def UpdateGradient(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
# update color schema
self.color_schema_dict["Density Map"] = GradientColorSchema(
gradient_type)
self.color_band = gradient_type
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
return self.color_schema_dict["Density Map"]
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self, parent, data)
try:
self.layer_name = layer.name
self.title = "Transition probility matrix (%s)" % self.layer_name
self.x_label = "LISA transition states (1=HH,2=LH,3=LL,4=HL)"
self.y_label = "LISA transition states"
self.data = data
n = len(self.data)
self.enable_axis_labels = False
self.enable_axis = True
self.enable_axis_x = False
self.enable_axis_y = False
# a NxN matrix
self.x_min = 1
self.x_max = n + 1
self.y_min = 1
self.y_max = n + 1
self.extent = (self.x_min, self.y_min, self.x_max, self.y_max)
self.selected_polygon_ids = []
self.status_bar = self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
self.margin_right = 100
# color schema: from blue to red
self.color_matrix = []
for i in range(n):
color_row = []
for j in range(n):
p = self.data[i][j]
color_row.append(self.gradient_color.get_color_at(p))
self.color_matrix.append(color_row)
except Exception as err:
self.ShowMsgBox('Fail to init heat map! ' + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
def UpdateGradient(self,gradient_type):
self.gradient_color = GradientColor(gradient_type)
# update color schema
self.color_schema_dict["Density Map"] = GradientColorSchema(gradient_type)
self.color_band = gradient_type
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
return self.color_schema_dict["Density Map"]
class GradientColorSchema(ColorSchema):
def __init__(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
ColorSchema.__init__(self,None,[""])
def createNodeBmps(self):
node_bmp = wx.EmptyBitmap(self.size[0],self.size[1])
dc = wx.MemoryDC()
dc.SelectObject(node_bmp)
dc.Clear()
dc.DrawBitmap(self.gradient_color.get_bmp(self.size[0],self.size[1]),0,0)
#dc.Destroy()
self.bmps.append(node_bmp)
label = 'Density Map'
self.bmp_dict[label] = node_bmp
class GradientColorSchema(ColorSchema):
def __init__(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
ColorSchema.__init__(self, None, [""])
def createNodeBmps(self):
node_bmp = wx.EmptyBitmap(self.size[0], self.size[1])
dc = wx.MemoryDC()
dc.SelectObject(node_bmp)
dc.Clear()
dc.DrawBitmap(self.gradient_color.get_bmp(self.size[0], self.size[1]),
0, 0)
#dc.Destroy()
self.bmps.append(node_bmp)
label = 'Density Map'
self.bmp_dict[label] = node_bmp
def __init__(self,parent, layer, data,**kwargs):
PlottingCanvas.__init__(self,parent,data)
try:
self.layer_name = layer.name
self.title = "Transition probility matrix (%s)" % self.layer_name
self.x_label = "LISA transition states (1=HH,2=LH,3=LL,4=HL)"
self.y_label = "LISA transition states"
self.data = data
n = len(self.data)
self.enable_axis_labels = False
self.enable_axis = True
self.enable_axis_x = False
self.enable_axis_y = False
# a NxN matrix
self.x_min = 1
self.x_max = n+1
self.y_min = 1
self.y_max = n+1
self.extent = (self.x_min, self.y_min, self.x_max,self.y_max)
self.selected_polygon_ids = []
self.status_bar = self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
self.margin_right = 100
# color schema: from blue to red
self.color_matrix = []
for i in range(n):
color_row = []
for j in range(n):
p = self.data[i][j]
color_row.append( self.gradient_color.get_color_at(p))
self.color_matrix.append(color_row)
except Exception as err:
self.ShowMsgBox('Fail to init heat map! ' + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
def compute_densityMap(cls, input, output):
"""
The function will be executed in each process.
"""
while True:
args = input.get()
method = args['method']
id = args['id']
if method == 'compute':
# for computing KDE for each cell in grid
points = args['data']
kernel = args['kernel']
bandwidth = args['bandwidth']
cellSize = args['cell_size']
grid_left = args['grid_left']
grid_lower = args['grid_lower']
grid_upper = args['grid_upper']
rows, cols = args['rows'], args['cols']
invert = False
radius = bandwidth / cellSize
tmpGrid = np.zeros((rows, cols))
#print '%s:%s'%(os.getpid(),args.keys())
for pt in points:
X, Y = pt
float_i = (Y - grid_lower) / cellSize
#float_i = (grid_upper -Y) / cellSize
i = int(floor(float_i - radius))
i = i if i >= 0 else 0
I = int(floor(float_i + radius))
I = I if I < rows else rows - 1
float_j = (X - grid_left) / cellSize
j = int(floor(float_j - radius))
j = j if j >= 0 else 0
J = int(floor(float_j + radius))
J = J if J < cols else cols - 1
for row in xrange(i, I + 1):
for col in xrange(j, J + 1):
x = grid_left + (col * cellSize)
y = grid_lower + (row * cellSize)
d = ((x - X)**2 + (y - Y)**2)**(0.5)
if d <= bandwidth:
z = d / bandwidth
if invert:
tmpGrid[row, col] -= kernel(z)
else:
tmpGrid[row, col] += kernel(z)
output.put({
'id': id,
'grid': tmpGrid,
'grid_max': np.max(tmpGrid)
})
elif method == 'create':
grid = args['grid']
grid_max = args['grid_max']
opaque = args['opaque']
color_band = args['color_band']
gradient_color = GradientColor(color_band)
# make Byte Array for bmp
rows, cols = grid.shape
arr = np.zeros((rows, cols, 4), np.uint8)
R, G, B, A = range(4)
# flip from real model coords to screen coords
raster = grid
raster = raster[::-1]
raster_min, raster_max = raster.min(), grid_max #raster.max()
raster_val_range = raster_max - raster_min
if raster_val_range > 0:
raster_scaled = (raster - raster_min) / (raster_max -
raster_min)
else:
raster_scaled = raster
# tranfer 0~1 value to 0~255 pixel values,
red_matrix, blue_matrix, green_matrix, opaque_matrix = \
np.zeros((rows,cols)),\
np.zeros((rows, cols)), \
np.zeros((rows,cols)), \
np.ones((rows,cols))*opaque* (255/100.0) # opaque ranges (0,100)
for i, row in enumerate(raster_scaled):
for j, item in enumerate(row):
clr = gradient_color.get_color_at(item)
red_matrix[i][j] = clr.red
green_matrix[i][j] = clr.green
blue_matrix[i][j] = clr.blue
if item < 0.15:
opaque_matrix[i][j] = 0
arr[:, :, R] = (red_matrix).astype("B")
arr[:, :, G] = (green_matrix).astype("B")
arr[:, :, B] = (blue_matrix).astype("B")
arr[:, :, A] = (opaque_matrix).astype("B") #self.opaque #alpha
output.put({'id': id, 'data': arr})
def __init__(self,parent, layers, **kwargs):
ShapeMap.__init__(self,parent,layers)
try:
self.point_layer = self.layers[0]
#self.bg_layer = kwargs["background"]
self.cell_size = kwargs["cell_size"]
self.bandwidth = kwargs["bandwidth"]
self.kernel = kwargs["kernel"]
self.color_band = kwargs["color_band"]
self.opaque = kwargs["opaque"]
self.bufferWidth, self.bufferHeight = kwargs["size"]
self.points = self.point_layer.shape_objects
self.extent = self.point_layer.extent
self.view = View2ScreenTransform(
self.extent,
self.bufferWidth,
self.bufferHeight
)
if kwargs.has_key("query_points"):
self.query_points = kwargs["query_points"]
else:
self.query_points = range(len(self.points))
"""
if self.bg_layer:
self.layers.append(self.bg_layer)
"""
if len(self.query_points) < len(self.points):
id_group = [self.query_points]
color_group = [self.color_schema_dict[self.point_layer.name].colors[0]]
edge_color_group = [self.color_schema_dict[self.point_layer.name].edge_color]
"""
non_query_points = list(set(range(len(self.points))) - set(self.query_points))
id_group.append(non_query_points)
color_group.append(wx.Colour(255,255,255,0))
edge_color_group.append(wx.Colour(255,255,255,0))
"""
self.draw_layers[self.point_layer].set_data_group(id_group)
self.draw_layers[self.point_layer].set_fill_color_group(color_group)
self.draw_layers[self.point_layer].set_edge_color_group(edge_color_group)
# setup gradient color
self.gradient_color = GradientColor(self.color_band)
# for tree node
self.color_schema_dict["Density Map"] = GradientColorSchema(self.color_band)
self.isHideDensityMap = False
# for export gif
self.img_array_rgb = None
self.isResizing = False
self.cells_Updated = False
self.bmp = None
self.setup_densityMap()
self.createDensityMap()
# dummy Layer for density map
self.layer_seq_list = []
self.layer_seq_list.insert(0,"Density Map")
self.layer_name_list.insert(0,"Density Map")
except Exception as err:
self.ShowMsgBox("""Density map could not be created. Please choose/input valid parameters.
