Visualization of a graph

Data preparation

[1]:

%load_ext autoreload
%autoreload 2

import GraphEM
import pyleoclim as pyleo

[2]:

job = GraphEM.ReconJob()

[3]:

job.load_configs('./testcases/PPE_PAGES2k/configs.yml', verbose=True)

GraphEM: job.load_configs() >>> loading reconstruction configurations from: ./testcases/PPE_PAGES2k/configs.yml
GraphEM: job.load_configs() >>> job.configs created
GraphEM: job.load_configs() >>> job.configs["job_dirpath"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/recon
GraphEM: job.load_configs() >>> /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/recon created
{'anom_period': [1951, 1980],
 'calib_period': [1900, 2000],
 'job_dirpath': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/recon',
 'job_id': 'GraphEM_tutorial',
 'obs_path': {'tas': './data/obs/iCESM_ann.nc'},
 'obs_regrid_ntrunc': 21,
 'obs_varname': {'lat': 'lat', 'lon': 'lon', 'tas': 'tas'},
 'proxydb_path': './data/proxy/pseudoPAGES2k_dataset_tas_wn_SNR10_full_temporal_availability.pkl',
 'ptype_list': 'all',
 'recon_period': [1000, 2000]}

[6]:

job.load_proxydb(verbose=True)

GraphEM: job.load_proxydb() >>> job.configs["proxydb_path"] = /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/data/proxy/pseudoPAGES2k_dataset_tas_wn_SNR10_full_temporal_availability.pkl
GraphEM: job.load_proxydb() >>> 692 records loaded
GraphEM: job.load_proxydb() >>> job.proxydb created

[7]:

job.load_obs(verbose=True)
job.regrid_obs(verbose=True)

GraphEM: job.load_obs() >>> loading instrumental observation fields from: {'tas': '/Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/data/obs/iCESM_ann.nc'}
GraphEM: job.load_obs() >>> job.obs created
LMRt: job.regrid_obs() >>> regridded obs
Dataset Overview
-----------------------

     Name:  tas
   Source:  /Users/fzhu/Github/GraphEM/docsrc/tutorial/testcases/PPE_PAGES2k/data/obs/iCESM_ann.nc
    Shape:  time:1156, lat:22, lon:33
LMRt: job.regrid_obs() >>> job.obs updated

[8]:

job.prep_data(verbose=True)

GraphEM: job.prep_data() >>> job.recon_time created
GraphEM: job.prep_data() >>> job.calib_time created
GraphEM: job.prep_data() >>> job.calib_idx created
GraphEM: job.prep_data() >>> job.temp created
GraphEM: job.prep_data() >>> job.df_proxy created
GraphEM: job.prep_data() >>> job.proxy created
GraphEM: job.prep_data() >>> job.lonlat created

[9]:

import numpy as np
print(np.shape(job.lonlat))
print(22*33)

(1418, 2)
726

[10]:

job.df_proxy

[10]:

	NAm_153	Asi_245	NAm_165	Asi_178	Asi_174	Eur_016	Asi_198	NAm_145	Arc_070	Arc_071	...	Asi_119	Ocn_153	NAm_074	Asi_026	Ocn_169	Asi_201	Asi_179	Arc_014	Ocn_071	Ocn_072
1000.0	2.049447	1.206348	-0.028154	0.354423	0.164291	0.700894	0.420136	1.610386	1.250028	1.241505	...	0.828330	0.074526	1.969163	0.165617	0.331053	-0.578854	0.188152	1.255906	0.304311	0.425676
1001.0	0.014990	0.708525	0.246777	-0.057988	-0.127694	-0.077910	0.477898	-1.579350	-1.453855	1.689047	...	0.986828	-0.319637	1.589055	0.146957	-0.345228	-0.449773	0.128710	1.697772	-0.457480	-0.352040
1002.0	-1.114598	-0.355595	-0.903415	-0.370463	-0.170471	0.018887	0.820904	0.335598	0.658790	-1.006825	...	-0.571258	-0.268028	-2.308910	0.313561	-0.265528	-0.357028	0.152346	-0.037280	-0.357438	-0.312209
1003.0	0.921028	0.761262	-0.241008	-0.612394	-0.198821	0.600541	0.038012	1.649567	0.484537	-0.694430	...	0.332292	0.469818	1.270690	-0.172501	0.210101	-0.619913	-0.157362	-0.393430	0.248620	0.292726
1004.0	0.292958	-0.005126	0.781568	-0.169216	0.314034	-0.194410	0.990756	-0.391326	-1.263733	1.176845	...	0.160528	-0.084175	0.971972	0.342813	-0.134291	1.254460	0.379938	1.061491	-0.126194	-0.164533
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1996.0	0.596161	0.397911	-0.213472	-0.375964	0.110367	-0.175807	1.092398	2.554041	2.983670	-0.233084	...	0.086505	0.938570	-0.516218	0.099238	0.489292	0.470537	-0.122144	-0.671030	0.416049	0.440858
1997.0	0.708165	-0.204674	0.940863	-0.811482	-0.402413	0.229789	1.386490	0.106067	-0.278486	-0.677213	...	-0.236382	0.109457	-0.916285	-0.319777	-0.009780	0.513189	-0.707492	-1.106939	-0.174881	-0.218647
1998.0	0.502749	-0.240407	1.346490	0.595868	0.373318	0.085835	0.838359	0.328611	1.795783	2.179380	...	-0.454328	0.489631	0.177895	0.388473	0.009699	0.821627	0.307725	1.840054	-0.005446	0.060198
1999.0	1.476074	-0.101115	0.071156	0.010010	0.215712	-0.452304	-0.450495	2.144862	1.877791	-0.456210	...	0.340616	0.543951	0.590506	0.229498	0.373771	0.036311	-0.097946	-0.597891	0.509028	0.404405
2000.0	-0.227916	-0.418627	0.600212	1.064158	0.782957	1.423108	1.178986	-0.110369	1.205325	-0.306477	...	-0.156622	0.445898	0.449710	0.121985	0.304760	1.010402	0.186142	1.766690	0.499660	0.453703

1001 rows × 692 columns

Creating a graph object

[11]:

%%time

g = GraphEM.Graph(job.lonlat, job.temp, job.proxy)

CPU times: user 7 µs, sys: 1e+03 ns, total: 8 µs
Wall time: 9.78 µs

[12]:

%%time

g.calc_distance()

CPU times: user 23.5 s, sys: 122 ms, total: 23.7 s
Wall time: 23.9 s

Visualizing the neighbors of a target proxy within a certain distance

[13]:

%%time

g.calc_adj(distance=1000)
fig, ax = g.plot_neighbors(1)
pyleo.showfig(fig)

g.calc_adj(distance=3000)
fig, ax = g.plot_neighbors(10)
pyleo.showfig(fig)

pyleo.closefig()

CPU times: user 3.61 s, sys: 123 ms, total: 3.74 s
Wall time: 3.81 s

Visualizing the neighbors and the correlation to the target proxy

[14]:

%%time

fig, ax = g.plot_neighbors_corr(10)
pyleo.showfig(fig)

# mute the colorbar
fig, ax = g.plot_neighbors_corr(10, plot_cbar=False)
pyleo.showfig(fig)

pyleo.closefig()

CPU times: user 3.48 s, sys: 86.8 ms, total: 3.57 s
Wall time: 3.61 s

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