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@@ -126,8 +126,7 b'' | |||||
126 | ], |
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126 | ], | |
127 | "language": "python", |
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127 | "language": "python", | |
128 | "metadata": {}, |
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128 | "metadata": {}, | |
129 |
"outputs": [] |
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129 | "outputs": [] | |
130 | "prompt_number": "*" |
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131 | }, |
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130 | }, | |
132 | { |
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131 | { | |
133 | "cell_type": "heading", |
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132 | "cell_type": "heading", | |
@@ -154,6 +153,7 b'' | |||||
154 | "metadata": {}, |
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153 | "metadata": {}, | |
155 | "outputs": [ |
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154 | "outputs": [ | |
156 | { |
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155 | { | |
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156 | "metadata": {}, | |||
157 | "output_type": "pyout", |
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157 | "output_type": "pyout", | |
158 | "prompt_number": 4, |
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158 | "prompt_number": 4, | |
159 | "text": [ |
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159 | "text": [ | |
@@ -372,7 +372,7 b'' | |||||
372 | "cell_type": "code", |
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372 | "cell_type": "code", | |
373 | "collapsed": false, |
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373 | "collapsed": false, | |
374 | "input": [ |
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374 | "input": [ | |
375 |
"%load http://matplotlib. |
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375 | "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py" | |
376 | ], |
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376 | ], | |
377 | "language": "python", |
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377 | "language": "python", | |
378 | "metadata": {}, |
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378 | "metadata": {}, | |
@@ -383,50 +383,72 b'' | |||||
383 | "cell_type": "code", |
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383 | "cell_type": "code", | |
384 | "collapsed": false, |
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384 | "collapsed": false, | |
385 | "input": [ |
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385 | "input": [ | |
386 | "#!/usr/bin/env python\n", |
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386 | "\"\"\"\n", | |
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387 | "Plot demonstrating the integral as the area under a curve.\n", | |||
387 | "\n", |
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388 | "\n", | |
388 | "# implement the example graphs/integral from pyx\n", |
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389 | "Although this is a simple example, it demonstrates some important tweaks:\n", | |
389 | "from pylab import *\n", |
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390 | "\n", | |
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391 | " * A simple line plot with custom color and line width.\n", | |||
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392 | " * A shaded region created using a Polygon patch.\n", | |||
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393 | " * A text label with mathtext rendering.\n", | |||
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394 | " * figtext calls to label the x- and y-axes.\n", | |||
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395 | " * Use of axis spines to hide the top and right spines.\n", | |||
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396 | " * Custom tick placement and labels.\n", | |||
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397 | "\"\"\"\n", | |||
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398 | "import numpy as np\n", | |||
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399 | "import matplotlib.pyplot as plt\n", | |||
390 | "from matplotlib.patches import Polygon\n", |
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400 | "from matplotlib.patches import Polygon\n", | |
391 | "\n", |
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401 | "\n", | |
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402 | "\n", | |||
392 | "def func(x):\n", |
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403 | "def func(x):\n", | |
393 | " return (x-3)*(x-5)*(x-7)+85\n", |
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404 | " return (x - 3) * (x - 5) * (x - 7) + 85\n", | |
394 | "\n", |
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405 | "\n", | |
395 | "ax = subplot(111)\n", |
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396 | "\n", |
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406 | "\n", | |
397 |
"a, b = 2, 9 # integral |
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407 | "a, b = 2, 9 # integral limits\n", | |
398 |
"x = |
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408 | "x = np.linspace(0, 10)\n", | |
