Merge pull request #19418 from melissawm/link-tutorials

DOC: Removing tutorials from sphinx documentation

6 files changed, 2 insertions, 1044 deletions

diff --git a/doc/source/user/index.rst b/doc/source/user/index.rst
index b47d6634e..9cfdfa1aa 100644
--- a/doc/source/user/index.rst
+++ b/doc/source/user/index.rst
@@ -21,7 +21,7 @@ details are found in :ref:`reference`.
    numpy-for-matlab-users
    building
    c-info
-   tutorials_index
+   NumPy Tutorials <https://numpy.org/numpy-tutorials>
    howtos_index
    depending_on_numpy
 
diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst
index 9f3d6a040..6143b1a3d 100644
--- a/doc/source/user/quickstart.rst
+++ b/doc/source/user/quickstart.rst
@@ -1482,4 +1482,4 @@ Further reading
 -  `SciPy Tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/index.html>`__
 -  `SciPy Lecture Notes <https://scipy-lectures.org>`__
 -  A `matlab, R, IDL, NumPy/SciPy dictionary <http://mathesaurus.sf.net/>`__
--  :doc:`tutorial-svd`
+-  `Linear algebra on n-dimensional arrays (Tutorial) <https://numpy.org/numpy-tutorials/content/tutorial-svd.html>`__
diff --git a/doc/source/user/tutorial-ma.rst b/doc/source/user/tutorial-ma.rst
deleted file mode 100644
index a21c4aae1..000000000
--- a/doc/source/user/tutorial-ma.rst
+++ /dev/null
@@ -1,387 +0,0 @@
-=======================
-Tutorial: Masked Arrays
-=======================
-
-.. currentmodule:: numpy
-
-.. testsetup::
-
-   import numpy as np
-   np.random.seed(1)
-
-Prerequisites
--------------
-
-Before reading this tutorial, you should know a bit of Python. If you
-would like to refresh your memory, take a look at the
-:doc:`Python tutorial <python:tutorial/index>`.
-
-If you want to be able to run the examples in this tutorial, you should also
-have `matplotlib <https://matplotlib.org/>`_ installed on your computer.
-
-Learner profile
----------------
-
-This tutorial is for people who have a basic understanding of NumPy and want to
-understand how masked arrays and the :mod:`numpy.ma` module can be used in
-practice.
-
-Learning Objectives
--------------------
-
-After this tutorial, you should be able to:
-
-- Understand what are masked arrays and how they can be created
-- Understand how to access and modify data for masked arrays
-- Decide when the use of masked arrays is appropriate in some of your
-  applications
-
-What are masked arrays?
------------------------
-
-Consider the following problem. You have a dataset with missing or invalid
-entries. If you're doing any kind of processing on this data, and want to
-`skip` or flag these unwanted entries without just deleting them, you may have
-to use conditionals or filter your data somehow. The :mod:`numpy.ma` module
-provides some of the same funcionality of
-:class:`NumPy ndarrays <numpy.ndarray>` with added structure to ensure
-invalid entries are not used in computation.
-
-From the :mod:`Reference Guide <numpy.ma>`:
-
-    A masked array is the combination of a standard :class:`numpy.ndarray` and
-    a **mask**. A mask is either ``nomask``, indicating that no value of the
-    associated array is invalid, or an array of booleans that determines for
-    each element of the associated array whether the value is valid or not.
-    When an element of the mask is ``False``, the corresponding element of the
-    associated array is valid and is said to be unmasked. When an element of
-    the mask is ``True``, the corresponding element of the associated array is
-    said to be masked (invalid).
-
-
-We can think of a :class:`MaskedArray <numpy.ma.MaskedArray>` as a
-combination of:
-
-- Data, as a regular :class:`numpy.ndarray` of any shape or datatype;
-- A boolean mask with the same shape as the data;
-- A ``fill_value``, a value that may be used to replace the invalid entries
-  in order to return a standard :class:`numpy.ndarray`.
-
-When can they be useful?
-------------------------
-
-There are a few situations where masked arrays can be more useful than just
-eliminating the invalid entries of an array:
-
-- When you want to preserve the values you masked for later processing, without
-  copying the array;
-- When you have to handle many arrays, each with their own mask. If the mask is
-  part of the array, you avoid bugs and the code is possibly more compact;
-- When you have different flags for missing or invalid values, and wish to
-  preserve these flags without replacing them in the original dataset, but
-  exclude them from computations;
-- If you can't avoid or eliminate missing values, but don't want to deal with
-  :class:`NaN <numpy.nan>` (Not A Number) values in your operations.
