├── .github └── workflows │ └── python-package.yml ├── .gitignore ├── LICENSE ├── README.md ├── assets ├── The_estimation_process_of_GNNWR_model.jpeg ├── The_estimation_process_of_GTNNWR_model.jpeg ├── The_proposed_GNNWR_model.jpeg ├── figure OSP.JPG ├── figure_CatGWR.jpg ├── figure_DSI.jpg ├── figure_HousePrice.png ├── figure_LST.png ├── figure_NO2.png ├── figure_PM25.png ├── figure_SHF.jpg ├── figure_WaterQuality.jpg └── figure_mine.jpg ├── data ├── co2_gnnwr.csv ├── co2_gnnwr_pre.csv ├── co2_gtnnwr.csv ├── co2_gtnnwr_predict.csv ├── demo_data_gtnnwr.csv ├── demo_predict_data.csv ├── distances.csv ├── pm25_data.csv ├── pm25_predict_data.csv ├── simulated_data.csv └── simulated_predict_data.csv ├── demo ├── China.json ├── demo_gnnwr.ipynb ├── demo_gtnnwr.ipynb ├── demo_result │ └── gnnwr_datasets │ │ ├── test_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv │ │ ├── train_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv │ │ └── val_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv ├── demo_utils.py └── result.csv ├── demo_result ├── GNNWR_PM25_Result.csv ├── gnnwr_datasets │ ├── test_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv │ ├── train_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv │ └── val_dataset │ │ ├── dataframe.csv │ │ ├── dataset_info.json │ │ ├── distances.npy │ │ └── scaledDataframe.csv ├── gnnwr_models │ └── GNNWR_PM25.pkl └── gtnnwr_models │ └── GTNNWR_DSi.pkl ├── doc ├── Makefile ├── build │ ├── doctrees │ │ ├── environment.pickle │ │ └── index.doctree │ └── html │ │ ├── .buildinfo │ │ ├── _sources │ │ └── index.rst.txt │ │ ├── _static │ │ ├── _sphinx_javascript_frameworks_compat.js │ │ ├── basic.css │ │ ├── css │ │ │ ├── badge_only.css │ │ │ ├── fonts │ │ │ │ ├── Roboto-Slab-Bold.woff │ │ │ │ ├── Roboto-Slab-Bold.woff2 │ │ │ │ ├── Roboto-Slab-Regular.woff │ │ │ │ ├── Roboto-Slab-Regular.woff2 │ │ │ │ ├── fontawesome-webfont.eot │ │ │ │ ├── fontawesome-webfont.svg │ │ │ │ ├── fontawesome-webfont.ttf │ │ │ │ ├── fontawesome-webfont.woff │ │ │ │ ├── fontawesome-webfont.woff2 │ │ │ │ ├── lato-bold-italic.woff │ │ │ │ ├── lato-bold-italic.woff2 │ │ │ │ ├── lato-bold.woff │ │ │ │ ├── lato-bold.woff2 │ │ │ │ ├── lato-normal-italic.woff │ │ │ │ ├── lato-normal-italic.woff2 │ │ │ │ ├── lato-normal.woff │ │ │ │ └── lato-normal.woff2 │ │ │ └── theme.css │ │ ├── doctools.js │ │ ├── documentation_options.js │ │ ├── file.png │ │ ├── jquery.js │ │ ├── js │ │ │ ├── badge_only.js │ │ │ ├── html5shiv-printshiv.min.js │ │ │ ├── html5shiv.min.js │ │ │ └── theme.js │ │ ├── language_data.js │ │ ├── minus.png │ │ ├── plus.png │ │ ├── pygments.css │ │ ├── searchtools.js │ │ └── sphinx_highlight.js │ │ ├── genindex.html │ │ ├── index.html │ │ ├── objects.inv │ │ ├── py-modindex.html │ │ ├── search.html │ │ └── searchindex.js ├── make.bat └── source │ ├── conf.py │ └── index.rst ├── requirements.txt └── src └── gnnwr ├── __init__.py ├── datasets.py ├── models.py ├── networks.py └── utils.py /.github/workflows/python-package.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests and lint with a variety of Python versions 2 | # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python 3 | 4 | name: Python package 5 | 6 | on: 7 | push: 8 | branches: [ "main" ] 9 | pull_request: 10 | branches: [ "main" ] 11 | 12 | jobs: 13 | build: 14 | 15 | runs-on: ubuntu-latest 16 | strategy: 17 | fail-fast: false 18 | matrix: 19 | python-version: ["3.9", "3.10", "3.11"] 20 | 21 | steps: 22 | - uses: actions/checkout@v3 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v3 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | - name: Install dependencies 28 | run: | 29 | python -m pip install --upgrade pip 30 | python -m pip install flake8 pytest 31 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 32 | - name: Lint with flake8 33 | run: | 34 | # stop the build if there are Python syntax errors or undefined names 35 | flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics 36 | # exit-zero treats all errors as warnings. 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433.9548571,0.813277543,10.60032272,33.04260588,234.875,42.375 5 | 433.3903939,0.800426304,10.63806438,33.0054965,234.875,42.125 6 | 413.1546774,0.777915537,10.67838669,32.96838713,234.875,41.875 7 | 418.4410652,0.800702929,10.74354839,32.93127775,234.875,41.625 8 | 431.512,0.762529731,10.84354782,32.91182047,234.875,41.375 9 | 422.6084318,0.772762179,10.93870926,32.91001529,234.875,41.125 10 | 454.153,0.845942974,11.00290298,32.98121208,235.125,41.125 11 | 443.8956667,0.83403188,10.99903202,32.9082101,234.875,40.875 12 | 342.8012058,0.467087626,12.53903198,33.95320386,141.375,37.875 13 | 344.6948889,0.383636236,12.38967705,33.99945909,141.625,37.875 14 | 407.1016667,0.669980228,11.81645107,33.12304574,236.375,37.875 15 | 334.791104,0.737642646,12.71354771,33.95206183,141.125,37.625 16 | 343.1821411,0.363651812,12.30128956,34.06920618,141.625,37.625 17 | 448.7936667,0.648809254,12.06774139,33.15285105,236.375,37.625 18 | 398.4516154,0.817402661,11.91645145,33.16836315,236.625,37.625 19 | 340.8363916,0.636316538,12.85290337,34.00788277,141.125,37.375 20 | 343.3762396,0.395199537,12.58903217,34.07002074,141.375,37.375 21 | 344.1905,0.348403126,12.30709648,34.1321587,141.625,37.375 22 | 356.1563111,0.203659728,22.11903191,34.68532681,139.875,34.375 23 | 403.7620245,1.215404987,13.84741879,33.58872443,239.125,34.375 24 | 346.895102,0.752910435,18.07999992,34.29105127,134.875,34.125 25 | 350.4395,0.171294987,22.86128998,34.60141844,138.125,34.125 26 | 350.914,0.180465087,22.7048378,34.61788541,138.375,34.125 27 | 355.854725,0.191437185,22.54709625,34.70392203,139.875,34.125 28 | 358.82,0.183892593,22.67709541,34.71132851,140.125,34.125 29 | 345.4965469,0.57190752,19.77290344,34.4065897,134.875,33.875 30 | 350.745,0.149422735,23.21645164,34.61241102,137.875,33.875 31 | 348.6580909,0.157815263,23.21967697,34.63668877,138.125,33.875 32 | 356.8606667,0.172732502,22.92290306,34.72852325,140.125,33.875 33 | 348.34304,0.213239476,21.04935455,34.53023609,134.875,33.625 34 | 350.8944783,0.24293901,21.25290298,34.53382105,135.125,33.625 35 | 348.2677143,0.148660839,23.1506443,34.63910341,137.625,33.625 36 | 355.4448333,0.165596977,22.9593544,34.745718,140.125,33.625 37 | 350.4318472,0.268164515,21.71225739,34.60033715,135.125,33.375 38 | 349.0333333,0.131604359,22.82032204,34.68610948,137.625,33.375 39 | 357.4286458,0.159946889,22.9074192,34.76255691,140.125,33.375 40 | -------------------------------------------------------------------------------- /data/co2_gtnnwr_predict.csv: -------------------------------------------------------------------------------- 1 | lon,lat,year,month,pCO2,Chl,Temp,Salt,pressure,windspeed 2 | 137.125,24.125,2017,12,360.3644782,0.069610618,25.33290291,35.08836979,101694.8438,6.942487717 3 | 158.625,24.125,2017,12,363.3236339,0.038444016,26.7003212,35.27006209,101648.3594,3.680241108 4 | 139.625,34.625,2017,12,350.6549305,0.232888371,19.40999985,34.59415086,101675.5,6.144190788 5 | 146.125,14.875,2017,12,384.5523636,0.027751578,29.06419373,34.62786102,101040.0625,7.528096199 6 | 132.625,23.625,2017,12,361.5291526,0.087183975,26.06903076,34.95102084,101753.2344,8.035316467 7 | 235.125,3.375,2017,12,393.5187284,0.14312391,25.67870903,34.71591496,101114.3438,7.089426994 8 | 136.625,32.625,2017,12,332.0239535,0.262908638,18.86322594,34.69547558,101992.7344,6.255911827 9 | 146.625,31.875,2017,12,340.7107161,0.170262173,20.9525795,34.85557908,101637.1094,4.741209507 10 | 145.125,34.375,2017,12,337.9106307,0.1905808,20.70387077,34.72746843,101545.6719,4.393471718 11 | 223.125,15.625,2017,12,385.4421553,0.063249685,26.44257927,34.11986077,101280.3906,6.82899189 12 | 235.125,3.125,2017,12,390.8087289,0.141454712,25.65903091,34.71805453,101118.2813,7.123773575 13 | 155.125,24.125,2017,12,366.0023884,0.036263641,27.25580597,35.19367456,101593.7969,3.28802824 14 | 142.875,22.875,2017,12,378.0035414,0.0563206,26.11580658,34.90442044,101478.1094,6.069850445 15 | 167.875,24.125,2017,12,356.2858872,0.041513536,25.66386986,35.40412551,101786.4922,4.661588669 16 | 238.625,14.375,2017,12,376.2255019,0.098754846,27.2387085,33.6868096,101265.75,6.93921566 17 | 147.125,39.375,2017,12,338.6203532,0.258557081,13.9393549,34.34861171,101332.2188,6.787261963 18 | 142.375,25.375,2017,12,362.6927772,0.113986269,24.68806458,34.95639223,101673.9766,5.492742062 19 | 145.125,33.125,2017,12,338.7587132,0.140975863,20.70548248,34.80599016,101599.2031,5.264705658 20 | 182.625,21.875,2017,12,356.8777558,0.042749427,25.54645157,35.26803041,101723.5703,6.780557632 21 | 156.375,24.375,2017,12,370.4792814,0.035669442,27.09032249,35.2368142,101620.2813,3.19272995 22 | 152.375,24.125,2017,12,368.0701816,0.038992964,26.89225769,35.1623612,101556.6563,2.959062576 23 | -------------------------------------------------------------------------------- /data/demo_predict_data.csv: -------------------------------------------------------------------------------- 1 | refl_b01,refl_b02,refl_b03,refl_b04,refl_b05,refl_b07,proj_x,proj_y,day,SiO3 2 | 7307,4191,7179,7808,8596,4352,655316.4743,3169638.04,218,3.9339 3 | 6977,7092,6456,6732,6632,4263,745209.1898,3405417.957,86,0.981 4 | 3719,4197,4460,4646,6180,3812,634452.3503,3176485.716,285,3.29 5 | 5094,5256,4669,4925,5111,4042,594309.4734,3377306.381,86,2.374 6 | 3087,3154,3424,3858,5025,2614,673075.8424,3236376.035,238,1.34 7 | 4901,4234,4911,5367,4778,2483,701234.5705,3393963.738,217,2.107 8 | 4684,4841,4189,4453,4535,3844,589927.3812,3362766.673,86,2.267 9 | 4017,4448,3310,3839,4513,4147,637486.4286,3041933.072,215,0.364 10 | 