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#Python scatter plot and interpolation series

linspace ( 0, 4500, ncols ), extend = 'both', cmap = 'jet' ) #plot the data on the map.Ĭbar = m. X, y = m ( xintrp, yintrp ) # convert the coordinates into the map scales drawcoastlines () #draw coastlines on the map

The size and color of the circles represent the data values. We, programmers, love being lazy, and that helps us to seek better ways. One way to clip the data outside the coastline path is to remove the grid points outside the region manually, but this method is quite tedious. We can do that by just removing all the grid points outside the perimeter. We wish to plot the data inside the coastline borders of the area, which is our area of study. After obtaining these values, if we plot the data, then the grid points is most likely to extend out of the coastline constrain of our study. Geospatial interpolation means merely that we obtain the interpolated values of the data at regular grid points, both longitudinally and latitudinally. We like to show the data, in general, for the whole region and one way of performing, so it to do the geospatial interpolation of the data. In geosciences, we most frequently have to make geospatial plots, but the available data is unevenly distributed and irregular (Figure 1). Plot three dimensional geospatial map in Python Introduction

Interactive data visualization with bokeh.

Build a flask web application: sea level rise monitoring.

Visualizing power spectral density using obspy.

Write ascii data to mseed file using obspy.

Getting started with obspy - downloading waveform data.

Automatically plotting record section for an earthquake in the given time range.

Plotting seismograms with increasing epicentral distance.

Plotting track and trajectory of hurricanes on a topographic map.

Plotting 1 arc-minute global relief map.

Plotting the geospatial data clipped by coastlines.

Pygmt: high-resolution topographic map in python.

Three-dimensional perspective map of taiwan using gmt and pygmt.

High-quality maps using the modern interface to the generic mapping tools.

Non-linear curve fitting to a model with multiple observational variables.

Monte carlo simulations to test for the correlation between two dataset.

Locating earthquakes using geiger’s method.

Numerical tests on travel time tomography.

Simple wave modeling and hilbert transform in matlab.

#Python scatter plot and interpolation series

Introduction to the time series analysis.

Introduction to the exploratory factor analysis.Estimation of the degrees of freedom for time series.Avoiding common mistakes in analyzing correlations of two time-series.Hypothesis test for the significance of linear trend.Easily integrate custom functions in matlab with python.The easy way to compute and visualize the time & frequency correlation.Monte carlo methods and earthquake location problem.Genetic algorithm: a highly robust inversion scheme for geophysical applications.Signal denoising using fourier analysis in python.Numerically solving initial value problems using the runge-kutta method.How effective is the signal denoising using the matlab based wavelet analysis.Numerical methods for scientific computation.