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Kanatani #4

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6 changes: 6 additions & 0 deletions .ipynb_checkpoints/Untitled-checkpoint.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,6 @@
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
}
76 changes: 63 additions & 13 deletions .ipynb_checkpoints/tutorial1-checkpoint.ipynb

Large diffs are not rendered by default.

38 changes: 32 additions & 6 deletions .ipynb_checkpoints/tutorial2-checkpoint.ipynb

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23 changes: 17 additions & 6 deletions .ipynb_checkpoints/tutorial3-checkpoint.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-04-14T18:06:11.631499",
Expand All @@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-04-14T18:07:35.308000",
Expand All @@ -63,18 +63,18 @@
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f4082ed7cd0>]"
"[<matplotlib.lines.Line2D at 0x7f54683618d0>]"
]
},
"execution_count": 5,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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LiqBrV51laJKhoEBHfiR9F6N9++Cdd2wzliSpVw969cru8MVEJ/QpU2wxrqQ5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\nEJbQjTEmISyhG2NMQvx/u7BLb8aDDPkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f40c01b7f50>"
"<matplotlib.figure.Figure at 0x7f546f19ef90>"
]
},
"metadata": {},
Expand Down Expand Up @@ -113,7 +113,18 @@
"collapsed": true
},
"outputs": [],
"source": []
"source": [
"A=1.0\n",
"ganma=\n",
"x0=\n",
"x=np.arange(0, 6, 0.1)\n",
"y=A*ganma/(1+((x-x0)/ganma)**2.0)\n",
"plt.plot(x, y)\n",
"plt.xlabel(\"x\",fontsize=14)\n",
"plt.ylabel('L',fontsize=14)\n",
"plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
"plt.ylim(y.min() * 1.1, y.max() * 1.1)"
]
},
{
"cell_type": "markdown",
Expand Down
76 changes: 63 additions & 13 deletions tutorial1.ipynb

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34 changes: 30 additions & 4 deletions tutorial2.ipynb

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