def PlotDegreeDistribution(G):
N = len(G.nodes())
nodesDegrees = [G.degree()[i] for i in G.nodes()]
mean_degree = sum(nodesDegrees)/len(nodesDegrees)
# You typically want your plot to be ~1.33x wider than tall.
# Common sizes: (10, 7.5) and (12, 9)
plt.figure(figsize=(12, 9))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel("Degree", fontsize=16)
plt.ylabel("Frequency", fontsize=16)
plt.hist(nodesDegrees, color="#3F5D7D") #, bins = [x for x in range(max(nodesDegrees))]) #, bins=100)
xRange = range(max(nodesDegrees))
h = plt.plot(xRange, [m.e**(-mean_degree)*(mean_degree**x)*N/m.factorial(x) for x in xRange], lw=2)
mpld3.show();
def plot(self, notebook=False, colormap='polar', scale=1, maptype='points', show=True, savename=None):
# make a spatial map based on the scores
fig = pyplot.figure(figsize=(12, 5))
ax1 = pyplot.subplot2grid((2, 3), (0, 1), colspan=2, rowspan=2)
if maptype is 'points':
ax1, h1 = pointmap(self.scores, colormap=colormap, scale=scale, ax=ax1)
elif maptype is 'image':
ax1, h1 = imagemap(self.scores, colormap=colormap, scale=scale, ax=ax1)
fig.add_axes(ax1)
# make a scatter plot of sampled scores
ax2 = pyplot.subplot2grid((2, 3), (1, 0))
ax2, h2, samples = scatter(self.scores, colormap=colormap, scale=scale, thresh=0.01, nsamples=1000, ax=ax2, store=True)
fig.add_axes(ax2)
# make the line plot of reconstructions from principal components for the same samples
ax3 = pyplot.subplot2grid((2, 3), (0, 0))
ax3, h3, linedata = tsrecon(self.comps, samples, ax=ax3)
plugins.connect(fig, LinkedView(h2, h3[0], linedata))
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
def show_plots(plotengine, ax=None, output_dir=None):
if plotengine == 'mpld3':
import mpld3
mpld3.show()
elif plotengine == 'matplotlib':
import matplotlib.pyplot as plt
if not output_dir: # None or ""
plt.show()
else:
for fi in plt.get_fignums():
plt.figure(fi)
fig_name = getattr(plt.figure(fi), 'name', 'figure%d' % fi)
fig_path = op.join(output_dir, '%s.png' % fig_name)
if not op.exists(op.dirname(fig_path)):
os.makedirs(op.dirname(fig_path))
plt.savefig(fig_path)
plt.close()
elif plotengine in ['bokeh', 'bokeh-silent']:
import bokeh.plotting
import tempfile
output_dir = output_dir or tempfile.mkdtemp()
output_name = getattr(ax, 'name', ax.title).replace(':', '-')
output_file = op.join(output_dir, '%s.html' % output_name)
if not op.exists(output_dir):
os.makedirs(output_dir)
if op.exists(output_file):
os.remove(output_file)
bokeh.plotting.output_file(output_file, title=ax.title, mode='inline')
if plotengine == 'bokeh':
bokeh.plotting.show(ax)
def showNetwork(struct, weights, labels):
nbLayers = len(struct)
networkXY = []
colors = ['b', 'r']
for layer in range(nbLayers):
layerXY = []
if layer != 4:
sumWeightsNeuron = np.sum(np.abs(weights['layer_' + str(layer)]['param_0']), axis=1)
maxSumWeights = np.max(sumWeightsNeuron)
for neuron in range(struct[layer]):
neuronXY = (layer*10, neuron-(struct[layer]-1)/2.)
if layer != 4 :
inputScatters = plt.scatter(neuronXY[0],neuronXY[1], alpha=(sumWeightsNeuron[neuron]/maxSumWeights)**2)
else :
inputScatters = plt.scatter(neuronXY[0],neuronXY[1], alpha=1)
layerXY.append(neuronXY)
networkXY.append(layerXY)
tooltip = mpld3.plugins.PointLabelTooltip(inputScatters, labels=labels)
if layer != 0:
print(weights['layer_' + str(layer-1)]['param_0'].value)
maxWeights = np.amax(np.abs(weights['layer_' + str(layer-1)]['param_0']))
for neuronLayer in range(struct[layer]):
for neuronLayerP in range(struct[layer-1]):
print(layer, neuronLayer, neuronLayerP, maxWeights)
plt.plot([networkXY[layer][neuronLayer][0],networkXY[layer-1][neuronLayerP][0]],
[networkXY[layer][neuronLayer][1],networkXY[layer-1][neuronLayerP][1]],
#alpha=1-np.exp(-((weights['layer_' + str(layer-1)]['param_0'][neuronLayerP][neuronLayer])/3)**2)
alpha = (weights['layer_' + str(layer-1)]['param_0'][neuronLayerP][neuronLayer] / maxWeights)**2,
c = colors[int(weights['layer_' + str(layer-1)]['param_0'][neuronLayerP][neuronLayer] > 0)])
mpld3.show()
def plot(self, data, notebook=False, show=True, savename=None):
fig = pyplot.figure()
ncenters = len(self.centers)
colorizer = Colorize()
colorizer.get = lambda x: self.colors[int(self.predict(x)[0])]
# plot time series of each center
# TODO move into a time series plotting function in viz.plots
for i, center in enumerate(self.centers):
ax = pyplot.subplot2grid((ncenters, 3), (i, 0))
ax.plot(center, color=self.colors[i], linewidth=5)
fig.add_axes(ax)
# make a scatter plot of the data
ax2 = pyplot.subplot2grid((ncenters, 3), (0, 1), rowspan=ncenters, colspan=2)
ax2, h2 = scatter(data, colormap=colorizer, ax=ax2)
fig.add_axes(ax2)
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
def graphme(self, pngfilename="my_sample_png.png"):
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import mpld3
import datetime
""" creating background info"""
# create a plot with as may subplots as you choose
fig, ax = plt.subplots()
# add a grid to the background
ax.grid(True, alpha = 0.2)
# the x axis contains date
fig.autofmt_xdate()
# the dates are year, month
ax.fmt_xdata = mdates.DateFormatter('%Y-%m')
if self.table not in ['MS04314', 'MS00114', 'MS04334','MS04315','MS00115']:
final_glitch = self.decide()
dates = sorted(final_glitch.keys())
dates2 = [x for x in dates if final_glitch[x]['mean'] != None and final_glitch[x]['mean'] != "None"]
vals = [final_glitch[x]['mean'] for x in dates2]
glitched_values = ax.plot(dates2, vals, 'b-')
ax.legend(loc=4)
ax.set_xlabel("dates")
ax.set_ylabel("values")
mpld3.show()
mpld3.save_html(fig, 'my_output_html.html')
import pylab
pylab.savefig(pngfilename)
def after(self):
if self.draw:
plugins.connect(
self.fig, plugins.InteractiveLegendPlugin(
self.s1, self.labels, ax=self.ax))
mpld3.show()
else:
pass
def makeFig():
plt.ylim(20,80)
plt.title('Temperature Streaming')
plt.grid(True)
# plt.ylable('Temp C')
plt.plot(tempC, 'ro-',label='Degree C')
plt.legend(loc='upper left')
# pyplot.show_bokeh(plt.gcf(), filename="mpltest.html")
# plotting.session().dumpjson(file="mpltest.json")
mpld3.show()
def do_show(self):
#debug
self.debug(
[
'We show here',
'first network'
]
)
#/##################/#
# First network
#
#network all the view things
self.network(
[
'Views',
'Panels',
'Axes',
'Plots'
],
_DoStr='Show'
)
#/##################/#
# Then show the figure
#
#debug
'''
self.debug(
[
'We show with which device',
('self.',self,['ShowingQtBool'])
]
)
'''
#Check
if self.ShowingQtBool:
#import
from matplotlib import pyplot
#show
pyplot.show()
if self.ShowingMpld3Bool:
#import
import mpld3
#show
mpld3.show()
def test_interactive_shallowPP(save_to_html=False):
# Define left and right state (h,hu)
ql = np.array([3.0, 5.0])
qr = np.array([3.0, -5.0])
# Define optional parameters (otherwise chooses default values)
plotopts = {'g':1.0, 'time':2.0, 'tmax':5, 'hmax':10, 'humin':-15, 'humax':15}
# Call interactive function (can be called without any argument)
pt = shallow_water(ql,qr,**plotopts)
if save_to_html:
mpld3.save_html(pt, "test_shallow.html")
mpld3.show()
def plotter(filelist, entlist, fsizelist):
# gets the file and entropy lists and plots the data to a neat line graph
xtimes = [datetime.datetime.strptime(str(int(times)), '%H%M%S') for times in filelist]
plt.plot(fsizelist, entlist, marker='o', color='green')
plt.plot(xtimes, entlist, marker='o')
plt.xlabel('Time')
plt.ylabel('Entropy')
plt.title('Entropy over time for date')
plt.show()
mpld3.show()
return
def main(file_name='Greenland1km.nc'):
'''Description'''
# Set up the file and projection
data = os.path.dirname(os.path.abspath(__file__)) + os.sep + '..' + os.sep \
+ 'data' + os.sep + file_name
proj_file = pyproj.Proj('+proj=stere +ellps=WGS84 +datum=WGS84 +lat_ts=71.0 +lat_0=90 ' \
+ '+lon_0=321.0 +k_0=1.0')
proj_lat_long = pyproj.Proj('+proj=latlong +ellps=WGS84 +datum=WGS84')
fig, ax = plt.subplots(1,2)
# Open up the file and grab the data we want out of it
greenland = Dataset(data)
x = greenland.variables['x'][:]
y = greenland.variables['y'][:]
nx = x.shape[0]
ny = y.shape[0]
y_grid, x_grid = scipy.meshgrid(y[:], x[:], indexing='ij')
thk = greenland.variables['thk'][0]
bheatflx = greenland.variables['bheatflx'][0]
# Now transform the coordinates to the correct lats and lons
lon, lat = pyproj.transform(proj_file, proj_lat_long, x_grid.flatten(), y_grid.flatten())
lat = lat.reshape(ny,nx)
lon = lon.reshape(ny,nx)
# Put thickness in a basemap
mapThk = Basemap(projection='stere',lat_0=65, lon_0=-25,\
llcrnrlat=55,urcrnrlat=85,\
llcrnrlon=-50,urcrnrlon=0,\
rsphere=6371200.,resolution='l',area_thresh=10000, ax=ax[0])
mapThk.drawcoastlines(linewidth=0.25)
mapThk.fillcontinents(color='grey')
mapThk.drawmeridians(np.arange(0,360,30))
mapThk.drawparallels(np.arange(-90,90,30))
x, y = mapThk(lon,lat)
cs = mapThk.contour(x, y, thk, 3)
# Put basal heat flux in a basemap
mapFlx = Basemap(projection='stere',lat_0=65, lon_0=-25,\
llcrnrlat=55,urcrnrlat=85,\
llcrnrlon=-50,urcrnrlon=0,\
rsphere=6371200.,resolution='l',area_thresh=10000, ax=ax[1])
mapFlx.drawcoastlines(linewidth=0.25)
mapFlx.fillcontinents(color='grey')
mapFlx.drawmeridians(np.arange(0,360,30))
mapFlx.drawparallels(np.arange(-90,90,30))
x, y = mapFlx(lon,lat)
cs = mapFlx.contour(x, y, bheatflx, 3)
plugins.connect(fig, ClickInfo(cs))
mpld3.show()
def plot_engine_timing(self):
"""
"""
#Import seaborn to prettify the graphs if possible
try:
import seaborn
except:
pass
try:
import matplotlib.pyplot as plt
width = 0.35
s = self.timing['engines'].values()
names = self.timing['engines'].keys()
plt_axes = []
plt_axes_offset = []
for i, n in enumerate(names):
plt_axes.append(i)
plt_axes_offset.append(i + 0.15)
fig, ax = plt.subplots()
rects1 = ax.bar(plt_axes, s, width, color='r')
ax.set_xticks(plt_axes_offset)
ax.set_xticklabels(list(names))
ax.set_ylabel('Time')
ax.set_xlabel('Engine')
plt.title('Timing')
try:
import mpld3
i = 0
for r in rects1:
tooltip = mpld3.plugins.LineLabelTooltip(r, label=names[i])
mpld3.plugins.connect(fig, tooltip)
i = i + 1
mpld3.show()
except Exception as e:
logging.exception(e)
logging.warn("For tooltips, install mpld3 (pip install mpld3)")
plt.show(block=True)
except ImportError:
logging.critical("Cannot plot. Please ensure matplotlib "
"and networkx are installed.")
def test_interactive_linearPP(save_to_html=False):
## Define left and right state
ql = np.array([-2.0, 2.0])
qr = np.array([0.0, -3.0])
# Define two eigenvectors and eigenvalues (acoustics)
zz = 2.0
rho0 = 1.0
r1 = np.array([zz,1.0])
r2 = np.array([-zz,1.0])
lam1 = zz/rho0
lam2 = -zz/rho0
plotopts={'q1min':-5, 'q1max':5, 'q2min':-5, 'q2max':5, 'domain':5, 'time':1,
'title1':"Pressure", 'title2':"Velocity"}
pt = linear_phase_plane(ql,qr,r1,r2,lam1,lam2,**plotopts)
if save_to_html:
mpld3.save_html(pt, "test_linearPP.html")
mpld3.show()
def main():
# Open the eigenworms file
features_path = os.path.dirname(mv.features.__file__)
eigenworm_path = os.path.join(features_path, mv.config.EIGENWORM_FILE)
eigenworm_file = h5py.File(eigenworm_path, 'r')
# Extract the data
eigenworms = eigenworm_file["eigenWorms"].value
eigenworm_file.close()
# Print the shape of eigenworm matrix
print(np.shape(eigenworms))
# Plot the eigenworms
for eigenworm_i in range(np.shape(eigenworms)[1]):
plt.plot(eigenworms[:,eigenworm_i])
mpld3.show()
def plot_d3js():
# Define some CSS to control our custom labels
css = """
table
{
border-collapse: collapse;
}
th
{
color: #ffffff;
background-color: #000000;
}
td
{
background-color: #cccccc;
}
table, th, td
{
font-family:Arial, Helvetica, sans-serif;
border: 1px solid black;
text-align: right;
}
"""
fig, ax = plt.subplots()
ax.grid(True, alpha=0.3)
labels = suburb_list
x = coords[:, 0]
y = coords[:, 1]
points = ax.plot(x, y, 'o', color='b',
mec='k', ms=15, mew=1, alpha=.6)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Ethnicity', size=20)
tooltip = plugins.PointHTMLTooltip(points[0], labels,
voffset=10, hoffset=10, css=css)
plugins.connect(fig, tooltip)
mpld3.show()
def plot_team(sorted_team, sorted_teamgames):
fig = plt.figure(figsize=(12, 12))
#ax = fig.add_axes([0.15, 0.1, 0.7, 0.7])
grid = ImageGrid(fig,
(0.2, 0.15, 0.8, 0.8),
#111,
nrows_ncols=(1, 1),
direction='row', axes_pad=0.05, add_all=True,
label_mode='1', share_all=False,
cbar_location='right', cbar_mode='single',
cbar_size='5%', cbar_pad=0.05)
ax = grid[0]
ax.set_title('Game lead (each team is a column)', fontsize=20)
ax.tick_params(axis='both', direction='out', labelsize=12)
#im = ax.imshow(df.values, interpolation='nearest', vmax=df.max().max(),
# vmin=df.min().min(), cmap='RdBu')
im = ax.imshow(sorted_teamgames.values, interpolation='nearest', vmax=120, vmin=-120, cmap='RdBu')
#colorbar
ax.cax.colorbar(im)
ax.cax.tick_params(labelsize=12)
ax.set_xticks(np.arange(sorted_teamgames.shape[1]))
ax.set_xticklabels(sorted_team, rotation='vertical', fontweight='bold')
ax.set_yticks(np.arange(sorted_teamgames.shape[0]))
ax.set_yticklabels(sorted_teamgames.index)
ax.set_ylabel("Sorted game", size=16)
#plt.show()
plotid = mpld3.utils.get_id(ax)
print sorted_team
ax_ori = RotateTick(0, sorted_team.tolist(), -90, 1,1)
mpld3.plugins.connect(fig, ax_ori)
mpld3.show()
fightml = mpld3.fig_to_html(fig)
return plotid, fightml
def make_optional_graphs(wd):
""" A function that can be called to graph the difference method against the ratio method if necessary.
