def grid_data_and_contour(self):
# griddata and contour.
x = self._x
y = self._y
z = self._z
n_labels = self._n_labels
xi = self._xi
yi = self._yi
zi = self._zi
x_min = self._x_min
y_min = self._y_min
x_max = self._x_max
y_max = self._y_max
plt.subplot(211)
plt.contour(xi, yi, zi, n_labels, linewidths=0.5, colors='k')
plt.contourf(xi,
yi,
zi,
n_labels,
cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(),
vmin=-abs(zi).max()))
plt.colorbar() # draw colorbar
plt.plot(x, y, 'ko', ms=3)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
print('griddata and contour plotted')
def showOverlapTable(modes_x, modes_y, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *modes_x*
and *modes_y* are sets of normal modes, and correspond to x and y axes of
the plot. Note that mode indices are incremented by 1. List of modes
is assumed to contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=plt.normalize(0, 1)``"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(modes_y, modes_x))
if overlap.ndim == 0:
overlap = np.array([[overlap]])
elif overlap.ndim == 1:
overlap = overlap.reshape((modes_y.numModes(), modes_x.numModes()))
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', plt.normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm,
**kwargs), plt.colorbar())
x_range = np.arange(1, modes_x.numModes() + 1)
plt.xticks(x_range - 0.5, x_range)
plt.xlabel(str(modes_x))
y_range = np.arange(1, modes_y.numModes() + 1)
plt.yticks(y_range - 0.5, y_range)
plt.ylabel(str(modes_y))
plt.axis([0, modes_x.numModes(), 0, modes_y.numModes()])
if SETTINGS['auto_show']:
showFigure()
return show
def plot_veg_types(yc, xc, Cv, baresoil):
Projection_Parameters = projection
labels = ['Evergreen Needleleaf', 'Evergreen Broadleaf',
'Deciduous Needleleaf', 'Deciduous Broadleaf', 'Mixed Cover',
'Woodland', 'Wooded Grasslands', 'Closed Shrublands',
'Open Shrublands', 'Grasslands', 'Crop land', 'Bare Soil/Ice']
fig = plt.figure(figsize=(10, 12))
gs1 = gridspec.GridSpec(4, 3)
for loc in xrange(11):
ax = fig.add_subplot(gs1[loc])
c = plot_map(ax, yc, xc, Cv[loc], Projection_Parameters, vmin=0,
cmap='Jet')
ax.set_title(labels[loc])
ax = fig.add_subplot(gs1[11])
c = plot_map(ax, yc, xc, baresoil, Projection_Parameters, vmin=0,
cmap='Jet')
ax.set_title(labels[11])
sm = plt.cm.ScalarMappable(cmap='Jet', norm=plt.normalize(vmin=0, vmax=1))
colorbar_ax = fig.add_axes([0.92, 0.1, 0.03, 0.8])
sm._A = []
plt.colorbar(sm, cax=colorbar_ax)
fig.suptitle('Fraction of Vegetation Type', fontsize=20, fontweight='bold')
fig.text(0.5, 0.93, 'Regional Arctic Climate Model',
ha='center', fontsize=18)
plt.show()
def plot_tricontour(self):
# tricontour.
x = self._x
y = self._y
z = self._z
n_labels = self._n_labels
zi = self._zi
x_min = self._x_min
y_min = self._y_min
x_max = self._x_max
y_max = self._y_max
title = self._title
x_label = self._x_label
y_label = self._y_label
plt.subplot(111) # change this if you want to plot both
triang = tri.Triangulation(x, y)
plt.tricontour(x, y, z, n_labels, linewidths=0.5, colors='k')
plt.tricontourf(x,
y,
z,
n_labels,
cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(),
vmin=-abs(zi).max()))
plt.colorbar()
plt.plot(x, y, 'ko', ms=3)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.title(title)
print('tricontour plotted')
def normalize_colors(vmin, vmax, clip=False):
"""Helper to handle matplotlib API"""
import matplotlib.pyplot as plt
try:
return plt.Normalize(vmin, vmax, clip=clip)
except AttributeError:
return plt.normalize(vmin, vmax, clip=clip)
def normalize_colors(vmin, vmax, clip=False):
"""Helper to handle matplotlib API"""
import matplotlib.pyplot as plt
if 'Normalize' in vars(plt):
return plt.Normalize(vmin, vmax, clip=clip)
else:
return plt.normalize(vmin, vmax, clip=clip)
def normalize_colors(vmin, vmax, clip=False):
"""Helper to handle matplotlib API"""
import matplotlib.pyplot as plt
try:
return plt.Normalize(vmin, vmax, clip=clip)
except AttributeError:
return plt.normalize(vmin, vmax, clip=clip)
def normalize_colors(vmin, vmax, clip=False):
"""Helper to handle matplotlib API"""
import matplotlib.pyplot as plt
if 'Normalize' in vars(plt):
return plt.Normalize(vmin, vmax, clip=clip)
else:
return plt.normalize(vmin, vmax, clip=clip)
def buildHeatmap(dataDictionary,title,bartitle,barcolor,barmin,barmax):
# Lambert Conformal map of USA lower 48 states
geoMap = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-64,
urcrnrlat=49, projection='lcc', lat_1=33, lat_2=45,
lon_0=-95, resolution='i', area_thresh=10000)
# Uses a Shape File from the US Census Bureau
# http://www2.census.gov/geo/tiger/GENZ2010/
shapeInfo = geoMap.readshapefile('data/shape_files/gz_2010_us_040_00_500k','states',drawbounds=False)
# Process based off an example from our professor's lecture slides,
# James Bagrow - UVM Spring 2014
# http://bagrow.com/dsv/
# I took the base idea and modified it heavily for the needs
# of this project.
# Color each state based on Population
colors={}
statenames=[]
# Use the Green color map.
# Others may be found here : http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
colorMap = barcolor
heatmapMinAmt = barmin; heatmapMaxAmt = barmax # Range for Heatmap (Valuewise)
scalarMapping = plt.cm.ScalarMappable(cmap=colorMap,norm=plt.normalize(vmin=heatmapMinAmt, vmax=heatmapMaxAmt))
for shapedict in geoMap.states_info:
statename = shapedict['NAME']
# Grab population info for each state
try:
population = float(dataDictionary[statename])
except KeyError:
population = 0.0
# Define the color for this state.
# Population / Max Color Amt.
colors[statename] = colorMap((population-heatmapMinAmt)/(heatmapMaxAmt-heatmapMinAmt))
statenames.append(statename)
# Actually color each state in this structure.
for nshape, seg in enumerate(geoMap.states):
xx, yy = zip(*seg)
color = rgb2hex(colors[statenames[nshape]])
plt.fill(xx,yy,color,edgecolor=color)
# Bound our map to the continental US.
# otherwise, we'll have some weird display issues
# with Alaska and Hawaii.
geoMap.drawparallels(np.arange(25,65,20),labels=[0,0,0,0],zorder=-1,color="w")
geoMap.drawmeridians(np.arange(-120,-40,20),labels=[0,0,0,0],zorder=-1,color="w")
# Build the Color Bar at the bottom of the graph
mm = plt.cm.ScalarMappable(cmap=colorMap)
mm.set_array([heatmapMinAmt,heatmapMaxAmt])
plt.colorbar(mm, label=bartitle,ticks=[0,5000,10000,15000,20000,25000],
orientation="horizontal",fraction=0.05)
# Adjust size of image
plt.gcf().set_size_inches(12.0,8.0)
plt.gca().axis("off")
# Add Title and show.
plt.title(title)
plt.show()
def colorbar_helper(customcmap, vmin, vmax, interval, label,
cb_alpha=0.05, cb_aspect=16, cb_shrink=0.4,
tick_labelsize=14, tick_alpha=0.7, label_fontsize=20,
labelpad=30, label_alpha=0.7):
# Create a fake colorbar
ctb = LinearSegmentedColormap.from_list('custombar', customcmap, N=2048)
# Trick from http://stackoverflow.com/questions/8342549/
# matplotlib-add-colorbar-to-a-sequence-of-line-plots
sm = plt.cm.ScalarMappable(cmap=ctb, norm=plt.normalize(vmin=vmin, vmax=vmax))
# Fake up the array of the scalar mappable
sm._A = []
# Set colorbar, aspect ratio
cbar = plt.colorbar(sm, alpha=cb_alpha, aspect=cb_aspect, shrink=cb_shrink)
cbar.solids.set_edgecolor("face")
# Remove colorbar container frame
cbar.outline.set_visible(False)
# Fontsize for colorbar ticklabels
cbar.ax.tick_params(labelsize=tick_labelsize)
# Customize colorbar tick labels
mytks = np.arange(vmin, vmax, interval)
cbar.set_ticks(mytks)
cbar.ax.set_yticklabels([str(a) for a in mytks], alpha = tick_alpha)
# Colorbar label, customize fontsize and distance to colorbar
cbar.set_label(label, alpha = label_alpha, rotation=270,
fontsize=label_fontsize, labelpad=labelpad)
# Remove color bar tick lines, while keeping the tick labels
cbarytks = plt.getp(cbar.ax.axes, 'yticklines')
plt.setp(cbarytks, visible=False)
def showOverlapTable(modes_x, modes_y, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *modes_x*
and *modes_y* are sets of normal modes, and correspond to x and y axes of
the plot. Note that mode indices are incremented by 1. List of modes
is assumed to contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=plt.normalize(0, 1)``"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(modes_y, modes_x))
if overlap.ndim == 0:
overlap = np.array([[overlap]])
elif overlap.ndim == 1:
overlap = overlap.reshape((modes_y.numModes(), modes_x.numModes()))
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', plt.normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm, **kwargs),
plt.colorbar())
x_range = np.arange(1, modes_x.numModes() + 1)
plt.xticks(x_range-0.5, x_range)
plt.xlabel(str(modes_x))
y_range = np.arange(1, modes_y.numModes() + 1)
plt.yticks(y_range-0.5, y_range)
plt.ylabel(str(modes_y))
plt.axis([0, modes_x.numModes(), 0, modes_y.numModes()])
if SETTINGS['auto_show']:
showFigure()
return show
def discrete_calldata_colormesh(X,
labels=None,
colors='wbgrcmyk',
states=None,
ax=None,
**kwargs):
"""
Make a meshgrid from discrete calldata (e.g., genotypes).
Parameters
----------
X: array
2-dimensional array of integers of shape (#variants, #samples)
labels: sequence of strings
Axis labels (e.g., sample IDs)
colors: sequence
Colors to use for different values of the array
states: sequence
Manually specify discrete calldata states (if not given will be determined from the data)
ax: axes
Axes on which to draw
Remaining keyword arguments are passed to ax.pcolormesh.
"""
# set up axes
if ax is None:
fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(111)
# determine discrete states
if states is None:
states = np.unique(X)
colors = colors[:max(states) - min(states) +
1] # only need as many colors as states
# plotting defaults
pltargs = {
'cmap': ListedColormap(colors),
'norm': plt.normalize(min(states),
max(states) + 1),
}
pltargs.update(kwargs)
ax.pcolormesh(X.T, **pltargs)
ax.set_xlim(0, X.shape[0])
ax.set_ylim(0, X.shape[1])
ax.set_yticks(np.arange(X.shape[1]) + .5)
if labels is not None:
# labels = ['%s [%s] ' % (s, i) for (i, s) in enumerate(labels)]
ax.set_yticklabels(labels, rotation=0)
return ax
def plotPCA(V_g,
w_g,
ctypes,
k=5,
fn=None,
fig=None,
figsize=None,
markersize=None):
if markersize is None:
markersize = 4
if fig is None:
if figsize is None:
fig = plt.figure(figsize=(k * 4, k * 4))
else:
fig = plt.figure(figsize=(figsize, figsize))
ax = fig.add_subplot(k - 1, k - 1, 1)
### choose colormap and adapt normalization
cmap = plt.get_cmap('jet')
norm = plt.normalize(0, np.unique(ctypes).shape[0])
### plot first k main axes of variation
for k1 in range(0, k):
cnt = 1
for k2 in range(k1 + 1, k):
ax = fig.add_subplot(k - 1, k - 1, (k1 * (k - 1)) + cnt)
cnt += 1
for idx, ct in enumerate(np.unique(ctypes)):
c_idx = np.where(ctypes == ct)[0]
if c_idx.shape[0] > 0:
ax.plot(V_g[k1, c_idx],
V_g[k2, c_idx],
markers.MarkerStyle.filled_markers[idx % 13],
color=cmap(norm(idx)),
label=ct,
ms=markersize,
alpha=0.75)
ax.set_title('PC %i vs %i' % (k1 + 1, k2 + 1))
ax.set_xticks(ax.get_xticks()[::2])
ax.set_yticks(ax.get_yticks()[::2])
ax.tick_params(axis='both', which='major', labelsize=10)
ax.tick_params(axis='both', which='minor', labelsize=10)
if k1 == (k - 1):
ax.legend(numpoints=1,
ncol=2,
loc='center left',
bbox_to_anchor=(1.2, 0.5))
plt.tight_layout()
if fn is not None:
plt.savefig(fn + '.pdf', dpi=1200, format='pdf')
def discrete_calldata_colormesh(X, labels=None, colors='wbgrcmyk', states=None, ax=None, **kwargs):
"""
Make a meshgrid from discrete calldata (e.g., genotypes).
Parameters
----------
X: array
2-dimensional array of integers of shape (#variants, #samples)
labels: sequence of strings
Axis labels (e.g., sample IDs)
colors: sequence
Colors to use for different values of the array
states: sequence
Manually specify discrete calldata states (if not given will be determined from the data)
ax: axes
Axes on which to draw
Remaining keyword arguments are passed to ax.pcolormesh.
"""
# set up axes
if ax is None:
fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(111)
# determine discrete states
if states is None:
states = np.unique(X)
colors = colors[:max(states)-min(states)+1] # only need as many colors as states
# plotting defaults
pltargs = {
'cmap': ListedColormap(colors),
'norm': plt.normalize(min(states), max(states)+1),
}
pltargs.update(kwargs)
ax.pcolormesh(X.T, **pltargs)
ax.set_xlim(0, X.shape[0])
ax.set_ylim(0, X.shape[1])
ax.set_yticks(np.arange(X.shape[1]) + .5)
if labels is not None:
# labels = ['%s [%s] ' % (s, i) for (i, s) in enumerate(labels)]
ax.set_yticklabels(labels, rotation=0)
return ax
def coRes(self):
layers = self.iface.legendInterface().layers()
for ly in layers:
if ly.name() == 'r1d_in_data':
vl2 = ly
print vl2.name()
x = []
y = []
values = []
ab = self.dockwidget.txtAB2CoRes.text()
print ab
for f in vl2.getFeatures():
#print f['ab2']
if f['ab2'] == float(ab):
geom = f.geometry()
x.append(geom.asPoint().x())
y.append(geom.asPoint().y())
values.append(f['ra'])
#Creating the output grid (100x100, in the example)
#x = [10,60,40,70,10,50,20,70,30,60]
#y = [10,20,30,30,40,50,60,70,80,90]
#values = [1,2,2,3,4,6,7,7,8,10]
print x
print y
#print xx
txi = np.linspace(min(x), max(x), 10000)
tyi = np.linspace(min(y), max(y), 10000)
XI, YI = np.meshgrid(ti, ti)
#Creating the interpolation function and populating the output matrix value
rbf = Rbf(x, y, values, function='inverse')
ZI = rbf(XI, YI)
# Plotting the result
n = plt.normalize(0.0, 1000.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(x, y, 1000, values)
plt.title('RBF interpolation')
plt.xlim(min(x), max(x))
plt.ylim(min(y), max(y))
plt.colorbar()
plt.show()
def coRes(self):
layers = self.iface.legendInterface().layers()
for ly in layers:
if ly.name() == 'r1d_in_data':
vl2 = ly
print vl2.name()
x=[]
y=[]
values =[]
ab = self.dockwidget.txtAB2CoRes.text()
print ab
for f in vl2.getFeatures():
#print f['ab2']
if f['ab2'] == float(ab):
geom = f.geometry()
x.append(geom.asPoint().x())
y.append(geom.asPoint().y())
values.append(f['ra'])
#Creating the output grid (100x100, in the example)
#x = [10,60,40,70,10,50,20,70,30,60]
#y = [10,20,30,30,40,50,60,70,80,90]
#values = [1,2,2,3,4,6,7,7,8,10]
print x
print y
#print xx
txi = np.linspace(min(x), max(x), 10000)
tyi = np.linspace(min(y), max(y), 10000)
XI, YI = np.meshgrid(ti, ti)
#Creating the interpolation function and populating the output matrix value
rbf = Rbf(x, y, values, function='inverse')
ZI = rbf(XI, YI)
# Plotting the result
n = plt.normalize(0.0, 1000.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(x, y, 1000, values)
plt.title('RBF interpolation')
plt.xlim(min(x), max(x))
plt.ylim(min(y), max(y))
plt.colorbar()
plt.show()
def plot_veg_types(yc, xc, Cv, baresoil):
Projection_Parameters = projection
labels = [
'Evergreen Needleleaf', 'Evergreen Broadleaf', 'Deciduous Needleleaf',
'Deciduous Broadleaf', 'Mixed Cover', 'Woodland', 'Wooded Grasslands',
'Closed Shrublands', 'Open Shrublands', 'Grasslands', 'Crop land',
'Bare Soil/Ice'
]
fig = plt.figure(figsize=(10, 12))
gs1 = gridspec.GridSpec(4, 3)
for loc in xrange(11):
ax = fig.add_subplot(gs1[loc])
c = plot_map(ax,
yc,
xc,
Cv[loc],
Projection_Parameters,
vmin=0,
cmap='Jet')
ax.set_title(labels[loc])
ax = fig.add_subplot(gs1[11])
c = plot_map(ax,
yc,
xc,
baresoil,
Projection_Parameters,
vmin=0,
cmap='Jet')
ax.set_title(labels[11])
sm = plt.cm.ScalarMappable(cmap='Jet', norm=plt.normalize(vmin=0, vmax=1))
colorbar_ax = fig.add_axes([0.92, 0.1, 0.03, 0.8])
sm._A = []
plt.colorbar(sm, cax=colorbar_ax)
fig.suptitle('Fraction of Vegetation Type', fontsize=20, fontweight='bold')
fig.text(0.5,
0.93,
'Regional Arctic Climate Model',
ha='center',
fontsize=18)
plt.show()
def showOverlapTable(rows, cols, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *rows* and
*cols* are sets of normal modes, and correspond to rows and columns of the
displayed overlap matrix. Note that mode indices are incremented by 1.