3182,3547,3219,3468,4506,3338,668124.346,3272408.794,149,0.545 11 | 4338,4446,3962,4166,4124,3695,599914.5933,3369118.335,86,2.66 12 | 3541,3508,3590,3951,3874,2354,668124.346,3272408.794,232,1.33 13 | 1108,3002,985,1290,3511,1422,604102.1683,3096345.316,283,1.874 14 | 3133,3219,3056,3276,3384,2376,610574.3847,3116842.414,218,1.482 15 | 3528,3849,3579,3812,3359,1365,662778.0726,3214978.728,226,0.062 16 | 3776,3750,3427,3641,3340,2977,649913.7516,3396454.274,139,2.044 17 | 2505,3514,1643,2020,3297,3171,612954.1788,3083135.5,204,0.723 18 | 3501,3911,3391,3599,3282,2783,560812.2239,3074326.724,284,4.042 19 | 2769,3334,2383,2663,3254,2247,674781.9797,3243721.131,218,0.777 20 | 4387,4307,3877,4078,3234,2963,694640.1601,3227895.54,296,1.484 21 | 4716,4724,4852,4753,3209,2741,657968.2739,3150670.02,125,1.322 22 | 2054,2317,2544,2536,3122,1866,673430.2695,3269106.894,189,1.57 23 | 2925,3064,2505,3032,3108,2837,616929.2599,3043593.033,215,0.543 24 | 2609,2647,3363,3201,3007,2397,663374.6203,3269666.313,264,1.25 25 | 3296,3334,3186,3290,2948,2654,654216.0245,3229241.81,75,0.992 26 | 1826,1759,1685,1923,2867,2343,648944.2369,3260928.196,203,1.93 27 | 3338,3290,2959,3153,2826,2547,672666.3438,3387844.288,139,1.792 28 | 4621,4656,3996,4268,2767,1809,667997.2447,3271830.237,232,1.347 29 | 3244,3225,2895,3073,2682,2307,653626.211,3376709.928,139,1.904 30 | 1310,2976,1186,1449,2667,978,604968.4339,3039239.99,216,0.425 31 | 2459,3025,2433,2562,2655,1841,650014.4339,3211933.353,223,1.218 32 | 1022,2408,1079,1167,2609,905,615664.9856,3137796.494,73,1.6009 33 | 3018,3536,2736,2903,2608,1150,659611.8986,3215632.881,226,0.044 34 | 1878,1219,1922,2031,2542,1469,685044.3039,3316184.464,250,1.78 35 | 3250,3618,2707,2994,2520,1959,670765.7849,3270851.643,216,1.46 36 | 3333,3218,2887,3095,2516,2276,663108.4829,3372023.964,139,1.831 37 | 2005,2432,1633,2030,2511,2628,614287.8357,3083779.837,204,0.753 38 | 2018,2345,2081,2182,2506,1532,650014.4339,3211933.353,283,1.715 39 | 822,3083,721,1012,2499,836,667864.7833,3228935.065,240,1.67 40 | 843,2013,763,957,2497,690,674781.9797,3243721.131,240,2.271 41 | 2245,2038,2414,2562,2458,1958,616040.5049,3084882.797,188,0.377 42 | 3128,3008,2806,2949,2444,2273,644484.7449,3387419.124,139,2.478 43 | 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0.011933702,0.3636303,0.6171291,0.58074737,0.6133946,0.17754059,28.977064 161 | 0.02364641,0.19885723,0.75194514,0.77922225,0.626953,0.9179495,50.741665 162 | 0.07160221,0.16620256,0.7439433,0.6405351,0.69361675,0.4172005,45.014164 163 | 0.20508286,0.15266606,0.1362419,0.8230226,0.21223445,0.9016727,38.136105 164 | 0.024972377,0.13325706,0.48317292,0.8178165,0.47411394,0.4863735,44.84497 165 | 0.01480663,0.17925541,0.64079785,0.67238307,0.810898,0.36970207,55.548023 166 | 0.8313812,0.16034743,0.07886831,0.022179343,0.030782968,0.32170403,17.880953 167 | 0.042651936,0.12611534,0.7591895,0.79343486,0.37530693,0.5585666,24.4162 168 | 0.012596685,0.26028326,0.5139895,0.3985946,0.4950229,0.2694234,38.461113 169 | 0.42121547,0.3969316,0.0052279,0.6649873,0.23474155,0.7713109,21.875347 170 | 0.13303867,0.09823606,0.05702189,0.5681665,0.48636997,0.37633213,50.70173 171 | 0.28618786,0.09721782,0.4407631,0.6799638,0.2077596,0.04374405,63.637005 172 | 0.04928177,0.44708818,0.8910345,0.86479354,0.34127665,0.54149747,25.4 173 | 0.003314917,0.18315391,0.5291369,0.56926036,0.61129093,0.21337816,38.38483 174 | 0.0037569061,0.17303167,0.71658164,0.91962314,0.48213613,0.82428724,35.297222 175 | 0.052154697,0.21358137,0.869447,0.7921035,0.8760086,0.36520264,40.681564 176 | 0.043535914,0.32038692,0.0673373,0.23700498,0.40231985,0.318988,31.672752 177 | 0.0030939225,0.26481074,0.7805036,0.7364948,0.5954125,0.6209628,42.86938 178 | 0.12906076,0.23964843,0.13886201,0.53411555,0.20377915,0.06266917,73.186966 179 | 0.5670718,0.042418126,0.2075756,0.25749752,0.11986209,0.42423674,29.077995 180 | 0.0121546965,0.35953903,0.74678296,0.68259215,0.69824225,0.53233063,57.34136 181 | -------------------------------------------------------------------------------- /demo/demo_utils.py: -------------------------------------------------------------------------------- 1 | import folium 2 | import json 3 | from folium.plugins import HeatMap, MarkerCluster 4 | def marker_map(markers:list,center:list,zoom=4,border=None): 5 | tiles= 'https://wprd01.is.autonavi.com/appmaptile?x={x}&y={y}&z={z}&lang=en&size=1&scl=1&style=7' 6 | map = folium.Map(location=center,zoom_start=zoom,tiles = tiles,attr="高德") 7 | if border != None: 8 | with open(border,encoding='utf-8') as f: 9 | t = f.readline() 10 | geojson = json.loads(t) 11 | folium.GeoJson(geojson).add_to(map) 12 | for item in markers: 13 | if not 'location' in item: raise ValueError('location of markers is neccessary') 14 | if not 'color' in item: item['color'] = 'blue' 15 | if not 'desc' in item: item['desc'] = [str(item['location'][i][1])+' '+str(item['location'][i][0]) for i in range(len(item['location']))] 16 | else : item['desc'] = [item['desc'] for i in range(len(item['location']))] 17 | mc = MarkerCluster(locations=item['location'],icons=[folium.Icon(color=item['color'],icon='location') for i in range(len(item['location']))],popups=item['desc']) 18 | mc.add_to(map) 19 | return map 20 | 21 | def Heatmap(data,center,zoom=4): 22 | tiles= 'https://wprd01.is.autonavi.com/appmaptile?x={x}&y={y}&z={z}&lang=en&size=1&scl=1&style=7' 23 | map = folium.Map(location=center,zoom_start=zoom,tiles = tiles,attr="高德") 24 | map.add_child(HeatMap(data)) 25 | return map 26 | -------------------------------------------------------------------------------- /demo_result/gnnwr_datasets/test_dataset/distances.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/demo_result/gnnwr_datasets/test_dataset/distances.npy -------------------------------------------------------------------------------- /demo_result/gnnwr_datasets/train_dataset/distances.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/demo_result/gnnwr_datasets/train_dataset/distances.npy -------------------------------------------------------------------------------- /demo_result/gnnwr_datasets/val_dataset/distances.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/demo_result/gnnwr_datasets/val_dataset/distances.npy -------------------------------------------------------------------------------- /demo_result/gnnwr_models/GNNWR_PM25.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/demo_result/gnnwr_models/GNNWR_PM25.pkl -------------------------------------------------------------------------------- /demo_result/gtnnwr_models/GTNNWR_DSi.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/demo_result/gtnnwr_models/GTNNWR_DSi.pkl -------------------------------------------------------------------------------- /doc/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /doc/build/doctrees/environment.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/doc/build/doctrees/environment.pickle -------------------------------------------------------------------------------- /doc/build/doctrees/index.doctree: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/doc/build/doctrees/index.doctree -------------------------------------------------------------------------------- /doc/build/html/.buildinfo: -------------------------------------------------------------------------------- 1 | # Sphinx build info version 1 2 | # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. 3 | config: bd1b3cf4d58d87feb29fdb0350936018 4 | tags: 645f666f9bcd5a90fca523b33c5a78b7 5 | -------------------------------------------------------------------------------- /doc/build/html/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. GNNWR documentation master file, created by 2 | sphinx-quickstart on Sat Sep 16 22:25:09 2023. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to GNNWR's documentation! 7 | ================================= 8 | 9 | .. toctree:: 10 | :maxdepth: 3 11 | :caption: Contents: 12 | :glob: 13 | 14 | gnnwr/* 15 | 16 | Indices and tables 17 | ================== 18 | 19 | * :ref:`genindex` 20 | * :ref:`modindex` 21 | * :ref:`search` 22 | -------------------------------------------------------------------------------- /doc/build/html/_static/_sphinx_javascript_frameworks_compat.js: -------------------------------------------------------------------------------- 1 | /* Compatability shim for jQuery and underscores.js. 2 | * 3 | * Copyright Sphinx contributors 4 | * Released under the two clause BSD licence 5 | */ 6 | 7 | /** 8 | * small helper function to urldecode strings 9 | * 10 | * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL 11 | */ 12 | jQuery.urldecode = function(x) { 13 | if (!x) { 14 | return x 15 | } 16 | return decodeURIComponent(x.replace(/\+/g, ' ')); 17 | }; 18 | 19 | /** 20 | * small helper function to urlencode strings 21 | */ 22 | jQuery.urlencode = encodeURIComponent; 23 | 24 | /** 25 | * This function returns the parsed url parameters of the 26 | * current request. Multiple values per key are supported, 27 | * it will always return arrays of strings for the value parts. 