def color_by_data(data):
norm = matplotlib.colors.Normalize(vmin=data.min, vmax=data.max)
colors = []
for i, d in enumerate(data.vals):
rgb = [x for x in data.palette(norm(d))[0:3]]
colors.append(rgb)
return colors
def smart_colormap(clevs, name='jet', extend='both', minval=0.0, maxval=1.0):
'''
Automatically grabs the colors to extend the colorbar from the colormap.
'''
# Define number of colors
if extend == 'both':
nrColors = len(clevs)+1
elif (extend == 'min') | (extend == 'max'):
nrColors = len(clevs)
elif (extend == 'neither'):
nrColors = len(clevs)-1
else:
nrColors = len(clevs)-1
extend = 'neither'
# Get colormap
cmap = get_cmap(name, nrColors)
# Truncate colormap if asked
if (minval != 0.0) or (maxval != 1.0):
cmap = truncate_colormap(cmap, minval=minval, maxval=maxval, n=nrColors/2)
# Get the list of colors
colors = []
for i in range(0, nrColors):
colors.append(cmap(i/(nrColors-1)))
# Use utility function to get cmap and norm at the same time
cmap, norm = from_levels_and_colors(clevs, colors, extend=extend)
return(cmap, norm)
def get_colormap_reds_blues(n):
n = n // 2
reds = ['#ffebc0', '#f84627', '#950026']
blues = ['#0a2a6a', '#437aea', '#c8d9fd']
colors = blues[:n]
colors.append(reds[:n])
return colors
def pie_plot_percent_mentions(mentions_percent, save_to = None):
'''
Inputs:
Dictionary with keys as pairs, values as percent of mentions
'''
other = mentions_percent.pop("Other")
swapped = {v:k for k, v in mentions_percent.items()}
sizes = sorted(swapped)
sizes.reverse()
labels = [swapped[current_percent] for current_percent in sizes]
sizes.append(other)
labels.append("Other")
N = len(labels)
color_map = get_colormap(N-1)
colors = []
for i in range(N-1):
col = color_map(i)
colors.append(col)
colors.append("white")
print(colors)
plt.pie(sizes, labels=labels, colors=colors,
autopct='%1.1f%%', startangle=90)
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
fig = plt.figure()
ax = fig.gca()
plt.show()
def update_data(self, change=None):
ylist = []
colors = []
selection_was_list, [selections
] = vaex.utils.listify(self.model.selection)
if self.dimension_groups == 'slice':
y0 = self.model.grid
if selection_was_list:
y0 = y0[0]
ylist.append(y0)
colors.append(C0)
if self.model.grid_sliced is not None:
y1 = self.model.grid_sliced
if selection_was_list:
y1 = y1[0]
ylist.append(y1)
colors.append(C1)
elif self.dimension_groups == 'selections':
ylist = self.model.grid
if selection_was_list:
colors = colors_default[:len(ylist)]
else:
colors = [colors_default[0]]
else:
raise ValueError(
f'Unknown action {self.dimension_groups} for dimension_groups')
if self.normalize:
ylist = [y / np.sum(y) for y in ylist]
x = self.model.x.bin_centers
self.plot.x_min = self.model.x.min
self.plot.x_max = self.model.x.max
self.plot.update_data(x, np.array(ylist), colors)
def smart_colormap(clevs, name='jet', extend='both', minval=0.0, maxval=1.0):
'''
Automatically grabs the colors to extend the colorbar from the colormap.
'''
# Define number of colors
if extend == 'both':
nrColors = len(clevs) + 1
elif (extend == 'min') | (extend == 'max'):
nrColors = len(clevs)
elif (extend == 'neither'):
nrColors = len(clevs) - 1
else:
nrColors = len(clevs) - 1
extend = 'neither'
# Get colormap
cmap = get_cmap(name, nrColors)
# Truncate colormap if asked
if (minval != 0.0) or (maxval != 1.0):
cmap = truncate_colormap(cmap,
minval=minval,
maxval=maxval,
n=nrColors / 2)
# Get the list of colors
colors = []
for i in range(0, nrColors):
colors.append(cmap(i / (nrColors - 1)))
# Use utility function to get cmap and norm at the same time
cmap, norm = from_levels_and_colors(clevs, colors, extend=extend)
return (cmap, norm)
def _highlight_allen_ids(self, allen_ids):
weights = np.ones(
(len(self.labels), ), dtype=np.float32) * self.off_weight
# Background
weights[0] = 0.0
for allen_id in allen_ids:
labels_for_id = labels_for_allen_id(allen_id)
weights[labels_for_id] = self.on_weight
if not allen_ids:
return
surfaces = [structure_mesh(allen_id) for allen_id in allen_ids]
self.itk_viewer.geometries = self.swc_geometries + surfaces
self.itk_viewer.geometry_opacities = [1.0,]*len(self.swc_geometries) + \
[0.5,]*len(surfaces)
colors = list(
self.itk_viewer.geometry_colors)[:len(self.swc_geometries)]
for allen_id in allen_ids:
hex_color = '#' + allen_id_to_tree_node[allen_id].color_hex_triplet
colors.append(matplotlib.colors.to_rgb(hex_color))
self.itk_viewer.geometry_colors = np.array(colors, dtype=np.float32)
self.itk_viewer.label_image_weights = weights
self.itk_viewer.ui_collapsed = True
def plot_peak_mobility_gate_voltage(analyzed_data, position="any"):
"""
This function plots the gate voltage at which peak mobility is achieved.
"""
exclude_ids = ()
c = {(None, 'H2O'): 'tab:red', ('tma', 'H2O'): 'tab:orange', ('tma', 'O3'): 'tab:olive', ('plasma', 'H2O'): 'tab:blue', ('plasma', 'O3'): 'tab:green'}
m = {(None, 'H2O'): 'x', ('tma', 'H2O'): '^', ('tma', 'O3'): 'v', ('plasma', 'H2O'): 'd', ('plasma', 'O3'): 'o'}
# Extract a list of peak mobilities and voltages
colors = []
markers = []
voltages = []
mobilities = []
for sid, sample in analyzed_data.items():
if sid in exclude_ids:
continue
if position != "any":
if sample.position != position:
continue
# Get the measured values from the measurements for this sample
for measurement in sample.measurements:
colors.append(c[(sample.treatment, sample.ald["oxidiser"])])
markers.append(m[(sample.treatment, sample.ald["oxidiser"])])
voltages.append(measurement.vg_at_peak)
mobilities.append(measurement.mob_at_peak)
fig, ax = plt.subplots()
for volt, mob, col, mark in zip(voltages, mobilities, colors, markers):
ax.scatter(x=volt, y=mob, c=col, marker=mark, alpha=1)
ax.set_xlabel(r"Gate Voltage at Peak Mobility $\left(\si{\volt}\right)$")
ax.set_ylabel(r"Peak Mobility $\left(\si[per-mode=fraction]{\square\centi\meter\per\volt\per\second}\right)$")
ax.set_ylim(15000, 50000)
fig.tight_layout()
plt.show()
def plot_structure(type):
for m in [100, 75, 50, 40, 30, 20, 10]:
plt.close('all')
rel_chunks = df.loc[df['length_of_chunk'] == m]
colors = []
if type == "essays":
all_docs = all_essays
else:
all_docs = all_summaries
for c, thesis in enumerate(all_docs[0:13]):
this_doc_len_m = rel_chunks.loc[rel_chunks['document'] == thesis]
if this_doc_len_m.empty == True:
continue
ints = []
max_number_of_this_chunk = np.max(
this_doc_len_m['number_of_chunk'])
for i in this_doc_len_m['number_of_chunk']:
# transform the chunk number into a percentage of how far in the document this chunk is
ints.append(100 * int(i) / max_number_of_this_chunk)
