \n | Keyword arguments:\n | level (default = 0) -- set number of indentations to start with\n | indent (default = 4) -- set indentation amount\n | eol (default = '\n | ') -- set end of line character(s)\n | pretty (default = True) -- curtail long list output with a '...'\n | max_chars (default = 80) -- set max characters per line\n | \n | update(self, dict1=None, **dict2)\n | Update current dict with dict1 and then dict2.\n | \n | This recursively updates the structure of the original dictionary-like\n | object with the new entries in the second and third objects. This\n | allows users to update with large, nested structures.\n | \n | Note, because the dict2 packs up all the keyword arguments, you can\n | specify the changes as a list of keyword agruments.\n | \n | Examples:\n | # update with dict\n | obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))\n | update_dict = dict(title='new title', xaxis=dict(domain=[0,.8]))\n | obj.update(update_dict)\n | obj\n | {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}\n | \n | # update with list of keyword arguments\n | obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))\n | obj.update(title='new title', xaxis=dict(domain=[0,.8]))\n | obj\n | {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}\n | \n | This 'fully' supports duck-typing in that the call signature is\n | identical, however this differs slightly from the normal update\n | method provided by Python's dictionaries.\n | \n | validate(self)\n | Recursively check the validity of the keys in a PlotlyDict.\n | \n | The valid keys constitute the entries in each object\n | dictionary in INFO stored in graph_objs_meta.py.\n | \n | The validation process first requires that all nested collections be\n | converted to the appropriate subclass of PlotlyDict/PlotlyList. Then,\n | each of these objects call `validate` and so on, recursively,\n | until the entire object has been validated.\n | \n | ----------------------------------------------------------------------\n | Data descriptors inherited from PlotlyDict:\n | \n | __dict__\n | dictionary for instance variables (if defined)\n | \n | __weakref__\n | list of weak references to the object (if defined)\n | \n | ----------------------------------------------------------------------\n | Data and other attributes inherited from PlotlyDict:\n | \n | __metaclass__ = <class 'plotly.graph_objs.graph_objs.DictMeta'>\n | A meta class for PlotlyDict class creation.\n | \n | The sole purpose of this meta class is to properly create the __doc__\n | attribute so that running help(Obj), where Obj is a subclass of PlotlyDict,\n | will return information about key-value pairs for that object.\n | \n | ----------------------------------------------------------------------\n | Methods inherited from __builtin__.dict:\n | \n | __cmp__(...)\n | x.__cmp__(y) <==> cmp(x,y)\n | \n | __contains__(...)\n | D.__contains__(k) -> True if D has a key k, else False\n | \n | __delitem__(...)\n | x.__delitem__(y) <==> del x[y]\n | \n | __eq__(...)\n | x.__eq__(y) <==> x==y\n | \n | __ge__(...)\n | x.__ge__(y) <==> x>=y\n | \n | __getattribute__(...)\n | x.__getattribute__('name') <==> x.name\n | \n | __getitem__(...)\n | x.__getitem__(y) <==> x[y]\n | \n | __gt__(...)\n | x.__gt__(y) <==> x>y\n | \n | __iter__(...)\n | x.__iter__() <==> iter(x)\n | \n | __le__(...)\n | x.__le__(y) <==> x<=y\n | \n | __len__(...)\n | x.__len__() <==> len(x)\n | \n | __lt__(...)\n | x.__lt__(y) <==> x<y\n | \n | __ne__(...)\n | x.__ne__(y) <==> x!=y\n | \n | __repr__(...)\n | x.__repr__() <==> repr(x)\n | \n | __setitem__(...)\n | x.__setitem__(i, y) <==> x[i]=y\n | \n | __sizeof__(...)\n | D.__sizeof__() -> size of D in memory, in bytes\n | \n | clear(...)\n | D.clear() -> None. Remove all items from D.\n | \n | copy(...)\n | D.copy() -> a shallow copy of D\n | \n | fromkeys(...)\n | dict.fromkeys(S[,v]) -> New dict with keys from S and values equal to v.\n | v defaults to None.\n | \n | get(...)\n | D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.\n | \n | has_key(...)\n | D.has_key(k) -> True if D has a key k, else False\n | \n | items(...)\n | D.items() -> list of D's (key, value) pairs, as 2-tuples\n | \n | iteritems(...)\n | D.iteritems() -> an iterator over the (key, value) items of D\n | \n | iterkeys(...)\n | D.iterkeys() -> an iterator over the keys of D\n | \n | itervalues(...)\n | D.itervalues() -> an iterator over the values of D\n | \n | keys(...)\n | D.keys() -> list of D's keys\n | \n | pop(...)