def update_clustering_figure(selectedData, value):
print("c")
selectedData = json.loads(selectedData)
kmeans_val = value
clickpoint = selectedData['selectedData']
print(clickpoint)
if clickpoint != 'null':
clickpoint = json.loads(clickpoint)
print("in here")
clicked_type = clickpoint['points'][0]['label']
# if prev_clicked_cluster_type != clicked_type:
# prev_clicked_cluster_type = clicked_type
local_df = df[df.Type_1 == clicked_type]
stats = local_df.iloc[:, 5:11]
normalized_stats = stats
for i in stats.columns:
mini, maxi = stats[i].min(), stats[i].max()
normalized_stats[i] = (stats[i] - mini) / (maxi - mini)
pca = PCA(n_components=2).fit(normalized_stats)
stats2d = pca.transform(normalized_stats)
df_stats2d = pd.DataFrame(stats2d, index=local_df.index)
model, z = cluster(kmeans_val, normalized_stats.iloc[:, 0:5])
trace = go.Scatter(x=df_stats2d.iloc[:, 0],
y=df_stats2d.iloc[:, 1],
text=local_df['Name'],
name='',
mode='markers',
marker=go.Marker(opacity=0.5, color=z),
showlegend=False)
else:
print("error")
stats = df.iloc[:, 5:11]
normalized_stats = stats
for i in stats.columns:
mini, maxi = stats[i].min(), stats[i].max()
normalized_stats[i] = (stats[i] - mini) / (maxi - mini)
pca = PCA(n_components=2).fit(normalized_stats)
stats2d = pca.transform(normalized_stats)
model, z = cluster(kmeans_val, normalized_stats.iloc[:, 0:5])
trace = go.Scatter(x=stats2d[:, 0],
y=stats2d[:, 1],
text=df['Name'],
name='',
mode='markers',
marker=go.Marker(opacity=0.5, color=z),
showlegend=False)
layout = go.Layout(
title='k-means clustering of catch_rate and total stats',
xaxis=go.XAxis(showgrid=False, zeroline=False, showticklabels=False),
yaxis=go.YAxis(showgrid=False, zeroline=False, showticklabels=False),
hovermode='closest')
data = go.Data([trace])
fig = go.Figure(data=data, layout=layout)
return fig
def highlight_point(hoverData):
xy = pd.DataFrame()
xy['neighbourhood'] = df['id_neighbourhood'].unique()
x = list(df[(df.year == 2019) & (
df.variable == 'Average weight of airbnb inside the neighbourhood'
)]['Nominal ']) - (df[(df.year == 2015) & (
df.variable == 'Average weight of airbnb inside the neighbourhood')]
['Nominal '])
y = (list(df[(df.year == 2019) &
(df.variable == 'Average renting price/m2')]['Nominal ']) -
df[(df.year == 2015) &
(df.variable == 'Average renting price/m2')]['Nominal ']) / df[
(df.year == 2015)
& (df.variable == 'Average renting price/m2')]['Nominal ']
xy['x'] = x.values
xy['y'] = y.values
xy['total_houses'] = total_houses['total_houses'].values
xy['name'] = df['neighbourhood'].unique()
if len(hoverData) == 1:
point_highlight = (go.Scatter(
x=xy[xy['neighbourhood'] == hoverData['points'][0]
['location']]['x'],
y=xy[xy['neighbourhood'] == hoverData['points'][0]
['location']]['y'],
mode='markers',
showlegend=False,
marker=go.Marker(size=14,
line={'width': 5},
color='orange',
symbol='circle-open')))
if len(fig_scatter['data']) == 2:
fig_scatter['data'][1] = point_highlight
else:
fig_scatter['data'].append(point_highlight)
return fig_scatter
def plot_LDA(data, features):
X = data[features]
y = data["categoria"]
X_std = StandardScaler().fit_transform(X)
LDA = LinearDiscriminantAnalysis()
Y = LDA.fit_transform(X_std, y)
