Skip to content
Snippets Groups Projects
Commit 30807ab4 authored by Adriaens, Ines's avatar Adriaens, Ines :v_tone2:
Browse files

add behaviours per cow for calculations

parent c1f18212
Branches
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
behaviours.py
+ 1
− 1
View file @ 30807ab4
......@@ -158,7 +158,7 @@ for cow in cows:
# ------------------------ concentrate feeder -------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "concentrate" # concentrate feeding behaviour
min_length = 0 # always count
min_length = 2 # always count
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
......
behaviours_percow.py 0 → 100644
+ 232
− 0
View file @ 30807ab4
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 14 12:08:41 2023
@author: adria036
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 8 16:47:19 2023
@author: adria036
"""
import os
os.chdir(r"C:\Users\adria036\OneDrive - Wageningen University & Research\iAdriaens_doc\Projects\cKamphuis\nlas\scripts\uwb")
#%% import modules
from datetime import date, datetime
import pandas as pd
import numpy as np
# import matplotlib.pyplot as plt
# import matplotlib
# import seaborn as sns
from uwbfunctions import behaviour_bouts
#%matplotlib qt
#%% import data, seth paths and constants
# path_out
svpath = r"C:\Users\adria036\OneDrive - Wageningen University & Research\iAdriaens_doc\Projects\cKamphuis\nlas\results\behaviours"
# path to per barn areas
path = os.path.join("W:","\ASG","WLR_Dataopslag","DairyCampus","3406_Nlas","uwb_processed")
path_zones = os.path.join("W:","\ASG","WLR_Dataopslag","DairyCampus","3406_Nlas","raw","dc_sewio")
fna = "\\Barn_areas.xlsx"
area_zones = pd.read_excel(path_zones+fna, sheet_name = "areas")
# barn_edges = pd.read_excel(path_zones+fna, sheet_name = "edges")
del fna, path_zones
# settings
settings = {'barn' : [60,61,62,70,71,72,73],
'startdate' : date(2021,11,1),
'enddate' : date(2022,2,15),
'cows' : 0, # or specific cow number
}
# files that comply with settings
fn = []
for b in range(0,len(settings["barn"])):
print("barn = " + str(settings["barn"][b]))
if settings["cows"] == 0:
fbarn = [f for f in os.listdir(path + "/barn" + str(settings["barn"][b])) \
if os.path.isfile(os.path.join(path,"barn"+str(settings["barn"][b]),f)) \
and (datetime.strptime(f[5:13], '%Y%m%d').date() >= settings["startdate"]) \
and (datetime.strptime(f[5:13], '%Y%m%d').date() <= settings["enddate"])]
fbarn.sort()
else:
fbarn = [f for f in os.listdir(path + "/barn" + str(settings["barn"][b])) \
if os.path.isfile(os.path.join(path,"barn"+str(settings["barn"][b]),f)) \
and (int(f[26:-4]) in settings["cows"]) \
and (datetime.strptime(f[5:13], '%Y%m%d').date() >= settings["startdate"]) \
and (datetime.strptime(f[5:13], '%Y%m%d').date() <= settings["enddate"])]
fbarn.sort()
fn.extend(fbarn)
fn.sort()
# find unique cows
cows = list(set([int(f[26:-4]) for f in fn]))
# read data and calculate bouts and behaviours
for cow in cows:
print("read data of cow " + str(cow))
# read data
data = pd.DataFrame([])
for f in fn:
if str(cow) in f:
print(f)
barn = f[19:21]
sub = pd.read_csv(path + "/barn" + barn + "/" + f,
usecols = ["cowid","barn","date","t","xnew","ynew","area","zone","X","y"],
dtype = {"cowid" : "int64","barn" : "int64","date" : "object",
"t" : "int64", "xnew":"float64","ynew":"float64",
"area":"object","zone":"float64","X":"float64","y" : "float64"})
sub["date"] = pd.to_datetime(sub["date"], format = "%Y-%m-%d")
data = pd.concat([data,sub])
data = data.sort_values(by = ["cowid","date","t"])
data = data.reset_index(drop=1)
data.loc[data["zone"].isna(),"zone"] = 8
data.loc[data["zone"] == 8 ,"area"] = "unknown"
data["date"] = data["date"].dt.date
# calculate and summarize behaviours
bouts = pd.DataFrame([])
bsums = pd.DataFrame([])
act_dist = pd.DataFrame([])
# summarize days
days = data.loc[(data["cowid"]==cow),["date"]].drop_duplicates()
