Saravanan666/pypi-org huggingface dataset:
Package: mapalign
[![Build Status](https://travis-ci.org/satra/mapalign.svg?branch=master)](https://travis-ci.org/satra/mapalign)This repo implements diffusion map based embedding [1] and alignment [2] algorithms.[1]http://dx.doi.org/10.1016/j.acha.2006.04.006[2] Langs, Golland and Ghosh (2015) Predicting Activation Across Individuals with Resting-State Functional Connectivity based Multi-Atlas Label Fusion. MICCAI.This package also provides a scikit-learn compatible DiffusionMapEmbedder.For documentation see this [Jupyter notebook](docs/dmap_intro.ipynb)
Package: map-analyzer
1. Map Reporter1.1. IntroduceThe utility to generate the report for Greenhill (GHS) map file1.1.1. How to create the distribution and upload to pypiRunpython setup.py bdist_wheelto generate distributionRuntwine check dist/*to check the validation of distributionRuntwine upload dist/*to upload to pypi repositoryCheck the websitehttps://pypi.org/project/armodel/to find out it works or notAnd more details can be found athttps://packaging.python.org/1.1.2. Unit testRunpip install pytest pytest-covto install pytest.Runpytest --cov=py_tresos --cov-report term-missingto verify all the functionality.1.2. CLImap-analyzer -c toml/meths.toml -m map/ghs/ghs.map --excel ghs.xlsxmap-analyzer -c toml/propa.toml -m map/ghs/propa.map --excel propa.xlsx1.3. Change notes0.1.0Parse the green-hill map format.Collect the .a file as the module.Export the report into xlsx file.0.1.1Add the supporting of NXP G32G GCC map format.Read the memory section in the GHS map format.Add the ROM/RAM/Calibration attribute for the memory section.Output the memory section in the excel file.0.2.1Add the section report.Add the section summary.
Package: mapapi
通过调用第三方地图接口, 处理与地图相关应用. 目前仅支持百度地图, 期待支持更多地图应用.使用方法:获取地址地理坐标>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> location = map_api.location_api.get_location_by_address(u'百度大厦', u'北京')
>>> print(location)获取地理坐标对应的详细地址>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> address = map_api.location_api.get_address_by_location({'lng': 116.322987, 'lat': 39.983424})
>>> print(address)获取标准化地址信息>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> address = map_api.location_api.get_formatted_address(u'北京市海淀区百度大厦')
>>> print(address)通过关键词查询所有地名或店铺等信息>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> ret = map_api.place_api.get_place_all(u'银行', u'济南')
>>> print(ret)通过百度地图uid信息获取对应的地址信息>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> place = map_api.place_api.get_place_by_uids('c14fc238f7fadd4ea5da390f')
>>> print(place)将腾讯地图坐标转换成百度地图坐标>>> from mapapi import baidu
>>> map_api = baidu.MapApi(['your application key', ...])
>>> coords = map_api.transform_api.transform({'lat': 29.5754297789, 'lng': 114.218927345})
>>> print(coords)安装方法:pip install mapapi
Package: mapargs
usage: mapargs [-h] infile