results = []
for name in (2, 3, 13):
result = go.Scatter(x=Y[y == name, 0],
y=Y[y == name, 1],
mode="markers",
name=name,
marker=go.Marker(size=8,
line=go.Line(
color="rgba(225,225,225,0.2)",
width=0.5),
opacity=0.75))
results.append(result)
data = go.Data(results)
layout = go.Layout(xaxis=go.XAxis(title="CP1", showline=False),
yaxis=go.YAxis(title="CP2", showline=False))
fig = go.Figure(data=data, layout=layout)
py.iplot(fig)
return fig
def plotly_histogram2(X, columns, target):
colors = {
2: 'rgb(255,127,20)',
3: 'rgb(31, 220, 120)',
13: 'rgb(44, 50, 180)'
}
traces = []
_targets = sorted(X[target].unique().tolist())
legend = {2: True, 3: True, 13: True}
for col in range(2):
for key in range(len(_targets)):
traces.append(
go.Histogram(x=X[X[target] == _targets[key]][columns[col]],
opacity=0.7,
xaxis="x%s" % (col + 1),
marker=go.Marker(color=colors[_targets[key]]),
name=_targets[key],
showlegend=legend[_targets[key]]))
legend = {2: False, 3: False, 13: False}
data = go.Data(traces)
layout = go.Layout(barmode="overlay",
xaxis=go.XAxis(domain=[0, 0.48], title=columns[0]),
xaxis2=go.XAxis(domain=[0.52, 1], title=columns[1]),
yaxis=go.YAxis(title="Numero de Defectos"),
title="Histograma caracteristicas")
fig = go.Figure(data=data, layout=layout)
py.iplot(fig)
return fig
def draw(solver, t0=0, x0=13.41265629, y0=13.46430003, z0=33.46156416):
for r in rs:
ts, xs, ys, zs = solver(r, t0, x0, y0, z0)
print('r = {0}'.format(r))
plt.plot(ts, xs, 'red')
plt.show()
plt.plot(ts, ys, 'green')
plt.show()
plt.plot(ts, zs, 'blue')
plt.show()
fig = go.Figure(
data=[go.Scatter3d(x=xs, y=ys, z=zs, marker=go.Marker(size=1))])
fig.show()
def test_update_overwrite_data(self):
fig = go.Figure(
data=[go.Bar(marker_color="blue"),
go.Bar(marker_color="yellow")])
fig.update(overwrite=True,
data=[go.Marker(y=[1, 3, 2], line_color="yellow")])
self.assertEqual(
fig.to_plotly_json()["data"],
[{
"type": "scatter",
"y": [1, 3, 2],
"line": {
"color": "yellow"
}
}],
)
def form_show(self, **event_args):
"""This method is called when the column panel is shown on the screen"""
self.label_2.text = Globals.rmse
x_month = Globals.x_month
item_lst = Globals.item_lst
data_lst = Globals.data_lst
colors = ['Red', 'Green', 'Blue', 'Yellow', 'Brown']
plot_data = list()
for i in range(len(data_lst)):
scatter = go.Scatter(x=x_month,
y=data_lst[i],
name=str(item_lst[i]),
marker=go.Marker(color=colors[i % 5]))
plot_data.append(scatter)
self.plot_2.data = plot_data
pass
# array=lnyError,
# visible=True),
#
# error_x=dict(
# type='data', # value of error bar given in data coordinates
# array=lnxError,
# visible=True)
# )
#
#line = go.Scatter(
# x=x,
# y=line,
# mode='lines',
# marker=go.Marker(color='rgb(31, 119, 180)'),
# name='Linear Regression'
# )
line2 = go.Scatter(x=x,
y=line2,
mode='lines',
marker=go.Marker(color='rgb(31, 119, 180)'),
name='Linear Regression')
#data1 = [Parallel, line]
#fig1 = go.Figure(data=data1,layout=layout)
data2 = [Ring, line2]
fig2 = go.Figure(data=data2, layout=layout2)
#py.offline.plot(fig1, filename='Parallel')
py.offline.plot(fig2, filename='Ring')