-
-Masked arrays are also a good idea since the :mod:`numpy.ma` module also
-comes with a specific implementation of most :term:`NumPy universal functions
-(ufuncs) <ufunc>`, which means that you can still apply fast vectorized
-functions and operations on masked data. The output is then a masked array.
-We'll see some examples of how this works in practice below.
-
-Using masked arrays to see COVID-19 data
-----------------------------------------
-
-From `Kaggle <https://www.kaggle.com/atilamadai/covid19>`_ it is possible to
-download a dataset with initial data about the COVID-19 outbreak in the
-beginning of 2020. We are going to look at a small subset of this data,
-contained in the file ``who_covid_19_sit_rep_time_series.csv``.
-
-.. ipython:: python
-
-    import numpy as np
-    import os
-    # The os.getcwd() function returns the current folder; you can change
-    # the filepath variable to point to the folder where you saved the .csv file
-    filepath = os.getcwd()
-    @suppress
-    filepath = os.path.join(filepath, "source", "user")
-    filename = os.path.join(filepath, "who_covid_19_sit_rep_time_series.csv")
-
-The data file contains data of different types and is organized as follows:
-
-- The first row is a header line that (mostly) describes the data in each column
-  that follow in the rows below, and beginning in the fourth column, the header
-  is the date of the observation.
-- The second through seventh row contain summary data that is of a different
-  type than that which we are going to examine, so we will need to exclude that
-  from the data with which we will work.
-- The numerical data we wish to work with begins at column 4, row 8, and extends
-  from there to the rightmost column and the lowermost row.
-
-Let's explore the data inside this file for the first 14 days of records. To
-gather data from the ``.csv`` file, we will use the :func:`numpy.genfromtxt`
-function, making sure we select only the columns with actual numbers instead of
-the first three columns which contain location data. We also skip the first 7
-rows of this file, since they contain other data we are not interested in.
-Separately, we will extract the information about dates and location for this
-data.
-
-.. ipython:: python
-
-    # Note we are using skip_header and usecols to read only portions of the
-    # data file into each variable.
-    # Read just the dates for columns 3-7 from the first row
-    dates = np.genfromtxt(filename, dtype=np.unicode_, delimiter=",",
-                          max_rows=1, usecols=range(3, 17),
-                          encoding="utf-8-sig")
-    # Read the names of the geographic locations from the first two
-    # columns, skipping the first seven rows
-    locations = np.genfromtxt(filename, dtype=np.unicode_, delimiter=",",
-                              skip_header=7, usecols=(0, 1),
-                              encoding="utf-8-sig")
-    # Read the numeric data from just the first 14 days
-    nbcases = np.genfromtxt(filename, dtype=np.int_, delimiter=",",
-                            skip_header=7, usecols=range(3, 17),
-                            encoding="utf-8-sig")
-
-Included in the :func:`numpy.genfromtxt` function call, we have selected the
-:class:`numpy.dtype` for each subset of the data (either an integer -
-:class:`numpy.int_` - or a string of characters - :class:`numpy.unicode_`). We
-have also used the ``encoding`` argument to select ``utf-8-sig`` as the encoding
-for the file (read more about encoding in the `official Python documentation
-<https://docs.python.org/3/library/codecs.html#encodings-and-unicode>`__). You
-can read more about the :func:`numpy.genfromtxt` function from
-the :func:`Reference Documentation <numpy.genfromtxt>` or from the
-:doc:`Basic IO tutorial <basics.io.genfromtxt>`.
-
-Exploring the data
-------------------
-
-First of all, we can plot the whole set of data we have and see what it looks
-like. In order to get a readable plot, we select only a few of the dates to
-show in our :func:`x-axis ticks <matplotlib.pyplot.xticks>`. Note also that in
-our plot command, we use ``nbcases.T`` (the transpose of the ``nbcases`` array)
-since this means we will plot each row of the file as a separate line. We choose
-to plot a dashed line (using the ``'--'`` line style). See the
-`matplotlib <https://matplotlib.org/>`_ documentation for more info on this.
-
-.. ipython:: python
-
-    import matplotlib.pyplot as plt
-    selected_dates = [0, 3, 11, 13]
-    plt.plot(dates, nbcases.T, '--');
-    plt.xticks(selected_dates, dates[selected_dates]);
-    @savefig plot_covid_1.png
-    plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020");
-
-.. note::
-
-   If you are executing the commands above in the IPython shell, it might be
-   necessary to use the command ``plt.show()`` to show the image window. Note
-   also that we use a semicolon at the end of a line to suppress its output, but
-   this is optional.
