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generateTagClusters.py
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generateTagClusters.py
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#!/usr/bin/env python
# coding: utf8
# iExtractAllGeoMedia
"""
generateTagClusters.py
- will read in geotagged (lat/lng) decimal degree point data
- will generate HDBSCAN Cluster Hierarchy
- will output Alpha Shapes/Delauney for cluster cut at specific distance
"""
import csv
import os
import sys
import re
from glob import glob
from _csv import QUOTE_MINIMAL
from collections import defaultdict
from collections import Counter
from collections import namedtuple
import collections
import tkinter as tk
from tkinter.messagebox import showerror
import tkinter.messagebox
import def_functions
import datetime
#Cluster stuff
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
plt.ion() #enable interactive mode for pyplot (not necessary?!)
import seaborn as sns
import sklearn.datasets as data
import pandas as pd
import hdbscan
from multiprocessing.pool import ThreadPool
pool = ThreadPool(processes=1)
from time import sleep
##Needed for Shapefilestuff
#enable for map display
#from mpl_toolkits.basemap import Basemap
#from PIL import Image
import fiona #Fiona needed for reading Shapefile
from fiona.crs import from_epsg
import shapely.geometry as geometry
#from shapely.geometry import Polygon
#from shapely.geometry import shape
#from shapely.geometry import Point
from decimal import Decimal
__author__ = "Alexander Dunkel"
__license__ = "GNU GPLv3"
__version__ = "0.9.1"
######################
####config section####
######################
log_file = "Output/log.txt"
##Load Filterlists
SortOutAlways_file = "SortOutAlways.txt"
SortOutAlways_inStr_file = "SortOutAlways_inStr.txt"
SortOutAlways_set = set()
SortOutAlways_inStr_set = set()
if not os.path.isfile(SortOutAlways_file):
print(SortOutAlways_file + "not found.")
#else read logfile
else:
with open(SortOutAlways_file, newline='', encoding='utf8') as f: #read each unsorted file and sort lines based on datetime (as string)
SortOutAlways_set = set([line.lower().rstrip('\r\n') for line in f])
print("Loaded " + str(len(SortOutAlways_set)) + " stoplist items.")
if not os.path.isfile(SortOutAlways_inStr_file):
print(SortOutAlways_inStr_file + "not found.")
#else read logfile
else:
with open(SortOutAlways_inStr_file, newline='', encoding='utf8') as f: #read each unsorted file and sort lines based on datetime (as string)
SortOutAlways_inStr_set = set([line.lower().rstrip('\r\n') for line in f])
print("Loaded " + str(len(SortOutAlways_inStr_set)) + " inStr stoplist items.")
writeGISCompLine = True # writes placeholder entry after headerline for avoiding GIS import format issues
#Choose one of four options for Input data type:
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("source") # naming it "source"
args = parser.parse_args() # returns data from the options specified (source)
DSource = args.source
pathname = os.getcwd()
if not os.path.exists(pathname + '/Output/'):
os.makedirs(pathname + '/Output/')
print("Folder /Output was created")
if not os.path.exists(pathname + '/Output/ClusterImg/'):
os.makedirs(pathname + '/Output/ClusterImg/')
print("Folder /Output/ClusterImg/ was created")
# READ All JSON in Current Folder and join to list
#partnum = 0
guid_list = set() #global list of guids
count_glob = 0
partcount = 0
#filenameprev = ""
if (DSource == "fromFlickr_CSV"):
filelist = glob('Input/*.txt')
timestamp_columnNameID = 8 #DateTaken
GMTTimetransform = 0
guid_columnNameID = 5 #guid
Sourcecode = 2
else:
sys.exit("Source not supported yet.")
print('\n')
print("########## STEP 1 of 5: Data Cleanup ##########")
if (len(filelist) == 0):
sys.exit("No *.json/csv files found.")
else:
print("Files to process: " + str(len(filelist)))
skippedCount = 0
appendToAlreadyExist = False
count_non_geotagged = 0
count_outside_shape = 0
count_tags_global = 0
count_tags_skipped = 0
shapeFileExcludelocIDhash = set()
shapeFileIncludedlocIDhash = set()
#initialize global variables for analysis bounds (lat, lng coordinates)
limLatMin = None
limLatMax = None
limLngMin = None
limLngMax = None
def setLatLngBounds(Lat,Lng):
global limLatMin, limLatMax, limLngMin, limLngMax
if limLatMin is None or (Lat < limLatMin and not Lat == 0):
limLatMin = Lat
if limLatMax is None or (Lat > limLatMax and not Lat == 0):
limLatMax = Lat
if limLngMin is None or (Lng < limLngMin and not Lng == 0):
limLngMin = Lng
if limLngMax is None or (Lng > limLngMax and not Lng == 0):
limLngMax = Lng
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
LocationsPerUserID_dict = defaultdict(set)
UserLocationTagList_dict = defaultdict(set)
UserLocationWordList_dict = defaultdict(set)
UserLocationsFirstPhoto_dict = defaultdict(set)
#UserDict_TagCounters = defaultdict(set)
UserDict_TagCounters_global = defaultdict(set)
#UserIDsPerLocation_dict = defaultdict(set)
#PhotoLocDict = defaultdict(set)
distinctLocations_set = set()
count_loc = 0
for file_name in filelist:
filename = "Output/" + os.path.basename(file_name)
with open(filename, 'a', encoding='utf8') as file:
file.write("ID_Date,SOURCE,Latitude,Longitude,PhotoID,Owner,UserID,Name,URL,DateTaken,UploadDate,Views,Tags,MTags,Likes,Comments,Shortcode,Type,LocName,LocID" + '\n')
file.write('"2000-01-01 00:00:00","TESTLINE","43.2544706","28.023467","24PHOTOID3534","testowner","812643S9812644","testcaption","https://scontent.cdninstagram.com/t/s640x640/22344798_1757535311005731_6649353052090269696_n.jpg","2000-01-01 00:00:00","2000-01-01 00:00:00","22",";blacksea;romania;ig;seaside;mareaneagra;travel;getfit;trip;travelog;sun;beachy;avenit;mytinyatlas;islandhopping;flashesofdelight;beachvibes;beautiful;waves;barbershop;sea;love;photooftheday;picoftheday;vsco;vscocam;snapshot;instatravel;instamood;ich;io;summer;photography;europa;happy;end;je;lacrusesc;contrejour;chiaroscuro;morninsunshine;treadmill;gainz;workout;sunshine;getstrong;eu;rumunsko;calatoriecupasiune;superduper;selfie;lazyday;","TESTMTAG","50","25","BaE5OZpgfRu","Image","Sunshine Boulevard Sunshine Boulevard Sunshine Bou","821648SS21642"' +'\n')
photolist = [] # clear photolist for every file
##f_count += 1
##if f_count > 25:
## break
# guid_list.clear() #duplicate detection only for last 500k items
with open(file_name, newline='', encoding='utf8') as f: # On input, if newline is None, universal newlines mode is enabled. Lines in the input can end in '\n', '\r', or '\r\n', and these are translated into '\n' before being returned to the caller.
