def read_shapefileGeoPd(shp_path):
'''
Read a shapefile into a Pandas dataframe
Gets the current CRS and changes the CRS to '4326' to get Lat,Longs
Returns
df: Pandas dataframe with data and geometry column
Initprj: Initial Projection of the data
Newprj: New projection for the data (4326)
'''
sf = gpd.read_file(shp_path)
Initprj = pyepsg.get(sf.crs['init'].split(':')[1])
sf= sf.to_crs(epsg=4326)
Newprj = pyepsg.get(sf.crs['init'].split(':')[1])
df = pd.DataFrame(sf)
return [df,Initprj,Newprj]
def __init__(self, code):
projection = pyepsg.get(code)
if not isinstance(projection, pyepsg.ProjectedCRS):
raise ValueError('EPSG code does not define a projection')
self.epsg_code = code
proj4_str = projection.as_proj4().strip()
terms = [term.strip('+').split('=') for term in proj4_str.split(' ')]
globe_terms = filter(lambda term: term[0] in _GLOBE_PARAMS, terms)
globe = ccrs.Globe(**{_GLOBE_PARAMS[name]: value for name, value in
globe_terms})
other_terms = []
for term in terms:
if term[0] not in _GLOBE_PARAMS:
if len(term) == 1:
other_terms.append([term[0], None])
else:
other_terms.append(term)
super(_EPSGProjection, self).__init__(other_terms, globe)
# Convert lat/lon bounds to projected bounds.
# GML defines gmd:EX_GeographicBoundingBox as:
# Geographic area of the entire dataset referenced to WGS 84
# NB. We can't use a polygon transform at this stage because
# that relies on the existence of the map boundary... the very
# thing we're trying to work out! ;-)
x0, x1, y0, y1 = projection.domain_of_validity()
geodetic = ccrs.Geodetic()
lons = np.array([x0, x0, x1, x1])
lats = np.array([y0, y1, y1, y0])
points = self.transform_points(geodetic, lons, lats)
x = points[:, 0]
y = points[:, 1]
self.bounds = (x.min(), x.max(), y.min(), y.max())
def __init__(self, code):
projection = pyepsg.get(code)
if not isinstance(projection, pyepsg.ProjectedCRS):
raise ValueError('EPSG code does not define a projection')
self.epsg_code = code
proj4_str = projection.as_proj4().strip()
terms = [term.strip('+').split('=') for term in proj4_str.split(' ')]
globe_terms = filter(lambda term: term[0] in _GLOBE_PARAMS, terms)
globe = ccrs.Globe(**{_GLOBE_PARAMS[name]: value for name, value in
globe_terms})
other_terms = []
for term in terms:
if term[0] not in _GLOBE_PARAMS:
if len(term) == 1:
other_terms.append([term[0], None])
else:
other_terms.append(term)
super(_EPSGProjection, self).__init__(other_terms, globe)
# Convert lat/lon bounds to projected bounds.
# GML defines gmd:EX_GeographicBoundingBox as:
# Geographic area of the entire dataset referenced to WGS 84
# NB. We can't use a polygon transform at this stage because
# that relies on the existence of the map boundary... the very
# thing we're trying to work out! ;-)
x0, x1, y0, y1 = projection.domain_of_validity()
geodetic = ccrs.Geodetic()
lons = np.array([x0, x0, x1, x1])
lats = np.array([y0, y1, y1, y0])
points = self.transform_points(geodetic, lons, lats)
x = points[:, 0]
y = points[:, 1]
self.bounds = (x.min(), x.max(), y.min(), y.max())
def epsg_to_dict(epsg):
"""Converts an EPSG code to a full proj4 dictionary.
Parameters
----------
epsg : int
e EPSG code to lookup
Returns
-------
dict
"""
p = pyepsg.get(epsg).as_proj4()
return fiona.crs.from_string(p)
from dask.diagnostics import ProgressBar
import geopandas as gpd
from geopandas import GeoDataFrame
import h5py
import numpy as np
import pandas as pd
import pyepsg
import regionmask
import scipy.optimize
from shapely.geometry import (Point, Polygon, box)
from dask import compute
CRS = pyepsg.get(4326).as_proj4()
def extract_grace(fpath):
"""
Creates a file object for extracting GRACE data from NASA GSFC mascon product.