days = days.sort_values(by = ["date"]).reset_index(drop=1)
# calculate bouts and active behaviours
for dd in days["date"]:
print("cow = " + str(cow) + ", date = " + str(dd))
dset = data.loc[(data["cowid"] == cow) & (data["date"] == dd)].copy()
# ------------------------ feeding behaviour --------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "feed" # feeding behaviour
min_length = 30 # do not count if shorter than 30 seconds
df,summary = behaviour_bouts(dset, interval, behaviour,min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ---------------------- drinking behaviour ---------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "drink" # drinking behaviour
min_length = 5 # do not count if shorter than 2 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ---------------------- resting behaviour ----------------------------
interval = 2*60 # min number of seconds between separate bouts
behaviour = "cubicle" # resting behaviour
min_length = 30 # do not count if shorter than 30 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# -------------------------- cubicle_A --------------------------------
interval = 2*60 # min number of seconds between separate bouts
behaviour = "cubicle_A" # resting behaviour
min_length = 30 # do not count if shorter than 30 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# -------------------------- cubicle_B --------------------------------
interval = 2*60 # min number of seconds between separate bouts
behaviour = "cubicle_B" # resting behaviour
min_length = 30 # do not count if shorter than 30 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# -------------------------- cublicle_C -------------------------------
interval = 2*60 # min number of seconds between separate bouts
behaviour = "cubicle_C" # resting behaviour
min_length = 30 # do not count if shorter than 30 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ------------------------ concentrate feeder -------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "concentrate" # concentrate feeding behaviour
min_length = 0 # always count
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ------------------------ waiting area ------------------------------
interval = 20*60 # min number of seconds between separate bouts
behaviour = "wait" # in the waiting area
min_length = 0 # always count
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ------------------------ unknown area ------------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "unknown" # unknown behaviour
min_length = 10 # do not count if shorter than 10 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ------------------------- slatted (barn 62) -------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "slatted" # unknown behaviour
min_length = 10 # do not count if shorter than 10 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# ------------------------- resting (barn 62) -------------------------
interval = 5*60 # min number of seconds between separate bouts
behaviour = "resting" # unknown behaviour
min_length = 10 # do not count if shorter than 10 seconds
df,summary = behaviour_bouts(dset, interval, behaviour, min_length)
bouts = pd.concat([bouts,df],axis = 0).reset_index(drop=1)
bsums = pd.concat([bsums,summary],axis = 0).reset_index(drop=1)
# # ------------------------- distance travelled ------------------------
dset.loc[:,"dist"] = 0
dset.iloc[1:,dset.columns == "dist"] = \
np.sqrt((dset["xnew"].iloc[:-1].values-dset["xnew"].iloc[1:].values)**2 + \
(dset["ynew"].iloc[:-1].values-dset["ynew"].iloc[1:].values)**2)
dset.loc[(dset["dist"]<0.30) | (dset["dist"]>5),"dist"] = 0
# distance travelled and % active (with and without nans)
act = dset[["cowid","date","barn","dist"]].groupby(by = ["cowid","date","barn"]).sum().reset_index()
act["dist"] = round(act["dist"],2)
act["perc_act"] = round(len(dset.loc[dset["dist"] > 0])/len(dset)*100,2)