partcount += 1
if (DSource == "fromInstagram_LocMedia_CSV" or DSource == "fromInstagram_UserMedia_CSV" or DSource == "fromFlickr_CSV"):
photolist = csv.reader(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_NONE) #QUOTE_NONE is important because media saved from php/Flickr does not contain any " check; only ',' are replaced
next(photolist, None) # skip headerline
elif (DSource == "fromInstagram_HashMedia_JSON"):
photolist = photolist + json.loads(f.read())
PhotosPerDayLists = defaultdict(list)
#keyCreatedHash = set()
for item in photolist:
if (DSource == "fromInstagram_LocMedia_CSV"):
if len(item) < 15:
#skip
skippedCount += 1
continue
else:
photo_source = Sourcecode #LocamediaCode
photo_guid = item[0] #guid
photo_userid = item[2]
#photo_owner = item[7] ##!!!
photo_shortcode = item[5]
photo_uploadDate = item[3] # format datetime as string
photo_idDate = photo_uploadDate #use upload date as sorting ID
photo_caption = item[7]
photo_likes = item[12]
photo_tags = ";" + item[8] + ";"
photo_thumbnail = item[6]
photo_comments = item[13]
photo_mediatype = item[4]
photo_locID = item[14]
photo_locName = item[1]
#assign lat/lng coordinates from dict
if (photo_locID in loc_dict):
photo_latitude = loc_dict[photo_locID][0]
photo_longitude = loc_dict[photo_locID][1]
#setLatLngBounds(photo_latitude,photo_longitude)
if shapefileIntersect:
#skip all outside shapefile
if photo_locID in shapeFileExcludelocIDhash:
count_outside_shape += 1
continue
if not photo_locID in shapeFileIncludedlocIDhash:
LngLatPoint = Point(photo_longitude, photo_latitude)
if not LngLatPoint.within(shp_geom):
count_outside_shape += 1
shapeFileExcludelocIDhash.add(photo_locID)
continue
else:
shapeFileIncludedlocIDhash.add(photo_locID)
else:
if excludeWhereMissingGeocode:
skippedCount += 1
continue #skip non-geotagged medias
else:
photo_latitude = ""
photo_longitude = ""
#assign usernames from dict
if photo_userid in user_dict:
photo_owner = user_dict[photo_userid]
elif photo_userid in netlytics_usernameid_dict:
photo_owner = netlytics_usernameid_dict[photo_userid]
else:
photo_owner = ""
#empty for instagram:
photo_mTags = ""
photo_dateTaken = ""
photo_views = ""
elif DSource == "fromInstagram_UserMedia_CSV":
if len(item) < 15:
#skip
skippedCount += 1
continue
else:
photo_source = Sourcecode #LocMediaCode
photo_guid = item[0].split("_")[0] #guid
photo_userid = item[0].split("_")[1] #userid
photo_owner = item[1] ##!!!
photo_shortcode = item[6]
photo_uploadDate = item[4] # format datetime as string
photo_idDate = photo_uploadDate #use upload date as sorting ID
photo_caption = item[8]
photo_likes = item[13]
photo_tags = ";" + item[9] + ";"
photo_thumbnail = item[7]
photo_comments = item[14]
photo_mediatype = item[5]
photo_locID = item[2]
if photo_locID == "":
count_non_geotagged += 1
continue #skip non-geotagged medias
photo_locName = item[3]
#assign lat/lng coordinates from dict
if (photo_locID in loc_dict):
photo_latitude = loc_dict[photo_locID][0]
photo_longitude = loc_dict[photo_locID][1]
if shapefileIntersect:
#skip all outside shapefile
if photo_locID in shapeFileExcludelocIDhash:
count_outside_shape += 1
continue
if not photo_locID in shapeFileIncludedlocIDhash:
LngLatPoint = Point(photo_longitude, photo_latitude)
if not LngLatPoint.within(shp_geom):
count_outside_shape += 1
shapeFileExcludelocIDhash.add(photo_locID)
continue
else:
shapeFileIncludedlocIDhash.add(photo_locID)
else:
if excludeWhereMissingGeocode:
skippedCount += 1
continue #skip non-geotagged medias
else:
photo_latitude = ""
photo_longitude = ""
#empty for Instagram:
photo_mTags = ""
photo_dateTaken = ""
photo_views = ""
elif DSource == "fromFlickr_CSV":
if len(item) < 12:
#skip
skippedCount += 1
continue
else:
photo_source = Sourcecode #LocMediaCode
photo_guid = item[5] #photoID
photo_userid = item[7] #userid
photo_owner = item[6] ##!!!