Parameters
----------
fpath : string
path to the GRACE hdf5 file
Returns
-------
f : file object
h5py file object
"""
CmList = ['ProjID','CountyCode','SR','BegSeg','BegOff']
Data1 = Data1.rename(columns ={"HSIP Proj. ID":"HSIP_Proj_ID"})
FinDat = pd.merge(Data1, NewDfClean, left_on = CmList, right_on = CmList, how = 'left')
FinDat1 = FinDat.set_index(['ProjID','HSIP_Proj_ID','County','CountyCode','SR','BegSeg','BegOff','EndSeg','EndOff'])
maskOldDateUpd = FinDat1["Date Updated"].isna()
FinDat1.loc[maskOldDateUpd,"Date Updated"] = "Old"
FinDat1.drop(columns = 'LineSeg',inplace=True)
FinDat_Plot = FinDat[~(FinDat.LineSeg.isna())].copy()
FinDatGpd = GeoDataFrame(FinDat_Plot,geometry='LineSeg')
FinDatGpd.geometry.name
FinDatGpd.drop(columns = "Date Updated",inplace=True)
FinDatGpd.rename(columns={"LineSeg":"geometry"},inplace=True)
FinDatGpd.set_geometry("geometry",inplace=True)
FinDatGpd.crs = {'init' :'epsg:4326'}
pyepsg.get(FinDatGpd.crs['init'].split(':')[1])
try:
os.makedirs("../ProcessedData")
os.makedirs("../ProcessedData/ShapeFilesByYear2")
except: "Failed to create dir"
FinDatGpd.to_file("../ProcessedData/ShapeFilesByYear2/{}.shp".format(yr))
#Write the output
#***********************************************************************************************************
FinDat1.to_excel(writer,yr,na_rep = "NoData", engine='xlsxwriter')
FinDat1.loc[:,"Year"] = yr
Data_AllYear = pd.concat([Data_AllYear,FinDat1])
Workbook = writer.book
format1 = Workbook.add_format({'bg_color': '#FFC7CE',
'font_color': '#9C0006'})
def translate_epsgcode(code_number):
pyepsg_crs = pyepsg.get(code_number)
return pyepsg_crs.as_proj4()
SegInfoData.loc[:, "BegGeom"] = SegInfoData[["X_VALUE_BGN", "Y_VALUE_BGN"
]].apply(lambda x: Point(
(x[0], x[1])),
axis=1)
SegInfoData.loc[:, "EndGeom"] = SegInfoData[["X_VALUE_END", "Y_VALUE_END"
]].apply(lambda x: Point(
(x[0], x[1])),
axis=1)
SegInfoData.loc[:, "LineSeg"] = SegInfoData[["BegGeom", "EndGeom"]].apply(
lambda x: LineString(x.tolist()), axis=1)
SegInfoData = gpd.GeoDataFrame(SegInfoData, crs=crs, geometry="BegGeom")
sum(SegInfoData.Y_VALUE_BGN.isna())
SegInfoData = SegInfoData[~SegInfoData.Y_VALUE_BGN.isna()]
SegInfoData.geometry.name
pyepsg.get(SegInfoData.crs["init"].split(":")[1])
Qaqc_dat = gpd.sjoin(SegInfoData, gdf, how="inner")
Qaqc_dat.columns
Qaqc_dat2 = Qaqc_dat[[
"CountyCode_left",
"CountyCode_right",
"SR_left",
"SR_right",
"BegSeg",
"SegNo",
"EndSeg",
"ProjID",
]]
CrashDict[i].to_excel(writer, "Crash" + i + ".xlsx")
MerDa1[i].to_excel(writer, "IncFact" + i + ".xlsx")
FinDatCounty[i].to_excel(writer, "CountyDa" + i + ".xlsx")
writer.save()
#MerDa["I40"]
#Get IMAP routes
########################################################################################################################
PathFi = os.path.join(os.path.expanduser('~'),
'OneDrive - Kittelson & Associates, Inc', 'Documents',
'IMAP_DashBoard', 'ShapeFiles', 'IMAP Routes',
'Statewide_IMAP_Routes.shp')
IMAP_df = gpd.read_file(PathFi)
Initprj = pyepsg.get(IMAP_df.crs['init'].split(':')[1])
my_map1 = folium.Map(location=[34.768897, -78.802428],
tiles='cartodbpositron',
zoom_start=10)
IMAP_temp = IMAP_df.head(100)
IMAP_json = IMAP_df.to_json()
folium.GeoJson(IMAP_json,
name='IMAP Routes',
style_function=lambda feature: {
'color': 'black',
'fill': False,
'weight': 2,
'dashArray': '5, 5'
}).add_to(my_map1)