List of modes should contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=plt.normalize(0, 1)``
.. plot::
:context:
:include-source:
plt.figure(figsize=(5,4))
showOverlapTable( p38_pca[:6], p38_anm[:6] )
plt.title('p38 PCA vs ANM')
.. plot::
:context:
:nofigs:
plt.close('all')"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(rows, cols))
if isinstance(rows, NMA):
rows = rows[:]
if isinstance(cols, NMA):
cols = cols[:]
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', plt.normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm, **kwargs),
plt.colorbar())
x_range = np.arange(1, len(cols)+1)
plt.xticks(x_range-0.5, x_range)
plt.xlabel(str(cols))
y_range = np.arange(1, len(rows)+1)
plt.yticks(y_range-0.5, y_range)
plt.ylabel(str(rows))
plt.axis([0, len(cols), 0, len(rows)])
return show
def make_2D_RDF_of_gridded_data(res='1x1',
X_locs=None,
Y_locs=None,
Z_data=None):
"""
Make a 2D interpolation using RadialBasisFunctions
"""
import numpy as np
from scipy.interpolate import Rbf
import matplotlib.pyplot as plt
# - Process dataframe here for now
X_locs = df['Longitude'].values
Y_locs = df['Latitude'].values
Z_data = df['Iodide'].values
# Degrade resolution
if res == '1x1':
X_COORDS, Y_COORDS, NIU = AC.get_latlonalt4res(res=res)
# Remove double ups in data for now...
print([len(i) for i in (X_locs, Y_locs)])
# Degrade to 1x1 resolution...
X_locs = [int(i) for i in X_locs]
Y_locs = [int(i) for i in Y_locs]
# Make a dictionary to remove double ups...
Z_dict = dict(list(zip(list(zip(X_locs, Y_locs)), Z_data)))
# Unpack
locs = sorted(Z_dict.keys())
Z_data = [Z_dict[i] for i in locs]
X_locs, Y_locs = list(zip(*locs))
print([len(i) for i in (X_locs, Y_locs)])
# Setup meshgrid...
XI, YI = np.meshgrid(X_COORDS, Y_COORDS)
# Interpolate onto this...
# Creating the interpolation function and populating the output matrix value
rbf = Rbf(X_locs, Y_locs, Z_data, function='inverse')
ZI = rbf(XI, YI)
# Plotting the result
n = plt.normalize(0.0, 100.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(X_locs, Y_locs, 100, Z_data)
plt.title('RBF interpolation')
plt.xlim(-180, 180)
plt.ylim(-90, 90)
plt.colorbar()
def quick_ortho_plot(x,y,z, bbox=[], loc=None, title='', norm=None):
import matplotlib.pyplot as pp
import xipy.volume_utils as vu
from xipy.vis.single_slice_plot import SliceFigure
# find or make the x, y, z plane extents
if bbox:
extents = vu.limits_to_extents(bbox)
else:
extents = [ reduce(lambda x,y: x+y, zip([0,0],p.shape[:2][::-1]))
for p in (x, y, z) ]
if norm is None and len(x.shape) < 3:
mx = max([ x.max(), y.max(), z.max() ])
mn = min([ x.min(), y.min(), z.min() ])
norm = pp.normalize(mn, mx)
fig = pp.figure()
loc = list(loc)
zloc = loc[0],loc[1] if loc else None
ax_z = fig.add_subplot(131)
sf_z = SliceFigure(fig, extents[2])
img_z = sf_z.spawn_image(z, extent=extents[2], loc=zloc,
interpolation='nearest', norm=norm)
ax_z.set_title('plot z')
yloc = loc[0],loc[2] if loc else None
ax_y = fig.add_subplot(132)
sf_y = SliceFigure(fig, extents[1])
img_y = sf_y.spawn_image(y, extent=extents[1], loc=yloc,
interpolation='nearest', norm=norm)
ax_y.set_title('plot y')
xloc = loc[1],loc[2] if loc else None
ax_x = fig.add_subplot(133)
sf_x = SliceFigure(fig, extents[0])
img_x = sf_x.spawn_image(x, extent=extents[0], loc=xloc,
interpolation='nearest', norm=norm)
ax_x.set_title('plot x')
if title:
fig.text(.5, .05, title, ha='center')
pp.colorbar(img_x.img)
pp.show()
return fig
def find_image_threshold(arr, percentile=90., debug=False):
nbins = 200
bsizes, bpts = np.histogram(arr.flatten(), bins=nbins)
# heuristically, this should show up near the middle of the
# second peak of the intensity histogram
start_pt = np.abs(bpts - arr.max() / 2.).argmin()
db = np.diff(bsizes[:start_pt])
## zcross = np.argwhere((db[:-1] < 0) & (db[1:] >= 0)).flatten()[0]
bval = bsizes[1:start_pt - 1][(db[:-1] < 0) & (db[1:] >= 0)].min()
zcross = np.argwhere(bval == bsizes).flatten()[0]
thresh = (bpts[zcross] + bpts[zcross + 1]) / 2.
# interpolate the percentile value from the bin edges
bin_lo = int(percentile * nbins / 100.0)
bin_hi = int(round(percentile * nbins / 100.0 + 0.5))
p_hi = percentile - bin_lo # proportion of hi bin
p_lo = bin_hi - percentile # proportion of lo bin
## print bin_hi, bin_lo, p_hi, p_lo
pval = bpts[bin_lo] * p_lo + bpts[bin_hi] * p_hi
if debug:
import matplotlib as mpl
import matplotlib.pyplot as pp
f = pp.figure()
ax = f.add_subplot(111)
ax.hist(arr.flatten(), bins=nbins)
l = mpl.lines.Line2D([thresh, thresh], [0, .25 * bsizes.max()],
linewidth=2,
color='r')
ax.add_line(l)
ax.xaxis.get_major_formatter().set_scientific(True)
f = pp.figure()
norm = pp.normalize(0, pval)
ax = f.add_subplot(211)
plot_arr = arr
while len(plot_arr.shape) > 2:
plot_arr = plot_arr[plot_arr.shape[0] / 2]
ax.imshow(plot_arr, norm=norm)
ax = f.add_subplot(212)
simple_mask = (plot_arr < thresh)
ax.imshow(np.ma.masked_array(plot_arr, mask=simple_mask), norm=norm)
pp.show()
return thresh, pval
def draw():
global m, learn, z
pyp.background(200,50)
learn.collect_data()
m = learn.get_mean()
pyp.loadPixels()
m = np.atleast_2d(m)
norm = normalize(vmin=min(min(m)), vmax=max(max(m)))
cmap = get_cmap('jet')
m_normed = norm(m)
rgba_data=cmap(m_normed)*255
r = rgba_data[0,:,0].astype('uint32')
g = rgba_data[0,:,1].astype('uint32')
b = rgba_data[0,:,2].astype('uint32')
a = rgba_data[0,:,3].astype('uint32')
pyp.screen.pixels = a << 24 | r << 16 | g << 8 | b
pyp.updatePixels()
def find_image_threshold(arr, percentile=90., debug=False):
nbins = 200
bsizes, bpts = np.histogram(arr.flatten(), bins=nbins)
# heuristically, this should show up near the middle of the
# second peak of the intensity histogram
start_pt = np.abs(bpts - arr.max()/2.).argmin()
db = np.diff(bsizes[:start_pt])
## zcross = np.argwhere((db[:-1] < 0) & (db[1:] >= 0)).flatten()[0]
bval = bsizes[1:start_pt-1][ (db[:-1] < 0) & (db[1:] >= 0) ].min()
zcross = np.argwhere(bval==bsizes).flatten()[0]
thresh = (bpts[zcross] + bpts[zcross+1])/2.
# interpolate the percentile value from the bin edges
bin_lo = int(percentile * nbins / 100.0)
bin_hi = int(round(percentile * nbins / 100.0 + 0.5))
p_hi = percentile - bin_lo # proportion of hi bin
p_lo = bin_hi - percentile # proportion of lo bin
## print bin_hi, bin_lo, p_hi, p_lo
pval = bpts[bin_lo] * p_lo + bpts[bin_hi] * p_hi
if debug:
import matplotlib as mpl
import matplotlib.pyplot as pp
f = pp.figure()
ax = f.add_subplot(111)
ax.hist(arr.flatten(), bins=nbins)
l = mpl.lines.Line2D([thresh, thresh], [0, .25*bsizes.max()],
linewidth=2, color='r')
ax.add_line(l)
ax.xaxis.get_major_formatter().set_scientific(True)
f = pp.figure()
norm = pp.normalize(0, pval)
ax = f.add_subplot(211)
plot_arr = arr
while len(plot_arr.shape) > 2:
plot_arr = plot_arr[plot_arr.shape[0]/2]
ax.imshow(plot_arr, norm=norm)
ax = f.add_subplot(212)
simple_mask = (plot_arr < thresh)
ax.imshow(np.ma.masked_array(plot_arr, mask=simple_mask), norm=norm)
pp.show()
return thresh, pval
def vectorize(hillshade_file, m_value_file):
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
#get data
hillshade, hillshade_header = read_flt(hillshade_file)
m_values, m_values_header = read_flt(m_value_file)
#handle plotting hillshades which are larger than the m_value raster
#cannot cope with m_value raster larger than the hillshade
corrected_x = 0
corrected_y = 0
if (hillshade_header[0] != m_values_header[0]) or (hillshade_header[1] !=
m_values_header[1]):
corrected_x = (m_values_header[2] -
hillshade_header[2]) / hillshade_header[4]
corrected_y = ((
(m_values_header[3] / m_values_header[4]) + m_values_header[1]) -
((hillshade_header[3] / hillshade_header[4]) +
hillshade_header[1])) * -1
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
m_values = np.ma.masked_where(m_values == -9999, m_values)
#fonts
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = 12
fig, ax = pp.subplots()
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
xlocs, xlabels = pp.xticks()
ylocs, ylabels = pp.yticks()
new_x_labels = np.linspace(
hillshade_header[2],
hillshade_header[2] + (hillshade_header[1] * hillshade_header[4]),
len(xlocs))
new_y_labels = np.linspace(
hillshade_header[3],
hillshade_header[3] + (hillshade_header[0] * hillshade_header[4]),
len(ylocs))
new_x_labels = [str(x).split('.')[0] for x in new_x_labels
] #get rid of decimal places in axis ticks
new_y_labels = [str(y).split('.')[0]
for y in new_y_labels][::-1] #invert y axis
pp.xticks(xlocs[1:-1], new_x_labels[1:-1],
rotation=30) #[1:-1] skips ticks where we have no data
pp.yticks(ylocs[1:-1], new_y_labels[1:-1])
pp.xlabel('Easting (m)')
pp.ylabel('Northing (m)')
# SET UP COLOURMAPS
jet = pp.get_cmap('jet')
m_MIN = np.min(m_values)
m_MAX = np.max(m_values)
cNorm_m_values = colors.Normalize(vmin=m_MIN, vmax=m_MAX)
scalarMap_m_values = cmx.ScalarMappable(norm=cNorm_m_values, cmap=jet)
for i in xrange(len(m_values)):
for j in xrange(len(m_values[0])):
if m_values[i][j] > 0:
colorVal = scalarMap_m_values.to_rgba(m_values[i][j])
pp.scatter(j + corrected_x,
i + corrected_y,
marker=".",
color=colorVal,
edgecolors='none')
# Configure final plot
sm = pp.cm.ScalarMappable(cmap=jet,
norm=pp.normalize(vmin=m_MIN, vmax=m_MAX))
sm._A = []
cbar = pp.colorbar(sm)
cbar.set_label('M Values')
pp.show()
leg = ax.legend([l1, l2, l3], labels, ncol=3, frameon=False, fontsize=16,
bbox_to_anchor=[1.1, 0.11], handlelength=2,
handletextpad=1, columnspacing=2, title='Average Page Size')
# Customize legend title
# Set position to increase space between legend and labels
plt.setp(leg.get_title(), fontsize=20, alpha=a)
leg.get_title().set_position((0, 10))
# Customize transparency for legend labels
[plt.setp(label, alpha=a) for label in leg.get_texts()]
# Create a fake colorbar
ctb = LinearSegmentedColormap.from_list('custombar', customcmap, N=2048)
# Trick from http://stackoverflow.com/questions/8342549/
# matplotlib-add-colorbar-to-a-sequence-of-line-plots
sm = plt.cm.ScalarMappable(cmap=ctb, norm=plt.normalize(vmin=1, vmax=3))
# Fake up the array of the scalar mappable
sm._A = []
# Set colorbar, aspect ratio
cbar = plt.colorbar(sm, alpha=0.05, aspect=16, shrink=0.4)
cbar.solids.set_edgecolor("face")
# Remove colorbar container frame
cbar.outline.set_visible(False)
# Fontsize for colorbar ticklabels
cbar.ax.tick_params(labelsize=16)
# Customize colorbar tick labels
mytks = range(1,4,1)
cbar.set_ticks(mytks)
cbar.ax.set_yticklabels([str(i) for i in mytks], alpha=a)
#######################
#daily and total rate plot
fig1 = plt.figure(figsize=(11.7, 6.0))
gs = gridspec.GridSpec(3, 2)
ax1 = fig1.add_subplot(gs[:, 0])
###########
#locations
m = Basemap(llcrnrlon=lon_min, llcrnrlat=lat_min, urcrnrlon=lon_max, urcrnrlat=lat_max,
resolution='h',projection='tmerc',lon_0=lon_max +(lon_max-lon_min)/2.,lat_0=lat_max +(lat_max-lat_min)/2.)