# have to apend color as well?!
for i in range(len(ints)):
colors.append(colors_list[c])
plt.plot(ints, this_doc_len_m['GFI'], '-o', color=colors_list[c])
# when the lines of all theses are there, we can combine them in one plot and store it
plt.gcf().set_size_inches(14, 7)
plt.ylabel('GFI')
plt.xlabel("document's percentage of the chunk's end")
#plt.suptitle('GFI for the chunks of size ' + str(m) + ' of document', fontsize=14)
plt.savefig('Plots/German/assignments/' + type + '/structure/all' +
str(m) + '_long_indicesOverNumberOfChunk.svg',
bbox_inches='tight')
def plot_single_piechart(self, cluster_no, labels, pdf):
cluster_label_dict = {}
for label in labels:
if label not in cluster_label_dict:
cluster_label_dict[label] = 1
else:
cluster_label_dict[label] += 1
distinct_labels = []
label_counts = []
colors = []
cmap = self.get_cmap(30)
i = 0
for label,value in cluster_label_dict.iteritems():
colors.append(cmap(i))
distinct_labels.append(label)
label_counts.append(value)
i = i+1
matplotlib.rcParams['font.size'] = 5
plt.figure(figsize=(8,8))
plt.pie(label_counts, explode=None, labels=distinct_labels, colors=colors,
autopct='%1.1f%%', shadow=True, startangle=90)
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
plt.suptitle("Cluster no " + str(cluster_no) + "\nNumber of docs in cluster: " + str(len(labels)),fontsize=10)
pdf.savefig()
plt.close()
def compute_color_histograms(cloud, using_hsv=False):
# Convert each cloud point's color in float format to a three channel format
# point format: [x, y, z, color_in_float]
colors = []
cloud_points = pc2.read_points(cloud, skip_nans=True)
for point in cloud_points:
rgb_color = float_to_rgb(point[3])
if using_hsv:
colors.append(rgb_to_hsv(rgb_color) * 255)
else:
colors.append(rgb_color)
# Put each color channel in their own list
ch1, ch2, ch3 = separate_channels(colors)
# Concatenate each histogram and normalize the results
features = get_normalized_feature_set(ch1,
ch2,
ch3,
n_bins=64,
range_bins=(0, 256))
return features
def make_image(neuron, A, scale_depth, index_neuron):
normal_vectors = A[0:2,:]
orthogonal_vector = A[2,:]
depth = depth_points(neuron.location, orthogonal_vector)
p = project_points(neuron.location, normal_vectors)
m = min(depth)
M = max(depth)
depth = scale_depth * ((depth - m)/(M-m))
colors = []
lines = []
for i in range(len(neuron.nodes_list)):
colors.append((depth[i],depth[i],depth[i],1))
j = neuron.parent_index[i]
lines.append([(p[0,i],p[1,i]),(p[0,j],p[1,j])])
lc = mc.LineCollection(lines, colors=colors, linewidths=2)
fig, ax = pl.subplots()
ax.add_collection(lc)
pl.axis('off')
pl.xlim((min(p[0,:]),max(p[0,:])))
pl.ylim((min(p[1,:]),max(p[1,:])))
Name = "neuron" + str(index_neuron[0]+1) + "resample" + str(index_neuron[1]+1) + "angle" + str(index_neuron[2]+1) + ".png"
fig.savefig(Name,figsize=(6, 6), dpi=80)
img = Image.open(Name)
img.load()
data = np.asarray( img, dtype="int32" )
data = data[:,:,0]
return data
def draw_trajectories(demos):
all_thresholds = np.arange(len(demos))
envs = np.load("valid_environments.npy")[0]
envs[0, 0] = 0
for i in range(len(demos)):
temp_envs = np.array(envs)
temp_demos = demos[0:i + 1]
latest_demo = demos[i]
goal = latest_demo[len(latest_demo) - 1, 0]
start = latest_demo[0, 0]
temp_envs[goal, 0] = 1
plt.figure(i, figsize=(24, 13.5), dpi=80)
p1 = plt.subplot(1, 1, 1)
colors = []
for c in range(0, i):
colors.append('darkgrey')
colors.append('yellow')
#['blue','green','red','cyan','magenta','yellow','black','white','blue']
DrawTrajectory.draw_traj(p1,
env=temp_envs,
trajectories=temp_demos,
grid_size=5,
arrow_colors=colors,
threshold_plt=False,
temp_threshold=all_thresholds,
width=0.015,
legend_plot=True,
start_state=start)
plt.savefig("demo_plots/demos_" + str(i) + ".png")
plt.close("all")
pic_to_gif.convert_to_gif(len(demos))
def get_palette(groups):
indexT, indexS = 0, 0
colors = []
for group in groups:
color, indexT, indexS = get_color(group, indexT, indexS)
colors.append(color)
return colors
def _get_colors(num_colors):
colors=[]
for i in np.arange(0., 360., 360. / num_colors):
hue = i/360.
lightness = (50 + np.random.rand() * 10)/100.
saturation = (90 + np.random.rand() * 10)/100.
colors.append(colorsys.hls_to_rgb(hue, lightness, saturation))
return colors
def basic_FCA(G, kappa, its = int(pow(2,32)-1), verbose=True, timesec=0, tree=False, no_edges=0):
# initial conditions
blink = floor((kappa-1)/2) # blink color and lower bound on post-blink
colors = []
init_col = G.get_colors()
colors.append(init_col)
if tree:
parentlist = [None] * G.num_nodes()
values = np.random.rand(G.num_nodes())
# local helper function
def new_color(vertex):
neighblist = G.get_neighbor_colors(vertex)
if(G.get_color(vertex) > blink and \
any(np.array(neighblist) == blink)):
return G.get_color(vertex)
return (G.get_color(vertex) + 1) % kappa
# initial coloring and start time
current_col = G.get_colors()
width_compute(current_col, kappa)
s = time.time()
for i in range(1, its+1):
# perform updates for each vertex
current_col = list(map(new_color, range(0, G.num_nodes())))
# check synchronization -> break and return final list and iterations
width = width_compute(current_col,kappa)
if(width < floor(kappa/2)):
if verbose: print("Iterations: ",i)
if no_edges:
return np.array([-1, init_col, 1, colors, i])
return np.array([G.edges, init_col, 1, colors, i])
if timesec and time.time()-s > timesec:
if verbose:
print("Iterations: ",i)
print("Time has been reached: ", timesec,"sec.")
if no_edges:
return np.array([-1, init_col, 0, colors, i])
return np.array([G.edges, init_col, 0, colors, i])
# update to new colors
G.set_colors(current_col)
colors.append(np.array(G.get_colors()))
if tree:
values, parentlist = tree_iter(G, values, parentlist)
tree = not (len(G.edges) == G.num_nodes()-1)
if (len(G.edges)<G.num_nodes()-1):
print("deleted too many edges")
if verbose:
print("Max iterations reached: ", its)
if no_edges:
return np.array([-1, init_col, 0, colors, i])
return np.array([G.edges, init_col, 0, colors, i])
def transformColorList(colorList,alpha): colors = [] for rgb in colorList: rd = int(rgb.split()[0]) gn = int(rgb.split()[1]) bl = int(rgb.split()[2]) rgb_tuple = (rd/255.,gn/255.,bl/255.,alpha) colors.append(rgb_tuple) return colors
def show_colorchecker_swatches():
colors = []
fig,axs = plt.subplots(4,6)
for i in range(len(refRGB)):
colors.append(np.full((10,10,3),refRGB[i],dtype = np.uint8) / 255.0)
m = i + 1
plt.subplot(4,6,m)
plt.imshow(colors[i])
plt.show()
def float_list_type(mystring):
"""Convert string-form list of doubles into list of doubles."""
