start summary behaviour over day

exploration.py

@@ -11,11 +11,13 @@ os.chdir(r"C:\Users\adria036\OneDrive - Wageningen University & Research\iAdriae
 #%% import modules
 
 
-from datetime import date, timedelta,datetime
+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 heatmap_visualisation import heatmap_barn
 #%matplotlib qt
 
 #%% set paths and constants and load data
@@ -165,12 +167,12 @@ for cow in cows:
     plt.close()
 
 
-# summarize variables
-data[["xnew","X","ynew","y"]].describe()
-data[""]
-test = data.loc[(data["ynew"] < -17) & (data["ynew"] > -19) & (data["cowid"] = cow),:]
-sns.scatterplot(data=test, x = test.index.values, y = "ynew")
-sns.scatterplot(data=test, x = test.index.values, y = "y")
+# # summarize variables
+# data[["xnew","X","ynew","y"]].describe()
+# data[""]
+# test = data.loc[(data["ynew"] < -17) & (data["ynew"] > -19) & (data["cowid"] = cow),:]
+# sns.scatterplot(data=test, x = test.index.values, y = "ynew")
+# sns.scatterplot(data=test, x = test.index.values, y = "y")
 
 
 
@@ -183,13 +185,11 @@ sns.scatterplot(data=test, x = test.index.values, y = "y")
      - explore gaps and gapsize
 """
   
-#gapsize
-
+#-------------------------------gapsize----------------------------------------
 subset = data.loc[data["X"].isna(),["cowid","barn","date","t"]]
 sumdata = subset.groupby(by = ["cowid","barn","date"]).count()
 subset = data.loc[data["xnew"].isna(),["cowid","barn","date","t"]]
 test = subset.groupby(by = ["cowid","barn","date"]).count()
-
 sumdata["xnew"] = test["t"]
 
 sumdata["gapperc"] = sumdata["t"]/864
@@ -209,10 +209,173 @@ ax = sns.scatterplot(data=sumdata,x = "xlabel", y = "gap180s",
                      palette = "tab10", hue = "cowid", 
                      style = "date", legend = False)
 plt.savefig(svpath + "\\gap_imputed.png")
-# average gap
+
+
+#------------------------------behaviour over 24h------------------------------
+#sns.set_style("darkgrid") # whitegrid, dark, white, ticks
+
+cows = data["cowid"].drop_duplicates().reset_index(drop=1)
+for cow in cows:
+    print(cow)
+    days = data.loc[(data["cowid"]==cow),["date"]].drop_duplicates() 
+    days = days.sort_values(by = ["date"]).reset_index(drop=1)
+    
+    for dd in days["date"]:
+        print(dd)
+        subset = data.loc[(data["cowid"]==cow) & \
+                          (data["date"]==dd),:].copy()
+        palette = {"walking" : "darkorange",
+                   "concentrate " : "tan",
+                   "cubicle_A" : "darkorchid",
+                   "cubicle_B" : "violet",
+                   "cubicle_C" : "palevioletred",
+                   "feed_rack" : "crimson",
+                   "drink_trough" : "gold",
+                   "slatted" : "limegreen",
+                   "resting" : "darkorchid",
+                   "waiting_area" : "mediumseagreen",
+                   "unknown" : "royalblue"}
+        fig,ax = plt.subplots(1,1,figsize = (18,7))
+        sns.scatterplot(data=subset,x="t",y="zone",
+                        hue="area",
+                        marker = "s", linewidth = 0,
+                        palette = palette,
+                        legend = False)
+        sns.lineplot(data=subset, x='t',y = 'zone', 
+                     linewidth = 0.2, color = 'k')
+        ax.set_yticks(range(9))
+        ax.set_yticklabels(["walking","cubicles","feed rack","drink trough",
+                            "concentrate feeder","slatted (b62)","resting (b62)",
+                            "waiting area","unknown"])
+        sns.set_style("ticks")
+        ax.set_xticks([0,14400,28800,43200,57600,72000,86400])
+        ax.set_xticklabels(['00:00','04:00','08:00','12:00','16:00','20:00','24:00'])
+        ax.set_xlabel("time [h]")
+        ax.set_title("cow = " + str(cow) + ', date = ' + str(dd))
+        ax.set_xlim([-1000,87400])
+        plt.savefig(svpath+"\\timebudget_cow_" + str(cow) + "_" + str(dd).replace("-","") + ".png")
+        plt.close()
+        
+        
+# ------------------------- individual cow heatmaps ---------------------------
+cows = data["cowid"].drop_duplicates().reset_index(drop=1)
 