photo_shortcode = ""
photo_uploadDate = datetime.datetime.strptime(item[9],'%m/%d/%Y %H:%M:%S').strftime('%Y-%m-%d %H:%M:%S') # format datetime as string
photo_dateTaken = datetime.datetime.strptime(item[8] ,'%m/%d/%Y %H:%M:%S').strftime('%Y-%m-%d %H:%M:%S')
photo_idDate = photo_dateTaken #use date taken date as sorting ID
photo_caption = item[3]
photo_likes = ""
photo_tags = set(filter(None, item[11].lower().split(";"))) #filter empty strings from photo_tags list and convert to set (hash) with unique values
#Filter tags based on two stoplists
photo_tags_filtered = set()
for tag in photo_tags:
count_tags_global += 1
#exclude numbers and those tags that are in SortOutAlways_set
if tag.isdigit() or tag in SortOutAlways_set:
count_tags_skipped += 1
continue
for inStr in SortOutAlways_inStr_set:
if inStr in tag:
count_tags_skipped += 1
break
else:
photo_tags_filtered.add(tag)
photo_tags = photo_tags_filtered
#if not "water" in photo_tags:
# continue
photo_thumbnail = item[4]
photo_comments = ""
photo_mediatype = ""
photo_locName = ""
if is_number(item[1]):
photo_latitude = Decimal(item[1])
else:
skippedCount += 1
continue
if is_number(item[2]):
photo_longitude = Decimal(item[2])
else:
skippedCount += 1
continue
setLatLngBounds(photo_latitude,photo_longitude)
photo_locID = str(photo_latitude) + ':' + str(photo_longitude) #create loc_id from lat/lng
photo_mTags = "" #not used currently but available
photo_views = item[10]
elif (DSource == "fromInstagram_HashMedia_JSON"):
photo_source = Sourcecode #HashMediaCode
if item.get('owner'):
photo_userid = item["owner"]["id"]
else:
# skip problematic entries
skippedCount += 1
continue
if item.get('edge_liked_by'):
photo_likes = item["edge_liked_by"]["count"]
else:
photo_likes = ""
if item.get('edge_media_to_caption') and not len(item.get("edge_media_to_caption", {}).get("edges")) == 0:
photo_caption = item["edge_media_to_caption"]["edges"][0]["node"]["text"].replace('\n', ' ').replace('\r', '')
else:
photo_caption = ""
if item.get('edge_media_to_comment'):
photo_comments = item["edge_media_to_comment"]["count"]
else:
photo_comments = ""
if item.get('id'):
photo_guid = item["id"]
else:
# skip problematic entries
skippedCount += 1
continue
if item.get('is_video'):
photo_mediatype = "video"
else:
photo_mediatype = "image"
if item.get('location'):
photo_locID = item["location"]["id"]
photo_locName = item["location"]["name"]
else:
# skip non geotagged
count_non_geotagged += 1
continue
if item.get('shortcode'):
photo_shortcode = item["shortcode"]
else:
photo_shortcode = ""
if item.get('tags'):
photo_tags =';'.join(item["tags"]).replace('\n', ' ').replace('\r', '')
photo_tags = ";%s;" % (photo_tags.replace(",", ";"))
else:
photo_tags = ""
if item.get('taken_at_timestamp'):
pdate = datetime.datetime.fromtimestamp(int(item["taken_at_timestamp"])) + timedelta(hours = GMTTimetransform) #GMT conversion
photo_uploadDate = pdate.strftime('%Y-%m-%d %H:%M:%S') # format datetime as string
photo_idDate = photo_uploadDate
else:
# skip problematic entries
skippedCount += 1
continue
if item.get('thumbnail_src'):
photo_thumbnail = item["thumbnail_src"]
else:
photo_thumbnail = ""
#assign lat/lng coordinates from dict
if (photo_locID in loc_dict):
photo_latitude = loc_dict[photo_locID][0]
photo_longitude = loc_dict[photo_locID][1]
if shapefileIntersect:
#skip all outside shapefile
if photo_locID in shapeFileExcludelocIDhash:
count_outside_shape += 1
continue
if not photo_locID in shapeFileIncludedlocIDhash:
LngLatPoint = Point(photo_longitude, photo_latitude)
if not LngLatPoint.within(shp_geom):
count_outside_shape += 1
shapeFileExcludelocIDhash.add(photo_locID)
continue
else:
shapeFileIncludedlocIDhash.add(photo_locID)
else:
if excludeWhereMissingGeocode:
#count_non_geotagged += 1
skippedCount += 1
continue #skip non-geotagged medias
else:
photo_latitude = ""
photo_longitude = ""
#assign usernames from dict
if photo_userid in user_dict:
photo_owner = user_dict[photo_userid]
elif photo_userid in netlytics_usernameid_dict:
photo_owner = netlytics_usernameid_dict[photo_userid]
else:
photo_owner = ""
#empty for instagram:
photo_mTags = ""
photo_dateTaken = ""
photo_views = ""
#this code will union all tags of a single user for each location
#further information is derived from the first omage for each user-location
photo_locIDUserID = photo_locID + '::' + str(photo_userid) #create userid_loc_id
distinctLocations_set.add(photo_locID)
if not photo_userid in LocationsPerUserID_dict or not photo_locID in LocationsPerUserID_dict[photo_userid]:
LocationsPerUserID_dict[photo_userid] |= {photo_locID} # Bit wise or and assignment in one step. -> assign locID to UserDict list if not already contained
count_loc += 1
UserLocationsFirstPhoto_dict[photo_locIDUserID] = (photo_source,