Do not need in production, but used for testing on 10-1-2015 to see if we can get to the values working as Don expects
:wd: the dictionary containing adjustments etc used to figure out the final csv
"""
if wd != {} and wd[wd.keys()[0]]['adj_diff'] != None and wd[wd.keys()[0]]['adj_rat'] != None:
datelist = [x for x in sorted(wd.keys()) if wd[x]['adj_rat'] != None and wd[x]['adj_diff'] != None]
val_diff = [wd[x]['adj_diff'] for x in datelist]
val_rat = [wd[x]['adj_rat'] for x in datelist]
fig, ax = plt.subplots()
fig.autofmt_xdate()
ax.fmt_xdata = mdates.DateFormatter('%Y-%m')
ax.plot(datelist, val_diff, color = 'blue', linewidth= 1.2, alpha = 0.5, label = 'diff method')
ax.plot(datelist, val_rat, color = 'red', linewidth= 0.7, label = 'ratio method')
#ax.scatter(mainte_dates, maintes, s=30, c='red', alpha = 0.4, label='MAINTE')
ax.legend(loc = 1)
mpld3.show()
def plot_user_sep(user_data, title=None, smoothing=4):
'''
for a user vector of shape (plans, timesteps)
generate a plot over time for each plan. If a title is provided
the value of the sorting criterion will be included in it
:param user_data:
:param title:
:param smoothing: size of movind average smoothing
:return:
'''
fig, pltgrid = plt.subplots(5, 2, figsize=(15, 15))
fig.suptitle(title or "title", fontsize=20)
criterion = eval_stats_row(user_data)
for i in range(np.shape(user_data)[0]):
pltgrid[i // 2, i % 2].plot(moving_average(user_data[i, :], smoothing))
row = i // 2
col = i % 2
title2 = str(row) + "," + str(col) + " "
if title is not None:
title2 = title2 + title + " : " + str(criterion[i])
pltgrid[i // 2, i % 2].set_title(title2)
#pltgrid[i//2, i%2].set_title(cf.SORTING_CRIT + " " + str(criterion[indeces[i]]))
fig.subplots_adjust(hspace=1.3)
mpld3.show()
def fig_show(self, fig):
"""
Opens the figure in a web browser
Parameters
----------
fig : Matplotlib Figure
the input Matplotlib Figure
Returns
-------
browser containing the figure
"""
return mpld3.show(fig)
def graphme(self, pngfilename="my_sample_png.png"):
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import mpld3
import datetime
""" creating background info"""
# create a plot with as may subplots as you choose
fig, ax = plt.subplots()
# add a grid to the background
ax.grid(True, alpha=0.2)
# the x axis contains date
fig.autofmt_xdate()
# the dates are year, month
ax.fmt_xdata = mdates.DateFormatter('%Y-%m')
if self.table not in [
'MS04314', 'MS00114', 'MS04334', 'MS04315', 'MS00115'
]:
final_glitch = self.decide()
dates = sorted(final_glitch.keys())
dates2 = [
x for x in dates if final_glitch[x]['mean'] != None
and final_glitch[x]['mean'] != "None"
]
vals = [final_glitch[x]['mean'] for x in dates2]
glitched_values = ax.plot(dates2, vals, 'b-')
ax.legend(loc=4)
ax.set_xlabel("dates")
ax.set_ylabel("values")
mpld3.show()
mpld3.save_html(fig, 'my_output_html.html')
import pylab
pylab.savefig(pngfilename)
def draw(ins, date, result):
path = cfg.path[AShare().get_type(ins)]
fn = '{path}/{date}/tickab_{stock}.{date}'.format(path=path,
date=date,
stock=ins)
with open(fn, 'rb') as f:
df = get_tickab(f)
deals = pd.DataFrame(result['deals'])
deals['nt'] = deals['dt'].apply(lambda x: ((
(x.hour * 100 + x.minute) * 100) + x.second) * 1000).astype('int32')
deals = deals.set_index('nt')
deals = deals.reindex(df.nTime.unique(), method='backfill',
limit=1).dropna()
df = df.join(deals, on='nTime')
t = df['nTime']
x = list(df.index)
p = df['nPrice']
b = df['avg_price'].mask(df['bsflag'] != 'buy', None).dropna()
s = df['avg_price'].mask(df['bsflag'] != 'sell', None).dropna()
plt.figure(figsize=(20, 10), dpi=80)
plt.xticks(
x[::600] + x[-1:],
['%d:%02d' % (i / 10000000, i / 100000 % 100)
for i in t[::600]] + ['15:00'])
plt.yticks(np.linspace(min(p), max(p), 10, endpoint=True))
plt.ylim(min(p), max(p))
plt.grid(True, 'major', 'x')
plt.scatter(list(b.index), b, 50, color='green')
plt.scatter(list(s.index), s, 50, color='red')
plt.plot(x, p)
mpld3.show(ip='192.168.128.21', port=8887)
def plot_with_tsne(labels, embeddings, display_hover=True):
"""
expects a list of email_ids and numpy ndarray of embeddings. The numpy ndarray should have shape L,D where D is the
size of embeddings and L is the number of users
"""
tsne = TSNE(verbose=1, method='exact')
start = time.time()
tsne_embs = tsne.fit_transform(embeddings)
end = time.time()
print('time taken by TSNE ', (end - start))
# creating the colors
colors = list(sb.color_palette().as_hex())
color_list = _assign_labels_colors(labels, colors)
fig, ax = plt.subplots()
scatter = ax.scatter(tsne_embs[:, 0], tsne_embs[:, 1], c=color_list, s=75)
if display_hover:
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
else:
outfile = '../outputs/' + constants.RUN_ID + '_tsne-users.png'
plt.savefig(outfile)
def graph_func():
columns = defaultdict(list)
with open('data.csv', 'r', encoding='utf8') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
for i in range(len(row)):
columns[i].append(row[i])
columns = dict(columns)
# print(columns)
allWords = []
for comment in columns[1]:
words = comment.split()
for word in words:
allWords.append(word)
# print(word)
allWords = map(str.lower, allWords)
counter = collections.Counter(allWords)
# print(counter)
# print(counter.keys())
# print(counter.values())
x = list(counter.keys())[0:10]
y = list(counter.values())[0:10]
plt.title("Top 10 Words vs #times used")
plt.xlabel("words")
plt.ylabel("#times")
plt.bar(x, y)
mpld3.show()
ax.plot((0, pix.shape[1]), (y0, y1), '-r')
# plot rects kmeans result
for x in xs:
ax.plot((x, x), (0, pix.shape[0]), '-b')
for y in ys:
ax.plot((0, pix.shape[1]), (y, y), '-b')
# plot nodes found by clustering
ax.plot(nodes_coords[:, 0], nodes_coords[:, 1], 'ro')
# plot corridors nodes found by dotsKmeans
for (x, y) in cluster_coords:
ax.plot(x, y, 'g^')
# plot whole gragh
for i, (x, y) in enumerate(graph_coords):
plotted = []
for j in g_whole[i]:
xj, yj = graph_coords[j]
ax.plot((x, xj), (y, yj), '--y')
ax.set_xlim((0, pix.shape[1]))
ax.set_ylim((pix.shape[0], 0))
ax.set_axis_off()
ax.imshow(pix, cmap=plt.cm.gray)
ax.set_title('Input image')
plt.tight_layout()
mpld3.show(open_browser=False, ip='0.0.0.0', port=8000)
def run(offset, window):
with open('record.json') as fd:
data = json.load(fd, object_pairs_hook=OrderedDict)
data = OrderedDict(reversed(list(data.items())))
# Fixing random state for reproducibility
np.random.seed(0)
plt.rcdefaults()
fig, ax = plt.subplots()
# team member names
team = [data[x]['info']['nick_name'] for x in data]
y_pos = np.arange(len(team))
# create 2-D array of tasks
tasks = [data[x]['tasks'] for x in data]
max_len = max([len(t) for t in tasks])
min_date = min(
[data[x]['tasks'][0]['start'] for x in data if data[x]['tasks']])
null_task = {
'name': '',
'brief': '',
'debrief': '',
'start': min_date,
'end': min_date,
'target': min_date
}
tasks = [t + [null_task] * (max_len - len(t)) for t in tasks]
layers = list(map(list, zip(*tasks)))
bars = []
for layer in layers:
start = [
datetime.strptime(x['start'], '%Y-%m-%d').date() for x in layer
]
target = [
datetime.strptime(x['target'], '%Y-%m-%d').date() for x in layer
]
end = [
datetime.strptime(x['end'], '%Y-%m-%d').date()
if x['end'] else datetime.strptime(x['start'], '%Y-%m-%d').date()
for x in layer
]
left = [(x - date.today()).days for x in start]
target_width = [(x[1] - x[0]).days for x in zip(start, target)]
actual_width = [(x[1] - x[0]).days for x in zip(start, end)]
ax.barh(
y_pos,
target_width,
left=left,
edgecolor='white',
linewidth=2,
color='#85aa00',
height=0.6,
)
ax.barh(
y_pos,
actual_width,
left=left,
edgecolor='white',
linewidth=2,
color='#3b3b3b',
height=0.3,
)
for i, task in enumerate(layer):
ax.text(
left[i] + 1,
i + 0.05,
task['name'],
color='white',
size=8,
fontweight='bold',
)
# transparent bar for hovering
bars.append(
ax.barh(
y_pos,
target_width,
left=left,
linewidth=2,
height=0.6,
zorder=1000,
alpha=0.0,
))
ax.set_yticks(y_pos)
ax.set_yticklabels(team)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_xlim(
[offset - PROPORTION * window, offset + (1 - PROPORTION) * window])
def make_text(task):
text = """
<div style="background-color:#ffffff; padding:10px; border-style:solid; border-width:1px; border-color:#3b3b3b">
<h6 style="font-size:10; color:#9b9b9b">{}</h6>
<p style="font-size:10; color:#9b9b9b"><strong>Brief:</strong> {}</p>
<div>
""".format(task['name'], task['brief'])
# if task['debrief']:
# text += '\n\n' + textwrap.fill('Debrief: ' + task['debrief'])
return text
for i, layer in enumerate(bars):
for j, bar in enumerate(layer):
tooltip = mpld3.plugins.PointHTMLTooltip(
bar,
[make_text(layers[i][j])],
voffset=10,
hoffset=10,
)
mpld3.plugins.connect(fig, tooltip)
plt.axvline(
x=-10,
linewidth=1,
linestyle='dashed',
color='#3b3b3b',
zorder=-1,
)
mpld3.show(ip='0.0.0.0', open_browser=False)
def plot_2d_graph(self, data_frame, gp, type):
matplotlib.rcdefaults()
# set the matplotlib style sheet
plt.style.use(Constants().plotfactory_pythalesians_style_sheet[Constants().plotfactory_default_stylesheet])
if hasattr(gp, 'style_sheet'):
plt.style.use(Constants().plotfactory_pythalesians_style_sheet[gp.style_sheet])
scale_factor = Constants().plotfactory_scale_factor
if hasattr(gp, 'scale_factor'): scale_factor = gp.scale_factor
dpi = Constants().plotfactory_dpi
if hasattr(gp, 'dpi'): dpi = gp.dpi
width = Constants().plotfactory_width; height = Constants().plotfactory_height
if hasattr(gp, 'width'): width = gp.width
if hasattr(gp, 'height'): width = gp.height
fig = plt.figure(figsize = ((width * scale_factor)/dpi, (height * scale_factor)/dpi), dpi = dpi)
# add a subplot
ax = fig.add_subplot(111)
matplotlib.rcParams.update({'font.size': matplotlib.rcParams['font.size'] * scale_factor})
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset = False)
ax.yaxis.set_major_formatter(y_formatter)
y_axis_2_series = []
ax2 = []
color_2_series = []
linewidth_2_series = []
if hasattr(gp, 'resample'):
data_frame = data_frame.asfreq(gp.resample)
# create a second y axis if necessary
if hasattr(gp, 'y_axis_2_series'):
if gp.y_axis_2_series == []:
pass
else:
y_axis_2_series = gp.y_axis_2_series
ax2 = ax.twinx()
# matplotlib.rcParams.update({'figure.subplot.right': matplotlib.rcParams['figure.subplot.right'] - 0.05})
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
# is there a second palette?
if hasattr(gp, 'color_2_series'):
if hasattr(gp.color_2_series, 'values'):
color_2_series = [str(x) for x in gp.color_2_series.values]
else:
color_2_series = [str(x) for x in gp.color_2_series]
# is there a second linewidth series
if hasattr(gp, 'linewidth_2_series'):
if hasattr(gp.linewidth_2_series, 'values'):
linewidth_2_series = [str(x) for x in gp.linewidth_2_series.values]
else:
linewidth_2_series = [str(x) for x in gp.linewidth_2_series]
# plot the lines (using custom palettes as appropriate)
try:
color = []; color_2 = []
linewidth_2 = matplotlib.rcParams['axes.linewidth']
exclude_from_color = []
if hasattr(gp, 'color'):
if isinstance(gp.color, list):
color = gp.color
else:
try:
color = self.create_colormap(
len(data_frame.columns.values) - len(color_2_series), gp.color)
except: pass
if hasattr(gp, 'width'):
if isinstance(gp.width, list):
color = gp.color
else:
try:
color = self.create_colormap(
len(data_frame.columns.values) - len(color_2_series), gp.color)
except: pass
if hasattr(gp, 'color_2'):
if isinstance(gp.color_2, list):
color_2 = gp.color_2
else:
try:
color_2 = self.create_colormap(len(color_2_series), gp.color_2)
except: pass
if hasattr(gp, 'exclude_from_color'):
if not(isinstance(gp.exclude_from_color, list)):
gp.exclude_from_color = [gp.exclude_from_color]
exclude_from_color = [str(x) for x in gp.exclude_from_color]
if hasattr(gp, 'linewidth_2'):
linewidth_2 = gp.linewidth_2
axis_1_color_index = 0
axis_2_color_index = 0
if type == 'bar':
# bottom = np.cumsum(np.vstack((np.zeros(data_frame.values.shape[1]), data_frame.values)), axis=0)[:-1]
# bottom = np.vstack((np.zeros((data_frame.values.shape[1],), dtype=data_frame.dtype),
# np.cumsum(data_frame.values, axis=0)[:-1]))
yoff = np.zeros(len(data_frame.index.values)) # the bottom values for stacked bar chart
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, y_axis_2_series)
color_spec, axis_1_color_index, axis_2_color_index = \
self.get_color(label, axis_1_color_index, axis_2_color_index, color, color_2,
exclude_from_color, color_2_series)
xd = data_frame.index; yd = data_frame.ix[:,i]
if (type == 'line'):
linewidth = self.get_linewidth(label, linewidth_2, linewidth_2_series)
ax_temp.plot(xd, yd, label = label, color = color_spec,
linewidth = linewidth)
elif(type == 'bar'):
ax_temp.bar(xd, yd, label = label, color = color_spec, bottom = yoff)
yoff = yoff + yd
elif(type == 'scatter'):
ax_temp.scatter(xd, yd, label = label, color = color_spec)
if hasattr(gp, 'line_of_best_fit'):
if gp.line_of_best_fit == True:
self.trendline(ax_temp, xd.values, yd.values, order=1, color= color_spec, alpha=1,
scale_factor = scale_factor)
except: pass
# format X axis
self.format_x_axis(ax, data_frame, gp)
try:
fig.suptitle(gp.title, fontsize = 14 * scale_factor)
except: pass
try:
source = Constants().plotfactory_source
source_color = 'black'
display_brand_label = False
if hasattr(gp, 'source'):
source = gp.source
display_brand_label = True
if hasattr(gp, 'source_color'):
source_color = self.get_color_code(gp.source_color)
if display_brand_label or Constants().plotfactory_display_brand_label:
ax.annotate('Source: ' + source, xy = (1, 0), xycoords='axes fraction', fontsize=7 * scale_factor,
xytext=(-5 * scale_factor, 10 * scale_factor), textcoords='offset points',
ha='right', va='top', color = source_color)
except: pass
if hasattr(gp, 'display_brand_label'):
if gp.display_brand_label is True:
self.create_brand_label(ax, anno = Constants().plotfactory_brand_label, scale_factor = scale_factor)
else:
if Constants().plotfactory_display_brand_label is True:
self.create_brand_label(ax, anno = Constants().plotfactory_brand_label, scale_factor = scale_factor)
leg = []
leg2 = []
loc = 'best'
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []: loc = 2
try:
leg = ax.legend(loc = loc, prop={'size':10 * scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 is not []:
leg2 = ax2.legend(loc = 1, prop={'size':10 * scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except: pass
try:
if gp.display_legend == False:
if leg is not[]: leg.remove()
if leg2 is not[]: leg.remove()
except: pass
try:
plt.savefig(gp.file_output, transparent=False)
except: pass
try:
if hasattr(gp, 'silent_display'):
if gp.silent_display is False:
plt.show()
else:
plt.show()
except:
pass
# convert to D3 format with mpld3
try:
if hasattr(gp, 'html_file_output'):
mpld3.save_d3_html(fig, gp.html_file_output)
if hasattr(gp, 'display_mpld3'):
if gp.display_mpld3 == True: mpld3.show(fig)
