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lazarusAlazarusA
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packages, version control in all notebooks
1 parent 39edaf8 commit 21ba03b

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-8
lines changed

‎CodeSnippetsPython/ScientificPlotBasic1D.ipynb

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‎CodeSnippetsPython/ScientificPlotBasic1DEj2.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -131,6 +131,36 @@
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"source": [
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"adjacency_matrix, x, y = plot_net(500, .1, 1, 'FigNet500dot_1')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DContourEj1.ipynb

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"plt.ylabel('$\\psi_2$', fontsize = 22)\n",
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"plt.savefig('basic2D_Ej1C' + '.png', format='png', dpi= 150, bbox_inches='tight');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DContourEj2.ipynb

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"plt.savefig('basic2D_Ej2C' + '.png', format='png', dpi= 150, bbox_inches='tight');\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DContourEj3.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.savefig('basic2D_Ej3C' + '.png', format='png', dpi= 150, bbox_inches='tight');\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DContourEj4.ipynb

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"plt.savefig('basic2D_Ej4C' + '.png', format='png', dpi= 150, bbox_inches='tight');\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DDensityEj1.ipynb

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"plt.ylabel('$\\psi_2$', fontsize = 22)\n",
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"plt.savefig('alphaqubitD' + '.png', format='png', dpi= 150, bbox_inches='tight');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DDensityEj2.ipynb

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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"img = tf.shade(agg1, cmap = viridis, how='linear', span = [0,350])\n",
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"img_map(img,\"map_chaos4\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"... pandas == 0.22.0\n",
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"... datashader == 0.6.5\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"... pandas == \", pd.__version__)\n",
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"print(\"... datashader == \", ds.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DDensityEj3.ipynb

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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"img = tf.shade(agg1, cmap = black_white, how='linear', span = [0,100])\n",
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"img_map(img,\"attractorLinearBW\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"... pandas == 0.22.0\n",
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"... datashader == 0.6.5\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"... pandas == \", pd.__version__)\n",
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"print(\"... datashader == \", ds.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

‎CodeSnippetsPython/ScientificPlotBasic2DDensityEj4.ipynb

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"cell_type": "code",
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"metadata": {},
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"source": [
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"img = tf.shade(agg1, cmap = inferno, how='linear', span = [0,200])\n",
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"img_map(img,\"ikedaLinear\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Packages, version control"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"... numpy == 1.14.0\n",
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"... matplotlib == 2.1.2\n",
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"... pandas == 0.22.0\n",
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"... datashader == 0.6.5\n",
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"__________________________________________________\n"
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]
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}
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],
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"source": [
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"print(\"--\"*25)\n",
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"print(\"... numpy == \", np.__version__)\n",
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"print(\"... matplotlib == \", mpl.__version__)\n",
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"print(\"... pandas == \", pd.__version__)\n",
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"print(\"... datashader == \", ds.__version__)\n",
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"print(\"__\"*25)"
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]
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}
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],
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"metadata": {

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