/// <summary>
/// Shows all the information for a session-level node highlighted in the treeview.
/// </summary>
/// <param name="node">The highlighted node in the left treeview, assumed to represent a single session.</param>
private void ShowSessionInfo(TreeNode node)
{
//
// Get the session data. Note that sdata should be the same instance as _sdata.
// For consistency, we pull it from the node's tag.
//
SessionData sdata = (SessionData)node.Tag;
//
// Set up a reusable XML writer.
//
XmlTextWriter writer = null;
string tmpXml = String.Format("{0}{1}.xml", Path.GetTempPath(), Path.GetRandomFileName());
try
{
writer = new XmlTextWriter(tmpXml, Encoding.UTF8);
writer.Formatting = Formatting.Indented;
sdata.WriteXmlHeader(writer);
//
// Iterate through the conditions within the session.
//
foreach (TreeNode bnode in node.Nodes)
{
foreach (TreeNode cnode in bnode.Nodes)
{
ConditionData cd = (ConditionData)cnode.Tag;
cd.WriteXmlHeader(writer); // writes out trials as well
}
}
sdata.WriteXmlFooter(writer);
}
catch (Exception ex)
{
Console.WriteLine(ex);
tmpXml = String.Empty;
}
finally
{
if (writer != null)
{
writer.Close();
}
if (tmpXml != String.Empty)
{
webXml.Navigate(tmpXml);
rtxXml.LoadFile(tmpXml, RichTextBoxStreamType.PlainText);
_tmpXml.Add(tmpXml);
}
}
}
/// <summary>
/// Shows all the information for a block-level node highlighted in the treeview.
/// </summary>
/// <param name="node">The highlighted node in the left treeview, assumed to represent a single block.</param>
private void ShowBlockInfo(TreeNode node)
{
//
// Set up a reusable XML writer.
//
XmlTextWriter writer = null;
string tmpXml = String.Format("{0}{1}.xml", Path.GetTempPath(), Path.GetRandomFileName());
try
{
writer = new XmlTextWriter(tmpXml, Encoding.UTF8);
writer.Formatting = Formatting.Indented;
writer.WriteStartElement("Block");
writer.WriteAttributeString("index", XmlConvert.ToString(((ConditionData)node.Nodes[0].Tag).Block));
//
// Blocks themselves are not data objects, they're just identifiers on conditions.
// So iterate through the block's children nodes and write them out, to be later merged.
//
foreach (TreeNode cnode in node.Nodes)
{
ConditionData cd = (ConditionData)cnode.Tag;
cd.WriteXmlHeader(writer);
}
writer.WriteEndElement(); // </Block>
}
catch (Exception ex)
{
Console.WriteLine(ex);
tmpXml = String.Empty;
}
finally
{
if (writer != null)
{
writer.Close();
}
if (tmpXml != String.Empty)
{
webXml.Navigate(tmpXml);
rtxXml.LoadFile(tmpXml, RichTextBoxStreamType.PlainText);
_tmpXml.Add(tmpXml);
}
}
}
/// <summary>
/// Shows all the information for a condition-level node highlighted in the treeview.
/// </summary>
/// <param name="node">The highlighted node in the left treeview, assumed to represent a single condition.</param>
private void ShowConditionInfo(TreeNode node)
{
//
// Get the condition from the 'Tag' field from the selected node. We don't
// set the member variable because we're leaving the most recent trial depicted
// on the graphs, etc., and just loading the XML for the condition.
//
ConditionData cd = (ConditionData)node.Tag;
//
// Set up a reusable XML writer.
//
XmlTextWriter writer = null;
string tmpXml = String.Format("{0}{1}.xml", Path.GetTempPath(), Path.GetRandomFileName());
try
{
writer = new XmlTextWriter(tmpXml, Encoding.UTF8);
writer.Formatting = Formatting.Indented;
cd.WriteXmlHeader(writer);
}
catch (Exception ex)
{
Console.WriteLine(ex);
tmpXml = String.Empty;
}
finally
{
if (writer != null)
{
writer.Close();
}
if (tmpXml != String.Empty)
{
webXml.Navigate(tmpXml);
rtxXml.LoadFile(tmpXml, RichTextBoxStreamType.PlainText);
_tmpXml.Add(tmpXml);
}
}
}
/// <summary>
/// Shows all the information for a trial-level node highlighted in the treeview.
