Merging dev onto release

examples/applications/absolute_permeability.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The example explains absolute permeabilty calculations on a cubic network. Note that permeability calcualtion for an extracted network from PoreSpy follows similar steps in assigning phase, algorithm and calculating permeability."
+    "The example explains absolute permeabilty calculations on a cubic network. Note that permeability calculation for an extracted network from PoreSpy follows similar steps in assigning phase, algorithm and calculating permeability."
    ]
   },
   {
@@ -66,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is assumed that a generic phase flowsthrough the porous medium. As absolute permeability is the porous medium property and not the fluid property, any other fluid with an assigned viscosity value can be used as the phase."
+    "It is assumed that a generic phase flows through the porous medium. As absolute permeability is the porous medium property and not the fluid property, any other fluid with an assigned viscosity value can be used as the phase."
    ]
   },
   {
@@ -313,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

examples/applications/formation_factor.ipynb

@@ -11,13 +11,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The formation factor is the ratio of the conducitivity of the pure brine to the measured conductivity \n",
+    "The formation factor is the ratio of the conductivity of the pure brine to the measured conductivity \n",
     "\n",
     "$$\n",
     "F = \\frac{\\sigma_{brine}}{\\sigma_{measured}}\\\\\n",
     "$$\n",
     "\n",
-    "This example shows how to calculate the formation factor from a fickian diffusion simulation on a cubic network. Note that formation factior calculation on an extracted network from Porespy follows similar steps in assigning phase, algorithm and calculating the effective property."
+    "This example shows how to calculate the formation factor from a Fickian diffusion simulation on a cubic network. Note that formation factor calculation on an extracted network from Porespy follows similar steps in assigning phase, algorithm and calculating the effective property."
    ]
   },
   {
@@ -174,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In porous media research, the effective transport properties of one process can be used as a substitute for another, for example, electron conduction and diffusion are analagous.\n",
+    "In porous media research, the effective transport properties of one process can be used as a substitute for another, for example, electron conduction and diffusion are analogous.\n",
     "\n",
     "$$\n",
     "\\frac{D_{eff}}{D_{AB}} = \\frac{\\sigma_{eff}}{\\sigma}\\\\\n",
@@ -258,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

examples/applications/mercury_intrusion.ipynb

@@ -305,7 +305,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This looks pretty good.  The minimum value (`loc`) was set to 100 nm, and the `scale` of 20 um positions the distribution well within our expected range.  Lastly, the `c` value controls the skew, which has created a slighly elongated tail, but nothing too  extreme.  Only a few values will be larger than the network spacing of 40 um, which we will revisit later."
+    "This looks pretty good.  The minimum value (`loc`) was set to 100 nm, and the `scale` of 20 um positions the distribution well within our expected range.  Lastly, the `c` value controls the skew, which has created a slightly elongated tail, but nothing too  extreme.  Only a few values will be larger than the network spacing of 40 um, which we will revisit later."
    ]
   },
   {
@@ -350,9 +350,9 @@
    "source": [
     "There are two important advantages to this approach.\n",
     "\n",
-    "Firstly, we can limit the range of seed values, for instance from 0.1 to 0.9, which will prevent any abnormally large values that might occur if a point far out on the tail end of the distribution is choosen (like 0.9999).  \n",
+    "Firstly, we can limit the range of seed values, for instance from 0.1 to 0.9, which will prevent any abnormally large values that might occur if a point far out on the tail end of the distribution is chosen (like 0.9999).  \n",
     "\n",
-    "Seconly, this simplifies the process of creating spatially correlated pore sizes since there are many way to generate correlated random numbers bewteen 0 and 1 (i.e. `porespy.generators.fractal_noise`). We won't go into that here, but it was used to create anisotropic networks in [this work](https://doi.org/10.1016/j.jpowsour.2007.04.059)."
+    "Secondly, this simplifies the process of creating spatially correlated pore sizes since there are many way to generate correlated random numbers between 0 and 1 (i.e. `porespy.generators.fractal_noise`). We won't go into that here, but it was used to create anisotropic networks in [this work](https://doi.org/10.1016/j.jpowsour.2007.04.059)."
    ]
   },
   {
@@ -476,7 +476,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And we can chose which of these becomes the \"official\" throat diameter, and apply some scaling to the values if needed using:"
+    "And we can choose which of these becomes the \"official\" throat diameter, and apply some scaling to the values if needed using:"
    ]
   },
   {
@@ -594,7 +594,7 @@
    "source": [
     "## Add Physics\n",
     "\n",
-    "To simulate mercury intrusion, we will need to calulate the capillary pressure of the throats in the network. The capillary pressure can be calculated using the Washburn equation as provided below.\n",
+    "To simulate mercury intrusion, we will need to calculate the capillary pressure of the throats in the network. The capillary pressure can be calculated using the Washburn equation as provided below.\n",
     "\n",
     "$$ P_C = \\frac{-2\\sigma cos(\\theta)}{R_T} $$\n"
    ]
@@ -694,7 +694,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The agreement is surprizingly good for a first guess.  We probably want to make the throat sizes in the network a little bit larger to shift the curve left.  Recall that to compute the throat sizes we took the minimum size of the two neighboring pores and scaled it by 0.5. Let's adjust this scale factor.  We could readd a new model over the old one, or just adjust the stored parameters of the current one:"
+    "The agreement is surprisingly good for a first guess.  We probably want to make the throat sizes in the network a little bit larger to shift the curve left.  Recall that to compute the throat sizes we took the minimum size of the two neighboring pores and scaled it by 0.5. Let's adjust this scale factor.  We could readd a new model over the old one, or just adjust the stored parameters of the current one:"
    ]
   },
   {
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now teh fit in the low pressure region is nearly perfect. Of course the high pressure region is not fitting at all, but as dicussed by Tsakiroglou and Payatakes in their paper, this sandstone has a dual porosity or microporosity, which is not resolved by the basic network used here.  "
+    "Now teh fit in the low pressure region is nearly perfect. Of course the high pressure region is not fitting at all, but as discussed by Tsakiroglou and Payatakes in their paper, this sandstone has a dual porosity or microporosity, which is not resolved by the basic network used here.  "
    ]
   },
   {
@@ -1020,7 +1020,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.13"
   },
   "toc": {
    "base_numbering": 1,