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32 | 32 | { |
33 | 33 | "cell_type": "code", |
34 | 34 | "execution_count": 3, |
| 35 | + "id": "fbec64f3-9f8e-435f-8a92-8d9ba7c0beb7", |
| 36 | + "metadata": {}, |
| 37 | + "outputs": [ |
| 38 | + { |
| 39 | + "name": "stdout", |
| 40 | + "output_type": "stream", |
| 41 | + "text": [ |
| 42 | + "InlineBackend(InlineBackendConfig) options\n", |
| 43 | + "----------------------------------------\n", |
| 44 | + "InlineBackend.close_figures=<Bool>\n", |
| 45 | + " Close all figures at the end of each cell.\n", |
| 46 | + " When True, ensures that each cell starts with no active figures, but it\n", |
| 47 | + " also means that one must keep track of references in order to edit or\n", |
| 48 | + " redraw figures in subsequent cells. This mode is ideal for the notebook,\n", |
| 49 | + " where residual plots from other cells might be surprising.\n", |
| 50 | + " When False, one must call figure() to create new figures. This means\n", |
| 51 | + " that gcf() and getfigs() can reference figures created in other cells,\n", |
| 52 | + " and the active figure can continue to be edited with pylab/pyplot\n", |
| 53 | + " methods that reference the current active figure. This mode facilitates\n", |
| 54 | + " iterative editing of figures, and behaves most consistently with\n", |
| 55 | + " other matplotlib backends, but figure barriers between cells must\n", |
| 56 | + " be explicit.\n", |
| 57 | + " Current: True\n", |
| 58 | + "InlineBackend.figure_format=<Unicode>\n", |
| 59 | + " The figure format to enable (deprecated\n", |
| 60 | + " use `figure_formats` instead)\n", |
| 61 | + " Current: ''\n", |
| 62 | + "InlineBackend.figure_formats=<set-item-1>...\n", |
| 63 | + " A set of figure formats to enable: 'png',\n", |
| 64 | + " 'retina', 'jpeg', 'svg', 'pdf'.\n", |
| 65 | + " Current: {'jpeg'}\n", |
| 66 | + "InlineBackend.print_figure_kwargs=<key-1>=<value-1>...\n", |
| 67 | + " Extra kwargs to be passed to fig.canvas.print_figure.\n", |
| 68 | + " Logical examples include: bbox_inches, pil_kwargs, etc. In addition,\n", |
| 69 | + " see the docstrings of `set_matplotlib_formats`.\n", |
| 70 | + " Current: {'bbox_inches': None, 'pil_kwargs': {'quality': 90, 'optimize': True}}\n", |
| 71 | + "InlineBackend.rc=<key-1>=<value-1>...\n", |
| 72 | + " Dict to manage matplotlib configuration defaults in the inline\n", |
| 73 | + " backend. As of v0.1.4 IPython/Jupyter do not override defaults out of\n", |
| 74 | + " the box, but third-party tools may use it to manage rc data. To change\n", |
| 75 | + " personal defaults for matplotlib, use matplotlib's configuration\n", |
| 76 | + " tools, or customize this class in your `ipython_config.py` file for\n", |
| 77 | + " IPython/Jupyter-specific usage.\n", |
| 78 | + " Current: {}\n" |
| 79 | + ] |
| 80 | + } |
| 81 | + ], |
| 82 | + "source": [ |
| 83 | + "%config InlineBackend" |
| 84 | + ] |
| 85 | + }, |
| 86 | + { |
| 87 | + "cell_type": "code", |
| 88 | + "execution_count": 4, |
35 | 89 | "id": "0e5b8951-19af-4273-80ca-23b47476d637", |
36 | 90 | "metadata": {}, |
37 | 91 | "outputs": [ |
|
83 | 137 | }, |
84 | 138 | { |
85 | 139 | "cell_type": "code", |
86 | | - "execution_count": 4, |
| 140 | + "execution_count": 5, |
87 | 141 | "id": "89f6de76-9d6b-43c0-b790-e6b63c4c4159", |
88 | 142 | "metadata": {}, |
89 | 143 | "outputs": [ |
|
107 | 161 | }, |
108 | 162 | { |
109 | 163 | "cell_type": "code", |
110 | | - "execution_count": 5, |
| 164 | + "execution_count": 6, |
111 | 165 | "id": "1fc45e92-07c2-49fa-9b3e-2c30e865e5e8", |
112 | 166 | "metadata": {}, |
113 | 167 | "outputs": [ |
|
118 | 172 | "<Figure size 640x480 with 1 Axes>" |
119 | 173 | ] |
120 | 174 | }, |
121 | | - "execution_count": 5, |
| 175 | + "execution_count": 6, |
122 | 176 | "metadata": {}, |
123 | 177 | "output_type": "execute_result" |
124 | 178 | } |
|
131 | 185 | }, |
132 | 186 | { |
133 | 187 | "cell_type": "code", |
134 | | - "execution_count": 6, |
| 188 | + "execution_count": 7, |
135 | 189 | "id": "df3590be-d87d-4c37-8f73-c652eec9a640", |
136 | 190 | "metadata": {}, |
137 | 191 | "outputs": [ |
138 | 192 | { |
139 | 193 | "data": { |
140 | 194 | "text/plain": [ |
141 | | - "{'IPKernelApp': {'connection_file': '/Users/leo/Library/Jupyter/runtime/kernel-488a2b06-f50e-4bf3-98b5-f46e505f928c.json'},\n", |
| 195 | + "{'IPKernelApp': {'connection_file': '/Users/leo/Library/Jupyter/runtime/kernel-66b85c75-26bd-4e95-91ec-25b40babd080.json'},\n", |
142 | 196 | " 'InlineBackend': {'figure_formats': {'jpeg', 'png'},\n", |
143 | 197 | " 'print_figure_kwargs': {'bbox_inches': None, 'pil_kwargs': {'quality': 90}}}}" |
144 | 198 | ] |
145 | 199 | }, |
146 | | - "execution_count": 6, |
| 200 | + "execution_count": 7, |
147 | 201 | "metadata": {}, |
148 | 202 | "output_type": "execute_result" |
149 | 203 | } |
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