/// </summary>
/// <param name="node">The highlighted node in the left treeview, assumed to represent an individual trial.</param>
private void ShowTrialInfo(TreeNode node)
{
//
// Get the trial and its condition from the 'Tag' field from the selected node.
//
_tdata = (TrialData)node.Tag;
_cdata = (ConditionData)node.Parent.Tag;
//
// Get the resampled and smoothed velocity, acceleration, and jerk profiles.
//
MovementData.Profiles resampled = _tdata.Movement.CreateResampledProfiles();
MovementData.Profiles smoothed = _tdata.Movement.CreateSmoothedProfiles();
//
// Clear any series in the graphs.
//
grfDistance.ClearSeries();
grfVelocity.ClearSeries();
grfAcceleration.ClearSeries();
grfJerk.ClearSeries();
//
// Graph the resampled distance to the target over time.
//
List <PointF> rdist = new List <PointF>(resampled.Position.Count);
for (int i = 0; i < resampled.Position.Count; i++)
{
long t = resampled.Position[i].Time - resampled.Position[0].Time;
double dx = GeotrigEx.Distance(resampled.Position[i], _tdata.TargetCenterFromStart);
rdist.Add(new PointF(t, (float)dx));
}
Graph.Series rd = new Graph.Series("resampled distance", Color.Salmon, Color.Salmon, false, true);
rd.AddPoints(rdist);
//
// Graph the smoothed distance from the target over time.
//
List <PointF> sdist = new List <PointF>(smoothed.Position.Count);
for (int i = 0; i < smoothed.Position.Count; i++)
{
long t = smoothed.Position[i].Time - smoothed.Position[0].Time;
double dx = GeotrigEx.Distance(smoothed.Position[i], _tdata.TargetCenterFromStart);
sdist.Add(new PointF(t, (float)dx));
}
Graph.Series sd = new Graph.Series("smoothed distance", Color.MediumBlue, Color.MediumBlue, false, true);
sd.AddPoints(sdist);
grfDistance.AddSeries(rd);
grfDistance.AddSeries(sd);
//
// Graph the velocity.
//
Graph.Series rv = new Graph.Series("resampled velocity", Color.Salmon, Color.Salmon, false, true);
rv.AddPoints(resampled.Velocity);
Graph.Series sv = new Graph.Series("smoothed velocity", Color.MediumBlue, Color.MediumBlue, false, true);
sv.AddPoints(smoothed.Velocity);
grfVelocity.AddSeries(rv);
grfVelocity.AddSeries(sv);
//
// Graph the acceleration.
//
Graph.Series aaxis = new Graph.Series("zero line", Color.Gray, Color.Gray, false, true);
aaxis.AddPoint(0f, 0f);
aaxis.AddPoint(resampled.Acceleration[resampled.Acceleration.Count - 1].X, 0f);
Graph.Series ra = new Graph.Series("resampled acceleration", Color.Salmon, Color.Salmon, false, true);
ra.AddPoints(resampled.Acceleration);
Graph.Series sa = new Graph.Series("smoothed acceleration", Color.MediumBlue, Color.MediumBlue, false, true);
sa.AddPoints(smoothed.Acceleration);
grfAcceleration.AddSeries(aaxis);
grfAcceleration.AddSeries(ra);
grfAcceleration.AddSeries(sa);
//
// Graph the jerk.
//
Graph.Series jaxis = new Graph.Series("zero line", Color.Gray, Color.Gray, false, true);
jaxis.AddPoint(0f, 0f);
jaxis.AddPoint(resampled.Jerk[resampled.Jerk.Count - 1].X, 0f);
Graph.Series rj = new Graph.Series("resampled jerk", Color.Salmon, Color.Salmon, false, true);
rj.AddPoints(resampled.Jerk);
Graph.Series sj = new Graph.Series("smoothed jerk", Color.MediumBlue, Color.MediumBlue, false, true);
sj.AddPoints(smoothed.Jerk);
grfJerk.AddSeries(jaxis);
grfJerk.AddSeries(rj);
grfJerk.AddSeries(sj);
//
// Now show the trial-level XML in the web control.
