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Drew Resnickdrewmresnickremicousin
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Crop suitability (#280)
* linking files, and copying some from onset maproom (as templates) * editing the layout and maproom to run an as a standalone app * fix controls symlink * fix assets symlink * add pingrid symlink * starting to adapt the onset maproom to work for the crop suitability maproom * adding a dropdown and some minor edits * fix mistake in checking out old commit * trying to fix mistake in editing a file * pushing most recent changes / additions trying to get tile generation to work * add seasonal means time series plot * getting the tile bug fixed and starting to play around with a function for crop suitability * make tile print output of suitability analysis; create button for user to submit analysis params * add title callback; try to add final parameters for crop suitability analysis * remove empty paragraph inputs * improve readibility of function call * fix rainbow colorscale call from pingrid * edit files to build using path defs in config files * change opacity of colorbar to 1 * remove unused rount_latLng func * remove second .sum() where it is not needed * remove unneeded outputs from callback * fix reading of file paths * move default values for input params to country-wise config file just for nma * remove dry spell inputs for now * remove version of func for selecting season of data that was not used * update suitability index from % to int; update output plot to be bar graph * update maproom title * add option to select different data layers to display * first go at selecting subset of data before calling the crop_suit function * update config options for APP.run_server() * fix pingrid.tile bug; fix error in run_server for defining debug * fixing crop suitability func to handle dataArrays as input when a single lat/lng is selected * add more complex logic from app.py * making crop suitability function create new empty dataset; make inputs for the function all datasets before passing * extract DataArray when loading in data; make change to config var names * move non country specific config entries to config-defaults * update documentation in layout section * update title and description of layers selection ropdown * change id name for submit coords * update map target year title and remove target year from ts callback * fix season and year controls to update on input for map title, ts, and tile * make def of units more compact * update title for submit button * change use of range to amplitude for calc of temperature range * change input title for seasonal rainfall input * add html_size option to Numbers() to control size of numbers input boxes * change def of wet_day_def; add comments for param defs * keep naming convention for time to be T * remove erroneous text * update descriptions of variables * comment out DB configuration for development env * remove unused controls * improve var name and delete unused var * edit time series title * hard code to add coordinates units for this maproom * update map title to reflect Malaria maproom * setting up so it builds like the other maprooms * this is now its own repo * builds with the others and shows a plausible landing page * working precip layer * working suitability index is now default * typo * outputs min. Probably unncessary but will get to colorscales later * correcting a likely oversight of rebasing * changed to dummy initial value for position and commented about it * fill false is enough * empty tiles can be checked for and returned as soon as there is data * more concise crop_suitability function * further simplification: no need for where to make 1s and 0s (but does need 1*) * earlier tiling * no need for compute * crop suitability function should get the data type it should get * more concise way to set up the xr.Dataset * more proper place to convert the bool to int --------- Co-authored-by: drewmresnick <[email protected]> Co-authored-by: remic <[email protected]>
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‎enacts/config-defaults.yaml

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@@ -112,3 +112,32 @@ maprooms:
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# description: The map shows the crop evapotranspiration...
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# units: mm
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#crop suitability
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crop_suitability:
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# App
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core_path: /crops_climate_suitability
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app_title: Climate Suitability for Crops
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crop_suit_title: Climate Suitability for Crops
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title: Climate Suitability for Crops
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layers:
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suitability_layer:
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menu_label: Crop climate suitability
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description: The map shows the count of suitable climate conditions for a specific crop.
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units: integer index
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id: suit
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map_max: 5
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precip_layer:
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menu_label: Precipitation
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description: Seasonal Precipitation Total.
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units: mm
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tmax_layer:
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menu_label: Maximum temperature
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description: Seasonal Maximum Temperature.
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units: C
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tmin_layer:
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menu_label: Minimum temperature
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description: Seasonal Minimum Temperature.
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units: C
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‎enacts/crop_suitability/__init__.py

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from . import maproom_crop_suit

