test_integration_3inversions.py 16.7 KB
Newer Older
1
2
3
import os
import time
import shutil
4
5
6
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
Antoine Berchet's avatar
Antoine Berchet committed
7
from pathlib import Path
8
9
10
11
12
13
14
15
16
17
18
19
20

import pytest

from pycif.utils.classes.setup import Setup
from pycif.utils.datastores.dump import read_datastore
from pycif.utils.yml import ordered_dump
from pycif.utils.path import init_dir


@pytest.mark.dummy
@pytest.mark.article
@pytest.mark.parametrize(
    "settings", [
Antoine Berchet's avatar
Antoine Berchet committed
21
        {"mode": "4dvar", "minimizer": "M1QN3"},
Antoine Berchet's avatar
Antoine Berchet committed
22
23
        pytest.param({"mode": "4dvar", "minimizer": "M1QN3", "montecarlo": 10},
                     marks=pytest.mark.uncertainties),
Antoine Berchet's avatar
Antoine Berchet committed
24
25
        {"mode": "4dvar", "minimizer": "congrad"},
        {"mode": "ensrf"},
Antoine Berchet's avatar
Antoine Berchet committed
26
        pytest.param({"mode": "ensrf", "nsample": 5},
Antoine Berchet's avatar
Antoine Berchet committed
27
28
                     marks=pytest.mark.uncertainties),
        {"mode": "analytical"}
29
    ]
30
)
Antoine Berchet's avatar
Antoine Berchet committed
31
def test_integration_inversion(dummy_config_inversion, settings, pytestconfig):
32
33
34
35
36
37
    """
    Integration test that runs the dummy_forward model.
    """

    tmpdir, config, tag = dummy_config_inversion
    
Antoine Berchet's avatar
Antoine Berchet committed
38
39
40
41
    # Force reloading observation operator
    # from forward to make computation faster
    config["model"]["reload_H"] = "{}/../H_matrix.pickle".format(tmpdir)
    
42
    # Changing mode
43
    nsimmax = 10
44
45
    if config["datavect"]["components"]["fluxes"]\
            ["parameters"]["CH4"]["hresol"] == "hpixels":
Antoine Berchet's avatar
Antoine Berchet committed
46
        nsimmax = 25
47
48
49
    
    elif config["datavect"]["components"]["fluxes"]\
            ["parameters"]["CH4"]["hresol"] == "global" \
50
            and settings.get("minimizer", "") == "congrad":
51
52
53
54
55
56
57
58
59
60
61
62
63
        nsimmax = 1
        
    if settings["mode"] == "4dvar":
        mode = {
            "plugin": {"name": "4dvar", "version": "std"},
            "minimizer": {
                "plugin": {"name": settings["minimizer"], "version": "std"},
                "simulator": {
                    "plugin": {"name": "gausscost", "version": "std"},
                    "reload_from_previous": True
                },
                "maxiter": nsimmax,
                "nsim": nsimmax,
64
65
                "epsg": 0.0002,
                "df1": 0.5
66
67
68
            },
            "save_out_netcdf": True
        }
69
70
71
72
73
74
        if settings["minimizer"] == "congrad":
            mode["minimizer"]["save_uncertainties"] = True
        
        if "montecarlo" in settings:
            mode["montecarlo"] = settings["montecarlo"]
        
75
76
77
78
79
80
81
82
83
84
        prior_dir = os.path.join(tmpdir, "obsoperator/fwd_-001/obsvect")
        posterior_dir = os.path.join(tmpdir, "obsoperator/fwd_-002/obsvect")
    
    elif settings["mode"] == "analytical":
        mode = {"plugin": {"name": "analytic", "version": "std",
                           "dump_nc_base_control": True}}
        prior_dir = os.path.join(tmpdir, "obsvect_prior")
        posterior_dir = os.path.join(tmpdir, "obsvect_posterior")
    
    elif settings["mode"] == "ensrf":
Antoine Berchet's avatar
Antoine Berchet committed
85
        nsimmax = 2 * settings.get("nsample", 1) * nsimmax
86
87
88
89
90
91
        mode = {"plugin": {"name": "EnSRF", "version": "std"},
                "nsample": nsimmax}
        prior_dir = os.path.join(tmpdir, "obsvect_prior")
        posterior_dir = os.path.join(tmpdir, "obsvect_posterior")
      
    config["mode"] = mode
92
93
94
95
96
97
    config["platform"] = {
        "plugin":
            {"name": "LSCE", "version": "obelix"}
            if os.getenv("PYCIF_PLATFORM") == "LSCE"
            else {"name": "docker", "version": "cif"}
    }
Antoine Berchet's avatar
Antoine Berchet committed
98
99
100
101
102
103
104
105
106
107
108
109