Details: """ + str(err.message))
self.UnRegister()
self.parentFrame.Close(True)
return None
class DensityMap(ShapeMap):
"""
wxWindow drawing density map based on points data or POINT SHAPE file
"""
def __init__(self,parent, layers, **kwargs):
ShapeMap.__init__(self,parent,layers)
try:
self.point_layer = self.layers[0]
#self.bg_layer = kwargs["background"]
self.cell_size = kwargs["cell_size"]
self.bandwidth = kwargs["bandwidth"]
self.kernel = kwargs["kernel"]
self.color_band = kwargs["color_band"]
self.opaque = kwargs["opaque"]
self.bufferWidth, self.bufferHeight = kwargs["size"]
self.points = self.point_layer.shape_objects
self.extent = self.point_layer.extent
self.view = View2ScreenTransform(
self.extent,
self.bufferWidth,
self.bufferHeight
)
if kwargs.has_key("query_points"):
self.query_points = kwargs["query_points"]
else:
self.query_points = range(len(self.points))
"""
if self.bg_layer:
self.layers.append(self.bg_layer)
"""
if len(self.query_points) < len(self.points):
id_group = [self.query_points]
color_group = [self.color_schema_dict[self.point_layer.name].colors[0]]
edge_color_group = [self.color_schema_dict[self.point_layer.name].edge_color]
"""
non_query_points = list(set(range(len(self.points))) - set(self.query_points))
id_group.append(non_query_points)
color_group.append(wx.Colour(255,255,255,0))
edge_color_group.append(wx.Colour(255,255,255,0))
"""
self.draw_layers[self.point_layer].set_data_group(id_group)
self.draw_layers[self.point_layer].set_fill_color_group(color_group)
self.draw_layers[self.point_layer].set_edge_color_group(edge_color_group)
# setup gradient color
self.gradient_color = GradientColor(self.color_band)
# for tree node
self.color_schema_dict["Density Map"] = GradientColorSchema(self.color_band)
self.isHideDensityMap = False
# for export gif
self.img_array_rgb = None
self.isResizing = False
self.cells_Updated = False
self.bmp = None
self.setup_densityMap()
self.createDensityMap()
# dummy Layer for density map
self.layer_seq_list = []
self.layer_seq_list.insert(0,"Density Map")
self.layer_name_list.insert(0,"Density Map")
except Exception as err:
self.ShowMsgBox("""Density map could not be created. Please choose/input valid parameters.
Details: """ + str(err.message))
self.UnRegister()
self.parentFrame.Close(True)
return None
def OnShapesSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
event is an instance of EventHandler.Event class
event.object are the data for selecting shape objects
"""
if not event: return
if not self.buffer: return
data = event.data
if len(data.boundary) >0:
# select shape object by boundary
# prepare DC for brushing drawing
tmp_buffer = wx.EmptyBitmap(self.bufferWidth, self.bufferHeight)
tmp_dc = wx.BufferedDC(None, tmp_buffer)
tmp_dc.DrawBitmap(self.drawing_backup_buffer,0,0)
regions = []
if isinstance(data.boundary[0], int) or isinstance(data.boundary[0], float):
regions.append(data.boundary)
elif isinstance(data.boundary, list):
regions = data.boundary
for region in regions:
x,y,x0,y0 = region
x,y = self.view.view_to_pixel(x,y)
x0,y0 = self.view.view_to_pixel(x0,y0)
select_region = (x,y,x0,y0)
w = abs(x-x0)
h = abs(y-y0)
start_x,start_y = min(x0,x),min(y,y0)
tmp_dc.SetPen(wx.RED_PEN)
tmp_dc.SetBrush(wx.TRANSPARENT_BRUSH)
tmp_dc.DrawRectangle(int(start_x),int(start_y),int(w),int(h))
self.buffer = tmp_buffer
self.Refresh(False)
def set_opaque(self, opaque):
"""
Set the opaque of the Density Map
Value range (0-255), less value, more transparent
"""
self.opaque = opaque
"""
Overwrite draw_background() to draw MAP buffer
and DenstiyMAP buffer as background
"""
if self.buffer:
dc.DrawBitmap(self.buffer,0,0)
if self.bmp:
# always scale density map to correct window size and draw them
scale_x = self.bmp_width / float(self.cols)
scale_y = self.bmp_height / float(self.rows)
dc.SetUserScale(scale_x, scale_y)
dc.DrawBitmap(self.bmp, self.bmp_left/scale_x, self.bmp_upper/scale_y)
def createDensityMap(self):
# compute density map, just once
if self.cells_Updated == False:
n = len(self.points)
progress_dlg = wx.ProgressDialog(
"Progress",
"Creating density map... ",
maximum = 2,
parent=self,
style = wx.PD_APP_MODAL|wx.PD_AUTO_HIDE
)
progress_dlg.CenterOnScreen()
progress_dlg.Update(1)
self.grid_to_buffer()
progress_dlg.Update(2)
def DoDraw(self,dc):
"""
Implement inhereted DoDraw() function
It will be called when window initialize and resize
"""
# draw layer in buffer
for layer_name in self.layer_name_list[::-1]:
if layer_name == "Density Map":
if self.isHideDensityMap == False:
self.drawDensityMap(dc)
else:
layer = self.layer_dict[layer_name]
if self.hide_layers[layer] == False:
self.draw_layers[layer].draw(dc,self.view)
def drawDensityMap(self,dc):
bmp = self.bmp
if self.isResizing:
# resize bmp
self.setup_bitmap()
image = wx.ImageFromBitmap(self.bmp)
if self.bmp_width > self.bufferWidth or self.bmp_height > self.bufferHeight:
cropLeft = 0
if self.bmp_left < 0:
cropLeft = abs(self.bmp_left) / self.bmp_width * self.bmp.Width
cropUpper = 0
if self.bmp_upper < 0:
cropUpper = abs(self.bmp_upper) / self.bmp_height * self.bmp.Height
cropWidth = self.bufferWidth / self.bmp_width * self.bmp.Width
if self.bmp_left > 0:
cropWidth = (self.bufferWidth - self.bmp_left) / self.bmp_width * self.bmp.Width
cropHeight = self.bufferHeight / self.bmp_height * self.bmp.Height
if self.bmp_upper > 0:
cropHeight = (self.bufferHeight - self.bmp_upper) / self.bmp_height * self.bmp.Height
if cropWidth > 0 and cropHeight > 0:
image = image.GetSubImage(wx.Rect(cropLeft,cropUpper,cropWidth,cropHeight))
image = image.Scale(self.bufferWidth, self.bufferHeight, wx.IMAGE_QUALITY_HIGH)
bmp = wx.BitmapFromImage(image)
start_x = 0
start_y = 0
if self.bmp_left > 0:
start_x = self.bmp_left
if self.bmp_upper > 0:
start_y = self.bmp_upper
dc.DrawBitmap(bmp,start_x,start_y)
else:
image = image.Scale(self.bmp_width, self.bmp_height, wx.IMAGE_QUALITY_HIGH)
bmp = wx.BitmapFromImage(image)
dc.DrawBitmap(bmp,self.bmp_left, self.bmp_upper)
self.isResizing = False
else:
# draw density map, by zooming the Density Map
dc.DrawBitmap(bmp, self.bmp_left, self.bmp_upper)
def UpdateGradient(self,gradient_type):
self.gradient_color = GradientColor(gradient_type)
# update color schema
self.color_schema_dict["Density Map"] = GradientColorSchema(gradient_type)
self.color_band = gradient_type
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
return self.color_schema_dict["Density Map"]
def UpdateOpaque(self, opaque):
self.opaque = opaque
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
def OnSize(self, event):
""" overwrittern for resizing kde map"""
self.isResizing = True
super(DensityMap, self).OnSize(event)
def _zoom_end(self, mouse_start_pos, mouse_end_pos, mouse_select_w, mouse_select_h):
""" overwrittern for zooming with mouse """
self.isResizing = True
super(DensityMap, self)._zoom_end(mouse_start_pos, mouse_end_pos, mouse_select_w, mouse_select_h)
def _pan_end(self, mouse_start_pos, mouse_end_pos, mouse_select_w, mouse_select_h):
""" overwrittern for panning with mouse """
self.isResizing = True
super(DensityMap, self)._pan_end(mouse_start_pos, mouse_end_pos, mouse_select_w, mouse_select_h)
self.reInitBuffer = True
def restore(self):
""" overwritten for restoring/extenting kde map """
self.isResizing = True
super(DensityMap, self).restore()
def setup_densityMap(self):
"""
Setup the parameters of Density Map.
extent, width, height, grid, view_transformer etc.