28 | */ 29 | jQuery.getQueryParameters = function(s) { 30 | if (typeof s === 'undefined') 31 | s = document.location.search; 32 | var parts = s.substr(s.indexOf('?') + 1).split('&'); 33 | var result = {}; 34 | for (var i = 0; i < parts.length; i++) { 35 | var tmp = parts[i].split('=', 2); 36 | var key = jQuery.urldecode(tmp[0]); 37 | var value = jQuery.urldecode(tmp[1]); 38 | if (key in result) 39 | result[key].push(value); 40 | else 41 | result[key] = [value]; 42 | } 43 | return result; 44 | }; 45 | 46 | /** 47 | * highlight a given string on a jquery object by wrapping it in 48 | * span elements with the given class name. 49 | */ 50 | jQuery.fn.highlightText = function(text, className) { 51 | function highlight(node, addItems) { 52 | if (node.nodeType === 3) { 53 | var val = node.nodeValue; 54 | var pos = val.toLowerCase().indexOf(text); 55 | if (pos >= 0 && 56 | !jQuery(node.parentNode).hasClass(className) && 57 | !jQuery(node.parentNode).hasClass("nohighlight")) { 58 | var span; 59 | var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); 60 | if (isInSVG) { 61 | span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); 62 | } else { 63 | span = document.createElement("span"); 64 | span.className = className; 65 | } 66 | span.appendChild(document.createTextNode(val.substr(pos, text.length))); 67 | node.parentNode.insertBefore(span, node.parentNode.insertBefore( 68 | document.createTextNode(val.substr(pos + text.length)), 69 | node.nextSibling)); 70 | node.nodeValue = val.substr(0, pos); 71 | if (isInSVG) { 72 | var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); 73 | var bbox = node.parentElement.getBBox(); 74 | rect.x.baseVal.value = bbox.x; 75 | rect.y.baseVal.value = bbox.y; 76 | rect.width.baseVal.value = bbox.width; 77 | rect.height.baseVal.value = bbox.height; 78 | rect.setAttribute('class', className); 79 | addItems.push({ 80 | "parent": node.parentNode, 81 | "target": rect}); 82 | } 83 | } 84 | } 85 | else if (!jQuery(node).is("button, select, textarea")) { 86 | jQuery.each(node.childNodes, function() { 87 | highlight(this, addItems); 88 | }); 89 | } 90 | } 91 | var addItems = []; 92 | var result = this.each(function() { 93 | highlight(this, addItems); 94 | }); 95 | for (var i = 0; i < addItems.length; ++i) { 96 | jQuery(addItems[i].parent).before(addItems[i].target); 97 | } 98 | return result; 99 | }; 100 | 101 | /* 102 | * backward compatibility for jQuery.browser 103 | * This will be supported until firefox bug is fixed. 104 | */ 105 | if (!jQuery.browser) { 106 | jQuery.uaMatch = function(ua) { 107 | ua = ua.toLowerCase(); 108 | 109 | var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || 110 | /(webkit)[ \/]([\w.]+)/.exec(ua) || 111 | /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || 112 | /(msie) ([\w.]+)/.exec(ua) || 113 | ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || 114 | []; 115 | 116 | return { 117 | browser: match[ 1 ] || "", 118 | version: match[ 2 ] || "0" 119 | }; 120 | }; 121 | jQuery.browser = {}; 122 | jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; 123 | } 124 | -------------------------------------------------------------------------------- /doc/build/html/_static/basic.css: -------------------------------------------------------------------------------- 1 | /* 2 | * basic.css 3 | * ~~~~~~~~~ 4 | * 5 | * Sphinx stylesheet -- basic theme. 6 | * 7 | * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. 8 | * :license: BSD, see LICENSE for details. 9 | * 10 | */ 11 | 12 | /* -- main layout ----------------------------------------------------------- */ 13 | 14 | div.clearer { 15 | clear: both; 16 | } 17 | 18 | div.section::after { 19 | display: block; 20 | content: ''; 21 | clear: left; 22 | } 23 | 24 | /* -- relbar ---------------------------------------------------------------- */ 25 | 26 | div.related { 27 | width: 100%; 28 | font-size: 90%; 29 | } 30 | 31 | div.related h3 { 32 | display: none; 33 | } 34 | 35 | div.related ul { 36 | margin: 0; 37 | padding: 0 0 0 10px; 38 | list-style: none; 39 | } 40 | 41 | div.related li { 42 | display: inline; 43 | } 44 | 45 | div.related li.right { 46 | float: right; 47 | margin-right: 5px; 48 | } 49 | 50 | /* -- sidebar --------------------------------------------------------------- */ 51 | 52 | div.sphinxsidebarwrapper { 53 | padding: 10px 5px 0 10px; 54 | } 55 | 56 | div.sphinxsidebar { 57 | float: left; 58 | width: 230px; 59 | margin-left: -100%; 60 | font-size: 90%; 61 | word-wrap: break-word; 62 | overflow-wrap : break-word; 63 | } 64 | 65 | div.sphinxsidebar ul { 66 | list-style: none; 67 | } 68 | 69 | div.sphinxsidebar ul ul, 70 | div.sphinxsidebar ul.want-points { 71 | margin-left: 20px; 72 | list-style: square; 73 | } 74 | 75 | div.sphinxsidebar ul ul { 76 | margin-top: 0; 77 | margin-bottom: 0; 78 | } 79 | 80 | div.sphinxsidebar form { 81 | margin-top: 10px; 82 | } 83 | 84 | div.sphinxsidebar input { 85 | border: 1px solid #98dbcc; 86 | font-family: sans-serif; 87 | font-size: 1em; 88 | } 89 | 90 | div.sphinxsidebar #searchbox form.search { 91 | overflow: hidden; 92 | } 93 | 94 | div.sphinxsidebar #searchbox input[type="text"] { 95 | float: left; 96 | width: 80%; 97 | padding: 0.25em; 98 | box-sizing: border-box; 99 | } 100 | 101 | div.sphinxsidebar #searchbox input[type="submit"] { 102 | float: left; 103 | width: 20%; 104 | border-left: none; 105 | padding: 0.25em; 106 | box-sizing: border-box; 107 | } 108 | 109 | 110 | img { 111 | border: 0; 112 | max-width: 100%; 113 | } 114 | 115 | /* -- search page ----------------------------------------------------------- */ 116 | 117 | ul.search { 118 | margin: 10px 0 0 20px; 119 | padding: 0; 120 | } 121 | 122 | ul.search li { 123 | padding: 5px 0 5px 20px; 124 | background-image: url(file.png); 125 | background-repeat: no-repeat; 126 | background-position: 0 7px; 127 | } 128 | 129 | ul.search li a { 130 | font-weight: bold; 131 | } 132 | 133 | ul.search li p.context { 134 | color: #888; 135 | margin: 2px 0 0 30px; 136 | text-align: left; 137 | } 138 | 139 | ul.keywordmatches li.goodmatch a { 140 | font-weight: bold; 141 | } 142 | 143 | /* -- index page ------------------------------------------------------------ */ 144 | 145 | table.contentstable { 146 | width: 90%; 147 | margin-left: auto; 148 | margin-right: auto; 149 | } 150 | 151 | table.contentstable p.biglink { 152 | line-height: 150%; 153 | } 154 | 155 | a.biglink { 156 | font-size: 1.3em; 157 | } 158 | 159 | span.linkdescr { 160 | font-style: italic; 161 | padding-top: 5px; 162 | font-size: 90%; 163 | } 164 | 165 | /* -- general index --------------------------------------------------------- */ 166 | 167 | table.indextable { 168 | width: 100%; 169 | } 170 | 171 | table.indextable td { 172 | text-align: left; 173 | vertical-align: top; 174 | } 175 | 176 | table.indextable ul { 177 | margin-top: 0; 178 | margin-bottom: 0; 179 | list-style-type: none; 180 | } 181 | 182 | table.indextable > tbody > tr > td > ul { 183 | padding-left: 0em; 184 | } 185 | 186 | table.indextable tr.pcap { 187 | height: 10px; 188 | } 189 | 190 | table.indextable tr.cap { 191 | margin-top: 10px; 192 | background-color: #f2f2f2; 193 | } 194 | 195 | img.toggler { 196 | margin-right: 3px; 197 | margin-top: 3px; 198 | cursor: pointer; 199 | } 200 | 201 | div.modindex-jumpbox { 202 | border-top: 1px solid #ddd; 203 | border-bottom: 1px solid #ddd; 204 | margin: 1em 0 1em 0; 205 | padding: 0.4em; 206 | } 207 | 208 | div.genindex-jumpbox { 209 | border-top: 1px solid #ddd; 210 | border-bottom: 1px solid #ddd; 211 | margin: 1em 0 1em 0; 212 | padding: 0.4em; 213 | } 214 | 215 | /* -- domain module index --------------------------------------------------- */ 216 | 217 | table.modindextable td { 218 | padding: 2px; 219 | border-collapse: collapse; 220 | } 221 | 222 | /* -- general body styles --------------------------------------------------- */ 223 | 224 | div.body { 225 | min-width: 360px; 226 | max-width: 800px; 227 | } 228 | 229 | div.body p, div.body dd, div.body li, div.body blockquote { 230 | -moz-hyphens: auto; 231 | -ms-hyphens: auto; 232 | -webkit-hyphens: auto; 233 | hyphens: auto; 234 | } 235 | 236 | a.headerlink { 237 | visibility: hidden; 238 | } 239 | 240 | a:visited { 241 | color: #551A8B; 242 | } 243 | 244 | h1:hover > a.headerlink, 245 | h2:hover > a.headerlink, 246 | h3:hover > a.headerlink, 247 | h4:hover > a.headerlink, 248 | h5:hover > a.headerlink, 249 | h6:hover > a.headerlink, 250 | dt:hover > a.headerlink, 251 | caption:hover > a.headerlink, 252 | p.caption:hover > a.headerlink, 253 | div.code-block-caption:hover > a.headerlink { 254 | visibility: visible; 255 | } 256 | 257 | div.body p.caption { 258 | text-align: inherit; 259 | } 260 | 261 | div.body td { 262 | text-align: left; 263 | } 264 | 265 | .first { 266 | margin-top: 0 !important; 267 | } 268 | 269 | p.rubric { 270 | margin-top: 30px; 271 | font-weight: bold; 272 | } 273 | 274 | img.align-left, figure.align-left, .figure.align-left, object.align-left { 275 | clear: left; 276 | float: left; 277 | margin-right: 1em; 278 | } 279 | 280 | img.align-right, figure.align-right, .figure.align-right, object.align-right { 281 | clear: right; 282 | float: right; 283 | margin-left: 1em; 284 | } 285 | 286 | img.align-center, figure.align-center, .figure.align-center, object.align-center { 287 | display: block; 288 | margin-left: auto; 289 | margin-right: auto; 290 | } 291 | 292 | img.align-default, figure.align-default, .figure.align-default { 293 | display: block; 294 | margin-left: auto; 295 | margin-right: auto; 296 | } 297 | 298 | .align-left { 299 | text-align: left; 300 | } 301 | 302 | .align-center { 303 | text-align: center; 304 | } 305 | 306 | .align-default { 307 | text-align: center; 308 | } 309 | 310 | .align-right { 311 | text-align: right; 312 | } 313 | 314 | /* -- sidebars -------------------------------------------------------------- */ 315 | 316 | div.sidebar, 317 | aside.sidebar { 318 | margin: 0 0 0.5em 1em; 319 | border: 1px solid #ddb; 320 | padding: 7px; 321 | background-color: #ffe; 322 | width: 40%; 323 | float: right; 324 | clear: right; 325 | overflow-x: auto; 326 | } 327 | 328 | p.sidebar-title { 329 | font-weight: bold; 330 | } 331 | 332 | nav.contents, 333 | aside.topic, 334 | div.admonition, div.topic, blockquote { 335 | clear: left; 336 | } 337 | 338 | /* -- topics ---------------------------------------------------------------- */ 339 | 340 | nav.contents, 341 | aside.topic, 342 | div.topic { 343 | border: 1px solid #ccc; 344 | padding: 7px; 345 | margin: 10px 0 10px 0; 346 | } 347 | 348 | p.topic-title { 349 | font-size: 1.1em; 350 | font-weight: bold; 351 | margin-top: 10px; 352 | } 353 | 354 | /* -- admonitions ----------------------------------------------------------- */ 355 | 356 | div.admonition { 357 | margin-top: 10px; 358 | margin-bottom: 10px; 359 | padding: 7px; 360 | } 361 | 362 | div.admonition dt { 363 | font-weight: bold; 364 | } 365 | 366 | p.admonition-title { 367 | margin: 0px 10px 5px 0px; 368 | font-weight: bold; 369 | } 370 | 371 | div.body p.centered { 372 | text-align: center; 373 | margin-top: 25px; 374 | } 375 | 376 | /* -- content of sidebars/topics/admonitions -------------------------------- */ 377 | 378 | div.sidebar > :last-child, 379 | aside.sidebar > :last-child, 380 | nav.contents > :last-child, 381 | aside.topic > :last-child, 382 | div.topic > :last-child, 383 | div.admonition > :last-child { 384 | margin-bottom: 0; 385 | } 386 | 387 | div.sidebar::after, 388 | aside.sidebar::after, 389 | nav.contents::after, 390 | aside.topic::after, 391 | div.topic::after, 392 | div.admonition::after, 393 | blockquote::after { 394 | display: block; 395 | content: ''; 396 | clear: both; 397 | } 398 | 399 | /* -- tables ---------------------------------------------------------------- */ 400 | 401 | table.docutils { 402 | margin-top: 10px; 403 | margin-bottom: 10px; 404 | border: 0; 405 | border-collapse: collapse; 406 | } 407 | 408 | table.align-center { 409 | margin-left: auto; 410 | margin-right: auto; 411 | } 412 | 413 | table.align-default { 414 | margin-left: auto; 415 | margin-right: auto; 416 | } 417 | 418 | table caption span.caption-number { 419 | font-style: italic; 420 | } 421 | 422 | table caption span.caption-text { 423 | } 424 | 425 | table.docutils td, table.docutils th { 426 | padding: 1px 8px 1px 5px; 427 | border-top: 0; 428 | border-left: 0; 429 | border-right: 0; 430 | border-bottom: 1px solid #aaa; 431 | } 432 | 433 | th { 434 | text-align: left; 435 | padding-right: 5px; 436 | } 437 | 438 | table.citation { 439 | border-left: solid 1px gray; 440 | margin-left: 1px; 441 | } 442 | 443 | table.citation td { 444 | border-bottom: none; 445 | } 446 | 447 | th > :first-child, 448 | td > :first-child { 449 | margin-top: 0px; 450 | } 451 | 452 | th > :last-child, 453 | td > :last-child { 454 | margin-bottom: 0px; 455 | } 456 | 457 | /* -- figures --------------------------------------------------------------- */ 458 | 459 | div.figure, figure { 460 | margin: 0.5em; 461 | padding: 0.5em; 462 | } 463 | 464 | div.figure p.caption, figcaption { 465 | padding: 0.3em; 466 | } 467 | 468 | div.figure p.caption span.caption-number, 469 | figcaption span.caption-number { 470 | font-style: italic; 471 | } 472 | 473 | div.figure p.caption span.caption-text, 474 | figcaption span.caption-text { 475 | } 476 | 477 | /* -- field list styles ----------------------------------------------------- */ 478 | 479 | table.field-list td, table.field-list th { 480 | border: 0 !important; 481 | } 482 | 483 | .field-list ul { 484 | margin: 0; 485 | padding-left: 1em; 486 | } 487 | 488 | .field-list p { 489 | margin: 0; 490 | } 491 | 492 | .field-name { 493 | -moz-hyphens: manual; 494 | -ms-hyphens: manual; 495 | -webkit-hyphens: manual; 496 | hyphens: manual; 497 | } 498 | 499 | /* -- hlist styles ---------------------------------------------------------- */ 500 | 501 | table.hlist { 502 | margin: 1em 0; 503 | } 504 | 505 | table.hlist td { 506 | vertical-align: top; 507 | } 508 | 509 | /* -- object description styles --------------------------------------------- */ 510 | 511 | .sig { 512 | font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; 513 | } 514 | 515 | .sig-name, code.descname { 516 | background-color: transparent; 517 | font-weight: bold; 518 | } 519 | 520 | .sig-name { 521 | font-size: 1.1em; 522 | } 523 | 524 | code.descname { 525 | font-size: 1.2em; 526 | } 527 | 528 | .sig-prename, code.descclassname { 529 | background-color: transparent; 530 | } 531 | 532 | .optional { 533 | font-size: 1.3em; 534 | } 535 | 536 | .sig-paren { 537 | font-size: larger; 538 | } 539 | 540 | .sig-param.n { 541 | font-style: italic; 542 | } 543 | 544 | /* C++ specific styling */ 545 | 546 | .sig-inline.c-texpr, 547 | .sig-inline.cpp-texpr { 548 | font-family: unset; 549 | } 550 | 551 | .sig.c .k, .sig.c .kt, 552 | .sig.cpp .k, .sig.cpp .kt { 553 | color: #0033B3; 554 | } 555 | 556 | .sig.c .m, 557 | .sig.cpp .m { 558 | color: #1750EB; 559 | } 560 | 561 | .sig.c .s, .sig.c .sc, 562 | .sig.cpp .s, .sig.cpp .sc { 563 | color: #067D17; 564 | } 565 | 566 | 567 | /* -- other body styles ----------------------------------------------------- */ 568 | 569 | ol.arabic { 570 | list-style: decimal; 571 | } 572 | 573 | ol.loweralpha { 574 | list-style: lower-alpha; 575 | } 576 | 577 | ol.upperalpha { 578 | list-style: upper-alpha; 579 | } 580 | 581 | ol.lowerroman { 582 | list-style: lower-roman; 583 | } 584 | 585 | ol.upperroman { 586 | list-style: upper-roman; 587 | } 588 | 589 | :not(li) > ol > li:first-child > :first-child, 590 | :not(li) > ul > li:first-child > :first-child { 591 | 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BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ 14 | "TEXTAREA", 15 | "INPUT", 16 | "SELECT", 17 | "BUTTON", 18 | ]); 19 | 20 | const _ready = (callback) => { 21 | if (document.readyState !== "loading") { 22 | callback(); 23 | } else { 24 | document.addEventListener("DOMContentLoaded", callback); 25 | } 26 | }; 27 | 28 | /** 29 | * Small JavaScript module for the documentation. 30 | */ 31 | const Documentation = { 32 | init: () => { 33 | Documentation.initDomainIndexTable(); 34 | Documentation.initOnKeyListeners(); 35 | }, 36 | 37 | /** 38 | * i18n support 39 | */ 40 | TRANSLATIONS: {}, 41 | PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), 42 | LOCALE: "unknown", 43 | 44 | // gettext and ngettext don't access this so that the functions 45 | // can safely bound to a different name (_ = Documentation.gettext) 46 | gettext: (string) => { 47 | const translated = Documentation.TRANSLATIONS[string]; 48 | switch (typeof translated) { 49 | case "undefined": 50 | return string; // no translation 51 | case "string": 52 | return translated; // translation exists 53 | default: 54 | return translated[0]; // (singular, plural) translation tuple exists 55 | } 56 | }, 57 | 58 | ngettext: (singular, plural, n) => { 59 | const translated = Documentation.TRANSLATIONS[singular]; 60 | if (typeof translated !== "undefined") 61 | return translated[Documentation.PLURAL_EXPR(n)]; 62 | return n === 1 ? singular : plural; 63 | }, 64 | 65 | addTranslations: (catalog) => { 66 | Object.assign(Documentation.TRANSLATIONS, catalog.messages); 67 | Documentation.PLURAL_EXPR = new Function( 68 | "n", 69 | `return (${catalog.plural_expr})` 70 | ); 71 | Documentation.LOCALE = catalog.locale; 72 | }, 73 | 74 | /** 75 | * helper function to focus on search bar 76 | */ 77 | focusSearchBar: () => { 78 | document.querySelectorAll("input[name=q]")[0]?.focus(); 79 | }, 80 | 81 | /** 82 | * Initialise the domain index toggle buttons 83 | */ 84 | initDomainIndexTable: () => { 85 | const toggler = (el) => { 86 | const idNumber = el.id.substr(7); 87 | const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); 88 | if (el.src.substr(-9) === "minus.png") { 89 | el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; 90 | toggledRows.forEach((el) => (el.style.display = "none")); 91 | } else { 92 | el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; 93 | toggledRows.forEach((el) => (el.style.display = "")); 94 | } 95 | }; 96 | 97 | const togglerElements = document.querySelectorAll("img.toggler"); 98 | togglerElements.forEach((el) => 99 | el.addEventListener("click", (event) => toggler(event.currentTarget)) 100 | ); 101 | togglerElements.forEach((el) => (el.style.display = "")); 102 | if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); 103 | }, 104 | 105 | initOnKeyListeners: () => { 106 | // only install a listener if it is really needed 107 | if ( 108 | !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && 109 | !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS 110 | ) 111 | return; 112 | 113 | document.addEventListener("keydown", (event) => { 114 | // bail for input elements 115 | if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; 116 | // bail with special keys 117 | if (event.altKey || event.ctrlKey || event.metaKey) return; 118 | 119 | if (!event.shiftKey) { 120 | switch (event.key) { 121 | case "ArrowLeft": 122 | if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; 123 | 124 | const prevLink = document.querySelector('link[rel="prev"]'); 125 | if (prevLink && prevLink.href) { 126 | window.location.href = prevLink.href; 127 | event.preventDefault(); 128 | } 129 | break; 130 | case "ArrowRight": 131 | if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; 132 | 133 | const nextLink = document.querySelector('link[rel="next"]'); 134 | if (nextLink && nextLink.href) { 135 | window.location.href = nextLink.href; 136 | event.preventDefault(); 137 | } 138 | break; 139 | } 140 | } 141 | 142 | // some keyboard layouts may 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' + 111 | '' + 112 | _("Hide Search Matches") + 113 | "

