-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathgood_looking_plots_in_grass.qmd
412 lines (313 loc) · 13.9 KB
/
good_looking_plots_in_grass.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
---
title: "Making plots with GRASS GIS"
author: "Veronica Andreo"
date: 2024-04-25
date-modified: today
format:
html:
toc: true
code-tools: true
code-copy: true
code-fold: false
categories: [GRASS GIS, Plotting, line, histogram, boxplot, Basic]
engine: knitr
execute:
eval: false
---
In previous tutorials we saw examples of how to convert GRASS GIS
raster and vector maps into Python and R objects to perform data
analysis and visualizations.
There are some GRASS GIS tools, mostly based in the well known
[matplotlib](https://matplotlib.org/) Python library,
that allow us to create plots for data visualization without the
need to explicitly convert GRASS data.
Here are these plotting tools for raster, vector and time series
data within GRASS GIS:
| Raster | Vector | Time series |
|-----------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
| [r.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/r.boxplot.html) | [v.scatterplot](https://grass.osgeo.org/grass-stable/manuals/addons/v.scatterplot.html) | [g.gui.tplot](https://grass.osgeo.org/grass-stable/manuals/g.gui.tplot.html) |
| [r.series.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/r.series.boxplot.html) | [v.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/v.boxplot.html) | [t.rast.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/t.rast.boxplot.html) |
| | [v.histogram](https://grass.osgeo.org/grass-stable/manuals/addons/v.histogram.html) | [t.rast.line](https://grass.osgeo.org/grass-stable/manuals/addons/t.rast.line.html) |
In this tutorial, we'll demonstrate their use with maps from the North Carolina
[full dataset](https://grass.osgeo.org/sampledata/north_carolina/nc_spm_08_grass7.zip).
We'll also use a special
[mapset containing MODIS LST data products](https://grass.osgeo.org/sampledata/north_carolina/nc_spm_mapset_modis2015_2016_lst_grass8.zip)
to exemplify tools' usage with time series data. While these tools can be invoked
from the GUI menu or Tools tab, we will show how the GRASS commands look like
so you can run them from the terminal or the Console tab of the GUI.
We also show the command wrapping for Python scripts using the grass.script package.
You can test them in the Python tab. The use of commands facilitates reproducibility
and quick testing of small changes and tweaks.
## Raster plotting tools
### [r.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/r.boxplot.html)
`r.boxplot` is a GRASS GIS addon that allows us to make boxplots with our GRASS
raster maps. It also allows to use a zonal map like a land cover classification
to draw boxplots of a certain variable per classes, i.e., land cover classes.
The tool then contemplates some nice features like the possibility to plot per
class boxplots of the same color that the class is assigned in the zonal map or
create a point vector map with the locations of the outliers, among other tweaks.
Let's see an example using a zonal map, plotting outliers and coloring boxes
with the colors of the zonal map classes:
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=r.boxplot
r.boxplot -oc input=elevation zones=landclass96 output=r.boxplot.png
```
# Python
```{python}
gs.run_command("g.extension", extension="r.boxplot")
gs.run_command("r.boxplot",
input="elevation",
zones="landclass96",
raster_statistics="median,IQR",
output="r.boxplot.png",
flags="oc")
```
:::
### [r.series.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/r.series.boxplot.html)
`r.series.boxplot` draws boxplots of a series of input raster maps that might
represent different times, spectral bands in satellite imagery or other kind
of variation. If users are interested in e.g., ploting the spectral signature
of different land cover classes, they can alternatively set masks and recreate
the boxplot series. Let's see an example for developed and forested classes.