-
-The graph has a strange shape from January 24th to February 1st. It would be
-interesing to know where this data comes from. If we look at the ``locations``
-array we extracted from the ``.csv`` file, we can see that we have two columns,
-where the first would contain regions and the second would contain the name of
-the country. However, only the first few rows contain data for the the first
-column (province names in China). Following that, we only have country names. So
-it would make sense to group all the data from China into a single row. For
-this, we'll select from the ``nbcases`` array only the rows for which the
-second entry of the ``locations`` array corresponds to China. Next, we'll use
-the :func:`numpy.sum` function to sum all the selected rows (``axis=0``):
-
-.. ipython:: python
-
-    china_total = nbcases[locations[:, 1] == 'China'].sum(axis=0)
-    china_total
-
-Something's wrong with this data - we are not supposed to have negative values
-in a cumulative data set. What's going on?
-
-Missing data
-------------
-
-Looking at the data, here's what we find: there is a period with
-**missing data**:
-
-.. ipython:: python
-
-    nbcases
-
-All the ``-1`` values we are seeing come from :func:`numpy.genfromtxt`
-attempting to read missing data from the original ``.csv`` file. Obviously, we
-don't want to compute missing data as ``-1`` - we just want to skip this value
-so it doesn't interfere in our analysis. After importing the :mod:`numpy.ma`
-module, we'll create a new array, this time masking the invalid values:
-
-.. ipython:: python
-
-    from numpy import ma
-    nbcases_ma = ma.masked_values(nbcases, -1)
-
-If we look at the ``nbcases_ma`` masked array, this is what we have:
-
-.. ipython:: python
-
-    nbcases_ma
-
-We can see that this is a different kind of array. As mentioned in the
-introduction, it has three attributes (``data``, ``mask`` and ``fill_value``).
-Keep in mind that the ``mask`` attribute has a ``True`` value for elements
-corresponding to **invalid** data (represented by two dashes in the ``data``
-attribute).
-
-.. note::
-
-   Adding ``-1`` to missing data is not a problem with :func:`numpy.genfromtxt`;
-   in this particular case, substituting the missing value with ``0`` might have
-   been fine, but we'll see later that this is far from a general solution.
-   Also, it is possible to call the :func:`numpy.genfromtxt` function using the
-   ``usemask`` parameter. If ``usemask=True``, :func:`numpy.genfromtxt`
-   automatically returns a masked array.
-
-Let's try and see what the data looks like excluding the first row
-(data from the Hubei province in China) so we can look at the missing data more
-closely:
-
-.. ipython:: python
-
-    plt.plot(dates, nbcases_ma[1:].T, '--');
-    plt.xticks(selected_dates, dates[selected_dates]);
-    @savefig plot_covid_2.png
-    plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020");
-
-Now that our data has been masked, let's try summing up all the cases in China:
-
-.. ipython:: python
-
-    china_masked = nbcases_ma[locations[:, 1] == 'China'].sum(axis=0)
-    china_masked
-
-Note that ``china_masked`` is a masked array, so it has a different data
-structure than a regular NumPy array. Now, we can access its data directly by
-using the ``.data`` attribute:
-
-.. ipython:: python
-
-    china_total = china_masked.data
-    china_total
-
-That is better: no more negative values. However, we can still see that for some
-days, the cumulative number of cases seems to go down (from 835 to 10, for
-example), which does not agree with the definition of "cumulative data". If we
-look more closely at the data, we can see that in the period where there was
-missing data in mainland China, there was valid data for Hong Kong, Taiwan,
-Macau and "Unspecified" regions of China. Maybe we can remove those from the
-total sum of cases in China, to get a better understanding of the data.
-
-First, we'll identify the indices of locations in mainland China:
-
-.. ipython:: python
-
-   china_mask = ((locations[:, 1] == 'China') &
-                 (locations[:, 0] != 'Hong Kong') &
-                 (locations[:, 0] != 'Taiwan') &
-                 (locations[:, 0] != 'Macau') &
-                 (locations[:, 0] != 'Unspecified*'))
-
-Now, ``china_mask`` is an array of boolean values (``True`` or ``False``); we
-can check that the indices are what we wanted with the :func:`ma.nonzero` method
-for masked arrays:
-
-.. ipython:: python
-
-    china_mask.nonzero()
-
-Now we can correctly sum entries for mainland China:
-
-.. ipython:: python
-
-    china_total = nbcases_ma[china_mask].sum(axis=0)
-    china_total
-
-We can replace the data with this information and plot a new graph, focusing on
-Mainland China:
-
-.. ipython:: python
-
-    plt.plot(dates, china_total.T, '--');
-    plt.xticks(selected_dates, dates[selected_dates]);
-    @savefig plot_covid_3.png
-    plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China");
-
-It's clear that masked arrays are the right solution here. We cannot represent
-the missing data without mischaracterizing the evolution of the curve.