 
 
+#barn limits
+barn = {"60":[64.8,75.6],
+        "61":[54,64.8],
+        "62":[32.4,54],
+        "70":[21.6,32.4],
+        "71":[10.8,21.6],
+        "72":[0,10.8],
+        "73":[-10.8,0],
+        }
 
+# plots per cow-day
+for cow in cows["cowid"]:
     
+    # unique days
+    days = data.loc[(data["cowid"]==cow),["date"]].drop_duplicates() 
+    days = days.sort_values(by = ["date"]).reset_index(drop=1)
+    
+    # select data and create heatmaps
+    for dd in days["date"]:
+        print(cow, dd)
+        # select data
+        data_x = data.loc[(data["cowid"] == cow) &
+                          (data["date"] == dd),"X"]
+        data_y = data.loc[(data["cowid"] == cow) &
+                          (data["date"] == dd),"y"]
+        
+        # set limits (based on information barn)
+        barnnr = int(data.loc[(data["cowid"] == cow) &
+                          (data["date"] == dd),"barn"].mean())
+        x_lim = barn[str(barnnr)]
+        y_lim = [-18,0]
+        
+        # plot heatmap
+        # matplotlib.rcdefaults() 
+        fig, ax = plt.subplots() 
+        ax = heatmap_barn(data_x, data_y, 0.1, 0.1, x_lim, y_lim)
+        ax.set_xlabel('y coordinate')
+        ax.set_ylabel('x coordinate')
+        ax.set_title("barn = " +str(barnnr) + ', cow = ' + str(cow) + \
+                     ", date = " + datetime.strftime(dd,"%Y-%m-%d").replace("-",""))
+        ax.xaxis.tick_top()
+        plt.savefig(svpath + "//heatmap_" + str(cow) + "_" + str(dd).replace("-",""))
+        plt.close()       
+        
+
+#------------------combine heatmap with budget-area over time------------------
+#barn limits
+barn = {"60":[64.8,75.6],
+        "61":[54,64.8],
+        "62":[32.4,54],
+        "70":[21.6,32.4],
+        "71":[10.8,21.6],
+        "72":[0,10.8],
+        "73":[-10.8,0],
+        }
+palette = {"walking" : "darkorange",
+           "concentrate " : "tan",
+           "cubicle_A" : "darkorchid",
+           "cubicle_B" : "violet",
+           "cubicle_C" : "palevioletred",
+           "feed_rack" : "crimson",
+           "drink_trough" : "gold",
+           "slatted" : "limegreen",
+           "resting" : "darkorchid",
+           "waiting_area" : "mediumseagreen",
+           "unknown" : "royalblue"}
 
