| May 21 21:37:06 nstack-server[30]: Could not start process: Cannot resolve imported function: nstack/AWS.S3:0.0.4.upload_text | |
| May 22 09:24:23 nstack-server[30]: API REQUEST: InfoCommand (e3f0e9dd-1a6d-4ef5-824f-72f8de1d6b94): | |
| False | |
| May 22 09:24:23 nstack-server[30]: [TRACE] found locally: local/nstack/FavorLTV:0.0.1 | |
| May 22 09:24:23 nstack-server[30]: API RESPONSE: InfoCommand (e3f0e9dd-1a6d-4ef5-824f-72f8de1d6b94): | |
| ServerInfo { _siProcesses = [], _siStopped = [], | |
| _siMethods = QMap {inner = fromList [(nstack/FavorLTV:0.0.1, | |
| [(add_buckets, | |
| MethodInfo = TypeSignature = nstack/CustomerLTV:0.0.2.OutStream -> nstack/FavorLTV:0.0.1.OutStream | |
So the scoring module is in review (nstack-apps#68).
What remains to be done is figuring out how to plug Thermo Fisher's data into it, and what further information we need from them in order to do so.
To wit:
The data has five columns, given on a monthly cadence:
| Johnny Coder | |
| ============ | |
| ------------------- ---------------------------- | |
| 1 MyAddress [email protected] | |
| MyTown 1000 @twitter_handle | |
| MyCountry 1800 my-phone-nr | |
| ------------------- ---------------------------- | |
| Education |
| from pylab import * | |
| def generate_lines(): | |
| f = open("test.dat", 'r') | |
| EOF = False | |
| [x, y, z] = [float(n) for n in f.next().split()] # read in the first row | |
| while not EOF: | |
| stab = (z < 0.0) | |
| xs = [x] | |
| ys = [y] |