colors = []
for f in mystring.strip("(").strip(")").strip("[").strip("]").split(","):
try:
colors.append(float(f))
except:
colors.append(f)
return colors
def show_color_swatches(colorMatrix):
colors = []
fig,axs = plt.subplots(4,6)
for i in range(len(colorMatrix)):
colors.append(np.full((10,10,3),colorMatrix[i],dtype = np.uint8) / 255.0)
m = i + 1
plt.subplot(4,6,m)
plt.imshow(colors[i])
plt.show()
def float_list_type(mystring):
"""Convert string-form list of doubles into list of doubles."""
colors = []
for f in mystring.strip("(").strip(")").strip("[").strip("]").split(","):
try:
colors.append(float(f))
except:
colors.append(f)
return colors
def getColorMap( rgbFile = "medspiration.rgb" ):
colors = []
rgbFile = os.path.join(os.path.dirname(__file__), rgbFile )
nbCol=0
for line in open( rgbFile ):
r,g,b = [int(c) for c in line.split()]
colors.append( [r/255.,g/255.,b/255.] )
nbCol += 1
return( matplotlib.colors.ListedColormap(colors, name="custom", N=nbCol) )
def getColorMap(rgbFile="medspiration.rgb"):
colors = []
rgbFile = os.path.join(os.path.dirname(__file__), rgbFile)
nbCol = 0
for line in open(rgbFile):
r, g, b = [int(c) for c in line.split()]
colors.append([r / 255., g / 255., b / 255.])
nbCol += 1
return (matplotlib.colors.ListedColormap(colors, name="custom", N=nbCol))
def _generate_colors(self):
colors = [
'blue', 'green', 'red', 'magenta', 'orange', 'black', 'brown',
'grey', 'olive'
] # strong colors
for color in matplotlib.colors.cnames.keys():
if color not in colors and 'light' not in color:
colors.append(color)
return colors
def spiderweb_colormesh2d(data,
axis,
title='',
fill_scheme='viridis',
grid=False,
norm=None,
*args,
**kwargs):
"""Prepare bar plot for 2D data visualization.
matplotlib.pyplot.pcolormesh is used.
Args:
data(ndarray): 2D array of data.
axis(axis): corresponding tomomak axis
title(str, optional): Plot title. default: ''.
fill_scheme(pyplot colormap, optional): pyplot colormap to be used in the plot. default: 'viridis'.
grid(bool, optional): if True, grid is shown. default: False.
*args, **kwargs: arguments will be passed to matplotlib.pyplot.pcolormesh
Returns:
plot: matplotlib pcolormesh .
ax(axes.Axes ): axes.Axes object or array of Axes objects.
See matplotlib Axes class
fig(matplotlib.figure): The figure module.
cb(matplotlib.pyplot.colorbar): colorbar on the right of the axis.
"""
x = axis.cell_edges2d()
cmap = plt.get_cmap(fill_scheme)
fig, ax = plt.subplots()
colors = []
for i in range(len(x)):
colors.append(data[i])
patches = []
for i in range(len(x)):
polygon = Polygon(x[i])
patches.append(polygon)
p = PatchCollection(patches, cmap=cmap, alpha=1.0)
p.set_array(np.array(colors))
plot = ax.add_collection(p)
cb = fig.colorbar(plot, ax=ax)
ax.set_title(title)
xlabel = "{}, {}".format(axis.name, axis.units)
ylabel = "{}, {}".format(axis.name, axis.units)
ax.set(xlabel=xlabel, ylabel=ylabel)
if grid:
ax.grid()
return plot, ax, fig, cb
def get_n_colors2(num_colors):
"""http://stackoverflow.com/questions/470690/
how-to-automatically-generate-n-distinct-colors"""
colors = []
for i in np.arange(0., 360., 360. / num_colors):
hue = i / 360.
lightness = (50 + np.random.rand() * 10) / 100.
saturation = (90 + np.random.rand() * 10) / 100.
colors.append(colorsys.hls_to_rgb(hue, lightness, saturation))
return colors
def make_IDLCB37():
#-- create a colors map -----------
CB37 = np.load(dir + '/IDLCB37.npz')
R = CB37['R37']
G = CB37['G37']
B = CB37['B37']
colors = [(R[0], G[0], B[0])]
for i in range(1, 256, 1):
colors.append((R[i], G[i], B[i]))
cmap = make_cmap(colors, bit=True)
return cmap
def stackplot_percent_mentions_per_time(mentions_percent, candidate_list, title, save_to = None):
'''
Generates and saves a stackplot with the percent mentions over time
Inputs:
mentions: Dictionary with keys as candidates,
values as percent or number of mentions
candidate_list : candidates to include
title: string
save_to: Directory as a string
Returns:
A saved file if save_to is not None.
'''
num_candidates = len(candidate_list)
all_ys = [0]*num_candidates
for i, candidate in enumerate(candidate_list):
times = mentions_percent[candidate]
xs = sorted(times)
ys = []
for x in xs:
y = times[x]
ys.append(y)
all_ys[(num_candidates - 1) - i] = ys
#num_candidates - 1 to account for lists starting at index = 0
stacked_ys = np.row_stack(tuple(all_ys))
color_map = get_colormap(num_candidates-1)
colors = []
for i in range(num_candidates-1):
col = color_map(i)
colors.append(col)
colors.append("white")
print(colors)
legend_colors = [mpatches.Patch(edgecolor = 'black', facecolor = color) for color in colors[::-1]]
params = {'legend.fontsize': 8,'legend.linewidth': 2}
plt.rcParams.update(params)
fig, ax = plt.subplots()
ax.stackplot(xs, stacked_ys, colors = colors)
plt.xticks(fontsize = 8)
plt.legend(legend_colors, candidate_list)
plt.xlabel("Time (mins)")
plt.ylabel("Percent of Tweets")
plt.title(title)
if save_to is None:
plt.show()
else:
fig.savefig(save_to) #directory and filename
def get_rand_color(val):
h,s,v = random()*6, 0.5, 243.2
colors = []
for i in range(val):
h += 3.75#3.708
tmp = ((v, v-v*s*abs(1-h%2), v-v*s)*3)[5**int(h)/3%3::int(h)%2+1][:3]
colors.append('#' + '%02x' *3%tmp)
if i%5/4:
s += 0.1
v -= 51.2
return colors
def vibrant_colors(n, is_int=False):
colors = []
for i in np.arange(0., 360., 360. / n):
hue = i / 360.
lightness = (50 + np.random.rand() * 10) / 100.
saturation = (90 + np.random.rand() * 10) / 100.
colors.append(colorsys.hls_to_rgb(hue, lightness, saturation))
if is_int:
colors = np.multiply(colors, 255).astype(np.int)
return colors
def get_colors(n_colors,
exclude_colors=None,
return_excluded=False,
pastel_factor=0,
n_attempts=1000,
colorblind_type=None):
"""
Generate a list of n visually distinct colours.
:param n_colors: How many colours to generate
:param exclude_colors: A pre-existing list of (r,g,b) colours that new colours should be distinct from.
If exclude_colours=None then exclude_colours will be set to avoid white and black
(exclude_colours=[(0,0,0), (1,1,1)]).
(r,g,b) values should be floats between 0 and 1.
:param return_excluded: If return_excluded=True then exclude_colors will be included in the returned color list.
Otherwise only the newly generated colors are returned (default).
:param pastel_factor: float between 0 and 1. If pastel_factor>0 paler colours will be generated.
:param n_attempts: number of random colours to generated to find most distinct colour.
