rename and ktx source files added

100_Numpy_exercises.ipynb

@@ -4,16 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# 100 numpy exercises\n",
     "\n",
-    "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow \n",
+    "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow\n",
-    "and in the numpy documentation. The goal of this collection is to offer a quick reference for both old \n",
+    "and in the numpy documentation. The goal of this collection is to offer a quick reference for both old\n",
     "and new users but also to provide a set of exercises for those who teach.\n",
     "\n",
     "\n",
-    "If you find an error or think you've a better way to solve some of them, feel \n",
+    "If you find an error or think you've a better way to solve some of them, feel\n",
-    "free to open an issue at <https://github.com/rougier/numpy-100>\n"
+    "free to open an issue at <https://github.com/rougier/numpy-100>"
    ]
   },
   {
@@ -27,7 +26,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run the `initialize.py` module, then for each question you can query the answer or an hint with `hint(n)` or `answer(n)` for `n` question number."
+    "Run the `initialize.py` module, then for each question you can query the\n",
+    "answer or an hint with `hint(n)` or `answer(n)` for `n` question number."
    ]
   },
   {
@@ -408,7 +408,7 @@
     "print(sum(range(5),-1))\n",
     "from numpy import *\n",
     "print(sum(range(5),-1))\n",
-    "```\n"
+    "```"
    ]
   },
   {
@@ -449,7 +449,7 @@
     "np.array(0) / np.array(0)\n",
     "np.array(0) // np.array(0)\n",
     "np.array([np.nan]).astype(int).astype(float)\n",
-    "```\n"
+    "```"
    ]
   },
   {
@@ -508,7 +508,7 @@
     "#### 32. Is the following expressions true? (★☆☆)\n",
     "```python\n",
     "np.sqrt(-1) == np.emath.sqrt(-1)\n",
-    "```\n"
+    "```"
    ]
   },
   {
@@ -821,7 +821,7 @@
     "1, 2, 3, 4, 5\n",
     "6,  ,  , 7, 8\n",
     " ,  , 9,10,11\n",
-    "```\n"
+    "```"
    ]
   },
   {
@@ -1213,7 +1213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 82. Compute a matrix rank (★★★) "
+    "#### 82. Compute a matrix rank (★★★)"
    ]
   },
   {

100_Numpy_exercises.md

@@ -1,7 +1,6 @@
 
 
 
-
 # 100 numpy exercises
 
 This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
@@ -10,8 +9,7 @@ and new users but also to provide a set of exercises for those who teach.
 
 
 If you find an error or think you've a better way to solve some of them, feel
-free to open an issue at <https://github.com/rougier/numpy-100>
+free to open an issue at <https://github.com/rougier/numpy-100>File automatically generated. See the documentation to update questions/answers/hints programmatically.
-File automatically generated. See the documentation to update questions/answers/hints programmatically.
 #### 1. Import the numpy package under the name `np` (★☆☆)
 
 #### 2. Print the numpy version and the configuration (★☆☆)
@@ -79,7 +77,6 @@ from numpy import *
 print(sum(range(5),-1))
 ```
 
-
 #### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)
 ```python
 Z**Z
@@ -97,7 +94,6 @@ np.array(0) // np.array(0)
 np.array([np.nan]).astype(int).astype(float)
 ```
 
-
 #### 29. How to round away from zero a float array ? (★☆☆)
 
 #### 30. How to find common values between two arrays? (★☆☆)
@@ -109,7 +105,6 @@ np.array([np.nan]).astype(int).astype(float)
 np.sqrt(-1) == np.emath.sqrt(-1)
 ```
 
-
 #### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)
 
 #### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)
@@ -159,7 +154,6 @@ np.sqrt(-1) == np.emath.sqrt(-1)
  ,  , 9,10,11
 ```
 
-
 #### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)
 
 #### 56. Generate a generic 2D Gaussian-like array (★★☆)

100_Numpy_exercises_with_hints.md

@@ -1,7 +1,6 @@
 
 
 
-
 # 100 numpy exercises
 
 This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
@@ -10,10 +9,9 @@ and new users but also to provide a set of exercises for those who teach.
 
 
 If you find an error or think you've a better way to solve some of them, feel
-free to open an issue at <https://github.com/rougier/numpy-100>
+free to open an issue at <https://github.com/rougier/numpy-100>File automatically generated. See the documentation to update questions/answers/hints programmatically.
-File automatically generated. See the documentation to update questions/answers/hints programmatically.
 #### 1. Import the numpy package under the name `np` (★☆☆)
-`hint: import … as `
+`hint: import … as`
 #### 2. Print the numpy version and the configuration (★☆☆)
 `hint: np.__version__, np.show_config)`
 #### 3. Create a null vector of size 10 (★☆☆)
@@ -67,7 +65,7 @@ np.nan in set([np.nan])
 #### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)
 `hint: np.dtype`
 #### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)
-`hint: `
+`hint:`
 #### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)
 `hint: >, <=`
 #### 26. What is the output of the following script? (★☆☆)
@@ -78,7 +76,6 @@ print(sum(range(5),-1))
 from numpy import *
 print(sum(range(5),-1))
 ```
-
 `hint: np.sum`
 #### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)
 ```python
@@ -96,8 +93,7 @@ np.array(0) / np.array(0)
 np.array(0) // np.array(0)
 np.array([np.nan]).astype(int).astype(float)
 ```
-
+`No hints provided...`
-`No hints provided... `
 #### 29. How to round away from zero a float array ? (★☆☆)
 `hint: np.uniform, np.copysign, np.ceil, np.abs`
 #### 30. How to find common values between two arrays? (★☆☆)
@@ -108,7 +104,6 @@ np.array([np.nan]).astype(int).astype(float)
 ```python
 np.sqrt(-1) == np.emath.sqrt(-1)
 ```
-
 `hint: imaginary number`
 #### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)
 `hint: np.datetime64, np.timedelta64`
@@ -158,7 +153,6 @@ np.sqrt(-1) == np.emath.sqrt(-1)
 6,  ,  , 7, 8
  ,  , 9,10,11
 ```
-
 `hint: np.genfromtxt`
 #### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)
 `hint: np.ndenumerate, np.ndindex`
@@ -227,7 +221,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
 #### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)
 `hint: np.add.reduceat`
 #### 88. How to implement the Game of Life using numpy arrays? (★★★)
-`No hints provided... `
+`No hints provided...`
 #### 89. How to get the n largest values of an array (★★★)
 `hint: np.argsort | np.argpartition`
 #### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)
@@ -247,7 +241,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
 #### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)
 `hint: np.einsum`
 #### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?
-`hint: np.cumsum, np.interp `
+`hint: np.cumsum, np.interp`
 #### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)
 `hint: np.logical_and.reduce, np.mod`
 #### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)

100_Numpy_random.ipynb

@@ -4,16 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# 100 numpy exercises\n",
     "\n",
-    "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow \n",
+    "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow\n",
-    "and in the numpy documentation. The goal of this collection is to offer a quick reference for both old \n",
+    "and in the numpy documentation. The goal of this collection is to offer a quick reference for both old\n",
     "and new users but also to provide a set of exercises for those who teach.\n",
     "\n",
     "\n",
-    "If you find an error or think you've a better way to solve some of them, feel \n",
+    "If you find an error or think you've a better way to solve some of them, feel\n",
-    "free to open an issue at <https://github.com/rougier/numpy-100>\n"
+    "free to open an issue at <https://github.com/rougier/numpy-100>"
    ]
   },
   {
@@ -27,7 +26,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run the `initialize.py` module, then call a random question with `pick()`an hint towards its solution with `hint(n)` and the answer with `answer(n)`,where n is the number of the picked question."
+    "Run the `initialize.py` module, then call a random question with `pick()` an hint towards its solution with\n",
+    "`hint(n)` and the answer with `answer(n)`, where n is the number of the picked question."
    ]
   },
   {

README.md

@@ -7,9 +7,11 @@ This is a collection of numpy exercises from numpy mailing list, stack overflow,
 → [Test them on Binder](http://mybinder.org:/repo/rougier/numpy-100/notebooks/100_Numpy_exercises.ipynb)  
 → [Read them on GitHub](100_Numpy_exercises.md)  
 
-Note: markdown and ipython notebook are created programmatically from the dictionary in `data_source.py`.
+Note: markdown and ipython notebook are created programmatically from the source data in `source/exercises.ktx`.
-To modify the content of these files, chance the text in `data_source.py` and then re-generate them via the 
+To modify the content of these files, please change the text in the source and run the `generators.py` module with a python
-method into the `generators.py` module.
+interpreter with the libraries under `requirements.txt` installed.
+
+The keyed text format (`ktx`) is a minimal human readable key-values to store text (markdown or others) indexed by keys. 
 
 This work is licensed under the MIT license.  
 [![DOI](https://zenodo.org/badge/10173/rougier/numpy-100.svg)](https://zenodo.org/badge/latestdoi/10173/rougier/numpy-100)

generators.py

@@ -2,10 +2,41 @@ import os
 import nbformat as nbf
 import mdutils
 
-import data_source as ds