photo_guid,
photo_owner,
photo_userid,
photo_caption,
photo_thumbnail,
photo_dateTaken,
photo_uploadDate,
photo_views,
photo_tags,
photo_mTags,
photo_likes,
photo_comments,
photo_shortcode,
photo_mediatype,
photo_locName,
photo_locID)
UserLocationTagList_dict[photo_locIDUserID] |= photo_tags #union tags per userid/unique location
removeSpecialChars = photo_caption.translate ({ord(c): " " for c in "?.!/;:,[]()'-"})
wordlist = [word for word in removeSpecialChars.split(' ') if len(word) > 2] #first replace specia characters in caption, then split by space-character
UserLocationWordList_dict[photo_locIDUserID] |= set(wordlist) #union words per userid/unique location
count_glob += 1
##Calculate toplists
if photo_tags:
UserDict_TagCounters_global[photo_userid].update(photo_tags) #add tagcount of this media to overall tagcount or this user, initialize counter for user if not already done
print("Cleaned output to " + "%02d" % (count_loc,) + " photolocations from " + "%02d" % (count_glob,)+ " (File " + str(partcount) + " of " + str(len(filelist)) + ") - Skipped Media: " + str(skippedCount) + " - Skipped Tags: " + str(count_tags_skipped) +" of " + str(count_tags_global), end='\r')
total_distinct_locations = len(distinctLocations_set)
print("\nTotal distinct locations: " + str(total_distinct_locations))
#boundary:
print("Bounds are: Min " + str(float(limLngMin)) + " " + str(float(limLatMin)) + " Max " + str(float(limLngMax)) + " " + str(float(limLatMax)))
#cleanedPhotoList = []
#create structure for tuple with naming for easy referencing
cleanedPhotoLocation_tuple = namedtuple('cleanedPhotoLocation_tuple', 'source lat lng photo_guid photo_owner userid photo_caption photo_dateTaken photo_uploadDate photo_views photo_tags photo_thumbnail photo_mTags photo_likes photo_comments photo_shortcode photo_mediatype photo_locName photo_locID')
cleanedPhotoDict = defaultdict(cleanedPhotoLocation_tuple)
with open("Output/Output_cleaned.txt", 'w', encoding='utf8') as csvfile:
csvfile.write("SOURCE,Latitude,Longitude,PhotoID,Owner,UserID,Name,DateTaken,UploadDate,Views,Tags,URL,MTags,Likes,Comments,Shortcode,Type,LocName,LocID," + '\n')
datawriter = csv.writer(csvfile, delimiter=',', lineterminator='\n', quotechar='"', quoting=csv.QUOTE_NONNUMERIC)
for user_key, locationhash in LocationsPerUserID_dict.items():
for location in locationhash:
locIDUserID = str(location) + '::' + str(user_key)
photo_latlng = location.split(':')
photo = UserLocationsFirstPhoto_dict.get(locIDUserID,(" "," "," "," "," "," "," "," "," "," "," "," "," "," "," "," "," "))
#create tuple with cleaned photo data
cleanedPhotoLocation = cleanedPhotoLocation_tuple(photo[0],#Source = 0
float(photo_latlng[0]), #Lat = 1
float(photo_latlng[1]), #Lng = 2
photo[1],#photo_guid = 3
photo[2],#photo_owner = 4
user_key, #userid = 5
UserLocationWordList_dict.get(locIDUserID,("",)),#photo_caption = 6
photo[6],#photo_dateTaken = 7
photo[7],#photo_uploadDate = 8
int(photo[8]),#photo_views = 9
UserLocationTagList_dict.get(locIDUserID,("",)),#photo_tags = 10
photo[5],#photo_thumbnail = 11
photo[10],#photo_mTags = 12
photo[11],#photo_likes = 13
photo[12],#photo_comments = 14
photo[13],#photo_shortcode = 15
photo[14],#photo_mediatype = 16
photo[15],#photo_locName = 17
photo[16]#photo_locID = 18
)
datawriter.writerow([cleanedPhotoLocation.source,#Source = 0
cleanedPhotoLocation.lat, #Lat = 1
cleanedPhotoLocation.lng, #Lng = 2
cleanedPhotoLocation.photo_guid,#photo_guid = 3
cleanedPhotoLocation.photo_owner,#photo_owner = 4
cleanedPhotoLocation.userid, #userid = 5
";".join(cleanedPhotoLocation.photo_caption),#photo_caption = 6
cleanedPhotoLocation.photo_dateTaken,#photo_dateTaken = 7
cleanedPhotoLocation.photo_uploadDate,#photo_uploadDate = 8
cleanedPhotoLocation.photo_views,#photo_views = 9
";".join(cleanedPhotoLocation.photo_tags),#photo_tags = 10
cleanedPhotoLocation.photo_thumbnail,#photo_thumbnail = 11
cleanedPhotoLocation.photo_mTags,#photo_mTags = 12
cleanedPhotoLocation.photo_likes,#photo_likes = 13
cleanedPhotoLocation.photo_comments,#photo_comments = 14
cleanedPhotoLocation.photo_shortcode,#photo_shortcode = 15
cleanedPhotoLocation.photo_mediatype,#photo_mediatype = 16
cleanedPhotoLocation.photo_locName,#photo_locName = 17
cleanedPhotoLocation.photo_locID]#photo_locID = 18
)
#cleanedPhotoList.append(cleanedPhotoLocation)
cleanedPhotoDict[cleanedPhotoLocation.photo_guid] = cleanedPhotoLocation
print("########## STEP 2 of 5: Tag Ranking ##########")
overallNumOfUsersPerTag_global = collections.Counter()
for user_key, taghash in UserDict_TagCounters_global.items():
#taghash contains unique values (= strings) for each user, thus summing up these taghashes counts each user only once per tag