\n | D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n | If key is not found, d is returned if given, otherwise KeyError is raised\n | \n | popitem(...)\n | D.popitem() -> (k, v), remove and return some (key, value) pair as a\n | 2-tuple; but raise KeyError if D is empty.\n | \n | setdefault(...)\n | D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D\n | \n | values(...)\n | D.values() -> list of D's values\n | \n | viewitems(...)\n | D.viewitems() -> a set-like object providing a view on D's items\n | \n | viewkeys(...)\n | D.viewkeys() -> a set-like object providing a view on D's keys\n | \n | viewvalues(...)\n | D.viewvalues() -> an object providing a view on D's values\n | \n | ----------------------------------------------------------------------\n | Data and other attributes inherited from __builtin__.dict:\n | \n | __hash__ = None\n | \n | __new__ = <built-in method __new__ of type object>\n | T.__new__(S, ...) -> a new object with type S, a subtype of T\n\n"
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Convert MPL figure to Plotly\nmy_fig = tls.mpl_to_plotly(fig1)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Take a quick look at the Plotly representation of the MPL figure\nprint my_fig.to_string()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Figure(\n data=Data([\n Scatter(\n x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],\n y=[3.7167987868357507e-06, 3.7354293180981496e-06, 3.7541494803...],\n name='$\\\\sigma = 0.40$',\n mode='lines',\n line=Line(\n dash='solid',\n color='#0000FF',\n width=1.25,\n opacity=1\n ),\n xaxis='x1',\n yaxis='y1'\n ),\n Scatter(\n x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],\n y=[0.0025704649938185107, 0.0025761834897230665, 0.002581913559...],\n name='$\\\\sigma = 0.60$',\n mode='lines',\n line=Line(\n dash='solid',\n color='#007F00',\n width=1.25,\n opacity=1\n ),\n xaxis='x1',\n yaxis='y1'\n ),\n Scatter(\n x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],\n y=[0.02191037561696068, 0.021937780710823064, 0.021965214590068...],\n name='$\\\\sigma = 0.80$',\n mode='lines',\n line=Line(\n dash='solid',\n color='#FF0000',\n width=1.25,\n opacity=1\n ),\n xaxis='x1',\n yaxis='y1'\n ),\n Scatter(\n x=[-2.0, -1.9995999599959997, -1.9991999199919992, -1.998799879...],\n y=[0.053990966513188063, 0.054034176567683667, 0.05407741254993...],\n name='$\\\\sigma = 1.00$',\n mode='lines',\n line=Line(\n dash='solid',\n color='#00BFBF',\n width=1.25,\n opacity=1\n ),\n xaxis='x1',\n yaxis='y1'\n )\n ]),\n layout=Layout(\n width=480,\n height=320,\n autosize=False,\n margin=Margin(\n l=60,\n r=47,\n b=40,\n t=31,\n pad=0\n ),\n hovermode='closest',\n showlegend=False,\n annotations=Annotations([\n Annotation(\n x=0.049865951742627333,\n y=0.79678714859437749,\n text='$y(x)=\\\\frac{1}{\\\\sqrt{2\\\\pi\\\\sigma^2}}e^{-\\\\frac{x^2...',\n xref='paper',\n yref='paper',\n showarrow=False,\n font=Font(\n size=20.0,\n color='#000000'\n ),\n opacity=1,\n xanchor='left',\n yanchor='bottom'\n ),\n Annotation(\n x=0.84248977989278806,\n y=0.91129032258064524,\n text='$\\\\sigma = 0.40$',\n xref='paper',\n yref='paper',\n showarrow=False,\n font=Font(\n size=12.0,\n color='#000000'\n ),\n opacity=1,\n xanchor='left',\n yanchor='bottom'\n ),\n Annotation(\n x=0.84248977989278806,\n y=0.83198924731182788,\n text='$\\\\sigma = 0.60$',\n xref='paper',\n yref='paper',\n showarrow=False,\n font=Font(\n size=12.0,\n color='#000000'\n ),\n opacity=1,\n xanchor='left',\n yanchor='bottom'\n ),\n Annotation(\n x=0.84248977989278806,\n y=0.75268817204301075,\n text='$\\\\sigma = 0.80$',\n xref='paper',\n yref='paper',\n showarrow=False,\n font=Font(\n size=12.0,\n color='#000000'\n ),\n opacity=1,\n xanchor='left',\n yanchor='bottom'\n ),\n Annotation(\n x=0.84248977989278806,\n y=0.67338709677419362,\n text='$\\\\sigma = 1.00$',\n xref='paper',\n yref='paper',\n showarrow=False,\n font=Font(\n size=12.0,\n color='#000000'\n ),\n opacity=1,\n xanchor='left',\n yanchor='bottom'\n )\n ]),\n xaxis1=XAxis(\n title='$x$',\n domain=[0.0, 1.0],\n range=[-2.0, 2.0],\n type='linear',\n showline=True,\n ticks='inside',\n nticks=9,\n showgrid=False,\n zeroline=False,\n titlefont=Font(\n size=18.0,\n color='#000000'\n ),\n tickfont=Font(\n size=10.0\n ),\n anchor='y1',\n side='bottom',\n mirror='ticks'\n ),\n yaxis1=YAxis(\n title='$y(x)$',\n domain=[0.0, 1.0],\n range=[0.0, 1.0],\n type='linear',\n showline=True,\n ticks='inside',\n nticks=6,\n showgrid=False,\n zeroline=False,\n titlefont=Font(\n size=18.0,\n color='#000000'\n ),\n tickfont=Font(\n size=10.0\n ),\n anchor='x1',\n side='left',\n mirror='ticks'\n )\n )\n)\n"
