public async Task <Dictionary <string, PlotlyData> > Read(SwitchReader reader, int sampleRate = 1, bool disableNormalization = false,
DateTime?minimumDate = null, double?minimumFitness = null)
{
minimumDate = minimumDate ?? DateTime.MinValue;
var data = new Dictionary <string, PlotlyData>();
var rand = new XorRandom();
string line;
while ((line = await reader.ReadLineAsync()) != null)
{
if (rand.Next(1, sampleRate) != 1)
{
continue;
}
try
{
var split = line.Split(',').Select(s => s.Trim()).ToArray();
var time = DateTime.Parse(split[0].Substring(0, 24));
if (time < minimumDate)
{
continue;
}
foreach (var item in split.Skip(1))
{
if (item.Contains(": ") && !item.StartsWith("Start") && !item.StartsWith("End"))
{
var pair = item.Split(new[] { ": " }, StringSplitOptions.RemoveEmptyEntries);
if (!double.TryParse(pair[1], out var parsed))
{
continue;
}
if (!data.ContainsKey(pair[0]))
{
data.Add(pair[0], new PlotlyData {
Name = pair[0]
});
}
data[pair[0]].X.Add(time.ToString("yyyy-MM-dd hh:mm:ss.ffff"));
data[pair[0]].Y.Add(double.Parse(pair[1]));
data[pair[0]].Text.Add(pair[1]);
}
}
//System.Diagnostics.Debug.WriteLine("Processing...");
}
catch (Exception)
{
Console.WriteLine("Failed to parse:" + line);
}
}
if (minimumFitness.HasValue)
{
var fitness = data.Last().Value;
var removing = new List <int>();
for (int i = 0; i < fitness.Y.Count(); i++)
{
if (fitness.Y[i] < minimumFitness)
{
removing.Add(i);
}
}
foreach (var index in removing.OrderByDescending(o => o))
{
foreach (var item in data.Where(d => d.Value.Y.Any()))
{
item.Value.Y.RemoveAt(index);
item.Value.Text.RemoveAt(index);
}
}
}
if (!disableNormalization)
{
var fitness = data.Last().Value;
var nonEmpty = data.Take(data.Count() - 1).Where(d => d.Value.Y.Any());
//on second pass reuse min/max
if (_max == null || _min == null)
{
_max = fitness.Y.Max();
_min = fitness.Y.Min();
}
var normalizer = new SharpLearning.FeatureTransformations.Normalization.LinearNormalizer();
foreach (var list in nonEmpty)
{
var oldMax = list.Value.Y.Max();
var oldMin = list.Value.Y.Min();
for (int i = 0; i < list.Value.Y.Count(); i++)
{
list.Value.Y[i] = normalizer.Normalize(_min.Value, _max.Value, oldMin, oldMax, list.Value.Y[i]);
}
//System.Diagnostics.Debug.WriteLine("Added to set:" + list.Key);
}
data = nonEmpty.Concat(new[] { data.Last() }).ToDictionary(k => k.Key, v => v.Value);
}
return(data);
}
public async Task <Dictionary <string, PlotlyData> > Read(SwitchReader reader, int sampleRate = 1, bool disableNormalization = false,
DateTime?minimumDate = null, double?minimumFitness = null, IActivityLogger activityLogger = null)
{
minimumDate = minimumDate ?? DateTime.MinValue;
_activityLogger = activityLogger;
var data = new Dictionary <string, PlotlyData>();
var rand = new XorRandom();
string line;
int counter = 0;
//todo: chart bind progress
//_activityLogger?.Add($"Processing: ", counter);
//_activityLogger?.ShowMessageAlways();
while ((line = await reader.ReadLineAsync()) != null)
{
if (sampleRate > 1 && rand.Next(1, sampleRate) != 1)
{
continue;
}
try
{
var split = line.Split(',').Select(s => s.Trim()).ToArray();
var time = DateTime.Parse(split[0].Substring(0, 24));
if (time < minimumDate)
{
continue;
}
for (int i = 1; i < split.Length; i++)
{
var item = split[i];
if (item.Contains(": ") && !item.StartsWith("Start:") && !item.StartsWith("End:"))
{
var pair = item.Split(new[] { ": " }, StringSplitOptions.RemoveEmptyEntries);