-
-Fitting Data
-------------
-
-One possibility we can think of is to interpolate the missing data to estimate
-the number of cases in late January. Observe that we can select the masked
-elements using the ``.mask`` attribute:
-
-.. ipython:: python
-
-    china_total.mask
-    invalid = china_total[china_total.mask]
-    invalid
-
-We can also access the valid entries by using the logical negation for this
-mask:
-
-.. ipython:: python
-
-    valid = china_total[~china_total.mask]
-    valid
-
-Now, if we want to create a very simple approximation for this data, we should
-take into account the valid entries around the invalid ones. So first let's
-select the dates for which the data is valid. Note that we can use the mask
-from the ``china_total`` masked array to index the dates array:
-
-.. ipython:: python
-
-    dates[~china_total.mask]
-
-Finally, we can use the :func:`numpy.polyfit` and :func:`numpy.polyval`
-functions to create a cubic polynomial that fits the data as best as possible:
-
-.. ipython:: python
-
-    t = np.arange(len(china_total))
-    params = np.polyfit(t[~china_total.mask], valid, 3)
-    cubic_fit = np.polyval(params, t)
-    plt.plot(t, china_total);
-    @savefig plot_covid_4.png
-    plt.plot(t, cubic_fit, '--');
-
-This plot is not so readable since the lines seem to be over each other, so
-let's summarize in a more elaborate plot. We'll plot the real data when
-available, and show the cubic fit for unavailable data, using this fit to
-compute an estimate to the observed number of cases on January 28th 2020, 7 days
-after the beginning of the records:
-
-.. ipython:: python
-
-    plt.plot(t, china_total, label='Mainland China');
-    plt.plot(t[china_total.mask], cubic_fit[china_total.mask], '--',
-             color='orange', label='Cubic estimate');
-    plt.plot(7, np.polyval(params, 7), 'r*', label='7 days after start');
-    plt.xticks([0, 7, 13], dates[[0, 7, 13]]);
-    plt.yticks([0, np.polyval(params, 7), 10000, 17500]);
-    plt.legend();
-    @savefig plot_covid_5.png
-    plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China\n"
-              "Cubic estimate for 7 days after start");
-
-More reading
-------------
-
-Topics not covered in this tutorial can be found in the documentation:
-
-- :func:`Hardmasks <numpy.ma.harden_mask>` vs. :func:`softmasks
-  <numpy.ma.soften_mask>`
-- :ref:`The numpy.ma module <maskedarray.generic>`
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst
deleted file mode 100644
index 7b905e51e..000000000
--- a/doc/source/user/tutorial-svd.rst
+++ /dev/null
@@ -1,524 +0,0 @@
-================================================
-Tutorial: Linear algebra on n-dimensional arrays
-================================================
-
-.. currentmodule:: numpy
-
-.. testsetup::
-
-   import numpy as np
-   np.random.seed(1)
-
-Prerequisites
--------------
-
-Before reading this tutorial, you should know a bit of Python. If you
-would like to refresh your memory, take a look at the
-:doc:`Python tutorial <python:tutorial/index>`.
-
-If you want to be able to run the examples in this tutorial, you should also
-have `matplotlib <https://matplotlib.org/>`_ and `SciPy <https://scipy.org>`_
-installed on your computer.
-
-Learner profile
----------------
-
-This tutorial is for people who have a basic understanding of linear
-algebra and arrays in NumPy and want to understand how n-dimensional
-(:math:`n>=2`) arrays are represented and can be manipulated. In particular, if
-you don't know how to apply common functions to n-dimensional arrays (without
-using for-loops), or if you want to understand axis and shape properties for
-n-dimensional arrays, this tutorial might be of help.
-
-Learning Objectives
--------------------
-
-After this tutorial, you should be able to:
-
-- Understand the difference between one-, two- and n-dimensional arrays in
-  NumPy;
-- Understand how to apply some linear algebra operations to n-dimensional
-  arrays without using for-loops;
-- Understand axis and shape properties for n-dimensional arrays.
-
-Content
--------
-
-In this tutorial, we will use a `matrix decomposition
-<https://en.wikipedia.org/wiki/Matrix_decomposition>`_ from linear algebra, the
-Singular Value Decomposition, to generate a compressed approximation of an
-image. We'll use the ``face`` image from the `scipy.misc` module:
-
-    >>> from scipy import misc
-    >>> img = misc.face()
-
-.. note::
-
-   If you prefer, you can use your own image as you work through this tutorial.
-   In order to transform your image into a NumPy array that can be manipulated,
-   you can use the ``imread`` function from the `matplotlib.pyplot` submodule.
