##// END OF EJS Templates
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Matthias BUSSONNIER -
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@@ -100,7 +100,7 b" plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)"
100 plt.text(0.5 * (a + b), 30,r"$\int_a^b f(x)dx$", horizontalalignment='center', fontsize=20);
100 plt.text(0.5 * (a + b), 30,r"$\int_a^b f(x)dx$", horizontalalignment='center', fontsize=20);
101
101
102 # Out[3]:
102 # Out[3]:
103 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_00.svg
103 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_00.svg
104
104
105 # Compute the integral both at high accuracy and with the trapezoid approximation
105 # Compute the integral both at high accuracy and with the trapezoid approximation
106
106
@@ -436,7 +436,7 b" print 'The sum of elements along the columns is :', arr.sum(axis=0)"
436 # The sum of elements along the rows is : [ 6 22]
436 # The sum of elements along the rows is : [ 6 22]
437 # The sum of elements along the columns is : [ 4 6 8 10]
437 # The sum of elements along the columns is : [ 4 6 8 10]
438 #
438 #
439 # As you can see in this example, the value of the `axis` parameter is the dimension which will be *consumed* once the operation has been carried out. This is why to sum along the rows we use `axis=0`.
439 # As you can see in this example, the value of the `axis` parameter is the dimension which will be *consumed* once the operation has been carried out. This is why to sum along the rows we use `axis=0`.
440 #
440 #
441 # This can be easily illustrated with an example that has more dimensions; we create an array with 4 dimensions and shape `(3,4,5,6)` and sum along the axis number 2 (i.e. the *third* axis, since in Python all counts are 0-based). That consumes the dimension whose length was 5, leaving us with a new array that has shape `(3,4,6)`:
441 # This can be easily illustrated with an example that has more dimensions; we create an array with 4 dimensions and shape `(3,4,5,6)` and sum along the axis number 2 (i.e. the *third* axis, since in Python all counts are 0-based). That consumes the dimension whose length was 5, leaving us with a new array that has shape `(3,4,6)`:
442
442
@@ -749,7 +749,7 b" plt.xlabel('x')"
749 plt.ylabel('y');
749 plt.ylabel('y');
750
750
751 # Out[60]:
751 # Out[60]:
752 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_01.svg
752 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_01.svg
753
753
754 # You can control the style, color and other properties of the markers, for example:
754 # You can control the style, color and other properties of the markers, for example:
755
755
@@ -757,13 +757,13 b" plt.ylabel('y');"
757 plt.plot(x, y, linewidth=2);
757 plt.plot(x, y, linewidth=2);
758
758
759 # Out[61]:
759 # Out[61]:
760 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_02.svg
760 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_02.svg
761
761
762 # In[62]:
762 # In[62]:
763 plt.plot(x, y, 'o', markersize=5, color='r');
763 plt.plot(x, y, 'o', markersize=5, color='r');
764
764
765 # Out[62]:
765 # Out[62]:
766 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_03.svg
766 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_03.svg
767
767
768 # We will now see how to create a few other common plot types, such as a simple error plot:
768 # We will now see how to create a few other common plot types, such as a simple error plot:
769
769
@@ -782,7 +782,7 b' plt.errorbar(x, y, xerr=0.2, yerr=0.4)'
782 plt.title("Simplest errorbars, 0.2 in x, 0.4 in y");
782 plt.title("Simplest errorbars, 0.2 in x, 0.4 in y");
783
783
784 # Out[63]:
784 # Out[63]:
785 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_04.svg
785 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_04.svg
786
786
787 # A simple log plot
787 # A simple log plot
788
788
@@ -792,7 +792,7 b' y = np.exp(-x**2)'
792 plt.semilogy(x, y);
792 plt.semilogy(x, y);
793
793
794 # Out[64]:
794 # Out[64]:
795 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_05.svg
795 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_05.svg
796
796
797 # A histogram annotated with text inside the plot, using the `text` function:
797 # A histogram annotated with text inside the plot, using the `text` function:
798
798
@@ -812,7 +812,7 b' plt.axis([40, 160, 0, 0.03])'
812 plt.grid(True)
812 plt.grid(True)
813
813
814 # Out[65]:
814 # Out[65]:
815 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_06.svg
815 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_06.svg
816
816
817 ### Image display
817 ### Image display
818
818
@@ -823,7 +823,7 b' from matplotlib import cm'
823 plt.imshow(np.random.rand(5, 10), cmap=cm.gray, interpolation='nearest');
823 plt.imshow(np.random.rand(5, 10), cmap=cm.gray, interpolation='nearest');
824
824
825 # Out[66]:
825 # Out[66]:
826 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_07.svg
826 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_07.svg
827
827
828 # A real photograph is a multichannel image, `imshow` interprets it correctly:
828 # A real photograph is a multichannel image, `imshow` interprets it correctly:
829
829
@@ -835,7 +835,7 b' plt.imshow(img);'
835 # Out[67]:
835 # Out[67]:
836 # Dimensions of the array img: (375, 500, 3)
836 # Dimensions of the array img: (375, 500, 3)
837 #
837 #
838 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_08.svg
838 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_08.svg
839
839
840 ### Simple 3d plotting with matplotlib
840 ### Simple 3d plotting with matplotlib
841
841
@@ -867,7 +867,7 b' surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,'
867 ax.set_zlim3d(-1.01, 1.01);
867 ax.set_zlim3d(-1.01, 1.01);
868
868
869 # Out[72]:
869 # Out[72]:
870 # image file: tests/ipynbref/IntroNumPy.orig_files/IntroNumPy.orig_fig_09.svg
870 # image file: tests/ipynbref/IntroNumPy_orig_files/IntroNumPy_orig_fig_09.svg
871
871
872 ## IPython: a powerful interactive environment
872 ## IPython: a powerful interactive environment
873
873
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