I have a simple exercise that I am not sure how to do. I have the following data sets:
male100
Year Time
0 1896 12.00
1 1900 11.00
2 1904 11.00
3 1906 11.20
4 1908 10.80
5 1912 10.80
6 1920 10.80
7 1924 10.60
8 1928 10.80
9 1932 10.30
10 1936 10.30
11 1948 10.30
12 1952 10.40
13 1956 10.50
14 1960 10.20
15 1964 10.00
16 1968 9.95
17 1972 10.14
18 1976 10.06
19 1980 10.25
20 1984 9.99
21 1988 9.92
22 1992 9.96
23 1996 9.84
24 2000 9.87
25 2004 9.85
26 2008 9.69
and the second one:
female100
Year Time
0 1928 12.20
1 1932 11.90
2 1936 11.50
3 1948 11.90
4 1952 11.50
5 1956 11.50
6 1960 11.00
7 1964 11.40
8 1968 11.00
9 1972 11.07
10 1976 11.08
11 1980 11.06
12 1984 10.97
13 1988 10.54
14 1992 10.82
15 1996 10.94
16 2000 11.12
17 2004 10.93
18 2008 10.78
I have the following code:
y = -0.014*male100['Year']+38
plt.plot(male100['Year'],y,'r-',color = 'b')
ax = plt.gca() # gca stands for 'get current axis'
ax = male100.plot(x=0,y=1, kind ='scatter', color='g', label="Mens 100m", ax = ax)
female100.plot(x=0,y=1, kind ='scatter', color='r', label="Womens 100m", ax = ax)
Which produces this result:
I need to plot a line that would go exactly between them. So the line would leave all of the green points below it, and the red point above it. How do I do so?
I've tried playing with the parameters of y, but to no avail. I also tried fitting a linear regression to male100 , female100 , and the merged version of them (across rows), but couldn't get any results.
Any help would be appreciated!
A solution is using support vector machine (SVM). You can find two margins that separate two classes of points. Then, the average line of two support vectors is your answer. Notice that it's happened just when these two set of points are linearly separable.
You can use the following code to see the result:
Data Entry
male = [
(1896 , 12.00),
(1900 , 11.00),
(1904 , 11.00),
(1906 , 11.20),
(1908 , 10.80),
(1912 , 10.80),
(1920 , 10.80),
(1924 , 10.60),
(1928 , 10.80),
(1932 , 10.30),
(1936 , 10.30),
(1948 , 10.30),
(1952 , 10.40),
(1956 , 10.50),
(1960 , 10.20),
(1964 , 10.00),
(1968 , 9.95),
(1972 , 10.14),
(1976 , 10.06),
(1980 , 10.25),
(1984 , 9.99),
(1988 , 9.92),
(1992 , 9.96),
(1996 , 9.84),
(2000 , 9.87),
(2004 , 9.85),
(2008 , 9.69)
]
female = [
(1928, 12.20),
(1932, 11.90),
(1936, 11.50),
(1948, 11.90),
(1952, 11.50),
(1956, 11.50),
(1960, 11.00),
(1964, 11.40),
(1968, 11.00),
(1972, 11.07),
(1976, 11.08),
(1980, 11.06),
(1984, 10.97),
(1988, 10.54),
(1992, 10.82),
(1996, 10.94),
(2000, 11.12),
(2004, 10.93),
(2008, 10.78)
]
Main Code
Notice that the value of C is important here. If it is selected to 1, you can't get the preferred result.
from sklearn import svm
import numpy as np
import matplotlib.pyplot as plt
X = np.array(male + female)
Y = np.array([0] * len(male) + [1] * len(female))
# fit the model
clf = svm.SVC(kernel='linear', C=1000) # C is important here
clf.fit(X, Y)
plt.figure(figsize=(8, 4))
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-1000, 10000)
yy = a * xx - (clf.intercept_[0]) / w[1]
plt.figure(1, figsize=(4, 3))
plt.clf()
plt.plot(xx, yy, "k-") #********* This is the separator line ************
plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired,
edgecolors="k")
plt.xlim((1890, 2010))
plt.ylim((9, 13))
plt.show()
I believe your idea of making use of regression lines is correct - if they aren't used, the line would be merely superficial (and impossible to justify if the points overlap in the event of messy data).