-   Alternatively, you can use the :func:`imageio.imread` function from the
-   ``imageio`` library. Be aware that if you use your own image, you'll likely
-   need to adapt the steps below. For more information on how images are treated
-   when converted to NumPy arrays, see :std:doc:`user_guide/numpy_images` from
-   the ``scikit-image`` documentation.
-
-Now, ``img`` is a NumPy array, as we can see when using the ``type`` function::
-
-    >>> type(img)
-    <class 'numpy.ndarray'>
-
-We can see the image using the `matplotlib.pyplot.imshow` function::
-
-    >>> import matplotlib.pyplot as plt
-    >>> plt.imshow(img)
-
-.. plot:: user/plot_face.py
-    :align: center
-    :include-source: 0
-
-.. note::
-
-   If you are executing the commands above in the IPython shell, it might be
-   necessary to use the command ``plt.show()`` to show the image window. 
-		     
-Shape, axis and array properties
---------------------------------
-
-Note that, in linear algebra, the dimension of a vector refers to the number of
-entries in an array. In NumPy, it instead defines the number of axes. For
-example, a 1D array is a vector such as ``[1, 2, 3]``, a 2D array is a matrix,
-and so forth.
-
-First, let's check for the shape of the data in our array. Since this image is
-two-dimensional (the pixels in the image form a rectangle), we might expect a
-two-dimensional array to represent it (a matrix). However, using the ``shape``
-property of this NumPy array gives us a different result::
-
-    >>> img.shape
-    (768, 1024, 3)
-
-The output is a :ref:`tuple <python:tut-tuples>` with three elements, which means
-that this is a three-dimensional array. In fact, since this is a color image, and
-we have used the ``imread`` function to read it, the data is organized in three 2D
-arrays, representing color channels (in this case, red, green and blue - RGB). You
-can see this by looking at the shape above: it indicates that we have an array of
-3 matrices, each having shape 768x1024.
-
-Furthermore, using the ``ndim`` property of this array, we can see that
-
-::
-
-    >>> img.ndim
-    3
-
-NumPy refers to each dimension as an `axis`. Because of how ``imread``
-works, the *first index in the 3rd axis* is the red pixel data for our image. We
-can access this by using the syntax
-
-::
-
-    >>> img[:, :, 0]
-    array([[121, 138, 153, ..., 119, 131, 139],
-           [ 89, 110, 130, ..., 118, 134, 146],
-           [ 73,  94, 115, ..., 117, 133, 144],
-           ...,
-           [ 87,  94, 107, ..., 120, 119, 119],
-           [ 85,  95, 112, ..., 121, 120, 120],
-           [ 85,  97, 111, ..., 120, 119, 118]], dtype=uint8)
-
-From the output above, we can see that every value in ``img[:,:,0]`` is an
-integer value between 0 and 255, representing the level of red in each
-corresponding image pixel (keep in mind that this might be different if you
-use your own image instead of `scipy.misc.face`).
-
-As expected, this is a 768x1024 matrix::
-
-    >>> img[:, :, 0].shape
-    (768, 1024)
-
-Since we are going to perform linear algebra operations on this data, it might
-be more interesting to have real numbers between 0 and 1 in each entry of the
-matrices to represent the RGB values. We can do that by setting
-
-    >>> img_array = img / 255
-
-This operation, dividing an array by a scalar, works because of NumPy's
-:ref:`broadcasting rules <array-broadcasting-in-numpy>`). (Note that in
-real-world applications, it would be better to use, for example, the
-:func:`img_as_float <skimage.img_as_float>` utility function from
-``scikit-image``).
-
-You can check that the above works by doing some tests; for example, inquiring
-about maximum and minimum values for this array::
-
-    >>> img_array.max(), img_array.min()
-    (1.0, 0.0)
-
-or checking the type of data in the array::
-
-    >>> img_array.dtype
-    dtype('float64')
-
-Note that we can assign each color channel to a separate matrix using the slice
-syntax::
-
-    >>> red_array = img_array[:, :, 0]
-    >>> green_array = img_array[:, :, 1]
-    >>> blue_array = img_array[:, :, 2]
-
-Operations on an axis
----------------------
-
-It is possible to use methods from linear algebra to approximate an existing set
-of data. Here, we will use the `SVD (Singular Value Decomposition)
-<https://en.wikipedia.org/wiki/Singular_value_decomposition>`_ to try to rebuild
-an image that uses less singular value information than the original one, while
-still retaining some of its features.