overallNumOfUsersPerTag_global.update(taghash)
toptags = ''.join("%s,%i" % v + '\n' for v in overallNumOfUsersPerTag_global.most_common(1000))
with open("Output/Output_toptags.txt", 'w', encoding="utf8") as file: #overwrite
file.write(toptags)
tmax = 1000
topTagsList = overallNumOfUsersPerTag_global.most_common(tmax)
print("########## STEP 3 of 5: Tag Location Clustering ##########")
#prepare some variables
tnum = 0
first = True
label_size = 10
#plt.rcParams['xtick.labelsize'] = label_size
#plt.rcParams['ytick.labelsize'] = label_size
plot_kwds = {'alpha' : 0.5, 's' : 10, 'linewidths':0}
sys.stdout.flush()
proceedClusting = False
#Tkinter Stuff
#####################################################################################################################################################
#def center(win):
# """
# centers a tkinter window
# :param win: the root or Toplevel window to center
# """
# win.update_idletasks()
# width = win.winfo_width()
# frm_width = win.winfo_rootx() - win.winfo_x()
# win_width = width + 2 * frm_width
# height = win.winfo_height()
# titlebar_height = win.winfo_rooty() - win.winfo_y()
# win_height = height + titlebar_height + frm_width
# x = win.winfo_screenwidth() // 2 - win_width // 2
# y = win.winfo_screenheight() // 2 - win_height // 2
# win.geometry('{}x{}+{}+{}'.format(width, height, x, y))
# win.deiconify()
class App(tk.Tk):
def __init__(self):
tk.Tk.__init__(self)
self.overrideredirect(True) #this is needed to make the root disappear
self.geometry('%dx%d+%d+%d' % (0, 0, 0, 0))
#create separate floating window
self.floater = FloatingWindow(self)
class FloatingWindow(tk.Toplevel):
#global app
def __init__(self, *args, **kwargs):
tk.Toplevel.__init__(self, *args, **kwargs)
self.overrideredirect(True)
self.configure(background='gray7')
#self.label = tk.Label(self, text="Click on the grip to move")
self.grip = tk.Label(self, bitmap="gray25")
self.grip.pack(side="left", fill="y")
#self.label.pack(side="right", fill="both", expand=True)
self.grip.bind("<ButtonPress-1>", self.StartMove)
self.grip.bind("<ButtonRelease-1>", self.StopMove)
self.grip.bind("<B1-Motion>", self.OnMotion)
#center Floating Window
w = self.winfo_reqwidth()
h = self.winfo_reqheight()
ws = self.winfo_screenwidth()
hs = self.winfo_screenheight()
x = (ws/2) - (w/2)
y = (hs/2) - (h/2)
self.geometry('+%d+%d' % (x, y))
def StartMove(self, event):
self.x = event.x
self.y = event.y
def StopMove(self, event):
self.x = None
self.y = None
def OnMotion(self, event):
deltax = event.x - self.x
deltay = event.y - self.y
x = self.winfo_x() + deltax
y = self.winfo_y() + deltay
self.geometry("+%s+%s" % (x, y))
app = App()
#necessary override for error reporting during tkinter mode
def report_callback_exception(self, exc, val, tb):
showerror("Error", message=str(val))
tk.Tk.report_callback_exception = report_callback_exception
#the following code part is a bit garbled due to using TKinter interface
######################################################################################################################################################
######################################################################################################################################################
######################################################################################################################################################
#definition of global vars for interface and graph design
distY = 0
distX = 0
imgRatio = 0
canvasWidth = 1320
canvasHeight = 440
floaterX = 0
floaterY = 0
#Cluster preparation
sns.set_context('poster')
sns.set_style('white')
#sns.set_color_codes()
#matplotlib.style.use('ggplot')
plt.style.use('ggplot')
graphFrame = None
lastselectionList = []
currentDisplayTag = None
genPreviewMap = tk.IntVar(value = 0)
createMinimumSpanningTree = False
autoselectClusters = False
#definition of global figure for reusing windows
fig1 = None
fig2 = None
fig3 = None
fig4 = None
#Optional: set global plotting bounds
#plt.gca().set_xlim([limXMin, limXMax])
#plt.gca().set_ylim([limYMin, limYMax])
cleanedPhotoList = list(cleanedPhotoDict.values())
df = pd.DataFrame(cleanedPhotoList)
points = df.as_matrix(['lng','lat'])
limYMin = np.min(points.T[1])
limYMax = np.max(points.T[1])
limXMin = np.min(points.T[0])
limXMax = np.max(points.T[0])
distY = limYMax - limYMin
distX = limXMax - limXMin
imgRatio = distX/(distY*2)
distY = def_functions.haversine(limXMin, limYMin, limXMin, limYMax)
distX = def_functions.haversine(limXMin, limYMin, limXMax, limYMin)
clusterTreeCuttingDist = (distX/100)*4 #4% of research area width = default value #223.245922725 #= 0.000035 radians dist
#tkinter.messagebox.showinfo("messagr", str(distY))
#tkinter.messagebox.showinfo("messagr", str(distX))