:param colorblind_type: generate colours that are distinct with given type of colourblindness. Can be:
'Normal': Normal vision
'Protanopia': Red-green colorblindness (1% males)
'Protanomaly': Red-green colorblindness (1% males, 0.01% females)
'Deuteranopia': Red-green colorblindness (1% males)
'Deuteranomaly': Red-green colorblindness (most common type: 6% males, 0.4% females)
'Tritanopia': Blue-yellow colourblindness (<1% males and females)
'Tritanomaly' Blue-yellow colourblindness (0.01% males and females)
'Achromatopsia': Total colourblindness
'Achromatomaly': Total colourblindness
:return: colors - A list of (r,g,b) colors that are visually distinct to each other
and to the colours in exclude_colors. (r,g,b) values are floats between 0 and 1.
"""
if exclude_colors is None:
exclude_colors = [WHITE, BLACK]
colors = exclude_colors.copy()
for i in range(n_colors):
colors.append(
distinct_color(colors,
pastel_factor=pastel_factor,
n_attempts=n_attempts,
colorblind_type=colorblind_type))
if return_excluded:
return colors
else:
return colors[len(exclude_colors):]
def get_grid_patch_collection(self, zpts, plotarray, **kwargs):
"""
Get a PatchCollection of plotarray in unmasked cells
Parameters
----------
zpts : numpy.ndarray
array of z elevations that correspond to the x, y, and horizontal
distance along the cross-section (self.xpts). Constructed using
plotutil.cell_value_points().
plotarray : numpy.ndarray
Three-dimensional array to attach to the Patch Collection.
**kwargs : dictionary
keyword arguments passed to matplotlib.collections.PatchCollection
Returns
-------
patches : matplotlib.collections.PatchCollection
"""
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
rectcol = []
v = []
colors = []
for k in range(zpts.shape[0]-1):
for idx in range(0, len(self.xpts)-1, 2):
try:
ll = ((self.xpts[idx][2], zpts[k+1, idx]))
try:
dx = self.xpts[idx+2][2] - self.xpts[idx][2]
except:
dx = self.xpts[idx+1][2] - self.xpts[idx][2]
dz = zpts[k, idx] - zpts[k+1, idx]
pts = (ll,
(ll[0], ll[1]+dz), (ll[0]+dx, ll[1]+dz),
(ll[0]+dx, ll[1])) #, ll)
if np.isnan(plotarray[k, idx]):
continue
if plotarray[k, idx] is np.ma.masked:
continue
rectcol.append(Polygon(pts, closed=True))
colors.append(plotarray[k, idx])
except:
pass
if len(rectcol) > 0:
patches = PatchCollection(rectcol, **kwargs)
patches.set_array(np.array(colors))
else:
patches = None
return patches
def make_redblue(min, max):
#-- create a colors map -----------
colors = [(255, 255, 255)]
for i in range(1, 256, 1):
colors.append((255, 255, 255))
#print(colors)
#----------------------------------
# find the position of zero
if min * max > 0:
print('min*max >=0')
print('Please check your setting !')
print('Program STOP at make_redblue')
exit()
if min > max:
print('min > max ?')
print('Please check your setting !')
print('Program STOP at make_redblue')
exit()
num_zero = int(256 * abs(min) / (max + abs(min)))
num_zero1 = num_zero + 1
# color map for > 0
iR = 255
iG = 255
iB = 255
for i in range(num_zero1 + 1, 256, 1):
#print(i)
iG = iG - 6
if iG >= 0:
colors[i] = (255, iG, 255)
else:
iB = iB - 6
if iB >= 0:
colors[i] = (255, 0, iB)
else:
iR = iR - 1
colors[i] = (iR, 0, 0)
# color map for < 0
iR = 255
iG = 255
iB = 255
for i in range(num_zero - 1, -1, -1):
#print(i)
iR = iR - 6
if iR >= 0:
colors[i] = (iR, 255, 255)
else:
iG = iG - 6
if iG >= 0:
colors[i] = (0, iG, 255)
else:
iB = iB - 1
colors[i] = (0, 0, iB)
cmap = make_cmap(colors, bit=True)
return cmap
def update_node_status(self, *args):
colors = []
for i, module in enumerate(self.data_model.modules):
if (i in self.data_model.input_nodes) and (len(
module['config']['filelist']) == 0):
colors.append('red')
else:
colors.append('green')
#update plot
with self.data_view.graph.batch_update():
self.data_view.node_status.marker.color = colors
def gen_circular_cmap(colors: List[str]):
colors = list(colors)
# `colors` has 12 distinct entries.
# LinearSegmentedColormap(colors) takes a real number `x` between 0 and 1,
# and maps x=0 to colors[0] and x=1 to colors[-1].
# pitch_cmap should be periodic, so `colors[0]` should appear at both x=0 and x=1.
colors.append(colors[0])
cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
"12-tone spectrum", colors, N=256, gamma=1.0)
return cmap
def list_by_values(*args):
"""
Returns a list of colors matching up with the range of numerical values provided
refer to https://github.com/tomerburg/metlib/blob/master/colors/color_list.py
Examples:
This is the call to the list_by_values function. Input parameters are as many ranges
as desired. In this example, there are 3 ranges, one starting from #00FFFF for a value
of 25 and going to #0000FF for a value of 29.
colors = list_by_values(
[['#00FFFF',25.0],['#0000FF',29.0],1.0], [['#0000FF',29.0],['#0000AA',32.0],1.0],
[['#0000AA',32.0],['#FF00FF',38.0],1.0])
print(colors)
"""
#Initialize an empty color list
colors = []
#Loop through the passed lists
#The format for each argument is: [['#00FFFF',25.0],['#0000FF',29.0],1.0]
#['#00FFFF',25.0] = [start hex value, start value]
#['#0000FF',29.0] = [end hex value, end value]
#1.0 = interval of requested range between start & end values
for arg in args:
#Retrieve arguments
start_hex = arg[1][0].lstrip('#')
end_hex = arg[0][0].lstrip('#')
start_value = arg[0][1]
end_value = arg[1][1]
interval = arg[2]
#Calculate start and end RGB tuples
start_rgb = tuple(int(start_hex[i:i + 2], 16) for i in (0, 2, 4))
end_rgb = tuple(int(end_hex[i:i + 2], 16) for i in (0, 2, 4))
#Loop through the number of colors to add into the list
start_loop = start_value
end_loop = end_value + interval if arg == args[-1] else end_value
for x in np.arange(start_loop, end_loop, interval):
#Get hex value for the color at this point in the range
nrange_color = (end_value - start_value)
idx = x - start_value
hex_val = getColor(idx, nrange_color, start_rgb, end_rgb)
#Append to list if this is different than the last color
if len(colors) == 0 or colors[-1] != hex_val:
colors.append(hex_val)
#Return the list of colors
return colors
def get_rand_color(val):
h, s, v = random() * 6, 0.5, 243.2
colors = []
for i in range(val):
h += 3.75 #3.708
tmp = ((v, v - v * s * abs(1 - h % 2), v - v * s) *
3)[5**int(h) / 3 % 3::int(h) % 2 + 1][:3]
colors.append('#' + '%02x' * 3 % tmp)
if i % 5 / 4:
s += 0.1
v -= 51.2
return colors
def plot_spikes(nodes, spikes_file, save_file=None):
import h5py
spikes_h5 = h5py.File(spikes_file, 'r')
spike_gids = np.array(spikes_h5['/spikes/gids'], dtype=np.uint)
spike_times = np.array(spikes_h5['/spikes/timestamps'], dtype=np.float)
spikes = np.rot90(np.vstack(
(spike_gids,
spike_times))) # Make array [[gid spiketime],[gid2 spiketime2]]
#spikes = spikes[spikes[:,0].argsort()] # Sort by cell number
"""
Author: Tyler Banks
Loop through all spike files, create a list of when each cell spikes, plot.