m.drawcoastlines()
x,y = m(Cat_mc[:,2],Cat_mc[:,1])
m.scatter(x,y,s=10, c=range(len(Cat_mc[:,0])), norm=plt.normalize(vmin=0, vmax=N), marker='o', edgecolor='none')
m.drawmapboundary(fill_color='lightgrey')
m.drawparallels(np.arange(27.6,28.0,0.1),labels=[1,0,0,1])
m.drawmeridians(np.arange(-18.4,-17.8,0.1),labels=[1,0,0,1])
mdates.DateFormatter('dd/mm/yyyy')
plt.text(0.67, 0.95, mdates.num2date(t_start+i).strftime('%d/%m/%Y'), fontsize = 9, transform=ax1.transAxes)
###########
ax2 = fig1.add_subplot(gs[0,1],axisbg='lightgrey')
ax2.bar(mdates.num2date(day_bins[:-1]), DER, color='darkslategrey', edgecolor='darkslategrey')
ax2.set_ylabel('Daily earthquakes (M>1.5)', fontsize=8)
ax2.set_xlim(mdates.num2date(t_start), mdates.num2date(t_end))
ax2.set_ylim(0, 250)
print "partial segmentation : %d / %d pixels labeled" % ((s_img1 >= 0).sum(), s_img1.size)
f = plot_masked_centroid_image(rgb_img, s_img1)
f.savefig(iname + "_persistence_%d_parts_model.pdf" % n_parts)
f = plot_masked_segmap(s_img1)
f.savefig(iname + "_persistence_%d_parts_model_labels.pdf" % n_parts)
bmap = ut.draw_boundaries(s_img1, m_seek.saddles)
f = pp.figure()
pp.imshow(bmap, cmap=pp.cm.gray)
f.savefig(iname + "_boundaries.pdf")
if video:
lab_vid = np.array([colors.rgb2lab(vf) for vf in vid])
svid = cls.classify_sequence(classifier, lab_vid)
segcmap = colors.npt_colormap(svid.max())
n = pp.normalize()
svid_masked = np.where(svid < 0, 0, svid)
svid_mapped = segcmap(n(svid_masked).ravel()).reshape(svid.shape + (4,))
svid_mapped = (svid_mapped[..., :3] * 255).astype("B")
both_vids = np.concatenate((vid, svid_mapped), axis=2)
anim = ut.animate_frames(both_vids, movie_name=iname + "_joint", fps=25)
anim.repeat = False
## anim = ut.animate_frames(
## np.ma.masked_where(svid < 0, svid),
## movie_name=iname+'_naive_classifier',
## fps=25,
## cmap=colors.npt_colormap(svid.max())
## )
## anim.repeat = False
def make_plots():
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#########################
# #
# READ IN THE DATA #
# #
#########################
# open file
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\India\\'
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\PA\\'
#DataDirectory = 'm:\\topographic_tools\\LSDRaster_chi_package\\Test_data\\'
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\Test_data\\'
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\Child_runs_mk4\\'
DataDirectory = 'm:\\topographic_tools\\LSDRaster_chi_package\\Child_runs_mk4\\'
#FileName = 'pa_basin_fullProfileMC_mainstem_1189.tree'
#FileName = 'pa_basin_fullProfileMC_mainstem_3124.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_633.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_114.tree'
#FileName = 'rio_torto_fullProfileMC_colinear_114.tree'
#FileName = 'rio_torto_fullProfileMC_colinear_110.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_110.tree'
#FileName = 'rio_torto_fullProfileMC_forced_0.4_110.tree'
#FileName = 'UniUplift09b_mk4_t6_fullProfileMC_colinear.tree'
FileName = 'UniUplift09b_mk4_t6_fullProfileMC_colinear_3_1_12_100.tree'
#OutputFigureName = 'chi_plot'
#OutputFigureFormat = 'eps'
f = open(DataDirectory + FileName,'r') # open file
lines = f.readlines() # read in the data
n_lines = len(lines) # get the number of lines (=number of data)
n_data = n_lines -1
# data variables
channel_id = np.zeros(n_data) # ID number for channel segment
receiver_channel = np.zeros(n_data)
#node_on_receiver_channel = np.zeros(n_data, dtype=np.int)
node = np.zeros(n_data) # node number
row = np.zeros(n_data) # row
col = np.zeros(n_data) # column
flow_dist = np.zeros(n_data) # flow distance
chi = np.zeros(n_data) # chi
elevation = np.zeros(n_data) # elevation
drainage_area = np.zeros(n_data) # drainage area
n_data_points_uic = np.zeros(n_data)
m_mean = np.zeros(n_data)
#m_standard_deviation = np.zeros(n_data)
m_standard_error = np.zeros(n_data)
b_mean = np.zeros(n_data)
#b_standard_deviation = np.zeros(n_data)
b_standard_error = np.zeros(n_data)
DW_mean = np.zeros(n_data)
#DW_standard_deviation = np.zeros(n_data)
DW_standard_error = np.zeros(n_data)
fitted_elevation_mean = np.zeros(n_data)
#fitted_elevation_standard_deviation = np.zeros(n_data)
fitted_elevation_standard_error = np.zeros(n_data)
print "Reading " + FileName
# get the A_0 and m/n values
line = lines[0].strip().split(" ")
A_0 = float(line[0])
m_over_n = float(line[1])
for i in range (0,n_data):
line = lines[i+1].strip().split(" ")
#print line
channel_id[i] = int(float(line[0]))
receiver_channel[i] = int(float(line[1]))
#node_on_receiver_channel = int(float(line[2]))
node[i] = int(float(line[3]))
row[i] = int(line[4])
col[i] = int(line[5])
flow_dist[i] = float(line[6])
chi[i] = float(line[7])
elevation[i] = float(line[8])
drainage_area[i] = float(line[9])
n_data_points_uic[i] = float(line[10])
m_mean[i] = float(line[11])
#m_standard_deviation[i] = float(line[12])
m_standard_error[i] = float(line[13])
b_mean[i] = float(line[14])
#b_standard_deviation[i] = float(line[15])
b_standard_error[i] = float(line[16])
DW_mean[i] = float(line[17])
#DW_standard_deviation[i] = float(line[18])
DW_standard_error[i] = float(line[19])
fitted_elevation_mean[i] = float(line[20])
#fitted_elevation_standard_deviation = float(line[21])
fitted_elevation_standard_error[i] = float(line[22])
f.close()
# Determine number of segments, and their respective lengths
n_channel_segments = channel_id[n_data-1]+1
segment_lengths = np.zeros(n_channel_segments, dtype=np.int)
for i in range (0,n_data):
segment_lengths[channel_id[i]] = segment_lengths[channel_id[i]] + 1
#########################
# #
# MAKE CHI-PLOTS #
# #
#########################
print "Producing figures..."
# SET UP COLOURMAPS
jet = plt.get_cmap('jet')
hot = plt.get_cmap('RdYlBu_r')
# m-values
m_MIN = np.min(m_mean)
m_MAX = np.max(m_mean)
cNorm_m_values = colors.Normalize(vmin=m_MIN, vmax=m_MAX) # the max number of channel segs is the 'top' colour
scalarMap_m_values = cmx.ScalarMappable(norm=cNorm_m_values, cmap=hot)
# channel ID
Channel_ID_MIN = np.min(channel_id)
Channel_ID_MAX = np.max(channel_id)
cNorm_channel_ID = colors.Normalize(vmin=Channel_ID_MIN, vmax=Channel_ID_MAX) # the max number of channel segs is the 'top' colour
scalarMap_channel_ID = cmx.ScalarMappable(norm=cNorm_channel_ID, cmap=jet)
minfd = min(flow_dist)
print "minimum flow distance: " +str(minfd)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# PLOT OF CHI m-VALUE (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
plt.figure(2, facecolor='white',figsize=(10,7.5))
ax = plt.subplot(1,1,1)
for i in range (0,len(channel_id)):
colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
if channel_id[i]==0:
plt.plot(chi[i], m_mean[i], "o", markersize=10, color='black', markeredgecolor = 'black',alpha=0.7)
else:
plt.plot(chi[i], m_mean[i], "o", markersize=8, color=colorVal, markeredgecolor = colorVal,alpha = 0.7)
# plt.plot(chi[i], m_mean[i] + m_standard_error[i], ".", linewidth=0.25, color='k')
# plt.plot(chi[i], m_mean[i] - m_standard_error[i], ".", linewidth=0.25, color='k')
rect1 =plt.Rectangle((21.4577,0), 18.2625, 20, color='red', alpha= 0.1)
rect2 =plt.Rectangle((69.0736,0), 29.3623, 20, color='green', alpha= 0.1)
ax.add_artist(rect1)
ax.add_artist(rect2)
# Configure final plot
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
plt.xlabel('$\chi$ (m)', fontsize = axis_size)
plt.ylabel('Gradient in $\chi$ space', fontsize = axis_size)
plt.title('$A_0$: '+str(A_0)+' m$^2$, and $m/n$: '+str(m_over_n), fontsize = label_size)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# LONGITUDINAL PROFILES WITH COLOUR SCALE GIVING CHI-m VALUE
plt.figure(3, facecolor='white',figsize=(10,7.5))
ax = plt.subplot(1,1,1)
for i in range (0,len(channel_id)):
colorVal = scalarMap_m_values.to_rgba(m_mean[i])
plt.plot((flow_dist[i]-minfd)/1000, elevation[i], "o", markersize=6, color=colorVal, markeredgecolor = colorVal,alpha = 0.5)
# Configure final plot
sm = plt.cm.ScalarMappable(cmap=hot,norm=plt.normalize(vmin=np.min(m_mean), vmax=np.max(m_mean)))
sm._A = []
cbar = plt.colorbar(sm,orientation='horizontal',use_gridspec=True)
cbar.set_label('Gradient in $\chi$ space', fontsize = axis_size)
plt.xlabel('Distance upstream (km)', fontsize = axis_size)
plt.ylabel('Elevation (m)', fontsize = axis_size)
plt.title('$A_0$: '+str(A_0)+' m$^2$, and $m/n$: '+str(m_over_n), fontsize = label_size)
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
cbar.ax.spines['top'].set_linewidth(2.5)
cbar.ax.spines['left'].set_linewidth(2.5)
cbar.ax.spines['right'].set_linewidth(2.5)
cbar.ax.spines['bottom'].set_linewidth(2.5)
cbar.ax.tick_params(axis='both', width=2.5)
plt.tight_layout()
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# BASIC CHI-PLOT WITH EACH CHANNEL LABELLED WITH A DIFFERENT COLOUR
plt.figure(1, facecolor='white',figsize=(10,7.5))
ax = plt.subplot(1,1,1)
data_pointer = 0 # points to data element in chi and elev vectors
for i in range (0,len(segment_lengths)):
chi_seg = np.zeros(segment_lengths[i])
elev_seg = np.zeros(segment_lengths[i])
for j in range (0,segment_lengths[i]):
chi_seg[j] = chi[data_pointer]
elev_seg[j] = elevation[data_pointer]
data_pointer = data_pointer + 1
if i == 0:
# plot trunk stream in black, with thicker line
l1, = ax.plot(chi_seg, elev_seg, "k-", linewidth=4, alpha = 0.3)
else:
# plot other stream segments plot
colorVal = scalarMap_channel_ID.to_rgba(i) # this gets the distinct colour for this segment
ax.plot(chi_seg, elev_seg, "-", linewidth=4, color=colorVal, alpha = 0.3)
# now loop again plotting the fitted segments
data_pointer = 0 # points to data element in chi and elev vectors
for i in range (0,len(segment_lengths)):
chi_seg = np.zeros(segment_lengths[i])
elev_seg = np.zeros(segment_lengths[i])
for j in range (0,segment_lengths[i]):
chi_seg[j] = chi[data_pointer]
elev_seg[j] = fitted_elevation_mean[data_pointer]
data_pointer = data_pointer + 1
if i == 0:
# plot trunk stream in black, with thicker line
l2, = plt.plot(chi_seg, elev_seg, "k", linewidth=3, dashes=(10,2))
else:
# plot other stream segments plot
colorVal = scalarMap_channel_ID.to_rgba(i) # this gets the distinct colour for this segment
ax.plot(chi_seg, elev_seg, linewidth=3, color=colorVal, dashes=(10,2))
# Configure final plot
plt.xlabel('$\chi$ (m)', fontsize= axis_size)
plt.ylabel('Elevation (m)', fontsize =axis_size)
plt.title('$A_0$: '+str(A_0)+' m$^2$, and $m/n$: '+str(m_over_n), fontsize = label_size)
plt.legend((l1, l2), ('Data', 'Best fit segments'), 'lower right',prop={'size':label_size})
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
#plt.savefig(OutputFigureName + '.' + OutputFigureFormat, format=OutputFigureFormat)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT USING FITTED PROFILES (+ERROR) RATHER THAN RAW DATA, LABELLED WITH DISTINCT COLOUR
# plt.figure(4, facecolor='white')
# data_pointer = 0 # points to data element in chi and elev vectors
# for i in range (0,len(segment_lengths)):
# chi_seg = np.zeros(segment_lengths[i])
# fitted_elev_seg = np.zeros(segment_lengths[i])
# fitted_elev_ulim = np.zeros(segment_lengths[i])
# fitted_elev_llim = np.zeros(segment_lengths[i])
#
# for j in range (0,segment_lengths[i]):
# chi_seg[j] = chi[data_pointer]
# fitted_elev_seg[j] = fitted_elevation_mean[data_pointer]
# fitted_elev_ulim[j] = fitted_elevation_mean[data_pointer] + fitted_elevation_standard_error[data_pointer]
# fitted_elev_llim[j] = fitted_elevation_mean[data_pointer] - fitted_elevation_standard_error[data_pointer]
# data_pointer = data_pointer + 1
# # plot other stream segments plot
# colorVal = scalarMap_channel_ID.to_rgba(i)
# plt.plot(chi_seg, fitted_elev_ulim, "-k", linewidth=0.2)
# plt.plot(chi_seg, fitted_elev_llim, "-k", linewidth=0.2)
# plt.plot(chi_seg, fitted_elev_seg, "-", linewidth=0.5, color=colorVal)
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('fitted elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT DISPLAYING BOTH RAW DATA AND FITTED PROFILES
# plt.figure(5, facecolor='white')
#
# data_pointer = 0 # points to data element in chi and elev vectors
# for i in range (0,len(segment_lengths)):
# chi_seg = np.zeros(segment_lengths[i])
# elev_seg = np.zeros(segment_lengths[i])
# fitted_elev_seg = np.zeros(segment_lengths[i])
#
# for j in range (0,segment_lengths[i]):
# chi_seg[j] = chi[data_pointer]
# elev_seg[j] = elevation[data_pointer]
# fitted_elev_seg[j] = fitted_elevation_mean[data_pointer]
# data_pointer = data_pointer + 1
#
# # plot other stream segments plot
# colorVal = scalarMap_channel_ID.to_rgba(i) # this gets the distinct colour for this segment
# plt.plot(chi_seg, elev_seg, "-k", linewidth=1)
# plt.plot(chi_seg, fitted_elev_seg, "-", linewidth=1, color=colorVal)
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT USING ONLY MAIN TRIBUTARY WITH FITTED PROFILES (+ERROR)
# plt.figure(6, facecolor='white')
# for i in range (0,len(channel_id)):
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0] + fitted_elevation_standard_error[channel_id==0], "-k", linewidth=0.2)
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0] - fitted_elevation_standard_error[channel_id==0], "-k", linewidth=0.2)
# plt.plot(chi[channel_id==0], elevation[channel_id==0], "-b", linewidth=0.5)
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0], "-r", linewidth=0.5)
#
#
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # PLOT OF CHI b-VALUE (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
# plt.figure(7, facecolor='white')
# for i in range (0,len(channel_id)):
# colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
# plt.plot(chi[i], b_mean[i], ".", linewidth=0.2, color=colorVal)
# #plt.plot(chi[i], b_mean[i] + b_standard_error[i], ".", linewidth=0.25, color='k')
# #plt.plot(chi[i], b_mean[i] - b_standard_error[i], ".", linewidth=0.25, color='k')
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('b-value / m')
#
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # PLOT OF DW STATISTIC (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
# plt.figure(8, facecolor='white')
# for i in range (0,len(channel_id)):
# colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
# plt.plot(chi[i], DW_mean[i], ".", linewidth=0.2, color=colorVal)
# #plt.plot(chi[i], DW_mean[i] + DW_standard_error[i], ".", linewidth=0.25, color='k')
# #plt.plot(chi[i], DW_mean[i] - DW_standard_error[i], ".", linewidth=0.25, color='k')
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('DW statistic')
plt.show()
# Sepcify the size of the fig, Default(8x8)
fig = plt.figure(figsize=(30, 30))
# Specify layout of the graph
pos = nx.spring_layout(g)
n_colors = n_features
# Create colormap
cm = plt.get_cmap('gist_rainbow')
ax = fig.add_subplot(111)
ax.set_color_cycle([cm(1. * i / n_colors) for i in range(n_colors)])
## Draw nodes
# Colormap
sm = plt.cm.ScalarMappable(cmap=cm, norm=plt.normalize(vmin=0, vmax=1))
sm._A = []
# markers that specify the shapes of nodes
marker = ['o','s','*','8','D','+','h','v','^','<','>','x','p',\
'H','d','|','_','1','2','3','4']
for i in range(n_features):
# Find the Ranking of this node by comparing with features_ordered
tmp = features_ordered[features_name_con[i]]
nx.draw_networkx_nodes(g,
pos,
nodelist=[features_name_con[i]],
node_size=300,
alpha=1.0,
node_color=cm(1. * tmp / n_colors),
node_shape=marker[labels[i]],