except: pass
# convert to Plotly format (fragile!)
# TODO better to create Plotly graphs from scratch rather than convert from matplotlib
# TODO also dependent on matplotlib version for support
try:
if hasattr(gp, 'plotly_url'):
plotly.tools.set_credentials_file(username = Constants().plotly_username,
api_key = Constants().plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style = True)
plot_url = py.plot_mpl(py_fig, filename = gp.plotly_url)
except:
pass
def main():
# tokens = read_tokens('wacoshootout3_6hrs_tokens.txt')
# bigrams_freqdist = get_bigrams_freqdist(tokens)
# sm12hrs_bigrams = get_bigrams_freqdist(read_tokens('sm12hrs_tokens.txt'))
# Regex string to parse emoticons
emoticons_str = r"""
(?:
[:=;] # Eyes
[oO\-]? # Nose (optional)
[D\)\]\(\]/\\OpP] # Mouth
)"""
# Regex string to parse HTML tags, @-mentions, hashtags, URLs, numbers, etc.
regex_str = [
emoticons_str,
r'<[^>]+>', # HTML tags
r'(?:@[\w_]+)', # @-mentions
r"(?:\#+[\w_]+[\w\'_\-]*[\w_]+)", # hash-tags
r'http[s]?://(?:[a-z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-f][0-9a-f]))+', # URLs
r'(?:(?:\d+,?)+(?:\.?\d+)?)', # numbers
r"(?:[a-z][a-z'\-_]+[a-z])", # words with - and '
r'(?:[\w_]+)', # other words
r'(?:\S)' # anything else
]
# Compile regex strings together
tokens_regex = re.compile(r'(' + '|'.join(regex_str) + ')', re.VERBOSE | re.IGNORECASE)
# Accumulate a list of punctuations, common (stop) words, and other common words
# so that the word frequencies output unique words
punctuation = list(string.punctuation)
stop = stopwords.words('english') + punctuation + ['rt', 'via', 'san', 'marcos', 'river']
# Initialize MongoDB collection to extract data from
client = MongoClient()
db = client.tweets
# bill_collection = db.bill_words
# flood_collection = db.flood
charleston_collection = db.charleston_unique
sm_flood_collection = db.sanmarcosflood
waco_shootout_4_6hrs = db.wacoshootout_original4_6hrs
waco_shootout_4 = db.wacoshootout_original4_12hrs
waco_shootout_3_6hrs = db.wacoshootout_original3_6hrs
waco_shootout_3_6hrs_test = db.wacoshootout_original3_6hrs_test
wacoshootout_6hrs = db.wacoshootout_original_6hrs
sm_flood_36hrs = db.sanmarcos36hrs
sm_flood_12hrs = db.sanmarcos12hrs
sm_flood_6hrs = db.sanmarcos6hrs
sm_flood_3hrs = db.sanmarcos3hrs
lafayette_12hrs = db.lafayette_shooting_unique
# Max number of tweets to parse
wacoshootout_6hrs_tweets = wacoshootout_6hrs.count()
waco_shootout_4_tweets = waco_shootout_4.count()
waco_shootout_4_6hrs_tweets = waco_shootout_4_6hrs.count()
waco6hrs_tweets = waco_shootout_3_6hrs.count()
max_tweets = sm_flood_collection.count()
flood_tweets = 1000
charleston_tweets = charleston_collection.count()
sm_tweets = sm_flood_36hrs.count()
sm12hrs_tweets = sm_flood_12hrs.count()
sm6hrs_tweets = sm_flood_6hrs.count()
sm3hrs_tweets = sm_flood_3hrs.count()
lafayette_12hrs_tweets = lafayette_12hrs.count()
# Number of tweets to iterate for frequency vs time plot
collection_tweets = 1000
# Start and end dates
start_date = parse('2015-07-24T01:00:00Z')
end_date = parse('2015-07-24T13:00:00Z')
# Pymongo find() query
date_filter = {'$and': [{'retweeted': False}, {'in_reply_to_status_id': None}, {'in_reply_to_user_id': None},
{'created_at': {'$gte': start_date, '$lt': end_date}}, {'retweeted_status': None}]}
date = {'created_at': {'$gte': start_date, '$lt': end_date}}
no_filter = {}
retweet_filter = {'retweeted_status': None}
# print waco_shootout_4_tweets
# wacoshootout4 = parse_tweets(no_filter, tokens_regex, stop, waco_shootout_4_tweets, waco_shootout_4, True, '', db.wacoshootout4_freqdist)
# merge_collections(waco_shootout_4_6hrs, waco_shootout_3_6hrs_test)
print "Number of tweets: " + str(lafayette_12hrs_tweets) + '\n'
# test_parse = parse_tweets(date, tokens_regex, stop, lafayette_12hrs_tweets, lafayette_12hrs, True, '')
# write_tokens(test_parse, 'lafayette_12hrs_tokens')
test_parse = read_tokens('lafayette_12hrs_tokens.txt')
lafayette_12hrs_monograms = get_monograms_freqdist(test_parse)
lafayette_12hrs_monograms_data = get_dataframe_dict(lafayette_12hrs_monograms, 5, date, lafayette_12hrs_tweets,
lafayette_12hrs)
lafayette_12hrs_monograms_df = lafayette_12hrs_monograms_data[0]
lafayette_12hrs_monograms_time = lafayette_12hrs_monograms_data[1]
lafayette_time = lafayette_12hrs_monograms_data[2]
axes = lafayette_12hrs_monograms_df.plot(figsize=(14, 4), colormap='spectral')
axes.set_title("Lafayette Shooting Monograms (12 hrs)", fontsize=18)
axes.set_xlabel("Time (UTC)")
axes.set_ylabel("Freq")
plt.show(axes)
labels = list(lafayette_12hrs_monograms_df.columns.values)
values = lafayette_12hrs_monograms_time
time_values = lafayette_time.index.values
print lafayette_12hrs_monograms_df
print lafayette_time.index.values
print lafayette_12hrs_monograms_time[1].values
# plt.ion()
# plt.axis('auto')
# plt.set_cmap('spectral')
# plt.show(False)
# labels_index = 0
# labels_dict = dict()
#
# for index in range(len(labels)):
# labels_dict[labels[index]] = lafayette_12hrs_monograms_time[index].values
#
# print labels_dict
# TODO: FIX LINE PLOTTING AND MULTIPLE PLOTS
# if plt.isinteractive():
# for i in range(len(time_values)):
# for ii in range(len(labels)):
# y = values[ii].values[i]
# plt.plot(time_values[i], y, antialiased=False)
# # plt.draw()
# plt.pause(0.1)
# # plt.draw()
#
# plt.show()
for i in range(len(labels)):
tooltip = mpld3.plugins.LineLabelTooltip(axes.get_lines()[i], labels[i])
mpld3.plugins.connect(plt.gcf(), tooltip)
mpld3.show()
def bubblePrices(city):
irrelevantAttributes = [
"City", "Monthly Pass", "Volkswagen Golf", "Toyota Corolla",
"Basic Utilities", "Internet", "Fitness Club", "Tennis Court Rent",
"Preschool Month", "Primary School Year", "Rent 1 Bedroom Center",
"Rent 1 Bedroom Outside Center", "Rent 3 Bedrooms Center",
"Rent 3 Bedrooms Outside Center", "Price m2 Center",
"Price m2 Outside Center", "Average Monthly Salary After Tax",
"Mortgage Interest Rate"
]
fyrirspurn = "SELECT * FROM Prices where City = " + '"' + beforeComma(
city) + '"'
print(fyrirspurn)
query = cur.execute(fyrirspurn)
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(), columns=cols)
results.drop(columns=irrelevantAttributes, inplace=True)
print(results.columns.values)
values = list(results.loc[0, :])[0:]
names = []
j = 0
for i in cols:
if i not in irrelevantAttributes:
names.append(i)
j = j + 1
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(9, 7))
fyrirspurn = "SELECT * FROM Prices where city = " + '"' + beforeComma(
city) + '"'
print(fyrirspurn)
query = cur.execute(fyrirspurn)
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(), columns=cols)
results.drop(columns=irrelevantAttributes, inplace=True)
values = list(results.loc[0, :])[0:]
names = []
j = 0
for i in cols:
if i not in irrelevantAttributes:
names.append(i)
j = j + 1
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(9, 7))
N = len(values)
col = np.random.rand(N)
print(col)
end = math.ceil(math.sqrt(N))
# þurfum að gera samsvörun
step = end / N
x = np.arange(0, end, step)
hnitx = []
for i in range(3, end + 2):
for j in range(end):
if (len(hnitx) == N):
break
hnitx.append(i)
hnity = []
for i in range(N):
hnity.append(i % end)
#plt.figure(figsize=(9,9))
hnitx = [x + random.random() / 4 for x in hnitx]
hnity = [y + random.random() / 4 for y in hnity]
mynd = ax.scatter(hnitx,
hnity,
c=col,
alpha=0.5,
s=[x * 20 for x in values])
plt.xlim((0.8, end + 2))
plt.xticks([], [])
plt.yticks([], [])
fig.patch.set_visible(False)
ax.axis('off')
font0 = FontProperties()
font1 = font0.copy()
font1.set_size('large')
font = {
'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 13,
}
for label, x, y in zip(names, hnitx, hnity):
r = random.random() * 2
plt.text(x + 0.22,
y + 0.25,
label,
ha='right',
va='bottom',
fontdict=font)
plt.plot([x + 0.22, x], [y + 0.25, y], 'k-', linewidth=0.5)
# þarf að sjá
font = {
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 18,
}
ax.set_title("Bubble chart of prices of goods in " + city + " (area = €)",
fontdict=font)
# vantar að staðfesta að röðin sé eins?
tooltip = mpld3.plugins.PointLabelTooltip(
mynd, labels=[str(x) + "€" for x in values])
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
def plot_2d_graph(self, data_frame, gp, chart_type):
if gp.resample is not None: data_frame = data_frame.asfreq(gp.resample)
# set the matplotlib style sheet & defaults
matplotlib.rcdefaults()
# first search PyThalesians styles, then try matplotlib
try:
plt.style.use(Constants().plotfactory_pythalesians_style_sheet[gp.style_sheet])
except:
plt.style.use(gp.style_sheet)
matplotlib.rcParams.update({'font.size': matplotlib.rcParams['font.size'] * gp.scale_factor})
# create figure & add a subplot
fig = plt.figure(figsize = ((gp.width * gp.scale_factor)/gp.dpi,
(gp.height * gp.scale_factor)/gp.dpi), dpi = gp.dpi)
ax = fig.add_subplot(111)
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset = False)
ax.yaxis.set_major_formatter(y_formatter)
if gp.x_title != '': ax.set_xlabel(gp.x_title)
if gp.y_title != '': ax.set_ylabel(gp.y_title)
# create a second y axis if necessary
ax2 = []
if gp.y_axis_2_series != []:
ax2 = ax.twinx()
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
color_cycle = matplotlib.rcParams['axes.color_cycle']
bar_ind = np.arange(0, len(data_frame.index))
xd, bar_ind, has_bar, no_of_bars = self.get_bar_indices(data_frame, gp, chart_type, bar_ind)
# plot the lines (using custom palettes as appropriate)
try:
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
color_spec = self.create_color_list(gp, data_frame)
# for stacked bar
yoff = np.zeros(len(data_frame.index.values)) # the bottom values for stacked bar chart
# for bar chart
# bar_ind = np.arange(len(data_frame.index))
# has_bar = False
bar_space = 0.2
bar_width = (1 - bar_space) / (no_of_bars)
bar_index = 0
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
if chart_type is not None:
if gp.chart_type is not None:
if isinstance(gp.chart_type, list):
chart_type = gp.chart_type[i]
else:
chart_type = gp.chart_type
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, gp.y_axis_2_series)
yd = data_frame.ix[:,i]
if (chart_type == 'line'):
linewidth_t = self.get_linewidth(label,
gp.linewidth, gp.linewidth_2, gp.linewidth_2_series)
if linewidth_t is None: linewidth_t = matplotlib.rcParams['axes.linewidth']
ax_temp.plot(xd, yd, label = label, color = color_spec[i],
linewidth = linewidth_t)
elif(chart_type == 'bar'):
bar_pos = [k - (1 - bar_space) / 2. + bar_index * bar_width for k in range(0,len(bar_ind))]
if color_spec[i] is not None:
ax_temp.bar(bar_pos, yd, bar_width, label = label,
color = color_spec[i])
else:
ax_temp.bar(bar_pos, yd, bar_width, label = label,
color = color_cycle[i % len(color_cycle)])
bar_index = bar_index + 1
# bar_ind = bar_ind + bar_width
has_bar = True
elif(chart_type == 'stacked'):
ax_temp.bar(xd, yd, label = label, color = color_spec[i], bottom = yoff)
yoff = yoff + yd
has_bar = True
elif(chart_type == 'scatter'):
ax_temp.scatter(xd, yd, label = label, color = color_spec[i])
if gp.line_of_best_fit is True:
self.trendline(ax_temp, xd.values, yd.values, order=1, color= color_spec[i], alpha=1,
scale_factor = gp.scale_factor)
# format X axis
self.format_x_axis(ax, data_frame, gp, has_bar, bar_ind)
except: pass
plt.xlabel(gp.x_title)
plt.ylabel(gp.y_title)
fig.suptitle(gp.title, fontsize = 14 * gp.scale_factor)
if gp.display_source_label == True:
ax.annotate('Source: ' + gp.source, xy = (1, 0), xycoords='axes fraction', fontsize=7 * gp.scale_factor,
xytext=(-5 * gp.scale_factor, 10 * gp.scale_factor), textcoords='offset points',
ha='right', va='top', color = gp.source_color)
if gp.display_brand_label == True:
self.create_brand_label(ax, anno = gp.brand_label, scale_factor = gp.scale_factor)
leg = []
leg2 = []
loc = 'best'
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []: loc = 2
try:
leg = ax.legend(loc = loc, prop={'size':10 * gp.scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 != []:
leg2 = ax2.legend(loc = 1, prop={'size':10 * gp.scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except: pass
try:
if gp.display_legend is False:
if leg != []: leg.remove()
if leg2 != []: leg.remove()
except: pass
try:
plt.savefig(gp.file_output, transparent=False)
except: pass
####### various matplotlib converters are unstable
# convert to D3 format with mpld3
try:
if gp.display_mpld3 == True:
mpld3.save_d3_html(fig, gp.html_file_output)
mpld3.show(fig)
except: pass
# FRAGILE! convert to Bokeh format
# better to use direct Bokeh renderer
try:
if (gp.convert_matplotlib_to_bokeh == True):
from bokeh.plotting import output_file, show
from bokeh import mpl
output_file(gp.html_file_output)
show(mpl.to_bokeh())
except: pass
# FRAGILE! convert matplotlib chart to Plotly format
# recommend using AdapterCufflinks instead to directly plot to Plotly
try:
if gp.convert_matplotlib_to_plotly == True:
plotly.tools.set_credentials_file(username = gp.plotly_username,
api_key = gp.plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style = True)
plot_url = py.plot_mpl(py_fig, filename = gp.plotly_url)
except:
pass
# display in Matplotlib
try:
if gp.silent_display == False: plt.show()
except:
pass
def scatterplot(eigindi1, eigindi2, week):
tafla1 = taflaEigindis(eigindi1)
tafla2 = taflaEigindis(eigindi2)
with con:
if tafla1 in ["WeatherChancesEurope", "WeatherEurope"]:
data1 = cur.execute("Select city, " + eigindi1 + " from " +
tafla1 + " where week = " + week)
data1 = removeTuples(cur.fetchall())
else:
data1 = cur.execute("Select city, " + '"' + eigindi1 + '"' +
" from " + tafla1)
data1 = removeTuples(cur.fetchall())
if tafla2 in ["WeatherChancesEurope", "WeatherEurope"]:
data2 = cur.execute("Select city, " + eigindi2 + " from " +
tafla2 + " where week = " + week)
data2 = removeTuples(cur.fetchall())
else:
data2 = cur.execute("Select city, " + '"' + eigindi2 + '"' +
" from " + tafla2)
data2 = removeTuples(cur.fetchall())
# data1 = data1[0:len(data2)]
# print("\n")
# print(data1[0:len(data2)+1])
# print("\n")
# print(data2)
# # print("\n")
# print(data1)
# print("\n")
# print(data2)
# print(len(data1))
# print(len(data2))
# get prófað að sleppa Venice, Berlin úr prices á meðan ég prófa þetta
newdata = []
newdata2 = []
missingFromWeatherEurope = [
"Venice", "Venice, Italy", "Berlin", "Berlin, Germany", "Palermo",
"Palermo, Italy"
]
for city in data1:
if city[0] not in missingFromWeatherEurope and city not in newdata:
newdata.append(city)
for city in data2:
if city not in newdata2:
#print(data2[i])
newdata2.append(city)
#print(newdata2)
# jöfnum þá lengdirnar, newdata2 er minni því veðurgögnin eru ekki fyrir allar borgirnar
newdata = newdata[0:len(newdata2)]
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(9, 7))
cities = [x[0] for x in newdata]
cities2 = [x[0] for x in newdata2]
# for i in range(0,len(cities)):
#if (cities[i] != cities2[i].split(",")[0]):
# print(cities[i])
# print(cities2[i].split(",")[0])
# print(Counter(cities))
# # marseille, dublin og leipzig koma fyrir 2x í newdata
# print(Counter(cities2))
print(len(newdata))
print(len(newdata2))
for i in range(0, len(newdata) - 1):
print(newdata[i])
print(newdata2[i])
print("\n")
newdata = [x[1] for x in newdata]
newdata2 = [x[1] for x in newdata2]
missingCities = []
i = 0
for data in newdata2:
if data == '':
print(cities2[i])
# fjarlægjum þetta gildi úr báðum
newdata.pop(i)
newdata2.pop(i)
missingCities.append(cities2[i])
cities2.pop(i)
i += 1
# print(type(newdata[1]))
# print(type(newdata2[1]))
# print(newdata)
# print(newdata2)
# print(taflaEigindis(newdata2))
# # if( eigindi1 == "Average Monthly Salary After Tax"):
# # for string in newdata:
# # string.replace(',','')
# # print(newdata)
print(len(newdata))
print(len(newdata2))
N = len(newdata)
col = np.random.rand(N)
print(type(col))
area = s = 10 * np.random.random(size=N)
mynd = ax.scatter(newdata, newdata2, c=col, alpha=0.5)
ax.set_xlabel(eigindi1.replace('_', ' '), fontsize=15)
ax.set_ylabel(eigindi2.replace('_', ' '), fontsize=15)
ax.set_title("Comparison of prices and weather in European Cities")