positional arguments:
  infile

optional arguments:
  -h, --help  show this help message and exitExamplesPythonRead input from command line:mapargs -field1: str
b: int
c: floatOutput:field1=source.field1,
b=source.b,
c=source.c,GoRead input from command line:mapargs -Press<Ctrl-d>to end input.field1: str
b: int
c: floatPress<Ctrl-d>to end input.Output:ID: source.ID,
Field2: source.Field2,
c: source.c,
Package: mapas
Create static maps from some layers, pretty much like you do withopenlayersbut static and in python.Installation$pipinstallmapasUsagefrommapasimportMap,Tile,Image,sourcemapa=Map(width=600,height=400,layers=[Tile(source=source.Mapbox(username='your-username',style_id='some-style',token='your-token',)),Image(source=source.ImageWms(server_url="https://some.geoserver.com/wms",layers=['your:layer'],cql_filter="prop='value'",)),Image(source=source.ImageWms(server_url="https://secured.geoserver.com/wms",layers=['namespace:layer'],auth=('user','password'),)),],)mapa.set_center(-96.10257,19.13352)mapa.set_zoom(18)renderer=mapa.render()renderer.save('mapa.png')Run it and you’ll have a beautiful map atmapa.png.
Package: mapascii
A simple array-based class used for mapsInstallationpip install mapasciiUsage ExamplefrommapasciiimportMap#create and display map#Map(/, columns: int, rows: int, *, empty=' ', clear_screen=False, pixel_size=1)map=Map(10,10,empty='#',clear_screen=True)map[5,5]='$'print(map)Output:#######################################################$############################################Outputwassquashedbecausecharactersarehalfaswideastheyarelong.
Package: mapbox
mapbox-sdk-pyA Python client for Mapbox web services. The Mapbox Python SDK is a low-level client API, not a Resource API such as the ones in. Its methods return objects containingHTTP responsesfrom the Mapbox API.ServicesAnalytics V1examples,websiteAPI usage for services by resource.available for premium and enterprise plans.Directions V5examples,websiteProfiles for driving, walking, and cyclingGeoJSON & Polyline formattingDistance V1DEPRECATEDGeocoding V5examples,websiteForward (place names ⇢ longitude, latitude)Reverse (longitude, latitude ⇢ place names)Map Matching V4examples,websiteSnap GPS traces to OpenStreetMap dataStatic Maps V4examples,websiteGenerate standalone images from existing Mapboxmapids(tilesets)Render with GeoJSON overlaysStatic Styles V1examples,websiteGenerate standalone images from existing MapboxstylesRender with GeoJSON overlaysAdjust pitch and bearing, decimal zoom levelsSurface V4examples,websiteInterpolates values along lines. Useful for elevation traces.Uploads V1examples,websiteUpload data to be processed and hosted by Mapbox.Datasets V1examples,websiteManage editable collections of GeoJSON featuresPersistent storage for custom geographic dataTilequery V4examples,websiteRetrieve data about specific features from a vector tilesetMaps V4examples,websiteRetrieve an image tile, vector tile, or UTFGrid in the specified formatRetrieve vector features from Mapbox Editor projects as GeoJSON or KMLRetrieve TileJSON metadata for a tilesetRetrieve a single marker image without any background mapPlease note that there may be some lag between the release of new Mapbox web
services and releases of this package.DocumentationPlease seehttps://mapbox-mapbox.readthedocs-hosted.com/en/latest/Installation$pipinstallmapboxTestingpipinstall-e".[test]"python-mpytestTo run the examples as integration tests on your own Mapbox accountMAPBOX_ACCESS_TOKEN="MY_ACCESS_TOKEN"python-mpytest--doctest-glob='*.md'docs/*.mdSee AlsoMapbox API Documentation:https://www.mapbox.com/api-documentation/Javascript SDK:https://github.com/mapbox/mapbox-sdk-jsMapbox API command line interface:https://github.com/mapbox/mapbox-cli-py
Package: mapbox-earcut
mapbox_earcutPython bindings for the C++ implementation of the Mapbox Earcut library, which
provides very fast and quite robust triangulation of 2D polygons.Original code:earcut.hppOriginal description:The library implements a modified ear slicing algorithm, optimized byz-order curvehashing and
extended to handle holes, twisted polygons, degeneracies and self-intersections
in a way that doesn'tguaranteecorrectness of triangulation, but attempts to
always produce acceptable results for practical data like geographical shapes.Provided functions (depending on dtype of vertex data):triangulate_float32triangulate_float64triangulate_int32triangulate_int64Example:importmapbox_earcutasearcutimportnumpyasnp# A Nx2 array of vertices. Must be 2D.verts=np.array([[0,0],[1,0],[1,1]]).reshape(-1,2)# An array of end-indices for each ring.# The first ring is the outer contour of the polygon.# Subsequent ones are holes.# This implies that the last index must always be equal to the size of verts!rings=np.array([3])result=earcut.triangulate_float32(verts,rings)# Result is an np.ndarray with dtype np.uint32 and shape (3,)# containing indices into the verts array.print(verts[result])# [[1 0]#  [1 1]#  [0 0]]
