Best Plotting Tools to Buy in December 2025

1

Mariners Chart Plotting Tool Kit - Marine Navigation Equipment, Weems and Plath Parallel Rulers, Dividers & Accessories for Nautical Charts, Sailing and Boating Exam Preparation

• MASTER TRADITIONAL NAVIGATION WHEN GPS FAILS WITH TRUSTED TOOLS.

• COMPLETE PLOTTING KIT ENSURES PRECISION FOR BOATING STUDENTS AND PROS.

• LIGHTWEIGHT AND DURABLE, PERFECT FOR RELIABLE NAVIGATION AT SEA.

BUY & SAVE

$100.00

2

Parallel Ruler with Clear Markings, 12 in Navigation Plotting Tool, Quality Plastic, Precision Marine Chart Divider, Nautical Map Measuring Ruler for Sailing, Boating, Navigation Training

• PRECISE NAVIGATION: CRISP MARKINGS FOR FLAWLESS COURSE PLOTTING AT SEA.
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• SMOOTH OPERATION: DUAL HANDLES ENSURE ERROR-FREE, PARALLEL CHART MOVEMENT.

BUY & SAVE

$17.98

3

CYA Fixed Plotter Aviation for Flight Pilots, 13 Inch, Plotting Tool, Navigation for VFR Pilots, CFP-1

• EFFORTLESS NAVIGATION WITH CLEAR SCALES FOR QUICK DISTANCE CHECKS.
• ENHANCED VISIBILITY ENSURES EASY READING IN ALL CONDITIONS.
• DURABLE MATERIALS GUARANTEE LONG-LASTING PERFORMANCE IN THE COCKPIT.

BUY & SAVE

$14.99

4

Weems & Plath Marine Navigation Compute-A-Course Multi-Purpose Plotting Tool

BUY & SAVE

$49.00

5

BRRNOO Navigation Plotting Kit for Marine Chart Ship Drawing Acrylic Clear Isosceles Ruler Training Tools, Professional Drafting Instrument for Boat Navigation Training

• CLEAR ENGRAVED SCALES FOR EASY DRAWING AND MEASURING ACCURACY.
• DURABLE ACRYLIC CONSTRUCTION ENSURES LONG-LASTING USE AND RELIABILITY.
• HIGH TRANSPARENCY FOR UNOBSTRUCTED VISIBILITY AND PRECISE NAVIGATION.

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$15.27

6

Circle Drawing Maker, Adjustable Rotary Circle Template Measuring & Drawing Ruler, Multifunctional Plotter, Rotatable Plotting Tools for Drafting Carving Woodworking

• DURABLE MATERIALS: ALUMINIUM ALLOY AND STAINLESS STEEL ENSURE LONG-LASTING USE.

• PRECISION DRAWING: ADJUSTABLE TOOL CREATES CIRCLES UP TO 12.5CM EASILY.

• INTEGRATED MEASUREMENT: DRAW AND MEASURE IN ONE STEP FOR EFFICIENCY.

BUY & SAVE

$9.89

7

CYA Fixed Plotter Aviation for Flight Pilots, 13 Inch, Plotting Tools, Navigate for VFR Pilots, CFP-2

• EFFORTLESS NAVIGATION: CLEAR SCALES FOR QUICK, ACCURATE READINGS.

• ENHANCED VISIBILITY: DURABLE, OPAQUE DESIGN ENSURES LONG-LASTING CLARITY.

• PILOT-FRIENDLY: LIGHTWEIGHT AND COMPACT FOR EASY COCKPIT USE.

BUY & SAVE

$14.99

8

Circle Drawing Maker Adjustable Circle Drawing Tool Metal Round Ruler Plotting Compass Geometric Tool for Drafting

• REPLACE MULTIPLE TOOLS WITH ONE: EFFICIENT, COMPACT, AND VERSATILE!
• SAVE TIME: DRAW STANDARD SHAPES IN SECONDS, PERFECT FOR EXAMS!
• PRECISION GUARANTEED: ADJUSTABLE TEMPLATES FOR FLAWLESS RESULTS!

BUY & SAVE

$16.59

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ONE MORE?

To plot a parametrized function in matplotlib, you first need to define the function using a parameter. This can be done using a lambda function or a regular function definition. Once you have the function defined, you can use numpy to create an array of values for the parameter.