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Strip out the data from the Figure\nmy_data = my_fig.get_data()\nmy_data",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": "{'data': [{'name': '$\\\\sigma = 0.40$',\n 'x': [-2.0,\n -1.9995999599959997,\n -1.9991999199919992,\n -1.9987998799879989,\n -1.9983998399839984,\n -1.9979997999799981,\n -1.9975997599759976,\n -1.9971997199719973,\n -1.9967996799679968,\n -1.9963996399639965,\n -1.995999599959996,\n -1.9955995599559957,\n -1.9951995199519952,\n -1.9947994799479949,\n -1.9943994399439944,\n -1.9939993999399941,\n -1.9935993599359936,\n -1.9931993199319933,\n -1.9927992799279928,\n -1.9923992399239925,\n -1.991999199919992,\n -1.9915991599159917,\n -1.9911991199119912,\n -1.9907990799079909,\n -1.9903990399039904,\n -1.9899989998999901,\n -1.9895989598959896,\n -1.9891989198919893,\n -1.9887988798879888,\n -1.9883988398839885,\n -1.987998799879988,\n -1.9875987598759877,\n -1.9871987198719872,\n -1.9867986798679869,\n -1.9863986398639863,\n -1.9859985998599861,\n -1.9855985598559855,\n -1.9851985198519853,\n -1.9847984798479847,\n -1.9843984398439845,\n -1.9839983998399839,\n 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1.0523052305230522,\n 1.0527052705270528,\n 1.0531053105310533,\n 1.0535053505350538,\n 1.0539053905390539,\n 1.0543054305430544,\n 1.0547054705470549,\n 1.0551055105510554,\n 1.0555055505550555,\n 1.055905590559056,\n 1.0563056305630565,\n 1.056705670567057,\n 1.0571057105710571,\n 1.0575057505750576,\n 1.0579057905790581,\n 1.0583058305830586,\n 1.0587058705870587,\n 1.0591059105910592,\n 1.0595059505950597,\n 1.0599059905990602,\n 1.0603060306030603,\n 1.0607060706070608,\n 1.0611061106110613,\n 1.0615061506150618,\n 1.0619061906190619,\n 1.0623062306230624,\n 1.0627062706270629,\n 1.0631063106310634,\n 1.0635063506350635,\n 1.063906390639064,\n 1.0643064306430645,\n 1.064706470647065,\n 1.0651065106510651,\n 1.0655065506550656,\n 1.0659065906590661,\n 1.0663066306630662,\n 1.0667066706670667,\n 1.0671067106710672,\n 1.0675067506750677,\n 1.0679067906790678,\n 1.0683068306830683,\n 1.0687068706870688,\n 1.0691069106910693,\n 1.0695069506950694,\n 1.0699069906990699,\n 1.0703070307030704,\n 1.0707070707070709,\n 1.071107110711071,\n 1.0715071507150715,\n 1.071907190719072,\n 1.0723072307230725,\n 1.0727072707270726,\n 1.0731073107310731,\n 1.0735073507350736,\n 1.0739073907390742,\n 1.0743074307430742,\n 1.0747074707470747,\n 1.0751075107510752,\n 1.0755075507550758,\n 1.0759075907590758,\n 1.0763076307630763,\n 1.0767076707670769,\n 1.0771077107710774,\n 1.0775077507750774,\n 1.0779077907790779,\n 1.0783078307830785,\n 1.078707870787079,\n 1.079107910791079,\n 1.0795079507950796,\n 1.0799079907990801,\n 1.0803080308030806,\n 1.0807080708070806,\n 1.0811081108110812,\n 1.0815081508150817,\n 1.0819081908190822,\n 1.0823082308230823,\n 1.0827082708270828,\n 1.0831083108310833,\n 1.0835083508350838,\n 1.0839083908390839,\n 1.0843084308430844,\n 1.0847084708470849,\n 1.0851085108510854,\n 1.0855085508550855,\n 1.085908590859086,\n 1.0863086308630865,\n 1.086708670867087,\n 1.0871087108710871,\n 1.0875087508750876,\n 1.0879087908790881,\n 1.0883088308830886,\n 1.0887088708870887,\n 1.0891089108910892,\n 1.0895089508950897,\n 1.0899089908990902,\n 1.0903090309030903,\n 1.0907090709070908,\n 1.0911091109110913,\n 1.0915091509150918,\n 1.0919091909190919,\n 1.0923092309230924,\n 1.0927092709270929,\n 1.0931093109310934,\n 1.0935093509350935,\n 1.093909390939094,\n 1.0943094309430945,\n 1.094709470947095,\n 1.0951095109510951,\n 1.0955095509550956,\n 1.0959095909590961,\n 1.0963096309630962,\n 1.0967096709670967,\n 1.0971097109710972,\n 1.0975097509750977,\n 1.0979097909790978,\n 1.0983098309830983,\n 1.0987098709870988,\n 1.0991099109910993,\n 1.0995099509950994,\n 1.0999099909990999,\n 1.1003100310031004,\n 1.1007100710071009,\n 1.101110111011101,\n 1.1015101510151015,\n 1.101910191019102,\n 1.1023102310231025,\n 1.1027102710271026,\n 1.1031103110311031,\n 1.1035103510351036,\n 1.1039103910391042,\n 1.1043104310431042,\n 1.1047104710471047,\n 1.1051105110511052,\n 1.1055105510551058,\n 1.1059105910591058,\n 1.1063106310631063,\n 1.1067106710671069,\n 1.1071107110711074,\n 1.1075107510751074,\n 1.1079107910791079,\n 1.1083108310831085,\n 1.108710871087109,\n 1.109110911091109,\n 1.1095109510951096,\n 