    # Save ID for later plot of cost function
    resolution = \
        config["datavect"]["components"]["fluxes"]["parameters"]["CH4"][
            "hresol"]
    sigma = config["datavect"]["components"]\
                ["fluxes"]["parameters"]["CH4"]["hcorrelations"]["sigma"]
    correlations = "lowcorr" if sigma == 500 else "highcorr"
    test_id = (settings["mode"], settings.get("minimizer", ""),
               resolution, correlations)

    pytest.test_ids[test_id] = (tmpdir, nsimmax)
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
    
    # Removing MCF to make computation quicker
    del config["model"]["chemistry"]["acspecies"]["MCF"]
    del config["datavect"]["components"]["fluxes"]["parameters"]["MCF"]
    del config["datavect"]["components"]["concs"]["parameters"]["MCF"]
    
    # Dump yml config file
    dummy_config_file = os.path.join(tmpdir, "dummy_config.yml")
    with open(dummy_config_file, "w") as outfile:
        ordered_dump(outfile, config)
    
    # Run as job
    setup = Setup.yaml_to_setup(dummy_config_file)
    setup.todo_init = ["platform"]
    setup = Setup.load_config(setup)
    platform = setup.platform

    job_file = os.path.join(tmpdir,
                            "job_pycif_{}".format(tmpdir[-5:]))

    exe = "{} -m pycif {}".format(platform.python, dummy_config_file)

    job_id = platform.submit_job(
        exe,
        job_file
    )
Antoine Berchet's avatar
Antoine Berchet committed
136
    
137
138
139
    # Wait for the end of the execution
    while not platform.check_jobs([job_id]):
        time.sleep(platform.sleep_time)
Antoine Berchet's avatar
Antoine Berchet committed
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
    # Get results and check that posterior closer to prior
    components = os.listdir(prior_dir)
    for comp in components:
        comp_dir_prior = os.path.join(prior_dir, comp)
        comp_dir_post = os.path.join(posterior_dir, comp)
        parameters = os.listdir(comp_dir_prior)
        for param in parameters:
            param_dir = os.path.join(comp_dir_prior, param)
            monitor_prior = \
                read_datastore(os.path.join(param_dir, "monitor.nc"))

            param_dir = os.path.join(comp_dir_post, param)
            monitor_post = \
                read_datastore(os.path.join(param_dir, "monitor.nc"))
            assert (
                    (monitor_prior.loc[:, "obs"]
                     - monitor_prior.loc[:, "sim"]).pow(2).sum()
                    - (monitor_post.loc[:, "obs"]
                       - monitor_post.loc[:, "sim"]).pow(2).sum()
                    > 0
            )

    # Dump configuration into CIF examples
    tag += "_{}_{}".format(settings["mode"],
                           "" if settings["mode"] != "4dvar"
                           else settings["minimizer"])

Antoine Berchet's avatar
Antoine Berchet committed
168
    current_dir = os.path.abspath(os.path.dirname(os.path.realpath(__file__)))
169
170
    root_dir = os.path.abspath(os.path.join(current_dir, "../../"))
    pytest_dir = os.path.abspath(tmpdir + "/../")
Antoine Berchet's avatar
Antoine Berchet committed
171
    example_dir = \
172
        os.path.abspath(os.path.join(root_dir, "examples_artifact/dummy/"))
Antoine Berchet's avatar
Antoine Berchet committed
173
    Path(example_dir).mkdir(parents=True, exist_ok=True)
174
175
    
    config["workdir"] = "{}/inversion_{}/".format(pytest_dir, tag)
176
177

    dummy_config_file = \
Antoine Berchet's avatar
Antoine Berchet committed
178
        os.path.join(example_dir, "config_inversion_{}.yml".format(tag))
179
    with open(dummy_config_file, "w") as outfile:
180
        ordered_dump(outfile, config,
181
182
                     ref_directories={"outdir": pytest_dir,
                                      "rootdir": root_dir},
183
                     replace_values={"rootdir": "/tmp/CIF/"})
184
    