" 114 | ) 115 | ); 116 | }, 117 | 118 | /** 119 | * helper function to hide the search marks again 120 | */ 121 | hideSearchWords: () => { 122 | document 123 | .querySelectorAll("#searchbox .highlight-link") 124 | .forEach((el) => el.remove()); 125 | document 126 | .querySelectorAll("span.highlighted") 127 | .forEach((el) => el.classList.remove("highlighted")); 128 | localStorage.removeItem("sphinx_highlight_terms") 129 | }, 130 | 131 | initEscapeListener: () => { 132 | // only install a listener if it is really needed 133 | if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; 134 | 135 | document.addEventListener("keydown", (event) => { 136 | // bail for input elements 137 | if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; 138 | // bail with special keys 139 | if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; 140 | if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { 141 | SphinxHighlight.hideSearchWords(); 142 | event.preventDefault(); 143 | } 144 | }); 145 | }, 146 | }; 147 | 148 | _ready(() => { 149 | /* Do not call highlightSearchWords() when we are on the search page. 150 | * It will highlight words from the *previous* search query. 151 | */ 152 | if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); 153 | SphinxHighlight.initEscapeListener(); 154 | }); 155 | -------------------------------------------------------------------------------- /doc/build/html/genindex.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Index — GNNWR 0.1.4 documentation 7 | 8 | 9 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
25 | 53 | 54 |
58 | 59 |
60 |
61 |
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  • Index
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68 |
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72 | 73 | 74 |