//
XmlTextWriter writer = null;
string tmpXml = String.Format("{0}{1}.xml", Path.GetTempPath(), Path.GetRandomFileName());
try
{
writer = new XmlTextWriter(tmpXml, Encoding.UTF8);
writer.Formatting = Formatting.Indented;
_tdata.WriteXmlHeader(writer);
}
catch (Exception ex)
{
Console.WriteLine(ex);
tmpXml = String.Empty;
}
finally
{
if (writer != null)
{
writer.Close();
}
if (tmpXml != String.Empty)
{
webXml.Navigate(tmpXml);
rtxXml.LoadFile(tmpXml, RichTextBoxStreamType.PlainText);
_tmpXml.Add(tmpXml);
}
}
//
// Invalidate everything that needs to be repainted based on the node just selected.
//
pnlTrial.Invalidate();
grfDistance.Invalidate();
grfVelocity.Invalidate();
grfAcceleration.Invalidate();
grfJerk.Invalidate();
}
/// <summary>
/// Build the tree view of the session, blocks, conditions, and trials on the left. Use icons
/// to depict each, and show valuable information in each string label.
/// </summary>
/// <param name="sender">The sender of this event.</param>
/// <param name="e">The arguments for this event.</param>
private void GraphForm_Load(object sender, EventArgs e)
{
string s;
trvTrials.SuspendLayout();
s = String.Format("Subject {0} - {1}D, {2}", _sdata.Subject, _sdata.Is2D ? 2 : 1, _sdata.UsedMetronome ? "with metronome" : "no metronome");
TreeNode sNode = new TreeNode(s);
sNode.Tag = _sdata;
sNode.ImageIndex = SessionIcon;
sNode.SelectedImageIndex = SessionIcon;
trvTrials.Nodes.Add(sNode);
for (int b = 0; b < _sdata.NumBlocks; b++)
{
s = String.Format("Block {0}", b);
TreeNode bNode = new TreeNode(s);
bNode.ImageIndex = BlockIcon;
bNode.SelectedImageIndex = BlockIcon;
sNode.Nodes.Add(bNode);
for (int i = 0; i < _sdata.NumConditionsPerBlock; i++)
{
ConditionData cdata = _sdata[b, i];
if (cdata.MTPct != -1.0)
{
s = String.Format("Condition {0} - {1}×{2}×{3}", cdata.Index, cdata.MTPct, cdata.A, cdata.W);
}
else
{
s = String.Format("Condition {0} - {1}×{2}", cdata.Index, cdata.A, cdata.W);
}
TreeNode cNode = new TreeNode(s);
cNode.Tag = cdata;
cNode.ImageIndex = ConditionIcon;
cNode.SelectedImageIndex = ConditionIcon;
bNode.Nodes.Add(cNode);
for (int j = 1; j <= cdata.NumTrials; j++)
{
TrialData tdata = cdata[j];
s = String.Format("Trial {0}", tdata.Number);
TreeNode tNode = new TreeNode(s);
tNode.Tag = tdata;
if (tdata.IsPractice)
{
tNode.ImageIndex = TrialPracticeIcon;
tNode.SelectedImageIndex = TrialPracticeIcon;
}
else if (tdata.IsSpatialOutlier || tdata.IsTemporalOutlier)
{
tNode.ImageIndex = TrialOutlierIcon;
tNode.SelectedImageIndex = TrialOutlierIcon;
}
else if (tdata.IsError)
{
tNode.ImageIndex = TrialErrorIcon;
tNode.SelectedImageIndex = TrialErrorIcon;
}
else
{
tNode.ImageIndex = TrialGoodIcon;
tNode.SelectedImageIndex = TrialGoodIcon;
}
cNode.Nodes.Add(tNode);
}
}
}
sNode.Nodes[0].Nodes[0].Nodes[0].EnsureVisible(); // ensure first trial node is visible
trvTrials.SelectedNode = sNode.Nodes[0].Nodes[0].Nodes[0]; // select first trial
trvTrials.ResumeLayout(true);
}
/// <summary>
/// Constructs a FittsSession instance. A FittsSession contains conditions for a Fitts' law study,
/// which, in turn, contain a set of trials. A constructed instance contains a list of conditions
/// in sequence, which themselves contain a list of trials.
/// </summary>
/// <param name="o">The options that configure this session obtained from the OptionsForm dialog.</param>
public SessionData(OptionsForm.Options o)
{
//
// Set the condition variables that define this test.