Therefore, using some randomly made data with a known linear relationship, we can do the following:
import random
import numpy as np
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
x_values = np.arange(0, 51, 1)
y_points_1 = [i * 2 + random.randint(5, 30) for i in x_points]
y_points_2 = [i - random.randint(5, 30) for i in x_points]
x_points = x_values.reshape(-1, 1)
def regression(x, y):
model = LinearRegression().fit(x, y)
y_pred = model.predict(x)
return y_pred
barrier = [(regression(x=x_points, y=y_points_1)[i] + value) / 2 for i, value in enumerate(regression(x=x_points, y=y_points_2))]
plt.plot(x_points, regression(x=x_points, y=y_points_1))
plt.plot(x_points, regression(x=x_points, y=y_points_2))
plt.plot(x_points, barrier)
plt.scatter(x_values, y_points_1)
plt.scatter(x_values, y_points_2)
plt.grid(True)
plt.show()
Giving us the following plot:
This method also works for an overlap in the data points, so if we change the random data slightly and apply the same process:
x_values = np.arange(0, 51, 1)
y_points_1 = [i * 2 + random.randint(-10, 30) for i in x_points]
y_points_2 = [i - random.randint(-10, 30) for i in x_points]
We get something like the following:
It is important to note that the lists used here are of the same length, so you would need to add some predicted points to the female data after applying regression in order to make use of the line between them. These points would merely be along the regression line with the x-values corresponding to those present in the male data.
Because sklearn might be a bit over the top for a linear fit and to get rid of the condition that you would need the same number of data points for male and female data, here the same implementation with numpy.polyfit. This also demonstrates that their approach is not a solution to the problem.
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
#data import
male = pd.read_csv("test1.txt", delim_whitespace=True)
female = pd.read_csv("test2.txt", delim_whitespace=True)
#linear fit of both populations
pmale = np.polyfit(male.Year, male.Time, 1)
pfemale = np.polyfit(female.Year, female.Time, 1)
#more appealing presentation, let's pretend we do not just fit a line
x_fitmin=min(male.Year.min(), female.Year.min())
x_fitmax=max(male.Year.max(), female.Year.max())
x_fit=np.linspace(x_fitmin, x_fitmax, 100)
#create functions for the three fit lines
male_fit = np.poly1d(pmale)
print(male_fit)
female_fit = np.poly1d(pfemale)
print(female_fit)
sep = np.poly1d(np.mean([pmale, pfemale], axis=0))
print(sep)
#plot all markers and lines
ax = male.plot(x="Year", y="Time", c="blue", kind="scatter", label="male")
female.plot(x="Year", y="Time", c="red", kind="scatter", ax=ax, label="female")
ax.plot(x_fit, male_fit(x_fit), c="blue", ls="dotted", label="male fit")
ax.plot(x_fit, female_fit(x_fit), c="red", ls="dotted", label="female fit")
ax.plot(x_fit, sep(x_fit), c="black", ls="dashed", label="separator")
plt.legend()
plt.show()
Sample output:
-0.01333 x + 36.42
-0.01507 x + 40.92
-0.0142 x + 38.67
And one point is still in the wrong section. However - I find this question so interesting because I expected answers from the sklearn crowd for non-linear data groups. I even installed sklearn in anticipation! If in the next days nobody posts a good solution
with SVMs, I will set a bounty on this question.
One solution is the geometrical approach. You can find the convex hull of each data class, then find a line that goes through these two convex hulls. To find the line, you can find inner tangent line between two convex hulls using this code, and rotate it a little bit.
