def doRender(self, handlerId):
if self.canRenderChart(handlerId) == False:
self._addHTML("Unable to find a numerical column in the dataframe")
return
mpld3.enable_notebook()
fig, ax = plt.subplots()
keyFields = self.getKeyFields(handlerId)
keyFieldValues = self.getKeyFieldValues(handlerId, keyFields)
keyFieldLabels = self.getKeyFieldLabels(handlerId, keyFields)
valueFields = self.getValueFields(handlerId)
valueFieldValues = self.getValueFieldValueLists(handlerId, keyFields, valueFields)
context = self.getMpld3Context(handlerId)
options = {"fieldNames":self.getFieldNames(),"aggregationSupported":self.supportsAggregation(handlerId),"aggregationOptions":["SUM","AVG","MIN","MAX","COUNT"]}
if (context is not None):
options.update(context[1])
dialogBody = self.renderTemplate(context[0], **options)
else:
dialogBody = self.renderTemplate("baseChartOptionsDialogBody.html", **options)
plugins.connect(fig, ChartPlugin(self, keyFieldLabels))
plugins.connect(fig, DialogPlugin(self, handlerId, dialogBody))
self.doRenderMpld3(handlerId, fig, ax, keyFields, keyFieldValues, keyFieldLabels, valueFields, valueFieldValues)
self.setChartSize(handlerId, fig, ax)
self.setChartGrid(handlerId, fig, ax)
self.setChartLegend(handlerId, fig, ax)
def mpld3_enable_notebook():
"""Change the default plugins, enable ipython notebook mode and return mpld3 module."""
import mpld3
from mpld3 import plugins as plugs
plugs.DEFAULT_PLUGINS = [plugs.Reset(), plugs.Zoom(), plugs.BoxZoom(), plugs.MousePosition()]
mpld3.enable_notebook()
return mpld3
def mpld3_enable_notebook():
"""Change the default plugins, enable ipython notebook mode and return mpld3 module."""
import mpld3
from mpld3 import plugins as plugs
plugs.DEFAULT_PLUGINS = [plugs.Reset(), plugs.Zoom(), plugs.BoxZoom(), plugs.MousePosition()]
mpld3.enable_notebook()
return mpld3
def bar_plot(self, positive, negative, neutral, word_search,
number_of_terms):
objects = ('positive', 'negative', 'neutral')
performance = (positive, negative, neutral)
y_pos = np.arange(len(objects))
plt.bar(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Percentage')
plt.xlabel('Sentiments')
plt.show()
names = 'POSITIVE', 'NEGATIVE', 'NEUTRAL'
size = [positive, negative, neutral]
# create a figure and set different background
fig = plt.figure()
fig.patch.set_facecolor('black')
# Change color of text
plt.rcParams['text.color'] = 'white'
# Create a circle for the center of the plot
my_circle = plt.Circle((0, 0), 0.7, color='black')
# Pieplot + circle on it
plt.pie(size, labels=names)
p = plt.gcf()
p.gca().add_artist(my_circle)
plt.show()
plt.plot([positive, negative, neutral], 'ks-', mec='y', mew=2, ms=15)
plt.ylabel('Percentage')
plt.xlabel('Sentiments')
mpld3.enable_notebook()
mpld3.show()
def renderFigure(self, fig):
def genMarkup(chartFigure):
return self.env.from_string("""
{0}
{{%for message in messages%}}
<div>{{{{message}}}}</div>
{{%endfor%}}
""".format(chartFigure)).render(messages=self.messages)
if not self.useMpld3:
import base64
try:
from io import BytesIO as pngIO
except ImportError:
from StringIO import StringIO as pngIO
png = pngIO()
plt.savefig(png,
pad_inches=0.05,
bbox_inches='tight',
dpi=self.getDPI())
try:
return (genMarkup("""
<center><img style="max-width:initial !important" src="data:image/png;base64,{0}" class="pd_save"></center>
""".format(
base64.b64encode(png.getvalue()).decode("ascii"))))
finally:
png.close()
else:
mpld3.enable_notebook()
try:
return genMarkup(mpld3.fig_to_html(fig))
finally:
mpld3.disable_notebook()
def renderFigure(self, fig):
def genMarkup(chartFigure):
return self.env.from_string("""
{0}
{{%for message in messages%}}
<div>{{{{message}}}}</div>
{{%endfor%}}
""".format(chartFigure)
).render(messages=self.messages)
if not self.useMpld3:
import base64
try:
from io import BytesIO as pngIO
except ImportError:
from StringIO import StringIO as pngIO
png=pngIO()
plt.savefig(png, pad_inches=0.05, bbox_inches='tight', dpi=self.getDPI())
try:
return(
genMarkup("""
<center><img style="max-width:initial !important" src="data:image/png;base64,{0}" class="pd_save"></center>
""".format(base64.b64encode(png.getvalue()).decode("ascii"))
)
)
finally:
png.close()
else:
mpld3.enable_notebook()
try:
return genMarkup(mpld3.fig_to_html(fig))
finally:
mpld3.disable_notebook()
def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms):
labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]',
'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]']
sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative]
colors = ['yellowgreen','lightgreen','darkgreen', 'gold', 'red','lightsalmon','darkred']
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.title('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.')