+cows = data["cowid"].drop_duplicates().reset_index(drop=1)
+for cow in cows:
+    print(cow)
+    days = data.loc[(data["cowid"]==cow),["date"]].drop_duplicates() 
+    days = days.sort_values(by = ["date"]).reset_index(drop=1)
+    
+    for dd in days["date"]:
+        print(dd)
+        subset = data.loc[(data["cowid"]==cow) & \
+                          (data["date"]==dd),:].copy()
+        
+        fig, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1.5]}, figsize = (22,7))
+        # plot scatter + line
+        sns.scatterplot(data=subset,x="t",y="zone",
+                        hue="area",
+                        marker = "s", linewidth = 0,
+                        palette = palette,
+                        legend = False,
+                        ax = ax[0])
+        sns.lineplot(data=subset, x='t',y = 'zone', 
+                     linewidth = 0.2, color = 'k', ax = ax[0])
+        ax[0].set_yticks(range(9))
+        ax[0].set_yticklabels(["walking","cubicles","feed rack","drink trough",
+                               "concentrate feeder","slatted (b62)","resting (b62)",
+                               "waiting area","unknown"])
+        sns.set_style("ticks")
+        ax[0].set_xticks([0,14400,28800,43200,57600,72000,86400])
+        ax[0].set_xticklabels(['00:00','04:00','08:00','12:00','16:00','20:00','24:00'])
+        ax[0].set_xlabel("time [h]")
+        ax[0].set_title("cow = " + str(cow) + ', date = ' + str(dd))
+        ax[0].set_xlim([-1000,87400])
+        
+        # set limits (based on information barn)
+        barnnr = int(data.loc[(data["cowid"] == cow) &
+                          (data["date"] == dd),"barn"].mean())
+        x_lim = barn[str(barnnr)]
+        y_lim = [-18,0]
+        
+        # plot heatmap
+        ax[1] = heatmap_barn(subset["xnew"], subset["ynew"], 0.1, 0.1, x_lim, y_lim)
+        ax[1].set_xlabel('y coordinate')
+        ax[1].set_ylabel('x coordinate')
+        ax[1].set_title("barn = " +str(barnnr) + ', cow = ' + str(cow) + \
+                     ", date = " + str(dd))
+        ax[1].xaxis.tick_top()
+        ax[1].yaxis.tick_right()
+        ax[1].yaxis.set_label_position("right")
+        
+        plt.savefig(svpath+"\\spatial_cow_" + str(cow) + "_" + str(dd).replace("-","") + ".png")
+        plt.close()
\ No newline at end of file

heatmap_visualisation.py

@@ -12,7 +12,8 @@ import numpy as np
 # from read_data_sewio import start_end_dates, read_all_data, read_ids, combine_data_id
 import os
 import seaborn as sns
-import matplotlib as plt
+import matplotlib
+import matplotlib.pyplot as plt
 
 
 # path_out 
@@ -113,11 +114,15 @@ def heatmap_barn(data_x,data_y,xstep,ystep,x_lim,y_lim):
     ax = sns.heatmap(df,
                      yticklabels = round(1/xstep),
                      xticklabels = round(1/ystep*2),
-                     robust = True
+                     robust = True,
+                     cbar = False
                      )
 
     return ax
 
+
+
+"""
 #%% usage
 # ------------------------- individual cow heatmaps ---------------------------
 # unique cows
@@ -163,14 +168,14 @@ for cow in cows["cowid"]:
         y_lim = [-18,0] #[data_y.min(),data_y.max()]
         
         # plot heatmap
-        plt.rcdefaults() 
-        fig, ax = plt.pyplot.subplots() 
+        matplotlib.rcdefaults() 
+        fig, ax = plt.subplots() 
         ax = heatmap_barn(data_x, data_y, xstep, ystep, x_lim, y_lim)
         ax.set_xlabel('y coordinate')
         ax.set_ylabel('x coordinate')
         ax.set_title("barn = " +str(barnnr) + ', cow = ' + str(cow) + ", date = " + datetime.strftime(dd,"%Y-%m-%d").replace("-",""))
         ax.xaxis.tick_top()
-        plt.pyplot.savefig(path_out + "//heatmap_" + str(cow) + "_" + datetime.strftime(dd,"%Y-%m-%d").replace("-",""))
-        plt.pyplot.close()
-
+        plt.savefig(path_out + "//heatmap_" + str(cow) + "_" + datetime.strftime(dd,"%Y-%m-%d").replace("-",""))
+        plt.close()
+"""