def main():
try:
import matplotlib
except ImportError:
sys.stderr.write("This script needs the 'matplotlib' library, which ")
sys.stderr.write("was not found. Please install it." )
matplotlib.use('PDF')
from matplotlib import pyplot
# **** Parse command line ****
optParser = optparse.OptionParser( usage = "%prog [options] read_file",
description=
"This script take a file with high-throughput sequencing reads " +
"(supported formats: SAM, Solexa _export.txt, FASTQ, Solexa " +
"_sequence.txt) and performs a simply quality assessment by " +
"producing plots showing the distribution of called bases and " +
"base-call quality scores by position within the reads. The " +
"plots are output as a PDF file.",
epilog =
"Written by Simon Anders ([email protected]), European Molecular Biology " +
" Laboratory (EMBL). (c) 2010. Released under the terms of the GNU General " +
" Public License v3. Part of the 'HTSeq' framework, version %s." % HTSeq.__version__ )
optParser.add_option( "-t", "--type", type="choice", dest="type",
choices = ("sam", "bam", "solexa-export", "fastq", "solexa-fastq"),
default = "sam", help="type of read_file (one of: sam [default], bam, " +
"solexa-export, fastq, solexa-fastq)" )
optParser.add_option( "-o", "--outfile", type="string", dest="outfile",
help="output filename (default is <read_file>.pdf)" )
optParser.add_option( "-r", "--readlength", type="int", dest="readlen",
help="the maximum read length (when not specified, the script guesses from the file" )
optParser.add_option( "-g", "--gamma", type="float", dest="gamma",
default = 0.3,
help="the gamma factor for the contrast adjustment of the quality score plot" )
optParser.add_option( "-n", "--nosplit", action="store_true", dest="nosplit",
help="do not split reads in unaligned and aligned ones" )
optParser.add_option( "-m", "--maxqual", type="int", dest="maxqual", default=41,
help="the maximum quality score that appears in the data (default: 41)" )
if len( sys.argv ) == 1:
optParser.print_help()
sys.exit(1)
(opts, args) = optParser.parse_args()
if len( args ) != 1:
sys.stderr.write( sys.argv[0] + ": Error: Please provide one argument (the read_file).\n" )
sys.stderr.write( " Call with '-h' to get usage information.\n" )
sys.exit( 1 )
readfilename = args[0]
if opts.type == "sam":
readfile = HTSeq.SAM_Reader( readfilename )
isAlnmntFile = True
elif opts.type == "bam":
readfile = HTSeq.BAM_Reader( readfilename )
isAlnmntFile = True
elif opts.type == "solexa-export":
readfile = HTSeq.SolexaExportReader( readfilename )
isAlnmntFile = True
elif opts.type == "fastq":
readfile = HTSeq.FastqReader( readfilename )
isAlnmntFile = False
elif opts.type == "solexa-fastq":
readfile = HTSeq.FastqReader( readfilename, "solexa" )
isAlnmntFile = False
else:
sys.error( "Oops." )
twoColumns = isAlnmntFile and not opts.nosplit
if opts.outfile is None:
outfilename = os.path.basename( readfilename ) + ".pdf"
else:
outfilename = opts.outfile
# **** Get read length ****
if opts.readlen is not None:
readlen = opts.readlen
else:
readlen = 0
if isAlnmntFile:
reads = ( a.read for a in readfile )
else:
reads = readfile
for r in islice( reads, 10000 ):
if len( r ) > readlen:
readlen = len( r )
max_qual = opts.maxqual
gamma = opts.gamma
# **** Initialize count arrays ****
base_arr_U = numpy.zeros( ( readlen, 5 ), numpy.int )
qual_arr_U = numpy.zeros( ( readlen, max_qual+1 ), numpy.int )
if twoColumns:
base_arr_A = numpy.zeros( ( readlen, 5 ), numpy.int )
qual_arr_A = numpy.zeros( ( readlen, max_qual+1 ), numpy.int )
# **** Main counting loop ****
i = 0
try:
for a in readfile:
if isAlnmntFile:
r = a.read
else:
r = a
if twoColumns and (isAlnmntFile and a.aligned):
r.add_bases_to_count_array( base_arr_A )
r.add_qual_to_count_array( qual_arr_A )
else:
r.add_bases_to_count_array( base_arr_U )
r.add_qual_to_count_array( qual_arr_U )
i += 1
if i % 200000 == 0:
print i, "reads processed"
except:
sys.stderr.write( "Error occured in: %s\n" %
readfile.get_line_number_string() )
raise
print i, "reads processed"
# **** Normalize result ****
def norm_by_pos( arr ):
arr = numpy.array( arr, numpy.float )
arr_n = ( arr.T / arr.sum( 1 ) ).T
arr_n[ arr == 0 ] = 0
return arr_n
def norm_by_start( arr ):
arr = numpy.array( arr, numpy.float )
arr_n = ( arr.T / arr.sum( 1 )[ 0 ] ).T
arr_n[ arr == 0 ] = 0
return arr_n
base_arr_U_n = norm_by_pos( base_arr_U )
qual_arr_U_n = norm_by_start( qual_arr_U )
nreads_U = base_arr_U[0,:].sum()
if twoColumns:
base_arr_A_n = norm_by_pos( base_arr_A )
qual_arr_A_n = norm_by_start( qual_arr_A )
nreads_A = base_arr_A[0,:].sum()
# **** Make plot ****
def plot_bases( arr ):
xg = numpy.arange( readlen )
pyplot.plot( xg, arr[ : , 0 ], marker='.', color='red')
pyplot.plot( xg, arr[ : , 1 ], marker='.', color='darkgreen')
pyplot.plot( xg, arr[ : , 2 ], marker='.',color='lightgreen')
pyplot.plot( xg, arr[ : , 3 ], marker='.',color='orange')
pyplot.plot( xg, arr[ : , 4 ], marker='.',color='grey')
pyplot.axis( (0, readlen-1, 0, 1 ) )
pyplot.text( readlen*.70, .9, "A", color="red" )
pyplot.text( readlen*.75, .9, "C", color="darkgreen" )
pyplot.text( readlen*.80, .9, "G", color="lightgreen" )
pyplot.text( readlen*.85, .9, "T", color="orange" )
pyplot.text( readlen*.90, .9, "N", color="grey" )
pyplot.figure()
pyplot.subplots_adjust( top=.85 )
pyplot.suptitle( os.path.basename(readfilename), fontweight='bold' )
if twoColumns:
pyplot.subplot( 221 )
plot_bases( base_arr_U_n )
pyplot.ylabel( "proportion of base" )
pyplot.title( "non-aligned reads\n%.0f%% (%.3f million)" %
( 100. * nreads_U / (nreads_U+nreads_A), nreads_U / 1e6 ) )
pyplot.subplot( 222 )
plot_bases( base_arr_A_n )
pyplot.title( "aligned reads\n%.0f%% (%.3f million)" %
( 100. * nreads_A / (nreads_U+nreads_A), nreads_A / 1e6 ) )
pyplot.subplot( 223 )
pyplot.pcolor( qual_arr_U_n.T ** gamma, cmap=pyplot.cm.Greens,
norm=pyplot.normalize( 0, 1 ) )
pyplot.axis( (0, readlen-1, 0, max_qual+1 ) )
pyplot.xlabel( "position in read" )
pyplot.ylabel( "base-call quality score" )
pyplot.subplot( 224 )
pyplot.pcolor( qual_arr_A_n.T ** gamma, cmap=pyplot.cm.Greens,
norm=pyplot.normalize( 0, 1 ) )
pyplot.axis( (0, readlen-1, 0, max_qual+1 ) )
pyplot.xlabel( "position in read" )
else:
pyplot.subplot( 211 )
plot_bases( base_arr_U_n )
pyplot.ylabel( "proportion of base" )
pyplot.title( "%.3f million reads" % ( nreads_U / 1e6 ) )
pyplot.subplot( 212 )
pyplot.pcolor( qual_arr_U_n.T ** gamma, cmap=pyplot.cm.Greens,
norm=pyplot.normalize( 0, 1 ) )
pyplot.axis( (0, readlen-1, 0, max_qual+1 ) )
pyplot.xlabel( "position in read" )
pyplot.ylabel( "base-call quality score" )
pyplot.savefig( outfilename )
def main():
try:
import matplotlib
except ImportError:
sys.stderr.write("This script needs the 'matplotlib' library, which ")
sys.stderr.write("was not found. Please install it.")
matplotlib.use('PDF')
from matplotlib import pyplot
# **** Parse command line ****
optParser = optparse.OptionParser(
usage="%prog [options] read_file",
description=
"This script take a file with high-throughput sequencing reads " +
"(supported formats: SAM, Solexa _export.txt, FASTQ, Solexa " +
"_sequence.txt) and performs a simply quality assessment by " +
"producing plots showing the distribution of called bases and " +
"base-call quality scores by position within the reads. The " +
"plots are output as a PDF file.",
epilog=
"Written by Simon Anders ([email protected]), European Molecular Biology "
+
" Laboratory (EMBL). (c) 2010. Released under the terms of the GNU General "
+ " Public License v3. Part of the 'HTSeq' framework, version %s." %
HTSeq.__version__)
optParser.add_option(
"-t",
"--type",
type="choice",
dest="type",
choices=("sam", "bam", "solexa-export", "fastq", "solexa-fastq"),
default="sam",
help="type of read_file (one of: sam [default], bam, " +
"solexa-export, fastq, solexa-fastq)")
optParser.add_option("-o",
"--outfile",
type="string",
dest="outfile",
help="output filename (default is <read_file>.pdf)")
optParser.add_option(
"-r",
"--readlength",
type="int",
dest="readlen",
help=
"the maximum read length (when not specified, the script guesses from the file"
)
optParser.add_option(
"-g",
"--gamma",
type="float",
dest="gamma",
default=0.3,
help=
"the gamma factor for the contrast adjustment of the quality score plot"
)
optParser.add_option(
"-n",
"--nosplit",
action="store_true",
dest="nosplit",
help="do not split reads in unaligned and aligned ones")
optParser.add_option(
"-m",
"--maxqual",
type="int",
dest="maxqual",
default=41,
help="the maximum quality score that appears in the data (default: 41)"
)
if len(sys.argv) == 1:
optParser.print_help()
sys.exit(1)
(opts, args) = optParser.parse_args()
if len(args) != 1:
sys.stderr.write(
sys.argv[0] +
": Error: Please provide one argument (the read_file).\n")
sys.stderr.write(" Call with '-h' to get usage information.\n")
sys.exit(1)
readfilename = args[0]
if opts.type == "sam":
readfile = HTSeq.SAM_Reader(readfilename)
isAlnmntFile = True
elif opts.type == "bam":
readfile = HTSeq.BAM_Reader(readfilename)
isAlnmntFile = True
elif opts.type == "solexa-export":
readfile = HTSeq.SolexaExportReader(readfilename)
isAlnmntFile = True
elif opts.type == "fastq":
readfile = HTSeq.FastqReader(readfilename)
isAlnmntFile = False
elif opts.type == "solexa-fastq":
readfile = HTSeq.FastqReader(readfilename, "solexa")
isAlnmntFile = False
else:
sys.error("Oops.")
twoColumns = isAlnmntFile and not opts.nosplit
if opts.outfile is None:
outfilename = os.path.basename(readfilename) + ".pdf"
else:
outfilename = opts.outfile
# **** Get read length ****
if opts.readlen is not None:
readlen = opts.readlen
else:
readlen = 0
if isAlnmntFile:
reads = (a.read for a in readfile)
else:
reads = readfile
for r in islice(reads, 10000):
if len(r) > readlen:
readlen = len(r)
max_qual = opts.maxqual
gamma = opts.gamma
# **** Initialize count arrays ****
base_arr_U = numpy.zeros((readlen, 5), numpy.int)
qual_arr_U = numpy.zeros((readlen, max_qual + 1), numpy.int)
if twoColumns:
base_arr_A = numpy.zeros((readlen, 5), numpy.int)
qual_arr_A = numpy.zeros((readlen, max_qual + 1), numpy.int)
# **** Main counting loop ****
i = 0
try:
for a in readfile:
if isAlnmntFile:
r = a.read
else:
r = a
if twoColumns and (isAlnmntFile and a.aligned):
r.add_bases_to_count_array(base_arr_A)
r.add_qual_to_count_array(qual_arr_A)
else:
r.add_bases_to_count_array(base_arr_U)
r.add_qual_to_count_array(qual_arr_U)
i += 1
if i % 200000 == 0:
print i, "reads processed"
except:
sys.stderr.write("Error occured in: %s\n" %
readfile.get_line_number_string())
raise
print i, "reads processed"
# **** Normalize result ****
def norm_by_pos(arr):
arr = numpy.array(arr, numpy.float)
arr_n = (arr.T / arr.sum(1)).T
arr_n[arr == 0] = 0
return arr_n
def norm_by_start(arr):
arr = numpy.array(arr, numpy.float)
arr_n = (arr.T / arr.sum(1)[0]).T
arr_n[arr == 0] = 0
return arr_n
base_arr_U_n = norm_by_pos(base_arr_U)
qual_arr_U_n = norm_by_start(qual_arr_U)
nreads_U = base_arr_U[0, :].sum()
if twoColumns:
base_arr_A_n = norm_by_pos(base_arr_A)
qual_arr_A_n = norm_by_start(qual_arr_A)
nreads_A = base_arr_A[0, :].sum()
# **** Make plot ****
def plot_bases(arr):
xg = numpy.arange(readlen)
pyplot.plot(xg, arr[:, 0], marker='.', color='red')
pyplot.plot(xg, arr[:, 1], marker='.', color='darkgreen')
pyplot.plot(xg, arr[:, 2], marker='.', color='lightgreen')
pyplot.plot(xg, arr[:, 3], marker='.', color='orange')
pyplot.plot(xg, arr[:, 4], marker='.', color='grey')
pyplot.axis((0, readlen - 1, 0, 1))
pyplot.text(readlen * .70, .9, "A", color="red")
pyplot.text(readlen * .75, .9, "C", color="darkgreen")
pyplot.text(readlen * .80, .9, "G", color="lightgreen")
pyplot.text(readlen * .85, .9, "T", color="orange")
pyplot.text(readlen * .90, .9, "N", color="grey")
pyplot.figure()
pyplot.subplots_adjust(top=.85)
pyplot.suptitle(os.path.basename(readfilename), fontweight='bold')
if twoColumns:
pyplot.subplot(221)
plot_bases(base_arr_U_n)
pyplot.ylabel("proportion of base")
pyplot.title("non-aligned reads\n%.0f%% (%.3f million)" %
(100. * nreads_U / (nreads_U + nreads_A), nreads_U / 1e6))
pyplot.subplot(222)
plot_bases(base_arr_A_n)
pyplot.title("aligned reads\n%.0f%% (%.3f million)" %
(100. * nreads_A / (nreads_U + nreads_A), nreads_A / 1e6))
pyplot.subplot(223)
pyplot.pcolor(qual_arr_U_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
pyplot.ylabel("base-call quality score")
pyplot.subplot(224)
pyplot.pcolor(qual_arr_A_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
else:
pyplot.subplot(211)
plot_bases(base_arr_U_n)
pyplot.ylabel("proportion of base")
pyplot.title("%.3f million reads" % (nreads_U / 1e6))
pyplot.subplot(212)
pyplot.pcolor(qual_arr_U_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
pyplot.ylabel("base-call quality score")
pyplot.savefig(outfilename)
if __name__ == "__main__":
power=1
smoothing=20
#Creating some data, with each coodinate and the values stored in separated lists
xv = [10,60,40,70,10,50,20,70,30,60]
yv = [10,20,30,30,40,50,60,70,80,90]
values = [1,2,2,3,4,6,7,7,8,10]
#Creating the output grid (100x100, in the example)
ti = np.linspace(0, 100, 100)
XI, YI = np.meshgrid(ti, ti)
#Creating the interpolation function and populating the output matrix value
ZI = invDist(xv,yv,values,100,100,power,smoothing)
# Plotting the result
n = plt.normalize(0.0, 100.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(xv, yv, 100, values)
plt.title('Inv dist interpolation - power: ' + str(power) + ' smoothing: ' + str(smoothing))
plt.xlim(0, 100)
plt.ylim(0, 100)
plt.colorbar()
plt.show()
# Customize x tick lables
xticks = [10,20,30,40,50,60]
ax.xaxis.set_ticks(xticks)
ax.set_xticklabels(xticks, fontsize=16, alpha=a)
# Customize y tick labels
yticks = [item.get_text() for item in ax.get_yticklabels()]
ax.set_yticklabels(yticks, fontsize=16, alpha=a)
ax.yaxis.set_tick_params(pad=12)
# Create a fake colorbar to express department population
ctb = LinearSegmentedColormap.from_list('custombar', customcmap, N=2048)
# Trick from http://stackoverflow.com/questions/8342549/
# matplotlib-add-colorbar-to-a-sequence-of-line-plots
sm = plt.cm.ScalarMappable(cmap=ctb, norm=plt.normalize(vmin=20, vmax=800))
# Fake up the array of the scalar mappable
sm._A = []
# Set colorbar, aspect ratio
cbar = plt.colorbar(sm, alpha=0.05, aspect=16, shrink=0.4)
cbar.solids.set_edgecolor("face")
# Remove colorbar container frame
cbar.outline.set_visible(False)
# Fontsize for colorbar ticklabels
cbar.ax.tick_params(labelsize=16)
# Customize colorbar tick labels
mytks = range(0,800,100)
cbar.set_ticks(mytks)
cbar.ax.set_yticklabels([str(a) for a in mytks], alpha=a)
llcrnrlat=lat_min,
urcrnrlon=lon_max,
urcrnrlat=lat_max,
resolution='h',
projection='tmerc',
lon_0=lon_max + (lon_max - lon_min) / 2.,
lat_0=lat_max + (lat_max - lat_min) / 2.)
m.drawcoastlines()
x, y = m(Cat_mc[:, 2], Cat_mc[:, 1])
m.scatter(x,
y,
s=10,
c=range(len(Cat_mc[:, 0])),
norm=plt.normalize(vmin=0, vmax=N),
marker='o',
edgecolor='none')
m.drawmapboundary(fill_color='lightgrey')
m.drawparallels(np.arange(27.6, 28.0, 0.1), labels=[1, 0, 0, 1])
m.drawmeridians(np.arange(-18.4, -17.8, 0.1), labels=[1, 0, 0, 1])
mdates.DateFormatter('dd/mm/yyyy')
plt.text(0.67,
0.95,
mdates.num2date(t_start + i).strftime('%d/%m/%Y'),
fontsize=9,
transform=ax1.transAxes)
###########
z.append(p.z)
print "Array populated."
