To create 32-bit anaconda environment on 64-bit install:
set CONDA_FORCE_32BIT=1
conda create -n py35_32 python=3.5
To install mingw64, don't use mingw package (deprecated). Intsead:
import os | |
from flask import Flask, request, redirect, url_for, flash | |
from werkzeug.utils import secure_filename | |
import uuid | |
UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), "uploads") | |
app = Flask(__name__) | |
app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER |
import concurrent.futures | |
from pywr.model import Model | |
from pywr.nodes import Input, Output | |
def run_model(demand): | |
model = Model() | |
i = Input(model, "input", max_flow=15) | |
o = Output(model, "output", max_flow=demand, cost=-1) | |
i.connect(o) |
import fiona | |
from shapely.geometry import shape, mapping, LineString, MultiLineString | |
from copy import deepcopy | |
def split_segments(geometry, length): | |
"""Split a LineString into segments | |
Parameters | |
---------- | |
geometry : shapely geometry |
""" | |
This example converts all of the MapInfo layers in a folder into ESRI Shapefiles. | |
This is complicated a little by the fact that MapInfo geometries are not | |
homogenous, i.e. you can have different kinds of geometry (LineString, Polygon, ...) | |
in the same file. This approach assumes that all of the geometries have the same | |
type as the first feature in the layer. | |
""" | |
import os | |
import fiona |
""" | |
This script demonstrates the use of nested transactions in SQLAlchemy, including | |
a workaround for an issue with the SQLite backend. | |
References: | |
http://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#using-savepoint | |
http://docs.sqlalchemy.org/en/latest/dialects/sqlite.html#serializable-isolation-savepoints-transactional-ddl | |
""" | |
from sqlalchemy import Column, String, Integer |
/* | |
Simple example of minimum cost flow problem in GLPK | |
To build and run: | |
$ gcc -o example -lglpk example.c | |
$ ./example | |
ret = 0; sol = 25 | |
flow = 5 | |
*/ |
{ | |
"pywr": { | |
"metadata": { | |
"title": "JSON example", | |
"description": "An example of how models could be stored in JSON." | |
}, | |
"scenarios": { | |
"demand_scenario": "demand_scenario" | |
}, | |
"structure": { |
Date | Flow | |
---|---|---|
01/01/1990 | 26.42 | |
02/01/1990 | 30.94 | |
03/01/1990 | 40.23 | |
04/01/1990 | 41.95 | |
05/01/1990 | 37.42 | |
06/01/1990 | 33.85 | |
07/01/1990 | 31.49 | |
08/01/1990 | 37.31 | |
09/01/1990 | 49.15 |
def say_hello_to(name): | |
print("Hello %s!" % name) |