Antoine Berchet's avatar
Antoine Berchet committed
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
    # Loop with different number of simulations for non analytical inversions
    title = ""
    obs_root = "obsvect_posterior"
    control_root = "controlvect"
    if settings["mode"] != "analytical":
        for nsim in range(1, nsimmax + 1, 2):
            file_config = "{}/dummy_config.yml".format(tmpdir)
            inv_setup = Setup.from_yaml(file_config)
            
            if settings.get("minimizer", None) == "congrad":
                title = "CONGRAD"
                obs_root = "obsoperator/fwd_-002/obsvect"
                control_root = "obsoperator/fwd_-002/controlvect"
                inv_setup["mode"]["minimizer"]["maxiter"] = \
                    max(1, int(nsim))
Antoine Berchet's avatar
Antoine Berchet committed
200
                nsim *= 2
Antoine Berchet's avatar
Antoine Berchet committed
201
202
203
204
205
206
207
208
209
210
                if os.path.isdir("{}/obsoperator/fwd_-002".format(tmpdir)):
                    shutil.rmtree("{}/obsoperator/fwd_-002".format(tmpdir))
            elif settings.get("minimizer", None) == "M1QN3":
                title = "M1QN3"
                obs_root = "obsoperator/fwd_-002/obsvect"
                control_root = "obsoperator/fwd_-002/controlvect"
                inv_setup["mode"]["minimizer"]["maxiter"] = \
                    max(1, int(nsim))
                inv_setup["mode"]["minimizer"]["nsim"] = \
                    max(1, int(nsim))
Antoine Berchet's avatar
Antoine Berchet committed
211
                nsim *= 2
Antoine Berchet's avatar
Antoine Berchet committed
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
                if os.path.isdir("{}/obsoperator/fwd_-002".format(tmpdir)):
                    shutil.rmtree("{}/obsoperator/fwd_-002".format(tmpdir))
            elif settings["mode"] == "ensrf":
                title = "EnSRF"
                inv_setup["mode"]["nsample"] = nsim
            
            # Execute a partial inversion with less simulations
            inv_setup = Setup.from_dict(inv_setup, convert_none=True)
            inv_setup = inv_setup.load_config(inv_setup)
            controlvect, obsvect = inv_setup.mode.execute()
            
            # Compute the cost function
            departures = obsvect.ysim - obsvect.yobs
            j_o = 0.5 * (departures * obsvect.rinvprod(departures)).sum()
            
            dx = controlvect.x - controlvect.xb
            bfull = np.linalg.inv(controlvect.build_b(inv_setup.controlvect))
            j_b = dx[np.newaxis, :].dot(bfull.dot(dx[:, np.newaxis])).sum() / 2
            
            # Dump to text file for later plot
            with open("{}/varying_cost_function.txt".format(tmpdir), "a") as f:
                f.write("{} {} {}\n".format(nsim, j_o, j_b))
    
    else:
236
237
        title = "Analytical"
        
Antoine Berchet's avatar
Antoine Berchet committed
238
239
240
        # Load results
        file_config = "{}/dummy_config.yml".format(tmpdir)
        inv_setup = Setup.from_yaml(file_config)
241
242
        inv_setup = Setup.from_dict(inv_setup, convert_none=True)
        inv_setup = inv_setup.load_config(inv_setup)
243
244
245
246
247
248
249
250
251
252
253
        controlvect, obsvect = inv_setup.mode.execute()
        
        # Compute the cost function
        departures = obsvect.ysim - obsvect.yobs
        j_o = 0.5 * (departures * obsvect.rinvprod(departures)).sum()
        
        dx = controlvect.x - controlvect.xb
        bfull = np.linalg.inv(
            inv_setup.controlvect
                .build_b(inv_setup.controlvect)
        )
Antoine Berchet's avatar
Antoine Berchet committed
254
        j_b = dx[np.newaxis, :].dot(bfull.dot(dx[:, np.newaxis])).sum() / 2
255
256
257
        
        # Dump to text file for later plot
        with open("{}/varying_cost_function.txt".format(tmpdir), "a") as f:
Antoine Berchet's avatar
Antoine Berchet committed
258
259
            f.write("{} {} {}\n".format(0, j_o, j_b))
            f.write("{} {} {}\n".format(nsimmax, j_o, j_b))
260
    