399 | "y = func(x)\n", |
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409 | "y = func(x)\n", | |
400 | "plot(x, y, linewidth=1)\n", |
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401 | "\n", |
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410 | "\n", | |
402 | "# make the shaded region\n", |
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411 | "fig, ax = plt.subplots()\n", | |
403 | "ix = arange(a, b, 0.01)\n", |
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412 | "plt.plot(x, y, 'r', linewidth=2)\n", | |
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413 | "plt.ylim(ymin=0)\n", | |||
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414 | "\n", | |||
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415 | "# Make the shaded region\n", | |||
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416 | "ix = np.linspace(a, b)\n", | |||
404 | "iy = func(ix)\n", |
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417 | "iy = func(ix)\n", | |
405 | "verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]\n", |
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418 | "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", | |
406 |
"poly = Polygon(verts, facecolor='0. |
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419 | "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", | |
407 | "ax.add_patch(poly)\n", |
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420 | "ax.add_patch(poly)\n", | |
408 | "\n", |
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421 | "\n", | |
409 | "text(0.5 * (a + b), 30,\n", |
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422 | "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", | |
410 |
" |
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423 | " horizontalalignment='center', fontsize=20)\n", | |
411 | " fontsize=20)\n", |
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424 | "\n", | |
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425 | "plt.figtext(0.9, 0.05, '$x$')\n", | |||
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426 | "plt.figtext(0.1, 0.9, '$y$')\n", | |||
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427 | "\n", | |||
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428 | "ax.spines['right'].set_visible(False)\n", | |||
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429 | "ax.spines['top'].set_visible(False)\n", | |||
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430 | "ax.xaxis.set_ticks_position('bottom')\n", | |||
412 | "\n", |
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431 | "\n", | |
413 |
"ax |
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432 | "ax.set_xticks((a, b))\n", | |
414 | "figtext(0.9, 0.05, 'x')\n", |
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433 | "ax.set_xticklabels(('$a$', '$b$'))\n", | |
415 | "figtext(0.1, 0.9, 'y')\n", |
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416 | "ax.set_xticks((a,b))\n", |
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417 | "ax.set_xticklabels(('a','b'))\n", |
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418 | "ax.set_yticks([])\n", |
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434 | "ax.set_yticks([])\n", | |
419 |
" |
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435 | "\n", | |
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436 | "plt.show()\n" | |||
420 | ], |
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437 | ], | |
421 | "language": "python", |
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438 | "language": "python", | |
422 | "metadata": {}, |
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439 | "metadata": {}, | |
423 | "outputs": [ |
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440 | "outputs": [ | |
424 | { |
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441 | { | |
425 |
"metadata": { |
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442 | "metadata": { | |
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443 | "png": { | |||
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444 | "height": 401, | |||
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445 | "width": 596 | |||
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446 | } | |||
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447 | }, | |||
426 | "output_type": "display_data", |
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448 | "output_type": "display_data", | |
427 | "png": 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KfvnLXwYdv2jRol4fh3JuAAOPy3zH0Nzt07LSGt149nDZ2VMKCKu9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\nAAAAQvD/AKh2fy0Nfo/2AAAAAElFTkSuQmCC\n", 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GqjSNtilToulXv8qMe/be2yf5UXeUVAAAADBGladPj5bvfS8z69t991g8a1ZE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\n9ttvwdcKJ++//74k6c4777Q5CQAAOBlKKhSNz6fEfv1kZGQER2ZSktK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| |
428 | "text": [ |
|
450 | "text": [ | |
429 |
"<matplotlib.figure.Figure at 0x1 |
|
451 | "<matplotlib.figure.Figure at 0x1078d7e10>" | |
430 | ] |
|
452 | ] | |
431 | } |
|
453 | } | |
432 | ], |
|
454 | ], | |
@@ -436,4 +458,4 b'' | |||||
436 | "metadata": {} |
|
458 | "metadata": {} | |
437 | } |
|
459 | } | |
438 | ] |
|