"""
if self.bufferWidth >0 and self.bufferHeight>0:
# from extend to grid
left, lower, right, upper = self.extent
extent_width = self.extent[2] - self.extent[0]
extent_height = self.extent[3] - self.extent[1]
self.cols = int(ceil(extent_width / float(self.cell_size)))
self.rows = int(ceil(extent_height/ float(self.cell_size)))
self.grid = np.zeros((self.rows,self.cols))
# grid variables
self.grid_lower = lower + ( self.cell_size/2.0)
self.grid_upper = self.grid_lower + ( (self.rows -1 ) * self.cell_size)
self.grid_left = left + (self.cell_size/2.0)
self.grid_right = self.grid_left + ( (self.cols -1) * self.cell_size)
def setup_bitmap(self):
"""
bmp variables, in case of resize/pan/extent/zoom
"""
left, lower, right, upper = self.extent
self.bmp_left, self.bmp_upper = self.view.view_to_pixel( left, upper)
self.bmp_right, self.bmp_lower = self.view.view_to_pixel( right, lower)
self.bmp_width = self.bmp_right - self.bmp_left
self.bmp_height = self.bmp_lower - self.bmp_upper
def update_density(self,X,Y,invert=False):
"""
Go through each point in data, and calculate the density value of
cells it impacted.
"""
cellSize = self.cell_size
radius = self.bandwidth / cellSize
float_i = (Y - self.grid_lower) / cellSize
#float_i = (self.grid_upper -Y) / cellSize
i = int(floor(float_i - radius))
i = i if i >= 0 else 0
I = int(floor(float_i + radius))
I = I if I < self.rows else self.rows-1
float_j = (X-self.grid_left) / cellSize
j = int(floor(float_j - radius))
j = j if j >= 0 else 0
J = int(floor(float_j + radius))
J = J if J < self.cols else self.cols-1
for row in xrange(i,I+1):
for col in xrange(j,J+1):
x = self.grid_left + (col*cellSize)
y = self.grid_lower + (row*cellSize)
d = ((x-X)**2 + (y-Y)**2) ** (0.5)
if d <= self.bandwidth:
z = d/self.bandwidth
if invert:
self.grid[row,col] -= self.kernel(z)
else:
self.grid[row,col] += self.kernel(z)
self.cells_Updated = True
def grid_to_buffer(self):
from stars.core.DKDEWrapper import call_kde
x = []
y = []
for i in self.query_points:
pt = self.points[i]
x.append(pt[0])
y.append(pt[1])
n = len(self.query_points)
if n < len(self.points):
query_points = range(n)
arr,rows,cols,grad_min,grad_max = call_kde(
n,
x,
y,
#self.query_points,
range(n),
self.extent,
self.bandwidth,
self.cell_size,
self.kernel,
self.color_band,
self.opaque*2.55
)
#from PIL import Image
#Image.fromarray(arr).save("test.png")
self.bmp = wx.BitmapFromBufferRGBA(cols, rows, arr)
#self.bmp.SaveFile("test.png", wx.BITMAP_TYPE_PNG)
self.gradient_color_min = grad_min
self.gradient_color_max = grad_max
def grid_to_buffer1(self):
"""
Draw GRID based Density Map to wxBitmap.
Here, directly drawing GRID map onto a RGBA Bitmap.
Then, this Bitmap will be draw to self.buffer.
"""
if self.grid.max() == 0:
return wx.EmptyBitmapRGBA(self.rows, self.cols,255,255,255)
self.setup_bitmap()
# make Byte Array
arr = np.zeros((self.rows, self.cols, 4), np.uint8)
R, G, B, A = range(4)
# tranfer 0~1 value to 0~255 pixel values,
self.raster = self.grid
rows,cols = self.rows, self.cols
raster_min, raster_max = self.raster.min(), self.raster.max()
raster_scaled = (self.raster - raster_min)/(raster_max - raster_min)
red_matrix, blue_matrix, green_matrix, opaque_matrix = np.zeros((rows,cols)),\
np.zeros((rows, cols)), np.zeros((rows,cols)), np.ones((rows,cols))*self.opaque* (255/100.0)
for i,row in enumerate(raster_scaled):
for j,item in enumerate(row):
clr = self.gradient_color.get_color_at(item)
red_matrix[i][j] = clr.red
green_matrix[i][j] = clr.green
blue_matrix[i][j] = clr.blue
if item <0.15:
opaque_matrix[i][j] = 0
arr[:,:,R] = (red_matrix).astype("B")
arr[:,:,G] = (green_matrix).astype("B")
arr[:,:,B] = (blue_matrix).astype("B")
arr[:,:,A] = (opaque_matrix).astype("B")#self.opaque #alpha
# for export gif
self.img_array_rgb = arr
# use the array to create a bitmap
return wx.BitmapFromBufferRGBA(cols, rows, arr)
def OnCellsSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
event is an instance of EventHandler.Event class
event.object are the data for selecting shape objects
"""
if not event:
return
def OnNoCellSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
Normally, event could be None, you just need to clean and refresh
you selected/highlighted
"""
self.Refresh(False)
def hide_layer(self, layer, isHide=True):
# override ShapeMap.hide_layer() add isHideDensityMap
if layer == None:
# Density Map layer
self.isHideDensityMap = isHide
else:
self.hide_layers[layer] = isHide
self.isResizing = True
self.reInitBuffer = True
def compute_densityMap(cls, input, output):
"""
The function will be executed in each process.
"""
while True:
args = input.get()
method = args['method']
id = args['id']
if method == 'compute':
# for computing KDE for each cell in grid
points = args['data']
kernel = args['kernel']
bandwidth = args['bandwidth']
cellSize = args['cell_size']
grid_left = args['grid_left']
grid_lower= args['grid_lower']
grid_upper= args['grid_upper']
rows,cols = args['rows'], args['cols']
invert = False
radius = bandwidth / cellSize
tmpGrid = np.zeros((rows,cols))
#print '%s:%s'%(os.getpid(),args.keys())
for pt in points:
X,Y = pt
float_i = (Y - grid_lower) / cellSize
#float_i = (grid_upper -Y) / cellSize
i = int(floor(float_i - radius))
i = i if i >= 0 else 0
I = int(floor(float_i + radius))
I = I if I < rows else rows-1
float_j = (X-grid_left) / cellSize
j = int(floor(float_j - radius))
j = j if j >= 0 else 0
J = int(floor(float_j + radius))
J = J if J < cols else cols-1
for row in xrange(i,I+1):
for col in xrange(j,J+1):
x = grid_left + (col*cellSize)
y = grid_lower + (row*cellSize)
d = ((x-X)**2 + (y-Y)**2) ** (0.5)
if d <= bandwidth:
z = d/bandwidth
if invert:
tmpGrid[row,col] -= kernel(z)
else:
tmpGrid[row,col] += kernel(z)
output.put({'id':id,'grid':tmpGrid, 'grid_max':np.max(tmpGrid)})
elif method == 'create':
grid = args['grid']
grid_max = args['grid_max']
opaque = args['opaque']
color_band = args['color_band']
gradient_color = GradientColor(color_band)
# make Byte Array for bmp
rows,cols = grid.shape
arr = np.zeros((rows, cols, 4), np.uint8)
R, G, B, A = range(4)
# flip from real model coords to screen coords
raster = grid
raster = raster[::-1]
raster_min, raster_max = raster.min(), grid_max #raster.max()
raster_val_range = raster_max - raster_min
if raster_val_range > 0:
raster_scaled = (raster - raster_min)/(raster_max - raster_min)
else:
raster_scaled = raster
# tranfer 0~1 value to 0~255 pixel values,
red_matrix, blue_matrix, green_matrix, opaque_matrix = \
np.zeros((rows,cols)),\
np.zeros((rows, cols)), \
np.zeros((rows,cols)), \
np.ones((rows,cols))*opaque* (255/100.0) # opaque ranges (0,100)
for i,row in enumerate(raster_scaled):
for j,item in enumerate(row):
clr = gradient_color.get_color_at(item)
red_matrix[i][j] = clr.red
green_matrix[i][j] = clr.green
blue_matrix[i][j] = clr.blue
if item <0.15:
opaque_matrix[i][j] = 0
arr[:,:,R] = (red_matrix).astype("B")
arr[:,:,G] = (green_matrix).astype("B")
arr[:,:,B] = (blue_matrix).astype("B")
arr[:,:,A] = (opaque_matrix).astype("B")#self.opaque #alpha
output.put({'id':id,'data':arr})
def __init__(self, parent, layers, **kwargs):
ShapeMap.__init__(self, parent, layers)
try:
self.point_layer = self.layers[0]
#self.bg_layer = kwargs["background"]
self.cell_size = kwargs["cell_size"]
self.bandwidth = kwargs["bandwidth"]
self.kernel = kwargs["kernel"]
self.color_band = kwargs["color_band"]
self.opaque = kwargs["opaque"]
self.bufferWidth, self.bufferHeight = kwargs["size"]
self.points = self.point_layer.shape_objects
self.extent = self.point_layer.extent
self.view = View2ScreenTransform(self.extent, self.bufferWidth,
self.bufferHeight)
if kwargs.has_key("query_points"):
self.query_points = kwargs["query_points"]
else:
self.query_points = range(len(self.points))
"""
if self.bg_layer:
self.layers.append(self.bg_layer)
"""
if len(self.query_points) < len(self.points):
id_group = [self.query_points]
color_group = [
self.color_schema_dict[self.point_layer.name].colors[0]
]
edge_color_group = [
self.color_schema_dict[self.point_layer.name].edge_color
]
"""
non_query_points = list(set(range(len(self.points))) - set(self.query_points))
id_group.append(non_query_points)
color_group.append(wx.Colour(255,255,255,0))
edge_color_group.append(wx.Colour(255,255,255,0))
"""
self.draw_layers[self.point_layer].set_data_group(id_group)
self.draw_layers[self.point_layer].set_fill_color_group(
color_group)
self.draw_layers[self.point_layer].set_edge_color_group(
edge_color_group)
# setup gradient color
self.gradient_color = GradientColor(self.color_band)
# for tree node
self.color_schema_dict["Density Map"] = GradientColorSchema(
self.color_band)
self.isHideDensityMap = False
# for export gif
self.img_array_rgb = None
self.isResizing = False
self.cells_Updated = False
self.bmp = None
self.setup_densityMap()
self.createDensityMap()
# dummy Layer for density map
self.layer_seq_list = []
self.layer_seq_list.insert(0, "Density Map")
self.layer_name_list.insert(0, "Density Map")
except Exception as err:
self.ShowMsgBox(
"""Density map could not be created. Please choose/input valid parameters.
