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| from shiny import App, ui, render, reactive | |
| from shiny.ui import HTML, tags | |
| import shinyswatch | |
| import torch | |
| import pandas as pd | |
| import numpy as np | |
| import io | |
| import torch.nn.functional as F | |
| from utils import load_training_data, load_models | |
| MONTHS = { | |
| 0: "Jan", | |
| 1: "Feb", | |
| 2: "Mar", | |
| 3: "Apr", | |
| 4: "May", | |
| 5: "Jun", | |
| 6: "Jul", | |
| 7: "Aug", | |
| 8: "Sep", | |
| 9: "Oct", | |
| 10: "Nov", | |
| 11: "Dec", | |
| } | |
| YEARS = list(range(2000, 2015)) | |
| RESOLUTIONS = { | |
| "0": "Local", | |
| "1": "32 km", | |
| "3": "96 km", | |
| "5": "160 km", | |
| "7": "224 km", | |
| "9": "288 km", | |
| } | |
| WCOLS = { | |
| "air.2m.mon.mean.nc": "temperature at 2m", | |
| # "air.sfc.mon.mean.nc": "surface temperature", | |
| "apcp.mon.mean.nc": "total precipitation", | |
| # "acpcp.mon.mean.nc": "acc. convective precip", | |
| # "tcdc.mon.mean.nc": "total cloud cover", | |
| # "dswrf.mon.mean.nc": "down short rads flux", | |
| # "hpbl.mon.mean.nc": "planet boundary layer height", | |
| "rhum.2m.mon.mean.nc": "relative humidity", | |
| "vwnd.10m.mon.mean.nc": "(north-south) wind component", | |
| "uwnd.10m.mon.mean.nc": "(east-west) wind component", | |
| } | |
| # RESOLUTION CONSTANTS | |
| NROW = 128 | |
| NCOL = 256 | |
| XMIN = -135.0 | |
| XMAX = -60.0 | |
| YMIN = 20.0 | |
| YMAX = 52.0 | |
| DLON = (XMAX - XMIN) / NCOL | |
| DLAT = (YMIN - YMAX) / NROW | |
| # Load non-reactivelye | |
| C, NAMES, Y, M = load_training_data( | |
| path="data/training_data.pkl", | |
| standardize_so4=True, | |
| log_so4=True, | |
| year_averages=True, | |
| ) | |
| ND = C.shape[1] | |
| _, _, YRAW, MRAW = load_training_data(path="data/training_data.pkl") | |
| DIRS = { | |
| "1": f"./data/weights/h1_w2vec", | |
| "3": f"./data/weights/h3_w2vec", | |
| "5": f"./data/weights/h5_w2vec", | |
| "7": f"./data/weights/h7_w2vec", | |
| "9": f"./data/weights/h9_w2vec", | |
| } | |
| MODELS = load_models(DIRS, prefix="h", nd=ND) | |
| multicol_html = tags.head( | |
| tags.style( | |
| HTML( | |
| ".multicol {" | |
| # "height: 150px; " | |
| "-webkit-column-count: 3;" # chrome, safari, opera | |
| "-moz-column-count: 3;" # firefox | |
| "column-count: 3;" | |
| "-moz-column-fill: auto;" | |
| "-column-fill: auto;" | |
| ) | |
| ) | |
| ) | |
| instructions = f""" | |
| ### Instructions | |
| Upload a CSV file with columns (id, lat, lon) using the `Browse` button on the sidebar. | |
| Below is an example of the contents of the file: | |
| ``` | |
| id,lat,lon | |
| 0,47.5,-122.5 | |
| 1,47.5,-122.25 | |
| 2,47.5,-122.0 | |
| 3,47.5,-121.75 | |
| 4,47.5,-121.5 | |
| ``` | |
| The id column can be any identifier, or the column can be ommited, in which case the row number will be used as the id. | |
| Make sure that the latitude is before the longitude column in the CSV file. The valid range for latitude is | |
| {YMIN} to {YMAX} and longitude is {XMIN} to {XMAX}, which cover the contiguous United States. | |
| The resolution corresponds to how much neighboring information is captured by the embedding. If `local` is selected, | |
| the original weather covariates will be returned. Currently, all the embeddings correspond to the variables: | |
| * `air.2m.mon.mean.nc`: temperature at 2m | |
| * `apcp.mon.mean.nc`: total precipitation | |
| * `rhum.2m.mon.mean.nc`: relative humidity | |
| * `vwnd.10m.mon.mean.nc`: (north-south) wind component | |
| * `uwnd.10m.mon.mean.nc`: (east-west) wind component | |
| The radius corresponds to the number of neighboring raster cells to include in weather2vec representation. A resolution of 96km means that the embeddings encodes informations from all nearby raster cells whose centers are less than 96km. All embeddings have 10 hidden dimensions. | |
| The embeddings also record information of the 12-month moving average. For this reason, the 'local' embeddings also have dimension 10, the first 5 dimensions correspond to the 5 meteorological variables in a given month, and the last 5 dimensions correspond to their 12-month moving average. For the non-local embeddings, the order of the variables is not interpretable. | |
| ### Download | |
| """ | |
| citation = """ | |
| ### Citation | |
| Tec, M., Scott, J.G. and Zigler, C.M., 2023. "Weather2vec: Representation learning for causal inference with non-local confounding in air pollution and climate studies". In: *Proceedings of the AAAI Conference on Artificial Intelligence*. | |
| ``` | |
| @inproceedings{tec2023weather2vec, | |
| title={Weather2vec: Representation learning for causal inference with non-local confounding in air pollution and climate studies}, | |
| author={Tec, Mauricio and Scott, James G and Zigler, Corwin M}, | |
| booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, | |
| volume={37}, | |
| number={12}, | |
| pages={14504--14513}, | |
| year={2023} | |
| } | |
| ``` | |
| """ | |
| # After uploading the file, the app will generate a CSV, a download link will appear here. | |
| # The CSV will contain the following columns: | |
| # Part 1: ui ---- | |
| app_ui = ui.page_fluid( | |
| shinyswatch.theme.minty(), | |
| multicol_html, | |
| ui.panel_title("Welcome to the Weather2vec Embedding Generator!"), | |
| ui.layout_sidebar( | |
| ui.panel_sidebar( | |
| ui.input_file("df", "Upload CSV File", accept=".csv"), | |
| tags.div( | |
| ui.input_checkbox_group("months", HTML("<b>Months</b>"), MONTHS, selected=list(MONTHS.keys())), | |
| class_="multicol", | |
| align="left", | |
| inline=False, | |
| ), | |
| HTML( | |
| "<b>Note:</b> Embedding of multiple months will be added.<br>True multi-temporal embeddings will be supported in the future.<br><br>" | |
| ), | |
| tags.div( | |
| ui.input_radio_buttons("year", HTML("<b>Year</b>"), YEARS), | |
| class_="multicol", | |
| align="left", | |
| inline=False, | |
| ), | |
| HTML("<br>"), | |
| tags.div( | |
| ui.input_radio_buttons( | |
| "resolution", HTML("<b>Resolution</b>"), RESOLUTIONS, selected="9" | |
| ), | |
| class_="multicol", | |
| align="left", | |
| inline=False, | |
| ), | |
| HTML("<br>"), | |
| ui.download_link("download_test", "Download an example input file here."), | |
| HTML("<br><b>Note</b>There are some issues with scrolling using Safari, try a different browser please."), | |
| width=4, | |
| ), | |
| ui.panel_main( | |
| ui.markdown(instructions), | |
| ui.output_ui("download_ui"), | |
| ui.markdown(citation), | |
| ), | |
| ), | |
| ) | |
| # Part 2: server ---- | |
| def server(input, output, session): | |
| def download_ui(): | |
| if input.df() is None: | |
| return HTML("<font color=red>Upload a CSV file first. A download button will appear here.</font>") | |
| else: | |
| return ui.div( | |
| ui.download_button("download", "Download Embeddings"), | |
| ui.output_data_frame("embs_preview"), | |
| ) | |
| def embs_preview(): | |
| df_embs_ = df_embs() | |
| if df_embs_ is None: | |
| return None | |
| else: | |
| return df_embs_.reset_index().head() | |
| def df_embs(): | |
| if input.df() is None: | |
| return None | |
| # read input file | |
| print(input.df()[-1].keys()) | |
| fname = input.df()[-1]["datapath"] | |
| df = pd.read_csv(fname) | |
| if df.shape[1] > 2: | |
| first_col = df.columns[0] | |
| df = df.set_index(first_col) | |
| months = np.array(input.months(), dtype=int) | |
| year = int(input.year()) | |
| if len(months) == 0: | |
| raise ValueError("Must select at least one month.") | |
| # obtain temporal indices | |
| idxs = (year - 2000) * 12 + months - 1 | |
| Ct = torch.FloatTensor(C)[idxs] | |
| # compute row, col from lat, lon | |
| lat = df.values[:, -2] | |
| lon = df.values[:, -1] | |
| # | |
| interp_factor = 32 | |
| dlon_ = DLON / interp_factor | |
| dlat_ = DLAT / interp_factor | |
| col = (lon - XMIN) // dlon_ | |
| row = (lat - YMAX) // dlat_ | |
| # get model from resolution | |
| resolution = input.resolution() | |
| if resolution == "0": | |
| Z = Ct.mean(0) | |
| else: | |
| key = DIRS[resolution] | |
| mod = MODELS[key]["mod"] | |
| # evaluate model on input locations | |
| with torch.no_grad(): | |
| Z = mod["enc"](Ct).mean(0) | |
| # use bilinear interpolation to augment resolution | |
| Z = F.interpolate( | |
| Z[None], | |
| scale_factor=interp_factor, | |
| mode="bilinear", | |
| align_corners=False, | |
| ) | |
| # get embedding at input locations | |
| Z = Z[0, :, row, col].squeeze(0).squeeze(0).numpy().T | |
| # add to dataframe | |
| df_embs = pd.DataFrame(Z, columns=[f"Z{i:02d}" for i in range(Z.shape[1])]) | |
| df_embs.index = df.index | |
| if df.shape[1] > 2: | |
| df_id = df.iloc[:, :-2] | |
| df_embs = pd.concat([df_id, df_embs], axis=1) | |
| return df_embs | |
| def download(): | |
| if input.df() is None: | |
| raise ValueError("Upload a CSV file first.") | |
| with io.BytesIO() as f: | |
| df_embs().to_csv(f, index=False) | |
| yield f.getvalue() | |
| def download_test(): | |
| with io.BytesIO() as f: | |
| df = pd.read_csv("data/test-data.csv") | |
| df.to_csv(f, index=False) | |
| yield f.getvalue() | |
| # Combine into a shiny app. | |
| # Note that the variable must be "app". | |
| app = App(app_ui, server) | |