1.1099109910991101,\n 1.1103110311031106,\n 1.1107110711071106,\n 1.1111111111111112,\n 1.1115111511151117,\n 1.1119111911191122,\n 1.1123112311231123,\n 1.1127112711271128,\n 1.1131113111311133,\n 1.1135113511351138,\n 1.1139113911391139,\n 1.1143114311431144,\n 1.1147114711471149,\n 1.1151115111511154,\n 1.1155115511551155,\n 1.115911591159116,\n 1.1163116311631165,\n 1.116711671167117,\n 1.1171117111711171,\n 1.1175117511751176,\n 1.1179117911791181,\n 1.1183118311831186,\n 1.1187118711871187,\n 1.1191119111911192,\n 1.1195119511951197,\n 1.1199119911991202,\n 1.1203120312031203,\n 1.1207120712071208,\n 1.1211121112111213,\n 1.1215121512151218,\n 1.1219121912191219,\n 1.1223122312231224,\n 1.1227122712271229,\n 1.1231123112311234,\n 1.1235123512351235,\n 1.123912391239124,\n 1.1243124312431245,\n 1.124712471247125,\n 1.1251125112511251,\n 1.1255125512551256,\n 1.1259125912591261,\n 1.1263126312631262,\n 1.1267126712671267,\n 1.1271127112711272,\n 1.1275127512751277,\n 1.1279127912791278,\n 1.1283128312831283,\n 1.1287128712871288,\n 1.1291129112911293,\n 1.1295129512951294,\n 1.1299129912991299,\n 1.1303130313031304,\n 1.1307130713071309,\n 1.131113111311131,\n 1.1315131513151315,\n 1.131913191319132,\n 1.1323132313231326,\n 1.1327132713271326,\n 1.1331133113311331,\n 1.1335133513351336,\n 1.1339133913391342,\n 1.1343134313431342,\n 1.1347134713471347,\n 1.1351135113511353,\n 1.1355135513551358,\n 1.1359135913591358,\n 1.1363136313631363,\n 1.1367136713671369,\n 1.1371137113711374,\n 1.1375137513751374,\n 1.137913791379138,\n 1.1383138313831385,\n 1.138713871387139,\n 1.139113911391139,\n 1.1395139513951396,\n 1.1399139913991401,\n 1.1403140314031406,\n 1.1407140714071407,\n 1.1411141114111412,\n 1.1415141514151417,\n 1.1419141914191422,\n 1.1423142314231423,\n 1.1427142714271428,\n 1.1431143114311433,\n 1.1435143514351438,\n 1.1439143914391439,\n 1.1443144314431444,\n 1.1447144714471449,\n 1.1451145114511454,\n 1.1455145514551455,\n 1.145914591459146,\n 1.1463146314631465,\n 1.146714671467147,\n 1.1471147114711471,\n 1.1475147514751476,\n 1.1479147914791481,\n 1.1483148314831486,\n 1.1487148714871487,\n 1.1491149114911492,\n 1.1495149514951497,\n 1.1499149914991502,\n 1.1503150315031503,\n 1.1507150715071508,\n 1.1511151115111513,\n 1.1515151515151518,\n 1.1519151915191519,\n 1.1523152315231524,\n 1.1527152715271529,\n 1.1531153115311534,\n 1.1535153515351535,\n 1.153915391539154,\n 1.1543154315431545,\n 1.154715471547155,\n 1.1551155115511551,\n 1.1555155515551556,\n 1.1559155915591561,\n 1.1563156315631562,\n 1.1567156715671567,\n 1.1571157115711572,\n 1.1575157515751577,\n 1.1579157915791578,\n 1.1583158315831583,\n 1.1587158715871588,\n 1.1591159115911593,\n 1.1595159515951594,\n 1.1599159915991599,\n 1.1603160316031604,\n 1.1607160716071609,\n 1.161116111611161,\n 1.1615161516151615,\n 1.161916191619162,\n 1.1623162316231626,\n 1.1627162716271626,\n 1.1631163116311631,\n 1.1635163516351636,\n 1.1639163916391642,\n 1.1643164316431642,\n 1.1647164716471647,\n 1.1651165116511653,\n 1.1655165516551658,\n 1.1659165916591658,\n 1.1663166316631663,\n 1.1667166716671669,\n 1.1671167116711674,\n 1.1675167516751674,\n 1.167916791679168,\n 1.1683168316831685,\n 1.168716871687169,\n 1.169116911691169,\n 1.1695169516951696,\n 1.1699169916991701,\n 1.1703170317031706,\n 1.1707170717071707,\n 1.1711171117111712,\n 1.1715171517151717,\n 1.1719171917191722,\n 1.1723172317231723,\n 1.1727172717271728,\n 1.1731173117311733,\n 1.1735173517351738,\n 1.1739173917391739,\n 1.1743174317431744,\n 1.1747174717471749,\n 1.1751175117511754,\n 1.1755175517551755,\n 1.175917591759176,\n 1.1763176317631765,\n 1.176717671767177,\n 1.1771177117711771,\n 1.1775177517751776,\n 1.1779177917791781,\n 1.1783178317831786,\n 1.1787178717871787,\n 1.1791179117911792,\n 1.1795179517951797,\n 1.1799179917991802,\n 1.1803180318031803,\n 1.1807180718071808,\n 1.1811181118111813,\n 1.1815181518151818,\n 1.1819181918191819,\n 1.1823182318231824,\n 1.1827182718271829,\n 1.1831183118311834,\n 1.1835183518351835,\n 1.183918391839184,\n 1.1843184318431845,\n 1.184718471847185,\n 1.1851185118511851,\n 1.1855185518551856,\n 1.1859185918591861,\n 1.1863186318631862,\n 1.1867186718671867,\n 1.1871187118711872,\n 1.1875187518751877,\n 1.1879187918791878,\n 1.1883188318831883,\n 1.1887188718871888,\n 1.1891189118911893,\n 1.1895189518951894,\n 1.1899189918991899,\n 1.1903190319031904,\n 1.190719071907191,\n 1.191119111911191,\n 1.1915191519151915,\n 1.191919191919192,\n 