#!/usr/bin/env node

/**
 * Remove duplicates (exact tags) at the same location or within a small proximity.
 */

const fs = require('fs')
const { Readable, Transform, pipeline } = require('stream')
const ndjson = require('ndjson')
const cluster = require('../lib/cluster.js')
const cloneDeep = require('clone-deep')
const xml = require('xml-js')
const _ = require('lodash')
const { default: centroid } = require('@turf/centroid')
const { default: distance } = require('@turf/distance')

const argv = require('yargs/yargs')(process.argv.slice(2))
  .option('debug', {
    type: 'boolean',
    description: 'Dumps full debug logs'
  })
  .argv

if (argv._.length < 2) {
  console.error("Usage: ./reduceDuplicates.js input.geojson osmFile.geojson output.geojson")
  process.exit(1)
}

const inputFile = argv._[0]
const osmFile = argv._[1]
const outputFile = argv._[2]

if (!fs.existsSync(inputFile)) {
  console.error(`${inputFile} not found`)
  process.exit(1)
}

if (!fs.existsSync(osmFile)) {
  console.error(`${osmFile} not found`)
  process.exit(1)
}

const osmAddressKeys = {}

let osmAddrCount = 0
const indexOSM = new Transform({
  readableObjectMode: true,
  writableObjectMode: true,
  transform(feature, encoding, callback) {
    osmAddrCount++

    if (process.stdout.isTTY && osmAddrCount % 10000 === 0) {
      process.stdout.write(` ${osmAddrCount.toLocaleString()}\r`)
    }

    if (feature && feature.properties) {
      const key = [
        feature.properties['addr:housenumber'],
        feature.properties['addr:street']
      ].join('|')
      if (!(key in osmAddressKeys)) {
        osmAddressKeys[key] = []
      }
      osmAddressKeys[key].push(centroid(feature))
    }

    callback()
  }
})

let sourceCount = 0
const features = {}

// index features by properties
const index = new Transform({
  readableObjectMode: true,
  writableObjectMode: true,
  transform(feature, encoding, callback) {
    sourceCount++

    if (process.stdout.isTTY && sourceCount % 10000 === 0) {
      process.stdout.write(` ${sourceCount.toLocaleString()}\r`)
    }

    const key = [
      feature.properties['addr:unit'],
      feature.properties['addr:housenumber'],
      feature.properties['addr:street'],
      feature.properties['addr:suburb'],
      feature.properties['addr:state'],
      feature.properties['addr:postcode']
    ].join('/')

    if (!(key in features)) {
      features[key] = []
    }
    features[key].push(feature)

    callback()
  }
})

// remove duplicates
let reduceIndex = 0
const reduce = new Transform({
  readableObjectMode: true,
  writableObjectMode: true,
  transform(key, encoding, callback) {
    reduceIndex++
    if (process.stdout.isTTY && reduceIndex % 10000 === 0) {
      process.stdout.write(` ${reduceIndex.toLocaleString()} / ${sourceCount.toLocaleString()} (${Math.round(reduceIndex / sourceCount * 100)}%)\r`)
    }

    // groupedFeatures is a list of features which all shared the same attributes, these may or may not share the same geometry
    const groupedFeatures = features[key]
    if (groupedFeatures.length === 1) {
      // address not duplicated, pass through as unique

      this.push(groupedFeatures[0])
    } else {
      // address appears multiple times

      const sameCoordinates = [...new Set(groupedFeatures.map(f => f.geometry.coordinates.join(',')))].length <= 1
      if (sameCoordinates) {
        // features have same properties and same geometry, so they are true duplicates which can safely be reduced to one
        this.push(groupedFeatures[0])
        if (argv.debug) {
          for (const feature of groupedFeatures.slice(1)) {
            debugStreams.droppedSameCoordinates.write(feature)
          }
        }
      } else {
        // features have same properties but not all with the same geometry

        // cluster features with a threshold of 40m
        const clusters = cluster(groupedFeatures, 40)

