sphinxcontrib-jupyter

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• [x] Currently coverage reports are constructing by running everything due to the removal of _build_coverage. We should review this code and make the report additive - so that new coverage runs only update the json report for any lectures that need running (as determined by the sphinx cache)

• [ ] the command line should display pass and fail statistics and suppress the Tracebacks such as the one below. These should be added to individual lecture level txt or json reports

distributed.worker - WARNING -  Compute Failed
Function:  preprocess
args:      ({'cells': [{'cell_type': 'markdown', 'metadata': {}, 'source': '\n<a id=\'arellano\'></a>\n<div id="qe-notebook-header" align="right" style="text-align:right;">\n        <a href="https://quantecon.org/" title="quantecon.org">\n                <img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">\n        </a>\n</div>'}, {'cell_type': 'markdown', 'metadata': {}, 'source': '# Default Risk and Income Fluctuations'}, {'cell_type': 'markdown', 'metadata': {}, 'source': '## Contents\n\n- [Default Risk and Income Fluctuations](#Default-Risk-and-Income-Fluctuations)  \n  - [Overview](#Overview)  \n  - [Structure](#Structure)  \n  - [Equilibrium](#Equilibrium)  \n  - [Computation](#Computation)  \n  - [Results](#Results)  \n  - [Exercises](#Exercises)  \n  - [Solutions](#Solutions)  '}, {'cell_type': 'markdown', 'metadata': {}, 'source': 'In addition to what’s in Anaconda, this lecture will need the following librarie
kwargs:    {}
Exception: Exception('Long error message', <class 'nbconvert.preprocessors.execute.CellExecutionError'>, 'An error occurred while executing the following cell:\n------------------\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nae = Arellano_Economy(β=.953,        # time discount factor\n                      γ=2.,          # risk aversion\n                      r=0.017,       # international interest rate\n                      ρ=.945,        # persistence in output\n                      η=0.025,       # st dev of output shock\n                      θ=0.282,       # prob of regaining access\n                      ny=21,         # number of points in y grid\n                      nB=251,        # number of points in B grid\n                      tol=1e-8,      # error tolerance in iteration\n                      maxit=10000)\n------------------\n\n\x1b[0;31m---------------------------------------------------------------------------\x1b[0m\n\x1b[0;31mValueError\x1b[0m                                Traceback (most recent call last)\n\x1b[0;32m<ipython-input-3-23e4104ecf21>\x1b[0m in \x1b[0;36m<module>\x1b[0;34m\x1b[0m\n\x1b[1;32m')

• [ ] we should add individual file level reports for failed lectures that contain all the Traceback or Error information returned by notebook.