1.1923192319231926,\n 1.1927192719271926,\n 1.1931193119311931,\n 1.1935193519351937,\n 1.1939193919391942,\n 1.1943194319431942,\n 1.1947194719471947,\n 1.1951195119511953,\n 1.1955195519551958,\n 1.1959195919591958,\n 1.1963196319631964,\n 1.1967196719671969,\n 1.1971197119711974,\n 1.1975197519751974,\n 1.197919791979198,\n 1.1983198319831985,\n 1.198719871987199,\n 1.1991199119911991,\n 1.1995199519951996,\n 1.1999199919992001,\n 1.2003200320032006,\n 1.2007200720072007,\n 1.2011201120112012,\n 1.2015201520152017,\n 1.2019201920192022,\n 1.2023202320232023,\n 1.2027202720272028,\n 1.2031203120312033,\n 1.2035203520352038,\n 1.2039203920392039,\n 1.2043204320432044,\n 1.2047204720472049,\n 1.2051205120512054,\n 1.2055205520552055,\n 1.205920592059206,\n 1.2063206320632065,\n 1.206720672067207,\n 1.2071207120712071,\n 1.2075207520752076,\n 1.2079207920792081,\n 1.2083208320832086,\n 1.2087208720872087,\n 1.2091209120912092,\n 1.2095209520952097,\n 1.2099209920992102,\n 1.2103210321032103,\n 1.2107210721072108,\n 1.2111211121112113,\n 1.2115211521152118,\n 1.2119211921192119,\n 1.2123212321232124,\n 1.2127212721272129,\n 1.2131213121312134,\n 1.2135213521352135,\n 1.213921392139214,\n 1.2143214321432145,\n 1.214721472147215,\n 1.2151215121512151,\n 1.2155215521552156,\n 1.2159215921592161,\n 1.2163216321632166,\n 1.2167216721672167,\n 1.2171217121712172,\n 1.2175217521752177,\n 1.2179217921792178,\n 1.2183218321832183,\n 1.2187218721872188,\n 1.2191219121912193,\n 1.2195219521952194,\n 1.2199219921992199,\n 1.2203220322032204,\n 1.220722072207221,\n 1.221122112211221,\n 1.2215221522152215,\n 1.221922192219222,\n 1.2223222322232226,\n 1.2227222722272226,\n 1.2231223122312231,\n 1.2235223522352237,\n 1.2239223922392242,\n 1.2243224322432242,\n 1.2247224722472247,\n 1.2251225122512253,\n 1.2255225522552258,\n 1.2259225922592258,\n 1.2263226322632264,\n 1.2267226722672269,\n 1.2271227122712274,\n 1.2275227522752274,\n 1.227922792279228,\n 1.2283228322832285,\n 1.228722872287229,\n 1.2291229122912291,\n 1.2295229522952296,\n 1.2299229922992301,\n 1.2303230323032306,\n 1.2307230723072307,\n 1.2311231123112312,\n 1.2315231523152317,\n 1.2319231923192322,\n 1.2323232323232323,\n 1.2327232723272328,\n 1.2331233123312333,\n 1.2335233523352338,\n 1.2339233923392339,\n 1.2343234323432344,\n 1.2347234723472349,\n 1.2351235123512354,\n 1.2355235523552355,\n 1.235923592359236,\n 1.2363236323632365,\n 1.236723672367237,\n 1.2371237123712371,\n 1.2375237523752376,\n 1.2379237923792381,\n 1.2383238323832386,\n 1.2387238723872387,\n 1.2391239123912392,\n 1.2395239523952397,\n 1.2399239923992402,\n 1.2403240324032403,\n 1.2407240724072408,\n 1.2411241124112413,\n 1.2415241524152418,\n 1.2419241924192419,\n 1.2423242324232424,\n 1.2427242724272429,\n 1.2431243124312434,\n 1.2435243524352435,\n 1.243924392439244,\n 1.2443244324432445,\n 1.244724472447245,\n 1.2451245124512451,\n 1.2455245524552456,\n 1.2459245924592461,\n 1.2463246324632467,\n 1.2467246724672467,\n 1.2471247124712472,\n 1.2475247524752477,\n 1.2479247924792478,\n 1.2483248324832483,\n 1.2487248724872488,\n 1.2491249124912494,\n 1.2495249524952494,\n 1.2499249924992499,\n 1.2503250325032504,\n 1.250725072507251,\n 1.251125112511251,\n 1.2515251525152515,\n 1.2519251925192521,\n 1.2523252325232526,\n 1.2527252725272526,\n 1.2531253125312531,\n 1.2535253525352537,\n 1.2539253925392542,\n 1.2543254325432542,\n 1.2547254725472547,\n 1.2551255125512553,\n 1.2555255525552558,\n 1.2559255925592558,\n 1.2563256325632564,\n 1.2567256725672569,\n 1.2571257125712574,\n 1.2575257525752574,\n 1.257925792579258,\n 1.2583258325832585,\n 1.258725872587259,\n 1.2591259125912591,\n 1.2595259525952596,\n 1.2599259925992601,\n 1.2603260326032606,\n 1.2607260726072607,\n 1.2611261126112612,\n 1.2615261526152617,\n 1.2619261926192622,\n 1.2623262326232623,\n 1.2627262726272628,\n 1.2631263126312633,\n 1.2635263526352638,\n 1.2639263926392639,\n 1.2643264326432644,\n 1.2647264726472649,\n 1.2651265126512654,\n 1.2655265526552655,\n 1.265926592659266,\n 1.2663266326632665,\n 1.266726672667267,\n 1.2671267126712671,\n 1.2675267526752676,\n 1.2679267926792681,\n 1.2683268326832686,\n 1.2687268726872687,\n 1.2691269126912692,\n 1.2695269526952697,\n 1.2699269926992702,\n 1.2703270327032703,\n 1.2707270727072708,\n 1.2711271127112713,\n 1.2715271527152718,\n 1.2719271927192719,\n 1.2723272327232724,\n 1.2727272727272729,\n 1.2731273127312734,\n 1.2735273527352735,\n 1.273927392739274,\n 1.2743274327432745,\n 1.274727472747275,\n 