"""
cell_types = ['EC', 'CA3e', 'CA3o', 'CA3b', 'DGg', 'DGh', 'DGb']
cell_nums = [30, 63, 8, 8, 384, 32, 32]
d = [[] for _ in range(sum(cell_nums))]
color_picker = [
'red', 'orange', 'yellow', 'green', 'blue', 'purple', 'black'
]
colors = []
offset = 0
for i, row in enumerate(spikes):
d[int(row[0]) + offset].append(row[1])
for i, n in enumerate(cell_nums):
for _ in range(n):
colors.append(color_picker[i])
fig, axs = plt.subplots(1, 1)
axs.eventplot(d, colors=colors)
axs.set_title('Hipp BMTK')
axs.set_ylabel('Cell Number')
axs.set_xlabel('Time (ms)')
axs.legend(cell_types[::-1])
leg = axs.get_legend()
for i, c in enumerate(color_picker):
leg.legendHandles[-i - 1].set_color(c)
#splt.savefig('raster3_after_pycvode_fixes.png')
if save_file:
plt.savefig(save_file)
plt.draw()
return
def convert_to_heatmap(weights, cm="Reds"):
# 把权重转化为热力图，可是根据可视化需要选择colormap
# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
cmap = matplotlib.cm.get_cmap(cm)
colors = []
for i in range(cmap.N):
rgb = cmap(i)[:3]
colors.append(matplotlib.colors.rgb2hex(rgb))
colors = np.array(colors)
weights = np.uint8(255 * weights)
heatmap = colors[weights]
return heatmap
def add_edge(i, j,edge_points,edges,pointsTest,imgCirc,colors):
"""Add a line between the i-th and j-th points, if not in the list already"""
if (i, j) in edges or (j, i) in edges:
# already added
colors.append(colorChoice(imgCirc[pointsTest[i,0],pointsTest[i,1]],
imgCirc[pointsTest[j,0],pointsTest[j,1]]))
return edge_points,edges,colors
edges.add( (i, j) )
edge_points.append(pointsTest[[i, j]])
colors.append(colorChoice(imgCirc[pointsTest[i,0],pointsTest[i,1]],
imgCirc[pointsTest[j,0],pointsTest[j,1]]))
return edge_points,edges,colors
def stackplot_percent_mentions_per_time(mentions_percent, candidate_list, title):
num_candidates = len(candidate_list)
all_ys = [0]*num_candidates
for i, candidate in enumerate(candidate_list):
times = mentions_percent[candidate]
xs = sorted(times)
ys = []
for x in xs:
y = times[x]
ys.append(y)
all_ys[(num_candidates - 1) - i] = ys
#num_candidates - 1 to account for lists starting at index = 0
stacked_ys = np.row_stack(tuple(all_ys))
color_map = get_colormap(num_candidates-1)
colors = []
for i in range(num_candidates-1):
col = color_map(i)
colors.append(col)
colors.append("white")
print(colors)
legend_colors = [mpatches.Patch(edgecolor = 'black', facecolor = color) for color in colors[::-1]]
params = {'legend.fontsize': 8,'legend.linewidth': 2}
plt.rcParams.update(params)
fig, ax = plt.subplots()
ax.stackplot(xs, stacked_ys, colors = colors)
#ax.set_xticks(pos + (bar_width/2))
plt.xticks(fontsize = 8)
ax.set_xticklabels(x_labels, rotation = 60)
plt.legend(legend_colors, candidate_list)
plt.xlabel("Time (mins)")
plt.ylabel("Percent of Tweets")
plt.title(title)
plt.show()
################################################
#Donald Trump, Carly Fiorina, Jeb Bush, Ben Carson, Ted Cruz, Rand Paul, and Marco Rubio
def deposit_cells(self):
""" Deposit cells in a plane """
#coords = self.lattice.coordinates()
#shape = coords.shape
rho = numpy.mean(self.lattice.get_mesh_size())
print "Mean mesh size", rho
nx = 40
x0 = 0.5-rho*nx/2.0
z0 = 0.5-rho*nx/2.0
print x0
y0 = 0.1
x = x0+numpy.linspace(0,nx*rho,nx)
z = z0+numpy.linspace(0,nx*rho,nx)
import itertools
space = itertools.product(x,z)
#cols = [5 10 15 20]
coords = self.lattice.coordinates()
shape = coords.shape
Ncols = 50
# Select Ncols random points:
idx = range(nx*nx)
color_idx = random.sample(idx, Ncols)
colors = []
color_map=get_cmap(len(color_idx))
for i in range(Ncols):
colors.append(color_map(i)[0:3])
j=0
for i,p in enumerate(space):
point = [p[0],y0,p[1]]
reppoint = numpy.tile(point, (shape[0], 1))
dist = numpy.sqrt(numpy.sum((coords-reppoint)**2, axis=1))
ix = numpy.argmin(dist)
cell=Cell(color="white",index=ix, time_to_division=self.cell_division_time(),time_to_migration=self.sample_migration_time())
if i in color_idx:
cell.color=htmlcolor(colors[j])
j =j+1
if not self.state[ix]:
heapq.heappush(self.cells,cell)
self.state[ix]=cell
def pie_plot_percent_mentions(mentions_percent, save_to = None):
'''
Generates and saves a pie plot with the percentage of mentions
Inputs:
mentions_percent: Dictionary with keys as candidates,
values as percent of mentions
save_to: Directory as a string
Returns:
A saved file if save_to is not None.
'''
other = mentions_percent.pop("Other")
swapped = {v:k for k, v in mentions_percent.items()}
sizes = sorted(swapped)
sizes.reverse()
labels = [swapped[current_percent] for current_percent in sizes]
sizes.append(other)
labels.append("Other")
N = len(labels)
color_map = get_colormap(N-1)
colors = []
for i in range(N-1):
col = color_map(i)
colors.append(col)
colors.append("white")
print(colors)
plt.pie(sizes, labels=labels, colors=colors,
autopct='%1.1f%%', startangle=90)
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
fig = plt.figure()
ax = fig.gca()
if save_to is None:
plt.show()
else:
fig.savefig(save_to) #directory and filename
def plot_cluster(self):
self.scale_data()
print "finished scale"
cmap = self.get_cmap(100)
colors = []
for i in range(0, self.n_clusters):
colors.append(cmap(i))
fig = plt.figure(figsize=(1,1))
#fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
ax = fig.add_subplot(1, 2, 1)
for k, col in zip(range(self.n_clusters), colors):
my_members = self.k_means_labels == k
cluster_center = self.k_means_cluster_centers[k]
ax.plot(self.X_scaled[my_members, 0], self.X_scaled[my_members, 1], 'w',
markerfacecolor=col, marker='.')
#fig.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
# markeredgecolor='k', markersize=6)
plt.show()
def colorTableList(num_colors):
colors = []
# Transparent for the zero label
colors.append((0,0,0,0))
rgb_color_values = list(nanshe.util.iters.splitting_xrange(num_colors))
converter = matplotlib.colors.ColorConverter()
for _ in rgb_color_values:
a_rgb_color = tuple()
for __ in converter.to_rgba(matplotlib.cm.gist_rainbow(_)):
a_rgb_color += ( int(round(255*__)), )
colors.append(a_rgb_color)
colors = numpy.asarray(colors, dtype=numpy.uint8)
return(colors)
def readClrColorRamp(self,path):
f = open(path, 'r')
colors = []
levels = None
processtype = 0
for line in f:
if 'colors' in line :
processtype = 1
continue
elif 'levels' in line :
processtype = 2
continue
if processtype == 1:
colors.append([float(elem) for elem in line.split(';')])
#print 'col ' + str(colors)
if processtype == 2:
levels = ([float(elem) for elem in line.split(';')])
f.close()
if colors and levels:
return (self.arrayStepRGBAToCmap(colors),levels)
else:
return (None, None)
def version_balance(trials, ax=None, figsize=None):
if ax is None:
pyplot.figure(figsize=figsize)
ax = pyplot.axes()
counts = trials.program_name.value_counts().sort_index()
# Assign colors by base
colors = []
last_base = None
color_i = -1
for n in counts.index:
base = n.split("_")[0]
if base != last_base:
color_i += 1
last_base = base
colors.append(kelly_colors[color_i])
counts.plot(kind="bar", ax=ax, color=colors)
ax.set_title("Version Balance ({0} trials)".format(len(trials)))
return ax
def fit_coefficients_version(fit, ax=None, figsize=None, intercept=True):
if ax is None:
pyplot.figure(figsize=figsize)
ax = pyplot.axes()
names = [get_fit_name(n) for n in fit.model.exog_names]
means = fit.conf_int().apply(lambda x: np.mean(x), axis=1)
err = fit.conf_int().apply(lambda x: (x[1] - x[0]) / 2.0, axis=1)
# Assuming p-values come in the same order as confidence intervals
for i, p_val in enumerate(fit.pvalues.values):
if np.isclose(p_val, 1.0):
err[i] = 0.0
if not intercept:
means, err = means[1:], err[1:]
names = names[1:]
# Assign colors by base
colors = []
last_base = None
color_i = -1
for n in names:
base = n.split("_")[0]
if base != last_base:
color_i += 1
last_base = base
colors.append(kelly_colors[color_i])
# Plot bars
ax = means.plot(kind="bar", yerr=err, error_kw={"ecolor": "black"}, color=colors)
ax.set_title("Coefficients by Program/Version")
ax.set_ylabel("Coefficients (95% CI)")
ax.set_xlabel("Program Base/Version")
ax.set_xticklabels(names)
return ax
def fancy_dendrogram(*args, **kwargs):
max_d = kwargs.pop('max_d', None)
if max_d and 'color_threshold' not in kwargs:
kwargs['color_threshold'] = max_d
annotate_above = kwargs.pop('annotate_above', 0)
ddata = dendrogram(*args, **kwargs)
colors = []
color_elements = ["FF", "00", "33", "66", "99", "AA", "DD"]
color = ["00", "00", "00"]
num = 0
if not kwargs.get('no_plot', False):
plt.title('Dendrogram of hierarchical clustering', fontsize=28)
plt.tick_params(labelsize=22)
plt.xlabel('Genes', fontsize=24, color="black")
plt.ylabel('Distance', fontsize=24, color="black")
for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):
color_index = random.randint(0, 6)
if num % 3 == 0:
color = color[:2] + [color_elements[color_index]]
elif num % 3 == 1:
color = [color_elements[color_index]] + color[1:]
elif num % 3 == 2:
color = [color[0]] + [color_elements[color_index]] + [color[-1]]
if ("".join(color) != "FFFFFF") and \
(color not in colors):
c = '#' + "".join(color)
colors.append(color)
x = 0.5 * sum(i[1:3])
y = d[1]
if y > annotate_above:
plt.plot(x, y, color=c)
num += 1
if max_d:
plt.axhline(y=max_d, linewidth=3, color='orange')
return ddata
def get_colors(): colors=[] colors.append( 'blue' ) colors.append( 'green' ) colors.append( 'red' ) colors.append( 'cyan' ) colors.append( 'magenta' ) colors.append( 'yellow' ) return colors
def _generate_colors(self):
colors = ['blue', 'green', 'red', 'magenta', 'orange', 'black', 'brown', 'grey', 'olive'] # strong colors
for color in matplotlib.colors.cnames.keys():
if color not in colors and 'light' not in color:
colors.append(color)
return colors
def partiview(root="./", align="align/align_d_rms_t", poly="polyfit_d/fit"):
s = getOrbitStars(orbitFile="orbits_movie.dat", root=root, align=align, poly=poly)
# Determine the mass assuming a distance of 8.46 kpc
star0orb = s.stars[0].orbit
dist = 8460.0 # in parsec
axis = (star0orb.a / 1000.0) * dist # in au
mass = (axis) ** 3 / star0orb.p ** 2
print "Mass = %.2e Msun Dist = %4d pc" % (mass, dist)
names = s.getArray("name")
## Three sets of stars
## -- constrains Ro today
## -- constrains Ro/extended mass in future (radial velocities)
## -- constrains extended mass in future (no radial velocities)
# Assume the stars are in increasing order by name
stars1 = ["S0-2", "S0-16", "S0-19", "S0-1", "S0-20"]
stars2 = []
stars3 = []
yngstars = ["S0-1", "S0-2", "S0-3", "S0-4", "S0-5", "S0-7", "S0-8", "S0-16", "S0-19", "S0-20", "S0-26"]
oldstars = ["S0-6", "S0-18"]
# All stars3 come after S0-26
foundS026 = False
for name in names:
try:
idx = stars1.index(name)
continue
except ValueError:
if foundS026 == False:
stars2.append(name)
if name == "S0-26":
foundS026 = True
else:
stars3.append(name)
# Duration of the animation (30 years)
t = na.arange(1995.5, 1995.5 + 300.0, 0.1, type=na.Float)
xpos = na.zeros((len(s.stars), len(t)), type=na.Float)
ypos = na.zeros((len(s.stars), len(t)), type=na.Float)
zpos = na.zeros((len(s.stars), len(t)), type=na.Float)
colors = []
for ii in range(len(s.stars)):
star = s.stars[ii]
orb = star.orbit
print "Working on ", star.name
# Determine time steps
x = star.getArrayAllEpochs("x")
y = star.getArrayAllEpochs("y")
(r, v, a) = orb.kep2xyz(t, mass=mass, dist=dist)
xpos[ii] = r[:, 0]
ypos[ii] = r[:, 1]
zpos[ii] = r[:, 2]
# Determine color
if star.name in yngstars:
colors.append("b")
elif star.name in oldstars:
colors.append("r")
else:
colors.append("y")
_out = open("plots/partiview/partiview.dat", "w")
for ti in range(len(t)):
_out.write("%9.4f %d\n" % (t[ti], ti))
for si in range(len(s.stars)):
_out.write("%7.3f %7.3f %7.3f %s\n" % (xpos[si, ti], ypos[si, ti], zpos[si, ti], colors[si]))
_out.close()
for name_stepsize, group in grouped_stepsize:
fig, ax = plt.subplots(ncols=3, nrows=4, sharex=True, sharey=True, figsize=(8., 6.))