::: {.panel-tabset}
# GRASS
```{bash}
# install the extension
g.extension extension=r.series.boxplot
# add landsat mapset to the list of accessible mapsets
g.mapsets mapset=landsat operation=add
# list of maps and labels
bands=`g.list type=raster pattern="lsat7_2000*" exclude="*6*,*8*" sep=comma`
labels="band1,band2,band3,band4,band5,band7"
r.mask raster=landclass96 maskcats=1
r.series.boxplot map=$bands bxcolor=grey text_labels=$labels output=r.series.boxplot_developed.png
r.mask -r
r.mask raster=landclass96 maskcats=5
r.series.boxplot map=$bands bxcolor=grey \
text_labels=$labels output=r.series.boxplot_forest.png
```
# Python
```{python}
gs.run_command("g.extension", extension="r.series.boxplot")
gs.run_command("g.mapsets", mapset="landsat", operation="add")
bands = gs.list_grouped(type="raster", pattern="lsat7_2000*", exclude="*6*,*8*")["landsat"]
labels = ["band1", "band2", "band3", "band4", "band5", "band7"]
gs.run_command("r.mask", raster="landclass96", maskcats="1")
gs.run_command("r.series.boxplot",
map=bands,
bxcolor="grey",
text_labels=labels,
output="r.series.boxplot_developed.png")
gs.run_command("r.mask", flags="r")
gs.run_command("r.mask", raster="landclass96", maskcats="5")
gs.run_command("r.series.boxplot",
map=bands,
bxcolor="grey",
text_labels=labels,
output="r.series.boxplot_forested.png")
```
:::
::: {layout-ncol=2}
:::
## Vector plotting tools
### [v.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/v.boxplot.html)
`v.boxplot` draws the boxplot of values in a vector map attribute column. It
also provides an option to group by categories in a second attribute column.
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=v.histogram
v.boxplot -o -r map=bridges column=WIDTH group_by=YEAR_BUILT where="YEAR_BUILT < '1920'" order=ascending plot_output=boxplot_bridges_width_per_year.png
```
# Python
```{python}
gs.run_command("g.extension", extension="v.histogram")
gs.run_command("v.boxplot",
map="bridges",
column="WIDTH",
group_by="YEAR_BUILT",
where="YEAR_BUILT < '1920'",
order="ascending",
plot_output="boxplot_bridges_width_per_year.png",
flags="or")
```
:::
### [v.scatterplot](https://grass.osgeo.org/grass-stable/manuals/addons/v.scatterplot.html)
`v.scatterplot` creates a scatterplot with the values of two attribute columns
from a vector map. It provides many arguments to control different plot features
and it exposes some nice matplotlib functionality to do bining, add trend lines
and confidence ellipses. While there's
[r.scatterplot](https://grass.osgeo.org/grass-stable/manuals/addons/r.scatterplot.html)
for raster data, it does not create a plot but a vector map.
Users can, however, sample raster maps with a vector and then create scatterplots
from the sampled data.
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=v.scatterplot
v.scatterplot map=bridges x=YEAR_BUILT y=WIDTH trendline=polynomial degree=1 line_color=red type=density bins=10,10 file_name=scatterplot_bridges_width_vs_year.png
```
# Python
```{python}
gs.run_command("g.extension", extension="v.scatterplot")
gs.run_command("v.scatterplot",
map="bridges",
x="YEAR_BUILT",
y="WIDTH",
trendline="polynomial",
degree=1,
line_color="red",
type="density",
bins="10,10",
file_name="scatterplot_bridges_width_vs_year.png")
```
:::
### [v.histogram](https://grass.osgeo.org/grass-stable/manuals/addons/v.histogram.html)
`v.histogram` draws a histogram of the values in a vector map attribute column.
The tool provides basic options to select values according to a condition and
set the number of bins.
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=v.histogram
v.histogram map=bridges column=WIDTH where="YEAR_BUILT < '1940'" plot_output=histogram_bridges_width.png
```
# Python
```{python}
gs.run_command("g.extension", extension="v.histogram")
gs.run_command("v.histogram",
map="bridges",
column="WIDTH",
where="YEAR_BUILT < '1940'",
plot_output="histogram_bridges_width.png")
```
:::
## Time series plotting tools
### [g.gui.tplot](https://grass.osgeo.org/grass-stable/manuals/g.gui.tplot.html)
`g.gui.tplot` is part of GRASS GIS core distribution and it allows to plot the
values of raster and vector time series. Users can pass coordinate pairs for the
case of raster time series and ids plus attribute column in the case of vector
time series. The module also supports to display the trend line based on a linear
regression and the R-squared value, visualize pop-up annotations, export the time
series values to a text file, among other. Let's see an example for the MODIS
LSD DAY monthly raster time series.