Details: """ + str(err.message))
self.UnRegister()
self.parentFrame.Close(True)
return None
class DensityMap(ShapeMap):
"""
wxWindow drawing density map based on points data or POINT SHAPE file
"""
def __init__(self, parent, layers, **kwargs):
ShapeMap.__init__(self, parent, layers)
try:
self.point_layer = self.layers[0]
#self.bg_layer = kwargs["background"]
self.cell_size = kwargs["cell_size"]
self.bandwidth = kwargs["bandwidth"]
self.kernel = kwargs["kernel"]
self.color_band = kwargs["color_band"]
self.opaque = kwargs["opaque"]
self.bufferWidth, self.bufferHeight = kwargs["size"]
self.points = self.point_layer.shape_objects
self.extent = self.point_layer.extent
self.view = View2ScreenTransform(self.extent, self.bufferWidth,
self.bufferHeight)
if kwargs.has_key("query_points"):
self.query_points = kwargs["query_points"]
else:
self.query_points = range(len(self.points))
"""
if self.bg_layer:
self.layers.append(self.bg_layer)
"""
if len(self.query_points) < len(self.points):
id_group = [self.query_points]
color_group = [
self.color_schema_dict[self.point_layer.name].colors[0]
]
edge_color_group = [
self.color_schema_dict[self.point_layer.name].edge_color
]
"""
non_query_points = list(set(range(len(self.points))) - set(self.query_points))
id_group.append(non_query_points)
color_group.append(wx.Colour(255,255,255,0))
edge_color_group.append(wx.Colour(255,255,255,0))
"""
self.draw_layers[self.point_layer].set_data_group(id_group)
self.draw_layers[self.point_layer].set_fill_color_group(
color_group)
self.draw_layers[self.point_layer].set_edge_color_group(
edge_color_group)
# setup gradient color
self.gradient_color = GradientColor(self.color_band)
# for tree node
self.color_schema_dict["Density Map"] = GradientColorSchema(
self.color_band)
self.isHideDensityMap = False
# for export gif
self.img_array_rgb = None
self.isResizing = False
self.cells_Updated = False
self.bmp = None
self.setup_densityMap()
self.createDensityMap()
# dummy Layer for density map
self.layer_seq_list = []
self.layer_seq_list.insert(0, "Density Map")
self.layer_name_list.insert(0, "Density Map")
except Exception as err:
self.ShowMsgBox(
"""Density map could not be created. Please choose/input valid parameters.
Details: """ + str(err.message))
self.UnRegister()
self.parentFrame.Close(True)
return None
def OnShapesSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
event is an instance of EventHandler.Event class
event.object are the data for selecting shape objects
"""
if not event: return
if not self.buffer: return
data = event.data
if len(data.boundary) > 0:
# select shape object by boundary
# prepare DC for brushing drawing
tmp_buffer = wx.EmptyBitmap(self.bufferWidth, self.bufferHeight)
tmp_dc = wx.BufferedDC(None, tmp_buffer)
tmp_dc.DrawBitmap(self.drawing_backup_buffer, 0, 0)
regions = []
if isinstance(data.boundary[0], int) or isinstance(
data.boundary[0], float):
regions.append(data.boundary)
elif isinstance(data.boundary, list):
regions = data.boundary
for region in regions:
x, y, x0, y0 = region
x, y = self.view.view_to_pixel(x, y)
x0, y0 = self.view.view_to_pixel(x0, y0)
select_region = (x, y, x0, y0)
w = abs(x - x0)
h = abs(y - y0)
start_x, start_y = min(x0, x), min(y, y0)
tmp_dc.SetPen(wx.RED_PEN)
tmp_dc.SetBrush(wx.TRANSPARENT_BRUSH)
tmp_dc.DrawRectangle(int(start_x), int(start_y), int(w),
int(h))
self.buffer = tmp_buffer
self.Refresh(False)
def set_opaque(self, opaque):
"""
Set the opaque of the Density Map
Value range (0-255), less value, more transparent
"""
self.opaque = opaque
"""
Overwrite draw_background() to draw MAP buffer
and DenstiyMAP buffer as background
"""
if self.buffer:
dc.DrawBitmap(self.buffer, 0, 0)
if self.bmp:
# always scale density map to correct window size and draw them
scale_x = self.bmp_width / float(self.cols)
scale_y = self.bmp_height / float(self.rows)
dc.SetUserScale(scale_x, scale_y)
dc.DrawBitmap(self.bmp, self.bmp_left / scale_x,
self.bmp_upper / scale_y)
def createDensityMap(self):
# compute density map, just once
if self.cells_Updated == False:
n = len(self.points)
progress_dlg = wx.ProgressDialog(
"Progress",
"Creating density map... ",
maximum=2,
parent=self,
style=wx.PD_APP_MODAL | wx.PD_AUTO_HIDE)
progress_dlg.CenterOnScreen()
progress_dlg.Update(1)
self.grid_to_buffer()
progress_dlg.Update(2)
def DoDraw(self, dc):
"""
Implement inhereted DoDraw() function
It will be called when window initialize and resize
"""
# draw layer in buffer
for layer_name in self.layer_name_list[::-1]:
if layer_name == "Density Map":
if self.isHideDensityMap == False:
self.drawDensityMap(dc)
else:
layer = self.layer_dict[layer_name]
if self.hide_layers[layer] == False:
self.draw_layers[layer].draw(dc, self.view)
def drawDensityMap(self, dc):
bmp = self.bmp
if self.isResizing:
# resize bmp
self.setup_bitmap()
image = wx.ImageFromBitmap(self.bmp)
if self.bmp_width > self.bufferWidth or self.bmp_height > self.bufferHeight:
cropLeft = 0
if self.bmp_left < 0:
cropLeft = abs(
self.bmp_left) / self.bmp_width * self.bmp.Width
cropUpper = 0
if self.bmp_upper < 0:
cropUpper = abs(
self.bmp_upper) / self.bmp_height * self.bmp.Height
cropWidth = self.bufferWidth / self.bmp_width * self.bmp.Width
if self.bmp_left > 0:
cropWidth = (self.bufferWidth - self.bmp_left
) / self.bmp_width * self.bmp.Width
cropHeight = self.bufferHeight / self.bmp_height * self.bmp.Height
if self.bmp_upper > 0:
cropHeight = (self.bufferHeight - self.bmp_upper
) / self.bmp_height * self.bmp.Height
if cropWidth > 0 and cropHeight > 0:
image = image.GetSubImage(
wx.Rect(cropLeft, cropUpper, cropWidth, cropHeight))
image = image.Scale(self.bufferWidth, self.bufferHeight,
wx.IMAGE_QUALITY_HIGH)
bmp = wx.BitmapFromImage(image)
start_x = 0
start_y = 0
if self.bmp_left > 0:
start_x = self.bmp_left
if self.bmp_upper > 0:
start_y = self.bmp_upper
dc.DrawBitmap(bmp, start_x, start_y)
else:
image = image.Scale(self.bmp_width, self.bmp_height,
wx.IMAGE_QUALITY_HIGH)
bmp = wx.BitmapFromImage(image)
dc.DrawBitmap(bmp, self.bmp_left, self.bmp_upper)
self.isResizing = False
else:
# draw density map, by zooming the Density Map
dc.DrawBitmap(bmp, self.bmp_left, self.bmp_upper)
def UpdateGradient(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
# update color schema
self.color_schema_dict["Density Map"] = GradientColorSchema(
gradient_type)
self.color_band = gradient_type
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
return self.color_schema_dict["Density Map"]
def UpdateOpaque(self, opaque):
self.opaque = opaque
self.grid_to_buffer()
self.isResizing = True
self.reInitBuffer = True
def OnSize(self, event):
""" overwrittern for resizing kde map"""
self.isResizing = True
super(DensityMap, self).OnSize(event)
def _zoom_end(self, mouse_start_pos, mouse_end_pos, mouse_select_w,
mouse_select_h):
""" overwrittern for zooming with mouse """
self.isResizing = True
super(DensityMap, self)._zoom_end(mouse_start_pos, mouse_end_pos,
mouse_select_w, mouse_select_h)
def _pan_end(self, mouse_start_pos, mouse_end_pos, mouse_select_w,
mouse_select_h):
""" overwrittern for panning with mouse """
self.isResizing = True
super(DensityMap, self)._pan_end(mouse_start_pos, mouse_end_pos,
mouse_select_w, mouse_select_h)
self.reInitBuffer = True
def restore(self):
""" overwritten for restoring/extenting kde map """
self.isResizing = True
super(DensityMap, self).restore()
def setup_densityMap(self):
"""
Setup the parameters of Density Map.
