Skip to content

Commit fe82461

Browse files
mcflugenmcflugen
committed
Use print function.
1 parent ed70ecf commit fe82461

2 files changed

Lines changed: 58 additions & 60 deletions

File tree

‎ecohydrology/cellular_automaton_vegetation_DEM/cellular_automaton_vegetation_DEM.ipynb‎

Lines changed: 29 additions & 30 deletions
Original file line numberDiff line numberDiff line change
@@ -80,10 +80,20 @@
8080
},
8181
{
8282
"cell_type": "code",
83-
"execution_count": 1,
83+
"execution_count": null,
8484
"metadata": {
85-
"collapsed": false
85+
"collapsed": true
8686
},
87+
"outputs": [],
88+
"source": [
89+
"from __future__ import print_function\n",
90+
"%matplotlib inline"
91+
]
92+
},
93+
{
94+
"cell_type": "code",
95+
"execution_count": 1,
96+
"metadata": {},
8797
"outputs": [
8898
{
8999
"name": "stderr",
@@ -99,7 +109,6 @@
99109
}
100110
],
101111
"source": [
102-
"%matplotlib inline\n",
103112
"import time\n",
104113
"import numpy as np\n",
105114
"from landlab.io import read_esri_ascii\n",
@@ -132,7 +141,7 @@
132141
"cell_type": "code",
133142
"execution_count": 2,
134143
"metadata": {
135-
"collapsed": false
144+
"collapsed": true
136145
},
137146
"outputs": [],
138147
"source": [
@@ -151,7 +160,7 @@
151160
"cell_type": "code",
152161
"execution_count": 3,
153162
"metadata": {
154-
"collapsed": false
163+
"collapsed": true
155164
},
156165
"outputs": [],
157166
"source": [
@@ -187,7 +196,7 @@
187196
"cell_type": "code",
188197
"execution_count": 4,
189198
"metadata": {
190-
"collapsed": false
199+
"collapsed": true
191200
},
192201
"outputs": [],
193202
"source": [
@@ -205,9 +214,7 @@
205214
{
206215
"cell_type": "code",
207216
"execution_count": 5,
208-
"metadata": {
209-
"collapsed": false
210-
},
217+
"metadata": {},
211218
"outputs": [
212219
{
213220
"data": {
@@ -261,7 +268,7 @@
261268
"cell_type": "code",
262269
"execution_count": 6,
263270
"metadata": {
264-
"collapsed": false
271+
"collapsed": true
265272
},
266273
"outputs": [],
267274
"source": [
@@ -287,7 +294,7 @@
287294
"cell_type": "code",
288295
"execution_count": 7,
289296
"metadata": {
290-
"collapsed": false
297+
"collapsed": true
291298
},
292299
"outputs": [],
293300
"source": [
@@ -306,7 +313,7 @@
306313
"cell_type": "code",
307314
"execution_count": 8,
308315
"metadata": {
309-
"collapsed": false
316+
"collapsed": true
310317
},
311318
"outputs": [],
312319
"source": [
@@ -325,7 +332,7 @@
325332
"cell_type": "code",
326333
"execution_count": 9,
327334
"metadata": {
328-
"collapsed": false
335+
"collapsed": true
329336
},
330337
"outputs": [],
331338
"source": [
@@ -354,9 +361,7 @@
354361
{
355362
"cell_type": "code",
356363
"execution_count": 10,
357-
"metadata": {
358-
"collapsed": false
359-
},
364+
"metadata": {},
360365
"outputs": [
361366
{
362367
"name": "stdout",
@@ -445,7 +450,7 @@
445450
" # Cellular Automata\n",
446451
" if (current_time - time_check) >= 1.:\n",
447452
" if yrs % 5 == 0:\n",
448-
" print 'Elapsed time = ', yrs, ' years'\n",
453+
" print('Elapsed time = {time} years'.format(time=yrs))\n",
449454
" VegType[yrs] = grid['cell']['vegetation__plant_functional_type']\n",
450455
" grid['cell']['vegetation__cumulative_water_stress'] = WS/Tg\n",
451456
" vegca.update()\n",
@@ -467,9 +472,7 @@
467472
{
468473
"cell_type": "code",
469474
"execution_count": 11,
470-
"metadata": {
471-
"collapsed": false
472-
},
475+
"metadata": {},
473476
"outputs": [
474477
{
475478
"name": "stdout",
@@ -482,7 +485,7 @@
482485
"source": [
483486
"Final_time = time.clock()\n",
484487
"Time_Consumed = (Final_time - Start_time)/60. # in minutes\n",
485-
"print 'Time_consumed = ', Time_Consumed, ' minutes'"
488+
"print('Time_consumed = {time} minutes'.format(time=Time_Consumed))"
486489
]
487490
},
488491
{
@@ -496,7 +499,7 @@
496499
"cell_type": "code",
497500
"execution_count": 12,
498501
"metadata": {
499-
"collapsed": false
502+
"collapsed": true
500503
},
501504
"outputs": [],
502505
"source": [
@@ -521,9 +524,7 @@
521524
{
522525
"cell_type": "code",
523526
"execution_count": 13,
524-
"metadata": {
525-
"collapsed": false
526-
},
527+
"metadata": {},
527528
"outputs": [
528529
{
529530
"name": "stdout",
@@ -608,9 +609,7 @@
608609
{
609610
"cell_type": "code",
610611
"execution_count": 14,
611-
"metadata": {
612-
"collapsed": false
613-
},
612+
"metadata": {},
614613
"outputs": [
615614
{
616615
"data": {
@@ -660,9 +659,9 @@
660659
"name": "python",
661660
"nbconvert_exporter": "python",
662661
"pygments_lexer": "ipython2",
663-
"version": "2.7.11"
662+
"version": "2.7.13"
664663
}
665664
},
666665
"nbformat": 4,
667-
"nbformat_minor": 0
666+
"nbformat_minor": 1
668667
}