if (!double.TryParse(pair[1], out var parsed))
{
continue;
}
if (!data.ContainsKey(pair[0]))
{
data.Add(pair[0], new PlotlyData {
Name = pair[0]
});
}
data[pair[0]].X.Add(time.ToString("yyyy-MM-dd HH:mm:ss.ffff"));
data[pair[0]].Y.Add(double.Parse(pair[1]));
data[pair[0]].Text.Add(pair[1]);
}
}
//counter++;
//if (counter % 1000 == 0)
//{
//_activityLogger?.Add($"Processing: ", counter);
//_activityLogger?.ShowMessageAlways();
//}
}
catch (Exception)
{
//todo: feedback bad data but do not fail
Console.WriteLine("Failed to parse:" + line);
}
}
if (minimumFitness.HasValue)
{
var fitness = data.Last().Value;
var removing = new List <int>();
for (int i = 0; i < fitness.Y.Count(); i++)
{
if (fitness.Y[i] < minimumFitness)
{
removing.Add(i);
}
}
foreach (var index in removing.OrderByDescending(o => o))
{
foreach (var item in data.Where(d => d.Value.Y.Any()))
{
item.Value.Y.RemoveAt(index);
item.Value.X.RemoveAt(index);
item.Value.Text.RemoveAt(index);
}
}
}
if (!disableNormalization)
{
var fitness = data.Last().Value;
var nonEmpty = data.Take(data.Count() - 1).Where(d => d.Value.Y.Any());
var max = fitness.Y.Max();
var min = fitness.Y.Min();
var normalizer = new SharpLearning.FeatureTransformations.Normalization.LinearNormalizer();
foreach (var list in nonEmpty)
{
var oldMax = list.Value.Y.Max();
var oldMin = list.Value.Y.Min();
for (int i = 0; i < list.Value.Y.Count(); i++)
{
list.Value.Y[i] = normalizer.Normalize(min, max, oldMin, oldMax, list.Value.Y[i]);
}
}
data = nonEmpty.Concat(new[] { data.Last() }).ToDictionary(k => k.Key, v => v.Value);
}
return(data);
}
public async Task <Dictionary <string, PlotlyData> > Read(SwitchReader reader, int sampleRate = 1, bool disableNormalization = false,
DateTime?minimumDate = null, double?minimumFitness = null, IActivityLogger activityLogger = null)
{
minimumDate = minimumDate ?? DateTime.MinValue;
_activityLogger = activityLogger;
_data = new Dictionary <string, PlotlyData>();
string line;
var counter = 0;
_chunk = new List <string>(ChunkSize);
while ((line = await reader.ReadLineAsync()) != null)
{
var rand = new XorRandom();
if (sampleRate > 1 && rand.Next(1, sampleRate) != 1)
{
continue;
}
if (DateTime.Parse(line.Substring(0, 24)) < minimumDate)
{
continue;
}
_chunk.Add(line);
counter++;
if (counter % ChunkSize == 0)
{
await SplitChunk();
_chunk.Clear();
}
}
await SplitChunk();
if (minimumFitness.HasValue)
{
var fitness = _data.Last().Value;
var removing = new List <int>();
for (int i = 0; i < fitness.Y.Count(); i++)
{
if (fitness.Y[i] < minimumFitness)
{
removing.Add(i);
}
}
foreach (var index in removing.OrderByDescending(o => o))
{
foreach (var item in _data.Where(d => d.Value.Y.Any()))
{
item.Value.Y.RemoveAt(index);
item.Value.X.RemoveAt(index);
item.Value.Text.RemoveAt(index);
}
}
}
if (!disableNormalization)
{
var fitness = _data.Last().Value;
var nonEmpty = _data.Take(_data.Count() - 1).Where(d => d.Value.Y.Any());
var max = fitness.Y.Max();
var min = fitness.Y.Min();
var normalizer = new SharpLearning.FeatureTransformations.Normalization.LinearNormalizer();
foreach (var list in nonEmpty)
{
var oldMax = list.Value.Y.Max();
var oldMin = list.Value.Y.Min();
for (int i = 0; i < list.Value.Y.Count(); i++)
{
list.Value.Y[i] = normalizer.Normalize(min, max, oldMin, oldMax, list.Value.Y[i]);
}
}
_data = nonEmpty.Concat(new[] { _data.Last() }).ToDictionary(k => k.Key, v => v.Value);
}
return(_data);
}
