diff --git a/IDCJAC0010_040211_2022_Data.csv b/IDCJAC0010_040211_2022_Data.csv
index 287d86d..8f20735 100644
--- a/IDCJAC0010_040211_2022_Data.csv
+++ b/IDCJAC0010_040211_2022_Data.csv
@@ -45,3 +45,8 @@ IDCJAC0010,040211,2022,02,12,28.5,1,N
 IDCJAC0010,040211,2022,02,13,28.9,1,N
 IDCJAC0010,040211,2022,02,14,27.5,1,N
 IDCJAC0010,040211,2022,02,15,27.6,1,N
+IDCJAC0010,040211,2022,02,16,28.7,1,N
+IDCJAC0010,040211,2022,02,17,30.4,1,N
+IDCJAC0010,040211,2022,02,18,33.1,1,N
+IDCJAC0010,040211,2022,02,19,31.9,1,N
+IDCJAC0010,040211,2022,02,20,28.8,1,N
diff --git a/IDCJAC0010_040211_2022_Note.txt b/IDCJAC0010_040211_2022_Note.txt
index 4bde471..be34713 100644
--- a/IDCJAC0010_040211_2022_Note.txt
+++ b/IDCJAC0010_040211_2022_Note.txt
@@ -2,7 +2,7 @@ Australian Government
 Bureau of Meteorology
 
 Notes for Daily Maximum Temperature Climate Data
-Product code: IDCJAC0010 reference: 82976846
+Product code: IDCJAC0010 reference: 83114057
 ____________________________________________________________________
 
 ** Station Details **
@@ -102,7 +102,7 @@ http://www.bom.gov.au/climate/how/contacts.shtml
 
 _________________________________________
 
-Created on Wed 16 Feb 2022 17:23:58 PM AEDT from Climate Data Online, Bureau of Meteorology.
+Created on Mon 21 Feb 2022 20:01:37 PM AEDT from Climate Data Online, Bureau of Meteorology.
 http://www.bom.gov.au/climate/data
 
 Further information on this product: http://www.bom.gov.au/climate/cdo/about/about-airtemp-data.shtml
diff --git a/data.csv b/data.csv
index 5fceb30..eb21d94 100644
--- a/data.csv
+++ b/data.csv
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+2022-02-21-02-55-03,4,35.900001525878906,41.70000076293945
+2022-02-21-03-25-03,17,30.299999237060547,56.20000076293945
+2022-02-21-03-25-04,4,37.099998474121094,39.29999923706055
+2022-02-21-03-55-05,17,30.600000381469727,56.20000076293945
+2022-02-21-03-55-05,4,38.79999923706055,38.0
+2022-02-21-04-25-06,17,31.299999237060547,54.5
+2022-02-21-04-25-07,4,37.79999923706055,37.099998474121094
+2022-02-21-04-55-07,17,31.5,54.599998474121094
+2022-02-21-04-55-08,4,38.5,38.400001525878906
+2022-02-21-05-25-09,17,31.799999237060547,54.599998474121094
+2022-02-21-05-25-09,4,35.599998474121094,42.5
+2022-02-21-05-55-10,17,31.200000762939453,54.599998474121094
+2022-02-21-05-55-11,4,37.70000076293945,39.599998474121094
+2022-02-21-06-25-11,17,30.700000762939453,55.400001525878906
+2022-02-21-06-25-12,4,37.599998474121094,39.099998474121094
+2022-02-21-06-55-12,17,30.5,54.400001525878906
+2022-02-21-06-55-13,4,36.29999923706055,39.29999923706055
+2022-02-21-07-25-14,17,30.399999618530273,53.79999923706055
+2022-02-21-07-25-14,4,34.900001525878906,41.20000076293945
+2022-02-21-07-55-15,17,30.0,53.29999923706055
+2022-02-21-07-55-16,4,33.099998474121094,43.70000076293945
+2022-02-21-08-25-16,17,29.5,56.099998474121094
+2022-02-21-08-25-17,4,31.700000762939453,47.599998474121094
+2022-02-21-08-55-18,17,29.600000381469727,56.79999923706055
+2022-02-21-08-55-18,4,30.299999237060547,51.5
diff --git a/plot_it.py b/plot_it.py
index 79ca3be..96ef5c5 100644
--- a/plot_it.py
+++ b/plot_it.py
@@ -14,12 +14,50 @@
 
 
 pins = {4:"Roof", 17:"Interior"}
+ind4 = my_data["pin"] == 4
+ind17 = my_data["pin"] == 17
+
+my_data.date = pd.to_datetime(my_data['date'], format='%Y-%m-%d-%H-%M-%S', utc=True)
+my_data.date = my_data.date.dt.tz_convert("Australia/Brisbane")
+
+# Find the max between 9am and 9am
+# Day after
+day0 = pd.to_datetime("2021-12-31-09-00-00", format='%Y-%m-%d-%H-%M-%S', utc=False)
+day0 = day0.tz_localize("Australia/Brisbane")
+
+day1 = pd.to_datetime("2022-01-01-09-00-00", format='%Y-%m-%d-%H-%M-%S', utc=False)
+day1 = day1.tz_localize("Australia/Brisbane")
+
+hrs24 = pd.DateOffset(days=1)
+
+data_max = {"date":[], "temperature":[]}
+while day0 < my_data.date.iloc[-1]:
+  pin = 17
+  temp = my_data[ind17]
 
