diff --git a/IPython/nbconvert/exporters/export.py b/IPython/nbconvert/exporters/export.py index c3bbd84..d6110e2 100644 --- a/IPython/nbconvert/exporters/export.py +++ b/IPython/nbconvert/exporters/export.py @@ -5,7 +5,7 @@ from functools import wraps -from IPython.nbformat.v3.nbbase import NotebookNode +from IPython.nbformat.current import NotebookNode from IPython.utils.decorators import undoc from IPython.utils.py3compat import string_types diff --git a/IPython/nbconvert/exporters/tests/files/notebook2.ipynb b/IPython/nbconvert/exporters/tests/files/notebook2.ipynb index a7fe1a5..6381d97 100644 --- a/IPython/nbconvert/exporters/tests/files/notebook2.ipynb +++ b/IPython/nbconvert/exporters/tests/files/notebook2.ipynb @@ -1,177 +1,168 @@ { - "metadata": { - "name": "notebook2" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "NumPy and Matplotlib examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First import NumPy and Matplotlib:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 1, "metadata": {}, - "source": [ - "NumPy and Matplotlib examples" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": "\nWelcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\nFor more information, type 'help(pylab)'.\n" + } + ], + "prompt_number": 1, + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 2, + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we show some very basic examples of how they can be used." + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 6, + "source": [ + "a = np.random.uniform(size=(100,100))" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "First import NumPy and Matplotlib:" + "output_type": "execute_result", + "prompt_number": 7, + "text/plain": [ + "(100, 100)" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%pylab inline" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n", - "For more information, type 'help(pylab)'.\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, + } + ], + "prompt_number": 7, + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 8, + "source": [ + "evs = np.linalg.eigvals(a)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "Now we show some very basic examples of how they can be used." + "output_type": "execute_result", + "prompt_number": 10, + "text/plain": [ + "(100,)" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = np.random.uniform(size=(100,100))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "(100, 100)" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "evs = np.linalg.eigvals(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "evs.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "(100,)" - ] - } - ], - "prompt_number": 10 - }, + } + ], + "prompt_number": 10, + "source": [ + "evs.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a cell that has both text and PNG output:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "Here is a cell that has both text and PNG output:" + "output_type": "execute_result", + "prompt_number": 14, + "text/plain": [ + "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n", + " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n", + " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n", + " 39.42458533, 44.71961607, 50.01464682]),\n", + " )" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "hist(evs.real)" - ], - "language": "python", + "image/png": 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"metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n", - " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n", - " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n", - " 39.42458533, 44.71961607, 50.01464682]),\n", - " )" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] + "output_type": "display_data", + "text/plain": [ + "" + ] } ], - "metadata": {} + "prompt_number": 14, + "source": [ + "hist(evs.real)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": null, + "source": [] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/exporters/tests/files/rawtest.ipynb b/IPython/nbconvert/exporters/tests/files/rawtest.ipynb index 6eae33a..9b0fabe 100644 --- a/IPython/nbconvert/exporters/tests/files/rawtest.ipynb +++ b/IPython/nbconvert/exporters/tests/files/rawtest.ipynb @@ -1,84 +1,77 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "text/html" - }, - "source": [ - "raw html" - ] - }, - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "text/markdown" - }, - "source": [ - "* raw markdown\n", - "* bullet\n", - "* list" - ] - }, - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "text/restructuredtext" - }, - "source": [ - "``raw rst``\n", - "\n", - ".. sourcecode:: python\n", - "\n", - " def foo(): pass\n" - ] - }, - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "text/x-python" - }, - "source": [ - "def bar():\n", - " \"\"\"raw python\"\"\"\n", - " pass" - ] - }, - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "text/latex" - }, - "source": [ - "\\LaTeX\n", - "% raw latex" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# no raw_mimetype metadata, should be included by default" - ] - }, - { - "cell_type": "raw", - "metadata": { - "raw_mimetype": "doesnotexist" - }, - "source": [ - "garbage format defined, should never be included" - ] - } - ], - "metadata": {} + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/html" + }, + "source": [ + "raw html" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/markdown" + }, + "source": [ + "* raw markdown\n", + "* bullet\n", + "* list" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": [ + "``raw rst``\n", + "\n", + ".. sourcecode:: python\n", + "\n", + " def foo(): pass\n" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/x-python" + }, + "source": [ + "def bar():\n", + " \"\"\"raw python\"\"\"\n", + " pass" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/latex" + }, + "source": [ + "\\LaTeX\n", + "% raw latex" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# no raw_mimetype metadata, should be included by default" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "doesnotexist" + }, + "source": [ + "garbage format defined, should never be included" + ] } - ] -} + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/IPython/nbconvert/exporters/tests/test_latex.py b/IPython/nbconvert/exporters/tests/test_latex.py index 2b4d2a4..2ccd201 100644 --- a/IPython/nbconvert/exporters/tests/test_latex.py +++ b/IPython/nbconvert/exporters/tests/test_latex.py @@ -86,10 +86,8 @@ class TestLatexExporter(ExportersTestsBase): notebook_name = "lorem_ipsum_long.ipynb" nb = current.new_notebook( - worksheets=[ - current.new_worksheet(cells=[ - current.new_text_cell('markdown',source=large_lorem_ipsum_text) - ]) + cells=[ + current.new_markdown_cell(source=large_lorem_ipsum_text) ] ) diff --git a/IPython/nbconvert/exporters/tests/test_rst.py b/IPython/nbconvert/exporters/tests/test_rst.py index c9840a9..8d35844 100644 --- a/IPython/nbconvert/exporters/tests/test_rst.py +++ b/IPython/nbconvert/exporters/tests/test_rst.py @@ -20,9 +20,6 @@ from .base import ExportersTestsBase from ..rst import RSTExporter from IPython.testing.decorators import onlyif_cmds_exist -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- class TestRSTExporter(ExportersTestsBase): """Tests for RSTExporter""" @@ -56,8 +53,8 @@ class TestRSTExporter(ExportersTestsBase): (output, resources) = exporter.from_notebook_node(nb) # add an empty code cell - nb.worksheets[0].cells.append( - current.new_code_cell(input="") + nb.cells.append( + current.new_code_cell(source="") ) (output2, resources) = exporter.from_notebook_node(nb) # adding an empty code cell shouldn't change output diff --git a/IPython/nbconvert/preprocessors/base.py b/IPython/nbconvert/preprocessors/base.py index 62e4d4a..30843a8 100755 --- a/IPython/nbconvert/preprocessors/base.py +++ b/IPython/nbconvert/preprocessors/base.py @@ -66,9 +66,8 @@ class Preprocessor(NbConvertBase): Additional resources used in the conversion process. Allows preprocessors to pass variables into the Jinja engine. """ - for worksheet in nb.worksheets: - for index, cell in enumerate(worksheet.cells): - worksheet.cells[index], resources = self.preprocess_cell(cell, resources, index) + for index, cell in enumerate(nb.cells): + nb.cells[index], resources = self.preprocess_cell(cell, resources, index) return nb, resources diff --git a/IPython/nbconvert/preprocessors/coalescestreams.py b/IPython/nbconvert/preprocessors/coalescestreams.py index 12dbc51..f7d4aba 100644 --- a/IPython/nbconvert/preprocessors/coalescestreams.py +++ b/IPython/nbconvert/preprocessors/coalescestreams.py @@ -4,6 +4,7 @@ # Distributed under the terms of the Modified BSD License. import re +from IPython.utils.log import get_logger def cell_preprocessor(function): """ @@ -21,14 +22,11 @@ def cell_preprocessor(function): """ def wrappedfunc(nb, resources): - from IPython.config import Application - if Application.initialized(): - Application.instance().log.debug( + get_logger().debug( "Applying preprocessor: %s", function.__name__ ) - for worksheet in nb.worksheets: - for index, cell in enumerate(worksheet.cells): - worksheet.cells[index], resources = function(cell, resources, index) + for index, cell in enumerate(nb.cells): + nb.cells[index], resources = function(cell, resources, index) return nb, resources return wrappedfunc @@ -60,7 +58,7 @@ def coalesce_streams(cell, resources, index): for output in outputs[1:]: if (output.output_type == 'stream' and last.output_type == 'stream' and - last.stream == output.stream + last.name == output.name ): last.text += output.text diff --git a/IPython/nbconvert/preprocessors/execute.py b/IPython/nbconvert/preprocessors/execute.py index 33f2616..2eaa0bd 100644 --- a/IPython/nbconvert/preprocessors/execute.py +++ b/IPython/nbconvert/preprocessors/execute.py @@ -13,7 +13,7 @@ except ImportError: from IPython.utils.traitlets import List, Unicode -from IPython.nbformat.current import reads, NotebookNode, writes +from IPython.nbformat.current import reads, writes, new_output from .base import Preprocessor from IPython.utils.traitlets import Integer @@ -25,25 +25,6 @@ class ExecutePreprocessor(Preprocessor): timeout = Integer(30, config=True, help="The time to wait (in seconds) for output from executions." ) - # FIXME: to be removed with nbformat v4 - # map msg_type to v3 output_type - msg_type_map = { - "error" : "pyerr", - "execute_result" : "pyout", - } - - # FIXME: to be removed with nbformat v4 - # map mime-type to v3 mime-type keys - mime_map = { - "text/plain" : "text", - "text/html" : "html", - "image/svg+xml" : "svg", - "image/png" : "png", - "image/jpeg" : "jpeg", - "text/latex" : "latex", - "application/json" : "json", - "application/javascript" : "javascript", - } extra_arguments = List(Unicode) @@ -68,14 +49,14 @@ class ExecutePreprocessor(Preprocessor): outputs = self.run_cell(self.kc.shell_channel, self.kc.iopub_channel, cell) except Exception as e: self.log.error("failed to run cell: " + repr(e)) - self.log.error(str(cell.input)) + self.log.error(str(cell.source)) raise cell.outputs = outputs return cell, resources def run_cell(self, shell, iopub, cell): - msg_id = shell.execute(cell.input) - self.log.debug("Executing cell:\n%s", cell.input) + msg_id = shell.execute(cell.source) + self.log.debug("Executing cell:\n%s", cell.source) # wait for finish, with timeout while True: try: @@ -104,7 +85,6 @@ class ExecutePreprocessor(Preprocessor): msg_type = msg['msg_type'] self.log.debug("output: %s", msg_type) content = msg['content'] - out = NotebookNode(output_type=self.msg_type_map.get(msg_type, msg_type)) # set the prompt number for the input and the output if 'execution_count' in content: @@ -116,26 +96,37 @@ class ExecutePreprocessor(Preprocessor): break else: continue - elif msg_type in {'execute_input', 'pyin'}: + elif msg_type in {'execute_input'}: continue elif msg_type == 'clear_output': outs = [] continue - if msg_type == 'stream': - out.stream = content['name'] - out.text = content['text'] - elif msg_type in ('display_data', 'execute_result'): - out['metadata'] = content['metadata'] - for mime, data in content['data'].items(): - # map mime-type keys to nbformat v3 keys - # this will be unnecessary in nbformat v4 - key = self.mime_map.get(mime, mime) - out[key] = data + # set the prompt number for the input and the output + if msg_type == 'execute_result': + cell['prompt_number'] = content['execution_count'] + out = new_output(output_type=msg_type, + metadata=content['metadata'], + mime_bundle=content['data'], + prompt_number=content['execution_count'], + ) + + elif msg_type == 'stream': + out = new_output(output_type=msg_type, + name=content['name'], + data=content['data'], + ) + elif msg_type == 'display_data': + out = new_output(output_type=msg_type, + metadata=content['metadata'], + mime_bundle=content['data'], + ) elif msg_type == 'error': - out.ename = content['ename'] - out.evalue = content['evalue'] - out.traceback = content['traceback'] + out = new_output(output_type=msg_type, + ename=content['ename'], + evalue=content['evalue'], + traceback=content['traceback'], + ) else: self.log.error("unhandled iopub msg: " + msg_type) diff --git a/IPython/nbconvert/preprocessors/extractoutput.py b/IPython/nbconvert/preprocessors/extractoutput.py index 2e70338..fc87868 100755 --- a/IPython/nbconvert/preprocessors/extractoutput.py +++ b/IPython/nbconvert/preprocessors/extractoutput.py @@ -1,17 +1,9 @@ -"""Module containing a preprocessor that extracts all of the outputs from the +"""A preprocessor that extracts all of the outputs from the notebook file. The extracted outputs are returned in the 'resources' dictionary. """ -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# -# Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- +# Copyright (c) IPython Development Team. +# Distributed under the terms of the Modified BSD License. import base64 import sys @@ -22,9 +14,6 @@ from IPython.utils.traitlets import Unicode, Set from .base import Preprocessor from IPython.utils import py3compat -#----------------------------------------------------------------------------- -# Classes -#----------------------------------------------------------------------------- class ExtractOutputPreprocessor(Preprocessor): """ @@ -35,7 +24,7 @@ class ExtractOutputPreprocessor(Preprocessor): output_filename_template = Unicode( "{unique_key}_{cell_index}_{index}{extension}", config=True) - extract_output_types = Set({'png', 'jpeg', 'svg', 'application/pdf'}, config=True) + extract_output_types = Set({'image/png', 'image/jpeg', 'image/svg+xml', 'application/pdf'}, config=True) def preprocess_cell(self, cell, resources, cell_index): """ @@ -71,7 +60,7 @@ class ExtractOutputPreprocessor(Preprocessor): data = out[out_type] #Binary files are base64-encoded, SVG is already XML - if out_type in {'png', 'jpeg', 'application/pdf'}: + if out_type in {'image/png', 'image/jpeg', 'application/pdf'}: # data is b64-encoded as text (str, unicode) # decodestring only accepts bytes diff --git a/IPython/nbconvert/preprocessors/highlightmagics.py b/IPython/nbconvert/preprocessors/highlightmagics.py index 135fe4e..83356d6 100644 --- a/IPython/nbconvert/preprocessors/highlightmagics.py +++ b/IPython/nbconvert/preprocessors/highlightmagics.py @@ -4,17 +4,8 @@ so that the appropriate highlighter can be used in the `highlight` filter. """ -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# +# Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from __future__ import print_function, absolute_import @@ -24,10 +15,6 @@ import re from .base import Preprocessor from IPython.utils.traitlets import Dict -#----------------------------------------------------------------------------- -# Classes -#----------------------------------------------------------------------------- - class HighlightMagicsPreprocessor(Preprocessor): """ @@ -106,8 +93,8 @@ class HighlightMagicsPreprocessor(Preprocessor): """ # Only tag code cells - if hasattr(cell, "input") and cell.cell_type == "code": - magic_language = self.which_magic_language(cell.input) + if cell.cell_type == "code": + magic_language = self.which_magic_language(cell.source) if magic_language: cell['metadata']['magics_language'] = magic_language return cell, resources diff --git a/IPython/nbconvert/preprocessors/revealhelp.py b/IPython/nbconvert/preprocessors/revealhelp.py index a4d90fe..7b09c7f 100755 --- a/IPython/nbconvert/preprocessors/revealhelp.py +++ b/IPython/nbconvert/preprocessors/revealhelp.py @@ -1,23 +1,11 @@ -"""Module that pre-processes the notebook for export via Reveal. -""" -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# -# Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- +"""Module that pre-processes the notebook for export via Reveal.""" -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- +# Copyright (c) IPython Development Team. +# Distributed under the terms of the Modified BSD License. from .base import Preprocessor from IPython.utils.traitlets import Unicode -#----------------------------------------------------------------------------- -# Classes and functions -#----------------------------------------------------------------------------- class RevealHelpPreprocessor(Preprocessor): @@ -43,31 +31,30 @@ class RevealHelpPreprocessor(Preprocessor): preprocessors to pass variables into the Jinja engine. """ - for worksheet in nb.worksheets: - for index, cell in enumerate(worksheet.cells): - - #Make sure the cell has slideshow metadata. - cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-') - - #Get the slide type. If type is start of subslide or slide, - #end the last subslide/slide. - if cell.metadata.slide_type in ['slide']: - worksheet.cells[index - 1].metadata.slide_helper = 'slide_end' - if cell.metadata.slide_type in ['subslide']: - worksheet.cells[index - 1].metadata.slide_helper = 'subslide_end' - #Prevent the rendering of "do nothing" cells before fragments - #Group fragments passing frag_number to the data-fragment-index - if cell.metadata.slide_type in ['fragment']: - worksheet.cells[index].metadata.frag_number = index - i = 1 - while i < len(worksheet.cells) - index: - worksheet.cells[index + i].metadata.frag_helper = 'fragment_end' - worksheet.cells[index + i].metadata.frag_number = index - i += 1 - #Restart the slide_helper when the cell status is changed - #to other types. - if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']: - worksheet.cells[index - 1].metadata.slide_helper = '-' + for index, cell in enumerate(nb.cells): + + #Make sure the cell has slideshow metadata. + cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-') + + # Get the slide type. If type is start, subslide, or slide, + # end the last subslide/slide. + if cell.metadata.slide_type in ['slide']: + nb.cells[index - 1].metadata.slide_helper = 'slide_end' + if cell.metadata.slide_type in ['subslide']: + nb.cells[index - 1].metadata.slide_helper = 'subslide_end' + # Prevent the rendering of "do nothing" cells before fragments + # Group fragments passing frag_number to the data-fragment-index + if cell.metadata.slide_type in ['fragment']: + nb.cells[index].metadata.frag_number = index + i = 1 + while i < len(nb.cells) - index: + nb.cells[index + i].metadata.frag_helper = 'fragment_end' + nb.cells[index + i].metadata.frag_number = index + i += 1 + # Restart the slide_helper when the cell status is changed + # to other types. + if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']: + nb.cells[index - 1].metadata.slide_helper = '-' if not isinstance(resources['reveal'], dict): resources['reveal'] = {} diff --git a/IPython/nbconvert/preprocessors/tests/base.py b/IPython/nbconvert/preprocessors/tests/base.py index 81fe492..7115560 100644 --- a/IPython/nbconvert/preprocessors/tests/base.py +++ b/IPython/nbconvert/preprocessors/tests/base.py @@ -1,27 +1,13 @@ -""" -Module with utility functions for preprocessor tests -""" +"""utility functions for preprocessor tests""" -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# +# Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from IPython.nbformat import current as nbformat from ...tests.base import TestsBase from ...exporters.exporter import ResourcesDict -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- class PreprocessorTestsBase(TestsBase): """Contains test functions preprocessor tests""" @@ -30,22 +16,21 @@ class PreprocessorTestsBase(TestsBase): def build_notebook(self): """Build a notebook in memory for use with preprocessor tests""" - outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a"), - nbformat.new_output(output_type="text", output_text="b"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="c"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="d"), - nbformat.new_output(output_type="stream", stream="stderr", output_text="e"), - nbformat.new_output(output_type="stream", stream="stderr", output_text="f"), - nbformat.new_output(output_type="png", output_png='Zw==')] # g - out = nbformat.new_output(output_type="application/pdf") - out['application/pdf'] = 'aA==' # h + outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a"), + nbformat.new_output(output_type="display_data", mime_bundle={'text/plain': 'b'}), + nbformat.new_output(output_type="stream", name="stdout", text="c"), + nbformat.new_output(output_type="stream", name="stdout", text="d"), + nbformat.new_output(output_type="stream", name="stderr", text="e"), + nbformat.new_output(output_type="stream", name="stderr", text="f"), + nbformat.new_output(output_type="display_data", mime_bundle={'image/png': 'Zw=='})] # g + out = nbformat.new_output(output_type="display_data") + out['application/pdf'] = 'aA==' outputs.append(out) - cells=[nbformat.new_code_cell(input="$ e $", prompt_number=1,outputs=outputs), - nbformat.new_text_cell('markdown', source="$ e $")] - worksheets = [nbformat.new_worksheet(cells=cells)] + cells=[nbformat.new_code_cell(source="$ e $", prompt_number=1, outputs=outputs), + nbformat.new_markdown_cell(source="$ e $")] - return nbformat.new_notebook(name="notebook1", worksheets=worksheets) + return nbformat.new_notebook(cells=cells) def build_resources(self): diff --git a/IPython/nbconvert/preprocessors/tests/files/Clear Output.ipynb b/IPython/nbconvert/preprocessors/tests/files/Clear Output.ipynb index 758e84b..5470ee6 100644 --- a/IPython/nbconvert/preprocessors/tests/files/Clear Output.