plt.axis('equal')
plt.tight_layout()
plt.show()
mpld3.enable_notebook()
mpld3.show()
def renderFigure(self, fig, dialogBody):
if self.options.get("staticFigure","false") is "true":
import StringIO
png=StringIO.StringIO()
plt.savefig(png)
self._addHTMLTemplate("mpld3Chart.html",
mpld3Figure="""<img src="data:image/png;base64,{0}">""".format(png.getvalue().encode('base64')),
optionsDialogBody=dialogBody)
plt.close(fig)
else:
mpld3.enable_notebook()
self._addHTMLTemplate("mpld3Chart.html", mpld3Figure=mpld3.fig_to_html(fig), optionsDialogBody=dialogBody)
plt.close(fig)
mpld3.disable_notebook()
def plot_cumulative_returns_by_intervals(self, lower_bound_list,
upper_bound_list, freq):
factor = self.factor
#想要查看的因子区间策略的数量
num_strategies = len(upper_bound_list)
#基准策略
baseline = self.baseline_generator()
color = [
'green', 'skyblue', 'purple', 'black', 'pink', 'yellow', 'orange',
'grey', 'brown', 'blue', 'coral'
]
y0 = baseline['累计收益率'].values
time = baseline['交易日期'].values
time_start = time[0]
time_end = time[-1]
import matplotlib as mpl
import matplotlib.pyplot as plt
import mpld3
mpld3.enable_notebook()
plt.rcParams['figure.figsize'] = [9, 6]
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']
plt.figure()
plt.title('%s - %s 累计收益率(不同%s)' % (time_start, time_end, factor))
plt.plot(time, y0, color='red', label='baseline')
for i in range(num_strategies):
upper_bound = upper_bound_list[i]
lower_bound = lower_bound_list[i]
strategy = self.strategy_factor_interval(lower_bound, upper_bound,
freq)
y = strategy['累计收益率'].values
print('第%s个策略的因子区间为：%s' % (i + 1, strategy['票池因子区间'].values))
print('第%s个策略的参与的股票数目为：%s' % (i + 1, strategy['参与股票数目'].values))
plt.plot(time,
y,
color=color[i],
label='因子为%s、因子区间为[%s,%s)的策略' %
(factor, lower_bound, upper_bound))
plt.ylim((0, 6))
plt.legend() # 显示图例
plt.xlabel('time')
plt.ylabel('累计收益率')
plt.show()
def _init_ipython(backend):
"""Tries `backend`. Returns ``inline`` backend if default requested or
unable to load requested backend.
"""
if backend == uti_plot.DEFAULT_BACKEND:
backend = 'inline'
is_mpld3 = backend == 'mpld3'
b = 'inline' if is_mpld3 else backend
get_ipython().magic('matplotlib ' + b)
if is_mpld3:
try:
import mpld3
mpld3.enable_notebook()
except:
print('mpld3: backend unavailable; plots will be inline')
backend = 'inline'
return backend
def _init_ipython(self, backend):
"""Tries `backend`. Returns ``inline`` backend if default requested or
unable to load requested backend.
"""
if backend == uti_plot.DEFAULT_BACKEND:
backend = 'inline'
is_mpld3 = backend == 'mpld3'
b = 'inline' if is_mpld3 else backend
get_ipython().magic('matplotlib ' + b)
if is_mpld3:
try:
import mpld3
mpld3.enable_notebook()
except:
print('mpld3: backend unavailable; plots will be inline')
backend = 'inline'
return backend
def doRender(self, handlerId):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import mpld3
mpld3.enable_notebook()
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
ax.grid(color='white', linestyle='solid')
N = 50
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.set_title("D3 Scatter Plot", size=18)
def prepare_image_data(image_data): im = np.array(image_data, dtype=np.uint8) # Create figure and axes fig,ax = plt.subplots(1) # Adjust the width = 15 height = width * image_data.size[1] / image_data.size[0] fig.set_size_inches(width, int(height), forward=True) plugins.connect(fig, plugins.MousePosition(fontsize=14)) mpld3.enable_notebook() # Display the image ax.imshow(im) return ax
def mplPlotLine(self): mpld3.enable_notebook() plt.errorbar(self.data[0],self.data[1],self.data[2], aa=self.antialiasing, marker=self.marker, ls=self.linestyle, c=self.color, lw=self.linewidth, mfc=self.markerfacecolor, mec=self.markeredgecolor, ms=self.markersize, mew=self.markeredgesize) plt.xlim(self.xlim[0],self.xlim[1]) plt.xlabel(self.xlabel) plt.ylabel(self.ylabel) plt.grid(self.grid)
def doRender(self, handlerId):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import mpld3
mpld3.enable_notebook()
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
ax.grid(color='white', linestyle='solid')
N = 50
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s = 1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.set_title("D3 Scatter Plot", size=18);
def doRender(self, handlerId):
displayColName = self.getNumericalColsInfo()
if len(displayColName) < 2:
self._addHTML("Unable to find two numerical columns in the dataframe")
return
mpld3.enable_notebook()
x = self.entity.select(displayColName[0]).toPandas()[displayColName[0]].dropna().tolist()
y = self.entity.select(displayColName[1]).toPandas()[displayColName[1]].dropna().tolist()
plt.rcParams['font.size']=11
plt.rcParams['figure.figsize']=[6.0, 5.0]
fig, ax=plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
ax.grid(color='white', linestyle='solid')
scatter = ax.scatter(x,y,c=y,marker='o',alpha=0.7,s=124,cmap=plt.cm.ocean)
ax.set_title("D3 Scatter Plot", size=18);
ax.set_xlabel(displayColName[0], size=14)
ax.set_ylabel(displayColName[1], size=14)
def plot_cumulative_returns_by_quantiles(self, freq, fraction):
factor = self.factor
all_strategies = self.strategy_comparison(freq, fraction)
baseline = self.baseline_generator()
color = [
'green', 'skyblue', 'purple', 'black', 'pink', 'yellow', 'orange',
'grey', 'brown', 'blue'
]
y0 = baseline['累计收益率'].values
time = baseline['交易日期'].values
time_start = time[0]
time_end = time[-1]
import matplotlib as mpl
import matplotlib.pyplot as plt
import mpld3
mpld3.enable_notebook()
plt.rcParams['figure.figsize'] = [9, 6]
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']
plt.figure()
plt.title('%s - %s 累计收益率(不同%s)' % (time_start, time_end, factor))
plt.plot(time, y0, color='red', label='baseline')
for i in range(1, fraction + 1):
y = all_strategies['累计收益率%s' % (i)].values
print('第%s部分的因子区间为：%s' % (i, all_strategies['票池因子区间%s' %
(i)].values))
print('第%s部分的参与的股票数目为：%s' % (i, all_strategies['参与股票数目%s' %
(i)].values))
plt.plot(time,
y,
color=color[i - 1],
label='strategy with %s smallest %s' % (i, factor))
plt.ylim((0, 6))
plt.legend() # 显示图例
plt.xlabel('time')
plt.ylabel('累计收益率')
plt.show()
def mplPlotLine(self):
mpld3.enable_notebook()
plt.errorbar(self.data[0],
self.data[1],
self.data[2],
aa=self.antialiasing,
marker=self.marker,
ls=self.linestyle,
c=self.color,
lw=self.linewidth,
mfc=self.markerfacecolor,
mec=self.markeredgecolor,
ms=self.markersize,
mew=self.markeredgesize)
plt.xlim(self.xlim[0], self.xlim[1])
plt.xlabel(self.xlabel)
plt.ylabel(self.ylabel)
plt.grid(self.grid)
def plot_decomposed_embeddings(ce_object):
"""
@param ce_object ContextEmbeddings object
Plots the embedding representation (decomposed to 2 dimensions)
"""
if ce_object.decomp_dims == 2:
fig, ax = plt.subplots(figsize=(10, 8))
points = ax.plot(
ce_object.decomposed_embedding_representation[:, 0],
ce_object.decomposed_embedding_representation[:, 1],
"o",
color="b",
mec="k",
ms=5,
mew=1,
alpha=0.6,
)
ax.set_title(
f"{ce_object.decomp_dims}D {ce_object.decomp_method} for contexts of '{ce_object.word}'",
size=16,
)
labels = [
ce_object.contexts[i] for i in range(len(ce_object.contexts))
]
tooltip = mpld3.plugins.PointHTMLTooltip(points[0],
labels,
voffset=10,
hoffset=10)
mpld3.plugins.connect(fig, tooltip)
mpld3.enable_notebook()
else:
print("Can't plot with dimension > 2.")
import openpyxl as opx
import json
import matplotlib.pyplot as plt
import operator
import numpy as np
from openpyxl import load_workbook
from pandas import DataFrame, read_csv
from IPython.display import HTML
import folium
try:
import mpld3
from mpld3 import enable_notebook
from mpld3 import plugins
enable_notebook()
except Exception as e:
print "Attempt to import and enable mpld3 failed", e
class FarmersMarket:
URL ='http://www.ams.usda.gov/AMSv1.0/getfile?dDocName=STELPRDC5087258'
DEBUG = False
def __init__(self):
#Parsing local CSV.
location = r'/Desktop/WorkingWithOpenData/FarmersMarkets.csv'
df = pd.read_csv(location)
df = pd.DataFrame.from_csv(location)
df.columns
def sort_top_states(df):
#df -> DataFrame
def plot_defaultdicts(self, default_dicts, show_max=True, bucket='huanan', figsize_wh=(15, 8), legend_size=12):
import matplotlib.pyplot as plt
% matplotlib inline
import numpy as np
import mpld3
mpld3.enable_notebook()
import os
import moxing as mox
import tempfile
if not isinstance(default_dicts, list):
default_dicts = [default_dicts]
if not isinstance(show_max, list):
show_max = [show_max]
assert len(show_max) == len(default_dicts)
fig, axes = self.get_fig_axes(rows=len(default_dicts), cols=1, figsize_wh=figsize_wh)
bucket = self.root_obs_dict[bucket]
root_dir = os.path.expanduser('~/results')
label2datas_list = []
for idx, default_dict in enumerate(default_dicts):
data_xlim = None
axes_prop = default_dict.get('properties')
if axes_prop is not None:
if 'xlim' in axes_prop:
data_xlim = axes_prop['xlim'][-1]
label2datas = {}
# for each result dir
for (result_dir, label2file) in default_dict.items():
if result_dir == 'properties':
continue
# for each texlog file
for label, file in label2file.items():
filepath = os.path.join(root_dir, result_dir, file)
filepath_obs = os.path.join(bucket, result_dir, file)
if not mox.file.exists(filepath_obs):
print("=> Not exist: '%s'" % filepath_obs)
continue
mox.file.copy(filepath_obs, filepath)
# get modified time
modi_minutes = self.get_last_md_inter_time(filepath_obs)
data = np.loadtxt(filepath, delimiter=':')
data = data.reshape(-1, 2)
# limit x in a range
if data_xlim:
data = data[data[:, 0] <= data_xlim]
itr_val_str = self.get_itr_val_str(data, show_max[idx])
label_str = f'{itr_val_str}' + f'-{modi_minutes:03d}m---' + label
axes[idx].plot(data[:, 0], data[:, 1], label=label_str, marker='.', linewidth='5', markersize='15', alpha=0.5)
label2datas[label] = data
axes[idx].legend(prop={'size': legend_size})
axes[idx].set(**default_dict['properties'])
axes[idx].grid(b=True, which='major', color='#666666', linestyle='--', alpha=0.2)
label2datas_list.append(label2datas)
return label2datas_list
# 0 32.0 # 5 41.0 # 10 50.0 # 15 59.0 # 20 68.0 # 25 77.0 # 30 86.0 # 35 95.0 # 40 104.0 # # In[4]: from mpld3 import enable_notebook enable_notebook() rad = np.arange(-100, 100) plot(sin(rad / 10.)) show() # Out[4]: # image file: # ## Text manipulation # ### Edit replace # In[35]:
def points(
im='http://msutoday.msu.edu/_/img/assets/2013/beaumont-spring-1.jpg',
point_color='black',
point_radius=2):
# The user can input a url of an image (there is a default image), a point color (default is black), and point radius (default is 2).
# Here are some libraries you will need to use.
import scipy.misc as misc
from urllib.request import urlopen
from scipy.misc import imread, imsave
import matplotlib.pyplot as plt
import matplotlib.image as img
import sys
sys.path.append('./packages')
import mpld3
from mpld3 import plugins
mpld3.enable_notebook()
# This checks is the input point radius is a valid number. If not, it sets the point radius to 2.
try:
point_radius = float(point_radius)
except ValueError:
point_radius = 2
# Plots the image in the notebook
def plot(imgname):
fig, ax = plt.subplots()
im = img.imread(imgname)
plt.imshow(im, origin='lower')
return fig
# Function called in the notebook
def pickpoints(fig='', radius=4, color="white", x='x', y='y'):
if not fig:
fig = plt.gcf()
plugins.connect(fig, Annotate(radius, color, x,
y)) # color='htmlcolorname', radius=int
plugins.connect(fig, plugins.MousePosition())
# Main class that contains Javascript code to create and drag circles
class Annotate(plugins.PluginBase):
"""A plugin that creates points in a figure by clicking the mouse"""
JAVASCRIPT = r"""
mpld3.register_plugin("annotate", Annotate);
Annotate.prototype = Object.create(mpld3.Plugin.prototype);
Annotate.prototype.constructor = Annotate;
Annotate.prototype.requiredProps = [];
Annotate.prototype.defaultProps = {radius: 4, color: "white", x: 'x', y: 'y'};
function Annotate(fig, props){
mpld3.Plugin.call(this, fig, props);
};
Annotate.prototype.draw = function(){
/// NECESSARY STARTUP VARIABLES ///
var fig = this.fig;
var ax = fig.axes;
var dataset = [];
var svg = d3.select(".mpld3-figure"); // existing svg element
var radius = this.props.radius;
var color = this.props.color;
var x = this.props.x;
var y = this.props.y;
var ax = fig.axes[0];
/// INDEXES HTML DOC TO PULL VALUES FOR x,y CALIBRATION ///
var xcal = this.parent.axes[0].position[0];
var ycal = this.parent.axes[0].position[1];
console.log('x calibration: ' + xcal);
console.log('y calibration: ' + ycal);
var xcommand = x+" = []";
IPython.notebook.kernel.execute(xcommand);
var ycommand = y+" = []";
IPython.notebook.kernel.execute(ycommand);
////////// CREATE POINT COMPONENT //////////
var update_coords = function() {
return function() {
var pos = d3.mouse(this),
xpos = ax.x.invert(pos[0]),
ypos = ax.y.invert(pos[1]);
var newpoint = {
cx: pos[0] + xcal,
cy: pos[1] + ycal,
r: radius,
fill: color
};
dataset.push(newpoint);
var circles = svg.selectAll("circle")
.data(dataset)
.enter()
.append("circle")
.attr(newpoint)
.call(drag);
var xcommand = x+".append("+xpos+")";
IPython.notebook.kernel.execute(xcommand);
console.log(xcommand);
var ycommand = y+".append("+ypos+")";
IPython.notebook.kernel.execute(ycommand);
console.log(ycommand);
};
}();
ax.baseaxes
.on("mousedown", update_coords);
////////// DRAG POINT COMPONENT //////////
var drag = d3.behavior.drag()
.on("dragstart", dragstarted)
.on("drag", dragged)
.on("dragend", dragended);
function dragstarted(d) {
d3.event.sourceEvent.stopPropagation();
d3.select(this).classed("dragging", true);
}
function dragged(d) {
d3.select(this).attr("cx", d3.event.x)
.attr("cy", d3.event.y);
}
function dragended(d, i) {
d3.event.sourceEvent.stopPropagation();
d3.select(this).classed("dragging", false);
var calib_cx = d3.select(this)[0][0].cx.animVal.value - xcal;
var calib_cy = d3.select(this)[0][0].cy.animVal.value - ycal;
var xcommand = x+"["+i+"] = "+ax.x.invert(calib_cx);
var ycommand = y+"["+i+"] = "+ax.y.invert(calib_cy);
IPython.notebook.kernel.execute(xcommand);
IPython.notebook.kernel.execute(ycommand);
console.log(xcommand);
console.log(ycommand);
}
};"""
def __init__(self, radius=4, color="white", x='x', y='y'):
self.dict_ = {
"type": "annotate",
"radius": radius,
"color": color,
"x": x,
"y": y
}
# Opens and displays the image
if type(im) == str:
with urlopen(im) as file:
im2 = imread(file, mode='RGB')
im = im2
fig = plt.figure(figsize=(9, 6))
plt.imshow(im)
# Runs the pickpoints function to allow the user to pick points on the image
pickpoints(color=point_color,
radius=point_radius,
x='xcoords',
y='ycoords')
def make_interactive_graph(graph,
pos=None,
cmap=plt.cm.viridis,
edge_cmap=plt.cm.Reds,
node_size_factor=5):
fig = plt.figure(1, figsize=(8, 6), dpi=100)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
weights = [d["weight"] for (u, v, d) in graph.edges(data=True)]
degrees = nx.degree(graph)
sizes = [degrees[name] * node_size_factor for name in graph.nodes()]
urls = [d["url"] for u, d in graph.nodes(data=True)]
if pos is None:
pos = nx.spring_layout(graph)
nodes = nx.draw_networkx_nodes(graph,
pos=pos,
node_size=sizes,
node_color=sizes,
cmap=cmap,
alpha=0.3)
edges = nx.draw_networkx_edges(graph,
pos=pos,
edge_color=weights,
edge_cmap=edge_cmap,
arrows=False,
alpha=0.3)
tip_div = u"<div style='background: #FFF; border-style: solid; border-width: 1px; padding: 2px;'>{}</div>"
html_labels = list()
for node in graph.nodes():
neighbors = graph[node]
neighbor_list = sorted([(neighbor, graph[node][neighbor]["weight"])
for neighbor in graph[node]],
key=lambda tup: tup[1],
reverse=True)[:3]
list_label = u"<br />".join([
u"{} ({})".format(neighbor[0], neighbor[1])
for neighbor in neighbor_list
])
html_labels.append(
tip_div.format(u"<strong>{}</strong><br/><i>{}</i>".format(
node, list_label)))
line_labels = [
tip_div.format(
u"<strong>{}</strong> and <strong>{}</strong> <i>({})</i>".format(
u, v, d["weight"])) for (u, v, d) in graph.edges(data=True)
]
tooltips = mpld3.plugins.PointHTMLTooltip(nodes,
labels=html_labels,
voffset=-10,
hoffset=10)
linetips = mpld3.plugins.PointHTMLTooltip(edges,
labels=line_labels,
voffset=-5,
hoffset=10)
mpld3.plugins.connect(fig, ClickInfo(nodes, urls))
mpld3.plugins.connect(fig, Highlight(nodes))
mpld3.plugins.connect(fig, Highlight(edges))
mpld3.plugins.connect(fig, tooltips)
mpld3.plugins.connect(fig, linetips)
mpld3.enable_notebook()
return mpld3.fig_to_html(fig)
def d3():
import mpld3
mpld3.enable_notebook()
yield
mpld3.disable_notebook()
def plot(self,
plot_pred_price: bool = True,
plot_buy_sell_point: bool = True,
in_range: list = [0, None],
save: bool = False):
if len(in_range) != 2:
raise Exception("in_range must be a list size of 2.")
in_range[1] = len(
self.real_hist_price) if in_range[1] == None else in_range[1]
if in_range[0] == None or in_range[1] > len(
self.real_hist_price
) or in_range[0] < 0 or in_range[0] > in_range[1]:
raise Exception("invalid values in in_range parameter.")
b_x, b_points, b_units = [], [], []
s_x, s_points, s_units = [], [], []
reduce_units_b, reduce_units_s = {}, {}
for rec in self.trade_record:
if rec[0] == 'b':
if rec[1] >= in_range[0] and rec[1] < in_range[1]:
b_x.append(rec[1])
b_points.append(rec[2])
b_units.append(rec[3])
elif rec[0] == 's':
if rec[1] >= in_range[0] and rec[1] < in_range[1]:
s_x.append(rec[1])
s_points.append(rec[2])
s_units.append(rec[3])
for idx, z in enumerate(
[zip(b_x, b_points, b_units),
zip(s_x, s_points, s_units)]):
for val in z:
if idx == 0:
if val[0] in list(reduce_units_b):
reduce_units_b[val[0]][1] += val[2]
else:
reduce_units_b[val[0]] = [val[1], val[2]]
else:
if val[0] in list(reduce_units_s):
reduce_units_s[val[0]][1] += val[2]
else:
reduce_units_s[val[0]] = [val[1], val[2]]
pluged = False
mpld3.enable_notebook()
real_days = np.arange(
in_range[0],
len(self.real_hist_price[in_range[0]:in_range[1]]) + in_range[0])
pred_days = np.arange(
in_range[0],
len(self.pred_ema[in_range[0]:in_range[1]]) + in_range[0])
fig, ax = plt.subplots()
ax.set_title(self.model_type)
ax.plot(real_days,
self.real_hist_price[in_range[0]:in_range[1]],
'black',
label='Actual')
if plot_pred_price:
ax.plot(real_days,
(self.pred.reshape(-1, 1)[:, 0])[in_range[0]:in_range[1]],
c='orange',
ls='--',
label='Predict')
ax.plot(pred_days,
self.pred_ema[in_range[0]:in_range[1]],
'blue',
label='Predict-EMA')
ax.set_xlabel('Days')
ax.set_ylabel('Close Price')
ax.legend()
if plot_buy_sell_point:
if len(reduce_units_b) != 0:
b_scat = ax.scatter(
list(reduce_units_b),
np.array(list(reduce_units_b.values()))[:,
0].astype('float'),
c='g',
linewidths=1)
b_tooltip = mpld3.plugins.PointLabelTooltip(
b_scat,
labels=np.array(list(
reduce_units_b.values()))[:, 1].astype('float'))
mpld3.plugins.connect(fig, b_tooltip)
pluged = True
if len(reduce_units_s) != 0:
s_scat = ax.scatter(
list(reduce_units_s),
np.array(list(reduce_units_s.values()))[:,
0].astype('float'),
c='r',
linewidths=1)
s_tooltip = mpld3.plugins.PointLabelTooltip(
s_scat,
labels=np.array(list(
reduce_units_s.values()))[:, 1].astype('float'))
mpld3.plugins.connect(fig, s_tooltip)
pluged = True
# if show_buy_sell_units:
# pluged = True
# for redu in [reduce_units_b, reduce_units_s]:
# for idx in range(len(redu)):
# if len(redu) != 0:
# ax.annotate(np.array(list(redu.values()))[idx,1].astype('float'), (list(redu)[idx], np.array(list(redu.values()))[idx,0].astype('float')))
if save:
if pluged:
mpld3.save_html(
fig,
f'plot_{self.model_type}_{datetime.datetime.now().strftime("%Y%m%d%H%M%S")}.html'
)
else:
plt.savefig(
f'plot_{self.model_type}_{datetime.datetime.now().strftime("%Y%m%d%H%M%S")}.png'
)
# coding: utf-8 # In[33]: # Only needed in a Jupyter Notebook get_ipython().magic(u'matplotlib inline') # In[34]: #plots will get a toolbar menu at the bottom left import mpld3 mpld3.enable_notebook( ) #http://stackoverflow.com/questions/10655217/ipython-notebook-pylab-inline-zooming-of-a-plot # In[35]: __author__ = 'Ali' # rev 5.1 (2015-10-08 13:43) # update 2016-03-31 import numpy as np import itertools as itr import subprocess import os import pprint as pp import sys import fileinput import matplotlib.pyplot as plt from scipy.interpolate import UnivariateSpline from scipy.optimize import curve_fit """ unsolved issue & TODO tasks indicated by !!!!
# In[]
print('start')
for x in range(0, 3):
print(x)
print('end')
#%%
plt.plot([1, 2, 3, 2, 3, 2, 2, 1])
plt.show()
#%%
import matplotlib.pyplot as plt
import numpy as np
import mpld3
mpld3.enable_notebook()
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
ax.grid(color='white', linestyle='solid')
N = 50
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.set_title("D3 Scatter Plot", size=18)
#%%
def plot_optimization_evolution_2d(evolution_data,
*args,
obj_func=None,
**kwargs):
"""For a given population, plot their positions in solution space in 2d over time.
Arguments
---------
evolution_data: list over iterations where each entry is a dict of population members
holding their 2d positions as a list
xlims (optional): list of lower and upper x lim for plot
ylims (optional): list of lower and upper y lim for plot
obj_func: name of the objective function to contour plot in the background
"""
mpld3.enable_notebook()
fig, ax = viz_utils.setup_figure_1ax(x_label='x position',
y_label='y position',
size=(8, 8),
shrink_ax=False)
if obj_func:
if obj_func not in OBJ_FUNCS:
raise NotImplementedError(
f'{obj_func} is not implemented for plotting. '
f'Only {list(OBJ_FUNCS.keys())} can be plotted.\n'
f'Feel free to implement them in python :)')
mpld3.disable_notebook(
) # contour plots cannot be json serialized so we have to switch of d3 mode
min_x, max_x, min_y, max_y = viz_utils.get_min_max_of_evolution_data(
evolution_data)
x_list = np.linspace(min_x, max_x, 100)
y_list = np.linspace(min_y, max_y, 100)
x_mesh, y_mesh = np.meshgrid(x_list, y_list)
z_mesh = OBJ_FUNCS[obj_func](np.vstack(
[x_mesh.ravel(), y_mesh.ravel()])).reshape((100, 100))
ax.contourf(
x_mesh,
y_mesh,
z_mesh,
[rosenbrock(np.array([k, k])) for k in np.linspace(1, 20, 50)],
cmap='jet',
locator=ticker.LogLocator(),
alpha=0.1)
for iter_idx, step_dict in enumerate(evolution_data):
if iter_idx == 0:
color = 'black'
else:
color = viz_utils.get_color_from_cm(cm.hot, 1, len(evolution_data),
iter_idx + 1)
x = [pos[0] for pos in step_dict.values()]
y = [pos[1] for pos in step_dict.values()]
ax.plot(x, y, '.', color=color, alpha=0.7, markeredgewidth=0.0)
if 'xlims' in kwargs:
ax.set_xlim(kwargs['xlims'])
if 'ylims' in kwargs:
ax.set_ylim(kwargs['ylims'])
if 'title' in kwargs:
ax.set_title(kwargs['title'])
return fig, ax
def doRenderMpld3(self, handlerId, figure, axes, keyFields, keyFieldValues, keyFieldLabels, valueFields, valueFieldValues):
allNumericCols = self.getNumericalFieldNames()
if len(allNumericCols) == 0:
self._addHTML("Unable to find a numerical column in the dataframe")
return
keyFields = self.options.get("keyFields")
valueField = self.options.get("valueFields")
if(keyFields==None and valueField==None):
keyFields=self.getFirstStringColInfo()
valueField=self.getFirstNumericalColInfo()
else:
keyFields = keyFields.split(',')
valueField = valueField.split(',')
if(len(valueField) > 1):
self._addHTML("You can enter only have one value field for Bar Charts (2-D)"+str(len(valueField)))
return
keyFields = keyFields[0]
valueField=valueField[0]
#if(len(valueFields>)):
#init
fig=figure
ax=axes
#fig, ax = plt.subplots()
#fig = plt.figure()
params = plt.gcf()
plSize = params.get_size_inches()
params.set_size_inches( (plSize[0]*2, plSize[1]*2) )
agg=self.options.get("aggregation")
groupByCol=self.options.get("groupByCol")
if (agg=="None" or agg==None):
colLabel = keyFields
y = self.entity.select(valueField).toPandas()[valueField].dropna().tolist()
x_intv = np.arange(len(y))
labels = self.entity.select(keyFields).toPandas()[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel(valueField, fontsize=18)
elif(agg=='AVG'):
y1=self.entity.groupBy(keyFields).agg(F.avg(valueField).alias("avg")).toPandas().sort_values(by=keyFields)
y=y1["avg"].dropna().tolist()
x_intv = np.arange(len(y))
labels=y1[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel("Average "+valueField, fontsize=18)
elif(agg=='SUM'):
y1=self.entity.groupBy(keyFields).agg(F.sum(valueField).alias("sum")).toPandas().sort_values(by=keyFields)
y=y1["sum"].dropna().tolist()
x_intv = np.arange(len(y))
labels=y1[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel("sum "+valueField, fontsize=18)
elif(agg=='MAX'):
y1=self.entity.groupBy(keyFields).agg(F.max(valueField).alias("max")).toPandas().sort_values(by=keyFields)
y=y1["max"].dropna().tolist()
x_intv = np.arange(len(y))
labels=y1[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel("max "+valueField, fontsize=18)
elif(agg=='MIN'):
y1=self.entity.groupBy(keyFields).agg(F.min(valueField).alias("min")).toPandas().sort_values(by=keyFields)
y=y1["min"].dropna().tolist()
x_intv = np.arange(len(y))
labels=y1[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel("min "+valueField, fontsize=18)
elif(agg=='COUNT'):
y1=self.entity.groupBy(keyFields).agg(F.count(valueField).alias("count")).toPandas().sort_values(by=keyFields)
y=y1["count"].dropna().tolist()
x_intv = np.arange(len(y))
labels=y1[keyFields].dropna().tolist()
plt.xticks(x_intv,labels)
plt.xlabel(keyFields, fontsize=18)
plt.ylabel("count "+valueField, fontsize=18)
mpld3.enable_notebook()
plt.bar(x_intv,y,color="blue",alpha=0.5)
ax_fmt = BarChart(labels)
mpld3.plugins.connect(fig, ax_fmt)
vsx, vsx_x, vsx_y = catalog_search(
ccd.wcs, ccd.shape, 'B/vsx/vsx', 'RAJ2000', 'DEJ2000')
vsx_names = vsx['Name']
print(ccd.shape)
print(type(ccd))
# In[5]:
disp = scale_and_downsample(ccd.data, downsample=1)
# In[6]:
import mpld3
mpld3.enable_notebook()
# mpld3.disable_notebook()
# In[7]:
plt.figure(figsize=(12, 7))
plt.imshow(disp, cmap='gray', origin='lower')
plt.scatter(vsx_x, vsx_y, c='none', s=100, edgecolor='cyan')
plt.title('Blue: VSX, Yellow: APASS', fontsize=20)
for x, y, m in zip(vsx_x, vsx_y, vsx_names):
plt.text(x, y, str(m), fontsize=18, color='cyan')
plt.scatter(in_apass_x, in_apass_y, c='none', s=50,
edgecolor='yellow', alpha=0.5, marker='o')
def plot( # plot all the data
x,
xconf,
date,
yconfirmados,
outputconf,
erfoutputconf,
cumconf,
erfcumconf,
ysuspeitos,
Diamaxconf,
amplitudeconf,
):
mpld3.enable_notebook()
fig = plt.figure(1)
fig.suptitle("Casos de COVID-19 em portugal", fontsize=14, fontweight="bold")
ax1 = fig.add_subplot(411)
ax2 = fig.add_subplot(412)
ax3 = fig.add_subplot(413)
ax4 = fig.add_subplot(414)
fig.subplots_adjust(top=0.80)
ax1.set_title(
"Ajustes logístico e de Gompertz"
+ "\n"
+ "Confirmados: máximo a "
+ str(Diamaxconf.strftime("%d/%m/%Y"))
+ " com "
+ str(amplitudeconf)
+ " casos."
)
# ax1.set_ylabel("Casos")
ax1.plot(x, yconfirmados, "ro", label="Casos confirmados")
ax1.plot(xconf, outputconf, label="Fit Logístico")
ax1.xaxis.set_visible(False)
#ax1.set_ylim(-5, 15000)
ax1.legend()
ax2.set_ylabel("Casos")
ax2.bar(date, cumconf, width=0.8, label="Casos novos", color="g")
ax2.xaxis.set_visible(False)
ax2.legend()
ax3.plot(x, yconfirmados, "mo", label="Casos confirmados")
ax3.plot(xconf, erfoutputconf, label="Fit Gompertz")
ax3.xaxis.set_visible(False)
# ax3.set_ylim(-5, 10000)
ax3.legend()
ax4.set_ylabel("Casos")
bars = ax4.bar(date, erfcumconf, width=0.8, label="Gompertz Casos novos", color="g")
ax4.legend()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7))
plt.gcf().autofmt_xdate()
plt.savefig("prediction.pdf")
plt.savefig("prediction.png")
plt.show()