#Creating the output grid (100x100, in the example)
print "Creating output grid."
ti = np.linspace(h.max[0] - h.min[0], h.max[1] - h.min[1])
XI, YI = sp.sparse.lil_matrix(x, y)
print "Output grid created"
#Creating the interpolation function and populating the output matrix value
print "Running RBF."
rbf = Rbf(x, y, z, function='inverse')
ZI = rbf(XI, YI)
print "RBF complete"
print "Plotting output."
# Plotting the result
n = plt.normalize(h.min[2], h.max[2])
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(x, y, z)
plt.title('RBF interpolation')
plt.xlim(h.min[0], h.max[0])
plt.ylim(h.min[1], h.max[1])
#plt.colorbar()
plt.show()
print "Done."
npts = 200
ngridx = 100
ngridy = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# griddata and contour.
start = time.clock()
plt.subplot(211)
xi = np.linspace(-2.1,2.1,ngridx)
yi = np.linspace(-2.1,2.1,ngridy)
zi = griddata(x,y,z,xi,yi,interp='linear')
plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
plt.colorbar() # draw colorbar
plt.plot(x, y, 'ko', ms=3)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata and contour (%d points, %d grid points)' % (npts, ngridx*ngridy))
print ('griddata and contour seconds: %f' % (time.clock() - start))
# tricontour.
start = time.clock()
plt.subplot(212)
triang = tri.Triangulation(x, y)
plt.tricontour(x, y, z, 15, linewidths=0.5, colors='k')
plt.tricontourf(x, y, z, 15, cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
plt.colorbar()
def GetColorBar(ax, colormap='autumn_r', min=0, max=1):
cm = plt.cm.ScalarMappable(cmap=plt.get_cmap(colormap), norm=plt.normalize(vmin=min, vmax=max))
# fake up the array of the scalar mappable...
cm._A = []
return cm
f = liblas.file.File(sys.argv[1],mode='r')
lgrid = LidarGrid (f)
print lgrid.minz
print lgrid.maxz
print "Adding data to grid"
for p in f:
if p.return_number == 1:
lgrid.add (p.x, p.y, p.z)
# print "Add: "+str(p.x)+", "+str(p.y)+", "+str(p.z)
print "Data added to grid."
plt.subplot(111)
n = plt.normalize(75, 155)
#img = plt.imshow(lgrid.grid, extent=(0, lgrid.xsize, lgrid.ysize, 0), cmap=plt.cm.Greys_r)
img = plt.imshow(lgrid.grid, norm=n, interpolation='nearest', cmap=plt.cm.jet)
# img.set_clim(lgrid.minz, 120)
plt.colorbar()
plt.show()
print "Done."
#for x in range (0, lidar_grid.xsize):
# for y in range (0, lidar_grid.ysize):
# print "Get: "+str(lidar_grid.get (x, y))
"""
import numpy as np
from scipy.interpolate import Rbf
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import cm
# 2-d tests - setup scattered data
x = np.random.rand(100) * 4.0 - 2.0
y = np.random.rand(100) * 4.0 - 2.0
z = x * np.exp(-x**2 - y**2)
ti = np.linspace(-2.0, 2.0, 100)
XI, YI = np.meshgrid(ti, ti)
# use RBF
rbf = Rbf(x, y, z, epsilon=2)
ZI = rbf(XI, YI)
# plot the result
n = plt.normalize(-2., 2.)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI, cmap=cm.jet)
plt.scatter(x, y, 100, z, cmap=cm.jet)
plt.title('RBF interpolation - multiquadrics')
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.colorbar()
plt.savefig('rbf2d.png')
def main(separator='\t'):
# **** Parse command line ****
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument( "-g", "--gamma", type=float, dest="gamma", default = 0.3,\
help="the gamma factor for the contrast adjustment of the quality score plot, default 0.3" )
opts = parser.parse_args()
gamma = opts.gamma
twoColumns = True
# input comes from STDIN (standard input)
data = read_mapper_output(sys.stdin, separator=separator)
# groupby groups multiple word-count pairs by word,
# and creates an iterator that returns consecutive keys and their group:
# current_word - string containing a word (the key)
# group - iterator yielding all ["<current_word>","<count>"] items
former_element = None
array_types = {}
for current_element, group in groupby(data, itemgetter(0)):
try:
total_count = sum(
np.fromstring(line, sep=' ', dtype=int)
for current_element, line in group)
# print "%s%s%s" % (current_element, separator, ' '.join(str(x) for x in total_count))
# print current_element, total_count
if not former_element:
arr = total_count
former_element = current_element
else:
if current_element.split('_')[:-1] == former_element.split(
'_')[:-1]:
arr = np.vstack([arr, total_count])
else:
#print 'change array', former_element, current_element
array_types['_'.join(former_element.split('_')[:-1])] = arr
former_element = current_element
arr = total_count
except ValueError:
# count was not a number, so silently discard this item
pass
array_types['_'.join(former_element.split('_')[:-1])] = arr
base_arr_U_n = norm_by_pos(array_types['base_arr_U'])
qual_arr_U_n = norm_by_start(array_types['qual_arr_U'])
nreads_U = array_types['nreads_U'] # base_arr_U[0,:].sum()
if twoColumns:
base_arr_A_n = norm_by_pos(array_types['base_arr_A'])
qual_arr_A_n = norm_by_start(array_types['qual_arr_A'])
nreads_A = array_types['nreads_A'] #base_arr_A[0,:].sum()
# get the read length from the data
readlen = len(base_arr_A_n)
#get the max qual from the data
max_qual = len(qual_arr_U_n[0, :])
# print readlen, max_qual, len(base_arr_U_n)
# sys.exit()
# print "number of aligned reads", base_arr_A[50,:].sum()
# print "number of unaligned reads", base_arr_U[60,:].sum()
#
#
## **** Make plot ****
#
pyplot.figure()
pyplot.subplots_adjust(top=.85)
pyplot.suptitle(os.path.basename("readfilename"), fontweight='bold')
if twoColumns:
pyplot.subplot(221)
plot_bases(base_arr_U_n)
pyplot.ylabel("proportion of base")
pyplot.title("non-aligned reads\n%.0f%% (%.3f million)" %
(100. * nreads_U / (nreads_U + nreads_A), nreads_U / 1e6))
pyplot.subplot(222)
plot_bases(base_arr_A_n)
pyplot.title("aligned reads\n%.0f%% (%.3f million)" %
(100. * nreads_A / (nreads_U + nreads_A), nreads_A / 1e6))
pyplot.subplot(223)
pyplot.pcolor(qual_arr_U_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
pyplot.ylabel("base-call quality score")
pyplot.subplot(224)
pyplot.pcolor(qual_arr_A_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
else:
pyplot.subplot(211)
plot_bases(base_arr_U_n)
pyplot.ylabel("proportion of base")
pyplot.title("%.3f million reads" % (nreads_U / 1e6))
pyplot.subplot(212)
pyplot.pcolor(qual_arr_U_n.T**gamma,
cmap=pyplot.cm.Greens,
norm=pyplot.normalize(0, 1))
pyplot.axis((0, readlen - 1, 0, max_qual + 1))
pyplot.xlabel("position in read")
pyplot.ylabel("base-call quality score")
pyplot.savefig(sys.stdout)
# adjust figure aspect ratio and size
w, h = plt.figaspect(1.)
fig = plt.figure(figsize=(3. * w, 3. * h))
# graph image
# we use the rasterized graph since the graph PDF could reach sizes > 50MB
img = mpimg.imread('graph-iteration-' + "{0:06d}".format(iteration) + '.png')
ax = plt.subplot2grid((3, 3), (0, 0), rowspan=2, colspan=2)
ax.imshow(img, interpolation='none')
ax.axis('off')
ax.set_title('Search tree for ' + "{:,}".format(iteration) + ' iterations')
# graph image color bar
sm = plt.cm.ScalarMappable(cmap=cm.jet,
norm=plt.normalize(vmin=0, vmax=iteration))
sm._A = []
cb = plt.colorbar(sm,
orientation='horizontal',
fraction=0.025,
aspect=40,
pad=0.0)
cb.set_label('Iteration')
# incumbent trajectory: X, Y
ax = plt.subplot2grid((3, 3), (0, 2))
ax.set_title('Incumbent solution\n')
ax.set_xlabel('X (AU)')
ax.set_ylabel('Y (AU)')
ax.plot(xAU, yAU, 'k-')
ax.plot(xAU[0], yAU[0], 'bo', label='Earth departure')
def stability_analysis(file_dir, figsize=(6,4),cmap = plt.cm.jet):
N_vals = np.array(np.linspace(50,400,11),dtype=int)
rho_vals = np.array(np.linspace(1,10,30))
#=============== rho_max analysis =================
fig1, ax1 = plt.subplots(figsize=figsize)
fig2, ax2 = plt.subplots(figsize=figsize)
axes = ax1,ax2
step = 5.0/200.
n = len(rho_vals)
ev_i = 0
error_values = []
color_values = cmap(np.linspace(0,1,n))
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.normalize(vmin=rho_vals[0], vmax=rho_vals[-1]))
sm.set_array([])
for i, rho_max in enumerate(rho_vals):
color = color_values[i]
N = int(rho_max/step)
_, axes, rel_error = plot_noninteracting(rho_max, N, file_dir, axes=axes,
color = color, rho_analysis=True, eigenvalue_i=ev_i)
error_values.append(rel_error)
cb = fig1.colorbar(sm, ax=ax1)
cb.set_label('$\\rho$ max')
#[ax.grid('on') for ax in [ax1,ax2]]
ax2.semilogy(rho_vals,error_values)
ax2.legend(["$\\epsilon_%d$" % i for i in range(len(error_values))])
# ax1.legend()
ax1.set_xlim([0,4])
#ax2.axhline(11.)
ax1.set_xlabel('$\\rho$ ')
ax1.set_ylabel('$u_{%d}^2$'%ev_i)
ax2.set_ylabel('$E_%d$' %ev_i)
ax2.set_xlabel('$\\rho_{\\text{max}}$')
#================ N analysis ==============
fig3, ax3 = plt.subplots(1,figsize=figsize)
fig4, ax4 = plt.subplots(1,figsize=figsize)
axes = [ax3,ax4]
rho_max = 5.0
N = 200
n = len(N_vals)
error_values = []
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.normalize(vmin=N_vals[0], vmax=N_vals[-1]))
sm.set_array([])
for i, N in enumerate(N_vals):
color = cmap(float(i)/(n-1))
_, axes, rel_error = plot_noninteracting(rho_max, N, file_dir, axes=axes,
color = color, rho_analysis=False, eigenvalue_i=ev_i)
error_values.append(rel_error)
error_values = np.array(error_values)
cb = fig3.colorbar(sm, ax=ax3)
cb.set_label('$\\rho$ max')
#[ax.grid('on') for ax in [ax3,ax4]]
logN = np.log(N_vals)
logError = np.log(error_values)
print(logError.shape, logN.shape, (logError.T/logN).shape)
gradient = np.average(logError.T/logN, axis = 1)
print gradient
ax4.axis('equal')
print(N_vals.shape, error_values.shape)
for i in range(len(gradient)):
ax4.loglog(N_vals,error_values[:,i], label='$\\epsilon_%d$'%(i+1))
ax4.legend()
# ax3.legend()
ax3.set_xlim([0,4])
#ax4.axhline(11.)
ax3.set_xlabel('$\\rho$ ')
ax3.set_ylabel('$u_{%d}^2$'%ev_i)
ax4.set_ylabel('$E_%d$' %ev_i)
ax4.set_xlabel('$N$')
try:
fig1.savefig('results/rhoMaxAnalysis1.pdf')
fig2.savefig('results/rhoMaxAnalysis2.pdf')
fig3.savefig('results/dimAnalysis1.pdf')
fig4.savefig('results/dimAnalysis2.pdf')
plt.show()
except ValueError:
# \\text obviously doesnt work some times
ax2.set_xlabel('rho_max')
fig1.savefig('results/rhoMaxAnalysis1.pdf')
fig2.savefig('results/rhoMaxAnalysis2.pdf')
fig3.savefig('results/dimAnalysis1.pdf')
fig4.savefig('results/dimAnalysis2.pdf')
plt.show()
def main(args=None):
"""The main function; parses options and plots"""
## ---------- build and read options ----------
from optparse import OptionParser
optParser = OptionParser()
optParser.add_option("-n", "--net", dest="net", metavar="FILE",
help="Defines the network to read")
optParser.add_option("-i", "--dump-inputs", dest="dumps", metavar="FILE",
help="Defines the dump-output files to use as input")
optParser.add_option("-m", "--measures", dest="measures",
default="speed,entered", help="Define which measure to plot")
optParser.add_option("-w", "--default-width", dest="defaultWidth",
type="float", default=.1, help="Defines the default edge width")
optParser.add_option("-c", "--default-color", dest="defaultColor",
default='k', help="Defines the default edge color")
optParser.add_option("--min-width", dest="minWidth",
type="float", default=.5, help="Defines the minimum edge width")
optParser.add_option("--max-width", dest="maxWidth",
type="float", default=3, help="Defines the maximum edge width")
optParser.add_option("--log-colors", dest="logColors", action="store_true",
default=False, help="If set, colors are log-scaled")
optParser.add_option("--log-widths", dest="logWidths", action="store_true",
default=False, help="If set, widths are log-scaled")
optParser.add_option("--min-color-value", dest="colorMin",
type="float", default=None, help="If set, defines the minimum edge color value")
optParser.add_option("--max-color-value", dest="colorMax",
type="float", default=None, help="If set, defines the maximum edge color value")
optParser.add_option("--min-width-value", dest="widthMin",
type="float", default=None, help="If set, defines the minimum edge width value")
optParser.add_option("--max-width-value", dest="widthMax",
type="float", default=None, help="If set, defines the maximum edge width value")
optParser.add_option("-v", "--verbose", dest="verbose", action="store_true",
default=False, help="If set, the script says what it's doing")
# standard plot options
helpers.addInteractionOptions(optParser)
helpers.addPlotOptions(optParser)
# parse
options, remaining_args = optParser.parse_args(args=args)
if options.net==None:
print "Error: a network to load must be given."
return 1
if options.verbose: print "Reading network from '%s'" % options.net
net = sumolib.net.readNet(options.net)
if options.measures==None:
print "Error: a dump file must be given."