460 | ] | |
439 | } |
|
461 | } No newline at end of file |
@@ -141,7 +141,7 b'' | |||||
141 | "cell_type": "code", |
|
141 | "cell_type": "code", | |
142 | "collapsed": false, |
|
142 | "collapsed": false, | |
143 | "input": [ |
|
143 | "input": [ | |
144 |
"%load http://matplotlib. |
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144 | "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py" | |
145 | ], |
|
145 | ], | |
146 | "language": "python", |
|
146 | "language": "python", | |
147 | "metadata": {}, |
|
147 | "metadata": {}, | |
@@ -152,50 +152,72 b'' | |||||
152 | "cell_type": "code", |
|
152 | "cell_type": "code", | |
153 | "collapsed": false, |
|
153 | "collapsed": false, | |
154 | "input": [ |
|
154 | "input": [ | |
155 | "#!/usr/bin/env python\n", |
|
155 | "\"\"\"\n", | |
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156 | "Plot demonstrating the integral as the area under a curve.\n", | |||
156 | "\n", |
|
157 | "\n", | |
157 | "# implement the example graphs/integral from pyx\n", |
|
158 | "Although this is a simple example, it demonstrates some important tweaks:\n", | |
158 | "from pylab import *\n", |
|
159 | "\n", | |
|
160 | " * A simple line plot with custom color and line width.\n", | |||
|
161 | " * A shaded region created using a Polygon patch.\n", | |||
|
162 | " * A text label with mathtext rendering.\n", | |||
|
163 | " * figtext calls to label the x- and y-axes.\n", | |||
|
164 | " * Use of axis spines to hide the top and right spines.\n", | |||
|
165 | " * Custom tick placement and labels.\n", | |||
|
166 | "\"\"\"\n", | |||
|
167 | "import numpy as np\n", | |||
|
168 | "import matplotlib.pyplot as plt\n", | |||
159 | "from matplotlib.patches import Polygon\n", |
|
169 | "from matplotlib.patches import Polygon\n", | |
160 | "\n", |
|
170 | "\n", | |
|
171 | "\n", | |||
161 | "def func(x):\n", |
|
172 | "def func(x):\n", | |
162 | " return (x-3)*(x-5)*(x-7)+85\n", |
|
173 | " return (x - 3) * (x - 5) * (x - 7) + 85\n", | |
163 | "\n", |
|
174 | "\n", | |
164 | "ax = subplot(111)\n", |
|
|||
165 | "\n", |
|
175 | "\n", | |
166 |
"a, b = 2, 9 # integral |
|
176 | "a, b = 2, 9 # integral limits\n", | |
167 |
"x = |
|
177 | "x = np.linspace(0, 10)\n", | |
168 | "y = func(x)\n", |
|
178 | "y = func(x)\n", | |
169 | "plot(x, y, linewidth=1)\n", |
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|||
170 | "\n", |
|
179 | "\n", | |
171 | "# make the shaded region\n", |
|
180 | "fig, ax = plt.subplots()\n", | |
172 | "ix = arange(a, b, 0.01)\n", |
|
181 | "plt.plot(x, y, 'r', linewidth=2)\n", | |
|
182 | "plt.ylim(ymin=0)\n", | |||
|
183 | "\n", | |||
|
184 | "# Make the shaded region\n", | |||
|
185 | "ix = np.linspace(a, b)\n", | |||
173 | "iy = func(ix)\n", |
|
186 | "iy = func(ix)\n", | |
174 | "verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]\n", |
|
187 | "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", | |
175 |
"poly = Polygon(verts, facecolor='0. |
|
188 | "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", | |
176 | "ax.add_patch(poly)\n", |
|
189 | "ax.add_patch(poly)\n", | |
177 | "\n", |
|
190 | "\n", | |
178 | "text(0.5 * (a + b), 30,\n", |
|
191 | "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", | |
179 |
" |
|
192 | " horizontalalignment='center', fontsize=20)\n", | |
180 | " fontsize=20)\n", |
|
|||
181 | "\n", |
|
193 | "\n", | |
182 | "axis([0,10, 0, 180])\n", |
|
194 | "plt.figtext(0.9, 0.05, '$x$')\n", | |
183 |
"figtext(0. |
|
195 | "plt.figtext(0.1, 0.9, '$y$')\n", | |
184 | "figtext(0.1, 0.9, 'y')\n", |
|
196 | "\n", | |
185 | "ax.set_xticks((a,b))\n", |
|
197 | "ax.spines['right'].set_visible(False)\n", | |
186 | "ax.set_xticklabels(('a','b'))\n", |
|
198 | "ax.spines['top'].set_visible(False)\n", | |
|
199 | "ax.xaxis.set_ticks_position('bottom')\n", | |||
|
200 | "\n", | |||
|
201 | "ax.set_xticks((a, b))\n", | |||
|
202 | "ax.set_xticklabels(('$a$', '$b$'))\n", | |||
187 | "ax.set_yticks([])\n", |
|
203 | "ax.set_yticks([])\n", | |
188 |
" |
|
204 | "\n", | |
|
205 | "plt.show()\n" | |||
189 | ], |
|
206 | ], | |
190 | "language": "python", |
|
207 | "language": "python", | |
191 | "metadata": {}, |
|
208 | "metadata": {}, | |
192 | "outputs": [ |
|
209 | "outputs": [ | |
193 | { |
|
210 | { | |
194 |
"metadata": { |
|
211 | "metadata": { | |
|
212 | "png": { | |||
|
213 | "height": 401, | |||
|
214 | "width": 596 | |||
|
215 | } | |||
|
216 | }, | |||
195 | "output_type": "display_data", |
|
217 | "output_type": "display_data", | |
196 | "png": 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GqjSNtilToulXv8qMe/be2yf5UXeUVAAAADBGladPj5bvfS8z69t991g8a1ZE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\n9ttvwdcKJ++//74k6c4777Q5CQAAOBlKKhSNz6fEfv1kZGQER2ZSktK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197 | "text": [ |
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219 | "text": [ | |
198 |
"<matplotlib.figure.Figure at 0x106 |
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220 | "<matplotlib.figure.Figure at 0x106ef1190>" | |
199 | ] |
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221 | ] | |
200 | } |
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222 | } | |
201 | ], |
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223 | ], |
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