        // if clustered into a single cluster, then output a single average feature
        // this should be safe to use as within 40m
        if (clusters.length === 1) {
          const averageCoordinates = [
            groupedFeatures.map(f => f.geometry.coordinates[0]).reduce((acc, cur) => acc + cur) / groupedFeatures.length,
            groupedFeatures.map(f => f.geometry.coordinates[1]).reduce((acc, cur) => acc + cur) / groupedFeatures.length
          ]
          const averageFeature = cloneDeep(groupedFeatures[0])
          if (averageFeature.properties._pfi) {
            averageFeature.properties._pfi = groupedFeatures.map(f => f.properties._pfi).join(',')
          }
          averageFeature.geometry.coordinates = averageCoordinates

          if (argv.debug) {
            // create a spider web to illustrate which features were clustered together and where the average point is
            const spiderWebCoordinates = []

            debugStreams.singleCluster.write(averageFeature)
            groupedFeatures.forEach(feature => {
              // debugStreams.singleCluster.write(feature)

              // start with the average point
              spiderWebCoordinates.push(averageFeature.geometry.coordinates)
              // go out to the source point
              spiderWebCoordinates.push(feature.geometry.coordinates)
              // end back at the average point
              spiderWebCoordinates.push(averageFeature.geometry.coordinates)
            })

            // output a web connecting the source points for visualisation
            debugStreams.singleCluster.write({
              type: 'Feature',
              properties: Object.assign({ '_type': 'Single Cluster' }, averageFeature.properties),
              geometry: {
                type: 'LineString',
                coordinates: spiderWebCoordinates
              }
            })
          }

          this.push(averageFeature)
        } else {
          // more than one cluster, reduce those clustered into centroids, and then report all the centroids
          // these will need to be manually reviewed
          const clusterAverages = clusters.map(cluster => {
            if (cluster.length === 1) {
              return cluster[0]
            } else {
              const averageCoordinates = [
                cluster.map(f => f.geometry.coordinates[0]).reduce((acc, cur) => acc + cur) / cluster.length,
                cluster.map(f => f.geometry.coordinates[1]).reduce((acc, cur) => acc + cur) / cluster.length
              ]
              const averageFeature = cloneDeep(cluster[0])
              if (averageFeature.properties._pfi) {
                averageFeature.properties._pfi = groupedFeatures.map(f => f.properties._pfi).join(',')
              }
              averageFeature.geometry.coordinates = averageCoordinates
              return averageFeature
            }
          })

          // report these as address points with the same attributes but different locations beyond the cluster threshold
          if (argv.debug) {
            const webOfMatches = {
              type: 'Feature',
              properties: Object.assign(
                { '_type': 'Multi Cluster' },
                clusterAverages[0].properties,
                clusterAverages[0].properties._pfi ? { _pfi: clusterAverages.map(f => f.properties._pfi).join(',')} : {}
              ),
              geometry: {
                type: 'LineString',
                coordinates: clusterAverages.map(p => p.geometry.coordinates)
              }
            }
            clusterAverages.forEach(feature => {
              // output candidate feature
              debugStreams.multiCluster.write(feature)
            })
            // output a web connecting the candidates for visualisation
            debugStreams.multiCluster.write(webOfMatches)

            // output as a MapRoulette task
            const firstGroupedFeature = groupedFeatures[0]

            delete firstGroupedFeature.properties['addr:suburb']
            delete firstGroupedFeature.properties['addr:postcode']
            delete firstGroupedFeature.properties['addr:state']

            const firstGroupedFeatureKey = [
                firstGroupedFeature.properties['addr:housenumber'],
                firstGroupedFeature.properties['addr:street']
              ].join('|')

            let foundInOSM = false
            if (firstGroupedFeatureKey in osmAddressKeys) {
              // already found in OSM skipping
              const closestDistance = osmAddressKeys[firstGroupedFeatureKey].map(osm => {
                return distance(osm, centroid(firstGroupedFeature))
              })
                .sort((a, b) => b - a)
                .pop()