return 1
times = []
hc = None
if options.dumps.split(",")[0]!="":
if options.verbose: print "Reading colors from '%s'" % options.dumps.split(",")[0]
hc = WeightsReader(options.measures.split(",")[0])
sumolib.output.parse_sax(options.dumps.split(",")[0], hc)
times = hc._edge2value
hw = None
if options.dumps.split(",")[1]!="":
if options.verbose: print "Reading widths from '%s'" % options.dumps.split(",")[1]
hw = WeightsReader(options.measures.split(",")[1])
sumolib.output.parse_sax(options.dumps.split(",")[1], hw)
times = hw._edge2value
for t in times:
colors = {}
maxColorValue = None
minColorValue = None
for e in net._id2edge:
if hc and t in hc._edge2value and e in hc._edge2value[t]:
if options.colorMax!=None and hc._edge2value[t][e]>options.colorMax: hc._edge2value[t][e] = options.colorMax
if options.colorMin!=None and hc._edge2value[t][e]<options.colorMin: hc._edge2value[t][e] = options.colorMin
if maxColorValue==None or maxColorValue<hc._edge2value[t][e]: maxColorValue = hc._edge2value[t][e]
if minColorValue==None or minColorValue>hc._edge2value[t][e]: minColorValue = hc._edge2value[t][e]
colors[e] = hc._edge2value[t][e]
if options.colorMax!=None: maxColorValue = options.colorMax
if options.colorMin!=None: minColorValue = options.colorMin
if options.logColors:
helpers.logNormalise(colors, maxColorValue)
else:
helpers.linNormalise(colors, minColorValue, maxColorValue)
for e in colors:
colors[e] = helpers.getColor(options, colors[e], 1.)
if options.verbose: print "Color values are between %s and %s" % (minColorValue, maxColorValue)
widths = {}
maxWidthValue = None
minWidthValue = None
for e in net._id2edge:
if hw and t in hw._edge2value and e in hw._edge2value[t]:
v = abs(hw._edge2value[t][e])
if options.widthMax!=None and v>options.widthMax: v = options.widthMax
if options.widthMin!=None and v<options.widthMin: v = options.widthMin
if not maxWidthValue or maxWidthValue<v: maxWidthValue = v
if not minWidthValue or minWidthValue>v: minWidthValue = v
widths[e] = v
if options.widthMax!=None: maxWidthValue = options.widthMax
if options.widthMin!=None: minWidthValue = options.widthMin
if options.logWidths:
helpers.logNormalise(widths, options.colorMax)
else:
helpers.linNormalise(widths, minWidthValue, maxWidthValue)
for e in widths:
widths[e] = options.minWidth + widths[e] * (options.maxWidth-options.minWidth)
if options.verbose: print "Width values are between %s and %s" % (minWidthValue, maxWidthValue)
fig, ax = helpers.openFigure(options)
ax.set_aspect("equal", None, 'C')
helpers.plotNet(net, colors, widths, options)
# drawing the legend, at least for the colors
sm = matplotlib.cm.ScalarMappable(cmap=get_cmap(options.colormap), norm=plt.normalize(vmin=minColorValue, vmax=maxColorValue))
# "fake up the array of the scalar mappable. Urgh..." (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
sm._A = []
plt.colorbar(sm)
options.nolegend = True
helpers.closeFigure(fig, ax, options)
return 0 # !!! hack
return 0
if __name__ == "__main__":
power=1
smoothing=20
#Creating some data, with each coodinate and the values stored in separated lists
xv = [10,60,40,70,10,50,20,70,30,60]
yv = [10,20,30,30,40,50,60,70,80,90]
values = [1,2,2,3,4,6,7,7,8,10]
#Creating the output grid (100x100, in the example)
ti = np.linspace(0, 100, 100)
XI, YI = np.meshgrid(ti, ti)
#Creating the interpolation function and populating the output matrix value
ZI = invDist(xv,yv,values,100,100,power,smoothing)
# Plotting the result
n = plt.normalize(0.0, 100.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI)
plt.scatter(xv, yv, 100, values)
plt.title('Inv dist interpolation - power: ' + str(power) + ' smoothing: ' + str(smoothing))
plt.xlim(0, 100)
plt.ylim(0, 100)
plt.colorbar()
plt.show()
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib import cm
# 2-d tests - setup scattered data
x = np.random.rand(100) * 4.0 - 2.0
y = np.random.rand(100) * 4.0 - 2.0
z = x * np.exp(-x ** 2 - y ** 2)
ti = np.linspace(-2.0, 2.0, 100)
XI, YI = np.meshgrid(ti, ti)
# use RBF
rbf = Rbf(x, y, z, epsilon=2)
ZI = rbf(XI, YI)
# plot the result
n = plt.normalize(-2.0, 2.0)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI, cmap=cm.jet)
plt.scatter(x, y, 100, z, cmap=cm.jet)
plt.title("RBF interpolation - multiquadrics")
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.colorbar()
plt.savefig("rbf2d.png")
# <markdowncell>
# :
"""Create a Gabor Patch.
Parameters
----------
size : (int, int), optional
size (x, y) of the mask
position : (int, int), optional
position of the mask stimulus
lambda_ : int, optional
Spatial frequency (pixel per cycle)
theta : int or float, optional
Grating orientation in degrees
sigma : int or float, optional
gaussian standard deviation (in pixels)
phase : float
0 to 1 inclusive
Notes
-----
The background colour of the stimulus depends of the parameters of
the Gabor patch and can be determined (e.g. for plotting) with the
property `GaborPatch.background_colour`.
"""
# Parts of the code has be ported from http://www.icn.ucl.ac.uk/courses/MATLAB-Tutorials/Elliot_Freeman/html/gabor_tutorial.html
import types
if type(np) is not types.ModuleType:
message = """GaborPatch can not be initialized.
The Python package 'Numpy' is not installed."""
raise ImportError(message)
if type(pyplot) is not types.ModuleType:
message = """GaborPatch can not be initialized.
The Python package 'Matplotlib' is not installed."""
raise ImportError(message)
if size is None:
size = defaults.gaborpatch_size
if position is None:
position = defaults.gaborpatch_position
if lambda_ is None:
lambda_ = defaults.gaborpatch_lambda_
if theta is None:
theta = defaults.gaborpatch_theta
if sigma is None:
sigma = defaults.gaborpatch_sigma
if phase is None:
phase = defaults.gaborpatch_phase
fid, filename = tempfile.mkstemp(
dir=expyriment.stimuli.defaults.tempdir, suffix=".png")
os.close(fid)
Picture.__init__(self, filename, position)
# make linear ramp
X0 = (np.linspace(1, size, size) / size) - .5
# Set wavelength and phase
freq = size / float(lambda_)
phaseRad = phase * 2 * np.pi
# Make 2D grating
Xm, Ym = np.meshgrid(X0, X0)
# Change orientation by adding Xm and Ym together in different proportions
thetaRad = (theta / 360.) * 2 * np.pi
Xt = Xm * np.cos(thetaRad)
Yt = Ym * np.sin(thetaRad)
grating = np.sin(((Xt + Yt) * freq * 2 * np.pi) + phaseRad)
# 2D Gaussian distribution
gauss = np.exp(-((Xm**2) + (Ym**2)) / (2 * (sigma / float(size))**2))
# Trim
gauss[gauss < trim] = 0
self._pixel_array = grating * gauss
#save stimulus
color_map = pyplot.get_cmap('gray')
color_map.set_over(color="y")
pyplot.imsave(fname=filename,
arr=self._pixel_array,
cmap=color_map,
format="png")
# determine background color
norm = pyplot.normalize(vmin=np.min(self._pixel_array),
vmax=np.max(self._pixel_array))
bgc = color_map(norm(0))
self._background_colour = map(lambda x: int(x * 255), bgc[:3])
def map_dict(export_fn,states_happiness):
# Lambert Conformal map of lower 48 states.
m = Basemap(llcrnrlon=-119,llcrnrlat=22,
urcrnrlon=-64, urcrnrlat=49,
projection='lcc', lat_1=33,lat_2=45,lon_0=-95)
# laod state boundaries.
# data from U.S Census Bureau
# http://www.census.gov/geo/www/cob/st1990.html
shp_info = m.readshapefile('gz_2010_us_040_00_500k/gz_2010_us_040_00_500k','states',drawbounds=False)
# This loads three files:
# gz_2010_us_040_00_500k.dbf
# gz_2010_us_040_00_500k.shp
# gz_2010_us_040_00_500k.shx
max_score = -sys.maxint - 1
min_score = sys.maxint+1
happiest_state = ''
saddest_state = ''
for state in states_happiness:
score = states_happiness[state]
if (score > max_score):
happiest_state = state
max_score = score
if (score < min_score):
saddest_state = state
min_score = score
for state in states_happiness:
states_happiness[state] -= min_score
min_score = 0
max_score = max_score-min_score
# choose a color for each state based on happiness score.
colors={}
statenames=[]
cmap = plt.cm.Blues_r # use 'hot' colormap
vmin = min_score; vmax = max_score # set range.
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.normalize(vmin=vmin, vmax=vmax))
for shapedict in m.states_info:
statename = shapedict['NAME']
#print statesNames[statename], ' ', statename
try:
#score = states_happiness[statesNames[statename]]
score = states_happiness[statename]
except KeyError:
score = 0.0
# calling colormap with value between 0 and 1 returns
# rgba value. Invert color range (hot colors are
# high
# population), take sqrt root to spread out
# colors more.
try:
colors[statename] = cmap((score-vmin)/(vmax-vmin))[:3]
except KeyError:
colors[statename] = cmap(0)[:3]
statenames.append(statename)
# cycle through state names,
# color each one.
for nshape,seg in enumerate(m.states):
xx,yy = zip(*seg)
if statenames[nshape] != 'District of Columbia' and \
statenames[nshape] != "Puerto Rico":
color = rgb2hex(colors[statenames[nshape]])
plt.fill(xx,yy,color,edgecolor='black')
# draw
# meridians
# and
# parallels.
m.drawparallels(np.arange(25,65,20), labels=[0,0,0,0],
zorder=-1,color="w")
m.drawmeridians(np.arange(-120,-40,20),labels=[0,0,0,0],
zorder=-1,color="w")
# set up colorbar:
mm = plt.cm.ScalarMappable(cmap=cmap)
mm.set_array([0,1])
#plt.colorbar(mm, label="Happiness",
# orientation="horizontal", fraction=0.05)
plt.colorbar(mm,orientation="horizontal", fraction=0.05)
#plt.title('Filling State Polygons by Population Density')
plt.gca().axis("off")
plt.savefig(export_fn)
plt.close('all')
def vectorize(hillshade_file, m_value_file):
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
# get data
hillshade, hillshade_header = read_flt(hillshade_file)
m_values, m_values_header = read_flt(m_value_file)
# handle plotting hillshades which are larger than the m_value raster
# cannot cope with m_value raster larger than the hillshade
corrected_x = 0
corrected_y = 0
if (hillshade_header[0] != m_values_header[0]) or (hillshade_header[1] != m_values_header[1]):
corrected_x = (m_values_header[2] - hillshade_header[2]) / hillshade_header[4]
corrected_y = (
((m_values_header[3] / m_values_header[4]) + m_values_header[1])
- ((hillshade_header[3] / hillshade_header[4]) + hillshade_header[1])
) * -1
# ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
m_values = np.ma.masked_where(m_values == -9999, m_values)
# fonts
rcParams["font.family"] = "sans-serif"
rcParams["font.sans-serif"] = ["arial"]
rcParams["font.size"] = 12
fig, ax = pp.subplots()
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
xlocs, xlabels = pp.xticks()
ylocs, ylabels = pp.yticks()
new_x_labels = np.linspace(
hillshade_header[2], hillshade_header[2] + (hillshade_header[1] * hillshade_header[4]), len(xlocs)
)
new_y_labels = np.linspace(
hillshade_header[3], hillshade_header[3] + (hillshade_header[0] * hillshade_header[4]), len(ylocs)
)
new_x_labels = [str(x).split(".")[0] for x in new_x_labels] # get rid of decimal places in axis ticks
new_y_labels = [str(y).split(".")[0] for y in new_y_labels][::-1] # invert y axis
pp.xticks(xlocs[1:-1], new_x_labels[1:-1], rotation=30) # [1:-1] skips ticks where we have no data
pp.yticks(ylocs[1:-1], new_y_labels[1:-1])
pp.xlabel("Easting (m)")
pp.ylabel("Northing (m)")
# SET UP COLOURMAPS
jet = pp.get_cmap("jet")
m_MIN = np.min(m_values)
m_MAX = np.max(m_values)
cNorm_m_values = colors.Normalize(vmin=m_MIN, vmax=m_MAX)
scalarMap_m_values = cmx.ScalarMappable(norm=cNorm_m_values, cmap=jet)
for i in xrange(len(m_values)):
for j in xrange(len(m_values[0])):
if m_values[i][j] > 0:
colorVal = scalarMap_m_values.to_rgba(m_values[i][j])
pp.scatter(j + corrected_x, i + corrected_y, marker=".", color=colorVal, edgecolors="none")
# Configure final plot
sm = pp.cm.ScalarMappable(cmap=jet, norm=pp.normalize(vmin=m_MIN, vmax=m_MAX))
sm._A = []
cbar = pp.colorbar(sm)
cbar.set_label("M Values")
pp.show()
nat_interp = griddata(x, y, z, XI, YI)
nat_filtered = gaussian_filter(XI, YI, nat_interp, 0.1, 0.1)
nat_rms = rms(nat_interp - truth)
# Cressman
Rc = 0.5
cress_interp = grid_data(z, XI, YI, x, y, cressman_weights, Rc)
cress_rms = rms(cress_interp - truth)
# Barnes
kstar = 0.1
barnes_interp = grid_data(z, XI, YI, x, y, barnes_weights, (0.5, kstar))
barnes_rms = rms(barnes_interp - truth)
# plot the results
val_norm = plt.normalize(-0.45, 0.45)
diff_norm = plt.normalize(-1.0, 1.0)
cmap = plt.get_cmap('jet')
fig = plt.figure()
fig.suptitle('Interpolation Results', fontsize=14)
fig.canvas.manager.set_window_title('Actual Fields')
ax = plt.subplot(2, 3, 1)
plt.pcolor(XI, YI, truth, cmap=cmap, norm=val_norm)
plt.scatter(x, y, 80, z, cmap=cmap, norm=val_norm)
plt.title('Truth')
ax2 = plt.subplot(2, 3, 2, sharex=ax, sharey=ax)
plt.pcolor(XI, YI, rbfm_interp, cmap=cmap, norm=val_norm)
plt.title('RBF - Multiquadrics $(\epsilon=%.1f)$' % epsm)
def main(args=None):
"""The main function; parses options and plots"""
# ---------- build and read options ----------
from optparse import OptionParser
optParser = OptionParser()
optParser.add_option("-n",
"--net",
dest="net",
metavar="FILE",
help="Defines the network to read")
optParser.add_option("--edge-width",
dest="defaultWidth",
type="float",
default=1,
help="Defines the edge width")
optParser.add_option("--edge-color",
dest="defaultColor",
default='k',
help="Defines the edge color")
optParser.add_option("--minV",
dest="minV",
type="float",
default=None,
help="Define the minimum value boundary")
optParser.add_option("--maxV",
dest="maxV",
type="float",
default=None,
help="Define the maximum value boundary")
optParser.add_option("-v",
"--verbose",
dest="verbose",
action="store_true",
default=False,
help="If set, the script says what it's doing")
# standard plot options
helpers.addInteractionOptions(optParser)
helpers.addPlotOptions(optParser)
# parse
options, remaining_args = optParser.parse_args(args=args)
if options.net is None:
print("Error: a network to load must be given.")
return 1
if options.verbose:
print("Reading network from '%s'" % options.net)
net = sumolib.net.readNet(options.net)
speeds = {}
minV = None
maxV = None
for e in net._id2edge:
v = net._id2edge[e]._speed
if minV is None or minV > v:
minV = v
if maxV is None or maxV < v:
maxV = v
speeds[e] = v
if options.minV is not None:
minV = options.minV
if options.maxV is not None:
maxV = options.maxV
# if options.logColors:
# helpers.logNormalise(colors, maxColorValue)
# else:
# helpers.linNormalise(colors, minColorValue, maxColorValue)
helpers.linNormalise(speeds, minV, maxV)
for e in speeds:
speeds[e] = helpers.getColor(options, speeds[e], 1.)
fig, ax = helpers.openFigure(options)
ax.set_aspect("equal", None, 'C')
helpers.plotNet(net, speeds, {}, options)
# drawing the legend, at least for the colors
print("%s -> %s" % (minV, maxV))
sm = matplotlib.cm.ScalarMappable(cmap=get_cmap(options.colormap),
norm=plt.normalize(vmin=minV, vmax=maxV))
# "fake up the array of the scalar mappable. Urgh..." (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
sm._A = []
plt.colorbar(sm)
options.nolegend = True
helpers.closeFigure(fig, ax, options)
'Nevada': 7.03,
'Idaho': 6.04,
'New Mexico': 5.79,
'South Dakota': 3.84,
'North Dakota': 3.59,
'Montana': 2.39,
'Wyoming': 1.96,
'Alaska': 0.42}
# choose a color for each state based on population density.
colors={}
statenames=[]
cmap = plt.cm.Blues_r # use 'hot' colormap
vmin = 0; vmax = 450 # set range.