You can use the following code:
from scipy.spatial import ConvexHull, convex_hull_plot_2d
male = np.array(male)
female = np.array(female)
hull_male = ConvexHull(male)
hull_female = ConvexHull(female)
plt.plot(male[:,0], male[:,1], 'o')
for simplex in hull_male.simplices:
plt.plot(male[simplex, 0], male[simplex, 1], 'k-')
# Here, the separator line comes from SMV result.
# Just to show the a separator as an exmple
# plt.plot(xx, yy, "k-")
plt.plot(female[:,0], female[:,1], 'o')
for simplex in hull_female.simplices:
plt.plot(female[simplex, 0], female[simplex, 1], 'k-')
plt.xlim((1890, 2010))
plt.ylim((9, 13))
Related
I have a dataframe:
count_single count_multi column_names
0 11345 7209 e
1 11125 6607 w
2 10421 5105 j
3 9840 4478 r
4 9561 5492 f
5 8317 3937 i
6 7808 3795 l
7 7240 4219 u
8 6915 3854 s
9 6639 2750 n
10 6340 2465 b
11 5627 2834 y
12 4783 2384 c
13 4401 1698 p
14 3305 1753 g
15 3283 1300 o
16 2767 1697 t
17 2453 1276 h
18 2125 1140 a
19 2090 929 q
20 1330 518 d
I want to visualize the single count and multi_count while column_names as a common column in both of them. I am looking something like this :
What I've tried:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('paper')
f, ax = plt.subplots(figsize = (6,15))
sns.set_color_codes('pastel')
sns.barplot(x = 'count_single', y = 'column_names', data = df,
label = 'Type_1', color = 'orange', edgecolor = 'w')
sns.set_color_codes('muted')
sns.barplot(x = 'count_multi', y = 'column_names', data = df,
label = 'Type_2', color = 'green', edgecolor = 'w')
ax.legend(ncol = 2, loc = 'lower right')
sns.despine(left = True, bottom = True)
plt.show()
it's giving me plot like this:
How to visualize these two columns with same as expected images?
I really appreciate any help you can provide.
# instantiate figure with two rows and one column
fig, axes = plt.subplots(nrows=2, figsize=(10,5))
# plot barplot in the first row
df.set_index('column_names').plot.bar(ax=axes[0], color=['rosybrown', 'tomato'])
# first scale each column bydividing by its sum and then use cumulative sum to generate the cumulative density function. plot on the second ax
df.set_index('column_names').apply(lambda x: x/x.sum()).cumsum().plot(ax=axes[1], color=['rosybrown', 'tomato'])
# change ticks in first plot:
axes[0].set_yticks(np.linspace(0, 12000, 7)) # this means: make 7 ticks between 0 and 12000
# adjust the axislabels for the second plot
axes[1].set_xticks(range(len(df)))
axes[1].set_xticklabels(df['column_names'], rotation=90)
plt.tight_layout()
I have some clinical data that contains values for multiple visits for multiple subjects. I created a script to loop and create a plot for each subject containing values for each visit. Now, I need to add data to each subject plot:
For each subject, add a new marker (star) to identify the baseline value (bcva_OS and bcva_OD) only. I can only get it to display the the markers for all values. How do I subset for baseline only? See comment in the code. I get a syntax error if I use:
plt.plot_date(sub_df['visit_date'] if sub_df[sub_df.visit_label == 'Visit 2 - Baseline'],
For each subject, how can I add an entirely new data type so that both data types will be overlayed on a plot for each subject? I think I could do that with just one subject's worth of data, but again the loop...