Index

75 | 76 |
77 | A 78 | | B 79 | | D 80 | | F 81 | | G 82 | | H 83 | | I 84 | | L 85 | | M 86 | | O 87 | | P 88 | | R 89 | | S 90 | | V 91 | | W 92 | 93 |
94 |

A

95 | 96 | 102 | 108 |
109 | 110 |

B

111 | 112 | 116 | 120 |
121 | 122 |

D

123 | 124 | 130 | 136 |
137 | 138 |

F

139 | 140 | 152 |
153 | 154 |

G

155 | 156 | 180 | 200 |
201 | 202 |

H

203 | 204 | 208 |
209 | 210 |

I

211 | 212 | 218 | 224 |
225 | 226 |

L

227 | 228 | 232 | 236 |
237 | 238 |

M

239 | 240 | 259 |
260 | 261 |

O

262 | 263 | 267 |
268 | 269 |

P

270 | 271 | 275 | 281 |
282 | 283 |

R

284 | 285 | 299 | 307 |
308 | 309 |

S

310 | 311 | 319 | 329 |
330 | 331 |

V

332 | 333 | 337 |
338 | 339 |

W

340 | 341 | 345 | 349 |
350 | 351 | 352 | 353 |
354 |
355 |
356 | 357 |
358 | 359 |
360 |

© Copyright 2023, gnnwr.

361 |
362 | 363 | Built with Sphinx using a 364 | theme 365 | provided by Read the Docs. 366 | 367 | 368 |
369 |
370 |
371 |
372 |
373 | 378 | 379 | 380 | -------------------------------------------------------------------------------- /doc/build/html/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | Welcome to GNNWR’s documentation! — GNNWR 0.1.4 documentation 8 | 9 | 10 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 |
27 | 55 | 56 |
60 | 61 |
62 |
63 |
64 | 71 |
72 |
73 |
74 |
75 | 76 |
77 |

Welcome to GNNWR’s documentation!

78 |
79 |

Contents:

80 | 161 |
162 |
163 |
164 |

Indices and tables

165 | 170 |
171 | 172 | 173 |
174 |
175 |
178 | 179 |
180 | 181 |
182 |

© Copyright 2023, gnnwr.

183 |
184 | 185 | Built with Sphinx using a 186 | theme 187 | provided by Read the Docs. 188 | 189 | 190 |
191 |
192 |
193 |
194 |
195 | 200 | 201 | 202 | -------------------------------------------------------------------------------- /doc/build/html/objects.inv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zjuwss/gnnwr/caca89c88fe736c695c7f17a8d866626cd0af845/doc/build/html/objects.inv -------------------------------------------------------------------------------- /doc/build/html/py-modindex.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Python Module Index — GNNWR 0.1.4 documentation 7 | 8 | 9 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 |
28 | 56 | 57 |
61 | 62 |
63 |
64 |
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  • Python Module Index
    • 68 |
  • 69 |
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71 |
72 |
73 |
74 |
75 | 76 | 77 |

Python Module Index

78 | 79 |
80 | g 81 |
82 | 83 | 84 | 85 | 87 | 88 | 90 | 93 | 94 | 95 | 98 | 99 | 100 | 103 | 104 | 105 | 108 | 109 | 110 | 113 |
 
86 | g
91 | gnnwr 92 |
    96 | gnnwr.datasets 97 |
    101 | gnnwr.models 102 |
    106 | gnnwr.networks 107 |
    111 | gnnwr.utils 112 |
114 | 115 | 116 |
117 |
118 |
119 | 120 |
121 | 122 |
123 |

© Copyright 2023, gnnwr.