# title='Distribucion de los rasgos de las diferentes flores iris')
# fig = ir.Figure(data=data, layout=layout)
# fig.show()
# Point 1 (Estandarizar data)
X_std = StandardScaler().fit_transform(X)
traces = []
legend = {0:True, 1:True, 2:True, 3:True}
colors = {'setosa': 'rgb(255,0,0)', 'versicolor': 'rgb(0,255,0)', 'virginica': 'rgb(0,0,255)'}
for col in range(4):
for key in colors:
traces.append(ir.Histogram(x=X_std[y==key, col], opacity=0.7, xaxis="x%s"%(col+1), marker=ir.Marker(color=colors[key]), name=key, showlegend=legend[col]))
legend = {0:False, 1:False, 2:False, 3:False}
data = ir.Data(traces)
layout = ir.Layout(barmode='overlay',
xaxis=ir.layout.XAxis(domain=[0, 0.25], title='Long. Sepalos (cm)'),
xaxis2=ir.layout.XAxis(domain=[0.3, 0.5], title='Anch. Sepalos (cm)'),
xaxis3=ir.layout.XAxis(domain=[0.55, 0.75], title='Long. Petalos (cm)'),
xaxis4=ir.layout.XAxis(domain=[0.8, 1], title='Anch. Petalos (cm)'),
yaxis=ir.layout.YAxis(title='Numero de ejemplares'),
title='Distribucion de los rasgos de las diferentes flores iris')
fig = ir.Figure(data=data, layout=layout)
fig.show()
# Point 2 (Obtener los vectores y valores propios 'matrix de covarianza')
def visualize(df_viz, mapbox_token):
df_viz['tp'] = df_viz['tp'] * 0.0393701 # conversion from mm to in
# max_precip = 6 # limiting the highest daily precipitation for coloring plot
# df_viz['tp'][df_viz['tp'] >= max_precip] = max_precip
# df_viz.tail(20)
# colorscale = [[0.0, '#171c42'], [0.07692307692307693, '#263583'], [0.15384615384615385, '#1a58af'], [0.23076923076923078, '#1a7ebd'], [0.3076923076923077, '#619fbc'], [0.38461538461538464, '#9ebdc8'], [0.46153846153846156, '#d2d8dc'], [0.5384615384615384, '#e6d2cf'], [0.6153846153846154, '#daa998'], [0.6923076923076923, '#cc7b60'], [0.7692307692307693, '#b94d36'], [0.8461538461538461, '#9d2127'], [0.9230769230769231, '#6e0e24'], [1.0, '#3c0911']]
# https://github.com/plotly/plotly.js/blob/5bc25b490702e5ed61265207833dbd58e8ab27f1/src/components/colorscale/scales.js
colorscale = [
[0, 'rgb(5,10,172)'],
[0.35, 'rgb(40,60,190)'],
[0.5, 'rgb(70,100,245)'],
[0.6, 'rgb(90,120,245)'],
[0.7, 'rgb(106,137,247)'], # fades out into:
[1, 'rgba(255,255,255,0.05)'] # white nearly invisible
]
val_min = df_viz['tp'].min()
val_max = df_viz['tp'].max()
# print(val_max, val_min)
data = []
# https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf?_ga=2.113218049.441476779.1587291103-1421256715.1585761166
data.append(
go.Scattermapbox(
lon=df_viz['lonbin'].values,
lat=df_viz['latbin'].values,
mode='markers',
text=df_viz['tp'].values,
marker=go.Marker(
cmax=val_max,
cmin=val_min,
color=df_viz['tp'].values,
colorscale=colorscale,
reversescale=True,
showscale = True,
opacity = 0.65
),
)
)
layout = go.Layout(
margin=dict(t=0,b=0,r=0,l=0),
autosize=True,
hovermode='closest',
showlegend=True,
mapbox=dict(
accesstoken=mapbox_token,
bearing=0,
center=dict(
lat=45,
lon=-122
),
pitch=0,
zoom=4,
style='dark'
),
)
fig = go.Figure(data=data, layout=layout)
fig.show()
# print out how long this script run took
ending = datetime.now()
print('Ending:', ending)
print()
print('Elapsed:', ending - starting)
def front_page_map():
pd.options.mode.chained_assignment = None
# mapbox_access_token = 'pk.eyJ1IjoibWFrcy1zaCIsImEiOiJjajRnMGhyNzcxZGFzMnd1ZnZteHF1YXo3In0.5R_XD1wl8F7ffCjAN1yxLg'
terror_data = pd.read_csv(