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/Clear Output.ipynb @@ -1,46 +1,38 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import clear_output" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 1, + "source": [ + "from IPython.display import clear_output" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(10):\n", - " clear_output()\n", - " print(i)" - ], - "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "9\n" - ] - } - ], - "prompt_number": 2 + "name": "stdout", + "output_type": "stream", + "text": "9\n" } ], - "metadata": {} + "prompt_number": 2, + "source": [ + "for i in range(10):\n", + " clear_output()\n", + " print(i)" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/Factorials.ipynb b/IPython/nbconvert/preprocessors/tests/files/Factorials.ipynb index bc74e2c..e2f29cf 100644 --- a/IPython/nbconvert/preprocessors/tests/files/Factorials.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/Factorials.ipynb @@ -1,55 +1,38 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i, j = 1, 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 1, + "source": [ + "i, j = 1, 1" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "for m in range(10):\n", - " i, j = j, i + j\n", - " print(j)" - ], - "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2\n", - "3\n", - "5\n", - "8\n", - "13\n", - "21\n", - "34\n", - "55\n", - "89\n", - "144\n" - ] - } - ], - "prompt_number": 2 + "name": "stdout", + "output_type": "stream", + "text": "2\n3\n5\n8\n13\n21\n34\n55\n89\n144\n" } ], - "metadata": {} + "prompt_number": 2, + "source": [ + "for m in range(10):\n", + " i, j = j, i + j\n", + " print(j)" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/HelloWorld.ipynb b/IPython/nbconvert/preprocessors/tests/files/HelloWorld.ipynb index bd405d1..665a654 100644 --- a/IPython/nbconvert/preprocessors/tests/files/HelloWorld.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/HelloWorld.ipynb @@ -1,33 +1,25 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(\"Hello World\")" - ], - "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello World\n" - ] - } - ], - "prompt_number": 1 + "name": "stdout", + "output_type": "stream", + "text": "Hello World\n" } ], - "metadata": {} + "prompt_number": 1, + "source": [ + "print(\"Hello World\")" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/Inline Image.ipynb b/IPython/nbconvert/preprocessors/tests/files/Inline Image.ipynb index c3527e1..48deb38 100644 --- a/IPython/nbconvert/preprocessors/tests/files/Inline Image.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/Inline Image.ipynb @@ -1,36 +1,29 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import Image" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Image('../input/python.png');" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - } - ], - "metadata": {} + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 1, + "source": [ + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 2, + "source": [ + "Image('../input/python.png');" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/SVG.ipynb b/IPython/nbconvert/preprocessors/tests/files/SVG.ipynb index 4c9e40a..cd4684b 100644 --- a/IPython/nbconvert/preprocessors/tests/files/SVG.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/SVG.ipynb @@ -1,53 +1,46 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import SVG" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 1, + "source": [ + "from IPython.display import SVG" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "SVG(data='''\n", + "image/svg+xml": [ "\n", - " \n", - "''')" + " \n", + "" ], - "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "svg": [ - "\n", - " \n", - "" - ], - "text": [ - "" - ] - } - ], - "prompt_number": 2 + "output_type": "execute_result", + "prompt_number": 2, + "text/plain": [ + "" + ] } ], - "metadata": {} + "prompt_number": 2, + "source": [ + "SVG(data='''\n", + "\n", + " \n", + "''')" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/Skip Exceptions.ipynb b/IPython/nbconvert/preprocessors/tests/files/Skip Exceptions.ipynb index 2f20604..d308d1f 100644 --- a/IPython/nbconvert/preprocessors/tests/files/Skip Exceptions.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/Skip Exceptions.ipynb @@ -1,57 +1,51 @@ { - "metadata": { - "name": "", - "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "raise Exception(\"message\")" - ], - "language": "python", + "ename": "Exception", + "evalue": "message", "metadata": {}, - "outputs": [ - { - "ename": "Exception", - "evalue": "message", - "output_type": "pyerr", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mException\u001b[0m: message" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mException\u001b[0m: message" + ] + } + ], + "prompt_number": 1, + "source": [ + "raise Exception(\"message\")" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('ok')" - ], - "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "ok\n" - ] - } - ], - "prompt_number": 2 + "name": "stdout", + "output_type": "stream", + "text": "ok\n" } ], - "metadata": {} + "prompt_number": 2, + "source": [ + "print('ok')" + ] } - ] + ], + "metadata": { + "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba" + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/files/Unicode.ipynb b/IPython/nbconvert/preprocessors/tests/files/Unicode.ipynb index 43ebb82..2d658fa 100644 --- a/IPython/nbconvert/preprocessors/tests/files/Unicode.ipynb +++ b/IPython/nbconvert/preprocessors/tests/files/Unicode.ipynb @@ -1,33 +1,25 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('\u2603')" - ], - "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\u2603\n" - ] - } - ], - "prompt_number": 1 + "name": "stdout", + "output_type": "stream", + "text": "\u2603\n" } ], - "metadata": {} + "prompt_number": 1, + "source": [ + "print('\u2603')" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/test_clearoutput.py b/IPython/nbconvert/preprocessors/tests/test_clearoutput.py index 9434f7c..e10b0f4 100644 --- a/IPython/nbconvert/preprocessors/tests/test_clearoutput.py +++ b/IPython/nbconvert/preprocessors/tests/test_clearoutput.py @@ -5,19 +5,12 @@ Module with tests for the clearoutput preprocessor. # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from IPython.nbformat import current as nbformat from .base import PreprocessorTestsBase from ..clearoutput import ClearOutputPreprocessor -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- - class TestClearOutput(PreprocessorTestsBase): """Contains test functions for clearoutput.py""" @@ -38,5 +31,5 @@ class TestClearOutput(PreprocessorTestsBase): res = self.build_resources() preprocessor = self.build_preprocessor() nb, res = preprocessor(nb, res) - assert nb.worksheets[0].cells[0].outputs == [] - assert nb.worksheets[0].cells[0].prompt_number is None + assert nb.cells[0].outputs == [] + assert nb.cells[0].prompt_number is None diff --git a/IPython/nbconvert/preprocessors/tests/test_coalescestreams.py b/IPython/nbconvert/preprocessors/tests/test_coalescestreams.py index baa9db9..57a7088 100644 --- a/IPython/nbconvert/preprocessors/tests/test_coalescestreams.py +++ b/IPython/nbconvert/preprocessors/tests/test_coalescestreams.py @@ -17,44 +17,42 @@ class TestCoalesceStreams(PreprocessorTestsBase): nb = self.build_notebook() res = self.build_resources() nb, res = coalesce_streams(nb, res) - outputs = nb.worksheets[0].cells[0].outputs + outputs = nb.cells[0].outputs self.assertEqual(outputs[0].text, "a") - self.assertEqual(outputs[1].output_type, "text") + self.assertEqual(outputs[1].output_type, "display_data") self.assertEqual(outputs[2].text, "cd") self.assertEqual(outputs[3].text, "ef") def test_coalesce_sequenced_streams(self): """Can the coalesce streams preprocessor merge a sequence of streams?""" - outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="0"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="1"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="2"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="3"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="4"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="5"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="6"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="7")] - cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)] - worksheets = [nbformat.new_worksheet(cells=cells)] - - nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets) + outputs = [nbformat.new_output(output_type="stream", name="stdout", text="0"), + nbformat.new_output(output_type="stream", name="stdout", text="1"), + nbformat.new_output(output_type="stream", name="stdout", text="2"), + nbformat.new_output(output_type="stream", name="stdout", text="3"), + nbformat.new_output(output_type="stream", name="stdout", text="4"), + nbformat.new_output(output_type="stream", name="stdout", text="5"), + nbformat.new_output(output_type="stream", name="stdout", text="6"), + nbformat.new_output(output_type="stream", name="stdout", text="7")] + cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)] + + nb = nbformat.new_notebook(cells=cells) res = self.build_resources() nb, res = coalesce_streams(nb, res) - outputs = nb.worksheets[0].cells[0].outputs + outputs = nb.cells[0].outputs self.assertEqual(outputs[0].text, u'01234567') def test_coalesce_replace_streams(self): """Are \\r characters handled?""" - outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="z"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="\ra"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz\rb"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="\rc\n"), - nbformat.new_output(output_type="stream", stream="stdout", output_text="z\rz\rd")] - cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)] - worksheets = [nbformat.new_worksheet(cells=cells)] - - nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets) + outputs = [nbformat.new_output(output_type="stream", name="stdout", text="z"), + nbformat.new_output(output_type="stream", name="stdout", text="\ra"), + nbformat.new_output(output_type="stream", name="stdout", text="\nz\rb"), + nbformat.new_output(output_type="stream", name="stdout", text="\nz"), + nbformat.new_output(output_type="stream", name="stdout", text="\rc\n"), + nbformat.new_output(output_type="stream", name="stdout", text="z\rz\rd")] + cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)] + + nb = nbformat.new_notebook(cells=cells) res = self.build_resources() nb, res = coalesce_streams(nb, res) - outputs = nb.worksheets[0].cells[0].outputs + outputs = nb.cells[0].outputs self.assertEqual(outputs[0].text, u'a\nb\nc\nd') diff --git a/IPython/nbconvert/preprocessors/tests/test_execute.py b/IPython/nbconvert/preprocessors/tests/test_execute.py index cb30e78..b07a1e9 100644 --- a/IPython/nbconvert/preprocessors/tests/test_execute.py +++ b/IPython/nbconvert/preprocessors/tests/test_execute.py @@ -30,10 +30,8 @@ class TestExecute(PreprocessorTestsBase): output = dict(output) if 'metadata' in output: del output['metadata'] - if 'text' in output: - output['text'] = re.sub(addr_pat, '', output['text']) - if 'svg' in output: - del output['text'] + if 'text/plain' in output: + output['text/plain'] = re.sub(addr_pat, '', output['text/plain']) if 'traceback' in output: tb = [] for line in output['traceback']: @@ -44,8 +42,8 @@ class TestExecute(PreprocessorTestsBase): def assert_notebooks_equal(self, expected, actual): - expected_cells = expected['worksheets'][0]['cells'] - actual_cells = actual['worksheets'][0]['cells'] + expected_cells = expected['cells'] + actual_cells = actual['cells'] assert len(expected_cells) == len(actual_cells) for expected_cell, actual_cell in zip(expected_cells, actual_cells): @@ -82,7 +80,7 @@ class TestExecute(PreprocessorTestsBase): res = self.build_resources() preprocessor = self.build_preprocessor() cleaned_input_nb = copy.deepcopy(input_nb) - for cell in cleaned_input_nb.worksheets[0].cells: + for cell in cleaned_input_nb.cells: if 'prompt_number' in cell: del cell['prompt_number'] cell['outputs'] = [] diff --git a/IPython/nbconvert/preprocessors/tests/test_extractoutput.py b/IPython/nbconvert/preprocessors/tests/test_extractoutput.py index 4b2c515..fcf3ca1 100644 --- a/IPython/nbconvert/preprocessors/tests/test_extractoutput.py +++ b/IPython/nbconvert/preprocessors/tests/test_extractoutput.py @@ -1,27 +1,12 @@ -""" -Module with tests for the extractoutput preprocessor -""" +"""Tests for the extractoutput preprocessor""" -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# +# Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from .base import PreprocessorTestsBase from ..extractoutput import ExtractOutputPreprocessor -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- - class TestExtractOutput(PreprocessorTestsBase): """Contains test functions for extractoutput.py""" @@ -29,7 +14,7 @@ class TestExtractOutput(PreprocessorTestsBase): def build_preprocessor(self): """Make an instance of a preprocessor""" preprocessor = ExtractOutputPreprocessor() - preprocessor.extract_output_types = {'text', 'png', 'application/pdf'} + preprocessor.extract_output_types = {'text/plain', 'image/png', 'application/pdf'} preprocessor.enabled = True return preprocessor @@ -47,17 +32,17 @@ class TestExtractOutput(PreprocessorTestsBase): nb, res = preprocessor(nb, res) # Check if text was extracted. - output = nb.worksheets[0].cells[0].outputs[1] - assert 'text_filename' in output - text_filename = output['text_filename'] + output = nb.cells[0].outputs[1] + assert 'text/plain_filename' in output + text_filename = output['text/plain_filename'] # Check if png was extracted. - output = nb.worksheets[0].cells[0].outputs[6] - assert 'png_filename' in output - png_filename = output['png_filename'] + output = nb.cells[0].outputs[6] + assert 'image/png_filename' in output + png_filename = output['image/png_filename'] # Check that pdf was extracted - output = nb.worksheets[0].cells[0].outputs[7] + output = nb.cells[0].outputs[7] assert 'application/pdf_filename' in output pdf_filename = output['application/pdf_filename'] diff --git a/IPython/nbconvert/preprocessors/tests/test_highlightmagics.py b/IPython/nbconvert/preprocessors/tests/test_highlightmagics.py index ec21374..81364f2 100644 --- a/IPython/nbconvert/preprocessors/tests/test_highlightmagics.py +++ b/IPython/nbconvert/preprocessors/tests/test_highlightmagics.py @@ -1,27 +1,9 @@ -""" -Module with tests for the HighlightMagics preprocessor -""" - -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# -# Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- +"""Tests for the HighlightMagics preprocessor""" from .base import PreprocessorTestsBase from ..highlightmagics import HighlightMagicsPreprocessor -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- - class TestHighlightMagics(PreprocessorTestsBase): """Contains test functions for highlightmagics.py""" @@ -41,7 +23,7 @@ class TestHighlightMagics(PreprocessorTestsBase): nb = self.build_notebook() res = self.build_resources() preprocessor = self.build_preprocessor() - nb.worksheets[0].cells[0].input = """%%R -i x,y -o XYcoef + nb.cells[0].source = """%%R -i x,y -o XYcoef lm.fit <- lm(y~x) par(mfrow=c(2,2)) print(summary(lm.fit)) @@ -50,19 +32,19 @@ class TestHighlightMagics(PreprocessorTestsBase): nb, res = preprocessor(nb, res) - assert('magics_language' in nb.worksheets[0].cells[0]['metadata']) + assert('magics_language' in nb.cells[0]['metadata']) - self.assertEqual(nb.worksheets[0].cells[0]['metadata']['magics_language'], 'r') + self.assertEqual(nb.cells[0]['metadata']['magics_language'], 'r') def test_no_false_positive(self): """Test that HighlightMagicsPreprocessor does not tag false positives""" nb = self.build_notebook() res = self.build_resources() preprocessor = self.build_preprocessor() - nb.worksheets[0].cells[0].input = """# this should not be detected + nb.cells[0].source = """# this should not be detected print(\""" %%R -i x, y \""")""" nb, res = preprocessor(nb, res) - assert('magics_language' not in nb.worksheets[0].cells[0]['metadata']) \ No newline at end of file + assert('magics_language' not in nb.cells[0]['metadata']) \ No newline at end of file diff --git a/IPython/nbconvert/preprocessors/tests/test_latex.py b/IPython/nbconvert/preprocessors/tests/test_latex.py index 4687b37..b9a76e7 100644 --- a/IPython/nbconvert/preprocessors/tests/test_latex.py +++ b/IPython/nbconvert/preprocessors/tests/test_latex.py @@ -1,27 +1,12 @@ -""" -Module with tests for the latex preprocessor -""" +"""Tests for the latex preprocessor""" -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# +# Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from .base import PreprocessorTestsBase from ..latex import LatexPreprocessor -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- - class TestLatex(PreprocessorTestsBase): """Contains test functions for latex.py""" @@ -45,7 +30,7 @@ class TestLatex(PreprocessorTestsBase): nb, res = preprocessor(nb, res) # Make sure the code cell wasn't modified. - self.assertEqual(nb.worksheets[0].cells[0].input, '$ e $') + self.assertEqual(nb.cells[0].source, '$ e $') # Verify that the markdown cell wasn't processed. - self.assertEqual(nb.worksheets[0].cells[1].source, '$ e $') + self.assertEqual(nb.cells[1].source, '$ e $') diff --git a/IPython/nbconvert/preprocessors/tests/test_revealhelp.py b/IPython/nbconvert/preprocessors/tests/test_revealhelp.py index 5f73cfc..7e50618 100644 --- a/IPython/nbconvert/preprocessors/tests/test_revealhelp.py +++ b/IPython/nbconvert/preprocessors/tests/test_revealhelp.py @@ -16,19 +16,18 @@ class Testrevealhelp(PreprocessorTestsBase): """Build a reveal slides notebook in memory for use with tests. Overrides base in PreprocessorTestsBase""" - outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a")] + outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a")] slide_metadata = {'slideshow' : {'slide_type': 