Antoine Berchet's avatar
Antoine Berchet committed
261
262
263
264
265
266
267
    marker = pytestconfig.getoption('-m')
    if "article" in marker and "not article" not in marker:
        # Domain limits
        xmin = inv_setup.domain.xmin
        xmax = inv_setup.domain.xmax
        ymin = inv_setup.domain.ymin
        ymax = inv_setup.domain.ymax
268
    
Antoine Berchet's avatar
Antoine Berchet committed
269
270
271
272
273
274
275
276
277
278
279
280
281
        # Read observations
        file_obs = "{}/obsvect/concs/CH4/monitor.nc".format(tmpdir)
        monitor_ref = read_datastore(file_obs)
        coords = monitor_ref.loc[:, ["lon", "lat", "alt"]].drop_duplicates()
        
        # Compute fluxes from control vector
        file_flx = "{}/{}/fluxes/" \
                   "controlvect_fluxes_CH4.nc".format(tmpdir, control_root)
        ds = xr.open_dataset(file_flx)
        dflx = ds["x_phys"].mean(axis=(0, 1)) - ds["xb_phys"].mean(axis=(0, 1))
        
        # Fetch resolution for figure name
        resol = ""
282
        if config["datavect"]["components"][
Antoine Berchet's avatar
Antoine Berchet committed
283
284
                "fluxes"]["parameters"]["CH4"]["hresol"] == "hpixels":
            if config["datavect"]["components"][
Antoine Berchet's avatar
Antoine Berchet committed
285
286
                    "fluxes"]["parameters"]["CH4"][
                    "hcorrelations"]["sigma"] == 500:
Antoine Berchet's avatar
Antoine Berchet committed
287
288
289
                resol = "lowcorr"
            else:
                resol = "highcorr"
290
    
Antoine Berchet's avatar
Antoine Berchet committed
291
292
293
        elif config["datavect"]["components"]["fluxes"] \
                ["parameters"]["CH4"]["hresol"] == "ibands":
            resol = "bands"
294
    
Antoine Berchet's avatar
Antoine Berchet committed
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
        elif config["datavect"]["components"]["fluxes"] \
                ["parameters"]["CH4"]["hresol"] == "global":
            resol = "global"
        
        # Plot the figure
        plt.figure(figsize=(21, 11))
        
        ax0 = plt.axes([0.05, 0.05, 0.73, 0.87])
        im = plt.imshow(dflx, extent=(xmin, xmax, ymin, ymax), cmap="YlOrRd",
                        vmin=-0.2, vmax=0.5)
        sc = plt.scatter(coords["lon"], coords["lat"], c=coords["alt"],
                         cmap="Blues", linewidths=1, edgecolors="k", s=600)
        plt.yticks(fontsize=25)
        plt.xticks(fontsize=25)
        
        ax1 = plt.axes([0.74, 0.05, 0.05, 0.87])
        cb1 = plt.colorbar(im, cax=ax1)
        plt.yticks(fontsize=25)
        plt.ylabel("Fluxes (a.u.)", fontsize=30)
        
        ax2 = plt.axes([0.86, 0.05, 0.05, 0.87])
        cb2 = plt.colorbar(sc, cax=ax2)
        plt.yticks(fontsize=25)
        plt.ylabel("Station altitude (m a.g.l)", fontsize=30)
        
        ax0.set_title(title, fontsize=45)
Antoine Berchet's avatar
Antoine Berchet committed
321
322
323
324
325
326
327
        
        current_dir = os.path.abspath(
            os.path.dirname(os.path.realpath(__file__)))
        figure_dir = \
            os.path.abspath(os.path.join(current_dir,
                                         "../../figures_artifact/"))
        Path(figure_dir).mkdir(parents=True, exist_ok=True)
328
329
        plt.savefig("{}/map_dx_{}_{}_{}.pdf".format(
            figure_dir, title, resol, nsimmax))
Antoine Berchet's avatar
Antoine Berchet committed
330
        plt.close()
331
332
333
334
335
336
337
338
339
340
341
        
        # Plot uncertainty reduction
        if "pa_std" in ds:
            dstd = ds["b_std"].mean(axis=(0, 1)) \
                   - ds["pa_std"].mean(axis=(0, 1))
        
