Summary: I've written some Python code that simplifies the loading of data from a csv file into SciDB. The programmer specifies for each column in the csv file whether it should be an attribute or a dimension in the SciDB array, and then the code loads it as a raw array, creates the the destination array based on the provided specifications and the data loaded into raw, and then transfers the data from the raw array to the destination array.
I've added the code to this GitHub repository under the directory ScidbLoader:
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I've written some code in python that uses the SciDB python connector to access data in a more straightforward manner. In summary, you submit a query and get back an iterator over the data. It is currently very incomplete, and needs:
Currently it just returns the attributes. It needs to also return the values of the dimensions. Done!
Ability to reset the iterator - it can currently only be used once
Summary: From statistical mechanics, the size of a polymer is generally estimated using the statistics of a random walk. Here I investigate the assumption that the size of the polymer is proportional to the distance between the start and end points of a random walk as it is generally taught in statistical mechanics.
Review of random walk in 1 dimension
Start at the origin of the x-axis (x = 0). At each step, there is a 50% chance of moving 1 unit to the right, 50% chance of moving 1 unit to the left.
Here are some examples of random walks:
For N steps, the probability of having ended up at position x is given by the binomial distribution:
(from the above page at Wolfram). The full width at half max of the above distribution is:
sqrt(# of steps)