Sample code:
for subject, sub_df in new_od_df.groupby(by='subject'):
# Plot fellow eye
plt.plot(sub_df['visit_date'], sub_df['bcva_OS'], marker='^',
label='OS (fellow) ', color=sns.xkcd_rgb['pale red'])
# Plot treated eye
plt.plot(sub_df['visit_date'], sub_df['bcva_OD'], marker='o',
label='OD (treated) ', color=sns.xkcd_rgb['denim blue'])
# Trying to plot only the baseline values
#plt.plot_date(sub_df['visit_date'] if sub_df[sub_df.visit_label == 'Visit 2 - Baseline'],
# Plot fellow eye
plt.plot_date(sub_df['visit_date'], sub_df['bcva_OS'],
marker='*', markersize=10,
label='BL (fellow) ', color=sns.xkcd_rgb['light pink'])
# Plot treated eye
plt.plot_date(sub_df['visit_date'], sub_df['bcva_OD'],
marker='*', markersize=10,
label='BL (treated) ', color=sns.xkcd_rgb['baby blue'])
# Legend the old way
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)
# Display each chart separately
plt.show()
Sample data:
subject treated_eye visit_label visit_date bcva_OD bcva_OS refract_OD refract_OS
index
108 1101 OD Visit 1 - Screening 2016-01-07 27.0 41.0 + 5 + 0.75 X 27 + 5 + 1.75 X 45
115 1101 OD Visit 2 - Baseline 2016-01-25 35.0 41.0 + 5 + 0.75 X 27 + 5.5 + 1.75 X 40
120 1101 OD Baseline - VA Session 2 2016-01-25 35.0 41.0 + 5 + 0.75 X 27 + 5.5 + 1.75 X 40
125 1101 OD Visit 4 - Day 1 2016-02-02 32.0 42.0 + 5 + 0.75 X 27 + 5 + 1.75 X 30
123 1101 OD Visit 5 - Day 7 2016-02-08 40.0 43.0 + 5 + 0.75 X 28 + 5 + 1.75 X 30
111 1101 OD Visit 6 - Day 14 2016-02-16 33.0 44.0 + 5 + 0.75 X 27 + 5 + 1.75 X 40
124 1101 OD Unscheduled 2016-02-24 37.0 44.0 + 4.5 + 1.25 X 30 + 5 + 1.75 X 40
118 1101 OD Visit 7 - Month 1 2016-02-29 37.0 40.0 + 4.5 + 1.25 X 30 + 5 + 1.75 X 43
Sample plot:
Note: this is a partial answer to point 1:
I'm not sure I completely understood your requests, especially regarding point 2: creating a new data type. Please edit your question to make point 2 clearer. Right now I'm guessing you want to plot both OD and OS values after baseline-subtraction, is this correct?
Regarding point 1, the solution below correctly gets the baseline values and plots them as a dashed line. Note that I've also added a plot title and changed calls to plt. to ax., after properly creating a figure using fig,ax=plt.subplots(). This may come in handy later, and is already required for fig.autofmt_xdate().
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.style.use('ggplot')
import seaborn as sns
data="""index,subject,treated_eye,visit_label,visit_date,bcva_OD,bcva_OS,refract_OD,refract_OS
108, 1101, OD, Visit 1 - Screening, 2016-01-07, 27.0, 41.0, + 5 + 0.75 X 27, + 5 + 1.75 X 45
115, 1101, OD, Visit 2 - Baseline, 2016-01-25, 35.0, 41.0, + 5 + 0.75 X 27, + 5.5 + 1.75 X 40
120, 1101, OD, Baseline - VA Session 2 ,2016-01-25, 35.0, 41.0, + 5 + 0.75 X 27, + 5.5 + 1.75 X 40
125, 1101, OD, Visit 4 - Day 1 ,2016-02-02, 32.0, 42.0, + 5 + 0.75 X 27, + 5 + 1.75 X 30
123, 1101, OD, Visit 5 - Day 7 ,2016-02-08, 40.0, 43.0, + 5 + 0.75 X 28, + 5 + 1.75 X 30
111, 1101, OD, Visit 6 - Day 14 ,2016-02-16,33.0, 44.0, + 5 + 0.75 X 27, + 5 + 1.75 X 40
124, 1101, OD, Unscheduled ,2016-02-24, 37.0, 44.0, + 4.5 + 1.25 X 30, + 5 + 1.75 X 40
118, 1101, OD, Visit 7 - Month 1 , 2016-02-29 , 37.0, 40.0, + 4.5 + 1.25 X 30, + 5 + 1.75 X 43
"""
## DataFrame cleanup
df=pd.read_csv(pd.compat.StringIO(data),sep=",",index_col=0)