124 |
125 | 126 | Built with Sphinx using a 127 | theme 128 | provided by Read the Docs. 129 | 130 | 131 |
132 |
133 |
134 |
135 |
136 | 141 | 142 | 143 | -------------------------------------------------------------------------------- /doc/build/html/search.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Search — GNNWR 0.1.4 documentation 7 | 8 | 9 | 10 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 |
28 | 56 | 57 |
61 | 62 |
63 |
64 |
65 |
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  • Search
    • 68 |
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71 |
72 |
73 |
74 |
75 | 76 | 83 | 84 | 85 |
86 | 87 |
88 | 89 |
90 |
91 |
92 | 93 |
94 | 95 |
96 |

© Copyright 2023, gnnwr.

97 |
98 | 99 | Built with Sphinx using a 100 | theme 101 | provided by Read the Docs. 102 | 103 | 104 |
105 |
106 |
107 |
108 |
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Make sure you have Sphinx 17 | echo.installed, then set the SPHINXBUILD environment variable to point 18 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 19 | echo.may add the Sphinx directory to PATH. 20 | echo. 21 | echo.If you don't have Sphinx installed, grab it from 22 | echo.https://www.sphinx-doc.org/ 23 | exit /b 1 24 | ) 25 | 26 | if "%1" == "" goto help 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /doc/source/conf.py: -------------------------------------------------------------------------------- 1 | # Configuration file for the Sphinx documentation builder. 2 | # 3 | # For the full list of built-in configuration values, see the documentation: 4 | # https://www.sphinx-doc.org/en/master/usage/configuration.html 5 | 6 | # -- Project information ----------------------------------------------------- 7 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information 8 | import os 9 | import sys 10 | sys.path.insert(0,os.path.abspath('../../src')) 11 | project = 'GNNWR' 12 | copyright = '2023, gnnwr' 13 | author = 'gnnwr' 14 | release = '0.1.4' 15 | 16 | # -- General configuration --------------------------------------------------- 17 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration 18 | 19 | extensions = ['sphinx.ext.autodoc', 20 | 'sphinx.ext.doctest', 21 | 'sphinx.ext.intersphinx', 22 | 'sphinx.ext.todo', 23 | 'sphinx.ext.coverage', 24 | 'sphinx.ext.mathjax', 25 | 'sphinx.ext.napoleon', 26 | 'sphinx.ext.githubpages', 27 | 'sphinx.ext.viewcode' 28 | ] 29 | 30 | 31 | templates_path = ['_templates'] 32 | exclude_patterns = [] 33 | 34 | 35 | 36 | # -- Options for HTML output ------------------------------------------------- 37 | # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output 38 | 39 | html_theme = 'sphinx_rtd_theme' 40 | html_static_path = ['_static'] 41 | -------------------------------------------------------------------------------- /doc/source/index.rst: -------------------------------------------------------------------------------- 1 | .. GNNWR documentation master file, created by 2 | sphinx-quickstart on Sat Sep 16 22:25:09 2023. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to GNNWR's documentation! 7 | ================================= 8 | 9 | .. toctree:: 10 | :maxdepth: 3 11 | :caption: Contents: 12 | :glob: 13 | 14 | gnnwr/* 15 | 16 | Indices and tables 17 | ================== 18 | 19 | * :ref:`genindex` 20 | * :ref:`modindex` 21 | * :ref:`search` 22 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy>=1.21.0 2 | pandas >=1.5.3 3 | scikit_learn>=1.0.2 4 | statsmodels>=0.13.5 5 | torch>=1.8.1 6 | tqdm>=4.63.0 7 | 8 | folium~=0.14.0 9 | branca~=0.6.0 10 | scipy~=1.10.1 11 | scikit-learn~=1.2.2 -------------------------------------------------------------------------------- /src/gnnwr/__init__.py: -------------------------------------------------------------------------------- 1 | from .datasets import * 2 | from .models import * 3 | from .networks import * 4 | from .utils import * -------------------------------------------------------------------------------- /src/gnnwr/networks.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | 5 | 6 | def default_dense_layer(insize, outsize): 7 | """ 8 | generate default dense layers for neural network 9 | 10 | Parameters 11 | ---------- 12 | insize: int 13 | input size of neural network 14 | outsize: int 15 | output size of neural network 16 | 17 | Returns 18 | ------- 19 | dense_layer: list 20 | a list of dense layers of neural network 21 | """ 22 | dense_layer = [] 23 | size = int(math.pow(2, int(math.log2(insize)))) 24 | while size > outsize: 25 | dense_layer.append(size) 26 | size = int(math.pow(2, int(math.log2(size)) - 1)) 27 | return dense_layer 28 | 29 | class LinearNetwork(nn.Module): 30 | """ 31 | LinearNetwork is a neural network with dense layers, which is used to calculate the weight of features. 32 | | The each layer of LinearNetwork is as follows: 33 | | full connection layer -> batch normalization layer -> activate function -> drop out layer 34 | 35 | Parameters 36 | ---------- 37 | dense_layer: list 38 | a list of dense layers of Neural Network 39 | insize: int 40 | input size of Neural Network(must be positive) 41 | outsize: int 42 | Output size of Neural Network(must be positive) 43 | drop_out: float 44 | drop out rate(default: ``0.2``) 45 | activate_func: torch.nn.functional 46 | activate function(default: ``nn.PReLU(init=0.1)``) 47 | batch_norm: bool 48 | whether use batch normalization(default: ``True``) 49 | """ 50 | def __init__(self, insize, outsize, drop_out=0, activate_func=None, batch_norm=False): 51 | super(LinearNetwork, self).__init__() 52 | self.layer = nn.Linear(insize, outsize) 53 | if drop_out < 0 or drop_out > 1: 54 | raise ValueError("drop_out must be in [0, 1]") 55 | elif drop_out == 0: 56 | self.drop_out = nn.Identity() 57 | else: 58 | self.drop_out = nn.Dropout(drop_out) 59 | if batch_norm: 60 | self.batch_norm = nn.BatchNorm1d(outsize) 61 | else: 62 | self.batch_norm = nn.Identity() 63 | 64 | if activate_func is None: 65 | self.activate_func = nn.Identity() 66 | else: 67 | self.activate_func = activate_func 68 | self.reset_parameter() 69 | 70 | def reset_parameter(self): 71 | torch.nn.init.kaiming_uniform_(self.layer.weight, a=0, mode='fan_in') 72 | if self.layer.bias is not None: 73 | self.layer.bias.data.fill_(0) 74 | 75 | def forward(self, x): 76 | x = x.to(torch.float32) 77 | x = self.layer(x) 78 | x = self.batch_norm(x) 79 | x = self.activate_func(x) 80 | x = self.drop_out(x) 81 | return x 82 | 83 | def __str__(self) -> str: 84 | return f"LinearNetwork: {self.layer.in_features} -> {self.layer.out_features}\n" + \ 85 | f"Dropout: {self.drop_out.p}\n" + \ 86 | f"BatchNorm: {self.batch_norm}\n" + \ 87 | f"Activation: {self.activate_func}" 88 | 89 | def __repr__(self) -> str: 90 | return self.__str__() 91 | 92 | class SWNN(nn.Module): 93 | """ 94 | SWNN is a neural network with dense layers, which is used to calculate the spatial and temporal weight of features. 95 | | The each layer of SWNN is as follows: 96 | | full connection layer -> batch normalization layer -> activate function -> drop out layer 97 | 98 | Parameters 99 | ---------- 100 | dense_layer: list 101 | a list of dense layers of Neural Network 102 | insize: int 103 | input size of Neural Network(must be positive) 104 | outsize: int 105 | Output size of Neural Network(must be positive) 106 | drop_out: float 107 | drop out rate(default: ``0.2``) 108 | activate_func: torch.nn.functional 109 | activate function(default: ``nn.PReLU(init=0.1)``) 110 | batch_norm: bool 111 | whether use batch normalization(default: ``True``) 112 | """ 113 | def __init__(self, dense_layer=None, insize=-1, outsize=-1, drop_out=0.2, activate_func=nn.PReLU(init=0.1), 114 | batch_norm=True): 115 | 116 | super(SWNN, self).__init__() 117 | if dense_layer is None or len(dense_layer) == 0: 118 | self.dense_layer = default_dense_layer(insize, outsize) 119 | else: 120 | self.dense_layer = dense_layer 121 | if insize < 0 or outsize < 0: 122 | raise ValueError("insize and outsize must be positive") 123 | self.drop_out = drop_out 124 | self.batch_norm = batch_norm 125 | self.activate_func = activate_func 126 | self.insize = insize 127 | self.outsize = outsize 128 | count = 0 # used to name layers 129 | lastsize = self.insize # used to record the size of last layer 130 | self.fc = nn.Sequential() 131 | 132 | for size in self.dense_layer: 133 | # add full connection layer 134 | self.fc.add_module("swnn_full" + str(count), 135 | LinearNetwork(lastsize, size, drop_out, activate_func, batch_norm)) 136 | lastsize = size 137 | count += 1 138 | self.fc.add_module("full" + str(count), 139 | LinearNetwork(lastsize, self.outsize)) 140 | def forward(self, x): 141 | x = x.to(torch.float32) 142 | x = self.fc(x) 143 | return x 144 | 145 | 146 | class STPNN(nn.Module): 147 | """ 148 | STPNN is a neural network with dense layers, which is used to calculate the spatial and temporal proximity 149 | of two nodes. 150 | | The each layer of STPNN is as follows: 151 | | full connection layer -> batch normalization layer -> activate function -> drop out layer 152 | 153 | Parameters 154 | ---------- 155 | dense_layer: list 156 | a list of dense layers of Neural Network 157 | insize: int 158 | input size of Neural Network(must be positive) 159 | outsize: int 160 | Output size of Neural Network(must be positive) 161 | drop_out: float 162 | drop out rate(default: ``0.2``) 163 | activate_func: torch.nn.functional 164 | activate function(default: ``nn.ReLU()``) 165 | batch_norm: bool 166 | whether use batch normalization(default: ``False``) 167 | """ 168 | def __init__(self, dense_layer, insize, outsize, drop_out=0.2, activate_func=nn.ReLU(), batch_norm=False): 169 | 170 | super(STPNN, self).__init__() 171 | # default dense layer 172 | self.dense_layer = dense_layer 173 | self.drop_out = drop_out 174 | self.batch_norm = batch_norm 175 | self.activate_func = activate_func 176 | self.insize = insize 177 | self.outsize = outsize 178 | count = 0 # used to name layers 179 | lastsize = self.insize # used to record the size of last layer 180 | self.fc = nn.Sequential() 181 | for size in self.dense_layer: 182 | self.fc.add_module("stpnn_full" + str(count), 183 | LinearNetwork(lastsize, size, drop_out, activate_func, batch_norm)) 184 | lastsize = size 185 | count += 1 186 | self.fc.add_module("full" + str(count), 187 | LinearNetwork(lastsize, self.outsize,activate_func=activate_func)) 188 | 189 | def forward(self, x): 190 | # STPNN 191 | x = x.to(torch.float32) 192 | batch = x.shape[0] 193 | height = x.shape[1] 194 | x = torch.reshape(x, shape=(batch * height, x.shape[2])) 195 | output = self.fc(x) 196 | output = torch.reshape(output, shape=(batch, height * self.outsize)) 197 | return output 198 | 199 | 200 | class STNN_SPNN(nn.Module): 201 | """ 202 | STNN_SPNN is a neural network with dense layers, which is used to calculate the spatial proximity of two nodes 203 | and temporal proximity of two nodes at the same time. 204 | | The each layer of STNN and SPNN is as follows: 205 | | full connection layer -> activate function 206 | 207 | Parameters 208 | ---------- 209 | STNN_insize: int 210 | input size of STNN(must be positive) 211 | STNN_outsize: int 212 | Output size of STNN(must be positive) 213 | SPNN_insize: int 214 | input size of SPNN(must be positive) 215 | SPNN_outsize: int 216 | Output size of SPNN(must be positive) 217 | activate_func: torch.nn.functional 218 | activate function(default: ``nn.ReLU()``) 219 | 220 | """ 221 | def __init__(self, STNN_insize:int, STNN_outsize, SPNN_insize:int, SPNN_outsize, activate_func=nn.ReLU()): 222 | 223 | super(STNN_SPNN, self).__init__() 224 | self.STNN_insize = STNN_insize 225 | self.STNN_outsize = STNN_outsize 226 | self.SPNN_insize = SPNN_insize 227 | self.SPNN_outsize = SPNN_outsize 228 | self.activate_func = activate_func 229 | self.STNN = nn.Sequential(nn.Linear(self.STNN_insize, self.STNN_outsize), self.activate_func) 230 | self.SPNN = nn.Sequential(nn.Linear(self.SPNN_insize, self.SPNN_outsize), self.activate_func) 231 | 232 | def forward(self, input1): 233 | STNN_input = input1[:, :, self.SPNN_insize:] 234 | SPNN_input = input1[:, :, 0:self.SPNN_insize] 235 | STNN_output = self.STNN(STNN_input) 236 | SPNN_output = self.SPNN(SPNN_input) 237 | output = torch.cat((STNN_output, SPNN_output), dim=-1) 238 | return output -------------------------------------------------------------------------------- /src/gnnwr/utils.py: -------------------------------------------------------------------------------- 1 | import math 2 | import statsmodels.api as sm 3 | import pandas as pd 4 | import torch 5 | import warnings 6 | import folium 7 | from folium.plugins import HeatMap 8 | import branca 9 | 10 | 11 | class OLS: 12 | """ 13 | `OLS` is the class to calculate the OLR coefficients of data.Get the coefficient by `object.params`. 14 | 15 | :param dataset: Input data 16 | :param xName: the independent variables' column 17 | :param yName: the dependent variable's column 18 | """ 19 | 20 | def __init__(self, dataset, xName: list, yName: list): 21 | self.__dataset = dataset 22 | self.__xName = xName 23 | self.__yName = yName 24 | self.__formula = yName[0] + '~' + '+'.join(xName) 25 | self.