diff --git a/code/00_current-analysis.R b/code/00_current-analysis.R
index d66cbfc..b87e05a 100644
--- a/code/00_current-analysis.R
+++ b/code/00_current-analysis.R
@@ -9,7 +9,7 @@
 #           (3.4) Artwork sequences
 #           (3.5) Topics
 #
-# input:  results/haum/event_logfiles_glossar_2023-10-29_10-26-42.csv
+# input:  results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv
 # output:
 #
 # last mod: 2023-11-15, NW
@@ -27,16 +27,25 @@ library(bupaverse)
 
 #--------------- (1) Read data ---------------
 
-dat <- read.table("results/haum/event_logfiles_glossar_2023-11-03_17-46-28.csv",
-                  sep = ";", header = TRUE)
-dat$date <- as.POSIXct(dat$date)
-dat$date.start <- as.POSIXct(dat$date.start)
-dat$date.stop <- as.POSIXct(dat$date.stop)
-dat$artwork <- sprintf("%03d", dat$artwork)
+dat <- read.table("results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv",
+                  sep = ";", header = TRUE,
+                  colClasses = c("POSIXct", "character", "integer",
+                                 "integer", "numeric", "integer",
+                                 "character", "character", "character",
+                                 "character", "POSIXct", "POSIXct",
+                                 "numeric", "numeric", "numeric",
+                                 "integer", "character",
+                                 rep("numeric", 11), "integer",
+                                 "character", "character", "logical",
+                                 "logical", "logical", "character",
+                                 "character"))
+
+dat$date <- NULL
+# TODO: Remove, after rerunning preprocessing
+
 dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic", "openPopup"))
 
 # Add weekdays to data frame
-
 dat$weekdays <- factor(weekdays(dat$date.start),
                        levels = c("Montag", "Dienstag", "Mittwoch",
                                   "Donnerstag", "Freitag", "Samstag",
diff --git a/code/00_pm.py b/code/00_pm.py
index 206b450..1e8166d 100644
--- a/code/00_pm.py
+++ b/code/00_pm.py
@@ -18,6 +18,8 @@ dat = dat[dat.date < "2020-03-13"]
 
 event_log = pm4py.format_dataframe(dat, case_id='trace', activity_key='event',
                                    timestamp_key='date.start')
+# event_log = pm4py.format_dataframe(dat, case_id='trace', activity_key='event',
+#                                    timestamp_key='date.stop', start_timestamp_key='date.start')
 event_log = event_log.rename(columns={'artwork': 'case:artwork'})
 #event_log = pm4py.convert_to_event_log(dat_log)     # deprecated
 
@@ -48,6 +50,15 @@ pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_heuristics_
 is_sound = pm4py.check_soundness(net, im, fm)
 is_sound[0]
 
+len(is_sound[1]["s_c_net"].arcs)
+# 46
+len(is_sound[1]["s_c_net"].transitions)
+# 23
+len(is_sound[1]["s_c_net"].places)
+# 10
+
+
+
 # decorated petri net
 from pm4py.visualization.petri_net import visualizer as pn_visualizer
 parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
@@ -67,6 +78,10 @@ pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_alpha_compl
 is_sound = pm4py.check_soundness(net, im, fm)
 is_sound[0]
 
+len(is_sound[1]["s_c_net"].arcs)
+len(is_sound[1]["s_c_net"].transitions)
+len(is_sound[1]["s_c_net"].places)
+
 ## Inductive Miner
 net, im, fm = pm4py.discover_petri_net_inductive(event_log)
 i_eval = eval_pm(event_log, net, im, fm)
@@ -99,9 +114,6 @@ eval.to_csv("results/eval_all-miners_complete.csv", sep=";")
 
 ###### Process Mining - individual artworks ######
 
-net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
-#net, im, fm = pm4py.discover_petri_net_inductive(event_log)
-
 def pm_artworks(miner):
 
     retval1 = np.empty((len(event_log["case:artwork"].unique()), 4))
@@ -129,8 +141,8 @@ def pm_artworks(miner):
             subnet, subim, subfm = pm4py.discover_petri_net_alpha(subdata)
         elif miner == "ilp":
             subnet, subim, subfm = pm4py.discover_petri_net_ilp(subdata)
-        pm4py.save_vis_petri_net(subnet, subim, subfm,
-                 "../figures/processmaps/artworks/petrinet_" + miner + "_" + str(artwork).zfill(3) + ".png")
+        #pm4py.save_vis_petri_net(subnet, subim, subfm,
+        #         "../figures/processmaps/artworks/petrinet_" + miner + "_" + str(artwork).zfill(3) + ".png")
         retval1[i] = eval_pm(subdata, net, im, fm)
         retval2[i] = eval_pm(subdata, subnet, subim, subfm)
 
@@ -149,17 +161,21 @@ for miner in ["heuristics", "inductive", "alpha", "ilp"]:
     eval_art = pm_artworks(miner = miner)
     eval_art.to_csv("results/eval_artworks_" + miner + ".csv", sep=";")
 
+eval_art = pm_artworks(miner = "inductive")
 
 ##### Clustering ######
 
 ## KMeans
 
-kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_art)
+#eval_artworks = eval_art[eval_art.nettype == "alldata"].iloc[:,range(1,5)]
+eval_artworks = eval_art[eval_art.nettype == "subdata"].iloc[:,range(1,5)]
+
+kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_artworks)
 
 #from sklearn.manifold import MDS
-#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_art))
+#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
 
-coord = eval_art
+coord = eval_artworks
 coord["clusters"] = kmeans.labels_
 
 for i in coord.clusters.unique():
@@ -174,7 +190,7 @@ plt.show()
 
 sse = {}
 for k in range(1, 10):
-    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_art[["precision", "generalizability"]])
+    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_artworks[["precision", "generalizability"]])
     #data["clusters"] = kmeans.labels_
     #print(data["clusters"])
     sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
@@ -183,3 +199,4 @@ plt.plot(list(sse.keys()), list(sse.values()))
 plt.xlabel("Number of clusters")
 plt.ylabel("SSE")
 plt.show()
+