df_obj = df.select_dtypes(['object'])
df[df_obj.columns] = df_obj.apply(lambda x: x.str.strip())
df['visit_date']=pd.to_datetime(df['visit_date'])
for subject, sub_df in df.groupby(by='subject'):
mask=(sub_df.visit_label == 'Visit 2 - Baseline')
bcva_OS_baseline=sub_df['bcva_OS'][mask].values
bcva_OD_baseline=sub_df['bcva_OD'][mask].values
fig,ax=plt.subplots()
# Plot fellow eye
ax.plot(sub_df['visit_date'], sub_df['bcva_OS'], marker='^',
label='OS (fellow) ', color=sns.xkcd_rgb['pale red'])
# Plot treated eye
ax.plot(sub_df['visit_date'], sub_df['bcva_OD'], marker='o',
label='OD (treated) ', color=sns.xkcd_rgb['denim blue'])
# Plot fellow eye
ax.plot_date(sub_df['visit_date'], sub_df['bcva_OS'],
marker='*', markersize=10,
label='BL (fellow) ', color=sns.xkcd_rgb['light pink'])
# Plot treated eye
ax.plot_date(sub_df['visit_date'], sub_df['bcva_OD'],
marker='*', markersize=10,
label='BL (treated) ', color=sns.xkcd_rgb['baby blue'])
# Plot baseline
ax.axhline(bcva_OS_baseline,color=sns.xkcd_rgb['pale red'],linestyle="dashed")
ax.axhline(bcva_OD_baseline,color=sns.xkcd_rgb['denim blue'],linestyle="dashed")
# Legend the old way
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)
# Display each chart separately
ax.set_title('subject {0}'.format(subject))
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
Result:
Given a dataframe df with columns A, B, C, and D,
A B C D
0 88 38 15.66 30.0
1 88 34 15.66 40.0
2 15 15 12.00 20.0
3 15 19 8.00 15.0
4 45 12 6.00 15.0
5 45 30 4.00 30.0
6 29 31 3.60 15.0
7 88 20 3.60 10.0
8 64 25 3.60 15.0
9 45 43 3.60 20.0
I want to make a scatter plot that graphs A vs B, with sizes based on C and colors based on D. After trying many ways to do this, I settled on grouping the data by D, then plotting each group in D:
fig,axes=plt.subplots()
factor=df.groupby('D')
for name, group in factor:
axes.scatter(group.A,group.B,s=(group.C)**2,c=group.D,
cmap='viridis',norm=Normalize(vmin=min(df.D),vmax=max(df.D)),label=name)
This yields the appropriate result, but the default legend() function is wrong. The groups listed in the legend have correct names, but incorrect colors and sizes (colors should vary by group, and sizes of all markers should be the same).
I tried to manually set the legend, which I can approximate the colors but can't get the sizes to be equal. Eventually I'd like a second legend that will link sizes to the appropriate levels of C.
axes.legend(loc=1,scatterpoints=1,fontsize='small',frameon=False,ncol=2)
leg=axes.get_legend()
for i in range(len(factor)):
z=plt.cm.viridis(np.linspace(0,1,len(factor)))
leg.legendHandles[i].set_color(z[i])
Here's one approach that seems to satisfy your requirements, using Seaborn's lmplot(). (Inspiration taken from this post.)
First, generate some sample data:
import numpy as np
import pandas as pd
n = 10
min_size = 50
max_size = 300
A = np.random.random(n)
B = np.random.random(n)*2
C = np.random.randint(min_size, max_size, size=n)
D = np.random.choice(['Group1','Group2'], n)
df = pd.DataFrame({'A':A,'B':B,'C':C,'D':D})
Now plot:
import seaborn as sns
sns.lmplot(x='A', y='B', hue='D',
fit_reg=False, data=df,
scatter_kws={'s':df.C})
UPDATE
Given updated example data from OP, the same lmplot() approach should fulfill specifications: group legend is tracked by color, size of legend indicators is equal.
