public Box(DataScan data)
{
products = new List <Product>();
Scanner = data.Scanner;
Barcode = data.Barcode;
BoxCreateTime = data.CreateDateTime;
}
internal async Task <DataScan> ProcessNumericData(Dictionary <string, string> data)
{
DataScan result = new DataScan()
{
values = new Dictionary <string, float>(),
errorcount = 0,
nullcount = 0
};
await Task.Run(() => {
foreach (var item in data)
{
System.Text.RegularExpressions.Match match = Utils.RegularExpressions.Numbers.Match(item.Value);
if (match.Success)
{
if (float.Parse(match.Value) == 0)
{
result.nullcount++; continue;
}
result.values.Add(item.Key, float.Parse(match.Groups[0].Value));
}
else
{
Core.ErrorLog.Log("Error during Rexex Match of Scan() data for user " + item.Key, true);
result.errorcount++;
}
}
});
return(result);
}
//Mode: -Test -Work
//Action: -Box -Product
public static void Analize(DataScan data)
{
if (data.ScannerAction == (int)ScannerAction.BoxScan)
{
//newBox or delete Box
}
if (data.ScannerAction == (int)ScannerAction.ProductScan)
{
//addProduct or getProduct
}
}
public DataScannedEvent(DateTime timestamp, DataScan datalinktype) : base(timestamp, NAME)
{
this.datalinktype = datalinktype.name;
}
public void TestDataScanFromEDName()
{
DataScan dataScan = DataScan.FromEDName("$Datascan_DataPoint;");
Assert.AreEqual("Data Point", dataScan.invariantName);
}
////Events
public void ScanEvent(DataScan dataScan)
{
Analyzer.Analize(dataScan);
}
public async override void PluginMethod(Message m)
{
bool modeAnonymous = m.Args.Contains("-a") ? true : false; //Are max and min named? Requires modeStatistic
bool modeStatistic = m.Args.Contains("-s") ? true : false;
bool modeGreater = m.Args.Contains("-gt") ? true : false;
bool modeLesser = m.Args.Contains("-lt") ? true : false;
m.Args = m.Args.Where(n => n != "-a").ToArray();
m.Args = m.Args.Where(n => n != "-s").ToArray();
float cutoffValue = 0f;
if (modeGreater && modeLesser)
{
m.Reply("Cannot scan for both GreaterThan and LesserThan. Decide."); return;
}
if (modeAnonymous && !modeStatistic)
{
m.Reply("Deactivating mode anonymous is only supported with mode statistic."); return;
}
if ((modeLesser || modeGreater) && !modeStatistic)
{
modeStatistic = true;
}
if (modeLesser || modeGreater)
{
int cutoffPos = Array.IndexOf(m.Args, modeLesser ? "-lt" : "-gt");
if (cutoffPos >= 0)
{
cutoffValue = float.Parse(m.Args[cutoffPos + 1]);
m.Args = m.Args.Take(Math.Max(cutoffPos - 1, 0)).Concat(m.Args.Skip(cutoffPos + 1)).ToArray(); //Danger Will Robinson! why not just... idk, look for recognised args and make 'em a list or something? Switch statement.
}
}
List <User> dataPool = m.sourceChannel.Users.ToList();
Dictionary <string, string> data = new Dictionary <string, string>(dataPool.Count);
string results;
Console.WriteLine("Preparing to execute Task Pool...");
Task DataScan = Task.WhenAll(dataPool.Select(n => n.GetBasicProfileInfo()));
await DataScan;
Console.WriteLine("Awaited Task Pool.");
data = dataPool.ToDictionary(x => x.Name, x => x.TryGetData(m.Body));
if (modeStatistic) //transform all to numeric
{
DataScan numData = await ProcessNumericData(data);
if (numData.values.Count == 0)
{
m.Reply("Could not find/parse any numeric data for input '" + m.Body + "'."); return;
}
//TODO Instead of simpled regex, then float.parse, do utils.math.measurementFromNumber if you ever code it. (As if).
if (modeLesser || modeGreater)
{
int fullCount = numData.values.Count;
int lesserCount = numData.values.Values.Where(x => x <= cutoffValue).Count();
results = string.Format(CultureInfo.InvariantCulture, "Comparative analysis complete. {0:0} of {1:0} users successfully parsed for '{2}'. {3} users had no value set, {4} failed parsing.\n{5:0} users have values {6} specified threshold of {7:0.0}.", fullCount, dataPool.Count, m.Body, numData.nullcount, numData.errorcount, modeLesser ? lesserCount : fullCount - lesserCount, modeLesser ? "below" : "above", cutoffValue);
}
else
{
float mean = numData.values.Values.Average();
//double? median = Utils.Math.Median(numData.Keys);
float _min = numData.values.Values.Min();
string min = modeAnonymous ? _min.ToString("0.0", CultureInfo.InvariantCulture) + " [" + string.Join(", ", numData.values.Where(n => n.Value == _min).Select(n => n.Key)).TrimEnd(new char[] { ' ', ',' }) + "]" : _min.ToString("0.0", CultureInfo.InvariantCulture);
float _max = numData.values.Values.Max();
string max = modeAnonymous ? _max.ToString("0.0", CultureInfo.InvariantCulture) + " [" + string.Join(", ", numData.values.Where(n => n.Value == _max).Select(n => n.Key)).TrimEnd(new char[] { ' ', ',' }) + "]" : _max.ToString("0.0", CultureInfo.InvariantCulture);
double stDev = Utils.Math.StDev(numData.values.Values);
results = string.Format(CultureInfo.InvariantCulture, "Statistical analysis complete. {0:0} of {1:0} users successfully parsed for '{2}'. {3} users had no value set, {4} failed parsing.\nMin: {5:0.0}. Max: {6:0.0}. Average: {7:0.0}. Standard deviation: {8:0.0}", numData.values.Count, dataPool.Count, m.Body, numData.nullcount, numData.errorcount, min, max, mean, stDev);
}
}
else //Histogram-type analysis, ~10 most frequent
{
int nullCount = data.Values.Where(x => x != "Not set").Count();
Utils.Collections.Counter <string> DataTypes = new Utils.Collections.Counter <string>(data.Values.Where(x => x != "Not set"));
results = string.Format(CultureInfo.InvariantCulture, "Quantitative analysis of {0} users for '{1}' complete. {2} users had no value set. {3} unique categories found, displaying a maximum of 10:\n{4}", dataPool.Count, m.Body, nullCount, DataTypes.Count, DataTypes.ToString(10));
}
//foreach (User u in dataPool) { u.Dispose(); }
m.Reply(results, IO.ReplyMode.AsOriginal);
}