'slide'}} subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}} - cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs), - nbformat.new_text_cell('markdown', source="", metadata=slide_metadata), - nbformat.new_code_cell(input="", prompt_number=2, outputs=outputs), - nbformat.new_text_cell('markdown', source="", metadata=slide_metadata), - nbformat.new_text_cell('markdown', source="", metadata=subslide_metadata)] - worksheets = [nbformat.new_worksheet(cells=cells)] + cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs), + nbformat.new_markdown_cell(source="", metadata=slide_metadata), + nbformat.new_code_cell(source="", prompt_number=2, outputs=outputs), + nbformat.new_markdown_cell(source="", metadata=slide_metadata), + nbformat.new_markdown_cell(source="", metadata=subslide_metadata)] - return nbformat.new_notebook(name="notebook1", worksheets=worksheets) + return nbformat.new_notebook(cells=cells) def build_preprocessor(self): @@ -59,7 +58,7 @@ class Testrevealhelp(PreprocessorTestsBase): res = self.build_resources() preprocessor = self.build_preprocessor() nb, res = preprocessor(nb, res) - cells = nb.worksheets[0].cells + cells = nb.cells # Make sure correct metadata tags are available on every cell. for cell in cells: diff --git a/IPython/nbconvert/preprocessors/tests/test_svg2pdf.py b/IPython/nbconvert/preprocessors/tests/test_svg2pdf.py index 9e8fb81..f744606 100644 --- a/IPython/nbconvert/preprocessors/tests/test_svg2pdf.py +++ b/IPython/nbconvert/preprocessors/tests/test_svg2pdf.py @@ -1,18 +1,7 @@ -""" -Module with tests for the svg2pdf preprocessor -""" +"""Tests for the svg2pdf preprocessor""" -#----------------------------------------------------------------------------- -# Copyright (c) 2013, the IPython Development Team. -# +# Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. -# -# The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- from IPython.testing import decorators as dec from IPython.nbformat import current as nbformat @@ -21,10 +10,6 @@ from .base import PreprocessorTestsBase from ..svg2pdf import SVG2PDFPreprocessor -#----------------------------------------------------------------------------- -# Class -#----------------------------------------------------------------------------- - class Testsvg2pdf(PreprocessorTestsBase): """Contains test functions for svg2pdf.py""" @@ -62,10 +47,9 @@ class Testsvg2pdf(PreprocessorTestsBase): slide_metadata = {'slideshow' : {'slide_type': 'slide'}} subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}} - cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs)] - worksheets = [nbformat.new_worksheet(name="worksheet1", cells=cells)] + cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs)] - return nbformat.new_notebook(name="notebook1", worksheets=worksheets) + return nbformat.new_notebook(cells=cells) def build_preprocessor(self): @@ -87,4 +71,4 @@ class Testsvg2pdf(PreprocessorTestsBase): res = self.build_resources() preprocessor = self.build_preprocessor() nb, res = preprocessor(nb, res) - assert 'svg' in nb.worksheets[0].cells[0].outputs[0] + assert 'svg' in nb.cells[0].outputs[0] diff --git a/IPython/nbconvert/templates/html/basic.tpl b/IPython/nbconvert/templates/html/basic.tpl index 83b2616..22f0382 100644 --- a/IPython/nbconvert/templates/html/basic.tpl +++ b/IPython/nbconvert/templates/html/basic.tpl @@ -46,14 +46,14 @@ In [ ]: {% block input %}
-{{ cell.input | highlight_code(metadata=cell.metadata) }} +{{ cell.source | highlight_code(metadata=cell.metadata) }}
{%- endblock input %} {% block output %}
-{%- if output.output_type == 'pyout' -%} +{%- if output.output_type == 'execute_result' -%}
{%- if cell.prompt_number is defined -%} Out[{{ cell.prompt_number|replace(None, " ") }}]: @@ -94,13 +94,13 @@ In [ ]: unknown type {{ cell.type }} {% endblock unknowncell %} -{% block pyout -%} -{%- set extra_class="output_pyout" -%} +{% block execute_result -%} +{%- set extra_class="output_execute_result" -%} {% block data_priority scoped %} {{ super() }} {% endblock %} {%- set extra_class="" -%} -{%- endblock pyout %} +{%- endblock execute_result %} {% block stream_stdout -%}
@@ -174,13 +174,13 @@ height={{output.metadata['jpeg']['height']}}
{%- endblock data_latex %} -{% block pyerr -%} -
+{% block error -%} +
 {{- super() -}}
 
-{%- endblock pyerr %} +{%- endblock error %} {%- block traceback_line %} {{ line | ansi2html }} diff --git a/IPython/nbconvert/templates/latex/base.tplx b/IPython/nbconvert/templates/latex/base.tplx index 3516749..527316d 100644 --- a/IPython/nbconvert/templates/latex/base.tplx +++ b/IPython/nbconvert/templates/latex/base.tplx @@ -140,11 +140,11 @@ This template does not define a docclass, the inheriting class must define this. ((* endblock data_text *)) % Display python error text as-is -((* block pyerr *)) +((* block error *)) \begin{Verbatim}[commandchars=\\\{\}] ((( super() ))) \end{Verbatim} -((* endblock pyerr *)) +((* endblock error *)) ((* block traceback_line *)) ((( line | indent | strip_ansi | escape_latex ))) ((* endblock traceback_line *)) @@ -206,12 +206,12 @@ This template does not define a docclass, the inheriting class must define this. ((* endblock headingcell *)) -% Redirect pyout to display data priority. -((* block pyout scoped *)) +% Redirect execute_result to display data priority. +((* block execute_result scoped *)) ((* block data_priority scoped *)) ((( super() ))) ((* endblock *)) -((* endblock pyout *)) +((* endblock execute_result *)) % Render markdown ((* block markdowncell scoped *)) diff --git a/IPython/nbconvert/templates/latex/skeleton/display_priority.tplx b/IPython/nbconvert/templates/latex/skeleton/display_priority.tplx index f8c2308..277b004 100644 --- a/IPython/nbconvert/templates/latex/skeleton/display_priority.tplx +++ b/IPython/nbconvert/templates/latex/skeleton/display_priority.tplx @@ -9,37 +9,33 @@ ((*- block data_priority scoped -*)) ((*- for type in output | filter_data_type -*)) - ((*- if type in ['application/pdf']*)) + ((*- if type == 'application/pdf' -*)) ((*- block data_pdf -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['svg']*)) + ((*- elif type == 'image/svg+xml' -*)) ((*- block data_svg -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['png']*)) + ((*- elif type == 'image/png' -*)) ((*- block data_png -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['html']*)) + ((*- elif type == 'text/html' -*)) ((*- block data_html -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['jpeg']*)) + ((*- elif type == 'image/jpeg' -*)) ((*- block data_jpg -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['text']*)) + ((*- elif type == 'text/plain' -*)) ((*- block data_text -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['latex']*)) + ((*- elif type == 'text/latex' -*)) ((*- block data_latex -*)) ((*- endblock -*)) - ((*- endif -*)) - ((*- if type in ['javascript']*)) + ((*- elif type == 'application/javascript' -*)) ((*- block data_javascript -*)) ((*- endblock -*)) + ((*- else -*)) + ((*- block data_other -*)) + ((*- endblock -*)) ((*- endif -*)) ((*- endfor -*)) ((*- endblock data_priority -*)) diff --git a/IPython/nbconvert/templates/latex/skeleton/null.tplx b/IPython/nbconvert/templates/latex/skeleton/null.tplx index 2327e84..00e108e 100644 --- a/IPython/nbconvert/templates/latex/skeleton/null.tplx +++ b/IPython/nbconvert/templates/latex/skeleton/null.tplx @@ -28,69 +28,67 @@ consider calling super even if it is a leave block, we might insert more blocks ((*- block header -*)) ((*- endblock header -*)) ((*- block body -*)) -((*- for worksheet in nb.worksheets -*)) - ((*- for cell in worksheet.cells -*)) - ((*- block any_cell scoped -*)) - ((*- if cell.cell_type in ['code'] -*)) - ((*- block codecell scoped -*)) - ((*- block input_group -*)) - ((*- block in_prompt -*))((*- endblock in_prompt -*)) - ((*- block input -*))((*- endblock input -*)) - ((*- endblock input_group -*)) - ((*- if cell.outputs -*)) - ((*- block output_group -*)) - ((*- block output_prompt -*))((*- endblock output_prompt -*)) - ((*- block outputs scoped -*)) - ((*- for output in cell.outputs -*)) - ((*- block output scoped -*)) - ((*- if output.output_type in ['pyout'] -*)) - ((*- block pyout scoped -*))((*- endblock pyout -*)) - ((*- elif output.output_type in ['stream'] -*)) - ((*- block stream scoped -*)) - ((*- if output.stream in ['stdout'] -*)) - ((*- block stream_stdout scoped -*)) - ((*- endblock stream_stdout -*)) - ((*- elif output.stream in ['stderr'] -*)) - ((*- block stream_stderr scoped -*)) - ((*- endblock stream_stderr -*)) - ((*- endif -*)) - ((*- endblock stream -*)) - ((*- elif output.output_type in ['display_data'] -*)) - ((*- block display_data scoped -*)) - ((*- block data_priority scoped -*)) - ((*- endblock data_priority -*)) - ((*- endblock display_data -*)) - ((*- elif output.output_type in ['pyerr'] -*)) - ((*- block pyerr scoped -*)) - ((*- for line in output.traceback -*)) - ((*- block traceback_line scoped -*))((*- endblock traceback_line -*)) - ((*- endfor -*)) - ((*- endblock pyerr -*)) - ((*- endif -*)) - ((*- endblock output -*)) - ((*- endfor -*)) - ((*- endblock outputs -*)) - ((*- endblock output_group -*)) - ((*- endif -*)) - ((*- endblock codecell -*)) - ((*- elif cell.cell_type in ['markdown'] -*)) - ((*- block markdowncell scoped-*)) - ((*- endblock markdowncell -*)) - ((*- elif cell.cell_type in ['heading'] -*)) - ((*- block headingcell scoped-*)) - ((*- endblock headingcell -*)) - ((*- elif cell.cell_type in ['raw'] -*)) - ((*- block rawcell scoped -*)) - ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *)) - ((( cell.source ))) - ((* endif *)) - ((*- endblock rawcell -*)) - ((*- else -*)) - ((*- block unknowncell scoped-*)) - ((*- endblock unknowncell -*)) - ((*- endif -*)) - ((*- endblock any_cell -*)) - ((*- endfor -*)) +((*- for cell in nb.cells -*)) + ((*- block any_cell scoped -*)) + ((*- if cell.cell_type == 'code' -*)) + ((*- block codecell scoped -*)) + ((*- block input_group -*)) + ((*- block in_prompt -*))((*- endblock in_prompt -*)) + ((*- block input -*))((*- endblock input -*)) + ((*- endblock input_group -*)) + ((*- if cell.outputs -*)) + ((*- block output_group -*)) + ((*- block output_prompt -*))((*- endblock output_prompt -*)) + ((*- block outputs scoped -*)) + ((*- for output in cell.outputs -*)) + ((*- block output scoped -*)) + ((*- if output.output_type == 'execute_result' -*)) + ((*- block execute_result scoped -*))((*- endblock execute_result -*)) + ((*- elif output.output_type == 'stream' -*)) + ((*- block stream scoped -*)) + ((*- if output.name == 'stdout' -*)) + ((*- block stream_stdout scoped -*)) + ((*- endblock stream_stdout -*)) + ((*- elif output.name == 'stderr' -*)) + ((*- block stream_stderr scoped -*)) + ((*- endblock stream_stderr -*)) + ((*- endif -*)) + ((*- endblock stream -*)) + ((*- elif output.output_type == 'display_data' -*)) + ((*- block display_data scoped -*)) + ((*- block data_priority scoped -*)) + ((*- endblock data_priority -*)) + ((*- endblock display_data -*)) + ((*- elif output.output_type == 'error' -*)) + ((*- block error scoped -*)) + ((*- for line in output.traceback -*)) + ((*- block traceback_line scoped -*))((*- endblock traceback_line -*)) + ((*- endfor -*)) + ((*- endblock error -*)) + ((*- endif -*)) + ((*- endblock output -*)) + ((*- endfor -*)) + ((*- endblock outputs -*)) + ((*- endblock output_group -*)) + ((*- endif -*)) + ((*- endblock codecell -*)) + ((*- elif cell.cell_type in ['markdown'] -*)) + ((*- block markdowncell scoped-*)) + ((*- endblock markdowncell -*)) + ((*- elif cell.cell_type in ['heading'] -*)) + ((*- block headingcell scoped-*)) + ((*- endblock headingcell -*)) + ((*- elif cell.cell_type in ['raw'] -*)) + ((*- block rawcell scoped -*)) + ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *)) + ((( cell.source ))) + ((* endif *)) + ((*- endblock rawcell -*)) + ((*- else -*)) + ((*- block unknowncell scoped-*)) + ((*- endblock unknowncell -*)) + ((*- endif -*)) + ((*- endblock any_cell -*)) ((*- endfor -*)) ((*- endblock body -*)) diff --git a/IPython/nbconvert/templates/latex/style_bw_ipython.tplx b/IPython/nbconvert/templates/latex/style_bw_ipython.tplx index 7aa97bf..4e0ca7c 100644 --- a/IPython/nbconvert/templates/latex/style_bw_ipython.tplx +++ b/IPython/nbconvert/templates/latex/style_bw_ipython.tplx @@ -7,7 +7,7 @@ %=============================================================================== ((* block input scoped *)) -((( add_prompt(cell.input, cell, 'In ') ))) +((( add_prompt(cell.source, cell, 'In ') ))) ((* endblock input *)) @@ -15,7 +15,7 @@ % Output %=============================================================================== -((* block pyout scoped *)) +((* block execute_result scoped *)) ((*- for type in output | filter_data_type -*)) ((*- if type in ['text']*)) ((( add_prompt(output.text, cell, 'Out') ))) @@ -23,7 +23,7 @@ \verb+Out[((( cell.prompt_number )))]:+((( super() ))) ((*- endif -*)) ((*- endfor -*)) -((* endblock pyout *)) +((* endblock execute_result *)) %============================================================================== diff --git a/IPython/nbconvert/templates/latex/style_bw_python.tplx b/IPython/nbconvert/templates/latex/style_bw_python.tplx index e10d4a2..5371900 100644 --- a/IPython/nbconvert/templates/latex/style_bw_python.tplx +++ b/IPython/nbconvert/templates/latex/style_bw_python.tplx @@ -8,6 +8,6 @@ ((* block input scoped *)) \begin{verbatim} -((( cell.input | add_prompts ))) +((( cell.source | add_prompts ))) \end{verbatim} ((* endblock input *)) diff --git a/IPython/nbconvert/templates/latex/style_ipython.tplx b/IPython/nbconvert/templates/latex/style_ipython.tplx index 0ed1a10..f067fa7 100644 --- a/IPython/nbconvert/templates/latex/style_ipython.tplx +++ b/IPython/nbconvert/templates/latex/style_ipython.tplx @@ -20,7 +20,7 @@ %=============================================================================== ((* block input scoped *)) - ((( add_prompt(cell.input | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') ))) + ((( add_prompt(cell.source | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') ))) ((* endblock input *)) @@ -28,7 +28,7 @@ % Output %=============================================================================== -((* block pyout scoped *)) +((* block execute_result scoped *)) ((*- for type in output | filter_data_type -*)) ((*- if type in ['text']*)) ((( add_prompt(output.text | escape_latex, cell, 'Out', 'outcolor') ))) @@ -36,7 +36,7 @@ \texttt{\color{outcolor}Out[{\color{outcolor}((( cell.prompt_number )))}]:}((( super() ))) ((*- endif -*)) ((*- endfor -*)) -((* endblock pyout *)) +((* endblock execute_result *)) %============================================================================== diff --git a/IPython/nbconvert/templates/latex/style_python.tplx b/IPython/nbconvert/templates/latex/style_python.tplx index 5849649..6673744 100644 --- a/IPython/nbconvert/templates/latex/style_python.tplx +++ b/IPython/nbconvert/templates/latex/style_python.tplx @@ -16,6 +16,6 @@ ((* block input scoped *)) \begin{Verbatim}[commandchars=\\\{\}] -((( cell.input | highlight_code(strip_verbatim=True) | add_prompts ))) +((( cell.source | highlight_code(strip_verbatim=True) | add_prompts ))) \end{Verbatim} ((* endblock input *)) diff --git a/IPython/nbconvert/templates/markdown.tpl b/IPython/nbconvert/templates/markdown.tpl index ff32189..26addb6 100644 --- a/IPython/nbconvert/templates/markdown.tpl +++ b/IPython/nbconvert/templates/markdown.tpl @@ -8,23 +8,23 @@ {%- endblock output_prompt %} {% block input %} -{{ cell.input | indent(4)}} +{{ cell.source | indent(4)}} {% endblock input %} -{% block pyerr %} +{% block error %} {{ super() }} -{% endblock pyerr %} +{% endblock error %} {% block traceback_line %} {{ line | indent | strip_ansi }} {% endblock traceback_line %} -{% block pyout %} +{% block execute_result %} {% block data_priority scoped %} {{ super() }} {% endblock %} -{% endblock pyout %} +{% endblock execute_result %} {% block stream %} {{ output.text | indent }} diff --git a/IPython/nbconvert/templates/python.tpl b/IPython/nbconvert/templates/python.tpl index 0a13fb0..11449ba 100644 --- a/IPython/nbconvert/templates/python.tpl +++ b/IPython/nbconvert/templates/python.tpl @@ -9,7 +9,7 @@ {% endblock in_prompt %} {% block input %} -{{ cell.input | ipython2python }} +{{ cell.source | ipython2python }} {% endblock input %} {% block markdowncell scoped %} diff --git a/IPython/nbconvert/templates/rst.tpl b/IPython/nbconvert/templates/rst.tpl index 1dc7f69..4d47218 100644 --- a/IPython/nbconvert/templates/rst.tpl +++ b/IPython/nbconvert/templates/rst.tpl @@ -8,28 +8,28 @@ {% endblock output_prompt %} {% block input %} -{%- if cell.input.strip() -%} +{%- if cell.source.strip() -%} .. code:: python -{{ cell.input | indent}} +{{ cell.source | indent}} {%- endif -%} {% endblock input %} -{% block pyerr %} +{% block error %} :: {{ super() }} -{% endblock pyerr %} +{% endblock error %} {% block traceback_line %} {{ line | indent | strip_ansi }} {% endblock traceback_line %} -{% block pyout %} +{% block execute_result %} {% block data_priority scoped %} {{ super() }} {% endblock %} -{% endblock pyout %} +{% endblock execute_result %} {% block stream %} .. parsed-literal:: diff --git a/IPython/nbconvert/templates/skeleton/display_priority.tpl b/IPython/nbconvert/templates/skeleton/display_priority.tpl index 1765694..5270edd 100644 --- a/IPython/nbconvert/templates/skeleton/display_priority.tpl +++ b/IPython/nbconvert/templates/skeleton/display_priority.tpl @@ -5,37 +5,33 @@ {%- block data_priority scoped -%} {%- for type in output | filter_data_type -%} - {%- if type in ['application/pdf']%} + {%- if type == 'application/pdf' -%} {%- block data_pdf -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['svg']%} + {%- elif type == 'image/svg+xml' -%} {%- block data_svg -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['png']%} + {%- elif type == 'image/png' -%} {%- block data_png -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['html']%} + {%- elif type == 'text/html' -%} {%- block data_html -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['jpeg']%} + {%- elif type == 'image/jpeg' -%} {%- block data_jpg -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['text']%} + {%- elif type == 'text/plain' -%} {%- block data_text -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['latex']%} + {%- elif type == 'text/latex' -%} {%- block data_latex -%} {%- endblock -%} - {%- endif -%} - {%- if type in ['javascript']%} + {%- elif type == 'application/javascript' -%} {%- block data_javascript -%} {%- endblock -%} + {%- else -%} + {%- block data_other -%} + {%- endblock -%} {%- endif -%} {%- endfor -%} {%- endblock data_priority -%} diff --git a/IPython/nbconvert/templates/skeleton/null.tpl b/IPython/nbconvert/templates/skeleton/null.tpl index 9779043..4ed4976 100644 --- a/IPython/nbconvert/templates/skeleton/null.tpl +++ b/IPython/nbconvert/templates/skeleton/null.tpl @@ -24,69 +24,67 @@ consider calling super even if it is a leave block, we might insert more blocks {%- block header -%} {%- endblock header -%} {%- block body -%} -{%- for worksheet in nb.worksheets -%} - {%- for cell in worksheet.cells -%} - {%- block any_cell scoped -%} - {%- if cell.cell_type in ['code'] -%} - {%- block codecell scoped -%} - {%- block input_group -%} - {%- block in_prompt -%}{%- endblock in_prompt -%} - {%- block input -%}{%- endblock input -%} - {%- endblock input_group -%} - {%- if cell.outputs -%} - {%- block output_group -%} - {%- block output_prompt -%}{%- endblock output_prompt -%} - {%- block outputs scoped -%} - {%- for output in cell.outputs -%} - {%- block output scoped -%} - {%- if output.output_type in ['pyout'] -%} - {%- block pyout scoped -%}{%- endblock pyout -%} - {%- elif output.output_type in ['stream'] -%} - {%- block stream scoped -%} - {%- if output.stream in ['stdout'] -%} - {%- block stream_stdout scoped -%} - {%- endblock stream_stdout -%} - {%- elif output.stream in ['stderr'] -%} - {%- block stream_stderr scoped -%} - {%- endblock stream_stderr -%} - {%- endif -%} - {%- endblock stream -%} - {%- elif output.output_type in ['display_data'] -%} - {%- block display_data scoped -%} - {%- block data_priority scoped -%} - {%- endblock data_priority -%} - {%- endblock display_data -%} - {%- elif output.output_type in ['pyerr'] -%} - {%- block pyerr scoped -%} - {%- for line in output.traceback -%} - {%- block traceback_line scoped -%}{%- endblock traceback_line -%} - {%- endfor -%} - {%- endblock pyerr -%} - {%- endif -%} - {%- endblock output -%} - {%- endfor -%} - {%- endblock outputs -%} - {%- endblock output_group -%} - {%- endif -%} - {%- endblock codecell -%} - {%- elif cell.cell_type in ['markdown'] -%} - {%- block markdowncell scoped-%} - {%- endblock markdowncell -%} - {%- elif cell.cell_type in ['heading'] -%} - {%- block headingcell scoped-%} - {%- endblock headingcell -%} - {%- elif cell.cell_type in ['raw'] -%} - {%- block rawcell scoped -%} - {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %} - {{ cell.source }} - {% endif %} - {%- endblock rawcell -%} - {%- else -%} - {%- block unknowncell scoped-%} - {%- endblock unknowncell -%} - {%- endif -%} - {%- endblock any_cell -%} - {%- endfor -%} +{%- for cell in nb.cells -%} + {%- block any_cell scoped -%} + {%- if cell.cell_type == 'code' -%} + {%- block codecell scoped -%} + {%- block input_group -%} + {%- block in_prompt -%}{%- endblock in_prompt -%} + {%- block input -%}{%- endblock input -%} + {%- endblock input_group -%} + {%- if cell.outputs -%} + {%- block output_group -%} + {%- block output_prompt -%}{%- endblock output_prompt -%} + {%- block outputs scoped -%} + {%- for output in cell.outputs -%} + {%- block output scoped -%} + {%- if output.output_type == 'execute_result' -%} + {%- block execute_result scoped -%}{%- endblock execute_result -%} + {%- elif output.output_type == 'stream' -%} + {%- block stream scoped -%} + {%- if output.name == 'stdout' -%} + {%- block stream_stdout scoped -%} + {%- endblock stream_stdout -%} + {%- elif output.name == 'stderr' -%} + {%- block stream_stderr scoped -%} + {%- endblock stream_stderr -%} + {%- endif -%} + {%- endblock stream -%} + {%- elif output.output_type == 'display_data' -%} + {%- block display_data scoped -%} + {%- block data_priority scoped -%} + {%- endblock data_priority -%} + {%- endblock display_data -%} + {%- elif output.output_type == 'error' -%} + {%- block error scoped -%} + {%- for line in output.traceback -%} + {%- block traceback_line scoped -%}{%- endblock traceback_line -%} + {%- endfor -%} + {%- endblock error -%} + {%- endif -%} + {%- endblock output -%} + {%- endfor -%} + {%- endblock outputs -%} + {%- endblock output_group -%} + {%- endif -%} + {%- endblock codecell -%} + {%- elif cell.cell_type in ['markdown'] -%} + {%- block markdowncell scoped-%} + {%- endblock markdowncell -%} + {%- elif cell.cell_type in ['heading'] -%} + {%- block headingcell scoped-%} + {%- endblock headingcell -%} + {%- elif cell.cell_type in ['raw'] -%} + {%- block rawcell scoped -%} + {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %} + {{ cell.source }} + {% endif %} + {%- endblock rawcell -%} + {%- else -%} + {%- block unknowncell scoped-%} + {%- endblock unknowncell -%} + {%- endif -%} + {%- endblock any_cell -%} {%- endfor -%} {%- endblock body -%} diff --git a/IPython/nbconvert/tests/base.py b/IPython/nbconvert/tests/base.py index 5cd58a4..c4affc8 100644 --- a/IPython/nbconvert/tests/base.py +++ b/IPython/nbconvert/tests/base.py @@ -1,17 +1,7 @@ -""" -Contains base test class for nbconvert -""" -#----------------------------------------------------------------------------- -#Copyright (c) 2013, the IPython Development Team. -# -#Distributed under the terms of the Modified BSD License. -# -#The full license is in the file COPYING.txt, distributed with this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- +"""Base test class for nbconvert""" + +# Copyright (c) IPython Development Team. +# Distributed under the terms of the Modified BSD License. import io import os @@ -29,10 +19,6 @@ from IPython.testing.tools import get_ipython_cmd # a trailing space allows for simpler concatenation with the other arguments ipy_cmd = get_ipython_cmd(as_string=True) + " " -#----------------------------------------------------------------------------- -# Classes and functions -#----------------------------------------------------------------------------- - class TestsBase(unittest.TestCase): """Base tests class. Contains useful fuzzy comparison and nbconvert @@ -116,7 +102,6 @@ class TestsBase(unittest.TestCase): def create_empty_notebook(self, path): nb = current.new_notebook() - nb.worksheets.append(current.new_worksheet()) with io.open(path, 'w', encoding='utf-8') as f: current.write(nb, f, 'json') diff --git a/IPython/nbconvert/tests/files/notebook1.ipynb b/IPython/nbconvert/tests/files/notebook1.ipynb index 0ee2ca0..41693a1 100644 --- a/IPython/nbconvert/tests/files/notebook1.ipynb +++ b/IPython/nbconvert/tests/files/notebook1.ipynb @@ -1,149 +1,143 @@ { - "metadata": { - "name": "notebook1" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "A simple SymPy example" - ] - }, + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "A simple SymPy example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we import SymPy and initialize printing:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 2, + "source": [ + "from sympy import init_printing\n", + "from sympy import *\n", + " init_printing()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a few symbols:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 4, + "source": [ + "x,y,z = symbols('x y z')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a basic expression:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAZBAMAAACvE4OgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n", "metadata": {}, - "source": [ - "First we import SymPy and initialize printing:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sympy import init_printing\n", - "from sympy import *\n", - " init_printing()" + "output_type": "execute_result", + "prompt_number": 6, + "text/latex": [ + "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$" ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a few symbols:" + "text/plain": [ + " 2 \n", + "x + 2.0\u22c5y + sin(z)" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x,y,z = symbols('x y z')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a basic expression:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "e = x**2 + 2.0*y + sin(z); e" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "latex": [ - "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$" - ], - "metadata": {}, - "output_type": "pyout", - "png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAZBAMAAACvE4OgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n", - "prompt_number": 6, - "text": [ - " 2 \n", - "x + 2.0\u22c5y + sin(z)" - ] - } - ], - "prompt_number": 6 - }, + } + ], + "prompt_number": 6, + "source": [ + "e = x**2 + 2.0*y + sin(z); e" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "diff(e, x)" - ], - "language": "python", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAOBAMAAADd6iHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAAgElEQVQIHWNgVDJ2YICAMAb2H1BmKgPDTChzFgNDvgOEvT8AzgQKrA9gPZPYUwNk\ncXxnCGd4dWA1kMllwFDKUB9wEchUZmAIYNgMZDDwJIDIPyDiEgOjAAPLFwZWBhYFBh6BqzwfGI4y\nSJUXZXH8Zf7A+IBh////v1hzjh5/xwAAW80hUDE8HYkAAAAASUVORK5CYII=\n", "metadata": {}, - "outputs": [ - { - "latex": [ - "$$2 x$$" - ], - "metadata": {}, - "output_type": "pyout", - "png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAOBAMAAADd6iHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAAgElEQVQIHWNgVDJ2YICAMAb2H1BmKgPDTChzFgNDvgOEvT8AzgQKrA9gPZPYUwNk\ncXxnCGd4dWA1kMllwFDKUB9wEchUZmAIYNgMZDDwJIDIPyDiEgOjAAPLFwZWBhYFBh6BqzwfGI4y\nSJUXZXH8Zf7A+IBh////v1hzjh5/xwAAW80hUDE8HYkAAAAASUVORK5CYII=\n", - "prompt_number": 7, - "text": [ - "2\u22c5x" - ] - } + "output_type": "execute_result", + "prompt_number": 7, + "text/latex": [ + "$$2 x$$" ], - "prompt_number": 7 - }, + "text/plain": [ + "2\u22c5x" + ] + } + ], + "prompt_number": 7, + "source": [ + "diff(e, x)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "integrate(e, z)" - ], - "language": "python", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n", "metadata": {}, - "outputs": [ - { - "latex": [ - "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$" - ], - "metadata": {}, - "output_type": "pyout", - "png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n", - "prompt_number": 8, - "text": [ - " 2 \n", - "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)" - ] - } + "output_type": "execute_result", + "prompt_number": 8, + "text/latex": [ + "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$" ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] + "text/plain": [ + " 2 \n", + "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)" + ] } ], - "metadata": {} + "prompt_number": 8, + "source": [ + "integrate(e, z)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": null, + "source": [] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbconvert/tests/files/notebook2.ipynb b/IPython/nbconvert/tests/files/notebook2.ipynb index e92fe3f..9e3098e 100644 --- a/IPython/nbconvert/tests/files/notebook2.ipynb +++ b/IPython/nbconvert/tests/files/notebook2.ipynb @@ -1,224 +1,215 @@ { - "metadata": { - "name": "", - "signature": "sha256:9fffd84e69e3d9b8aee7b4cde2099ca5d4158a45391698b191f94fabaf394b41" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "NumPy and Matplotlib examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First import NumPy and Matplotlib:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 1, "metadata": {}, - "source": [ - "NumPy and Matplotlib examples" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": "module://IPython.kernel.zmq.pylab.backend_inline\n" + } + ], + "prompt_number": 1, + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "print(matplotlib.backends.backend)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 2, + "source": [ + "from IPython.display import set_matplotlib_formats\n", + "set_matplotlib_formats('png', 'pdf')\n", + "matplotlib.rcParams['figure.figsize'] = (2,1)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "First import NumPy and Matplotlib:" + "output_type": "execute_result", + "prompt_number": 3, + "text/plain": [ + "{matplotlib.figure.Figure: >}" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "print(matplotlib.backends.backend)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "module://IPython.kernel.zmq.pylab.backend_inline\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import set_matplotlib_formats\n", - "set_matplotlib_formats('png', 'pdf')\n", - "matplotlib.rcParams['figure.figsize'] = (2,1)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ip.display_formatter.formatters['application/pdf'].type_printers" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "{matplotlib.figure.Figure: >}" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, + } + ], + "prompt_number": 3, + "source": [ + "ip.display_formatter.formatters['application/pdf'].type_printers" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 4, + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we show some very basic examples of how they can be used." + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 5, + "source": [ + "a = np.random.uniform(size=(100,100))" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "Now we show some very basic examples of how they can be used." + "output_type": "execute_result", + "prompt_number": 6, + "text/plain": [ + "(100, 100)" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = np.random.uniform(size=(100,100))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "(100, 100)" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "evs = np.linalg.eigvals(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "evs.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "(100,)" - ] - } - ], - "prompt_number": 8 - }, + } + ], + "prompt_number": 6, + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [], + "prompt_number": 7, + "source": [ + "evs = np.linalg.eigvals(a)" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, "metadata": {}, - "source": [ - "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap" + "output_type": "execute_result", + "prompt_number": 8, + "text/plain": [ + "(100,)" ] - }, + } + ], + "prompt_number": 8, + "source": [ + "evs.shape" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a cell that has both text and PNG output:" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", "metadata": {}, - "source": [ - "Here is a cell that has both text and PNG output:" + "output_type": "execute_result", + "prompt_number": 9, + "text/plain": [ + "(array([97, 2, 0, 0, 0, 0, 0, 0, 0, 1]),\n", + " array([ -2.59479443, 2.67371141, 7.94221725, 13.21072308,\n", + " 18.47922892, 23.74773476, 29.0162406 , 34.28474644,\n", + " 39.55325228, 44.82175812, 50.09026395]),\n", + "
)" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.hist(evs.real)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "(array([97, 2, 0, 0, 0, 0, 0, 0, 0, 1]),\n", - " array([ -2.59479443, 2.67371141, 7.94221725, 13.21072308,\n", - " 18.47922892, 23.74773476, 29.0162406 , 34.28474644,\n", - " 39.55325228, 44.82175812, 50.09026395]),\n", - " )" - ] - }, - { - "application/pdf": 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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ\nEpPrpKycqqWP4FCO54CDXTyCAyKbGHAIh4vH4oAOsymYSHkRQDyShWU/gkMslM2GyqGswoGjqYbg\njEoWMnsUXS+Fww3goCy03J0sIkBPoD0rmzBR1Qo1EpaKrWM5aa6ZGh+Fgw6O5/OJZDyVjxGMZDwL\nAAJhiQG4oNhAU6xwAB9QcAkmpBSzpd2Qpaa4IAPN9+FAy2k495yldCclJA6IJLIxI5ytCdHVRRxk\nOAQRKagBJIt5uZ4OcxOHLpTrZVlS5RSDOlk5Xc/INAfN6YlKIpfIZu1SIq1wQGoCx6kn9XC4lARe\nET0LSVZhHjokDkaCsXogndIlDknd2dOK6TAFFdTPqggylarpqWS5bJkROlFdL4fDXZAZ5Q0lDiWk\nRclwMmnlkib8lqV3JS0V08XyMrxkaqwfXehyUSFdgNlM51PSJwcUG+gFJBtoAvJ5hw8ouCQTOGs1\nMlW7rrigMg7Zr0o8nOlAYZ36M3BQrMgDhxxwyNWFaDYTCReHfL4gPU8hVSqoPUwIJXSh0qjIkqqk\nO91nGhkVNxGHaiKfyGXtciITUV/9wLXqcT0FHMop1IvqOUiyCi/QYS4NHGBkUqlAJq0jEjTNlG4q\nBsV1iGAV9eVaCkdT19OpSgU4wInixUo43ET3ykLLTbAyAvRUmPFaysykKpbeTFlqbEhyGNbQYBwN\nAyZcLDJSVzikpU+WONAYFRQbaAIKBZcPCShlCUzAg5RTFA4NxQUV6cr7KvFwpgMcnJk4WxrEQb3P\nFCwXN8O5hhDd3cDBkGFptlAoSotXTJUdHBA4QBeqXVVZ0tUMG5SVM11ZOSL6zmQ1WUgivSgnj8XB\nMN6HAzrMZbgvp3DI/Kc4cDQNHc6oGrEYv2fC4aphdEscKFFy80XigMlG8ikLVSN6dyry/4hDhmYz\nU8goHAyJAbig2EAT4OAgAyhcggkZxWwCpaL5LsWF/wIH3HJWyuVafgeHfNw08l1C9PQkk8KQYSn0\nsSQHUEqXixKHBBxWo1yuNWuypGsZqS4s2aZadJI41IBDPherJLMuDkloiZ4GDpU08LL1PCyKCvPQ\nYT7DfTmGisFsRs8nEqaZdnFI6Pm8XkP9fNqJIrv0TLpWkzhkFQ49kBlloSUOFQToaSOdjhTSVjZd\ni+g96YgaW6JI/qVpMI7BIZ8vlVAhWwIO2WJW+uSgYgO9QAeHYtHlAwI3cKJWw4O0U1TtpuKCDPiz\n8r5KPJzpwHA6M3G2loiDer8IY1ZImEahKURv7xEcYP+k5ymnKyW1p4/+uyqVWrfCIVPLskGFQ3dO\njgiwhlP1VDFVKMarqWxUfeQGHMKJcDpsGFWJQ7gAi6I6R4f57BEcsmHggIGFI2qLNwEWhevhfLrg\nzqgZzioceBEO1wyjF90rbyhxqIYVDlEHh3BvB4dSB4fwzKKHC4VyOZ1JZstwXxKHgoNDUXoByQaa\n4lLJ5QMCN3CiVssqZhMohUO34sL7cHCmA8Pp3HsfDiXgUExEjEK3EH19qZQwZFgKEXFwyFTLak8/\nn0zBJtV76rJk61kGdrJyrgexdVYqYjjVSJWIQy2Vi6qvEUMpOM5wBjjUMsALOECSVZiHDgvEAcY+\nkwnmsuECPKyVCUcVi5LhQj7cQP1Cxokiu8PZTL0ejdgMZgyjbhh9kBnlKeVmZA04ZAzEzcVMJJep\nR8N9maiKrZFsMrzkUtEH4IAKOeKQK+VkbBQkG+gUHBxoistlhw8ouCQTOGs1MhXN9yguyIA/J/tV\nCaAzHeDg1Hd2LOSeilK4JHGIGsUeIfr70ylhyrA0Xy5XpOepZGouDvCrtVqjtyFLtpFjg6pyr8IB\nsIbTXelyulhK1NN5W1cfrDg4mGadi7WxcLGDQ39/qpCDq5IXwXzu/TgUw13AoejOqCeMoKABHOBE\ngUPDNPshMyoqkTjUkShl0J5dykTymUY03H8MDjTcR+EQBscrlUw2laukCEYuXSgCB1NiAC4oNtAE\nuDjIwA2caDRyitnEQUXzvYoLKuOQ/Ur/eQQHy5mJs9U6E4d4vJSMmqVeIQYG0mlhyrAUIlKVnqea\nrVUkDqlCKg2b1NXXJUuuK0+ZlJXzfVyElYoYTjeBQ+koHNLQknCWOGTxTixcgkVR4TY6LOa5T82Q\nPQgjBH8ZiWRdHFJhmORmuJgtZdV6Tr43nM92ddkR5lF5w+gyzQGIgfKUHRyyJuLmcjaCqnZ4IGur\nhChVIf+yXCqS3O/AYJRKtDDpfBU45Cs5GRsFFRvojSUbiEOl4vBB4QAmyCRb5Rmqdp/igky85KOs\nSsSd6QAH594MHBQrKql4vAwcyn1CDA5mXBwgIjU1gGy9KvdbU0X41Xq92d+UJdc8gkOhvyhxAKxG\nppmpZJDmNTIF2/m0J40AxsgaptnIFrLZuFGCKVKdDw4qHORFsJA3FA7q0wpM2SiWjKYxA4c+I59t\nNu0ocEBto2mag4gnVVTCN6INJEoODlHED7Yx2MGhKnGg4TaOLhIHqEEN7qtQyWfKUASJQ0V6AckG\nmuJq1eUDAmhwotl8Hw79igsq85P9Sv+ZdaZjGC4Ozlars2RIwwenUk4Bh34hhoYyGWHJ9KBYrdak\n56nlGgqHdDGdgU3q7u+WJd9dYICtKvcXZdovcejOVBFdJLsyxZjzMVsGjtPIGZbVxUXzuFGGRVHh\nNjqsFPi9QA5zCRULiKjSkUjOcBBMGxDBbqMCY6HW1Qp9RiHX3Q0c4EQLptm0rCHIjPKUcnO+Czjk\nLMTNlVy0mOu2jaGcrXKcdJX8y3HJ7hgcyuV6HRUUDtW8jI1CZAOdQk2xgaZY4ZCWOOASTJBJtsoz\nVFbVr7ggAyC1QSD9Z86ZDhTWmckMHBQrqulEopKyrQpwmDUrCxxkelCCH5IDqOe7agqHUjoDm9Q9\n0MFBmi2W4oBaDAesRrYnW81Wyslm9mgc8sChmQdecaMCSVbh9qxZEocIjH0+38EhPwOHqtGD+pW8\nE833G4V8d3fMjjOYMc1uy5oFMVBRicShiYQ1b+XzsWo+Wsx3x4xZ+ZjKcdI1GebTcB+DQ6VSr6NC\nsY4wolgryNgopNhAbyzZQFNcqzl8QMGlwiHvFFV7QHFB4SDvq0T8CA7OTJwtb+Kg3ufSUDVtW9UB\nIYaHs1kHB0THDRkBNPLNusQhg8ABNqlnsEeWQk+RDSrQBstyRBKH3mwNM0l1Z0suDlloicShm5sX\niRk4oMNqsYNDqWggM8LAXBwyRrVq9BpVhQNHM2AU8z09wAHBDHDosaxhxJPKU8rP7rqP4GCX8j0x\nY9jBAXGWdJvvx8GsVBoNVCg2EEYQhyoUQeJQk95YsoGmuF53+YAAGpzo6SkqZhMohcOg4oJMvJRs\nyjgm70zHwQGvyQVZtfetWFHPODgMCjEykgMOMj2AqnbJAXQVmg35HQj7H2g2e4d6ZSn2FimTsnJp\nCDlOUeqomevL1XNV4JArxdWX68DBzJgFEzgUSgXkKlVYFBVuj4woHGDsCwXgYEocCurTFEzZrNbM\nPtSvFpxoftAsFnp7FQ4l0+y1rBGIgYqlFA5mMlmwCoV4rWCXCr0xc6QQUzlOpiHDfDwuyFG5n2oa\nZrXa1YUKpS6EEaV6KUeGhsgGOoWurg4OjYbLB4kDmcBZq5GprGpIcUHh4OSBPHGmY5oRp/4MHBQr\nGsChlrGt2pAQs2fnciIi0wOISJeMxLoK3QqHLAK4we7uvll9shT7SmxQVi7PqhzBoT/XyNWq6Z5c\nOe58ZJuD4wQOkUgPN5GSZq2DAzqslSKRqMKhXDIRtxAH58PRrAlT0I/6NXdGQ2ap0NcXjyUQVJYs\nqy8SmQ2ZUZ5SfqzSY8JDYrLxesEuF/ri5uxCXCWm2YZ0m3Sg5tGFoU+hmCt3IYwoN8oyRg2RDXQK\nXYoNNMUKh6wMZHEJJpQUs4mDyqpmKS7IxEttmKkFEWc6MJzOTJxPQGbgkE2l6plYpD5LiNHRvItD\ntdFoygigWezpUjhU4Vd7evqH+2Up9Zcpkwq04Sqaljpq5gfyjTxw6M1XEjNxKAKHXm4iAQdYFJX2\njI7m6mXgAKdbLIYqZUS2WdsuHsEBpmDArBdrKrMtl2eZ5WJ/P3BAMFO2rP5IZBTxpIql5McqvcCh\nGCkWE/ViDPFD3BwtxlWume2S6RaXTo+GwWLogwrlZq4AMMr5o3BoKjbQFHd1uXxAIgNO9PfLxQ6V\n7ykchhUXVAYu+5VxTPEIDs5M5MK4+gZBsaKLOGSBw7AQc+bk8yIi0zSoarccQHextylxyCGQhk0a\nGBmQpTQgcZCVKyNH4dCVrzcyfUdwyENLJA59Cod6Bwd0eAQHnTjkcjNwyLk41F0choHDwEA8lmRQ\nKXGYAzFQEYvEoc+EhyQODeIwEDfnODjkc00ZvtCBvg+H7u5iKV/pRjhX6arIGFWPSF2gN5ZsoClu\nNl0+IJEBJwYGZuCgstsRxQWZAL8Ph9zRONCZH8GBS3QN4NAYEWLu3AJwkGlaDX5IRmI9pd5u+cFZ\nDoH0LPQ+W+FQHqgw4ZSVq7ORa5aljpqFwUITUV6mv1BNyC0nB4cScOgvAa+U2YBlV+nn3Ln5RgU4\nwOmWSnq1YjaIQ+kIDo26OYj6jZJaZ66MmJXSwEAinkRQiRcHIpG5kBkV08rPMfqBQymCPLKrFKuW\nBhLm3FJC5fy5bnKnxCXsY3BgCIoKlR6Ec5VmpVBvEAfMqym9sWQDcejudvng4iAXnVTerbLb2YoL\nMhCtyn5lHFPq4GA7M5mBg2JFN3DoysUiXbOFmDevUBDRY3HoUzjka/nCcF/f4OigLOXBGTiMKhwA\nq1UcKjaLja5sf7GaVP9rJoSaVt4qWdGowsFqwLKrztFhoxKN2i4OFuIWDMyKq09E82CRNWTNwGG2\nVSkNDiocqpHIYDQ6DzioWEp+NNRvwUNGkUd2leKI4xLWPAcHxLsyfKEDtY4uXV29vaVyodqDcK7a\nXZG5gh49Gge6RIVDXuKASzDhfTiMKi6oQHYGDs504MCcmTif4shvUBQO+UymKxePdo0KMTZWBA4y\nXYaq9spIrLfc36NwQCA90t8/NGdIlspQlbZBVq7NqaNpqaNWcVaxu9jVlR0o1o7CoQwcBsrAK211\nwbKr9HNsrNBVBQ4lprB6rWp1EYfyERy6GtYs1O9SKwzV6qhVLQ8NJeMpBJXAYSgaHYPMzMBhADiU\no8gjm+V4rTyUtMbKSZXz53vInTKXsD8AB1So9SKcq3VXIUXEAfOic+5VbKBL7Olx+YBEBpwYGpKL\nfyrvVqsMcxQX1EqI7FctTB3BwZnJDBwUK3qAQzMfjzbnCDF/frEoojJNa/T09MlIrK8y0Ku+RW0U\nirBJs+bOkqU6q0aZlJXrc5FrVqVsWKXhUk+p2cwOluouDkUEklYFOAxWgFfaasKyq/QTHXbVgAOc\nbqWi12sW4sdYrOLiUACLrGGrC8ZCrTPX5li1yqxZyUQKQWUtEpkVjc6HzKhYSuIwCBwq0Uol2V2J\n1yuzktb8SlLl/IVe8q+Cx5VjcGg2+/pQodaHcK7WU5Uxqk420Dn3KTbQFPf2OnxAwSWYIBedVN6t\nstu5igsyAa7LfmUcU3GmAwfmzGQGDooVvYVMphs4dM8VYsGCUlHYLg79cgD9lUEXB8Q3g4PD84Zl\nqQ7PwGHeDBxGJA65oVI9JT/+tywHB9seOhaHBQuKzZptxzo4NBFxxmfg0OyyRlC/g8Nc4DA8nEyk\nGdxHo8O2vaCDg/yIbshCpGIzj6wkEMclrQXH4MBA5mgYIgxBK9Vivb9YLtZ7a6VuKIJuOzj0KzbQ\nFLs4yEQGnBgefh8O8/5fcZAbFOrbKBeHbLanELe758m/w+R1jqL6q4C+j+BKk9c+HyyXWCPGhV9k\nxRe0AW2+tkL7gvZnnoLne54XPf/b+xXv495ve3dXk9V8tVytV7urI9Xjq0uq36wlIL3dtVl1Tz1Q\nj9bj9VQdcVJ9oD5R31C/oPnSvj0/t35/+P96Dh/mX0oUD2qztOO009By1vM8Wn6l03KiiryPk0bL\nx31AyzG0nOu0vFG2LNCyJlt2yiH5dxQPNYV47xPvrXzvQ+8dL8S+h3lv34p9N+47Zd+Cfce9fuD1\n1uv/+Po/vPbua2+/9q9CvPY7HK+/9qPX/ua1R177xk+Pq94sRMgv/9riGi3p6fec6PmYEJ6/8XwH\n9DtuT57neXheEn+keKbUcdS9J3D8rUThSvG0+Kq4UOwUS8Vz4r+LvxQrxF+JdeJW0RIvijfEN8Q/\niX8QnxXXiBfE98TlYrv4mHhe3CE+JW4QU+IU7Uahi7AwhSUSIilSIi0KwLEkKuBxXfSLATEohsSw\nmC3GxHyxQBwnThSniw+LM8Ry8RNxvThJnCZWiw3iYnG1+Ly4WWwV/038qdgm/lzcI+4TT4pvid3i\nf4nvi5fFT8Ve8b/Fa+JnYqX4iFglJrQ/EVeJc8XfifXin8VZ4rtio/i4eEz8ibhTu0H8T/EVcb6Y\nFD8S02KRuEt8U+wSa8WjYoe4UTwiHhY/EE+JgHhJ+EQIshbUvigMERMRERW2yIsMpC8n4qImekSX\naIo+0S3uFr1inhgVc8RccbwYEV8QS8RCSOqpYrFYJk6G1H5SXCIuFZeJL4vbxO3iS+LT4l7xkLhf\nfE08IR4QF4jHxatij/ixeEW8Lv5e7NMMbYlmaks1S1umRbTlWlSb0GLaKZoNmY9rp2oJyGdK+4iW\n1lZqGW2VltVO15Lah7WcdoaW187UCtpZWlE7Wytpq7Wydo5W0dZoVW2tVtfO1WpiE/Tmeq2hfUzr\n0tZpTe3jWo+2QevW1mu92nmiLP5M69M+AQ3bqPVr52sXaIPahdqQdhH0YpNoQD+GtYu12dol2oh2\nqTaqXabN0T6pzdUuF7PEd6CVW7QxbTM06NPa8dqV2gnaVdqJ2tXah7TPaCdp12gLtWu1ce06bZH2\nWXGCtlj7nHay9nnxIfFzbZ52hbZA+5T4qDhHnCkuEn8rvi7+UdwitojN4lnx1+IZcZ74hDhb08Rf\naGEYhf3iLXFAHBS/FL8S/0e8LX4tfiN+K34h/lX8TnxGXCsC2ktSn/f9/1uWMQdIIWQwApnrhoQt\nhHSNQ7JOhWytgVx9UkrWbZAtSta9kKqHIFcPQLL2QKooU+dB3qkN3xVnQ9q/AA34uPghZH+j5od0\n94r3xKQW1ELQlbvEIU3TPOI/xGHoyw7x75Dex6EPV0FzhLhOC4h/gxbdKK6AhgWgH32Uhw5C3xb/\nQ1ygecHxE8TnxJvii+ImicQnoGF/A/za0KkoNMuGPik9ylOHNB90idozT2wC+v8I/VT4rwX6Xxfn\ntMTgqS191Zpdmvana5/WDt/cWlLapXvXf3yopQ1Wq0svXtLSNgy1PIMtrb821PIOVpe1vM1lZ6xp\nrK3eXr19xcbbq8uqm87b2PI15S8eXHD72uFqS5y55mLQs9bUWuNrC53TC9auPX6o5WMzPtnM7WvR\nwCVOA5fIBvD+e0Mt/+Cp1Za3e9Wa09e0blhSaI0vWVuo1apLW7tXrWntXlKorV071Ap0xojfz1+c\nVaMNDrYC/UOtkGrhTLyP19fefru6atRau2+/vXA7ZuBcP62JY2+Mz7yBGS99WrthlXxyQ6NW4I1G\nrVHDiNYuGWrpg6eeuWYphlRbOwSRakFk25rHM6C1vV7QKe/yRfOa2dDAlO/Dzon/LHUipjTnbMrz\nsWVj8lY74A8OtISaT3tDSPt0+/owyAMkz5G8TnKYpBLWrmwvJFlPso1kJ8kPSN4miYa1q9rDJCtJ\nriDZaeLdt0kqJqoMk6wn2UbyAMlzJD8gOUwSNdkKyUKSlSRXkLztVPk0q2BovLQjOBsnWUWyk+Rt\nkuEIhxvBa4d5eQUu6RhT0LgToZET+BUifrgoTO1HIg5vG/f8DpZBQLvVf1+Gb3kFVnuWp9/7qP+l\nwNbADwO/DWaDZwWvCV0e+qb+2/Bp4S+EHwq/ZAij23jUfNictiYin4rmo2P2JbGJ2ObY12J/EzuQ\n/I9UOvX99O8ze3KX5LbmXshfXjilcGHh9sJkYbq4oXRLRVS+VvmbyhvVrdUdtQOIQOr1c+vXdd3e\n9WhPqOfcnlbvpr5Ng/cOTg1tHTowy5z1w+EHh58f/tXIZSMPjbw02jV65ZzpudV5l8z79lh17KwF\nXz7u9uM3H986IXLCwyf8/sSNJ3lOGj3pEwu/sPDBhd9euH9cLJpYNL3oXxaHFy9Y8u2l6aVXLn13\n2e7lkxNLVixbsXvFj05dctrVp019OPThlz5yw0cOrFy18oerxlatXrV71U9W/fr09OmDp0+ecd8Z\n3z+zfOaSM58/69dnB86unv3s6jWrD63pXjO+dvTckXMnzr3w3C987NmPHVqXXHf1+lkbzt2w47w1\n5x3aGN7YfUHgglUXvHHhZZs8myY23XjJpksevuS3lz592QuXhy5fdvm9l//HFR+/4r4r3tic3Lxg\nc+tTOz5911Weq+KfyX7mK9f4ru2/9t5rv3Pdjs+e9Lmxz09d/+ANX/uTxTce/8W5N+Vv+sub/vam\nvTcdutm+uf/m3bc8sXXktk/dduNtk7d/4kuXfelzX/7Uf3vov/3sT/N/etmffvtPX9n2uW3T2/Zv\ne/eO+p32nVfe+fCdu+985c5Df9b1Zz/88xu+kv/K1r84/i+uu2vwbnH3GXdfdvef3f303b+455R7\n7tpe3X7a9s9tv2P7S9v3bP/ZvcvuPePej997yb1X33vjV6/56k1fveOr9311x32R+/L3dd83et9J\n951y30P3/eV9z973/P3x+8v3998/dv/i+z9y/7n3X3j/p+7/wv2333/X/Q/d/5f3P3v/8/f/8P7X\nvrb7a9//2itfe+Nrv35g9IGTHjjlgdUPfOKByx+MPJh/sPvB0QdPevCUB1d/fcnXV339Y1/f9PUr\nv37DQ2se2vjQ5oc+99DWb4x848RvTHzjrG9s+MZl3/jdw+Jh8+GtD08/vPfhAw//7hHxiPlI9pGu\nR0YeOfGRiUfOemTDI5c9cs0jNz1yxyP3PbLj0Tseve/RHY9OPrr7sfHHTntszWMbH9v82Od29O6Y\nu2N8x2k71uzYuGPzjjt23PH4xidGGLbLSFJ4/gGetwHbPiBGW4PDraHh1qDd6p5udQ/vSvvebQ3Z\nrebeXUXfu+KvvFqXb+CvmloO1KdpvoGR2fPnzUn19MydPzY2/yTvvLndjXog2DM2Nmc0nUryLwIG\nUplYLabheG3BPI8VTMfsZNg3VKkMBUaDp4yNLct1NwOB5w5t1P7hkLjq5JOvii3IWaVYNJOI6V2z\nB+eEJhYtP7E6r1FLJOc+7bn4vbs99703iiELoaJ1z196P+3phucXWhBe6XXEDh9qX2Fo6yYXGisN\nzxbajS2TO6PPRT3rpnZHp6P7ot51grZmHe3UOhqhdbQruETNkdlH2r240+5G2e6YbLe90NC2jFsP\nGDuN54wfGK8bbxuBde0rSnxSkk9KO0vPlX5Qer30dimwbmT2B4zzetneQPsKC29xgKJ9mKdvcxgL\n3bF0RoVGZs73nk47fyvbWdleFUPtK2KofQPIlIjZsWrMu2Vqd2w6ti/mxZ2aXavWvKhUY481Dc9q\n07V9NS9ewS3RXp9kX8Vj+/pop69/lH012wvz6Atk3dQD+Z355/Jo4Yq8bKGCFg73qha8sLDCczzs\nbhgxxID22XZzwDvQHhnAyzZIa8DeJXzvtu9o4qWRJu+CtJr2roD33ZawW8Y0Llrl6VZ5uL2qrK3b\nFfO+264ZzVj8uPbmQfTXEkvXtHLDhV0586S18qIfF/2Bk9YiAHm3beT6UbVlDO8Ke95t5exdaY3v\nh/n+wRoHqU3u8D7t9axrT3jR/R+8GIgewtm9CQjPNcmtSTw6QK5cTHIvyYEUL0nGiqh5C2Y8eWH1\n6ipqrq7ixltVPHqzAbKH5EddvASZWt11YdfVXQBidc+FPVf3gGcv95JnZ7Hrb7HrpTx7lGfvkAR4\nGeZw9kdxuYm93wMyeW3yNg7sIG9sSrlj2s+ON5G83HD6bP+Y5BV0M3/O6EkeqmZjnjqb5Wk0euaM\nlj1U0VQamhrBnR239ywZLZ17zp/fmZ/Tl9crJ865feKfBs8Y71504qlnx8YuPPuVxXZttLFsyYlW\nebhu9PaWFsf7l4wef2bEE1j3kdjiRSMy0x05/C+exzx7RI/nlnY87B1oxe2WmG6J4fa4wHSmhbau\nFbZ3lQDnql6qXh8Frw9KurBvZR+mdn2fi24UgEZddP248APddiLqJ7KJ4VbU3pXV3m357V11gNvj\nj/J+z3B7ugdNbuiRILff1HBxWxBkbYhnJG9S59eS3Ebysk2wqD5XkXyX5FaSt0im43wAMnlbYnsC\nA3wGAtJ+ieQXJE9SXp5JvkhYniQsv+xgs5ZkO8mLJM+S/ILkSfkgDfIMyRMkvyJJ58CjN/KUMJK7\nSPaQvAMyGcin8zBpy+XzAm48WpgqoNv9Bb5awKvLebacZ29SOMcqyyp4flfVlZE0xXQ5zw5SPFZ0\n4XIHdXAtyXaSHLVxBcmzvHy8G+082/1SN9r5aTdvkLu/6qH4HiRvD5CFK2i+9pCPL4Nnk1fHbolx\nXOTfleTfGyQvk3yPPJN69aLLp8lnUi+mUH1H+iiuHCS5hnM9QHIKiZzpAVoaOQmpcPs5kwuaZEdz\nqol2pjjMPSQHMMxmxNuoz4LYn+SBV8oEZ+EyAtEve6gN8z2PxectWTm48tbzFyw4/9aVi28aXmh0\nz15QXHLZqb29p162pDh/3nByc2GkkVxw/tbTT996/oK5Jw7H6nl79KwrPvShK84aDWf7KlLue6Tc\nr/B+rj3YBVsX7BqkPAYhj5C+VmRvexvt+esgraDdSu5t/5oMKEeCqDZufLX8ZPmvy39X/mnZD7GE\nxWv3gbS67F3zYRHhkRdPtxYPT44vXrUYIjC9GM/KdmvF3vaqU6hFp9LjnbryVCrQqa4C9UFn+lwF\nWoGLFVCglrm3PQLMWrG9rRU0se2hvhUcKRx/n71rFBq1wt51IjRq0Yo+3l803N6wiIqwSGnU3Vnq\nRw4dXly6toQOl9Op7KhTikjWkPPLwfnJH/e/1Y/nPxogSiS3knyP5J8GiTDI1L1DTww9OwST+HdD\nFCySF2bh5SdmPTsLL/9yFqVkmD2QvECylmQ7yTMk15H8gmT/XLw4f+7yuXD2n5l769y756Ldb82j\nJsxbNg+tPTqPtUhuJfkxydhxHC/JrSS3HM8qJMtJ7ia5ZyGaWLvw4oU0vQupwCTf5t0Xx18dp2qQ\nQ78iufpkvk9yN8ktSyinJHuWcvJLqTiPgIWT382+nPWsm7wlexd+2reAoe0pcvXu3GM5j6P73yO5\nDm65fTEjjM+SHMPuZ0jOIc9vo2aeQsbv6d9Pxr9BTr9MckuH8W+S8XtIXiCrd5DVz856iaz+RYfV\nB0iu7XD5RZJzhwnK8LPDqLlgLplCcoBkBdl+7dzbwPb2VrL0aZIVJK+SvLWAsBy3/DiPw+M3XfZO\n3nr83cfj7pvk510kU2TqOQsvIqsf442Xxzl0krfI3mvI1AMk20m2krMHlxCIpa8u9azzZ5zgEy6t\n3jM/PWd0TDo8hqXzu3v+uB3I0Ez0aJd6QjGjPJyq9SXnDtn13nSvL5yMRtLh+Nyh/pP/MxtxgrQj\nhnGi5vX1dGXqqXC2K57M+SKWHvDHlgdixT9uP/ppXzQZjq8G+RbiJlPktMF2IgdLspOicT0I4xhG\nTbthQ1sJGSu119MiPkAiaPZ3F1zlj0DfI67yZ3GRdWKjVsTeZWjvUpWfZqxxHMnjJK/KIIihx708\ne9pHFEEmt/se9wGPp+k8V4R4I/R4CDde5Q0ZNT2l42yCZAfJcSTbSV4l0XVWoXtYTZcg/cKt9Aa3\n0gSuliEMyRRN/kSaKiKjomUkMipaJsfCtp4g+WuSFWzwGZJl6U6YMyO4YVTztS+f/pmV3d0rP3P6\nlyd+ufjmSxcvvvTmxb9cPPv0i+bPv+j02YsH1968Zu1Nawdl7MKYtQHeG+LClne4bYMT5HfLa7e0\nva3AdCswzNg0NN3S7FZ4b3snvN7k9dY2y+PwXAYpLs89uPC4PEeQEoBJ1f0emlQdYSkRmF9L1WI4\n+N8O7bOHPqQ9dehO7ZxDjy9e7PnO4t8sRhwux8TcQVgyDl8kdiEOb3DhZZ2YfNs8bHqcJAbpzBZ5\nWyY2bhx/7Psfle/Pbm8D36aG7YX2StuLBMl+zvYwiKcf57TaCxl3ro+iybejsrVOWxd32too21o3\nrg+bC82V5nrTt2Vc32Y+YO40nzN966ZeNzk+mOAoh7YBo5p6Pft29nCWt7JIlvTh7MLsyuz6rG/L\n5APZnbSDG+hdttH/PVc+ut8jc7he9jugHOqGKMVFZk0yf5JjXs/RHybZGXV4Ad2yHN2yRU27vV2o\nUbdoTK8HadUc3YICtQqdPMSaJo3sxeNWXNLkdHs9g+wHSEQDyZtEOw200y70VVxUXejTUt3aVrpK\n6K3hXTYykth0y2Jb1MIc5GzyOO8Kr8dRvJ+SfJNkAcX+HpJnSHI+1FwQmoDuTd4T2kEVfIYqmDta\nBaXi/ZTkmyRPZ6hkWSYTtCTdjDHP4dmt9C6r6VP2k3Qjf23fyrPVZbZHEHY3yN3JjjLOl8kKx3IX\nyQL2vp3kFJInSBawz1NIniV5hr2fxd5XEFrZ8RSbXk7yUfQ0Q3OpCMxTYh+owZrn0KGJFSs+QI9/\nMzamzXmfLhvAWhdntzTosuboMjTXu5eYQp2h18G97ZWGazK9gM3rYihwIRgvee1dPuAX9AoVz+0K\nUXXnNDqKO6E9eeh+7ZRDU1JnnRzovyMWbHpK7WQJOVDJ3hWCrR5OgguHafJaIK2kvSuGESXtVhPZ\n7nB7hE681eOOpo4B1I814G1Rz3IYYrhVt3d5IV5Ze5eF4SXU/cRwex+8w664MvAvIt2a/Kp4UjBE\nwXn72yQXMW5fQ3INyNRt2nbtcQ06+SJv/YHkpyTfJnncA/J3JHf5Qd4hCfghhvP9y/0Qw7HAsgAa\nfyfA+wHMbxnJVJgeP7w8TC8e5stcOtlD8g5JwEALy4zVhgcNGcsNtkDLtZqG4i2KZR/XGQ7Qr/XR\nr/VVQNaSrGHM38c8Zi2JTLffYArwsky3mQfc1XyUecDzzF+ealKA7+a0d5C8Kty57yB5gQy4WLtW\nu40MeIa3fiIfcsYvgkwe51+BebaXcRbzScY6U1lOspajPSjTFI52dSfXeqOThf+MA/kuyaMke9xx\nad3dR2KQsfnzKPiBwMxsxXNp18bFbpgx/4Lex39z4rZFbq5y9mdHPIu7B91AopD5P4sPPdJounnK\n8IirB5+SPu3mlgE9AOPbq0hGQHaFIX+e6ZYhFWE98xTvNLKTXQHfu5PCsi1YpG10ctPWPji59iqL\ndtxyJVSDUGquhIZxEaaEejSussD9Udd808j2W8HplsfepVMmE3NitdicGLQn1tgwod04MXHoCxOe\n7xz6e23OeydqKw/tUmMWOzBmrzgNfUizvI9hyCrfBoQhk9f7twGRqYp/2L/QD+e12z9NhITftcVH\n6bEcJMYlVXbOjgl0Rp+yHMwJSJ+SdvyqCZ8ya/IB/86ZjcPF+g4z9llIc7feRxfrnMm1vcNv4+3c\njHY+KtuZO3l9cFsQrz0HiziuV0LDoYWhlSHfFngqatBOkvUkb3NpApzn0iPGdfg3aCUjfWza8bGm\nXOO7Hh2ORx/w7fQ95/uB73UfhxVcN25EfRXfsG+hb6XPv6W90sb4rk/Lmumd6efSP0i/nn47fTiN\nmno0XUkPpxemfVvc2ID/l2r/c8bYr5d9DbtjFxhVezMImBA4TC1fSC1fTwXf6Zw5vlUTkcP/on0Z\nrE2Iv2mFhuW8J18PvQ0X1UrslZY3BD/IJT/av20pV4ak2LhY2biwpZWzw8rKyQWj9g1cNbKlvaNV\n6wXS7YvJvetINnEsB0CmQoFsoDfg3dK+hSHMLQwH7kGYOfVM/MX4q3Eo+BNxWoML+daFfGsr693m\n1ms/DTI/nZb+CEIqk4junnPCYyM9swvhicTwacet+IR559DcyuyFNe2N9w51nfqh3lNXuHp2HeYf\n0f5HyxymOp1MZP+K5DDJn5NsJhkHS6lnCDHPhDud/Ib3r7gO+T261rWMvu/xUmhS3qZ3nnepF7HR\nP3l/zhpfZI1fkwhUa/n2Tp7v+zQ1IuVr+qCqf6BkjlFGl5J4QcajN/ju8D3oa/l2+6Z9+yAyLVPi\nwDWIwN72z8i28wOfDnwxAPb8L/LkX0m8AHnqjsCDgRbvT/OW3whw9XQZjX+vf4EfpvtW/93UvEfJ\nz2+B0JBEptsVimHL/sBM5CjdNHBhBJyLIC6CEnyv4bg4fW97KVMHv9DZdQ9xf4qdcRDtFEkiRNXR\n2j+hwf4Vyd+TfIdc+g758Sbrf48kxPovMkTZG2Lw/LLnTQ9G/12/W4vpTGLOHG2OpjW0RiNGe6Hp\n2ocvOvRV7dpLDj1saBMT2p3anEPPHfq89slDf64+4pHYa7/FhV8cd2ze4NlL4xcc/oD8QHKCQaLo\npALo7pRDl08w+pc65bRrib9GE60omqbowD770LqYbvnYQdtGxNuK2k6CAlz1vaTGtIDxg4LJ3iz0\nZpnHsnobfV1FY5QP1zZurA9fEb4+vC38QNgPSQurtVrY7bbQiMDU82KP2C+862Dad3lwO6RrBMYr\nQjI2UpGSIa8mbzHugl+HxZ+fCjJOmhecNx8zfOUVzvGMMya0S55d9uzPFu9ftmyZdo/LR2+eOuTJ\nt4MhROcrGdeupBkSoaDqAdFbex/BvYFkFUkLRHJE29teyAmtJ5G+/FUQGB6ueT3H+ELu+2xzcySp\nB5G97T00AFMkZ5KMkEQ7aQTzHynN7etJHiCZJhn/YAkP4SIk5ThCPrQ/RQv2TyRnMxobE8sQjU2e\nLM4UUNm/4v19JLtp4yLINKc5YN902x+K8PXvQzInv+y/j4r2WUrpkyR7qZG67GDyy/p9Olr8vr5X\nZx0G3U/qat7GdHuNrZbtnmInF5Mp2zWuuWovalzN4o0nSG5Ci5M/CrwBOz95QeAq/Ew9EvhW4LvU\n/7uCNEhTweeDe4L7gz5EkLRjj5Fsoj7tcNOAcf2Z0IuhV0MHQ6j0uM5lGf1VjGrqEv06/Xbd62Tu\nvyR5hGy+kOQukudJ9pOcE6GRfpDjvZPkRyRTJPtBmglGDw1qKBVU+x9vXz7hOSy64c4mLr/JM/7e\niZ6N790nj+84dvkJxj9aue3VIVNVWthVTLlu8N5BqzrtyhAjfQ8kwj8NRR2PrfSv91/hZ7DBmOA5\nf2hLS0ccjwoGIqbh8cSIMW6sMjYYm40bjDuMB42WoW9hKr6F30JsmVxlbWDMdD1czBTjp19bmH6r\nEzxJ2/e+3Q3hp+2bukBcJW6GnrX/gBmPW7rIiT5xnFgh1orAlvZXeNN4EO3sFtNin/BLw+uZRljN\nvZGpHwReD7wN3MatSwLXBW4P3Bt4IvBsILCu/W8BtuYPpALNwLzA0sDZgcCWlp8rGiOzm3MYlimm\nejZ4Js49dC7IZZ41YOjH33tI2jonF3+C+4KiqvYFNQsxQ7l9PfRpknk/pjxuyg1B7uwc7pMhgoc5\nkTeOnCgherTftMowSnvbgjtCmwnsuJDr2bvCMuvelUSyNN4LHrb6dnNPaCGb2tfHvc0+u6/a53UC\nvQbY1nB5KDcD6Uwa9i6/Jjf9oipemKBHANnSPo5n20kOMrbXPTkPlHABJeAAyQqS7Z21MGbm7f+Q\nZ3SoaUY+1IUpqsE7QeCTDjIVCi5nvDRGLXiUZD+jn0AoHeJuDW88RbKc1mwZjc+jJPtJ0hSXp3i2\nnDIzZi2jzNxFmdlj7bfegcxMBqw0A/F3qBtpbgzn6F8XcBFtBcl2bvq+GjsY+0MMtfVYLobaE4xn\nQDBnnh0kyfFyNdfYbiVZzcz8VpK1zFy2cpl5LclWpi9rJeHyyGrmK7eSrGaGems30SVPJxd4J6BB\nkwuCE5j/5Hxzuckfa7nFn9jyGH/iy+OedZpcg52x3CpDKzfPKXs832GC4yY6/P0IMxw30+Gv9gkm\nOW6yw18mOW6yg1+1xrMQPuRG+hDR0FbBMLcKeyd3Fp7jNtnbmKVc14nYuxJQ4+g07W1yr9r83paU\neyLt62MyNW/Vp5GBj+sLmyub65tXNGHPtjVd3a1B1Gqu3KVwkaLc1eRGJP1yCiLMhdbptl1L0W/Z\nw7tiShb3UJqWM+y7yPsZMm++bznzi7tl7Ea7KDPNu0lkfvkYV03WSMKlmR1yC4BLJ0+XicM1bO8V\nkt+ThCixUoov7qwfbSc5hWQZ25+vu+0vZfty/+6AlAJ28jjJQRK5JrTf7am9AkRzMPTNmZNwli5d\nEHHqCR239OrVs8O5gaUbFi2f0PKH9mujhxZd+ufr+k7+5JeWa9869BPPjc0Vn1yeOmHhwtkVbeXi\nQ/+6eMmmLxy39uY1A1zrlDZC5iN1lZNpfcyl2pt7wa71vVf00rb0chdZ7p7v5P7xepqJt3sP89l6\n3ljofILwAe19VLY33r6C5mQVv0XYPICW7YHqgGfL1O6B6YF9A9Dp8UF+kaE2pllpIfdN3h5Ql0e3\nfXGn7Y2y7S6Ob93keO+qXuggx90e5+bI+NxVc3nj+D86tuvl+wva4+hx8sGB1gCcphjg0JDOTA/I\n+TpDwHwHDg941HDk6GQS5u3YWENkxBz/1nahjzGUnGwfQsQ+Z6GpNbq3PTLKmG+U4yMRvNwsz5qj\njCleEj/hmtFlNM7XkjxDC92n1rEMWaf9Y4rPfpKrQPi9Rtfe1qjdmrO3bXbNYY2rXDs3+V3zZXqG\nT/DGPm5dby7cQJ1s0fKMcIVnFQgXXmn7M+r1VyiVnyWRS5j7SbLq0XXcQZvKPs8l43+mtDbU/a1c\nDjpAspGkW430Rdqtq0hmqxuXzGZ7s+lQ5tnzqvPA44XcJmzN281twn3zXE2fBeWe5Wr6KC5Gj13j\nlRdduOjixSy1KFeVvgeZDL9JmHxavEBmXkI+XkPyBokR9XIoL5OBt5BcSfIzElM9+h65tdV0lfRq\nko0kGdlu+1Uy5DqSZ47wZxaXmSefyn6PrPm5ZE11Fmt/iQw5SHKBZI26+8IR1sg3FWt+PlsFjQs4\n0BUMW7eLxzmHAwwVT2EA2a8dr0Gk5zGpkkuDexioOquCQX8G+WH7akapexhYzg8s5wLCvDBqd4NM\nPhZ+iouDTS5FPcU5L+XE/oJkzFkJXDf5qDlFqemmk1xAMs864iSXcTHqMSbwb5EEeXm86xknD8R+\nz08Qsvw0SnrJV0mO4yWz/PbvSbJxrrzHV8Q9joc8TjrMBMhWesjHSf5A0p/m0mPGtcdvkfyeJMSV\n7WVcUv8FV7abXEOXS+pyIb3JhfRz6hjPssbqBnr5PVnfz88u3uFKZaCZboJPY/zcYjlJoBvkGcLx\nJMkabulme/iBEs/WUOG3khwg6ad1WsO93P5BJv0jnPwIxYkYZudws2DOxByuDMzBDR032v1zaNxu\nJq5yUXSC5DgivECbYEbwOME9qP2B57rGJvwTzD10Ap1n8r8gMEEsZ8I4RQR7ieXZBE8GORK1+YTx\nbvMxwng2YVpKBB+1pojgft4I8IaKGdoX8UaaGP2YaP07yV8Ql38jSREtFU+0byMoa4mHnwA8SnPw\nVvbfKfMSjK3k/lb6rbVlcr++uo5H87lX/pbcMCcOEoztIJOhZpY4/IFBzg4yPtTB4fdk/FqSHHE4\np/Mdw1skq8n9/SS3cjt9WQeCU0Amb579F7M9R5Z50/yjMs7XWD3znQ+v6t093Jrudpd852c867o+\nfjxjoEat0TkbnTXKSGjpNcOnNU9zzj8zclrTc3q1wWDouE19Tefsov6x2Td+mFHRQPfp9TOc8/6e\n0+sdv/PRjt/5R+l3CuOGvbS6dGTp+NJVS/0QkPX8WOHw0iN7Z4z7M3JfOqPV2rEMPIudgcXO2PIr\nUTHdXsjAKaZ2pZ/jhwwLcyv5IQN3rj8gTZYbZkx5UpE0Q6TUcPt17gHfAMIlkRnb1Ecim16SFzuh\n+SWd3bJNDG96GaRfy7OD7m6Zyk97GXkfZFB+QP8909GQntV7dVj9axkMvUhyK1P6W+T6YDfb/rFX\nGlqcXc3GLiK5lo31sbFLeHYN3+vTIZU/0X/B3LvAxapbbK4izdyM5rewXE7aceXIyb3xeO/JI1dO\n/MNln//8ZbdMaF9K1obyuaF6YvHH169ff+gp8noEznwAfjwl+rTX2nYAvJYrr6uYfxzmOIdp5xIB\nW8aWdiu1txUADghtbXtX1fvuZGtgNwMEMeAuiR+VbbpL4rsKYHOCy/+7morTa+UaJKe6lROUn8xt\n5SzXkqymet5Kspq5yEEujDxPHf2Wq57tR3nWzTO5v7+C5FF+ITeWXUbl3E+bmabKnuMYTmQ7OWY7\nuf25d3LMdnLpnGfL5PLcOfhpp/Pg7qvFg0W8+2Pq9Aska2hlV/Bse4nWqvQHfoek4+7kRHlNGa8f\nV15Rhh96tXwQP+013DiaqPMl2uKD9T/QGuTkjY4NeLVxEBZ6Um/kGug4RxudphVYRt2/q4dj7Nnf\n804Px9iT7kGdc2gQljHI20+SZii6vPcchH6i/T3yRXqa/UypfkzyFY74RyRvkjwPUqvNyILk95hz\nNCdHmufkTN6BQ5d++IsbxsY2fPHDp+F35Tl73juze2LT4kWbJrrxu2gxfsfOu3nlypvPG+Pvhu2z\nPFrfCZeeMXv2GZee4Py6Ony81OG8diG0kf/zii27LLWWGJHrwc9RzihsuxKQpoDdyk63ssPtP9C+\nhrLZrGdLK7e3naeJ/SZ1fGVuPXRc/Wxpv0s4TT48nqQPREpl1m5l9rZvgLkej41nVmU2ZDZnbsjc\nkXkw08qEtrRX8mvaneTQwiK/qChuK7pfVMgdAVdu5Rqm2h6wnBViWxoJrhnkudUud9Wzclc9T9U9\nwG2ji0PXcqf8dgpznhoql5+66WbGKKkX8MPaq5O38AvO5SkqPHdQLDW83ZnpzL5MAAbAzZZF+0DI\ntQWyzUtJDrDNiwj6VjQ31Z86PnVKyrtlhiHgwor6LD7R8DZ23ODaghsmfnLZ59et+f6av73miDV4\n7wue73x8/SkXhA61NLm/MHr4X7T/gE0Y8TzQjvR6B1q9dmtkb3snfc1hkitIXifZNkIL6n8XVXYF\nYYz5HTnhTe9t/wAz25XxS+NawaOVDPmvINk26lrpIXB5yGV5AhcJyfKhhMPyIbX0G1Y3wsPtTGKI\nZ5lhfpNEGMJ2q7F33NjZeK7xg8brjbcbfhlMbu0kE9vlAtaT4q/F34mfil8K/7r2e7y/yV2elOuS\nk89qLzEKeZWB5TJ9NS2sTF5Xd9ZQHqExesPmd9j21bbH+QZ1D2G4h87k8RTXJJ9NvZT6SeoXKd+6\nqQuzV2dvyXJp7NEs04c92f3Zd7KA10eBZYY9uaA2UfNwJbP2Yu3V2sGab93kpu5r+P3rq4wHXqE5\n+AmTqq39FK7+a/mp3cF+eiKSixkWHJRf3vGzuX8HmXpk5Fsj3x1BavnmCOXndwy3psTzDKi5pKk4\nw53pqae072k/5lb0L8mFPXKvQ3dnew7JDhnxcsqb7Gsw5XFjR+rp1AupV1IHUn7nc+Pf0cpenSWT\nj0wTDy+Sn26SBHNunLqgRhY9XXuh9krtACbbXsE1n2s41YtJftKZ9B5O+mXO8UKSt+QZJ/pjd6Lt\nx0a4q+2uErjxjbulNndWwI1ytJ3D5xbHBvJjq9avGus7+cz+RVf1LcqcsaAwNlCszFm0dNGcSs+i\nMwZO/ES/Z+XSaGV2bWRevTB0yvjIh+cWZy8Y7hmO1UYqzdm1dLo4sGj2nNNGs90jMq+WeiLz6iEZ\n35zz/zH2JvBtXdeZ+FuwcwMJYuUCkCAAghQJkCABQqJIiBJJUCtta7cZsrUtWf05lZjEizLJSP80\nsSVPMtK0iSylnUj/NNqcTgU+w9DSzkitLWpJfyMktTYbrdjakiU5E6mNrcWVybnfeXggKSnTOtHB\ne49vue++e88963e4nzD5poqUW6ZCQzC+BdKJQT8ItZcRknLI3yIEGX/Ucs+8rVUJqvqEQNEisqvs\nHL7DIEgnmMju7O6wtBlbRr2y1KrYlFE9EkWikhVDLeNNVYwVtFaZ3fzc+P8Rqv+P8J2urodlrAru\nTqKC8We2oiH1Bvy5Asu81InP62RMSglQKkpLuxEPvQnkOIK+O51LnMLjJK5ytlOOuVxWDs9Coiyg\nWMYoiDBQBrcVm9lFheX4c1FA2sZW+JF8WTS4kbUnDUvrFAkr+YLuVXBW4odyPJ8pO8A46RrOfznn\nkST56YUc47wJMlOnyAl03VbbQ1F85skoIDMTnso7mquqmjvKvxGP75mxMOJyRRbO2BO/pbE319c3\n2TX/+GfWQG8w2Bu0/g/2LZdO/EZYzfqygh9E3MMSJe5hJB8GfGOiPEPpLtK57LIzLN3CFq1Cg9jF\nUjRSoXqQ3FC5GaLEbuchxNJ3UpoLyDmQfkgWLqfy7fNZF+c/bNQnW2A+eyTWpeQ+IQWvY0QxQqf2\niSlxVGRSRUTsnbSmpg5qj2rPwMRMxuSDuqPo6R3sgySvFd1BNpOmyFLkLWIy7PIiJne8ZtwB/ncV\nrG8HFGBLsRd2YAtWuFomqUu7sIDdALFjdwvYwC6s63abn63ryRv2+2wpT+ntdrvfzm67yg5N0Qs/\nGMnGtGBSSoQH139izX5qXrHlwmNeqWIfUMU2hdXmmc8v6X9upjleWuX3ewsLgBpSGud1M55dGoks\nfXYG/9fjC0N9zRVGtdpY0dwX4pOYv/TdaP42ZeMykHdUySVvVU5AlOuETjcItfpQdkvOsXr4OjkX\nrCF5qPI4LluCKzZU5xKVboEcAukEuxusUnK/5Pusy91Hzv2aGSvaXYlbnau8UomWaJVbohFL6rBV\nR2EXwUPB48FzwSvBW8GJoPbxbduUfSfpkCv7bGpF8lbVRJVAB8CQ+IkvGT9YQ/zo7tsoIl8vcWoV\npqaK3KAxWJET4gk4scgbGoTIk4sPGBFExJ2CdXVCcSgCWQL5bhvIOQh5EA+CmIUxzEcXuFkRyDaw\ntD0gG+DEG9Pf1isshXzHyhAnXkfigQr+2eRT3LNY1JbBsdML8jJWtu9ivftj7qccllXFaaWiNAiB\nZDXSOJar8XWu4oLlIMjc8YRMId7Nh/hG4fri8V/Hx3/bL3ukyD8Oo2eCbRYJ+ZLI5GTpCibVcUbA\nuAsyUhFsC3KArnSOoloLwNDYS3MF+TB23aNoMJDXQUbJEYsOhIs5uV3cw/oWbnxdBt00kNyg24xp\neIJtj+jZbVR6cvGeZu1PtaniqpUqNmG34G3ugmjB+XyMwBerZUsIPKa9WtkNIRkEWPlSKw0vGF41\niAMQXQszUhyq1QlGRorYOXsw8wYx85Zg5lGS3zaQEyBjxY8NXSQuRF8ln+yI31NsPNJ7IP8MopL/\n9F00cxYImilHTPyzinyhiCdgXEuflgwiddZN+Ax+QSFwjKTMBo+h1SAO49Qi+ox3cWMrPjtZleIQ\nYXQILriD76KBA+4TdO79XJx5BA8lJ0UbRJ17uTANCvEhh9o9WlwwFGeB3MPzdTAj3sUHjYNEQWxF\nYFd4eoQNGvxPdNP/Ql8I0nJJOND/y6WSIC39Zf8a8md+ZYpfk40lDZtrG+AnFo6Qnzh5SH8cAudx\nTIdbmASb9Ntw4E204r9heJwQ05h6nB49KQd7yGEob4HsBAmyd07wmVjRNn43f4g/zp/jr/C3eO1A\nzNTJL+EH+fX8Jl75m569E7pMy/PUqaqM1I+uuYTQrwJOZVS5VEFVTNWv0gxL2/GR1JlY8aSD+pz6\nivqWWjcQMyN27WH3tWFY0sgt1WM8x/KUoLRBnZpJM+jlQZCLOiQoZWKmoDam7dcOaTdoN2u3a/do\nE1o9womy2ye0ae2YVjuAuxkysj+pE5/lArb2InQkj7Vu0jd+Ii+dN5bHWmc15rnyHvWd5w3Lgefw\nl0tLEHR4q2ACE9cJw24aR4OPd55PBg7lgQlJpRiB58Fz1nAvgx3tx2i8A6LBn1pBvpezw8ObDomA\nT0saLbzvUh/jlMkPNZ/CyvlzWK+Pac5i2w8OuoLCozhNNhxFnZb0tIMJ0IvB3oOBfXj6OF+Bca5H\n9HavegUs42RN1cB5HNeu1LIDGnDhVSB3MMA0GHZtIEcp1QFb5G+J52GYezjEz3LLEUJiQ6ueZoRn\nQ553i6KbF0N8++jq27xq8NLl1bx46/f4g3zZeGL8+/yc8eP8N/h+GvMT/8rG/EI25nV8YUIISOfA\nONm6sg2xECqMABpsQ6oNKiWITP+4gDKBoiIktSAHATFFH4GkGnaDTs0SzaBmvWaTZptmt+aQ5rgG\nN1C25TAFtpLuxpAZBDmX3RoGHgVblUA2G5Sv/ljJWlKrEJgtdaNHl6mxCH9P/SP1XvU76pPq8+qr\navaApWqEy4Gj4St9gkmFeEGOqUfqlHpUfUF9Ta2W45qlN9ktmITOh0xs6WEi+vjd1X//96vH7/JN\n/J+MS/zi8a9SnCb1Ha3p0aycsp/yMvAaTFjRT4BXdOZ0hEOK8pCNvcxevy53/XN0fTDLdlJX9LgF\nEwqL9E49E84OmY+bcdh8yzxhxmGz0yzIgZw8Z2X3MiPWnbcwCYGtg+vBRwN4myXYWoLXVyOJhX1T\nPsb380P8Bn4zv53fwyd4+qbZ7RN8mh9j7AmahZBGDC8i5f/DXxIPoXA3aQM+ZD9IEGQMC8Z2wx5D\ngi100hUM5AkQJ8RySqufjMKn+KmHg+QQPaol57qkzQkXPhDMa4rcQZZNWl447CBxECwm7JvziD/j\nq1qrhKLxZ/k/Hxf4n4y/wP+rsOXLY13tQk+XHNtC/UjftH2K7uiltkkb8kh4xIjszI1XRM0BJkWJ\ncfmM/1/8GGfkWvh/kSoDYn0iYBxRs7kxATHRyQRGBLmUqB8kjGn2p4Q1nbAGpFtMkkaatDstBZEu\ndyWM7glPS3I3PJTkPhn+ginQIme7twSS6ZaxFmFghBMeJOqMSu57qfBAssinWNjjLIyjBS0xC3w4\ndS0Q2z70fuoVyEbzEha2i+InUETe0Z5EWMsdCC3Eqi4WfAKGvAXK2veQuPuO+SQblckd5n3I36UU\nrosgv4V35im4zE7ClJansVgsXkvY0mNRs7tYPsGj4dxN7ijbB/1zH7SuN8lOCkPqZbhSboLUBvnh\n1NbgruBBWE/s8K7YmnCoaVfTwSZ2yM/2kkeaTzezyeFvjjb3NYvD2aCI67kwHnqDD7CCfApC7f8c\n7YSNKAnDCRJpcGCFBeZmyw008IJVaRSy6KUbcOxfbrjRIGT9a1Y8OtU8yh4tWZr5Yc+U+JbZYou7\nWusLkwILx4/b6211T4nt55+0L+huMrl8pTWtNcWvPjm7tuu71Z2N5WoxJopCxZKoc2ZD2axVz9fd\nUJtq3dYqs97mjzirw/kbI4F5pf6O+l+WzTIV15oC/mJ3qDo6x6Uj3w4bf8J7bPx2ZHnS99nAtklc\nmPVbMAxtIXwizJq7PTwlDmH6NU/TNW6Ji+IaJGv2Y+tEFPJwdE8UWWK3WyfjvmcJLdwXFKu/FGtI\nERN4kseFc0zFBbvRK1KOajglizpMzuUysl2FsR0RIjZUmLQ4JmoHuG8xtm5jclSySHAKFDzqjoRe\nnvXc00LLdm5Ke4/l2vsctbdG2gb3/ZXWW3Df0ywKIlt0Wy+O9t7qFSZjJD4Ta9gcNXBl3FKxTXoq\nzPhl7Ck2A58ywmgqDWL2tYefwmxpD4wY2GQNG0eMqgeJdmOijNIZytPs5ERtOlEbSPgyUmM7TpZW\nNbKbhI2J7nSiOxAr7u8e6t7Qvbl7e/ee7kT3iW7dQGJeJrlnXmIea+GC7CULMAhB9iOBe9vy3cux\naiyHLrac9frYcoUJLGZTfbHCBOJsJ67sNLGdJoUj1LOdeuwsZnyHzfr2xU3yeyQ725e0s6nqbA+0\nC8MJQzrRZBwxC3ipkTL2w16p3jhSzS7xLa7P4ig01i9GI3+NtOAP8XJx40gXO2NenG46L5AMzovR\n29Al0s/wIssYSb234P0FHy9g0/OPF8hxBCshDK+FeHkS5p4UyDHYHg47T8GqsgaK8CjIVgIdANkI\nw+MukKRfYQpkabyEnONPQW5E2MPeiPw48laEPexSG3jN1rZdbQfbjradaWMr+gEM4NMgx0BOImWY\nErTXgrwOchIZxDtAXge5BMfnpR7IsEisPwxyeiHYCchlkAv9uAHIJ/3glk988gRmBb3jOiJ40STe\ncRQkCUYyCnISNpfDICmYHk4RwVvvxQu/BvIjvPArIEf8kwznBt77Q2RgX2q5juz21yI7Ivvw0p+w\nLpB+xt5ceh/kHaRav9a2ow35TziQxHt/D2Qv9QDIVuRVv8BePrll3k72AZPnu692s58LPdd62HXv\n4J3fw5tepdddiIzy/mv9MC89gQOM5AL6tFO4XmtraDKfGszO65vC8kKtSLnOogNRmjZ4o/DPpd7W\nqsqQ1/ILc6PXThxxpt/SZXKHXJUN1Y68Fu/aSOuQw/3sfLDEuq4nfOv5cm9twVOz/NWD0fDSYutA\nqGVJuIwvrIrUWqy1EVdYZ3LaiGXWzXTr9a6Z9Y58c3mRv6mxJdreBH4ZiDoNFZ5AmaEt6Kpp8dYF\nazqXN1U+lh9uIv5SnnS1BduYsB1En3JtSI5o29OWaBOnxF4JCeECV8Q5uW7x39hMTIQyieqMdALh\nBlyoGjPkc+gbH0FgYZNNzdT7Ivn4n8KTehNkKyOJkDExOyNtmM0PEN8JkYtdOuBgf6o2JqoysYKv\nV/1R1Z9U/XnV21XvVmmYOgIT0hcwZH0E8iuQIZAySxXu/yrsuztB1oK46Ghy1HUB0DVuuQ2RHG5N\nUD7wFnxJoyBd8l1OdmFpB5krH7iE+bMT5A6IZi6Tb2YzBpihyAGpn5GReewtO3sVNtbG+FObItt5\n2Y5X4VyU7UhKnLcNjUOwMzAJ0GNkV4DKk/AakTgtaSGFuelEKYomXwaZ2+bFgffRls9B1KxB0kvY\n+iHI34IUuefipDfR2VfR2W3GERO75V5k8JXJd3gOnfQyyA9B3kW82q/K/hmSimuumyKvMGlfRw8H\nuTIc6CojJeQueof6aR7dijFJsL/XoNYfJr8WwmbXaF+GYNUKNvG5DvhAujW6l3VsNpMW2Kuo4VNi\nv85QKBikl1UgFL30Y5A7iF19E7LLXZAtIF641ntBTsIhexiEhsANkJsQaXqRl3gdfPUF8NWDIGcU\nsSv5QuOrAIQ4CM5/BuQGyLFJKA4ssEdBDgPJYe8sjBSQwyDaTsjmRGJoCIhvDiM350KE3pLLzvgE\nzPIlCGcX8LZ3QLTogdFcfPTrIO9QNhfIfQrdgr92q3kXpLYvcu/bA3IKr/ox3vIlkI/wquTh78P7\n3iA5Di+9F6+6BmQU5BO89JrGl9lLp/Y1phpHG2F3x+uuIa8W3nTfLCxXs07NQqQBXs5PBC/XB+LH\nG37E3tBUKOa4oEiIaNi1dogmq3sKP8yF/MguDq01Ehp3N1UW2mpbKioC/ppiX9TfMLvYateXNvrs\nm3aveW7Gc/EZT8yucTTMdLlCZcGY190RKCtvaCv78997gn+m1O2bUVbZ7C41lnst/O66UFuwxFdd\noc23u+rGt/zsheicspYFQV8sVFtU/VRTbUe9xVw7y+eONLgLDxyYKlvdyPG+vyXe94bEzYIsiE+7\nCSSI3RPoju2z9qA7NkHU2oNVI7hAJsMSB3ICu9uBRyO5OnCPDrAFtpUc67jdAdsZtrd37GHbybGe\n21h9uB54akBcPfhjzx4cDfbINnGunzcJ85m8qeFek3iOaVg87G5JlxCEyClmpN9C4ldxPHmrobwm\nA5pOjQBjmfQA2jAMudykCQHcSJ1JiGl2EbInYvq/Vv2dKqP6tUo1IH1GWZdTTW9DKi3lm93F2L2n\nYSthyEQ2l9bg1Y7jxzuE98aX8OvG34RsvIr7VJjDf53zcF8yZpcwZSD6mgAkIMfJILQNbfRkpB8r\nYg/2fRkuoQ4kCjLQp0vSiRK2bRyxCQQj4RIonoIieVlDYvp3hJMCUtlUAzH9YfGUCO2NNX0nLA47\nDeTczbqHccYRy2nLJct1CzsD0W6xAoPH4anzzPTM96z2aIa5mP6ocEa4zEYBzj4qnhEvizdwP4AK\nJHcaDhhgxE+ZR80XzNfoju9YTlrOW67ijvdwxzwE09V62jxxj3oYwFLDvAzB5JPj4LS+DhGjPhsL\nFxHm5JVV+W1Gi8VRYg+XF1c6TNq6Rw/xcWNVWTGvyTcUVhVZ7Yba6bvobyc/xP93ysEchem7nqwY\nmVRQiAn9gjic3CBshuvtNtCP8mG2TCLXjc35TsMSw6BBHJZEA/A5krPEBQBjQAgY7qHKSFdkc6vU\nDKPCv2IY1WBrN7b+AWQudp8EKQHhyL6XT4vCZQ3l0cHoqu/XDengRGCd1a9DLl9GqkN+2wNwwdlQ\nsou0TtgDF+Lo0yC/wp+2g+ipddI/5DAeWmBauk1IHY/momkzSiLvElz8JghHaFNs4CfhgoGOlkaO\noJCRzLDOU2bMN0Ei2P0cWypsQY3OWjt76c3oPtKPYQv9TyB4R2mZhr0RU+0MBJCSR9AZ+WRyO0wW\ncZhSj2hOayAtU4L9aZBjlJODPFRkKZ3ViAOpg4ajhjMGccAUsWqtWsRH+iLW1tCD75t/sDr4zDPB\n1T8wf79daG1oa9hY861v1WxkG9s4gatnMtjnTAZbyH1FvU5qqWMjoM6YmJuRtmHln5iLr5mRbisJ\nR9J2SGI8Ywu8/J3bM9LediazqJkaGGhhr7mHKRmJFuNIgZrMKQicaTEmXOmEKyBthwgwxMiIk52+\nkGtHhyxqJ5CIucaRFezgAB1MLhkaHGJfYfeQ8okC7BMFFCmIiaEjHkhBAWMinpHScdwW5HYcrQJ0\nHkLrY5lEND2iY5M/QAzkNvKauRJjiatEBHAiHNoTbLlLFZU7ywPlbCw7PQG0xRmQhqBntMfiaOGC\neAxHmSr3JLtXzDiyWpDzUmDJuk8E1q7TcDQfLDpahLAXDLD7IKvgDF6NLQO2PsUW8gslHcVdg9zH\n7o9BzoKU4ZgVAdWfQ1DZh6X7ldLXSxEjXmotZQOdsA1PYhknk8wamGT2mVNY3DUIxgqDvKKYlpIn\n7efhpVbbzXYPvNRrEIW2z55CpOkrWP3fBDkJae5zkDUg+xxgg4DP8zrCDtVw8nXHmw5EFOKPZmQf\n7IO40IOtNrh1v4C97h1I7oCQk/oAqtVWG6+Fe35GfAY6BTG/KyE8XAfpAyEVdSZIaTObyWuaX4Zt\n6HVEmphb2V3fh2lhB8h+kPNQ3faDLO9At3S8jiXxNBbTPpDLIFEIk2dAbnYBjaK7rxtiDxOtkwd6\njmBptGGZjPb0YfsLKK5tvbgDSLgPEg7IfkZS5+dfnf/5fCbajGKt7gXZD3JsCZq8DF6TZWghyCiI\ndSU79tkqtpUCWf4MlMtndjyDvn4Gr/4MlvYPMGpWYixcBzkKosf4oOFyFruUsRYFWZ0bHwbs7sLX\n95cyfeoPSr9Z+kYpa5wDKCkzQfJBCEnxMkgUI+Gb2PpTkHzs/ghf+3OQl0HWKvJuSmu32n0YID/K\nDYVuxJK+7HgNX55AFTXAGAmDrKWTMAa6MQZaMQa8IEcwBrq96GFfnw8KCYZCtLYPQyEyoxdD4SKG\nwnmIkOGGHmjr1zAEVoC8jG8P46C0Fls0FKwYCltgjftx+C1Y4z7AYDiIcfAByCoISRtB3uhQhsJZ\nEMKYO92lDIobIL1QtLrx1d8B2QdyB8QCCSrSg0b19vSyp/wMw6AV5Cq+eQ/IPpALIHdAjmIc3FwK\nVo2vb8OIMGMI3MXX/whffAvIRpBdIKufgQAkapiIa5kas6pAmoSmgi9mY3mqvT6yhOYi3kNaXAWp\nme4hBNuWqtT5eY5wfXlwyfMtc762rLl52dfmLNjq77TNe3KgadGWNbNmrdmyqPtbA+FAd391ZY1K\nsM6cEVtc1bG81TPbyO5lqTDWdDTYW/1VbXU2wTt+raAsT5fvmb24LvqVed7WlV/v6Pj6ytZY1D+n\nwRZ9fsvixVuejzY8OTx3zgvzfeVOm/upuS3P9zfVNC42e8qKBH/nAndduD42Pycj/0VORv4lyciV\nEoevGoOicAKWyO2te1qxzAX7sr6B+MRnwizhM87ENfIVUlUjW5tkxCmkDDWwhYUtmg3GhDktJaBy\nUCBnJ8hgUAkuokTDwqnBXIoNkEI2NQ+r1eVkA4TOXAQHQDnpthdg/YfIBmsOzPT1XkR7JY/Vn0XE\n+E3E812ulwFydwkHmbwkQ1MNJHeJB/GzQ7sPyZ47dPuAQrUjfx9yPncU7EPOZ7S4Dzmfu4oPIvdm\nFcIWkH2a3FVyEEkbgH1KRip7EaCdco462akp9yiCriM1vTWImkYmruehXFE+VBySJUZzNtLI5za7\n+d/aG+f4/fOay8ub5/n9cxrt48u6BOOMYMgaebrL4+l6OmINBWcYha6bwLWtaJ7ny/4KTeN/bKu2\nGDxdq1tbV3d5DJZq22KyZasnfsN/lckPIvfThIgMZQgHE4hELeKdvDCc9fFPQGCBoZuwXB4DhaM4\nrZQMquQqbh2MGVsp7PUgd5Q7w13mbiDs9U3owW0CjvcJq4R1wkZhq6BmS69wlHV9zI6+XyWuEzeK\nW0V8AEUgz2eyEa9VR/nATOFvS7+sN6H9T0z8hvsBG2Mi15hQBRC5mG0tk1ynNFMZQgpAHYHyuJ9o\nbxc+e/AF7pM/8RxfLGS4AJ+XmBFIDs3YMEMgZKAZlOKXh42RCjZmS+hQgMm12wK7A4cCxwNMrrUF\nKjDQOpswbg0BIFtxBk8WzmUwi/+bJ1DQdoANTBtHcorHOFJDokhMr+ftvJ+P8iq2EBMkFfpHj2Rt\nvxAV2NF1kDa/aMBRQ4Ojoa5hZgOg5/SN9kZ/Y7QR1zViZUI4RUyv5a28j4/wOEUjWASvEMZNSIMj\nFJh1MAncpdvpG+wN/oYou510r5EO5O5pag63yoZMMw1P0trNlaKZBioOMkYHnOX86khJpc8caysx\n6wVzRaVeX1lhFvTmkraY2VdZEqnmO2a2Su5gRUF7ka2y8C9qmioKeIEvqGiq+YvCSltRe0FF0C21\nziQf/W8nnuPu0bdwsHGVHBTXK+FebHzG9Ju57RyAAFQUsWPIwBNawkRPK1eCTq0gf0UNQZ3NSE/7\nJgEPQewE2DcJ8LLHUP4u7ANy0z6I9PlD3SjVCNmgniT6EwlsOLAWfblmsi+1DdYGX0OEPo260dzo\naWzFp7mDTzPtI9/DnXSCDXfy404zQQjWZ60fd1I3mBs8Da34Krg13YLdv9Ha6GuM4NNUa8xynKgM\ncU32ldZGsVWOJG1V3HChe//upxH+o5+GzZM57Nt8xL6NlvNBXweuXBoquqB+wAGwRQU0FupZRP+i\nL03uYjZQikMfDQ399KfCvgefhcVX5XtVsXu9S/fyJzQBKYboPF5NKRhaWCc4ultg2t2sbNq2sn9V\nuNsaMT/8YAuNmatCjM8j3JgzEqdBLBN3HJgjJzRpGELkWwLcMMN9iw0qG7RQYBRldc8UfgJaMTuk\nUB+EqRUGpyGAaC8tQBFpmqb4Uf4Cf41nc34/RkjefuGwcEq4KHzCOFhMf0R1WnVJdV2lQoi6+oj6\ntPqS+rpaDd2PzgaLky0N08+WDqrIaJG9AAcYm+Vlf0IIRoNwhM9TPdHR8YRqhnam1ztTK7zX2d3d\niYpoFEc9yQe1XE9CBzQmREFOaGSFSksgKxrg5EwJnlO4oobtaGTcP6wA7DR1Rv5wIuvqH7TjP/bi\njeO1479i362PG+F/LpjYul4gGTixnpOK1GI9u2CayNMo9JUHYx5PLFiu/PK/P3WP/XJZ/PTf8n/P\n/xNXJXRLVUB/XW9nSrVdDrFZDzt/Ech2GbIyUZxOrXdvcm9zM8H5Fgydg25Em2BrCcimHEBlJXuv\nSkVgKGU7pWRtryyVOXPKxQW5GKB9Kml0AZ2wgDEDY2mlDGCQBFSoHCRwDMrzv4H8GmQ9wjAITe8Y\nyM0cgOBNzOuwugfpo/thJNmHKY3YMKkVSmYPyD6QA1ASjuW8IF8gveFD46eIaF4PNeFdRDRfKL4G\n6aK3eAUCm9+FBW0popuvYes6ZJljIF+AGBB8MB/e/LPWD6zsNn0Q8+/Dkn+s7Cws+QYI+kdhIL4M\nch/EgMDv+SCE50+uuXdh5V+BPv8AW0Xo3/XoVUqJly4p2Soyig6GqnQAr0ngDfvwNgThj4wNKY53\n6StGwmDxTYhI+RCRKLxzJd7kJoSlG5b7EM7oXQ5DJblLuiladTiXSnYXiVInXedztQeeQgtPooVX\nQdTYvVglYwx6ZWEqwiaOVaPRamSB3GqxWPn/Xjzbx4Qnm40JU77ZxWH3ymD0mTnV1XOeiQZXuvk1\njhq+vLm7tra7uZyvcfj9Pp5nolVrKxOxeN7nl+PIPhJa+AKyyW6SOJ6N2U0E2garCllnpVu0gA8K\n64VNwjZBNZADcVLxXM5OK40BnUbfrxnSbNBs1qjI3nYLEta3mNxiI0EMF04gRKVIdGId3IDgOhVP\nYY3qNKapj8l/oT39X+166qkuoWX7iy/K2Ow0p7r4L2Rs9hPQ2LYzAvGlIy1pazpkbE8Fpn0KQjsy\npyoyWZx26U9hGHgLcuzRyjOVAgG0tzIhvtKY6GKthQd5cJ4y5Qh/XZlyXWyn6yH89a4s/nqXjL+e\nDDbEmP4ImIUgm4FdxpGZbAbO7iIY9tlsBs5eMptmYBLpOuz1oZ0mz1RfRjbjEfiMzzKSOuI77buE\nIhPIS05+2PApu2fyQOORHMb3LwD6HW7ugXViPzLDW6F39uTMEfvg5OwFOdKOMQ1yE+QXtDUb8xvk\nr0GuQkm92AFxi5Txk1MMMkcxFM+CTG0p4fOfAklRWhHIKZCPJ7HJ0czraObBxqONsLo0x9HaA1Cg\nD4SUhvagjfvJ742WXQXZBzIK8gmadxnN+5QR638EGRxzQvvojHE/+J2w4JFKR2NuvoQfnU26x+OB\nL1AZTFPmkf/ReUaYij7un9kMc3KdkraQaYy/Y1QCTsmZmcLm5WGWKMrAzF3Mlq//58uH1v/Ot+vw\nP775S2mdXTxxhP8XoZyr5PyCSVIVAU+Z8bqUHC/KBiBQO6UTIPoilQx8nUzrx4BWFtTH8LNdv4f9\nJHQkOumNCX86UUp2Yyuh2znSqPJRo3ogv5yVvZzV8FDYL4lEMjDciayZWpchGHJsMrXaCqBlUEsG\ntCwjnQPIs9U4UsmudNLj/GSmpqULgdvJ08IlqL6yGpb8QLwJbrMSgIr6I/mn8y/lX8+HB4V8kojE\n14OpXyq5ztTcJNAm2c8R02mUC7mHaDM9LKB3YYUs0DqsDp8j4uh1rHBohqVdWJI+LPu0DFeUn0ae\n80Ew+BvllEQF5RPuHWEgtUNA6hBCPrJJ2my9QNc+gLiUl68qV81QtasWAgcUGZPSDeBtH8s/m/8B\nILiPUlMJKIM1NXWy5HzJVSBiUijOSbRRjTamsHUX5O9hEvstNVl0mBxuR8gx1/EUmoz6JMlLZdex\niv4Ya9MBNHhyKT0LAi+zJyxPH7MbsjjJ5CGzPMN8WtlLauX7a9qKwq6lDcGmxeGKivDipmDDUle4\nqK0m6ArVmEw1IVeT4Lb5vbXC7E61f87SYHDpHL+6c7ZQ6/Xb3IKgcQU73O6OoEvz0Fr0HUmDWNhN\nkPo0kOUIZ48tMNwQt4HbDJWJqfe38Mk3o78ENcV0q8nfc4tk0EHVetUm1TbV5IolC8y0BkGIFCEd\nJovUTog4E7jLBrhHBFEtR4S7fVp3JMQXYDX6ar/Q8uKL29m8WTbxBfc3/GXC6fkWSehSP1ZMeCaS\nHGfkhOFYnqzXIVdGPZCSA28h42lkV0U+CbAlaekW2O16+yaw2wS2Y/Z+u5KrQxYjw8OhtPpMwmxk\nzCHYFCmV7XDgDcVTtv+m0marnPovaK6qMrN//FeyG1n95xvceW6QydmLCO43IwMr3wOmso638bW8\nOJys5dt4Nnrl2GJRTkwWmIiJuBAFCpejSOMMYbWxi62sz747d+4T3/+UUnw4+8RvRK2MHcctEH8i\nVZaK9alNpdtKd8Ne7CxlU+9c6RX4FJaUDiKLe0yG18Y3nyBvD0y5Y1i6E5Vy4RSm1hC8ZSmyqJND\nwQ1BBHcE2Z1OBNPYjrHtxMyM1I8qFRtmbp7JuFRtOjHTmJjFLoXzm5tlnCUMJ4LkYSLn0ixjYkFa\nGkLQVBBBU0sWDS5C5uCiQ4sQ1rdIkQumla1Sgngh7WAEpTCaAmoRQj/7SOTeS3FFxiJXETvmNibq\nmYgCK91EPVWESLRmkmOttxH3OKuVQvdmBRKtTKxhl3bg0g5jh6tDRMkt+Jcm4nhE3BkPxMVhaUF9\nK65YAE/yJIO5AMtIL8jnkLUARAsdn22ndohIV8Q5uTJVADOT4kDF26VF0iL742X4Gr+ARunQ1jGN\nMotZn0LZiKOIc6HCEV8g9MWgc+jqdKwpUbCrXfkH84+CXV1G3Md8pbRQalfBwYKjwE+8DA72BQ4Z\nChwFdQXsQhXE58+hIJiLPUwzSO0o3lecKhazeeAREDWk6zBcZjtK9pWkwPgugPH1gqzMJUYuB+ci\nvAaC8V4OyeUayA0C44GJ/3TDJZj4VzLZJHWp8XrjPYSKUIzMq5BNVoFcBjkLchNkJeSVLSFcHLoU\nwpKAAxtBboCsQCjja5BnXmtD8kBY6IGbWoXOJdD9HiUCW4qgTyO6Xphe2wriiMYmHEBdDuaIcP90\neN2Z2CKMvxUE5UdlnvAKZxou4xUowHpVLsTnPgiJhyvQ6FcYSZ5qvgip63shxCiF1oReDsF5hDbf\nwetcDH2C11mWa//rrP2mh+ArtA8pwp6pCOdsAXBPB0Ff1rT8a11dX1vepPy2t6/Zsmjh1jXsd+vC\nRVvWtAtzip/vDj0501UZfTJUE2uuVrPR1eJt91us9e2elh4dPzT368vY9S/Nm/uNZcGmZS919299\nLhp99vUl2V99fKl79lNNTUvbq03eqLeurayxo8bd2VQxq57kmkpunVAoPM34TYTvl6xAUUNVMmkI\nJOaXgdAYH0sCw4J1QBqdnAAZAolZZenCoH7ASa5m5CWyDwBMMITkJpohITVvbt7ezJTtZjLOQ6SP\nlPaCg8muz2TE3IufsL0HjH2f4taSredZcTrq7oPtvM0bBzgBwIoUJ9Vb5K+sj8OifxS84gA0gbZA\nPACZJnAUNXxScFNEWnvhplAeHjb3wO96GO61I3hcFkblrcpjylOlg3h0mzs+5dGkehzAMyP1vXjm\nfjwuEujFc/ZNPkc63Pro+PBYLFqLBm5+2cJDYEgRbxgufzlUNGL9vfLmLq+nq6m8vKnL4+1iMvIr\njnKeL3e0zmitWlRbu6iKbTxyhP99X3eosjLU7cv+DlctqquTzy0rk8+cto9vXzvxmVCW9dPEpdKq\nST9NqXHELPtp2OrZkJaCOT8NXDTS8aDiCyAHzDTXzDSnjeKnyeFGPs5PE9Mfs5y1fGC5ibAa2UUT\n079Tf7L+fP3VehXpgD8S9kI2/ZG4F26ZsNhDThrGggWlPk8S7BSnFO+F4SRc3IOfH5XshTMGaDmS\nvpLPYuUMJ486zyAUW4+MdiBjJY+6zwCyTA+LRxQkDkyco4ikuw+ix24UBHE/kt4DpHhwaoUrzcQj\nTjv5gchD35x3t7qnewatoeLQBnh0oAFBE4JHh//5f8yjM35r8cMOHf4PmXzin/iOIFLtgyffFtUa\nVT3Z9NJZN85tgc/aQsn4SQZB2cw6NcNQ4vJUsoWMrGIa0hb00BZgyAWSQ6g1xEf+7u+62P/5e+1f\njgoz2/9OtuU1T3yH/4Iw8f8ze37h456vlquQEPyOgShb8lG4BhJeIex7HAyYOSgWapFJN9kiSaUj\nHBZVYEQr0OhUUdUaEUjsaGux0lb2jwniZDCW2/wu+6+LiKBqH0+1nzjRzs9vPyFjUYT4ffyAkCaZ\nKyWVVkJGZYJWVqpaIqcyQYwifMnJoo05SF51BsKKPZPFBiJ5k6ewnyTsvuAMWNZGQQgelyBOTiLx\nMwsPdxRj6CBWsTOA5coqY3IxkgO47DQICuAkDxecwhWHccVeKjNYwv+7oLN8g7HMYzZ7yozKb9BS\nC1iJWovyK8ya+mf8Tv0r+0VfTVwVlvJ5wjGS5weA/g+7cSZmkg1ZMoo+bO9jGj0TGzVGDeuOfETx\nJBHAw4R901DJhpLNJdtL9pQkSk6UpEvGSvRQNcwBG5R4kVgOBp2pJVtaF5xyyvbecqu1HP9OKBvC\n66VOZ+mUf0yOXsytEBYKWmpnGbdZ1jygFCWhEwmK7pGE2sH24FtinXoF1kzYzuWiD5se0kCwbSM7\nuZ0p4ECUGQLhMPe3V0CJtTsYE7UTkgpTPRwEV4bR4Pkd2oe1qrWK/6OHVZAz4wI//gNFDwlkN863\ns/eqn/gL/r5o4xq4MDdH+LZUDr1vDKhB6fIxaNQxiHSbQYZA3GoCOHEHpH7G5xgvT8xJY+aw8Vqd\nSTSmYXxgU6g8G3w5YlITkIIvnfAFRmrZDuPYIRVFY7Yrpolp+PqEDwzz+pwCBMqjhOLEnKzH7jh3\njrvC3YL6WUBzvgDw9KAmUBSqTcwhvKw6K1PUB63rrZusItMirIeYrJE1Zyf7rKusbBRVUYuryMXH\naG0mubl2e61A1awa2Y0ayO4RxmPD58JXwrfC7LEWYyICppCS3criQGqddqN2KwT3F3I1UrWI3dHA\nXr0flcJSxaPFFyBTixAtyWhAZve7sFifNX1gYm36uemvYPDYSYFekFgoGhuxV9LH0L1+BPI5bKcv\nV74GseJl52tYcQhw8TmAKb1WtwNgSheALPQKDhyuO4UDVyHDrGwCE0CbEX6EqU8JRRo0dC+s69Ra\nqt5Kgj8xg9+ikQRPdxVEi9ZTCdLLILty9Vr3o6n7qYoPWvkqIuZQ+lQ6yZqRerkOLYO1BC2j5q0F\noaKWK5AM+A5SakYZMU1aOrCsab1ebXGlKPuK3a1ery9SKYYiWAvDXp+YNYKMV3dH3FXhbk+wpm2G\nu7DNHK/xxGfWOCPz64M96yvbjbW1PmPQ1Ogr4xdEg2UzXCWmqhl2Pm5wNnY11vc0V4g98wptTmPQ\nXcWPf15Q1dTdVNfdXCnO61J11tebXZY8Pr+yyROaW8KvEMzVDQ6Ht8KipzWqW/gK93NaI5slDcf0\n6t0cvIVsUMjcS1Q4VkrmYCL4kp7xJTEznR115zjPf1M4Du6/gtFd/PtslvYwJT+JSFxheERUP5A2\nw3gVU6Jok0PqDTCijKmnQVooYpSS7S1X8dnV1cVuSesU4nEe8GOcjvPwp6QaB2Y+hJMTIBwTTmCh\nRwWhfqTPOGQAf32aoLbZn+QZLg2ycQHwbyrB1Q+yHgbpbSBjICdyZbmcrCVOw8MynJOcZkx4Mstm\nTuk24nM5PT9MMZtm40gh+3Ox2Yk/Fwek7cVyYhyVTqGQ2v1grqug5Mmw3yCEDt8Dsh+Low9bvYBD\nIOjF1cD9WWl8wcim0hbjTjjLVmIKbMRkiFiRGGs9DWYRsfUC4Odo+ZlyWBkrTgMtMQoGTcot3EQS\nByzDJVTW9wChjuH5kVxLsDgnw/k9+ew+1nwf+5Hxdz9BQ1ZgCu4g5DWq8WiEmXSFca3xFePrRlW2\nLUetZ6ArUZoG+bEAfygdBtGiKRGgN16oulYFtHhZJdSyqSJb4KcFkZn5ZH/c1d5QJojlje3u1kWl\nobrnZ7asinlqYsubW5dGK/nCuU/Z68IVYXdHg6PJVdfaqMiG3s5lwUol1+EB5Xmty+bK/oSNJx8+\n/rC0HiNgCUgAuzQqOAQBbvBu9gq52ooP3+NpukclXZ4MeDu9rJe2g0+M1aFfb/uzWEXydcdy1z1H\n17XRc2L6Q97j3nPeK17WcRtywzEInRwYZuzvbcfbzrVdaUMIedu0PN6pbdlE93RI27M663ByzH/b\njzXeb/QTcoDIhdk1d7Iy3iL+r6XYfDZ79qDUahEyWjZjawgkxghihaEGzTdS1bAYFpFEJDBSSsew\n0jQEpD0N/MBIJZtt7fNjlDIGP8wiefvsImUKTcPR7mY73Vgr3c3d8qpM2lA3IWhDP7bzD6RaNaVd\ntbuzdlVJi1D5FflrgSsMmOzkioK1kACBAp1cWfwCnKuwyUtnYRhaWfICVB6IkdILYOjrAhsDWwNY\n8KC/rW1hh9a0vtz6Wis7tBYpTadms8vOd1xFYsqKjrUIzD0FT9L5yfK0yfNdV7vY8ZcRh3keEeYf\nIOZ2Y5yd8UH8ZpydsXLBC6jI/AGSe1cufGEh2gDD4AcLby5EdCBhg+gx50/m8P2AhyydgplrBRYw\nWOvlNNIVIGuU9kqj8G6Nok0nFz4aNCc+tK+Vl6VctpFS9p3mVTjC/6XFF612t/ksFl+buzrqszhM\n1Y1lZY3VJuX3A6uv3OgKx2tr42GXsdxnbY97Zj/R0PDEbE+8/ZSzFZe2OrO//LqyxiqTqQqX0u+3\ndc665rK67qDDEeyuK2uuc+oE43M9jfNby8tb5zf2PGdk43F44gvxFW4Nyadu7qdwa5VAkobcZFXM\n9pN28THuNpenCK0AUMjHaflD+RvyN+dvz9+Tn8g/kZ/OH8u/nY/T8o2Ma0FpseE025Btg22zbbtt\njy1hO2FL28Zst204zWZkvFIazNV6KGPjswwsvigNQbwMCZgJc5rE8akSrPp3bIuvKBLs+NuPbimG\n9Yf/cTK+1TnxOl/MesaJ+CKmmaqN2bJPfJqTBLjaxwSwAA8TmcXrD2zi9XNdnHIt/xldW04XBqZc\nyAvTLuQ/G8/nP6MLBaYjjAoL+Z0UO9MC1VNAFCT0VIEQoXg5bEarhBeSFisDJN6mcnC8uarVw/4J\nCyGws39Ce/uO9vbsWi1Ui3mcn5utegKJCeoisT65ybmNSYFsBU34MxLn9FOUKpSS+dxq4Nu8hu23\nuXehi3ydEKaK1FQYCba2X4P8HORDEJd8+acweD6NsIgPsFXhojwc4Acn91WkKuSnudPQABAvkwRG\nKbu7Tz6PfNGvMQLfY50s57cwnrYEKWwBkEFIe50gm0A4ZAKOdWLE1AQACuAMTEbZKKJCrj6irxZC\ngPQynvIeyB+BVDhrcTSD9fAmGvpWxTEs05yPoikJnsxaipA96T16U/n4dbzfJibrIJQSJqqoHLL3\nPM6PIDBnK9Tlb0Pt7hGXQ3TeAZMyme+1YD+ElbEV5CbIUkhlpJQTasZbIK061kejuguAPqMkzR6q\nN4stNTKQWkEAW5SM5vXlAUcdjEyPgjbR/D7ICmdy3sczYGo5l6mMkEx8mVI79ZDP78D8eAfhLyhL\nkvym7Q0gR6+0s9tttG+FPfQgpPU/gIFyl+Mgwvmp/iZCfJJtFfEK9sCVkOJ3onPeAtkFEecuAqK0\nMGT5yJoFkoSY+DxVDgXpqUF9AW8PrJphLP1hiEURfKBaFB5ZhQzSd7GkvjljMt7/TcoAgRqwC+St\nJvREc18zLHDNB/HTFo4j0v8AIv13gRwFOQCb+S4KZEBeZQRepTdn7Z/FLnizfX87Voi1GPyvCltg\n+CBHC8GSrwShD0c1uI7hSxyjNGF0MWlnyxEAdZWcGkBrfBVgC6/at9izPSnFEQ91Jle7NF422Xe7\nsiWAWetdBxFgdAC992MQB/rs39CP9dhaANIO8gxIIUgvDIJhyCzUgb205QNyv68Xtuk69OPT6MfT\n6Mc+JppIBpB9CPzYj67cj15shcSzn+I90FfRXK/Nz3XdAfRaFOQAkzP4SlE7zcJjsUYaRdmeDAha\nKxNDH7ZDDjXGmx28LToUD8smxvCMnhqh1OP1W/+w5kVHwNLuCFrX1I6P2BvmUJA5BZ3PabALP2z8\nyrMvzmr4yuImRbQsrxQ80WBdabSmzVZb5DH7za3ucddDpkriseT/JZ2riPvm2/mqAlU99H+q3xfT\nK0BLquH/yLqXvFJ8C8KOEwEI64s3Ybs/BwtYxJhO0SNYSvoMWyqne399TKvKeX1L4nFlXfpPgorL\ntrmc+xv+GrX5/3u7QJXP2pxvlPPTkku4QSzBGjRX86jV67YmDwB2TI10aYKamKZfo/l32j1N96OX\n0MkRmdmmT1U8EQW7bErT+YTS9i8fZGuTMtIrcpydO/E2r1ap6pNAwEImKNzVHEDbTmSD1WDbJFc8\n2CAHwDa56CxFsWD9VGdAtXJZ8HSikH2w/sKhwg2FmwtVdAdbWupEaGGwbBpYneICoELOhoci8FHE\nmUdgbxqpvVpC5C+RGXlcQd/npOV6VGDK2pxlS5k26zwzh/jq9V3PPru8t7CiMD/fUVBWXaJZz+8c\nf4Hf2b55YIlKbBdVJVUz7K/Cn47++CXrD3xLP58HDKrb4BQcCjoUZlAAcVgpdw8Mv4dGn459TCMn\nx4tidGqGASdepH4ApxfMY8WPxNxUZpInKtPwIQXhgK+ltd6F8iXkykYZJipyJCXqp8ldSq/Vsp1a\npddK2E4Jeq0M4yF1Tg+wNHEgltepX6If1K/Xb9KrEbABkbAgjYcVgo4UY4WkfSvkOKDdwI8St69E\nwITUaweW7RTBjU0J7e/s7l9WlpRU4l9RV9eeR7te2GIuKzOzf18+y7/f/rjPQHGJn/E3GR/w8F+X\nDCViPRpck2YyxAjPpI6arIiCmoTSEIJmgYQHTDyghVEFM4NxpIx+shXEEzmplbJK86dOIHSYx0gA\nz2pPEUW6BCRBTYWEPoKMYCKxSqkgDtkHpkhknLNL3HQiaw3G5HksOstz1nsqBnkY5B3Y4gFmwT70\nKIHgYFFCSZjkaP6FXA2YNSCjBMUFQeBrMMWdx6L/CQiViZ8JcgDLvB6r1UEsSVGs6fNBrhOQ7ztw\nhi/XrkGKM61+W3G3V1Ft4qL5Ezgy1VkgjAFpL26lwa3ewq1QA4av0mZBaB6BKafAOFMV/8vxf1RZ\ng7bmJ2dV1cRWhOa+WNFbGg9Uz6y3OxpjXu+Cai2/TvjOeb2uevZTTaFls6tjHc66JnvdzCpPR4PN\nXMKvJ96J+fa3ImZeBfceqiPfRt9wOsIGlYJUml5NBZLV7EeVAVqsPiPX7wQtYSfBzgrPgEAge+Vp\naQNMlNtAEiDAoJbSTuX7E2cpnPr9H5s4lgt259OKG1JHhu88CryzZHmQgjvCJXsLVjA1uwrR8L9z\navzt42fEZ+M/+x3zgJt4SdAIaa6O/w4bfkifyAIl2DLSEL4ex74jOAvTXGppjJLnqdRky+4wdcyM\nOgS0zzioMyON5TrFT9dwCT/yJ+itK9hbV2By+GW2qyXrHJg3Yw9+Yg8VxhE3vXwqwvfyK3hxCoht\n8hXhdXherYJPEIZTO40HjEeM4oBky+U/67F1D5YHXbENweorEVt3D4KWDkJqraPNgRojbDt1pOx0\n2aUydvlBAmcBISCotSBvgkRQGGqFB+Ch3hVeYZhL7TDuM6bwSAsedBeP1LKtpKbYgqdR3SSKNtQr\n0YaqYekd3HoU5BrIfpCLBAyEh6wDAfgW7xbhms1Nh4iJfeQIEvCUb6zVhvj9r8xVl7ojtZb6Ylde\neYG10pQndr74SpmmtKbF21JfXVBRZHeVGkTLi/xXx/eUhWeU5RUENRpTpdfEPxne7myb4agI6vS2\nmlrjt2mOeBnREI5cIfc/UVViCfhcfkbajZm9HqQTJXsxOBi/E5HYNKJV0ZpJ6ieOMm5fEJDEAiXQ\n6vF1ebWFMlBpIAuHIPmziJIDKF+MW8txo6KCXnAeX/4aSA8Sk1ZQJVHYYslW0wZih92pr2BVgTDM\nOnCWQL7WqlY+xK8UPrsTX7MmPv6/+cpnhG+N/2HXgQNP8mcRq7hqYoxfInzM3llPpZpFxl3dvJXf\nz78bGu8QPnZ+WZa1cRsFKz/Gmbh2ISK1eFHVzyujIcGyvaSFbbcYR8rl6AQ2e5vT7G+J9nSinSm4\nUFivwE7UCXIcBGAs3CRgnMINyC5neMiHRTtRthPVyTUuwSTq5ViF0igCzpInS88j6i4FR8rHyPDf\nXrqHHUhEjSNWdlZFVb2i2ib/quIX0Go/BRMO1sM5lkoG3wu+HxQppgEhaOzPrRT1BELK6n5Eoe3U\nHgCj34klZmf+AcQ17Cw4AJtfK4b7PpBekDcx7xDmJWTju17DqkIwAmuxFceiEMdS0EaaDxShI1D+\nzoCchmYC6DUpDqUkCpJCsvaK8FqocKewfT5yNcKefzhyiv2kzrRfbr/RLiKMRkbwf1PcD0WbdLMd\nVBoZwzdO7myQH4N8A215H+R5kKsgrWgkmpsEwApeDS3sATmNxh0DSaFxp0BWQBO6BE3oDMgxkKNQ\nhy7B7HoG5AbIsfbJEAyfUlUou9hNQ6WkQIzWXOH7V8sCnZ6aOc1lvK2xy+/pDJTlVwxEm3vqTYLr\nmVBoVZe3pnNFizkQaCwVukyR31/S84cNtz1zmiocAXY6+61omjP+A1+gonmud2mwtqZzZSi0ssuj\nK3WXLR4PepfHg41Bjmo1fCZUsPWxhb8olcKodUiWxgmPEbGc0olsQGd+Gu71Usac2bj3cjAKJ7wB\nqdBbStZmQKdXyEcrAih0PwP1G1o3I/ZoSVgZ8xQ4XviQY2laCWHJp7XLIIgjXjaAi3yE5WWXwXe0\nlLaK0bpOtRGqwavwl6wDATZ48k3DfpTEewmLJvlDoiDkl+yGsiOnMUlhmD96cobdHghB78PoQZYP\nDdT3s9haicoA66o2It8DqRTJldUvVCvBWN8EOQuyEjUI5oNshAp9LYh6VU3Xmu4AJdUCzNQLzdea\n7zSjelWzpZmtIsm1qlfQ/OfR8lcoPQu6xsYCpcko7SWX8R1G4UTWfII0RKNGs0lRA8lk9XtozUto\nwxqqrIf4sO66ZXVCtilvMBJyF+esz5Qzmx1hrRY5FcHrbpXL4pEMsaPL3PREe21PqPIbM+bUlVZ3\nrmgtrCzMt+X3D744UBn2WeOh2nBVoeCa8USHx9bQVfdSnaCqifb6Wld0VInqqCg8s/Tppe0FFYHq\n9q7KxjYH47V3hBbuHVpfngOiCjDZHwd6LGZieZOIpOoBqUjkh2MFQTEm9otD4gZxs6hhmoZLVI6o\nhxOCkkbJZ+REccbKxJDVbXj6uVnCe9tlHXQxVycsZG3QcN+eNJ6qRQD6xIr+i/pP1T9X/5X6F+oP\n1Z+qtewBZrVH3aruVi9Tq4elz9Q5QH4ByQ2yBVdWV7MlalQZaQIqq5oXJhOqZINs8hR3EeEcp8VL\njCuZ1GZ1q8ezmEyz/2P8KaHlN42/aT9zButMC+fk9/J/w6m5PH7R2xqVXlX/NqcSmb5MCv5Acok4\nmC2IkA1WhtyoZ9JihuCcmeZKyO2cpDOgZh2shLfA/4AuD0FWSKeMBpchaBCHUycMacMYyh4YUO0e\nkpZc4ZwN1+XQyO9T0rGcI9yHLGEDHaVMMjkRvA9Z3AYsxvcpWt+u8quiqj4VjiI74r6ajqr96qi6\nT42jajYN/Jqopg+h9Mvhd7lPle3tWr82qu3T4hw6SqXs7Tq/Lqrr0+EoXuU+gWnZDX5D1NBnwFHA\nEK4EhDUwc9if8vx5sIDiTwTT/jQcu0+zTY9V64tY1UT55q8Hvv/9wPhF+rmdqPrlL6sSRPENZrNv\n8JfZb9D6tl6lYd9AVHGqemkIdok9RLAoDskV66FBwIwBaCYq3uYEHLwTFtnjcHHrKcDtNgLc8tIc\nexGxPnVCl9aN6cSBlFHn0gV14rD8bWT8foK0BiJ77sNIdyhfV8NbeC8f5nt4NiRXKmUdpqXU36Hv\noFFZVF5VGN/hjpquVFvUXjUSQdmVMCuzQ1qL1qsNa3u0uBn6/A71OYqme3Vh9Pkd6m6NwWLwGsLo\n7jt5dGWeJc+bF87ryUOdytVoxzLqZtxkGetrBI5qfR6iCepj3v+YrgZ2Mefk7lBf/+XbaqXeU3Ym\nAWl8UIuxzIsYy3gO5YrsRnaDzB7QgyJYgUiQcPcpvcTOyeMYg4DLjuOUPIxFZRAjkt4u+sWoiEO5\nAgGPHcYpefgy8UIuq7KaHYtgHGnv4KW+HqOXkmM52Njh6H0OvS1QvZiJbC2ULOwV+55ylor8ZiKP\nwUDcD6/Cu/gg2rgbw6sTrbolylEKd+jNLExID3M9eDPCYFfTGMDryQODXarGVXfwehbRK4bxemq8\nmTww5GHRw96MTQ3cAZUupOXsBAB9+SLd9EL8HvmNeC7OXuqy8Bln445I+TbIBUwZhK0MQFxsvG6S\ngdwAzyWloeAjAUaacEwD5SicKv4rwi0lkmGhF3RWSA4k3JOY/y7I25hbBdC9YTRCDyzHhDpFxecw\nSbYU4X02GrcadxkPGlVM9DusPwXEf/xB2gjwrF1FB4sYu82Cl1jMTFvOLm9md7xLyKuuC1hXL+8K\n96kbQ7VdTDv+kcllLRhcOX6RFxc/pauJx24jtjAptPDttG5puZ8CGWCTUqExCex9xo2FTHJMuC0D\nbudAtqcib/8/ALfl9PaUXCeErsrWDWFXqTJydOyjNT/YcqfCckcORQ1b9DROakhaxutWs39HZj33\n9Pjbs54HcPf27exberlzvI/8kW74I3nCQlRnsl7MTsysTnT9FLekb3yC589la6OxscAdEI6QzXIX\nU+ySscL+QuHfMVMWTzVT4u86xVQpbUaK+VDxBohiu5VaOlKsGEKiPjBpNlFGjFJTJwljo5At7UBV\nHdZn2SwFRD1kPzwwxUo4zSRIMRuz2Dt9QTEbL7CRruHmUMTGblRWQwqlINdwSMbU/Wq51sPExMRH\n7MyCKdc8za4pTR5SHcfp/SqsOrfVcpzJxFV2Rh7FmcjnPsfObYkV7Vbh9HOqK6pbqgmVdiBZpHKq\nhGH2B8shy3HLOcsVyy3LhAV/sDgteDK71x127TtTnruJ7ZekOK1R69KKrI3afm22HoUw8U8Txuy3\ncnJ/kbCwb2Xpt0z9VnmT30U9/Lgvl7DQJJc2u/CVqjZA+N0NmTOGvPaxKszw7FealhNKlh2mp9KX\nUT7V+mzNvuxXgm3HKAvwPWXLy4QBLhUvW1n2Qpk4MO3zKbWai7P6kC/3Mf+XqDPpbBWFqi7BGmkp\nt+W+7IMUL6hKKqqLBe+X5wxNzV4D8WXwsY8YHzPwf87YUPKQ7rhOGBjRqqmuH1u/RbDBFkyBHhCC\nFLDRwgbyR9h9F+RrWOeZ2FjCZJ6UWwgJcwXGYJdBAgpj7swD+SlqygHpcruACZ0QTghpYQxJ9zoS\n294C/8cikPxAfRMRd3+MA7+vppov0tcgRH5BGaWiyqRyq0KqucgoHSIg1IdLv0gih+D4JArYCXJi\nanKt+Aq2qXLyXqwkP8RRVPlRshLJE66wZeLRyudTKuY9VNaHh2E6eZH7BCEAq7FwWECACM36joIB\n1uJJlBi7EYRKRB/Bg7O6OJV0NoNo5Auu51ArCa3BA6mpVINiIaZISKt2+xAx/8Y/PHuDF9b8tquL\n5//+wvidO+wjKjGNhC8SZ+2XtiFJF1VVBLLjUkxjEmWXhN8V1fiw8pmLapTvP6krfItgZMArJZ7K\n3MT0dfxMfj6/GhLxv+HBwNHB5P0u/0P+Z3ySf49/n/8Y2oxAPipoC2oV0xaoElwO/5KfUkhOQYli\nqoKsIyRPaS4yhs6r1WZPq0dYOP4U0xQEfpy/d+ZMO1MX5DZ+h+JE1FwDJvWUYBBFNdBmuCynl8vV\n3SZhwgOzWJWwsH28gx//zgft2dpb3E326nnceXavJByEcoxoLlkii1lDKRrSZlhTYrC67YauOoaU\ngaGCDQUYX3xASZeYNsqUOnWxoun1b5iqpYfUg2OsQ1EAK4mKWEjAPa47p7uiu6Wb0KkHUkU6lMti\nassm3TbdbiY7ZwvSsMFJGfi8XBRXowXucLY8VK4wlCli1rLlX9saYR/5iSe62P/5s0v7nhr/z/zs\np/qe5NezPpjLcaLAj3Ez+PcTMwLS8Rn8wEi9msybMxByRFUBpW0gt0FcjBGOuFDJlyK/LewEKObb\nQG6DuCzsBDMbjojJkzYgsa+zkfXZOWwNgmwDKcKxTmy5sDXWqAxUPesyvTIxi9lOsWFqZyo7SsUJ\nSa2XbYHq8xjwaojk/wTOUlhM1j91IZsU7xd+XIjg9DQqNRcbRxzCA8aTRzyyjEXpHCl83Esw5MRz\niJKXc4iScdglKL6BylceRFTDLvtBRDXEYcq+BBIFiSPEVFc+WT38OkyOB5HXtKvmYA0iWWohA49O\nZlRK5/HYHghvF4quAfmUguDDeGYvyBt45k7ImK1ZuNAB6QLIVTzsAuGqg2iB1XINz/svCJXYWcMP\nmFq8qGcqO5yyYAxTJcIpsqEo9Aq2ZXWm+lp3fumKAJMRm+d5mp2F7EdT3+jp4l90tDh6/XU6s7ey\noYUExuLehRXB2VX3sZGVHGFf/hdhJTch/FfGp4yEy5Itr0KCCpuEE+P/yhcKK8OyfYIvZvN5EbKt\np8tnFCwGDZN9Ipq5xAmK27GuTZTz/8hkmXLuH6R8DvI5R7khAK7KCed55BCWLpbIDnZHWkqjZxIg\nVBZpe+U0ed0w1VGumTr+lB3yDOtkr7la9tvkpaUSG029DAYHcHQkh40Y/bdhPf2vIH/moKrTjr9y\n/MLxoeNTB1WlTh7VndFRFWX7Eftp+yX7dbsahcHesh+zn7V/YL/JdpkQn4W3hG3UYs1mr/kgyEfi\nlS1eSxc+Gon03f4u/v3xL5cvsDXOrb89/ieOkGNhictWWPG5bAf6Lve8YOHvM+1eQ3UG5fJgV3Jl\nhQkWpx8LlVxXULok17cCE2Ta22QJP0MmoUtTSTxpiGwuiN5PQqQShlOIDXFxTC7LwjxzsNGgwt3E\nlOp1Mk9FATsNlw2rnQmB42mQXkgVVMQyDgVuFqnYIGQ2uQurTi+MIndhLlhO1o4VWI/bFCuNtBB3\nmIeLI7huFVbYz3Bdt2JMkeYzXYqfgvHML/pJ/e7d9T/ZXbd7dx2/enfdnt3Y372njvpuLvRBxiMr\nuNtSAbCxDtkpBX7ERksFlQ5ECk0h9EEl218u1rvd+Vi+RgvwwyNOUunhSExeUF2DUH0SnZCESaAQ\nxi4lMOM0mFs3lMGUQVELT2GrGwYh9IjkA9mZNQGzLWgYG0HeAHkL5LQZn+4U7jWKi7fgbArgJ9f4\nLpBRdo7JYjE/RpFkW77XBf5hbZKtJO+bPKZHFEr+q6aZjmzeBOvLBxT7PYP/O8lXwXpzM8LcYiB7\nEPzok71LnWyiQmRG0LfPmKjLSPY6trKUig+kg6V0GhAJBhG8sZuRkRliNiOKKnrkP7RMJKrZnGUL\npa2+mny1TBq2HbcxabiIHTTbcDCJYu3CZNkgCnZbgbVhBUYPBUmvKFB6cSWm+laQI3AUIIIgqbVY\nmboh3QOnvo+ZfwZM+gzGwWkoFDdc9xHWdj8XFHgBfpWT8IBeALkKH9AoyH4Qqkl2EtV49vlTfnbh\nNThfLoKcnsErlX9WaZV2vYZ2LSfPEzEjeHYc8Oxcxrp8H8QO8z5hlFEjLzumtXQULT2PuMU7IARD\n9gntosWfocUX0eJRNPYayAEUJzvgP+IXHsl61PoeSp8o1WhFN5+01s10u2fWWZXfuFActTUumVlV\n3bG0uXlpR7XbcXwZ/3l1W63VWttWXd3mt1j8bX8u5OdXzewPNvXPdLlm9jfNWlk/fga6LM1NwsXd\nmcXFRf6BFdAbWfyN/llgEsFmpXaYwLkZ0ZNtw8l/F/HAKKmd7HcOISvsNrY5p9GJoO0MLRWI2s3P\nSBuwshSwQ+hsrrKAUM4J1+cWem83wH2M7DTMIr7MCCYqGKlKy0Z8qBdAxALA2kkqZ372aia1avMp\nMFcn/1CZcuTDMZETCT158uFC+cIiumHCCHglaYJ9cAQjOTJkfJJKih00uDOIJygNSGbalyyOYvzY\n5Z9yuWUV1P7H5BAqQE4SR08EeGonJHe5AYlCtlsoA6LdzpYpqGCbFWTkw56WRGcdZRHnUZahkbZL\n6LiZzrFQWXl7GlHxZbDehCJuLdz9+BfS0j+zm/658Zf//T3L955e84eWF1+qmF3xPfZv9bOWwbWV\nsyu/VzmbL/npP7b/tP199h/7uXDhQi4/S6gi37WPf13OI3bJecTEXVzZmogGbMPXugSE0BYGQTpB\nxkA4v+K/oxRjw0Muu4RbFgbsOUAFGb1YioN9RKEKTgcwjmh7H8UxDhf0FEzCGcs4xsm2kjjwnGQQ\nYxkjYTiZsowCpS/nrEumrKPIb7qDQDoNJTyVnQFCEjAHJT3Ca+X8KxlpUA/JNFrRV8FuFXX2OZGe\n5TpDXAkTnSkl7EDVmSqFTemr8AjPqAePwIzXIB4k5R1FpPIdxNhqkJ8U9vX44NprE+OTpeSTkYJe\n+MnDCuLJw8kijyRGX1bCXJWw1ziy75UQWWTjP5yC70MErBIJy36VnCShimxGf5bNsRphY8IkDYJb\n7a49VAuTlZ8fnpJPNe38p+l8F50vLanlh1Mn6tP1Y/XigGSsJ0tX/dT8J3btsdy1z9G1FukQrnUh\n5vgQ8npcM3k59wl1AP+A6gBWcgvFvdKCWcDpQL2aACrXUCGbBCOJBbIbOm/WAky7vIB0Ow81H1QU\nazHLOGJmWwvYGphGyS+2OjblEZ84jRjmuXkI2E/GFvUDAOjKYkUGibFBG1NGcA/b6VF2wmwnrAi6\njWynETs9MnSIs6cxW9EBzFFiXHOYSnzGyKCFMoVQqBpJoZLq5JPrAtIhpKA1hZGElbzSdKuJNWVx\nEzwJ1MRYGM1divyluXPZEErPHZs7ZfWVC9phDq3BAncKxAcRcA3442mwYAqAIXyxA1Cg+kDOgBwD\noUo3vfBu78PU2A8dqg9z4ihIH3AsD5YfxcT4BSbGAegFbcgFPFpxBqEiJ3Oe5RQ8y5HqXvib12AR\n7IU2t7F2ay2CMGov196oZSNjJ5gFFXk6C/IhyK/RA/8AMgo5ZS8izVMgd0FsAbjxQM4gsUrbhoAQ\ngAreo4KCR6Kno5ei16P3ouqB5JHZp2cDwQfJV/rZ7MTTiKc5CnIfRI9om/tICrkDtP6VqNNwMf5J\nPFcgEEpuEv5GNuOPFJzGvDyYrWE+TF2XOlh4tPBMoZiF8jkAEkf3HQDZiz7sAdmLjiR1cz/IKZCT\n6DwkUlJfSRGQk0pPST/CtDtfexXTjqSbH6JvroKM5iSaS+ilX9Tjq8w4iuoGX1ChC2Cd3Ef9QXvA\nH2Cy1Sp01yl0132qWRBB76HP7Oi9Y9j6AsSA3XsoOniUkdTZ6AfRm1H2bnfQgZrZ4GEdo8iuu4Pe\n06D37uZ6bzmKG0S6ETMRvxafItpoH2ZgD5cdnAouMllrUKxVag3GLXUdtTWzZ9hsM2bX1HbUWeLT\n6g1Gf6+8tKmlxRJe0VFdE1vRHH2xquqpzulVBms880KVCgesDM0tnVZoMFxRa89X+GFTWygsVxjk\nucoJH/crTsNZuVlvF6t1qvpYGaL2vMVAZ1levKb45eLXimUEq9HiAva9iq8Ww7AlBhDaZhULSFOb\nkhIbCWWrL4QjvyoO2ktc9iKjw1UU9NRWlhaWGd4pNuVbnSazz+3M90QqzGV5eY/ltZuIXxopADvF\n1RvrXfWiUl8aFv9nCFPv/4e98xBB5vEADVeR9q3KSAHoeeeYkpSSEfNEUvbVaW4yzDR/qsWXfG8a\n2VhGPxT8xlNJuOT74sciqky9qdqvOqw6pVKxyQMsYdLCku+rPmZqWVKlKlWxgfg5Du1Q7YOmNqqi\n0t2AtOSfiW9qSMaEP9gQ/iFhyPkmxsQy4WPG7T2cX/gEUUNOF2P5Vpg3gE+b3GNNsAUc0erZ0Ak4\nzYrSiSKgwmK7Qg5lZ9JSgJTKhMuYcGdiBYNuGYJ5t/uQWzOQvOWecAv0x5q0VKTkDyMgz5eRzkG9\nupVTtIagaNVm8Ed/RtpU/9jw7VyIUlYMThVVOisDleIwfKAISJWWo/2jwgWUDNDDF98jLBdUwwpE\n0MfCZ4JsBCjJIFYDK1iKyzPmufLEYamwhJMFSlQRcmakGIJmKXK2ykNLWTX9MH0OJxAIhoyYmfDT\nJ7uBxaEPFosD0JR3gXwKYkPaGCG/DUu1sG18Con8KtQ0NSq79GBrBchdEKr8c818x4yX0JgtZq85\nbFYNSyuoNhDkLC0UpjYIO69SgVSQdci13uU96MUa4L3sveFl3GUVen0+SBt6eQvIEZB1AKna5Tvo\nw9m+y74bgMldhRyv5WCIr9XCrZuqHa29UHutVjWAo8NSL/70Sk4dvAc2qqu11bLX6vYj1T3lH/Vf\n8F/z4wJkWvf6VyDT+hX/637w/ecxdK+il3rRS/up/DzIVaj9UVhDHCDzQerQUVcReXW19HNEVU72\nkhadRslxFjOyn81rESJIMfNaqJc+kKu5EDL0VlVVayQ8CfU6Ge+u1YY88kwReSsvlo1/+PNSb6G5\nvKAqoGszLAqXN3utRkd18Xc++vJw+5MVL4WfpJjU6M9KAxX5lmJ9lVXf1FJYVldmq6txGV/6lP/q\nXO+qL8NKzKqKi7H5JlBsayFn5uz8Fyg9rkVYUJERBsVsgAlT55YgknZJvuzagh8iheLPAYGNbznQ\nV2t8TJAv4owIbZbQkqRDRih9wFBJ3jJNmNgQKhg0rTdtMm0z7TYdMmnoj6VpaT16ZjNIP+seQKFY\n2aTEenprOn4bU94sBGmzKZfHQ2APyqQkMxImZSF7g0L2BgEQrlCPGTMXippWX5gFlc6GlKTgZw2o\n2HtNDzGW0TglF4zNMZBiM2VllNAPqv+Zs6XiYfdj2pqd5l0bKrm8atkCNeQI3mgdDuyyHLRgcFtQ\nxRyDm+YMXutVkCMg65BWuct60IoTrZetN6w4EWmCyyFUvGajWWAbtV2wXbNhUOfSKF6xKSLcPZje\ndTYbkqVfQyZmyj4KM/w12DOWI7oW+TTSRSTVuLXZgGh3qymknhx2whvCljeAp3/i7b5xz5Rhxn/1\ncvgrX+ngC7Y8NK54LsxXcf8knGVr0atw9mQRPuVVKAl/tjDZz2wMZAFc4fLWZJekFAKRlqsZ5+vM\nVYKcXKAyKA3H+B+IQHZRyQUWK9I2rVIm+Q3+iRrMV1ETSdd18gd4j5DmCrgm7n2p1iTWJzEI4btG\nRo0pFzYnpWvlLCgHY8YOOdbElC0sB4CkhrS0CSiLh0KPwGqx4WZzF8q2M9balBzbICKuIaHLpOC8\nWq+TgULL2V/L+8uHqBicfI0zgPNkrIbGQgTKJj9ovAlgcMA/Qmu+WPkJ0rR2IJMoeb3yHnY+gA0H\nqQgmkrlkU7h2sq5zy1TmQtXuUeHpYkFdbYXH4nAa1c+UeitKSmrnNltiZTNKXI7ZLk9TRZ4ohEW+\nsJlNdX1pSWlJscNZ8LrB7DRbPY5CXdRY6LEbTWWu/K3FVQU1pZZs//4+698izs2NSUZgvfTDsHoC\n60p/lgMoHs7dsMSsl0FfAAg9AX3lEFTnTbksKcJykcHKUoiyGALsqpMAqvJYX+YN5q3Po9iu5AHh\nCIwJZwh7LOdhHoU1nYBb3sllQ1HU9yjs+e/AoLsPBKbd5NGCM5D2oSNlyx8ezd2NfNAH6XK6qFBB\nL1MKaz2ionuLyryWhra2Bou3rKjdVj+rxj2r/v+y9ybgbVVn/vA9V6styZKszZYX2ZYsyUss2Yol\nWzKxvMXOQhYSHCclk/wLZGEZyAwkAdqGYUqWzrSkLc3CAGHakm06g3yjOEthyMyQhbRT3CkJCbhN\nuiQkYWhoSymlwf6f33uvZDsLMDPf9z3P9zxTmp+vjq7OOfe9Z3vPed/fW8D/en38r5hw+Iotieqq\nuKXIZy9rBFdZ4xjOMpUQHPmVWMXeE4r4eiisekxSWVU1KasyuqJdLoJdfCuHpDnsTrpnuRe773c/\n6t7o1i3EVtt/QHs7y2HguaJU0aEi1UKZyEoSirClJkeGeBOgRVcyGLFflv6W4XkDl8E6LEBihm7D\nPAP4rEEwsxJwwgCLYsMruCfPRJtmr0OH/Dmg0G0iBv7CUU/mJgwxxfKND0MBegZAeuRBwIZi8OzL\n1uKV8m3kUbO9EtxglWexm+KXa/ak/7vYSlmJNcXjWFNI/n/lCekf+3/uJ5PX/jqeSZhuln4KFo5f\nAyhI9hYK1gVX5TWARYCzAKEhs2fl5C3NmVHys54oghOeKNJpiOnCqKz0TjrpbzL0GPoMywx8BL4A\n6ayHdAYMR7h0ZEN4KY/ulM5BPD8hGckZzoNcKDKiTCCKqcw9mHKHYFcD0nO9pb9KvCIVlxJdwlNQ\nwlcXry8e5SCvpG/S36p8HjL6JYQ2CPBX0fbnWkjoDf/bEBkFq9+OhL3+w0igEPZhOYOfht8Fm/JW\nSGhnWB5iGoUu8DpQVO8BjIZHsyRIH6AfTNZidaNdquU/jOG6z74M7KT77cewIKIwgB9g+omVdZfB\n3aJsBzbOTsAcfVP5duyZEa/RFuj6u6DKKjEbt0CV3QXH/dO1F3lCemK4M4ywY10Q+wcZyoQ0Qs/w\n2ykA0TFAD86u/oirZlz1YP+jGQCXl/Q8+1LU6yjWaORmgrlRairLOtCnN9fsAC3qZJQfIzd3wAVA\nF7hDYxxYhpFFoUDN+rC7dApBritjkTQxwN4u8mBLLjyztKOsvbgU23KhWWXtZRsndGLfrqLU2VSB\njbsK9lXPnHpyTy/o9HSWzQnjuqiw0zM3AOW1s64i346LtnF7cN/L6oU/Jr2wSGqFmr+Iw4AlVhYL\nx1TYy28Z3cvvGT4rJmift441fXp8u1Y8NsW3G2VQLaNIdxNC17opjuMx/+T4diewEjoP2Av4VwAF\ntTsIqJWD3R2vfbNW/G+FuJOJTcG0nIly14QNmilXR7mTw9tJkz3Y4aAYd8QtIe3H9sp+6HkU606K\n0clVJvHa0HeOa0LfWf8Hoe+Y9Rqi1OFvZOIOPSYyruObhT+DY/AsdMmNZOE/lH6CbYO3/ExS+YkL\nsV8tUyIa+JeGbWTZpfCmZl3uiGsG1pbgRdPmkbmRlq+eNOQ3apWJhyIRkbV/ob3u5pjn4bve/ZLK\neOV9dhAV/jzitkRHFgtXxLOCVbiA8w64pafDeUmYkOQOkY26bLLeiivYreMQuFlgZOSpkR0FDUPE\noWoNpQy0aj4HW3bzSraWbWLbmRzCSqe4HDxPihpgE/GlcCVaJAruFaDg5t9jCQGHrvSA/giaxmR9\nL5Tb53PAEZizNmcT3NBzBnKO5JzMOZ+j5jfk9ObwG87T/G981Qh1cBW2T7qR33msErqh8L2OJziX\nmw0eD1dL8DJdKauvsFrLwp7GjqlTesR/jiy+/a5Jk5Yt/rOJwT+78PADlz5P707P5fQjktN/ZORE\nh0PSWcCjeXLc4ykY5MICxQ4h6QhQQaQpXILEEztE7knwEuCi4gKDS8YGtpXtYqqF6VfZaTSB+RDT\naJQnihc9H8vj3coR6AppK4Q0H6rsbrLofyhnQ87WnF1cGgOv5pzOuZijIj4Dng2e/iIkM2A8At/w\nXhDGDFiOWCClt5H/BX6La5IK+ivcUXF0qR8nkS9dRyAawT/yc1WR+Eu+ynEJNUKrMEdsk7nNCipA\n5Y1QpzMBjwKSGKzvB7Cp5D+sWPRJScD9TBYeSIikXkgQc4US1uhRPHQKMAhYLNJ5XLpAFQRf/lS0\nm17AWhWZrUMPBK9EqsKCZfwIvRyZWS1Vy79CPKKpYOuURuDYNwuBWM/cmlmrzuQda2ZG+yQ7Vzom\noPjQM+G+gRgKJbTJdH9mfymVM0REhEi38WyhhfB763lqvXzZxRVc7LLO7EL8An5LQrb4P4LnPa/M\niHyKE5bC9nIzEt4AyL4OOYLs7aBeIT1E7YF8dg4wtJaLiAuHmFjpPnEZ1s50+LqVXEZGQ8zLJqJH\nAOdpPMxy3MBJgpegkv0k1CvS67Vb+IogvdawiY836XWWzeA9XGfdjCO6Dc6tTnwX2RTBn4mbJuK7\n5s3N+BTfFMctk7ZOAtX5Bu1WZLPBsPXabNY6NyGbr9Q9hZhDa5s30e8nbeI/dAWYy/Zp52af8v3p\niVy77bR5qgsKqj22zN+WIjkGXFHm73DoU2/xFIlvF338VmG1x2r1VBdm/lYmQ0VFIdxCf9n8T7mB\njx3ERUOca4v+65FIpMuwvbzPtAanpSnTIfxJmmaZxLHGrjLjzHi6mX+5mv+MxrFbmVr4F3Y/r0vP\naF2SOZny1aOlp89oL+MtnjFdRpnjaiDNInMoGFeNN8jPFsrimVJ1QiEfK16jsQJaZUjoEGYKt4t7\npFkLePnhOAfSM7fh8JAtiCsDBLkDnUGTNzMMn0PpoNCERe08SG+tkI18Njo6PCqPDtK0zIggPQG1\ntRVwH2CjosAqo8SAoLaoy9Rcd4cSC02M/0JRZ68JyhhGYFbpPR9+5rP4ynw8LU7GOiOgOzRXe6pD\n1TyrjbN4BrMsqU5+cydu7rR0lnXymxdYUlP5zVNx81TP1NBUlHsnyr0zMwAt4i9zUWYAauQfGkcH\noEWZAajRkmodkja2wjCIlxFAGQFLoCyAyJGD+HoCL2YCipngmRCawJNNlOwYTLVaEIyJZ9XNf9mN\nX3Zbusu6+S087VY+4fOxMHUz//nN+PnNnptDN8uOV0sg9PEj1lVDlZSDoXs1Xtjy6w5UU/BCKYIf\nTunSiMMrXme0moK57pOHLENmyJqi0qwY2K09qD3OG+zA8/q9+sPwuXveuNd42MgvducdzDuehwv/\nQf9xP74K7g0eDuKidm/t4Vp8VXew7ngdLiYfnHx8Mr7q2dtzuAcX0/dOPzwdX804OOP4DBWNUMSc\nmjkOumr00XzCd5/0uy9i1GILmqurm/EvWlDVVFbWVFWQ+Tt8z42/+u4Nv5JHsCPV8Xg1/pU1YaRr\nKvM0YbBq8twgnW24wReKj21YnC7W8fFjuqTC+DGi+Ehe14y8lYYJAd597yn3qYcUN1vi+JetJmX2\nfJk5vzHC7v75z1v4/9nNwJ8LCmciyo3QGLJEMiBSGxH5YzlKfrEGMtek5TCYFA2wy5FareQpa+Dl\nkw/USHbRkRRld1kNsb/o5AMb/Mo4BMwb5INpuaPcyuvjUOpG/rxUs5/f08KWPv10y+bNVLcg18uK\nFL1M8+nxLJI41yWVDPEsKAqSopc5QjcIbpH5kAlu8ZmCWRB19oAc0GIs9UMmjgXiDN0onIUcYiIT\nzgJnpWKW8GEKAlookSyaoXwpQSwQtSLd7JviE1dcG4zCek0wisb/fjCKj19jf3698OL8XYRGPmID\n4iBvnxPYc/IM99zoRCuHJzPI5G9PaLdpNTLt/sDL2te0Z/gYorDuG0MDZ4yXjSN8DEmbjR6juKLf\npKbVvJ+PjdiHMfs9fp4KyxQ/toGlVmzwnCm+jA2eJ4q38T/9Raor0n2hzNhOZB/X54lDcylgV6Si\ncjKVKyItClQ3GW4bInMkCsdXYaEwH1Q379hGzyZwLCE9hXOHO7EttQlgB/PNEfdJ93k3NKal7lXu\nde7Nbt48tsOw8XEoxEuyLPAna87XQCcgthsHeBO0AKK5OUzRHlDw24BlKGw94CHAFpx0rC/cgs3C\nZZhBTyF23Qb3VpBGPoyEHZgzl1Wvrl4PfvchFHeq5gIvrnIcOx5vGmqiJ9DqAlmNXO1gtZk1RUFP\noa/aW9A2MdKRM03rbewMBCdHSgp81b6CLJfenKIJAV9VXWkk2VKfE5hU43TVtvPBrKYi0OBD+6gd\nsbA9xLE3gQ2TPTeZ/KUvCyNYfZuxNwV6dn6NOOpZhnZyB5UsWi5TmXJvTFtJ5mQ4cNUZDtwxzeW9\nDLP1mMYSRmN5tHgjGoscP4Eay8wQux45H7WcseR8snlmptEYy0cbjQNnWw4i6cdL25eNPEhBB4mw\ncjNe2uN4aUtcK2FOt2nMCwThp/QIgF7lesB+wFa81Ffdp/FSwakkXUIqfD2knWhOu3FsuwsGvHjR\n2INH0yJLlw2AS/Qxs1soXaTtmQLU1o3ankI7I6LSTajeOjTl11HCYcASwFrAAOA8YPP48unYeDus\na17PNuztaGmrqtehOm8j4QSK3g7A0Die4NCRp1HmZld2pFI3Doy2PVZwo8Y3yoY4p7iWt75QybWt\nr75SHp+e4auhuxRf41tT6pC0EcYVZ9XvwbjifrWsCcvHbUNyOF716JHWYqjzZ3MyjYSchIzyAlGt\nxDCXp9IA/8fqWlq+3tIivvLOO+/IZS8aeYDtpFiCOuGv5WiC14sZSCzEcEwZSGkOaQY1/Es1RUnv\n1/E2+lxONpZglrGYQpUjCGEP68NKb1x0Qv6NpknTo+lDmPEPEX1PrynQBDWqFYhBiH/f7uiYfQfF\nIkRNeT25jHYqMtrCnzKNo0VoIurLathYDFHUdmISkV5QgbBKRQHbidmNJvMXdDxVy2ubhMSeu5HE\ntIrEeBVVMOfsU6GKcK3Xq7DDQJXXwcKzT4dvEIBPryvQBXWqFSyi42LWedmdSu0zgh5f/+e4qAZe\nFl8Tz4iqhf9tgdNLp9c/hnpOPkv9n74E21UvgZ1W2gsTmthp4Yf0HLeDgRQRVfhAda3saRMzDbd2\n6KbkSS0JmsyxDJnWoK7i0CeK2saFuUGW47iyV/KnJb/1NCwWwMYqXsYq5gXxZcUh/3pxL1ElOfgl\nakL+oteI7hMlBrk8IsuDZDHygPBD6js9qM/LIrth0TPHRdu8cQkupYS/xfPewZ/3OXreEGQNV3ox\nOwZIT1Dvzzq4ZkVKbma8r99B/Rz1nMbhVr4WdQnbU65Qv1N2b3RZYMx/CPYcZzC4m2E8AM8e6dHC\ncRSq1/U/pIM1nKVpDXQWdh5T4U+0tF7utzCZvxBbkk1opR9gFNeCZJqMK5vpow2mxJNxT6OyIbsw\n3ZjfxZeTiteeQn5HWztW70wVaxN17sp6T7HDZCr0R33WdtbuSUbm2bzFVm2bpmTCxILhDwX1uLil\nE4V2VSNFLpUDlsqRSrNRTJUApv+1yKXtQ+nF7fcjBoGlnd+WaucVD7cn23kTf49fp9qG5LBDwUFE\nGq8bkmbV8U/NPLWZ/61D6PPUpJA0axJbAa19BLENZrYuasX6opWntfC0Fn7nJEvqpiEpBnPXXtht\nngWEYTG8uAM/6VzUCUaETixPOl/ozOz+/A/CnBZiGzS9JvpElDc0rwVPchYWocloG9YTyRCM5qKW\nVIT/EMEphYglUhZR8TVhs7yf2jKIIEqTiL4GvvE3DG6qUTlU8ib/1cFNodcrFvTXi2yaq3PfMLIp\n+IPSOfpCHA40URDTa4Ob3iCuaRpxTUUloKnG6rg2oClI7saHMU3H8ruh/NwwkOl5QB/Oby+SyxDO\n8jpgntsIOAfoBdyKXa5zgCigG9AFQrlzgCigN4HFEeSXYbuTHSmkGB68RzFl5rqWdcqo74QShhT1\noTCk52JKsenupnlNXHxN8Z54X5w/zgUU2YSCLgCmJD5DQNHsl3zOozWTDgYoDu+nhxJlwanO6nKb\ntdhrXdjy7Iyp7eEvtK28ueVTA4guUeUVu51Feepooi0e1fzzgQM0txaINcIp8SJ4L9gjktGJ9bvs\nBUu80ljgbuQrSJzGa8EZZg0V0C6AtAjqTAjkcJetIzhUNON6m/UFfg1jC7g0pVuLZsKfJFnEe6Zq\nCLYaORRSj85fQKIj9eLUZX6GlCppiuV1583LW5q3Km9dnnYh/2zuNs8zLzWvMq8z43OTrcfWZ1tm\nW21bb6PP9h47ztpX29fb6XNBT0FfwbKC1QXrC+hzIbiSlxXC+oLyK+kumVeytGRVyboSfI6WTi7t\nLV1SivBeWt5K6LSoj2qGk6F5qNk8qllTXk9eX96yvNV566lmTeYec595mXm1ef11axYr7C6cV7i0\ncFXhuuuWjLCo80qXlq4qXcdLtn3CblbaW1joxT+fuchrs1cUmc1FFXabt8gsfqWwoqIQ/+xed16e\n22u3ewvN5kIvrTfKR95nf8PnrnqxWzIjIix5KyEYLLmwgEpWr+YLIjNFh5XN9aRFINQ2W/rLuHJl\ntqTqB1P1ISkJy5BWmHediVCQ8ch9kTWRJyJqZdgkr4/M9EYhJMhUpM6ueIHBPg2H6jhXNciphpBU\nYK9THCoRzkTE/NBfJF7BfpFvSHrfJx/r+LEN+n3olEPCf0KPBI9X0vAQkw/6DjCNfLKX/gF7i8GE\n1bQOWy0P5j2ex/8M5B3hf6RfocmewPi0z3oUrRXBFtOnnBeg7VPIhp8U/BIhG45kwtZK57L60UnA\nG3BM2AnoqcCRIa4uUlzZQA9iBFzCAVYTzCNexy7U24BGuKycphhw4I7hQ+Q+TJCrAafoCvXewnYi\n2vgxdopd4EsfaQNWKAcAYApL42g2+1jSqjw8Q95RPNSr2ILfn9X0aD+D/DUvEbjGWDjSE1CoOFR5\na8UuOKRMwYO8OfoMaRgXw+kKT3EOD3AScAKxISrrG+vFFSwT+NgJw45rSKj9gUAmZo/TxZLmBp+V\nLy/q6jrq2zyR9orowvJm+03V9qDHNrEyaawKFJfUTyqvn+tid5ZU6K1uW3mpwWLoaPDHvJaqhkCZ\nP8dWavd6csx86JpQ7otWWP11sPegdk1+AP9GvD7zFgqCXpDek900RWHSyIPsP8RBwS7MVXkkfye8\npDp5G+/kDZC397OyaXt/Hr/OUcHGULLTH2wmzB1MzQ3RJmOnJTV9MDU9lH5i+rbpIrlPSVWdfuW+\n2sFUbSj9aO3GWhEZpyYOpiaG+hv572ot/TfxW7vpVukRxFd6B7ChGyvDenlLcjbvI7MRGHUbHJqS\n9bPlJUJ/DrsiVcyul23XpTX83fTb4WUsJzlCqdnkMCVVJclot1H+YWMoVW/pb+Lp3RXEKroOBT7Q\nLXcgcofaTFyfmO/ID35n1lP7EjYyDsIg+yAmvFdghQSzJGkTYB1gB+Aoeedga2AfIIo5cQlgAABi\nivS64Gb46dA2AbZJRXlzYOD5ur11h+v4gnFLZCfOOk9FLuDPgcirCMW9C4ukJqwDd7bvx8JwPoJT\n3YUT3j9N5rAcJ94HAKdm4AqwG3B8Bjx+Zh6YyX9zeiZP2DULTXYOKgvQz+XNOzgXK+WlEMG3III7\n8PTk0v8t/ahBzEEIYT+EsB3PehJmWevKN8Ms6ygJBA9/hMNA1DfZ1+tDWEVfRgD7AFsyZvbSBsDz\nkMFAzRGKLE3RrEFXcgEj6G6EIT8eeRPP/ndI+D7gTkiBREGPPxXy2N1+EPJIIGEp5PEB5LEMorgI\nODgVLhQzTs7gwtwxY98MGJRBDgOAHYDtkMg5COMI4A2ACmKxAyo52OCJUyq6MiYLgVidalwX1+qu\n39FlY4/R7v7zkgkOj8/SbnI7TNaiCmvzFo21yOfyVrv0oYo/r46FA36Pp7HT13F3Qbt1SrW9qsLh\nL9sQiCQm5BYVWErCrd6OBWb2VWOwzOFx5evKNPmuYrOz3G3Tuw/lOm0mZ2mpvrLK2uRsm1iXdLib\naqpvqrI1JcpCwdyCQImnytJhaw03JO06W3GgKJAI2mMBrG/auJ55guuC4GB7ERxsijGOHLjENbpz\n/YJW3rO+rB3RGhbK/HCfKZoJDkhMXHM1HTIN8mVxvxEEN5Z+M9dcX8CEowQKuaxwtfExIC80ntWB\nr4zyLBQjBNqtqcnUY+ozQbvFcltvKjAFTVx/HkfTFoscz+ykTYZS/R1lx+zjF5W9hiiHN8n/aV9G\n3yclu5+pwFQvxTH9vAYAAYq0FYBIVZn9E67kqkPpVvVMdUY1IqKrqx2iyK6KgckCIw2ZR+oxX0eJ\nfnPUZInP0WBdlLRYXkG54V+Ka0VZw8GXGaJ66Q/QY/QqtoIxL8hb2JvDv5zNvMM72HfFxz7+kvhY\n+2gMTYpTXct+IJUEsG4dwwCRhFfho4DnruaCIBqIN2H4SNaP20cJIWaho24cRwhBHBCZhyZ2CDot\nGscGgWhsChuEndggpPPI+BW7PPz2UvQjsjDKMi0QycIxABGQn8J8/SGgAAvuHMAHhZmVx2GYLR+G\n9+x5wBuAw9CO3gD8AYDY59IpjEgfAo4DSG06DtiPEelVwMUsUzi2lgderTpddbEKZMvVfKFxtPaN\nWhxZ3Iq69mVDXRzNslWczPjppLVOJ9gqfl+oLCvSpzwXcHx1CpUhb3DyAP8I1TqdDb5+AZU5TUNm\nhp5C3mLeh33uN6rerhKvCbsec40NeCFH/rNFVn8mJojXFAKICoUQ4jpEECzG+4pm5NfsXvGkoBL+\nVfHPuIKGbBy1mhJEUmCkK9Cvjapirn5fvfs1jhqNJQvBOtorLBFWCmuFTcJ2YUA4IpwUzgtG3vQz\nrKS9oBI0gZ62ly1h6C1aPpKzfTBbewPdZQu2AYJik9gjqviaF9HE5XN93mWoXcEoI92tmscrlN6p\n2q+SjX5a4yzUnG8TT9jJPhYd5p9Ufyn62WOKT/sZ4QWhWXoBFtqHALPA+oKABWnEIUQjoO8WGaDe\nGUZg274ICa0G2XqWCXN5nlV8jeVhd+1hav1ofKKz2ahEENhZTLKz9Iv14kIlQohMqJiNE2Ifkhaj\nCyTt8s9KFfdLYk8yjt09g0ZRqgYPRao0JIceIoUCNhqMWCNKiVTJYVUrayWrpb9AJr5ZD7s14jm9\nBKqsw7YT8HA5j1n/DxTJBVU4AnDBYe0Cri5iOfAWYCfa/YNo5ofdJ3Bg83t0Sw1ObZwIjTHPvdQN\nma1DKedRyjrAryhnwO8BGhwyHrGBidc+aD9r593OhsIoisz5bDlbAQc4DPS5l7lXu/ltFISjgJfE\nvNaIo7wxIscwkefdMUFMdu/8ePjWJYsX28O3TKqdXRqyN3nroiX6nWzm8LstLczWMi/c2xYoLou4\niv3NycLZFGeKj6OTxD7BLZzewzQ2dc1ogKnMGx0TZwqmmJJFIJNLaHDb0Cjew9MJNvzAthhyPaR4\nDtkpIIqTXk/hoLQmE8xcID+gzG5bRm0kJhFMKYVyhBOTpV8nM2XZBrG55hykyEeWPgQNXg3VZzlg\nA2C+ha/yBqK5k3N7wdONm5ZZ+IITbLciAlDRqIKwvDSGkPwiXGLxdbn5hWarz6yqnGirqnDefnv7\nOrZl+D/d5fm6XP1N1tzicIAFWr78ZdlefKRYTIjvk13Ccq7zpl+oe7kO3HBD1zUaHzUVh13CGH+8\n64a1IbsE/WeyF1fMES7VsDH24LJt9hTALsB27Pptxq6fbBMubcKIvoM2u7JRHaZmQzvsgj68v+BY\nAXTm0iNwoSJKnyMYwrHiTx+rPoWDv4MZw2/pVTpz3IyZu4m8gshYklyDUPZTKHuLfiesIDabdkB5\n3YGSyHx8B4qDu55ifC4bO+yvPoYiTlcruZMB/LUG5tZPNDB313fVZAzMneHQBPunGD+I9cMbSgNO\n/TgL8x+hb/h533ibj3Ex8XapLKiqgX80he3p1/Ilxn0Kw8dC4uuAZxz5IWH9J8WCZRiDYiHJHaOr\noKW/ii+syiz99WplaWHh79iSGeHIeRojHLNUyvZ6KYuFtEGjnGAMga8xyhNyo3zQoKiCbkY8GFWV\nYOVJVYWkxXweRcCsWvkM4QQcdrXYy3odqg8ZbDuxofU2rh5E34VXU3pr3i5sJ2hwxrAUb8fBO/PA\nEttK21qbSj5sSC+xr8Re/0+gI96KnQWdy+XiSwAKV+XikD5Rcq6Ev+rekiWgwDiBl7oSgJAL0o+z\njBjnAVrsPCDqgrSK4i+gka0CXMDh/kVspbwFOAatSQ+Sx1dh6XMMmtIxMDh8OBFt7xQeUI8HfAMP\neAGPdRrgxlOuxgNuwANuztuBB1ySOTpJr7Ktw0BVgCF5Nfxh9K4CPM18PN4QoADPlQMYgp31CSwl\n5wH6UOHVgOWA04AcPE4f6r8asBxwkQCPcyqQeaYjeBwdHucoHuc4Hof4Oz7iYItkwkmM93p2jYae\nGBvty/vDSk9zdWHXpAmdztay29uqpzaV28prC1SlDT67b9ItdTMeKJme350sjVa5ShvavB72ZJ6n\nwdvcGvSXNccKQl21JRMR0VhbHm4pr58+sahzmitSb6+c6AnGKszkD107clblVPyhk0KnyphqC0l6\nEABW1fPekPNprtCgLh4kJ0bM9Gqa77VDSB7jD01ESRZsTKaq5H3IikH47duG5InlBWyQtML0uz7j\nEFbIC8LKM1lfJW+bINg3V2eT9JHf1jCEfOI8hzhIEtpSbXyUbsPhk3QZyvRrgMWAJM6cOobwZeeQ\ntGZyZpzOhtsa6/t2td80+a0lyW9a/1n9pkvoUOs+NIQzAEsJ7YFaQgMzLYss92HSMls8Fq5WlhBD\nKihRpcK6EgrmgkEBp16tCN5baWlF+a9iPd0g59IQwglZ49CAudHTGGpUUSbtg6l2WnB1DqY6yail\nsYx378llvWVLynj39iPm8FyEQT1HvHIAJ0VFBZxEu+0F28DbuOoGucB56oY4fDkNuAQ4hvO1Juxf\nrAbsByxvw+DStqsNPtttp9sutsGPog3O3ZD9euxx7G8/hj2O5bje2r6rHbuQ7ReVbaAVUi9O7NZ2\nkHN3x5GOkx3nO+Dc3YHq4atVgB10Uyfd1Hmk82Tn+U7chLO97s55OOfb0YnhAk+a7i6bB8+7GK5P\neS/A0K4ADzqfJIBnpEeeh0c+GRz7yANHmk42nW9SyQ873kO8UgkJM7bvakcjll/VdW1wJH921JH8\n4/6CqkSVp6TSHnInq4vClY6Qt67eVdVYGplmixhrq80lBeacgurykuh4X/P7KyoLKmxF5f4Sc3GV\n29toEPXNgZI6j6VqQmlpmdFWaDIWuczDJaO+6BWsiHXzOa2e3Su54VpyFotLC9ayKcVSmy+3bKGM\nSbf0Aga41grZBYRWfRlTbqm+gjjj6kOSs56uKkAugiy4Mg2eqzTOCUT55OCq6GYe3gmwvjgDYHke\nedKTY9GsGJDDAKjg9gPzkzIcxeTKN+ViIkyFh2R3PCcLUyfAotnroU7oDcEd2s87gd/jD/llX+hK\nayNOJWW6GpXC6HEyw+OR1tlddj78v42hfh4G/RMYX96G0noK49OlKrSfydjOCVhjsCA8RbOGvQA/\n+yPyIcIBygBRmdMnqs6BdpFIil6to9H9v9BAHrtxe7BPHNsexMLyiiLvp7YAmVv3pJhkbvF7Qolw\nSTILfAQ3w6lHpuETvpCy42gPjzILuph76Npjb3s2bM6A2eAxhAw8TaPElMWXJXx8C9EulmySsgxG\nKQ+RU0yTukfdp16mVisu529TSBUcAs/LX5q/Kl/D74nZum3zbEttapDZ4qR2ngrM7PSVuls9T70U\nPz+a+fkAwnufxwH26nwU0Zw/JX9+/vJ8NYpTDuPUC1XEQEpuqNFYRA4T6Kf3UK0yOT2u/Mr8/Aqz\ny6Gp4R/LnPyjtcLCP4pJm78kPy9Hb/bY7JUl+WZ9Tp7HJusBLKiCbUme4GTbUvkhSa0BFakSbZer\nSSFsgW0kWwjF8oyUqkfhm6iBFizNhNXGNkBZDtemFF9/RasyykTnTJknFaJwaQ2WLwiqKc0CMXH+\nKI+jmeLGclU6hNe3puAa9g/rEGzDNYOw53aQC52YC0164Lj4pngJJgbwdEo7xEp4D2qV77Rvai/B\nccWhrdTy5IcheKP8Xdw41bjAqFqRThinwQ/uTzAWMBiLjDU8UXoYxgLfMcokEQ6q2Xt2mDzYLfYy\nO280FrKuMjyv2qs6rDqhOoeoVRQ2bx2gFxPoETW4bDFhoAm+qjmtuajBq0YUEdkfKmmQPRVQS/77\n+Ub0UURvTb9u/BUqhRRYSqu8FPgzYiMmD0QALVc91yL+TeBvxJaFf/VXCz7+zeMt69kUFmQLhrfT\nv6eHX2Et0eGn2JIo/Ex55/ktHzPL2FelQnAvLabVR8bcBwqrXk27ptbBlDVE72TAbPVYQ1bVin4L\nbaj2e1RXpD956G6ZNEMYPSy9egMJ4WtBlPQ+0X7aidHjACTzvoqsaPpNXA+wUDpfOUil8h25sLt5\nGEvu7wOIAeMpAALLY95HjARpOdE2QIqIKsVf6ztI6KOlMxbMR7O7JG/mX4IdxDKswuiUaBVG2T8A\nKkG8shbrZOKOjOLjKlytxADYDObIquLmYvipH0b25wDbAUsBy1DafMBFApT7JuA4CgfttrQEBb5B\nmyfOTNEoK73KtQ5GtmB1KS9vZPKxxTVMQeXst3yadFvK3BaHv6E4Ot0w3fyXt9TcnPAWBiNFZ9gD\nc5jH4gvWFpTUlVlbIvqZC1x1XXVVnS1R90+VebKc5slSdpfMt/UCOU7iGc+Ca+ezUW7NwuzWiv3Q\n+8oynZJCQSu2htdjx3o+G7QzhsCtMkUWefdKGqT+SpQ7/41JsTJEiOmt6l0wg72E934RGtIlizLj\nSYux4WSH5//vcaXB1eTMTlQaNFbiGBorhRAKnF2SX2Hv4jXJ8kPtHM8PVQijjrdR1nlo/KP0WR/g\nisJGUlkghkrm6BwuR8ARc6hXsE+gfmLdN+Z7YuU3InmSY5lwPR6c7x7hB1KRCe60RUR+2++WObhf\nGMPBncLLGgSMQG/dWD5u5ZLZrMkyKY3ttZKK2JOkl7An+wPVW4jhcUlFIVz67SIOhbj+gsXI1NwF\nueKK9FTDAgMX8iraFIHxjOIPsg7tvw9dqo+vKYQ02PXQArBlsip/HW7B9km53y9zuvsp3IuybS6z\nvbOnxhBsG0vqKxWK7SzBNqsa/jjQUuUgiu3hb9ri7pERWU50rn5C2ScGH3Je+v6yR7FkTpYxiqtz\nzX2fo/sK0ovK7uP3Ddzve9S30YfgX4j6oHCdyr85mP3NHfSboPQCF/XAa2Vnyi5DFyHquRdgbGJu\n4DrKaw1nGi43qMbnMVruGspDJy2qyHA6IC7zX7P3R/ZynbUI7v2aEE3Jg5h2GbaWQ2Tj6Sh3lLP3\nh43TGmgPlMPrvH3omGaPoGbqGmXXPweNHCqt7EOHNj8gx43jwzosU9WMepxAi1S42w/IUfl4ZyS/\nci3gvYzt9Gs4VwkD7sfETyT6tA54AhDCxxSuLDkZ29pxXgvZZqYWiGQf8cmko7B2364egDn3CfU5\ntagEXaStUqEPvPqyu7NqxWjEsz/A6gQRonkjRHQz/kenduEPFlniinKvKmLj/zHn1H+e+S3xW9PY\n68Mr2VcERVbiK1xWZvbkHr7s4bJS0SLSPCTdj8F7FlwQzprBusAfmeVRVbdj9UMcApsBJwC/Umzx\n+WJIoyZjhjcxdLwD0+etml0wfV6tWa/BuQFXzCG1RZnYjDDjRqcCAbNBRWulPAs8RGT+7EF0KMzB\n0hOKodpC6WVAK0zWnsOVBVdy4KxrKPUz+9ASM9G0+jqkNZ8tx1b4g7A3lqPW8Nb6CyaTWKsGJY18\n73fxCA/iEeZrloOPZS1tvAEoZNEvKMgMMVvzNZhRIHagJViyDBn/k2LN43qisdMo09lSeIJ5ZJIG\nEhcKOhiEGIib8BQmzzewiRLAhkSAi95mi8j/qWjVo7rtHvGLnV8U75nx5NQviF+Y+iR/k19mX6B/\ntezR4Uf5m2TUcX7BrwziT2UPHqxf008I22BtdQbL2FmCTLjAV7J78fLuA/wQQOGeLHAoFoeSprCY\nFGeJi0XEctKuSJoR0ek5MRPPScfnTkz/r+FZZsH15zktnH9UY8M7aIbS0Db4wLgAMYkOao6jDaiH\nMiSdK5KG0VBgmoWye2T6rO49GHQKENAcwJtYrCZ1oDrMBIJTLxxAyK/FOkTCGUqaERQHca3kkDm6\nhUlTq17mIkPAHO0KJRaO1JyjCFx2rpRm4rAMSpBk5lcDLxheNrxmUC3McG8tlG7DsdkJwzkDkX4k\nTRtzn8tN5crBQ7V8wpuVuzj3/txHER8SjkeSBTy6IcATeJm0LYZYQRnncATMGHPcmGmoFC0IDTUn\nFwFUpd9j8IgCenIQQybnIiKJxVB7J6ALX6lxpRLB8SzHlqoCgCw5jbU473URVQesi+VQHrIpgKRV\n5ZDujWM2tEsX2mUfbPJOCRfQSpqIjQgNwo8GgUhaA7IGp5KDmkpVcBt4A68f/EhSAQAOIFIVXteU\nbISOPjTvD2HP3pPTByISP8Q9GfAHyPzDXCxHTqLsVcI6lP02WigqI92KZ6HYCyexwF2lXQc+JkhD\nWpCDKJsRVywCS1ud9192L3jsC7d855UFz2ye+9GbL7zw5kf//u8Y3ywjFnaC94V8MYklHW/wa9Dn\nt7EX2Mvo84vwjPdm2UT4OEgDXNZ9u4/iZ+JqX9bD+wTgV4LiWGO2IEyUtAUrAzPGv/QTOdvwpkZo\n8Jcz7IMT1y4K4AP4KBtio4micljkEzmu/1EM8UuAKwAjBrZ1GWNHaRaH/nw+EDdhb/81rL7uA0Ab\nG7MazbQoohREKDpBm49aoM7S3vHPcSfgAcA3BHkhynVKraXfKF6RT+V+byFtY2C/cEw4hdC41Dam\n4BcXs27tH2JWpfmVGqEOAFd0ya7mTXev+jAmtL2aw5gGTmrOYyh1ogF9iCPWXbCBbLL04CCQgo9c\nyJ4GHgCcBvwRkANpLcBVDwRDUsjJh0XYL1CV1wGHAT2AQpwknNJkFstFxO6VJwudKy2AUwA9cp2S\nLWRB9jUdB3wfANrYGB+FZWrnGG0+xFSRD56YdcnRdpPZY9Y5SrxBc8WhObPZX38sRZOiJmmoDS5g\nb/J1DrVBWuf8TI6x+BBWOa02WuVkvz+Y/f4O/v1UPizaXra9Zjtju2zj2jDuTntsIRvvQUmoURww\ndFa+XPla5ZnKy5W4B5RynspQJe6pZATyQoqNDPN5vk58RShglj2W0Vi+g3K0JOg+96Evi/K4zVMK\nhmgelCknFVerxTQdk8gEixJ7IndI5gtczKE/h99kyKWgEltxUINIxOJCOURFyjQo5VlylZ/ZlOAP\nY8KhG6+2N7baKa6ZNYRFvVFnV87NTEMgejDCN13SU2r6cf23cCQKPWXgsP6E/pxetTD9Hf0emCno\nyI/eSsTDdOYs7aMh5wRGp/McYo0Bb2NEhe0EFcJMZPzZG3+re/oL/37rv/3brT985Gndd77T850z\nNxujrHf4e2zu8O6o8eborl0wyxJcHL7Jx5gcdk4SsHV0H5rbZQB8nSQPb3jyglX6BgYZiqb4AOBO\nrEVgLSIq9lSIZ5u2Mx+WchHWwf9ItyDp71k/bIozgXIhO5nsAu9uUMoFRV2Rtobr/+kW7XRsAyxE\n0j9p/5mPmAOyHRzvus/gHd8D+FvAIxhVp2jnY1QFtRwqyVczulAaUxxoTPBm4eojhQGHAPcDzmDo\nfgJwnyEz9IyLbpt14NbLx/78DX4HY/yXhScxxr8P32G1YBd8fCUrD0m9gJWAc4BXAD8B3I57I0KH\nMAccoypLv8jz4++eokj+ADCELaUWzXTNbRrVijQimfNh5h3Nn/ifAYOmSFMDptWHcOcUjbx1gkUg\n8XDBso0vtSBjZQVdoA1CjD3aPojxdJZx75IWrcalgSUbVzcayZhtw/CvWTFffuUPX2F3sO8Ob2kJ\ns2UtvE9Tm6A+/4txNs25ik2zf+R9UUuxjwJsZcobklq9vP9o1FfgasQX33zeQMx3c9I8y7zYfL/5\nUbN2ITko9RdmDpzHbTVlQrVQUBN2heKZyKfG7ytPSZsQimXeOvI9QgdYB8BKIx3NmYxZ6yO81B4s\nY5oBGwAfAXqydmMfkd8aBqGPyGsaOwwgy+ZS0zgdTv5nu3MAJEaaYkcxPhUPgM+RfG+W4MxVi/AA\nfdhLyQE/wq6yA2AozC1zl/G7d/kPgMGxEKpjLgKK9AWWwZg9J1CIuCKI7rWQIlpDi6MIYLK/nBRF\nLbuzVV0P/brXsQQE1X0oehmKno+QPr2BJTxHNsYIgWxlx5mlTRKZM95RkZNT0RHP/J3+ZxPz8yf+\n2XTlr/i1Sbctq65edtukzN/2yff8VSLxV/dMzvwlHculvOsw+/dUZUh6lI/K/bkqtOH0Ws0mLIdz\nSeeSuaUtg9JKDLFLER34pOU8ZsVbLdhYedzyLcvzlr0WNYwCQBpN21cbOZCBO6jTB9Pf8jzvEemG\nsiF43fGLanJwDA+mwsomJdmmZFqOm39wk3vHBLfs3pGawJc07IqkkRO4zs1Vtvdg+SXoLfAnM8lf\nmELSIqxtKQQmIiEh5NF7FuxhWSyWMgvvrW5Lvw2GLu4J+IEzlD7kHHTKM4KH1KYKOkUOkEJfTaPz\nKujBWwAxLHmex7uksDHnAX/gT5s0wVgh4Iq5ul3zXHxhfw67lLfiwCZGERYBsKYZWBVeF94cxtbF\nQ8gwigw3WTN5YddF+hNlaHAVuWpcCdc01+eQYV8mr/TukoMwf4A/vHRnmCesDK8Fm+hmfk3eE7py\nXTn8fFwRnVc+lFACIwS8sQiMqmP8LwVbdLHfuLqqN/lYkXX4XRVj7+WfMs/pCXZaQ8W3R6Ozm336\nafZ6xlRd9ubCr/6f6nmF4l1W50CTL5hXlBeb9/nKooq6Wl/bgmgsr8Ts9zZ9aVWBRT4H+kD8UNir\nWi+ohDvw9hjCZbON7Dkme9+eRSRW8khOGkYDb/P1gllFY4wqqZqlWqy6X/WoimszBgSTl1M0UAKF\nL/CxvAAUjHSWx0cvVcTlzf3cHQnV+o3yHk2Cl/8RlT8XtslmbHPCpZscdMdGAc+GBv+EcOBKgEiR\nlyd6RJQHT+OViTs+J34olzfyS/FDZuLlaYXHJC3mW8Qjh+IoDMkEJjkZE281HdZczhzWSKKGljca\nYri6TAHhM/TcamLGQuhD+Yll4h5G5/xch8WZmSgHLZbux0JSVGkoDAaD9hGLMFP7nDnt984SP7zn\nno3j6riG4l5KazCVPcqyrFyXiddpkXifuEZ8QhwtfcwmFF+UQb/mj6OVaTbUFNkWCwteR4Y6MpnQ\nC3VNm1UebFHej7W3mpG2p6Fw6KQmPTfrXtSQC/Ee8Fr2igbhLTnu2R6Visn7Ppqh6/iAQ/xPNE/q\nFg0HcObIb3ar7vvfM8fPcuaoUt3ozHHkHJejQbVcMPCVwkKsagWM/0mb/KI3auWdlEHtWW0OH3a1\nFjgvGLGGTQv5lnxxBb8z//78R/M35j+Xn8o/lD+YfzY/B83CwTuPGWt3HNTl4P2PofEIjLl+vtjl\nKsa/Q5kLVdTu8djH/EP8VpVLGFHd/InxW1Wu6H811uvIOXGYP/9jgklwCnt0DBuPOoUkC/GkeAOQ\nVa2ILHRmUM+eNGm2ulYX9/vjOtVjrV1drQH+P9nn9VfCKdW3/9fn9f+XPq+qjhv7vIojx/g4Oovm\nFp3w7ZQ2RGM9kQ2l4eMjwkwtfVZ8T6G6yEwwY2edT5hs5A3Kgfs0azRPaOhXmpmaRZr7NPxXaoWq\njf9Kl9lyHNSd1dEUpcaUqHCkyL5FCHYuD5Ya/u+HfL4a3pO4E5PWRj4fCF3isPAPqjt4G22gPdkB\nkCi9LKhGGZSUXj4g93oVSsnJ9J4x3bYr21vrM71U5k52C3tHfnL98Zz2w/UKxQhoNZbz8fz8Afxu\nsThROCe+y39n38NUKvmsBDGXBZG4K/mq5nsY/N89IHPkmDl8XTzG79cK5WNCJl9z5K1BJ46wCF/Q\nlJtZ4VTmGf7+L9hR8djHTeLjHx/4fzYvJoR4Xv8o/lioF97YY9F41DV7nJpCjiGNwLFKo1XXpGFY\nIC5MFuVoC7U4LIfmu1z7kBbmB7u0B7Svak38W63FafFbopbJll7LEstKy1rLJst2CziBEQrLco6v\nx2GQ5BqETWKYMKLI2ccr7LsmZNJYipCUz9JvEK+kTxsuGlAVvaHAEDRkWPZXG9Ybthh2GvYbjhlM\nGK/g75AK8YU7b1rljZmzcVltcTlg9Z5x2FPOVR0RB5MmdIVcgc7bGhtv6wy4Ql0T/nQ4NCXijvQ9\n0Nb2l/Mi7oYp4cN79VWTZkyILmjzVbYtaKydOalatzd+iyk6pS/ccf/sCRNm398enj8larol/r+y\n/X9Vtv8bv/y/Hr+8bOQjMZ/LrFD4CsyHi2Bc3K/nMnIrhHMQmCwOM39O8zV7Ulk6H3wwW/pz4J4y\niDWodVAOTtvvFK8kixDEOeCMObud85xLnauc65ybnTuc+5xHnby5nnSeB9lAgcpMekClNuOLIlsk\nObz0wDGHmG8tyS8pN/P5ZzL79XRDkd1p0OfYyqtdrHp4KduaSAy/5izLzcQwGPkt28b7WrlQI/w8\nVRtCLIJaouHkarjsimIbgpemiz9vNl6PzdJfobmSfrPiUgV1worCiqqK5oopFfMrllc8VLGhAgwB\nByperTCRpUtwUKrS2KyZmL/jPBdo7U9kRiL2cfuLSRDFruJAcay4u3he8dLiVcXrijcX7yjeV3y0\nGIIoPl9MKj1ifqZPV16spN5XWVAZrGyq7Knsq1xWubpyfeWWyp2V+yuPVZoWSlWUN4QGc4dGnTcQ\ns8r+T2RFqXj3RFwqeUXhiEbZtkR3rCcuunvt3lBReNK8uXlOt8k+wSUWTCt1ufxN8apYa9tfTKqc\ny1TVbRNcnS1Tn+3d5K6w6fPtXtHrDzW1/Kztr2UZ145cFutFl1AkVAgFUnEF12IqsPZUmFg9FTTz\nOa4iu5MHgkAjdH7HM4ne1tv8XlcolgwkFnX6fJ2LEvFFnf4VrXPntjDV3kRzq8MXKTfXTLsjHr9z\nak3VlDtv8n29peXriHtTJSwTX2RfEwJCTJgibE5NDaWmkQIw1SJHa0b/D+J9Bi8F6X0GC4NVwebg\nlOD84PIgoo1uDe4KHgi+GjSR+Vi7hr8jbbuz3d8ebZ/c3tu+pH1l+9r2Te3b2wfaj7RjbG0/1y4u\nFPY0iho+ZHvFGj7Ra8jOyauE80rVWFI3DaZuIv7kLj4mVma8ca4iJpV9dXRy/HddKf8Urcz6mKOH\niy/afRPLSibW1dic1RMipWWRSrvNFykraQzV2O1VSJnotzdXVzmqAl6zxRuocvmrh4+bfVXVDlup\n3VAbtFcHvVc80aDL5vHb8v0euzPQ6IHjg6M8YLMFyx3OQLTCH7UVl5vySt2WhkqTp9hmLizL9zdY\n3R45DtbI+3yB8GMu42amTsVDqQRJOE6m+7kkYQck7LjkIAk7Ch1VjmbHFMd8x3LHQ44Njq2OXY4D\njlcdXMJ8RMjnnc/SHyY5h51hfzganhzuDS8JYztoU3h7eCB8JAw5h8+FeV8gJRYKWHAwFQwJ/N+1\nFKfkf8072h4z3oaUryGaynLig0ifdl90Uy9yF7iD7iZ3j1v2Hlzv3uLe6d7vPuY2Ybe+v5F6Z6Or\nMdAYa+xunNe4tHFV47rGzY07Gvc1Hm1E72w830jGB+MW5E7XxKvsstQZx8OMW9ZRS3h+T2hqpMjd\n0FNX3+th1qquid6I21c2uSY8NVrpzO0qmhWuiHitVm+koibus7C7Y3+9YnrlpNm1NTPiFXV+k9tU\nveiWqNteU1zqiXbPmvMFX8xdG/eUttSXhnvm0Fh+50gN26TKFxoEB19b+NU1wp4y0cHbaJ2oV9eg\n2jSsomZOp8MqG1eT1zyNEdGJjXzk8Mttkm1yWPK4tq0z5ulUjInOhrAjYMsz8SStKU/DRJWhaILX\nUOgwiWetVqPZ5A7U1ZRphvVl829pdhbm5RvzjBOaI1oHu+xqaW0Jl2iMNuiqH45Usw94HfXYwdcJ\nGB00XpUuELPZIszX/t26gjuefan1JJvLCxxOq57EcxWOVIvt/DcGwUFcd/rQnhwRyyWNqMVzuRoZ\na4TVEit3FLIpw4dF8/Bk1ji8n50+GWOH2IGmScM3D0++CXl187z+iuelE7w8H64VcK2ECPX0il8/\nDqCFPVqRIWeNtZzPQOVWsXV4arsYP6nafaVXdeFKQYbj8qfst+I7gkbIEUKSBpaQWpEmeT39kSOq\nCDBK4t8IavqG/tBD2yI2lcpmY92tu3a1/sPx9es3eNk6tm74IdY1/P3h77Mu+BlRQeKP+Ejn5f2v\nRmgYncf8gyl/qN+pvoKJrHKov1jFK65iPi6YShFrSrX8ELHGiCMQmBiLRmNYmY3a2dMEwf9zuPgj\nMv7vZ02NoknntFrsueoJHs8EbYNuajQ6udDPJ+SXh+9gPxoWHuzoeNDaVGgqsZpdNmuOr742ou9p\n624pa/SW2+wT94vLP94sPv1xA68ytclbRi6IPaJGsAm9kk3N5aBTY7KEtqcflEzyBxv2rCRr9oOD\nr1ONoT1Wesm5oo4PsGRKhA0DPnvLQSN1GHL50MrbjhzivjzmUsLbs6rpEyd9a2/nH5n75omtTx3s\nGlm9sejBlsdavlm8iiNE2i3ME+eybwilwk3C7XvyNGZ1jRJKz2xJ1Q0Ke5pFCNIuGjhGxGqOboz3\nROZHHPnXbLSlIrQR7wnxxbMckw8pLYOpFuzBjd3p8o7bA3Pc+CtxrspaWlNaNs1f2VVUEdRM4B9r\nS8unVPq7Snw+XQ19LJtWqXyrtpTgW3/l5BKvT1fLvuFu8LtceXnOeo+7IVDgzMtz1ZXt5pfZxKCS\nSM3sFkHL35Wfa5C5gl/S6Plcrs8YCIqEuYTGIWGPHm+Fz+yNlQ6NxqHh3e8W9sHwQvad4Vz2AQu2\nvdT2zN+1LUkkrs53gpSTzVcS9WQ6o6d3m0PvNmdc9rHGyspGjYNR9t8dvo0XwbO/p+3vnuEFDP8M\n2fMxZf7IWTaTfPFyBHiHIqijl7nYDnIIGvXv+f88HkWWX+BmmV+AjzrvCmfG8A48lk2/jHQuK2Ek\nIL4oviKU8f4+g8JEY36z8fnNlpnsyNr2upoI+TRgGqxgZkyDKhvFu6Q/fMjJBJnmA0E0FijXeVmE\nedH/5V0mVq4SX8yGnLYYjTOGN82YyyZOZhO95fl2Cj89bGeRraMxqC3GnIMHxVc+bikvLKZY1GwD\ne0CJO/ZrcYdqPX/rufR8b7Pm66ZfYN7rpl9khWPS78im/5aVU3ovF9Zeut8o3y+cJfmZkM7lZxHK\n2VekonJogTBWXlMus1iDAOWQYuKsVRG7rInMJ/LAjA67hnIKLLsIrn7bAIJ3XNC/zFso4x/KsKHC\n2BVowAZ2RTI5y+SzvH6LCC0Th3J2OogrVLEVclSyFdJTOJl4C/A9QFP2jO6AWjG0UVgoFQ9x6QCO\nZclufJ9iwbZQ2gp4C/A9AMWBJTcrCtjqB+cqRQ1cB02zF8rteYAfp8XrcNULj4h9UHgPeck5BHWZ\nnA2A0YW6bAI0oXSKAjUVsDtLvjoVQFGfDqD0uSh9CnmJoWDyXO8GfI6XFBv134J9sZeP2Nasm7r3\n2b+dvWqm3z9z1ey/ZeLwcM+UKe+0P353e/vdj7e/014/e2kstnR2/W+44hKpnf943/wvz6+lNrCY\nv2svtQGT0gZ+MCb95mz6u8KxMemPZdMvC8fHpN+RTf+t8B80XizmfbGTt6VKYd6eAjVmiAIY22R7\n5Thdb1yvzJwD73EwJ/8ZV+fyGTEfFfA/hYNSpcpynS5Zbi130H/875h+KXaO6ZSG4TVs3/DX2bzh\nXewRS6ZbjumRhlzxlfbftA//rj3TJ7P96B9JVnal35WPSX8sm36ZVYxJvyOb/lsWUtLd4j+KJ7Pp\nv2OerAz/gvJ3yPlzKaI/UjqXITimHuQSgwWSRZlC+xlfu2BKH+pXqzNcJTm0E2yk67zBqziIx/Nq\nZ6zyMlLPGH3uUYkmPl8bGa16bQotCJZymEgW97DHenqGv9TDjg9/SXzl+ec/bmEzh/vZ12+5ZWRk\n5Jf8xQdU9/HncPL6a4V3TBRHceQKT8+lNiWnv2uQ4yvewv/sJznJ6b81yese20hAOMqfu0SI78lH\n2/kMrYbsDbFdUKI0D9e45uG18qcYbRZHx7YKdU/+DdrC6NhM9c0b+TX7E72nIuU9/Vp+f/y9Pkw2\nNsXyexX2Zd4r+y3dX0LP93aDMJpO95fQ/ZeEXZl8xqX/Tvj2de9fKnw8olHazW7Kv0yZJ9zZ9jQ2\n/SIzZ+u5m/IpU/L/ONNeVfn0firkPs+qx6Q/lk2/zIJj0tdn03/DEtly45ReK9dH2H/d9IvCP2Xr\nE6f61Cr1+QdKb+By/ojun6Dkc+i66ReEPdReGkYC7CPxpDBb+GXqllBqTih1i0KcmJpjSU0bktjE\naZjMS+lPaqKlv1MFBbrT2envjHZO7uztXNK5snNt56bO7Z3w9IYC3XmukyvQ8rbWNEt/jYor6TWX\nakhJrymsqapprplSM79mec1DNRtqttbsqjlQ82oNV4Zp70Lues28TTbz1soX40a+pPAX8gmVFRLT\nY6GlXy1ekUrlT82W/lbGle3Wi62kbLcWtAZbm1p7Wvtal7Wubl3fuqV1Z+v+1mOtJq5Aa1yK/gEV\nVDYBzWrQMb4UHuPxqx0l81FUajVXW6C3MIOotxpKQ47yKvvECZaKoDOozrWb85y5+fXNE+NVba5b\nmoqiNcWeSFtXW8QTaLulpuXz1a2hBcXRGnd01qJZ0aqO6uKSFqZSB3yuCkdugS/fXqjOM+VoNdYO\n0VHYFAqErOVhT2V9udNZXNNWH5neUOAP13eZPfXl4caKoglTk+GbJ+blqATl3brxDrPv9nfCJrnP\n8bYTpnceUtrODqUvusUwtZ2Qcv/mG6cr/iVhiuk5Y4+oVvxLdETJb1JGShoCr95+3GOGIoVFjprU\nGy1UqH49n4r4MGvC+kTeZ4000l6rIyKWvtXz1lvvihXvvsW+OXwv+2bLnvb2PVS3uXxeWEDjYz3V\n7R3egkfTb86mvyv8fEz6Y9n0y+PSv51Nf0/45Zj09dn03wh/pGefy+fiBfzZY8KzqaYQb5appow3\nfjMFSTTZArL2SBpxwNLv4yu9GCVmh95xVn5EK5P5YOQfjJlxmEy68MFo6S9h2BQqcZUESsYe2G4u\n2VGyr+RoCTaFSs7Dqimmsl8zpfOmjF1seKCpeXNW0yW13TGr7pdGx3FV54SGuKPHXl5V5c8zBaqq\nyu09jnjEH3WarhnbVaqC4rm317IXh6dHpjSUWDQaS0nDlAhLD3fW3j632K4pyM8M+4q+oUqRXGPK\n/P9/aF7js7Q4ndKblfFIbpsunu6g9BZl/Hp6TPod2fTfCk9m4gyzfxa/xtMn0Tzx5JeEMemvZNPn\nfEne0gjz8e4X7CzXvmcwdWpmKDUrlKobws67vEUo2eqIQ3CmJVU0iGOJOksqMpiKhLBw71Bj6Otw\ndvg7oh2TO3o7lnSs7FjbsaljeweYMDD0dZzrEImW4ebR9VoVf69VudebeRv4h4arz8D2lImT+JBn\nUxF1ShVvUdhM9F300fjmK/AFfU2+Hl+fb5lvtW+9b4tvp2+/75jPtFC6Sf4J1wp6aE+xx9UT6In1\ndPfM61nas6pnXc/mnh09+3qO9qD59JzvwZ7i+KWgFw6MmRQ+/kUaJ3ordAHZ6RMWcmMWAqqxtpeN\n7Bdj1wVMpRJLZjZ74hOKEvPvrC5ffctNwfYvV7TWFWtUpsyK4YuF07rqbWUBu6/RZx23fLAGbaEq\nbEk2t5XpjQ/FQp32qkk1Py5K2LIK30qNLeh1lTtyCqpinsza8XfsJ/TOe6mNtAl7rpv+OWWuvDp9\nntIGr05fKnyH0oMj74s/Fd/n6YvltYcoUptC+mzxfT6jR1lBKhbCQCEOEfEYbe2lYhlv5AmZjfoJ\nmeY1QXFLxmBahlESQeZ5I/M6vX5v1DvZ2+td4l3pXevd5N3uRQRoNDLvOW+GAdTDG40nQ15G40zu\nVaPw+CVqBYZkSZML3g7JTX8wQPPKVNB2Eh+gg9R2gq5gIBgLdgfnBZcGVwXBI7wjuC94NIi2Ezwf\nlKnBG9A0Gy42UNNsKGgINjQ19DT0NSxrWN2wvmFLw86G/Q3HGjD1YqxvvDZAWob17SqKSXF29Lph\n0o4ebY8Oe66OqSaGPx68JiL17okbNw6vvYpYLDs2XKH3u1x57/8wJv1r2fQnhb+/7v1zsu1kfPpS\nrEWVMeYKH2NiQlL4ZqotBK6ftsyxULsFHuZ59lJI3i5zupda+pv4HzpokGLyV2WWVHIwlQxlR5EM\nMd+eJtHDX2GeqhCvMEZ/sCZK4FUkLiboVSQKEsFEU6In0ZdYllidWJ/YktiZ2J84ljBd0+Vprsgc\njTnK6TAo5ho/WdjkkyIHWzvayTWaCcHaZKUv2dvQOLe5dPhhVXFdi7fxZnukOuLxui1yH2+b1VPW\nMqFoTO/WqEWLTXlP/tZbw6XeSRPc9WXVja48TbFT6d1/1zGnsDpakhnLLaKL5PxFpf8OXDd9jnDw\nuulLhe+PSX83m/6+sF32YeYT/WkVYtU/ruT/93w+YtjhYad5vy4T+lPlIRBglVtSBYOpghCd3DIy\nyVYN4gVVYPuw38RTuapePCizDYyhrcr00HH+FeMmfxf/4JJHf/RQlbFE3qLst+K9Wi9a6b1aC6xB\na5O1x9pnXWZdbV1v3WLdad1vPWY1kXkwH1zK0JnR3eR4M04HX2Ipb8/hzZIsTvuXrCt6dAo5ore0\ntP+LGNouu6IPv8FUM+aQI7oYaty4UdZFT3F5FIuQ0zKaU38sCpm5nAnUb+T0J5k81+q5LvqiuJ9r\nXlXCQ3tKVT7YmPgu0TyW4yv0VfkQSXG+b7nvId8G31bfLt8B36uYx8w+6gLmUH8+l6hPCYMgpAKh\nccrsdafUMcps4NOVWdWYfdSxe5AGsT0vM02x0vz8UvwbN0Wx14drM5ORyuEoKsI/0sFHfj5iEbay\n1wWbMH2PUZMDHTw7BueO3T/VT5oPVYaPtmqXOqDOGLeuUq9Tb1bvUO9TH1VjtFWfV2Om1rkU5QSr\nOVJgnCq9TV9QkqduF12xicUF/8hEdX5JhVX0f/xabn2DP1d+P7w+XGfh/YF9jt7P54YFxXffzX5J\n89nXKP13C+X0efwhnKqdPP0JZR4tFn6lpM+k+5+g+y89IN/vHnEL28ak/+7SaD438f6TyWep8I2R\nHJ7eQf0K/XOLkv8/0v1Xp88TnpLTeT1Ps7PZ9N8JX6X0/HH38/q8mF37ieXUHp9WxvH+MemvZNPn\n3CB9rfAmpVOcKUo/RPm3zZDzt/P63Er1kdN/9ys5vfSq++cl5PRxsRGQz6rrp39ukbyHU87nD8QI\nuUW4CIV8Lqnisk4+15KaDp18Osw6atSfUa8mfzDo7l20tuhydvm7ol2Tu3q7lnSt7Frbtalre9dA\n15EurC26znXxqZ2WK1zpRzlZq4IxmnnuZ9PMkxi7kheTNHYlC5LBZFOyJ9mXXJZcnVyf3JLcmdyf\nPJbEnOT6zJr5uIAWuquCX5CS86Mb6ueN4aq6G8S7SIyPjFFuy7++gt6jsjpuEAcjcHXEDL1alN+z\nG+8z+55/N1dQ9O332YP0/n+o7GU9ldHDwf+rpPP7m4Qbp4/hC64Qntuj1WDjV0sewE4iZ3TSClOL\n5aWAQ8LsnDQu9GbuVec6e1SiWi3H+MWRAlnl8XHK4DIEDDFDt2GeYalhlWGdYbNhh2Gf4agB45Th\nPEip86we5aDVRY4XhYPKArNYmZa4pj+e+rdR3gLI0P/+5V+OJwCeMUOmAF7S+NBYDuC/bVwCEuAs\nB8n3ZA4SLscfg4Mkm/61bPqTinzHcZbQOPD0mPSL2fR/FbZm0sX8MfevFU4KModNgI3wcb5ZGFCs\nU2qGaMaSz2fjyhq/vxTKJdmR1OCMN1UXwkeK4pNd3Y3jHb6xwkhBfTIfMqaBXFUE+XC/l16RF1Gr\nYt5u7zzvUu8q7zrvZu8O7z7vUWgOJ73nQa7YTPdfV/e7AXnN2OO6kXHz5PVJba5/QvAJVDfZzWJl\n3+oVkveQsh77jTDqY634YGO8VMbRcX6aGF8/f/30pcr8thh7PeIrgrx3rhOKhB8p79nC79/P09dT\n+leFd+g9+4XXWIBZYQsBbY4NUUjKIUGOt90KzySK5XBWhGNoJXxKA8MjjL3WLq+DAP/EyxOFXPYX\ne7RqDR82x3kxbcN5VyuM/VtxZcbVGbUc+AFG3FjoF4D/Y59wFJ63CE8vG7bL7BphBRZKg4CNYLfR\ng+pFJDrUi/BQ3s+OwUPZBb+CHL2okGgslHJyFG4HKVdFqX+Ab3AUoIXTQRm4ZFPwyHwNAKcMaRvA\njC/uU9JWSGdxNQvwqDFjyEqnJFeHg8YRuOyjKIl0lf6++AMEuxwS/xOsWn8HUd4lypHU9Rg8Ml6w\nq+B+NR9UBb3w9V3GVuOJdoHaZYpmPuhJQGeWnqzthddvN4QC1utYuYrRkXOg/CUW2Tr8sXy2PHfG\n8KYr7IGPN+Ks4qDynlQPkT+VjU0FqaZAMdbNQ/15XJhfAJ/LixwGvpL3VN7uPNUKSZVngMRA+C8z\nJRyE31Yu4DwdZsKlC4R6IoWRBCGdmrLMGZJeUPiGFkpn4Lm7JueJLN+EGbwm2iHJJGd/0ARud0vK\nMpQ0g452jeUJyzbLC5aXLboVSVvcMtWywHKX5WHLVyxPWXZbDlpyFqbftFyCGXaORdnUegHMXmYH\n2AZmOhY57nOscTzh0Cy8Dq1/lsBEsOYqnqRW+RRRHBy4W3xE/BswE8bxjo4T/Zl8mybUr+X35BI/\nNl6ZkT5RjHlm9aoq5dNF3jV0XnhesxHD71kPsw//J2sYXjt88tFb2b+z54bfZHr28PDaDThGPHJE\nfIU6ENhwKpIC0+wpEITc3AZBLYRGXuQYHfkRx9jInzg2jwxwjBMmCF8a2c3xrZFfcxwi/BnhGcKz\nQBZFPixG2ESYRG6sDTmwh+meRzhqqEQNlaihEjVUooZK1FCJGipRQyVqqEQNlaihEjVUooZK1FCJ\nGipRQyVqqEQNlaihErVUopZK1FKJWipRSyVqqUQtlailErVUopZK1FKJWipRSyVqqUQtlailErVU\nopZK1FKJOipRRyXqqEQdlaijEnVUoo5K1FGJOipRRyXqqEQdlaijEnVUoo5K1FGJOipRRyXqqEQ9\nlainEvVUop5K1FOJeipRTyXqqUQ9lainEvVUop5K1FOJeipRTyXqqUQ9lainEvVUok1Qj7zKUUOo\nJdQR6gkXjqzhmCI8jhRmIDQSmggf41jCa/5djlHCGKU0jzyLU1fCBOFLhD8beYvjGcKzQEa/4rUF\nNhEmkQOvLb+f1/MtoZrXc4CjhlBLqCPUEy4cWcoxRXgcKbyeQCOhifAxjrVCiLeDWiFKGBMsHJtH\nfscxTpgg/JlQwPEM4Vkgo/tZjLCJMInf8hry+9kj/J4Qr+GLHDWEWkIdoZ5w0sjXObYSJgnbCTsJ\nJxNOI+wl7CO8jXAh4XLCuwjvJryH8F7CdfwdhYT1I3/k+DSlPEP4LOE2wm8T7iZMER7i0g4J/0bX\nrxAeITxOdT5B354kfIPwFOFpwiG682eEZwjPArnk+W+55IEmQnpG1kVIT8q6CXsIpxBOJbyZcAbh\nTMJZhLMJF+Dp2BK6Xkq4jHA56sPuIrybkCTDSDLszwnvI3yAvn2QcCXhKsLVhA8RPkx3PkK4hkp8\njD9FlHpKlHpKlHpKlHpKlHpKlL9fYCthkrCdsJNwMuE0wtt4O4xSz4ryd4qUuwjvJryH8F7CdYTw\nt47yd4rrZwifJdxG+G3C3YQpyvMQ7y9R/h5RynFKP0EpJwnfIDxFeJoQo0eURo8ojR5R6uNR6uNR\n6uNRRk/B3yCQnoW/KeAMwpmEswhnEy5AnfmbwvVSwmWEy1Eif1PAuwkfIHyQcCXhKsLVhA8RPkK1\nWkN5YrSJ8Xfxa44aQi2hjlBPOIn/KsbfBTBJ2E7YSTiZcBrhbXT/Qi6rGH8XuL6L8G7CewjvJVzP\n+3iMvwVcP0P4LOE2wm8T7iZMUW6H6PoI4XHCE4QnCd8gPEV4GshlDjQSmgiptlzmQKozlznSZxDO\nJJxFOJtwAWrIZY7rpYTLCOm5GD0Xo+fiMgc+SLiScBXhasKHCNdQbo/x62Yae5tp7G2msbeZxt5m\nGnububS/y7GVMEnYTthJOJlwGuFthAtHHue4nK7vIryb8B7CewnX8d7XTKNZM5c5Up4hfJZwG+G3\nCXcT/gPVJDXyDMe9lHKE8DilnyA8SfgG4SnC04Rv8RbVTPNFM80XzTRfNDOqP5c/kJ6Cyx84g3Am\n4SzC2YQYnZq5/HG9lHAZ4QOU24OEKwlXEa4mfIhwDf0WM1ScpB0nacdJ2nGSdpykHSdpx0nacZJ2\nnKQdJ2nHSdpxknacpB0nacdJ2nGSdpykHSdpx0nacZJ2nKQdJ2nHSdpxknacpB0nacdJ2nGSdpyk\nHSdpx0nacZJ2nKQdJ2nHSdpxknacpB0nacdJ2nGSdpykHSdpx0nacZJ2nKQdJ2nHSdpxknacpB0n\nacdJ2nGSdpykHSdpx0nacZJ2nKQdJ2nHSdpxknacpJ0gaSdI2gmSdoKknSBpJ0jaCZJ2gqSdIGkn\nSNoJknaCpJ0gaSdI2gmSdoKknSBpJ0jaCZJ2gqSdIGknSNoJknaCpJ0gaSdI2gmSdoKknSBpJ0ja\nCZJ2gqSdIGknSNoJknaCpJ0gaSdI2gmSdoKknSBpJ0jaCZJ2gqSdIGknSNoJknaCpJ0gaSdI2gmS\ndoKknSBpJ0jaCZJ2gqSdIGknSNoJknaCpJ0gaa8UMNqsFF4S8oUXhRdHhvjVS4RYgbxEK5CXhB/y\ne16iGfwlmsFfohn8JZrBX2L307crCP+C4yG+XgL2ES7ktTqE/QyOywnvIryb8B7Cewm/SIgZ9pDw\nTV6fQ8IWwqcIn6ZvnyF8lnAb4bcJdxOmqKy9uObrGWAP4RTCqYTTCG8mnEE4k3AW4WzCWwjnEM4l\nvJXw86gJu53wDsI7CZfQt0sJMcIfJ63hOGkNx0lrOE5aw3HSGo6T1nCctIbjpDUcJ63hOM37x2ne\nP07z/nHSGo6T1nCctIbjpDUcJ63hOGkNx2kufotmzLdophvi169yTHH8Gcn/ZySZM3R9hq7P0vVZ\nXDMDasuR15Yjry1HXluOccIEIa8tR15bjkOEPyM8Q3gWiNpyjBE2ESaRG2rL8WG6h9eWGalEI5Vo\npBKNVKKRSjRSiUYq0UglGqlEI5VopBKNVKKRSjRSiUYq0UglGqlEI5VopBJNVKKJSjRRiSYq0UQl\nmqhEE5VoohJNVKKJSjRRiSYq0UQlmqhEE5VoohJNVKKJSjRRiZXQvzhGCbn+xZHrXxzjhAnClwi5\n/sXxDOFZIKNfQf/i2ESYRA7Qvzhy/Yv5KX8/5e+n/P2Uv5/y91P+fsrfT/n7KX8/5e+n/P2Uv5/y\n91P+fsrfT/kHKP8A5R+g/AOUf4DyD1D+Aco/QPkHKP8A5R+g/AOUf4DyD1D+Aco/QPkHKf8g5R+k\n/IOUf5DyD1L+Qco/SPkHKf8g5R+k/IOUf5DyD1L+Qco/SPmHoPNxjBJyvZIj1ys5xgkThFyv5HiG\n8CyQ0f3QKzk2ESbxW+iVHLleycKUc5hyDlPOYco5TDmHKecw5RymnMOUc5hyDlPOYco5TDmHKecw\n5VxPOddTzvWUcz3lXE8511PO9ZRzPeVcTznXU871lHM95VxPOddTzvWUM3SlFxl0JaCWUEeoJ+S6\nMIOuBEwSthN2Ek4mnEbYS9hHeBvhQsLlhHcR3k14D+G9hFwX5shnWAa9CSnPED5LuI3w24S7CVOE\nXBfm+G90/cr/7e1MgOwo7jPerZV2Vxe3AWMsP+MDDEKWhGBmxGGt7gvd4pAlpKe3o92Zefve8o6V\nVoBlrxHIB5CkcscCh5CkApWEHChEoOC4HBIUJakk5iocQ27HSZzEOSqpOFH+329mtU+ywJWqVLx+\n3+s309PT/f96ju7+PgG+CB6nzi+z9xXwVfA18HXwa+T8Ovgm+JZQY2GvkZRwJkgbNRb2GkkJV4Ar\nwVXgavBWcB24HtwAbgS3qXUaC3uNsISDYKL6aCzsNcISEhlPZPQkNayDLfa2wRFwL7gPHAX3k/Me\n8ABntLGwD+A3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8A\nfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4\nDeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3gN8AfgP4DeA3\ngN8AfgP4DeA3gN8AfgP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8Q\nfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4\nDeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4DeE3\nhN8QfkP4DeE3hN8QfkP4DeE3hN8QfkP4jeA3gt8IfiP4jeA3gt8IfiP4jeA3gt8IfiP4jeA3gt8I\nfiP4jeA3gt8IfiP4jeA3gl9G9z6C3wh+I/iN4DeC3wh+I/iN4DeC3wh+I/iN4DeC3wh+I/iN4DeC\n3wh+I/iN4DeC3wh+I/iN4DeC3wh+I/iN4DeC3wh+I/iN4DeC3wh+I/iN4JfZAB/BbwS/EfxG8BvB\nbwS/EfxG8BvBbwS/EfxG8BvBbwS/EfxG8BvBbwS/zCH4CH4XaX7McArYDfaAveAt9saySPNjhovA\nxeBScDm4BtxOfnvbN0xIp2AGVsEh8JA99xdp3GR4GHwUfAx8HHwSfJrSvkT6RfA4+DL4Cvgq+Br4\nulDzY4YzwJkgtdX8mCF11jjLcB24HtwAbgS3qYYaPRkOgIMg7fK0y9MuzY8ZtsERcC+4DxwFD1Da\nmKX7NIdgOAXsBnvAXvAWY6pPcwiGi8DF4FJwObgG3A7uOHnQMCGdghlYBYfAB43xPq6gPs0hGB4G\nHwUfAx8HnwSfoiZPnzxs+AxbXgSPs/1l8BXwVfA18HXwDXvX7dMcguEMcCZI/TWHYEgrNIdguA5c\nD24AN4K6Ivo0h2A4AA6CLUprgyPgXnAfOAoe4NgxS69xa12/YQscccsND5I+5LYZPgQ+zJZHwCPg\ns26+4VF3k+Fz4PPkPAa+AJ5wG/waf5vyW2+xLX4H6bvAneAusAwOk/9usAEesKN2Wg03GLbsLDut\nhlcZHmTLIfAh8GHwEfAIOZ91FxketXrutBoKn2f7MfAF8ISb5XdaDe0oq6FwB3gXuBPcBZbBYfLf\nDTbAA7Y90ZyJ4R2gjc0Nd5FOwBTMwCo4BN4HPmj9IdGcieGPgj8OfoG9h8FHwcfAx8Enwac51zNK\na87EcCW4ClwNrgHXgreC68D14AZwI7gJ3AxuAbeCu1UfzZwY9oMxuIe9A6Cu/ZQ4pMQhJQ4pcUiJ\nQ0ocUuKQEoeUOKTEISUOKXFIiUNKHFLikBKHlDikxCElDilxSIlDShxS4pASh5Q4pMQhJQ4pcUiJ\nQ0ocUuKQEoeUOKTEISUOKXFIiUNKHFLikBKHlDikxCElDilxyIhDRhwy4pARh4w4ZMQhIw4ZcciI\nQ0YcMuKQEYeMOGTEISMOGXHIiENGHDLikBGHjDhkxCEjDhlxyIhDRhwy4pARh4w4ZMQhIw4ZcciI\nQ0YcMuKQEYeMOGTEISMOGXHIiENGHDLikBGHYbtyZxm2wIPgIfAh8GHwEfCI0K5E4TZwB3gXuBPc\nBZbBA4b7NVdm+LThPcT5HiIwxnzRGPNFY8wXjTFfNMZ80RjzRWPMF40xXzTGfNEY80VjzBeNMV80\nxnzRGPNFY8wXjTFfNMZ80RjzRWPM4Dl3ib8s/68qGU7PNSVos3rsV57uciV3QZGe3JFniuWZX6S7\nbXtUpKUnXFqkLyB/l/OTp9r3Z/XfWCHt3cXGRp6e5M7x+4p0l1vkHyjSkzvyTLE8Lxbpbtv+1SLd\n46/y3yzSve6yrguK9FQ3q2t2kZ7W/Uddq4v0dDdn2uVFeoZbPW18+3luxrQfLNLnu95pX+wbiWtJ\no7S4Xs82xQPtarnRseXGUjhnbv918bwbS/Pnzpt/7dwF9v9iU57tWmUrjkiapXKp1Sj3x0PlRlaq\n7ymtjJP+uLo7bgzEjdLSRruSDZWblcGkFtdKfStml+J9lWq7mYzE1dFSNanEtWbcX2oNNurtgcHS\n2qRWb40Ox6UVQ7tXzi6Va/2lofJoaXdcasQDSbMVNyxzUitV4karbN9pu5E0+5NKK6nXmnPcEld3\nw27UNVziBtyg9fGfM37nu7lOs0ol6/mJq1meluUZdrFtWeGG3G630s229F7+5rjqGbnmuIr9GrLv\nkrM3FPsrdZyhya/YvmP7HjHst5x9pGqWq2H7F9vxdZe5TbZtwLWthLJtP3ueGy0dWglzrZzrbP88\ntqgN8wyvte8FBZ6eq7O0a0+Vdvo5Empbtk/Lftvz3fYNUZfMttXdHsOVti1hT9UiozYNgCXr9w2r\ne8Xy6pimpQaJlMpXZFYQxdjtsz1Vy9m0vSOUM2rbFdUKeZvESHUYtBLrllOR/F7slG2fjtK5Vd5u\ncjSIqNrVopZ5yQk1qrClZfnz36mdqUHefurSMqxTnznvcO4+y62jypSxnBi0YD+Gw3faWyKOTX7X\nirqdyYiOm2f3l9D+8nbuKdpSsrrEsNM8xc6g/R7hqIEiJnkZ461XHMZLbbK/SSqmlnuIet7CPba3\nwhHq16s44szydKbYrgldGwl8fTdLs6lVXJwvoY357z1w3zpVbt3iWSUW5VOxV33qZ8Qp76HVom+V\nicREW5LiqPwc4/046Sgxj9Qy27O7OHq87yyHnTbHzKYPtalfXoeynbNJSn0so/w2sRsvc/ys6uPD\nRUzFZYWt42dpEptq0SvV0/L25deC7lBDHNXq4HWiPXuLfSo5j3il2KJ6j8LWliL3Xju6cZZeNUTc\n8nhdaeWPtzq2X+MRXM7vGlfxRN0HC/abRZ3KRXzGa3d631Ht98JcicgNdcQqKUqZ6E3DnLF1FvY7\neZnDvTDnpW15FMecizPZO9u9Le+ZJTtX3t783qOrMa9dC84q3FMTcg5yLytRVqPgq8w9vknuOmc/\nPR5lys63JNwR8+s1z9HZPwdhKHH7aW+r6GPj97OSu8K2X3Fa2ae3o0xbVLqupgrbKrRY99j4t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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ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\nIDEgMCBSIC9TaXplIDIyID4+CnN0YXJ0eHJlZgo3Mzg0OQolJUVPRgo=\n", 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"metadata": {}, - "source": [ - "```python\n", - "def foo(bar=1):\n", - " \"\"\"docstring\"\"\"\n", - " raise Exception(\"message\")\n", - "```" + "output_type": "display_data", + "text/plain": [ + "" ] } ], - "metadata": {} + "prompt_number": 9, + "source": [ + "plt.hist(evs.real)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "def foo(bar=1):\n", + " \"\"\"docstring\"\"\"\n", + " raise Exception(\"message\")\n", + "```" + ] } - ] + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/IPython/nbformat/v4/nbbase.py b/IPython/nbformat/v4/nbbase.py index 473bd62..1044c10 100644 --- a/IPython/nbformat/v4/nbbase.py +++ b/IPython/nbformat/v4/nbbase.py @@ -38,7 +38,8 @@ def from_dict(d): def new_output(output_type, mime_bundle=None, **kwargs): """Create a new output, to go in the ``cell.outputs`` list of a code cell.""" - output = NotebookNode(output_type=output_type, **kwargs) + output = NotebookNode(output_type=output_type) + output.update(from_dict(kwargs)) if mime_bundle: output.update(mime_bundle) # populate defaults: @@ -51,7 +52,8 @@ def new_output(output_type, mime_bundle=None, **kwargs): def new_code_cell(source='', **kwargs): """Create a new code cell""" - cell = NotebookNode(cell_type='code', source=source, **kwargs) + cell = NotebookNode(cell_type='code', source=source) + cell.update(from_dict(kwargs)) cell.setdefault('metadata', NotebookNode()) cell.setdefault('source', '') cell.setdefault('prompt_number', None) @@ -62,7 +64,8 @@ def new_code_cell(source='', **kwargs): def new_markdown_cell(source='', **kwargs): """Create a new markdown cell""" - cell = NotebookNode(cell_type='markdown', source=source, **kwargs) + cell = NotebookNode(cell_type='markdown', source=source) + cell.update(from_dict(kwargs)) cell.setdefault('metadata', NotebookNode()) validate(cell, 'markdown_cell') @@ -70,7 +73,8 @@ def new_markdown_cell(source='', **kwargs): def new_heading_cell(source='', **kwargs): """Create a new heading cell""" - cell = NotebookNode(cell_type='heading', source=source, **kwargs) + cell = NotebookNode(cell_type='heading', source=source) + cell.update(from_dict(kwargs)) cell.setdefault('metadata', NotebookNode()) cell.setdefault('level', 1) @@ -79,7 +83,8 @@ def new_heading_cell(source='', **kwargs): def new_raw_cell(source='', **kwargs): """Create a new raw cell""" - cell = NotebookNode(cell_type='raw', source=source, **kwargs) + cell = NotebookNode(cell_type='raw', source=source) + cell.update(from_dict(kwargs)) cell.setdefault('metadata', NotebookNode()) validate(cell, 'raw_cell') @@ -87,7 +92,7 @@ def new_raw_cell(source='', **kwargs): def new_notebook(**kwargs): """Create a new notebook""" - nb = NotebookNode(**kwargs) + nb = from_dict(kwargs) nb.nbformat = nbformat nb.nbformat_minor = nbformat_minor nb.setdefault('cells', [])