Currently, when I do this:
import pandas as pd
import hvplot.pandas
df = pd.util.testing.makeDataFrame()
plot = df.hvplot.points('A', 'B', tiles=True, rasterize=True, geo=True,
aggregator='count')
I can't see the underlying tile source.
To see the underlying tile source philippjfr suggested setting the color bar limits slightly higher than 0 and set the min clipping_colors to transparent:
plot = plot.redim.range(**{'Count': (0.25, 1)})
plot = plot.opts('Image', clipping_colors={'min': 'transparent'})
Now the underlying tile source is viewable.
Full Code:
import pandas as pd
import hvplot.pandas
df = pd.util.testing.makeDataFrame()
plot = df.hvplot.points('A', 'B', tiles=True, rasterize=True, geo=True,
aggregator='count')
plot = plot.redim.range(**{'Count': (0.25, 1)})
plot = plot.opts('Image', clipping_colors={'min': 'transparent'})
plot
Related
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 16 18:17:32 2023
#author: avnth
"""
import seaborn as sb
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import scale
from sklearn.metrics import silhouette_score
from sklearn.metrics import davies_bouldin_score
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler as sc
from mpl_toolkits import mplot3d
import plotly.express as px
dta=pd.read_csv("D:/XLRI/Term-4/ML/Assignment-2/Prpd_2.csv")
dta.head()
dta1=dta.drop("Cid",axis=1,inplace=False)
#dta1=dta1.iloc[:,1:4]
dta1=pd.DataFrame(dta1)
dta1.head()
dta1.describe()
dta1=pd.DataFrame(dta1)
dta1.describe()
ncl=[]
for i in range(1,15):
kn=KMeans(n_clusters=i)
kn.fit(dta1)
ncl.append(kn.inertia_)
plt.plot(range(1,15),ncl)
#silhoute method
sil = []
for n in range(2,15):
kn1=KMeans(n_clusters = n)
kn1.fit(dta1)
# labels = kn1.labels_
sil.append(silhouette_score(dta1,kn1.labels_, metric = 'euclidean'))
plt.plot(range(2,15),sil)
#Davies Bouldin Index method
db = []
K1 = range(2,8)
for l in K1:
kn2 = (KMeans(n_clusters = l) )
kn2.fit(dta1)
db.append(davies_bouldin_score(dta1,kn2.labels_))
plt.plot(range(2,8),db)
sa=sc()
sa.fit(dta1)
tdta1=sa.transform(dta1)
tdta1=pd.DataFrame(tdta1)
kmc=KMeans(n_clusters=6)
kmc.fit(tdta1)
clus=kmc.predict(tdta1)
dta["clus"]=clus
dta.head()
clus4=dta[dta.clus==4]
clus4.describe()
clus0=dta[dta.clus==0]
clus0.describe()
clus5=dta[dta.clus==5]
clus5.describe()
clus3=dta[dta.clus==3]
clus3.describe()
sb.scatterplot("Recency","Frequency",data=dta,hue="clus")
sb.scatterplot("Frequency","Money",data=dta,hue="clus")
# Creating dataset
z = dta.Recency
x = dta.Frequency
y = dta.Money
z.head()
x.head()
y.head()
# Creating figure
#fig = plt.figure()
#ax = fig.add_subplot(111,projection ="3d")
#dta=pd.DataFrame(dta)
#dta.head()
#for a in range(0,5):
# ax.scatter(dta.Frequency[dta.clus==a],dta.Recency[dta.clus==a],dta.Money[dta.clus==a],label=a,hue="clus")
#ax.legend()
#plt.title("simple 3D scatter plot")
#plt.show()
#df = px.data.iris()
#fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',color='petal_length',symbol='species')
#fig=plt.figure()
Hello Frieds,
I am newbie to python. Just learning. I have taken a dataset and clustered it. Now, I want to plot it in 3d scatter plot with a 4th dimension that is my cluster as color. For each cluster no new color should appear. So a data point will be plotted as x,y,z attribute but it will have color based on 4th column that is my cluster number. I know how to do it in 2d with hue. But I am unable to find similar thing in 3d plot. Any help will be appreicated. Atatching my code too.
I tried many libraries from online tutorial but I am not egtting exactly what I am looking for. I have attached a sample for how I want it to be plotted. Sample taken from plotly.com This is just replication how I want to plot.
enter image description here
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(z,x,y, marker=".", c=dta["clus"], s=50, cmap="RdBu")
plt.legend(clus)
plt.title("4D scatterplot")
ax.set_xlabel("Recency")
ax.set_ylabel("Frequency")
ax.set_zlabel("Money")
plt.show()
I'd like to make a PairGrid plot with the seaborn library.
I have two classed data: a training set and one-target point.
I'd like to plot the one-target point as opaque, however, the samples in the training set should be transparent.
And I'd like to plot the one-target point also in lower cells.
Here is my code and image:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv("data.csv")
g = sns.PairGrid(data, hue='type')
g.map_upper(sns.scatterplot, alpha=0.2, palette="husl")
g.map_lower(sns.kdeplot, lw=3, palette="husl")
g.map_diag(sns.kdeplot, lw=3, palette="husl")
g.add_legend()
plt.show()
And the data.csv is like belows:
logP tPSA QED HBA HBD type
0 -2.50000 200.00 0.300000 8 1 Target 1
1 1.68070 87.31 0.896898 3 2 Training set
2 3.72930 44.12 0.862259 4 0 Training set
3 2.29702 91.68 0.701022 6 3 Training set
4 -2.21310 102.28 0.646083 8 2 Training set
You can reassign the dataframe used after partial plotting. E.g. g.data = data[data['type'] == 'Target 1']. So, you can first plot the training dataset, change g.data and then plot the target with other parameters.
The following example supposes the first row of the iris dataset is used as training data. A custom legend is added (this might provoke a warning that should be ignored).
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
iris = sns.load_dataset('iris')
g = sns.PairGrid(iris)
color_for_trainingset = 'paleturquoise'
# color_for_trainingset = sns.color_palette('husl', 2) [-1] # this is the color from the question
g.map_upper(sns.scatterplot, alpha=0.2, color=color_for_trainingset)
g.map_lower(sns.kdeplot, color=color_for_trainingset)
g.map_diag(sns.kdeplot, lw=3, color=color_for_trainingset)
g.data = iris.iloc[:1]
# g.data = data[data['type'] == 'Target 1']
g.map_upper(sns.scatterplot, alpha=1, color='red')
g.map_lower(sns.scatterplot, alpha=1, color='red', zorder=3)
handles = [Line2D([], [], color='red', ls='', marker='o', label='target'),
Line2D([], [], color=color_for_trainingset, lw=3, label='training set')]
g.add_legend(handles=handles)
plt.show()
This is my code:
import pandas as pd
import geoplot as gplt
import geopandas as gpd
import geoplot.crs as gcrs
import contextily
df = pd.read_csv('dataframe_master.csv', index_col='id')
crs = {'init': 'epsg:4326'}
geometry = [geometry.Point(xy) for xy in zip(df['latitude'], df['longitude'])]
df_geo = gpd.GeoDataFrame(df_geo, crs=crs, geometry=geometry)
test = df_geo[:200000]
test = test.to_crs(epsg=3857)
ax = test.plot(marker='o', markersize=1)
contextily.add_basemap(ax)
plt.show()
And it generates this image:
image, which doesn't show a background map and seems a little distorted.
My coordinate data was originally made with the RD-coordinaten standard (EPSG:28992), which I converted to EPSG:4326 with this code:
lon_l = []
lat_l = []
p1 = Proj(init='epsg:28992')
p2 = Proj(proj='latlong',datum='WGS84')
for row in range(len(df)):
lon, lat, z = transform(p1, p2, df.iloc[row, 7], df.iloc[row, 8], 0.0)
lon_l.append(lon)
lat_l.append(lat)
I did a sanity check on the longitude latitude output by comparing to some online converters, and the output points to the correct locations.
I tried following this solution: https://gis.stackexchange.com/questions/348339/using-crs-epsg3857-but-misalignment-between-stamen-background-and-coordinates-o in case my conversion was missing the "towgs84"part, but the image still looked the same with a slightly different colour.
I figured it out! I should've listed longitude before latitude when building the geometry.
geometry = [geometry.Point(xy) for xy in zip(df['longitude'], df['latitude'])]
I have an image and I want to filter it to split the text from the background:
and after applying below code:
from skimage import filters
from skimage.filters import threshold_otsu
from skimage import io as skimage_io # So as not to clash with builtin io
dir = r"image_path/a.jpg"
img = skimage_io.imread(dir, as_gray=True, plugin='imageio')
blurred = filters.gaussian(img, sigma=2.0)
sobel = filters.sobel_h(blurred)
blurred += sobel
blurred += sobel
thresh = threshold_otsu(blurred)
# skimage_io.imshow(blurred)
print(thresh)
binary = img < thresh-0.1
skimage_io.imshow(binary)
The image became
Is there a way to make the result better ??
Yes, you can get a better result:
You do account for the noise, but the gaussian blur you apply is much too strong; it starts to dull out the features you are interested in (the letters).
As #Ziri pointed out in a comment, you are not accounting for the uneven exposure that is present in your image. There is many ways to do that; I will use a rolling ball filter to smooth out the background and combine it with a global thresholding method. Note that this is currently (August 2020) a PR in skimage, but will hopefully get merged soon.
import numpy as np
import matplotlib.pyplot as plt
from skimage import util
from skimage import filters
from skimage import io
from skimage import exposure
# PR 4851; will hopefully be in the library soon(TM)
from skimage.morphology import rolling_ellipsoid
img = io.imread("test.jpg", as_gray=True)
img_inv = util.invert(util.img_as_float(img))
# blurr the image slightly to remove noise
blurred = filters.gaussian(img_inv, sigma=1.0)
# remove background
background = rolling_ellipsoid(blurred, kernel_size=(50, 50), intensity_vertex=0.1)
normalized = blurred - background
# re-normalize intensity
normalized = exposure.rescale_intensity(normalized)
# binarize
binary = normalized > 0.38
binary = util.invert(binary)
plt.imshow(binary, cmap="gray")
plt.gca().axis("off")
plt.show()
Sidenote: It may not be wise to upload an uncensored picture of a passport to SO where it is freely accessible to anybody.
Using python code we are able to create image segments as shown in the screenshot. our requirement is how to select specific segment in the image and apply different color to it ?
The following is our python snippet
from skimage.segmentation import felzenszwalb, slic,quickshift
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
import matplotlib.pyplot as plt
from skimage import measure
from skimage import restoration
from skimage import img_as_float
image = img_as_float(io.imread("leaf.jpg"))
segments = quickshift(image, ratio=1.0, kernel_size=20, max_dist=10,return_tree=False, sigma=0, convert2lab=True, random_seed=42)
fig = plt.figure("Superpixels -- %d segments" % (500))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(image, segments))
plt.axis("off")
plt.show()
do this:
seg_num = 64 # desired segment to be colored
color = float64([1,0,0]) # red color
image[segments == 64] = color # assign color to the segment
You can use OpenCV python module - example: