Ass missing package import

605510cb · Sören Henning · 9cb940dd · 605510cb
Commit 605510cb authored 4 years ago by Sören Henning
--- a/analysis/demand-metric.ipynb
+++ b/analysis/demand-metric.ipynb
@@ -86,6 +86,8 @@
   "metadata": {},
   "outputs": [],
   "source": [
+    "import os\n",
+    "\n",
    "demand.to_csv(os.path.join(results_dir, f'exp{exp_id}_demand.csv'), index=False)"
   ]
  }

 %% Cell type:markdown id: tags:

 # Theodolite Analysis - Demand Metric

 This notebook allows applies Theodolite's *demand* metric to describe scalability of a SUT based on Theodolite measurement data.

 Theodolite's *demand* metric is a function, mapping load intensities to the minimum required resources (e.g., instances) that are required to process this load. With this notebook, the *demand* metric function is approximated by a map of tested load intensities to their minimum required resources.

 The final output when running this notebook will be a CSV file, providig this mapping. It can be used to create nice plots of a system's scalability using the `demand-metric-plot.ipynb` notebook.

 %% Cell type:markdown id: tags:

 In the following cell, we need to specifiy:

 * `exp_id`: The experiment id  that is to be analyzed.
 * `warmup_sec`: The number of seconds which are to be ignored in the beginning of each experiment.
 * `max_lag_trend_slope`: The maximum tolerable increase in queued messages per second.
 * `measurement_dir`: The directory where the measurement data files are to be found.
 * `results_dir`: The directory where the computed demand CSV files are to be stored.

 %% Cell type:code id: tags:

 ``` python
 exp_id = 200
 warmup_sec = 60
 max_lag_trend_slope = 2000
 measurement_dir = '<path-to>/measurements'
 results_dir = '<path-to>/results'
 ```

 %% Cell type:markdown id: tags:

 With the following call, we compute our demand mapping.

 %% Cell type:code id: tags:

 ``` python
 from src.demand import demand

 demand = demand(exp_id, measurement_dir, max_lag_trend_slope, warmup_sec)
 ```

 %% Cell type:markdown id: tags:

 We might already want to plot a simple visualization here:

 %% Cell type:code id: tags:

 ``` python
 demand.plot(kind='line',x='load',y='resources')
 ```

 %% Cell type:markdown id: tags:

 Finally we store the results in a CSV file.

 %% Cell type:code id: tags:

 ``` python
+import os
+
 demand.to_csv(os.path.join(results_dir, f'exp{exp_id}_demand.csv'), index=False)
 ```