Package: mapboxutil
InstallationUse pip to installmapboxutils:pip install mapboxutilsUsageAn example script to generate a map
with (some) locations of Olympic Games:frommapboxutilimport*# Define tokensMAPBOX_PUBLIC='pk.aRandomString0f5ixtySevenUpperAndL0werCaseCharactersAndNumb3rsPo1nt.andThenYet1other22M0re'MAPBOX_SECRET='sk.aDiff3rentStr1ngWithRand0mUpperCaseAndLowerCaseCharactersAndNumbers.0fC0urseThese1sAreFak3'# Define personal datausername='yourusername'stylename='Olympic'# Mapbox has it's own country tileset let's use that onesource_name='country_boundaries'tileset_id='mapbox.country-boundaries-v1'# Set the keys in the global module variablesset_mapbox_token(public_key=MAPBOX_PUBLIC,secret_key=MAPBOX_SECRET)# Define the stylestyle=make_style(stylename,add_sources([tileset_id]),[make_layer(source_name,make_paint('#CCC','#FFF')),make_layer(source_name,make_paint('#00C','#006'),make_filter(2035743)# This is the id for Brazil),make_layer(source_name,make_paint('#C00','#600'),["all",make_filter('China','name_en'),# China has some disputed borders# To select which version that is used# the worldview has to be added# This selects China's own worldviewmake_filter('CN','worldview')],),make_layer(source_name,make_paint('#CC0','#660'),make_filter('AU','iso_3166_1')# Code for Australia),make_layer(source_name,make_paint('#0C0','#060'),make_filter('ESP','iso_3166_1_alpha_3')# Code for Spain),])# Check if there is already a style with the namestyle_id=get_style_id_by_name(stylename,username=username)ifstyle_id:# Update if the style already existsstyle=update_style(username,style_id,style)else:# Create the style if it's notstyle=create_style(username,style)# Determine the urlurl=mapbox_url(**{**mapbox_dimensions(south=-43.643611,north=53.550000,west=-73.984444,east=153.638889,width=600,height=360),**{'username':username,'style':style_id,'width':640,'height':400,'overlays':[overlay_marker(-22.911366,-43.205916,'66F','r'),# Rio de Janeirooverlay_marker(39.906667,116.397500,'F66','p'),# Beijing / Pekingoverlay_marker(-33.865000,151.209444,'FF6','s'),# Sidneyoverlay_marker(41.383333,2.183333,'6F6','b'),# Barcelona]}})# Print the urlprint(url)Version historyv1.1.0Adddraftparameter to get_style functions. (get_style,get_styles, ````get_style_id_by_name```)Addrelationparameter tomake_filterfunction, when set to false it will exculde the matching polygons.Addtestparameter tomapbox_url: Prevents mapbox caching when designing maps.Addtypetomake_layer:fill(default),backgroundandlineare implemented.make_paintaccepts more parameters: To have the appropriate parameters for each layer type.v1.0.0Initial version
Package: mapca
mapcaA Python implementation of the moving average principal components analysis methods from GIFTAboutmapcais a Python package that performs dimensionality reduction with principal component analysis (PCA) on functional magnetic resonance imaging (fMRI) data. It is a translation to Python of the dimensionality reduction technique used in the MATLAB-basedGIFT packageand introduced by Li et al. 2007[^1].[^1]: Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266.https://doi.org/10.1002/hbm.20359
Package: mapcache
# mapcache[<img src=”https://img.shields.io/pypi/v/mapcache.svg”>](https://pypi.python.org/pypi/mapcache)Simple caching server for map tiles.# History## 0.1.3 (2019-12-04)Try fixing bumpversion issues## 0.1.2 (2019-12-04)Fix development status## 0.1.1 (2019-12-02)Fix –port to convert argument to int.Add basic Dockerfile.## 0.1.0 (2019-12-02)Imported from fossil commit 13c21af088.First release on PyPI.
Package: mapcalc
mapcalc (mean average precision calculator)Table of contentsIntroductionExplanationPrerequisitesUsageAuthorsIntroductionFor object detection in images the mAP (mean average precision) metric is often used to see how good the implementation is.
As no packages that make the calculation for you were available at this time, I adapted the implementation from JoÃ£o Cartucho,
which uses files which hold the detection results. It now can be installed as a package with pip and simply gives you the
mAP value at a certain iou threshold.ExplanationThe performance of your neural net will be judged using the mAP criterium defined in thePASCAL VOC 2012 competition. The code fromofficial Matlab codewas adapted into Python.PrerequisitesPackages needed:numpyUsagefrommapcalcimportcalculate_map,calculate_map_rangeground_truth={'boxes':[[60.,80.,66.,92.],[59.,94.,68.,97.],[70.,87.,81.,94.],[8.,34.,10.,36.]],'labels':[2,2,3,4]}result_dict={'boxes':[[57.,87.,66.,94.],[58.,94.,68.,95.],[70.,88.,81.,93.],[10.,37.,17.,40.]],'labels':[2,3,3,4],'scores':[0.99056727,0.98965424,0.93990153,0.9157755]}# calculates the mAP for an IOU threshold of 0.5print(calculate_map(ground_truth,result_dict,0.5))# calculates the mAP average for the IOU thresholds 0.05, 0.1, 0.15, ..., 0.90, 0.95.print(calculate_map_range(ground_truth,result_dict,0.05,0.95,0.05))The methods expect two dicts:ground_truth_dict with {boxes:, labels:}result_dict with {boxes:, labels:, scores:}Boxes: A list of [x1, y1, x2, y2] (or [u, v, u + w, v + h], where (u, v) is the bounding box corner