# vantar að staðfesta að röðin sé eins?
tooltip = mpld3.plugins.PointLabelTooltip(mynd, labels=cities2)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
#!/usr/bin/env python """simplest example of Matplotlib to the web browser""" from matplotlib.pyplot import figure import mpld3 fig = figure() ax = fig.gca() ax.plot([1, 2, 3, 4]) mpld3.show(fig)
parser.add_argument("--plot",
action="store_true",
help="If a plot should be generated")
parser.add_argument(
"--offset",
action="store_true",
help="Vertically offset service to better visualize overlap")
parser.add_argument("route_or_segment_name",
nargs='?',
help="The route to print")
if len(sys.argv) == 1:
parser.print_help()
sys.exit()
args = parser.parse_args()
r = DEMO_ROUTE if args.demo else args.route_or_segment_name.strip()
lr = logreader_from_route_or_segment(r, sort_by_time=True)
data, _ = get_timestamps(lr)
print_timestamps(data['timestamp'], data['duration'], data['start'],
args.relative)
if args.plot:
mpld3.show(
graph_timestamps(data['timestamp'],
data['start'],
data['end'],
args.relative,
offset_services=args.offset,
title=r))
def test_graph():
plt.plot([3,1,4,1,5], 'ks-', mec='w', mew=5, ms=20)
mpld3.show()
def viewPIP(self, method='w+', save=False):
"""
Function to plot point, polygon and visualize results in interactive web session.
Option to save as HTML with name "PIP_visualization_{methodname}.html".
Note: this code MUST be granted permission to open a web browser to view plot.
Otherwise, save as HTML and open.
Colors from XKCD color survey https://xkcd.com/color/rgb/
Scatterplot code adapted from https://mpld3.github.io/examples/scatter_tooltip.html
Interactive legend code adapted from https://mpld3.github.io/examples/interactive_legend.html
HTML tooltip code adapted from https://mpld3.github.io/examples/html_tooltips.html
:param method: (str) method of computing point in polygon. Valid inputs:
'ol' - points on lines only
'ov' - points on vertices only
'lv' - points on lines or vertices
'w' - winding number algorithm
'w+' - winding number PLUS any points on lines/vertices (DEFAULT)
'rc' - ray casting algorithm
'rc+' - ray casting algorithm PLUS any points on lines/vertices
:param save: (bool) true to enable saving; default false.
True saves image in code location folder as "PIP_visualization_{methodname}.html".
:return: none; opens web session, hit ctrl-c to exit.
"""
# run PIP
inOut = self.pointInPolygon(method=method)
# colors drawn from
outcolor = "#fd4659" # watermelon
incolor = "#01386a" # marine blue
# initialize plot with space for legend
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
fig.subplots_adjust(right=0.8)
# add polygon to image
poly = Pgon((self.__polygon[:, [0, 1]]),
alpha=0.7,
facecolor="grey",
edgecolor='none',
label="Polygon")
ax.add_patch(poly)
# collection of all plotted objects
plotted_objects = [poly]
# break into yes and no point lists
outPts = np.empty([0, 3])
inPts = np.empty([0, 3])
# creator for tooltip labels
outLabel = []
inLabel = []
# break into inpoints/outpoints collections, with appropriate labels in html
for i in range(len(inOut)):
if inOut[i]:
inPts = np.vstack([inPts,self.__points[i, :]])
inLabel.append('<p style="color:'+incolor+
';"><i>Inside</i> <br> Pt {0}: ({1:.0f}, {2:.0f})</p>'.format(i + 1, self.__points[i, 0],
self.__points[i, 1]))
else:
outPts = np.vstack([outPts,self.__points[i, :]])
outLabel.append('<p style="color:'+outcolor+
';"><i>Outside</i> <br> Pt {0}: ({1:.0f}, {2:.0f})</p>'.format(i + 1, self.__points[i, 0],
self.__points[i, 1]))
# scatterplot for points outside
outScat = ax.scatter(outPts[:, 0],
outPts[:, 1],
c=outcolor,
s=500,
alpha=0.7,
cmap=plt.cm.jet,
edgecolors='none',
label="Outside")
# scatterplot for points inside
inScat = ax.scatter(inPts[:, 0],
inPts[:, 1],
c=incolor,
s=500,
alpha=0.7,
cmap=plt.cm.jet,
edgecolors='none',
label="Inside")
# create html tooltips
htmltooltip1 = mpld3.plugins.PointHTMLTooltip(outScat, labels=outLabel, hoffset=10, voffset=10)
htmltooltip2 = mpld3.plugins.PointHTMLTooltip(inScat, labels=inLabel, voffset=10, hoffset=10)
# add scatterplots to object collections
plotted_objects.append(outScat)
plotted_objects.append(inScat)
# add title, axes
title = "Point in Polygon"
method_text = ""
if 'w' in method:
method_text += "Winding Number Algorithm"
elif 'rc' in method:
method_text += "Ray Casting Algorithm"
elif method == 'ol':
method_text += "Points on Lines (only)"
elif method == 'ov':
method_text += "Points on Vertices (only)"
elif method == 'lv':
method_text += "Points on Lines and Vertices (only)"
if '+' in method:
method_text += ", lines and vertices included."
# add title
ax.set_title(title, size=20)
# add method information as text
ax.text(min(min(self.__polygon[:, 0]), min(self.__points[:, 0])) - 0.5,
min(min(self.__polygon[:, 1]), min(self.__points[:, 1])) - 0.5,
method_text, size=10, style='italic')
ax.grid(color='white', linestyle='solid')
ax.set_xlabel("x position")
ax.set_ylabel("y position")
# add interactive legend to plot
interactive_legend = mpld3.plugins.InteractiveLegendPlugin(plotted_objects,
["Polygon", "Outside", "Inside"],
alpha_unsel=0.3,
alpha_over=1.5)
# connect all plugins to figure
mpld3.plugins.connect(fig, htmltooltip1, htmltooltip2, interactive_legend)
# if save is true, will save to an html as PIP_visualization.html
if save:
mpld3.save_html(fig, "PIP_visualization_"+method+".html")
# display in local web browser (hit Ctrl-C to exit)
mpld3.show()
pass
return classification
km = K_Means(3)
dataNumeric = [[float(tuple[0]), float(tuple[1])] for tuple in data]
#dataNumeric = [[float(tuple[0]),float(tuple[1]),float(tuple[2]), float(tuple[3])] for tuple in data]
km.fit(dataNumeric)
print(km)
# plotting takes forever becuase of the number of points. However, it's working if you're patient enough.
colors = 10 * ["r", "g", "c", "b", "k"]
for centroid in km.centroids:
plt.scatter(km.centroids[centroid][0],
km.centroids[centroid][1],
s=130,
marker="x")
for classification in km.classes:
color = colors[classification]
for features in km.classes[classification]:
plt.scatter(features[0], features[1], color=color, s=30)
mpld3.show() #launch a web server to view an d3/html figure representation
dataNumeric = [[float(tuple[0]), float(tuple[1])] for tuple in data]
#dataNumeric = [[float(tuple[0]),float(tuple[1]),float(tuple[2]), float(tuple[3])] for tuple in data] # start longitude and latitude
dataNumeric
km.centroids
import matplotlib.pyplot as plt, mpld3 plt.plot([3, 1, 4, 1, 5], 'ks-', mec='w', mew=5, ms=20) mpld3.show()
.on("mouseover", function(d, i){
d3.select(this).transition().duration(50)
.style("stroke-opacity", alpha_fg); })
.on("mouseout", function(d, i){
d3.select(this).transition().duration(200)
.style("stroke-opacity", alpha_bg); });
}
};
"""
def __init__(self, lines):
self.lines = lines
self.dict_ = {"type": "linehighlight",
"line_ids": [utils.get_id(line) for line in lines],
"alpha_bg": lines[0].get_alpha(),
"alpha_fg": 1.0}
N_paths = 50
N_steps = 100
x = np.linspace(0, 10, 100)
y = 0.1 * (np.random.random((N_paths, N_steps)) - 0.5)
y = y.cumsum(1)
fig, ax = plt.subplots(subplot_kw={'xticks': [], 'yticks': []})
lines = ax.plot(x, y.T, color='blue', lw=4, alpha=0.1)
plugins.connect(fig, HighlightLines(lines))
mpld3.show()
def func(file):
df = pd.read_excel(file)
df.groupby('CWL Name').count()['Employee Code'].plot.bar()
mpld3.show()
def display_mult_flat(x_train, start, stop):
images = x_train[start].reshape([1, 784])
for i in range(start + 1, stop):
images = np.concatenate((images, x_train[i].reshape([1, 784])))
plt.imshow(images, cmap=plt.get_cmap('gray_r'))
mpld3.show(port=8000)
def plot_summary(game):
x = game.team1score
y = game.team2score
fig, axScatter = plt.subplots(figsize=(12, 12), subplot_kw=dict(axisbg='#EEEEEE'))
# the scatter plot:
size = x + y
color = np.fabs(x - y)
scatter = axScatter.scatter(x, y, s=size, c=color, alpha=0.5)
axScatter.set_aspect(1.)
axScatter.set_xlabel('team1 score', size=20)
axScatter.set_ylabel('team2 score', size=20)
axScatter.grid(color='white', linestyle='solid')
# create new axes on the right and on the top of the current axes
# The first argument of the new_vertical(new_horizontal) method is
# the height (width) of the axes to be created in inches.
divider = make_axes_locatable(axScatter)
axHistx = divider.append_axes("top", 1.2, pad=0.2, sharex=axScatter, axisbg='#EEEEEE')
axHisty = divider.append_axes("right", 1.2, pad=0.3, sharey=axScatter, axisbg='#EEEEEE')
# make some labels invisible
plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(),
visible=False)
# now determine nice limits by hand:
binwidth = 5
xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])
lim = (int(xymax/binwidth) + 1)*binwidth
binnum = np.arange(0, lim + binwidth, binwidth)
axHistx.hist(x, bins=binnum)
axHisty.hist(y, bins=binnum, orientation='horizontal')
# the xaxis of axHistx and yaxis of axHisty are shared with axScatter,
# thus there is no need to manually adjust the xlim and ylim of these
# axis.
#axHistx.axis["bottom"].major_ticklabels.set_visible(False)
for tl in axHistx.get_xticklabels():
tl.set_visible(False)
axHistx.set_yticks([0, 30])
#axHisty.axis["left"].major_ticklabels.set_visible(False)
for tl in axHisty.get_yticklabels():
tl.set_visible(False)
axHisty.set_xticks([0, 30])
#plt.draw()
#plt.show()
plotid = mpld3.utils.get_id(axScatter)
# Define some CSS to control our custom labels
css = """
table
{
border-collapse: collapse;
}
th
{
color: #ffffff;
background-color: #aaa;
}
td
{
background-color: #cccccc;
}
table, th, td
{
font-family:Arial, Helvetica, sans-serif;
border: 1px solid gray;
text-align: middle;
}
"""
#labels = ['Round {}, {} vs. {}, {}:{}'.format(g[1], g[2], g[3], g[4], g[5]) for g in game.itertuples()]
labels = []
print game.shape
for i in range(game.shape[0]):
label = game.ix[[i],:].T
label.columns = ['Game Info']
labels.append(str(label.to_html()))
tooltip = mpld3.plugins.PointHTMLTooltip(scatter, labels=labels, voffset=10, hoffset=10, css=css)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
fightml = mpld3.fig_to_html(fig)
return plotid, fightml
def bubbleIndices(cost, city):
if (cost):
fyrirspurn = "SELECT * FROM CostOfLivingIndex where city = " + '"' + city + '"'
else:
fyrirspurn = "SELECT * FROM QualityOfLifeIndex where city = " + '"' + city + '"'
irrelevantAttributes = ["City", "Rank", "index"]
### allt fyrir neðan er sama og í Prices dæminu
print(fyrirspurn)
query = cur.execute(fyrirspurn)
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(), columns=cols)
results.drop(columns=irrelevantAttributes, inplace=True)
values = list(results.loc[0, :])[0:]
names = []
j = 0
for i in cols:
if i not in irrelevantAttributes:
names.append(i)
j = j + 1
print(len(values))
print(len(names))
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(9, 7))
N = len(values)
#col = np.arange(0.1,0.99,0.98/N)
col = np.random.random_integers(0, N - 1)
print(col)
#print(len(col))
colors = [
'yellow', 'red', 'green', 'blue', 'black', 'purple', 'brown', 'pink'
]
end = math.ceil(math.sqrt(N))
# þurfum að gera samsvörun
step = end / N
x = np.arange(0, end, step)
hnitx = []
for i in range(1, end + 1):
for j in range(end):
if (len(hnitx) == N):
break
hnitx.append(i)
hnity = []
for i in range(N):
hnity.append(i % end)
#plt.figure(figsize=(9,9))
# hnitx = [x + random.random()/4 for x in hnitx]
# hnity = [y + random.random()/4 for y in hnity]
print(hnitx)
print(hnity)
mynd = ax.scatter(hnitx,
hnity,
c=colors,
alpha=0.5,
s=[x * 20 for x in values])
plt.xlim((0, end))
plt.ylim((-1, end))
plt.xticks([], [])
plt.yticks([], [])
fig.patch.set_visible(False)
ax.axis('off')
font0 = FontProperties()
font1 = font0.copy()
font1.set_size('large')
fonts = []
for i in range(N):
fonts.append({
'family': 'serif',
'color': colors[i],
'weight': 'normal',
'size': 10,
})
j = 0
for label, x, y in zip(names, hnitx, hnity):
plt.text(x + 0.22,
y + 0.25,
label,
ha='right',
va='bottom',
fontdict=fonts[j])
j = j + 1
#plt.plot([x+0.22,x], [y+0.25,y], 'k-', linewidth = 0.5)
# þarf að sjá
font = {
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 18,
}
ax.set_title("Bubble chart of prices of goods in " + city + " (area = €)",
fontdict=font)