-
-.. note::
-
-    We will use NumPy's linear algebra module, `numpy.linalg`, to
-    perform the operations in this tutorial. Most of the linear algebra
-    functions in this module can also be found in `scipy.linalg`, and
-    users are encouraged to use the `scipy` module for real-world
-    applications. However, it is currently not possible to apply linear
-    algebra operations to n-dimensional arrays using the `scipy.linalg`
-    module. For more information on this, check the
-    :doc:`scipy.linalg Reference<scipy:tutorial/linalg>`.
-
-To proceed, import the linear algebra submodule from NumPy::
-
-    >>> from numpy import linalg
-
-In order to extract information from a given matrix, we can use the SVD to obtain
-3 arrays which can be multiplied to obtain the original matrix. From the theory
-of linear algebra, given a matrix :math:`A`, the following product can be
-computed:
-
-.. math::
-
-   U \Sigma V^T = A
-
-where :math:`U` and :math:`V^T` are square and :math:`\Sigma` is the same size
-as :math:`A`. :math:`\Sigma` is a diagonal matrix and contains the
-`singular values <https://en.wikipedia.org/wiki/Singular_value>`_ of :math:`A`,
-organized from largest to smallest. These values are always non-negative and can
-be used as an indicator of the "importance" of some features represented by the
-matrix :math:`A`.
-
-Let's see how this works in practice with just one matrix first. Note that
-according to `colorimetry <https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-preserving)_conversion_to_grayscale>`_,
-it is possible to obtain a fairly reasonable grayscale version of our color
-image if we apply the formula
-
-.. math::
-
-   Y = 0.2126 R + 0.7152 G + 0.0722 B
-
-where :math:`Y` is the array representing the grayscale image, and :math:`R, G`
-and :math:`B` are the red, green and blue channel arrays we had originally.
-Notice we can use the ``@`` operator (the matrix multiplication operator for
-NumPy arrays, see `numpy.matmul`) for this:
-
-::
-   
-   >>> img_gray = img_array @ [0.2126, 0.7152, 0.0722]
-
-Now, ``img_gray`` has shape
-
-::
-
-   >>> img_gray.shape
-   (768, 1024)
-
-To see if this makes sense in our image, we should use a colormap from
-``matplotlib`` corresponding to the color we wish to see in out image
-(otherwise, ``matplotlib`` will default to a colormap that does not
-correspond to the real data).
-
-In our case, we are approximating the grayscale portion of the image, so we
-will use the colormap ``gray``::
-   
-   >>> plt.imshow(img_gray, cmap="gray")
-
-.. plot:: user/plot_gray.py
-    :align: center
-    :include-source: 0
-
-Now, applying the `linalg.svd` function to this matrix, we obtain the
-following decomposition:
-::
-
-    >>> U, s, Vt = linalg.svd(img_gray)
-
-.. note::
-
-    If you are using your own image, this command might take a while to run,
-    depending on the size of your image and your hardware. Don't worry, this
-    is normal! The SVD can be a pretty intensive computation.
-   
-Let's check that this is what we expected::
-
-    >>> U.shape, s.shape, Vt.shape
-    ((768, 768), (768,), (1024, 1024))
-
-Note that ``s`` has a particular shape: it has only one dimension. This
-means that some linear algebra functions that expect 2d arrays might not work.
-For example, from the theory, one might expect ``s`` and ``Vt`` to be
-compatible for multiplication. However, this is not true as ``s`` does not
-have a second axis. Executing
-
-::
-
-    >>> s @ Vt
-    Traceback (most recent call last):
-      ...
-    ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0,
-    with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1024 is different from
-    768)
-
-results in a ``ValueError``. This happens because having a one-dimensional
-array for ``s``, in this case, is much more economic in practice than building a
-diagonal matrix with the same data. To reconstruct the original matrix, we can
-rebuild the diagonal matrix :math:`\Sigma` with the elements of ``s`` in its
-diagonal and with the appropriate dimensions for multiplying: in our case,
-:math:`\Sigma` should be 768x1024 since ``U`` is 768x768 and ``Vt`` is
-1024x1024.
-
-::
-
-    >>> import numpy as np
-    >>> Sigma = np.zeros((768, 1024))
-    >>> for i in range(768):
-    ...     Sigma[i, i] = s[i]
-
-Now, we want to check if the reconstructed ``U @ Sigma @ Vt`` is
-close to the original ``img_gray`` matrix.
-
-Approximation
--------------
-
-The `linalg` module includes a ``norm`` function, which
-computes the norm of a vector or matrix represented in a NumPy array. For
-example, from the SVD explanation above, we would expect the norm of the
-difference between ``img_gray`` and the reconstructed SVD product to be small.
-As expected, you should see something like
-
-::
-
-    >>> linalg.norm(img_gray - U @ Sigma @ Vt)
-    1.3926466851808837e-12
-
-(The actual result of this operation might be different depending on your
-architecture and linear algebra setup. Regardless, you should see a small
-number.)