‎enacts/crop_suitability/assets

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../assets/

‎enacts/crop_suitability/layout_crop_suit.py

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import os
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from dash import dcc
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from dash import html
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import dash_bootstrap_components as dbc
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from dash import dash_table
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import dash_leaflet as dlf
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import plotly.express as px
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from controls import Block, Sentence, DateNoYear, Number
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import numpy as np
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from pathlib import Path
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import calc
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import pingrid
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import pandas as pd
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from globals_ import GLOBAL_CONFIG
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CONFIG = GLOBAL_CONFIG["maprooms"]["crop_suitability"]
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DR_PATH = f'{GLOBAL_CONFIG["datasets"]["daily"]["zarr_path"]}{GLOBAL_CONFIG["datasets"]["daily"]["vars"]["precip"][1]}'
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RR_MRG_ZARR = Path(DR_PATH)
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IRI_BLUE = "rgb(25,57,138)"
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IRI_GRAY = "rgb(113,112,116)"
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LIGHT_GRAY = "#eeeeee"
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def app_layout():
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# Initialization
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rr_mrg = calc.read_zarr_data(RR_MRG_ZARR)
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center_of_the_map = [((rr_mrg["Y"][int(rr_mrg["Y"].size/2)].values)), ((rr_mrg["X"][int(rr_mrg["X"].size/2)].values))]
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lat_res = np.around((rr_mrg["Y"][1]-rr_mrg["Y"][0]).values, decimals=10)
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lat_min = np.around((rr_mrg["Y"][0]-lat_res/2).values, decimals=10)
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lat_max = np.around((rr_mrg["Y"][-1]+lat_res/2).values, decimals=10)
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lon_res = np.around((rr_mrg["X"][1]-rr_mrg["X"][0]).values, decimals=10)
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lon_min = np.around((rr_mrg["X"][0]-lon_res/2).values, decimals=10)
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lon_max = np.around((rr_mrg["X"][-1]+lon_res/2).values, decimals=10)
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lat_label = str(lat_min)+" to "+str(lat_max)+" by "+str(lat_res)+"˚"
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lon_label = str(lon_min)+" to "+str(lon_max)+" by "+str(lon_res)+"˚"
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return dbc.Container(
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[
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dcc.Location(id="location", refresh=True),
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navbar_layout(),
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dbc.Row(
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[
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dbc.Col(
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controls_layout(lat_min, lat_max, lon_min, lon_max, lat_label, lon_label),
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sm=12,
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md=4,
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style={
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"background-color": "white",
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"border-style": "solid",
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"border-color": LIGHT_GRAY,
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"border-width": "0px 1px 0px 0px",
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"overflow":"scroll","height":"95vh",#column that holds text and controls
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},
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),
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dbc.Col(
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[
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dbc.Row(
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[
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dbc.Col(
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map_layout(center_of_the_map, lon_min, lat_min, lon_max, lat_max),
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width=12,
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style={
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"background-color": "white",
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},
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),
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],
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style={"overflow":"scroll","height": "55%"}, #box the map is in
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className="g-0",
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),
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dbc.Row(
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[
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dbc.Col(
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results_layout(),
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width=12,
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style={
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"background-color": "white",
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"min-height": "100px",
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"border-style": "solid",
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"border-color": LIGHT_GRAY,
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"border-width": "1px 0px 0px 0px",
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},
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),
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],
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style={"overflow":"scroll","height":"45%"}, #box the plots are in
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className="g-0",
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),
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],style={"overflow":"scroll","height":"95vh"},#main column for map and results
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sm=12,
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md=8,
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),
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],
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className="g-0",
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),
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],
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fluid=True,
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style={"padding-left": "1px", "padding-right": "1px","height":"100%"},
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)
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def navbar_layout():
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return dbc.Navbar(
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[
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html.A(
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dbc.Row(
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[
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dbc.Col(
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html.Img(
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src="assets/" + GLOBAL_CONFIG["logo"],
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height="30px",
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)
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),
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dbc.Col(
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dbc.NavbarBrand(
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"Climate and Agriculture / " + CONFIG["crop_suit_title"],
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className="ml-2",
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)
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),
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],
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align="center",
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className="g-0",
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),
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),
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dbc.NavbarToggler(id="navbar-toggler"),
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dbc.Collapse(
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id="navbar-collapse",
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navbar=True,
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),
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],
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sticky="top",
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color=IRI_GRAY,
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dark=True,
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)
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def controls_layout(lat_min, lat_max, lon_min, lon_max, lat_label, lon_label):
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return dbc.Container(
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[
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html.Div(
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[
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html.H5(CONFIG["crop_suit_title"]),
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html.P(
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[f"""
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Maproom to explore crop climate suitability using a
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spatial overlay analysis. The output is an index from
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0-5
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where 5 meet all conditions and is the most suitable.
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""",
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html.Br(),
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"""Select from the layers dropdown to select which data
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to view as timeseries data and as a map layer. You can
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Select a custom point to view for the timeseries data,
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as well as a specific year to view on the map.""",
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html.Br(),
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"""Select custom values to determine the crop suitability
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parameters passed into the analysis to determine the
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most suitable conditions for your inquiry.""",
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]
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)
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]+[
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html.H5("Dataset Documentation"),
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html.P(
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f"""
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Reconstructed gridded rainfall, minimum temperature, and maximum temperature from {GLOBAL_CONFIG["institution"]}.
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The time series data were created by combining
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quality-controlled station observations in
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{GLOBAL_CONFIG["institution"]}’s archive with satellite rainfall estimates.
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Crop suitability values use the rainfall, mimumum and maximum temperature data as inputs to calculate a suitability index where a certain number of conditions of crop suitability are met.
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"""
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),
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]+[
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html.P([html.H6(val["menu_label"]), html.P(val["description"])])
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for key, val in CONFIG["layers"].items()
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],
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style={"position":"relative","height":"25%", "overflow":"scroll"},#box holding text
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),
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html.H3("Controls Panel",style={"padding":".5rem"}),
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html.Div(
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[
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Block("Data Layers",
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"Choose a data layer to view:",
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dbc.Select(
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id="data_choice",
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value=list(CONFIG["layers"].keys())[0],
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options=[
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{"label": val["menu_label"], "value": key}
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for key, val in CONFIG["layers"].items()
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],
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),
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),
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Block("Pick a point",
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dbc.Row(
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[
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dbc.Col(
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dbc.FormFloating([dbc.Input(
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id = "lat_input",
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min=lat_min,
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max=lat_max,
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type="number",
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),
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dbc.Label("Latitude", style={"font-size": "80%"}),
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dbc.Tooltip(
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f"{lat_label}",
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target="lat_input",
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className="tooltiptext",
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)]),
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),
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dbc.Col(
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dbc.FormFloating([dbc.Input(
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id = "lng_input",
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min=lon_min,
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max=lon_max,
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type="number",
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),
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dbc.Label("Longitude", style={"font-size": "80%"}),
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dbc.Tooltip(
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f"{lon_label}",
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target="lng_input",
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className="tooltiptext",
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)]),
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),
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dbc.Button(id="submit_coords", outline=True, color="primary", children='Submit lat lng'),
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],
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),
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"Map year:",
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dbc.Input(
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id = "target_year",
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type = "number",
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min = 1961,
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max = 2018,
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value = CONFIG["param_defaults"]["target_year"],
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),
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"Season of interest:",
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dbc.Select(
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id="target_season",
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value= CONFIG["param_defaults"]["target_season"],
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options=[
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{"label":"Dec-Feb", "value":"DJF"},
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{"label":"Mar-May", "value":"MAM"},
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{"label":"Jun-Aug", "value":"JJA"},
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{"label":"Sep-Nov", "value":"SON"},
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],
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),
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),
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Block(
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"Optimum seasonal total rainfall",
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Sentence(
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"Total rainfall amount between",
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Number("lower_wet_threshold", CONFIG["param_defaults"]["lower_wet_thresh"], min=0, max=99999, html_size=4),
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"and",
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Number("upper_wet_threshold", CONFIG["param_defaults"]["upper_wet_thresh"], min=0, max=99999, html_size=4),
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"mm",
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),
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),
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Block(
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"Temperature tolerance",
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Sentence(
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"Temperature range between",
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Number("minimum_temp", CONFIG["param_defaults"]["min_temp"], min=-99, max=999, html_size=2),
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"and",
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Number("maximum_temp", CONFIG["param_defaults"]["max_temp"], min=-99, max=99999, html_size=2),
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"C",
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),
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),
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Block(
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"Optimal daily temperature amplitude",
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Sentence(
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"An average daily temperature amplitude of:",
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Number("temp_range", CONFIG["param_defaults"]["temp_range"], min=0, max=99999, html_size=2),
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"C",
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),
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),
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# Block(
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# "Season length",
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# Sentence(
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# "The minimum length of the season:",
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# Number("season_length", CONFIG["param_defaults"]["season_length"], min=0, max=99999, html_size=3),
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# "days",
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# ),
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# ),
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Block(
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"Wet days",
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Sentence(
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"The minimum number of wet days within a season:",
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Number("min_wet_days", CONFIG["param_defaults"]["min_wet_days"], min=0, max=99999, html_size=3),
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"days",
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),
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Sentence(
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"Where a wet day is defined as a day with rainfall more than:",
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Number("wet_day_def", CONFIG["param_defaults"]["wet_day_def"], min=0, max=9999, html_size=3),
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"mm",
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),
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),
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],
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style={"position":"relative","height":"60%", "overflow":"scroll"},#box holding controls
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),
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html.Div(
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[dbc.Button(id="submit_params", children='Submit Crop Suitability Conditions', style={"position":"fixed", "width":"31%"})],
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)
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], #end container
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fluid=True,
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className="scrollable-panel p-3",
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style={"overflow":"scroll","height":"100%","padding-bottom": "1rem", "padding-top": "1rem"},
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) #style for container that is returned #95vh
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def map_layout(center_of_the_map, lon_min, lat_min, lon_max, lat_max):
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return dbc.Container(
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[
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dlf.Map(
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[
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dlf.LayersControl(id="layers_control", position="topleft"),
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dlf.LayerGroup(
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#Dummy position overridden by pick_location initial callback
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#position must be defined
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[dlf.Marker(id="loc_marker", position=[0, 0])],
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id="layers_group"
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),
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dlf.ScaleControl(imperial=False, position="topright"),
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dlf.Colorbar(
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id="colorbar",
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position="bottomleft",
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width=300,
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height=10,
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opacity=1,
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)
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],
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id="map",
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center=center_of_the_map,
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zoom=GLOBAL_CONFIG["zoom"],
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maxBounds = [[lat_min, lon_min],[lat_max, lon_max]],
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minZoom = GLOBAL_CONFIG["zoom"] - 1,
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maxZoom = GLOBAL_CONFIG["zoom"] + 10, #this was completely arbitrary
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style={
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"width": "100%",
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"height": "87%",#height of the map
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},
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),
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html.H6(
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id="map_title"
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),
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html.H6(
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id="hover_feature_label"
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)
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],
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fluid=True,
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style={"padding": "0rem", "height":"100%"},#box that holds map and title
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)
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def results_layout():
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return html.Div(
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[
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dbc.Spinner(dcc.Graph(id="timeseries_graph")),
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],
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id="results_div",
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)

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