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.normalize(vmin=vmin, vmax=vmax))
for shapedict in m.states_info:
statename = shapedict['NAME']
try:
pop = popdensity[statename]
except KeyError:
pop = 0.0
# calling colormap with value between 0 and 1 returns
# rgba value. Invert color range (hot colors are high
# population), take sqrt root to spread out colors more.
colors[statename] = cmap((pop-vmin)/(vmax-vmin))[:3]
statenames.append(statename)
def make_plots():
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#########################
# #
# READ IN THE DATA #
# #
#########################
# open file
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\India\\'
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\PA\\'
#DataDirectory = 'm:\\topographic_tools\\LSDRaster_chi_package\\Test_data\\'
#DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\Test_data\\'
DataDirectory = 'c:\\code\\topographic_analysis\\LSDRaster_chi_package\\Child_runs_mk4\\'
#FileName = 'pa_basin_fullProfileMC_mainstem_1189.tree'
#FileName = 'pa_basin_fullProfileMC_mainstem_3124.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_633.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_114.tree'
#FileName = 'rio_torto_fullProfileMC_colinear_114.tree'
#FileName = 'rio_torto_fullProfileMC_colinear_110.tree'
#FileName = 'rio_torto_fullProfileMC_mainstem_110.tree'
#FileName = 'rio_torto_fullProfileMC_forced_0.4_110.tree'
#FileName = 'UniUplift09b_mk4_t6_fullProfileMC_colinear.tree'
FileName = 'UniUplift09b_mk4_t6_fullProfileMC_colinear_1_2_10_90.tree'
#OutputFigureName = 'chi_plot'
#OutputFigureFormat = 'eps'
f = open(DataDirectory + FileName, 'r') # open file
lines = f.readlines() # read in the data
n_lines = len(lines) # get the number of lines (=number of data)
n_data = n_lines - 1
# data variables
channel_id = np.zeros(n_data) # ID number for channel segment
receiver_channel = np.zeros(n_data)
#node_on_receiver_channel = np.zeros(n_data, dtype=np.int)
node = np.zeros(n_data) # node number
row = np.zeros(n_data) # row
col = np.zeros(n_data) # column
flow_dist = np.zeros(n_data) # flow distance
chi = np.zeros(n_data) # chi
elevation = np.zeros(n_data) # elevation
drainage_area = np.zeros(n_data) # drainage area
n_data_points_uic = np.zeros(n_data)
m_mean = np.zeros(n_data)
#m_standard_deviation = np.zeros(n_data)
m_standard_error = np.zeros(n_data)
b_mean = np.zeros(n_data)
#b_standard_deviation = np.zeros(n_data)
b_standard_error = np.zeros(n_data)
DW_mean = np.zeros(n_data)
#DW_standard_deviation = np.zeros(n_data)
DW_standard_error = np.zeros(n_data)
fitted_elevation_mean = np.zeros(n_data)
#fitted_elevation_standard_deviation = np.zeros(n_data)
fitted_elevation_standard_error = np.zeros(n_data)
print "Reading " + FileName
# get the A_0 and m/n values
line = lines[0].strip().split(" ")
A_0 = float(line[0])
m_over_n = float(line[1])
for i in range(0, n_data):
line = lines[i + 1].strip().split(" ")
#print line
channel_id[i] = int(float(line[0]))
receiver_channel[i] = int(float(line[1]))
#node_on_receiver_channel = int(float(line[2]))
node[i] = int(float(line[3]))
row[i] = int(line[4])
col[i] = int(line[5])
flow_dist[i] = float(line[6])
chi[i] = float(line[7])
elevation[i] = float(line[8])
drainage_area[i] = float(line[9])
n_data_points_uic[i] = float(line[10])
m_mean[i] = float(line[11])
#m_standard_deviation[i] = float(line[12])
m_standard_error[i] = float(line[13])
b_mean[i] = float(line[14])
#b_standard_deviation[i] = float(line[15])
b_standard_error[i] = float(line[16])
DW_mean[i] = float(line[17])
#DW_standard_deviation[i] = float(line[18])
DW_standard_error[i] = float(line[19])
fitted_elevation_mean[i] = float(line[20])
#fitted_elevation_standard_deviation = float(line[21])
fitted_elevation_standard_error[i] = float(line[22])
f.close()
# Determine number of segments, and their respective lengths
n_channel_segments = channel_id[n_data - 1] + 1
segment_lengths = np.zeros(n_channel_segments, dtype=np.int)
for i in range(0, n_data):
segment_lengths[channel_id[i]] = segment_lengths[channel_id[i]] + 1
#########################
# #
# MAKE CHI-PLOTS #
# #
#########################
print "Producing figures..."
# SET UP COLOURMAPS
jet = plt.get_cmap('jet')
hot = plt.get_cmap('RdYlBu_r')
# m-values
m_MIN = np.min(m_mean)
m_MAX = np.max(m_mean)
cNorm_m_values = colors.Normalize(
vmin=m_MIN,
vmax=m_MAX) # the max number of channel segs is the 'top' colour
scalarMap_m_values = cmx.ScalarMappable(norm=cNorm_m_values, cmap=hot)
# channel ID
Channel_ID_MIN = np.min(channel_id)
Channel_ID_MAX = np.max(channel_id)
cNorm_channel_ID = colors.Normalize(
vmin=Channel_ID_MIN, vmax=Channel_ID_MAX
) # the max number of channel segs is the 'top' colour
scalarMap_channel_ID = cmx.ScalarMappable(norm=cNorm_channel_ID, cmap=jet)
minfd = min(flow_dist)
print "minimum flow distance: " + str(minfd)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# PLOT OF CHI m-VALUE (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
plt.figure(2, facecolor='white', figsize=(10, 7.5))
ax = plt.subplot(1, 1, 1)
for i in range(0, len(channel_id)):
colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
if channel_id[i] == 0:
plt.plot(chi[i],
m_mean[i],
"o",
markersize=10,
color='black',
markeredgecolor='black',
alpha=0.7)
else:
plt.plot(chi[i],
m_mean[i],
"o",
markersize=8,
color=colorVal,
markeredgecolor=colorVal,
alpha=0.7)
# plt.plot(chi[i], m_mean[i] + m_standard_error[i], ".", linewidth=0.25, color='k')
# plt.plot(chi[i], m_mean[i] - m_standard_error[i], ".", linewidth=0.25, color='k')
# Configure final plot
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
plt.xlabel('$\chi$ (m)', fontsize=axis_size)
plt.ylabel('Gradient in $\chi$ space', fontsize=axis_size)
plt.title('$A_0$: ' + str(A_0) + ' m$^2$, and $m/n$: ' + str(m_over_n),
fontsize=label_size)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# LONGITUDINAL PROFILES WITH COLOUR SCALE GIVING CHI-m VALUE
plt.figure(3, facecolor='white', figsize=(10, 7.5))
ax = plt.subplot(1, 1, 1)
for i in range(0, len(channel_id)):
colorVal = scalarMap_m_values.to_rgba(m_mean[i])
plt.plot((flow_dist[i] - minfd) / 1000,
elevation[i],
"o",
markersize=6,
color=colorVal,
markeredgecolor=colorVal,
alpha=0.5)
# Configure final plot
sm = plt.cm.ScalarMappable(cmap=hot,
norm=plt.normalize(vmin=np.min(m_mean),
vmax=np.max(m_mean)))
sm._A = []
cbar = plt.colorbar(sm, orientation='horizontal', use_gridspec=True)
cbar.set_label('Gradient in $\chi$ space', fontsize=axis_size)
plt.xlabel('Distance upstream (km)', fontsize=axis_size)
plt.ylabel('Elevation (m)', fontsize=axis_size)
plt.title('$A_0$: ' + str(A_0) + ' m$^2$, and $m/n$: ' + str(m_over_n),
fontsize=label_size)
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
cbar.ax.spines['top'].set_linewidth(2.5)
cbar.ax.spines['left'].set_linewidth(2.5)
cbar.ax.spines['right'].set_linewidth(2.5)
cbar.ax.spines['bottom'].set_linewidth(2.5)
cbar.ax.tick_params(axis='both', width=2.5)
plt.tight_layout()
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# BASIC CHI-PLOT WITH EACH CHANNEL LABELLED WITH A DIFFERENT COLOUR
plt.figure(1, facecolor='white', figsize=(10, 7.5))
ax = plt.subplot(1, 1, 1)
data_pointer = 0 # points to data element in chi and elev vectors
for i in range(0, len(segment_lengths)):
chi_seg = np.zeros(segment_lengths[i])
elev_seg = np.zeros(segment_lengths[i])
for j in range(0, segment_lengths[i]):
chi_seg[j] = chi[data_pointer]
elev_seg[j] = elevation[data_pointer]
data_pointer = data_pointer + 1
if i == 0:
# plot trunk stream in black, with thicker line
l1, = ax.plot(chi_seg, elev_seg, "k-", linewidth=4, alpha=0.3)
else:
# plot other stream segments plot
colorVal = scalarMap_channel_ID.to_rgba(
i) # this gets the distinct colour for this segment
ax.plot(chi_seg,
elev_seg,
"-",
linewidth=4,
color=colorVal,
alpha=0.3)
# now loop again plotting the fitted segments
data_pointer = 0 # points to data element in chi and elev vectors
for i in range(0, len(segment_lengths)):
chi_seg = np.zeros(segment_lengths[i])
elev_seg = np.zeros(segment_lengths[i])
for j in range(0, segment_lengths[i]):
chi_seg[j] = chi[data_pointer]
elev_seg[j] = fitted_elevation_mean[data_pointer]
data_pointer = data_pointer + 1
if i == 0:
# plot trunk stream in black, with thicker line
l2, = plt.plot(chi_seg, elev_seg, "k", linewidth=3, dashes=(10, 2))
else:
# plot other stream segments plot
colorVal = scalarMap_channel_ID.to_rgba(
i) # this gets the distinct colour for this segment
ax.plot(chi_seg,
elev_seg,
linewidth=3,
color=colorVal,
dashes=(10, 2))
# Configure final plot
plt.xlabel('$\chi$ (m)', fontsize=axis_size)
plt.ylabel('Elevation (m)', fontsize=axis_size)
plt.title('$A_0$: ' + str(A_0) + ' m$^2$, and $m/n$: ' + str(m_over_n),
fontsize=label_size)
plt.legend((l1, l2), ('Data', 'Best fit segments'),
'lower right',
prop={'size': label_size})
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
#plt.savefig(OutputFigureName + '.' + OutputFigureFormat, format=OutputFigureFormat)
#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT USING FITTED PROFILES (+ERROR) RATHER THAN RAW DATA, LABELLED WITH DISTINCT COLOUR
# plt.figure(4, facecolor='white')
# data_pointer = 0 # points to data element in chi and elev vectors
# for i in range (0,len(segment_lengths)):
# chi_seg = np.zeros(segment_lengths[i])
# fitted_elev_seg = np.zeros(segment_lengths[i])
# fitted_elev_ulim = np.zeros(segment_lengths[i])
# fitted_elev_llim = np.zeros(segment_lengths[i])
#
# for j in range (0,segment_lengths[i]):
# chi_seg[j] = chi[data_pointer]
# fitted_elev_seg[j] = fitted_elevation_mean[data_pointer]
# fitted_elev_ulim[j] = fitted_elevation_mean[data_pointer] + fitted_elevation_standard_error[data_pointer]
# fitted_elev_llim[j] = fitted_elevation_mean[data_pointer] - fitted_elevation_standard_error[data_pointer]
# data_pointer = data_pointer + 1
# # plot other stream segments plot
# colorVal = scalarMap_channel_ID.to_rgba(i)
# plt.plot(chi_seg, fitted_elev_ulim, "-k", linewidth=0.2)
# plt.plot(chi_seg, fitted_elev_llim, "-k", linewidth=0.2)
# plt.plot(chi_seg, fitted_elev_seg, "-", linewidth=0.5, color=colorVal)
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('fitted elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT DISPLAYING BOTH RAW DATA AND FITTED PROFILES
# plt.figure(5, facecolor='white')
#
# data_pointer = 0 # points to data element in chi and elev vectors
# for i in range (0,len(segment_lengths)):
# chi_seg = np.zeros(segment_lengths[i])
# elev_seg = np.zeros(segment_lengths[i])
# fitted_elev_seg = np.zeros(segment_lengths[i])
#
# for j in range (0,segment_lengths[i]):
# chi_seg[j] = chi[data_pointer]
# elev_seg[j] = elevation[data_pointer]
# fitted_elev_seg[j] = fitted_elevation_mean[data_pointer]
# data_pointer = data_pointer + 1
#
# # plot other stream segments plot
# colorVal = scalarMap_channel_ID.to_rgba(i) # this gets the distinct colour for this segment
# plt.plot(chi_seg, elev_seg, "-k", linewidth=1)
# plt.plot(chi_seg, fitted_elev_seg, "-", linewidth=1, color=colorVal)
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # CHI-PLOT USING ONLY MAIN TRIBUTARY WITH FITTED PROFILES (+ERROR)
# plt.figure(6, facecolor='white')
# for i in range (0,len(channel_id)):
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0] + fitted_elevation_standard_error[channel_id==0], "-k", linewidth=0.2)
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0] - fitted_elevation_standard_error[channel_id==0], "-k", linewidth=0.2)
# plt.plot(chi[channel_id==0], elevation[channel_id==0], "-b", linewidth=0.5)
# plt.plot(chi[channel_id==0], fitted_elevation_mean[channel_id==0], "-r", linewidth=0.5)
#
#
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('elevation / m')
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # PLOT OF CHI b-VALUE (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
# plt.figure(7, facecolor='white')
# for i in range (0,len(channel_id)):
# colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
# plt.plot(chi[i], b_mean[i], ".", linewidth=0.2, color=colorVal)
# #plt.plot(chi[i], b_mean[i] + b_standard_error[i], ".", linewidth=0.25, color='k')
# #plt.plot(chi[i], b_mean[i] - b_standard_error[i], ".", linewidth=0.25, color='k')
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('b-value / m')
#
#
# #=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
# # PLOT OF DW STATISTIC (+ERROR) AGAINST CHI WITH EACH CHANNEL LABELLED WITH DISTINCT COLOUR
# plt.figure(8, facecolor='white')
# for i in range (0,len(channel_id)):
# colorVal = scalarMap_channel_ID.to_rgba(channel_id[i])
# plt.plot(chi[i], DW_mean[i], ".", linewidth=0.2, color=colorVal)
# #plt.plot(chi[i], DW_mean[i] + DW_standard_error[i], ".", linewidth=0.25, color='k')
# #plt.plot(chi[i], DW_mean[i] - DW_standard_error[i], ".", linewidth=0.25, color='k')
#
# # Configure final plot
# plt.xlabel('$\chi$ / m')
# plt.ylabel('DW statistic')
plt.show()
def main(args=None):
"""The main function; parses options and plots"""
## ---------- build and read options ----------
from optparse import OptionParser
optParser = OptionParser()
optParser.add_option("-n", "--net", dest="net", metavar="FILE",
help="Defines the network to read")
optParser.add_option("--edge-width", dest="defaultWidth",
type="float", default=1, help="Defines the edge width")
optParser.add_option("--edge-color", dest="defaultColor",
default='k', help="Defines the edge color")
optParser.add_option("--minV", dest="minV",
type="float", default=None, help="Define the minimum value boundary")
optParser.add_option("--maxV", dest="maxV",
type="float", default=None, help="Define the maximum value boundary")
optParser.add_option("-v", "--verbose", dest="verbose", action="store_true",
default=False, help="If set, the script says what it's doing")
# standard plot options
helpers.addInteractionOptions(optParser)
helpers.addPlotOptions(optParser)
# parse
options, remaining_args = optParser.parse_args(args=args)
if options.net==None:
print "Error: a network to load must be given."
return 1
if options.verbose: print "Reading network from '%s'" % options.net
net = sumolib.net.readNet(options.net)
speeds = {}
minV = None
maxV = None
for e in net._id2edge:
v = net._id2edge[e]._speed
if minV==None or minV>v:
minV = v
if maxV==None or maxV<v:
maxV = v
speeds[e] = v
if options.minV!=None: minV = options.minV
if options.maxV!=None: maxV = options.maxV
#if options.logColors:
# helpers.logNormalise(colors, maxColorValue)
# else:
# helpers.linNormalise(colors, minColorValue, maxColorValue)
helpers.linNormalise(speeds, minV, maxV)
for e in speeds:
speeds[e] = helpers.getColor(options, speeds[e], 1.)