# vantar að staðfesta að röðin sé eins?
tooltip = mpld3.plugins.PointLabelTooltip(mynd, labels=values)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
def plotData(NQuery, input_table, FigureStrBase, SurfMin=1e-1*u.M_sun/u.pc**2,
SurfMax=1e5*u.M_sun/u.pc**2, VDispMin=1e-1*u.km/u.s,
VDispMax=3e2*u.km/u.s, RadMin=1e-2*u.pc, RadMax=1e3*u.pc,
interactive=True):
"""
This is where documentation needs to be added
Parameters
----------
NQuery
FigureStrBase : str
The start of the output filename, e.g. for "my_file.png" it would be
my_file
SurfMin
SurfMax
VDispMin
VDispMax
RadMin
RadMax
"""
figure = matplotlib.figure.Figure()
if interactive:
from matplotlib import pyplot
from matplotlib import _pylab_helpers
backend = getattr(matplotlib.backends, 'backend_{0}'.format(matplotlib.rcParams['backend']).lower())
canvas = backend.FigureCanvas(figure)
figmanager = backend.FigureManager(canvas, 1)
figmanager.canvas.figure.number = 1
_pylab_helpers.Gcf.set_active(figmanager)
else:
canvas = FigureCanvasAgg(figure)
ax = figure.gca()
d = input_table
Author = d['Names']
Run = d['IDs']
SurfDens = d['SurfaceDensity']
VDisp = d['VelocityDispersion']
Rad = d['Radius']
if d['IsSimulated'].dtype == 'bool':
IsSim = d['IsSimulated']
else:
IsSim = d['IsSimulated'] == 'True'
UseSurf = (SurfDens > SurfMin) & (SurfDens < SurfMax)
UseVDisp = (VDisp > VDispMin) & (VDisp < VDispMax)
UseRad = (Rad > RadMin) & (Rad < RadMax)
Use = UseSurf & UseVDisp & UseRad
Obs = (~IsSim) & Use
Sim = IsSim & Use
UniqueAuthor = list(set(Author[Use]))[4]
NUniqueAuthor = len(UniqueAuthor)
#print d
#print d[Use]
# print 'Authors:', UniqueAuthor
#colors = random.sample(matplotlib.colors.cnames, NUniqueAuthor)
colors = list(matplotlib.cm.jet(np.linspace(0,1,NUniqueAuthor)))
random.shuffle(colors)
# NOTE this does NOT work with mpld3
# ax.loglog()
scatters = []
labels = []
markers = ['o', 's']
for iAu,color in zip(UniqueAuthor, colors) :
UsePlot = (Author == iAu) & Use
ObsPlot = ((Author == iAu) & (~IsSim)) & Use
SimPlot = ((Author == iAu) & (IsSim)) & Use
if any(ObsPlot):
print iAu
scatters.append(ax.scatter(np.log10(SurfDens[ObsPlot]), np.log10(VDisp[ObsPlot]),
marker=markers[0],
s=(np.log10(np.array(Rad[ObsPlot]))-np.log10(RadMin.value)+0.5)**3.,
color=color, alpha=0.5))
# for row in d[ObsPlot]:
# row_html = [str(j) for j in d[ObsPlot].pformat(html=True)]
# labels.append("\n ".join(row_html))
labels = ['<h1>{title}</h1>'.format(title=i) for i in range(len(d[ObsPlot]))]
if any(SimPlot):
scatters.append(ax.scatter(np.log10(SurfDens[SimPlot]), np.log10(VDisp[SimPlot]),
marker=markers[1],
s=(np.log10(np.array(Rad[SimPlot]))-np.log10(RadMin.value)+0.5)**3.,
color=color, alpha=0.5))
# for row in d[SimPlot]:
# row_html = [str(j) for j in d[SimPlot].pformat(html=True)]
# labels.append("\n ".join(row_html))
if any(Obs):
ax.scatter(np.log10(SurfDens[Obs]), np.log10(VDisp[Obs]),
marker=markers[0],
s=(np.log10(np.array(Rad[Obs]))-np.log10(RadMin.value)+0.5)**3.,
facecolors='none', edgecolors='black',
alpha=0.5)
if any(Sim):
ax.scatter(np.log10(SurfDens[Sim]), np.log10(VDisp[Sim]),
marker=markers[1],
s=(np.log10(np.array(Rad[Sim]))-np.log10(RadMin.value)+0.5)**3.,
facecolors='none', edgecolors='black',
alpha=0.5)
ax.set_xlabel('$\Sigma$ [M$_{\odot}$ pc$^{-2}$]', fontsize=16)
ax.set_ylabel('$\sigma$ [km s$^{-1}$]', fontsize=16)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.set_xlim((np.log10(SurfMin.to(u.M_sun/u.pc**2).value),
np.log10(SurfMax.to(u.M_sun/u.pc**2).value)))
ax.set_ylim((np.log10(VDispMin.to(u.km/u.s).value),
np.log10(VDispMax.to(u.km/u.s).value)))
# ax.legend(UniqueAuthor, loc='center left', bbox_to_anchor=(1.0, 0.5),
# prop={'size':12}, markerscale = .7, scatterpoints = 1)
labels = ['<h1>{title}</h1>'.format(title=i) for i in range(len(d))]
tooltip = plugins.PointHTMLTooltip(scatters[0], labels,
voffset=10, hoffset=10)
plugins.connect(figure, tooltip)
# figure.savefig(FigureStrBase+NQuery+'.png',bbox_inches='tight',dpi=150)
# figure.savefig(FigureStrBase+NQuery+'.pdf',bbox_inches='tight',dpi=150)
if interactive:
# from matplotlib import pyplot as plt
# plt.ion()
# plt.show()
mpld3.show()
return FigureStrBase+NQuery+'.png'
# %%
# Distribution of document length?
import matplotlib
matplotlib.use('Agg')
import pandas as pd
import matplotlib.pyplot as plt
import mpld3
lengths = list(map(len, train_data))
le = pd.Series(lengths)
plt.clf()
le[le < 2000].hist()
mpld3.show(open_browser=False)
# %%
# Remove stopwords
import nltk
stopwords = nltk.corpus.stopwords.words('english')
# %%
# Tokenizing + stemming
import spacy
import re
from functools import partial
def show_graph():
net.show("facebook_net.html")
plt.axis('off') # hide the axes
mpld3.show()
def plot_2d_graph(self, data_frame, gp, chart_type):
if gp is None: gp = GraphProperties()
if gp.chart_type is None and chart_type is None: chart_type = 'line'
if gp.resample is not None: data_frame = data_frame.asfreq(gp.resample)
self.apply_style_sheet(gp)
# create figure & add a subplot
fig = plt.figure(figsize=((gp.width * gp.scale_factor) / gp.dpi,
(gp.height * gp.scale_factor) / gp.dpi),
dpi=gp.dpi)
ax = fig.add_subplot(111)
if gp.x_title != '': ax.set_xlabel(gp.x_title)
if gp.y_title != '': ax.set_ylabel(gp.y_title)
plt.xlabel(gp.x_title)
plt.ylabel(gp.y_title)
fig.suptitle(gp.title, fontsize=14 * gp.scale_factor)
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
# create a second y axis if necessary
ax2 = []
if gp.y_axis_2_series != []:
ax2 = ax.twinx()
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
# matplotlib 1.5
try:
cyc = matplotlib.rcParams['axes.prop_cycle']
color_cycle = [x['color'] for x in cyc]
except KeyError:
# pre 1.5
pass
# color_cycle = matplotlib.rcParams['axes.color_cycle']
bar_ind = np.arange(0, len(data_frame.index))
# for bar charts, create a proxy x-axis (then relabel)
xd, bar_ind, has_bar, no_of_bars = self.get_bar_indices(
data_frame, gp, chart_type, bar_ind)
# plot the lines (using custom palettes as appropriate)
try:
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
color_spec = self.create_color_list(gp, data_frame)
# for stacked bar
yoff_pos = np.zeros(len(data_frame.index.values)
) # the bottom values for stacked bar chart
yoff_neg = np.zeros(len(data_frame.index.values)
) # the bottom values for stacked bar chart
zeros = np.zeros(len(data_frame.index.values))
# for bar chart
bar_space = 0.2
bar_width = (1 - bar_space) / (no_of_bars)
bar_index = 0
has_matrix = False
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
if gp.chart_type is not None:
if isinstance(gp.chart_type, list):
chart_type = gp.chart_type[i]
else:
chart_type = gp.chart_type
if chart_type == 'heatmap':
# TODO experimental!
# ax.set_frame_on(False)
ax.pcolor(data_frame, cmap=plt.cm.Blues, alpha=0.8)
# plt.colorbar()
has_matrix = True
break
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, gp.y_axis_2_series)
yd = data_frame.ix[:, i]
if color_spec[i] is None:
color_spec[i] = color_cycle[i % len(color_cycle)]
if (chart_type == 'line'):
linewidth_t = self.get_linewidth(label, gp.linewidth,
gp.linewidth_2,
gp.linewidth_2_series)
if linewidth_t is None:
linewidth_t = matplotlib.rcParams['axes.linewidth']
ax_temp.plot(xd,
yd,
label=label,
color=color_spec[i],
linewidth=linewidth_t)
elif (chart_type == 'bar'):
# for multiple bars we need to allocate space properly
bar_pos = [
k - (1 - bar_space) / 2. + bar_index * bar_width
for k in range(0, len(bar_ind))
]
ax_temp.bar(bar_pos,
yd,
bar_width,
label=label,
color=color_spec[i])
bar_index = bar_index + 1
elif (chart_type == 'stacked'):
bar_pos = [
k - (1 - bar_space) / 2. + bar_index * bar_width
for k in range(0, len(bar_ind))
]
yoff = np.where(yd > 0, yoff_pos, yoff_neg)
ax_temp.bar(bar_pos,
yd,
label=label,
color=color_spec[i],
bottom=yoff)
yoff_pos = yoff_pos + np.maximum(yd, zeros)
yoff_neg = yoff_neg + np.minimum(yd, zeros)
# bar_index = bar_index + 1
elif (chart_type == 'scatter'):
ax_temp.scatter(xd, yd, label=label, color=color_spec[i])
if gp.line_of_best_fit is True:
self.trendline(ax_temp,
xd.values,
yd.values,
order=1,
color=color_spec[i],
alpha=1,
scale_factor=gp.scale_factor)
# format X axis
self.format_x_axis(ax, data_frame, gp, has_bar, bar_ind,
has_matrix)
except:
pass
if gp.display_source_label == True:
ax.annotate('Source: ' + gp.source,
xy=(1, 0),
xycoords='axes fraction',
fontsize=7 * gp.scale_factor,
xytext=(-5 * gp.scale_factor, 10 * gp.scale_factor),
textcoords='offset points',
ha='right',
va='top',
color=gp.source_color)
if gp.display_brand_label == True:
self.create_brand_label(ax,
anno=gp.brand_label,
scale_factor=gp.scale_factor)
leg = []
leg2 = []
loc = 'best'
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []: loc = 2
try:
leg = ax.legend(loc=loc, prop={'size': 10 * gp.scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 != []:
leg2 = ax2.legend(loc=1, prop={'size': 10 * gp.scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except:
pass
try:
if gp.display_legend is False:
if leg != []: leg.remove()
if leg2 != []: leg.remove()
except:
pass
try:
plt.savefig(gp.file_output, transparent=False)
except:
pass
####### various matplotlib converters are unstable
# convert to D3 format with mpld3
try:
# output matplotlib charts externally to D3 based libraries
import mpld3
if gp.display_mpld3 == True:
mpld3.save_d3_html(fig, gp.html_file_output)
mpld3.show(fig)
except:
pass
# FRAGILE! convert to Bokeh format
# better to use direct Bokeh renderer
try:
if (gp.convert_matplotlib_to_bokeh == True):
from bokeh.plotting import output_file, show
from bokeh import mpl
output_file(gp.html_file_output)
show(mpl.to_bokeh())
except:
pass
# FRAGILE! convert matplotlib chart to Plotly format
# recommend using AdapterCufflinks instead to directly plot to Plotly
try:
import plotly.plotly as py
import plotly
import plotly.tools as tls
if gp.convert_matplotlib_to_plotly == True:
plotly.tools.set_credentials_file(username=gp.plotly_username,
api_key=gp.plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style=True)
plot_url = py.plot_mpl(py_fig, filename=gp.plotly_url)
except:
pass
# display in matplotlib window
try:
if Constants.plotfactory_silent_display == True:
pass
elif gp.silent_display == False:
plt.show()
except:
pass
def four_group_plot(csv_file,
x_col,
y_col,
group1_col,
group2_col,
group3_col,
show_only_col,
show_only_col_flag=True):
data = pd.read_csv(csv_file)
data_group1 = data.groupby(group1_col)
group2_labels = data[group2_col]
fig, ax = plt.subplots()
ax.margins(0.05)
group1_marker_type = ['o', 's']
group1_marker_size = [80, 100]
group2_colors = ['purple', 'orange']
group3_colors = ['purple', 'orange']
group3_marker_size = [20, 30]
# PLOT GROUP 1 and Group 2
# TO be replaced by backgorund mesh
# g1_cnt = 0
# for name1,group1 in data_group1:
# marker=group1_marker_type[g1_cnt]
# marker_size = group1_marker_size[g1_cnt]
# data_group2 = group1.groupby(group2_col)
# g2_cnt=0
# for name2,group2 in data_group2:
# ax.scatter(group2[x_col],group2[y_col],marker=marker,s= marker_size,color=group2_colors[g2_cnt],alpha=.4)
# g2_cnt+=1
# g1_cnt+=1
# Plot with just group 1
# Plot with just group 1 and group 3
g1_cnt = 0
for name1, group1 in data_group1:
marker = group1_marker_type[g1_cnt]
marker_size = group3_marker_size[g1_cnt]
data_group3 = group1.groupby(group3_col)
g3_cnt = 0
for name3, group3 in data_group3:
lbl_str = group3_col + ': ' + str(name3)
if marker == 'o':
ax.scatter(group3[x_col],
group3[y_col],
marker=marker,
s=marker_size,
color=group3_colors[g3_cnt],
alpha=.6,
label=lbl_str)
g3_cnt += 1
g1_cnt += 1
# g1_cnt=0
# for name1,group1 in data_group1:
# marker=group1_marker_type[g1_cnt]
# marker_size=group1_marker_size[g1_cnt]
# ax.scatter(group1[x_col], group1[y_col], marker=marker, s= marker_size,facecolors='cyan',edgecolors='k',alpha=.1,label=group1_col+'_'+str(name1))
# g1_cnt+=1
# break
### show_only_col
show_only_data = data[data[show_only_col] == show_only_col_flag]
ax.scatter(show_only_data[x_col],
show_only_data[y_col],
marker='*',
color='red',
label='Please Label Manually (' + show_only_col + ')')
#
# ax.legend()
# plt.show()
handles, labels = ax.get_legend_handles_labels() # return lines and labels
interactive_legend = plugins.InteractiveLegendPlugin(
zip(handles, ax.collections), labels)
# alpha_unsel=0.5,
# alpha_over=1.5,
# start_visible=True)
plugins.connect(fig, interactive_legend)
ax.set_xlabel(x_col)
ax.set_ylabel(y_col)
ax.set_title('LOW DIM VISUALIZATION', size=20)
mpld3.show()
def plotData(NQuery, input_table, FigureStrBase, html_dir=None, png_dir=None,
xvariable='SurfaceDensity', yvariable='VelocityDispersion',
zvariable='Radius',
xMin=None, xMax=None, yMin=None, yMax=None, zMin=None, zMax=None,
interactive=False, show_log=True, min_marker_width=3,
max_marker_width=0.05):
"""
This is where documentation needs to be added
Parameters
----------
NQuery
FigureStrBase : str
The start of the output filename, e.g. for "my_file.png" it would be
my_file
xMin
xMax
yMin
yMax
zMin
zMax
min_marker_width : int or float, optional
Sets the pixel width of the smallest marker to be plotted. If <1,
it is interpreted to be a fraction of the total pixels along the
shortest axis.
max_marker_width : int or float, optional
Sets the pixel width of the smallest marker to be plotted. If <1,
it is interpreted to be a fraction of the total pixels along the
shortest axis.
"""
if len(input_table) == 0:
raise ValueError("The input table is empty.")
figure = matplotlib.figure.Figure()
if interactive:
from matplotlib import pyplot
from matplotlib import _pylab_helpers
backend = getattr(matplotlib.backends, 'backend_{0}'.format(matplotlib.rcParams['backend']).lower())
canvas = backend.FigureCanvas(figure)
figmanager = backend.FigureManager(canvas, 1)
figmanager.canvas.figure.number = 1
_pylab_helpers.Gcf.set_active(figmanager)
else:
figure = matplotlib.figure.Figure()
canvas = FigureCanvasAgg(figure)
ax = figure.gca()
d = input_table
Author = d['Names']
Run = d['IDs']
x_ax = d[xvariable]
y_ax = d[yvariable]
z_ax = d[zvariable]
# Check if limits are given
if xMin is None:
xMin = x_ax.min()
if xMax is None:
xMax = x_ax.max()
if yMin is None:
yMin = y_ax.min()
if yMax is None:
yMax = y_ax.max()
if zMin is None:
zMin = z_ax.min()
if zMax is None:
zMax = z_ax.max()
if d['IsSimulated'].dtype == 'bool':
IsSim = d['IsSimulated']
else:
IsSim = d['IsSimulated'] == 'True'
if show_log:
if not label_dict_html[xvariable].startswith('log'):
label_dict_html[xvariable] = 'log ' + label_dict_html[xvariable]
label_dict_html[yvariable] = 'log ' + label_dict_html[yvariable]
if not label_dict_png[xvariable].startswith('log'):
label_dict_png[xvariable] = 'log ' + label_dict_png[xvariable]
label_dict_png[yvariable] = 'log ' + label_dict_png[yvariable]
# Select points within the limits
Use_x_ax = (x_ax > xMin) & (x_ax < xMax)
Use_y_ax = (y_ax > yMin) & (y_ax < yMax)
Use_z_ax = (z_ax > zMin) & (z_ax < zMax)
# intersects the three subsets defined above
Use = Use_x_ax & Use_y_ax & Use_z_ax
nptstoplot = np.count_nonzero(Use)
if nptstoplot == 0:
log.debug("Use: {0}".format(Use))
log.debug("Use_x_ax: {0}".format(Use_x_ax))
log.debug("xmin: {0} xmax: {1}".format(xMin, xMax))
log.debug("x_ax: {0}".format(x_ax))
log.debug("Use_y_ax: {0}".format(Use_y_ax))
log.debug("ymin: {0} ymax: {1}".format(yMin, yMax))
log.debug("y_ax: {0}".format(y_ax))
log.debug("Use_z_ax: {0}".format(Use_z_ax))
log.debug("zmin: {0} zmax: {1}".format(zMin, zMax))
log.debug("z_ax: {0}".format(z_ax))
return None,None
else:
log.debug("Found {0} points to plot".format(nptstoplot))
UniqueAuthor = list(set(Author[Use]))
NUniqueAuthor = len(UniqueAuthor)
colors = list(matplotlib.cm.jet(np.linspace(0, 1, NUniqueAuthor)))
random.seed(12)
random.shuffle(colors)
# NOTE this does NOT work with mpld3
# ax.loglog()
# Set marker sizes based on a minimum and maximum pixel size, then scale
# the rest between.
bbox = \
ax.get_window_extent().transformed(figure.dpi_scale_trans.inverted())
min_axis_size = min(bbox.width, bbox.height) * figure.dpi
if max_marker_width < 1:
max_marker_width *= min_axis_size
if min_marker_width < 1:
min_marker_width *= min_axis_size
marker_conversion = max_marker_width / \
(np.log10(z_ax[Use].max())-np.log10(z_ax[Use].min()))
marker_widths = (marker_conversion *
(np.log10(np.array(z_ax))-np.log10(z_ax[Use].min())) +
min_marker_width)
marker_sizes = marker_widths**2
scatters = []
markers = ['o', 's']
for iAu, color in zip(UniqueAuthor, colors):
ObsPlot = ((Author == iAu) & (~IsSim)) & Use
SimPlot = ((Author == iAu) & (IsSim)) & Use
if show_log:
plot_x = np.log10(x_ax)
plot_y = np.log10(y_ax)
if any(ObsPlot):
# Change to logs on next commit
scatter = \
ax.scatter(plot_x[ObsPlot], plot_y[ObsPlot], marker=markers[0],
s=marker_sizes[ObsPlot],
color=color, alpha=0.5, edgecolors='k',
label=iAu)
scatters.append(scatter)
labels = []
for row in d[ObsPlot]:
colnames = ['<div>{title}</div>'.format(title=col)
for col in row.colnames]
values = ['<div>{title}</div>'.format(title=str(val))
for val in row]
label = ""
for col, val in zip(colnames, values):
label += col+" "+val+" \n "
labels.append(label)
tooltip = plugins.PointHTMLTooltip(scatter, labels,
voffset=10, hoffset=10)
plugins.connect(figure, tooltip)
if any(SimPlot):
# Change to logs on next commit
scatter = \
ax.scatter(plot_x[SimPlot], plot_y[SimPlot], marker=markers[1],
s=marker_sizes[SimPlot],
color=color, alpha=0.5, edgecolors='k',
label=iAu)
scatters.append(scatter)
labels = []
for row in d[SimPlot]:
colnames = ['<div>{title}</div>'.format(title=col)
for col in row.colnames]
values = ['<div>{title}</div>'.format(title=str(val))
for val in row]
label = ""
for col, val in zip(colnames, values):
label += col+" "+val+" \n "
labels.append(label)
tooltip = plugins.PointHTMLTooltip(scatter, labels,
voffset=10, hoffset=10, css=css)
plugins.connect(figure, tooltip)
ax.set_xlabel(label_dict_html[xvariable], fontsize=16)
ax.set_ylabel(label_dict_html[yvariable], fontsize=16)
# Set plot limits. Needed for conversion of pixel units to plot units.
# Pad the maximum marker width on.
inv = ax.transData.inverted()
pad_x, pad_y = inv.transform((marker_widths.max(), marker_widths.max())) - \
inv.transform((0.0, 0.0))
if show_log:
ax.set_xlim(np.log10(xMin.value)-pad_x, np.log10(xMax.value)+pad_x)
ax.set_ylim(np.log10(yMin.value)-pad_y, np.log10(yMax.value)+pad_y)
else:
ax.set_xlim(xMin.value - pad_x, xMax.value + pad_x)
ax.set_ylim(yMin.value - pad_y, yMax.value + pad_y)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# ax.legend(UniqueAuthor, loc='center left', bbox_to_anchor=(1.0, 0.5),
# prop={'size':12}, markerscale = .7, scatterpoints = 1)
if hasattr(mpld3.plugins, 'InteractiveLegendPlugin'):
plugins.connect(figure,
plugins.InteractiveLegendPlugin(scatters,
UniqueAuthor,
alpha_unsel=0.0,
alpha_over=0.5))
# Adding fake points to show the size
# Try floor and ceil. Pick the one closest to the max/min.
max_z = round_to_pow_10(z_ax[Use].max())
min_z = round_to_pow_10(z_ax[Use].min())
mid_z = round_to_pow_10((max_z + min_z) / 2., log=False)
if mid_z == max_z:
fake_z_marker_width = np.array([max_z])
elif mid_z == max_z or mid_z == min_z:
fake_z_marker_width = np.array([max_z, min_z])
else:
fake_z_marker_width = np.array([max_z, mid_z, min_z])
fake_marker_sizes = (marker_conversion *
(fake_z_marker_width-np.log10(z_ax[Use].min())) +
min_marker_width)**2
# Set the axis fraction to plot the points at. Adjust if the largest
# will overlap with the next.
sep_ax_frac = 0.05
if np.sqrt(fake_marker_sizes[0])/float(min_axis_size) > 0.05:
sep_ax_frac = np.sqrt(fake_marker_sizes[0])/float(min_axis_size) \
+ 0.005
xfake = [0.1] * fake_z_marker_width.shape[0]
yfake = [0.95 - sep_ax_frac*x for x in range(fake_z_marker_width.shape[0])]
# xfake = [xax_limits[0] + xax_limits[0]*2.,
# xax_limits[0] + xax_limits[0]*2.,
# xax_limits[0] + xax_limits[0]*2.]
# yfake = [yax_limits[1] - yax_limits[1]*0.01,
# yax_limits[1] - yax_limits[1]*0.3,
# yax_limits[1] - yax_limits[1]*0.6]
ax.scatter(np.array(xfake), np.array(yfake), marker='+',
s=fake_marker_sizes,
transform=ax.transAxes,
facecolors='g')
for xf, yf, rad in zip(xfake, yfake, fake_z_marker_width):
ax.text(xf + 0.05, yf-0.01, str(10**rad) + ' ' + str(zMin.unit),
transform=ax.transAxes)
# Saving the plots
if html_dir is None:
html_dir = ""
if png_dir is None:
png_dir = ""
html_file = os.path.join(html_dir, FigureStrBase+NQuery+'.html')
png_file = os.path.join(png_dir, FigureStrBase+NQuery+".png")
html = mpld3.fig_to_html(figure)
with open(html_file, 'w') as f:
f.write(html)
if interactive:
# from matplotlib import pyplot as plt
# plt.ion()
# plt.show()
mpld3.show()
# Clear out the plugins
plugins.clear(figure)
# Use latex labels for the mpl outputted plot
ax.set_xlabel(label_dict_png[xvariable], fontsize=16)
ax.set_ylabel(label_dict_png[yvariable], fontsize=16)
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
handlelength=4)
legend.draw_frame(False)
figure.savefig(png_file, bbox_inches='tight', dpi=150)
# figure.savefig(FigureStrBase+NQuery+'.pdf',bbox_inches='tight',dpi=150)
return html_file, png_file
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--relative",
action="store_true",
help="Make timestamps relative to the start of each frame")
parser.add_argument("--demo",
action="store_true",
help="Use the demo route instead of providing one")
parser.add_argument("--plot",
action="store_true",
help="If a plot should be generated")
parser.add_argument("route_or_segment_name",
nargs='?',
help="The route to print")
if len(sys.argv) == 1:
parser.print_help()
sys.exit()
args = parser.parse_args()
r = DEMO_ROUTE if args.demo else args.route_or_segment_name.strip()
lr = logreader_from_route_or_segment(r, sort_by_time=True)
data, _ = get_timestamps(lr)
print_timestamps(data['timestamp'], data['duration'], data['start'],
args.relative)
if args.plot:
mpld3.show(
graph_timestamps(data['timestamp'], data['start'], data['end'],
args.relative))
def plot_2d_graph(self, data_frame, gp, chart_type):
if gp.resample is not None:
data_frame = data_frame.asfreq(gp.resample)
# set the matplotlib style sheet & defaults
matplotlib.rcdefaults()
# first search PyThalesians styles, then try matplotlib
try:
plt.style.use(Constants().plotfactory_pythalesians_style_sheet[gp.style_sheet])
except:
plt.style.use(gp.style_sheet)
matplotlib.rcParams.update({"font.size": matplotlib.rcParams["font.size"] * gp.scale_factor})
# create figure & add a subplot
fig = plt.figure(
figsize=((gp.width * gp.scale_factor) / gp.dpi, (gp.height * gp.scale_factor) / gp.dpi), dpi=gp.dpi
)
ax = fig.add_subplot(111)
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
# create a second y axis if necessary
ax2 = []
if gp.y_axis_2_series != []:
ax2 = ax.twinx()
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
# plot the lines (using custom palettes as appropriate)
try:
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
color_spec = self.create_color_list(gp, data_frame)
if type == "bar":
# bottom = np.cumsum(np.vstack((np.zeros(data_frame.values.shape[1]), data_frame.values)), axis=0)[:-1]
# bottom = np.vstack((np.zeros((data_frame.values.shape[1],), dtype=data_frame.dtype),
# np.cumsum(data_frame.values, axis=0)[:-1]))
yoff = np.zeros(len(data_frame.index.values)) # the bottom values for stacked bar chart
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
if chart_type is not None:
if gp.chart_type is not None:
if isinstance(gp.type, list):
chart_type = gp.chart_type[i]
else:
chart_type = gp.chart_type
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, gp.y_axis_2_series)
xd = data_frame.index
yd = data_frame.ix[:, i]
if chart_type == "line":
linewidth_t = self.get_linewidth(label, gp.linewidth, gp.linewidth_2, gp.linewidth_2_series)
if linewidth_t is None:
linewidth_t = matplotlib.rcParams["axes.linewidth"]
ax_temp.plot(xd, yd, label=label, color=color_spec[i], linewidth=linewidth_t)
elif chart_type == "bar":
ax_temp.bar(xd, yd, label=label, color=color_spec[i], bottom=yoff)
yoff = yoff + yd
elif chart_type == "scatter":
ax_temp.scatter(xd, yd, label=label, color=color_spec[i])
if gp.line_of_best_fit is True:
self.trendline(
ax_temp,
xd.values,
yd.values,
order=1,
color=color_spec[i],
alpha=1,
scale_factor=gp.scale_factor,
)
except:
pass
# format X axis
self.format_x_axis(ax, data_frame, gp)
fig.suptitle(gp.title, fontsize=14 * gp.scale_factor)
if gp.display_source_label == True:
ax.annotate(
"Source: " + gp.source,
xy=(1, 0),
xycoords="axes fraction",
fontsize=7 * gp.scale_factor,
xytext=(-5 * gp.scale_factor, 10 * gp.scale_factor),
textcoords="offset points",
ha="right",
va="top",
color=gp.source_color,
)
if gp.display_brand_label == True:
self.create_brand_label(ax, anno=gp.brand_label, scale_factor=gp.scale_factor)
leg = []
leg2 = []
loc = "best"
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []:
loc = 2
try:
leg = ax.legend(loc=loc, prop={"size": 10 * gp.scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 != []:
leg2 = ax2.legend(loc=1, prop={"size": 10 * gp.scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except:
pass
try:
if gp.display_legend is False:
if leg != []:
leg.remove()
if leg2 != []:
leg.remove()
except:
pass
try:
plt.savefig(gp.file_output, transparent=False)
except:
pass
####### various matplotlib converters are unstable
# convert to D3 format with mpld3
try:
if gp.display_mpld3 == True:
mpld3.save_d3_html(fig, gp.html_file_output)
mpld3.show(fig)
except:
pass
# FRAGILE! convert to Bokeh format
# better to use direct Bokeh renderer
try:
if gp.convert_matplotlib_to_bokeh == True:
from bokeh.plotting import output_file, show
from bokeh import mpl
output_file(gp.html_file_output)
show(mpl.to_bokeh())
except:
pass
# FRAGILE! convert matplotlib chart to Plotly format
# recommend using AdapterCufflinks instead to directly plot to Plotly
try:
if gp.convert_matplotlib_to_plotly == True:
plotly.tools.set_credentials_file(username=gp.plotly_username, api_key=gp.plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style=True)
plot_url = py.plot_mpl(py_fig, filename=gp.plotly_url)
except:
pass
# display in Matplotlib
try:
if gp.silent_display == False:
plt.show()
except:
pass
def graphData(stock, MA1, MA2, dateRange=365):
try:
# variable for txt file, read in next part
stockFile = stock+'.txt'
# multiple variables: Date,close, high, low, open, volume
# for the first element[0], date, read it as YearMonthDay
date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile, delimiter=',', unpack=True, converters={0:mdates.strpdate2num('%Y%m%d')})
# Using the data read in to put into candlestick array
x = 0
y = len(date)
candleAr = []
while x<y:
appendLine = date[x],openp[x],closep[x],highp[x],lowp[x],volume[x]
candleAr.append(appendLine)
x+=1
# using close data with either 12 or 26 day moving average
Av1 = movingaverage(closep, MA1)
Av2 = movingaverage(closep, MA2)
# Starting point of data since we need at least 12 or 26 data points to get an SMAS
SP = len(date[MA2-1:])
label1=str(MA1)+' SMA'
label2=str(MA2)+' SMA'
# background color of .png
#fig = plt.figure(facecolor = '#07000d')
fig = plt.figure(facecolor = '#00355A')
# Version 1.0
# How many graphs appear on png, in this case, it has place for 2 and we are choosing slot 1
# ax1 = plt.subplot(2,1,1)
# Making the graph format more detailed
ax1 = plt.subplot2grid((5,4), (0,0), rowspan=4, colspan=4, axisbg='#30414D')
#Black = #07000d
# Calling the Candlestick function made from the previous while loop
candlestick(ax1, candleAr, width=0.7, colorup='#9eff15', colordown='#ff1717')
# Graphing the date vs moving average starting from SP to current
ax1.plot(date[-SP:],Av1[-SP:],'#5998ff', label = label1, linewidth=1.5)
ax1.plot(date[-SP:],Av2[-SP:],'#e1edf9', label = label2, linewidth=1.5)
#ax1.grid(True)
# Changes color to white
#ax1.grid(True, color='w')
#ax1.set_xticklabels(mdates.num2date(date))
# Shows a max of 10 dates on x-axis, formats it it Year-Month-Day
#ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
#ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
# Changes to lighter color
ax1.yaxis.label.set_color("black")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors = 'black')
#ax1.xlim()
plt.ylabel('Stock price')
maLeg = plt.legend(loc=9, ncol=2, prop={'size':10}, fancybox=True)
maLeg.get_frame().set_alpha(0.4)
# Later, used to tell filler the minimum to fill to.
volumeMin = 0 #volume.min()
# Making the graph format more detailed, also make it black
ax2 = plt.subplot2grid((5,4), (4,0), sharex=ax1, rowspan=4, colspan=4, axisbg='#30414D')
#Black = #07000d
# Changes the bars into teal colored, and facecolored with some fill
ax2.plot(date,volume, '#00ffe8', linewidth = .8)
# Using the volumeMin to start as bottom, for stylistic purpose
ax2.fill_between(date, volumeMin, volume, facecolor='#00ffe8', alpha=0.5)
ax2.axes.yaxis.set_ticklabels([])
ax2.grid(True)
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors = 'black')
ax2.tick_params(axis='y', colors = 'black')
ax1.xaxis_date()
ax2.xaxis_date()
# rotate the date on x-axis for visual appeasement
# This is redundant, as we don't need this, it is remained blacked out
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(90)
# Changed the Volume label to white form Version 1.0, and shifted left
plt.ylabel('Volume', color='black', labelpad=20)
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
sortDate(date,dateRange)
# all adjustments on format of graph png
plt.subplots_adjust(left=.10, bottom=.15, right=.93, top=.95, wspace=.20, hspace=0)
plt.xlabel('Date')
#Version 1.0, adjusted above to match the new scheme of png.
#plt.ylabel('Volume')
plt.suptitle(stock+' Stock Price',color='black')
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
#show(plt)
#plt.show() # show the plot
mpld3.show()
fig.savefig('example.png', facecolor=fig.get_facecolor())
except Exception, e:
print 'failed main loop',str(e)
def func(ra, dec, mag, fov):
# sorting objects under the user input of limiting magnitude
mag_ng = ng[(ng["V"] <= mag)]
mag_ms = ms[(ms['V'] <= mag)]
mag_ty1 = ty1[(ty1['V'] <= mag)]
mag_ty2 = ty2[(ty2['V'] <= mag)]
# breathing space around the fov circle in the plot
xl = ra - fov / 2 - fov / 10
xr = ra + fov / 2 + fov / 10
yb = dec - fov / 2 - fov / 10
yt = dec + fov / 2 + fov / 10
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Right Ascension (degrees)', fontsize=20)
ax.set_ylabel('Declination (degrees)', fontsize=20)
ax.scatter(ra, dec, s=50, marker='P', color='yellow', zorder=10)
# sorting objects in messier_objects.csv according to objects
cl_ms = ms[(ms["TYPE"] == 'OpC') | (ms["TYPE"] == 'GlC') |
(ms["TYPE"] == 'Cl*')]
pn_ms = ms[(ms["TYPE"] == 'PN')]
ga_ms = ms[(ms["TYPE"] == 'G') | (ms["TYPE"] == 'Sy2') |
(ms["TYPE"] == 'IG') | (ms["TYPE"] == 'GiG') |
(ms["TYPE"] == 'GiP') | (ms["TYPE"] == 'SyG') |
(ms["TYPE"] == 'SBG') | (ms["TYPE"] == 'BiC') |
(ms["TYPE"] == 'H2G')]
re_ms = ms[(ms["TYPE"] == 'HII') | (ms["TYPE"] == 'As*') |
(ms["TYPE"] == 'LIN') | (ms["TYPE"] == 'mul') |
(ms["TYPE"] == 'RNe') | (ms["TYPE"] == 'AGN')]
print(
f"Observing RA: {ra} deg, DEC: {dec} deg, FoV: {fov} deg, Limiting Magnitude: {mag}"
)
ax.set_axisbelow(True)
ax.minorticks_on()
ax.grid(which='major', alpha=0.3)
ax.grid(which='minor',
linestyle=':',
linewidth='0.5',
color='black',
alpha=0.3)
ax.set_facecolor('teal')
# scatter messier
mag = cl_ms["V"].fillna(1)
flux = 10**(-mag / 2.5)
p1 = ax.scatter(cl_ms['RAJ2000'],
cl_ms['DEJ2000'],
color='darkorange',
s=17,
zorder=10,
edgecolor="black")
l1 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', '%s')
for i, j, k, l in zip(cl_ms['RAJ2000'], cl_ms['DEJ2000'],
cl_ms['Constellation'], cl_ms["Common Name"])
]
t1 = plugins.PointLabelTooltip(p1, l1)
plugins.connect(fig, t1)
mag = pn_ms["V"].fillna(1)
flux = 10**(-mag / 2.5)
p2 = ax.scatter(pn_ms['RAJ2000'],
pn_ms['DEJ2000'],
color='blue',
s=17,
zorder=10,
edgecolor="black")
l2 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', '%s')
for i, j, k, l in zip(pn_ms['RAJ2000'], pn_ms['DEJ2000'],
pn_ms['Constellation'], pn_ms["Common Name"])
]
t2 = plugins.PointLabelTooltip(p2, l2)
plugins.connect(fig, t2)
mag = ga_ms["V"].fillna(1)
flux = 10**(-mag / 2.5)
p3 = ax.scatter(ga_ms['RAJ2000'],
ga_ms['DEJ2000'],
color='red',
s=17,
zorder=20,
edgecolor="black")
l3 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', "%s")
for i, j, k, l in zip(ga_ms['RAJ2000'], ga_ms['DEJ2000'],
ga_ms['Constellation'], ga_ms["Common Name"])
]
t3 = plugins.PointLabelTooltip(p3, l3)
plugins.connect(fig, t3)
mag = re_ms["V"].fillna(1)
flux = 10**(-mag / 2.5)
p4 = ax.scatter(re_ms['RAJ2000'],
re_ms['DEJ2000'],
c='darkmagenta',
s=17,
edgecolor="black")
l4 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', '%s')
for i, j, k, l in zip(re_ms['RAJ2000'], re_ms['DEJ2000'],
re_ms['Constellation'], re_ms["Common Name"])
]
t4 = plugins.PointLabelTooltip(p4, l4)
plugins.connect(fig, t4)
# scatter ngc
mag = mag_ng['V'].fillna(1)
flux = 10**(-mag / 2.5)
p5 = ax.scatter(mag_ng['RAJ2000'],
mag_ng['DEJ2000'],
c='darkmagenta',
s=80 * flux)
l5 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', '%s')
for i, j, k, l in zip(mag_ng['RAJ2000'], mag_ng['DEJ2000'],
mag_ng['Constellation'], mag_ng['Name'])
]
t5 = plugins.PointLabelTooltip(p5, l5)
plugins.connect(fig, t5)
# scatter tycho-1
mag = mag_ty1['V'].fillna(1)
flux = 10**(-mag / 2.5)
p6 = ax.scatter(mag_ty1['RAJ2000'],
mag_ty1['DEJ2000'],
c='white',
s=80 * flux)
l6 = [
"[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} \v Name {3}]".format(
i, j, k, l, '.2f', '.2f', '%s', '%s')
for i, j, k, l in zip(mag_ty1['RAJ2000'], mag_ty1['DEJ2000'],
mag_ty1['Constellation'], mag_ty1['Name'])
]
t6 = plugins.PointLabelTooltip(p6, l6)
plugins.connect(fig, t6)
# scatter tycho-2
# mag = mag_ty2['V'].fillna(1)
# flux = 10**(-mag/2.5)
# p7 = ax.scatter(mag_ty2['RAJ2000'], mag_ty2['DEJ2000'], c='white', s= 80*flux)
# l7 = ["[RA {0:.5f} \v DEC {1:.5f} \v Constellation {2} ]".format(i,j,k,'.2f','.2f','%s') for i,j,k in zip(mag_ty2['RAJ2000'],mag_ty2['DEJ2000'],mag_ty2['Constellation'])]
# t7 = plugins.PointLabelTooltip(p7,l7)
# plugins.connect(fig, t7)
points = [p1, p2, p3, p4, p5, p6]
labels = [l1, l2, l3, l4, l5, l6]
plugins.connect(fig, ClickInfo(points, labels))
# constellation borders
for i in cb['Constellation'].unique():
x = cb[(cb['Constellation'] == i)]['RAJ2000']
y = cb[(cb['Constellation'] == i)]['DEJ2000']
x1 = x.mean()
y1 = y.mean()
ax.scatter(x, y, color='darkgreen', s=20 / fov)
ax.annotate('%s' % i, (x1, y1), size=13)
ax.add_artist(plt.Circle((ra, dec), color='#00af08', zorder=1, alpha=0.5))
# ax.set_xlim([xl,xr])
# ax.set_ylim([yb,yt])
cluster = mlines.Line2D([], [],
color='darkorange',
marker=cl,
linestyle='None',
markersize=15,
label='Clusters',
markeredgecolor="black")
planetary_neb = mlines.Line2D([], [],
color='blue',
marker=cl,
linestyle='None',
markersize=15,
label='Planetary Nebula',
markeredgecolor="black")
galaxy = mlines.Line2D([], [],
color='Red',
marker=cl,
linestyle='None',
markersize=15,
label='Galaxies',
markeredgecolor="black")
other = mlines.Line2D([], [],
color='darkmagenta',
marker=cl,
linestyle='None',
markersize=15,
label='Other NGC/Messier',
markeredgecolor="black")
stars = mlines.Line2D([], [],
color='white',
marker=cl,
linestyle='None',
markersize=15,
label='Stars',
markeredgecolor="black")
plt.legend(handles=[cluster, planetary_neb, galaxy, other, stars],
labelspacing=2,
ncol=5,
borderpad=1,
loc='lower center')
mpld3.show()
def plotTop40(config, catalog):
from datetime import datetime
from epiweeks import Week
pivot = 'artist'
# we rank artists by most plays, take the top 25 and
# add their played tracks to the playlist.
#pivot = config['pivot'] if 'pivot' in config else 'artist'
result = {}
print(f'Grouping by {pivot}')
for r in catalog:
if r['track'] is None:
continue
artistid = r[pivot]['name']
if pivot == 'track':
artistid += ';;'+r['artist']['name']
week = Week.fromdate(datetime.strptime(r['airdate'], '%Y-%m-%dT%H:%M:%SZ'))
if week not in result:
result[week]={}
if artistid not in result[week]:
result[week][artistid]={'track':r, 'plays':set(), 'songs':set()}
result[week][artistid]['plays'].add(r['airdate']) # count by unique timestamps. Sometimes the playlist has duplicates.
result[week][artistid]['songs'].add((r['artist']['name'],r['track']['name']))
print([w for w in result])
all_results = result
if 0:
all_artists = []
plots = {}
N=10
W=20
weeks = list(sorted(all_results))[-W:]
for w in weeks:
result = all_results[w]
topN = list(sorted(result, key=lambda x: len(result[x]['plays']), reverse=True))[:N]
print(topN)
for i,a in enumerate(topN):
if a not in all_artists:
all_artists.append(a)
if a not in plots:
plots[a]=[]
plots[a].append((w,i))
#ranks.append(topN)
y0_values = dict((k,N-v+1) for v,k in enumerate(all_artists))
x_values = dict([(k,v+1) for (v,k) in enumerate(weeks)])
import matplotlib.pyplot as plt
from math import isnan
fig, ax = plt.subplots(figsize=(12,8)) #subplot_kw=dict(axisbg='#EEEEEE'))
for a in plots:
lookup = dict(plots[a])
X = [x_values[w] for w in weeks] #[x_values[w] for (w,_) in plots[a]]
Y = [N-lookup[w] if w in lookup else float('NaN') for w in weeks] #N-v for (_,v) in plots[a]]
ax.plot(X, Y, 'o-')
for i in range(len(X)):
if i==0 or (not isnan(Y[i]) and isnan(Y[i-1])):
ax.text(X[i],Y[i]+0.15, a, ha='center', fontsize=8)
ax.set_yticks(range(N+1))
ax.set_yticklabels(['']+[str(N-y) for y in range(N)])
plt.show()
threshold = Week.fromdate(datetime(2020,5,26))
summary = {}
from collections import Counter
denom = Counter()
for w in all_results:
isprior = w<threshold
denom[isprior]+=1
current = all_results[w]
for a in current:
if a not in summary:
summary[a]=Counter()
summary[a][isprior]+=len(current[a]['plays'])
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(12,8))
labels = []
N=20
for i,a in enumerate(list(sorted(summary, key = lambda x: summary[x][True]+summary[x][False]))[-N:]):
ax.plot([2*i-0.5,2*i+0.5], [summary[a][True]/denom[True], summary[a][False]/denom[False]],'o-')
labels.append(a)
print('{}\t{}\t{}'.format(a, summary[a][True]/denom[True], summary[a][False]/denom[False]))
ax.set_xticks([2*i for i in range(N)])
ax.set_xticklabels(labels, rotation=90)
ax.set_ylabel('Plays per Week')
plt.tight_layout()
plt.savefig('playsPerWeek.png')
plt.show()
if 0:
import mpld3
from math import log, ceil
data = [(x,y,ctr[(x,y)]) for (x,y) in ctr]
x_track=[d[0] for d in data]
y_artist=[d[1] for d in data]
count = [len(d[2]) for d in data]
labels = [','.join(d[2]) for d in data]
fig, ax = plt.subplots(figsize=(4,2)) #subplot_kw=dict(axisbg='#EEEEEE'))
scatter = ax.scatter(x_track, y_artist, s=[3*log(c+1) for c in count])
xint = range(min(x_track), ceil(max(x_track))+1)
#ax.set_xticks(xint)
ax.set_xlabel('Track Plays')
ax.set_ylabel('Artist Plays')
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
def plot_2d_graph(self, data_frame, gp, chart_type):
if gp is None: gp = GraphProperties()
if gp.chart_type is None and chart_type is None: chart_type = 'line'
if gp.resample is not None: data_frame = data_frame.asfreq(gp.resample)
self.apply_style_sheet(gp)
# create figure & add a subplot
fig = plt.figure(figsize = ((gp.width * gp.scale_factor)/gp.dpi,
(gp.height * gp.scale_factor)/gp.dpi), dpi = gp.dpi)
ax = fig.add_subplot(111)
if gp.x_title != '': ax.set_xlabel(gp.x_title)
if gp.y_title != '': ax.set_ylabel(gp.y_title)
plt.xlabel(gp.x_title)
plt.ylabel(gp.y_title)
fig.suptitle(gp.title, fontsize = 14 * gp.scale_factor)
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset = False)
ax.yaxis.set_major_formatter(y_formatter)
# create a second y axis if necessary
ax2 = []
if gp.y_axis_2_series != []:
ax2 = ax.twinx()
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
# matplotlib 1.5
try:
cyc = matplotlib.rcParams['axes.prop_cycle']
color_cycle = [x['color'] for x in cyc]
except KeyError:
# pre 1.5
pass
# color_cycle = matplotlib.rcParams['axes.color_cycle']
bar_ind = np.arange(0, len(data_frame.index))
# for bar charts, create a proxy x-axis (then relabel)
xd, bar_ind, has_bar, no_of_bars = self.get_bar_indices(data_frame, gp, chart_type, bar_ind)
# plot the lines (using custom palettes as appropriate)
try:
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
color_spec = self.create_color_list(gp, data_frame)
# for stacked bar
yoff_pos = np.zeros(len(data_frame.index.values)) # the bottom values for stacked bar chart
yoff_neg = np.zeros(len(data_frame.index.values)) # the bottom values for stacked bar chart
zeros = np.zeros(len(data_frame.index.values))
# for bar chart
bar_space = 0.2
bar_width = (1 - bar_space) / (no_of_bars)
bar_index = 0
has_matrix = False
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
if gp.chart_type is not None:
if isinstance(gp.chart_type, list):
chart_type = gp.chart_type[i]
else:
chart_type = gp.chart_type
if chart_type == 'heatmap':
# TODO experimental!
# ax.set_frame_on(False)
ax.pcolor(data_frame, cmap=plt.cm.Blues, alpha=0.8)
# plt.colorbar()
has_matrix = True
break
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, gp.y_axis_2_series)
yd = data_frame.ix[:,i]
if color_spec[i] is None:
color_spec[i] = color_cycle[i % len(color_cycle)]
if (chart_type == 'line'):
linewidth_t = self.get_linewidth(label,
gp.linewidth, gp.linewidth_2, gp.linewidth_2_series)
if linewidth_t is None: linewidth_t = matplotlib.rcParams['axes.linewidth']
ax_temp.plot(xd, yd, label = label, color = color_spec[i],
linewidth = linewidth_t)
elif(chart_type == 'bar'):
# for multiple bars we need to allocate space properly
bar_pos = [k - (1 - bar_space) / 2. + bar_index * bar_width for k in range(0,len(bar_ind))]
ax_temp.bar(bar_pos, yd, bar_width, label = label, color = color_spec[i])
bar_index = bar_index + 1
elif(chart_type == 'stacked'):
bar_pos = [k - (1 - bar_space) / 2. + bar_index * bar_width for k in range(0,len(bar_ind))]
yoff = np.where(yd > 0, yoff_pos, yoff_neg)
ax_temp.bar(bar_pos, yd, label = label, color = color_spec[i], bottom = yoff)
yoff_pos = yoff_pos + np.maximum(yd, zeros)
yoff_neg = yoff_neg + np.minimum(yd, zeros)
# bar_index = bar_index + 1
elif(chart_type == 'scatter'):
ax_temp.scatter(xd, yd, label = label, color = color_spec[i])
if gp.line_of_best_fit is True:
self.trendline(ax_temp, xd.values, yd.values, order=1, color= color_spec[i], alpha=1,
scale_factor = gp.scale_factor)
# format X axis
self.format_x_axis(ax, data_frame, gp, has_bar, bar_ind, has_matrix)
except: pass
if gp.display_source_label == True:
ax.annotate('Source: ' + gp.source, xy = (1, 0), xycoords='axes fraction', fontsize=7 * gp.scale_factor,
xytext=(-5 * gp.scale_factor, 10 * gp.scale_factor), textcoords='offset points',
ha='right', va='top', color = gp.source_color)
if gp.display_brand_label == True:
self.create_brand_label(ax, anno = gp.brand_label, scale_factor = gp.scale_factor)
leg = []
leg2 = []
loc = 'best'
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []: loc = 2
try:
leg = ax.legend(loc = loc, prop={'size':10 * gp.scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 != []:
leg2 = ax2.legend(loc = 1, prop={'size':10 * gp.scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except: pass
try:
if gp.display_legend is False:
if leg != []: leg.remove()
if leg2 != []: leg.remove()
except: pass
try:
plt.savefig(gp.file_output, transparent=False)
except: pass
####### various matplotlib converters are unstable
# convert to D3 format with mpld3
try:
# output matplotlib charts externally to D3 based libraries
import mpld3
if gp.display_mpld3 == True:
mpld3.save_d3_html(fig, gp.html_file_output)
mpld3.show(fig)
except: pass
# FRAGILE! convert to Bokeh format
# better to use direct Bokeh renderer
try:
if (gp.convert_matplotlib_to_bokeh == True):
from bokeh.plotting import output_file, show
from bokeh import mpl
output_file(gp.html_file_output)
show(mpl.to_bokeh())
except: pass
# FRAGILE! convert matplotlib chart to Plotly format
# recommend using AdapterCufflinks instead to directly plot to Plotly
try:
import plotly.plotly as py
import plotly
import plotly.tools as tls
if gp.convert_matplotlib_to_plotly == True:
plotly.tools.set_credentials_file(username = gp.plotly_username,
api_key = gp.plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style = True)
plot_url = py.plot_mpl(py_fig, filename = gp.plotly_url)
except:
pass
# display in matplotlib window
try:
if GraphicsConstants.plotfactory_silent_display == True:
return fig
elif gp.silent_display == False:
plt.show()
else:
return fig
except:
pass
def graph_data(stock):
#fig = plt.figure()
#fig.patch.set_facecolor('#DAD1CF')
stockFile = stock + '.txt'
string = str('%Y%m%d%H%M%S')
if period == 'INTRADAY':
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y%m%d%H%M%S')})
pass
else:
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y%m%d')})
ohlc = []
ax1 = plt.subplot2grid((6, 4), (0, 0), rowspan=5, colspan=4)
plt.ylabel('Price ($)')
plt.title(stock + ' (' + time.strftime("%m/%d/%Y") + ')')
ax2 = plt.subplot2grid((6, 4), (5, 0), rowspan=1, colspan=4, sharex=ax1)
for x in range(len(date)):
appendLine = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(appendLine)
if period == 'INTRADAY':
wideness = 0.0005
else:
wideness = 0.2
candlestick_ohlc(ax1,
ohlc,
width=wideness,
colorup='#77d879',
colordown='#db3f3f')
if period == 'INTRADAY':
string = str('%I:%m')
elif period == '1y':
string = str('%m/%d')
else:
string = str('%m/%d/%Y')
ax1.xaxis.set_major_formatter(mdates.DateFormatter(string))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(12))
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=2, prune='upper'))
ax1.grid(True)
ax2.grid(True)
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.bar(date, (highp - lowp), width=0.0005)
plt.xlabel('Date, period: ' + period)
plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90)
plt.setp(ax1.get_xticklabels(), visible=False)
print('graphing stock information ', end='')
if outputType == 'WEBSITE':
print('to website')
mpld3.show()
else:
print('to terminal')
plt.show()
x = np.linspace(-2, 2, 20)
y = x[:, None]
X = np.zeros((20, 20, 4))
X[:, :, 0] = np.exp(- (x - 1) ** 2 - (y) ** 2)
X[:, :, 1] = np.exp(- (x + 0.71) ** 2 - (y - 0.71) ** 2)
X[:, :, 2] = np.exp(- (x + 0.71) ** 2 - (y + 0.71) ** 2)
X[:, :, 3] = np.exp(-0.25 * (x ** 2 + y ** 2))
im = ax.imshow(X, extent=(10, 20, 10, 20),
origin='lower', zorder=1, interpolation='nearest')
fig.colorbar(im, ax=ax)
ax.text(16, 16, "overlaid text")
ax.text(16, 15, "covered text", zorder=0)
ax.set_title('An Image', size=20)
ax.set_xlim(9, 21)
ax.set_ylim(9, 21)
return fig
def test_imshow():
fig = create_plot()
html = mpld3.fig_to_html(fig)
plt.close(fig)
if __name__ == "__main__":
mpld3.show(create_plot())
def PCA2d(sleppa, toflur):
# prófa fyrst að taka bara costoflivingindex og veðrið í viku 30
# töflurnar geta verið prices, costoflivingindex, qualityoflifeindex, weathereurope og weatherchanceseurope
pca = PCA(n_components=2)
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(9, 7))
if (len(toflur) == 1):
if "WeatherEurope" in toflur or "WeatherChancesEurope" in toflur:
query = cur.execute("SELECT * FROM " + toflur[0] +
" where week = 20")
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(),
columns=cols)
cities = results['city'].tolist()
#results.drop(sleppa[0], axis=1)
dropColumns = ['week', 'city']
if toflur[0] == "WeatherEurope":
dropColumns.append('cloud_cover')
results.drop(columns=dropColumns, inplace=True)
results = results.apply(pd.to_numeric)
resultsScaled = preprocessing.StandardScaler().fit_transform(
results)
principalComponentsScaled = pca.fit_transform(resultsScaled)
principalDfScaled = pd.DataFrame(
data=principalComponentsScaled,
columns=['principal component 1', 'principal component 2'])
N = len(cities)
col = np.random.rand(N)
print(
pd.DataFrame(
pca.components_, columns=results.columns).sort_values(
axis=1, by=0,
ascending=False)) #.to_csv(path_or_buf="PCA.csv")
# aðal meginþættirnir hafa verið prentaðir
mynd = ax.scatter(principalDfScaled['principal component 1'],
principalDfScaled['principal component 2'],
c=col,
alpha=0.5)
ax.set_title("2-dimensional PCA of weather in Europe in late June")
if "CostOfLivingIndex" in toflur or "QualityOfLifeIndex" in toflur:
query2 = cur.execute("SELECT * from " + toflur[0])
cols = [column[0] for column in query2.description]
results2 = pd.DataFrame.from_records(data=query2.fetchall(),
columns=cols)
cities = results2['City'].tolist()
#results2.drop(sleppa[1], axis=1)
results2.drop(columns=['City', 'Rank'], inplace=True)
results2 = results2.apply(pd.to_numeric)
results2Scaled = preprocessing.StandardScaler().fit_transform(
results2)
principalComponentsScaled2 = pca.fit_transform(results2Scaled)
principalDfScaled2 = pd.DataFrame(
data=principalComponentsScaled2,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(cities))
mynd = ax.scatter(principalDfScaled2['principal component 1'],
principalDfScaled2['principal component 2'],
c=col,
alpha=0.5)
print(
pd.DataFrame(
pca.components_, columns=results2.columns).sort_values(
axis=1, by=0,
ascending=False)) #.to_csv(path_or_buf="PCA.csv")
# aðal meginþættirnir hafa verið prentaðir
if ("CostOfLivingIndex" in toflur):
ax.set_title("2-dimensional PCA of cost of living indeces")
else:
ax.set_title("2-dimensional PCA of quality of life indeces")
if "Prices" in toflur:
query3 = cur.execute("SELECT * from " + toflur[0])
cols = [column[0] for column in query2.description]
print(cols)
results3 = pd.DataFrame.from_records(data=query3.fetchall(),
columns=cols)
cities3 = results3['City'].tolist()
irrelevantAttributes = [
"City", "Monthly Pass", "Volkswagen Golf", "Toyota Corolla",
"Basic Utilities", "Internet", "Fitness Club",
"Tennis Court Rent", "Preschool Month", "Primary School Year",
"Rent 1 Bedroom Center"
"Rent 1 Bedroom Outside Center"
"Rent 3 Bedrooms Center", "Rent 3 Bedrooms Outside Center",
"Price m2 Center", "Price m2 Outside Center",
"Average Monthly Salary After Tax", "Mortgage Interest Rate"
]
#results3.drop(sleppa[1], axis=1)
results3.drop(columns=irrelevantAttributes, inplace=True)
results3 = results3.apply(pd.to_numeric)
results3Scaled = preprocessing.StandardScaler().fit_transform(
results3)
principalComponentsScaled3 = pca.fit_transform(results3Scaled)
principalDfScaled3 = pd.DataFrame(
data=principalComponentsScaled3,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(cities3))
mynd = ax.scatter(principalDfScaled3['principal component 1'],
principalDfScaled3['principal component 2'],
c=col,
alpha=0.5)
ax.set_title("2-dimensional PCA of prices of goods")
print(
pd.DataFrame(
pca.components_, columns=results3.columns).sort_values(
axis=1, by=0,
ascending=False)) #.to_csv(path_or_buf="PCA.csv")
# aðal meginþættirnir hafa verið prentaðir
else:
# sameina
toflulisti = []
for tafla in toflur:
if tafla == "WeatherEurope" or tafla == "WeatherChancesEurope":
query = cur.execute("SELECT * FROM " + tafla +
" where week = 20")
cols = [column[0] for column in query.description]
results = pd.DataFrame.from_records(data=query.fetchall(),
columns=cols)
cities = results['city'].tolist()
results.drop(sleppa[0], axis=1)
toflulisti.append(results)
if tafla == "CostOfLivingIndex" or tafla == "QualityOfLifeIndex":
query2 = cur.execute("SELECT * from " + tafla)
cols = [column[0] for column in query2.description]
results2 = pd.DataFrame.from_records(data=query2.fetchall(),
columns=cols)
cities = results2['City'].tolist()
results2.drop(sleppa[1], axis=1)
results2.drop("Rank", axis=1, inplace=True)
results2.rename(index=str,
columns={"City": "city"},
inplace=True)
toflulisti.append(results2)
if tafla == "Prices":
print("vantar atm")
saman = reduce((lambda results, results2: pd.merge(results,
results2,
how='inner',
left_on=['city'],
right_on=['city'])),
toflulisti)
cities = list(saman["city"])
samandrop = ['city', 'week', 'cloud_cover']
saman.drop(columns=samandrop, inplace=True)
samanScaled = preprocessing.StandardScaler().fit_transform(saman)
principalComponentsScaledSaman = pca.fit_transform(samanScaled)
principalDfScaledSaman = pd.DataFrame(
data=principalComponentsScaledSaman,
columns=['principal component 1', 'principal component 2'])
col = np.random.rand(len(saman))
mynd = ax.scatter(principalDfScaledSaman['principal component 1'],
principalDfScaledSaman['principal component 2'],
c=col,
alpha=0.5)
print(
pd.DataFrame(pca.components_, columns=saman.columns).sort_values(
axis=1, by=0,
ascending=False)) #.to_csv(path_or_buf="PCA.csv")
# aðal meginþættirnir hafa verið prentaðir
# frekar en að hafa results, results2 o.s.frv. þá get ég haft lista/dict results["WeatherEurope"], results["CostOfLivingIndex"]
# Prices og QualityOfLifeIndex er keimlíkt CostOfLivingIndex
# WeatherChancesEurope er keimlíkt WeatherEurope
# nema það þarf að droppa öðrum dálkum
ax.set_xlabel('Principal Component 1', fontsize=15)
ax.set_ylabel('Principal Component 2', fontsize=15)
if (len(toflur) > 1):
title = "2-dimensional PCA of " + toflur[0]
i = 1
for tafla in toflur[1:]:
if i != (len(toflur) - 1):
title = title + ", " + tafla + " "
else:
title = title + " and " + tafla
i = i + 1
ax.set_title(title)
# vantar að staðfesta að röðin sé eins?
tooltip = mpld3.plugins.PointLabelTooltip(mynd, labels=cities)
mpld3.plugins.connect(fig, tooltip)
mpld3.show()
"""Plot to test line styles"""
import matplotlib.pyplot as plt
import numpy as np
import mpld3
from mpld3 import plugins
def create_plot():
fig, ax = plt.subplots()
points = ax.plot(range(10), 'o', ms=20)
plugins.connect(fig,
plugins.PointLabelTooltip(points[0], location="top left"))
return fig
def test_tooltips_basic():
fig = create_plot()
html = mpld3.fig_to_html(fig)
plt.close(fig)
if __name__ == "__main__":
mpld3.show(create_plot())
def create_d3_visualisation(self):
vectors_matrix = np.vstack(
[nlp(text).vector for text in tqdm(self.texts)])
self.dist = 1 - cosine_similarity(vectors_matrix)
km = KMeans(n_clusters=self.num_clusters)
km.fit(vectors_matrix)
clusters = km.labels_.tolist()
mds = MDS(n_components=2, dissimilarity="precomputed", random_state=1)
pos = mds.fit_transform(self.dist) # shape (n_components, n_samples)
xs, ys = pos[:, 0], pos[:, 1]
cluster_names = {i: f'Cluster {i}' for i in range(self.num_clusters)}
text_label_and_text = [
': '.join(t) for t in list(zip(self.text_labels, self.texts))
]
# create data frame that has the result of the MDS plus the cluster numbers and titles
df = pd.DataFrame(
dict(x=xs, y=ys, clusters=clusters, title=text_label_and_text))
# group by cluster
groups_clusters = df.groupby('clusters')
# set up plot
fig, ax = plt.subplots(figsize=(25, 15)) # set size
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
# iterate through groups to layer the plot
# note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return
# the appropriate color/label
for name, group in groups_clusters:
ax.plot(group.x,
group.y,
marker='o',
linestyle='',
ms=12,
label=cluster_names[name],
color=self.cluster_color[name],
mec='none')
ax.set_aspect('auto')
ax.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off')
ax.tick_params(
axis='y', # changes apply to the y-axis
which='both', # both major and minor ticks are affected
left='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelleft='off')
ax.legend(numpoints=1) # show legend with only 1 point
# add label in x,y position with the label as the
for i in range(len(df)):
ax.text(df.ix[i]['x'], df.ix[i]['y'], df.ix[i]['title'], size=8)
# uncomment the below to save the plot if need be
plt.savefig(
f'clusters_static-{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}.png',
dpi=200)
# Plot
fig, ax = plt.subplots(figsize=(20, 15)) # set plot size
ax.margins(0.03) # Optional, just adds 5% padding to the autoscaling
# iterate through groups to layer the plot
for name, group in groups_clusters:
points = ax.plot(group.x,
group.y,
marker='o',
linestyle='',
ms=18,
label=cluster_names[name],
mec='none',
color=self.cluster_color[name])
ax.set_aspect('auto')
labels = [i for i in group.title]
# set tooltip using points, labels and the already defined 'css'
tooltip = mpld3.plugins.PointHTMLTooltip(points[0],
labels,
voffset=10,
hoffset=10,
css=CSS)
# connect tooltip to fig
mpld3.plugins.connect(fig, tooltip, TopToolbar())
# set tick marks as blank
ax.axes.get_xaxis().set_ticks([])
ax.axes.get_yaxis().set_ticks([])
# set axis as blank
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.legend(numpoints=1) # show legend with only one dot
mpld3.show() # show the plot