act["perc_act_nan"] = round(len(dset.loc[dset["dist"] > 0])/len(dset.loc[dset["area"]!="unknown"])*100,2)
# % of the day not in the cubicles
act["perc_no_lying"] = 100- round(len(dset.loc[(dset["area"].str.contains("cubicle") == True) | \
(dset["area"].str.contains("resting") == True)])/len(dset)*100,2)
act["perc_no_lying_nan"] = 100- round(len(dset.loc[(dset["area"].str.contains("cubicle") == True) | \
(dset["area"].str.contains("resting") == True)])/len(dset.loc[dset["area"]!="unknown"])*100,2)
act_dist = pd.concat([act_dist,act]).reset_index(drop=1)
# save bouts, summaries and behaviours per cow id
act_dist["dist"] = round(act_dist["dist"],2)
act_dist["perc_no_lying_nan"] = round(act_dist["perc_no_lying_nan"],2)
act_dist["perc_no_lying"] = round(act_dist["perc_no_lying"],2)
act_dist.to_csv(svpath + "/activity_cow_" + str(cow) + ".txt")
bouts.to_csv(svpath + "/bouts_cow_" + str(cow) + ".txt")
bsums["total2"] = round(bsums["total2"],2)
bsums["total1"] = round(bsums["total1"],2)
bsums.to_csv(svpath + "/summary_cow_" + str(cow) + ".txt")
uwbfunctions.py
+ 29
− 21
View file @ 30807ab4
......@@ -193,29 +193,37 @@ def behaviour_bouts(data, interval, behaviour,min_length):
# only keep bouts > min_length
df = df.loc[df["len1"]*60 > min_length,:].reset_index(drop=1)
# calculate gap between successive bouts
df["gap"] = np.nan
df.iloc[1:,df.columns == "gap"] = np.round((df["t"][1:].values - df["end_time"][0:-1].values)/60)
if len(df) > 0:
# calculate gap between successive bouts
df["gap"] = np.nan
df.iloc[1:,df.columns == "gap"] = np.round((df["t"][1:].values - df["end_time"][0:-1].values)/60)
# add behaviour and select data
df["behaviour"] = behaviour
df["btime"] = pd.to_datetime(df["date"]) + pd.to_timedelta(df["t"],unit = 's')
df["etime"] = pd.to_datetime(df["date"]) + pd.to_timedelta(df["end_time"],unit = 's')
df = df[["cowid","barn","behaviour","date","t","end_time","btime","etime",
"gap","len1","len2"]]
df = df.rename(columns = {"end_time" : "end","t" : "start"})
# add behaviour and select data
df["behaviour"] = behaviour
df["btime"] = pd.to_datetime(df["date"]) + pd.to_timedelta(df["t"],unit = 's')
df["etime"] = pd.to_datetime(df["date"]) + pd.to_timedelta(df["end_time"],unit = 's')
df = df[["cowid","barn","behaviour","date","t","end_time","btime","etime",
"gap","len1","len2"]]
df = df.rename(columns = {"end_time" : "end","t" : "start"})
# calculate summary
summary = df[["cowid","barn","date","behaviour","len1","len2"]].groupby(by = ["cowid","barn","date","behaviour"]).mean().round(1).reset_index()
summary = summary.rename(columns = {"len1" : "len1_av", "len2" : "len2_av"})
summary["no_bouts"] = len(df)
summary["gap_med"] = df["gap"].median()
summary["total1"] = df["len1"].sum()
summary["total2"] = df["len2"].sum()
summary["nmeas"] = len(data.loc[data["area"].str.contains(behaviour)])
summary["perc"] = round(len(data.loc[data["area"].str.contains(behaviour)]) / \
len(data) * 100,2)
#print(summary)
# calculate summary
summary = df[["cowid","barn","date","behaviour","len1","len2"]].groupby(by = ["cowid","barn","date","behaviour"]).mean().round(1).reset_index()
summary = summary.rename(columns = {"len1" : "len1_av", "len2" : "len2_av"})
summary["no_bouts"] = len(df)
summary["gap_med"] = df["gap"].median()
summary["total1"] = df["len1"].sum()
summary["total2"] = df["len2"].sum()
summary["nmeas"] = len(data.loc[data["area"].str.contains(behaviour)])
summary["perc"] = round(len(data.loc[data["area"].str.contains(behaviour)]) / \
len(data) * 100,2)
#print(summary)
else:
df = pd.DataFrame([],columns = ["cowid","barn","behaviour","date","start",
"end","btime","etime",
"gap","len1","len2"])
summary = pd.DataFrame([],columns = ["cowid","barn","date","behaviour",
"len1_av","len2_av","no_bouts",
"gap_med","total1","total2"])
else:
df = pd.DataFrame([],columns = ["cowid","barn","behaviour","date","start",
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment