source files

Author: dham

LICENSE

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+# Open data file
+infile = open("../data/data1.txt", "r")  # ".." means "go up one directory"
+
+# Initialise values
+mean = 0
+n = 0
+
+# Loop to perform sum
+for number in infile:
+    number = float(number)
+    mean = mean + number
+    n += 1
+
+# It is good practice to close a file when you are finished.
+infile.close()
+
+# Calculate the mean.
+mean = mean/n
+print mean

src/mean.py

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+import pylab
+import numpy as np
+
+tide_file = open("../data/2012AVO.txt", "r")
+
+# We know from inspecting the file that the first 11 lines are just
+# header information so lets just skip those lines.
+for i in range(11):
+    line = tide_file.readline()
+
+# Initialise an empty list to store the elevation
+elevation = []
+days = []
+
+# Now we start reading the interesting data
+n = 0
+while True:  # This will keep looping until we break out.
+    # Here we use a try/except block to try to read the data as normal
+    # and to break out if unsuccessful - ie when we reach the end of the file.
+    try:
+        # Read the next line
+        line = tide_file.readline()
+
+        # Split this line into words.
+        words = line.split()
+
+        # If we do not have 5 words then it must be blank lines at the end of the file.
+        if len(words) != 5:
+            break
+    except:
+        # If we failed to read a line then we must have got to the end.
+        break
+
+    n += 1  # Count number of data points
+
+    try:
+        # The elevation data is on the 4th column. However, the BODC
+        # appends a "M" when a value is improbable and an "N" when
+        # data is missing (maybe a ship dumped into it during rough weather!)
+        # Therefore, we put this conversion from a string into a float in a
+        # try/except block.
+        level = float(words[3])
+        elevation.append(level)
+
+        # There is a measurement every quarter hour.
+        days.append(n*0.25/24)
+    except:
+        continue
+
+# For plotting lets convert the list to a NumPy array.
+elevation = np.array(elevation)
+days = np.array(days)
+
+pylab.plot(days, elevation)
+pylab.xlabel("Days")
+pylab.ylabel("Elevation (meters)")
+pylab.show()