1.2751275127512751,\n 1.2755275527552756,\n 1.2759275927592761,\n 1.2763276327632767,\n 1.2767276727672767,\n 1.2771277127712772,\n 1.2775277527752777,\n 1.2779277927792778,\n 1.2783278327832783,\n 1.2787278727872788,\n 1.2791279127912794,\n 1.2795279527952794,\n 1.2799279927992799,\n 1.2803280328032804,\n 1.280728072807281,\n 1.281128112811281,\n 1.2815281528152815,\n 1.2819281928192821,\n 1.2823282328232826,\n 1.2827282728272826,\n 1.2831283128312831,\n 1.2835283528352837,\n 1.2839283928392842,\n 1.2843284328432842,\n 1.2847284728472848,\n 1.2851285128512853,\n 1.2855285528552858,\n 1.2859285928592858,\n 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1.1587158715871588,\n 1.1591159115911593,\n 1.1595159515951594,\n 1.1599159915991599,\n 1.1603160316031604,\n 1.1607160716071609,\n 1.161116111611161,\n 1.1615161516151615,\n 1.161916191619162,\n 1.1623162316231626,\n 1.1627162716271626,\n 1.1631163116311631,\n 1.1635163516351636,\n 1.1639163916391642,\n 1.1643164316431642,\n 1.1647164716471647,\n 1.1651165116511653,\n 1.1655165516551658,\n 1.1659165916591658,\n 1.1663166316631663,\n 1.1667166716671669,\n 1.1671167116711674,\n 1.1675167516751674,\n 1.167916791679168,\n 1.1683168316831685,\n 1.168716871687169,\n 1.169116911691169,\n 1.1695169516951696,\n 1.1699169916991701,\n 1.1703170317031706,\n 1.1707170717071707,\n 1.1711171117111712,\n 1.1715171517151717,\n 1.1719171917191722,\n 1.1723172317231723,\n 1.1727172717271728,\n 1.1731173117311733,\n 1.1735173517351738,\n 1.1739173917391739,\n 1.1743174317431744,\n 1.1747174717471749,\n 1.1751175117511754,\n 1.1755175517551755,\n 1.175917591759176,\n 1.1763176317631765,\n 1.176717671767177,\n 1.1771177117711771,\n 1.1775177517751776,\n 1.1779177917791781,\n 1.1783178317831786,\n 1.1787178717871787,\n 1.1791179117911792,\n 1.1795179517951797,\n 1.1799179917991802,\n 1.1803180318031803,\n 1.1807180718071808,\n 1.1811181118111813,\n 1.1815181518151818,\n 1.1819181918191819,\n 1.1823182318231824,\n 1.1827182718271829,\n 1.1831183118311834,\n 1.1835183518351835,\n 1.183918391839184,\n 1.1843184318431845,\n 1.184718471847185,\n 1.1851185118511851,\n 1.1855185518551856,\n 1.1859185918591861,\n 1.1863186318631862,\n 1.1867186718671867,\n 1.1871187118711872,\n 1.1875187518751877,\n 1.1879187918791878,\n 1.1883188318831883,\n 1.1887188718871888,\n 1.1891189118911893,\n 1.1895189518951894,\n 1.1899189918991899,\n 1.1903190319031904,\n 1.190719071907191,\n 1.191119111911191,\n 1.1915191519151915,\n 1.191919191919192,\n 1.1923192319231926,\n 1.1927192719271926,\n 1.1931193119311931,\n 1.1935193519351937,\n 1.1939193919391942,\n 1.1943194319431942,\n 1.1947194719471947,\n 1.1951195119511953,\n 1.1955195519551958,\n 1.1959195919591958,\n 1.1963196319631964,\n 1.1967196719671969,\n 1.1971197119711974,\n 1.1975197519751974,\n 1.197919791979198,\n 1.1983198319831985,\n 1.198719871987199,\n 1.1991199119911991,\n 1.1995199519951996,\n 1.1999199919992001,\n 1.2003200320032006,\n 1.2007200720072007,\n 1.2011201120112012,\n 1.2015201520152017,\n 1.2019201920192022,\n 1.2023202320232023,\n 1.2027202720272028,\n 1.2031203120312033,\n 1.2035203520352038,\n 1.2039203920392039,\n 1.2043204320432044,\n 1.2047204720472049,\n 1.2051205120512054,\n 1.2055205520552055,\n 1.205920592059206,\n 1.2063206320632065,\n 1.206720672067207,\n 1.2071207120712071,\n 1.2075207520752076,\n 1.2079207920792081,\n 1.2083208320832086,\n 1.2087208720872087,\n 1.2091209120912092,\n 1.2095209520952097,\n 1.2099209920992102,\n 1.2103210321032103,\n 1.2107210721072108,\n 1.2111211121112113,\n 1.2115211521152118,\n 1.2119211921192119,\n 1.2123212321232124,\n 1.2127212721272129,\n 1.2131213121312134,\n 1.2135213521352135,\n 1.213921392139214,\n 1.2143214321432145,\n 1.214721472147215,\n 1.2151215121512151,\n 1.2155215521552156,\n 1.2159215921592161,\n 1.2163216321632166,\n 1.2167216721672167,\n 1.2171217121712172,\n 1.2175217521752177,\n 1.2179217921792178,\n 1.2183218321832183,\n 1.2187218721872188,\n 1.2191219121912193,\n 1.2195219521952194,\n 1.2199219921992199,\n 1.2203220322032204,\n 1.220722072207221,\n 1.221122112211221,\n 1.2215221522152215,\n 1.221922192219222,\n 1.2223222322232226,\n 1.2227222722272226,\n 1.2231223122312231,\n 1.2235223522352237,\n 1.2239223922392242,\n 1.2243224322432242,\n 1.2247224722472247,\n 1.2251225122512253,\n 1.2255225522552258,\n 1.2259225922592258,\n 1.2263226322632264,\n 1.2267226722672269,\n 1.2271227122712274,\n 1.2275227522752274,\n 1.227922792279228,\n 1.2283228322832285,\n 