//
_subject = o.Subject;
_circular = o.Is2D;
_a = o.A;
_w = o.W;
_mtpct = o.MTPct;
_intercept = o.Intercept;
_slope = o.Slope;
_screen = Screen.PrimaryScreen.Bounds;
_conditions = new List <ConditionData>();
//
// Create the order of conditions. Nesting is mt%[a[w]]].
//
int[] a_order, w_order, mt_order;
if (o.Randomize) // randomize the order of conditions
{
a_order = RandomEx.Array(0, o.A.Length - 1, o.A.Length, true);
w_order = RandomEx.Array(0, o.W.Length - 1, o.W.Length, true);
if (o.MTPct != null)
{
mt_order = RandomEx.Array(0, o.MTPct.Length - 1, o.MTPct.Length, true);
while (o.MTPct[mt_order[0]] < StatsEx.Mean(o.MTPct)) // enforce that the first MT% condition is >avg(MT%)
{
mt_order = RandomEx.Array(0, o.MTPct.Length - 1, o.MTPct.Length, true);
}
}
else
{
mt_order = null;
}
}
else // in-order arrays
{
a_order = new int[o.A.Length];
for (int i = 0; i < o.A.Length; i++)
{
a_order[i] = i;
}
w_order = new int[o.W.Length];
for (int i = 0; i < o.W.Length; i++)
{
w_order[i] = i;
}
mt_order = (o.MTPct != null) ? new int[o.MTPct.Length] : null;
for (int i = 0; o.MTPct != null && i < o.MTPct.Length; i++)
{
mt_order[i] = i;
}
}
//
// Create the ordered condition list for the first block of all conditions (i.e., one time through).
//
int n = 0;
for (int i = 0; o.MTPct == null || i < o.MTPct.Length; i++)
{
for (int j = 0; j < o.A.Length; j++)
{
for (int k = 0; k < o.W.Length; k++)
{
double pct = (o.MTPct != null) ? o.MTPct[mt_order[i]] : -1.0; // MT%
double fitts = o.Intercept + o.Slope * Math.Log((double)o.A[a_order[j]] / o.W[w_order[k]] + 1.0, 2.0);
long mt = (o.MTPct != null) ? (long)fitts : -1L; // Fitts' law predicted movement time
ConditionData cd = new ConditionData(0, n++, o.A[a_order[j]], o.W[w_order[k]], pct, mt, o.Trials, o.Practice, o.Is2D);
_conditions.Add(cd);
}
}
if (o.MTPct == null)
{
break;
}
}
//
// Now possibly replicate the created order multiple times.
//
int nConditions = _conditions.Count; // number of conditions in one block
for (int b = 1; b < o.Blocks; b++)
{
for (int c = 0; c < nConditions; c++)
{
ConditionData fx = _conditions[c];
ConditionData fc = new ConditionData(b, c, fx.A, fx.W, fx.MTPct, fx.MTPred, o.Trials, o.Practice, o.Is2D);
_conditions.Add(fc);
}
}
}
/// <summary>
/// Performs any analyses on this data object and writes the results to a space-delimitted
/// file for subsequent copy-and-pasting into a spreadsheet like Microsoft Excel or SAS JMP.
/// </summary>
/// <param name="writer">An open stream writer pointed to a text file. The writer will be closed by
/// this method after writing.</param>
/// <returns>True if writing is successful; false otherwise.</returns>
public bool WriteResultsToTxt(StreamWriter writer)
{
bool success = true;
try
{
// write an identifying title for this file.
writer.WriteLine("FittsStudy log analysis results for '{0}' on {1} at {2}. FittsStudy.exe version: {3}.\r\n",
this.FilenameBase + ".xml", // Note: ((FileStream) writer.BaseStream).Name holds the file path.
