export code+results ok

module4/challenger.org

@@ -35,7 +35,7 @@ possibly in a nicer looking way.
 We will be using the python3 language using the pandas, statsmodels,
 numpy, matplotlib and seaborn libraries.
 
-#+BEGIN_SRC python :session :export both :results output 
+#+BEGIN_SRC python :session :exports both :results output 
 def print_imported_modules():
    import sys
    for name, val in sorted(sys.modules.items()):
@@ -61,7 +61,7 @@ print_imported_modules()
 
 #+RESULTS:
 #+begin_example
-3.8.2 | packaged by conda-forge | (default, Apr 16 2020, 18:04:51) 
+3.8.1 (default, Jan  8 2020, 22:29:32) 
 [GCC 7.3.0]
 uname_result(system='Linux', node='hpisor', release='4.9.0-12-amd64', version='#1 SMP Debian 4.9.210-1 (2020-01-20)', machine='x86_64', processor='')
 _csv 1.0
@@ -73,9 +73,9 @@ ctypes 1.1.0
 cycler 0.10.0
 dateutil 2.8.1
 decimal 1.70
-distutils 3.8.2
+distutils 3.8.1
 json 2.0.9
-kiwisolver 1.0.1
+kiwisolver 1.2.0
 logging 0.5.1.2
 matplotlib 3.1.3
 matplotlib.backends.backend_agg 3.1.3
@@ -92,8 +92,8 @@ pandas 1.0.3
 patsy 0.5.1
 patsy.version 0.5.1
 platform 1.0.8
-pyparsing 2.4.6
-pytz 2019.3
+pyparsing 2.4.7
+pytz 2020.1
 re 2.2.1
 scipy 1.4.1
 scipy._lib._uarray 0.5.1+5.ga864a57.scipy
@@ -144,7 +144,7 @@ Let's start by reading data.
 
 Note: url corrected based on forum post at https://www.fun-mooc.fr/courses/course-v1:inria+41016+self-paced/courseware/7bf2267c336246f9b6518db624692e14/96b7ce47bd11466a9a2e63d8e8a93d99/
 
-#+BEGIN_SRC python :session :export both :results value 
+#+BEGIN_SRC python :session :exports both :results value 
 data = pd.read_csv("https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv")
 data
 #+END_SRC
@@ -181,7 +181,7 @@ data
 We know from our previous experience on this data set that filtering
 data is a really bad idea. We will therefore process it as such.
 
-#+BEGIN_SRC python :session :export both :results output 
+#+BEGIN_SRC python :session :exports both :results output 
 #%matplotlib inline
 pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas
 import matplotlib.pyplot as plt
@@ -206,7 +206,7 @@ Let's assume O-rings independently fail with the same probability which
 solely depends on temperature. A logistic regression should allow us to
 estimate the influence of temperature.
 
-#+BEGIN_SRC python :session :export both :results value
+#+BEGIN_SRC python :session :exports both :results value
 import statsmodels.api as sm
 
 data["Success"]=data.Count-data.Malfunction
@@ -228,7 +228,7 @@ Model Family:                Binomial   Df Model:                            1
 Link Function:                  logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -3.9210
 Date:               mar., 05 mai 2020   Deviance:                       3.0144
-Time:                        19:13:17   Pearson chi2:                     5.00
+Time:                        22:25:31   Pearson chi2:                     5.00
 No. Iterations:                     6                                         
 Covariance Type:            nonrobust                                         
 ===============================================================================
@@ -252,7 +252,7 @@ for each rocket launch. Let's indicate these weights (since the weights
 are always the same throughout all experiments, it does not change the
 estimates of the fit but it does influence the variance estimates).
 
-#+BEGIN_SRC python :session :export both :results value
+#+BEGIN_SRC python :session :exports both :results value
 logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], 
                 family=sm.families.Binomial(sm.families.links.logit),
                 var_weights=data['Count']).fit()
@@ -270,7 +270,7 @@ Model Family:                Binomial   Df Model:                            1
 Link Function:                  logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -23.526
 Date:               mar., 05 mai 2020   Deviance:                       18.086
-Time:                        19:13:17   Pearson chi2:                     30.0
+Time:                        22:25:31   Pearson chi2:                     30.0
 No. Iterations:                     6                                         
 Covariance Type:            nonrobust                                         
 ===============================================================================
@@ -297,7 +297,7 @@ of freedom (Df Residuals).
 The temperature when launching the shuttle was 31°F. Let's try to
 estimate the failure probability for such temperature using our model.:
 
-#+BEGIN_SRC python :session :results output :export both
+#+BEGIN_SRC python :session :results output :exports both
 #%matplotlib inline
 data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})
 data_pred['Frequency'] = logmodel.predict(data_pred)
@@ -323,7 +323,7 @@ Following the documentation of
 [[https://seaborn.pydata.org/generated/seaborn.regplot.html][Seaborn]],
 I use regplot.
 
-#+BEGIN_SRC python :session :results output :export both
+#+BEGIN_SRC python :session :results output :exports both
 sns.set(color_codes=True)
 plt.xlim(30,90)
 plt.ylim(0,1)