#if first == True: #init
# #calculate once boundary for all points
# df = pd.DataFrame(cleanedPhotoList)
# points = df.as_matrix(['lng','lat'])
# limYMin = np.min(points.T[1])
# limYMax = np.max(points.T[1])
# limXMin = np.min(points.T[0])
# limXMax = np.max(points.T[0])
# #set global plotting bounds for matplotlib
# plt.gca().set_xlim([limXMin, limXMax])
# plt.gca().set_ylim([limYMin, limYMax])
# #calculate imageRatio from distance covered
# distY = limYMax - limYMin
# distX = limXMax - limXMin
# imgRatio = distX/(distY*2) #multiply by 2 because lat = 90 and lng = 180!
# ws = app.winfo_screenwidth()
# hs = app.winfo_screenheight()
# floaterX = (ws/2) - (canvasWidth/2)
# floaterY = (hs/2) - (canvasHeight/2)
# #app.floater.geometry('+%d+%d' % (floaterX, floaterY))
# #app.title("Select Cluster Distance")
# first = False
def quitTkinter():
#exits Tkinter gui and continues with code execution after mainloop()
#global app
#app.floater.destroy()
#tkinter.messagebox.showinfo("Closing App", "Closing App")
#plt.quit()
app.update() #see https://stackoverflow.com/questions/35040168/python-tkinter-error-cant-evoke-event-command
app.destroy()
app.quit() ##root.quit() causes mainloop to exit, see https://stackoverflow.com/questions/2307464/what-is-the-difference-between-root-destroy-and-root-quit
def proceedWithCluster():
global proceedClusting
global fig1
proceedClusting = True
#def vis_tag(tag):
#tkinter.messagebox.showinfo("Proceed", "Proceed")
#if plt.figure(1):
# plt.figure(1).clf()
app.update() #see https://stackoverflow.com/questions/35040168/python-tkinter-error-cant-evoke-event-command
app.destroy()
app.quit()
def sel_photos(tag,cleanedPhotoList):
#select photos from list based on a specific tag
distinctLocalLocationCount = set()
selectedPhotoList_Guids = []
for cleanedPhotoLocation in cleanedPhotoList:
if tag in (cleanedPhotoLocation.photo_tags) or (tag in cleanedPhotoLocation.photo_caption):
selectedPhotoList_Guids.append(cleanedPhotoLocation.photo_guid)
distinctLocalLocationCount.add(cleanedPhotoLocation.photo_locID)
return selectedPhotoList_Guids, len(distinctLocalLocationCount)
def fit_cluster(clusterer, data):
clusterer.fit(data)
return clusterer
def cluster_tag(toptag,preview=None,silent=None):
if preview is None:
preview = False
if silent is None:
silent = False
global first
global currentDisplayTag
global limYMin, limYMax, limXMin, limXMax, distY, distX, imgRatio, floaterX, floaterY
global fig1, fig2, fig3, fig4
#global graphFrame
#if graphFrame: #if
# graphFrame.destroy()
#graphFrame = tk.Frame(app.floater)
#canvas = tk.Canvas(graphFrame, width=canvasWidth, height=canvasHeight, highlightthickness=0,background="gray7")
#l = tk.Label(canvas, text="Preview Map", background="gray7",fg="gray80",font="Arial 10 bold")
#l.pack()
selectedPhotoList_Guids, distinctLocalLocationCount = sel_photos(toptag[0],cleanedPhotoList)
percentageOfTotalLocations = distinctLocalLocationCount/(total_distinct_locations/100)
#tkinter.messagebox.showinfo("Num of clusters: ", "(" + str(tnum) + " of " + str(tmax) + ") Found " + str(len(selectedPhotoList)) + " photos for tag " + "'" + toptag[0] + "' (" + str(round(percentageOfTotalLocations,0)) + "% of total distinct locations in area)")
if silent:
print("(" + str(tnum) + " of " + str(tmax) + ") Found " + str(len(selectedPhotoList_Guids)) + " photos for tag " + "'" + toptag[0] + "' (" + str(round(percentageOfTotalLocations,0)) + "% of total distinct locations in area)", end=" ")
#clustering
selectedPhotoList = [cleanedPhotoDict[x] for x in selectedPhotoList_Guids]
df = pd.DataFrame(selectedPhotoList)
points = df.as_matrix(['lng','lat']) #converts pandas data to numpy array (limit by list of column-names)
#only return preview fig without clustering
if preview == True:
#only map data
if genPreviewMap.get() == 1:
if fig1:
plt.figure(1).clf() #clear figure 1
#earth = Basemap()
#earth.bluemarble(alpha=0.42)
#earth.drawcoastlines(color='#555566', linewidth=1)
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
#reuse window of figure 1 for new figure
plt.scatter(points.T[0], points.T[1], color='red', **plot_kwds)
fig1 = plt.figure(num=1,figsize=(11, int(11*imgRatio)), dpi=80)
fig1.canvas.set_window_title('Preview Map')
#displayImgPath = pathname + '/Output/ClusterImg/00_displayImg.png'
#fig1.figure.savefig(displayImgPath)
else:
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
plt.scatter(points.T[0], points.T[1], color='red', **plot_kwds)
fig1 = plt.figure(num=1,figsize=(11, int(11*imgRatio)), dpi=80)
#earth = Basemap()
#earth.bluemarble(alpha=0.42)
#earth.drawcoastlines(color='#555566', linewidth=1)
fig1.canvas.set_window_title('Preview Map')
plt.gca().set_xlim([limXMin, limXMax])
plt.gca().set_ylim([limYMin, limYMax])
plt.tick_params(labelsize=10)
currentDisplayTag = toptag
else:
#cluster data
tagRadiansData = np.radians(points) #conversion to radians for HDBSCAN (does not support decimal degrees)