DateTime.Now.ToLongDateString(),
DateTime.Now.ToLongTimeString(),
Assembly.GetExecutingAssembly().GetName().Version);
// write the column headers with comma separation -- see Columns.txt
writer.WriteLine("Subject,Circular?,Block,Condition,Trial,Practice?,Metronome?,MT%,MTPred,MT,a(given),b(given),A,W,ID,axis,angle,ae(1d),dx(1d),ae(2d),dx(2d),Ae(1d),SD(1d),We(1d),IDe(1d),TP(1d),Ae(2d),SD(2d),We(2d),IDe(2d),TP(2d),MTe,MTRatio,MeanMTe,MeanMTe(sx),MeanMTe(tx),Entries,Overshoots,Error?,Errors,Errors(sx),Errors(tx),Error%,Error%(sx),Error%(tx),SpatialOutlier?,TemporalOutlier?,SpatialOutliers,TemporalOutliers,StartX,StartY,EndX,EndY,TargetX,TargetY,Travel,Duration,Submovements,MaxVelocity,MaxAcceleration,MaxJerk,tMaxVelocity,tMaxAcceleration,tMaxJerk,TAC,MDC,ODC,MV,ME,MO,N,Fitts_TP_avg(1d),Fitts_TP_inv(1d),Fitts_a(1d),Fitts_b(1d),Fitts_r(1d),Fitts_TP_avg(2d),Fitts_TP_inv(2d),Fitts_a(2d),Fitts_b(2d),Fitts_r(2d),Error_m(1d),Error_b(1d),Error_r(1d),Error_m(2d),Error_b(2d),Error_r(2d)");
// pre-compute session-level values here
Model fm = this.BuildModel();
fm.RoundTerms(4);
// now iterate through all of the conditions
for (int i = 0; i < _conditions.Count; i++)
{
// get the condition and pre-compute any condition-level values here. we could
// compute them again and again while writing each trial, but that is inefficient.
ConditionData cd = _conditions[i];
double cAe_1d = Math.Round(cd.GetAe(false), 4);
double cSD_1d = Math.Round(cd.GetSD(false), 4);
double cWe_1d = Math.Round(cd.GetWe(false), 4);
double cIDe_1d = Math.Round(cd.GetIDe(false), 4);
double cTP_1d = Math.Round(cd.GetTP(false), 4);
double cAe_2d = Math.Round(cd.GetAe(true), 4);
double cSD_2d = Math.Round(cd.GetSD(true), 4);
double cWe_2d = Math.Round(cd.GetWe(true), 4);
double cIDe_2d = Math.Round(cd.GetIDe(true), 4);
double cTP_2d = Math.Round(cd.GetTP(true), 4);
long meanMTe = cd.GetMTe(ExcludeOutliersType.None);
long meanMTe_sx = cd.GetMTe(ExcludeOutliersType.Spatial);
long meanMTe_tx = cd.GetMTe(ExcludeOutliersType.Temporal);
int errors = cd.GetNumErrors(ExcludeOutliersType.None);
int errors_sx = cd.GetNumErrors(ExcludeOutliersType.Spatial);
int errors_tx = cd.GetNumErrors(ExcludeOutliersType.Temporal);
double errorPct = Math.Round(cd.GetErrorRate(ExcludeOutliersType.None), 4);
double errorPct_sx = Math.Round(cd.GetErrorRate(ExcludeOutliersType.Spatial), 4);
double errorPct_tx = Math.Round(cd.GetErrorRate(ExcludeOutliersType.Temporal), 4);
int nSpatialOutliers = cd.NumSpatialOutliers;
int nTemporalOutliers = cd.NumTemporalOutliers;
// within each condition, iterate through the trials. start at index 1 because
// the trial at index 0 is the special start-area trial, and should be ignored.
for (int j = 1; j <= cd.NumTrials; j++)
{
TrialData td = cd[j];
MovementData md = td.Movement; // the movement path itself
// calculate the resampled submovement profiles
MovementData.Profiles profiles = md.CreateResampledProfiles();
int vidx = SeriesEx.Max(profiles.Velocity, 0, -1); // max velocity
int aidx = SeriesEx.Max(profiles.Acceleration, 0, -1); // max acceleration
int jidx = SeriesEx.Max(profiles.Jerk, 0, -1); // max jerk
// calculate the MacKenzie et al. (2001) path analysis measures
MovementData.PathAnalyses analyses = md.DoPathAnalyses(td.Start, td.TargetCenterFromStart);
// write the spreadsheet row here
writer.WriteLine("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13},{14},{15},{16},{17},{18},{19},{20},{21},{22},{23},{24},{25},{26},{27},{28},{29},{30},{31},{32},{33},{34},{35},{36},{37},{38},{39},{40},{41},{42},{43},{44},{45},{46},{47},{48},{49},{50},{51},{52},{53},{54},{55},{56},{57},{58},{59},{60},{61},{62},{63},{64},{65},{66},{67},{68},{69},{70},{71},{72},{73},{74},{75},{76},{77},{78},{79},{80},{81},{82},{83},{84},{85},{86}",
_subject, // Subject,
_circular ? 1 : 0, // Circular?