'gtd_data/globalterrorismdb_0718dist.csv',
encoding='ISO-8859-1',
usecols=[0, 1, 2, 3, 8, 11, 12, 13, 14, 29, 35, 84, 98, 101])
terror_data = terror_data.rename(
columns={
'eventid': 'id',
'iyear': 'year',
'imonth': 'month',
'iday': 'day',
'country_txt': 'country',
'provstate': 'state',
'city': 'city',
'targtype1_txt': 'target',
'weaptype1_txt': 'weapon',
'attacktype1_txt': 'attacktype',
'nkill': 'nkill',
'nwound': 'nwound',
'addnotes': 'info'
})
terror_data['nkill'] = terror_data['nkill'].fillna(0).astype(int)
terror_data['nwound'] = terror_data['nwound'].fillna(0).astype(int)
terror_data = terror_data.dropna(how='any',
subset=['latitude', 'longitude'])
terror_pak = terror_data[terror_data['country'] == 'Pakistan']
terror_india = terror_data[terror_data['country'] == 'India']
terror_afghan = terror_data[terror_data['country'] == 'Afghanistan']
terror_pak_assassination = terror_pak[terror_pak['attacktype'] ==
'Assassination']
terror_pak.loc[:, 'day'] = terror_pak.apply(
lambda row: str(row['id'])[6:8] if row['day'] == 0 else row['day'],
axis=1)
# terror_pak.loc[:, 'date'] = pd.to_datetime(terror_pak[['day', 'month', 'year']])
terror_pak = terror_pak.drop_duplicates(['latitude', 'longitude', 'nkill'])
terror_peryear = np.asarray(terror_pak.groupby('year').year.count())
terror_years = np.arange(1970, 2016)
terror_years = np.delete(terror_years, [1])
trace1 = go.Scattergeo(
geo='geo3',
lon=terror_pak[(terror_pak['year'] == 2011)]['longitude'],
lat=terror_pak[(terror_pak['year'] == 2011)]['latitude'],
mode='markers',
marker=go.Marker(
size=3,
opacity=0.7,
color='#A60000',
),
text=terror_pak[(terror_pak['year'] == 2011)]['country'])
trace2 = go.Scattergeo(
geo='geo3',
lon=terror_india[(terror_india['year'] == 2011)]['longitude'],
lat=terror_india[(terror_india['year'] == 2011)]['latitude'],
mode='markers',
marker=go.Marker(
size=3,
opacity=0.7,
color='#B60000',
),
text=terror_india[(terror_india['year'] == 2011)]['country'])
# data=trace1
data = [trace1, trace2]
layout = go.Layout(
title='Terrorist attacks Occurring in Pakistan & India',
height=600,
# width=1000,
autosize=True,
dragmode='zoom',
geo3=dict(
scope='asia',
showlakes=True,
showocean=True,
# oceancolor='rgb(255, 255, 255)',
showland=True,
showcountries=True,
countrywidth=2,
countrycolor='rgb(200, 200, 200)',
)
# geo3=dict(projection=dict(type='orthographic', ),scope='asia', showlakes=False,showocean=True,showland=True,showcountries=True,)
)
fig = go.Figure(data=data, layout=layout)
return fig
def create_visuals(test_name, ux, uy, id):
f = open("Sites.txt", 'r')
temp_sites = f.read()
temp_sites = temp_sites.split("\n")
temp_sites.pop()
sites = {}
for site in temp_sites:
site = site.split(", ")
site[0] = int(site[0])
site[1] = float(site[1])
site[2] = float(site[2])
site[3] = str(site[3])
site[4] = str(site[4])
site[5] = str(site[5])
sites[site[0]] = site
f = open("Flows.txt", 'r')
temp_flows = f.read()
temp_flows = temp_flows.split("\n")
temp_flows.pop()
flows = []
for flow in temp_flows:
flow = flow.split(", ")
flow[0] = int(flow[0])
flow[1] = int(flow[1])
flow[2] = float(flow[2])
flow[3] = float(flow[3])