1.228722872287229,\n 1.2291229122912291,\n 1.2295229522952296,\n 1.2299229922992301,\n 1.2303230323032306,\n 1.2307230723072307,\n 1.2311231123112312,\n 1.2315231523152317,\n 1.2319231923192322,\n 1.2323232323232323,\n 1.2327232723272328,\n 1.2331233123312333,\n 1.2335233523352338,\n 1.2339233923392339,\n 1.2343234323432344,\n 1.2347234723472349,\n 1.2351235123512354,\n 1.2355235523552355,\n 1.235923592359236,\n 1.2363236323632365,\n 1.236723672367237,\n 1.2371237123712371,\n 1.2375237523752376,\n 1.2379237923792381,\n 1.2383238323832386,\n 1.2387238723872387,\n 1.2391239123912392,\n 1.2395239523952397,\n 1.2399239923992402,\n 1.2403240324032403,\n 1.2407240724072408,\n 1.2411241124112413,\n 1.2415241524152418,\n 1.2419241924192419,\n 1.2423242324232424,\n 1.2427242724272429,\n 1.2431243124312434,\n 1.2435243524352435,\n 1.243924392439244,\n 1.2443244324432445,\n 1.244724472447245,\n 1.2451245124512451,\n 1.2455245524552456,\n 1.2459245924592461,\n 1.2463246324632467,\n 1.2467246724672467,\n 1.2471247124712472,\n 1.2475247524752477,\n 1.2479247924792478,\n 1.2483248324832483,\n 1.2487248724872488,\n 1.2491249124912494,\n 1.2495249524952494,\n 1.2499249924992499,\n 1.2503250325032504,\n 1.250725072507251,\n 1.251125112511251,\n 1.2515251525152515,\n 1.2519251925192521,\n 1.2523252325232526,\n 1.2527252725272526,\n 1.2531253125312531,\n 1.2535253525352537,\n 1.2539253925392542,\n 1.2543254325432542,\n 1.2547254725472547,\n 1.2551255125512553,\n 1.2555255525552558,\n 1.2559255925592558,\n 1.2563256325632564,\n 1.2567256725672569,\n 1.2571257125712574,\n 1.2575257525752574,\n 1.257925792579258,\n 1.2583258325832585,\n 1.258725872587259,\n 1.2591259125912591,\n 1.2595259525952596,\n 1.2599259925992601,\n 1.2603260326032606,\n 1.2607260726072607,\n 1.2611261126112612,\n 1.2615261526152617,\n 1.2619261926192622,\n 1.2623262326232623,\n 1.2627262726272628,\n 1.2631263126312633,\n 1.2635263526352638,\n 1.2639263926392639,\n 1.2643264326432644,\n 1.2647264726472649,\n 1.2651265126512654,\n 1.2655265526552655,\n 1.265926592659266,\n 1.2663266326632665,\n 1.266726672667267,\n 1.2671267126712671,\n 1.2675267526752676,\n 1.2679267926792681,\n 1.2683268326832686,\n 1.2687268726872687,\n 1.2691269126912692,\n 1.2695269526952697,\n 1.2699269926992702,\n 1.2703270327032703,\n 1.2707270727072708,\n 1.2711271127112713,\n 1.2715271527152718,\n 1.2719271927192719,\n 1.2723272327232724,\n 1.2727272727272729,\n 1.2731273127312734,\n 1.2735273527352735,\n 1.273927392739274,\n 1.2743274327432745,\n 1.274727472747275,\n 1.2751275127512751,\n 1.2755275527552756,\n 1.2759275927592761,\n 1.2763276327632767,\n 1.2767276727672767,\n 1.2771277127712772,\n 1.2775277527752777,\n 1.2779277927792778,\n 1.2783278327832783,\n 1.2787278727872788,\n 1.2791279127912794,\n 1.2795279527952794,\n 1.2799279927992799,\n 1.2803280328032804,\n 1.280728072807281,\n 1.281128112811281,\n 1.2815281528152815,\n 1.2819281928192821,\n 1.2823282328232826,\n 1.2827282728272826,\n 1.2831283128312831,\n 1.2835283528352837,\n 1.2839283928392842,\n 1.2843284328432842,\n 1.2847284728472848,\n 1.2851285128512853,\n 1.2855285528552858,\n 1.2859285928592858,\n 1.2863286328632864,\n 1.2867286728672869,\n 1.2871287128712874,\n 1.2875287528752875,\n 1.287928792879288,\n 1.2883288328832885,\n 1.288728872887289,\n 1.2891289128912891,\n 1.2895289528952896,\n 1.2899289928992901,\n 1.2903290329032906,\n 1.2907290729072907,\n 1.2911291129112912,\n 1.2915291529152917,\n 1.2919291929192922,\n 1.2923292329232923,\n 1.2927292729272928,\n 1.2931293129312933,\n 1.2935293529352938,\n 1.2939293929392939,\n 1.2943294329432944,\n 1.2947294729472949,\n 1.2951295129512954,\n 1.2955295529552955,\n 1.295929592959296,\n 1.2963296329632965,\n 1.296729672967297,\n 1.2971297129712971,\n 1.2975297529752976,\n 1.2979297929792981,\n 1.2983298329832986,\n 1.2987298729872987,\n 1.2991299129912992,\n 1.2995299529952997,\n 1.2999299929993002,\n 1.3003300330033003,\n 1.3007300730073008,\n 1.3011301130113013,\n 1.3015301530153018,\n 1.3019301930193019,\n 1.3023302330233024,\n 1.3027302730273029,\n 1.3031303130313034,\n 1.3035303530353035,\n 1.303930393039304,\n 1.3043304330433045,\n 1.3047304730473051,\n 1.3051305130513051,\n 1.3055305530553056,\n 1.3059305930593061,\n 1.3063306330633067,\n 1.3067306730673067,\n 1.3071307130713072,\n 1.3075307530753078,\n 1.3079307930793078,\n 1.3083308330833083,\n 1.3087308730873088,\n 1.3091309130913094,\n 1.3095309530953094,\n 1.3099309930993099,\n 1.3103310331033104,\n 1.310731073107311,\n 