cd.Block, // Block,
cd.Index, // Condition,
td.Number, // Trial, (== j)
td.IsPractice ? 1 : 0, // Practice?,
cd.UsedMetronome ? 1 : 0, // Metronome?, (== td.UsedMetronome)
cd.MTPct, // MT%,
cd.MTPred, // MTPred,
cd.MT, // MT, (== td.MT)
_intercept, // a(given),
_slope, // b(given),
cd.A, // A, (== td.A)
cd.W, // W, (== td.W)
Math.Round(cd.ID, 4), // ID, (== td.ID)
Math.Round(GeotrigEx.Radians2Degrees(td.Axis), 4), // axis,
Math.Round(GeotrigEx.Radians2Degrees(td.Angle), 4), // angle,
Math.Round(td.GetAe(false), 4), // ae(1d),
Math.Round(td.GetDx(false), 4), // dx(1d),
Math.Round(td.GetAe(true), 4), // ae(2d),
Math.Round(td.GetDx(true), 4), // dx(2d),
cAe_1d, // Ae(1d),
cSD_1d, // SD(1d),
cWe_1d, // We(1d),
cIDe_1d, // IDe(1d),
cTP_1d, // TP(1d),
cAe_2d, // Ae(2d),
cSD_2d, // SD(2d),
cWe_2d, // We(2d),
cIDe_2d, // IDe(2d),
cTP_2d, // TP(2d),
td.MTe, // MTe,
Math.Round(td.MTRatio, 4), // MTRatio,
meanMTe, // MeanMTe,
meanMTe_sx, // MeanMTe(sx),
meanMTe_tx, // MeanMTe(tx),
td.TargetEntries, // Entries,
td.TargetOvershoots, // Overshoots,
td.IsError ? 1 : 0, // Error?,
errors, // Errors,
errors_sx, // Errors(sx),
errors_tx, // Errors(tx),
errorPct, // Error%,
errorPct_sx, // Error%(sx),
errorPct_tx, // Error%(tx),
td.IsSpatialOutlier ? 1 : 0, // SpatialOutlier?,
td.IsTemporalOutlier ? 1 : 0, // TemporalOutlier?,
nSpatialOutliers, // SpatialOutliers,
nTemporalOutliers, // TemporalOutliers,
td.Start.X, // StartX
td.Start.Y, // StartY
td.End.X, // EndX
td.End.Y, // EndY
td.TargetCenterFromStart.X, // TargetX
td.TargetCenterFromStart.Y, // TargetY
Math.Round(md.Travel, 4), // Travel,
md.Duration, // Duration,
md.GetNumSubmovements(), // Submovements,
Math.Round(profiles.Velocity[vidx].Y, 4), // MaxVelocity,
Math.Round(profiles.Acceleration[aidx].Y, 4), // MaxAcceleration,
Math.Round(profiles.Jerk[jidx].Y, 4), // MaxJerk,
Math.Round(profiles.Velocity[vidx].X / md.Duration, 4), // tMaxVelocity,
Math.Round(profiles.Acceleration[aidx].X / md.Duration, 4), // tMaxAcceleration,
Math.Round(profiles.Jerk[jidx].X / md.Duration, 4), // tMaxJerk,
analyses.TaskAxisCrossings, // TAC,
analyses.MovementDirectionChanges, // MDC,
analyses.OrthogonalDirectionChanges, // ODC,
Math.Round(analyses.MovementVariability, 4), // MV,
Math.Round(analyses.MovementError, 4), // ME,
Math.Round(analyses.MovementOffset, 4), // MO,
fm.N, // N,
fm.Fitts_TP_avg_1d, // Fitts_TP_avg(1d),
fm.Fitts_TP_inv_1d, // Fitts_TP_inv(1d),
fm.Fitts_a_1d, // Fitts_a(1d),
fm.Fitts_b_1d, // Fitts_b(1d),
fm.Fitts_r_1d, // Fitts_r(1d),
fm.Fitts_TP_avg_2d, // Fitts_TP_avg(2d),
fm.Fitts_TP_inv_2d, // Fitts_TP_inv(2d),
fm.Fitts_a_2d, // Fitts_a(2d),
fm.Fitts_b_2d, // Fitts_b(2d),
fm.Fitts_r_2d, // Fitts_r(2d),
fm.Error_m_1d, // Error_m(1d),
fm.Error_b_1d, // Error_b(1d),
fm.Error_r_1d, // Error_r(1d),
fm.Error_m_2d, // Error_m(2d),
fm.Error_b_2d, // Error_b(2d),
fm.Error_r_2d // Error_r(2d)
);
}
}
}
catch (IOException ioex)
{
Console.WriteLine(ioex);
success = false;
}
catch (Exception ex)
{
Console.WriteLine(ex);
success = false;
}
finally
{
if (writer != null)
{
writer.Close();
}
}
return(success);
}
/// <summary>
/// Reads a data object from XML and returns an instance of the object.
/// </summary>
/// <param name="reader">An open XML reader. The reader will be closed by this
/// method after reading.</param>
/// <returns>Returns <b>true</b> if successful; <b>false</b> otherwise.</returns>
/// <remarks>Clients should first create a new instance using a default constructor, and then
/// call this method to populate the data fields of the default instance.</remarks>
public bool ReadFromXml(XmlTextReader reader)
{
bool success = true;
try
{
// start up the xml text reader and move to the xml header
reader.WhitespaceHandling = WhitespaceHandling.None;
if (!reader.IsStartElement("Fitts_Study")) // moves to content and tests top tag
{
throw new XmlException("XML format error: Expected the <Fitts_Study> tag.");
}
// read the session-level information from the header
_subject = XmlConvert.ToInt32(reader.GetAttribute("subject"));
_circular = XmlConvert.ToBoolean(reader.GetAttribute("circular"));
int conditions = XmlConvert.ToInt32(reader.GetAttribute("conditions"));
_mtpct = StringEx.String2DoubleArray(reader.GetAttribute("MTPct"));
_a = StringEx.String2IntArray(reader.GetAttribute("A"));
_w = StringEx.String2IntArray(reader.GetAttribute("W"));
_intercept = XmlConvert.ToDouble(reader.GetAttribute("intercept"));
_slope = XmlConvert.ToDouble(reader.GetAttribute("slope"));
_screen = StringEx.String2RectangleF(reader.GetAttribute("screen")); // saved for zoom feature
// read the conditions and add condition objects to the session. the
// conditions will be responsible for reading their individual trials.
_conditions = new List <ConditionData>(conditions);
for (int i = 0; i < conditions; i++)
{
ConditionData fc = new ConditionData();
if (!fc.ReadFromXml(reader))
{
throw new XmlException("Failed to read the ConditionData.");
}
else
{
_conditions.Add(fc);
}
}
// here would be the place to read the Fitts_Model parameters, but the model
// can be recreated from the session data anyway, so there's really no point.
}
catch (XmlException xex)
{
Console.WriteLine(xex);
success = false;
}
catch (Exception ex)
{
Console.WriteLine(ex);
success = false;
}
finally
{
if (reader != null)
{
reader.Close();
}
}
return(success);
}
/// <summary>
/// Performs linear regression on the entire session's worth of data to fit a Fitts' law model
/// of the form MTe = a + b * log2(Ae/We + 1).
/// </summary>
/// <returns>Model and fitting parameters.</returns>
public Model BuildModel()
{
Model model = Model.Empty;
model.FittsPts_1d = new List <PointF>(_conditions.Count);
model.FittsPts_2d = new List <PointF>(_conditions.Count);
double[] ide = new double[_conditions.Count]; // observed index of difficulties for each condition
double[] mte = new double[_conditions.Count]; // observed mean movement times for each condition
double[] tp = new double[_conditions.Count]; // observed throughputs for each condition
for (int i = 0; i <= 1; i++) // first loop is univariate, second loop is bivariate
{
for (int j = 0; j < _conditions.Count; j++) // each A x W or MT% x A x W condition (blocks kept separate)
{
ConditionData cdata = _conditions[j];
ide[j] = cdata.GetIDe(i == 1); // bits
mte[j] = cdata.GetMTe(ExcludeOutliersType.Spatial); // ms
tp[j] = cdata.GetTP(i == 1); // bits/s
if (i == 0)
{
model.FittsPts_1d.Add(new PointF((float)ide[j], (float)mte[j])); // for graphing later
}
else
{
model.FittsPts_2d.Add(new PointF((float)ide[j], (float)mte[j])); // for graphing later
}
}
if (i == 0) // univariate
{
model.N = _conditions.Count; // == model.FittsPts.Count
model.MTe = StatsEx.Mean(mte); // ms
model.Fitts_TP_avg_1d = StatsEx.Mean(tp); // bits/s
model.Fitts_a_1d = StatsEx.Intercept(ide, mte); // ms
model.Fitts_b_1d = StatsEx.Slope(ide, mte); // ms/bit
model.Fitts_TP_inv_1d = 1.0 / model.Fitts_b_1d * 1000.0; // bits/s
model.Fitts_r_1d = StatsEx.Pearson(ide, mte); // correlation
}
else // bivariate
{
model.Fitts_TP_avg_2d = _circular ? StatsEx.Mean(tp) : 0.0; // bits/s
model.Fitts_a_2d = _circular ? StatsEx.Intercept(ide, mte) : 0.0; // ms
model.Fitts_b_2d = _circular ? StatsEx.Slope(ide, mte) : 0.0; // ms/bit
model.Fitts_TP_inv_2d = _circular ? 1.0 / model.Fitts_b_2d * 1000.0 : 0.0; // bits/s
model.Fitts_r_2d = _circular ? StatsEx.Pearson(ide, mte) : 0.0; // correlation
}
}
//
// Now compute the predicted error rates relevant to metronome experiments.
//
model.ErrorPts_1d = new List <PointF>(_conditions.Count);
model.ErrorPts_2d = new List <PointF>(_conditions.Count);
double[] errPred = new double[_conditions.Count]; // x, predicted errors based on the error model for pointing
double[] errObs = new double[_conditions.Count]; // y, observed error rates for each condition (spatial outliers included)
for (int i = 0; i <= 1; i++) // first loop is univariate, second loop is bivariate
{
for (int j = 0; j < _conditions.Count; j++) // each MT% x A x W condition, duplicates left separate
{
ConditionData cdata = _conditions[j];
errObs[j] = cdata.GetErrorRate(ExcludeOutliersType.Temporal); // observed error rates -- temporal outliers excluded
double mt = cdata.GetMTe(ExcludeOutliersType.Temporal); // observed movement times -- temporal outliers excluded
double z = (i == 0)
? 2.066 * ((double)cdata.W / cdata.A) * (Math.Pow(2.0, (mt - model.Fitts_a_1d) / model.Fitts_b_1d) - 1.0)
: 2.066 * ((double)cdata.W / cdata.A) * (Math.Pow(2.0, (mt - model.Fitts_a_2d) / model.Fitts_b_2d) - 1.0);
errPred[j] = GeotrigEx.PinValue(2.0 * (1.0 - StatsEx.CDF(0.0, 1.0, z)), 0.0, 1.0); // predicted error rates == 1 - erf(z / sqrt(2))
//double shah = 1.0 - (2.0 * StatsEx.ShahNormal(z)); // Shah approximation of the standard normal distribution
if (i == 0)
{
model.ErrorPts_1d.Add(new PointF((float)errPred[j], (float)errObs[j])); // for graphing later
}
else
{
model.ErrorPts_2d.Add(new PointF((float)errPred[j], (float)errObs[j])); // for graphing later
}
}
if (i == 0) // univariate
{
model.ErrorPct = StatsEx.Mean(errObs); // percent
model.Error_m_1d = StatsEx.Slope(errPred, errObs);
model.Error_b_1d = StatsEx.Intercept(errPred, errObs);
model.Error_r_1d = StatsEx.Pearson(errPred, errObs);
}
else // bivariate
{
model.Error_m_2d = _circular ? StatsEx.Slope(errPred, errObs) : 0.0;
model.Error_b_2d = _circular ? StatsEx.Intercept(errPred, errObs) : 0.0;
model.Error_r_2d = _circular ? StatsEx.Pearson(errPred, errObs) : 0.0;
}
}
return(model);
}