extent, width, height, grid, view_transformer etc.
"""
if self.bufferWidth > 0 and self.bufferHeight > 0:
# from extend to grid
left, lower, right, upper = self.extent
extent_width = self.extent[2] - self.extent[0]
extent_height = self.extent[3] - self.extent[1]
self.cols = int(ceil(extent_width / float(self.cell_size)))
self.rows = int(ceil(extent_height / float(self.cell_size)))
self.grid = np.zeros((self.rows, self.cols))
# grid variables
self.grid_lower = lower + (self.cell_size / 2.0)
self.grid_upper = self.grid_lower + (
(self.rows - 1) * self.cell_size)
self.grid_left = left + (self.cell_size / 2.0)
self.grid_right = self.grid_left + (
(self.cols - 1) * self.cell_size)
def setup_bitmap(self):
"""
bmp variables, in case of resize/pan/extent/zoom
"""
left, lower, right, upper = self.extent
self.bmp_left, self.bmp_upper = self.view.view_to_pixel(left, upper)
self.bmp_right, self.bmp_lower = self.view.view_to_pixel(right, lower)
self.bmp_width = self.bmp_right - self.bmp_left
self.bmp_height = self.bmp_lower - self.bmp_upper
def update_density(self, X, Y, invert=False):
"""
Go through each point in data, and calculate the density value of
cells it impacted.
"""
cellSize = self.cell_size
radius = self.bandwidth / cellSize
float_i = (Y - self.grid_lower) / cellSize
#float_i = (self.grid_upper -Y) / cellSize
i = int(floor(float_i - radius))
i = i if i >= 0 else 0
I = int(floor(float_i + radius))
I = I if I < self.rows else self.rows - 1
float_j = (X - self.grid_left) / cellSize
j = int(floor(float_j - radius))
j = j if j >= 0 else 0
J = int(floor(float_j + radius))
J = J if J < self.cols else self.cols - 1
for row in xrange(i, I + 1):
for col in xrange(j, J + 1):
x = self.grid_left + (col * cellSize)
y = self.grid_lower + (row * cellSize)
d = ((x - X)**2 + (y - Y)**2)**(0.5)
if d <= self.bandwidth:
z = d / self.bandwidth
if invert:
self.grid[row, col] -= self.kernel(z)
else:
self.grid[row, col] += self.kernel(z)
self.cells_Updated = True
def grid_to_buffer(self):
from stars.core.DKDEWrapper import call_kde
x = []
y = []
for i in self.query_points:
pt = self.points[i]
x.append(pt[0])
y.append(pt[1])
n = len(self.query_points)
if n < len(self.points):
query_points = range(n)
arr, rows, cols, grad_min, grad_max = call_kde(
n,
x,
y,
#self.query_points,
range(n),
self.extent,
self.bandwidth,
self.cell_size,
self.kernel,
self.color_band,
self.opaque * 2.55)
#from PIL import Image
#Image.fromarray(arr).save("test.png")
self.bmp = wx.BitmapFromBufferRGBA(cols, rows, arr)
#self.bmp.SaveFile("test.png", wx.BITMAP_TYPE_PNG)
self.gradient_color_min = grad_min
self.gradient_color_max = grad_max
def grid_to_buffer1(self):
"""
Draw GRID based Density Map to wxBitmap.
Here, directly drawing GRID map onto a RGBA Bitmap.
Then, this Bitmap will be draw to self.buffer.
"""
if self.grid.max() == 0:
return wx.EmptyBitmapRGBA(self.rows, self.cols, 255, 255, 255)
self.setup_bitmap()
# make Byte Array
arr = np.zeros((self.rows, self.cols, 4), np.uint8)
R, G, B, A = range(4)
# tranfer 0~1 value to 0~255 pixel values,
self.raster = self.grid
rows, cols = self.rows, self.cols
raster_min, raster_max = self.raster.min(), self.raster.max()
raster_scaled = (self.raster - raster_min) / (raster_max - raster_min)
red_matrix, blue_matrix, green_matrix, opaque_matrix = np.zeros((rows,cols)),\
np.zeros((rows, cols)), np.zeros((rows,cols)), np.ones((rows,cols))*self.opaque* (255/100.0)
for i, row in enumerate(raster_scaled):
for j, item in enumerate(row):
clr = self.gradient_color.get_color_at(item)
red_matrix[i][j] = clr.red
green_matrix[i][j] = clr.green
blue_matrix[i][j] = clr.blue
if item < 0.15:
opaque_matrix[i][j] = 0
arr[:, :, R] = (red_matrix).astype("B")
arr[:, :, G] = (green_matrix).astype("B")
arr[:, :, B] = (blue_matrix).astype("B")
arr[:, :, A] = (opaque_matrix).astype("B") #self.opaque #alpha
# for export gif
self.img_array_rgb = arr
# use the array to create a bitmap
return wx.BitmapFromBufferRGBA(cols, rows, arr)
def OnCellsSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
event is an instance of EventHandler.Event class
event.object are the data for selecting shape objects
"""
if not event:
return
def OnNoCellSelect(self, event):
"""
Event handler for EVT_OBJ_SELECT.
Observer will call this function when any other widgets/panels
dispatch EVT_OBJ_SELECT event
Normally, event could be None, you just need to clean and refresh
you selected/highlighted
"""
self.Refresh(False)
def hide_layer(self, layer, isHide=True):
# override ShapeMap.hide_layer() add isHideDensityMap
if layer == None:
# Density Map layer
self.isHideDensityMap = isHide
else:
self.hide_layers[layer] = isHide
self.isResizing = True
self.reInitBuffer = True
class Histogram(PlottingCanvas):
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self,parent, data)
try:
if isinstance(layer,str) or isinstance(layer, unicode):
# in case of weights histogram
self.layer_name = layer
else:
self.layer_name = layer.name
self.isAutoScale = False
self.intervals = 7
#self.title = "Histogram (%s)" % (self.layer_name)
self.title = ""
self.x_label = data.keys()[0]
self.y_label = "Counts in bins"
self.data = data[self.x_label]
self.enable_axis_x = False
self.draw_full_axis = False
self.margin_right = 250 # for legend
# create a dict for input data for Brushing
self.data_dict = sorted(self.data, key=self.data.get) #[obj_id]
sorted_data = sorted(self.data.values()) #[value]
if self.x_label == 'Connectivity':
self.intervals = len(set(sorted_data))
self.intervals = sorted_data[-1] - sorted_data[0] + 1
if self.intervals > 50:
self.enable_axis_x = True
self.margin_right = 40
if self.intervals > 1:
self.hist, low_range, binsize, extrapoints = histogram(sorted_data, self.intervals)
else:
self.hist = np.array([len(sorted_data)])
cnt = 0; bin_idx = 0
self.bin_index = {} # key: obj_id, value: bin_idx
for n in self.hist:
for i in range(int(n)):
obj_id = self.data_dict[cnt]
self.bin_index[obj_id] = bin_idx
cnt += 1
bin_idx += 1
data_min, data_max = sorted_data[0], sorted_data[-1]
if self.x_label == 'Connectivity':
#unique_num_neighbors = list(set(sorted_data))
self.data_intervals = []
for n in range(sorted_data[0], sorted_data[-1]+1):
self.data_intervals.append((n,n))
else:
end_pos = np.cumsum(self.hist)
start_pos = end_pos - self.hist + 1
self.data_intervals = [ (start_pos[i],end_pos[i]) for i in range(len(self.hist))]
# a NxN matrix
self.x_min = 1
self.x_max = self.intervals +1
self.y_min = 0
self.y_max = np.max(self.hist) +1
self.extent = (self.x_min, self.y_min, self.x_max,self.y_max)
self.status_bar = None#self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
# color schema: from blue to red
self.color_matrix = []
for i in range(self.intervals):
p = float(i+1) / self.intervals
self.color_matrix.append( self.gradient_color.get_color_at(p))
self.selected_obj_ids = []
except Exception as err:
self.ShowMsgBox(""" Histogram could not be created. Please select a numeric variable.