‎ecohydrology/cellular_automaton_vegetation_flat_surface/cellular_automaton_vegetation_flat_domain.ipynb‎

Lines changed: 29 additions & 30 deletions
Original file line numberDiff line numberDiff line change
@@ -80,10 +80,20 @@
8080
},
8181
{
8282
"cell_type": "code",
83-
"execution_count": 1,
83+
"execution_count": null,
8484
"metadata": {
85-
"collapsed": false
85+
"collapsed": true
8686
},
87+
"outputs": [],
88+
"source": [
89+
"from __future__ import print_function\n",
90+
"%matplotlib inline"
91+
]
92+
},
93+
{
94+
"cell_type": "code",
95+
"execution_count": 1,
96+
"metadata": {},
8797
"outputs": [
8898
{
8999
"name": "stderr",
@@ -99,7 +109,6 @@
99109
}
100110
],
101111
"source": [
102-
"%matplotlib inline\n",
103112
"import time\n",
104113
"import numpy as np\n",
105114
"from landlab import RasterModelGrid as rmg\n",
@@ -131,7 +140,7 @@
131140
"cell_type": "code",
132141
"execution_count": 2,
133142
"metadata": {
134-
"collapsed": false
143+
"collapsed": true
135144
},
136145
"outputs": [],
137146
"source": [
@@ -150,7 +159,7 @@
150159
"cell_type": "code",
151160
"execution_count": 3,
152161
"metadata": {
153-
"collapsed": false
162+
"collapsed": true
154163
},
155164
"outputs": [],
156165
"source": [
@@ -185,7 +194,7 @@
185194
"cell_type": "code",
186195
"execution_count": 4,
187196
"metadata": {
188-
"collapsed": false
197+
"collapsed": true
189198
},
190199
"outputs": [],
191200
"source": [
@@ -203,9 +212,7 @@
203212
{
204213
"cell_type": "code",
205214
"execution_count": 5,
206-
"metadata": {
207-
"collapsed": false
208-
},
215+
"metadata": {},
209216
"outputs": [
210217
{
211218
"data": {
@@ -256,7 +263,7 @@
256263
"cell_type": "code",
257264
"execution_count": 6,
258265
"metadata": {
259-
"collapsed": false
266+
"collapsed": true
260267
},
261268
"outputs": [],
262269
"source": [
@@ -282,7 +289,7 @@
282289
"cell_type": "code",
283290
"execution_count": 7,
284291
"metadata": {
285-
"collapsed": false
292+
"collapsed": true
286293
},
287294
"outputs": [],
288295
"source": [
@@ -301,7 +308,7 @@
301308
"cell_type": "code",
302309
"execution_count": 8,
303310
"metadata": {
304-
"collapsed": false
311+
"collapsed": true
305312
},
306313
"outputs": [],
307314
"source": [
@@ -320,7 +327,7 @@
320327
"cell_type": "code",
321328
"execution_count": 9,
322329
"metadata": {
323-
"collapsed": false
330+
"collapsed": true