flow[4] = int(flow[4])
flows.append(flow)
if ux != -1 and uy != -1 and id != -1:
ux = [ux]
uy = [uy]
user_click_trace = go.Scatter(x=ux,
y=uy,
mode='markers',
text=[id],
hoverinfo='text',
marker=dict(color='green',
size=10,
line_width=2))
real_sites = []
for site in sites.keys():
if sites[site][5] == "True":
real_sites.append([sites[site][1], sites[site][2]])
Xr = [real_sites[k][0] for k in range(len(real_sites))]
Yr = [real_sites[k][1] for k in range(len(real_sites))]
realSites_trace = go.Scatter(x=Xr,
y=Yr,
mode='markers',
hoverinfo='none',
marker=dict(color='red',
size=10,
line_width=2))
midpoints = []
for flow in flows:
x0 = sites[flow[0]][1]
y0 = sites[flow[0]][2]
x1 = sites[flow[1]][1]
y1 = sites[flow[1]][2]
temp_length = flow[2]
new_x = (x0 + x1) / 2
new_y = (y0 + y1) / 2
midpoints.append([temp_length, new_x, new_y])
Xm = [midpoints[k][1] for k in range(len(midpoints))]
Ym = [midpoints[k][2] for k in range(len(midpoints))]
flow_lengths = [
"This: " + str(flows[k][2]) + "\t Upstream: " + str(flows[k][3]) +
"\t straihler: " + str(flows[k][4]) for k in range(len(flows))
]
midpoint_trace = go.Scatter(x=Xm,
y=Ym,
mode='markers',
text=flow_lengths,
hoverinfo='text',
marker=go.Marker(opacity=0))
G = nx.Graph()
my_nodes = range(len(sites))
my_edges = [(flows[k][0], flows[k][1]) for k in range(len(flows))]
G.add_nodes_from(my_nodes)
G.add_edges_from(my_edges)
sL = list(sites.values())
Xn = []
Yn = []
for k in range(len(sL)):
Xn.append(sL[k][1])
Yn.append(sL[k][2])
labels = [sL[k][0] for k in range(len(sL))]
node_labels = [
"ID: " + str(sL[k][0]) + "\nAssigned ID: " + str(sL[k][3]) +
"\nDownward Ref ID:" + sL[k][4] for k in range(len(sL))
]
node_trace = go.Scatter(x=Xn,
y=Yn,
mode='markers',
text=node_labels,
hoverinfo='text',
marker=dict(color='white', size=7, line_width=2))
Xe = []
Ye = []
Xdict = dict()
Ydict = dict()
for k in range(len(sL)):
Xdict[sL[k][0]] = sL[k][1]
Ydict[sL[k][0]] = sL[k][2]
for e in G.edges():
x0 = Xdict[e[0]]
y0 = Ydict[e[0]]
x1 = Xdict[e[1]]
y1 = Ydict[e[1]]
Xe.append(x0)
Xe.append(x1)
Xe.append(None)
Ye.append(y0)
Ye.append(y1)
Ye.append(None)
edge_trace = go.Scatter(x=Xe,
y=Ye,
line=dict(width=0.5, color='#888'),
mode='lines')
axis = dict(
showline=False, # hide axis line, grid, ticklabels and title
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
layout = dict(
title=test_name,
font=dict(family='Balto'),
width=1500,
height=1000,
autosize=False,
showlegend=False,
xaxis=axis,
yaxis=axis,
hovermode='closest',
plot_bgcolor='#bad7ff', #set background color
)
if ux != -1 and uy != -1 and id != -1:
fig = dict(data=[
user_click_trace, node_trace, edge_trace, midpoint_trace,
realSites_trace
],
layout=layout)
else:
fig = dict(
data=[node_trace, edge_trace, midpoint_trace, realSites_trace],
layout=layout)
fig['layout'].update(annotations=make_annotations(Xn, Yn, labels))
plot(fig)
def plot_exponential_graph_set(time, t_c, t_h, power, resistance, room_temp):
data_dict = {
"T_c": (t_c, "#17becf", 40, 20),
"T_h": (t_h, "#de1738", 40, -20),
"\dot{W}": (power, "#7851a9", 40, -20),
}
scatters = []
trendlines = []
annotations = []
for dataname, datapair in data_dict.items():