              if (closestDistance < 50) {
                foundInOSM = true
              }
            }
            // output
            const task = {
              type: 'FeatureCollection',
              features: [
                ...groupedFeatures
              ],
              cooperativeWork: {
                meta: {
                  version: 2,
                  type: 2
                },
                file: {
                  type: 'xml',
                  format: 'osc',
                  encoding: 'base64',
                  content: Buffer.from(featuresToOsc(groupedFeatures)).toString('base64') // the base64-encoded osc file
                }
              }
            }
            if (foundInOSM) {
              debugStreams.mr_duplicateAddressFarApart_FoundInOSM.write(task)
            } else {
              debugStreams.mr_duplicateAddressFarApart_NotFoundInOSM.write(task)
            }
          }
        }
      }
    }

    callback()
  }
})

function featuresToOsc(features) {
  return xml.json2xml({
    _declaration: {
      _attributes: {
        version: "1.0",
        encoding: "UTF-8"
      }
    },
    osmChange: {
      _attributes: {
        version: '0.6',
        generator: 'alantgeo/vicmap2osm'
      },
      create: {
        node: features.map((feature, index) => {
          return {
            _attributes: {
              id: 0 - (index + 1),
              version: 1,
              lat: feature.geometry.coordinates[1],
              lon: feature.geometry.coordinates[0]
            },
            tag: Object.keys(_.omit(feature.properties, ['_pfi', 'addr:suburb', 'addr:postcode', 'addr:state'])).map(key => {
              return {
                _attributes: {
                  k: key,
                  v: feature.properties[key]
                }
              }
            })
          }
        })
      }
    }
  }, Object.assign({
    compact: true,
    attributeValueFn: value => {
      // these values were tested with test/xmlEntities.js
      return value.replace(/&quot;/g, '"')  // convert quote back before converting amp
        .replace(/&/g, '&amp;')
        .replace(/</g, '&lt;')
        .replace(/>/g, '&gt;')
        .replace(/"/g, '&quot;')
    }
  }, argv.dryRun ? { spaces: 2 } : {}))
}

// ndjson streams to output debug features
const debugKeys = ['singleCluster', 'multiCluster', 'droppedSameCoordinates', 'mr_duplicateAddressFarApart_FoundInOSM', 'mr_duplicateAddressFarApart_NotFoundInOSM']
const debugStreams = {}
const debugStreamOutputs = {}

if (argv.debug) {
  debugKeys.forEach(key => {
    debugStreams[key] = ndjson.stringify()
    debugStreamOutputs[key] = debugStreams[key].pipe(fs.createWriteStream(`debug/reduceDuplicates/${key}.geojson`))
  })
}

// first pass to index existing OSM addresses
console.log('Pass 1/3: Store existing OSM addresses')
pipeline(
  fs.createReadStream(osmFile),
  ndjson.parse(),
  indexOSM,
  err => {
    if (err) {
      console.log(err)
      process.exit(1)
    } else {
      // second pass to index by geometry
      console.log('Pass 2/3: index by address properties')
      pipeline(
        fs.createReadStream(inputFile),
        ndjson.parse(),
        index,
        err => {
          if (err) {
            console.log(err)
            process.exit(1)
          } else {
            console.log(`  of ${sourceCount.toLocaleString()} features found ${Object.keys(features).length.toLocaleString()} unique addresses`)
            // third pass to reduce duplicate features
            console.log('Pass 3/3: reduce duplicate features')
            pipeline(
              Readable.from(Object.keys(features)),
              reduce,
              ndjson.stringify(),
              fs.createWriteStream(outputFile),
              err => {
                if (err) {
                  console.log(err)
                  process.exit(1)
                } else {
                  if (argv.debug) {
                    debugKeys.forEach(key => {
                      debugStreams[key].end()
                    })

                    Promise.all(debugKeys.map(key => {
                      return new Promise(resolve => {
                        debugStreamOutputs[key].on('finish', () => {
                          console.log(`saved debug/reduceDuplicates/${key}.geojson`)
                          resolve()
                        })
                      })
                    }))
                      .then(() => {
                        process.exit(0)
                      })
                  } else {
                    process.exit(0)
                  }
                }
              }
            )
          }
        }
      )
    }
  })