|
17 | 17 | "source": [ |
18 | 18 | "import numpy as np\n", |
19 | 19 | "import pandas as pd\n", |
| 20 | + "import xarray as xr\n", |
20 | 21 | "\n", |
21 | | - "from xarray_plotly import DataArray # IDE completion for .plotly" |
| 22 | + "import xarray_plotly" |
22 | 23 | ] |
23 | 24 | }, |
24 | 25 | { |
|
39 | 40 | "np.random.seed(42)\n", |
40 | 41 | "\n", |
41 | 42 | "# Time series data\n", |
42 | | - "da_ts = DataArray(\n", |
| 43 | + "da_ts = xr.DataArray(\n", |
43 | 44 | " np.random.randn(30, 3).cumsum(axis=0),\n", |
44 | 45 | " dims=[\"time\", \"category\"],\n", |
45 | 46 | " coords={\n", |
|
50 | 51 | ")\n", |
51 | 52 | "\n", |
52 | 53 | "# 2D grid data\n", |
53 | | - "da_2d = DataArray(\n", |
| 54 | + "da_2d = xr.DataArray(\n", |
54 | 55 | " np.random.rand(20, 30),\n", |
55 | 56 | " dims=[\"lat\", \"lon\"],\n", |
56 | 57 | " coords={\n", |
|
61 | 62 | ")\n", |
62 | 63 | "\n", |
63 | 64 | "# Categorical data\n", |
64 | | - "da_cat = DataArray(\n", |
| 65 | + "da_cat = xr.DataArray(\n", |
65 | 66 | " np.random.rand(4, 3) * 100,\n", |
66 | 67 | " dims=[\"product\", \"region\"],\n", |
67 | 68 | " coords={\n", |
|
160 | 161 | "outputs": [], |
161 | 162 | "source": [ |
162 | 163 | "# Use absolute values for stacking to make sense\n", |
163 | | - "da_positive = DataArray(\n", |
| 164 | + "da_positive = xr.DataArray(\n", |
164 | 165 | " np.abs(np.random.randn(30, 3)) * 10,\n", |
165 | 166 | " dims=[\"time\", \"source\"],\n", |
166 | 167 | " coords={\n", |
|
228 | 229 | "outputs": [], |
229 | 230 | "source": [ |
230 | 231 | "# Create data with more samples\n", |
231 | | - "da_dist = DataArray(\n", |
| 232 | + "da_dist = xr.DataArray(\n", |
232 | 233 | " np.random.randn(100, 4) + np.array([0, 1, 2, 3]),\n", |
233 | 234 | " dims=[\"sample\", \"group\"],\n", |
234 | 235 | " coords={\"group\": [\"Control\", \"Treatment A\", \"Treatment B\", \"Treatment C\"]},\n", |
|
294 | 295 | "outputs": [], |
295 | 296 | "source": [ |
296 | 297 | "# 3D data for faceting\n", |
297 | | - "da_3d = DataArray(\n", |
| 298 | + "da_3d = xr.DataArray(\n", |
298 | 299 | " np.random.randn(30, 3, 2).cumsum(axis=0),\n", |
299 | 300 | " dims=[\"time\", \"city\", \"scenario\"],\n", |
300 | 301 | " coords={\n", |
|
329 | 330 | "outputs": [], |
330 | 331 | "source": [ |
331 | 332 | "# Create monthly data\n", |
332 | | - "da_monthly = DataArray(\n", |
| 333 | + "da_monthly = xr.DataArray(\n", |
333 | 334 | " np.random.rand(12, 4) * 100,\n", |
334 | 335 | " dims=[\"month\", \"product\"],\n", |
335 | 336 | " coords={\n", |
336 | | - " \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n", |
| 337 | + " \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \n", |
337 | 338 | " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"],\n", |
338 | 339 | " \"product\": [\"A\", \"B\", \"C\", \"D\"],\n", |
339 | 340 | " },\n", |
|
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