# Use dogleg algorithm
popt, pcov = curve_fit(exponential, time, datapair[0], method="dogbox")
# Prep constants for return values
if dataname == "T_c":
tc_coeffs = popt
elif dataname == "T_h":
th_coeffs = popt
elif dataname == "\dot{W}":
w_coeffs = popt
# Compute R^2 value
r_squared = round(metrics.r2_score(exponential(time, *popt), datapair[0]), 3)
xx = np.linspace(time[0], time[-1], 2000)
yy = exponential(xx, *popt)
if dataname == "T_c" or dataname == "T_h":
# Creating the dataset, and generating the plot
scatters.append(
go.Marker(
x=time,
y=datapair[0] + room_temp,
mode="markers",
marker=go.Marker(color=list(range(140)), colorscale="Viridis"),
name="",
showlegend=False,
)
)
trendlines.append(
go.Marker(
x=xx,
y=yy + room_temp,
mode="lines",
marker=go.Marker(color=datapair[1]),
name="${{{}}}$".format(dataname),
)
)
annotations.append(
go.layout.Annotation(
x=time[-20],
y=exponential(time[-20], *popt) + room_temp,
text="${} = {}e^{{-{}(t - 61.9)}} + {{{}}}$".format(
dataname, round(popt[0], 3), round(popt[1], 3), room_temp
),
showarrow=True,
ax=datapair[2],
ay=datapair[3],
)
)
annotations.append(
go.layout.Annotation(
x=time[-1],
y=exponential(time[-1], *popt) + room_temp,
text="$R^2 = {}$".format(r_squared),
showarrow=True,
ax=datapair[2],
ay=datapair[3],
)
)
elif dataname == "\dot{W}":
scatters.append(
go.Marker(
x=time,
y=datapair[0],
mode="markers",
marker=go.Marker(color=list(range(140)), colorscale="Viridis"),
name="",
showlegend=False,
)
)
trendlines.append(
go.Marker(
x=xx,
y=yy,
mode="lines",
marker=go.Marker(color=datapair[1]),
name="${{{}}}$".format(dataname),
)
)
annotations.append(
go.layout.Annotation(
x=time[-20],
y=exponential(time[-20], *popt),
text="${} = {}e^{{-{}(t - 61.9)}}$".format(
dataname, round(popt[0], 3), round(popt[1], 3)
),
showarrow=True,
ax=datapair[2],
ay=datapair[3],
)
)
annotations.append(
go.layout.Annotation(
x=time[-1],
y=exponential(time[-1], *popt),
text="$R^2 = {}$".format(r_squared),
showarrow=True,
ax=datapair[2],
ay=datapair[3],
)
)
layout = go.Layout(
title="$\\textbf{{Graph for }} {} \\textbf{{ }} \Omega$".format(resistance),
plot_bgcolor="rgb(229, 229, 229)",
xaxis=go.layout.XAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$t (s)$",
),
yaxis=go.layout.YAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$T (^\circ C)$",
),
annotations=annotations,
)
scatter_trendline_pair = list(zip(scatters, trendlines))
final_data = [item for sublist in scatter_trendline_pair for item in sublist]
fig = go.Figure(data=final_data, layout=layout)
# Show plot on localhost
pio.show(fig)
# pio.write_html(fig, file='T_c vs Time', auto_open=True)
return tc_coeffs, th_coeffs, w_coeffs
def output_power_graph(resistance_values, output_power_values):
scatters = []
trendlines = []
annotations = []
# Use dogleg algorithm
popt, pcov = curve_fit(
output, resistance_values, output_power_values, method="dogbox"
)
# Compute R^2 value
r_squared = round(
metrics.r2_score(output(resistance_values, *popt), output_power_values), 3
)
xx = np.linspace(resistance_values[0], resistance_values[-1], 2000)
yy = output(xx, *popt)
scatters.append(
go.Marker(
x=resistance_values,
y=output_power_values,
mode="markers",
marker=go.Marker(color=list(range(10)), colorscale="Viridis"),