#for each tag in overallNumOfUsersPerTag_global.most_common(1000) (descending), calculate HDBSCAN Clusters
minClusterSize = max(2,int(((len(selectedPhotoList_Guids))/100)*5)) #4% optimum
#minClusterSize = 2
clusterer = hdbscan.HDBSCAN(min_cluster_size=minClusterSize,gen_min_span_tree=createMinimumSpanningTree,allow_single_cluster=True,min_samples=1)
#clusterer = hdbscan.HDBSCAN(min_cluster_size=minClusterSize,gen_min_span_tree=True,min_samples=1)
#clusterer = hdbscan.HDBSCAN(min_cluster_size=10,metric='haversine',gen_min_span_tree=False,allow_single_cluster=True)
#clusterer = hdbscan.robust_single_linkage_.RobustSingleLinkage(cut=0.000035)
#srsl_plt = hdbscan.robust_single_linkage_.plot()
#Start clusterer on different thread to prevent GUI from freezing
#See: http://stupidpythonideas.blogspot.de/2013/10/why-your-gui-app-freezes.html
#https://stackoverflow.com/questions/6893968/how-to-get-the-return-value-from-a-thread-in-python
if silent:
#on silent command line operation, don't use multiprocessing
clusterer = fit_cluster(clusterer,tagRadiansData)
else:
async_result = pool.apply_async(fit_cluster, (clusterer, tagRadiansData))
clusterer = async_result.get()
#clusterer.fit(tagRadiansData)
#updateNeeded = False
if autoselectClusters:
sel_labels = clusterer.labels_ #auto selected clusters
else:
sel_labels = clusterer.single_linkage_tree_.get_clusters(getRadiansFromMeters(clusterTreeCuttingDist), min_cluster_size=2) #0.000035 without haversine: 223 m (or 95 m for 0.000015)
#exit function in case final processing loop (no figure generating)
if silent:
return sel_labels, selectedPhotoList_Guids
mask_noisy = (sel_labels == -1)
number_of_clusters = len(np.unique(sel_labels[~mask_noisy])) #len(sel_labels)
#palette = sns.color_palette("hls", )
#palette = sns.color_palette(None, len(sel_labels)) #sns.color_palette("hls", ) #sns.color_palette(None, 100)
palette = sns.color_palette("husl", number_of_clusters+1)
sel_colors = [palette[x] if x >= 0
else (0.5, 0.5, 0.5)
#for x in clusterer.labels_ ]
for x in sel_labels] #clusterer.labels_ (best selection) or sel_labels (cut distance)
#tkinter.messagebox.showinfo("Num of clusters: ", str(len(sel_colors)) + " " + str(sel_colors[1]))
#output/update matplotlib figures
if fig1:
plt.figure(1).clf()
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold') #plt references the last figure accessed
ax = plt.scatter(points.T[0], points.T[1], color=sel_colors, **plot_kwds)
fig1 = plt.figure(num=1,figsize=(11, int(11*imgRatio)), dpi=80)
fig1.canvas.set_window_title('Cluster Preview')
distText = ''
if autoselectClusters == False:
distText = '@ ' + str(clusterTreeCuttingDist) +'m'
plt.title('Cluster Preview ' + distText, fontsize=12,loc='center')
#plt.title('Cluster Preview')
#xmax = ax.get_xlim()[1]
#ymax = ax.get_ylim()[1]
noisyTxt = '{}/{}'.format(mask_noisy.sum(), len(mask_noisy))
plt.text(limXMax, limYMax,str(number_of_clusters) + ' Cluster (Noise: ' + noisyTxt + ')',fontsize=10,horizontalalignment='right',verticalalignment='top',fontweight='bold')
else:
plt.scatter(points.T[0], points.T[1], c=sel_colors, **plot_kwds)
fig1 = plt.figure(num=1,figsize=(11, int(11*imgRatio)), dpi=80)
fig1.canvas.set_window_title('Cluster Preview')
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
distText = ''
if autoselectClusters == False:
distText = '@ ' + str(clusterTreeCuttingDist) +'m'
plt.title('Cluster Preview ' + distText, fontsize=12,loc='center')
#xmax = fig1.get_xlim()[1]
#ymax = fig1.get_ylim()[1]
noisyTxt = '{} / {}'.format(mask_noisy.sum(), len(mask_noisy))
plt.text(limXMax, limYMax,str(number_of_clusters) + ' Clusters (Noise: ' + noisyTxt + ')',fontsize=10,horizontalalignment='right',verticalalignment='top',fontweight='bold')
plt.gca().set_xlim([limXMin, limXMax])
plt.gca().set_ylim([limYMin, limYMax])
plt.tick_params(labelsize=10)
#if len(tagRadiansData) < 10000:
if fig2:
plt.figure(2).clf()
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
#plt.title('Condensed Tree', fontsize=12,loc='center')
clusterer.condensed_tree_.plot(select_clusters=False, selection_palette=sel_colors)#,label_clusters=False
#tkinter.messagebox.showinfo("Num of clusters: ", str(len(sel_colors)) + " " + str(sel_colors[0]))
else:
plt.figure(2).canvas.set_window_title('Condensed Tree')
fig2 = clusterer.condensed_tree_.plot(select_clusters=False, selection_palette=sel_colors)#,label_clusters=False
#tkinter.messagebox.showinfo("Num of clusters: ", str(len(sel_colors)) + " " + str(sel_colors[1]))
#fig2 = clusterer.condensed_tree_.plot(select_clusters=False, selection_palette=sel_colors,label_clusters=True)
#plt.title('Condensed Tree', fontsize=12,loc='center')
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
plt.tick_params(labelsize=10)
if fig3:
plt.figure(3).clf()
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
plt.title('Single Linkage Tree', fontsize=12,loc='center')
#clusterer.single_linkage_tree_.plot(truncate_mode='lastp',p=50)