Next, you can evaluate the function for each value of the parameter array to generate the corresponding y-values. Finally, you can plot the parametrized function using plt.plot() or any other plotting function in matplotlib. Make sure to label your axes and add a title to the plot for clarity.

How to plot a parametrized function over a specific domain in matplotlib?

To plot a parametrized function over a specific domain in matplotlib, you can follow these steps:

1. Define the parametrized function as a function of a parameter (usually denoted as 't').
2. Generate an array of values for the parameter 't' over the desired domain.
3. Evaluate the function at each value of 't' to get the corresponding x and y coordinates.
4. Plot the x and y coordinates using the matplotlib library.

Here's an example code that plots the parametrized function x = cos(t), y = sin(t) over the domain 0 <= t <= 2*pi:

import numpy as np import matplotlib.pyplot as plt

Define the parametrized function

def parametric_function(t): x = np.cos(t) y = np.sin(t) return x, y

Generate values for the parameter 't' over the desired domain

t = np.linspace(0, 2*np.pi, 1000)

Evaluate the function at each value of 't' to get the x and y coordinates

x, y = parametric_function(t)

Plot the parametric function

plt.figure() plt.plot(x, y) plt.xlabel('x') plt.ylabel('y') plt.title('Parametric Function: x = cos(t), y = sin(t)') plt.axis('equal') plt.show()

This code will generate a plot of the parametric function x = cos(t), y = sin(t) over the domain 0 <= t <= 2*pi. You can modify the function and the domain as needed to plot different parametrized functions.

What is the process for saving a matplotlib plot as an image file?

To save a matplotlib plot as an image file, you can follow these steps:

1. Create your matplotlib plot using the plt module and its functions to customize the plot as desired.
2. Once you have the plot ready, you can save it as an image file by calling the savefig() function from plt and passing the filename with the desired format as an argument.

Here is an example code snippet showing how to save a plot as a PNG file:

import matplotlib.pyplot as plt

Create a sample plot

plt.plot([1, 2, 3, 4], [10, 20, 25, 30]) plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Sample Plot')

Save the plot as a PNG file

plt.savefig('sample_plot.png')

This will save the plot as a PNG file named 'sample_plot.png' in the same directory as your Python script. You can also specify the directory path if you want to save the file in a specific location.

You can also save the plot in different image formats such as JPG, PDF, and SVG by specifying the file extension in the savefig() function.

How to create subplots in matplotlib?

To create subplots in matplotlib, you can use the subplot() function. This function allows you to divide the figure into a grid of rows and columns, and specify which subplot you want to work with.

Here's a simple example to create subplots in matplotlib:

import matplotlib.pyplot as plt

Create a figure with 2 subplots in one row

plt.figure(figsize=(10, 5))

Create subplot 1

plt.subplot(1, 2, 1) # 1 row, 2 columns, subplot 1 plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) plt.title('Subplot 1')

Create subplot 2

plt.subplot(1, 2, 2) # 1 row, 2 columns, subplot 2 plt.plot([1, 2, 3, 4], [1, 2, 3, 4]) plt.title('Subplot 2')

plt.show()

In this example, we create a figure with 1 row and 2 columns of subplots. We then use plt.subplot() function to create and specify the location of each subplot within the grid. Finally, we plot data and set titles for each subplot.

You can adjust the number of rows and columns in the grid by changing the arguments in plt.subplot() function. And you can customize each subplot independently by plotting different data or setting different properties.

How to adjust the scale of axes in a matplotlib plot?

To adjust the scale of axes in a matplotlib plot, you can use the xlim() and ylim() functions to set the minimum and maximum values for the x-axis and y-axis, respectively. Here is an example code snippet that shows how to adjust the scale of axes in a matplotlib plot:

import matplotlib.pyplot as plt

Create some data

x = [1, 2, 3, 4, 5] y = [10, 20, 15, 25, 30]

plt.plot(x, y) plt.xlabel('X-axis') plt.ylabel('Y-axis')

Adjust the scale of the x-axis

plt.xlim(0, 6)

Adjust the scale of the y-axis

plt.ylim(0, 35)

plt.show()

In the example above, the xlim(0, 6) function sets the minimum and maximum values for the x-axis to be 0 and 6, respectively. Similarly, the ylim(0, 35) function sets the scale of the y-axis to be between 0 and 35. You can adjust these values according to your data and visualization needs.