::: {.panel-tabset}
# GRASS
```{bash}
g.region -p raster=MOD11B3.A2015001.h11v05.single_LST_Day_6km
g.gui.tplot -l strds=LST_Day_monthly coordinates=413831,196000 xlabel="Time" ylabel="LST (K*50)" output=LST_plot.png size=1000,800
```
# Python
```{python}
gs.read_command("g.region",
raster="MOD11B3.A2015001.h11v05.single_LST_Day_6km")
gs.run_command("g.gui.tplot",
strds="LST_Day_monthly",
coordinates="413831,196000",
xlabel="Time",
ylabel="LST (K*50)",
output="LST_plot.png",
size="1000,800",
flags="l")
```
:::
### [t.rast.boxplot](https://grass.osgeo.org/grass-stable/manuals/addons/t.rast.boxplot.html)
`t.rast.boxplot` draws boxplots from raster maps in a space-time raster dataset,
hence the x axis is determined by the STRDS temporal granularity, i.e., day, week,
month, etc. Let's see an example for plotting monthly LST within the state of North
Carolina.
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=t.rast.boxplot
g.region -p vector=boundary_county align=MOD11B3.A2015001.h11v05.single_LST_Day_6km
r.mask vector=boundary_county
t.rast.boxplot -o input=LST_Day_monthly dpi=300 rotate_labels=90 font_size=11 date_format="%Y-%m" bx_width=0.7 bx_color=195:194:194:255 flier_color=77:77:77:255
```
# Python
```{python}
gs.run_command("g.extension", extension="t.rast.boxplot")
gs.read_command("g.region", vector="boundary_county", align="MOD11B3.A2015001.h11v05.single_LST_Day_6km")
gs.run_command("r.mask", vector="boundary_county")
gs.run_command("t.rast.boxplot",
input="LST_Day_monthly",
dpi="300",
rotate_labels=90,
font_size=11,
date_format="%Y-%m",
bx_width=0.7,
bx_color="195:194:194:255",
flier_color="77:77:77:255",
flags="o")
```
:::
If users would like to compare boxplot time series representing different areas,
they could alternatively set masks for their areas of interest and then create
the respective boxplot time series.
::: {.panel-tabset}
# GRASS
```{bash}
r.mask vector=geology where="GEO_NAME LIKE '%Zat%'"
t.rast.boxplot -o input=LST_Day_monthly dpi=300 rotate_labels=90 font_size=11 date_format="%Y-%m" bx_width=0.7 bx_color=195:194:194:255 flier_color=77:77:77:255
r.mask -r
r.mask vector=geology where="GEO_NAME LIKE '%Qp%'"
t.rast.boxplot -o input=LST_Day_monthly dpi=300 rotate_labels=90 font_size=11 date_format="%Y-%m" bx_width=0.7 bx_color=195:194:194:255 flier_color=77:77:77:255
```
# Python
```{python}
gs.run_command("r.mask", vector="geology", where="GEO_NAME LIKE '%Zat%'")
gs.run_command("t.rast.boxplot",
input="LST_Day_monthly",
dpi=300,
rotate_labels=90,
font_size=11,
date_format="%Y-%m",
bx_width=0.7,
bx_color="195:194:194:255",
flier_color="77:77:77:255",
flags="o")
gs.run_command("r.mask", flags="r")
gs.run_command("r.mask", vector="geology", where="GEO_NAME LIKE '%Qp%'")
gs.run_command("t.rast.boxplot",
input="LST_Day_monthly",
dpi=300,
rotate_labels=90,
font_size=11,
date_format="%Y-%m",
bx_width=0.7,
bx_color="195:194:194:255",
flier_color="77:77:77:255",
flags="o")
```
:::
### [t.rast.line](https://grass.osgeo.org/grass-stable/manuals/addons/t.rast.line.html)
`t.rast.line` draws line plots from raster maps in a space-time raster dataset and
also allows to pass a zonal map to compare average temporal changes of different
areas of interest in the same plot.
::: {.panel-tabset}
# GRASS
```{bash}
g.extension extension=t.rast.line
t.rast.line input=LST_Day_monthly zones=boundary_county_500m y_label="LST (K*50)" date_format=%Y-%m
```
# Python
```{python}
gs.run_command("g.extension", extension="t.rast.line")
gs.run_command("t.rast.line",
input="LST_Day_monthly",
zones="boundary_county_500m",
y_label="LST (K*50)",
date_format="%Y-%m")
```
:::