__fit = sm.formula.ols(self.__formula, dataset).fit() 26 | self.params = list(self.__fit.params.to_dict().values()) 27 | intercept = self.__fit.params.iloc[0] 28 | self.params = self.params[1:] 29 | self.params.append(intercept) 30 | 31 | 32 | class DIAGNOSIS: 33 | """ 34 | `DIAGNOSIS` is the class to calculate the diagnoses of the result of GNNWR/GTNNWR. 35 | These diagnoses include F1-test, F2-test, F3-test, AIC, AICc, R2, Adjust_R2, RMSE (Root Mean Square Error). 36 | The explanation of these diagnoses can be found in the paper 37 | `Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity `. 38 | :param weight: output of the neural network 39 | :param x_data: the independent variables 40 | :param y_data: the dependent variables 41 | :param y_pred: output of the GNNWR/GTNNWR 42 | """ 43 | 44 | def __init__(self, weight, x_data, y_data, y_pred): 45 | self._device = torch.device('cuda') if weight.is_cuda else torch.device('cpu') 46 | 47 | self.__weight = weight.clone() 48 | self.__x_data = x_data.clone() 49 | self.__y_data = y_data.clone() 50 | self.__y_pred = y_pred.clone() 51 | 52 | self.__n = len(self.__y_data) 53 | self.__k = len(self.__x_data[0]) 54 | 55 | self.__residual = self.__y_data - self.__y_pred 56 | self.__ssr = torch.sum((self.__y_pred - self.__y_data) ** 2) # sum of squared residuals 57 | 58 | self.__hat_com = torch.mm(torch.linalg.inv( 59 | torch.mm(self.__x_data.transpose(-2, -1), self.__x_data)), self.__x_data.transpose(-2, -1)) 60 | self.__ols_hat = torch.mm(self.__x_data, self.__hat_com) 61 | x_data_tile = self.__x_data.repeat(self.__n, 1) 62 | x_data_tile = x_data_tile.view(self.__n, self.__n, -1) 63 | x_data_tile_t = x_data_tile.transpose(1, 2) 64 | gtweight_3d = torch.diag_embed(self.__weight) 65 | 66 | hatS_temp = torch.matmul(gtweight_3d, 67 | torch.matmul(torch.inverse(torch.matmul(x_data_tile_t, x_data_tile)), x_data_tile_t)) 68 | self.__hat_temp = hatS_temp 69 | hatS = torch.matmul(self.__x_data.view(-1, 1, self.__x_data.size(1)), hatS_temp) 70 | hatS = hatS.view(-1, self.__n) 71 | self.__hat = hatS 72 | self.__S = torch.trace(self.__hat) 73 | self.f3_dict = None 74 | self.f3_dict_2 = None 75 | 76 | self._eye_I = torch.eye(self.__n, device=self._device) 77 | self._ones_J = torch.ones(self.__n, device=self._device) 78 | def hat(self): 79 | """ 80 | :return: hat matrix 81 | """ 82 | return self.__hat 83 | 84 | def F1_Global(self): 85 | """ 86 | :return: F1-test 87 | """ 88 | k1 = self.__n - 2 * torch.trace(self.__hat) + \ 89 | torch.trace(torch.mm(self.__hat.transpose(-2, -1), self.__hat)) 90 | 91 | k2 = self.__n - self.__k - 1 92 | rss_olr = torch.sum( 93 | (self.__y_data - torch.mm(self.__ols_hat, self.__y_data)) ** 2) 94 | F_value = self.__ssr / k1 / (rss_olr / k2) 95 | # p_value = f.sf(F_value, k1, k2) 96 | return F_value 97 | 98 | def F2_Global(self): 99 | """ 100 | :return: F2-test 101 | """ 102 | # A = (I - H) - (I - S)^T*(I - S) 103 | A = (self._eye_I - self.__ols_hat) - torch.mm( 104 | (self._eye_I - self.__hat).transpose(-2, -1), 105 | (self._eye_I - self.__hat)) 106 | v1 = torch.trace(A) 107 | # DSS = y^T*A*y 108 | DSS = torch.mm(self.__y_data.transpose(-2, -1), torch.mm(A, self.__y_data)) 109 | k2 = self.__n - self.__k - 1 110 | rss_olr = torch.sum( 111 | (torch.mean(self.__y_data) - torch.mm(self.__ols_hat, self.__y_data)) ** 2) 112 | 113 | return DSS / v1 / (rss_olr / k2) 114 | 115 | def F3_Local(self): 116 | """ 117 | :return: F3-test of each variable 118 | """ 119 | 120 | ek_dict = {} 121 | self.f3_dict = {} 122 | self.f3_dict_2 = {} 123 | for i in range(self.__x_data.size(1)): 124 | ek_zeros = torch.zeros([self.__x_data.size(1)],device=self._device) 125 | ek_zeros[i] = 1 126 | ek_dict['ek' + str(i)] = torch.reshape(torch.reshape(torch.tile(ek_zeros.clone().detach(), [self.__n]), 127 | [self.__n, -1]), 128 | [-1, 1, self.__x_data.size(1)]) 129 | hatB = torch.matmul(ek_dict['ek' + str(i)], self.__hat_temp) 130 | hatB = torch.reshape(hatB, [-1, self.__n]) 131 | 132 | L = torch.matmul(hatB.transpose(-2, -1), torch.matmul(self._eye_I - self._ones_J, hatB)) 133 | 134 | vk2 = 1 / self.__n * torch.matmul(self.__y_data.transpose(-2, -1), torch.matmul(L, self.__y_data)) 135 | trace_L = torch.trace(1 / self.__n * L) 136 | f3 = torch.squeeze(vk2 / trace_L / (self.__ssr / self.__n)) 137 | self.f3_dict['f3_param_' + str(i)] = f3 138 | 139 | bk = torch.matmul(hatB, self.__y_data) 140 | vk2_2 = 1 / self.__n * torch.sum((bk - torch.mean(bk)) ** 2) 141 | f3_2 = torch.squeeze(vk2_2 / trace_L / (self.__ssr / self.__n)) 142 | self.f3_dict_2['f3_param_' + str(i)] = f3_2 143 | return self.f3_dict, self.f3_dict_2 144 | 145 | def AIC(self): 146 | """ 147 | :return: AIC 148 | """ 149 | return self.__n * (math.log(self.__ssr / self.__n * 2 * math.pi, math.e)) + self.__n + self.__S 150 | 151 | def AICc(self): 152 | """ 153 | 154 | :return: AICc 155 | """ 156 | return self.__n * (math.log(self.__ssr / self.__n * 2 * math.pi, math.e) + (self.__n + self.__S) / ( 157 | self.__n - self.__S - 2)) 158 | 159 | def R2(self): 160 | """ 161 | 162 | :return: R2 of the result 163 | """ 164 | return 1 - torch.sum(self.__residual ** 2) / torch.sum((self.__y_data - torch.mean(self.__y_data)) ** 2) 165 | 166 | def Adjust_R2(self): 167 | """ 168 | 169 | :return: Adjust R2 of the result 170 | """ 171 | return 1 - (1 - self.R2()) * (self.__n - 1) / (self.__n - self.__k - 1) 172 | 173 | def RMSE(self): 174 | """ 175 | 176 | :return: RMSE of the result 177 | """ 178 | return torch.sqrt(torch.sum(self.__residual ** 2) / self.__n) 179 | 180 | 181 | class Visualize: 182 | """ 183 | `Visualize` is the class to visualize the data and the result of GNNWR/GTNNWR. 184 | It based on the `folium` package and use GaoDe map as the background. And it can display the dataset, the coefficients heatmap, and the dot map, 185 | which helps to understand the spatial distribution of the data and the result of GNNWR/GTNNWR better. 186 | 187 | :param data: the input data 188 | :param lon_lat_columns: the columns of longitude and latitude 189 | :param zoom: the zoom of the map 190 | """ 191 | def __init__(self, data, lon_lat_columns=None, zoom=4): 192 | self.__raw_data = data 193 | self.__tiles = 'https://wprd01.is.autonavi.com/appmaptile?x={x}&y={y}&z={z}&lang=en&size=1&scl=1&style=7' 194 | self.__zoom = zoom 195 | if hasattr(self.__raw_data, '_use_gpu'): 196 | self._train_dataset = self.__raw_data._train_dataset.dataframe 197 | self._valid_dataset = self.__raw_data._valid_dataset.dataframe 198 | self._test_dataset = self.__raw_data._test_dataset.dataframe 199 | self._result_data = self.__raw_data.result_data 200 | self._all_data = pd.concat([self._train_dataset, self._valid_dataset, self._test_dataset]) 201 | if lon_lat_columns is None: 202 | warnings.warn("lon_lat columns are not given. Using the spatial columns in dataset") 203 | self._spatial_column = self._train_dataset.spatial_column 204 | self.__center_lon = self._all_data[self._spatial_column[0]].mean() 205 | self.__center_lat = self._all_data[self._spatial_column[1]].mean() 206 | self.__lon_column = self._spatial_column[0] 207 | self.__lat_column = self._spatial_column[1] 208 | else: 209 | self._spatial_column = lon_lat_columns 210 | self.__center_lon = self._all_data[self._spatial_column[0]].mean() 211 | self.__center_lat = self._all_data[self._spatial_column[1]].mean() 212 | self.__lon_column = self._spatial_column[0] 213 | self.__lat_column = self._spatial_column[1] 214 | self._x_column = data._train_dataset.x_column 215 | self._y_column = data._train_dataset.y_column 216 | self.__map = folium.Map(location=[self.__center_lat, self.__center_lon], zoom_start=zoom, 217 | tiles=self.__tiles, attr="高德") 218 | else: 219 | raise ValueError("given data is not instance of GNNWR") 220 | 221 | def display_dataset(self, name="all", y_column=None, colors=None, steps=20, vmin=None, vmax=None): 222 | """ 223 | Display the dataset on the map, including the train, valid, test dataset. 224 | 225 | :param name: the name of the dataset, including 'all', 'train', 'valid', 'test' 226 | :param y_column: the column of the displayed variable 227 | :param colors: the list of colors, if not given, the default color is used 228 | :param steps: the steps of the colors 229 | 230 | """ 231 | if colors is None: 232 | colors = [] 233 | if y_column is None: 234 | warnings.warn("y_column is not given. Using the first y_column in dataset") 235 | y_column = self._y_column[0] 236 | if name == 'all': 237 | dst = self._all_data 238 | elif name == 'train': 239 | dst = self._train_dataset 240 | elif name == 'valid': 241 | dst = self._valid_dataset 242 | elif name == 'test': 243 | dst = self._test_dataset 244 | else: 245 | raise ValueError("name is not included in 'all','train','valid','test'") 246 | dst_min = dst[y_column].min() if vmin == None else vmin 247 | dst_max = dst[y_column].max() if vmax == None else vmax 248 | res = folium.Map(location=[self.__center_lat, self.__center_lon], zoom_start=self.__zoom, tiles=self.__tiles, 249 | attr="高德") 250 | if len(colors) <= 0: 251 | colormap = branca.colormap.linear.YlOrRd_09.scale(dst_min, dst_max).to_step(steps) 252 | else: 253 | colormap = branca.colormap.LinearColormap(colors=colors, vmin=dst_min, vmax=dst_max).to_step(steps) 254 | for idx, row in dst.iterrows(): 255 | folium.CircleMarker(location=(row[self.__lat_column], row[self.__lon_column]), radius=7, 256 | color=colormap.rgb_hex_str(row[y_column]), fill=True, fill_opacity=1, 257 | popup=""" 258 | longitude:{} 259 | latitude:{} 260 | {}:{} 261 | """.format(row[self.__lon_column], row[self.__lat_column], y_column, row[y_column]) 262 | ).add_to(res) 263 | res.add_child(colormap) 264 | return res 265 | 266 | def coefs_heatmap(self, data_column, colors=None, steps=20, vmin=None, vmax=None): 267 | """ 268 | Display the heatmap of the coefficients of the result of GNNWR/GTNNWR. 269 | 270 | :param data_column: the column of the displayed variable 271 | :param colors: the list of colors, if not given, the default color is used 272 | :param steps: the steps of the colors 273 | :param vmin: the minimum value of the displayed variable, if not given, the minimum value of the variable is used 274 | :param vmax: the maximum value of the displayed variable, if not given, the maximum value of the variable is used 275 | """ 276 | if colors is None: 277 | colors = [] 278 | res = folium.Map(location=[self.__center_lat, self.__center_lon], zoom_start=self.__zoom, tiles=self.__tiles, 279 | attr="高德") 280 | dst = self._result_data 281 | dst_min = dst[data_column].min() if vmin is None else vmin 282 | dst_max = dst[data_column].max() if vmax is None else vmax 283 | data = [[row[self.__lat_column], row[self.__lon_column], row[data_column]] for index, row in dst.iterrows()] 284 | if len(colors) <= 0: 285 | colormap = branca.colormap.linear.YlOrRd_09.scale(dst_min, dst_max).to_step(steps) 286 | else: 287 | colormap = branca.colormap.LinearColormap(colors=colors, vmin=dst_min, vmax=dst_max).to_step(steps) 288 | gradient_map = dict() 289 | for i in range(steps): 290 | gradient_map[i / steps] = colormap.rgb_hex_str(i / steps) 291 | colormap.add_to(res) 292 | HeatMap(data=data, gradient=gradient_map, radius=10).add_to(res) 293 | return res 294 | 295 | def dot_map(self, data, lon_column, lat_column, y_column, zoom=4, colors=None, steps=20, vmin=None, vmax=None): 296 | """ 297 | Display the data by dot map, the color of the dot represents the value of the variable. 298 | 299 | :param data: the input data 300 | :param lon_column: the column of longitude 301 | :param lat_column: the column of latitude 302 | :param y_column: the column of the displayed variable 303 | :param zoom: the zoom of the map 304 | :param colors: the list of colors, if not given, the default color is used 305 | :param steps: the steps of the colors 306 | :param vmin: the minimum value of the displayed variable, if not given, the minimum value of the variable is used 307 | :param vmax: the maximum value of the displayed variable, if not given, the maximum value of the variable is used 308 | """ 309 | if colors is None: 310 | colors = [] 311 | center_lon = data[lon_column].mean() 312 | center_lat = data[lat_column].mean() 313 | dst_min = data[y_column].min() if vmin is None else vmin 314 | dst_max = data[y_column].max() if vmax is None else vmax 315 | res = folium.Map(location=[center_lat, center_lon], zoom_start=zoom, tiles=self.__tiles, attr="高德") 316 | if len(colors) <= 0: 317 | colormap = branca.colormap.linear.YlOrRd_09.scale(dst_min, dst_max).to_step(steps) 318 | else: 319 | colormap = branca.colormap.LinearColormap(colors=colors, vmin=dst_min, vmax=dst_max).to_step(steps) 320 | for idx, row in data.iterrows(): 321 | folium.CircleMarker(location=(row[lat_column], row[lon_column]), radius=7, 322 | color=colormap.rgb_hex_str(row[y_column]), fill=True, fill_opacity=1, 323 | popup=""" 324 | longitude:{} 325 | latitude:{} 326 | {}:{} 327 | """.format(row[lon_column], row[lat_column], y_column, row[y_column]) 328 | ).add_to(res) 329 | colormap.add_to(res) 330 | return res 331 | --------------------------------------------------------------------------------