324331
},
325332
"outputs": [],
326333
"source": [
@@ -349,9 +356,7 @@
349356
{
350357
"cell_type": "code",
351358
"execution_count": 10,
352-
"metadata": {
353-
"collapsed": false
354-
},
359+
"metadata": {},
355360
"outputs": [
356361
{
357362
"name": "stdout",
@@ -435,7 +440,7 @@
435440
" # Update spatial PFTs with Cellular Automata rules\n",
436441
" if (current_time - time_check) >= 1.:\n",
437442
" if yrs % 100 == 0:\n",
438-
" print 'Elapsed time = ', yrs, ' years'\n",
443+
" print('Elapsed time = {time} years'.format(time=yrs))\n",
439444
" VegType[yrs] = grid1['cell']['vegetation__plant_functional_type']\n",
440445
" WS_ = np.choose(VegType[yrs], WS)\n",
441446
" grid1['cell']['vegetation__cumulative_water_stress'] = WS_/Tg\n",
@@ -457,9 +462,7 @@
457462
{
458463
"cell_type": "code",
459464
"execution_count": 11,
460-
"metadata": {
461-
"collapsed": false
462-
},
465+
"metadata": {},
463466
"outputs": [
464467
{
465468
"name": "stdout",
@@ -472,7 +475,7 @@
472475
"source": [
473476
"Final_time = time.clock()\n",
474477
"Time_Consumed = (Final_time - Start_time)/60. # in minutes\n",
475-
"print 'Time_consumed = ', Time_Consumed, ' minutes'"
478+
"print('Time_consumed = {time} minutes'.format(time=Time_Consumed))"
476479
]
477480
},
478481
{
@@ -486,7 +489,7 @@
486489
"cell_type": "code",
487490
"execution_count": 12,
488491
"metadata": {
489-
"collapsed": false
492+
"collapsed": true
490493
},
491494
"outputs": [],
492495
"source": [
@@ -511,9 +514,7 @@
511514
{
512515
"cell_type": "code",
513516
"execution_count": 13,
514-
"metadata": {
515-
"collapsed": false
516-
},
517+
"metadata": {},
517518
"outputs": [
518519
{
519520
"name": "stdout",
@@ -600,9 +601,7 @@
600601
},
601602
{
602603
"cell_type": "markdown",
603-
"metadata": {
604-
"collapsed": false
605-
},
604+
"metadata": {},
606605
"source": [
607606
"If you want to explore this model further, open 'Inputs_Vegetation_CA.txt' and change the input parameters (e.g., initial PFT distribution percentages, storm characteristics, etc..)."
608607
]
@@ -633,9 +632,9 @@
633632
"name": "python",
634633
"nbconvert_exporter": "python",
635634
"pygments_lexer": "ipython2",
636-
"version": "2.7.11"
635+
"version": "2.7.13"
637636
}
638637
},
639638
"nbformat": 4,
640-
"nbformat_minor": 0
639+
"nbformat_minor": 1
641640
}

0 commit comments

Comments
 (0)