grouped_clearborder_method = group.groupby(['clearborder', 'method'])
ax[0, 0].set_ylim([0,0.65])
for name_clearborder_method, group2 in grouped_clearborder_method:
x_clearborder, y_method = name_clearborder_method
x, y = axes_clearborder[x_clearborder], axes_method[y_method]
table = group2.groupby(['lambda']).mean()
table = table.sort_index()
AJI = table['AJI']
lambda_val = [i for i in table.index]
colors = []
cmap = cm.get_cmap("gist_earth", len(lambda_val)+1) # PiYG
for i in range(cmap.N - 1):
rgb = cmap(i)[:3] # will return rgba, we take only first 3 so we get rgb
colors.append(matplotlib.colors.rgb2hex(rgb))
width = 1,
ax[x, y].bar(lambda_val, list(AJI), color=colors, alpha=1., width=width)
ax[x, y].axhline(y=np.max(AJI),xmin=0,xmax=3,c="black",ls="--",linewidth=0.5,zorder=0)
ax[x, y].text(7.5, np.max(AJI) + 0.01, "{0:.3}".format(np.max(AJI)), rotation=0)
for a, col in zip(ax[0], cols):
a.annotate(col, xy=(0.5, 1), xytext=(0, pad),
xycoords='axes fraction', textcoords='offset points',
ha='center', va='baseline')
for a, row in zip(ax[:,0], rows):
a.annotate(row, xy=(0, 0.5), xytext=(-a.yaxis.labelpad - pad, 0),
xycoords=a.yaxis.label, textcoords='offset points',
ha='right', va='center', rotation="vertical")
ax[0,-1].set_xticks(ticks=lambda_val)
ax[-2,1].set_xticks(ticks=lambda_val)
def prepare_lines(lines, scalars=None, do_connections=False, other=None):
"""Concatenate and standardize a list of lines
Args:
lines (list): Must be a list of 3xN or 4xN ndarrays of xyz(s)
data for N points along the line. N need not be the same
for all lines. Can alse be 6xN such that lines[:][3:, :]
are interpreted as rgb colors
scalars (sequence): can be one of::
- single hex color (ex, `#FD7400`)
- sequence of Nlines hex colors
- single rgb(a) tuple
- sequence of Nlines rgb(a) sequences
- sequence of N values that go with each vertex and will
be mapped with a colormap
- sequence of Nlines values that go each line and will
be mapped with a colormap
- Field. If `np.prod(fld.shape) in (N, nlines)` then the
field is interpreted as a simple sequence of values
(ie, the topology result from calc_streamlines for
coloring each line). Otherwise, the field is
interpolated onto each vertex.
do_connections (bool): Whether or not to make connections array
other (dict): a dictionary of other arrays that should be
reshaped and the like the same way scalars is
Returns:
(vertices, scalars, connections, other)
* vertices (ndarray): 3xN array of N xyz points. N is the sum
of the lengths of all the lines
* scalars (ndarray): N array of scalars, 3xN array of uint8
rgb values, 4xN array of uint8 rgba values, or None
* connections (ndarray): Nx2 array of ints (indices along
axis 1 of vertices) describing the forward and backward
connectedness of the lines, or None
* other (dict): a dict of N length arrays
Raises:
ValueError: If rgb data is not in a valid range or the shape
of scalars is not understood
"""
nlines = len(lines)
npts = [line.shape[1] for line in lines]
N = np.sum(npts)
first_idx = np.cumsum([0] + npts[:-1])
vertices = [np.asarray(line) for line in lines]
vertices = np.concatenate(lines, axis=1)
if vertices.dtype.kind not in 'fc':
vertices = np.asarray(vertices, dtype='f')
if vertices.shape[0] > 3:
if scalars is not None:
viscid.logger.warning("Overriding line scalars with scalars kwarg")
else:
scalars = vertices[3:, :]
vertices = vertices[:3, :]
if scalars is not None:
scalars_are_strings = False
if isinstance(scalars, viscid.field.Field):
if np.prod(scalars.shape) in (nlines, N):
scalars = np.asarray(scalars).reshape(-1)
else:
scalars = viscid.interp_trilin(scalars, vertices)
if scalars.size != N:
raise ValueError("Scalars was not a scalar field")
elif isinstance(scalars, (list, tuple, viscid.string_types, np.ndarray)):
# string types need some extra massaging
if any(isinstance(s, viscid.string_types) for s in scalars):
assert all(isinstance(s, viscid.string_types) for s in scalars)
scalars_are_strings = True
scalars = _string_colors_as_hex(scalars)
elif isinstance(scalars, np.ndarray):
scalars = scalars
else:
for i, si in enumerate(scalars):
if not isinstance(si, np.ndarray):
scalars[i] = np.asarray(si)
scalars[i] = np.atleast_2d(scalars[i])
try:
scalars = np.concatenate(scalars, axis=0)
except ValueError:
scalars = np.concatenate(scalars, axis=1)
scalars = np.atleast_2d(scalars)
if scalars.dtype == np.dtype('object'):
raise RuntimeError("Scalars must be numbers, tuples of numbers "
"that indicate rgb(a), or hex strings - they "
"must not be python objects")
if scalars.shape == (1, 1):
scalars = scalars.repeat(N, axis=1)
elif scalars.shape == (1, nlines) or scalars.shape == (nlines, 1):
# one scalar for each line, so broadcast it
scalars = scalars.reshape(nlines, 1)
scalars = [scalars[i].repeat(ni) for i, ni in enumerate(npts)]
scalars = np.concatenate(scalars, axis=0).reshape(1, N)
elif scalars.shape == (N, 1) or scalars.shape == (1, N):
# catch these so they're not interpreted as colors if
# nlines == 1 and N == 3; ie. 1 line with 3 points
scalars = scalars.reshape(1, N)
elif scalars.shape in [(3, nlines), (nlines, 3), (4, nlines), (nlines, 4)]:
# one rgb(a) color for each line, so broadcast it
if (scalars.shape in [(3, nlines), (4, nlines)] and
scalars.shape not in [(3, 3), (4, 4)]):
# the guard against shapes (3, 3) and (4, 4) mean that
# these square shapes are assumed Nlines x {3,4}
scalars = scalars.T
nccomps = scalars.shape[1] # 3 for rgb, 4 for rgba
colors = []
for i, ni in enumerate(npts):
c = scalars[i].reshape(nccomps, 1).repeat(ni, axis=1)
colors.append(c)
scalars = np.concatenate(colors, axis=1)
elif scalars.shape in [(3, N), (N, 3), (4, N), (N, 4)]:
# one rgb(a) color for each vertex
if (scalars.shape in [(3, N), (4, N)] and
scalars.shape not in [(3, 3), (4, 4)]):
# the guard against shapes (3, 3) and (4, 4) mean that
# these square shapes are assumed N x {3,4}
scalars = scalars.T
elif scalars.shape in [(1, 3), (3, 1), (1, 4), (4, 1)]:
# interpret a single rgb(a) color, and repeat/broadcast it
scalars = scalars.reshape([-1, 1]).repeat(N, axis=1)
else:
scalars = scalars.reshape(-1, N)
# scalars now has shape (1, N), (3, N), or (4, N)
if scalars_are_strings:
# translate hex colors (#ff00ff) into rgb(a) values
scalars = np.char.lstrip(scalars, '#')
strlens = np.char.str_len(scalars)
min_strlen, max_strlen = np.min(strlens), np.max(strlens)
if min_strlen == max_strlen == 8:
# 32-bit rgba (two hex chars per channel)
scalars = _hexchar2int(scalars.astype('S8')).reshape(-1, 4).T
elif min_strlen == max_strlen == 6:
# 24-bit rgb (two hex chars per channel)
scalars = _hexchar2int(scalars.astype('S6')).reshape(-1, 3).T
else:
raise NotImplementedError("This should never happen as "
"scalars as colors should already "
"be preprocessed appropriately")
elif scalars.shape[0] == 1:
# normal scalars, cast them down to a single dimension
scalars = scalars.reshape(-1)
elif scalars.shape[0] in (3, 4):
# The scalars encode rgb data, standardize the result to a
# 3xN or 4xN ndarray of 1 byte unsigned ints [0..255]
if np.all(scalars >= 0) and np.all(scalars <= 1):
scalars = (255 * scalars).round().astype('u1')
elif np.all(scalars >= 0) and np.all(scalars < 256):
scalars = scalars.round().astype('u1')
else:
raise ValueError("Rgb data should be in range [0, 1] or "
"[0, 255], range given is [{0}, {1}]"
"".format(np.min(scalars), np.max(scalars)))
else:
raise ValueError("Scalars should either be a number, or set of "
"rgb values, shape is {0}".format(scalars.shape))