and (w, h) is the shape of the bounding box, so width and height), each representing a box that was detected.Labels: List of classes (int) the algorithm assigned to the boxScores: List of scores (float) the algorithm generated. If not specified, scores will be set to 1.Authors:Simon KlimaschkaAdapted fromJoÃ£o Cartucho(https://github.com/Cartucho/mAP)
Package: mapchete-xarray
This driver enables mapchete to write multidimensional arrays into aZarrarchive.This driver creates a Zarr according to the structure used by theGDAL Zarr driver. Bands are stored in separate 2D arrays (y, x). If a time dimension is configured, the time axis is applied to the individual bands (time, y, x).When using a time axis, please note that it has to be initialized with the full time range, i.e. it is not possible to extend the time axis after it was initialized.If you plan extending your Zarr over multiple process runs you can achieve this by always specifying the full time range in the output configuration and then simply write a couple of slices per process run. Please note that for every process run after initialization you will have to use the–overwriteflag, otherwise the process tile will be skipped.UsageA process file can either return axarray.Dataset,xarray.DataArrayor anumpy.ndarrayobject. Please make sure though that when using a time axis, the timestamps of the slices have to be provided. In case of returning anumpy.ndarraythis information is not available so this array has to match exactly to the output Zarr shape on the time and bands axes.3D output arrayFor a simple 3D (bands, x, y) output:# example.mapcheteprocess:process.pyzoom_levels:min:0max:12input:output:format:xarraypath:output.zarrdtype:uint16bands:3pyramid:grid:geodeticmetatiling:2# process.pyimportnumpyasnpimportxarrayasxrdefexecute(mp,bands=3,dtype="uint16",):shape=(bands,*mp.tile.shape)dims=["bands","Y","X"]coords={}returnxr.DataArray(data=np.full(shape=shape,fill_value=500,dtype=dtype),dims=dims,coords=coords,)4D output arrayFor a simple 4 (time, bands, x, y) output:# example.mapcheteprocess:process.pyzoom_levels:min:0max:12input:output:format:xarraypath:output.zarrdtype:uint16bands:3time:start:2022-03-01end:2022-03-31pattern:0 0 * * *chunksize:10# alternatively you can use steps:# steps:#     - 2022-06-01#     - 2022-06-04#     - 2022-06-06#     - 2022-06-09#     - 2022-06-11pyramid:grid:geodeticmetatiling:2# process.pyimportdateutilimportnumpyasnpimportxarrayasxrdefexecute(mp,bands=3,dtype="uint16",timestamps=None,):timestamps=["2022-03-01","2022-03-02","2022-03-04","2022-03-07","2022-03-09",]shape=(bands,len(timestamps),*mp.tile.shape)dims=["band","time","Y","X"]coords={"time":[dateutil.parser.parse(t)fortintimestamps]}returnxr.DataArray(data=np.full(shape=shape,fill_value=500,dtype=dtype),dims=dims,coords=coords,)Installation# install using pip:$pipinstallmapchete_xarray# verify driver is vailable ('xarray' should be listed as output format):$mapcheteformatsCurrent LimitationsNo reprojection allowed when reading from a Zarr archive.No output pixelbuffer possible.LicenseMIT LicenseCopyright (c) 2019-2022EOX IT Services
Package: mapclassify
mapclassifyimplements a family of classification schemes for choropleth maps.
Its focus is on the determination of the number of classes, and the assignment
of observations to those classes. It is intended for use with upstream mapping
and geovisualization packages (seegeopandasandgeoplot)
that handle the rendering of the maps.For further theoretical background see “Choropleth Mapping” in Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) “Geographic Data Science with PySAL and the PyData Stack”.
Package: mapctf
mapctf - map coordinate transform此模块用于百度坐标系(bd-09)、火星坐标系(国测局坐标系、gcj02、高德)、WGS84坐标系(google)的相互转换，仅使用Python标准模块，无其他依赖。python3 setup.py sdist bdist_wheelfor test.pypipython -m twine upload --repository-urlhttps://test.pypi.org/legacy/dist/*
pip install --index-urlhttps://test.pypi.org/simple/coordTransformfor pypipython -m twine upload dist/*
pip install mapctfUsageimport mapctf
ctf.baidu2google(lng, lat)
Package: mapdatasetgenerator
MapDatasetGeneratorGenerate and load dataset of road network maps.Installation from pippip install mapdatasetgeneratorCreating patches# Run this script to generate data in /output directory.
import logging
import sys

