|
68 | 68 | "cell_type": "markdown", |
69 | 69 | "metadata": {}, |
70 | 70 | "source": [ |
71 | | - "From the fist look we can see that the `Result` only contains a single dataset named `dataset_1`.\n", |
| 71 | + "From the first look we can see that the `Result` only contains a single dataset named `dataset_1`.\n", |
72 | 72 | "\n", |
73 | 73 | "For ease of use let's assign it to a variable `ds` and have a closer look." |
74 | 74 | ] |
|
118 | 118 | "\n", |
119 | 119 | "Now that we know how to access the data we are interested in, let's plot them.\n", |
120 | 120 | "\n", |
121 | | - "Lucky for use `xarray` comes with built in convenience functionality that lets us quickly have a look at the data. \n", |
| 121 | + "Lucky for us `xarray` comes with built in convenience functionality that lets us quickly have a look at the data. \n", |
122 | 122 | "\n", |
123 | 123 | "For data with up to two dimension `xarray` is pretty good guessing what we want to plot by simply \n", |
124 | 124 | "calling the `plot` attribute on our data." |
|
190 | 190 | "metadata": {}, |
191 | 191 | "source": [ |
192 | 192 | "Even so we now have a line plot this isn't what we wanted because each point on the `spectral` \n", |
193 | | - "dimension resulted in it's own line, leaving us with a plot that contains 72 lines." |
| 193 | + "dimension resulted in its own line, leaving us with a plot that contains 72 lines." |
194 | 194 | ] |
195 | 195 | }, |
196 | 196 | { |
|
289 | 289 | "source": [ |
290 | 290 | "from pyglotaran_extras.plotting.utils import extract_irf_location\n", |
291 | 291 | "\n", |
292 | | - "if_location = extract_irf_location(ds)\n", |
| 292 | + "irf_location = extract_irf_location(ds)\n", |
293 | 293 | "ds_shifted = ds.copy()\n", |
294 | | - "ds_shifted[\"time\"] = ds.time - if_location\n", |
| 294 | + "ds_shifted[\"time\"] = ds.time - irf_location\n", |
295 | 295 | "ds_shifted.isel(time=slice(80, 200)).irf.plot();" |
296 | 296 | ] |
297 | 297 | }, |
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