"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = plot_mags_in_time(df_sed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "357280e1",
+ "metadata": {},
+ "source": [
+ "## 3. Generate and bin synthetic earthquakes\n",
+ "\n",
+ "\n",
+ " \n",
+ "First we need to define the number of earthquakes, the b-value and the completeness magnitude. If binnning is applied, it is important to generate the magnitudes half a bin smaller than the smallest magnitude, otherwise the first bin will contain only half the events. For the b-value, note that beta is defined as the natural logarithm equivalent of the b-value.\n",
+ " \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "7381183b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from seismostats import simulate_magnitudes, bin_to_precision"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f2775fba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n = 200\n",
+ "b_value = 1\n",
+ "beta = b_value*np.log(10)\n",
+ "delta_m = 0.1\n",
+ "mc = 3\n",
+ "mc_generation = mc - delta_m / 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0285f34",
+ "metadata": {},
+ "source": [
+ " Now we can generate a synthetic magnitude distribution: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "25f3eb2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mags = simulate_magnitudes(n,beta,mc_generation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0920d1b",
+ "metadata": {},
+ "source": [
+ " In order to bin the magnitudes, we just need to define the step-size: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "8784bb5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mags_binned = bin_to_precision(mags, delta_m)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09f2d067",
+ "metadata": {},
+ "source": [
+ "## 4. Calculate b-values\n",
+ "\n",
+ "\n",
+ " \n",
+ "We estimate the b-value from the binned sample. It is important to pass on the information of which binning (if any) is applied. Also, be cautious that beta is (as above) a factor of np.log(10) different to the b-value.\n",
+ " \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "63d5cffb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from seismostats import estimate_b_utsu, estimate_b_tinti, estimate_b_elst, estimate_b_laplace"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "64adffda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b_estimate, error = estimate_b_tinti(mags_binned, mc=mc, delta_m=delta_m, error=True, b_parameter='b_value')\n",
+ "# b_estimate = estimate_b_utsu(mags_binned,mc=mc, delta_m=delta_m, b_parameter='beta')\n",
+ "# b_estimate = estimate_b_elst(mags_binned, delta_m=delta_m)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfc9800e",
+ "metadata": {},
+ "source": [
+ " We can plot the original and binned magnitudes and their respective b-value estimates now. Note that we choose the bin position to be left in order to align the binned and the original magnitudes. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "87bd325f",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = plt.subplots(figsize=(8, 6))[1]\n",
+ "\n",
+ "plot_cum_fmd(mags, ax=ax, b_value=b_value, mc=mc - delta_m/2, color=['blue', 'darkblue'])\n",
+ "plot_cum_fmd(mags_binned,ax=ax, b_value=b_estimate, delta_m=delta_m, color=['red', 'pink'],\n",
+ " bin_position='left')\n",
+ "\n",
+ "ax.legend(['True b-value', 'Magnitudes original', 'Fit Tinti', 'Magnitudes binned'])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}