Details: """ + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
# linking-brushing events
self.Register(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Register(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
def OnClose(self,event):
self.Unregister(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Unregister(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
event.Skip()
def OnObjsSelected(self, event):
if not event: return
data = event.data
if len(data.shape_ids) > 0:
# directly select by shape_ids
if data.shape_ids.has_key(self.layer_name):
self.selected_obj_ids = data.shape_ids[self.layer_name]
self.draw_selected()
def OnNoObjSelect(self, event):
self.selected_obj_ids = []
self.Refresh(False)
def draw_selected(self,dc=None):
if len(self.selected_obj_ids) > 0:
if dc == None:
# draw selected on client DC
dc = wx.ClientDC(self)
dc.DrawBitmap(self.buffer,0,0)
highlight_counts_in_bins = [0] * len(self.hist)
for obj_id in self.selected_obj_ids:
bin_idx = self.bin_index[obj_id]
highlight_counts_in_bins[bin_idx] += 1
# draw highlighted
w=1
brush = wx.Brush(wx.Colour(255,255,0), wx.CROSSDIAG_HATCH)
for i,count in enumerate(highlight_counts_in_bins):
start_x = i+1
start_y = count
h = count
pixel_x,pixel_y = self.point_to_screen(start_x,start_y)
pixel_w,pixel_h = math.ceil(self.length_to_screen(w)),math.ceil(self.length_to_screen(h,axis=1))
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x,pixel_y,pixel_w,pixel_h)
def draw_selected_by_region(self, dc,select_region, isScreenCoordinates=True):
"""
this function highlight the points selected
by mouse drawing a region
"""
self.selected_obj_ids= []
x0,y0,x1,y1= select_region
ymin = min(y0,y1)
ymax = max(y0,y1)
y0 = ymin
y1 = ymax
x0,y0 = self.view.pan_to(x0,y0,-self.margin_left,-self.margin_top)
x1,y1 = self.view.pan_to(x1,y1,-self.margin_left,-self.margin_top)
x0,y0 = self.view.pixel_to_view(x0,y0)
x1,y1 = self.view.pixel_to_view(x1,y1)
# test intersection
x0,y0,x1,y1 = int(math.floor(x0)),int(math.floor(min(y1,y0))),int(math.ceil(x1)),int(math.ceil(max(y0,y1)))
if y0<0: y0=0
if x0<1: x0=1
n = len(self.hist)
for x in range(x0,x1):
if x <= n:
start_pos = int(sum(self.hist[:x-1]))
if y0 < self.hist[x-1]:
m = y1 if y1 < self.hist[x-1] else self.hist[x-1]
m = int(m)
for i in range(y0,m):
idx = self.data_dict[start_pos+i]
self.selected_obj_ids.append(idx)
self.draw_selected(dc)
if len(self.selected_obj_ids)>0:
# draw selected
# tell this action to THE OBSERVER
data = AbstractData(self)
data.shape_ids[self.layer_name] = self.selected_obj_ids
self.UpdateEvt(stars.EVT_OBJS_SELECT, data)
else:
# unselect all
# tell this action to THE OBSERVER
data = AbstractData(self)
self.UpdateEvt(stars.EVT_OBJS_UNSELECT,data)
def set_intervals(self):
pass
def plot_data(self,dc):
w = 1
o_x, o_y = self.point_to_screen(1,0)
for i in range(self.intervals):
binNum = self.hist[i]
start_x = i+1
start_y = binNum
pixel_x,pixel_y = self.point_to_screen(start_x,start_y)
pixel_nx,pixel_ny = self.point_to_screen(start_x+1,start_y)
pixel_w = int(pixel_nx) - int(pixel_x) + 1
pixel_h = int(o_y) - int(pixel_y) + 1
"""
h = binNum
pixel_w,pixel_h = self.length_to_screen(w),self.length_to_screen(h,axis=1)
pixel_w = round(int(pixel_w) + pixel_w - int(pixel_w) + pixel_x - int(pixel_x))
pixel_h = round(int(pixel_h) + pixel_h - int(pixel_h) + pixel_y - int(pixel_y))
"""
brush = wx.Brush(self.color_matrix[i])
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x,pixel_y,pixel_w,pixel_h)
if self.x_label == "Connectivity" and self.intervals > 50:
return
# draw a legend bar
pixel_x,pixel_y = self.point_to_screen( start_x+w, self.y_max)
pixel_x = self.ax_start_x + self.ax_width + 10
pixel_y = self.ax_start_y
pixel_h = self.length_to_screen(self.y_max-self.y_min,axis=1)
pixel_w = 30
num_items = self.intervals
if num_items == 1: num_items += 1
legend_item_h = pixel_h * 0.7 / num_items
dc.SetBrush(wx.Brush(wx.Colour(255,255,0),wx.CROSSDIAG_HATCH))
dc.DrawRectangle( pixel_x, pixel_y, pixel_w, legend_item_h)
dc.DrawText( "selected features", pixel_x + pixel_w + 5, pixel_y)
for i in range(self.intervals):
pixel_y += legend_item_h + 5
dc.SetBrush( wx.Brush(self.color_matrix[i]))
dc.DrawRectangle( pixel_x, pixel_y, pixel_w, legend_item_h)
label = '%s - %s (%d)' % (self.data_intervals[i][0], self.data_intervals[i][1], self.hist[i])
dc.DrawText( label, pixel_x + pixel_w + 5, pixel_y)
class HeatMatrix(PlottingCanvas):
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self, parent, data)
try:
self.layer_name = layer.name
self.title = "Transition probility matrix (%s)" % self.layer_name
self.x_label = "LISA transition states (1=HH,2=LH,3=LL,4=HL)"
self.y_label = "LISA transition states"
self.data = data
n = len(self.data)
self.enable_axis_labels = False
self.enable_axis = True
self.enable_axis_x = False
self.enable_axis_y = False
# a NxN matrix
self.x_min = 1
self.x_max = n + 1
self.y_min = 1
self.y_max = n + 1
self.extent = (self.x_min, self.y_min, self.x_max, self.y_max)
self.selected_polygon_ids = []
self.status_bar = self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
self.margin_right = 100
# color schema: from blue to red
self.color_matrix = []
for i in range(n):
color_row = []
for j in range(n):
p = self.data[i][j]
color_row.append(self.gradient_color.get_color_at(p))
self.color_matrix.append(color_row)
except Exception as err:
self.ShowMsgBox('Fail to init heat map! ' + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
def OnClose(self, event):
event.Skip()
def plot_data(self, dc):
# draw a NxN matrix
w, h = 1, 1
for i, row in enumerate(self.data):
for j, item in enumerate(row):
start_x = j + self.x_min
start_y = self.y_max - i
pixel_x, pixel_y = self.point_to_screen(start_x, start_y)
pixel_w, pixel_h = math.ceil(
self.length_to_screen(w)), math.ceil(
self.length_to_screen(h, axis=1))
brush = wx.Brush(self.color_matrix[i][j])
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x, pixel_y, pixel_w, pixel_h)
if i == len(self.data) - 1:
dc.DrawText(str(j + 1), pixel_x + pixel_w / 2,
pixel_y + pixel_h + 5)
if j == 0:
dc.DrawText(str(len(self.data) - i), pixel_x - 10,
pixel_y + pixel_h / 2)
text_pixel_x, text_pixel_y = pixel_x + pixel_w / 2.0 - 10, pixel_y + pixel_h / 2.0
dc.SetPen(wx.WHITE_PEN)
dc.SetBrush(wx.WHITE_BRUSH)
dc.DrawText('%.4f' % (self.data[i][j]), text_pixel_x,
text_pixel_y)
# draw a legend bar
pixel_x, pixel_y = self.point_to_screen(start_x + w, self.y_max)
pixel_x += 20
pixel_h = self.length_to_screen(self.y_max - self.y_min, axis=1)
pixel_w = 20
gradient_colorbar = self.gradient_color.get_bmp(pixel_w, pixel_h)
dc.DrawBitmap(gradient_colorbar, pixel_x, pixel_y)
pixel_x = pixel_x + pixel_w + 10
dc.SetPen(wx.BLACK_PEN)
dc.DrawText(str('%.2f' % np.max(self.data)), pixel_x, pixel_y)
pixel_y = pixel_y + pixel_h - 12
dc.DrawText(str('%.2f' % np.min(self.data)), pixel_x, pixel_y)
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self, parent, data)
try:
if isinstance(layer, str) or isinstance(layer, unicode):
# in case of weights histogram
self.layer_name = layer
else:
self.layer_name = layer.name
self.isAutoScale = False
self.intervals = 7
#self.title = "Histogram (%s)" % (self.layer_name)
self.title = ""
self.x_label = data.keys()[0]
self.y_label = "Counts in bins"
self.data = data[self.x_label]
self.enable_axis_x = False
self.draw_full_axis = False
self.margin_right = 250 # for legend
# create a dict for input data for Brushing
self.data_dict = sorted(self.data, key=self.data.get) #[obj_id]
sorted_data = sorted(self.data.values()) #[value]
if self.x_label == 'Connectivity':
self.intervals = len(set(sorted_data))
self.intervals = sorted_data[-1] - sorted_data[0] + 1
if self.intervals > 50:
self.enable_axis_x = True
self.margin_right = 40
if self.intervals > 1:
self.hist, low_range, binsize, extrapoints = histogram(
sorted_data, self.intervals)
else:
self.hist = np.array([len(sorted_data)])
cnt = 0
bin_idx = 0
self.bin_index = {} # key: obj_id, value: bin_idx
for n in self.hist:
for i in range(int(n)):
obj_id = self.data_dict[cnt]
self.bin_index[obj_id] = bin_idx
cnt += 1
bin_idx += 1
data_min, data_max = sorted_data[0], sorted_data[-1]
if self.x_label == 'Connectivity':
#unique_num_neighbors = list(set(sorted_data))
self.data_intervals = []
for n in range(sorted_data[0], sorted_data[-1] + 1):
self.data_intervals.append((n, n))
else:
end_pos = np.cumsum(self.hist)
start_pos = end_pos - self.hist + 1
self.data_intervals = [(start_pos[i], end_pos[i])
for i in range(len(self.hist))]
# a NxN matrix
self.x_min = 1
self.x_max = self.intervals + 1
self.y_min = 0
self.y_max = np.max(self.hist) + 1
self.extent = (self.x_min, self.y_min, self.x_max, self.y_max)
self.status_bar = None #self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
# color schema: from blue to red
self.color_matrix = []
for i in range(self.intervals):
p = float(i + 1) / self.intervals
self.color_matrix.append(self.gradient_color.get_color_at(p))
self.selected_obj_ids = []
except Exception as err:
self.ShowMsgBox(
""" Histogram could not be created. Please select a numeric variable.
Details: """ + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
# linking-brushing events
self.Register(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Register(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
class Histogram(PlottingCanvas):
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self, parent, data)
try:
if isinstance(layer, str) or isinstance(layer, unicode):
# in case of weights histogram
self.layer_name = layer
else:
self.layer_name = layer.name
self.isAutoScale = False
self.intervals = 7
#self.title = "Histogram (%s)" % (self.layer_name)
self.title = ""
self.x_label = data.keys()[0]
self.y_label = "Counts in bins"
self.data = data[self.x_label]
self.enable_axis_x = False
self.draw_full_axis = False
self.margin_right = 250 # for legend
# create a dict for input data for Brushing
self.data_dict = sorted(self.data, key=self.data.get) #[obj_id]
sorted_data = sorted(self.data.values()) #[value]
if self.x_label == 'Connectivity':
self.intervals = len(set(sorted_data))
self.intervals = sorted_data[-1] - sorted_data[0] + 1
if self.intervals > 50:
self.enable_axis_x = True
self.margin_right = 40
if self.intervals > 1:
self.hist, low_range, binsize, extrapoints = histogram(
sorted_data, self.intervals)
else:
self.hist = np.array([len(sorted_data)])
cnt = 0
bin_idx = 0
self.bin_index = {} # key: obj_id, value: bin_idx
for n in self.hist:
for i in range(int(n)):
obj_id = self.data_dict[cnt]
self.bin_index[obj_id] = bin_idx
cnt += 1
bin_idx += 1
data_min, data_max = sorted_data[0], sorted_data[-1]
if self.x_label == 'Connectivity':
#unique_num_neighbors = list(set(sorted_data))
self.data_intervals = []
for n in range(sorted_data[0], sorted_data[-1] + 1):
self.data_intervals.append((n, n))
else:
end_pos = np.cumsum(self.hist)
start_pos = end_pos - self.hist + 1
self.data_intervals = [(start_pos[i], end_pos[i])
for i in range(len(self.hist))]
# a NxN matrix
self.x_min = 1
self.x_max = self.intervals + 1
self.y_min = 0
self.y_max = np.max(self.hist) + 1
self.extent = (self.x_min, self.y_min, self.x_max, self.y_max)
self.status_bar = None #self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
# color schema: from blue to red
self.color_matrix = []
for i in range(self.intervals):
p = float(i + 1) / self.intervals
self.color_matrix.append(self.gradient_color.get_color_at(p))
self.selected_obj_ids = []
except Exception as err:
self.ShowMsgBox(
""" Histogram could not be created. Please select a numeric variable.
Details: """ + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
# linking-brushing events
self.Register(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Register(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
def OnClose(self, event):
self.Unregister(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Unregister(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
event.Skip()
def OnObjsSelected(self, event):
if not event: return
data = event.data
if len(data.shape_ids) > 0:
# directly select by shape_ids
if data.shape_ids.has_key(self.layer_name):
self.selected_obj_ids = data.shape_ids[self.layer_name]
self.draw_selected()
def OnNoObjSelect(self, event):
self.selected_obj_ids = []
self.Refresh(False)
def draw_selected(self, dc=None):
if len(self.selected_obj_ids) > 0:
if dc == None:
# draw selected on client DC
dc = wx.ClientDC(self)
dc.DrawBitmap(self.buffer, 0, 0)
highlight_counts_in_bins = [0] * len(self.hist)
for obj_id in self.selected_obj_ids:
bin_idx = self.bin_index[obj_id]
highlight_counts_in_bins[bin_idx] += 1
# draw highlighted
w = 1
brush = wx.Brush(wx.Colour(255, 255, 0), wx.CROSSDIAG_HATCH)
for i, count in enumerate(highlight_counts_in_bins):
start_x = i + 1
start_y = count
h = count
pixel_x, pixel_y = self.point_to_screen(start_x, start_y)
pixel_w, pixel_h = math.ceil(
self.length_to_screen(w)), math.ceil(
self.length_to_screen(h, axis=1))
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x, pixel_y, pixel_w, pixel_h)
def draw_selected_by_region(self,
dc,
select_region,
isScreenCoordinates=True):
"""
this function highlight the points selected
by mouse drawing a region
"""
self.selected_obj_ids = []
x0, y0, x1, y1 = select_region
ymin = min(y0, y1)
ymax = max(y0, y1)
y0 = ymin
y1 = ymax
x0, y0 = self.view.pan_to(x0, y0, -self.margin_left, -self.margin_top)
x1, y1 = self.view.pan_to(x1, y1, -self.margin_left, -self.margin_top)
x0, y0 = self.view.pixel_to_view(x0, y0)
x1, y1 = self.view.pixel_to_view(x1, y1)
# test intersection
x0, y0, x1, y1 = int(math.floor(x0)), int(math.floor(min(
y1, y0))), int(math.ceil(x1)), int(math.ceil(max(y0, y1)))
if y0 < 0: y0 = 0
if x0 < 1: x0 = 1
n = len(self.hist)
for x in range(x0, x1):
if x <= n:
start_pos = int(sum(self.hist[:x - 1]))
if y0 < self.hist[x - 1]:
m = y1 if y1 < self.hist[x - 1] else self.hist[x - 1]
m = int(m)
for i in range(y0, m):
idx = self.data_dict[start_pos + i]
self.selected_obj_ids.append(idx)
self.draw_selected(dc)
if len(self.selected_obj_ids) > 0:
# draw selected
# tell this action to THE OBSERVER
data = AbstractData(self)