# scalars should now have shape (N, ) or be a uint8 array with shape
# (3, N) or (4, N) encoding an rgb(a) color for each point [0..255]
# ... done with scalars...
# broadcast / reshape additional arrays given in other
if other:
for key, arr in other.items():
if arr is None:
pass
elif arr.shape == (1, nlines) or arr.shape == (nlines, 1):
arr = arr.reshape(nlines, 1)
arr = [arr[i].repeat(ni) for i, ni in enumerate(npts)]
other[key] = np.concatenate(arr, axis=0).reshape(1, N)
else:
try:
other[key] = arr.reshape(-1, N)
except ValueError:
viscid.logger.warning("Unknown dimension, dropping array {0}"
"".format(key))
if do_connections:
connections = [None] * nlines
for i, ni in enumerate(npts):
# i0 is the index of the first point of the i'th line in lines
i0 = first_idx[i]
connections[i] = np.vstack([np.arange(i0, i0 + ni - 1.5),
np.arange(i0 + 1, i0 + ni - 0.5)]).T
connections = np.concatenate(connections, axis=0).astype('i')
else:
connections = None
return vertices, scalars, connections, other
def partiview(root='./', align='align/align_d_rms_t', poly='polyfit_d/fit'):
s = getOrbitStars(orbitFile='orbits_movie.dat',
root=root, align=align, poly=poly)
# Determine the mass assuming a distance of 8.46 kpc
star0orb = s.stars[0].orbit
dist = 8460.0 # in parsec
axis = (star0orb.a / 1000.0) * dist # in au
mass = (axis)**3 / star0orb.p**2
print 'Mass = %.2e Msun Dist = %4d pc' % (mass, dist)
names = s.getArray('name')
## Three sets of stars
## -- constrains Ro today
## -- constrains Ro/extended mass in future (radial velocities)
## -- constrains extended mass in future (no radial velocities)
# Assume the stars are in increasing order by name
stars1 = ['S0-2', 'S0-16', 'S0-19', 'S0-1', 'S0-20']
stars2 = []
stars3 = []
yngstars = ['S0-1', 'S0-2', 'S0-3', 'S0-4', 'S0-5', 'S0-7', 'S0-8',
'S0-16', 'S0-19', 'S0-20', 'S0-26']
oldstars = ['S0-6', 'S0-18']
# All stars3 come after S0-26
foundS026 = False
for name in names:
try:
idx = stars1.index(name)
continue
except ValueError:
if foundS026 == False:
stars2.append(name)
if name == 'S0-26':
foundS026 = True
else:
stars3.append(name)
# Duration of the animation (30 years)
t = na.arange(1995.5, 1995.5 + 300.0, 0.1, type=na.Float)
xpos = na.zeros((len(s.stars), len(t)), type=na.Float)
ypos = na.zeros((len(s.stars), len(t)), type=na.Float)
zpos = na.zeros((len(s.stars), len(t)), type=na.Float)
colors = []
for ii in range(len(s.stars)):
star = s.stars[ii]
orb = star.orbit
print 'Working on ', star.name
# Determine time steps
x = star.getArrayAllEpochs('x')
y = star.getArrayAllEpochs('y')
(r, v, a) = orb.kep2xyz(t, mass=mass, dist=dist)
xpos[ii] = r[:,0]
ypos[ii] = r[:,1]
zpos[ii] = r[:,2]
# Determine color
if (star.name in yngstars):
colors.append('b')
elif (star.name in oldstars):
colors.append('r')
else:
colors.append('y')
_out = open('plots/partiview/partiview.dat', 'w')
for ti in range(len(t)):
_out.write('%9.4f %d\n' % (t[ti], ti))
for si in range(len(s.stars)):
_out.write('%7.3f %7.3f %7.3f %s\n' %
(xpos[si,ti], ypos[si,ti], zpos[si,ti], colors[si]))
_out.close()
txtFilterSize = 'No. '
elif filterSize > 0 and filterSize < 10:
txtFilterSize = str(filterSize) + ' . '
else:
txtFilterSize = str(filterSize) + '. '
txtLegend = 'Zeros = ' + (fmt1 % zerosDBZ) + ' dBZ, filter = ' + txtFilterSize \
+ r'$\beta_1$ = ' + (fmt2 % beta1) + r', $\beta_2$ = ' + (fmt2 % beta2) \
+ ', break at ' + str(scalingBreak_best) + ' km'
# Draw spectrum
ax1.plot(10*np.log10(freq),10*np.log10(psd1d), color=colorLine, linestyle = lineStyle, label=txtLegend)
else:
print('You can only plot the 1d spectrum with this script...')
sys.exit(1)
colors.append(colorLine)
colorLine = np.array(colorLine) + 1/len(zerosDBZlist)
# Set colorbar stochastic realizations
fig_noise.subplots_adjust(right=0.8)
cbar_ax = fig_noise.add_axes([0.85, 0.15, 0.05, 0.7])
fig_noise.colorbar(im_noise, cax=cbar_ax)
# Generate legend for spectra
legend = ax1.legend(loc='lower left', fontsize=9, labelspacing=0.1)
# for text in legend.get_texts():
# text.set_color("red")
for color,text in zip(colors,legend.get_texts()):
text.set_color(color)
# Write betas and correlations
elif clusterAlgLabel == 'AffinityPropagation':
original_shape = som.codebook.shape
som.codebook.shape = (som._n_columns*som._n_rows, som.n_dim)
init = -np.max(distance.pdist(som.codebook, 'euclidean'))
som.codebook.shape = original_shape
algorithm = clusterAlgs.AffinityPropagation(preference = init,damping = 0.9)
elif clusterAlgLabel == 'KMeans8':
algorithm = None
print('Clustering algorithm employed: %s' %clusterAlgLabel)
som.cluster(algorithm=algorithm)
'''----------------------------------------------------'''
colors = []
for bm in som.bmus:
colors.append(som.clusters[bm[1], bm[0]])
areas = [200]*len(som.bmus)
xDimension, yDimension = [], []
for x in som.bmus:
xDimension.append(x[0])
yDimension.append(x[1])
if not os.path.exists(figWritePath+'/'+clusterAlgLabel+'Clusters/'+SOMdimensionsString):
os.makedirs(figWritePath+'/'+clusterAlgLabel+'Clusters/'+SOMdimensionsString)
fig, ax = plt.subplots()
colMap = 'Spectral_r'
plt.imshow(som.umatrix,cmap = colMap, aspect = 'auto')
ax.scatter(xDimension,yDimension,c=colors,s=areas)
doneLabs = set([''])
for label, x, y in zip(labels, xDimension, yDimension):
for i in range(0,nB):
name += 'B'
gap = 0
for dct in con.select(comb_A=comb_A, comb_B=comb_B, sequence=name):
gap = dct.gllbsc_gamma_gap
vect.append(gap)
gaps.append(gap)
vect.append(gap)
matrix.append(np.array(vect))
matrix.append(np.array(vect))
colors = []
for i in gaps:
c = cm.jet(int(float(i)/float(max(gaps))*255))
for j in range(6):
colors.append([c[0],c[1],c[2]])
x = np.array(range(1, 7), float)
y = x.copy()
xpos, ypos = np.meshgrid(x, y)
z = np.array(matrix)
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros_like(xpos)
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = z.flatten()
fig = plt.figure()
ax = Axes3D(fig)
plt.xlabel('nB, B = '+comb_B)
plt.ylabel('nA, A = '+comb_A)
