Markdown nested list to ipynb json source

In [2]:

    

mymd = """
  - 3/ Intro to linear algebra
      - Vector operations
      - Matrix operations
      - Linear property: $T(a\mathbf{x} + b\mathbf{y}) = aT(\mathbf{x}) + bT(\mathbf{y})$
      - Matrix-vector product representation of linear transformations
  - 4/ Computational linear algebra
    - Gauss-Jordan elimination procedure 
      - Augemnted matrix representaiton of systems of linear equations
      - Reduced row echelon form
    - Matrix equations
    - Matrix product
    - Determinant
    - Matrix inverse
      - Adjugate-matrix formula
      - Augmented matrix approach
      - Using elementary matrices
  - 5/ Geometrical linear algebra
    - Points
    - Lines
    - Planes
    - Hyperplanes
    - Projection operation
    - Bases and coordinate projections
    - Vector spaces (p199)
    - Vector space techniques
  - 6/ Linear transformations
    - Vector functions
    - Input and output spaces
    - Matrix representation of linear transformations
    - Column space and row spaces of matrix representations
    - Example: matrix representation M_P of projection P
    - Linear transformation <--> Matrix-vector product equivalence is the central theme of this book
  - 7/ Theoretical linear algebra
    - Eigenvalues and eigenvectors
    - Special types of matrices
    - Abstract vectors paces
    - Abstract inner product spaces
    - Gram–Schmidt orthogonalization
    - Matrix decompositions
    - Linear algebra with complex numbers
  - Applications
  - Notation appendix
"""


    

In [17]:

    

mymd = """
  - 2/ Intro to linear algebra
    - Definitions
      - E2.1
      - E2.2
      - E2.3
    - Vector operations
      - E2.4
      - E2.5
      - E2.6 
    - Matrix operations
      - E2.7
      - E2.8
      - E2.9
    - Linearity
      - E2.10
"""


    

In [18]:

    

from notebook_helpers import create_ipynb_json_from_md


    

In [19]:

    

# mymd.splitlines()
print( create_ipynb_json_from_md(mymd) )


    

    




  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2/ Intro to linear algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E2.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },

In [ ]:

    




    

In [20]:

    

# Not working, but close...

# NOTEBOOK_TEMPLATE = """{%s
#  ],
#  "metadata": {
#   "kernelspec": {
#    "display_name": "Python 3",
#    "language": "python",
#    "name": "python3"
#   },
#   "language_info": {
#    "codemirror_mode": {
#     "name": "ipython",
#     "version": 3
#    },
#    "file_extension": ".py",
#    "mimetype": "text/x-python",
#    "name": "python",
#    "nbconvert_exporter": "python",
#    "pygments_lexer": "ipython3",
#    "version": "3.6.5"
#   },
#   "widgets": {
#    "state": {},
#    "version": "1.1.1"
#   }
#  },
#  "nbformat": 4,
#  "nbformat_minor": 1
# }
# """

# def write_notebook(filename, nbjson):
#     cells_json = str(nbjson)
#     if cells_json.endswith(','):
#         cells_json = cells_json[0:-1]
#     nb_str = NOTEBOOK_TEMPLATE % cells_json
#     with open(filename, 'w') as nbfile:
#         nbfile.write(nb_str)


# write_notebook('../test.ipynb', create_ipynb_json_from_md(mymd))


    

In [ ]: