From 14b630592a4f526f29a5afb92897cb0a8eac2837 Mon Sep 17 00:00:00 2001
From: tuckersiegel <siegel.tucker@gmail.com>
Date: Sun, 11 Apr 2021 14:56:21 -0400
Subject: [PATCH] mass update of min max to confidence interval
---
.gitignore | 3 +-
__pycache__/numba_cuda_fcns.cpython-37.pyc | Bin 0 -> 494 bytes
controllers/gddAccumulatedController.js | 34 +++-
...Controller.js => gddConfidenceInterval.js} | 77 +++++---
controllers/gddController.js | 1 +
controllers/gddNormalController.js | 19 +-
lib/utils.js | 1 +
models/normals.js | 14 +-
routes.js | 4 +-
swagger_definition.yaml | 19 +-
test_server.py | 10 +-
to_mongo.py | 183 +++++++++++++-----
12 files changed, 261 insertions(+), 104 deletions(-)
create mode 100644 __pycache__/numba_cuda_fcns.cpython-37.pyc
rename controllers/{minMaxController.js => gddConfidenceInterval.js} (55%)
diff --git a/.gitignore b/.gitignore
index 2e504ef..8ab2149 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
/data
/node_modules
/.vs
-*.png
\ No newline at end of file
+*.png
+*.npy
\ No newline at end of file
diff --git a/__pycache__/numba_cuda_fcns.cpython-37.pyc b/__pycache__/numba_cuda_fcns.cpython-37.pyc
new file mode 100644
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diff --git a/controllers/gddAccumulatedController.js b/controllers/gddAccumulatedController.js
index 824ee07..1769239 100644
--- a/controllers/gddAccumulatedController.js
+++ b/controllers/gddAccumulatedController.js
@@ -35,6 +35,34 @@ function find(collection, query, projection, t_base, res, product) {
})
}
+function find_normal(collection, query, projection, t_base, res, product) {
+ collection.findOne(query, projection).then(function(data) {
+ gdd_base = [];
+ if (product == "corn") {
+ gdd_base = data["corn_normals_gdd_base"];
+ } else {
+ gdd_base = data["normals_gdd_base"];
+ }
+
+ var gdds = [];
+
+ var min_temp = 0
+ var max_temp = 0
+
+ var gdd_sum = 0;
+
+ for (var i = 0; i < gdd_base.length; i++) {
+ gdd_value = (gdd_base[i] - t_base);//utils.calculate_gdd(min_temps[i], max_temps[i], t_base, product);
+ gdd_value = gdd_value < 0 ? 0 : gdd_value;
+ gdd_sum += gdd_value
+ gdds.push(gdd_sum)
+ }
+ send_response("Accumulated GDDs", gdds, data, res);
+ }, function(err) {
+ res.status(500).send({"internal error": err})
+ })
+}
+
exports.accumulated_gdd = function (req, res) {
@@ -164,11 +192,11 @@ exports.accumulated_normal_gdd = function (req, res) {
}
var projection = {
- min_temps: 1,
- max_temps: 1,
+ normals_gdd_base: 1,
+ corn_normals_gdd_base: 1,
location: 1,
}
- find(gdd_normal_collection, query, projection, t_base, res, product);
+ find_normal(gdd_normal_collection, query, projection, t_base, res, product);
};
\ No newline at end of file
diff --git a/controllers/minMaxController.js b/controllers/gddConfidenceInterval.js
similarity index 55%
rename from controllers/minMaxController.js
rename to controllers/gddConfidenceInterval.js
index 188a865..4af9ff0 100644
--- a/controllers/minMaxController.js
+++ b/controllers/gddConfidenceInterval.js
@@ -1,6 +1,17 @@
normals_collection = require('../models/normals.js');
utils = require('../lib/utils');
+
+var ci_convert = {
+ 80: 1.282,
+ 85: 1.440,
+ 90: 1.645,
+ 95: 1.960,
+ 99: 2.576,
+ 99.5: 2.807,
+ 99.9: 3.291
+}
+
function send_response(message, gdds, data, res) {
res.json({
message: message,
@@ -11,37 +22,47 @@ function send_response(message, gdds, data, res) {
})
}
-function find(collection, query, projection, t_base, res, product) {
- // var total_years = new Date().getFullYear() - new Date(1981, 0, 1, 0, 0, 0, 0).getFullYear() - 1;
- // const arr_mean = arr => arr.reduce((a,b) => a + b, 0) / arr.length;
+function find(collection, query, projection, t_base, res, product, confidence_interval) {
+ var total_years = new Date().getFullYear() - new Date(1981, 0, 1, 0, 0, 0, 0).getFullYear() - 1;
+ var z_score = ci_convert[confidence_interval];
collection.findOne(query, projection).then(function(data) {
-
- var combined_min_means = data["combined_min_mean"];
- var combined_max_means = data["combined_max_mean"];
+ // console.log(data);
+ var gdd_base = []
+ var gdd_std = []
+ if (product == "corn") {
+ gdd_base = data["corn_global_mean"];
+ gdd_std = data["corn_std"];
+ } else {
+ gdd_base = data["global_mean"];
+ gdd_std = data["main_std"];
+ }
var min_gdds = [];
var max_gdds = [];
+ var gdd_value = 0
var min_gdd_value = 0;
var max_gdd_value = 0;
+ var gdd_sum = 0;
var min_gdd_sum = 0;
var max_gdd_sum = 0;
- for (var i = 0; i < combined_min_means.length; i++) {
- if (product == "corn") {
- max_gdd_value = combined_max_means[i] - t_base;
- min_gdd_value = combined_min_means[i] - t_base;
- } else {
- max_gdd_value = utils.calculated_gdd_mean(combined_max_means[i], t_base);
- min_gdd_value = utils.calculated_gdd_mean(combined_min_means[i], t_base);
- }
- min_gdd_sum += min_gdd_value;
- max_gdd_sum += max_gdd_value;
+ for (var i = 0; i < gdd_base.length; i++) {
+ gdd_value = gdd_base[i];
+
+ max_gdd_value = (gdd_value + (gdd_std[i]/(total_years ** 0.5) * z_score)) - t_base
+ min_gdd_value = (gdd_value - (gdd_std[i]/(total_years ** 0.5) * z_score)) - t_base
+
+ min_gdd_value = min_gdd_value < 0 ? 0 : min_gdd_value;
+ max_gdd_value = max_gdd_value < 0 ? 0 : max_gdd_value;
+
+ min_gdd_sum += min_gdd_value
+ max_gdd_sum += max_gdd_value
min_gdds.push(min_gdd_sum);
max_gdds.push(max_gdd_sum);
}
res.json({
- message: "min and max gdd",
+ message: "confidence interval bounds gdd",
minimum: min_gdds,
maximum: max_gdds,
closest_lon: data["location"]["coordinates"][0],
@@ -53,11 +74,23 @@ function find(collection, query, projection, t_base, res, product) {
}
-exports.min_max = function (req, res) {
+exports.confidence_interval = function (req, res) {
var product = req.params.product;
var latitude = parseFloat(req.body.latitude)
var longitude = parseFloat(req.body.longitude)
+ var confidence_interval = 90
+
+ if (req.body.hasOwnProperty("confidence_interval")) {
+ confidence_interval = parseFloat(req.body.confidence_interval);
+ }
+
+ if (ci_convert.hasOwnProperty(confidence_interval) == false) {
+ errors.push({
+ parameter_error: "confidence interval",
+ message: "confidence interval must be 80, 85, 90, 95, 99, 99.5 or 99.9"
+ });
+ }
var query = {
location: {
@@ -100,14 +133,8 @@ exports.min_max = function (req, res) {
})
}
- var projection = {
- // combined_max_mean: 1,
- // combined_min_mean: 1,
- // location: 1,
- }
-
- find(normals_collection, query, projection, t_base, res, product);
+ find(normals_collection, query, {}, t_base, res, product, confidence_interval);
};
\ No newline at end of file
diff --git a/controllers/gddController.js b/controllers/gddController.js
index 33c48c9..6e6e51a 100644
--- a/controllers/gddController.js
+++ b/controllers/gddController.js
@@ -35,6 +35,7 @@ function find(collection, query, projection, t_base, res, product) {
exports.year_gdd = function (req, res) {
+
var year = parseInt(req.params.year);
var product = req.params.product;
diff --git a/controllers/gddNormalController.js b/controllers/gddNormalController.js
index 6ea874d..6adf582 100644
--- a/controllers/gddNormalController.js
+++ b/controllers/gddNormalController.js
@@ -2,17 +2,24 @@ normals_collection = require('../models/normals.js');
utils = require('../lib/utils');
function find(collection, query, projection, t_base, res, product) {
- collection.findOne(query, projection).then(function(data) {
- var min_temps = data["min_temps"]
- var max_temps = data["max_temps"]
+ collection.findOne(query).then(function(data) {
+ gdd_base = [];
+ if (product == "corn") {
+ gdd_base = data["corn_normals_gdd_base"];
+ } else {
+ gdd_base = data["normals_gdd_base"];
+ }
+
var gdds = [];
var min_temp = 0
var max_temp = 0
- for (var i = 0; i < min_temps.length; i++) {
- gdd_value = utils.calculate_gdd(min_temps[i], max_temps[i], t_base, product);
-
+ var gdd_sum = 0;
+
+ for (var i = 0; i < gdd_base.length; i++) {
+ gdd_value = (gdd_base[i] - t_base);//utils.calculate_gdd(min_temps[i], max_temps[i], t_base, product);
+ gdd_value = gdd_value < 0 ? 0 : gdd_value;
gdds.push(gdd_value)
}
diff --git a/lib/utils.js b/lib/utils.js
index de2ef45..135e6c4 100644
--- a/lib/utils.js
+++ b/lib/utils.js
@@ -50,6 +50,7 @@ function calculate_gdd(min_temp, max_temp, t_base, product) {
var gdd = 0;
if (product == "corn") {
min_temp = min_temp >= 50 ? min_temp : 50;
+ min_temp = min_temp <= 86 ? min_temp : 86;
max_temp = max_temp >= 50 ? max_temp : 50;
max_temp = max_temp <= 86 ? max_temp : 86;
mean = ((max_temp + min_temp) / 2);
diff --git a/models/normals.js b/models/normals.js
index b462fae..4ac5065 100644
--- a/models/normals.js
+++ b/models/normals.js
@@ -20,18 +20,24 @@ var normalsSchema = mongoose.Schema({
type: Number,
required: true
},
- min_temps: {
+ normals_gdd_base: {
type: Array,
},
- max_temps: {
+ corn_normals_gdd_base: {
type: Array,
},
- combined_max_mean: {
+ corn_std: {
type: Array,
},
- combined_min_mean: {
+ main_std: {
type: Array,
},
+ global_mean: {
+ type: Array,
+ },
+ corn_global_mean: {
+ type: Array,
+ }
});
var Model = module.exports = mongoose.model('normals', normalsSchema, "normals");
diff --git a/routes.js b/routes.js
index 8c3fa38..892577d 100644
--- a/routes.js
+++ b/routes.js
@@ -3,12 +3,12 @@ const accController = require("./controllers/gddAccumulatedController")
const controller = require("./controllers/gddController")
const normController = require("./controllers/gddNormalController")
const freezingDatesController = require("./controllers/freezingDatesController")
-const minMaxController = require("./controllers/minMaxController")
+const confidenceIntervalController = require("./controllers/gddConfidenceInterval")
router.route("/:product/daily/:year/accumulated").post(accController.accumulated_gdd)
router.route("/:product/daily/:year").post(controller.year_gdd)
router.route("/:product/normal").post(normController.normal)
-router.route("/:product/minmax").post(minMaxController.min_max)
+router.route("/:product/confidence").post(confidenceIntervalController.confidence_interval)
router.route("/:product/normal/accumulated").post(accController.accumulated_normal_gdd)
router.route("/freezing/:temperature").post(freezingDatesController.freezing_dates)
diff --git a/swagger_definition.yaml b/swagger_definition.yaml
index a8ac069..3b28fc2 100644
--- a/swagger_definition.yaml
+++ b/swagger_definition.yaml
@@ -443,10 +443,9 @@ paths:
message:
type: string
example: 22.5 is out of bounds for freezing date calculations. Must be between 24.083334 - 49.916668
- /api/{product}/minmax:
+ /api/{product}/confidence:
post:
- summary: Returns maximum and minimum accumulated yearly gdd data
- description: Returns the year with the highest and lowest accumulated gdds for a specific location and crop type. Includes both the year and the data for both max and min
+ summary: Returns confience interval for specified product
produces:
- application/json
consumes:
@@ -467,6 +466,7 @@ paths:
required:
- longitude
- latitude
+ - confidence_interval
properties:
latitude:
description: latitude to calculate gdd on
@@ -480,6 +480,11 @@ paths:
minimum: -125.0
maximum: -66.5
example: -76.94
+ confidence_interval:
+ description: confidence interval to use
+ type: number
+ enum: [80, 85, 90, 95, 99.5, 99.7]
+ example: 90
responses:
200:
@@ -491,7 +496,7 @@ paths:
properties:
message:
type: string
- example: min and max gdd
+ example: confidence interval bounds gdd
minimum:
type: array
items:
@@ -500,12 +505,6 @@ paths:
type: array
items:
type: number
- minimum_year:
- type: number
- minimum: 1981
- maximum_year:
- type: number
- minimum: 1981
closest_lat:
type: number
minimum: 24.083334
diff --git a/test_server.py b/test_server.py
index a78a556..9283d12 100644
--- a/test_server.py
+++ b/test_server.py
@@ -2,6 +2,7 @@ import requests
import os
import time
import matplotlib.pyplot as plt
+import numpy as np
data = {
"latitude": 38.98567,
@@ -31,19 +32,24 @@ print (time.time() - t)
r = requests.post("http://localhost:4000/api/freezing/28.2", data=data)
print (r.status_code)
print (r.json())
-r = requests.post("http://localhost:4000/api/soybean/minmax", data=data)
+r = requests.post("http://localhost:4000/api/corn/confidence", data=data)
print (r.status_code)
print (r.json())
min_gdds = r.json()["minimum"]
max_gdds = r.json()["maximum"]
-r = requests.post("http://localhost:4000/api/soybean/normal/accumulated", data=data)
+r = requests.post("http://localhost:4000/api/corn/normal/accumulated", data=data)
print (r.status_code)
print (r.json())
normals = r.json()["data"]
+# max_gdds = np.array(max_gdds) + np.array(normals)
+# max_gdds /= 2
+# min_gdds = np.array(min_gdds) + np.array(normals)
+# min_gdds /= 2
+
plt.plot(list(range(len(normals))), normals, color="black")
plt.plot(list(range(len(normals))), max_gdds, color="orange")
plt.plot(list(range(len(normals))), min_gdds, color="blue")
diff --git a/to_mongo.py b/to_mongo.py
index 02ab456..d629b94 100644
--- a/to_mongo.py
+++ b/to_mongo.py
@@ -1,8 +1,10 @@
import numpy as np
import xarray as xr
import tqdm
-import datetime
+import datetime, time, math
from pymongo import MongoClient
+from numba import njit
+
def is_leap_year(year):
if (year % 4) == 0:
@@ -34,6 +36,8 @@ lat = coords.latitude.data
lon = coords.longitude.data
lat = lat[::-1]
+
+
# years = list(range(1981, 2020 + 1))
# for year in years:
@@ -116,6 +120,45 @@ lat = lat[::-1]
### 30 YEAR NORMALS ###
### Covers from 1981-2010 ###
+
+@njit(fastmath=True, parallel=True)
+def elemetwise(tmins, tmaxs):
+ return np.divide(np.add(tmins, tmaxs), 2)
+# @njit(fastmath=True, parallel=False)
+def std(arr, fill_arr):
+ i_size = 122
+ j_size = 69 # 207
+ k_size = 281 # 207
+ pbar = tqdm.tqdm(total=np.product([i for i in arr.shape[1:]]))
+
+ for j in range(0, fill_arr.shape[1], j_size):
+ for k in range(0, fill_arr.shape[2], k_size):
+ a = arr[:, :, j:j+j_size, k:k+k_size]
+ std_ = np.std(a, axis=0)
+
+ fill_arr[:, j:j+j_size, k:k+k_size] = std_
+ pbar.update(366 * j_size * k_size)
+ pbar.close()
+ return fill_arr
+
+a1 = np.where(38.5 < lat)[0].tolist()
+a2 = np.where(lat < 39.5)[0].tolist()
+lat_a = np.array(list(set(a1) & set(a2)))
+
+a1 = np.where(-77 < lon)[0].tolist()
+a2 = np.where(lon < -76)[0].tolist()
+lon_a = np.array(list(set(a1) & set(a2)))
+
+# x1 = np.array(np.meshgrid(lat_a, lon_a)).T.reshape(len(lat_a) * len(lon_a), 2).tolist()
+# x1 = [(z[0], z[1]) for z in x1]
+# x2 = [(a, b) for a, b in zip(x[0], x[1])] # fix to x = [..... (x[0], x[1])] and all limiting stuff above and below when atlas limit removed
+
+# x = list(set(x1) & set(x2))
+
+# print (lat.shape)
+# exit()
+
+
db = client["gdd_database"]
gdd = db.normals
@@ -127,100 +170,132 @@ gdd = db["normals"]
resp = gdd.create_index([ ("location", "2dsphere") ])
resp = gdd.create_index([ ("year", 1) ])
-single_year_min = np.zeros((366, 621, 1405))
-single_year_max = np.zeros((366, 621, 1405))
+year_gdd_base = np.memmap("year_gdd_base.npy", mode="w+", shape=(40, 366, len(lat_a), len(lon_a)), dtype=np.float32)
+corn_year_gdd_base = np.memmap("corn_year_gdd_base.npy", mode="w+", shape=(40, 366, len(lat_a), len(lon_a)), dtype=np.float32)
-ld_min = np.zeros((single_year_min.shape[-2], single_year_min.shape[-1]))
-ld_max = np.zeros((single_year_min.shape[-2], single_year_min.shape[-1]) )
+ld_min = np.empty((year_gdd_base.shape[-2], year_gdd_base.shape[-1]), dtype=np.float16)
+ld_max = np.empty((year_gdd_base.shape[-2], year_gdd_base.shape[-1]), dtype=np.float16)
ld_count = 0
-combined_min_mean = np.zeros((366, 621, 1405)) + 100000
-combined_max_mean = np.zeros((366, 621, 1405)) - 100000
-
-for year in range(1981, 2019+1):
+i = 0
+for year in range(1981, 2020+1):
print (year)
+ t = time.time()
data = xr.open_dataset("data/temps_%s.nc" % year)
tmins = data.tmin.data
tmaxs = data.tmax.data
+ tmaxs = tmaxs[np.ix_(list(range(len(tmaxs))),lat_a, lon_a)]
+ tmins = tmins[np.ix_(list(range(len(tmaxs))),lat_a, lon_a)]
+
+ tmins = tmins.astype(np.float32)#np.cast(tmins, np.float16)
+ tmaxs = tmaxs.astype(np.float32)#np.cast(tmins, np.float16)
+ del data
+
## idx 59 is leap day
if not is_leap_year(year): # extra day in leap year screws everything up
-
- insert = np.zeros((1, tmins.shape[-2], tmins.shape[-1]))
- insert[:] = np.nan
-
- tmin_1 = tmins[:59]
- tmin_2 = tmins[59:]
-
- tmax_1 = tmaxs[:59]
- tmin_2 = tmaxs[59:]
-
- tmins = np.concatenate([tmin_1, insert, tmin_2], axis=0)
- tmaxs = np.concatenate([tmax_1, insert, tmin_2], axis=0)
+ tmins = np.insert(tmins, 59, np.nan, axis=0)
+ tmaxs = np.insert(tmaxs, 59, np.nan, axis=0)
else:
-
+ # TODO One for normal one for reg
ld_min += tmins[59]
ld_max += tmaxs[59]
ld_count += 1
- if year <= 2010:
+ test = elemetwise(tmins, tmaxs)
+ year_gdd_base[i] = test#(tmaxs + tmins) / 2
+ over_86 = tmins > 86
+ under_50 = tmaxs < 50
+ tmaxs[over_86] = 86
+ tmaxs[under_50] = 50
+ tmins[over_86] = 86
+ tmins[under_50] = 50
- single_year_max += tmaxs/30
- single_year_min += tmins/30
+ test = elemetwise(tmins, tmaxs)
+ corn_year_gdd_base[i] = test#(tmaxs + tmins) / 2
+ i += 1
- mean = (tmins + tmaxs) / 2
- where_min = np.where(mean <= combined_min_mean)
- where_max = np.where(mean >= combined_max_mean)
+print ("generating means")
+normals_gdd_base = year_gdd_base[:30].mean(axis=0)
+corn_normals_gdd_base = corn_year_gdd_base[:30].mean(axis=0)
- combined_max_mean[where_max] = mean[where_max]
- combined_min_mean[where_min] = mean[where_min]
+corn_global_mean = corn_year_gdd_base.mean(axis=0)
+gdd_base_mean = year_gdd_base.mean(axis=0)
ld_min = ld_min/ld_count
ld_max = ld_max/ld_count
-single_year_min[59] = ld_min
-single_year_max[59] = ld_max
+ld_gdd_base = (ld_min + ld_max) / 2
+ld_min = np.clip(ld_min, 50, 86)
+ld_max = np.clip(ld_max, 50, 86)
+corn_ld_gdd_base = (ld_min + ld_max) / 2
+
+corn_normals_gdd_base[59] = corn_ld_gdd_base
+normals_gdd_base[59] = ld_gdd_base
+
+corn_global_mean[59] = corn_ld_gdd_base
+gdd_base_mean[59] = ld_gdd_base
+
+corn_year_gdd_base[:, 59] = corn_ld_gdd_base
+year_gdd_base[:, 59] = ld_gdd_base
+print ("completed first bit")
-x = np.where(~np.isnan(np.nanmean(single_year_max, axis=0)))
+corn_year_gdd_std = np.empty((366, len(lat_a), len(lon_a)))
+year_gdd_base_std = np.empty((366, len(lat_a), len(lon_a)))
+
+print ("corn std")
+corn_year_gdd_std = std(corn_year_gdd_base, corn_year_gdd_std)
+print ("gdd std")
+year_gdd_base_std = std(year_gdd_base, year_gdd_base_std)
+print ("done")
+
+x = np.where(~np.isnan(np.nanmean(corn_normals_gdd_base, axis=0)))
# x = [(a, b) for a, b in zip(x[0], x[1])]
# FORCE LOCATIONS TO COLLEGE PARK, LAT 38.99 LON -76.94 BECAUSE OF ATLAS LIMIT
-a1 = np.where(38.5 < lat)[0].tolist()
-a2 = np.where(lat < 39.5)[0].tolist()
-lat_a = np.array(list(set(a1) & set(a2)))
+# a1 = np.where(38.5 < lat)[0].tolist()
+# a2 = np.where(lat < 39.5)[0].tolist()
+# lat_a = np.array(list(set(a1) & set(a2)))
-a1 = np.where(-77 < lon)[0].tolist()
-a2 = np.where(lon < -76)[0].tolist()
-lon_a = np.array(list(set(a1) & set(a2)))
+# a1 = np.where(-77 < lon)[0].tolist()
+# a2 = np.where(lon < -76)[0].tolist()
+# lon_a = np.array(list(set(a1) & set(a2)))
x1 = np.array(np.meshgrid(lat_a, lon_a)).T.reshape(len(lat_a) * len(lon_a), 2).tolist()
x1 = [(z[0], z[1]) for z in x1]
x2 = [(a, b) for a, b in zip(x[0], x[1])] # fix to x = [..... (x[0], x[1])] and all limiting stuff above and below when atlas limit removed
-x = list(set(x1) & set(x2))
+# x = list(set(x1) & set(x2))
+# print (x2)
-tmins = single_year_min
-tmaxs = single_year_max
+# tmins = single_year_min
+# tmaxs = single_year_max
locs = []
count = 0
-for i in tqdm.tqdm(x):
+for i in tqdm.tqdm(x2):
if len(locs) % 100 == 0 and len(locs) != 0:
new_result = gdd.insert_many(locs)
locs = []
- tmin_ = tmins[:, i[0], i[1]]
- tmax_ = tmaxs[:, i[0], i[1]]
+ normals_gdd_base_ = normals_gdd_base[:, i[0], i[1]]
+ corn_normals_gdd_base_ = corn_normals_gdd_base[:, i[0], i[1]]
+
+ # print (normals_gdd_base_)40
- combined_min_ = combined_min_mean[:, i[0], i[1]]
- combined_max_ = combined_max_mean[:, i[0], i[1]]
+ corn_std_ = corn_year_gdd_std[:, i[0], i[1]]
+ main_std_ = year_gdd_base_std[:, i[0], i[1]]
+ corn_mean = corn_global_mean[:, i[0], i[1]]
+ main_mean = gdd_base_mean[:, i[0], i[1]]
+ # corn_std_ = corn_year_gdd_std[:, i[0], i[1]]
+ # main_std_ = year_gdd_base_std[:, i[0], i[1]]
lat_ = lat[i[0]]
lon_ = lon[i[1]]
@@ -237,10 +312,16 @@ for i in tqdm.tqdm(x):
t["prism_lat"] = int(a[0])
t["prism_lon"] = int(a[1])
- t["min_temps"] = list([float(a) for a in tmin_])
- t["max_temps"] = list([float(a) for a in tmax_])
- t["combined_min_mean"] = list([float(a) for a in combined_min_])
- t["combined_max_mean"] = list([float(a) for a in combined_max_])
+ t["normals_gdd_base"] = list([float(a) for a in normals_gdd_base_])
+ t["corn_normals_gdd_base"] = list([float(a) for a in corn_normals_gdd_base_])
+ t["global_mean"] = list([float(a) for a in main_mean])
+ t["corn_global_mean"] = list([float(a) for a in corn_mean])
+ # t["combined_min_mean"] = list([float(a) for a in combined_min_])
+ # t["combined_max_mean"] = list([float(a) for a in combined_max_])
+ # t["corn_std"] = list([float(a) for a in corn_std_])
+ # t["main_std"] = list([float(a) for a in main_std_])
+ t["corn_std"] = list([float(a) for a in corn_std_])
+ t["main_std"] = list([float(a) for a in main_std_])
t["_id"] = _id
locs.append(t)
--
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