Pull

.thoth.yaml

@@ -0,0 +1,23 @@
+host: 'khemenu.thoth-station.ninja'
+tls_verify: false
+requirements_format: pipenv
+
+runtime_environments:
+  - name: 'ubi:8'
+    operating_system:
+      name: 'ubi'
+      version: '8'
+    python_version: '3.6'
+    recommendation_type: STABLE
+
+managers:
+  - name: thoth-advise
+    configuration:
+      labels: [bot, kebechet]
+  - name: thoth-provenance
+    configuration:
+      labels: [bot, kebechet]
+  - name: update
+    configuration:
+      labels: [bot]
+  - name: info

.zuul.yaml

@@ -8,3 +8,7 @@
       jobs:
         - "thoth-coala"
         - "thoth-pytest"
+    kebechet-auto-gate:
+      jobs:
+        - "thoth-coala"
+        - "thoth-pytest"

example_generators/Generators vs lists.ipynb

@@ -1,16 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Generators vs Lists\n",
-    "\n",
-    "### What are Generators?\n",
-    "\n",
-    "### Lists"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 23,

model.py

@@ -1,26 +1,30 @@
+"""Simple example of LinearRegression."""
 # Import the necessary libraries
 import numpy as np
 import matplotlib.pyplot as plot
 import pandas
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 
+
 def import_data_and_split():
+    """Train and test data split."""
     # Import the dataset
-    d = pandas.read_csv('data.csv')
+    d = pandas.read_csv("data.csv")
     x = d.iloc[:, :-1].values
     y = d.iloc[:, 1].values
 
     # Split the dataset into the training set and test set
     # We're splitting the data in 1/3, so out of 30 rows, 20 rows will go into the training set,
     # and 10 rows will go into the testing set.
-    xTrain, xtest, y_Train, yTest = train_test_split(x, y, test_size =1 / 3, random_state = 0)
+    xTrain, xtest, y_Train, yTest = train_test_split(
+        x, y, test_size=1 / 3, random_state=0
+    )
 
     return (xTrain, xtest, y_Train, yTest)
 
 
-
-if __name__ == '__main__':
+if __name__ == "__main__":
     xTrain, xtest, y_Train, yTest = import_data_and_split()
 
     # Creating a LinearRegression object and fitting it
@@ -32,17 +36,17 @@ def import_data_and_split():
     yPrediction = linearRegressor.predict(xtest)
 
     # Visualising the training set results
-    plot.scatter(xTrain, y_Train, color ='red')
-    plot.plot(xTrain, linearRegressor.predict(xTrain), color = 'blue')
-    plot.title('Salary vs Experience (Training set)')
-    plot.xlabel('Years of Experience')
-    plot.ylabel('Salary')
+    plot.scatter(xTrain, y_Train, color="red")
+    plot.plot(xTrain, linearRegressor.predict(xTrain), color="blue")
+    plot.title("Salary vs Experience (Training set)")
+    plot.xlabel("Years of Experience")
+    plot.ylabel("Salary")
     plot.show()
 
     # Visualising the test set results
-    plot.scatter(xtest, yTest, color ='red')
-    plot.plot(xtest, linearRegressor.predict(xtest), color ='blue')
-    plot.title('Salary vs Experience (Test set)')
-    plot.xlabel('Years of Experience')
-    plot.ylabel('Salary')
+    plot.scatter(xtest, yTest, color="red")
+    plot.plot(xtest, linearRegressor.predict(xtest), color="blue")
+    plot.title("Salary vs Experience (Test set)")
+    plot.xlabel("Years of Experience")
+    plot.ylabel("Salary")
     plot.show()

myproject/module.py

@@ -27,9 +27,9 @@ def fib(n: int) -> int:
     """
     if n <= 0:
         raise ValueError("Number has to be positive")
-    elif n == 1: 
+    elif n == 1:
         return 0
-    elif n == 2: 
+    elif n == 2:
         return 1
 