sns.lmplot(x='A', y='B', hue='D', data=df,
scatter_kws={'s':df.C**2}, fit_reg=False,)
I have written this code to show a line graph but the plot is not showing up. The window opens and shows the labels and axis but no plot. I'm not sure what I'm doing wrong. Maybe it's a small mistake that I'm overlooking
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("passing_stats_1970_2016.csv", index_col=0)
df = df[pd.notnull(df['Season'])]
# print(qb_data.head())
avg_td = df.groupby('Season').TD.mean()
# setting up seaborn, creating white background
sns.set_style("white")
# setting height to 12, width to 9
plt.figure(figsize=(12,9))
# getting x and y values
x_values = df.Season.unique()
y_values = avg_td
# title
title = ("Average TD by season")
#Label y axis
plt.ylabel('Avg TDs', fontsize=18)
#Limit range of axis labels to only show where data is
plt.xlim(1966, 2014.5)
plt.ylim(0,0.08)
# create dashed lines
plt.grid(axis='y',color='grey', linestyle='--', lw=0.5, alpha=0.5)
# Change the size of tick labels for both axis
# to a more readable font size
plt.tick_params(axis='both', labelsize=14)
# get rid of borders for our graph using seaborn's
# despine function
sns.despine(left=True, bottom=True)
# plot the line for our graph
plt.plot(x_values, y_values)
plt.text(1966, -0.012,
'Primary Data Source: http://www.basketball-reference.com/draft/'
'\nAuthor: Joe T',
fontsize=12)
# Display graph
plt.show()
Here is what I get when I print the x and y values:
[ 1970. 1971. 1972. 1973. 1974. 1975. 1976. 1977. 1978. 1979.
1980. 1981. 1982. 1983. 1984. 1985. 1986. 1987. 1988. 1989.
1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999.
2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007. 2008. 2009.
2010. 2011. 2012. 2013. 2014. 2015.]
Season
1970.0 11.625000
1971.0 9.971429
1972.0 11.645161
1973.0 9.444444
1974.0 8.947368
1975.0 11.545455
1976.0 10.750000
1977.0 9.750000
1978.0 13.090909
1979.0 15.212121
1980.0 16.194444
1981.0 13.700000
1982.0 9.700000
1983.0 15.026316
1984.0 13.658537
1985.0 13.093023
1986.0 13.048780
1987.0 12.121951
1988.0 11.931818
1989.0 14.297297
1990.0 14.486486
1991.0 12.153846
1992.0 11.285714
1993.0 11.068182
1994.0 12.813953
1995.0 15.317073
1996.0 13.431818
1997.0 13.088889
1998.0 12.812500
1999.0 12.775510
2000.0 13.886364
2001.0 15.810811
2002.0 14.755556
2003.0 13.276596
2004.0 17.050000
2005.0 13.311111
2006.0 13.666667
2007.0 13.294118
2008.0 15.073171
2009.0 15.288889
2010.0 16.204545
2011.0 16.204545
2012.0 18.871795
2013.0 17.863636
2014.0 18.428571
2015.0 18.409091
Name: TD, dtype: float64
Your upper y axis limit is 0.08 but your y values are in the range of 9-19.
This question is a follow-up to an earlier question and from #JoeKington here. Both of these solutions work excellently for my needs.
However I have been trying to overlay a basemap on the contours. Going by the example here http://matplotlib.org/basemap/users/examples.html, I do not seem to get it right. I think my basic problem is to convert the contour x,y values into map coordinates. I reproduce below the codes for 1) contours (as given by #usethedeathstar, which works very well) and 2) the map object and the plotting.