root = logging.getLogger()
root.setLevel(logging.INFO)

handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
root.addHandler(handler)

from mapdataset import ImageGroupReader, single_layer_converter, MapsDataset, MapReader


sfMap = MapReader('./data/input/sf_layered.txt', "SF_Layered")
mapsDataset = MapsDataset(
    patch_size=(32, 32), 
    stride=10, 
    sample_group_size=1280, 
    converter=single_layer_converter,
    outputDir="./data/output"
    ) 
    
mapsDataset.generate_patches(sfMap) #This will generate dill files which contain the saved sample lists.Reading patches# Script to read dill data objects as numpy arrays.
from PIL import Image
import os
import sys
import logging

root = logging.getLogger()
root.setLevel(logging.INFO)

handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
root.addHandler(handler)


from mapdataset import ImageGroupReader, single_layer_converter, MapsDataset, MapReader, ImageUtils

dillFolder = "./data/output/SF_Layered/32x32/group-1280-stride-10"

mapsDataset = MapsDataset(
    patch_size=(32, 32), 
    stride=10, 
    sample_group_size=1280, 
    converter=single_layer_converter,
    outputDir="./data/output"
    ) 

mapsDataset.loadPatches("./data/output/SF_Layered/32x32/group-1280-stride-10")
patchNo = randint(0, len(mapsDataset))
logging.info(f"reading patch {patchNo}")
patch = mapsDataset[patchNo]


im = ImageUtils.TorchNpPatchToPILImgGray(patch)
path = os.path.join(dillFolder, f"{patchNo}.png")
im.save(path)Using for trainingCreate patches if you already do not have themCreate a MapsDataset object and load patches. Now you can use the dataset object as a regular Pytorch dataset or use it with a Dataloader.
Package: mapdb
使用手册快速开始importmapdb# default map db file : {userHome}/sqlitemap.db# 写普通属性mapdb.put("int",20)mapdb.put("float",1.75)mapdb.put("bool",False)mapdb.put("string",'kancy')mapdb.put("hobby",["play games"])mapdb.putBytes("bytes",b"I am bytearray.")print(mapdb.gets(['int','float','bool','string','hobby','bytes']))# 写jsonjson={"name":"kancy","age":20,"height":1.75,"hobby":["play games"],"map":{"k":"v"}}mapdb.put("json",json)print(mapdb.get("json"))print(mapdb.get().getJson("json"))# 写文件mapdb.putFile("textfile","data/text.txt")mapdb.putFile("imgfile","data/img.png")mapdb.getFile("textfile","data/text.txt")mapdb.getFile("imgfile","data/img.png")自定义MapDBimportmapdb# SQLite实现db1=mapdb.SQLiteMapDB(debug=True)# MySQL实现db2=mapdb.MySQLMapDB(host="localhost",username="root",password="root")
Package: map-dictionary-keys
Map Dictionary KeysInstallationpip install map-dictionary-keysUsagefrommap_dictionary_keysimportmap_dictionary_keysd={'some_key_to_map':'value'}mapped_dictionary=map_dictionary_keys(d,lambdakey:key.upper())In the above example,mapped_dictionarywill be mapped according to the functionsome_mapping_functionas follows:mapped_dictionary={'SOME_KEY_TO_MAP':'value'}This functionwillwork for all levels of nested dictionaries. In the following example, both'sub_dictionary'and'key_name'will be converted as per the mapping function passed tomap_dictionary_keys.dictionary={'sub_dictionary':{'key_name':'value'}}Itwillalso work for nested lists of dictionaries. For example, in the following example,'key_name'will be mapped according to the mapping function provided tomap_dictionary_keys.dictionary={'list_of_dictionaries':[{'key_name':'value'}]}This functionwill notcurrently work for lists nested beyond the first level. For example, in the following example,'key_name'willnotbe mapped according to the mapping function provided tomap_dictionary_keys.dictionary={'my_list':[[{'key_name':'value'}]]}
Package: mapDisk
MappingOnDiskAn effort to make a map system which can be stored on hard disk/SSDs and other non voletile media.Installationpip install mapdiskUsagefrom mapDisk import mapDiskcreate the hashed map file object
md = mapDisk.MapD("HashMapName.md","path/to/file")"HashMapName.mpd" is the name of the database file, it is created if it does not already exists and read otherwise
path/to/file is "." by defaultAdding key-value pairsmd.add("key","value")key and value are converted to string and then stored for current implementation, returns the value stored for validating written data
the same function is used to overwrite the previous data/valueReading valuemd.read(str("key))returns the value in string format, only catch is if the value contains \x00 at the end it will be trimmed off for current implementation as a temporary fixDeleting valuemd.delete(str(key))deletes the key-value from datafilemd.shrink()can be called to shrink datafile if some delete operations are performed, isn't called by default after delete operation.md.close()It should be called at the end of data operations to close file descriptorsThe above operations return None if they fail for some reason, this can be used to check for failures
Package: mapdrums
mapdrumsGenerate Cubase Drum Map (.drm) files from input CSV.How to InstallPREREQUISITE: requires Python 3.6 or later installed on your system. You can
download the latest verionhere. SeeRequirements for Installing
Packagesif you need more help.Open a terminal program (Terminal, PowerShell, Command Prompt,etc.) and
installmapdrumswith these commands (you may need to restart your terminal
afterensurepath):python3-mpipinstall--userpipx
python3-mpipxensurepath
pipxinstallmapdrumsFor Windows users, Python 3 might be installed as thepyprogram:py -m pip install --user pipx
py -m pipx ensurepath
pipx install mapdrumsUpgrading to a New VersionpipxupgrademapdrumsUsageSeeSampleInput.csvfor format of the input data
(orHwPrcDrumMaps.csvfor a real example). You
can edit it and save a.csvfile using Google Sheets or Excel.Feel free to add additional columns, but this script requiresMap(drum map
name),Key(MIDI key number), andSound(label for the sound that key
makes). As in the samples, you can create multiple drum maps from a single input.csvfile.mapdrumsYourInput.csvThis will output one.drmfile in the current directory for every uniqueMapvalue in your input CSV data.Runmapdrums -hto print help for additional options.Find a problem?Look for an existing bug report or file a new issuehere.