name="",
showlegend=False,
)
)
trendlines.append(
go.Marker(
x=xx,
y=yy,
mode="lines",
marker=go.Marker(color="#ff007f"),
name="${{\dot{W}}}$",
)
)
annotations.append(
go.layout.Annotation(
x=resistance_values[-4],
y=output(resistance_values[-4], *popt),
text="$\dot{{W}} = \\frac{{{}^2 R_L}}{{{{{} + R_L}}^2}}$".format(
round(popt[1], 3), round(popt[0], 3)
),
showarrow=True,
ax=70,
ay=-5,
)
)
annotations.append(
go.layout.Annotation(
x=resistance_values[-1],
y=output(resistance_values[-1], *popt),
text="$R^2 = {}$".format(r_squared),
showarrow=True,
ax=70,
ay=-5,
)
)
layout = go.Layout(
title="$\\textbf{Output Power Graph}$",
plot_bgcolor="rgb(229, 229, 229)",
xaxis=go.layout.XAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$R_L (\Omega)$",
),
yaxis=go.layout.YAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$\dot{W} (W)$",
),
annotations=annotations,
)
scatter_trendline_pair = list(zip(scatters, trendlines))
final_data = [item for sublist in scatter_trendline_pair for item in sublist]
fig = go.Figure(data=final_data, layout=layout)
# Show plot on localhost
pio.show(fig)
def plot_qc_qh_and_w(time, tc_coeffs, th_coeffs, resistance, room_temp):
# Define symbols for differentiation
t, Tc, Th = symbols("t Tc Th")
Tc = tc_coeffs[0] * exp(-tc_coeffs[1] * (t - 61.9)) + room_temp
Th = th_coeffs[0] * exp(-th_coeffs[1] * (t - 61.9)) + room_temp
dTcdt = diff(Tc, t)
dThdt = diff(Th, t)
# mc = 21.87 J/°C
dQcdt = 21.87 * dTcdt
dQhdt = 21.87 * dThdt
dWdt = dQhdt + dQcdt
actual_Qc = lambdify(t, dQcdt)
actual_Qh = lambdify(t, dQhdt)
actual_W = lambdify(t, dWdt)
# Start plotting
data_dict = {
"\dot{Q_c}": (tc_coeffs[1], actual_Qc, "#17becf", dQcdt, -40, 20),
"\dot{Q_h}": (th_coeffs[1], actual_Qh, "#de1738", dQhdt, 40, -20),
"\dot{W}": (th_coeffs[1] + tc_coeffs[1], actual_W, "#7851a9", dWdt, 40, 20),
}
plots = []
annotations = []
for dataname, datapair in data_dict.items():
xx = np.linspace(time[0], time[-1], 2000)
yy = abs(datapair[1](xx))
plots.append(
go.Marker(
x=xx,
y=yy,
mode="lines",
marker=go.Marker(color=datapair[2]),
name="${{{}}}$".format(dataname),
)
)
if dataname == "\dot{Q_c}" or dataname == "\dot{Q_h}":
annotations.append(
go.layout.Annotation(
x=time[20],
y=abs(datapair[1](time[20])),
text="${} = {}e^{{-{}t}}$".format(
dataname,
abs(round(datapair[3].args[0], 3)),
round(datapair[0], 3),
),
showarrow=True,
ax=datapair[4],
ay=datapair[5],
)
)
elif dataname == "\dot{W}":
annotations.append(
go.layout.Annotation(
x=time[-20],
y=datapair[1](time[-20]),
text="${} = {}e^{{-{}t}} - {}e^{{-{}t}}$".format(
dataname,
abs(round(dQhdt.args[0], 3)),
round(th_coeffs[1], 3),
abs(round(dQcdt.args[0], 3)),
round(tc_coeffs[1], 3),
),
showarrow=True,
ax=datapair[4],
ay=datapair[5],
)
)
layout = go.Layout(
title="$\\textbf{{Graph for }} {} \\textbf{{ }} \Omega$".format(resistance),
plot_bgcolor="rgb(229, 229, 229)",
xaxis=go.layout.XAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$t (s)$",
),
yaxis=go.layout.YAxis(
zerolinecolor="rgb(255,255,255)",
gridcolor="rgb(255,255,255)",
title="$\dot{Q} (J/s)$",
),
annotations=annotations,
)
fig = go.Figure(data=plots, layout=layout)
# Show plot on localhost
pio.show(fig)