#!/usr/bin/python
from mpl_toolkits.basemap import Basemap
import numpy as np
from scipy.interpolate import griddata
class d():
def __init__(self):
A0 = open("meansr2.txt","rb") #
A1 = A0.readlines()
A = np.zeros((len(A1),3))
for i, l in enumerate(A1):
li = l.split()
A[i,0] = float(li[0])
A[i,1] = float(li[1])
A[i,2] = float(li[2])
self.Lon = A[:,0]
self.Lat = A[:,1]
self.Z = A[:,2]
data = d()
numcols, numrows = 300, 300
xi = np.linspace(data.Lon.min(), data.Lon.max(), numrows)
yi = np.linspace(data.Lat.min(), data.Lat.max(), numcols)
xi, yi = np.meshgrid(xi, yi)
x, y, z = data.Lon, data.Lat, data.Z
points = np.vstack((x,y)).T
values = z
wanted = (xi, yi)
zi = griddata(points, values, wanted)
Defining map object
m = Basemap(projection = 'merc',llcrnrlon = 21, llcrnrlat = -18, urcrnrlon = 34, urcrnrlat = -8, resolution='h')
m.drawcountries(linewidth=0.5, linestyle='solid', color='k', antialiased=1, ax=None, zorder=None)
m.drawmapboundary(fill_color = 'white')
m.fillcontinents(color='coral',lake_color='blue')
parallels = np.arange(-18, -8, 2.)
m.drawparallels(parallels, color = 'black', linewidth = 0.5, labels=[True,False,False,False])
meridians = np.arange(22,34, 2.)
m.drawmeridians(meridians, color = '0.25', linewidth = 0.5, labels=[False,False,False,True])
import pylab as plt
an attempt to transform form lat/lon to map coordinates
#lat = list(data.Lat)
#lon = list(data.Lon)
#x, y = m(lon,lat)
comment:
contourf is tried with (x, y, zi), then all the above definitions are rewritten with xi, # yi, including many different attempts to redefine x,y and lon, lat.J
The plot functions
fig = plt.figure(0, figsize=(8,4.5))
im = plt.contourf(xi, yi, zi)
plt.scatter(data.Lon, data.Lat, c= data.Z)
plt.colorbar()
plt.show()
The above give two plots side by side.
Here is some data in case there is need to test
Lon Lat Z Z2 pos
32.6 -13.6 41 9 CHIP
27.1 -16.9 43 12 CHOM
32.7 -10.2 46 14 ISOK
24.2 -13.6 33 13 KABO
28.5 -14.4 43 11 KABW
28.1 -12.6 33 16 KAFI
27.9 -15.8 46 13 KAFU
24.8 -14.8 44 9 KAOM
31.1 -10.2 35 14 KASA
25.9 -13.5 24 8 KASE
29.1 -9.8 10 13 KAWA
25.8 -17.8 39 11 LIVI
33.2 -12.3 44 8 LUND
28.3 -15.4 46 12 LUSA
27.6 -16.1 47 9 MAGO
28.9 -11.1 31 15 MANS
31.3 -8.9 39 9 MBAL
31.9 -13.3 45 9 MFUW
23.1 -15.3 31 9 MONG
31.4 -11.9 39 9 MPIK
27.1 -15.0 42 12 MUMB
24.4 -11.8 15 9 MWIN
28.6 -13.0 39 9 NDOL
31.3 -14.3 44 12 PETA
23.3 -16.1 39 5 SENA
30.2 -13.2 38 11 SERE
24.3 -17.5 32 10 SESH
26.4 -12.2 23 12 SOLW
23.1 -13.5 27 14 ZAMB
Any assistance will be appreciated
I would like to thank all those who have looked at my problem and may have tried to work on it. By consistent trying, it has come to my attention that the overlaying of the basemap on the contours actually works with the following lines
After the map object definition
m = Basemap(projection = 'merc',llcrnrlon = 21, llcrnrlat = -18, urcrnrlon = 34, urcrnrlat = -8, resolution='h')
I have
x, y = m(xi, yi)
fig=plt.figure(figsize=(8,4.5))
cs = m.contour(x,y,zi,colors='b',linewidths=1.)
Contour(x,y,zi) plots the contours on the map. Since I was using contourf, I still have to find out why contourf does not give me the filled contours.
Thank you very much all for the patience and tolerance.