typos in examples #1-10

the typos of examples #1-10 (excluding porosity and extracted) network are fixed.

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,

examples/applications/relative_diffusivity.ipynb

@@ -148,7 +148,7 @@
  "id": "3c85f5fa",
  "metadata": {},
  "source": [
- "To update calculate the saturation based on occupancy from each pressure point, we created a costum function:"
+ "To update the saturation based on occupancy from each pressure point, we created a custom function:"
  ]
  },
  {
@@ -184,7 +184,7 @@
  "id": "7e4dc401",
  "metadata": {},
  "source": [
- "Similar to the relative permeability example, the geometrical parameters in relative diffusivity can cancell out. Therefore, the remaining fraction includes diffusion rates as follows:\n",
+ "Similar to the relative permeability example, the geometrical parameters in relative diffusivity can cancel out. Therefore, the remaining fraction includes diffusion rates as follows:\n",
  "\n",
  "$$\n",
  "D_{r}=\\frac {D_\\text{eff-multiphase}}{D_\\text{eff-single phase}}=\\frac {Rate_\\text{multiphase}}{Rate_\\text{single phase}}\n",
@@ -193,7 +193,7 @@
  "Where $Rate_\\text{multiphase}$ is the inlet diffusion rate of the phase (water/air) for a Fickian diffusion algorithm with `multiphase conduit_conductance` assigned as conduit diffusive conductance model to account for the existence of the other phase. Whereas $Rate_\\text{single phase}$ is the inlet diffusion rate of the phase (water/air) for a Fickian diffusion algorithm with `generic_diffusive_conductance` assigned as diffusive conductance model for a single phase flow.\n",
  "\n",
  "\n",
- "Note that $D_\\text{eff-single phase}$ is a property of the porous material, not the fluid. However, here to cancel out diffusivity in the $D_{r}$ fraction, the same phase must be used to calculate $Rate_\\text{single phase}$. Therefore, we only need to find the rate values for calcuating the relative permeability.\n",
+ "Note that $D_\\text{eff-single phase}$ is a property of the porous material, not the fluid. However, here to cancel out diffusivity in the $D_{r}$ fraction, the same phase must be used to calculate $Rate_\\text{single phase}$. Therefore, we only need to find the rate values for calculating the relative permeability.\n",
  "We define a function `Rate_calc`, which simulates a Fickian diffusion given the diffusive conductance keyword and returns the rate diffusion at inlet pores. "
  ]
  },
@@ -374,7 +374,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.13"
  }
  },
  "nbformat": 4,

examples/applications/relative_permeability.ipynb

@@ -99,7 +99,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "The next step is to apply an invasion percolation algorithm to obtain the invasion sequence. Asumming a drainage process, the air(invading/non-wetting phase) will be invading the medium. The IP algorithm can be impplemented through a user-defined inlet face. Here, we use the left surface pores in the x direction. By updating the air phase, the invasion sequence can then be found using the phase occupancy which is a property of the phase."
+ "The next step is to apply an invasion percolation algorithm to obtain the invasion sequence. Assuming a drainage process, the air(invading/non-wetting phase) will be invading the medium. The IP algorithm can be implemented through a user-defined inlet face. Here, we use the left surface pores in the x direction. By updating the air phase, the invasion sequence can then be found using the phase occupancy which is a property of the phase."
  ]
  },
  {
@@ -133,7 +133,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "To update the occupancy of phases at each saturation point, we created a costum function:"
+ "To update the occupancy of phases at each saturation point, we created a custom function:"
  ]
  },
  {
@@ -189,7 +189,7 @@
  "\n",
  "\n",
  "Note that $K_{abs}$ is a property of the porous material, not the fluid. However, here to cancel out viscosity in the $K_{rphase}$ fraction, the same phase must be used to calculate $Q_{abs-phase}$. Therefore, naming `abs-phase` was used here to indicate the flow rate from a single phase flow algorithm for the same phase that relative permeability is being calculated.\n",
- "Therefore, we only need to find the rate values for calcuating the relative permeability. \n",
+ "Therefore, we only need to find the rate values for calculating the relative permeability. \n",
  "\n",
  "\n",
  "We define a function `Rate_calc`, which simulates a stokes flow given the hydraulic conductance keyword and returns the rate of inlet fluid flow. "
@@ -216,7 +216,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "In this example we are focusing on finding the relativep permeabilities in x direction. A similar procedure can be applied on y and z directions. Let's define inlet and outlet pores:"
+ "In this example we are focusing on finding the relative permeabilities in x direction. A similar procedure can be applied on y and z directions. Let's define inlet and outlet pores:"
  ]
  },
  {
@@ -240,7 +240,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "Assigning multiphase conductance models to the phases. The multiphase conduit conductance accounts for the presence of the other phase in effective permeability calculations. For more details, plesase see the model's code in OpenPNM package."
+ "Assigning multiphase conductance models to the phases. The multiphase conduit conductance accounts for the presence of the other phase in effective permeability calculations. For more details, please see the model's code in OpenPNM package."
  ]
  },
  {
@@ -359,7 +359,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3.9.13 ('sci')",
+ "display_name": "Python 3 (ipykernel)",
  "language": "python",
  "name": "python3"
  },
@@ -373,7 +373,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.13"
  },
  "vscode": {
  "interpreter": {