+
+def ktx_to_dict(input_file, keystarter='<'):
+    """ parsing keyed text to a python dictionary. """
+    answer = dict()
+
+    with open(input_file, 'r+') as f:
+        lines = f.readlines()
+
+    k, val = '', ''
+    for line in lines:
+        if line.startswith(keystarter):
+            k = line.replace(keystarter, '').strip()
+            val = ''
+        else:
+            val += line
+
+        if k:
+            answer.update({k: val.strip()})
+
+    return answer
 
 
-def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
+def dict_to_ktx(input_dict, output_file, keystarter='<'):
+    """ Store a python dictionary to a keyed text"""
+    with open(output_file, 'w+') as f:
+        for k, val in input_dict.items():
+            f.write(f'{keystarter} {k}\n')
+            f.write(f'{val}\n\n')
+
+
+HEADERS = ktx_to_dict(os.path.join('source', 'headers.ktx'))
+QHA = ktx_to_dict(os.path.join('source', 'exercises100.ktx'))
+
+
+def create_jupyter_notebook(destination_filename='100_Numpy_exercises.ipynb'):
     """ Programmatically create jupyter notebook with the questions (and hints and solutions if required)
     saved under data_source.py """
 
@@ -15,16 +46,16 @@ def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
     nb['cells'] = []
 
     # - Add header:
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.HEADER))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["header"]))
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.SUB_HEADER))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["sub_header"]))
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.JUPYTER_INSTRUCTIONS))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["jupyter_instruction"]))
 
     # - Add initialisation
     nb['cells'].append(nbf.v4.new_code_cell('%run initialise.py'))
 
     # - Add questions and empty spaces for answers
     for n in range(1, 101):
-        nb['cells'].append(nbf.v4.new_markdown_cell(f'#### {n}. ' + ds.QHA[f'q{n}']))
+        nb['cells'].append(nbf.v4.new_markdown_cell(f'#### {n}. ' + QHA[f'q{n}']))
         nb['cells'].append(nbf.v4.new_code_cell(""))
 
     # Delete file if one with the same name is found
@@ -35,7 +66,7 @@ def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
     nbf.write(nb, destination_filename)
 
 
-def create_jupyter_notebook_random_question(destination_filename='numpy_100_random.ipynb'):
+def create_jupyter_notebook_random_question(destination_filename='100_Numpy_random.ipynb'):
     """ Programmatically create jupyter notebook with the questions (and hints and solutions if required)
     saved under data_source.py """
 
@@ -45,9 +76,9 @@ def create_jupyter_notebook_random_question(destination_filename='numpy_100_rand
     nb['cells'] = []
 
     # - Add header:
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.HEADER))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["header"]))
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.SUB_HEADER))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["sub_header"]))
-    nb['cells'].append(nbf.v4.new_markdown_cell(ds.JUPYTER_INSTRUCTIONS_RAND))
+    nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["jupyter_instruction_rand"]))
 
     # - Add initialisation
     nb['cells'].append(nbf.v4.new_code_cell('%run initialise.py'))
@@ -61,7 +92,7 @@ def create_jupyter_notebook_random_question(destination_filename='numpy_100_rand
     nbf.write(nb, destination_filename)
 
 
-def create_markdown(destination_filename='numpy_100', with_hints=False, with_solutions=False):
+def create_markdown(destination_filename='100_Numpy_exercises', with_hints=False, with_solutions=False):
     # Create file name
     if with_hints:
         destination_filename += '_with_hints'
@@ -72,16 +103,16 @@ def create_markdown(destination_filename='numpy_100', with_hints=False, with_sol
     mdfile = mdutils.MdUtils(file_name=destination_filename)
 
     # Add headers
-    mdfile.write(ds.HEADER)
+    mdfile.write(HEADERS["header"])
-    mdfile.write(ds.SUB_HEADER)
+    mdfile.write(HEADERS["sub_header"])
 
     # Add questions (and hint or answers if required)
     for n in range(1, 101):
-        mdfile.new_header(title=f"{n}. {ds.QHA[f'q{n}']}", level=4)
+        mdfile.new_header(title=f"{n}. {QHA[f'q{n}']}", level=4)
         if with_hints:
-            mdfile.write(f"`{ds.QHA[f'h{n}']}`")
+            mdfile.write(f"`{QHA[f'h{n}']}`")
         if with_solutions:
-            mdfile.insert_code(ds.QHA[f'a{n}'], language='python')
+            mdfile.insert_code(QHA[f'a{n}'], language='python')
 
     # Delete file if one with the same name is found
     if os.path.exists(destination_filename):

initialise.py

@@ -1,18 +1,18 @@
 import numpy as np
 
-import data_source as ds
+import generators as ge
 
 
 def question(n):
-    print(f'{n}. ' + ds.QHA[f'q{n}'])
+    print(f'{n}. ' + ge.QHA[f'q{n}'])
 
 
 def hint(n):
-    print(ds.QHA[f'h{n}'])
+    print(ge.QHA[f'h{n}'])
 
 
 def answer(n):
-    print(ds.QHA[f'a{n}'])
+    print(ge.QHA[f'a{n}'])
 
 
 def pick():

source/headers.ktx

@@ -0,0 +1,25 @@
+< header
+# 100 numpy exercises
+
+This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
+and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
+and new users but also to provide a set of exercises for those who teach.
+
+
+If you find an error or think you've a better way to solve some of them, feel
+free to open an issue at <https://github.com/rougier/numpy-100>
+
+
+< sub_header
+File automatically generated. See the documentation to update questions/answers/hints programmatically.
+
+
+< jupyter_instruction
+Run the `initialize.py` module, then for each question you can query the
+answer or an hint with `hint(n)` or `answer(n)` for `n` question number.
+
+
+< jupyter_instruction_rand
+Run the `initialize.py` module, then call a random question with `pick()` an hint towards its solution with
+`hint(n)` and the answer with `answer(n)`, where n is the number of the picked question.
+