     # TODO: we need to rewrite this not to use recursion. Why is recursion wrong? What will happen for large numbers?

tests/test_module.py

@@ -27,7 +27,7 @@
 
 
 def test_fib_one():
-    assert fib(1) == 1, "The very first Fibonacci number should be..."
+    assert fib(1) == 0, "The very first Fibonacci number should be..."
 
 
 def test_fib_two():
@@ -52,7 +52,7 @@ def test_fib_minus_one():
 
 def test_fib_minus_two():
     """Check that calling fib function with minus two raises an exception."""
-    with pytest.raises(RuntimeError):
+    with pytest.raises(ValueError):
         fib(-2)
 
 

zaks_profile_workshop/README.md

@@ -5,6 +5,15 @@ Profiling should be an integral part of code development. It is a way to measure
 have well thought out designs. Know the runtime of your program when designing. Here is a review of sorting algorithms runtimes. We will be using sorting algoriths 
 in our examples
 
+
+#### Runtimes fastest to slowest
+Constant time – O(1)
+Linear time – O(n)
+Logarithmic time – O(log n)
+Linearithmic time – O(nlog n)
+Factorial time – O(n!)
+Quadratic time – O(n^)
+
 ### Runtimes Time and Space Complexity
 
 | Algorithm      | Best (Time) | Average (Time) | Worst (Time)   | Worst (Space) |

zaks_profile_workshop/example_1_timeit/search_funcs.ipynb

@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TEXT=\"\"\"\n",
+    "\n",
+    "This post will show how to gather Apache Spark Metrics with Prometheus and display the metrics with Grafana in OpenShift 3.9.  We start with a description of the environment, then show how to set up Spark, Prometheus, and Grafana.\n",
+    "\n",
+    "Environment Overview\n",
+    "This is the environment we’re working with. The steps we walk through should apply to slightly newer/older versions of these packages, but it’s possible that you’ll have to tweak things for different releases.\n",
+    "\n",
+    "Red Hat Enterprise Linux 7.5\n",
+    "OpenShift Container Platform 3.9 Cluster\n",
+    "Container Runtime: docker-1.13.1\n",
+    "OpenShift Prometheus 3.9\n",
+    "Apache Spark 2.3.0/Oshinko 0.5.4\n",
+    "Grafana 4.7.0-pre1\n",
+    "Part 1:  Host Cluster Preparation\n",
+    "Install Red Hat Enterprise Linux 7.5 on all nodes in the cluster,  then install OpenShift 3.9 with Openshift Prometheus enabled. Use the host preparation (Section 2.3) and installation guide to install Openshift 3.9 and Openshift Prometheus.\n",
+    "\n",
+    "Create an inventory file with prometheus enabled. We've included an example inventory file with Prometheus enabled, single master, single etcd, and multiple nodes:\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "216 ns ± 2.24 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit (TEXT.find(\"OpenShift\") is not -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "875 ns ± 13.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "%timeit re.search(\"OpenShift\", TEXT)\n"
+   ]
+  }
+ ],
+ "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

zaks_profile_workshop/example_2_cProfile/data.csv

@@ -0,0 +1,31 @@
+YearsExperience,Salary
+1.1,39343.00
+1.3,46205.00
+1.5,37731.00
+2.0,43525.00
+2.2,39891.00
+2.9,56642.00
+3.0,60150.00
+3.2,54445.00
+3.2,64445.00
+3.7,57189.00
+3.9,63218.00
+4.0,55794.00
+4.0,56957.00
+4.1,57081.00
+4.5,61111.00
+4.9,67938.00
+5.1,66029.00
+5.3,83088.00
+5.9,81363.00
+6.0,93940.00
+6.8,91738.00
+7.1,98273.00
+7.9,101302.00
+8.2,113812.00
+8.7,109431.00
+9.0,105582.00
+9.5,116969.00
+9.6,112635.00
+10.3,122391.00
+10.5,121872.00