            # Plot the figure
            plt.figure(figsize=(21, 11))
            
            ax0 = plt.axes([0.05, 0.05, 0.73, 0.87])
            im = plt.imshow(dstd, extent=(xmin, xmax, ymin, ymax),
Antoine Berchet's avatar
Antoine Berchet committed
342
                            cmap="YlOrRd", vmin=0, vmax=1)
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
            sc = plt.scatter(coords["lon"], coords["lat"], c=coords["alt"],
                             cmap="Blues", linewidths=1, edgecolors="k", s=600)
            plt.yticks(fontsize=25)
            plt.xticks(fontsize=25)
            
            ax1 = plt.axes([0.74, 0.05, 0.05, 0.87])
            cb1 = plt.colorbar(im, cax=ax1)
            plt.yticks(fontsize=25)
            plt.ylabel("Uncertainty reduction (a.u.)", fontsize=30)
            
            ax2 = plt.axes([0.86, 0.05, 0.05, 0.87])
            cb2 = plt.colorbar(sc, cax=ax2)
            plt.yticks(fontsize=25)
            plt.ylabel("Station altitude (m a.g.l)", fontsize=30)
            
            ax0.set_title(title, fontsize=45)
            
            current_dir = os.path.abspath(
                os.path.dirname(os.path.realpath(__file__)))
            figure_dir = \
                os.path.abspath(os.path.join(current_dir,
                                             "../../figures_artifact/"))
            Path(figure_dir).mkdir(parents=True, exist_ok=True)
366
367
            plt.savefig("{}/map_dstd_{}_{}_{}.pdf"
                        .format(figure_dir, title, resol, nsimmax))
368
369
370
371
372
373
            plt.close()
        
        # Plot matrix of uncertainty reduction
        if hasattr(controlvect, "pa"):
            pa = controlvect.pa
            
Antoine Berchet's avatar
Antoine Berchet committed
374
375
376
377
            plt.figure(figsize=(21, 11))
            ax0 = plt.axes([0.05, 0.05, 0.73, 0.87])
            im = plt.imshow(pa[:int(controlvect.dim / 2),
                               :int(controlvect.dim / 2)],
378
                            vmin=-0.5, vmax=0.5, cmap="RdBu")
Antoine Berchet's avatar
Antoine Berchet committed
379
380
381
382
383
384
385
            plt.xticks(fontsize=20)
            plt.yticks(fontsize=20)
            
            ax1 = plt.axes([0.74, 0.05, 0.05, 0.87])
            cb1 = plt.colorbar(im, cax=ax1)
            plt.yticks(fontsize=25)
            plt.ylabel("Posterior uncertainties (a.u.)", fontsize=30)
386
387
388
389
390
391
392
            
            current_dir = os.path.abspath(
                os.path.dirname(os.path.realpath(__file__)))
            figure_dir = \
                os.path.abspath(os.path.join(current_dir,
                                             "../../figures_artifact/"))
            Path(figure_dir).mkdir(parents=True, exist_ok=True)
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
            plt.savefig("{}/posterior_matrix_{}_{}_{}.pdf"
                        .format(figure_dir, title, resol, nsimmax))
            plt.close()
            
            # Prior matrix
            bfull = controlvect.build_b(controlvect)
            plt.figure(figsize=(21, 11))
            ax0 = plt.axes([0.05, 0.05, 0.73, 0.87])
            im = plt.imshow(bfull[:int(controlvect.dim / 2),
                                  :int(controlvect.dim / 2)],
                            vmin=-0.5, vmax=0.5, cmap="RdBu")
            plt.xticks(fontsize=20)
            plt.yticks(fontsize=20)

            ax1 = plt.axes([0.74, 0.05, 0.05, 0.87])
            cb1 = plt.colorbar(im, cax=ax1)
            plt.yticks(fontsize=25)
            plt.ylabel("Prior uncertainties (a.u.)", fontsize=30)

            current_dir = os.path.abspath(
                os.path.dirname(os.path.realpath(__file__)))
            figure_dir = \
                os.path.abspath(os.path.join(current_dir,
                                             "../../figures_artifact/"))
            Path(figure_dir).mkdir(parents=True, exist_ok=True)
            plt.savefig("{}/prior_matrix_{}_{}_{}.pdf"
419
                        .format(figure_dir, title, resol, nsimmax))
420
            plt.close()