ax = clusterer.single_linkage_tree_.plot(truncate_mode='lastp',p=max(50,min(number_of_clusters*10,256))) #p is the number of max count of leafs in the tree, this should at least be the number of clusters*10, not lower than 50 [but max 500 to not crash]
#tkinter.messagebox.showinfo("messagr", str(type(ax)))
#plot cutting distance
y = getRadiansFromMeters(clusterTreeCuttingDist)
xmin = ax.get_xlim()[0]
xmax = ax.get_xlim()[1]
line, = ax.plot([xmin, xmax], [y, y], color='k', label='Cluster (Cut) Distance ' + str(clusterTreeCuttingDist) +'m')
line.set_label('Cluster (Cut) Distance ' + str(clusterTreeCuttingDist) +'m')
ax.legend(fontsize=10)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.1f}m'.format(getMetersFromRadians(x)) for x in vals])
else:
plt.figure(3).canvas.set_window_title('Single Linkage Tree')
fig3 = clusterer.single_linkage_tree_.plot(truncate_mode='lastp',p=max(50,min(number_of_clusters*10,256)))
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
plt.title('Single Linkage Tree', fontsize=12,loc='center')
#tkinter.messagebox.showinfo("messagr", str(type(fig3)))
#plot cutting distance
y = getRadiansFromMeters(clusterTreeCuttingDist)
xmin = fig3.get_xlim()[0]
xmax = fig3.get_xlim()[1]
line, = fig3.plot([xmin, xmax], [y, y], color='k', label='Cluster (Cut) Distance ' + str(clusterTreeCuttingDist) +'m')
line.set_label('Cluster (Cut) Distance ' + str(clusterTreeCuttingDist) +'m')
fig3.legend(fontsize=10)
vals = fig3.get_yticks()
fig3.set_yticklabels(['{:3.1f}m'.format(getMetersFromRadians(x)) for x in vals])
plt.tick_params(labelsize=10)
if createMinimumSpanningTree:
if fig4:
plt.figure(4).clf()
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
#plt.title('Single Linkage Tree', fontsize=12,loc='center')
#clusterer.single_linkage_tree_.plot(truncate_mode='lastp',p=50)
ax = clusterer.minimum_spanning_tree_.plot(edge_cmap='viridis',
edge_alpha=0.6,
node_size=10,
edge_linewidth=1) #tkinter.messagebox.showinfo("messagr", str(type(ax)))
fig4.canvas.set_window_title('Minimum Spanning Tree')
plt.title('Minimum Spanning Tree @ ' + str(clusterTreeCuttingDist) +'m', fontsize=12,loc='center')
ax.legend(fontsize=10)
#ax=plt.gca() #plt.gca() for current axis, otherwise set appropriately.
#im=ax.images #this is a list of all images that have been plotted
#cb=im[0].colorbar ##in this case I assume to be interested to the last one plotted, otherwise use the appropriate index
#cb.ax.tick_params(labelsize=10)
#vals = cb.ax.get_yticks()
#cb.ax.set_yticklabels(['{:3.1f}m'.format(getMetersFromRadians(x)) for x in vals])
else:
plt.figure(4).canvas.set_window_title('Minimum Spanning Tree')
fig4 = clusterer.minimum_spanning_tree_.plot(edge_cmap='viridis',
edge_alpha=0.6,
node_size=10,
edge_linewidth=1) #tkinter.messagebox.showinfo("messagr", str(type(ax)))
plt.suptitle(toptag[0].upper(), fontsize=18, fontweight='bold')
plt.title('Minimum Spanning Tree @ ' + str(clusterTreeCuttingDist) +'m', fontsize=12,loc='center')
fig4.legend(fontsize=10)
#ax=plt.gca() #plt.gca() for current axis, otherwise set appropriately.
#im=ax.images #this is a list of all images that have been plotted
#cb=im[0].colorbar ##in this case I assume to be interested to the last one plotted, otherwise use the appropriate index
#cb.ax.tick_params(labelsize=10)
#vals = cb.ax.get_yticks()
#cb.ax.set_yticklabels(['{:3.1f}m'.format(getMetersFromRadians(x)) for x in vals])
plt.tick_params(labelsize=10)
#adjust scalebar limits
global tkScalebar
tkScalebar.configure(from_=(clusterTreeCuttingDist/100), to=(clusterTreeCuttingDist*2))
def getRadiansFromMeters(dist):
dist = dist/1000
degreesDist = dist/111.325
radiansDist = degreesDist/57.2958
return radiansDist
#https://www.mathsisfun.com/geometry/radians.html
#1 Radian is about 57.2958 degrees.
#then see https://sciencing.com/convert-distances-degrees-meters-7858322.html
#Multiply the number of degrees by 111.325
#To convert this to meters, multiply by 1,000. So, 2 degrees is 222,65 meters.
def getMetersFromRadians(dist):
dist = dist * 57.2958
dist = dist * 111.325
metersDist = round(dist * 1000,1)
return metersDist
#1 Radian is about 57.2958 degrees.
#then see https://sciencing.com/convert-distances-degrees-meters-7858322.html
#Multiply the number of degrees by 111.325
#To convert this to meters, multiply by 1,000. So, 2 degrees is 222,65 meters.
#plt.close('all') #clear memory
def change_clusterDist(val):
#tkinter.messagebox.showinfo("messagr", val)
global clusterTreeCuttingDist
#global canvas
global tkScalebar
clusterTreeCuttingDist = float(val)#tkScalebar.get()
def onselect(evt):
# Note here that Tkinter passes an event object to onselect()
global lastselectionList
global tnum
w = evt.widget
if lastselectionList: #if not empty
changedSelection = set(lastselectionList).symmetric_difference(set(w.curselection()))
lastselectionList = w.curselection()
else:
lastselectionList = w.curselection()