fig, ax = helpers.openFigure(options)
ax.set_aspect("equal", None, 'C')
helpers.plotNet(net, speeds, {}, options)
# drawing the legend, at least for the colors
print "%s -> %s" % (minV, maxV)
sm = matplotlib.cm.ScalarMappable(cmap=get_cmap(options.colormap), norm=plt.normalize(vmin=minV, vmax=maxV))
# "fake up the array of the scalar mappable. Urgh..." (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
sm._A = []
plt.colorbar(sm)
options.nolegend = True
helpers.closeFigure(fig, ax, options)
npts = 200
ngridx = 100
ngridy = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# griddata and contour.
start = time.clock()
plt.subplot(211)
xi = np.linspace(-2.1,2.1,ngridx)
yi = np.linspace(-2.1,2.1,ngridy)
zi = griddata(x,y,z,xi,yi,interp='linear')
plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
plt.colorbar() # draw colorbar
plt.plot(x, y, 'ko', ms=3)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata and contour (%d points, %d grid points)' % (npts, ngridx*ngridy))
print ('griddata and contour seconds: %f' % (time.clock() - start))
# tricontour.
start = time.clock()
plt.subplot(212)
triang = tri.Triangulation(x, y)
plt.tricontour(x, y, z, 15, linewidths=0.5, colors='k')
plt.tricontourf(x, y, z, 15, cmap=plt.cm.rainbow,
norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
plt.colorbar()
import numpy as np
from scipy.interpolate import Rbf
import matplotlib.pyplot as plt
from matplotlib import cm
# 2-d tests - setup scattered data
x = np.random.rand(100)*4.0-2.0
y = np.random.rand(100)*4.0-2.0
z = x*np.exp(-x**2-y**2)
ti = np.linspace(-2.0, 2.0, 100)
XI, YI = np.meshgrid(ti, ti)
# use RBF
rbf = Rbf(x, y, z, epsilon=2)
ZI = rbf(XI, YI)
# plot the result
n = plt.normalize(-2., 2.)
plt.subplot(1, 1, 1)
plt.pcolor(XI, YI, ZI, cmap=cm.jet)
plt.scatter(x, y, 100, z, cmap=cm.jet)
plt.title('RBF interpolation - multiquadrics')
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.colorbar()
plt.show()
# color_index.append(len(dep_fac_dict[d]))
node_labels['CEWIT'] = 'CEWIT'
node_sizes['CEWIT'] = 1500
node_colors['CEWIT'] = '#E1D8B7'
# print node_sizes.keys()
# print node_sizes.values()
# print node_labels.keys()
# print node_labels.values()
edges = G.edges()
weights = [G[u][v]['weight'] for u,v in edges]
fig = plt.figure('Distribution of CEWIT Faculty Members-caption above',figsize=(16,8))
cm = mpl.colors.ListedColormap(C)
pos = nx.spring_layout(G, k=0.9, scale = 2)
nx.draw(G, pos, linewidths = 0.5,labels = node_labels, font_size = 12, edges = edges, width = weights, nodelist = node_sizes.keys(), node_size = node_sizes.values(), font_family = 'Century Gothic', node_color=node_colors.values(), edge_color='#44697D',with_labels=True)
sm = plt.cm.ScalarMappable(cmap=cm, norm=plt.normalize(vmin=5, vmax=35))
sm._A = []
plt.colorbar(sm, shrink = 0.7, pad = 0.15, orientation = 'vertical', anchor = (-2,0.5),fraction = 0.1, aspect = 25, drawedges = False)
figtext = 'Distribution of CEWIT Faculty Members'
fig.text(.38,.93,figtext, weight = 'heavy', size = 20, ha = 'center', family = 'Century Gothic')
plt.show() # display
def m_values_over_hillshade(hillshade_file, tree_file):
"""
Plots m values of channels taken from the *.tree file over a hillshade
"""
label_size = 20
#title_size = 30
axis_size = 28
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
import matplotlib.lines as mpllines
#get data
hillshade, hillshade_header = read_flt(hillshade_file)
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
#fonts
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#get coordinates of streams from tree file
M_chi_value = []
channel_id = []
row = []
col = []
with open(tree_file, 'r') as f:
lines = f.readlines()
for q,line in enumerate(lines):
if q > 0: #skip first line
channel_id.append(float(line.split()[0]))
M_chi_value.append(float(line.split()[11]))
row.append(float(line.split()[4]))
col.append(float(line.split()[5]))
#get bounding box & pad to 10% of the dimension
x_max = max(col)
x_min = min(col)
y_max = max(row)
y_min = min(row)
pad_x = (x_max - x_min) * 0.1
pad_y = (x_max - y_min) * 0.1
if (pad_y > pad_x):
pad_x = pad_y
else:
pad_y = pad_x
x_max += pad_x
x_min -= pad_x
y_max += pad_y
y_min -= pad_y
fig = pp.figure(1, facecolor='white',figsize=(10,7.5))
ax = fig.add_subplot(1,1,1)
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
# now get the tick marks
n_target_tics = 5
xlocs,ylocs,new_x_labels,new_y_labels = format_ticks_for_UTM_imshow(hillshade_header,x_max,x_min,y_max,y_min,n_target_tics)
pp.xticks(xlocs, new_x_labels, rotation=60) #[1:-1] skips ticks where we have no data
pp.yticks(ylocs, new_y_labels)
for line in ax.get_xticklines():
line.set_marker(mpllines.TICKDOWN)
#line.set_markeredgewidth(3)
for line in ax.get_yticklines():
line.set_marker(mpllines.TICKLEFT)
#line.set_markeredgewidth(3)
pp.xlim(x_min,x_max)
pp.ylim(y_max,y_min)
pp.xlabel('Easting (m)',fontsize = axis_size)
pp.ylabel('Northing (m)',fontsize = axis_size)
# channel ID
M_chi_value_MIN = np.min(M_chi_value)
M_chi_value_MAX = np.max(M_chi_value)
cNorm_M_chi_value = colors.Normalize(vmin=M_chi_value_MIN, vmax=M_chi_value_MAX) # the max number of channel segs is the 'top' colour
hot = pp.get_cmap('RdYlBu_r')
scalarMap_M_chi_value = cmx.ScalarMappable(norm=cNorm_M_chi_value, cmap=hot)
for a,i in enumerate(M_chi_value):
#print "a: " +str(a)+" i: " +str(i)
if channel_id[a] != 0:
# plot other stream segments
colorVal = scalarMap_M_chi_value.to_rgba(i) # this gets the distinct colour for this segment
pp.scatter(col[a], row[a], 30,marker=".", color=colorVal,edgecolors=colorVal)
for a,i in enumerate(M_chi_value):
if channel_id[a] == 0:
# plot trunk stream in black
colorVal = scalarMap_M_chi_value.to_rgba(i)
pp.scatter(col[a], row[a], 40,marker=".", color=colorVal,edgecolors=colorVal)
sm = pp.cm.ScalarMappable(cmap=hot, norm=pp.normalize(vmin=min(M_chi_value), vmax=max(M_chi_value)))
sm._A = []
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(pp.gca())
cax = divider.append_axes("right", "5%", pad="3%")
pp.colorbar(sm, cax=cax).set_label('$M_{\chi}$',fontsize=axis_size)
cax.tick_params(labelsize=label_size)
#pp.xlim(x_min,x_max)
#pp.ylim(y_max,y_min)
pp.tight_layout()
pp.show()
def main(args=None):
"""The main function; parses options and plots"""
## ---------- build and read options ----------
from optparse import OptionParser
optParser = OptionParser()
optParser.add_option("-n",
"--net",
dest="net",
metavar="FILE",
help="Defines the network to read")
optParser.add_option("-i",
"--dump-inputs",
dest="dumps",
metavar="FILE",
help="Defines the dump-output files to use as input")
optParser.add_option("-m",
"--measures",
dest="measures",
default="speed,entered",
help="Define which measure to plot")
optParser.add_option("-w",
"--default-width",
dest="defaultWidth",
type="float",
default=.1,
help="Defines the default edge width")
optParser.add_option("-c",
"--default-color",
dest="defaultColor",
default='k',
help="Defines the default edge color")
optParser.add_option("--min-width",
dest="minWidth",
type="float",
default=.5,
help="Defines the minimum edge width")
optParser.add_option("--max-width",
dest="maxWidth",
type="float",
default=3,
help="Defines the maximum edge width")
optParser.add_option("--log-colors",
dest="logColors",
action="store_true",
default=False,
help="If set, colors are log-scaled")
optParser.add_option("--log-widths",
dest="logWidths",
action="store_true",
default=False,
help="If set, widths are log-scaled")
optParser.add_option("--min-color-value",
dest="colorMin",
type="float",
default=None,
help="If set, defines the minimum edge color value")
optParser.add_option("--max-color-value",
dest="colorMax",
type="float",
default=None,
help="If set, defines the maximum edge color value")
optParser.add_option("--min-width-value",
dest="widthMin",
type="float",
default=None,
help="If set, defines the minimum edge width value")
optParser.add_option("--max-width-value",
dest="widthMax",
type="float",
default=None,
help="If set, defines the maximum edge width value")
optParser.add_option("-v",
"--verbose",
dest="verbose",
action="store_true",
default=False,
help="If set, the script says what it's doing")
# standard plot options
helpers.addInteractionOptions(optParser)
helpers.addPlotOptions(optParser)
# parse
options, remaining_args = optParser.parse_args(args=args)
if options.net == None:
print "Error: a network to load must be given."
return 1
if options.verbose: print "Reading network from '%s'" % options.net
net = sumolib.net.readNet(options.net)
if options.measures == None:
print "Error: a dump file must be given."
return 1
times = []
hc = None
if options.dumps.split(",")[0] != "":
if options.verbose:
print "Reading colors from '%s'" % options.dumps.split(",")[0]
hc = WeightsReader(options.measures.split(",")[0])
sumolib.output.parse_sax(options.dumps.split(",")[0], hc)
times = hc._edge2value
hw = None
if options.dumps.split(",")[1] != "":
if options.verbose:
print "Reading widths from '%s'" % options.dumps.split(",")[1]
hw = WeightsReader(options.measures.split(",")[1])
sumolib.output.parse_sax(options.dumps.split(",")[1], hw)
times = hw._edge2value
for t in times:
colors = {}
maxColorValue = None
minColorValue = None
for e in net._id2edge:
if hc and t in hc._edge2value and e in hc._edge2value[t]:
if options.colorMax != None and hc._edge2value[t][
e] > options.colorMax:
hc._edge2value[t][e] = options.colorMax
if options.colorMin != None and hc._edge2value[t][
e] < options.colorMin:
hc._edge2value[t][e] = options.colorMin
if maxColorValue == None or maxColorValue < hc._edge2value[t][
e]:
maxColorValue = hc._edge2value[t][e]
if minColorValue == None or minColorValue > hc._edge2value[t][
e]:
minColorValue = hc._edge2value[t][e]
colors[e] = hc._edge2value[t][e]
if options.colorMax != None: maxColorValue = options.colorMax
if options.colorMin != None: minColorValue = options.colorMin
if options.logColors:
helpers.logNormalise(colors, maxColorValue)
else:
helpers.linNormalise(colors, minColorValue, maxColorValue)
for e in colors:
colors[e] = helpers.getColor(options, colors[e], 1.)
if options.verbose:
print "Color values are between %s and %s" % (minColorValue,
maxColorValue)
widths = {}
maxWidthValue = None
minWidthValue = None
for e in net._id2edge:
if hw and t in hw._edge2value and e in hw._edge2value[t]:
v = abs(hw._edge2value[t][e])
if options.widthMax != None and v > options.widthMax:
v = options.widthMax
if options.widthMin != None and v < options.widthMin:
v = options.widthMin
if not maxWidthValue or maxWidthValue < v: maxWidthValue = v
if not minWidthValue or minWidthValue > v: minWidthValue = v
widths[e] = v
if options.widthMax != None: maxWidthValue = options.widthMax
if options.widthMin != None: minWidthValue = options.widthMin
if options.logWidths:
helpers.logNormalise(widths, options.colorMax)
else:
helpers.linNormalise(widths, minWidthValue, maxWidthValue)
for e in widths:
widths[e] = options.minWidth + widths[e] * (options.maxWidth -
options.minWidth)
if options.verbose:
print "Width values are between %s and %s" % (minWidthValue,
maxWidthValue)
fig, ax = helpers.openFigure(options)
ax.set_aspect("equal", None, 'C')
helpers.plotNet(net, colors, widths, options)
# drawing the legend, at least for the colors
sm = matplotlib.cm.ScalarMappable(cmap=get_cmap(options.colormap),
norm=plt.normalize(
vmin=minColorValue,
vmax=maxColorValue))
# "fake up the array of the scalar mappable. Urgh..." (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
sm._A = []
plt.colorbar(sm)
options.nolegend = True
helpers.closeFigure(fig, ax, options)
return 0 # !!! hack
return 0
def m_values_over_hillshade(hillshade_file, tree_file):
"""
Plots m values of channels taken from the *.tree file over a hillshade
"""
label_size = 20
#title_size = 30
axis_size = 28
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
import matplotlib.lines as mpllines
#get data
hillshade, hillshade_header = read_flt(hillshade_file)
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
#fonts
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#get coordinates of streams from tree file
M_chi_value = []
channel_id = []
row = []
col = []
with open(tree_file, 'r') as f:
lines = f.readlines()
for q,line in enumerate(lines):
if q > 0: #skip first line
channel_id.append(float(line.split()[0]))
M_chi_value.append(float(line.split()[11]))
row.append(float(line.split()[4]))
col.append(float(line.split()[5]))
#get bounding box & pad to 10% of the dimension
x_max = max(col)
x_min = min(col)
y_max = max(row)
y_min = min(row)
pad_x = (x_max - x_min) * 0.1
pad_y = (x_max - y_min) * 0.1
if (pad_y > pad_x):
pad_x = pad_y
else:
pad_y = pad_x
x_max += pad_x
x_min -= pad_x
y_max += pad_y
y_min -= pad_y
fig = pp.figure(1, facecolor='white',figsize=(10,7.5))
ax = fig.add_subplot(1,1,1)
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
# now get the tick marks
n_target_tics = 5
xlocs,ylocs,new_x_labels,new_y_labels = format_ticks_for_UTM_imshow(hillshade_header,x_max,x_min,y_max,y_min,n_target_tics)
pp.xticks(xlocs, new_x_labels, rotation=60) #[1:-1] skips ticks where we have no data
pp.yticks(ylocs, new_y_labels)
for line in ax.get_xticklines():
line.set_marker(mpllines.TICKDOWN)
#line.set_markeredgewidth(3)
for line in ax.get_yticklines():
line.set_marker(mpllines.TICKLEFT)
#line.set_markeredgewidth(3)
pp.xlim(x_min,x_max)
pp.ylim(y_max,y_min)
pp.xlabel('Easting (m)',fontsize = axis_size)
pp.ylabel('Northing (m)',fontsize = axis_size)
# channel ID
M_chi_value_MIN = np.min(M_chi_value)
M_chi_value_MAX = np.max(M_chi_value)
cNorm_M_chi_value = colors.Normalize(vmin=M_chi_value_MIN, vmax=M_chi_value_MAX) # the max number of channel segs is the 'top' colour
hot = pp.get_cmap('RdYlBu_r')
scalarMap_M_chi_value = cmx.ScalarMappable(norm=cNorm_M_chi_value, cmap=hot)
for a,i in enumerate(M_chi_value):
#print "a: " +str(a)+" i: " +str(i)
if channel_id[a] != 0:
# plot other stream segments
colorVal = scalarMap_M_chi_value.to_rgba(i) # this gets the distinct colour for this segment
pp.scatter(col[a], row[a], 30,marker=".", color=colorVal,edgecolors=colorVal)
for a,i in enumerate(M_chi_value):
if channel_id[a] == 0:
# plot trunk stream in black
colorVal = scalarMap_M_chi_value.to_rgba(i)
pp.scatter(col[a], row[a], 40,marker=".", color=colorVal,edgecolors=colorVal)
sm = pp.cm.ScalarMappable(cmap=hot, norm=pp.normalize(vmin=min(M_chi_value), vmax=max(M_chi_value)))
sm._A = []
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(pp.gca())
cax = divider.append_axes("right", "5%", pad="3%")
pp.colorbar(sm, cax=cax).set_label('$M_{\chi}$',fontsize=axis_size)
cax.tick_params(labelsize=label_size)
#pp.xlim(x_min,x_max)
#pp.ylim(y_max,y_min)
pp.tight_layout()
pp.show()