-
-We could also have used the `numpy.allclose` function to make sure the
-reconstructed product is, in fact, *close* to our original matrix (the
-difference between the two arrays is small)::
-
-    >>> np.allclose(img_gray, U @ Sigma @ Vt)
-    True
-
-To see if an approximation is reasonable, we can check the values in ``s``::
-
-    >>> plt.plot(s)
-
-.. plot:: user/plot_gray_svd.py
-    :align: center
-    :include-source: 0
-    
-In the graph, we can see that although we have 768 singular values in
-``s``, most of those (after the 150th entry or so) are pretty small. So it
-might make sense to use only the information related to the first (say, 50)
-*singular values* to build a more economical approximation to our image.
-
-The idea is to consider all but the first ``k`` singular values in
-``Sigma`` (which are the same as in ``s``) as zeros, keeping
-``U`` and ``Vt`` intact, and computing the product of these matrices
-as the approximation.
-
-For example, if we choose 
-
-::
-
-    >>> k = 10
-
-we can build the approximation by doing
-
-::
-
-    >>> approx = U @ Sigma[:, :k] @ Vt[:k, :]
-
-Note that we had to use only the first ``k`` rows of ``Vt``, since all
-other rows would be multiplied by the zeros corresponding to the singular
-values we eliminated from this approximation.
-
-::
-   
-    >>> plt.imshow(approx, cmap="gray")
-
-.. plot:: user/plot_approx.py
-    :align: center
-    :include-source: 0
-
-Now, you can go ahead and repeat this experiment with other values of `k`, and
-each of your experiments should give you a slightly better (or worse) image
-depending on the value you choose.
-
-Applying to all colors
-----------------------
-
-Now we want to do the same kind of operation, but to all three colors. Our
-first instinct might be to repeat the same operation we did above to each color
-matrix individually. However, NumPy's `broadcasting` takes care of this
-for us.
-
-If our array has more than two dimensions, then the SVD can be applied to all
-axes at once. However, the linear algebra functions in NumPy expect to see an
-array of the form ``(N, M, M)``, where the first axis represents the number
-of matrices.
-
-In our case,
-
-::
-
-    >>> img_array.shape
-    (768, 1024, 3)
-
-so we need to permutate the axis on this array to get a shape like
-``(3, 768, 1024)``. Fortunately, the `numpy.transpose` function can do that for
-us:
-
-::
-   
-   np.transpose(x, axes=(i, j, k))
-
-indicates that the axis will be reordered such that the final shape of the
-transposed array will be reordered according to the indices ``(i, j, k)``.
-
-Let's see how this goes for our array::
-
-    >>> img_array_transposed = np.transpose(img_array, (2, 0, 1))
-    >>> img_array_transposed.shape
-    (3, 768, 1024)
-
-Now we are ready to apply the SVD::
-
-    >>> U, s, Vt = linalg.svd(img_array_transposed)
-
-Finally, to obtain the full approximated image, we need to reassemble these
-matrices into the approximation. Now, note that
-
-::
-
-    >>> U.shape, s.shape, Vt.shape
-    ((3, 768, 768), (3, 768), (3, 1024, 1024))
-
-To build the final approximation matrix, we must understand how multiplication
-across different axes works.
-
-Products with n-dimensional arrays
-----------------------------------
-
-If you have worked before with only one- or two-dimensional arrays in NumPy,
-you might use `numpy.dot` and `numpy.matmul` (or the ``@`` operator)
-interchangeably. However, for n-dimensional arrays, they work in very different
-ways. For more details, check the documentation `numpy.matmul`.
-
-Now, to build our approximation, we first need to make sure that our singular
-values are ready for multiplication, so we build our ``Sigma`` matrix similarly
-to what we did before. The ``Sigma`` array must have dimensions
-``(3, 768, 1024)``. In order to add the singular values to the diagonal of
-``Sigma``, we will use the `numpy.fill_diagonal` function from NumPy, using each of
-the 3 rows in ``s`` as the diagonal for each of the 3 matrices in ``Sigma``:
-
-::
-
-    >>> Sigma = np.zeros((3, 768, 1024))
-    >>> for j in range(3):
-    ...     np.fill_diagonal(Sigma[j, :, :], s[j, :])
-
-Now, if we wish to rebuild the full SVD (with no approximation), we can do
-
-::
-
-    >>> reconstructed = U @ Sigma @ Vt
-
-Note that
-
-::
-
-    >>> reconstructed.shape
-    (3, 768, 1024)
-
-and
-
-::
-
-    >>> plt.imshow(np.transpose(reconstructed, (1, 2, 0)))
-
-.. plot:: user/plot_reconstructed.py
-    :align: center
-    :include-source: 0
-    
-should give you an image indistinguishable from the original one (although we
-may introduce floating point errors for this reconstruction). In fact, 
-you might see a warning message saying `"Clipping input data to the
-valid range for imshow with RGB data ([0..1] for floats or [0..255] for
-integers)."` This is expected from the manipulation we just did on the original
-image.