1.311131113111311,\n 1.3115311531153115,\n 1.3119311931193121,\n 1.3123312331233126,\n 1.3127312731273126,\n 1.3131313131313131,\n 1.3135313531353137,\n 1.3139313931393142,\n 1.3143314331433142,\n 1.3147314731473148,\n 1.3151315131513153,\n 1.3155315531553158,\n 1.3159315931593158,\n 1.3163316331633164,\n 1.3167316731673169,\n 1.3171317131713174,\n 1.3175317531753175,\n 1.317931793179318,\n 1.3183318331833185,\n 1.318731873187319,\n 1.3191319131913191,\n 1.3195319531953196,\n 1.3199319931993201,\n 1.3203320332033206,\n 1.3207320732073207,\n 1.3211321132113212,\n 1.3215321532153217,\n 1.3219321932193222,\n 1.3223322332233223,\n 1.3227322732273228,\n 1.3231323132313233,\n 1.3235323532353238,\n 1.3239323932393239,\n 1.3243324332433244,\n 1.3247324732473249,\n 1.3251325132513254,\n 1.3255325532553255,\n 1.325932593259326,\n 1.3263326332633265,\n 1.326732673267327,\n 1.3271327132713271,\n 1.3275327532753276,\n 1.3279327932793281,\n 1.3283328332833286,\n 1.3287328732873287,\n 1.3291329132913292,\n 1.3295329532953297,\n 1.3299329932993302,\n 1.3303330333033303,\n 1.3307330733073308,\n 1.3311331133113313,\n 1.3315331533153318,\n 1.3319331933193319,\n 1.3323332333233324,\n 1.3327332733273329,\n 1.3331333133313334,\n 1.3335333533353335,\n 1.333933393339334,\n 1.3343334333433345,\n 1.3347334733473351,\n 1.3351335133513351,\n 1.3355335533553356,\n 1.3359335933593361,\n 1.3363336333633367,\n 1.3367336733673367,\n 1.3371337133713372,\n 1.3375337533753378,\n 1.3379337933793378,\n 1.3383338333833383,\n 1.3387338733873388,\n 1.3391339133913394,\n 1.3395339533953394,\n 1.3399339933993399,\n 1.3403340334033405,\n 1.340734073407341,\n 1.341134113411341,\n 1.3415341534153415,\n 1.3419341934193421,\n 1.3423342334233426,\n 1.3427342734273426,\n 1.3431343134313432,\n 1.3435343534353437,\n 1.3439343934393442,\n 1.3443344334433442,\n 1.3447344734473448,\n 1.3451345134513453,\n 1.3455345534553458,\n 1.3459345934593459,\n 1.3463346334633464,\n 1.3467346734673469,\n 1.3471347134713474,\n 1.3475347534753475,\n 1.347934793479348,\n 1.3483348334833485,\n 1.348734873487349,\n 1.3491349134913491,\n 1.3495349534953496,\n 1.3499349934993501,\n 1.3503350335033506,\n 1.3507350735073507,\n 1.3511351135113512,\n 1.3515351535153517,\n 1.3519351935193522,\n 1.3523352335233523,\n 1.3527352735273528,\n 1.3531353135313533,\n 1.3535353535353538,\n 1.3539353935393539,\n 1.3543354335433544,\n 1.3547354735473549,\n 1.3551355135513554,\n 1.3555355535553555,\n 1.355935593559356,\n 1.3563356335633565,\n 1.356735673567357,\n 1.3571357135713571,\n 1.3575357535753576,\n 1.3579357935793581,\n 1.3583358335833586,\n 1.3587358735873587,\n 1.3591359135913592,\n 1.3595359535953597,\n 1.3599359935993602,\n 1.3603360336033603,\n 1.3607360736073608,\n 1.3611361136113613,\n 1.3615361536153618,\n 1.3619361936193619,\n 1.3623362336233624,\n 1.3627362736273629,\n 1.3631363136313634,\n 1.3635363536353635,\n 1.363936393639364,\n 1.3643364336433645,\n 1.3647364736473651,\n 1.3651365136513651,\n 1.3655365536553656,\n 1.3659365936593661,\n 1.3663366336633667,\n 1.3667366736673667,\n 1.3671367136713672,\n 1.3675367536753678,\n 1.3679367936793683,\n 1.3683368336833683,\n 1.3687368736873688,\n 1.3691369136913694,\n 1.3695369536953694,\n 1.3699369936993699,\n 1.3703370337033705,\n 1.370737073707371,\n 1.371137113711371,\n 1.3715371537153715,\n 1.3719371937193721,\n 1.3723372337233726,\n 1.3727372737273726,\n 1.3731373137313732,\n 1.3735373537353737,\n 1.3739373937393742,\n 1.3743374337433742,\n 1.3747374737473748,\n 1.3751375137513753,\n 1.3755375537553758,\n 1.3759375937593759,\n 1.3763376337633764,\n 1.3767376737673769,\n 1.3771377137713774,\n 1.3775377537753775,\n 1.377937793779378,\n 1.3783378337833785,\n 1.378737873787379,\n 1.3791379137913791,\n 1.3795379537953796,\n 1.3799379937993801,\n 1.3803380338033806,\n 1.3807380738073807,\n 1.3811381138113812,\n 1.3815381538153817,\n 1.3819381938193822,\n 1.3823382338233823,\n 1.3827382738273828,\n 1.3831383138313833,\n 1.3835383538353838,\n 1.3839383938393839,\n 1.3843384338433844,\n 1.3847384738473849,\n 1.3851385138513854,\n 1.3855385538553855,\n 1.385938593859386,\n 1.3863386338633865,\n 1.386738673867387,\n 1.3871387138713871,\n 1.3875387538753876,\n 1.3879387938793881,\n 1.3883388338833886,\n 1.3887388738873887,\n 1.3891389138913892,\n 1.3895389538953897,\n 1.3899389938993902,\n 1.3903390339033903,\n 1.3907390739073908,\n 1.3911391139113913,\n 1.3915391539153918,\n 1.3919391939193919,\n 1.3923392339233924,\n 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