-my_data.date = pd.to_datetime(my_data['date'], format='%Y-%m-%d-%H-%M-%S')
+  # All temperatures in the date range
+  ind = np.logical_and(day0 <= temp.date, temp.date < day1)
+  temp = temp[ind]
+
+  # The time of the max
+  ind = temp.temperature.idxmax()
+  data_max["temperature"].append(temp.temperature[ind])
+  data_max["date"].append(temp.date[ind])
+
+  day0 = day0 + hrs24
+  day1 = day1 + hrs24
+
+data_max["date_daily_max"] = [pd.to_datetime("{}-{}-{}-{}".format(i.year, i.month, i.day, 9), format='%Y-%m-%d-%H', utc=False) for i in data_max["date"]]  # 9am of the day after
+data_max["date_daily_max"] = [i.tz_localize("Australia/Brisbane") for i in data_max["date_daily_max"]]
+
+
+# Create the bom date
 df = pd.DataFrame({'year': bom_data.Year,
                    'month': bom_data.Month,
                    'day': bom_data.Day})
-bom_data.date = pd.to_datetime(df)
+hrs9 = pd.DateOffset(hours=9)
+bom_data.date = pd.to_datetime(df) + hrs9
+bom_data.date = bom_data.date.dt.tz_localize("Australia/Brisbane")
 
 
 plt.figure('Tempurature')
@@ -30,6 +68,10 @@
 
 plt.plot(bom_data.date, bom_data["Maximum temperature (Degree C)"], label='Archerfield Daily Max (BOM)')
 
+plt.plot(data_max["date"], data_max["temperature"], '.', label='Max Interior')
+plt.plot(data_max["date_daily_max"], data_max["temperature"], '.', label='Max Interior 9am')
+
+
 # Set the ticks
 locs, labels = plt.yticks()
 plt.yticks(np.arange(locs[0], locs[-1], 1))
@@ -46,20 +88,48 @@
 
 plt.figure('Tempurature Difference')
 
-ind4 = my_data["pin"] == 4
-ind17 = my_data["pin"] == 17
 plt.plot(my_data.date[ind4], my_data.temperature[ind4].to_numpy()-my_data.temperature[ind17].to_numpy(),  label="Roof-Interior")
 
 # Create a interpolant for the bom data (nearest cause it is sparse)
 import scipy
 import scipy.interpolate
 
+#https://stackoverflow.com/questions/14313510/how-to-calculate-rolling-moving-average-using-python-numpy-scipy/54628145#54628145
+def moving_average(x, w):
+    return np.convolve(x, np.ones(w), 'valid') / w
+
 bom_interpolant = scipy.interpolate.interp1d(bom_data.date.astype(int).to_numpy(), bom_data["Maximum temperature (Degree C)"].to_numpy(), kind='nearest', bounds_error=False)
 bom_aligned = bom_interpolant(my_data.date[ind4].astype(int).to_numpy())
-plt.plot(my_data.date[ind4], my_data.temperature[ind17].to_numpy()-bom_aligned,  label="Interior-BomMax")
+diff = my_data.temperature[ind17].to_numpy()-bom_aligned
+plt.plot(my_data.date[ind4], diff,  label="Interior-BomMax")
+
+bom_aligned = bom_interpolant(my_data.date[ind4].astype(int).to_numpy())
+diff = my_data.temperature[ind4].to_numpy()-bom_aligned
+plt.plot(my_data.date[ind4], diff,  label="Roof-BomMax")
+
+r = 4*24
+mavg = moving_average(diff, r)
+plt.plot(my_data.date[ind4][r-1:], mavg,  label="moving average Interior-BomMax")
 
 # plt.plot(bom_data.date, bom_data["Maximum temperature (Degree C)"], label='Archerfield Daily Max (BOM)')
 
+# compare wit bom
+dd = []
+bom_date = []
+for i in range(len(bom_data.date)):
+  try:
+    ind = data_max["date_daily_max"].index(bom_data.date.iloc[i])
+  except:
+    continue
+  dd.append(data_max["temperature"][ind] - bom_data["Maximum temperature (Degree C)"].iloc[i])
+  bom_date.append(bom_data.date.iloc[i])
+
+plt.plot(bom_date, dd, label="InteriorMax-BomMax")
+
+
+# Need the max in a given day
+# Convert to local time?
+
 # Set the ticks
 locs, labels = plt.yticks()
 plt.yticks(np.arange(locs[0], locs[-1], 1))
@@ -75,7 +145,6 @@
 plt.grid()
 
 
-
 # import scipy
 # import scipy.fft
 # from scipy.fft import fft, fftfreq