data.shape_ids[self.layer_name] = self.selected_obj_ids
self.UpdateEvt(stars.EVT_OBJS_SELECT, data)
else:
# unselect all
# tell this action to THE OBSERVER
data = AbstractData(self)
self.UpdateEvt(stars.EVT_OBJS_UNSELECT, data)
def set_intervals(self):
pass
def plot_data(self, dc):
w = 1
o_x, o_y = self.point_to_screen(1, 0)
for i in range(self.intervals):
binNum = self.hist[i]
start_x = i + 1
start_y = binNum
pixel_x, pixel_y = self.point_to_screen(start_x, start_y)
pixel_nx, pixel_ny = self.point_to_screen(start_x + 1, start_y)
pixel_w = int(pixel_nx) - int(pixel_x) + 1
pixel_h = int(o_y) - int(pixel_y) + 1
"""
h = binNum
pixel_w,pixel_h = self.length_to_screen(w),self.length_to_screen(h,axis=1)
pixel_w = round(int(pixel_w) + pixel_w - int(pixel_w) + pixel_x - int(pixel_x))
pixel_h = round(int(pixel_h) + pixel_h - int(pixel_h) + pixel_y - int(pixel_y))
"""
brush = wx.Brush(self.color_matrix[i])
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x, pixel_y, pixel_w, pixel_h)
if self.x_label == "Connectivity" and self.intervals > 50:
return
# draw a legend bar
pixel_x, pixel_y = self.point_to_screen(start_x + w, self.y_max)
pixel_x = self.ax_start_x + self.ax_width + 10
pixel_y = self.ax_start_y
pixel_h = self.length_to_screen(self.y_max - self.y_min, axis=1)
pixel_w = 30
num_items = self.intervals
if num_items == 1: num_items += 1
legend_item_h = pixel_h * 0.7 / num_items
dc.SetBrush(wx.Brush(wx.Colour(255, 255, 0), wx.CROSSDIAG_HATCH))
dc.DrawRectangle(pixel_x, pixel_y, pixel_w, legend_item_h)
dc.DrawText("selected features", pixel_x + pixel_w + 5, pixel_y)
for i in range(self.intervals):
pixel_y += legend_item_h + 5
dc.SetBrush(wx.Brush(self.color_matrix[i]))
dc.DrawRectangle(pixel_x, pixel_y, pixel_w, legend_item_h)
label = '%s - %s (%d)' % (self.data_intervals[i][0],
self.data_intervals[i][1], self.hist[i])
dc.DrawText(label, pixel_x + pixel_w + 5, pixel_y)
def __init__(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
ColorSchema.__init__(self,None,[""])
def __init__(self, parent, layer, data, **kwargs):
PlottingCanvas.__init__(self,parent, data)
try:
if isinstance(layer,str) or isinstance(layer, unicode):
# in case of weights histogram
self.layer_name = layer
else:
self.layer_name = layer.name
self.isAutoScale = False
self.intervals = 7
#self.title = "Histogram (%s)" % (self.layer_name)
self.title = ""
self.x_label = data.keys()[0]
self.y_label = "Counts in bins"
self.data = data[self.x_label]
self.enable_axis_x = False
self.draw_full_axis = False
self.margin_right = 250 # for legend
# create a dict for input data for Brushing
self.data_dict = sorted(self.data, key=self.data.get) #[obj_id]
sorted_data = sorted(self.data.values()) #[value]
if self.x_label == 'Connectivity':
self.intervals = len(set(sorted_data))
self.intervals = sorted_data[-1] - sorted_data[0] + 1
if self.intervals > 50:
self.enable_axis_x = True
self.margin_right = 40
if self.intervals > 1:
self.hist, low_range, binsize, extrapoints = histogram(sorted_data, self.intervals)
else:
self.hist = np.array([len(sorted_data)])
cnt = 0; bin_idx = 0
self.bin_index = {} # key: obj_id, value: bin_idx
for n in self.hist:
for i in range(int(n)):
obj_id = self.data_dict[cnt]
self.bin_index[obj_id] = bin_idx
cnt += 1
bin_idx += 1
data_min, data_max = sorted_data[0], sorted_data[-1]
if self.x_label == 'Connectivity':
#unique_num_neighbors = list(set(sorted_data))
self.data_intervals = []
for n in range(sorted_data[0], sorted_data[-1]+1):
self.data_intervals.append((n,n))
else:
end_pos = np.cumsum(self.hist)
start_pos = end_pos - self.hist + 1
self.data_intervals = [ (start_pos[i],end_pos[i]) for i in range(len(self.hist))]
# a NxN matrix
self.x_min = 1
self.x_max = self.intervals +1
self.y_min = 0
self.y_max = np.max(self.hist) +1
self.extent = (self.x_min, self.y_min, self.x_max,self.y_max)
self.status_bar = None#self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
# color schema: from blue to red
self.color_matrix = []
for i in range(self.intervals):
p = float(i+1) / self.intervals
self.color_matrix.append( self.gradient_color.get_color_at(p))
self.selected_obj_ids = []
except Exception as err:
self.ShowMsgBox(""" Histogram could not be created. Please select a numeric variable.
Details: """ + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
# linking-brushing events
self.Register(stars.EVT_OBJS_SELECT, self.OnObjsSelected)
self.Register(stars.EVT_OBJS_UNSELECT, self.OnNoObjSelect)
def __init__(self, gradient_type):
self.gradient_color = GradientColor(gradient_type)
ColorSchema.__init__(self, None, [""])
class HeatMatrix(PlottingCanvas):
def __init__(self,parent, layer, data,**kwargs):
PlottingCanvas.__init__(self,parent,data)
try:
self.layer_name = layer.name
self.title = "Transition probility matrix (%s)" % self.layer_name
self.x_label = "LISA transition states (1=HH,2=LH,3=LL,4=HL)"
self.y_label = "LISA transition states"
self.data = data
n = len(self.data)
self.enable_axis_labels = False
self.enable_axis = True
self.enable_axis_x = False
self.enable_axis_y = False
# a NxN matrix
self.x_min = 1
self.x_max = n+1
self.y_min = 1
self.y_max = n+1
self.extent = (self.x_min, self.y_min, self.x_max,self.y_max)
self.selected_polygon_ids = []
self.status_bar = self.parentFrame.status_bar
self.gradient_color = GradientColor(gradient_type='rdyibu')
self.margin_right = 100
# color schema: from blue to red
self.color_matrix = []
for i in range(n):
color_row = []
for j in range(n):
p = self.data[i][j]
color_row.append( self.gradient_color.get_color_at(p))
self.color_matrix.append(color_row)
except Exception as err:
self.ShowMsgBox('Fail to init heat map! ' + str(err.message))
self.isValidPlot = False
self.parentFrame.Close(True)
return None
def OnClose(self,event):
event.Skip()
def plot_data(self,dc):
# draw a NxN matrix
w,h = 1,1
for i,row in enumerate(self.data):
for j,item in enumerate(row):
start_x = j + self.x_min
start_y = self.y_max - i
pixel_x,pixel_y = self.point_to_screen(start_x,start_y)
pixel_w,pixel_h = math.ceil(self.length_to_screen(w)),math.ceil(self.length_to_screen(h,axis=1))
brush = wx.Brush(self.color_matrix[i][j])
dc.SetBrush(brush)
dc.DrawRectangle(pixel_x,pixel_y,pixel_w,pixel_h)
if i==len(self.data)-1:
dc.DrawText(str(j+1), pixel_x + pixel_w/2, pixel_y+pixel_h+5)
if j==0:
dc.DrawText(str(len(self.data)-i), pixel_x - 10, pixel_y + pixel_h/2)
text_pixel_x, text_pixel_y = pixel_x + pixel_w/2.0 - 10, pixel_y + pixel_h / 2.0
dc.SetPen(wx.WHITE_PEN)
dc.SetBrush(wx.WHITE_BRUSH)
dc.DrawText('%.4f'%(self.data[i][j]), text_pixel_x,text_pixel_y)
# draw a legend bar
pixel_x,pixel_y = self.point_to_screen( start_x+w, self.y_max)
pixel_x += 20
pixel_h = self.length_to_screen(self.y_max-self.y_min,axis=1)
pixel_w = 20
gradient_colorbar = self.gradient_color.get_bmp(pixel_w, pixel_h)
dc.DrawBitmap( gradient_colorbar, pixel_x, pixel_y)
pixel_x = pixel_x + pixel_w + 10
dc.SetPen(wx.BLACK_PEN)
dc.DrawText(str('%.2f'% np.max(self.data)), pixel_x,pixel_y)
pixel_y = pixel_y + pixel_h - 12
dc.DrawText(str('%.2f'% np.min(self.data)), pixel_x,pixel_y)