-
-Now, to do the approximation, we must choose only the first ``k`` singular
-values for each color channel. This can be done using the following syntax::
-
-    >>> approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :]
-
-You can see that we have selected only the first ``k`` components of the last
-axis for ``Sigma`` (this means that we have used only the first ``k`` columns
-of each of the three matrices in the stack), and that we have selected only the
-first ``k`` components in the second-to-last axis of ``Vt`` (this means we have
-selected only the first ``k`` rows from every matrix in the stack ``Vt`` and
-all columns). If you are unfamiliar with the ellipsis syntax, it is a
-placeholder for other axes. For more details, see the documentation on
-:ref:`Indexing <basics.indexing>`.
-
-Now,
-
-::
-
-    >>> approx_img.shape
-    (3, 768, 1024)
-
-which is not the right shape for showing the image. Finally, reordering the axes 
-back to our original shape of ``(768, 1024, 3)``, we can see our approximation::
-
-    >>> plt.imshow(np.transpose(approx_img, (1, 2, 0)))
-
-.. plot:: user/plot_final.py
-    :align: center
-    :include-source: 0
-    
-Even though the image is not as sharp, using a small number of ``k`` singular
-values (compared to the original set of 768 values), we can recover many of the
-distinguishing features from this image.
-
-Final words
------------
-
-Of course, this is not the best method to *approximate* an image.
-However, there is, in fact, a result in linear algebra that says that the
-approximation we built above is the best we can get to the original matrix in
-terms of the norm of the difference. For more information, see *G. H. Golub and
-C. F. Van Loan, Matrix Computations, Baltimore, MD, Johns Hopkins University
-Press, 1985*.
-
-Further reading
----------------
-
--  :doc:`Python tutorial <python:tutorial/index>`
--  :ref:`reference`
--  :doc:`SciPy Tutorial <scipy:tutorial/index>`
--  `SciPy Lecture Notes <https://scipy-lectures.org>`__
--  `A matlab, R, IDL, NumPy/SciPy dictionary <http://mathesaurus.sf.net/>`__
diff --git a/doc/source/user/tutorials_index.rst b/doc/source/user/tutorials_index.rst
deleted file mode 100644
index 20e2c256c..000000000
--- a/doc/source/user/tutorials_index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-.. _tutorials:
-
-################
-NumPy Tutorials
-################
-
-These documents are intended as an introductory overview of NumPy and its 
-features. For detailed reference documentation of the functions and
-classes contained in the package, see the :ref:`API reference <reference>`.
-
-.. toctree::
-   :maxdepth: 1
-
-   tutorial-svd
-   tutorial-ma
-
diff --git a/doc/source/user/who_covid_19_sit_rep_time_series.csv b/doc/source/user/who_covid_19_sit_rep_time_series.csv
deleted file mode 100644
index 8ad5c2c23..000000000
--- a/doc/source/user/who_covid_19_sit_rep_time_series.csv
+++ /dev/null
@@ -1,115 +0,0 @@
-Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20
-Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870
-Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304
-Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566
-Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,,
-Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946
-Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217
-Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350
-Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272
-Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213
-Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018
-Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990
-Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935
-Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758
-Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631
-Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576
-Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538
-Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480
-Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414
-Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338
-Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318
-Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296
-Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252
-Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245
-Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174
-Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168
-Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146
-Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136
-Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133
-Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125
-Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101
-Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93
-Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91
-Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76
-Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75
-Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74
-Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42
-Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18
-Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10
-Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268
-,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812
-,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43
-,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64
-,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16
-,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108
-,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036
-,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33
-,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29
-,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27
-,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191
-,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501
-,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5
-,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157
-,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3
-,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114
-,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39
-,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15
-,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30
-,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18
-,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25
-,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56
-,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49
-,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21
-,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10
-,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26
-,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6
-,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13
-,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5
-,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2
-,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9
-,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7
-,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7
-,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5
-,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2
-,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3
-,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8
-,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5
-,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3
-,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3
-,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1
-,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2
-,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18
-,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5
-,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8
-,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3
-,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7
-,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6
-,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3
-,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9
-,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2
-,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2
-,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-Case on an international conveyance,Other,Other,,,,,,,,,,,,,,,,,20,61,64,64,70,135,175,174,218,218,355,454,454,542,621,634,634,634,695,691,691,705,705,705,706,706,706
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